High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		95.70 1	96.40 1	93.61 3	98.67 1	85.39 37	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 16	85.07 71	99.27 1	99.54 1

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 51	97.23 2	95.32 2	99.01 2	97.26 9	80.16 152	98.99 1	95.15 1	99.14 2	96.47 35

TDRefinement		93.52 3	93.39 5	93.88 2	95.94 15	90.26 4	95.70 4	96.46 3	90.58 9	92.86 54	96.29 22	88.16 36	94.17 106	86.07 56	98.48 18	97.22 18

LTVRE_ROB	86.10 1	93.04 4	93.44 4	91.82 22	93.73 69	85.72 34	96.79 1	95.51 10	88.86 16	95.63 10	96.99 13	84.81 80	93.16 150	91.10 2	97.53 81	96.58 33
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
reproduce_model		92.89 5	93.18 8	92.01 13	94.20 54	88.23 9	92.87 13	94.32 22	90.25 11	95.65 9	95.74 33	87.75 43	95.72 39	89.60 5	98.27 28	92.08 239

reproduce-ours		92.86 6	93.22 6	91.76 23	94.39 46	87.71 11	92.40 28	94.38 20	89.82 13	95.51 12	95.49 42	89.64 22	95.82 29	89.13 7	98.26 30	91.76 250

our_new_method		92.86 6	93.22 6	91.76 23	94.39 46	87.71 11	92.40 28	94.38 20	89.82 13	95.51 12	95.49 42	89.64 22	95.82 29	89.13 7	98.26 30	91.76 250

HPM-MVS_fast		92.50 8	92.54 10	92.37 6	95.93 16	85.81 33	92.99 12	94.23 28	85.21 44	92.51 62	95.13 52	90.65 10	95.34 59	88.06 16	98.15 39	95.95 46

lecture		92.43 9	93.50 3	89.21 66	94.43 44	79.31 84	92.69 19	95.72 8	88.48 22	94.43 20	95.73 34	91.34 4	94.68 82	90.26 4	98.44 20	93.63 149

SR-MVS-dyc-post		92.41 10	92.41 11	92.39 5	94.13 60	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	88.83 27	95.51 50	87.16 38	97.60 75	92.73 195

SR-MVS		92.23 11	92.34 12	91.91 17	94.89 38	87.85 10	92.51 25	93.87 52	88.20 24	93.24 43	94.02 101	90.15 17	95.67 41	86.82 43	97.34 85	92.19 234

HPM-MVS		92.13 12	92.20 14	91.91 17	95.58 26	84.67 46	93.51 8	94.85 16	82.88 72	91.77 76	93.94 109	90.55 13	95.73 38	88.50 12	98.23 33	95.33 61
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS_3200maxsize		92.05 13	92.24 13	91.48 25	93.02 88	85.17 39	92.47 27	95.05 15	87.65 28	93.21 47	94.39 81	90.09 18	95.08 70	86.67 45	97.60 75	94.18 115

COLMAP_ROB	83.01 3	91.97 14	91.95 15	92.04 11	93.68 70	86.15 24	93.37 10	95.10 14	90.28 10	92.11 68	95.03 54	89.75 21	94.93 74	79.95 134	98.27 28	95.04 74
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMP		91.91 15	91.87 20	92.03 12	95.53 27	85.91 28	93.35 11	94.16 33	82.52 75	92.39 65	94.14 93	89.15 26	95.62 42	87.35 33	98.24 32	94.56 91
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
mPP-MVS		91.69 16	91.47 27	92.37 6	96.04 13	88.48 8	92.72 18	92.60 119	83.09 69	91.54 78	94.25 87	87.67 46	95.51 50	87.21 37	98.11 40	93.12 178

CP-MVS		91.67 17	91.58 24	91.96 14	95.29 31	87.62 13	93.38 9	93.36 75	83.16 68	91.06 88	94.00 102	88.26 33	95.71 40	87.28 36	98.39 23	92.55 209

XVS		91.54 18	91.36 29	92.08 9	95.64 24	86.25 22	92.64 20	93.33 79	85.07 45	89.99 110	94.03 100	86.57 59	95.80 31	87.35 33	97.62 73	94.20 112

MTAPA		91.52 19	91.60 23	91.29 30	96.59 4	86.29 21	92.02 38	91.81 146	84.07 55	92.00 71	94.40 80	86.63 58	95.28 62	88.59 11	98.31 26	92.30 226

UA-Net		91.49 20	91.53 25	91.39 27	94.98 35	82.95 58	93.52 7	92.79 110	88.22 23	88.53 147	97.64 6	83.45 95	94.55 90	86.02 60	98.60 13	96.67 30

ACMMPR		91.49 20	91.35 31	91.92 16	95.74 20	85.88 30	92.58 23	93.25 85	81.99 78	91.40 80	94.17 92	87.51 47	95.87 20	87.74 22	97.76 60	93.99 124

LPG-MVS_test		91.47 22	91.68 21	90.82 37	94.75 41	81.69 63	90.00 67	94.27 25	82.35 76	93.67 38	94.82 60	91.18 5	95.52 48	85.36 67	98.73 7	95.23 66

region2R		91.44 23	91.30 35	91.87 19	95.75 19	85.90 29	92.63 22	93.30 83	81.91 80	90.88 95	94.21 88	87.75 43	95.87 20	87.60 27	97.71 63	93.83 134

HFP-MVS		91.30 24	91.39 28	91.02 33	95.43 29	84.66 47	92.58 23	93.29 84	81.99 78	91.47 79	93.96 106	88.35 32	95.56 45	87.74 22	97.74 62	92.85 192

ZNCC-MVS		91.26 25	91.34 32	91.01 34	95.73 21	83.05 56	92.18 32	94.22 30	80.14 101	91.29 84	93.97 103	87.93 42	95.87 20	88.65 10	97.96 51	94.12 120

APDe-MVS		91.22 26	91.92 16	89.14 68	92.97 90	78.04 96	92.84 16	94.14 37	83.33 66	93.90 29	95.73 34	88.77 28	96.41 3	87.60 27	97.98 48	92.98 188
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
PGM-MVS		91.20 27	90.95 45	91.93 15	95.67 23	85.85 31	90.00 67	93.90 49	80.32 98	91.74 77	94.41 79	88.17 35	95.98 13	86.37 49	97.99 46	93.96 127

SteuartSystems-ACMMP		91.16 28	91.36 29	90.55 41	93.91 65	80.97 70	91.49 45	93.48 73	82.82 73	92.60 61	93.97 103	88.19 34	96.29 6	87.61 26	98.20 36	94.39 106
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		91.14 29	90.91 46	91.83 20	96.18 11	86.88 17	92.20 31	93.03 100	82.59 74	88.52 148	94.37 82	86.74 57	95.41 57	86.32 50	98.21 34	93.19 173
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		90.96 30	91.01 42	90.82 37	95.45 28	82.73 59	91.75 43	93.74 59	80.98 91	91.38 81	93.80 113	87.20 51	95.80 31	87.10 40	97.69 65	93.93 128

MP-MVS-pluss		90.81 31	91.08 39	89.99 50	95.97 14	79.88 77	88.13 110	94.51 19	75.79 159	92.94 51	94.96 55	88.36 31	95.01 72	90.70 3	98.40 22	95.09 73
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMH+	77.89 11	90.73 32	91.50 26	88.44 83	93.00 89	76.26 122	89.65 80	95.55 9	87.72 27	93.89 31	94.94 56	91.62 3	93.44 141	78.35 156	98.76 4	95.61 55

ACMMP_NAP		90.65 33	91.07 41	89.42 62	95.93 16	79.54 82	89.95 71	93.68 64	77.65 136	91.97 72	94.89 57	88.38 30	95.45 55	89.27 6	97.87 56	93.27 168

ACMM	79.39 9	90.65 33	90.99 43	89.63 58	95.03 34	83.53 51	89.62 81	93.35 78	79.20 114	93.83 32	93.60 123	90.81 8	92.96 157	85.02 74	98.45 19	92.41 216
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LS3D		90.60 35	90.34 55	91.38 28	89.03 204	84.23 49	93.58 6	94.68 18	90.65 8	90.33 104	93.95 108	84.50 82	95.37 58	80.87 124	95.50 158	94.53 95

ACMP	79.16 10	90.54 36	90.60 53	90.35 45	94.36 51	80.98 69	89.16 92	94.05 42	79.03 117	92.87 53	93.74 118	90.60 12	95.21 65	82.87 102	98.76 4	94.87 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DPE-MVS		90.53 37	91.08 39	88.88 71	93.38 79	78.65 90	89.15 93	94.05 42	84.68 49	93.90 29	94.11 95	88.13 37	96.30 5	84.51 84	97.81 58	91.70 254
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS		90.48 38	91.14 36	88.50 80	94.38 48	76.12 126	92.12 33	93.85 53	83.72 60	93.24 43	93.18 132	87.06 52	95.85 24	84.99 75	97.69 65	93.54 159

SED-MVS		90.46 39	91.64 22	86.93 109	94.18 55	72.65 156	90.47 60	93.69 62	83.77 58	94.11 27	94.27 83	90.28 15	95.84 27	86.03 57	97.92 52	92.29 228

SMA-MVS		90.31 40	90.48 54	89.83 55	95.31 30	79.52 83	90.98 52	93.24 86	75.37 168	92.84 55	95.28 48	85.58 72	96.09 8	87.92 18	97.76 60	93.88 131
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
SF-MVS		90.27 41	90.80 48	88.68 78	92.86 94	77.09 111	91.19 49	95.74 6	81.38 86	92.28 67	93.80 113	86.89 56	94.64 85	85.52 66	97.51 82	94.30 111

v7n		90.13 42	90.96 44	87.65 99	91.95 122	71.06 190	89.99 69	93.05 97	86.53 35	94.29 23	96.27 23	82.69 104	94.08 109	86.25 53	97.63 71	97.82 8

ME-MVS		90.09 43	90.66 51	88.38 85	92.82 97	76.12 126	89.40 90	93.70 61	83.72 60	92.39 65	93.18 132	88.02 40	95.47 53	84.99 75	97.69 65	93.54 159

PMVS	80.48 6	90.08 44	90.66 51	88.34 87	96.71 3	92.97 2	90.31 64	89.57 223	88.51 21	90.11 106	95.12 53	90.98 7	88.92 283	77.55 170	97.07 92	83.13 412
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS++		90.07 45	91.09 38	87.00 107	91.55 139	72.64 158	96.19 2	94.10 40	85.33 42	93.49 40	94.64 68	81.12 139	95.88 18	87.41 31	95.94 137	92.48 212

DVP-MVS		90.06 46	91.32 33	86.29 121	94.16 58	72.56 162	90.54 57	91.01 174	83.61 63	93.75 35	94.65 65	89.76 19	95.78 35	86.42 47	97.97 49	90.55 291
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
PS-CasMVS		90.06 46	91.92 16	84.47 173	96.56 6	58.83 363	89.04 94	92.74 112	91.40 6	96.12 5	96.06 29	87.23 50	95.57 44	79.42 144	98.74 6	99.00 2

PEN-MVS		90.03 48	91.88 19	84.48 172	96.57 5	58.88 360	88.95 95	93.19 88	91.62 5	96.01 7	96.16 27	87.02 54	95.60 43	78.69 152	98.72 9	98.97 3

OurMVSNet-221017-0		90.01 49	89.74 60	90.83 36	93.16 86	80.37 74	91.91 41	93.11 93	81.10 89	95.32 14	97.24 10	72.94 257	94.85 76	85.07 71	97.78 59	97.26 16

DTE-MVSNet		89.98 50	91.91 18	84.21 182	96.51 7	57.84 372	88.93 96	92.84 108	91.92 4	96.16 4	96.23 24	86.95 55	95.99 12	79.05 148	98.57 15	98.80 6

XVG-ACMP-BASELINE		89.98 50	89.84 58	90.41 43	94.91 37	84.50 48	89.49 86	93.98 44	79.68 106	92.09 69	93.89 111	83.80 90	93.10 153	82.67 106	98.04 41	93.64 148

TestfortrainingZip a		89.97 52	90.77 49	87.58 100	94.38 48	73.21 150	92.12 33	93.85 53	77.53 140	93.24 43	93.18 132	87.06 52	95.85 24	87.89 19	97.69 65	93.68 143

3Dnovator+	83.92 2	89.97 52	89.66 61	90.92 35	91.27 148	81.66 66	91.25 47	94.13 38	88.89 15	88.83 139	94.26 86	77.55 181	95.86 23	84.88 78	95.87 143	95.24 65

WR-MVS_H		89.91 54	91.31 34	85.71 138	96.32 9	62.39 301	89.54 84	93.31 82	90.21 12	95.57 11	95.66 37	81.42 136	95.90 17	80.94 123	98.80 3	98.84 5

OPM-MVS		89.80 55	89.97 56	89.27 64	94.76 40	79.86 78	86.76 137	92.78 111	78.78 120	92.51 62	93.64 122	88.13 37	93.84 120	84.83 80	97.55 78	94.10 121
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
mvs_tets		89.78 56	89.27 67	91.30 29	93.51 73	84.79 44	89.89 73	90.63 184	70.00 260	94.55 19	96.67 17	87.94 41	93.59 132	84.27 86	95.97 133	95.52 56

anonymousdsp		89.73 57	88.88 77	92.27 8	89.82 184	86.67 18	90.51 59	90.20 205	69.87 261	95.06 15	96.14 28	84.28 85	93.07 154	87.68 24	96.34 115	97.09 20

test_djsdf		89.62 58	89.01 71	91.45 26	92.36 107	82.98 57	91.98 39	90.08 208	71.54 236	94.28 25	96.54 19	81.57 134	94.27 96	86.26 51	96.49 109	97.09 20

XVG-OURS-SEG-HR		89.59 59	89.37 65	90.28 46	94.47 43	85.95 27	86.84 133	93.91 48	80.07 102	86.75 199	93.26 129	93.64 2	90.93 220	84.60 83	90.75 319	93.97 126

APD-MVS		89.54 60	89.63 62	89.26 65	92.57 100	81.34 68	90.19 66	93.08 96	80.87 93	91.13 86	93.19 131	86.22 66	95.97 14	82.23 112	97.18 90	90.45 293
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
jajsoiax		89.41 61	88.81 80	91.19 32	93.38 79	84.72 45	89.70 76	90.29 202	69.27 268	94.39 21	96.38 21	86.02 69	93.52 137	83.96 88	95.92 139	95.34 60

CPTT-MVS		89.39 62	88.98 73	90.63 40	95.09 33	86.95 16	92.09 37	92.30 128	79.74 105	87.50 182	92.38 169	81.42 136	93.28 146	83.07 98	97.24 88	91.67 255

