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.

CP-MVS		87.11 34	86.92 35	87.68 34	94.20 31	73.86 6	93.98 1	92.82 58	76.62 68	83.68 77	94.46 25	67.93 84	95.95 52	84.20 44	94.39 55	93.23 78

APDe-MVS		89.15 5	89.63 5	87.73 27	94.49 18	71.69 55	93.83 2	93.96 14	75.70 88	91.06 12	96.03 1	76.84 12	97.03 12	89.09 3	95.65 28	94.47 23

SteuartSystems-ACMMP		88.72 9	88.86 9	88.32 6	92.14 75	72.96 24	93.73 3	93.67 19	80.19 14	88.10 23	94.80 14	73.76 36	97.11 10	87.51 15	95.82 20	94.90 6
Skip Steuart: Steuart Systems R&D Blog.
test0726					95.27 5	71.25 59	93.60 4	94.11 6	77.33 45	92.81 3	95.79 3	80.98 7

SED-MVS		90.08 1	90.85 1	87.77 23	95.30 2	70.98 65	93.57 5	94.06 10	77.24 47	93.10 1	95.72 6	82.99 1	97.44 2	89.07 6	96.63 2	94.88 7

OPU-MVS				89.06 1	94.62 13	75.42 2	93.57 5				94.02 45	82.45 3	96.87 16	83.77 48	96.48 6	94.88 7

DVP-MVS		89.60 2	90.35 2	87.33 42	95.27 5	71.25 59	93.49 7	92.73 59	77.33 45	92.12 8	95.78 4	80.98 7	97.40 4	89.08 4	96.41 8	93.33 75
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				87.71 31	95.34 1	71.43 58	93.49 7	94.23 5					97.49 1	89.08 4	96.41 8	94.21 32

3Dnovator+	77.84 4	85.48 57	84.47 72	88.51 4	91.08 87	73.49 15	93.18 9	93.78 18	80.79 10	76.66 179	93.37 57	60.40 184	96.75 22	77.20 109	93.73 63	95.29 2

HFP-MVS		87.58 22	87.47 24	87.94 15	94.58 14	73.54 13	93.04 10	93.24 33	76.78 63	84.91 52	94.44 28	70.78 58	96.61 29	84.53 37	94.89 42	93.66 58

ACMMPR		87.44 25	87.23 30	88.08 11	94.64 11	73.59 10	93.04 10	93.20 35	76.78 63	84.66 61	94.52 21	68.81 80	96.65 26	84.53 37	94.90 41	94.00 42

ZNCC-MVS		87.94 17	87.85 19	88.20 9	94.39 24	73.33 18	93.03 12	93.81 17	76.81 61	85.24 47	94.32 33	71.76 51	96.93 15	85.53 26	95.79 21	94.32 28

region2R		87.42 27	87.20 31	88.09 10	94.63 12	73.55 11	93.03 12	93.12 38	76.73 66	84.45 64	94.52 21	69.09 77	96.70 23	84.37 40	94.83 46	94.03 39

MSP-MVS		89.51 3	89.91 4	88.30 7	94.28 27	73.46 16	92.90 14	94.11 6	80.27 12	91.35 11	94.16 39	78.35 10	96.77 20	89.59 1	94.22 60	94.67 16
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
XVS		87.18 33	86.91 36	88.00 13	94.42 20	73.33 18	92.78 15	92.99 45	79.14 21	83.67 78	94.17 38	67.45 89	96.60 31	83.06 55	94.50 52	94.07 37

X-MVStestdata		80.37 141	77.83 175	88.00 13	94.42 20	73.33 18	92.78 15	92.99 45	79.14 21	83.67 78	12.47 363	67.45 89	96.60 31	83.06 55	94.50 52	94.07 37

mPP-MVS		86.67 41	86.32 43	87.72 29	94.41 22	73.55 11	92.74 17	92.22 81	76.87 60	82.81 90	94.25 36	66.44 98	96.24 39	82.88 59	94.28 58	93.38 72

