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		86.90 1	88.67 1	81.57 25	91.50 1	63.30 120	84.80 32	87.77 9	86.18 1	96.26 1	96.06 1	90.32 1	84.49 66	68.08 89	97.05 1	96.93 1

TDRefinement		86.32 2	86.33 2	86.29 1	88.64 32	81.19 5	88.84 2	90.72 1	78.27 8	87.95 14	92.53 13	79.37 13	84.79 62	74.51 48	96.15 2	92.88 7

abl_6		84.92 3	85.70 3	82.57 17	86.72 47	79.27 8	87.56 5	86.08 26	77.48 12	88.12 13	91.53 27	81.18 8	84.31 71	78.12 24	94.47 35	84.15 120

test1172		84.85 4	85.39 5	83.21 3	88.34 37	80.50 6	85.12 28	85.22 39	81.06 2	87.20 27	90.28 67	79.20 14	85.58 45	78.04 27	94.08 54	83.55 130

SR-MVS-dyc-post		84.75 5	85.26 7	83.21 3	86.19 53	79.18 9	87.23 7	86.27 21	77.51 10	87.65 18	90.73 45	79.20 14	85.58 45	78.11 25	94.46 36	84.89 92

HPM-MVS_fast		84.59 6	85.10 8	83.06 6	88.60 33	75.83 26	86.27 23	86.89 17	73.69 22	86.17 35	91.70 24	78.23 20	85.20 55	79.45 13	94.91 25	88.15 45

SR-MVS		84.51 7	85.27 6	82.25 21	88.52 34	77.71 16	86.81 15	85.25 38	77.42 14	86.15 36	90.24 68	81.69 5	85.94 31	77.77 29	93.58 65	83.09 144

ACMMP		84.22 8	84.84 10	82.35 20	89.23 23	76.66 25	87.65 4	85.89 29	71.03 42	85.85 40	90.58 49	78.77 17	85.78 38	79.37 16	95.17 17	84.62 102
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
LTVRE_ROB	75.46 1	84.22 8	84.98 9	81.94 23	84.82 75	75.40 29	91.60 1	87.80 7	73.52 23	88.90 10	93.06 6	71.39 74	81.53 117	81.53 3	92.15 87	88.91 37
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
HPM-MVS		84.12 10	84.63 11	82.60 15	88.21 38	74.40 34	85.24 27	87.21 14	70.69 45	85.14 52	90.42 57	78.99 16	86.62 14	80.83 6	94.93 24	86.79 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		84.12 10	84.55 12	82.80 12	89.42 18	79.74 7	88.19 3	84.43 62	71.96 39	84.70 59	90.56 50	77.12 26	86.18 25	79.24 18	95.36 13	82.49 163

mPP-MVS		84.01 12	84.39 13	82.88 8	90.65 4	81.38 4	87.08 11	82.79 89	72.41 33	85.11 53	90.85 41	76.65 29	84.89 59	79.30 17	94.63 32	82.35 166

COLMAP_ROB	72.78 3	83.75 13	84.11 17	82.68 14	82.97 104	74.39 35	87.18 9	88.18 6	78.98 6	86.11 38	91.47 29	79.70 12	85.76 39	66.91 105	95.46 11	87.89 47
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
ACMMPR		83.62 14	83.93 19	82.69 13	89.78 11	77.51 21	87.01 13	84.19 71	70.23 46	84.49 61	90.67 48	75.15 42	86.37 19	79.58 11	94.26 49	84.18 119

APD-MVS_3200maxsize		83.57 15	84.33 14	81.31 33	82.83 107	73.53 43	85.50 26	87.45 12	74.11 19	86.45 33	90.52 53	80.02 11	84.48 67	77.73 30	94.34 47	85.93 72

region2R		83.54 16	83.86 21	82.58 16	89.82 10	77.53 19	87.06 12	84.23 70	70.19 48	83.86 70	90.72 47	75.20 41	86.27 22	79.41 15	94.25 50	83.95 123

XVS		83.51 17	83.73 22	82.85 10	89.43 16	77.61 17	86.80 16	84.66 55	72.71 26	82.87 79	90.39 61	73.86 55	86.31 20	78.84 20	94.03 55	84.64 100

LPG-MVS_test		83.47 18	84.33 14	80.90 38	87.00 42	70.41 64	82.04 57	86.35 18	69.77 50	87.75 15	91.13 34	81.83 3	86.20 23	77.13 34	95.96 5	86.08 68

HFP-MVS		83.39 19	84.03 18	81.48 27	89.25 21	75.69 27	87.01 13	84.27 66	70.23 46	84.47 62	90.43 55	76.79 27	85.94 31	79.58 11	94.23 51	82.82 153

MTAPA		83.19 20	83.87 20	81.13 35	91.16 2	78.16 14	84.87 30	80.63 123	72.08 36	84.93 54	90.79 42	74.65 48	84.42 68	80.98 4	94.75 27	80.82 191

MP-MVS		83.19 20	83.54 26	82.14 22	90.54 5	79.00 11	86.42 21	83.59 80	71.31 40	81.26 98	90.96 38	74.57 50	84.69 63	78.41 22	94.78 26	82.74 157
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		83.12 22	83.68 23	81.45 29	89.14 25	73.28 45	86.32 22	85.97 28	67.39 61	84.02 68	90.39 61	74.73 47	86.46 16	80.73 7	94.43 40	84.60 105

PGM-MVS		83.07 23	83.25 33	82.54 18	89.57 14	77.21 23	82.04 57	85.40 35	67.96 59	84.91 57	90.88 39	75.59 37	86.57 15	78.16 23	94.71 30	83.82 124

SteuartSystems-ACMMP		83.07 23	83.64 24	81.35 31	85.14 71	71.00 58	85.53 25	84.78 48	70.91 43	85.64 43	90.41 58	75.55 39	87.69 3	79.75 8	95.08 20	85.36 81
Skip Steuart: Steuart Systems R&D Blog.
zzz-MVS		83.01 25	83.63 25	81.13 35	91.16 2	78.16 14	82.72 53	80.63 123	72.08 36	84.93 54	90.79 42	74.65 48	84.42 68	80.98 4	94.75 27	80.82 191

APDe-MVS		82.88 26	84.14 16	79.08 57	84.80 77	66.72 92	86.54 19	85.11 41	72.00 38	86.65 32	91.75 23	78.20 21	87.04 9	77.93 28	94.32 48	83.47 133

GST-MVS		82.79 27	83.27 32	81.34 32	88.99 27	73.29 44	85.94 24	85.13 40	68.58 57	84.14 67	90.21 70	73.37 60	86.41 17	79.09 19	93.98 58	84.30 118

ACMP	69.50 8	82.64 28	83.38 29	80.40 43	86.50 49	69.44 72	82.30 54	86.08 26	66.80 66	86.70 31	89.99 73	81.64 6	85.95 30	74.35 49	96.11 3	85.81 74
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MP-MVS-pluss		82.54 29	83.46 28	79.76 47	88.88 31	68.44 81	81.57 60	86.33 20	63.17 106	85.38 49	91.26 33	76.33 31	84.67 64	83.30 1	94.96 23	86.17 67
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
#test#		82.40 30	82.71 40	81.48 27	89.25 21	75.69 27	84.47 34	84.27 66	64.45 91	84.47 62	90.43 55	76.79 27	85.94 31	76.01 36	94.23 51	82.82 153

ACMMP_NAP		82.33 31	83.28 31	79.46 53	89.28 19	69.09 79	83.62 42	84.98 44	64.77 88	83.97 69	91.02 37	75.53 40	85.93 34	82.00 2	94.36 45	83.35 138

SMA-MVS		82.12 32	82.68 41	80.43 42	88.90 30	69.52 70	85.12 28	84.76 49	63.53 101	84.23 66	91.47 29	72.02 66	87.16 7	79.74 10	94.36 45	84.61 103
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
ACMM	69.25 9	82.11 33	83.31 30	78.49 66	88.17 39	73.96 37	83.11 49	84.52 61	66.40 71	87.45 22	89.16 91	81.02 9	80.52 140	74.27 50	95.73 7	80.98 187
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPE-MVS		82.00 34	83.02 36	78.95 60	85.36 68	67.25 88	82.91 50	84.98 44	73.52 23	85.43 48	90.03 72	76.37 30	86.97 11	74.56 47	94.02 57	82.62 159
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		81.78 35	83.48 27	76.67 86	86.12 57	61.06 133	83.62 42	84.72 51	72.61 29	87.38 24	89.70 78	77.48 24	85.89 36	75.29 42	94.39 41	83.08 145

PMVS	70.70 6	81.70 36	83.15 34	77.36 81	90.35 6	82.82 2	82.15 55	79.22 146	74.08 20	87.16 29	91.97 19	84.80 2	76.97 201	64.98 116	93.61 63	72.28 278
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
UA-Net		81.56 37	82.28 43	79.40 54	88.91 29	69.16 77	84.67 33	80.01 137	75.34 16	79.80 115	94.91 2	69.79 85	80.25 145	72.63 60	94.46 36	88.78 41

CPTT-MVS		81.51 38	81.76 46	80.76 40	89.20 24	78.75 12	86.48 20	82.03 98	68.80 53	80.92 103	88.52 101	72.00 67	82.39 104	74.80 44	93.04 71	81.14 182

ACMH+	66.64 10	81.20 39	82.48 42	77.35 82	81.16 130	62.39 124	80.51 65	87.80 7	73.02 25	87.57 20	91.08 36	80.28 10	82.44 103	64.82 117	96.10 4	87.21 56

testtj		81.19 40	81.70 48	79.67 51	83.95 90	69.77 69	83.58 45	84.63 57	72.13 35	82.85 81	88.36 105	75.00 45	86.79 12	71.99 69	92.84 74	82.44 164

DVP-MVS		81.15 41	83.12 35	75.24 106	86.16 55	60.78 138	83.77 40	80.58 126	72.48 31	85.83 41	90.41 58	78.57 18	85.69 41	75.86 39	94.39 41	79.24 215
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
APD-MVS		81.13 42	81.73 47	79.36 55	84.47 82	70.53 63	83.85 38	83.70 78	69.43 52	83.67 72	88.96 97	75.89 35	86.41 17	72.62 61	92.95 72	81.14 182
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
3Dnovator+	73.19 2	81.08 43	80.48 56	82.87 9	81.41 127	72.03 48	84.38 35	86.23 24	77.28 15	80.65 106	90.18 71	59.80 172	87.58 4	73.06 57	91.34 100	89.01 33

DeepC-MVS	72.44 4	81.00 44	80.83 55	81.50 26	86.70 48	70.03 68	82.06 56	87.00 16	59.89 129	80.91 104	90.53 51	72.19 64	88.56 1	73.67 54	94.52 34	85.92 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPM-MVS		80.99 45	81.63 50	79.07 58	86.86 46	69.39 73	79.41 81	84.00 76	65.64 75	85.54 46	89.28 84	76.32 32	83.47 84	74.03 51	93.57 66	84.35 115

LS3D		80.99 45	80.85 54	81.41 30	78.37 160	71.37 54	87.45 6	85.87 30	77.48 12	81.98 88	89.95 75	69.14 88	85.26 51	66.15 106	91.24 102	87.61 51