ACMH	76.49 14	89.34 63	91.14 36	83.96 190	92.50 103	70.36 199	89.55 82	93.84 56	81.89 81	94.70 17	95.44 44	90.69 9	88.31 301	83.33 94	98.30 27	93.20 172
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
testf1		89.30 64	89.12 68	89.84 53	88.67 215	85.64 35	90.61 55	93.17 89	86.02 38	93.12 48	95.30 46	84.94 77	89.44 275	74.12 223	96.10 128	94.45 100

APD_test2		89.30 64	89.12 68	89.84 53	88.67 215	85.64 35	90.61 55	93.17 89	86.02 38	93.12 48	95.30 46	84.94 77	89.44 275	74.12 223	96.10 128	94.45 100

CP-MVSNet		89.27 66	90.91 46	84.37 174	96.34 8	58.61 366	88.66 103	92.06 135	90.78 7	95.67 8	95.17 51	81.80 131	95.54 47	79.00 149	98.69 10	98.95 4

XVG-OURS		89.18 67	88.83 79	90.23 47	94.28 52	86.11 26	85.91 152	93.60 67	80.16 100	89.13 135	93.44 125	83.82 89	90.98 217	83.86 90	95.30 166	93.60 152

DeepC-MVS	82.31 4	89.15 68	89.08 70	89.37 63	93.64 71	79.07 86	88.54 106	94.20 31	73.53 193	89.71 118	94.82 60	85.09 76	95.77 37	84.17 87	98.03 43	93.26 170
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet_ETH3D		89.12 69	90.72 50	84.31 180	97.00 2	64.33 272	89.67 79	88.38 244	88.84 17	94.29 23	97.57 7	90.48 14	91.26 207	72.57 254	97.65 70	97.34 15

MSP-MVS		89.08 70	88.16 87	91.83 20	95.76 18	86.14 25	92.75 17	93.90 49	78.43 125	89.16 133	92.25 178	72.03 271	96.36 4	88.21 13	90.93 310	92.98 188
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
SD-MVS		88.96 71	89.88 57	86.22 125	91.63 133	77.07 112	89.82 74	93.77 58	78.90 118	92.88 52	92.29 176	86.11 67	90.22 248	86.24 54	97.24 88	91.36 263
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
HPM-MVS++		88.93 72	88.45 83	90.38 44	94.92 36	85.85 31	89.70 76	91.27 166	78.20 128	86.69 203	92.28 177	80.36 150	95.06 71	86.17 55	96.49 109	90.22 297

Elysia		88.71 73	88.89 75	88.19 90	91.26 149	72.96 152	88.10 111	93.59 68	84.31 51	90.42 100	94.10 96	74.07 234	94.82 77	88.19 14	95.92 139	96.80 27

StellarMVS		88.71 73	88.89 75	88.19 90	91.26 149	72.96 152	88.10 111	93.59 68	84.31 51	90.42 100	94.10 96	74.07 234	94.82 77	88.19 14	95.92 139	96.80 27

test_0402		88.65 75	89.58 64	85.88 134	92.55 101	72.22 170	84.01 201	89.44 226	88.63 20	94.38 22	95.77 32	86.38 65	93.59 132	79.84 135	95.21 167	91.82 248

DP-MVS		88.60 76	89.01 71	87.36 102	91.30 146	77.50 104	87.55 119	92.97 104	87.95 26	89.62 122	92.87 151	84.56 81	93.89 117	77.65 168	96.62 104	90.70 283

APD_test1		88.40 77	87.91 89	89.88 52	89.50 190	86.65 20	89.98 70	91.91 141	84.26 53	90.87 96	93.92 110	82.18 119	89.29 279	73.75 231	94.81 186	93.70 142

Anonymous20231211		88.40 77	89.62 63	84.73 164	90.46 169	65.27 261	88.86 97	93.02 101	87.15 30	93.05 50	97.10 11	82.28 117	92.02 183	76.70 181	97.99 46	96.88 26

PS-MVSNAJss		88.31 79	87.90 90	89.56 60	93.31 81	77.96 99	87.94 115	91.97 138	70.73 249	94.19 26	96.67 17	76.94 195	94.57 88	83.07 98	96.28 117	96.15 38

OMC-MVS		88.19 80	87.52 94	90.19 48	91.94 124	81.68 65	87.49 122	93.17 89	76.02 153	88.64 144	91.22 218	84.24 86	93.37 144	77.97 166	97.03 93	95.52 56

CS-MVS		88.14 81	87.67 93	89.54 61	89.56 188	79.18 85	90.47 60	94.77 17	79.37 112	84.32 271	89.33 286	83.87 88	94.53 91	82.45 108	94.89 182	94.90 76

TSAR-MVS + MP.		88.14 81	87.82 91	89.09 69	95.72 22	76.74 115	92.49 26	91.19 169	67.85 295	86.63 204	94.84 59	79.58 158	95.96 15	87.62 25	94.50 195	94.56 91
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
tt0805		88.09 83	89.79 59	82.98 221	93.26 83	63.94 276	91.10 50	89.64 220	85.07 45	90.91 92	91.09 224	89.16 25	91.87 188	82.03 113	95.87 143	93.13 175

EC-MVSNet		88.01 84	88.32 86	87.09 104	89.28 195	72.03 173	90.31 64	96.31 4	80.88 92	85.12 245	89.67 279	84.47 83	95.46 54	82.56 107	96.26 120	93.77 140

RPSCF		88.00 85	86.93 108	91.22 31	90.08 177	89.30 5	89.68 78	91.11 170	79.26 113	89.68 119	94.81 63	82.44 108	87.74 312	76.54 186	88.74 356	96.61 32

AllTest		87.97 86	87.40 98	89.68 56	91.59 134	83.40 52	89.50 85	95.44 11	79.47 108	88.00 164	93.03 142	82.66 105	91.47 197	70.81 268	96.14 125	94.16 117

TranMVSNet+NR-MVSNet		87.86 87	88.76 81	85.18 150	94.02 63	64.13 273	84.38 193	91.29 162	84.88 48	92.06 70	93.84 112	86.45 62	93.73 122	73.22 245	98.66 11	97.69 9

nrg030		87.85 88	88.49 82	85.91 132	90.07 179	69.73 207	87.86 116	94.20 31	74.04 185	92.70 60	94.66 64	85.88 70	91.50 195	79.72 137	97.32 86	96.50 34

CNVR-MVS		87.81 89	87.68 92	88.21 89	92.87 92	77.30 110	85.25 170	91.23 167	77.31 143	87.07 192	91.47 208	82.94 100	94.71 81	84.67 82	96.27 119	92.62 203

HQP_MVS		87.75 90	87.43 97	88.70 77	93.45 75	76.42 119	89.45 87	93.61 65	79.44 110	86.55 205	92.95 148	74.84 221	95.22 63	80.78 126	95.83 145	94.46 98

sc_t1		87.70 91	88.94 74	83.99 188	93.47 74	67.15 238	85.05 175	88.21 251	86.81 32	91.87 74	97.65 5	85.51 74	87.91 307	74.22 217	97.63 71	96.92 25

MM		87.64 92	87.15 100	89.09 69	89.51 189	76.39 121	88.68 102	86.76 282	84.54 50	83.58 290	93.78 115	73.36 252	96.48 2	87.98 17	96.21 121	94.41 105

MVSMamba_PlusPlus		87.53 93	88.86 78	83.54 207	92.03 120	62.26 305	91.49 45	92.62 116	88.07 25	88.07 161	96.17 26	72.24 266	95.79 34	84.85 79	94.16 208	92.58 207

NCCC		87.36 94	86.87 109	88.83 72	92.32 110	78.84 89	86.58 141	91.09 172	78.77 121	84.85 257	90.89 235	80.85 142	95.29 60	81.14 121	95.32 163	92.34 224

DeepPCF-MVS	81.24 5	87.28 95	86.21 119	90.49 42	91.48 143	84.90 42	83.41 224	92.38 124	70.25 257	89.35 130	90.68 245	82.85 103	94.57 88	79.55 141	95.95 136	92.00 243

SixPastTwentyTwo		87.20 96	87.45 96	86.45 118	92.52 102	69.19 217	87.84 117	88.05 252	81.66 83	94.64 18	96.53 20	65.94 308	94.75 80	83.02 100	96.83 98	95.41 58

fmvsm_s_conf0.5_n_9		87.04 97	87.02 105	87.08 105	89.67 186	75.87 129	84.60 186	89.74 215	74.40 182	89.92 114	93.41 126	80.45 148	90.63 235	86.66 46	94.37 201	94.73 88

SPE-MVS-test		87.00 98	86.43 115	88.71 76	89.46 191	77.46 105	89.42 89	95.73 7	77.87 134	81.64 332	87.25 331	82.43 109	94.53 91	77.65 168	96.46 111	94.14 119

UniMVSNet (Re)		86.87 99	86.98 107	86.55 116	93.11 87	68.48 227	83.80 211	92.87 106	80.37 96	89.61 124	91.81 194	77.72 177	94.18 104	75.00 210	98.53 16	96.99 24

Vis-MVSNet		86.86 100	86.58 112	87.72 97	92.09 117	77.43 107	87.35 123	92.09 134	78.87 119	84.27 276	94.05 99	78.35 169	93.65 125	80.54 130	91.58 294	92.08 239
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		86.84 101	87.06 103	86.17 128	92.86 94	67.02 242	82.55 251	91.56 152	83.08 70	90.92 90	91.82 193	78.25 170	93.99 111	74.16 221	98.35 24	97.49 13

DU-MVS		86.80 102	86.99 106	86.21 126	93.24 84	67.02 242	83.16 234	92.21 129	81.73 82	90.92 90	91.97 184	77.20 189	93.99 111	74.16 221	98.35 24	97.61 10

casdiffmvs_mvg		86.72 103	87.51 95	84.36 176	87.09 268	65.22 262	84.16 197	94.23 28	77.89 132	91.28 85	93.66 121	84.35 84	92.71 163	80.07 131	94.87 185	95.16 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmconf0.01_n		86.68 104	86.52 113	87.18 103	85.94 308	78.30 92	86.93 130	92.20 130	65.94 315	89.16 133	93.16 137	83.10 98	89.89 263	87.81 21	94.43 199	93.35 163

tt0320-xc		86.67 105	88.41 84	81.44 267	93.45 75	60.44 337	83.96 203	88.50 240	87.26 29	90.90 94	97.90 3	85.61 71	86.40 339	70.14 280	98.01 45	97.47 14

IS-MVSNet		86.66 106	86.82 111	86.17 128	92.05 119	66.87 246	91.21 48	88.64 237	86.30 37	89.60 125	92.59 161	69.22 289	94.91 75	73.89 228	97.89 55	96.72 29

tt0320		86.63 107	88.36 85	81.41 268	93.57 72	60.73 334	84.37 194	88.61 239	87.00 31	90.75 97	97.98 2	85.54 73	86.45 337	69.75 285	97.70 64	97.06 22

v10		86.54 108	87.10 102	84.84 158	88.16 231	63.28 283	86.64 140	92.20 130	75.42 167	92.81 57	94.50 72	74.05 237	94.06 110	83.88 89	96.28 117	97.17 19

pmmvs6		86.52 109	88.06 88	81.90 253	92.22 113	62.28 304	84.66 185	89.15 231	83.54 65	89.85 115	97.32 8	88.08 39	86.80 330	70.43 277	97.30 87	96.62 31

NormalMVS		86.47 110	85.32 143	89.94 51	94.43 44	80.42 72	88.63 104	93.59 68	74.56 177	85.12 245	90.34 258	66.19 305	94.20 101	76.57 184	98.44 20	95.19 68

PHI-MVS		86.38 111	85.81 129	88.08 92	88.44 224	77.34 108	89.35 91	93.05 97	73.15 206	84.76 260	87.70 320	78.87 163	94.18 104	80.67 128	96.29 116	92.73 195

CSCG		86.26 112	86.47 114	85.60 140	90.87 161	74.26 139	87.98 114	91.85 142	80.35 97	89.54 128	88.01 307	79.09 161	92.13 179	75.51 203	95.06 174	90.41 294

DeepC-MVS_fast	80.27 8	86.23 113	85.65 135	87.96 95	91.30 146	76.92 113	87.19 125	91.99 137	70.56 250	84.96 252	90.69 244	80.01 154	95.14 68	78.37 155	95.78 149	91.82 248
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v8		86.22 114	86.83 110	84.36 176	87.82 239	62.35 303	86.42 144	91.33 161	76.78 147	92.73 59	94.48 74	73.41 249	93.72 123	83.10 97	95.41 159	97.01 23

Anonymous20240529		86.20 115	87.13 101	83.42 209	90.19 174	64.55 269	84.55 188	90.71 181	85.85 40	89.94 113	95.24 50	82.13 120	90.40 243	69.19 292	96.40 114	95.31 62

fmvsm_s_conf0.5_n_3		86.19 116	87.27 99	82.95 223	86.91 276	70.38 198	85.31 169	92.61 118	75.59 163	88.32 155	92.87 151	82.22 118	88.63 292	88.80 9	92.82 255	89.83 307

test_fmvsmconf0.1_n		86.18 117	85.88 127	87.08 105	85.26 324	78.25 93	85.82 156	91.82 144	65.33 330	88.55 146	92.35 175	82.62 107	89.80 265	86.87 42	94.32 203	93.18 174

CDPH-MVS		86.17 118	85.54 136	88.05 94	92.25 111	75.45 132	83.85 208	92.01 136	65.91 317	86.19 216	91.75 198	83.77 91	94.98 73	77.43 173	96.71 102	93.73 141

NR-MVSNet		86.00 119	86.22 118	85.34 147	93.24 84	64.56 268	82.21 266	90.46 190	80.99 90	88.42 151	91.97 184	77.56 180	93.85 118	72.46 255	98.65 12	97.61 10

train_agg		85.98 120	85.28 144	88.07 93	92.34 108	79.70 80	83.94 204	90.32 197	65.79 319	84.49 265	90.97 229	81.93 126	93.63 127	81.21 120	96.54 107	90.88 277

KinetiMVS		85.95 121	86.10 122	85.50 144	87.56 249	69.78 205	83.70 214	89.83 214	80.42 95	87.76 175	93.24 130	73.76 243	91.54 194	85.03 73	93.62 229	95.19 68

FC-MVSNet-test		85.93 122	87.05 104	82.58 235	92.25 111	56.44 383	85.75 157	93.09 95	77.33 142	91.94 73	94.65 65	74.78 223	93.41 143	75.11 209	98.58 14	97.88 7

test_fmvsmconf_n		85.88 123	85.51 137	86.99 108	84.77 333	78.21 94	85.40 167	91.39 159	65.32 331	87.72 177	91.81 194	82.33 112	89.78 266	86.68 44	94.20 206	92.99 186