ACMMP		85.89 52	85.39 57	87.38 41	93.59 46	72.63 32	92.74 17	93.18 37	76.78 63	80.73 115	93.82 50	64.33 119	96.29 37	82.67 65	90.69 92	93.23 78
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
MP-MVS		87.71 20	87.64 22	87.93 18	94.36 26	73.88 5	92.71 19	92.65 64	77.57 38	83.84 75	94.40 32	72.24 47	96.28 38	85.65 25	95.30 36	93.62 65
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SF-MVS		88.46 10	88.74 10	87.64 35	92.78 64	71.95 50	92.40 20	94.74 2	75.71 86	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

SMA-MVS		89.08 6	89.23 6	88.61 3	94.25 28	73.73 8	92.40 20	93.63 20	74.77 106	92.29 6	95.97 2	74.28 31	97.24 8	88.58 10	96.91 1	94.87 9
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
GST-MVS		87.42 27	87.26 28	87.89 22	94.12 34	72.97 23	92.39 22	93.43 29	76.89 59	84.68 58	93.99 47	70.67 61	96.82 18	84.18 45	95.01 38	93.90 47

HPM-MVS++		89.02 7	89.15 7	88.63 2	95.01 8	76.03 1	92.38 23	92.85 54	80.26 13	87.78 26	94.27 34	75.89 16	96.81 19	87.45 16	96.44 7	93.05 86

SR-MVS		86.73 38	86.67 39	86.91 49	94.11 35	72.11 48	92.37 24	92.56 67	74.50 111	86.84 33	94.65 18	67.31 91	95.77 56	84.80 34	92.85 70	92.84 94

#test#		87.33 30	87.13 32	87.94 15	94.58 14	73.54 13	92.34 25	93.24 33	75.23 97	84.91 52	94.44 28	70.78 58	96.61 29	83.75 49	94.89 42	93.66 58

EPP-MVSNet		83.40 82	83.02 82	84.57 100	90.13 105	64.47 196	92.32 26	90.73 132	74.45 114	79.35 126	91.10 101	69.05 79	95.12 85	72.78 150	87.22 134	94.13 34

PHI-MVS		86.43 44	86.17 48	87.24 43	90.88 92	70.96 67	92.27 27	94.07 9	72.45 146	85.22 48	91.90 81	69.47 73	96.42 35	83.28 53	95.94 16	94.35 26

testtj		87.78 19	87.78 20	87.77 23	94.55 16	72.47 37	92.23 28	93.49 26	74.75 107	88.33 21	94.43 30	73.27 39	97.02 13	84.18 45	94.84 44	93.82 52

HPM-MVS		87.11 34	86.98 34	87.50 38	93.88 38	72.16 46	92.19 29	93.33 32	76.07 80	83.81 76	93.95 48	69.77 71	96.01 49	85.15 28	94.66 48	94.32 28
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MTMP							92.18 30	32.83 366

HPM-MVS_fast		85.35 61	84.95 67	86.57 58	93.69 43	70.58 80	92.15 31	91.62 107	73.89 126	82.67 92	94.09 42	62.60 141	95.54 64	80.93 77	92.93 68	93.57 67

CPTT-MVS		83.73 75	83.33 78	84.92 91	93.28 50	70.86 72	92.09 32	90.38 140	68.75 220	79.57 123	92.83 70	60.60 180	93.04 179	80.92 78	91.56 83	90.86 155

APD-MVS_3200maxsize		85.97 50	85.88 51	86.22 64	92.69 67	69.53 98	91.93 33	92.99 45	73.54 133	85.94 37	94.51 24	65.80 108	95.61 59	83.04 57	92.51 75	93.53 70

SR-MVS-dyc-post		85.77 53	85.61 54	86.23 63	93.06 57	70.63 77	91.88 34	92.27 77	73.53 134	85.69 42	94.45 26	65.00 116	95.56 61	82.75 60	91.87 78	92.50 103

RE-MVS-def			85.48 55		93.06 57	70.63 77	91.88 34	92.27 77	73.53 134	85.69 42	94.45 26	63.87 123		82.75 60	91.87 78	92.50 103

