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.

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	81.53 115	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
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 63	74.51 48	96.15 2	92.88 7

CP-MVS		84.12 10	84.55 12	82.80 12	89.42 18	79.74 7	88.19 3	84.43 60	71.96 39	84.70 59	90.56 50	77.12 26	86.18 25	79.24 18	95.36 13	82.49 161

ACMMP		84.22 8	84.84 10	82.35 20	89.23 23	76.66 25	87.65 4	85.89 28	71.03 42	85.85 40	90.58 49	78.77 17	85.78 38	79.37 16	95.17 17	84.62 101
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
abl_6		84.92 3	85.70 3	82.57 17	86.72 47	79.27 8	87.56 5	86.08 25	77.48 12	88.12 13	91.53 27	81.18 8	84.31 71	78.12 24	94.47 35	84.15 119

LS3D		80.99 45	80.85 54	81.41 30	78.37 159	71.37 53	87.45 6	85.87 29	77.48 12	81.98 88	89.95 75	69.14 87	85.26 52	66.15 105	91.24 102	87.61 51

SR-MVS-dyc-post		84.75 5	85.26 7	83.21 3	86.19 53	79.18 9	87.23 7	86.27 20	77.51 10	87.65 18	90.73 45	79.20 14	85.58 46	78.11 25	94.46 36	84.89 91

RE-MVS-def			85.50 4		86.19 53	79.18 9	87.23 7	86.27 20	77.51 10	87.65 18	90.73 45	81.38 7		78.11 25	94.46 36	84.89 91

COLMAP_ROB	72.78 3	83.75 13	84.11 17	82.68 14	82.97 103	74.39 35	87.18 9	88.18 6	78.98 6	86.11 38	91.47 29	79.70 12	85.76 39	66.91 104	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
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	57.10 198	87.10 8	79.75 8	83.87 217	84.31 115
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
mPP-MVS		84.01 12	84.39 13	82.88 8	90.65 4	81.38 4	87.08 11	82.79 87	72.41 33	85.11 53	90.85 41	76.65 29	84.89 60	79.30 17	94.63 32	82.35 164

region2R		83.54 16	83.86 21	82.58 16	89.82 10	77.53 19	87.06 12	84.23 68	70.19 48	83.86 70	90.72 47	75.20 41	86.27 22	79.41 15	94.25 50	83.95 122

HFP-MVS		83.39 19	84.03 18	81.48 27	89.25 21	75.69 27	87.01 13	84.27 64	70.23 46	84.47 62	90.43 55	76.79 27	85.94 31	79.58 11	94.23 51	82.82 151

ACMMPR		83.62 14	83.93 19	82.69 13	89.78 11	77.51 21	87.01 13	84.19 69	70.23 46	84.49 61	90.67 48	75.15 42	86.37 19	79.58 11	94.26 49	84.18 118

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	83.09 142

XVS		83.51 17	83.73 22	82.85 10	89.43 16	77.61 17	86.80 16	84.66 54	72.71 26	82.87 79	90.39 61	73.86 55	86.31 20	78.84 20	94.03 55	84.64 99

X-MVStestdata		76.81 82	74.79 101	82.85 10	89.43 16	77.61 17	86.80 16	84.66 54	72.71 26	82.87 79	9.95 359	73.86 55	86.31 20	78.84 20	94.03 55	84.64 99

TSAR-MVS + MP.		79.05 63	78.81 68	79.74 48	88.94 28	67.52 85	86.61 18	81.38 107	51.71 216	77.15 146	91.42 32	65.49 122	87.20 6	79.44 14	87.17 175	84.51 109
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
APDe-MVS		82.88 26	84.14 16	79.08 57	84.80 77	66.72 91	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	83.47 132

CPTT-MVS		81.51 38	81.76 46	80.76 40	89.20 24	78.75 12	86.48 20	82.03 96	68.80 53	80.92 103	88.52 101	72.00 67	82.39 102	74.80 44	93.04 71	81.14 181

MP-MVS		83.19 20	83.54 26	82.14 22	90.54 5	79.00 11	86.42 21	83.59 78	71.31 40	81.26 98	90.96 38	74.57 50	84.69 64	78.41 22	94.78 26	82.74 155
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 27	67.39 61	84.02 68	90.39 61	74.73 47	86.46 16	80.73 7	94.43 40	84.60 104

HPM-MVS_fast		84.59 6	85.10 8	83.06 6	88.60 33	75.83 26	86.27 23	86.89 16	73.69 22	86.17 35	91.70 24	78.23 20	85.20 56	79.45 13	94.91 25	88.15 45

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	84.30 117

SteuartSystems-ACMMP		83.07 23	83.64 24	81.35 31	85.14 71	71.00 57	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.
APD-MVS_3200maxsize		83.57 15	84.33 14	81.31 33	82.83 106	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

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
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 46	78.04 27	94.08 54	83.55 129

SMA-MVS		82.12 32	82.68 41	80.43 42	88.90 30	69.52 69	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	84.61 102
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
MTAPA		83.19 20	83.87 20	81.13 35	91.16 2	78.16 14	84.87 30	80.63 121	72.08 36	84.93 54	90.79 42	74.65 48	84.42 68	80.98 4	94.75 27	80.82 189

MTMP							84.83 31	19.26 364

LCM-MVSNet		86.90 1	88.67 1	81.57 25	91.50 1	63.30 119	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

UA-Net		81.56 37	82.28 43	79.40 54	88.91 29	69.16 76	84.67 33	80.01 135	75.34 16	79.80 115	94.91 2	69.79 84	80.25 143	72.63 60	94.46 36	88.78 41

#test#		82.40 30	82.71 40	81.48 27	89.25 21	75.69 27	84.47 34	84.27 64	64.45 91	84.47 62	90.43 55	76.79 27	85.94 31	76.01 36	94.23 51	82.82 151

3Dnovator+	73.19 2	81.08 43	80.48 56	82.87 9	81.41 126	72.03 48	84.38 35	86.23 23	77.28 15	80.65 106	90.18 71	59.80 171	87.58 4	73.06 57	91.34 100	89.01 33

MVSFormer		69.93 165	69.03 177	72.63 150	74.93 200	59.19 150	83.98 36	75.72 187	52.27 209	63.53 289	76.74 263	43.19 269	80.56 135	72.28 65	78.67 271	78.14 227

test_djsdf		78.88 65	78.27 75	80.70 41	81.42 125	71.24 55	83.98 36	75.72 187	52.27 209	87.37 26	92.25 16	68.04 100	80.56 135	72.28 65	91.15 105	90.32 20

APD-MVS		81.13 42	81.73 47	79.36 55	84.47 82	70.53 62	83.85 38	83.70 76	69.43 52	83.67 72	88.96 97	75.89 35	86.41 17	72.62 61	92.95 72	81.14 181
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test0726					86.16 55	60.78 137	83.81 39	85.10 42	72.48 31	85.27 51	89.96 74	78.57 18

DVP-MVS		81.15 41	83.12 35	75.24 105	86.16 55	60.78 137	83.77 40	80.58 124	72.48 31	85.83 41	90.41 58	78.57 18	85.69 41	75.86 39	94.39 41	79.24 213
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
test_0728_SECOND				76.57 87	86.20 52	60.57 140	83.77 40	85.49 32					85.90 35	75.86 39	94.39 41	83.25 139

SED-MVS		81.78 35	83.48 27	76.67 85	86.12 57	61.06 132	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	83.08 143

OPU-MVS				78.65 64	83.44 98	66.85 90	83.62 42				86.12 155	66.82 110	86.01 29	61.72 138	89.79 137	83.08 143

ACMMP_NAP		82.33 31	83.28 31	79.46 53	89.28 19	69.09 78	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	83.35 137

testtj		81.19 40	81.70 48	79.67 51	83.95 90	69.77 68	83.58 45	84.63 56	72.13 35	82.85 81	88.36 105	75.00 45	86.79 12	71.99 69	92.84 74	82.44 162

HPM-MVS++		79.89 56	79.80 62	80.18 45	89.02 26	78.44 13	83.49 46	80.18 132	64.71 90	78.11 135	88.39 103	65.46 123	83.14 90	77.64 32	91.20 103	78.94 216

SF-MVS		80.72 47	81.80 45	77.48 78	82.03 117	64.40 111	83.41 47	88.46 5	65.28 82	84.29 64	89.18 88	73.73 58	83.22 88	76.01 36	93.77 60	84.81 95

SD-MVS		80.28 54	81.55 51	76.47 90	83.57 94	67.83 84	83.39 48	85.35 37	64.42 92	86.14 37	87.07 121	74.02 54	80.97 128	77.70 31	92.32 85	80.62 196
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
ACMM	69.25 9	82.11 33	83.31 30	78.49 66	88.17 39	73.96 37	83.11 49	84.52 59	66.40 71	87.45 22	89.16 91	81.02 9	80.52 138	74.27 50	95.73 7	80.98 186
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 87	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	82.62 157
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HQP_MVS		78.77 66	78.78 70	78.72 62	85.18 69	65.18 103	82.74 51	85.49 32	65.45 77	78.23 132	89.11 92	60.83 162	86.15 26	71.09 71	90.94 110	84.82 93

plane_prior2							82.74 51		65.45 77

zzz-MVS		83.01 25	83.63 25	81.13 35	91.16 2	78.16 14	82.72 53	80.63 121	72.08 36	84.93 54	90.79 42	74.65 48	84.42 68	80.98 4	94.75 27	80.82 189

ACMP	69.50 8	82.64 28	83.38 29	80.40 43	86.50 49	69.44 71	82.30 54	86.08 25	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
PMVS	70.70 6	81.70 36	83.15 34	77.36 81	90.35 6	82.82 2	82.15 55	79.22 143	74.08 20	87.16 29	91.97 19	84.80 2	76.97 197	64.98 115	93.61 63	72.28 274
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepC-MVS	72.44 4	81.00 44	80.83 55	81.50 26	86.70 48	70.03 67	82.06 56	87.00 15	59.89 128	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
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	83.82 123

LPG-MVS_test		83.47 18	84.33 14	80.90 38	87.00 42	70.41 63	82.04 57	86.35 17	69.77 50	87.75 15	91.13 34	81.83 3	86.20 23	77.13 34	95.96 5	86.08 68

F-COLMAP		75.29 95	73.99 112	79.18 56	81.73 122	71.90 49	81.86 59	82.98 84	59.86 129	72.27 217	84.00 181	64.56 131	83.07 93	51.48 217	87.19 174	82.56 160