SF-MVS		80.72 47	81.80 45	77.48 78	82.03 118	64.40 112	83.41 47	88.46 5	65.28 82	84.29 64	89.18 88	73.73 58	83.22 90	76.01 36	93.77 60	84.81 96

XVG-ACMP-BASELINE		80.54 48	81.06 52	78.98 59	87.01 41	72.91 46	80.23 73	85.56 31	66.56 70	85.64 43	89.57 80	69.12 89	80.55 139	72.51 62	93.37 67	83.48 132

MSP-MVS		80.49 49	79.67 64	82.96 7	89.70 12	77.46 22	87.16 10	85.10 42	64.94 87	81.05 100	88.38 104	57.10 201	87.10 8	79.75 8	83.87 220	84.31 116
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
PEN-MVS		80.46 50	82.91 37	73.11 135	89.83 9	39.02 297	77.06 113	82.61 92	80.04 4	90.60 5	92.85 9	74.93 46	85.21 54	63.15 134	95.15 18	95.09 2

PS-CasMVS		80.41 51	82.86 39	73.07 136	89.93 7	39.21 294	77.15 111	81.28 111	79.74 5	90.87 3	92.73 11	75.03 44	84.93 58	63.83 126	95.19 16	95.07 3

DTE-MVSNet		80.35 52	82.89 38	72.74 147	89.84 8	37.34 312	77.16 110	81.81 101	80.45 3	90.92 2	92.95 7	74.57 50	86.12 28	63.65 127	94.68 31	94.76 6

xxxxxxxxxxxxxcwj		80.31 53	80.94 53	78.42 68	87.00 42	67.23 89	79.24 82	88.61 4	56.65 162	84.29 64	89.18 88	73.73 58	83.22 90	76.01 36	93.77 60	84.81 96

SD-MVS		80.28 54	81.55 51	76.47 91	83.57 94	67.83 85	83.39 48	85.35 37	64.42 92	86.14 37	87.07 121	74.02 54	80.97 130	77.70 31	92.32 85	80.62 198
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
WR-MVS_H		80.22 55	82.17 44	74.39 113	89.46 15	42.69 270	78.24 98	82.24 95	78.21 9	89.57 8	92.10 18	68.05 100	85.59 44	66.04 108	95.62 9	94.88 5

HPM-MVS++		79.89 56	79.80 62	80.18 45	89.02 26	78.44 13	83.49 46	80.18 134	64.71 90	78.11 135	88.39 103	65.46 125	83.14 92	77.64 32	91.20 103	78.94 218

XVG-OURS-SEG-HR		79.62 57	79.99 60	78.49 66	86.46 50	74.79 33	77.15 111	85.39 36	66.73 67	80.39 110	88.85 99	74.43 53	78.33 181	74.73 46	85.79 190	82.35 166

XVG-OURS		79.51 58	79.82 61	78.58 65	86.11 60	74.96 32	76.33 120	84.95 46	66.89 63	82.75 82	88.99 96	66.82 112	78.37 179	74.80 44	90.76 119	82.40 165

CP-MVSNet		79.48 59	81.65 49	72.98 139	89.66 13	39.06 296	76.76 114	80.46 128	78.91 7	90.32 6	91.70 24	68.49 95	84.89 59	63.40 132	95.12 19	95.01 4

OMC-MVS		79.41 60	78.79 69	81.28 34	80.62 134	70.71 62	80.91 63	84.76 49	62.54 110	81.77 90	86.65 138	71.46 72	83.53 83	67.95 94	92.44 82	89.60 23

v7n		79.37 61	80.41 57	76.28 93	78.67 159	55.81 171	79.22 84	82.51 94	70.72 44	87.54 21	92.44 14	68.00 102	81.34 118	72.84 58	91.72 89	91.69 10

ETH3D-3000-0.1		79.14 62	79.80 62	77.16 85	80.67 133	64.57 109	80.26 71	87.60 11	60.74 122	82.47 85	88.03 113	71.73 69	81.81 113	73.12 56	93.61 63	85.09 86

TSAR-MVS + MP.		79.05 63	78.81 68	79.74 48	88.94 28	67.52 86	86.61 18	81.38 109	51.71 220	77.15 147	91.42 32	65.49 124	87.20 6	79.44 14	87.17 177	84.51 110
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
mvs_tets		78.93 64	78.67 72	79.72 49	84.81 76	73.93 38	80.65 64	76.50 184	51.98 218	87.40 23	91.86 21	76.09 34	78.53 171	68.58 84	90.20 125	86.69 64

test_djsdf		78.88 65	78.27 75	80.70 41	81.42 126	71.24 56	83.98 36	75.72 190	52.27 213	87.37 26	92.25 16	68.04 101	80.56 137	72.28 65	91.15 105	90.32 20

HQP_MVS		78.77 66	78.78 70	78.72 62	85.18 69	65.18 104	82.74 51	85.49 32	65.45 77	78.23 132	89.11 92	60.83 163	86.15 26	71.09 71	90.94 110	84.82 94

anonymousdsp		78.60 67	77.80 79	81.00 37	78.01 166	74.34 36	80.09 74	76.12 186	50.51 235	89.19 9	90.88 39	71.45 73	77.78 193	73.38 55	90.60 121	90.90 16

OurMVSNet-221017-0		78.57 68	78.53 74	78.67 63	80.48 135	64.16 113	80.24 72	82.06 97	61.89 114	88.77 11	93.32 4	57.15 199	82.60 102	70.08 77	92.80 76	89.25 27

jajsoiax		78.51 69	78.16 77	79.59 52	84.65 79	73.83 40	80.42 67	76.12 186	51.33 226	87.19 28	91.51 28	73.79 57	78.44 175	68.27 87	90.13 130	86.49 65

CNVR-MVS		78.49 70	78.59 73	78.16 71	85.86 64	67.40 87	78.12 101	81.50 105	63.92 96	77.51 144	86.56 142	68.43 97	84.82 61	73.83 52	91.61 94	82.26 169

DeepPCF-MVS	71.07 5	78.48 71	77.14 85	82.52 19	84.39 86	77.04 24	76.35 118	84.05 74	56.66 161	80.27 111	85.31 168	68.56 94	87.03 10	67.39 100	91.26 101	83.50 131

DP-MVS		78.44 72	79.29 66	75.90 97	81.86 121	65.33 102	79.05 86	84.63 57	74.83 18	80.41 109	86.27 148	71.68 70	83.45 85	62.45 138	92.40 83	78.92 219

ETH3D cwj APD-0.16		78.38 73	78.72 71	77.38 80	80.09 138	66.16 97	79.08 85	86.13 25	57.55 152	80.93 102	87.76 116	71.98 68	82.73 100	72.11 68	92.83 75	83.25 140

NCCC		78.25 74	78.04 78	78.89 61	85.61 65	69.45 71	79.80 78	80.99 120	65.77 74	75.55 177	86.25 150	67.42 106	85.42 47	70.10 76	90.88 116	81.81 175

test_0402		78.17 75	79.48 65	74.24 117	83.50 95	59.15 154	72.52 157	74.60 203	75.34 16	88.69 12	91.81 22	75.06 43	82.37 105	65.10 114	88.68 152	81.20 180

AllTest		77.66 76	77.43 81	78.35 69	79.19 151	70.81 59	78.60 90	88.64 2	65.37 80	80.09 113	88.17 109	70.33 80	78.43 176	55.60 191	90.90 114	85.81 74

PS-MVSNAJss		77.54 77	77.35 82	78.13 73	84.88 74	66.37 95	78.55 91	79.59 142	53.48 202	86.29 34	92.43 15	62.39 145	80.25 145	67.90 95	90.61 120	87.77 48

ACMH	63.62 14	77.50 78	80.11 59	69.68 186	79.61 142	56.28 168	78.81 87	83.62 79	63.41 104	87.14 30	90.23 69	76.11 33	73.32 236	67.58 97	94.44 39	79.44 213
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDPH-MVS		77.33 79	77.06 86	78.14 72	84.21 87	63.98 115	76.07 123	83.45 81	54.20 191	77.68 143	87.18 117	69.98 83	85.37 48	68.01 91	92.72 80	85.08 88

DeepC-MVS_fast	69.89 7	77.17 80	76.33 90	79.70 50	83.90 92	67.94 83	80.06 76	83.75 77	56.73 160	74.88 185	85.32 167	65.54 123	87.79 2	65.61 112	91.14 106	83.35 138
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_part1		76.97 81	78.21 76	73.25 132	77.87 168	45.76 246	78.27 96	87.26 13	66.69 68	85.31 50	91.43 31	55.95 210	84.24 73	65.71 110	95.43 12	89.75 22

X-MVStestdata		76.81 82	74.79 102	82.85 10	89.43 16	77.61 17	86.80 16	84.66 55	72.71 26	82.87 79	9.95 364	73.86 55	86.31 20	78.84 20	94.03 55	84.64 100

UniMVSNet_ETH3D		76.74 83	79.02 67	69.92 185	89.27 20	43.81 258	74.47 144	71.70 223	72.33 34	85.50 47	93.65 3	77.98 22	76.88 204	54.60 201	91.64 92	89.08 31

test_prior3		76.71 84	77.19 83	75.27 104	82.15 116	59.85 147	75.57 127	84.33 64	58.92 137	76.53 166	86.78 129	67.83 103	83.39 87	69.81 79	92.76 78	82.58 160

train_agg		76.38 85	76.55 87	75.86 98	85.47 66	69.32 75	76.42 116	78.69 155	54.00 195	76.97 150	86.74 132	66.60 114	81.10 124	72.50 63	91.56 95	77.15 239

TranMVSNet+NR-MVSNet		76.13 86	77.66 80	71.56 161	84.61 80	42.57 272	70.98 182	78.29 164	68.67 56	83.04 76	89.26 85	72.99 62	80.75 136	55.58 194	95.47 10	91.35 11

agg_prior1		75.89 87	76.41 88	74.31 116	84.44 84	66.02 98	76.12 122	78.62 158	54.40 187	76.95 152	86.85 126	66.44 116	80.34 142	72.45 64	91.42 98	76.57 243

SixPastTwentyTwo		75.77 88	76.34 89	74.06 120	81.69 124	54.84 176	76.47 115	75.49 192	64.10 95	87.73 17	92.24 17	50.45 235	81.30 120	67.41 99	91.46 97	86.04 70

RPSCF		75.76 89	74.37 108	79.93 46	74.81 207	77.53 19	77.53 105	79.30 145	59.44 132	78.88 124	89.80 77	71.26 75	73.09 238	57.45 174	80.89 252	89.17 30

ETH3 D test6400		75.73 90	76.00 92	74.92 107	81.75 122	56.93 165	78.31 94	84.60 59	52.83 208	77.15 147	85.14 170	68.59 93	84.03 74	65.44 113	90.20 125	83.82 124

v10		75.69 91	76.20 91	74.16 118	74.44 218	48.69 212	75.84 126	82.93 88	59.02 136	85.92 39	89.17 90	58.56 184	82.74 99	70.73 73	89.14 147	91.05 13

Anonymous20231211		75.54 92	77.19 83	70.59 170	77.67 173	45.70 248	74.73 140	80.19 133	68.80 53	82.95 78	92.91 8	66.26 117	76.76 207	58.41 170	92.77 77	89.30 26