APD-MVS		87.44 25	87.52 23	87.19 44	94.24 29	72.39 40	91.86 36	92.83 55	73.01 143	88.58 19	94.52 21	73.36 37	96.49 34	84.26 42	95.01 38	92.70 96
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		88.06 13	88.50 12	86.71 54	92.60 71	72.71 28	91.81 37	93.19 36	77.87 33	90.32 13	94.00 46	74.83 24	93.78 140	87.63 14	94.27 59	93.65 63
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
test1172		86.20 49	86.22 45	86.12 67	93.95 37	69.89 91	91.79 38	92.28 76	75.07 101	86.40 35	94.58 20	65.00 116	95.56 61	84.34 41	92.60 73	92.90 92

DPE-MVS		89.48 4	89.98 3	88.01 12	94.80 9	72.69 30	91.59 39	94.10 8	75.90 84	92.29 6	95.66 8	81.67 4	97.38 6	87.44 17	96.34 11	93.95 44
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
QAPM		80.88 122	79.50 138	85.03 85	88.01 177	68.97 109	91.59 39	92.00 91	66.63 241	75.15 218	92.16 76	57.70 197	95.45 68	63.52 222	88.76 115	90.66 161

IS-MVSNet		83.15 85	82.81 85	84.18 115	89.94 110	63.30 221	91.59 39	88.46 203	79.04 25	79.49 124	92.16 76	65.10 113	94.28 114	67.71 190	91.86 80	94.95 5

9.14			88.26 14		92.84 63		91.52 42	94.75 1	73.93 125	88.57 20	94.67 17	75.57 20	95.79 55	86.77 20	95.76 24

ETH3D-3000-0.1		88.09 12	88.29 13	87.50 38	92.76 65	71.89 53	91.43 43	94.70 3	74.47 112	88.86 18	94.61 19	75.23 21	95.84 54	86.62 23	95.92 17	94.78 13

TSAR-MVS + MP.		88.02 16	88.11 15	87.72 29	93.68 44	72.13 47	91.41 44	92.35 74	74.62 110	88.90 17	93.85 49	75.75 17	96.00 50	87.80 12	94.63 49	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DeepC-MVS_fast	79.65 3	86.91 37	86.62 40	87.76 26	93.52 47	72.37 41	91.26 45	93.04 39	76.62 68	84.22 69	93.36 58	71.44 54	96.76 21	80.82 79	95.33 34	94.16 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP_MVS		83.64 77	83.14 79	85.14 82	90.08 107	68.71 117	91.25 46	92.44 69	79.12 23	78.92 131	91.00 107	60.42 182	95.38 74	78.71 92	86.32 147	91.33 138

plane_prior2							91.25 46		79.12 23

NCCC		88.06 13	88.01 17	88.24 8	94.41 22	73.62 9	91.22 48	92.83 55	81.50 6	85.79 41	93.47 56	73.02 42	97.00 14	84.90 30	94.94 40	94.10 35

API-MVS		81.99 103	81.23 107	84.26 113	90.94 90	70.18 88	91.10 49	89.32 170	71.51 164	78.66 136	88.28 172	65.26 111	95.10 89	64.74 218	91.23 88	87.51 259

EPNet		83.72 76	82.92 84	86.14 66	84.22 246	69.48 99	91.05 50	85.27 251	81.30 7	76.83 174	91.65 85	66.09 103	95.56 61	76.00 121	93.85 62	93.38 72
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_part1		82.78 92	82.08 96	84.89 92	90.66 95	66.97 153	90.96 51	92.93 51	77.19 50	80.53 117	90.04 124	63.44 126	95.39 73	76.04 120	76.90 248	92.31 110

ACMMP_NAP		88.05 15	88.08 16	87.94 15	93.70 42	73.05 21	90.86 52	93.59 21	76.27 77	88.14 22	95.09 13	71.06 56	96.67 25	87.67 13	96.37 10	94.09 36

CSCG		86.41 46	86.19 47	87.07 47	92.91 60	72.48 36	90.81 53	93.56 23	73.95 123	83.16 83	91.07 103	75.94 15	95.19 83	79.94 87	94.38 56	93.55 68

abl_6		85.23 62	84.95 67	86.07 68	92.23 74	70.48 81	90.80 54	92.08 86	73.51 136	85.26 46	94.16 39	62.75 140	95.92 53	82.46 67	91.30 87	91.81 126