MP-MVS-pluss		82.54 29	83.46 28	79.76 47	88.88 31	68.44 80	81.57 60	86.33 19	63.17 106	85.38 49	91.26 33	76.33 31	84.67 65	83.30 1	94.96 23	86.17 67
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPM_NR		73.91 111	74.16 110	73.16 133	81.90 119	53.50 185	81.28 61	81.40 106	66.17 72	73.30 205	83.31 191	59.96 167	83.10 92	58.45 164	81.66 240	82.87 149

API-MVS		70.97 154	71.51 154	69.37 186	75.20 197	55.94 169	80.99 62	76.84 178	62.48 111	71.24 232	77.51 258	61.51 154	80.96 132	52.04 213	85.76 189	71.22 283

OMC-MVS		79.41 60	78.79 69	81.28 34	80.62 133	70.71 61	80.91 63	84.76 49	62.54 110	81.77 90	86.65 138	71.46 72	83.53 82	67.95 94	92.44 82	89.60 23

mvs_tets		78.93 64	78.67 72	79.72 49	84.81 76	73.93 38	80.65 64	76.50 181	51.98 214	87.40 23	91.86 21	76.09 34	78.53 168	68.58 84	90.20 125	86.69 64

ACMH+	66.64 10	81.20 39	82.48 42	77.35 82	81.16 129	62.39 123	80.51 65	87.80 7	73.02 25	87.57 20	91.08 36	80.28 10	82.44 101	64.82 116	96.10 4	87.21 56

EPP-MVSNet		73.86 112	73.38 123	75.31 102	78.19 161	53.35 187	80.45 66	77.32 174	65.11 85	76.47 167	86.80 127	49.47 236	83.77 77	53.89 205	92.72 80	88.81 40

jajsoiax		78.51 69	78.16 77	79.59 52	84.65 79	73.83 40	80.42 67	76.12 183	51.33 222	87.19 28	91.51 28	73.79 57	78.44 172	68.27 87	90.13 130	86.49 65

PHI-MVS		74.92 101	74.36 108	76.61 86	76.40 184	62.32 124	80.38 68	83.15 82	54.16 191	73.23 206	80.75 216	62.19 147	83.86 76	68.02 90	90.92 113	83.65 128

QAPM		69.18 177	69.26 173	68.94 196	71.61 248	52.58 190	80.37 69	78.79 151	49.63 240	73.51 200	85.14 170	53.66 215	79.12 157	55.11 192	75.54 289	75.11 250

9.14			80.22 58		80.68 131		80.35 70	87.69 10	59.90 127	83.00 77	88.20 108	74.57 50	81.75 113	73.75 53	93.78 59

ETH3D-3000-0.1		79.14 62	79.80 62	77.16 84	80.67 132	64.57 108	80.26 71	87.60 11	60.74 122	82.47 85	88.03 113	71.73 69	81.81 111	73.12 56	93.61 63	85.09 86

OurMVSNet-221017-0		78.57 68	78.53 74	78.67 63	80.48 134	64.16 112	80.24 72	82.06 95	61.89 114	88.77 11	93.32 4	57.15 196	82.60 100	70.08 77	92.80 76	89.25 27

XVG-ACMP-BASELINE		80.54 48	81.06 52	78.98 59	87.01 41	72.91 46	80.23 73	85.56 30	66.56 70	85.64 43	89.57 80	69.12 88	80.55 137	72.51 62	93.37 67	83.48 131

anonymousdsp		78.60 67	77.80 79	81.00 37	78.01 164	74.34 36	80.09 74	76.12 183	50.51 231	89.19 9	90.88 39	71.45 73	77.78 189	73.38 55	90.60 121	90.90 16

Gipuma		69.55 170	72.83 135	59.70 281	63.63 312	53.97 181	80.08 75	75.93 185	64.24 94	73.49 201	88.93 98	57.89 192	62.46 305	59.75 157	91.55 96	62.67 332
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
plane_prior						65.18 103	80.06 76		61.88 115						89.91 134

DeepC-MVS_fast	69.89 7	77.17 80	76.33 90	79.70 50	83.90 92	67.94 82	80.06 76	83.75 75	56.73 159	74.88 183	85.32 167	65.54 121	87.79 2	65.61 111	91.14 106	83.35 137
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
NCCC		78.25 74	78.04 78	78.89 61	85.61 65	69.45 70	79.80 78	80.99 118	65.77 74	75.55 175	86.25 150	67.42 104	85.42 48	70.10 76	90.88 116	81.81 174

IS-MVSNet		75.10 98	75.42 99	74.15 117	79.23 148	48.05 219	79.43 79	78.04 164	70.09 49	79.17 122	88.02 114	53.04 217	83.60 80	58.05 167	93.76 62	90.79 17

AdaColmap		74.22 109	74.56 103	73.20 132	81.95 118	60.97 134	79.43 79	80.90 119	65.57 76	72.54 214	81.76 207	70.98 77	85.26 52	47.88 247	90.00 131	73.37 262

OPM-MVS		80.99 45	81.63 50	79.07 58	86.86 46	69.39 72	79.41 81	84.00 74	65.64 75	85.54 46	89.28 84	76.32 32	83.47 83	74.03 51	93.57 66	84.35 114

xxxxxxxxxxxxxcwj		80.31 53	80.94 53	78.42 68	87.00 42	67.23 88	79.24 82	88.61 4	56.65 161	84.29 64	89.18 88	73.73 58	83.22 88	76.01 36	93.77 60	84.81 95

save fliter					87.00 42	67.23 88	79.24 82	77.94 166	56.65 161

v7n		79.37 61	80.41 57	76.28 92	78.67 158	55.81 170	79.22 84	82.51 92	70.72 44	87.54 21	92.44 14	68.00 101	81.34 116	72.84 58	91.72 89	91.69 10

ETH3D cwj APD-0.16		78.38 73	78.72 71	77.38 80	80.09 137	66.16 96	79.08 85	86.13 24	57.55 151	80.93 102	87.76 116	71.98 68	82.73 98	72.11 68	92.83 75	83.25 139

DP-MVS		78.44 72	79.29 66	75.90 96	81.86 120	65.33 101	79.05 86	84.63 56	74.83 18	80.41 109	86.27 148	71.68 70	83.45 84	62.45 135	92.40 83	78.92 217

ACMH	63.62 14	77.50 78	80.11 59	69.68 184	79.61 141	56.28 167	78.81 87	83.62 77	63.41 104	87.14 30	90.23 69	76.11 33	73.32 232	67.58 96	94.44 39	79.44 211
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RRT_MVS		73.80 114	71.19 158	81.60 24	71.04 251	70.33 65	78.78 88	74.91 197	56.96 155	77.83 138	85.56 165	32.82 316	87.39 5	71.16 70	91.68 91	87.07 60

MSLP-MVS++		74.48 107	75.78 94	70.59 168	84.66 78	62.40 122	78.65 89	84.24 67	60.55 124	77.71 141	81.98 203	63.12 137	77.64 191	62.95 132	88.14 153	71.73 279

AllTest		77.66 76	77.43 81	78.35 69	79.19 150	70.81 58	78.60 90	88.64 2	65.37 80	80.09 113	88.17 109	70.33 79	78.43 173	55.60 187	90.90 114	85.81 74

PS-MVSNAJss		77.54 77	77.35 82	78.13 73	84.88 74	66.37 94	78.55 91	79.59 140	53.48 200	86.29 34	92.43 15	62.39 144	80.25 143	67.90 95	90.61 120	87.77 48

Effi-MVS+-dtu		75.43 93	72.28 141	84.91 2	77.05 174	83.58 1	78.47 92	77.70 168	57.68 146	74.89 182	78.13 252	64.80 128	84.26 72	56.46 179	85.32 195	86.88 61

3Dnovator	65.95 11	71.50 149	71.22 157	72.34 154	73.16 232	63.09 120	78.37 93	78.32 159	57.67 148	72.22 219	84.61 173	54.77 209	78.47 170	60.82 147	81.07 245	75.45 246

ETH3 D test6400		75.73 90	76.00 92	74.92 106	81.75 121	56.93 164	78.31 94	84.60 58	52.83 205	77.15 146	85.14 170	68.59 92	84.03 74	65.44 112	90.20 125	83.82 123

OpenMVS	62.51 15	68.76 182	68.75 182	68.78 201	70.56 258	53.91 182	78.29 95	77.35 173	48.85 247	70.22 243	83.52 185	52.65 220	76.93 198	55.31 191	81.99 233	75.49 245

test_part1		76.97 81	78.21 76	73.25 131	77.87 166	45.76 243	78.27 96	87.26 13	66.69 68	85.31 50	91.43 31	55.95 207	84.24 73	65.71 109	95.43 12	89.75 22

WR-MVS_H		80.22 55	82.17 44	74.39 112	89.46 15	42.69 267	78.24 97	82.24 93	78.21 9	89.57 8	92.10 18	68.05 99	85.59 45	66.04 107	95.62 9	94.88 5

114514_t		73.40 120	73.33 126	73.64 123	84.15 89	57.11 163	78.20 98	80.02 134	43.76 281	72.55 213	86.07 158	64.00 134	83.35 87	60.14 152	91.03 109	80.45 199

PLC	62.01 16	71.79 147	70.28 166	76.33 91	80.31 136	68.63 79	78.18 99	81.24 110	54.57 184	67.09 268	80.63 218	59.44 172	81.74 114	46.91 254	84.17 212	78.63 218
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNVR-MVS		78.49 70	78.59 73	78.16 71	85.86 64	67.40 86	78.12 100	81.50 103	63.92 96	77.51 143	86.56 142	68.43 96	84.82 62	73.83 52	91.61 94	82.26 167

TAPA-MVS	65.27 12	75.16 97	74.29 109	77.77 75	74.86 204	68.08 81	77.89 101	84.04 73	55.15 174	76.19 171	83.39 187	66.91 108	80.11 148	60.04 154	90.14 129	85.13 85
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_prior4						70.14 66	77.57 102

EPNet		69.10 178	67.32 202	74.46 109	68.33 274	61.27 131	77.56 103	63.57 275	60.95 120	56.62 319	82.75 196	51.53 226	81.24 119	54.36 202	90.20 125	80.88 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
RPSCF		75.76 89	74.37 107	79.93 46	74.81 205	77.53 19	77.53 104	79.30 142	59.44 131	78.88 124	89.80 77	71.26 75	73.09 234	57.45 169	80.89 248	89.17 30

CSCG		74.12 110	74.39 106	73.33 128	79.35 145	61.66 129	77.45 105	81.98 97	62.47 112	79.06 123	80.19 225	61.83 149	78.79 163	59.83 156	87.35 168	79.54 210