Effi-MVS+-dtu		75.43 93	72.28 143	84.91 2	77.05 176	83.58 1	78.47 92	77.70 171	57.68 147	74.89 184	78.13 255	64.80 130	84.26 72	56.46 183	85.32 197	86.88 61

Regformer-2		75.32 94	74.47 106	77.88 74	74.22 220	66.65 93	72.77 155	77.54 173	68.47 58	80.44 108	72.08 305	70.60 79	80.97 130	70.08 77	84.02 218	86.01 71

F-COLMAP		75.29 95	73.99 113	79.18 56	81.73 123	71.90 49	81.86 59	82.98 86	59.86 130	72.27 219	84.00 181	64.56 133	83.07 95	51.48 221	87.19 176	82.56 162

HQP-MVS		75.24 96	75.01 101	75.94 96	82.37 111	58.80 157	77.32 107	84.12 72	59.08 133	71.58 228	85.96 160	58.09 189	85.30 50	67.38 101	89.16 144	83.73 128

DROMVSNet		75.22 97	75.51 98	74.34 115	75.38 197	53.32 189	78.27 96	87.07 15	60.17 126	72.14 223	82.58 199	67.65 105	84.67 64	67.83 96	88.49 153	85.04 90

TAPA-MVS	65.27 12	75.16 98	74.29 110	77.77 75	74.86 206	68.08 82	77.89 102	84.04 75	55.15 175	76.19 173	83.39 187	66.91 110	80.11 150	60.04 158	90.14 129	85.13 85
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS-MVSNet		75.10 99	75.42 100	74.15 119	79.23 149	48.05 221	79.43 79	78.04 167	70.09 49	79.17 122	88.02 114	53.04 220	83.60 80	58.05 172	93.76 62	90.79 17

v8		75.07 100	75.64 96	73.35 129	73.42 230	47.46 230	75.20 131	81.45 107	60.05 127	85.64 43	89.26 85	58.08 191	81.80 114	69.71 81	87.97 160	90.79 17

UniMVSNet (Re)		75.00 101	75.48 99	73.56 127	83.14 100	47.92 223	70.41 190	81.04 119	63.67 99	79.54 117	86.37 147	62.83 139	81.82 112	57.10 177	95.25 15	90.94 15

PHI-MVS		74.92 102	74.36 109	76.61 87	76.40 186	62.32 125	80.38 68	83.15 84	54.16 193	73.23 208	80.75 218	62.19 148	83.86 76	68.02 90	90.92 113	83.65 129

DU-MVS		74.91 103	75.57 97	72.93 141	83.50 95	45.79 244	69.47 200	80.14 135	65.22 83	81.74 92	87.08 119	61.82 151	81.07 126	56.21 186	94.98 21	91.93 8

UniMVSNet_NR-MVSNet		74.90 104	75.65 95	72.64 150	83.04 102	45.79 244	69.26 203	78.81 152	66.66 69	81.74 92	86.88 125	63.26 137	81.07 126	56.21 186	94.98 21	91.05 13

nrg030		74.87 105	75.99 93	71.52 162	74.90 205	49.88 208	74.10 148	82.58 93	54.55 186	83.50 74	89.21 87	71.51 71	75.74 216	61.24 144	92.34 84	88.94 36

Vis-MVSNet		74.85 106	74.56 104	75.72 99	81.63 125	64.64 108	76.35 118	79.06 148	62.85 108	73.33 206	88.41 102	62.54 143	79.59 156	63.94 125	82.92 228	82.94 149
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Regformer-4		74.64 107	73.67 119	77.55 76	74.74 209	64.49 111	72.91 153	75.42 194	67.45 60	80.24 112	72.07 307	68.98 90	80.19 149	70.29 75	80.91 250	87.98 46

MSLP-MVS++		74.48 108	75.78 94	70.59 170	84.66 78	62.40 123	78.65 89	84.24 69	60.55 124	77.71 142	81.98 205	63.12 138	77.64 195	62.95 135	88.14 155	71.73 283

Regformer-1		74.28 109	73.63 121	76.21 95	74.22 220	64.12 114	72.77 155	75.46 193	66.86 65	79.27 120	72.08 305	69.29 87	78.74 167	68.73 83	84.02 218	85.77 79

AdaColmap		74.22 110	74.56 104	73.20 133	81.95 119	60.97 135	79.43 79	80.90 121	65.57 76	72.54 216	81.76 209	70.98 77	85.26 51	47.88 251	90.00 131	73.37 266

CSCG		74.12 111	74.39 107	73.33 130	79.35 146	61.66 130	77.45 106	81.98 99	62.47 112	79.06 123	80.19 227	61.83 150	78.79 166	59.83 160	87.35 170	79.54 212

PAPM_NR		73.91 112	74.16 111	73.16 134	81.90 120	53.50 186	81.28 61	81.40 108	66.17 72	73.30 207	83.31 192	59.96 168	83.10 94	58.45 169	81.66 244	82.87 151

EPP-MVSNet		73.86 113	73.38 125	75.31 103	78.19 162	53.35 188	80.45 66	77.32 177	65.11 85	76.47 168	86.80 127	49.47 239	83.77 77	53.89 209	92.72 80	88.81 40

mvs-test1		73.81 114	70.69 165	83.18 5	77.05 176	81.39 3	75.39 129	77.70 171	57.68 147	71.19 237	74.72 281	64.80 130	83.66 79	56.46 183	81.19 248	84.50 111

RRT_MVS		73.80 115	71.19 160	81.60 24	71.04 254	70.33 66	78.78 88	74.91 200	56.96 156	77.83 139	85.56 165	32.82 320	87.39 5	71.16 70	91.68 91	87.07 60

K. test v3		73.67 116	73.61 122	73.87 122	79.78 140	55.62 174	74.69 142	62.04 290	66.16 73	84.76 58	93.23 5	49.47 239	80.97 130	65.66 111	86.67 183	85.02 91

NR-MVSNet		73.62 117	74.05 112	72.33 156	83.50 95	43.71 259	65.65 256	77.32 177	64.32 93	75.59 176	87.08 119	62.45 144	81.34 118	54.90 197	95.63 8	91.93 8

DP-MVS Recon		73.57 118	72.69 138	76.23 94	82.85 106	63.39 118	74.32 145	82.96 87	57.75 146	70.35 244	81.98 205	64.34 134	84.41 70	49.69 235	89.95 133	80.89 189

CNLPA		73.44 119	73.03 134	74.66 108	78.27 161	75.29 30	75.99 124	78.49 160	65.39 79	75.67 175	83.22 196	61.23 159	66.77 292	53.70 211	85.33 196	81.92 174

MCST-MVS		73.42 120	73.34 127	73.63 126	81.28 128	59.17 153	74.80 138	83.13 85	45.50 271	72.84 211	83.78 184	65.15 127	80.99 128	64.54 118	89.09 149	80.73 196

v1192		73.40 121	73.42 123	73.32 131	74.65 215	48.67 213	72.21 160	81.73 102	52.76 209	81.85 89	84.56 174	57.12 200	82.24 109	68.58 84	87.33 171	89.06 32

114514_t		73.40 121	73.33 128	73.64 125	84.15 89	57.11 164	78.20 99	80.02 136	43.76 285	72.55 215	86.07 158	64.00 135	83.35 89	60.14 156	91.03 109	80.45 201

FC-MVSNet-test		73.32 123	74.78 103	68.93 200	79.21 150	36.57 314	71.82 170	79.54 144	57.63 151	82.57 84	90.38 63	59.38 176	78.99 162	57.91 173	94.56 33	91.23 12

v1144		73.29 124	73.39 124	73.01 137	74.12 224	48.11 220	72.01 163	81.08 118	53.83 199	81.77 90	84.68 172	58.07 192	81.91 111	68.10 88	86.86 179	88.99 35

GeoE		73.14 125	73.77 118	71.26 165	78.09 164	52.64 192	74.32 145	79.56 143	56.32 165	76.35 171	83.36 191	70.76 78	77.96 189	63.32 133	81.84 239	83.18 143

baseline		73.10 126	73.96 114	70.51 172	71.46 252	46.39 241	72.08 161	84.40 63	55.95 168	76.62 162	86.46 145	67.20 107	78.03 188	64.22 121	87.27 174	87.11 59

hse-mvs3		73.08 127	71.61 153	77.48 78	83.89 93	72.89 47	70.47 188	71.12 238	54.28 189	77.89 136	83.41 186	49.04 242	80.98 129	63.62 128	90.77 118	78.58 223

TSAR-MVS + GP.		73.08 127	71.60 154	77.54 77	78.99 157	70.73 61	74.96 133	69.38 249	60.73 123	74.39 194	78.44 250	57.72 196	82.78 98	60.16 155	89.60 139	79.11 217

v1240		73.06 129	73.14 130	72.84 144	74.74 209	47.27 233	71.88 169	81.11 115	51.80 219	82.28 87	84.21 178	56.22 208	82.34 106	68.82 82	87.17 177	88.91 37

casdiffmvs		73.06 129	73.84 115	70.72 168	71.32 253	46.71 237	70.93 183	84.26 68	55.62 171	77.46 145	87.10 118	67.09 108	77.81 191	63.95 123	86.83 180	87.64 50

IterMVS-LS		73.01 131	73.12 132	72.66 149	73.79 227	49.90 205	71.63 171	78.44 161	58.22 141	80.51 107	86.63 139	58.15 188	79.62 154	62.51 136	88.20 154	88.48 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CANet		73.00 132	71.84 148	76.48 90	75.82 194	61.28 131	74.81 136	80.37 131	63.17 106	62.43 297	80.50 222	61.10 161	85.16 57	64.00 122	84.34 214	83.01 148

v144192		72.99 133	73.06 133	72.77 145	74.58 216	47.48 229	71.90 168	80.44 129	51.57 222	81.46 96	84.11 180	58.04 193	82.12 110	67.98 92	87.47 167	88.70 42

MVS_111021_HR		72.98 134	72.97 136	72.99 138	80.82 131	65.47 101	68.81 210	72.77 214	57.67 149	75.76 174	82.38 202	71.01 76	77.17 199	61.38 143	86.15 186	76.32 244

v1921920		72.96 135	72.98 135	72.89 143	74.67 212	47.58 228	71.92 167	80.69 122	51.70 221	81.69 94	83.89 182	56.58 206	82.25 108	68.34 86	87.36 169	88.82 39

CLD-MVS		72.88 136	72.36 142	74.43 112	77.03 178	54.30 180	68.77 213	83.43 82	52.12 215	76.79 160	74.44 286	69.54 86	83.91 75	55.88 189	93.25 70	85.09 86
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Regformer-3		72.86 137	72.28 143	74.62 109	74.74 209	60.18 144	72.91 153	71.76 222	64.74 89	78.42 130	72.07 307	67.00 109	76.28 211	67.97 93	80.91 250	87.39 53

EI-MVSNet-Vis-set		72.78 138	71.87 147	75.54 101	74.77 208	59.02 155	72.24 159	71.56 226	63.92 96	78.59 126	71.59 313	66.22 118	78.60 169	67.58 97	80.32 258	89.00 34