Effi-MVS+-dtu		85.82 124	83.38 198	93.14 4	87.13 263	91.15 3	87.70 118	88.42 243	74.57 176	83.56 291	85.65 355	78.49 168	94.21 100	72.04 257	92.88 251	94.05 123

TAPA-MVS	77.73 12	85.71 125	84.83 154	88.37 86	88.78 214	79.72 79	87.15 127	93.50 72	69.17 269	85.80 228	89.56 280	80.76 144	92.13 179	73.21 250	95.51 157	93.25 171
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
sasdasda		85.50 126	86.14 120	83.58 203	87.97 233	67.13 239	87.55 119	94.32 22	73.44 196	88.47 149	87.54 323	86.45 62	91.06 215	75.76 199	93.76 220	92.54 210

canonicalmvs		85.50 126	86.14 120	83.58 203	87.97 233	67.13 239	87.55 119	94.32 22	73.44 196	88.47 149	87.54 323	86.45 62	91.06 215	75.76 199	93.76 220	92.54 210

fmvsm_s_conf0.5_n_8		85.48 128	85.75 132	84.68 167	87.10 266	69.98 203	84.28 195	92.68 113	74.77 173	87.90 168	92.36 174	73.94 238	90.41 242	85.95 62	92.74 257	93.66 144

EPP-MVSNet		85.47 129	85.04 149	86.77 113	91.52 142	69.37 212	91.63 44	87.98 255	81.51 85	87.05 193	91.83 192	66.18 307	95.29 60	70.75 271	96.89 95	95.64 53

GeoE		85.45 130	85.81 129	84.37 174	90.08 177	67.07 241	85.86 155	91.39 159	72.33 225	87.59 179	90.25 264	84.85 79	92.37 173	78.00 164	91.94 283	93.66 144

MGCNet		85.37 131	84.58 166	87.75 96	85.28 323	73.36 144	86.54 143	85.71 299	77.56 139	81.78 330	92.47 167	70.29 283	96.02 11	85.59 65	95.96 134	93.87 132

FIs		85.35 132	86.27 117	82.60 234	91.86 126	57.31 376	85.10 174	93.05 97	75.83 158	91.02 89	93.97 103	73.57 245	92.91 161	73.97 227	98.02 44	97.58 12

test_fmvsmvis_n_1920		85.22 133	85.36 142	84.81 160	85.80 311	76.13 125	85.15 173	92.32 127	61.40 367	91.33 82	90.85 238	83.76 92	86.16 345	84.31 85	93.28 238	92.15 237

casdiffmvs		85.21 134	85.85 128	83.31 212	86.17 300	62.77 290	83.03 236	93.93 47	74.69 175	88.21 158	92.68 160	82.29 116	91.89 187	77.87 167	93.75 223	95.27 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.5_n_10		85.20 135	85.25 145	85.02 155	86.01 306	71.31 185	84.96 176	91.76 148	69.10 271	88.90 136	92.56 164	73.84 241	90.63 235	86.88 41	93.26 239	93.13 175

baseline		85.20 135	85.93 125	83.02 219	86.30 295	62.37 302	84.55 188	93.96 45	74.48 179	87.12 187	92.03 183	82.30 114	91.94 184	78.39 154	94.21 205	94.74 87

SSM_0404		85.16 137	85.09 147	85.36 146	90.14 176	69.52 210	86.17 149	91.58 150	74.41 180	86.55 205	91.49 205	78.54 164	93.97 113	73.71 232	93.21 243	92.59 206

K. test v3		85.14 138	84.73 156	86.37 119	91.13 155	69.63 209	85.45 165	76.68 385	84.06 56	92.44 64	96.99 13	62.03 335	94.65 84	80.58 129	93.24 240	94.83 83

mmtdpeth		85.13 139	85.78 131	83.17 217	84.65 335	74.71 135	85.87 154	90.35 196	77.94 131	83.82 283	96.96 15	77.75 175	80.03 404	78.44 153	96.21 121	94.79 86

EI-MVSNet-Vis-set		85.12 140	84.53 169	86.88 110	84.01 349	72.76 155	83.91 207	85.18 309	80.44 94	88.75 141	85.49 359	80.08 153	91.92 185	82.02 114	90.85 315	95.97 44

fmvsm_l_conf0.5_n_3		85.11 141	84.96 151	85.56 141	87.49 252	75.69 131	84.71 183	90.61 186	67.64 299	84.88 255	92.05 182	82.30 114	88.36 299	83.84 91	91.10 303	92.62 203

MGCFI-Net		85.04 142	85.95 124	82.31 245	87.52 250	63.59 279	86.23 148	93.96 45	73.46 194	88.07 161	87.83 318	86.46 61	90.87 225	76.17 193	93.89 216	92.47 214

EI-MVSNet-UG-set		85.04 142	84.44 172	86.85 111	83.87 353	72.52 164	83.82 209	85.15 310	80.27 99	88.75 141	85.45 361	79.95 155	91.90 186	81.92 117	90.80 318	96.13 39

X-MVStestdata		85.04 142	82.70 217	92.08 9	95.64 24	86.25 22	92.64 20	93.33 79	85.07 45	89.99 110	16.05 481	86.57 59	95.80 31	87.35 33	97.62 73	94.20 112

MSLP-MVS++		85.00 145	86.03 123	81.90 253	91.84 129	71.56 183	86.75 138	93.02 101	75.95 156	87.12 187	89.39 284	77.98 172	89.40 278	77.46 171	94.78 187	84.75 384

F-COLMAP		84.97 146	83.42 196	89.63 58	92.39 106	83.40 52	88.83 98	91.92 140	73.19 205	80.18 354	89.15 290	77.04 193	93.28 146	65.82 326	92.28 271	92.21 233

SSM_0407		84.89 147	84.85 153	85.01 156	89.13 199	68.97 220	85.60 161	91.58 150	74.41 180	85.68 229	91.49 205	78.54 164	93.69 124	73.71 232	93.47 231	92.38 221

balanced_conf03		84.80 148	85.40 140	83.00 220	88.95 207	61.44 314	90.42 63	92.37 126	71.48 238	88.72 143	93.13 138	70.16 285	95.15 67	79.26 146	94.11 209	92.41 216

3Dnovator	80.37 7	84.80 148	84.71 159	85.06 153	86.36 293	74.71 135	88.77 100	90.00 210	75.65 161	84.96 252	93.17 136	74.06 236	91.19 210	78.28 158	91.09 304	89.29 318

SymmetryMVS		84.79 150	83.54 190	88.55 79	92.44 105	80.42 72	88.63 104	82.37 345	74.56 177	85.12 245	90.34 258	66.19 305	94.20 101	76.57 184	95.68 153	91.03 271

E4		84.75 151	85.46 138	82.61 233	88.17 229	61.55 313	81.39 280	93.55 71	73.13 208	86.83 196	92.83 153	84.17 87	91.48 196	76.92 180	92.19 275	94.80 85

IterMVS-LS		84.73 152	84.98 150	83.96 190	87.35 256	63.66 277	83.25 229	89.88 213	76.06 151	89.62 122	92.37 172	73.40 251	92.52 168	78.16 161	94.77 189	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_111021_HR		84.63 153	84.34 177	85.49 145	90.18 175	75.86 130	79.23 323	87.13 272	73.35 198	85.56 236	89.34 285	83.60 94	90.50 239	76.64 183	94.05 213	90.09 303

HQP-MVS		84.61 154	84.06 182	86.27 122	91.19 151	70.66 193	84.77 178	92.68 113	73.30 201	80.55 346	90.17 269	72.10 267	94.61 86	77.30 175	94.47 197	93.56 156

v1192		84.57 155	84.69 161	84.21 182	87.75 241	62.88 287	83.02 237	91.43 156	69.08 272	89.98 112	90.89 235	72.70 261	93.62 130	82.41 109	94.97 179	96.13 39

fmvsm_s_conf0.5_n_11		84.56 156	84.69 161	84.15 185	86.53 282	71.29 186	85.53 162	92.62 116	70.54 251	82.75 307	91.20 220	77.33 184	88.55 295	83.80 92	91.93 284	92.61 205

fmvsm_s_conf0.5_n_5		84.56 156	84.71 159	84.11 186	87.92 236	72.09 172	84.80 177	88.64 237	64.43 340	88.77 140	91.78 196	78.07 171	87.95 306	85.85 63	92.18 276	92.30 226

FMVSNet1		84.55 158	85.45 139	81.85 255	90.27 173	61.05 324	86.83 134	88.27 248	78.57 124	89.66 121	95.64 38	75.43 213	90.68 232	69.09 293	95.33 162	93.82 135

v1144		84.54 159	84.72 158	84.00 187	87.67 245	62.55 294	82.97 239	90.93 177	70.32 255	89.80 116	90.99 228	73.50 246	93.48 139	81.69 119	94.65 193	95.97 44

Gipuma		84.44 160	86.33 116	78.78 314	84.20 345	73.57 143	89.55 82	90.44 191	84.24 54	84.38 268	94.89 57	76.35 208	80.40 401	76.14 194	96.80 100	82.36 422
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
fmvsm_s_conf0.5_n_4		84.38 161	84.27 178	84.74 163	87.25 259	70.84 192	83.55 219	88.45 242	68.64 281	86.29 215	91.31 214	74.97 219	88.42 297	87.87 20	90.07 334	94.95 75

MCST-MVS		84.36 162	83.93 186	85.63 139	91.59 134	71.58 181	83.52 220	92.13 132	61.82 360	83.96 281	89.75 277	79.93 156	93.46 140	78.33 157	94.34 202	91.87 247

VDDNet		84.35 163	85.39 141	81.25 270	95.13 32	59.32 351	85.42 166	81.11 356	86.41 36	87.41 183	96.21 25	73.61 244	90.61 237	66.33 318	96.85 96	93.81 138

ETV-MVS		84.31 164	83.91 187	85.52 142	88.58 220	70.40 197	84.50 192	93.37 74	78.76 122	84.07 279	78.72 435	80.39 149	95.13 69	73.82 230	92.98 249	91.04 270

v1240		84.30 165	84.51 170	83.65 200	87.65 246	61.26 320	82.85 243	91.54 153	67.94 292	90.68 99	90.65 249	71.71 275	93.64 126	82.84 103	94.78 187	96.07 41

MVS_111021_LR		84.28 166	83.76 188	85.83 136	89.23 197	83.07 55	80.99 290	83.56 333	72.71 217	86.07 219	89.07 292	81.75 133	86.19 344	77.11 177	93.36 234	88.24 337

h-mvs33		84.25 167	82.76 216	88.72 75	91.82 131	82.60 60	84.00 202	84.98 316	71.27 239	86.70 201	90.55 254	63.04 332	93.92 116	78.26 159	94.20 206	89.63 310

v144192		84.24 168	84.41 173	83.71 199	87.59 248	61.57 312	82.95 240	91.03 173	67.82 296	89.80 116	90.49 255	73.28 253	93.51 138	81.88 118	94.89 182	96.04 43

dcpmvs_2		84.23 169	85.14 146	81.50 265	88.61 219	61.98 309	82.90 242	93.11 93	68.66 280	92.77 58	92.39 168	78.50 167	87.63 315	76.99 179	92.30 268	94.90 76

v1921920		84.23 169	84.37 175	83.79 195	87.64 247	61.71 311	82.91 241	91.20 168	67.94 292	90.06 107	90.34 258	72.04 270	93.59 132	82.32 110	94.91 180	96.07 41

VDD-MVS		84.23 169	84.58 166	83.20 215	91.17 154	65.16 264	83.25 229	84.97 317	79.79 104	87.18 186	94.27 83	74.77 224	90.89 223	69.24 289	96.54 107	93.55 158

v2v482		84.09 172	84.24 179	83.62 201	87.13 263	61.40 315	82.71 246	89.71 218	72.19 228	89.55 126	91.41 209	70.70 281	93.20 148	81.02 122	93.76 220	96.25 37

EG-PatchMatch MVS		84.08 173	84.11 181	83.98 189	92.22 113	72.61 161	82.20 268	87.02 278	72.63 218	88.86 137	91.02 227	78.52 166	91.11 213	73.41 240	91.09 304	88.21 338

E2		84.06 174	84.61 163	82.40 243	87.49 252	61.31 317	81.03 288	93.36 75	71.83 233	86.02 221	91.87 186	82.91 101	91.37 204	75.66 201	91.33 298	94.53 95

E3		84.06 174	84.61 163	82.40 243	87.49 252	61.30 318	81.03 288	93.36 75	71.83 233	86.01 222	91.87 186	82.91 101	91.36 205	75.66 201	91.33 298	94.53 95

fmvsm_s_conf0.5_n_6		84.05 176	84.14 180	83.81 193	87.75 241	71.17 188	83.42 223	91.10 171	67.90 294	84.53 263	90.70 243	73.01 256	88.73 289	85.09 70	93.72 225	91.53 260

DP-MVS Recon		84.05 176	83.22 201	86.52 117	91.73 132	75.27 133	83.23 231	92.40 122	72.04 230	82.04 321	88.33 303	77.91 174	93.95 115	66.17 319	95.12 172	90.34 296

viewmacassd2359aftdt		84.04 178	84.78 155	81.81 258	86.43 287	60.32 339	81.95 270	92.82 109	71.56 235	86.06 220	92.98 144	81.79 132	90.28 244	76.18 192	93.24 240	94.82 84

TransMVSNet (Re)		84.02 179	85.74 133	78.85 313	91.00 158	55.20 396	82.29 262	87.26 267	79.65 107	88.38 153	95.52 41	83.00 99	86.88 328	67.97 307	96.60 105	94.45 100

Baseline_NR-MVSNet		84.00 180	85.90 126	78.29 325	91.47 144	53.44 408	82.29 262	87.00 281	79.06 116	89.55 126	95.72 36	77.20 189	86.14 346	72.30 256	98.51 17	95.28 63

fmvsm_l_conf0.5_n_9		83.98 181	84.46 171	82.53 238	86.11 303	70.65 195	82.45 256	89.17 230	67.72 298	86.74 200	91.49 205	79.20 159	85.86 355	84.71 81	92.60 261	91.07 269

TSAR-MVS + GP.		83.95 182	82.69 218	87.72 97	89.27 196	81.45 67	83.72 213	81.58 354	74.73 174	85.66 232	86.06 350	72.56 263	92.69 165	75.44 205	95.21 167	89.01 331