HQP-NCC					82.37 110		77.32 106		59.08 132	71.58 225

ACMP_Plane					82.37 110		77.32 106		59.08 132	71.58 225

HQP-MVS		75.24 96	75.01 100	75.94 95	82.37 110	58.80 156	77.32 106	84.12 70	59.08 132	71.58 225	85.96 160	58.09 186	85.30 51	67.38 100	89.16 144	83.73 127

DTE-MVSNet		80.35 52	82.89 38	72.74 146	89.84 8	37.34 308	77.16 109	81.81 99	80.45 3	90.92 2	92.95 7	74.57 50	86.12 28	63.65 126	94.68 31	94.76 6

PS-CasMVS		80.41 51	82.86 39	73.07 135	89.93 7	39.21 290	77.15 110	81.28 109	79.74 5	90.87 3	92.73 11	75.03 44	84.93 59	63.83 125	95.19 16	95.07 3

XVG-OURS-SEG-HR		79.62 57	79.99 60	78.49 66	86.46 50	74.79 33	77.15 110	85.39 36	66.73 67	80.39 110	88.85 99	74.43 53	78.33 178	74.73 46	85.79 188	82.35 164

PEN-MVS		80.46 50	82.91 37	73.11 134	89.83 9	39.02 293	77.06 112	82.61 90	80.04 4	90.60 5	92.85 9	74.93 46	85.21 55	63.15 131	95.15 18	95.09 2

CP-MVSNet		79.48 59	81.65 49	72.98 138	89.66 13	39.06 292	76.76 113	80.46 126	78.91 7	90.32 6	91.70 24	68.49 94	84.89 60	63.40 130	95.12 19	95.01 4

SixPastTwentyTwo		75.77 88	76.34 89	74.06 118	81.69 123	54.84 175	76.47 114	75.49 189	64.10 95	87.73 17	92.24 17	50.45 232	81.30 118	67.41 98	91.46 97	86.04 70

TEST9					85.47 66	69.32 74	76.42 115	78.69 152	53.73 198	76.97 149	86.74 132	66.84 109	81.10 122

train_agg		76.38 85	76.55 87	75.86 97	85.47 66	69.32 74	76.42 115	78.69 152	54.00 193	76.97 149	86.74 132	66.60 112	81.10 122	72.50 63	91.56 95	77.15 235

Vis-MVSNet		74.85 105	74.56 103	75.72 98	81.63 124	64.64 107	76.35 117	79.06 145	62.85 108	73.33 204	88.41 102	62.54 142	79.59 154	63.94 124	82.92 225	82.94 147
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
DeepPCF-MVS	71.07 5	78.48 71	77.14 85	82.52 19	84.39 86	77.04 24	76.35 117	84.05 72	56.66 160	80.27 111	85.31 168	68.56 93	87.03 10	67.39 99	91.26 101	83.50 130

XVG-OURS		79.51 58	79.82 61	78.58 65	86.11 60	74.96 32	76.33 119	84.95 46	66.89 63	82.75 82	88.99 96	66.82 110	78.37 176	74.80 44	90.76 119	82.40 163

test_8					85.09 72	67.89 83	76.26 120	78.66 154	54.00 193	76.89 154	86.72 134	66.60 112	80.89 133

agg_prior1		75.89 87	76.41 88	74.31 114	84.44 84	66.02 97	76.12 121	78.62 155	54.40 186	76.95 151	86.85 126	66.44 114	80.34 140	72.45 64	91.42 98	76.57 239

CDPH-MVS		77.33 79	77.06 86	78.14 72	84.21 87	63.98 114	76.07 122	83.45 79	54.20 189	77.68 142	87.18 117	69.98 82	85.37 49	68.01 91	92.72 80	85.08 88

CNLPA		73.44 118	73.03 132	74.66 107	78.27 160	75.29 30	75.99 123	78.49 157	65.39 79	75.67 173	83.22 195	61.23 158	66.77 288	53.70 207	85.33 194	81.92 173

UGNet		70.20 161	69.05 176	73.65 122	76.24 186	63.64 115	75.87 124	72.53 214	61.48 116	60.93 303	86.14 154	52.37 221	77.12 196	50.67 224	85.21 196	80.17 204
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
v10		75.69 91	76.20 91	74.16 116	74.44 216	48.69 210	75.84 125	82.93 86	59.02 135	85.92 39	89.17 90	58.56 181	82.74 97	70.73 73	89.14 147	91.05 13

test_prior3		76.71 84	77.19 83	75.27 103	82.15 115	59.85 146	75.57 126	84.33 62	58.92 136	76.53 165	86.78 129	67.83 102	83.39 85	69.81 79	92.76 78	82.58 158

test_prior2							75.57 126		58.92 136	76.53 165	86.78 129	67.83 102		69.81 79	92.76 78

mvs-test1		73.81 113	70.69 163	83.18 5	77.05 174	81.39 3	75.39 128	77.70 168	57.68 146	71.19 234	74.72 277	64.80 128	83.66 79	56.46 179	81.19 244	84.50 110

PAPR		69.20 176	68.66 185	70.82 165	75.15 199	47.77 223	75.31 129	81.11 113	49.62 241	66.33 270	79.27 237	61.53 153	82.96 94	48.12 245	81.50 242	81.74 175

v8		75.07 99	75.64 96	73.35 127	73.42 228	47.46 228	75.20 130	81.45 105	60.05 126	85.64 43	89.26 85	58.08 188	81.80 112	69.71 81	87.97 158	90.79 17

tttt0517		69.46 171	67.79 197	74.46 109	75.34 195	52.72 189	75.05 131	63.27 277	54.69 181	78.87 125	84.37 176	26.63 345	81.15 120	63.95 122	87.93 159	89.51 24

TSAR-MVS + GP.		73.08 125	71.60 152	77.54 77	78.99 156	70.73 60	74.96 132	69.38 245	60.73 123	74.39 192	78.44 248	57.72 193	82.78 96	60.16 151	89.60 139	79.11 215

MAR-MVS		67.72 196	66.16 211	72.40 153	74.45 215	64.99 106	74.87 133	77.50 172	48.67 248	65.78 274	68.58 333	57.01 200	77.79 188	46.68 256	81.92 234	74.42 255
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
无先验							74.82 134	70.94 236	47.75 258				76.85 201	54.47 198		72.09 276

CANet		73.00 130	71.84 146	76.48 89	75.82 192	61.28 130	74.81 135	80.37 129	63.17 106	62.43 293	80.50 220	61.10 160	85.16 58	64.00 121	84.34 211	83.01 146

PVSNet_Blended_VisFu		70.04 162	68.88 179	73.53 126	82.71 107	63.62 116	74.81 135	81.95 98	48.53 250	67.16 267	79.18 240	51.42 227	78.38 175	54.39 201	79.72 263	78.60 219

MCST-MVS		73.42 119	73.34 125	73.63 124	81.28 127	59.17 152	74.80 137	83.13 83	45.50 267	72.84 209	83.78 184	65.15 125	80.99 126	64.54 117	89.09 148	80.73 194

原ACMM2							74.78 138

Anonymous20231211		75.54 92	77.19 83	70.59 168	77.67 171	45.70 245	74.73 139	80.19 131	68.80 53	82.95 78	92.91 8	66.26 115	76.76 203	58.41 165	92.77 77	89.30 26

Effi-MVS+		72.10 144	72.28 141	71.58 159	74.21 221	50.33 199	74.72 140	82.73 88	62.62 109	70.77 237	76.83 262	69.96 83	80.97 128	60.20 149	78.43 273	83.45 134

K. test v3		73.67 115	73.61 120	73.87 120	79.78 139	55.62 173	74.69 141	62.04 286	66.16 73	84.76 58	93.23 5	49.47 236	80.97 128	65.66 110	86.67 181	85.02 90

MG-MVS		70.47 159	71.34 156	67.85 211	79.26 147	40.42 285	74.67 142	75.15 196	58.41 139	68.74 258	88.14 112	56.08 206	83.69 78	59.90 155	81.71 239	79.43 212

UniMVSNet_ETH3D		76.74 83	79.02 67	69.92 183	89.27 20	43.81 255	74.47 143	71.70 220	72.33 34	85.50 47	93.65 3	77.98 22	76.88 200	54.60 197	91.64 92	89.08 31

DP-MVS Recon		73.57 117	72.69 136	76.23 93	82.85 105	63.39 117	74.32 144	82.96 85	57.75 145	70.35 241	81.98 203	64.34 133	84.41 70	49.69 231	89.95 133	80.89 187

ambc				70.10 179	77.74 169	50.21 201	74.28 145	77.93 167		79.26 121	88.29 107	54.11 214	79.77 151	64.43 118	91.10 107	80.30 201

nrg030		74.87 104	75.99 93	71.52 161	74.90 203	49.88 206	74.10 146	82.58 91	54.55 185	83.50 74	89.21 87	71.51 71	75.74 212	61.24 141	92.34 84	88.94 36

canonicalmvs		72.29 143	73.38 123	69.04 192	74.23 217	47.37 229	73.93 147	83.18 81	54.36 187	76.61 162	81.64 209	72.03 65	75.34 216	57.12 172	87.28 171	84.40 112

CS-MVS		71.24 150	70.57 164	73.26 130	74.93 200	52.00 191	73.59 148	85.55 31	55.58 170	68.88 254	70.17 319	64.37 132	85.62 44	57.19 171	84.83 204	82.17 168

CANet_DTU		64.04 231	63.83 228	64.66 238	68.39 271	42.97 265	73.45 149	74.50 201	52.05 213	54.78 326	75.44 272	43.99 264	70.42 263	53.49 209	78.41 274	80.59 197

ETV-MVS		72.72 137	72.16 144	74.38 113	76.90 180	55.95 168	73.34 150	84.67 53	62.04 113	72.19 220	70.81 312	65.90 119	85.24 54	58.64 162	84.96 202	81.95 172

PCF-MVS	63.80 13	72.70 138	71.69 148	75.72 98	78.10 162	60.01 145	73.04 151	81.50 103	45.34 271	79.66 116	84.35 177	65.15 125	82.65 99	48.70 239	89.38 143	84.50 110
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Regformer-3		72.86 135	72.28 141	74.62 108	74.74 207	60.18 143	72.91 152	71.76 219	64.74 89	78.42 130	72.07 302	67.00 107	76.28 207	67.97 93	80.91 246	87.39 53

Regformer-4		74.64 106	73.67 117	77.55 76	74.74 207	64.49 110	72.91 152	75.42 191	67.45 60	80.24 112	72.07 302	68.98 89	80.19 147	70.29 75	80.91 246	87.98 46

Regformer-1		74.28 108	73.63 119	76.21 94	74.22 218	64.12 113	72.77 154	75.46 190	66.86 65	79.27 120	72.08 300	69.29 86	78.74 164	68.73 83	84.02 215	85.77 79