ETV-MVS		72.72 139	72.16 146	74.38 114	76.90 182	55.95 169	73.34 151	84.67 54	62.04 113	72.19 222	70.81 317	65.90 121	85.24 53	58.64 167	84.96 204	81.95 173

PCF-MVS	63.80 13	72.70 140	71.69 150	75.72 99	78.10 163	60.01 146	73.04 152	81.50 105	45.34 275	79.66 116	84.35 177	65.15 127	82.65 101	48.70 243	89.38 143	84.50 111
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EI-MVSNet-UG-set		72.63 141	71.68 151	75.47 102	74.67 212	58.64 160	72.02 162	71.50 227	63.53 101	78.58 128	71.39 316	65.98 119	78.53 171	67.30 103	80.18 260	89.23 28

Anonymous20240529		72.56 142	73.79 117	68.86 202	76.89 183	45.21 250	68.80 212	77.25 179	67.16 62	76.89 155	90.44 54	65.95 120	74.19 233	50.75 227	90.00 131	87.18 58

FIs		72.56 142	73.80 116	68.84 203	78.74 158	37.74 308	71.02 181	79.83 138	56.12 166	80.88 105	89.45 82	58.18 186	78.28 182	56.63 179	93.36 68	90.51 19

v2v482		72.55 144	72.58 139	72.43 153	72.92 242	46.72 236	71.41 174	79.13 147	55.27 173	81.17 99	85.25 169	55.41 211	81.13 123	67.25 104	85.46 192	89.43 25

hse-mvs2		72.32 145	70.66 166	77.31 83	83.10 101	71.77 50	69.19 205	71.45 229	54.28 189	77.89 136	78.26 252	49.04 242	79.23 158	63.62 128	89.13 148	80.92 188

canonicalmvs		72.29 146	73.38 125	69.04 195	74.23 219	47.37 231	73.93 149	83.18 83	54.36 188	76.61 163	81.64 211	72.03 65	75.34 220	57.12 176	87.28 173	84.40 113

Effi-MVS+		72.10 147	72.28 143	71.58 160	74.21 223	50.33 201	74.72 141	82.73 90	62.62 109	70.77 240	76.83 265	69.96 84	80.97 130	60.20 153	78.43 277	83.45 135

MVS_111021_LR		72.10 147	71.82 149	72.95 140	79.53 144	73.90 39	70.45 189	66.64 261	56.87 157	76.81 159	81.76 209	68.78 91	71.76 256	61.81 139	83.74 222	73.18 268

pmmvs6		71.82 149	73.66 120	66.31 233	75.94 193	42.01 275	66.99 239	72.53 217	63.45 103	76.43 169	92.78 10	72.95 63	69.69 270	51.41 222	90.46 122	87.22 55

PLC	62.01 16	71.79 150	70.28 168	76.33 92	80.31 137	68.63 80	78.18 100	81.24 112	54.57 185	67.09 271	80.63 220	59.44 173	81.74 116	46.91 258	84.17 215	78.63 221
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VDDNet		71.60 151	73.13 131	67.02 226	86.29 51	41.11 280	69.97 192	66.50 262	68.72 55	74.74 187	91.70 24	59.90 169	75.81 214	48.58 245	91.72 89	84.15 120

3Dnovator	65.95 11	71.50 152	71.22 159	72.34 155	73.16 234	63.09 121	78.37 93	78.32 162	57.67 149	72.22 221	84.61 173	54.77 212	78.47 173	60.82 150	81.07 249	75.45 250

WR-MVS		71.20 153	72.48 140	67.36 221	84.98 73	35.70 322	64.43 270	68.66 253	65.05 86	81.49 95	86.43 146	57.57 197	76.48 209	50.36 231	93.32 69	89.90 21

V42		71.06 154	70.83 163	71.72 159	67.25 288	47.14 234	65.94 250	80.35 132	51.35 225	83.40 75	83.23 194	59.25 177	78.80 165	65.91 109	80.81 254	89.23 28

FMVSNet1		71.06 154	72.48 140	66.81 227	77.65 174	40.68 285	71.96 164	73.03 209	61.14 118	79.45 119	90.36 64	60.44 165	75.20 222	50.20 232	88.05 157	84.54 106

API-MVS		70.97 156	71.51 156	69.37 189	75.20 200	55.94 170	80.99 62	76.84 181	62.48 111	71.24 235	77.51 261	61.51 155	80.96 134	52.04 217	85.76 191	71.22 287

VDD-MVS		70.81 157	71.44 157	68.91 201	79.07 156	46.51 238	67.82 226	70.83 242	61.23 117	74.07 198	88.69 100	59.86 170	75.62 217	51.11 224	90.28 124	84.61 103

EG-PatchMatch MVS		70.70 158	70.88 162	70.16 178	82.64 110	58.80 157	71.48 172	73.64 207	54.98 176	76.55 164	81.77 208	61.10 161	78.94 163	54.87 198	80.84 253	72.74 273

Baseline_NR-MVSNet		70.62 159	73.19 129	62.92 262	76.97 179	34.44 330	68.84 208	70.88 241	60.25 125	79.50 118	90.53 51	61.82 151	69.11 274	54.67 200	95.27 14	85.22 82

alignmvs		70.54 160	71.00 161	69.15 194	73.50 228	48.04 222	69.85 195	79.62 139	53.94 198	76.54 165	82.00 204	59.00 180	74.68 228	57.32 175	87.21 175	84.72 98

MG-MVS		70.47 161	71.34 158	67.85 215	79.26 148	40.42 289	74.67 143	75.15 199	58.41 140	68.74 260	88.14 112	56.08 209	83.69 78	59.90 159	81.71 243	79.43 214

AUN-MVS		70.22 162	67.88 200	77.22 84	82.96 105	71.61 51	69.08 206	71.39 230	49.17 248	71.70 226	78.07 256	37.62 307	79.21 159	61.81 139	89.15 146	80.82 191

UGNet		70.20 163	69.05 179	73.65 124	76.24 188	63.64 116	75.87 125	72.53 217	61.48 116	60.93 307	86.14 154	52.37 224	77.12 200	50.67 228	85.21 198	80.17 206
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
PVSNet_Blended_VisFu		70.04 164	68.88 183	73.53 128	82.71 108	63.62 117	74.81 136	81.95 100	48.53 254	67.16 270	79.18 242	51.42 230	78.38 178	54.39 205	79.72 267	78.60 222

Fast-Effi-MVS+-dtu		70.00 165	68.74 187	73.77 123	73.47 229	64.53 110	71.36 175	78.14 166	55.81 170	68.84 259	74.71 282	65.36 126	75.75 215	52.00 218	79.00 271	81.03 184

DPM-MVS		69.98 166	69.22 178	72.26 157	82.69 109	58.82 156	70.53 187	81.23 113	47.79 261	64.16 286	80.21 225	51.32 231	83.12 93	60.14 156	84.95 205	74.83 256

MVSFormer		69.93 167	69.03 181	72.63 151	74.93 203	59.19 151	83.98 36	75.72 190	52.27 213	63.53 293	76.74 266	43.19 273	80.56 137	72.28 65	78.67 275	78.14 231

MVS_Test		69.84 168	70.71 164	67.24 222	67.49 287	43.25 266	69.87 194	81.22 114	52.69 210	71.57 231	86.68 135	62.09 149	74.51 230	66.05 107	78.74 273	83.96 122

cl_fuxian		69.82 169	69.89 170	69.61 187	66.24 296	43.48 262	68.12 223	79.61 141	51.43 224	77.72 141	80.18 228	54.61 215	78.15 187	63.62 128	87.50 166	87.20 57

TransMVSNet (Re)		69.62 170	71.63 152	63.57 252	76.51 185	35.93 320	65.75 255	71.29 233	61.05 119	75.02 182	89.90 76	65.88 122	70.41 268	49.79 234	89.48 141	84.38 114

EI-MVSNet		69.61 171	69.01 182	71.41 164	73.94 225	49.90 205	71.31 177	71.32 231	58.22 141	75.40 180	70.44 319	58.16 187	75.85 212	62.51 136	79.81 264	88.48 43

Gipuma		69.55 172	72.83 137	59.70 285	63.63 316	53.97 182	80.08 75	75.93 188	64.24 94	73.49 203	88.93 98	57.89 195	62.46 309	59.75 161	91.55 96	62.67 336
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tttt0517		69.46 173	67.79 201	74.46 110	75.34 198	52.72 191	75.05 132	63.27 281	54.69 182	78.87 125	84.37 176	26.63 349	81.15 122	63.95 123	87.93 161	89.51 24

eth_miper_zixun_eth		69.42 174	68.73 188	71.50 163	67.99 281	46.42 239	67.58 228	78.81 152	50.72 233	78.13 134	80.34 224	50.15 237	80.34 142	60.18 154	84.65 207	87.74 49

BH-untuned		69.39 175	69.46 173	69.18 193	77.96 167	56.88 166	68.47 220	77.53 174	56.77 159	77.79 140	79.63 234	60.30 167	80.20 148	46.04 262	80.65 255	70.47 292

v148		69.38 176	69.39 174	69.36 190	69.14 274	44.56 253	68.83 209	72.70 215	54.79 180	78.59 126	84.12 179	54.69 213	76.74 208	59.40 163	82.20 234	86.79 62

CS-MVS		69.29 177	69.70 172	68.07 213	70.59 259	42.36 274	69.70 198	84.56 60	53.13 204	67.96 263	76.74 266	59.41 174	83.56 81	60.33 152	84.84 206	78.28 228

1121		69.23 178	68.26 192	72.12 158	88.36 36	71.40 53	68.59 215	62.06 288	43.80 284	74.75 186	86.18 151	52.92 221	76.85 205	54.47 202	83.27 226	68.12 309

PAPR		69.20 179	68.66 189	70.82 167	75.15 202	47.77 225	75.31 130	81.11 115	49.62 245	66.33 273	79.27 239	61.53 154	82.96 96	48.12 249	81.50 246	81.74 176

QAPM		69.18 180	69.26 176	68.94 199	71.61 251	52.58 193	80.37 69	78.79 154	49.63 244	73.51 202	85.14 170	53.66 218	79.12 160	55.11 196	75.54 293	75.11 254

CS-MVS-test		69.16 181	69.05 179	69.48 188	72.07 248	46.22 242	69.71 197	84.69 53	52.43 212	65.28 279	74.51 284	59.18 178	83.41 86	59.11 165	84.48 212	78.80 220

LCM-MVSNet-Re		69.10 182	71.57 155	61.70 269	70.37 264	34.30 332	61.45 293	79.62 139	56.81 158	89.59 7	88.16 111	68.44 96	72.94 239	42.30 278	87.33 171	77.85 236

EPNet		69.10 182	67.32 206	74.46 110	68.33 278	61.27 132	77.56 104	63.57 279	60.95 120	56.62 323	82.75 197	51.53 229	81.24 121	54.36 206	90.20 125	80.88 190
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		68.83 184	68.31 190	70.38 173	70.55 263	48.31 216	63.78 277	82.13 96	54.00 195	68.96 256	75.17 277	58.95 181	80.06 151	58.55 168	82.74 230	82.76 155
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
Fast-Effi-MVS+		68.81 185	68.30 191	70.35 174	74.66 214	48.61 214	66.06 249	78.32 162	50.62 234	71.48 234	75.54 273	68.75 92	79.59 156	50.55 230	78.73 274	82.86 152