LuminaMVS		83.94 183	83.51 191	85.23 148	89.78 185	71.74 176	84.76 181	87.27 266	72.60 219	89.31 131	90.60 253	64.04 321	90.95 218	79.08 147	94.11 209	92.99 186

alignmvs		83.94 183	83.98 184	83.80 194	87.80 240	67.88 234	84.54 190	91.42 158	73.27 204	88.41 152	87.96 308	72.33 264	90.83 226	76.02 196	94.11 209	92.69 199

Effi-MVS+		83.90 185	84.01 183	83.57 205	87.22 261	65.61 260	86.55 142	92.40 122	78.64 123	81.34 337	84.18 381	83.65 93	92.93 159	74.22 217	87.87 370	92.17 236

fmvsm_s_conf0.1_n_2		83.82 186	83.49 193	84.84 158	85.99 307	70.19 201	80.93 291	87.58 262	67.26 305	87.94 167	92.37 172	71.40 277	88.01 303	86.03 57	91.87 285	96.31 36

mvs5depth		83.82 186	84.54 168	81.68 261	82.23 378	68.65 225	86.89 131	89.90 212	80.02 103	87.74 176	97.86 4	64.19 320	82.02 389	76.37 188	95.63 156	94.35 107

CANet		83.79 188	82.85 215	86.63 114	86.17 300	72.21 171	83.76 212	91.43 156	77.24 144	74.39 410	87.45 327	75.36 214	95.42 56	77.03 178	92.83 254	92.25 232

pm-mvs1		83.69 189	84.95 152	79.91 299	90.04 181	59.66 348	82.43 257	87.44 263	75.52 165	87.85 171	95.26 49	81.25 138	85.65 359	68.74 299	96.04 130	94.42 104

AdaColmap		83.66 190	83.69 189	83.57 205	90.05 180	72.26 169	86.29 146	90.00 210	78.19 129	81.65 331	87.16 333	83.40 96	94.24 99	61.69 362	94.76 190	84.21 394

viewdifsd2359ckpt09		83.64 191	83.18 204	85.03 154	87.26 258	66.99 244	85.32 168	93.83 57	65.57 325	84.99 251	89.40 283	77.30 185	93.57 135	71.16 267	93.80 219	94.54 94

MIMVSNet1		83.63 192	84.59 165	80.74 281	94.06 62	62.77 290	82.72 245	84.53 325	77.57 138	90.34 103	95.92 31	76.88 201	85.83 356	61.88 360	97.42 83	93.62 150

fmvsm_s_conf0.5_n_2		83.62 193	83.29 200	84.62 168	85.43 321	70.18 202	80.61 298	87.24 268	67.14 306	87.79 173	91.87 186	71.79 274	87.98 305	86.00 61	91.77 288	95.71 50

test_fmvsm_n_1920		83.60 194	82.89 212	85.74 137	85.22 325	77.74 102	84.12 199	90.48 188	59.87 387	86.45 214	91.12 223	75.65 211	85.89 353	82.28 111	90.87 313	93.58 154

WR-MVS		83.56 195	84.40 174	81.06 275	93.43 78	54.88 397	78.67 332	85.02 314	81.24 87	90.74 98	91.56 203	72.85 258	91.08 214	68.00 306	98.04 41	97.23 17

CNLPA		83.55 196	83.10 207	84.90 157	89.34 194	83.87 50	84.54 190	88.77 234	79.09 115	83.54 292	88.66 300	74.87 220	81.73 391	66.84 313	92.29 270	89.11 324

viewcassd2359sk11		83.53 197	83.96 185	82.25 246	86.97 275	61.13 322	80.80 295	93.22 87	70.97 246	85.36 240	91.08 225	81.84 130	91.29 206	74.79 212	90.58 330	94.33 109

LCM-MVSNet-Re		83.48 198	85.06 148	78.75 315	85.94 308	55.75 389	80.05 304	94.27 25	76.47 148	96.09 6	94.54 71	83.31 97	89.75 269	59.95 373	94.89 182	90.75 280

hse-mvs2		83.47 199	81.81 233	88.47 82	91.03 157	82.27 61	82.61 247	83.69 331	71.27 239	86.70 201	86.05 351	63.04 332	92.41 171	78.26 159	93.62 229	90.71 282

V42		83.47 199	83.37 199	83.75 197	83.16 372	63.33 282	81.31 282	90.23 204	69.51 265	90.91 92	90.81 240	74.16 233	92.29 177	80.06 132	90.22 332	95.62 54

VPA-MVSNet		83.47 199	84.73 156	79.69 304	90.29 172	57.52 375	81.30 284	88.69 236	76.29 149	87.58 181	94.44 75	80.60 147	87.20 322	66.60 316	96.82 99	94.34 108

mamba_0408		83.44 202	82.88 213	85.11 151	89.13 199	68.97 220	72.73 408	91.28 163	72.90 211	85.68 229	90.61 251	76.78 202	93.97 113	73.37 242	93.47 231	92.38 221

viewdifsd2359ckpt07		83.41 203	84.35 176	80.56 288	85.84 310	58.93 359	79.47 315	91.28 163	73.01 210	87.59 179	92.07 181	85.24 75	88.68 290	73.59 237	91.11 302	94.09 122

PAPM_NR		83.23 204	83.19 203	83.33 211	90.90 160	65.98 256	88.19 109	90.78 180	78.13 130	80.87 342	87.92 312	73.49 248	92.42 170	70.07 281	88.40 359	91.60 257

CLD-MVS		83.18 205	82.64 219	84.79 161	89.05 203	67.82 235	77.93 342	92.52 120	68.33 284	85.07 248	81.54 410	82.06 123	92.96 157	69.35 288	97.91 54	93.57 155
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ANet_high		83.17 206	85.68 134	75.65 364	81.24 390	45.26 452	79.94 306	92.91 105	83.83 57	91.33 82	96.88 16	80.25 151	85.92 349	68.89 296	95.89 142	95.76 48

FA-MVS(test-final)		83.13 207	83.02 208	83.43 208	86.16 302	66.08 255	88.00 113	88.36 245	75.55 164	85.02 249	92.75 158	65.12 314	92.50 169	74.94 211	91.30 300	91.72 252

114514_t		83.10 208	82.54 222	84.77 162	92.90 91	69.10 219	86.65 139	90.62 185	54.66 419	81.46 334	90.81 240	76.98 194	94.38 94	72.62 253	96.18 123	90.82 279

E3new		83.08 209	83.39 197	82.14 248	86.49 284	61.00 327	80.64 296	93.12 92	70.30 256	84.78 259	90.34 258	80.85 142	91.24 208	74.20 220	89.83 339	94.17 116

RRT-MVS		82.97 210	83.44 194	81.57 263	85.06 328	58.04 370	87.20 124	90.37 194	77.88 133	88.59 145	93.70 120	63.17 329	93.05 155	76.49 187	88.47 358	93.62 150

viewmanbaseed2359cas		82.95 211	83.43 195	81.52 264	85.18 326	60.03 344	81.36 281	92.38 124	69.55 264	84.84 258	91.38 210	79.85 157	90.09 257	74.22 217	92.09 278	94.43 103

BP-MVS1		82.81 212	81.67 235	86.23 123	87.88 238	68.53 226	86.06 151	84.36 326	75.65 161	85.14 244	90.19 266	45.84 422	94.42 93	85.18 69	94.72 191	95.75 49

FE-MVSNET2		82.80 213	83.51 191	80.67 286	89.08 202	58.46 367	82.40 259	89.26 228	71.25 242	88.24 157	94.07 98	75.75 210	89.56 270	65.91 324	95.67 155	93.98 125

UGNet		82.78 214	81.64 236	86.21 126	86.20 299	76.24 123	86.86 132	85.68 300	77.07 145	73.76 414	92.82 154	69.64 286	91.82 190	69.04 295	93.69 226	90.56 290
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
LF4IMVS		82.75 215	81.93 231	85.19 149	82.08 379	80.15 76	85.53 162	88.76 235	68.01 289	85.58 235	87.75 319	71.80 273	86.85 329	74.02 226	93.87 217	88.58 334

EI-MVSNet		82.61 216	82.42 224	83.20 215	83.25 369	63.66 277	83.50 221	85.07 311	76.06 151	86.55 205	85.10 367	73.41 249	90.25 245	78.15 163	90.67 325	95.68 52

QAPM		82.59 217	82.59 221	82.58 235	86.44 286	66.69 247	89.94 72	90.36 195	67.97 291	84.94 254	92.58 163	72.71 260	92.18 178	70.63 274	87.73 373	88.85 332

fmvsm_s_conf0.1_n_a		82.58 218	81.93 231	84.50 171	87.68 244	73.35 145	86.14 150	77.70 374	61.64 365	85.02 249	91.62 200	77.75 175	86.24 341	82.79 104	87.07 381	93.91 130

Fast-Effi-MVS+-dtu		82.54 219	81.41 245	85.90 133	85.60 316	76.53 118	83.07 235	89.62 222	73.02 209	79.11 364	83.51 386	80.74 145	90.24 247	68.76 298	89.29 346	90.94 274

MVS_Test		82.47 220	83.22 201	80.22 295	82.62 377	57.75 374	82.54 252	91.96 139	71.16 244	82.89 303	92.52 166	77.41 182	90.50 239	80.04 133	87.84 372	92.40 218

viewdifsd2359ckpt11		82.46 221	82.98 210	80.88 278	83.53 356	61.00 327	79.46 316	85.97 295	69.48 266	87.89 169	91.31 214	82.10 121	88.61 293	74.28 215	92.86 252	93.02 182

viewmsd2359difaftdt		82.46 221	82.99 209	80.88 278	83.52 357	61.00 327	79.46 316	85.97 295	69.48 266	87.89 169	91.31 214	82.10 121	88.61 293	74.28 215	92.86 252	93.02 182

v148		82.31 223	82.48 223	81.81 258	85.59 317	59.66 348	81.47 278	86.02 293	72.85 213	88.05 163	90.65 249	70.73 280	90.91 222	75.15 208	91.79 286	94.87 78

API-MVS		82.28 224	82.61 220	81.30 269	86.29 296	69.79 204	88.71 101	87.67 261	78.42 126	82.15 317	84.15 382	77.98 172	91.59 193	65.39 329	92.75 256	82.51 421

MVSFormer		82.23 225	81.57 241	84.19 184	85.54 318	69.26 214	91.98 39	90.08 208	71.54 236	76.23 390	85.07 370	58.69 357	94.27 96	86.26 51	88.77 354	89.03 329

viewdifsd2359ckpt13		82.22 226	81.98 230	82.95 223	85.48 320	64.44 270	83.17 233	92.11 133	65.97 314	83.72 286	89.73 278	77.60 179	90.80 228	70.61 275	89.42 344	93.59 153

fmvsm_s_conf0.5_n_a		82.21 227	81.51 244	84.32 179	86.56 281	73.35 145	85.46 164	77.30 378	61.81 361	84.51 264	90.88 237	77.36 183	86.21 343	82.72 105	86.97 386	93.38 162

EIA-MVS		82.19 228	81.23 252	85.10 152	87.95 235	69.17 218	83.22 232	93.33 79	70.42 252	78.58 369	79.77 426	77.29 186	94.20 101	71.51 263	88.96 352	91.93 246

GDP-MVS		82.17 229	80.85 260	86.15 130	88.65 217	68.95 223	85.65 160	93.02 101	68.42 282	83.73 285	89.54 281	45.07 433	94.31 95	79.66 139	93.87 217	95.19 68

fmvsm_s_conf0.1_n		82.17 229	81.59 239	83.94 192	86.87 279	71.57 182	85.19 172	77.42 377	62.27 359	84.47 267	91.33 212	76.43 205	85.91 351	83.14 95	87.14 379	94.33 109

PCF-MVS	74.62 15	82.15 231	80.92 258	85.84 135	89.43 192	72.30 168	80.53 299	91.82 144	57.36 403	87.81 172	89.92 274	77.67 178	93.63 127	58.69 379	95.08 173	91.58 258
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	73.85 16	82.09 232	80.31 267	87.45 101	90.86 162	80.29 75	85.88 153	90.65 183	68.17 287	76.32 389	86.33 345	73.12 255	92.61 167	61.40 365	90.02 336	89.44 313
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n		82.06 233	81.54 243	83.60 202	83.94 350	73.90 141	83.35 226	86.10 289	58.97 389	83.80 284	90.36 257	74.23 231	86.94 327	82.90 101	90.22 332	89.94 305

fmvsm_s_conf0.5_n_7		82.04 234	82.05 228	82.01 251	86.98 274	71.07 189	78.70 330	89.45 225	68.07 288	78.14 372	91.61 201	74.19 232	85.92 349	79.61 140	91.73 289	89.05 328

GBi-Net		82.02 235	82.07 226	81.85 255	86.38 290	61.05 324	86.83 134	88.27 248	72.43 220	86.00 223	95.64 38	63.78 325	90.68 232	65.95 321	93.34 235	93.82 135

test1		82.02 235	82.07 226	81.85 255	86.38 290	61.05 324	86.83 134	88.27 248	72.43 220	86.00 223	95.64 38	63.78 325	90.68 232	65.95 321	93.34 235	93.82 135

OpenMVS	76.72 13	81.98 237	82.00 229	81.93 252	84.42 340	68.22 229	88.50 107	89.48 224	66.92 309	81.80 328	91.86 189	72.59 262	90.16 251	71.19 266	91.25 301	87.40 354

KD-MVS_self_test		81.93 238	83.14 206	78.30 324	84.75 334	52.75 412	80.37 301	89.42 227	70.24 258	90.26 105	93.39 127	74.55 230	86.77 331	68.61 301	96.64 103	95.38 59

fmvsm_s_conf0.5_n		81.91 239	81.30 249	83.75 197	86.02 305	71.56 183	84.73 182	77.11 381	62.44 356	84.00 280	90.68 245	76.42 206	85.89 353	83.14 95	87.11 380	93.81 138

SDMVSNet		81.90 240	83.17 205	78.10 328	88.81 212	62.45 300	76.08 376	86.05 292	73.67 190	83.41 293	93.04 140	82.35 111	80.65 398	70.06 282	95.03 175	91.21 265

tfpnnormal		81.79 241	82.95 211	78.31 323	88.93 208	55.40 392	80.83 294	82.85 340	76.81 146	85.90 227	94.14 93	74.58 228	86.51 335	66.82 314	95.68 153	93.01 185

AstraMVS		81.67 242	81.40 246	82.48 240	87.06 271	66.47 250	81.41 279	81.68 351	68.78 277	88.00 164	90.95 233	65.70 310	87.86 311	76.66 182	92.38 265	93.12 178

c3_l		81.64 243	81.59 239	81.79 260	80.86 396	59.15 356	78.61 333	90.18 206	68.36 283	87.20 185	87.11 335	69.39 287	91.62 192	78.16 161	94.43 199	94.60 90

guyue		81.57 244	81.37 248	82.15 247	86.39 288	66.13 254	81.54 277	83.21 335	69.79 262	87.77 174	89.95 272	65.36 313	87.64 314	75.88 197	92.49 263	92.67 200