Regformer-2		75.32 94	74.47 105	77.88 74	74.22 218	66.65 92	72.77 154	77.54 170	68.47 58	80.44 108	72.08 300	70.60 78	80.97 128	70.08 77	84.02 215	86.01 71

test_0402		78.17 75	79.48 65	74.24 115	83.50 95	59.15 153	72.52 156	74.60 200	75.34 16	88.69 12	91.81 22	75.06 43	82.37 103	65.10 113	88.68 151	81.20 179

EU-MVSNet		60.82 256	60.80 254	60.86 275	68.37 272	41.16 275	72.27 157	68.27 252	26.96 353	69.08 250	75.71 267	32.09 322	67.44 280	55.59 189	78.90 268	73.97 257

EI-MVSNet-Vis-set		72.78 136	71.87 145	75.54 100	74.77 206	59.02 154	72.24 158	71.56 223	63.92 96	78.59 126	71.59 308	66.22 116	78.60 166	67.58 96	80.32 254	89.00 34

v1192		73.40 120	73.42 121	73.32 129	74.65 213	48.67 211	72.21 159	81.73 100	52.76 206	81.85 89	84.56 174	57.12 197	82.24 107	68.58 84	87.33 169	89.06 32

baseline		73.10 124	73.96 113	70.51 170	71.46 249	46.39 239	72.08 160	84.40 61	55.95 166	76.62 161	86.46 145	67.20 105	78.03 185	64.22 120	87.27 172	87.11 59

EI-MVSNet-UG-set		72.63 139	71.68 149	75.47 101	74.67 210	58.64 159	72.02 161	71.50 224	63.53 101	78.58 128	71.39 311	65.98 117	78.53 168	67.30 102	80.18 256	89.23 28

v1144		73.29 123	73.39 122	73.01 136	74.12 222	48.11 218	72.01 162	81.08 116	53.83 197	81.77 90	84.68 172	58.07 189	81.91 109	68.10 88	86.86 177	88.99 35

GBi-Net		68.30 188	68.79 180	66.81 223	73.14 233	40.68 281	71.96 163	73.03 206	54.81 176	74.72 186	90.36 64	48.63 244	75.20 218	47.12 251	85.37 191	84.54 105

test1		68.30 188	68.79 180	66.81 223	73.14 233	40.68 281	71.96 163	73.03 206	54.81 176	74.72 186	90.36 64	48.63 244	75.20 218	47.12 251	85.37 191	84.54 105

FMVSNet1		71.06 152	72.48 138	66.81 223	77.65 172	40.68 281	71.96 163	73.03 206	61.14 118	79.45 119	90.36 64	60.44 164	75.20 218	50.20 228	88.05 155	84.54 105

v1921920		72.96 133	72.98 133	72.89 142	74.67 210	47.58 226	71.92 166	80.69 120	51.70 217	81.69 94	83.89 182	56.58 203	82.25 106	68.34 86	87.36 167	88.82 39

v144192		72.99 131	73.06 131	72.77 144	74.58 214	47.48 227	71.90 167	80.44 127	51.57 218	81.46 96	84.11 180	58.04 190	82.12 108	67.98 92	87.47 165	88.70 42

v1240		73.06 127	73.14 128	72.84 143	74.74 207	47.27 231	71.88 168	81.11 113	51.80 215	82.28 87	84.21 178	56.22 205	82.34 104	68.82 82	87.17 175	88.91 37

FC-MVSNet-test		73.32 122	74.78 102	68.93 197	79.21 149	36.57 310	71.82 169	79.54 141	57.63 150	82.57 84	90.38 63	59.38 174	78.99 159	57.91 168	94.56 33	91.23 12

IterMVS-LS		73.01 129	73.12 130	72.66 148	73.79 225	49.90 203	71.63 170	78.44 158	58.22 140	80.51 107	86.63 139	58.15 185	79.62 152	62.51 133	88.20 152	88.48 43
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EG-PatchMatch MVS		70.70 156	70.88 160	70.16 176	82.64 109	58.80 156	71.48 171	73.64 204	54.98 175	76.55 163	81.77 206	61.10 160	78.94 160	54.87 194	80.84 249	72.74 269

LF4IMVS		67.50 198	67.31 203	68.08 209	58.86 336	61.93 125	71.43 172	75.90 186	44.67 276	72.42 216	80.20 224	57.16 195	70.44 262	58.99 161	86.12 185	71.88 277

v2v482		72.55 142	72.58 137	72.43 152	72.92 240	46.72 234	71.41 173	79.13 144	55.27 172	81.17 99	85.25 169	55.41 208	81.13 121	67.25 103	85.46 190	89.43 25

Fast-Effi-MVS+-dtu		70.00 163	68.74 183	73.77 121	73.47 227	64.53 109	71.36 174	78.14 163	55.81 168	68.84 257	74.71 278	65.36 124	75.75 211	52.00 214	79.00 267	81.03 183

新几何2							71.33 175

EI-MVSNet		69.61 169	69.01 178	71.41 163	73.94 223	49.90 203	71.31 176	71.32 227	58.22 140	75.40 178	70.44 314	58.16 184	75.85 208	62.51 133	79.81 260	88.48 43

CVMVSNet		59.21 268	58.44 271	61.51 267	73.94 223	47.76 224	71.31 176	64.56 270	26.91 354	60.34 305	70.44 314	36.24 306	67.65 278	53.57 208	68.66 327	69.12 302

thisisatest0530		67.05 205	65.16 216	72.73 147	73.10 236	50.55 198	71.26 178	63.91 273	50.22 233	74.46 191	80.75 216	26.81 344	80.25 143	59.43 158	86.50 182	87.37 54

旧先验2							71.17 179		45.11 273	78.54 129			61.28 310	59.19 160

FIs		72.56 140	73.80 115	68.84 200	78.74 157	37.74 304	71.02 180	79.83 136	56.12 164	80.88 105	89.45 82	58.18 183	78.28 179	56.63 175	93.36 68	90.51 19

TranMVSNet+NR-MVSNet		76.13 86	77.66 80	71.56 160	84.61 80	42.57 269	70.98 181	78.29 161	68.67 56	83.04 76	89.26 85	72.99 62	80.75 134	55.58 190	95.47 10	91.35 11

casdiffmvs		73.06 127	73.84 114	70.72 166	71.32 250	46.71 235	70.93 182	84.26 66	55.62 169	77.46 144	87.10 118	67.09 106	77.81 187	63.95 122	86.83 178	87.64 50

CR-MVSNet		58.96 269	58.49 270	60.36 278	66.37 289	48.24 216	70.93 182	56.40 308	32.87 338	61.35 297	86.66 136	33.19 314	63.22 304	48.50 242	70.17 319	69.62 297

RPMNet		65.77 213	65.08 223	67.84 212	66.37 289	48.24 216	70.93 182	86.27 20	54.66 182	61.35 297	86.77 131	33.29 313	85.67 43	55.93 184	70.17 319	69.62 297

LFMVS		67.06 204	67.89 195	64.56 239	78.02 163	38.25 299	70.81 185	59.60 292	65.18 84	71.06 235	86.56 142	43.85 265	75.22 217	46.35 257	89.63 138	80.21 203

DPM-MVS		69.98 164	69.22 175	72.26 156	82.69 108	58.82 155	70.53 186	81.23 111	47.79 257	64.16 282	80.21 223	51.32 228	83.12 91	60.14 152	84.95 203	74.83 252

hse-mvs3		73.08 125	71.61 151	77.48 78	83.89 93	72.89 47	70.47 187	71.12 234	54.28 188	77.89 136	83.41 186	49.04 239	80.98 127	63.62 127	90.77 118	78.58 220

MVS_111021_LR		72.10 144	71.82 147	72.95 139	79.53 143	73.90 39	70.45 188	66.64 257	56.87 156	76.81 158	81.76 207	68.78 90	71.76 252	61.81 136	83.74 219	73.18 264

UniMVSNet (Re)		75.00 100	75.48 98	73.56 125	83.14 100	47.92 221	70.41 189	81.04 117	63.67 99	79.54 117	86.37 147	62.83 138	81.82 110	57.10 173	95.25 15	90.94 15

TinyColmap		67.98 192	69.28 172	64.08 243	67.98 278	46.82 233	70.04 190	75.26 194	53.05 202	77.36 145	86.79 128	59.39 173	72.59 242	45.64 260	88.01 157	72.83 267

VDDNet		71.60 148	73.13 129	67.02 222	86.29 51	41.11 276	69.97 191	66.50 258	68.72 55	74.74 185	91.70 24	59.90 168	75.81 210	48.58 241	91.72 89	84.15 119

EPNet_dtu		58.93 270	58.52 269	60.16 280	67.91 279	47.70 225	69.97 191	58.02 296	49.73 238	47.28 347	73.02 296	38.14 298	62.34 306	36.57 312	85.99 187	70.43 289
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_Test		69.84 166	70.71 162	67.24 218	67.49 283	43.25 263	69.87 193	81.22 112	52.69 207	71.57 228	86.68 135	62.09 148	74.51 226	66.05 106	78.74 269	83.96 121

alignmvs		70.54 158	71.00 159	69.15 191	73.50 226	48.04 220	69.85 194	79.62 137	53.94 196	76.54 164	82.00 202	59.00 177	74.68 224	57.32 170	87.21 173	84.72 97

GG-mvs-BLEND				52.24 306	60.64 327	29.21 347	69.73 195	42.41 345		45.47 349	52.33 354	20.43 360	68.16 275	25.52 351	65.42 334	59.36 339

pmmvs-eth3d		64.41 227	63.27 235	67.82 213	75.81 193	60.18 143	69.49 196	62.05 285	38.81 310	74.13 194	82.23 201	43.76 266	68.65 272	42.53 273	80.63 253	74.63 253

DU-MVS		74.91 102	75.57 97	72.93 140	83.50 95	45.79 241	69.47 197	80.14 133	65.22 83	81.74 92	87.08 119	61.82 150	81.07 124	56.21 182	94.98 21	91.93 8

EIA-MVS		68.59 185	67.16 204	72.90 141	75.18 198	55.64 172	69.39 198	81.29 108	52.44 208	64.53 278	70.69 313	60.33 165	82.30 105	54.27 203	76.31 284	80.75 193

PAPM		61.79 251	60.37 257	66.05 230	76.09 189	41.87 272	69.30 199	76.79 180	40.64 302	53.80 332	79.62 233	44.38 262	82.92 95	29.64 340	73.11 305	73.36 263

UniMVSNet_NR-MVSNet		74.90 103	75.65 95	72.64 149	83.04 101	45.79 241	69.26 200	78.81 149	66.66 69	81.74 92	86.88 125	63.26 136	81.07 124	56.21 182	94.98 21	91.05 13