OpenMVS	62.51 15	68.76 186	68.75 186	68.78 204	70.56 262	53.91 183	78.29 95	77.35 176	48.85 251	70.22 246	83.52 185	52.65 223	76.93 202	55.31 195	81.99 236	75.49 249

VPA-MVSNet		68.71 187	70.37 167	63.72 251	76.13 190	38.06 306	64.10 272	71.48 228	56.60 164	74.10 197	88.31 106	64.78 132	69.72 269	47.69 253	90.15 128	83.37 137

BH-RMVSNet		68.69 188	68.20 196	70.14 179	76.40 186	53.90 184	64.62 267	73.48 208	58.01 143	73.91 200	81.78 207	59.09 179	78.22 183	48.59 244	77.96 282	78.31 226

EIA-MVS		68.59 189	67.16 208	72.90 142	75.18 201	55.64 173	69.39 201	81.29 110	52.44 211	64.53 282	70.69 318	60.33 166	82.30 107	54.27 207	76.31 288	80.75 195

pm-mvs1		68.40 190	69.85 171	64.04 249	73.10 238	39.94 291	64.61 268	70.50 243	55.52 172	73.97 199	89.33 83	63.91 136	68.38 278	49.68 236	88.02 158	83.81 126

miper_ehance_all_eth		68.36 191	68.16 197	68.98 197	65.14 307	43.34 264	67.07 238	78.92 151	49.11 249	76.21 172	77.72 258	53.48 219	77.92 190	61.16 146	84.59 209	85.68 80

GBi-Net		68.30 192	68.79 184	66.81 227	73.14 235	40.68 285	71.96 164	73.03 209	54.81 177	74.72 188	90.36 64	48.63 248	75.20 222	47.12 255	85.37 193	84.54 106

test1		68.30 192	68.79 184	66.81 227	73.14 235	40.68 285	71.96 164	73.03 209	54.81 177	74.72 188	90.36 64	48.63 248	75.20 222	47.12 255	85.37 193	84.54 106

cl-mvsnet1		68.27 194	68.26 192	68.29 209	64.98 308	43.67 260	65.89 251	74.67 201	50.04 240	76.86 157	82.43 200	48.74 246	75.38 218	60.94 148	89.81 135	85.81 74

cl-mvsnet____		68.26 195	68.26 192	68.29 209	64.98 308	43.67 260	65.89 251	74.67 201	50.04 240	76.86 157	82.42 201	48.74 246	75.38 218	60.92 149	89.81 135	85.80 78

TinyColmap		67.98 196	69.28 175	64.08 247	67.98 282	46.82 235	70.04 191	75.26 197	53.05 205	77.36 146	86.79 128	59.39 175	72.59 246	45.64 264	88.01 159	72.83 271

xiu_mvs_v1_base_debu		67.87 197	67.07 209	70.26 175	79.13 153	61.90 127	67.34 232	71.25 234	47.98 257	67.70 265	74.19 292	61.31 156	72.62 243	56.51 180	78.26 279	76.27 245

xiu_mvs_v1_base		67.87 197	67.07 209	70.26 175	79.13 153	61.90 127	67.34 232	71.25 234	47.98 257	67.70 265	74.19 292	61.31 156	72.62 243	56.51 180	78.26 279	76.27 245

xiu_mvs_v1_base_debi		67.87 197	67.07 209	70.26 175	79.13 153	61.90 127	67.34 232	71.25 234	47.98 257	67.70 265	74.19 292	61.31 156	72.62 243	56.51 180	78.26 279	76.27 245

MAR-MVS		67.72 200	66.16 215	72.40 154	74.45 217	64.99 107	74.87 134	77.50 175	48.67 252	65.78 277	68.58 337	57.01 203	77.79 192	46.68 260	81.92 237	74.42 259
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
IterMVS-SCA-FT		67.68 201	66.07 216	72.49 152	73.34 232	58.20 162	63.80 276	65.55 267	48.10 256	76.91 154	82.64 198	45.20 260	78.84 164	61.20 145	77.89 283	80.44 202

LF4IMVS		67.50 202	67.31 207	68.08 212	58.86 340	61.93 126	71.43 173	75.90 189	44.67 280	72.42 218	80.20 226	57.16 198	70.44 266	58.99 166	86.12 187	71.88 281

FMVSNet2		67.48 203	68.21 195	65.29 238	73.14 235	38.94 298	68.81 210	71.21 237	54.81 177	76.73 161	86.48 144	48.63 248	74.60 229	47.98 250	86.11 188	82.35 166

MSDG		67.47 204	67.48 205	67.46 220	70.70 258	54.69 178	66.90 241	78.17 165	60.88 121	70.41 243	74.76 279	61.22 160	73.18 237	47.38 254	76.87 285	74.49 258

diffmvs		67.42 205	67.50 204	67.20 223	62.26 321	45.21 250	64.87 264	77.04 180	48.21 255	71.74 225	79.70 233	58.40 185	71.17 260	64.99 115	80.27 259	85.22 82

cl-mvsnet2		67.14 206	66.51 213	69.03 196	63.20 317	43.46 263	66.88 242	76.25 185	49.22 247	74.48 192	77.88 257	45.49 259	77.40 197	60.64 151	84.59 209	86.24 66

ANet_high		67.08 207	69.94 169	58.51 293	57.55 345	27.09 354	58.43 311	76.80 182	63.56 100	82.40 86	91.93 20	59.82 171	64.98 300	50.10 233	88.86 151	83.46 134

LFMVS		67.06 208	67.89 199	64.56 243	78.02 165	38.25 303	70.81 186	59.60 296	65.18 84	71.06 238	86.56 142	43.85 269	75.22 221	46.35 261	89.63 138	80.21 205

thisisatest0530		67.05 209	65.16 220	72.73 148	73.10 238	50.55 200	71.26 179	63.91 277	50.22 237	74.46 193	80.75 218	26.81 348	80.25 145	59.43 162	86.50 184	87.37 54

bset_n11_16_dypcd		66.91 210	65.84 218	70.12 180	72.95 241	53.54 185	63.64 278	68.65 254	48.54 253	72.54 216	74.28 289	40.58 290	78.54 170	63.52 131	87.82 162	78.29 227

MIMVSNet1		66.57 211	69.23 177	58.59 292	81.26 129	37.73 309	64.06 273	57.62 302	57.02 155	78.40 131	90.75 44	62.65 140	58.10 322	41.77 283	89.58 140	79.95 207

tfpnnormal		66.48 212	67.93 198	62.16 267	73.40 231	36.65 313	63.45 280	64.99 270	55.97 167	72.82 212	87.80 115	57.06 202	69.10 275	48.31 248	87.54 164	80.72 197

DIV-MVS_2432*1600		66.38 213	67.51 203	62.97 261	61.76 323	34.39 331	58.11 313	75.30 196	50.84 232	77.12 149	85.42 166	56.84 204	69.44 271	51.07 225	91.16 104	85.08 88

Anonymous202405211		66.02 214	66.89 212	63.43 255	74.22 220	38.14 304	59.00 308	66.13 263	63.33 105	69.76 250	85.95 161	51.88 225	70.50 265	44.23 270	87.52 165	81.64 177

miper_enhance_ethall		65.86 215	65.05 228	68.28 211	61.62 325	42.62 271	64.74 265	77.97 168	42.52 294	73.42 205	72.79 302	49.66 238	77.68 194	58.12 171	84.59 209	84.54 106

RRT_test8_iter05		65.80 216	65.13 223	67.80 218	67.02 291	40.85 284	67.13 237	75.33 195	49.73 242	72.69 213	81.32 212	24.45 359	77.37 198	61.69 142	86.82 181	85.18 84

RPMNet		65.77 217	65.08 227	67.84 216	66.37 293	48.24 218	70.93 183	86.27 21	54.66 183	61.35 301	86.77 131	33.29 317	85.67 43	55.93 188	70.17 323	69.62 301

VPNet		65.58 218	67.56 202	59.65 286	79.72 141	30.17 348	60.27 302	62.14 285	54.19 192	71.24 235	86.63 139	58.80 182	67.62 283	44.17 271	90.87 117	81.18 181

PVSNet_BlendedMVS		65.38 219	64.30 229	68.61 205	69.81 267	49.36 209	65.60 258	78.96 149	45.50 271	59.98 310	78.61 248	51.82 226	78.20 184	44.30 268	84.11 216	78.27 229

TAMVS		65.31 220	63.75 233	69.97 184	82.23 115	59.76 149	66.78 243	63.37 280	45.20 276	69.79 249	79.37 238	47.42 254	72.17 249	34.48 326	85.15 200	77.99 235

test_yl		65.11 221	65.09 225	65.18 239	70.59 259	40.86 282	63.22 285	72.79 212	57.91 144	68.88 257	79.07 245	42.85 276	74.89 226	45.50 265	84.97 201	79.81 208

DCV-MVSNet		65.11 221	65.09 225	65.18 239	70.59 259	40.86 282	63.22 285	72.79 212	57.91 144	68.88 257	79.07 245	42.85 276	74.89 226	45.50 265	84.97 201	79.81 208

mvs_anonymous		65.08 223	65.49 219	63.83 250	63.79 314	37.60 310	66.52 246	69.82 247	43.44 289	73.46 204	86.08 157	58.79 183	71.75 257	51.90 219	75.63 292	82.15 170

FMVSNet3		65.00 224	65.16 220	64.52 244	69.47 270	37.56 311	66.63 244	70.38 244	51.55 223	74.72 188	83.27 193	37.89 306	74.44 231	47.12 255	85.37 193	81.57 178

BH-w/o		64.81 225	64.29 230	66.36 232	76.08 192	54.71 177	65.61 257	75.23 198	50.10 239	71.05 239	71.86 312	54.33 216	79.02 161	38.20 304	76.14 289	65.36 324

cascas		64.59 226	62.77 244	70.05 182	75.27 199	50.02 204	61.79 292	71.61 224	42.46 295	63.68 291	68.89 334	49.33 241	80.35 141	47.82 252	84.05 217	79.78 210

TR-MVS		64.59 226	63.54 236	67.73 219	75.75 196	50.83 199	63.39 281	70.29 245	49.33 246	71.55 232	74.55 283	50.94 232	78.46 174	40.43 290	75.69 291	73.89 263

PM-MVS		64.49 228	63.61 235	67.14 225	76.68 184	75.15 31	68.49 219	42.85 349	51.17 229	77.85 138	80.51 221	45.76 255	66.31 295	52.83 216	76.35 287	59.96 342

jason		64.47 229	62.84 243	69.34 192	76.91 181	59.20 150	67.15 236	65.67 264	35.29 329	65.16 280	76.74 266	44.67 264	70.68 262	54.74 199	79.28 270	78.14 231
jason: jason.
xiu_mvs_v2_base		64.43 230	63.96 231	65.85 237	77.72 172	51.32 197	63.63 279	72.31 220	45.06 279	61.70 298	69.66 327	62.56 141	73.93 235	49.06 240	73.91 305	72.31 277

pmmvs-eth3d		64.41 231	63.27 239	67.82 217	75.81 195	60.18 144	69.49 199	62.05 289	38.81 314	74.13 196	82.23 203	43.76 270	68.65 276	42.53 277	80.63 257	74.63 257