PVSNet_Blended_VisFu		81.55 245	80.49 265	84.70 166	91.58 137	73.24 149	84.21 196	91.67 149	62.86 349	80.94 340	87.16 333	67.27 299	92.87 162	69.82 284	88.94 353	87.99 344

fmvsm_l_conf0.5_n_a		81.46 246	80.87 259	83.25 213	83.73 355	73.21 150	83.00 238	85.59 302	58.22 395	82.96 302	90.09 271	72.30 265	86.65 333	81.97 116	89.95 337	89.88 306

SSM_04072		81.44 247	82.88 213	77.10 344	89.13 199	68.97 220	72.73 408	91.28 163	72.90 211	85.68 229	90.61 251	76.78 202	69.94 441	73.37 242	93.47 231	92.38 221

DELS-MVS		81.44 247	81.25 250	82.03 250	84.27 344	62.87 288	76.47 370	92.49 121	70.97 246	81.64 332	83.83 383	75.03 217	92.70 164	74.29 214	92.22 274	90.51 292
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
FMVSNet2		81.31 249	81.61 238	80.41 291	86.38 290	58.75 364	83.93 206	86.58 284	72.43 220	87.65 178	92.98 144	63.78 325	90.22 248	66.86 311	93.92 215	92.27 230

TinyColmap		81.25 250	82.34 225	77.99 331	85.33 322	60.68 335	82.32 261	88.33 246	71.26 241	86.97 194	92.22 180	77.10 192	86.98 326	62.37 354	95.17 169	86.31 367

diffmvs_AUTHOR		81.24 251	81.55 242	80.30 293	80.61 401	60.22 340	77.98 341	90.48 188	67.77 297	83.34 295	89.50 282	74.69 226	87.42 318	78.78 151	90.81 317	93.27 168

AUN-MVS		81.18 252	78.78 290	88.39 84	90.93 159	82.14 62	82.51 253	83.67 332	64.69 339	80.29 350	85.91 354	51.07 397	92.38 172	76.29 191	93.63 228	90.65 287

IMVS_0407		81.08 253	81.23 252	80.62 287	85.76 312	62.46 296	82.46 254	87.91 256	65.23 332	82.12 318	87.92 312	77.27 187	90.18 250	71.67 259	90.74 320	89.20 319

tttt0517		81.07 254	79.58 280	85.52 142	88.99 206	66.45 251	87.03 129	75.51 393	73.76 189	88.32 155	90.20 265	37.96 454	94.16 108	79.36 145	95.13 170	95.93 47

Fast-Effi-MVS+		81.04 255	80.57 262	82.46 241	87.50 251	63.22 284	78.37 336	89.63 221	68.01 289	81.87 324	82.08 404	82.31 113	92.65 166	67.10 310	88.30 365	91.51 261

BH-untuned		80.96 256	80.99 256	80.84 280	88.55 221	68.23 228	80.33 302	88.46 241	72.79 216	86.55 205	86.76 339	74.72 225	91.77 191	61.79 361	88.99 351	82.52 420

IMVS_0403		80.93 257	81.00 255	80.72 283	85.76 312	62.46 296	81.82 271	87.91 256	65.23 332	82.07 320	87.92 312	75.91 209	90.50 239	71.67 259	90.74 320	89.20 319

eth_miper_zixun_eth		80.84 258	80.22 271	82.71 231	81.41 388	60.98 330	77.81 344	90.14 207	67.31 304	86.95 195	87.24 332	64.26 318	92.31 175	75.23 207	91.61 292	94.85 82

xiu_mvs_v1_base_debu		80.84 258	80.14 273	82.93 226	88.31 225	71.73 177	79.53 311	87.17 269	65.43 326	79.59 356	82.73 398	76.94 195	90.14 254	73.22 245	88.33 361	86.90 361

xiu_mvs_v1_base		80.84 258	80.14 273	82.93 226	88.31 225	71.73 177	79.53 311	87.17 269	65.43 326	79.59 356	82.73 398	76.94 195	90.14 254	73.22 245	88.33 361	86.90 361

xiu_mvs_v1_base_debi		80.84 258	80.14 273	82.93 226	88.31 225	71.73 177	79.53 311	87.17 269	65.43 326	79.59 356	82.73 398	76.94 195	90.14 254	73.22 245	88.33 361	86.90 361

IterMVS-SCA-FT		80.64 262	79.41 281	84.34 178	83.93 351	69.66 208	76.28 372	81.09 357	72.43 220	86.47 212	90.19 266	60.46 342	93.15 151	77.45 172	86.39 392	90.22 297

BH-RMVSNet		80.53 263	80.22 271	81.49 266	87.19 262	66.21 253	77.79 345	86.23 287	74.21 184	83.69 287	88.50 301	73.25 254	90.75 229	63.18 350	87.90 369	87.52 352

VortexMVS		80.51 264	80.63 261	80.15 297	83.36 365	61.82 310	80.63 297	88.00 254	67.11 307	87.23 184	89.10 291	63.98 322	88.00 304	73.63 236	92.63 260	90.64 288

Anonymous202405211		80.51 264	81.19 254	78.49 320	88.48 222	57.26 377	76.63 365	82.49 343	81.21 88	84.30 274	92.24 179	67.99 295	86.24 341	62.22 355	95.13 170	91.98 245

DIV-MVS_self_test		80.43 266	80.23 269	81.02 276	79.99 406	59.25 353	77.07 358	87.02 278	67.38 301	86.19 216	89.22 287	63.09 330	90.16 251	76.32 189	95.80 147	93.66 144

cl____		80.42 267	80.23 269	81.02 276	79.99 406	59.25 353	77.07 358	87.02 278	67.37 302	86.18 218	89.21 288	63.08 331	90.16 251	76.31 190	95.80 147	93.65 147

diffmvs		80.40 268	80.48 266	80.17 296	79.02 419	60.04 342	77.54 349	90.28 203	66.65 312	82.40 311	87.33 330	73.50 246	87.35 320	77.98 165	89.62 342	93.13 175
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EPNet		80.37 269	78.41 298	86.23 123	76.75 433	73.28 147	87.18 126	77.45 376	76.24 150	68.14 444	88.93 294	65.41 312	93.85 118	69.47 287	96.12 127	91.55 259
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_ehance_all_eth		80.34 270	80.04 276	81.24 272	79.82 409	58.95 358	77.66 346	89.66 219	65.75 322	85.99 226	85.11 366	68.29 294	91.42 201	76.03 195	92.03 279	93.33 164

MG-MVS		80.32 271	80.94 257	78.47 321	88.18 228	52.62 415	82.29 262	85.01 315	72.01 231	79.24 363	92.54 165	69.36 288	93.36 145	70.65 273	89.19 349	89.45 312

mvsmamba		80.30 272	78.87 287	84.58 170	88.12 232	67.55 236	92.35 30	84.88 319	63.15 347	85.33 241	90.91 234	50.71 399	95.20 66	66.36 317	87.98 368	90.99 272

VPNet		80.25 273	81.68 234	75.94 360	92.46 104	47.98 439	76.70 363	81.67 352	73.45 195	84.87 256	92.82 154	74.66 227	86.51 335	61.66 363	96.85 96	93.33 164

MAR-MVS		80.24 274	78.74 292	84.73 164	86.87 279	78.18 95	85.75 157	87.81 260	65.67 324	77.84 376	78.50 436	73.79 242	90.53 238	61.59 364	90.87 313	85.49 377
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PM-MVS		80.20 275	79.00 286	83.78 196	88.17 229	86.66 19	81.31 282	66.81 449	69.64 263	88.33 154	90.19 266	64.58 315	83.63 380	71.99 258	90.03 335	81.06 440

Anonymous20240521		80.18 276	81.25 250	76.95 346	83.15 373	60.84 332	82.46 254	85.99 294	68.76 278	86.78 197	93.73 119	59.13 354	77.44 415	73.71 232	97.55 78	92.56 208

LFMVS		80.15 277	80.56 263	78.89 312	89.19 198	55.93 385	85.22 171	73.78 405	82.96 71	84.28 275	92.72 159	57.38 367	90.07 259	63.80 344	95.75 150	90.68 284

DPM-MVS		80.10 278	79.18 285	82.88 229	90.71 165	69.74 206	78.87 328	90.84 178	60.29 383	75.64 399	85.92 353	67.28 298	93.11 152	71.24 265	91.79 286	85.77 373

MSDG		80.06 279	79.99 278	80.25 294	83.91 352	68.04 233	77.51 350	89.19 229	77.65 136	81.94 322	83.45 388	76.37 207	86.31 340	63.31 349	86.59 389	86.41 365

FE-MVS		79.98 280	78.86 288	83.36 210	86.47 285	66.45 251	89.73 75	84.74 323	72.80 215	84.22 278	91.38 210	44.95 434	93.60 131	63.93 342	91.50 295	90.04 304

sd_testset		79.95 281	81.39 247	75.64 365	88.81 212	58.07 369	76.16 375	82.81 341	73.67 190	83.41 293	93.04 140	80.96 141	77.65 414	58.62 380	95.03 175	91.21 265

ab-mvs		79.67 282	80.56 263	76.99 345	88.48 222	56.93 379	84.70 184	86.06 291	68.95 275	80.78 343	93.08 139	75.30 215	84.62 367	56.78 389	90.90 311	89.43 314

VNet		79.31 283	80.27 268	76.44 354	87.92 236	53.95 404	75.58 382	84.35 327	74.39 183	82.23 315	90.72 242	72.84 259	84.39 372	60.38 371	93.98 214	90.97 273

thisisatest0530		79.07 284	77.33 308	84.26 181	87.13 263	64.58 267	83.66 216	75.95 388	68.86 276	85.22 243	87.36 329	38.10 451	93.57 135	75.47 204	94.28 204	94.62 89

cl22		78.97 285	78.21 300	81.24 272	77.74 423	59.01 357	77.46 353	87.13 272	65.79 319	84.32 271	85.10 367	58.96 356	90.88 224	75.36 206	92.03 279	93.84 133

patch_mono-2		78.89 286	79.39 282	77.41 341	84.78 332	68.11 231	75.60 380	83.11 337	60.96 375	79.36 360	89.89 275	75.18 216	72.97 430	73.32 244	92.30 268	91.15 267

RPMNet		78.88 287	78.28 299	80.68 285	79.58 410	62.64 292	82.58 249	94.16 33	74.80 172	75.72 397	92.59 161	48.69 406	95.56 45	73.48 239	82.91 428	83.85 399

PAPR		78.84 288	78.10 301	81.07 274	85.17 327	60.22 340	82.21 266	90.57 187	62.51 351	75.32 403	84.61 375	74.99 218	92.30 176	59.48 376	88.04 367	90.68 284

viewmambaseed2359dif		78.80 289	78.47 297	79.78 300	80.26 405	59.28 352	77.31 355	87.13 272	60.42 381	82.37 312	88.67 299	74.58 228	87.87 310	67.78 309	87.73 373	92.19 234

PVSNet_BlendedMVS		78.80 289	77.84 302	81.65 262	84.43 338	63.41 280	79.49 314	90.44 191	61.70 364	75.43 400	87.07 336	69.11 290	91.44 199	60.68 369	92.24 272	90.11 302

FMVSNet3		78.80 289	78.55 294	79.57 306	82.89 376	56.89 381	81.76 272	85.77 298	69.04 273	86.00 223	90.44 256	51.75 395	90.09 257	65.95 321	93.34 235	91.72 252

test_yl		78.71 292	78.51 295	79.32 309	84.32 342	58.84 361	78.38 334	85.33 306	75.99 154	82.49 309	86.57 341	58.01 361	90.02 261	62.74 351	92.73 258	89.10 325

DCV-MVSNet		78.71 292	78.51 295	79.32 309	84.32 342	58.84 361	78.38 334	85.33 306	75.99 154	82.49 309	86.57 341	58.01 361	90.02 261	62.74 351	92.73 258	89.10 325

test1111		78.53 294	78.85 289	77.56 337	92.22 113	47.49 441	82.61 247	69.24 437	72.43 220	85.28 242	94.20 89	51.91 393	90.07 259	65.36 330	96.45 112	95.11 72

FE-MVSNET		78.46 295	79.36 283	75.75 362	86.53 282	54.53 399	78.03 338	85.35 305	69.01 274	85.41 239	90.68 245	64.27 317	85.73 357	62.59 353	92.35 267	87.00 360

icg_test_0407_2		78.46 295	79.68 279	74.78 372	85.76 312	62.46 296	68.51 437	87.91 256	65.23 332	82.12 318	87.92 312	77.27 187	72.67 431	71.67 259	90.74 320	89.20 319

ECVR-MVS		78.44 297	78.63 293	77.88 333	91.85 127	48.95 435	83.68 215	69.91 433	72.30 226	84.26 277	94.20 89	51.89 394	89.82 264	63.58 345	96.02 131	94.87 78

pmmvs-eth3d		78.42 298	77.04 311	82.57 237	87.44 255	74.41 138	80.86 293	79.67 365	55.68 412	84.69 261	90.31 263	60.91 340	85.42 360	62.20 356	91.59 293	87.88 348

mvs_anonymous		78.13 299	78.76 291	76.23 359	79.24 416	50.31 431	78.69 331	84.82 321	61.60 366	83.09 301	92.82 154	73.89 240	87.01 323	68.33 305	86.41 391	91.37 262

TAMVS		78.08 300	76.36 318	83.23 214	90.62 166	72.87 154	79.08 324	80.01 364	61.72 363	81.35 336	86.92 338	63.96 324	88.78 287	50.61 428	93.01 248	88.04 343

miper_enhance_ethall		77.83 301	76.93 312	80.51 289	76.15 440	58.01 371	75.47 384	88.82 233	58.05 397	83.59 289	80.69 414	64.41 316	91.20 209	73.16 251	92.03 279	92.33 225

Vis-MVSNet (Re-imp)		77.82 302	77.79 303	77.92 332	88.82 211	51.29 425	83.28 227	71.97 421	74.04 185	82.23 315	89.78 276	57.38 367	89.41 277	57.22 388	95.41 159	93.05 181

CANet_DTU		77.81 303	77.05 310	80.09 298	81.37 389	59.90 346	83.26 228	88.29 247	69.16 270	67.83 447	83.72 384	60.93 339	89.47 272	69.22 291	89.70 341	90.88 277