MVP-Stereo		61.56 252	59.22 263	68.58 203	79.28 146	60.44 141	69.20 201	71.57 222	43.58 284	56.42 320	78.37 249	39.57 293	76.46 206	34.86 321	60.16 343	68.86 304
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AUN-MVS		70.22 160	67.88 196	77.22 83	82.96 104	71.61 50	69.08 202	71.39 226	49.17 244	71.70 223	78.07 253	37.62 303	79.21 156	61.81 136	89.15 146	80.82 189

gg-mvs-nofinetune		55.75 282	56.75 282	52.72 305	62.87 314	28.04 349	68.92 203	41.36 352	71.09 41	50.80 339	92.63 12	20.74 359	66.86 285	29.97 338	72.41 307	63.25 329

Baseline_NR-MVSNet		70.62 157	73.19 127	62.92 258	76.97 177	34.44 326	68.84 204	70.88 237	60.25 125	79.50 118	90.53 51	61.82 150	69.11 270	54.67 196	95.27 14	85.22 82

v148		69.38 174	69.39 171	69.36 187	69.14 270	44.56 250	68.83 205	72.70 212	54.79 179	78.59 126	84.12 179	54.69 210	76.74 204	59.40 159	82.20 231	86.79 62

FMVSNet2		67.48 199	68.21 191	65.29 234	73.14 233	38.94 294	68.81 206	71.21 233	54.81 176	76.73 160	86.48 144	48.63 244	74.60 225	47.98 246	86.11 186	82.35 164

MVS_111021_HR		72.98 132	72.97 134	72.99 137	80.82 130	65.47 100	68.81 206	72.77 211	57.67 148	75.76 172	82.38 200	71.01 76	77.17 195	61.38 140	86.15 184	76.32 240

Anonymous20240529		72.56 140	73.79 116	68.86 199	76.89 181	45.21 247	68.80 208	77.25 176	67.16 62	76.89 154	90.44 54	65.95 118	74.19 229	50.75 223	90.00 131	87.18 58

Anonymous20240521		63.55 233	66.07 212	55.99 296	66.18 294	44.04 254	68.77 209	68.80 247	46.99 262	72.57 212	85.84 162	39.87 290	50.22 327	53.40 211	92.23 86	73.71 261

CLD-MVS		72.88 134	72.36 140	74.43 111	77.03 176	54.30 179	68.77 209	83.43 80	52.12 211	76.79 159	74.44 281	69.54 85	83.91 75	55.88 185	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
1314		59.83 264	58.86 267	62.74 259	65.71 297	44.78 249	68.59 211	72.63 213	33.54 337	61.05 301	67.29 339	43.62 267	71.26 255	49.49 233	67.84 330	72.19 275

1121		69.23 175	68.26 188	72.12 157	88.36 36	71.40 52	68.59 211	62.06 284	43.80 280	74.75 184	86.18 151	52.92 218	76.85 201	54.47 198	83.27 223	68.12 305

MVS		60.62 259	59.97 259	62.58 260	68.13 276	47.28 230	68.59 211	73.96 203	32.19 339	59.94 308	68.86 331	50.48 231	77.64 191	41.85 278	75.74 286	62.83 330

OpenMVS_ROB	54.93 17	63.23 237	63.28 234	63.07 255	69.81 263	45.34 246	68.52 214	67.14 254	43.74 282	70.61 239	79.22 238	47.90 248	72.66 238	48.75 238	73.84 303	71.21 284

PM-MVS		64.49 224	63.61 231	67.14 221	76.68 182	75.15 31	68.49 215	42.85 344	51.17 225	77.85 137	80.51 219	45.76 251	66.31 291	52.83 212	76.35 283	59.96 338

BH-untuned		69.39 173	69.46 170	69.18 190	77.96 165	56.88 165	68.47 216	77.53 171	56.77 158	77.79 139	79.63 232	60.30 166	80.20 146	46.04 258	80.65 251	70.47 288

testdata1							68.34 217		57.24 153

tpm2		56.12 280	54.64 293	60.55 277	66.24 292	36.01 314	68.14 218	56.77 307	33.60 336	58.25 314	75.52 271	30.25 337	74.33 228	33.27 327	69.76 323	71.32 281

cl_fuxian		69.82 167	69.89 168	69.61 185	66.24 292	43.48 259	68.12 219	79.61 139	51.43 220	77.72 140	80.18 226	54.61 212	78.15 184	63.62 127	87.50 164	87.20 57

CMPMVS	48.73 20	61.54 253	60.89 253	63.52 249	61.08 324	51.55 193	68.07 220	68.00 253	33.88 332	65.87 272	81.25 212	37.91 301	67.71 277	49.32 234	82.60 228	71.31 282
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test222					87.30 40	69.15 77	67.85 221	59.59 293	41.06 298	73.05 208	85.72 164	48.03 247			80.65 251	66.92 311

VDD-MVS		70.81 155	71.44 155	68.91 198	79.07 155	46.51 236	67.82 222	70.83 238	61.23 117	74.07 196	88.69 100	59.86 169	75.62 213	51.11 220	90.28 124	84.61 102

ab-mvs		64.11 230	65.13 219	61.05 272	71.99 246	38.03 303	67.59 223	68.79 248	49.08 246	65.32 275	86.26 149	58.02 191	66.85 286	39.33 290	79.79 262	78.27 225

eth_miper_zixun_eth		69.42 172	68.73 184	71.50 162	67.99 277	46.42 237	67.58 224	78.81 149	50.72 229	78.13 134	80.34 222	50.15 234	80.34 140	60.18 150	84.65 205	87.74 49

CostFormer		57.35 278	56.14 286	60.97 273	63.76 311	38.43 296	67.50 225	60.22 290	37.14 318	59.12 311	76.34 265	32.78 317	71.99 249	39.12 292	69.27 324	72.47 271

Patchmtry		60.91 255	63.01 238	54.62 299	66.10 295	26.27 353	67.47 226	56.40 308	54.05 192	72.04 221	86.66 136	33.19 314	60.17 312	43.69 268	87.45 166	77.42 233

USDC		62.80 242	63.10 237	61.89 264	65.19 300	43.30 262	67.42 227	74.20 202	35.80 324	72.25 218	84.48 175	45.67 253	71.95 250	37.95 302	84.97 199	70.42 290

xiu_mvs_v1_base_debu		67.87 193	67.07 205	70.26 173	79.13 152	61.90 126	67.34 228	71.25 230	47.98 253	67.70 262	74.19 287	61.31 155	72.62 239	56.51 176	78.26 275	76.27 241

xiu_mvs_v1_base		67.87 193	67.07 205	70.26 173	79.13 152	61.90 126	67.34 228	71.25 230	47.98 253	67.70 262	74.19 287	61.31 155	72.62 239	56.51 176	78.26 275	76.27 241

xiu_mvs_v1_base_debi		67.87 193	67.07 205	70.26 173	79.13 152	61.90 126	67.34 228	71.25 230	47.98 253	67.70 262	74.19 287	61.31 155	72.62 239	56.51 176	78.26 275	76.27 241

Vis-MVSNet (Re-imp)		62.74 243	63.21 236	61.34 270	72.19 244	31.56 339	67.31 231	53.87 315	53.60 199	69.88 245	83.37 189	40.52 287	70.98 257	41.40 280	86.78 180	81.48 178

jason		64.47 225	62.84 239	69.34 189	76.91 179	59.20 149	67.15 232	65.67 260	35.29 325	65.16 276	76.74 263	44.67 260	70.68 258	54.74 195	79.28 266	78.14 227
jason: jason.
RRT_test8_iter05		65.80 212	65.13 219	67.80 214	67.02 287	40.85 280	67.13 233	75.33 192	49.73 238	72.69 211	81.32 210	24.45 355	77.37 194	61.69 139	86.82 179	85.18 84

miper_ehance_all_eth		68.36 187	68.16 193	68.98 194	65.14 303	43.34 261	67.07 234	78.92 148	49.11 245	76.21 170	77.72 255	53.48 216	77.92 186	61.16 143	84.59 207	85.68 80

pmmvs6		71.82 146	73.66 118	66.31 229	75.94 191	42.01 271	66.99 235	72.53 214	63.45 103	76.43 168	92.78 10	72.95 63	69.69 266	51.41 218	90.46 122	87.22 55

PatchmatchNet		54.60 288	54.27 294	55.59 297	65.17 302	39.08 291	66.92 236	51.80 325	39.89 304	58.39 312	73.12 295	31.69 325	58.33 316	43.01 272	58.38 349	69.38 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MSDG		67.47 200	67.48 201	67.46 216	70.70 255	54.69 177	66.90 237	78.17 162	60.88 121	70.41 240	74.76 275	61.22 159	73.18 233	47.38 250	76.87 281	74.49 254

cl-mvsnet2		67.14 202	66.51 209	69.03 193	63.20 313	43.46 260	66.88 238	76.25 182	49.22 243	74.48 190	77.88 254	45.49 255	77.40 193	60.64 148	84.59 207	86.24 66

TAMVS		65.31 216	63.75 229	69.97 182	82.23 114	59.76 148	66.78 239	63.37 276	45.20 272	69.79 246	79.37 236	47.42 250	72.17 245	34.48 322	85.15 198	77.99 231

test_post1							66.63 240				2.08 361	30.66 335	59.33 314	40.34 287

FMVSNet3		65.00 220	65.16 216	64.52 240	69.47 266	37.56 307	66.63 240	70.38 240	51.55 219	74.72 186	83.27 192	37.89 302	74.44 227	47.12 251	85.37 191	81.57 177

mvs_anonymous		65.08 219	65.49 215	63.83 246	63.79 310	37.60 306	66.52 242	69.82 243	43.44 285	73.46 202	86.08 157	58.79 180	71.75 253	51.90 215	75.63 288	82.15 169

wuyk23d		61.97 248	66.25 210	49.12 316	58.19 340	60.77 139	66.32 243	52.97 321	55.93 167	90.62 4	86.91 124	73.07 61	35.98 355	20.63 357	91.63 93	50.62 346

tpm cat1		54.02 292	52.63 300	58.19 290	64.85 306	39.86 288	66.26 244	57.28 302	32.16 340	56.90 317	70.39 316	32.75 318	65.30 294	34.29 323	58.79 346	69.41 299

Fast-Effi-MVS+		68.81 181	68.30 187	70.35 172	74.66 212	48.61 212	66.06 245	78.32 159	50.62 230	71.48 231	75.54 269	68.75 91	79.59 154	50.55 226	78.73 270	82.86 150