MSLP-MVS++		85.43 59	85.76 53	84.45 104	91.93 78	70.24 82	90.71 55	92.86 53	77.46 44	84.22 69	92.81 72	67.16 93	92.94 181	80.36 83	94.35 57	90.16 179

3Dnovator	76.31 5	83.38 83	82.31 92	86.59 57	87.94 178	72.94 27	90.64 56	92.14 85	77.21 49	75.47 204	92.83 70	58.56 191	94.72 105	73.24 146	92.71 72	92.13 118

ETH3 D test6400		87.50 24	87.44 25	87.70 32	93.71 41	71.75 54	90.62 57	94.05 13	70.80 174	87.59 29	93.51 53	77.57 11	96.63 28	83.31 50	95.77 22	94.72 15

OpenMVS	72.83 10	79.77 151	78.33 164	84.09 118	85.17 231	69.91 89	90.57 58	90.97 127	66.70 237	72.17 250	91.91 80	54.70 219	93.96 128	61.81 241	90.95 90	88.41 243

CNVR-MVS		88.93 8	89.13 8	88.33 5	94.77 10	73.82 7	90.51 59	93.00 43	80.90 9	88.06 24	94.06 44	76.43 13	96.84 17	88.48 11	95.99 15	94.34 27

MVSFormer		82.85 91	82.05 97	85.24 80	87.35 196	70.21 83	90.50 60	90.38 140	68.55 223	81.32 106	89.47 140	61.68 156	93.46 158	78.98 90	90.26 98	92.05 120

test_djsdf		80.30 142	79.32 143	83.27 146	83.98 251	65.37 180	90.50 60	90.38 140	68.55 223	76.19 190	88.70 158	56.44 209	93.46 158	78.98 90	80.14 218	90.97 151

xxxxxxxxxxxxxcwj		87.88 18	87.92 18	87.77 23	93.80 39	72.35 42	90.47 62	89.69 162	74.31 115	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

save fliter					93.80 39	72.35 42	90.47 62	91.17 123	74.31 115

nrg030		83.88 73	83.53 75	84.96 88	86.77 211	69.28 104	90.46 64	92.67 61	74.79 105	82.95 85	91.33 96	72.70 43	93.09 175	80.79 81	79.28 228	92.50 103

canonicalmvs		85.91 51	85.87 52	86.04 69	89.84 112	69.44 103	90.45 65	93.00 43	76.70 67	88.01 25	91.23 97	73.28 38	93.91 135	81.50 72	88.80 114	94.77 14

plane_prior						68.71 117	90.38 66		77.62 36						86.16 150

DeepC-MVS	79.81 2	87.08 36	86.88 37	87.69 33	91.16 86	72.32 44	90.31 67	93.94 15	77.12 53	82.82 89	94.23 37	72.13 49	97.09 11	84.83 33	95.37 31	93.65 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Vis-MVSNet		83.46 80	82.80 86	85.43 76	90.25 103	68.74 115	90.30 68	90.13 150	76.33 76	80.87 114	92.89 68	61.00 173	94.20 120	72.45 153	90.97 89	93.35 74
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
zzz-MVS		87.53 23	87.41 26	87.90 19	94.18 32	74.25 3	90.23 69	92.02 88	79.45 19	85.88 38	94.80 14	68.07 82	96.21 40	86.69 21	95.34 32	93.23 78

PGM-MVS		86.68 40	86.27 44	87.90 19	94.22 30	73.38 17	90.22 70	93.04 39	75.53 90	83.86 74	94.42 31	67.87 86	96.64 27	82.70 64	94.57 51	93.66 58

LPG-MVS_test		82.08 100	81.27 106	84.50 102	89.23 134	68.76 113	90.22 70	91.94 95	75.37 94	76.64 180	91.51 90	54.29 222	94.91 95	78.44 96	83.78 171	89.83 200

Anonymous20231211		78.97 172	77.69 182	82.81 170	90.54 98	64.29 200	90.11 72	91.51 111	65.01 260	76.16 194	88.13 182	50.56 261	93.03 180	69.68 176	77.56 241	91.11 145