OpenMVS_ROB	70.19 17	77.77 304	77.46 305	78.71 316	84.39 341	61.15 321	81.18 286	82.52 342	62.45 355	83.34 295	87.37 328	66.20 304	88.66 291	64.69 337	85.02 408	86.32 366

SSC-MVS		77.55 305	81.64 236	65.29 438	90.46 169	20.33 485	73.56 401	68.28 439	85.44 41	88.18 160	94.64 68	70.93 279	81.33 393	71.25 264	92.03 279	94.20 112

MDA-MVSNet-bldmvs		77.47 306	76.90 313	79.16 311	79.03 418	64.59 266	66.58 449	75.67 391	73.15 206	88.86 137	88.99 293	66.94 300	81.23 394	64.71 336	88.22 366	91.64 256

jason		77.42 307	75.75 324	82.43 242	87.10 266	69.27 213	77.99 340	81.94 349	51.47 439	77.84 376	85.07 370	60.32 344	89.00 281	70.74 272	89.27 348	89.03 329
jason: jason.
CDS-MVSNet		77.32 308	75.40 328	83.06 218	89.00 205	72.48 165	77.90 343	82.17 347	60.81 376	78.94 366	83.49 387	59.30 352	88.76 288	54.64 408	92.37 266	87.93 347
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IMVS_0404		77.24 309	77.75 304	75.73 363	85.76 312	62.46 296	70.84 423	87.91 256	65.23 332	72.21 422	87.92 312	67.48 297	75.53 423	71.67 259	90.74 320	89.20 319

xiu_mvs_v2_base		77.19 310	76.75 315	78.52 319	87.01 272	61.30 318	75.55 383	87.12 276	61.24 372	74.45 409	78.79 434	77.20 189	90.93 220	64.62 339	84.80 415	83.32 408

MVSTER		77.09 311	75.70 325	81.25 270	75.27 448	61.08 323	77.49 352	85.07 311	60.78 377	86.55 205	88.68 297	43.14 443	90.25 245	73.69 235	90.67 325	92.42 215

PS-MVSNAJ		77.04 312	76.53 317	78.56 318	87.09 268	61.40 315	75.26 385	87.13 272	61.25 371	74.38 411	77.22 448	76.94 195	90.94 219	64.63 338	84.83 414	83.35 407

IterMVS		76.91 313	76.34 319	78.64 317	80.91 394	64.03 274	76.30 371	79.03 368	64.88 338	83.11 299	89.16 289	59.90 348	84.46 370	68.61 301	85.15 406	87.42 353
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
D2MVS		76.84 314	75.67 326	80.34 292	80.48 403	62.16 308	73.50 402	84.80 322	57.61 401	82.24 314	87.54 323	51.31 396	87.65 313	70.40 278	93.19 244	91.23 264

CL-MVSNet_self_test		76.81 315	77.38 307	75.12 368	86.90 277	51.34 423	73.20 405	80.63 361	68.30 285	81.80 328	88.40 302	66.92 301	80.90 395	55.35 402	94.90 181	93.12 178

TR-MVS		76.77 316	75.79 323	79.72 303	86.10 304	65.79 258	77.14 356	83.02 338	65.20 336	81.40 335	82.10 402	66.30 303	90.73 231	55.57 399	85.27 402	82.65 415

MonoMVSNet		76.66 317	77.26 309	74.86 370	79.86 408	54.34 401	86.26 147	86.08 290	71.08 245	85.59 234	88.68 297	53.95 385	85.93 348	63.86 343	80.02 444	84.32 390

USDC		76.63 318	76.73 316	76.34 356	83.46 360	57.20 378	80.02 305	88.04 253	52.14 435	83.65 288	91.25 217	63.24 328	86.65 333	54.66 407	94.11 209	85.17 379

BH-w/o		76.57 319	76.07 322	78.10 328	86.88 278	65.92 257	77.63 347	86.33 285	65.69 323	80.89 341	79.95 423	68.97 292	90.74 230	53.01 418	85.25 403	77.62 451

Patchmtry		76.56 320	77.46 305	73.83 378	79.37 415	46.60 445	82.41 258	76.90 382	73.81 188	85.56 236	92.38 169	48.07 409	83.98 377	63.36 348	95.31 165	90.92 275

PVSNet_Blended		76.49 321	75.40 328	79.76 302	84.43 338	63.41 280	75.14 386	90.44 191	57.36 403	75.43 400	78.30 437	69.11 290	91.44 199	60.68 369	87.70 375	84.42 389

miper_lstm_enhance		76.45 322	76.10 321	77.51 339	76.72 434	60.97 331	64.69 453	85.04 313	63.98 343	83.20 298	88.22 304	56.67 371	78.79 411	73.22 245	93.12 245	92.78 194

lupinMVS		76.37 323	74.46 338	82.09 249	85.54 318	69.26 214	76.79 361	80.77 360	50.68 446	76.23 390	82.82 396	58.69 357	88.94 282	69.85 283	88.77 354	88.07 340

cascas		76.29 324	74.81 334	80.72 283	84.47 337	62.94 286	73.89 398	87.34 264	55.94 410	75.16 405	76.53 453	63.97 323	91.16 211	65.00 333	90.97 309	88.06 342

SD_0403		76.08 325	76.77 314	73.98 376	87.08 270	49.45 434	83.62 217	84.68 324	63.31 344	75.13 406	87.47 326	71.85 272	84.56 368	49.97 430	87.86 371	87.94 346

WB-MVS		76.06 326	80.01 277	64.19 441	89.96 183	20.58 484	72.18 412	68.19 440	83.21 67	86.46 213	93.49 124	70.19 284	78.97 409	65.96 320	90.46 331	93.02 182

thres600view7		75.97 327	75.35 330	77.85 335	87.01 272	51.84 421	80.45 300	73.26 410	75.20 169	83.10 300	86.31 347	45.54 424	89.05 280	55.03 405	92.24 272	92.66 201

GA-MVS		75.83 328	74.61 335	79.48 308	81.87 381	59.25 353	73.42 403	82.88 339	68.68 279	79.75 355	81.80 407	50.62 400	89.46 273	66.85 312	85.64 399	89.72 308

MVP-Stereo		75.81 329	73.51 347	82.71 231	89.35 193	73.62 142	80.06 303	85.20 308	60.30 382	73.96 412	87.94 309	57.89 365	89.45 274	52.02 422	74.87 462	85.06 381
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_fmvs3		75.72 330	75.20 331	77.27 342	75.01 451	69.47 211	78.93 325	84.88 319	46.67 453	87.08 191	87.84 317	50.44 402	71.62 436	77.42 174	88.53 357	90.72 281

FE-MVSNET3		75.70 331	75.08 332	77.56 337	84.10 348	55.50 391	73.58 400	84.89 318	62.48 352	78.16 371	84.24 379	58.14 360	87.47 317	59.34 377	90.82 316	89.72 308

thres100view900		75.45 332	75.05 333	76.66 352	87.27 257	51.88 420	81.07 287	73.26 410	75.68 160	83.25 297	86.37 344	45.54 424	88.80 284	51.98 423	90.99 306	89.31 316

ET-MVSNet_ETH3D		75.28 333	72.77 356	82.81 230	83.03 375	68.11 231	77.09 357	76.51 386	60.67 379	77.60 381	80.52 418	38.04 452	91.15 212	70.78 270	90.68 324	89.17 323

thres400		75.14 334	74.23 340	77.86 334	86.24 297	52.12 417	79.24 321	73.87 403	73.34 199	81.82 326	84.60 376	46.02 417	88.80 284	51.98 423	90.99 306	92.66 201

wuyk23d		75.13 335	79.30 284	62.63 444	75.56 444	75.18 134	80.89 292	73.10 412	75.06 171	94.76 16	95.32 45	87.73 45	52.85 476	34.16 474	97.11 91	59.85 472

EU-MVSNet		75.12 336	74.43 339	77.18 343	83.11 374	59.48 350	85.71 159	82.43 344	39.76 473	85.64 233	88.76 295	44.71 436	87.88 309	73.86 229	85.88 398	84.16 395

HyFIR lowres test		75.12 336	72.66 358	82.50 239	91.44 145	65.19 263	72.47 410	87.31 265	46.79 452	80.29 350	84.30 378	52.70 390	92.10 182	51.88 427	86.73 387	90.22 297

CMPMVS	59.41 20	75.12 336	73.57 345	79.77 301	75.84 443	67.22 237	81.21 285	82.18 346	50.78 444	76.50 386	87.66 321	55.20 381	82.99 383	62.17 358	90.64 329	89.09 327
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs4		74.92 339	72.98 354	80.73 282	84.95 329	71.71 180	76.23 373	77.59 375	52.83 429	77.73 380	86.38 343	56.35 374	84.97 364	57.72 387	87.05 382	85.51 376

tfpn200view9		74.86 340	74.23 340	76.74 351	86.24 297	52.12 417	79.24 321	73.87 403	73.34 199	81.82 326	84.60 376	46.02 417	88.80 284	51.98 423	90.99 306	89.31 316

1112_ss		74.82 341	73.74 343	78.04 330	89.57 187	60.04 342	76.49 369	87.09 277	54.31 420	73.66 415	79.80 424	60.25 345	86.76 332	58.37 381	84.15 419	87.32 355

EGC-MVSNET		74.79 342	69.99 386	89.19 67	94.89 38	87.00 15	91.89 42	86.28 286	1.09 482	2.23 484	95.98 30	81.87 129	89.48 271	79.76 136	95.96 134	91.10 268

ppachtmachnet_test		74.73 343	74.00 342	76.90 348	80.71 399	56.89 381	71.53 418	78.42 370	58.24 394	79.32 362	82.92 395	57.91 364	84.26 374	65.60 328	91.36 297	89.56 311

Patchmatch-RL test		74.48 344	73.68 344	76.89 349	84.83 331	66.54 248	72.29 411	69.16 438	57.70 399	86.76 198	86.33 345	45.79 423	82.59 384	69.63 286	90.65 328	81.54 431

PatchMatch-RL		74.48 344	73.22 351	78.27 326	87.70 243	85.26 38	75.92 378	70.09 431	64.34 341	76.09 393	81.25 412	65.87 309	78.07 413	53.86 410	83.82 421	71.48 460

XXY-MVS		74.44 346	76.19 320	69.21 413	84.61 336	52.43 416	71.70 415	77.18 380	60.73 378	80.60 344	90.96 231	75.44 212	69.35 444	56.13 394	88.33 361	85.86 372

test2506		74.12 347	73.39 348	76.28 357	91.85 127	44.20 455	84.06 200	48.20 480	72.30 226	81.90 323	94.20 89	27.22 479	89.77 267	64.81 335	96.02 131	94.87 78

reproduce_monomvs		74.09 348	73.23 350	76.65 353	76.52 435	54.54 398	77.50 351	81.40 355	65.85 318	82.86 305	86.67 340	27.38 477	84.53 369	70.24 279	90.66 327	90.89 276

CR-MVSNet		74.00 349	73.04 353	76.85 350	79.58 410	62.64 292	82.58 249	76.90 382	50.50 447	75.72 397	92.38 169	48.07 409	84.07 376	68.72 300	82.91 428	83.85 399

SSC-MVS3.2		73.90 350	75.67 326	68.61 421	84.11 347	41.28 463	64.17 455	72.83 413	72.09 229	79.08 365	87.94 309	70.31 282	73.89 429	55.99 395	94.49 196	90.67 286

Test_1112_low_res		73.90 350	73.08 352	76.35 355	90.35 171	55.95 384	73.40 404	86.17 288	50.70 445	73.14 416	85.94 352	58.31 359	85.90 352	56.51 391	83.22 425	87.20 357

test20.03		73.75 352	74.59 337	71.22 399	81.11 392	51.12 427	70.15 429	72.10 420	70.42 252	80.28 352	91.50 204	64.21 319	74.72 427	46.96 448	94.58 194	87.82 350

test_fmvs2		73.57 353	72.80 355	75.90 361	72.74 465	68.84 224	77.07 358	84.32 328	45.14 459	82.89 303	84.22 380	48.37 407	70.36 440	73.40 241	87.03 383	88.52 335

SCA		73.32 354	72.57 360	75.58 366	81.62 385	55.86 387	78.89 327	71.37 426	61.73 362	74.93 407	83.42 389	60.46 342	87.01 323	58.11 385	82.63 433	83.88 396

baseline1		73.26 355	73.54 346	72.43 392	84.92 330	47.79 440	79.89 307	74.00 401	65.93 316	78.81 367	86.28 348	56.36 373	81.63 392	56.63 390	79.04 451	87.87 349

1314		73.22 356	72.56 361	75.20 367	80.41 404	57.84 372	81.64 275	85.36 304	51.68 438	73.10 417	76.65 452	61.45 337	85.19 362	63.54 346	79.21 449	82.59 416

MVS		73.21 357	72.59 359	75.06 369	80.97 393	60.81 333	81.64 275	85.92 297	46.03 457	71.68 425	77.54 443	68.47 293	89.77 267	55.70 398	85.39 400	74.60 457

HY-MVS	64.64 18	73.03 358	72.47 362	74.71 373	83.36 365	54.19 402	82.14 269	81.96 348	56.76 409	69.57 439	86.21 349	60.03 346	84.83 366	49.58 435	82.65 431	85.11 380

thisisatest0515		73.00 359	70.52 378	80.46 290	81.45 387	59.90 346	73.16 406	74.31 400	57.86 398	76.08 394	77.78 440	37.60 455	92.12 181	65.00 333	91.45 296	89.35 315

EPNet_dtu		72.87 360	71.33 372	77.49 340	77.72 424	60.55 336	82.35 260	75.79 389	66.49 313	58.39 475	81.06 413	53.68 386	85.98 347	53.55 413	92.97 250	85.95 370
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CVMVSNet		72.62 361	71.41 371	76.28 357	83.25 369	60.34 338	83.50 221	79.02 369	37.77 477	76.33 388	85.10 367	49.60 405	87.41 319	70.54 276	77.54 457	81.08 438

CHOSEN 1792x2688		72.45 362	70.56 377	78.13 327	90.02 182	63.08 285	68.72 436	83.16 336	42.99 467	75.92 395	85.46 360	57.22 369	85.18 363	49.87 433	81.67 435	86.14 368

testgi		72.36 363	74.61 335	65.59 435	80.56 402	42.82 460	68.29 438	73.35 409	66.87 310	81.84 325	89.93 273	72.08 269	66.92 458	46.05 452	92.54 262	87.01 359

thres200		72.34 364	71.55 370	74.70 374	83.48 359	51.60 422	75.02 387	73.71 406	70.14 259	78.56 370	80.57 417	46.20 415	88.20 302	46.99 447	89.29 346	84.32 390