V42		71.06 152	70.83 161	71.72 158	67.25 284	47.14 232	65.94 246	80.35 130	51.35 221	83.40 75	83.23 193	59.25 175	78.80 162	65.91 108	80.81 250	89.23 28

cl-mvsnet_		68.26 191	68.26 188	68.29 206	64.98 304	43.67 257	65.89 247	74.67 198	50.04 236	76.86 156	82.42 199	48.74 242	75.38 214	60.92 146	89.81 135	85.80 78

cl-mvsnet1		68.27 190	68.26 188	68.29 206	64.98 304	43.67 257	65.89 247	74.67 198	50.04 236	76.86 156	82.43 198	48.74 242	75.38 214	60.94 145	89.81 135	85.81 74

tpmvs		55.84 281	55.45 291	57.01 293	60.33 328	33.20 333	65.89 247	59.29 294	47.52 260	56.04 321	73.60 290	31.05 332	68.06 276	40.64 285	64.64 335	69.77 295

lupinMVS		63.36 234	61.49 249	68.97 195	74.93 200	59.19 150	65.80 250	64.52 271	34.68 330	63.53 289	74.25 285	43.19 269	70.62 259	53.88 206	78.67 271	77.10 236

TransMVSNet (Re)		69.62 168	71.63 150	63.57 248	76.51 183	35.93 316	65.75 251	71.29 229	61.05 119	75.02 180	89.90 76	65.88 120	70.41 264	49.79 230	89.48 141	84.38 113

NR-MVSNet		73.62 116	74.05 111	72.33 155	83.50 95	43.71 256	65.65 252	77.32 174	64.32 93	75.59 174	87.08 119	62.45 143	81.34 116	54.90 193	95.63 8	91.93 8

BH-w/o		64.81 221	64.29 226	66.36 228	76.08 190	54.71 176	65.61 253	75.23 195	50.10 235	71.05 236	71.86 307	54.33 213	79.02 158	38.20 300	76.14 285	65.36 320

PVSNet_BlendedMVS		65.38 215	64.30 225	68.61 202	69.81 263	49.36 207	65.60 254	78.96 146	45.50 267	59.98 306	78.61 246	51.82 223	78.20 181	44.30 264	84.11 213	78.27 225

thres100view900		61.17 254	61.09 251	61.39 269	72.14 245	35.01 322	65.42 255	56.99 305	55.23 173	70.71 238	79.90 228	32.07 323	72.09 246	35.61 318	81.73 236	77.08 237

CDS-MVSNet		64.33 228	62.66 241	69.35 188	80.44 135	58.28 160	65.26 256	65.66 261	44.36 277	67.30 266	75.54 269	43.27 268	71.77 251	37.68 303	84.44 210	78.01 230
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SCA		58.57 273	58.04 273	60.17 279	70.17 261	41.07 277	65.19 257	53.38 319	43.34 288	61.00 302	73.48 291	45.20 256	69.38 268	40.34 287	70.31 318	70.05 292

HY-MVS	49.31 19	57.96 275	57.59 276	59.10 285	66.85 288	36.17 313	65.13 258	65.39 264	39.24 307	54.69 328	78.14 251	44.28 263	67.18 283	33.75 326	70.79 315	73.95 258

ET-MVSNet_ETH3D		63.32 235	60.69 255	71.20 164	70.15 262	55.66 171	65.02 259	64.32 272	43.28 289	68.99 252	72.05 306	25.46 351	78.19 183	54.16 204	82.80 226	79.74 209

diffmvs		67.42 201	67.50 200	67.20 219	62.26 317	45.21 247	64.87 260	77.04 177	48.21 251	71.74 222	79.70 231	58.40 182	71.17 256	64.99 114	80.27 255	85.22 82

miper_enhance_ethall		65.86 211	65.05 224	68.28 208	61.62 321	42.62 268	64.74 261	77.97 165	42.52 290	73.42 203	72.79 297	49.66 235	77.68 190	58.12 166	84.59 207	84.54 105

thres600view7		61.82 250	61.38 250	63.12 254	71.81 247	34.93 323	64.64 262	56.99 305	54.78 180	70.33 242	79.74 230	32.07 323	72.42 244	38.61 296	83.46 221	82.02 170

BH-RMVSNet		68.69 184	68.20 192	70.14 177	76.40 184	53.90 183	64.62 263	73.48 205	58.01 142	73.91 198	81.78 205	59.09 176	78.22 180	48.59 240	77.96 278	78.31 223

pm-mvs1		68.40 186	69.85 169	64.04 245	73.10 236	39.94 287	64.61 264	70.50 239	55.52 171	73.97 197	89.33 83	63.91 135	68.38 274	49.68 232	88.02 156	83.81 125

pmmvs4		60.78 257	59.04 265	66.00 231	73.06 238	57.67 162	64.53 265	60.22 290	36.91 319	65.96 271	77.27 259	39.66 292	68.54 273	38.87 293	74.89 295	71.80 278

WR-MVS		71.20 151	72.48 138	67.36 217	84.98 73	35.70 318	64.43 266	68.66 249	65.05 86	81.49 95	86.43 146	57.57 194	76.48 205	50.36 227	93.32 69	89.90 21

tpmrst		50.15 307	51.38 306	46.45 323	56.05 346	24.77 355	64.40 267	49.98 329	36.14 321	53.32 333	69.59 324	35.16 308	48.69 329	39.24 291	58.51 348	65.89 317

VPA-MVSNet		68.71 183	70.37 165	63.72 247	76.13 188	38.06 302	64.10 268	71.48 225	56.60 163	74.10 195	88.31 106	64.78 130	69.72 265	47.69 249	90.15 128	83.37 136

MIMVSNet1		66.57 207	69.23 174	58.59 288	81.26 128	37.73 305	64.06 269	57.62 298	57.02 154	78.40 131	90.75 44	62.65 139	58.10 318	41.77 279	89.58 140	79.95 205

MVS_0304		62.51 245	62.27 243	63.25 252	69.39 267	48.47 213	64.05 270	62.48 279	59.69 130	54.10 331	81.04 214	45.71 252	66.31 291	41.38 281	82.58 229	74.96 251

IterMVS		63.12 238	62.48 242	65.02 237	66.34 291	52.86 188	63.81 271	62.25 280	46.57 264	71.51 230	80.40 221	44.60 261	66.82 287	51.38 219	75.47 290	75.38 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		67.68 197	66.07 212	72.49 151	73.34 230	58.20 161	63.80 272	65.55 263	48.10 252	76.91 153	82.64 197	45.20 256	78.84 161	61.20 142	77.89 279	80.44 200

DELS-MVS		68.83 180	68.31 186	70.38 171	70.55 259	48.31 214	63.78 273	82.13 94	54.00 193	68.96 253	75.17 273	58.95 178	80.06 149	58.55 163	82.74 227	82.76 153
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
bset_n11_16_dypcd		66.91 206	65.84 214	70.12 178	72.95 239	53.54 184	63.64 274	68.65 250	48.54 249	72.54 214	74.28 284	40.58 286	78.54 167	63.52 129	87.82 160	78.29 224

xiu_mvs_v2_base		64.43 226	63.96 227	65.85 233	77.72 170	51.32 195	63.63 275	72.31 217	45.06 275	61.70 294	69.66 323	62.56 140	73.93 231	49.06 236	73.91 301	72.31 273

tfpnnormal		66.48 208	67.93 194	62.16 263	73.40 229	36.65 309	63.45 276	64.99 266	55.97 165	72.82 210	87.80 115	57.06 199	69.10 271	48.31 244	87.54 162	80.72 195

TR-MVS		64.59 222	63.54 232	67.73 215	75.75 194	50.83 197	63.39 277	70.29 241	49.33 242	71.55 229	74.55 279	50.94 229	78.46 171	40.43 286	75.69 287	73.89 259

PS-MVSNAJ		64.27 229	63.73 230	65.90 232	77.82 168	51.42 194	63.33 278	72.33 216	45.09 274	61.60 295	68.04 334	62.39 144	73.95 230	49.07 235	73.87 302	72.34 272

tfpn200view9		60.35 261	59.97 259	61.51 267	70.78 253	35.35 320	63.27 279	57.47 299	53.00 203	68.31 259	77.09 260	32.45 320	72.09 246	35.61 318	81.73 236	77.08 237

thres400		60.77 258	59.97 259	63.15 253	70.78 253	35.35 320	63.27 279	57.47 299	53.00 203	68.31 259	77.09 260	32.45 320	72.09 246	35.61 318	81.73 236	82.02 170

test_yl		65.11 217	65.09 221	65.18 235	70.59 256	40.86 278	63.22 281	72.79 209	57.91 143	68.88 254	79.07 243	42.85 272	74.89 222	45.50 261	84.97 199	79.81 206

DCV-MVSNet		65.11 217	65.09 221	65.18 235	70.59 256	40.86 278	63.22 281	72.79 209	57.91 143	68.88 254	79.07 243	42.85 272	74.89 222	45.50 261	84.97 199	79.81 206

baseline1		57.82 276	58.36 272	56.19 295	69.17 269	30.76 343	62.94 283	55.21 311	46.04 266	63.83 285	78.47 247	41.20 280	63.68 301	39.44 289	68.99 325	74.13 256

baseline2		55.57 285	52.74 299	64.05 244	65.26 299	44.11 253	62.38 284	54.43 314	39.03 308	51.21 337	67.35 338	33.66 312	72.45 243	37.14 308	64.22 337	75.60 244

FPMVS		59.43 267	60.07 258	57.51 292	77.62 173	71.52 51	62.33 285	50.92 326	57.40 152	69.40 248	80.00 227	39.14 294	61.92 308	37.47 306	66.36 332	39.09 355

DWT-MVSNet_test		53.04 295	51.12 308	58.77 287	61.23 322	38.67 295	62.16 286	57.74 297	38.24 311	51.76 336	59.07 350	21.36 358	67.40 281	44.80 263	63.76 338	70.25 291

PatchMatch-RL		58.68 272	57.72 275	61.57 266	76.21 187	73.59 42	61.83 287	49.00 333	47.30 261	61.08 299	68.97 328	50.16 233	59.01 315	36.06 317	68.84 326	52.10 345

cascas		64.59 222	62.77 240	70.05 180	75.27 196	50.02 202	61.79 288	71.61 221	42.46 291	63.68 287	68.89 330	49.33 238	80.35 139	47.82 248	84.05 214	79.78 208

LCM-MVSNet-Re		69.10 178	71.57 153	61.70 265	70.37 260	34.30 328	61.45 289	79.62 137	56.81 157	89.59 7	88.16 111	68.44 95	72.94 235	42.30 274	87.33 169	77.85 232