ETH3D cwj APD-0.16		87.31 31	87.27 27	87.44 40	91.60 82	72.45 39	90.02 73	94.37 4	71.76 157	87.28 30	94.27 34	75.18 22	96.08 46	85.16 27	95.77 22	93.80 55

ACMM	73.20 8	80.78 132	79.84 130	83.58 135	89.31 131	68.37 125	89.99 74	91.60 108	70.28 185	77.25 165	89.66 133	53.37 230	93.53 154	74.24 134	82.85 186	88.85 230
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	74.13 6	81.51 115	80.57 117	84.36 108	89.42 122	68.69 120	89.97 75	91.50 114	74.46 113	75.04 222	90.41 116	53.82 227	94.54 107	77.56 105	82.91 185	89.86 199
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LFMVS		81.82 106	81.23 107	83.57 136	91.89 79	63.43 219	89.84 76	81.85 297	77.04 56	83.21 81	93.10 62	52.26 238	93.43 160	71.98 154	89.95 104	93.85 49

MCST-MVS		87.37 29	87.25 29	87.73 27	94.53 17	72.46 38	89.82 77	93.82 16	73.07 141	84.86 57	92.89 68	76.22 14	96.33 36	84.89 32	95.13 37	94.40 24

MAR-MVS		81.84 105	80.70 115	85.27 79	91.32 85	71.53 57	89.82 77	90.92 128	69.77 195	78.50 139	86.21 233	62.36 147	94.52 109	65.36 212	92.05 76	89.77 203
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
MP-MVS-pluss		87.67 21	87.72 21	87.54 36	93.64 45	72.04 49	89.80 79	93.50 24	75.17 100	86.34 36	95.29 10	70.86 57	96.00 50	88.78 9	96.04 12	94.58 19
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
UA-Net		85.08 66	84.96 66	85.45 75	92.07 76	68.07 132	89.78 80	90.86 131	82.48 2	84.60 63	93.20 60	69.35 74	95.22 81	71.39 159	90.88 91	93.07 85

alignmvs		85.48 57	85.32 59	85.96 71	89.51 119	69.47 100	89.74 81	92.47 68	76.17 78	87.73 28	91.46 93	70.32 63	93.78 140	81.51 71	88.95 111	94.63 18

VDDNet		81.52 113	80.67 116	84.05 121	90.44 100	64.13 203	89.73 82	85.91 246	71.11 169	83.18 82	93.48 54	50.54 262	93.49 155	73.40 143	88.25 122	94.54 22

CANet		86.45 43	86.10 49	87.51 37	90.09 106	70.94 69	89.70 83	92.59 66	81.78 4	81.32 106	91.43 94	70.34 62	97.23 9	84.26 42	93.36 66	94.37 25

114514_t		80.68 133	79.51 137	84.20 114	94.09 36	67.27 147	89.64 84	91.11 125	58.75 318	74.08 232	90.72 111	58.10 193	95.04 91	69.70 175	89.42 109	90.30 175

DeepPCF-MVS	80.84 1	88.10 11	88.56 11	86.73 53	92.24 73	69.03 105	89.57 85	93.39 31	77.53 42	89.79 14	94.12 41	78.98 9	96.58 33	85.66 24	95.72 25	94.58 19

UGNet		80.83 126	79.59 136	84.54 101	88.04 175	68.09 131	89.42 86	88.16 205	76.95 57	76.22 189	89.46 142	49.30 276	93.94 131	68.48 185	90.31 96	91.60 129
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
AdaColmap		80.58 137	79.42 139	84.06 120	93.09 56	68.91 110	89.36 87	88.97 188	69.27 204	75.70 201	89.69 132	57.20 205	95.77 56	63.06 228	88.41 121	87.50 260

mvs-test1		80.88 122	79.40 140	85.29 78	85.13 234	69.75 94	89.28 88	88.10 207	74.99 102	76.44 185	86.72 213	57.27 203	94.26 119	73.53 139	83.18 182	91.87 123