FPMVS		72.29 365	72.00 364	73.14 383	88.63 218	85.00 40	74.65 391	67.39 443	71.94 232	77.80 378	87.66 321	50.48 401	75.83 421	49.95 431	79.51 445	58.58 474

FMVSNet5		72.10 366	71.69 366	73.32 381	81.57 386	53.02 411	76.77 362	78.37 371	63.31 344	76.37 387	91.85 190	36.68 456	78.98 408	47.87 444	92.45 264	87.95 345

our_test_3		71.85 367	71.59 367	72.62 389	80.71 399	53.78 405	69.72 432	71.71 425	58.80 391	78.03 373	80.51 419	56.61 372	78.84 410	62.20 356	86.04 397	85.23 378

PAPM		71.77 368	70.06 384	76.92 347	86.39 288	53.97 403	76.62 366	86.62 283	53.44 424	63.97 464	84.73 374	57.79 366	92.34 174	39.65 464	81.33 439	84.45 388

ttmdpeth		71.72 369	70.67 375	74.86 370	73.08 462	55.88 386	77.41 354	69.27 436	55.86 411	78.66 368	93.77 117	38.01 453	75.39 424	60.12 372	89.87 338	93.31 166

IB-MVS	62.13 19	71.64 370	68.97 396	79.66 305	80.80 398	62.26 305	73.94 397	76.90 382	63.27 346	68.63 443	76.79 450	33.83 460	91.84 189	59.28 378	87.26 377	84.88 382
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
UnsupCasMVSNet_eth		71.63 371	72.30 363	69.62 410	76.47 437	52.70 414	70.03 430	80.97 358	59.18 388	79.36 360	88.21 305	60.50 341	69.12 445	58.33 383	77.62 456	87.04 358

testing3		71.53 372	70.79 374	73.77 379	88.89 210	41.86 462	76.60 368	59.12 469	72.83 214	80.97 338	82.08 404	19.80 485	87.33 321	65.12 332	91.68 291	92.13 238

test_vis3_rt		71.42 373	70.67 375	73.64 380	69.66 472	70.46 196	66.97 448	89.73 216	42.68 469	88.20 159	83.04 391	43.77 438	60.07 470	65.35 331	86.66 388	90.39 295

Anonymous20231206		71.38 374	71.88 365	69.88 407	86.31 294	54.37 400	70.39 427	74.62 396	52.57 431	76.73 385	88.76 295	59.94 347	72.06 433	44.35 456	93.23 242	83.23 410

test_vis1_n_1920		71.30 375	71.58 369	70.47 402	77.58 426	59.99 345	74.25 392	84.22 329	51.06 441	74.85 408	79.10 430	55.10 382	68.83 447	68.86 297	79.20 450	82.58 417

MIMVSNet		71.09 376	71.59 367	69.57 411	87.23 260	50.07 432	78.91 326	71.83 422	60.20 385	71.26 426	91.76 197	55.08 383	76.09 419	41.06 461	87.02 384	82.54 419

test_fmvs1_n		70.94 377	70.41 381	72.53 391	73.92 453	66.93 245	75.99 377	84.21 330	43.31 466	79.40 359	79.39 428	43.47 439	68.55 449	69.05 294	84.91 411	82.10 425

MS-PatchMatch		70.93 378	70.22 382	73.06 384	81.85 382	62.50 295	73.82 399	77.90 372	52.44 432	75.92 395	81.27 411	55.67 378	81.75 390	55.37 401	77.70 455	74.94 456

pmmvs5		70.73 379	70.07 383	72.72 387	77.03 431	52.73 413	74.14 393	75.65 392	50.36 448	72.17 423	85.37 364	55.42 380	80.67 397	52.86 419	87.59 376	84.77 383

testing3-2		70.72 380	70.97 373	69.95 406	88.93 208	34.80 476	69.85 431	66.59 450	78.42 126	77.58 382	85.55 356	31.83 466	82.08 388	46.28 449	93.73 224	92.98 188

PatchT		70.52 381	72.76 357	63.79 443	79.38 414	33.53 477	77.63 347	65.37 454	73.61 192	71.77 424	92.79 157	44.38 437	75.65 422	64.53 340	85.37 401	82.18 424

test_vis1_n		70.29 382	69.99 386	71.20 400	75.97 442	66.50 249	76.69 364	80.81 359	44.22 462	75.43 400	77.23 447	50.00 403	68.59 448	66.71 315	82.85 430	78.52 450

N_pmnet		70.20 383	68.80 398	74.38 375	80.91 394	84.81 43	59.12 466	76.45 387	55.06 415	75.31 404	82.36 401	55.74 377	54.82 475	47.02 446	87.24 378	83.52 403

tpmvs		70.16 384	69.56 389	71.96 395	74.71 452	48.13 437	79.63 309	75.45 394	65.02 337	70.26 434	81.88 406	45.34 429	85.68 358	58.34 382	75.39 461	82.08 426

new-patchmatchnet		70.10 385	73.37 349	60.29 452	81.23 391	16.95 487	59.54 464	74.62 396	62.93 348	80.97 338	87.93 311	62.83 334	71.90 434	55.24 403	95.01 178	92.00 243

YYNet1		70.06 386	70.44 379	68.90 415	73.76 455	53.42 409	58.99 467	67.20 445	58.42 393	87.10 189	85.39 363	59.82 349	67.32 455	59.79 374	83.50 424	85.96 369

MVStest1		70.05 387	69.26 390	72.41 393	58.62 484	55.59 390	76.61 367	65.58 452	53.44 424	89.28 132	93.32 128	22.91 483	71.44 438	74.08 225	89.52 343	90.21 301

MDA-MVSNet_test_wron		70.05 387	70.44 379	68.88 416	73.84 454	53.47 407	58.93 468	67.28 444	58.43 392	87.09 190	85.40 362	59.80 350	67.25 456	59.66 375	83.54 423	85.92 371

CostFormer		69.98 389	68.68 399	73.87 377	77.14 429	50.72 429	79.26 320	74.51 398	51.94 437	70.97 429	84.75 373	45.16 432	87.49 316	55.16 404	79.23 448	83.40 406

testing91		69.94 390	68.99 395	72.80 386	83.81 354	45.89 448	71.57 417	73.64 408	68.24 286	70.77 432	77.82 439	34.37 459	84.44 371	53.64 412	87.00 385	88.07 340

baseline2		69.77 391	66.89 408	78.41 322	79.51 412	58.09 368	76.23 373	69.57 434	57.50 402	64.82 462	77.45 445	46.02 417	88.44 296	53.08 415	77.83 453	88.70 333

PatchmatchNet		69.71 392	68.83 397	72.33 394	77.66 425	53.60 406	79.29 319	69.99 432	57.66 400	72.53 420	82.93 394	46.45 414	80.08 403	60.91 368	72.09 465	83.31 409
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_fmvs1		69.57 393	69.05 393	71.14 401	69.15 473	65.77 259	73.98 396	83.32 334	42.83 468	77.77 379	78.27 438	43.39 442	68.50 450	68.39 304	84.38 418	79.15 448

JIA-IIPM		69.41 394	66.64 412	77.70 336	73.19 459	71.24 187	75.67 379	65.56 453	70.42 252	65.18 458	92.97 147	33.64 462	83.06 381	53.52 414	69.61 471	78.79 449

Syy-MVS		69.40 395	70.03 385	67.49 426	81.72 383	38.94 468	71.00 420	61.99 460	61.38 368	70.81 430	72.36 464	61.37 338	79.30 406	64.50 341	85.18 404	84.22 392

testing99		69.27 396	68.15 403	72.63 388	83.29 367	45.45 450	71.15 419	71.08 427	67.34 303	70.43 433	77.77 441	32.24 465	84.35 373	53.72 411	86.33 393	88.10 339

UnsupCasMVSNet_bld		69.21 397	69.68 388	67.82 424	79.42 413	51.15 426	67.82 442	75.79 389	54.15 421	77.47 383	85.36 365	59.26 353	70.64 439	48.46 441	79.35 447	81.66 429

test_cas_vis1_n_1920		69.20 398	69.12 391	69.43 412	73.68 456	62.82 289	70.38 428	77.21 379	46.18 456	80.46 349	78.95 432	52.03 392	65.53 463	65.77 327	77.45 458	79.95 446

gg-mvs-nofinetune		68.96 399	69.11 392	68.52 422	76.12 441	45.32 451	83.59 218	55.88 474	86.68 33	64.62 463	97.01 12	30.36 470	83.97 378	44.78 455	82.94 427	76.26 453

WBMVS		68.76 400	68.43 400	69.75 409	83.29 367	40.30 466	67.36 444	72.21 419	57.09 406	77.05 384	85.53 358	33.68 461	80.51 399	48.79 439	90.90 311	88.45 336

WB-MVSnew		68.72 401	69.01 394	67.85 423	83.22 371	43.98 456	74.93 388	65.98 451	55.09 414	73.83 413	79.11 429	65.63 311	71.89 435	38.21 469	85.04 407	87.69 351

tpm2		68.45 402	66.83 409	73.30 382	78.93 420	48.50 436	79.76 308	71.76 423	47.50 451	69.92 436	83.60 385	42.07 445	88.40 298	48.44 442	79.51 445	83.01 413

tpm		67.95 403	68.08 404	67.55 425	78.74 421	43.53 458	75.60 380	67.10 448	54.92 416	72.23 421	88.10 306	42.87 444	75.97 420	52.21 421	80.95 443	83.15 411

WTY-MVS		67.91 404	68.35 401	66.58 431	80.82 397	48.12 438	65.96 450	72.60 414	53.67 423	71.20 427	81.68 409	58.97 355	69.06 446	48.57 440	81.67 435	82.55 418

testing11		67.38 405	65.93 413	71.73 397	83.37 364	46.60 445	70.95 422	69.40 435	62.47 354	66.14 451	76.66 451	31.22 467	84.10 375	49.10 437	84.10 420	84.49 386

test-LLR		67.21 406	66.74 410	68.63 419	76.45 438	55.21 394	67.89 439	67.14 446	62.43 357	65.08 459	72.39 462	43.41 440	69.37 442	61.00 366	84.89 412	81.31 433

testing222		66.93 407	65.30 420	71.81 396	83.38 363	45.83 449	72.06 413	67.50 442	64.12 342	69.68 438	76.37 454	27.34 478	83.00 382	38.88 465	88.38 360	86.62 364

sss		66.92 408	67.26 406	65.90 433	77.23 428	51.10 428	64.79 452	71.72 424	52.12 436	70.13 435	80.18 421	57.96 363	65.36 464	50.21 429	81.01 441	81.25 435

KD-MVS_2432*1600		66.87 409	65.81 416	70.04 404	67.50 474	47.49 441	62.56 458	79.16 366	61.21 373	77.98 374	80.61 415	25.29 481	82.48 385	53.02 416	84.92 409	80.16 444

miper_refine_blended		66.87 409	65.81 416	70.04 404	67.50 474	47.49 441	62.56 458	79.16 366	61.21 373	77.98 374	80.61 415	25.29 481	82.48 385	53.02 416	84.92 409	80.16 444

dmvs_re		66.81 411	66.98 407	66.28 432	76.87 432	58.68 365	71.66 416	72.24 417	60.29 383	69.52 440	73.53 461	52.38 391	64.40 466	44.90 454	81.44 438	75.76 454

tpm cat1		66.76 412	65.21 421	71.42 398	77.09 430	50.62 430	78.01 339	73.68 407	44.89 460	68.64 442	79.00 431	45.51 426	82.42 387	49.91 432	70.15 468	81.23 437

UWE-MVS		66.43 413	65.56 419	69.05 414	84.15 346	40.98 464	73.06 407	64.71 456	54.84 417	76.18 392	79.62 427	29.21 472	80.50 400	38.54 468	89.75 340	85.66 374

PVSNet	58.17 21	66.41 414	65.63 418	68.75 417	81.96 380	49.88 433	62.19 460	72.51 416	51.03 442	68.04 445	75.34 458	50.84 398	74.77 425	45.82 453	82.96 426	81.60 430

tpmrst		66.28 415	66.69 411	65.05 439	72.82 464	39.33 467	78.20 337	70.69 430	53.16 427	67.88 446	80.36 420	48.18 408	74.75 426	58.13 384	70.79 467	81.08 438

Patchmatch-test		65.91 416	67.38 405	61.48 449	75.51 445	43.21 459	68.84 435	63.79 458	62.48 352	72.80 419	83.42 389	44.89 435	59.52 472	48.27 443	86.45 390	81.70 428

ADS-MVSNet2		65.87 417	63.64 426	72.55 390	73.16 460	56.92 380	67.10 446	74.81 395	49.74 449	66.04 453	82.97 392	46.71 412	77.26 416	42.29 458	69.96 469	83.46 404

myMVS_eth3d28		65.83 418	65.85 414	65.78 434	83.42 362	35.71 474	67.29 445	68.01 441	67.58 300	69.80 437	77.72 442	32.29 464	74.30 428	37.49 470	89.06 350	87.32 355

test_vis1_rt		65.64 419	64.09 423	70.31 403	66.09 478	70.20 200	61.16 461	81.60 353	38.65 474	72.87 418	69.66 467	52.84 388	60.04 471	56.16 393	77.77 454	80.68 442

mvsany_test3		65.48 420	62.97 429	73.03 385	69.99 471	76.17 124	64.83 451	43.71 482	43.68 464	80.25 353	87.05 337	52.83 389	63.09 469	51.92 426	72.44 464	79.84 447

test-mter		65.00 421	63.79 425	68.63 419	76.45 438	55.21 394	67.89 439	67.14 446	50.98 443	65.08 459	72.39 462	28.27 475	69.37 442	61.00 366	84.89 412	81.31 433

ETVMVS		64.67 422	63.34 428	68.64 418	83.44 361	41.89 461	69.56 434	61.70 465	61.33 370	68.74 441	75.76 456	28.76 473	79.35 405	34.65 473	86.16 396	84.67 385

myMVS_eth3d		64.66 423	63.89 424	66.97 429	81.72 383	37.39 471	71.00 420	61.99 460	61.38 368	70.81 430	72.36 464	20.96 484	79.30 406	49.59 434	85.18 404	84.22 392

test0.0.03 1		64.66 423	64.36 422	65.57 436	75.03 450	46.89 444	64.69 453	61.58 466	62.43 357	71.18 428	77.54 443	43.41 440	68.47 451	40.75 463	82.65 431	81.35 432