1112_ss		59.48 266	58.99 266	60.96 274	77.84 167	42.39 270	61.42 290	68.45 251	37.96 314	59.93 309	67.46 336	45.11 258	65.07 295	40.89 284	71.81 310	75.41 247

IB-MVS	49.67 18	59.69 265	56.96 280	67.90 210	68.19 275	50.30 200	61.42 290	65.18 265	47.57 259	55.83 323	67.15 340	23.77 356	79.60 153	43.56 270	79.97 258	73.79 260
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
PVSNet_Blended		62.90 241	61.64 246	66.69 226	69.81 263	49.36 207	61.23 292	78.96 146	42.04 292	59.98 306	68.86 331	51.82 223	78.20 181	44.30 264	77.77 280	72.52 270

GA-MVS		62.91 240	61.66 245	66.66 227	67.09 286	44.49 251	61.18 293	69.36 246	51.33 222	69.33 249	74.47 280	36.83 304	74.94 221	50.60 225	74.72 296	80.57 198

MS-PatchMatch		55.59 284	54.89 292	57.68 291	69.18 268	49.05 209	61.00 294	62.93 278	35.98 322	58.36 313	68.93 329	36.71 305	66.59 289	37.62 305	63.30 339	57.39 341

MVSTER		63.29 236	61.60 248	68.36 204	59.77 333	46.21 240	60.62 295	71.32 227	41.83 293	75.40 178	79.12 241	30.25 337	75.85 208	56.30 181	79.81 260	83.03 145

thisisatest0515		60.48 260	57.86 274	68.34 205	67.25 284	46.42 237	60.58 296	62.14 281	40.82 300	63.58 288	69.12 326	26.28 347	78.34 177	48.83 237	82.13 232	80.26 202

tpm		50.60 305	52.42 302	45.14 328	65.18 301	26.29 352	60.30 297	43.50 342	37.41 316	57.01 316	79.09 242	30.20 339	42.32 348	32.77 329	66.36 332	66.81 314

VPNet		65.58 214	67.56 198	59.65 282	79.72 140	30.17 344	60.27 298	62.14 281	54.19 190	71.24 232	86.63 139	58.80 179	67.62 279	44.17 267	90.87 117	81.18 180

MIMVSNet		54.39 289	56.12 287	49.20 314	72.57 241	30.91 342	59.98 299	48.43 335	41.66 294	55.94 322	83.86 183	41.19 281	50.42 326	26.05 347	75.38 292	66.27 316

HyFIR lowres test		63.01 239	60.47 256	70.61 167	83.04 101	54.10 180	59.93 300	72.24 218	33.67 335	69.00 251	75.63 268	38.69 296	76.93 198	36.60 311	75.45 291	80.81 192

Patchmatch-RL test		59.95 263	59.12 264	62.44 261	72.46 242	54.61 178	59.63 301	47.51 337	41.05 299	74.58 189	74.30 283	31.06 331	65.31 293	51.61 216	79.85 259	67.39 308

PatchT		53.35 294	56.47 284	43.99 332	64.19 308	17.46 361	59.15 302	43.10 343	52.11 212	54.74 327	86.95 123	29.97 340	49.98 328	43.62 269	74.40 299	64.53 328

JIA-IIPM		54.03 291	51.62 303	61.25 271	59.14 335	55.21 174	59.10 303	47.72 336	50.85 227	50.31 343	85.81 163	20.10 361	63.97 299	36.16 316	55.41 354	64.55 327

Anonymous202405211		66.02 210	66.89 208	63.43 251	74.22 218	38.14 300	59.00 304	66.13 259	63.33 105	69.76 247	85.95 161	51.88 222	70.50 261	44.23 266	87.52 163	81.64 176

MDTV_nov1_ep13			54.05 295		65.54 298	29.30 346	59.00 304	55.22 310	35.96 323	52.44 334	75.98 266	30.77 334	59.62 313	38.21 299	73.33 304

thres200		57.55 277	57.02 279	59.17 284	67.89 280	34.93 323	58.91 306	57.25 303	50.24 232	64.01 283	71.46 310	32.49 319	71.39 254	31.31 332	79.57 264	71.19 285

ANet_high		67.08 203	69.94 167	58.51 289	57.55 341	27.09 350	58.43 307	76.80 179	63.56 100	82.40 86	91.93 20	59.82 170	64.98 296	50.10 229	88.86 150	83.46 133

ppachtmachnet_test		60.26 262	59.61 262	62.20 262	67.70 281	44.33 252	58.18 308	60.96 289	40.75 301	65.80 273	72.57 298	41.23 279	63.92 300	46.87 255	82.42 230	78.33 222

DIV-MVS_2432*1600		66.38 209	67.51 199	62.97 257	61.76 319	34.39 327	58.11 309	75.30 193	50.84 228	77.12 148	85.42 166	56.84 201	69.44 267	51.07 221	91.16 104	85.08 88

Test_1112_low_res		58.78 271	58.69 268	59.04 286	79.41 144	38.13 301	57.62 310	66.98 256	34.74 328	59.62 310	77.56 257	42.92 271	63.65 302	38.66 295	70.73 316	75.35 249

VNet		64.01 232	65.15 218	60.57 276	73.28 231	35.61 319	57.60 311	67.08 255	54.61 183	66.76 269	83.37 189	56.28 204	66.87 284	42.19 275	85.20 197	79.23 214

DSMNet-mixed		43.18 324	44.66 325	38.75 340	54.75 353	28.88 348	57.06 312	27.42 362	13.47 359	47.27 348	77.67 256	38.83 295	39.29 354	25.32 352	60.12 344	48.08 348

CL-MVSNet_2432*1600		62.44 246	63.40 233	59.55 283	72.34 243	32.38 335	56.39 313	64.84 267	51.21 224	67.46 265	81.01 215	50.75 230	63.51 303	38.47 298	88.12 154	82.75 154

D2MVS		62.58 244	61.05 252	67.20 219	63.85 309	47.92 221	56.29 314	69.58 244	39.32 305	70.07 244	78.19 250	34.93 309	72.68 237	53.44 210	83.74 219	81.00 185

FMVSNet5		55.08 287	55.54 290	53.71 300	65.80 296	33.50 332	56.22 315	52.50 323	43.72 283	61.06 300	83.38 188	25.46 351	54.87 321	30.11 337	81.64 241	72.75 268

MVS-HIRNet		45.53 317	47.29 317	40.24 338	62.29 316	26.82 351	56.02 316	37.41 357	29.74 349	43.69 356	81.27 211	33.96 311	55.48 320	24.46 353	56.79 350	38.43 356

pmmvs3		46.71 315	45.09 322	51.55 308	56.76 344	48.25 215	55.78 317	39.53 356	24.13 357	50.35 342	63.40 344	15.90 365	51.08 325	29.29 342	70.69 317	55.33 344

pmmvs5		52.49 299	52.58 301	52.21 307	54.99 352	32.38 335	55.45 318	53.84 316	32.15 341	55.49 325	74.81 274	38.08 299	57.37 319	34.02 324	74.40 299	66.88 312

our_test_3		56.46 279	56.51 283	56.30 294	67.70 281	39.66 289	55.36 319	52.34 324	40.57 303	63.85 284	69.91 322	40.04 289	58.22 317	43.49 271	75.29 294	71.03 287

EPMVS		45.74 316	46.53 319	43.39 333	54.14 356	22.33 358	55.02 320	35.00 359	34.69 329	51.09 338	70.20 318	25.92 349	42.04 350	37.19 307	55.50 353	65.78 318

dp		44.09 323	44.88 324	41.72 337	58.53 338	23.18 357	54.70 321	42.38 347	34.80 327	44.25 354	65.61 342	24.48 354	44.80 340	29.77 339	49.42 356	57.18 342

CHOSEN 1792x2688		58.09 274	56.30 285	63.45 250	79.95 138	50.93 196	54.07 322	65.59 262	28.56 350	61.53 296	74.33 282	41.09 282	66.52 290	33.91 325	67.69 331	72.92 266

MDTV_nov1_ep13_2view						18.41 360	53.74 323		31.57 345	44.89 351		29.90 341		32.93 328		71.48 280

test-LLR		50.43 306	50.69 311	49.64 312	60.76 325	41.87 272	53.18 324	45.48 340	43.41 286	49.41 344	60.47 348	29.22 342	44.73 341	42.09 276	72.14 308	62.33 334

TESTMET0.1,1		45.17 318	44.93 323	45.89 325	56.02 347	38.31 297	53.18 324	41.94 350	27.85 351	44.86 352	56.47 351	17.93 363	41.50 352	38.08 301	68.06 328	57.85 340

test-mter		48.56 311	48.20 316	49.64 312	60.76 325	41.87 272	53.18 324	45.48 340	31.91 344	49.41 344	60.47 348	18.34 362	44.73 341	42.09 276	72.14 308	62.33 334

Anonymous20231206		54.13 290	55.82 288	49.04 317	70.89 252	35.96 315	51.73 327	50.87 327	34.86 326	62.49 292	79.22 238	42.52 275	44.29 343	27.95 345	81.88 235	66.88 312

XXY-MVS		55.19 286	57.40 278	48.56 318	64.45 307	34.84 325	51.54 328	53.59 317	38.99 309	63.79 286	79.43 234	56.59 202	45.57 335	36.92 310	71.29 312	65.25 321

test20.03		55.74 283	57.51 277	50.42 309	59.89 332	32.09 337	50.63 329	49.01 332	50.11 234	65.07 277	83.23 193	45.61 254	48.11 331	30.22 336	83.82 218	71.07 286

UnsupCasMVSNet_eth		52.26 300	53.29 298	49.16 315	55.08 351	33.67 331	50.03 330	58.79 295	37.67 315	63.43 291	74.75 276	41.82 277	45.83 334	38.59 297	59.42 345	67.98 307

testgi		54.00 293	56.86 281	45.45 326	58.20 339	25.81 354	49.05 331	49.50 331	45.43 270	67.84 261	81.17 213	51.81 225	43.20 347	29.30 341	79.41 265	67.34 310

Patchmatch-test		47.93 312	49.96 313	41.84 335	57.42 342	24.26 356	48.75 332	41.49 351	39.30 306	56.79 318	73.48 291	30.48 336	33.87 356	29.29 342	72.61 306	67.39 308

UnsupCasMVSNet_bld		50.01 308	51.03 310	46.95 319	58.61 337	32.64 334	48.31 333	53.27 320	34.27 331	60.47 304	71.53 309	41.40 278	47.07 332	30.68 334	60.78 342	61.13 336

PVSNet	43.83 21	51.56 304	51.17 307	52.73 304	68.34 273	38.27 298	48.22 334	53.56 318	36.41 320	54.29 329	64.94 343	34.60 310	54.20 324	30.34 335	69.87 321	65.71 319