CDS-MVSNet		64.33 232	62.66 245	69.35 191	80.44 136	58.28 161	65.26 260	65.66 265	44.36 281	67.30 269	75.54 273	43.27 272	71.77 255	37.68 307	84.44 213	78.01 234
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		64.27 233	63.73 234	65.90 236	77.82 170	51.42 196	63.33 282	72.33 219	45.09 278	61.60 299	68.04 338	62.39 145	73.95 234	49.07 239	73.87 306	72.34 276

ab-mvs		64.11 234	65.13 223	61.05 276	71.99 249	38.03 307	67.59 227	68.79 252	49.08 250	65.32 278	86.26 149	58.02 194	66.85 290	39.33 294	79.79 266	78.27 229

CANet_DTU		64.04 235	63.83 232	64.66 242	68.39 275	42.97 268	73.45 150	74.50 204	52.05 217	54.78 330	75.44 276	43.99 268	70.42 267	53.49 213	78.41 278	80.59 199

VNet		64.01 236	65.15 222	60.57 280	73.28 233	35.61 323	57.60 315	67.08 259	54.61 184	66.76 272	83.37 189	56.28 207	66.87 288	42.19 279	85.20 199	79.23 216

Anonymous20240521		63.55 237	66.07 216	55.99 300	66.18 298	44.04 257	68.77 213	68.80 251	46.99 266	72.57 214	85.84 162	39.87 294	50.22 331	53.40 215	92.23 86	73.71 265

lupinMVS		63.36 238	61.49 253	68.97 198	74.93 203	59.19 151	65.80 254	64.52 275	34.68 334	63.53 293	74.25 290	43.19 273	70.62 263	53.88 210	78.67 275	77.10 240

ET-MVSNet_ETH3D		63.32 239	60.69 259	71.20 166	70.15 266	55.66 172	65.02 263	64.32 276	43.28 293	68.99 255	72.05 311	25.46 355	78.19 186	54.16 208	82.80 229	79.74 211

MVSTER		63.29 240	61.60 252	68.36 207	59.77 337	46.21 243	60.62 299	71.32 231	41.83 297	75.40 180	79.12 243	30.25 341	75.85 212	56.30 185	79.81 264	83.03 147

OpenMVS_ROB	54.93 17	63.23 241	63.28 238	63.07 259	69.81 267	45.34 249	68.52 218	67.14 258	43.74 286	70.61 242	79.22 240	47.90 252	72.66 242	48.75 242	73.84 307	71.21 288

IterMVS		63.12 242	62.48 246	65.02 241	66.34 295	52.86 190	63.81 275	62.25 284	46.57 268	71.51 233	80.40 223	44.60 265	66.82 291	51.38 223	75.47 294	75.38 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HyFIR lowres test		63.01 243	60.47 260	70.61 169	83.04 102	54.10 181	59.93 304	72.24 221	33.67 339	69.00 254	75.63 272	38.69 300	76.93 202	36.60 315	75.45 295	80.81 194

GA-MVS		62.91 244	61.66 249	66.66 231	67.09 290	44.49 254	61.18 297	69.36 250	51.33 226	69.33 252	74.47 285	36.83 308	74.94 225	50.60 229	74.72 300	80.57 200

PVSNet_Blended		62.90 245	61.64 250	66.69 230	69.81 267	49.36 209	61.23 296	78.96 149	42.04 296	59.98 310	68.86 335	51.82 226	78.20 184	44.30 268	77.77 284	72.52 274

USDC		62.80 246	63.10 241	61.89 268	65.19 304	43.30 265	67.42 231	74.20 205	35.80 328	72.25 220	84.48 175	45.67 257	71.95 254	37.95 306	84.97 201	70.42 294

Vis-MVSNet (Re-imp)		62.74 247	63.21 240	61.34 274	72.19 246	31.56 343	67.31 235	53.87 319	53.60 201	69.88 248	83.37 189	40.52 291	70.98 261	41.40 284	86.78 182	81.48 179

D2MVS		62.58 248	61.05 256	67.20 223	63.85 313	47.92 223	56.29 318	69.58 248	39.32 309	70.07 247	78.19 253	34.93 313	72.68 241	53.44 214	83.74 222	81.00 186

MVS_0304		62.51 249	62.27 247	63.25 256	69.39 271	48.47 215	64.05 274	62.48 283	59.69 131	54.10 335	81.04 216	45.71 256	66.31 295	41.38 285	82.58 232	74.96 255

CL-MVSNet_2432*1600		62.44 250	63.40 237	59.55 287	72.34 245	32.38 339	56.39 317	64.84 271	51.21 228	67.46 268	81.01 217	50.75 233	63.51 307	38.47 302	88.12 156	82.75 156

MDA-MVSNet-bldmvs		62.34 251	61.73 248	64.16 245	61.64 324	49.90 205	48.11 339	57.24 308	53.31 203	80.95 101	79.39 237	49.00 244	61.55 313	45.92 263	80.05 261	81.03 184

miper_lstm_enhance		61.97 252	61.63 251	62.98 260	60.04 333	45.74 247	47.53 341	70.95 239	44.04 282	73.06 209	78.84 247	39.72 295	60.33 315	55.82 190	84.64 208	82.88 150

wuyk23d		61.97 252	66.25 214	49.12 320	58.19 344	60.77 140	66.32 247	52.97 326	55.93 169	90.62 4	86.91 124	73.07 61	35.98 359	20.63 361	91.63 93	50.62 350

thres600view7		61.82 254	61.38 254	63.12 258	71.81 250	34.93 327	64.64 266	56.99 309	54.78 181	70.33 245	79.74 232	32.07 327	72.42 248	38.61 300	83.46 224	82.02 171

PAPM		61.79 255	60.37 261	66.05 234	76.09 191	41.87 276	69.30 202	76.79 183	40.64 306	53.80 336	79.62 235	44.38 266	82.92 97	29.64 344	73.11 309	73.36 267

MVP-Stereo		61.56 256	59.22 267	68.58 206	79.28 147	60.44 142	69.20 204	71.57 225	43.58 288	56.42 324	78.37 251	39.57 297	76.46 210	34.86 325	60.16 347	68.86 308
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CMPMVS	48.73 20	61.54 257	60.89 257	63.52 253	61.08 328	51.55 195	68.07 224	68.00 257	33.88 336	65.87 275	81.25 214	37.91 305	67.71 281	49.32 238	82.60 231	71.31 286
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
thres100view900		61.17 258	61.09 255	61.39 273	72.14 247	35.01 326	65.42 259	56.99 309	55.23 174	70.71 241	79.90 230	32.07 327	72.09 250	35.61 322	81.73 240	77.08 241

Patchmtry		60.91 259	63.01 242	54.62 303	66.10 299	26.27 357	67.47 230	56.40 312	54.05 194	72.04 224	86.66 136	33.19 318	60.17 316	43.69 272	87.45 168	77.42 237

EU-MVSNet		60.82 260	60.80 258	60.86 279	68.37 276	41.16 279	72.27 158	68.27 256	26.96 357	69.08 253	75.71 271	32.09 326	67.44 284	55.59 193	78.90 272	73.97 261

pmmvs4		60.78 261	59.04 269	66.00 235	73.06 240	57.67 163	64.53 269	60.22 294	36.91 323	65.96 274	77.27 262	39.66 296	68.54 277	38.87 297	74.89 299	71.80 282

thres400		60.77 262	59.97 263	63.15 257	70.78 256	35.35 324	63.27 283	57.47 303	53.00 206	68.31 261	77.09 263	32.45 324	72.09 250	35.61 322	81.73 240	82.02 171

MVS		60.62 263	59.97 263	62.58 264	68.13 280	47.28 232	68.59 215	73.96 206	32.19 343	59.94 312	68.86 335	50.48 234	77.64 195	41.85 282	75.74 290	62.83 334

thisisatest0515		60.48 264	57.86 278	68.34 208	67.25 288	46.42 239	60.58 300	62.14 285	40.82 304	63.58 292	69.12 330	26.28 351	78.34 180	48.83 241	82.13 235	80.26 204

tfpn200view9		60.35 265	59.97 263	61.51 271	70.78 256	35.35 324	63.27 283	57.47 303	53.00 206	68.31 261	77.09 263	32.45 324	72.09 250	35.61 322	81.73 240	77.08 241

ppachtmachnet_test		60.26 266	59.61 266	62.20 266	67.70 285	44.33 255	58.18 312	60.96 293	40.75 305	65.80 276	72.57 303	41.23 283	63.92 304	46.87 259	82.42 233	78.33 225

Patchmatch-RL test		59.95 267	59.12 268	62.44 265	72.46 244	54.61 179	59.63 305	47.51 342	41.05 303	74.58 191	74.30 288	31.06 335	65.31 297	51.61 220	79.85 263	67.39 312

1314		59.83 268	58.86 271	62.74 263	65.71 301	44.78 252	68.59 215	72.63 216	33.54 341	61.05 305	67.29 343	43.62 271	71.26 259	49.49 237	67.84 334	72.19 279

IB-MVS	49.67 18	59.69 269	56.96 284	67.90 214	68.19 279	50.30 202	61.42 294	65.18 269	47.57 263	55.83 327	67.15 344	23.77 360	79.60 155	43.56 274	79.97 262	73.79 264
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
1112_ss		59.48 270	58.99 270	60.96 278	77.84 169	42.39 273	61.42 294	68.45 255	37.96 318	59.93 313	67.46 340	45.11 262	65.07 299	40.89 288	71.81 314	75.41 251

FPMVS		59.43 271	60.07 262	57.51 296	77.62 175	71.52 52	62.33 289	50.92 331	57.40 153	69.40 251	80.00 229	39.14 298	61.92 312	37.47 310	66.36 336	39.09 359

CVMVSNet		59.21 272	58.44 275	61.51 271	73.94 225	47.76 226	71.31 177	64.56 274	26.91 358	60.34 309	70.44 319	36.24 310	67.65 282	53.57 212	68.66 331	69.12 306

CR-MVSNet		58.96 273	58.49 274	60.36 282	66.37 293	48.24 218	70.93 183	56.40 312	32.87 342	61.35 301	86.66 136	33.19 318	63.22 308	48.50 246	70.17 323	69.62 301

EPNet_dtu		58.93 274	58.52 273	60.16 284	67.91 283	47.70 227	69.97 192	58.02 300	49.73 242	47.28 351	73.02 301	38.14 302	62.34 310	36.57 316	85.99 189	70.43 293
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Test_1112_low_res		58.78 275	58.69 272	59.04 290	79.41 145	38.13 305	57.62 314	66.98 260	34.74 332	59.62 314	77.56 260	42.92 275	63.65 306	38.66 299	70.73 320	75.35 253

PatchMatch-RL		58.68 276	57.72 279	61.57 270	76.21 189	73.59 42	61.83 291	49.00 338	47.30 265	61.08 303	68.97 332	50.16 236	59.01 319	36.06 321	68.84 330	52.10 349