UBG		64.34 425	63.35 427	67.30 427	83.50 358	40.53 465	67.46 443	65.02 455	54.77 418	67.54 449	74.47 460	32.99 463	78.50 412	40.82 462	83.58 422	82.88 414

test_f		64.31 426	65.85 414	59.67 453	66.54 477	62.24 307	57.76 470	70.96 428	40.13 471	84.36 269	82.09 403	46.93 411	51.67 477	61.99 359	81.89 434	65.12 468

pmmvs3		62.47 427	60.02 440	69.80 408	71.58 468	64.00 275	70.52 426	58.44 472	39.77 472	66.05 452	75.84 455	27.10 480	72.28 432	46.15 451	84.77 416	73.11 458

EPMVS		62.47 427	62.63 431	62.01 445	70.63 470	38.74 469	74.76 389	52.86 476	53.91 422	67.71 448	80.01 422	39.40 449	66.60 459	55.54 400	68.81 473	80.68 442

ADS-MVSNet		61.90 429	62.19 433	61.03 450	73.16 460	36.42 473	67.10 446	61.75 463	49.74 449	66.04 453	82.97 392	46.71 412	63.21 467	42.29 458	69.96 469	83.46 404

PMMVS		61.65 430	60.38 437	65.47 437	65.40 481	69.26 214	63.97 456	61.73 464	36.80 478	60.11 470	68.43 469	59.42 351	66.35 460	48.97 438	78.57 452	60.81 471

E-PMN		61.59 431	61.62 434	61.49 448	66.81 476	55.40 392	53.77 473	60.34 468	66.80 311	58.90 473	65.50 472	40.48 448	66.12 461	55.72 397	86.25 394	62.95 470

TESTMET0.1,1		61.29 432	60.32 438	64.19 441	72.06 466	51.30 424	67.89 439	62.09 459	45.27 458	60.65 469	69.01 468	27.93 476	64.74 465	56.31 392	81.65 437	76.53 452

MVS-HIRNet		61.16 433	62.92 430	55.87 456	79.09 417	35.34 475	71.83 414	57.98 473	46.56 454	59.05 472	91.14 222	49.95 404	76.43 418	38.74 466	71.92 466	55.84 475

EMVS		61.10 434	60.81 436	61.99 446	65.96 479	55.86 387	53.10 474	58.97 471	67.06 308	56.89 477	63.33 473	40.98 446	67.03 457	54.79 406	86.18 395	63.08 469

DSMNet-mixed		60.98 435	61.61 435	59.09 455	72.88 463	45.05 453	74.70 390	46.61 481	26.20 479	65.34 457	90.32 262	55.46 379	63.12 468	41.72 460	81.30 440	69.09 464

dp		60.70 436	60.29 439	61.92 447	72.04 467	38.67 470	70.83 424	64.08 457	51.28 440	60.75 468	77.28 446	36.59 457	71.58 437	47.41 445	62.34 475	75.52 455

dmvs_testset		60.59 437	62.54 432	54.72 458	77.26 427	27.74 481	74.05 395	61.00 467	60.48 380	65.62 456	67.03 471	55.93 376	68.23 453	32.07 477	69.46 472	68.17 465

CHOSEN 280x420		59.08 438	56.52 444	66.76 430	76.51 436	64.39 271	49.62 475	59.00 470	43.86 463	55.66 478	68.41 470	35.55 458	68.21 454	43.25 457	76.78 460	67.69 466

mvsany_test1		58.48 439	56.47 445	64.50 440	65.90 480	68.21 230	56.95 471	42.11 483	38.30 475	65.69 455	77.19 449	56.96 370	59.35 473	46.16 450	58.96 476	65.93 467

UWE-MVS-28		58.44 440	57.71 442	60.65 451	73.58 457	31.23 478	69.68 433	48.80 479	53.12 428	61.79 466	78.83 433	30.98 468	68.40 452	21.58 480	80.99 442	82.33 423

PVSNet_0	51.08 22	56.10 441	54.97 446	59.48 454	75.12 449	53.28 410	55.16 472	61.89 462	44.30 461	59.16 471	62.48 474	54.22 384	65.91 462	35.40 472	47.01 477	59.25 473

new_pmnet		55.69 442	57.66 443	49.76 459	75.47 446	30.59 479	59.56 463	51.45 477	43.62 465	62.49 465	75.48 457	40.96 447	49.15 479	37.39 471	72.52 463	69.55 463

PMMVS2		55.64 443	59.27 441	44.74 460	64.30 482	12.32 488	40.60 476	49.79 478	53.19 426	65.06 461	84.81 372	53.60 387	49.76 478	32.68 476	89.41 345	72.15 459

MVE	40.22 23	51.82 444	50.47 447	55.87 456	62.66 483	51.91 419	31.61 478	39.28 484	40.65 470	50.76 479	74.98 459	56.24 375	44.67 480	33.94 475	64.11 474	71.04 462
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		41.90 445	42.65 448	39.67 461	70.86 469	21.11 483	61.01 462	21.42 488	57.36 403	57.97 476	50.06 477	16.40 486	58.73 474	21.03 481	27.69 481	39.17 477

kuosan		30.83 446	32.17 449	26.83 463	53.36 485	19.02 486	57.90 469	20.44 489	38.29 476	38.01 480	37.82 479	15.18 487	33.45 482	7.74 483	20.76 482	28.03 478

test_method		30.46 447	29.60 450	33.06 462	17.99 487	3.84 490	13.62 479	73.92 402	2.79 481	18.29 483	53.41 476	28.53 474	43.25 481	22.56 478	35.27 479	52.11 476

cdsmvs_eth3d_5k		20.81 448	27.75 451	0.00 468	0.00 491	0.00 493	0.00 480	85.44 303	0.00 486	0.00 487	82.82 396	81.46 135	0.00 487	0.00 486	0.00 485	0.00 483

tmp_tt		20.25 449	24.50 452	7.49 465	4.47 488	8.70 489	34.17 477	25.16 486	1.00 483	32.43 482	18.49 480	39.37 450	9.21 484	21.64 479	43.75 478	4.57 480

ab-mvs-re		6.65 450	8.87 453	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	79.80 424	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

pcd_1.5k_mvsjas		6.41 451	8.55 454	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	76.94 195	0.00 487	0.00 486	0.00 485	0.00 483

test123		6.27 452	8.08 455	0.84 466	1.11 490	0.57 491	62.90 457	0.82 490	0.54 484	1.07 486	2.75 485	1.26 488	0.30 485	1.04 484	1.26 484	1.66 481

testmvs		5.91 453	7.65 456	0.72 467	1.20 489	0.37 492	59.14 465	0.67 491	0.49 485	1.11 485	2.76 484	0.94 489	0.24 486	1.02 485	1.47 483	1.55 482

mmdepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

monomultidepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

test_blank		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet_test		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

DCPMVS		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

sosnet-low-res		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

sosnet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uncertanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

Regformer		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

MED-MVS test				88.50 80	94.38 48	76.12 126	92.12 33	93.85 53	77.53 140	93.24 43	93.18 132		95.85 24	84.99 75	97.69 65	93.54 159

TestfortrainingZip							92.12 33

WAC-MVS						37.39 471								52.61 420

FOURS1					96.08 12	87.41 14	96.19 2	95.83 5	92.95 3	96.57 3

MSC_two_6792asdad				88.81 73	91.55 139	77.99 97		91.01 174					96.05 9	87.45 29	98.17 37	92.40 218

PC_three_1452									58.96 390	90.06 107	91.33 212	80.66 146	93.03 156	75.78 198	95.94 137	92.48 212

No_MVS				88.81 73	91.55 139	77.99 97		91.01 174					96.05 9	87.45 29	98.17 37	92.40 218

test_one_0601					93.85 67	73.27 148		94.11 39	86.57 34	93.47 42	94.64 68	88.42 29

eth-test2					0.00 491

eth-test					0.00 491

ZD-MVS					92.22 113	80.48 71		91.85 142	71.22 243	90.38 102	92.98 144	86.06 68	96.11 7	81.99 115	96.75 101

RE-MVS-def			92.61 9		94.13 60	88.95 6	92.87 13	94.16 33	88.75 18	93.79 33	94.43 76	90.64 11		87.16 38	97.60 75	92.73 195

IU-MVS					94.18 55	72.64 158		90.82 179	56.98 407	89.67 120				85.78 64	97.92 52	93.28 167

OPU-MVS				88.27 88	91.89 125	77.83 100	90.47 60				91.22 218	81.12 139	94.68 82	74.48 213	95.35 161	92.29 228

test_241102_TWO								93.71 60	83.77 58	93.49 40	94.27 83	89.27 24	95.84 27	86.03 57	97.82 57	92.04 241

test_241102_ONE					94.18 55	72.65 156		93.69 62	83.62 62	94.11 27	93.78 115	90.28 15	95.50 52

9.14			89.29 66		91.84 129		88.80 99	95.32 13	75.14 170	91.07 87	92.89 150	87.27 49	93.78 121	83.69 93	97.55 78

save fliter					93.75 68	77.44 106	86.31 145	89.72 217	70.80 248

test_0728_THIRD									85.33 42	93.75 35	94.65 65	87.44 48	95.78 35	87.41 31	98.21 34	92.98 188

test_0728_SECOND				86.79 112	94.25 53	72.45 166	90.54 57	94.10 40					95.88 18	86.42 47	97.97 49	92.02 242

test0726					94.16 58	72.56 162	90.63 54	93.90 49	83.61 63	93.75 35	94.49 73	89.76 19

GSMVS																83.88 396

test_part2					93.86 66	77.77 101				92.84 55

sam_mvs1												46.11 416				83.88 396

sam_mvs												45.92 421

ambc				82.98 221	90.55 168	64.86 265	88.20 108	89.15 231		89.40 129	93.96 106	71.67 276	91.38 203	78.83 150	96.55 106	92.71 198

MTGPA								91.81 146

test_post1							78.85 329				3.13 482	45.19 431	80.13 402	58.11 385

test_post											3.10 483	45.43 427	77.22 417

patchmatchnet-post											81.71 408	45.93 420	87.01 323

GG-mvs-BLEND				67.16 428	73.36 458	46.54 447	84.15 198	55.04 475		58.64 474	61.95 475	29.93 471	83.87 379	38.71 467	76.92 459	71.07 461

MTMP							90.66 53	33.14 485

gm-plane-assit					75.42 447	44.97 454			52.17 433		72.36 464		87.90 308	54.10 409

test9_res														80.83 125	96.45 112	90.57 289

TEST9					92.34 108	79.70 80	83.94 204	90.32 197	65.41 329	84.49 265	90.97 229	82.03 124	93.63 127

test_8					92.09 117	78.87 88	83.82 209	90.31 199	65.79 319	84.36 269	90.96 231	81.93 126	93.44 141

agg_prior2														79.68 138	96.16 124	90.22 297

agg_prior					91.58 137	77.69 103		90.30 200		84.32 271			93.18 149

TestCases				89.68 56	91.59 134	83.40 52		95.44 11	79.47 108	88.00 164	93.03 142	82.66 105	91.47 197	70.81 268	96.14 125	94.16 117

test_prior4						78.97 87	84.59 187

test_prior2							83.37 225		75.43 166	84.58 262	91.57 202	81.92 128		79.54 142	96.97 94

test_prior				86.32 120	90.59 167	71.99 174		92.85 107					94.17 106			92.80 193

旧先验2							81.73 273		56.88 408	86.54 211			84.90 365	72.81 252

新几何2							81.72 274

新几何1				82.95 223	93.96 64	78.56 91		80.24 362	55.45 413	83.93 282	91.08 225	71.19 278	88.33 300	65.84 325	93.07 246	81.95 427

旧先验1					91.97 121	71.77 175		81.78 350			91.84 191	73.92 239			93.65 227	83.61 402

无先验							82.81 244	85.62 301	58.09 396				91.41 202	67.95 308		84.48 387

原ACMM2							82.26 265

原ACMM1				84.60 169	92.81 98	74.01 140		91.50 154	62.59 350	82.73 308	90.67 248	76.53 204	94.25 98	69.24 289	95.69 152	85.55 375

test222					93.31 81	76.54 116	79.38 318	77.79 373	52.59 430	82.36 313	90.84 239	66.83 302			91.69 290	81.25 435

testdata2													86.43 338	63.52 347

segment_acmp												81.94 125

testdata				79.54 307	92.87 92	72.34 167		80.14 363	59.91 386	85.47 238	91.75 198	67.96 296	85.24 361	68.57 303	92.18 276	81.06 440

testdata1							79.62 310		73.95 187

test12				86.57 115	90.74 163	72.63 160		90.69 182		82.76 306		79.20 159	94.80 79		95.32 163	92.27 230

plane_prior7					93.45 75	77.31 109

plane_prior6					92.61 99	76.54 116						74.84 221

plane_prior5								93.61 65					95.22 63	80.78 126	95.83 145	94.46 98

plane_prior4											92.95 148

plane_prior3						76.85 114			77.79 135	86.55 205

plane_prior2							89.45 87		79.44 110

plane_prior1					92.83 96

plane_prior						76.42 119	87.15 127		75.94 157						95.03 175

n2								0.00 492

nn								0.00 492

door-mid								74.45 399

lessismore_v0				85.95 131	91.10 156	70.99 191		70.91 429		91.79 75	94.42 78	61.76 336	92.93 159	79.52 143	93.03 247	93.93 128

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 25	82.35 76	93.67 38	94.82 60	91.18 5	95.52 48	85.36 67	98.73 7	95.23 66

test11								91.46 155

door								72.57 415

HQP5-MVS						70.66 193

HQP-NCC					91.19 151		84.77 178		73.30 201	80.55 346

ACMP_Plane					91.19 151		84.77 178		73.30 201	80.55 346

BP-MVS														77.30 175

HQP4-MVS										80.56 345			94.61 86			93.56 156

HQP3-MVS								92.68 113							94.47 197

HQP2-MVS												72.10 267

NP-MVS					91.95 122	74.55 137					90.17 269

MDTV_nov1_ep13_2view						27.60 482	70.76 425		46.47 455	61.27 467		45.20 430		49.18 436		83.75 401

MDTV_nov1_ep13			68.29 402		78.03 422	43.87 457	74.12 394	72.22 418	52.17 433	67.02 450	85.54 357	45.36 428	80.85 396	55.73 396	84.42 417

ACMMP++_ref															95.74 151

ACMMP++															97.35 84

Test By Simon												79.09 161

ITE_SJBPF				90.11 49	90.72 164	84.97 41		90.30 200	81.56 84	90.02 109	91.20 220	82.40 110	90.81 227	73.58 238	94.66 192	94.56 91

DeepMVS_CX				24.13 464	32.95 486	29.49 480		21.63 487	12.07 480	37.95 481	45.07 478	30.84 469	19.21 483	17.94 482	33.06 480	23.69 479

High-resolution multi-view benchmark