PS-MVSNAJss		82.07 101	81.31 105	84.34 110	86.51 214	67.27 147	89.27 89	91.51 111	71.75 158	79.37 125	90.22 120	63.15 134	94.27 115	77.69 104	82.36 193	91.49 134

jajsoiax		79.29 163	77.96 170	83.27 146	84.68 240	66.57 158	89.25 90	90.16 149	69.20 208	75.46 206	89.49 139	45.75 300	93.13 173	76.84 114	80.80 208	90.11 183

mvs_tets		79.13 167	77.77 178	83.22 151	84.70 239	66.37 160	89.17 91	90.19 148	69.38 202	75.40 209	89.46 142	44.17 308	93.15 171	76.78 115	80.70 210	90.14 180

HQP-NCC					89.33 126		89.17 91		76.41 70	77.23 167

ACMP_Plane					89.33 126		89.17 91		76.41 70	77.23 167

HQP-MVS		82.61 95	82.02 98	84.37 107	89.33 126	66.98 151	89.17 91	92.19 83	76.41 70	77.23 167	90.23 119	60.17 185	95.11 86	77.47 106	85.99 153	91.03 148

LS3D		76.95 216	74.82 228	83.37 143	90.45 99	67.36 146	89.15 95	86.94 232	61.87 294	69.52 279	90.61 113	51.71 250	94.53 108	46.38 333	86.71 142	88.21 245

OPM-MVS		83.50 79	82.95 83	85.14 82	88.79 151	70.95 68	89.13 96	91.52 110	77.55 41	80.96 113	91.75 83	60.71 176	94.50 110	79.67 88	86.51 145	89.97 195

TSAR-MVS + GP.		85.71 55	85.33 58	86.84 50	91.34 84	72.50 35	89.07 97	87.28 226	76.41 70	85.80 40	90.22 120	74.15 34	95.37 77	81.82 70	91.88 77	92.65 100

test_prior4						72.60 33	89.01 98

GeoE		81.71 108	81.01 112	83.80 132	89.51 119	64.45 197	88.97 99	88.73 198	71.27 167	78.63 137	89.76 130	66.32 100	93.20 167	69.89 173	86.02 152	93.74 56

Anonymous20240529		80.19 145	78.89 151	84.10 117	90.60 96	64.75 189	88.95 100	90.90 129	65.97 249	80.59 116	91.17 100	49.97 267	93.73 146	69.16 181	82.70 190	93.81 53

VDD-MVS		83.01 90	82.36 91	84.96 88	91.02 89	66.40 159	88.91 101	88.11 206	77.57 38	84.39 67	93.29 59	52.19 239	93.91 135	77.05 111	88.70 116	94.57 21

Effi-MVS+		83.62 78	83.08 80	85.24 80	88.38 166	67.45 142	88.89 102	89.15 179	75.50 91	82.27 93	88.28 172	69.61 72	94.45 111	77.81 103	87.84 124	93.84 51

ACMH+	68.96 14	76.01 230	74.01 237	82.03 188	88.60 158	65.31 181	88.86 103	87.55 220	70.25 186	67.75 290	87.47 194	41.27 323	93.19 169	58.37 270	75.94 264	87.60 256

test_prior3		86.73 38	86.86 38	86.33 60	92.61 69	69.59 96	88.85 104	92.97 48	75.41 92	84.91 52	93.54 51	74.28 31	95.48 66	83.31 50	95.86 18	93.91 45

test_prior2							88.85 104		75.41 92	84.91 52	93.54 51	74.28 31		83.31 50	95.86 18

CS-MVS		84.53 70	84.97 65	83.23 150	87.54 195	63.27 222	88.82 106	93.50 24	75.98 83	83.07 84	89.73 131	70.29 64	95.23 80	82.07 69	93.70 64	91.18 142

DP-MVS Recon		83.11 88	82.09 95	86.15 65	94.44 19	70.92 71	88.79 107	92.20 82	70.53 181	79.17 127	91.03 106	64.12 121	96.03 47	68.39 187	90.14 100	91.50 133

Effi-MVS+-dtu		80.03 147	78.57 156	84.42 105	85.13 234	68.74 115	88.77 108	88.10 207	74.99 102	74.97 223	83.49 277	57.27 203	93.36 161	73.53 139	80.88 206	91.18 142