SCA		58.57 277	58.04 277	60.17 283	70.17 265	41.07 281	65.19 261	53.38 324	43.34 292	61.00 306	73.48 296	45.20 260	69.38 272	40.34 291	70.31 322	70.05 296

CHOSEN 1792x2688		58.09 278	56.30 289	63.45 254	79.95 139	50.93 198	54.07 326	65.59 266	28.56 354	61.53 300	74.33 287	41.09 286	66.52 294	33.91 329	67.69 335	72.92 270

HY-MVS	49.31 19	57.96 279	57.59 280	59.10 289	66.85 292	36.17 317	65.13 262	65.39 268	39.24 311	54.69 332	78.14 254	44.28 267	67.18 287	33.75 330	70.79 319	73.95 262

baseline1		57.82 280	58.36 276	56.19 299	69.17 273	30.76 347	62.94 287	55.21 315	46.04 270	63.83 289	78.47 249	41.20 284	63.68 305	39.44 293	68.99 329	74.13 260

thres200		57.55 281	57.02 283	59.17 288	67.89 284	34.93 327	58.91 310	57.25 307	50.24 236	64.01 287	71.46 315	32.49 323	71.39 258	31.31 336	79.57 268	71.19 289

CostFormer		57.35 282	56.14 290	60.97 277	63.76 315	38.43 300	67.50 229	60.22 294	37.14 322	59.12 315	76.34 269	32.78 321	71.99 253	39.12 296	69.27 328	72.47 275

our_test_3		56.46 283	56.51 287	56.30 298	67.70 285	39.66 293	55.36 323	52.34 329	40.57 307	63.85 288	69.91 326	40.04 293	58.22 321	43.49 275	75.29 298	71.03 291

tpm2		56.12 284	54.64 297	60.55 281	66.24 296	36.01 318	68.14 222	56.77 311	33.60 340	58.25 318	75.52 275	30.25 341	74.33 232	33.27 331	69.76 327	71.32 285

tpmvs		55.84 285	55.45 295	57.01 297	60.33 332	33.20 337	65.89 251	59.29 298	47.52 264	56.04 325	73.60 295	31.05 336	68.06 280	40.64 289	64.64 339	69.77 299

gg-mvs-nofinetune		55.75 286	56.75 286	52.72 309	62.87 318	28.04 353	68.92 207	41.36 357	71.09 41	50.80 343	92.63 12	20.74 363	66.86 289	29.97 342	72.41 311	63.25 333

test20.03		55.74 287	57.51 281	50.42 313	59.89 336	32.09 341	50.63 333	49.01 337	50.11 238	65.07 281	83.23 194	45.61 258	48.11 335	30.22 340	83.82 221	71.07 290

MS-PatchMatch		55.59 288	54.89 296	57.68 295	69.18 272	49.05 211	61.00 298	62.93 282	35.98 326	58.36 317	68.93 333	36.71 309	66.59 293	37.62 309	63.30 343	57.39 345

baseline2		55.57 289	52.74 303	64.05 248	65.26 303	44.11 256	62.38 288	54.43 318	39.03 312	51.21 341	67.35 342	33.66 316	72.45 247	37.14 312	64.22 341	75.60 248

XXY-MVS		55.19 290	57.40 282	48.56 322	64.45 311	34.84 329	51.54 332	53.59 321	38.99 313	63.79 290	79.43 236	56.59 205	45.57 339	36.92 314	71.29 316	65.25 325

FMVSNet5		55.08 291	55.54 294	53.71 304	65.80 300	33.50 336	56.22 319	52.50 328	43.72 287	61.06 304	83.38 188	25.46 355	54.87 325	30.11 341	81.64 245	72.75 272

PatchmatchNet		54.60 292	54.27 298	55.59 301	65.17 306	39.08 295	66.92 240	51.80 330	39.89 308	58.39 316	73.12 300	31.69 329	58.33 320	43.01 276	58.38 353	69.38 304
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MIMVSNet		54.39 293	56.12 291	49.20 318	72.57 243	30.91 346	59.98 303	48.43 340	41.66 298	55.94 326	83.86 183	41.19 285	50.42 330	26.05 351	75.38 296	66.27 320

Anonymous20231206		54.13 294	55.82 292	49.04 321	70.89 255	35.96 319	51.73 331	50.87 332	34.86 330	62.49 296	79.22 240	42.52 279	44.29 347	27.95 349	81.88 238	66.88 316

JIA-IIPM		54.03 295	51.62 307	61.25 275	59.14 339	55.21 175	59.10 307	47.72 341	50.85 231	50.31 347	85.81 163	20.10 365	63.97 303	36.16 320	55.41 358	64.55 331

tpm cat1		54.02 296	52.63 304	58.19 294	64.85 310	39.86 292	66.26 248	57.28 306	32.16 344	56.90 321	70.39 321	32.75 322	65.30 298	34.29 327	58.79 350	69.41 303

testgi		54.00 297	56.86 285	45.45 330	58.20 343	25.81 358	49.05 335	49.50 336	45.43 274	67.84 264	81.17 215	51.81 228	43.20 351	29.30 345	79.41 269	67.34 314

PatchT		53.35 298	56.47 288	43.99 336	64.19 312	17.46 365	59.15 306	43.10 348	52.11 216	54.74 331	86.95 123	29.97 344	49.98 332	43.62 273	74.40 303	64.53 332

DWT-MVSNet_test		53.04 299	51.12 312	58.77 291	61.23 326	38.67 299	62.16 290	57.74 301	38.24 315	51.76 340	59.07 354	21.36 362	67.40 285	44.80 267	63.76 342	70.25 295

new-patchmatchnet		52.89 300	55.76 293	44.26 335	59.94 335	6.31 368	37.36 358	50.76 333	41.10 301	64.28 285	79.82 231	44.77 263	48.43 334	36.24 319	87.61 163	78.03 233

YYNet1		52.58 301	53.50 300	49.85 314	54.15 359	36.45 316	40.53 352	46.55 344	38.09 317	75.52 178	73.31 299	41.08 287	43.88 348	41.10 286	71.14 318	69.21 305

MDA-MVSNet_test_wron		52.57 302	53.49 301	49.81 315	54.24 358	36.47 315	40.48 353	46.58 343	38.13 316	75.47 179	73.32 298	41.05 288	43.85 349	40.98 287	71.20 317	69.10 307

pmmvs5		52.49 303	52.58 305	52.21 311	54.99 356	32.38 339	55.45 322	53.84 320	32.15 345	55.49 329	74.81 278	38.08 303	57.37 323	34.02 328	74.40 303	66.88 316

UnsupCasMVSNet_eth		52.26 304	53.29 302	49.16 319	55.08 355	33.67 335	50.03 334	58.79 299	37.67 319	63.43 295	74.75 280	41.82 281	45.83 338	38.59 301	59.42 349	67.98 311

N_pmnet		52.06 305	51.11 313	54.92 302	59.64 338	71.03 57	37.42 357	61.62 292	33.68 338	57.12 319	72.10 304	37.94 304	31.03 361	29.13 348	71.35 315	62.70 335

KD-MVS_2432*1600		52.05 306	51.58 308	53.44 305	52.11 361	31.20 344	44.88 348	64.83 272	41.53 299	64.37 283	70.03 324	15.61 371	64.20 301	36.25 317	74.61 301	64.93 328

miper_refine_blended		52.05 306	51.58 308	53.44 305	52.11 361	31.20 344	44.88 348	64.83 272	41.53 299	64.37 283	70.03 324	15.61 371	64.20 301	36.25 317	74.61 301	64.93 328

PVSNet	43.83 21	51.56 308	51.17 311	52.73 308	68.34 277	38.27 302	48.22 338	53.56 322	36.41 324	54.29 333	64.94 347	34.60 314	54.20 328	30.34 339	69.87 325	65.71 323

tpm		50.60 309	52.42 306	45.14 332	65.18 305	26.29 356	60.30 301	43.50 347	37.41 320	57.01 320	79.09 244	30.20 343	42.32 352	32.77 333	66.36 336	66.81 318

test-LLR		50.43 310	50.69 315	49.64 316	60.76 329	41.87 276	53.18 328	45.48 345	43.41 290	49.41 348	60.47 352	29.22 346	44.73 345	42.09 280	72.14 312	62.33 338

tpmrst		50.15 311	51.38 310	46.45 327	56.05 350	24.77 359	64.40 271	49.98 334	36.14 325	53.32 337	69.59 328	35.16 312	48.69 333	39.24 295	58.51 352	65.89 321

UnsupCasMVSNet_bld		50.01 312	51.03 314	46.95 323	58.61 341	32.64 338	48.31 337	53.27 325	34.27 335	60.47 308	71.53 314	41.40 282	47.07 336	30.68 338	60.78 346	61.13 340

WTY-MVS		49.39 313	50.31 316	46.62 326	61.22 327	32.00 342	46.61 344	49.77 335	33.87 337	54.12 334	69.55 329	41.96 280	45.40 341	31.28 337	64.42 340	62.47 337

ADS-MVSNet2		48.76 314	47.25 322	53.29 307	55.90 352	40.54 288	47.34 342	54.99 317	31.41 350	50.48 344	72.06 309	31.23 332	54.26 327	25.93 352	55.93 355	65.07 326

test-mter		48.56 315	48.20 320	49.64 316	60.76 329	41.87 276	53.18 328	45.48 345	31.91 348	49.41 348	60.47 352	18.34 366	44.73 345	42.09 280	72.14 312	62.33 338

Patchmatch-test		47.93 316	49.96 317	41.84 339	57.42 346	24.26 360	48.75 336	41.49 356	39.30 310	56.79 322	73.48 296	30.48 340	33.87 360	29.29 346	72.61 310	67.39 312

test0.0.03 1		47.72 317	48.31 319	45.93 328	55.53 354	29.39 349	46.40 345	41.21 358	43.41 290	55.81 328	67.65 339	29.22 346	43.77 350	25.73 354	69.87 325	64.62 330

sss		47.59 318	48.32 318	45.40 331	56.73 349	33.96 333	45.17 347	48.51 339	32.11 347	52.37 339	65.79 345	40.39 292	41.91 355	31.85 334	61.97 344	60.35 341

pmmvs3		46.71 319	45.09 326	51.55 312	56.76 348	48.25 217	55.78 321	39.53 361	24.13 361	50.35 346	63.40 348	15.90 370	51.08 329	29.29 346	70.69 321	55.33 348

EPMVS		45.74 320	46.53 323	43.39 337	54.14 360	22.33 362	55.02 324	35.00 364	34.69 333	51.09 342	70.20 323	25.92 353	42.04 354	37.19 311	55.50 357	65.78 322

MVS-HIRNet		45.53 321	47.29 321	40.24 342	62.29 320	26.82 355	56.02 320	37.41 362	29.74 353	43.69 360	81.27 213	33.96 315	55.48 324	24.46 357	56.79 354	38.43 360

TESTMET0.1,1		45.17 322	44.93 327	45.89 329	56.02 351	38.31 301	53.18 328	41.94 355	27.85 355	44.86 356	56.47 355	17.93 367	41.50 356	38.08 305	68.06 332	57.85 344

E-PMN		45.17 322	45.36 325	44.60 334	50.07 363	42.75 269	38.66 355	42.29 353	46.39 269	39.55 361	51.15 359	26.00 352	45.37 342	37.68 307	76.41 286	45.69 356