MDA-MVSNet-bldmvs		62.34 247	61.73 244	64.16 241	61.64 320	49.90 203	48.11 335	57.24 304	53.31 201	80.95 101	79.39 235	49.00 240	61.55 309	45.92 259	80.05 257	81.03 183

PMMVS		44.69 320	43.95 327	46.92 320	50.05 360	53.47 186	48.08 336	42.40 346	22.36 358	44.01 355	53.05 353	42.60 274	45.49 336	31.69 331	61.36 341	41.79 353

miper_lstm_enhance		61.97 248	61.63 247	62.98 256	60.04 329	45.74 244	47.53 337	70.95 235	44.04 278	73.06 207	78.84 245	39.72 291	60.33 311	55.82 186	84.64 206	82.88 148

ADS-MVSNet2		48.76 310	47.25 318	53.29 303	55.90 348	40.54 284	47.34 338	54.99 313	31.41 346	50.48 340	72.06 304	31.23 328	54.26 323	25.93 348	55.93 351	65.07 322

ADS-MVSNet		44.62 321	45.58 320	41.73 336	55.90 348	20.83 359	47.34 338	39.94 355	31.41 346	50.48 340	72.06 304	31.23 328	39.31 353	25.93 348	55.93 351	65.07 322

WTY-MVS		49.39 309	50.31 312	46.62 322	61.22 323	32.00 338	46.61 340	49.77 330	33.87 333	54.12 330	69.55 325	41.96 276	45.40 337	31.28 333	64.42 336	62.47 333

test0.0.03 1		47.72 313	48.31 315	45.93 324	55.53 350	29.39 345	46.40 341	41.21 353	43.41 286	55.81 324	67.65 335	29.22 342	43.77 346	25.73 350	69.87 321	64.62 326

test123		4.43 334	5.78 337	0.39 346	0.97 366	0.28 367	46.33 342	0.45 367	0.31 362	0.62 363	1.50 363	0.61 369	0.11 364	0.56 361	0.63 361	0.77 360

sss		47.59 314	48.32 314	45.40 327	56.73 345	33.96 329	45.17 343	48.51 334	32.11 343	52.37 335	65.79 341	40.39 288	41.91 351	31.85 330	61.97 340	60.35 337

KD-MVS_2432*1600		52.05 302	51.58 304	53.44 301	52.11 357	31.20 340	44.88 344	64.83 268	41.53 295	64.37 279	70.03 320	15.61 366	64.20 297	36.25 313	74.61 297	64.93 324

miper_refine_blended		52.05 302	51.58 304	53.44 301	52.11 357	31.20 340	44.88 344	64.83 268	41.53 295	64.37 279	70.03 320	15.61 366	64.20 297	36.25 313	74.61 297	64.93 324

testmvs		4.06 335	5.28 338	0.41 345	0.64 367	0.16 368	42.54 346	0.31 368	0.26 363	0.50 364	1.40 364	0.77 368	0.17 363	0.56 361	0.55 362	0.90 359

PVSNet_0	36.71 22	41.12 326	40.78 329	42.14 334	59.97 330	40.13 286	40.97 347	42.24 349	30.81 348	44.86 352	49.41 356	40.70 285	45.12 339	23.15 354	34.96 358	41.16 354

YYNet1		52.58 297	53.50 296	49.85 310	54.15 355	36.45 312	40.53 348	46.55 339	38.09 313	75.52 176	73.31 294	41.08 283	43.88 344	41.10 282	71.14 314	69.21 301

MDA-MVSNet_test_wron		52.57 298	53.49 297	49.81 311	54.24 354	36.47 311	40.48 349	46.58 338	38.13 312	75.47 177	73.32 293	41.05 284	43.85 345	40.98 283	71.20 313	69.10 303

new_pmnet		37.55 328	39.80 331	30.79 341	56.83 343	16.46 362	39.35 350	30.65 360	25.59 355	45.26 350	61.60 347	24.54 353	28.02 359	21.60 355	52.80 355	47.90 349

E-PMN		45.17 318	45.36 321	44.60 330	50.07 359	42.75 266	38.66 351	42.29 348	46.39 265	39.55 357	51.15 355	26.00 348	45.37 338	37.68 303	76.41 282	45.69 352

EMVS		44.61 322	44.45 326	45.10 329	48.91 361	43.00 264	37.92 352	41.10 354	46.75 263	38.00 359	48.43 357	26.42 346	46.27 333	37.11 309	75.38 292	46.03 351

N_pmnet		52.06 301	51.11 309	54.92 298	59.64 334	71.03 56	37.42 353	61.62 288	33.68 334	57.12 315	72.10 299	37.94 300	31.03 357	29.13 344	71.35 311	62.70 331

new-patchmatchnet		52.89 296	55.76 289	44.26 331	59.94 331	6.31 364	37.36 354	50.76 328	41.10 297	64.28 281	79.82 229	44.77 259	48.43 330	36.24 315	87.61 161	78.03 229

CHOSEN 280x420		41.62 325	39.89 330	46.80 321	61.81 318	51.59 192	33.56 355	35.74 358	27.48 352	37.64 360	53.53 352	23.24 357	42.09 349	27.39 346	58.64 347	46.72 350

PMMVS2		37.74 327	40.87 328	28.36 342	42.41 363	5.35 365	24.61 356	27.75 361	32.15 341	47.85 346	70.27 317	35.85 307	29.51 358	19.08 358	67.85 329	50.22 347

MVE	27.91 23	36.69 329	35.64 332	39.84 339	43.37 362	35.85 317	19.49 357	24.61 363	24.68 356	39.05 358	62.63 346	38.67 297	27.10 360	21.04 356	47.25 357	56.56 343
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		11.98 331	14.73 334	3.72 344	2.28 365	4.62 366	19.44 358	14.50 365	0.47 361	21.55 361	9.58 360	25.78 350	4.57 362	11.61 359	27.37 359	1.96 358

uanet_test		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

cdsmvs_eth3d_5k		17.71 330	23.62 333	0.00 347	0.00 368	0.00 369	0.00 359	70.17 242	0.00 364	0.00 365	74.25 285	68.16 98	0.00 365	0.00 363	0.00 363	0.00 361

pcd_1.5k_mvsjas		5.20 333	6.93 336	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	62.39 144	0.00 365	0.00 363	0.00 363	0.00 361

sosnet-low-res		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

sosnet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uncertanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

Regformer		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ab-mvs-re		5.62 332	7.50 335	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	67.46 336	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

uanet		0.00 336	0.00 339	0.00 347	0.00 368	0.00 369	0.00 359	0.00 369	0.00 364	0.00 365	0.00 365	0.00 370	0.00 365	0.00 363	0.00 363	0.00 361

ZD-MVS					83.91 91	69.36 73		81.09 115	58.91 138	82.73 83	89.11 92	75.77 36	86.63 13	72.73 59	92.93 73

IU-MVS					86.12 57	60.90 136		80.38 128	45.49 269	81.31 97				75.64 41	94.39 41	84.65 98

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	83.25 139

test_241102_ONE					86.12 57	61.06 132		84.72 51	72.64 28	87.38 24	89.47 81	77.48 24	85.74 40

test_0728_THIRD									74.03 21	85.83 41	90.41 58	75.58 38	85.69 41	77.43 33	94.74 29	84.31 115

GSMVS																70.05 292

test_part2					85.90 61	66.44 93				84.61 60

sam_mvs1												31.41 326				70.05 292

sam_mvs												31.21 330

MTGPA								80.63 121

test_post											1.99 362	30.91 333	54.76 322

patchmatchnet-post											68.99 327	31.32 327	69.38 268

gm-plane-assit					62.51 315	33.91 330			37.25 317		62.71 345		72.74 236	38.70 294

test9_res														72.12 67	91.37 99	77.40 234

agg_prior2														70.70 74	90.93 112	78.55 221

agg_prior					84.44 84	66.02 97		78.62 155		76.95 151			80.34 140

TestCases				78.35 69	79.19 150	70.81 58		88.64 2	65.37 80	80.09 113	88.17 109	70.33 79	78.43 173	55.60 187	90.90 114	85.81 74

test_prior				75.27 103	82.15 115	59.85 146		84.33 62					83.39 85			82.58 158

新几何1				69.99 181	88.37 35	71.34 54		62.08 283	43.85 279	74.99 181	86.11 156	52.85 219	70.57 260	50.99 222	83.23 224	68.05 306

旧先验1					84.55 81	60.36 142		63.69 274			87.05 122	54.65 211			83.34 222	69.66 296

原ACMM1				73.90 119	85.90 61	65.15 105		81.67 101	50.97 226	74.25 193	86.16 153	61.60 152	83.54 81	56.75 174	91.08 108	73.00 265

testdata2													67.30 282	48.34 243

segment_acmp												68.30 97

testdata				64.13 242	85.87 63	63.34 118		61.80 287	47.83 256	76.42 169	86.60 141	48.83 241	62.31 307	54.46 200	81.26 243	66.74 315

test12				76.51 88	82.28 113	60.94 135		81.64 102		73.60 199		64.88 127	85.19 57		90.42 123	83.38 135

plane_prior7					85.18 69	66.21 95

plane_prior6					84.18 88	65.31 102						60.83 162

plane_prior5								85.49 32					86.15 26	71.09 71	90.94 110	84.82 93

plane_prior4											89.11 92

plane_prior3						65.67 99			63.82 98	78.23 132

plane_prior1					84.46 83

n2								0.00 369

nn								0.00 369

door-mid								55.02 312

lessismore_v0				72.75 145	79.60 142	56.83 166		57.37 301		83.80 71	89.01 95	47.45 249	78.74 164	64.39 119	86.49 183	82.69 156

LGP-MVS_train				80.90 38	87.00 42	70.41 63		86.35 17	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 89

door								52.91 322

HQP5-MVS						58.80 156

BP-MVS														67.38 100

HQP4-MVS										71.59 224			85.31 50			83.74 126

HQP3-MVS								84.12 70							89.16 144

HQP2-MVS												58.09 186

NP-MVS					83.34 99	63.07 121					85.97 159

ACMMP++_ref															89.47 142

ACMMP++															91.96 88

Test By Simon												62.56 140

ITE_SJBPF				80.35 44	76.94 178	73.60 41		80.48 125	66.87 64	83.64 73	86.18 151	70.25 81	79.90 150	61.12 144	88.95 149	87.56 52

DeepMVS_CX				11.83 343	15.51 364	13.86 363		11.25 366	5.76 360	20.85 362	26.46 358	17.06 364	9.22 361	9.69 360	13.82 360	12.42 357

High-resolution multi-view benchmark