PMMVS		44.69 324	43.95 331	46.92 324	50.05 364	53.47 187	48.08 340	42.40 351	22.36 362	44.01 359	53.05 357	42.60 278	45.49 340	31.69 335	61.36 345	41.79 357

ADS-MVSNet		44.62 325	45.58 324	41.73 340	55.90 352	20.83 363	47.34 342	39.94 360	31.41 350	50.48 344	72.06 309	31.23 332	39.31 357	25.93 352	55.93 355	65.07 326

EMVS		44.61 326	44.45 330	45.10 333	48.91 365	43.00 267	37.92 356	41.10 359	46.75 267	38.00 363	48.43 361	26.42 350	46.27 337	37.11 313	75.38 296	46.03 355

dp		44.09 327	44.88 328	41.72 341	58.53 342	23.18 361	54.70 325	42.38 352	34.80 331	44.25 358	65.61 346	24.48 358	44.80 344	29.77 343	49.42 360	57.18 346

DSMNet-mixed		43.18 328	44.66 329	38.75 344	54.75 357	28.88 352	57.06 316	27.42 367	13.47 363	47.27 352	77.67 259	38.83 299	39.29 358	25.32 356	60.12 348	48.08 352

CHOSEN 280x420		41.62 329	39.89 334	46.80 325	61.81 322	51.59 194	33.56 359	35.74 363	27.48 356	37.64 364	53.53 356	23.24 361	42.09 353	27.39 350	58.64 351	46.72 354

PVSNet_0	36.71 22	41.12 330	40.78 333	42.14 338	59.97 334	40.13 290	40.97 351	42.24 354	30.81 352	44.86 356	49.41 360	40.70 289	45.12 343	23.15 358	34.96 362	41.16 358

PMMVS2		37.74 331	40.87 332	28.36 346	42.41 367	5.35 369	24.61 360	27.75 366	32.15 345	47.85 350	70.27 322	35.85 311	29.51 362	19.08 362	67.85 333	50.22 351

new_pmnet		37.55 332	39.80 335	30.79 345	56.83 347	16.46 366	39.35 354	30.65 365	25.59 359	45.26 354	61.60 351	24.54 357	28.02 363	21.60 359	52.80 359	47.90 353

MVE	27.91 23	36.69 333	35.64 336	39.84 343	43.37 366	35.85 321	19.49 361	24.61 368	24.68 360	39.05 362	62.63 350	38.67 301	27.10 364	21.04 360	47.25 361	56.56 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.26 334	19.12 338	19.71 347	9.09 369	1.91 371	7.79 363	53.44 323	1.42 365	10.27 367	35.80 362	17.42 368	25.11 365	12.44 363	24.38 364	32.10 361

cdsmvs_eth3d_5k		17.71 335	23.62 337	0.00 352	0.00 373	0.00 374	0.00 364	70.17 246	0.00 369	0.00 370	74.25 290	68.16 99	0.00 370	0.00 368	0.00 368	0.00 366

tmp_tt		11.98 336	14.73 339	3.72 349	2.28 370	4.62 370	19.44 362	14.50 370	0.47 366	21.55 365	9.58 365	25.78 354	4.57 367	11.61 364	27.37 363	1.96 363

ab-mvs-re		5.62 337	7.50 340	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	67.46 340	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

pcd_1.5k_mvsjas		5.20 338	6.93 341	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	62.39 145	0.00 370	0.00 368	0.00 368	0.00 366

test123		4.43 339	5.78 342	0.39 351	0.97 371	0.28 372	46.33 346	0.45 372	0.31 367	0.62 368	1.50 368	0.61 374	0.11 369	0.56 366	0.63 366	0.77 365

testmvs		4.06 340	5.28 343	0.41 350	0.64 372	0.16 373	42.54 350	0.31 373	0.26 368	0.50 369	1.40 369	0.77 373	0.17 368	0.56 366	0.55 367	0.90 364

uanet_test		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

sosnet-low-res		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

sosnet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

uncertanet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

Regformer		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

uanet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

eth-test2					0.00 373

eth-test					0.00 373

ZD-MVS					83.91 91	69.36 74		81.09 117	58.91 139	82.73 83	89.11 92	75.77 36	86.63 13	72.73 59	92.93 73

RE-MVS-def			85.50 4		86.19 53	79.18 9	87.23 7	86.27 21	77.51 10	87.65 18	90.73 45	81.38 7		78.11 25	94.46 36	84.89 92

IU-MVS					86.12 57	60.90 137		80.38 130	45.49 273	81.31 97				75.64 41	94.39 41	84.65 99

OPU-MVS				78.65 64	83.44 98	66.85 91	83.62 42				86.12 155	66.82 112	86.01 29	61.72 141	89.79 137	83.08 145

test_241102_TWO								84.80 47	72.61 29	84.93 54	89.70 78	77.73 23	85.89 36	75.29 42	94.22 53	83.25 140

test_241102_ONE					86.12 57	61.06 133		84.72 51	72.64 28	87.38 24	89.47 81	77.48 24	85.74 40

9.14			80.22 58		80.68 132		80.35 70	87.69 10	59.90 128	83.00 77	88.20 108	74.57 50	81.75 115	73.75 53	93.78 59

save fliter					87.00 42	67.23 89	79.24 82	77.94 169	56.65 162

test_0728_THIRD									74.03 21	85.83 41	90.41 58	75.58 38	85.69 41	77.43 33	94.74 29	84.31 116

test_0728_SECOND				76.57 88	86.20 52	60.57 141	83.77 40	85.49 32					85.90 35	75.86 39	94.39 41	83.25 140

test0726					86.16 55	60.78 138	83.81 39	85.10 42	72.48 31	85.27 51	89.96 74	78.57 18

GSMVS																70.05 296

test_part2					85.90 61	66.44 94				84.61 60

sam_mvs1												31.41 330				70.05 296

sam_mvs												31.21 334

ambc				70.10 181	77.74 171	50.21 203	74.28 147	77.93 170		79.26 121	88.29 107	54.11 217	79.77 153	64.43 119	91.10 107	80.30 203

MTGPA								80.63 123

test_post1							66.63 244				2.08 366	30.66 339	59.33 318	40.34 291

test_post											1.99 367	30.91 337	54.76 326

patchmatchnet-post											68.99 331	31.32 331	69.38 272

GG-mvs-BLEND				52.24 310	60.64 331	29.21 351	69.73 196	42.41 350		45.47 353	52.33 358	20.43 364	68.16 279	25.52 355	65.42 338	59.36 343

MTMP							84.83 31	19.26 369

gm-plane-assit					62.51 319	33.91 334			37.25 321		62.71 349		72.74 240	38.70 298

test9_res														72.12 67	91.37 99	77.40 238

TEST9					85.47 66	69.32 75	76.42 116	78.69 155	53.73 200	76.97 150	86.74 132	66.84 111	81.10 124

test_8					85.09 72	67.89 84	76.26 121	78.66 157	54.00 195	76.89 155	86.72 134	66.60 114	80.89 135

agg_prior2														70.70 74	90.93 112	78.55 224

agg_prior					84.44 84	66.02 98		78.62 158		76.95 152			80.34 142

TestCases				78.35 69	79.19 151	70.81 59		88.64 2	65.37 80	80.09 113	88.17 109	70.33 80	78.43 176	55.60 191	90.90 114	85.81 74

test_prior4						70.14 67	77.57 103

test_prior2							75.57 127		58.92 137	76.53 166	86.78 129	67.83 103		69.81 79	92.76 78

test_prior				75.27 104	82.15 116	59.85 147		84.33 64					83.39 87			82.58 160

旧先验2							71.17 180		45.11 277	78.54 129			61.28 314	59.19 164

新几何2							71.33 176

新几何1				69.99 183	88.37 35	71.34 55		62.08 287	43.85 283	74.99 183	86.11 156	52.85 222	70.57 264	50.99 226	83.23 227	68.05 310

旧先验1					84.55 81	60.36 143		63.69 278			87.05 122	54.65 214			83.34 225	69.66 300

无先验							74.82 135	70.94 240	47.75 262				76.85 205	54.47 202		72.09 280

原ACMM2							74.78 139

原ACMM1				73.90 121	85.90 61	65.15 106		81.67 103	50.97 230	74.25 195	86.16 153	61.60 153	83.54 82	56.75 178	91.08 108	73.00 269

test222					87.30 40	69.15 78	67.85 225	59.59 297	41.06 302	73.05 210	85.72 164	48.03 251			80.65 255	66.92 315

testdata2													67.30 286	48.34 247

segment_acmp												68.30 98

testdata				64.13 246	85.87 63	63.34 119		61.80 291	47.83 260	76.42 170	86.60 141	48.83 245	62.31 311	54.46 204	81.26 247	66.74 319

testdata1							68.34 221		57.24 154

test12				76.51 89	82.28 114	60.94 136		81.64 104		73.60 201		64.88 129	85.19 56		90.42 123	83.38 136

plane_prior7					85.18 69	66.21 96

plane_prior6					84.18 88	65.31 103						60.83 163

plane_prior5								85.49 32					86.15 26	71.09 71	90.94 110	84.82 94

plane_prior4											89.11 92

plane_prior3						65.67 100			63.82 98	78.23 132

plane_prior2							82.74 51		65.45 77

plane_prior1					84.46 83

plane_prior						65.18 104	80.06 76		61.88 115						89.91 134

n2								0.00 374

nn								0.00 374

door-mid								55.02 316

lessismore_v0				72.75 146	79.60 143	56.83 167		57.37 305		83.80 71	89.01 95	47.45 253	78.74 167	64.39 120	86.49 185	82.69 158

LGP-MVS_train				80.90 38	87.00 42	70.41 64		86.35 18	69.77 50	87.75 15	91.13 34	81.83 3	86.20 23	77.13 34	95.96 5	86.08 68

test11								82.71 91

door								52.91 327

HQP5-MVS						58.80 157

HQP-NCC					82.37 111		77.32 107		59.08 133	71.58 228

ACMP_Plane					82.37 111		77.32 107		59.08 133	71.58 228

BP-MVS														67.38 101

HQP4-MVS										71.59 227			85.31 49			83.74 127

HQP3-MVS								84.12 72							89.16 144

HQP2-MVS												58.09 189

NP-MVS					83.34 99	63.07 122					85.97 159

MDTV_nov1_ep13_2view						18.41 364	53.74 327		31.57 349	44.89 355		29.90 345		32.93 332		71.48 284

MDTV_nov1_ep13			54.05 299		65.54 302	29.30 350	59.00 308	55.22 314	35.96 327	52.44 338	75.98 270	30.77 338	59.62 317	38.21 303	73.33 308

ACMMP++_ref															89.47 142

ACMMP++															91.96 88

Test By Simon												62.56 141

ITE_SJBPF				80.35 44	76.94 180	73.60 41		80.48 127	66.87 64	83.64 73	86.18 151	70.25 82	79.90 152	61.12 147	88.95 150	87.56 52

DeepMVS_CX				11.83 348	15.51 368	13.86 367		11.25 371	5.76 364	20.85 366	26.46 363	17.06 369	9.22 366	9.69 365	13.82 365	12.42 362

High-resolution multi-view benchmark