TEST9					93.26 51	72.96 24	88.75 109	91.89 97	68.44 225	85.00 50	93.10 62	74.36 30	95.41 71

train_agg		86.43 44	86.20 46	87.13 46	93.26 51	72.96 24	88.75 109	91.89 97	68.69 221	85.00 50	93.10 62	74.43 27	95.41 71	84.97 29	95.71 26	93.02 88

ETV-MVS		84.90 69	84.67 70	85.59 74	89.39 124	68.66 121	88.74 111	92.64 65	79.97 17	84.10 71	85.71 241	69.32 75	95.38 74	80.82 79	91.37 85	92.72 95

PVSNet_Blended_VisFu		82.62 94	81.83 102	84.96 88	90.80 94	69.76 93	88.74 111	91.70 106	69.39 201	78.96 129	88.46 167	65.47 110	94.87 100	74.42 131	88.57 117	90.24 177

RRT_MVS		79.88 150	78.38 161	84.38 106	85.42 228	70.60 79	88.71 113	88.75 197	72.30 151	78.83 133	89.14 148	44.44 306	92.18 204	78.50 95	79.33 227	90.35 173

test_8					93.13 53	72.57 34	88.68 114	91.84 100	68.69 221	84.87 56	93.10 62	74.43 27	95.16 84

CS-MVS-test		84.27 71	84.52 71	83.55 137	87.94 178	64.55 191	88.65 115	93.57 22	75.26 96	81.78 101	88.17 176	70.27 65	95.22 81	81.41 73	93.62 65	90.87 154

ACMH	67.68 16	75.89 231	73.93 238	81.77 193	88.71 155	66.61 157	88.62 116	89.01 185	69.81 193	66.78 302	86.70 218	41.95 322	91.51 225	55.64 289	78.14 236	87.17 268
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
agg_prior1		86.22 48	86.09 50	86.62 56	92.85 61	71.94 51	88.59 117	91.78 103	68.96 216	84.41 65	93.18 61	74.94 23	94.93 93	84.75 35	95.33 34	93.01 89

CDPH-MVS		85.76 54	85.29 61	87.17 45	93.49 48	71.08 63	88.58 118	92.42 72	68.32 226	84.61 62	93.48 54	72.32 46	96.15 45	79.00 89	95.43 30	94.28 30

DP-MVS		76.78 218	74.57 230	83.42 140	93.29 49	69.46 102	88.55 119	83.70 271	63.98 274	70.20 267	88.89 155	54.01 226	94.80 102	46.66 330	81.88 198	86.01 292

Regformer-1		86.41 46	86.33 42	86.64 55	89.33 126	70.93 70	88.43 120	91.39 116	82.14 3	86.65 34	90.09 122	74.39 29	95.01 92	83.97 47	90.63 93	93.97 43

Regformer-2		86.63 42	86.53 41	86.95 48	89.33 126	71.24 62	88.43 120	92.05 87	82.50 1	86.88 32	90.09 122	74.45 26	95.61 59	84.38 39	90.63 93	94.01 41

WR-MVS_H		78.51 181	78.49 157	78.56 254	88.02 176	56.38 307	88.43 120	92.67 61	77.14 52	73.89 233	87.55 191	66.25 101	89.24 266	58.92 264	73.55 296	90.06 189

F-COLMAP		76.38 226	74.33 235	82.50 180	89.28 132	66.95 155	88.41 123	89.03 183	64.05 272	66.83 301	88.61 162	46.78 290	92.89 182	57.48 277	78.55 230	87.67 254

GBi-Net		78.40 182	77.40 187	81.40 201	87.60 190	63.01 228	88.39 124	89.28 171	71.63 160	75.34 211	87.28 197	54.80 215	91.11 233	62.72 229	79.57 221	90.09 185

test1		78.40 182	77.40 187	81.40 201	87.60 190	63.01 228	88.39 124	89.28 171	71.63 160	75.34 211	87.28 197	54.80 215	91.11 233	62.72 229	79.57 221	90.09 185

FMVSNet1		77.44 207	76.12 213	81.40 201	86.81 210	63.01 228	88.39 124	89.28 171	70.49 182	74.39 229	87.28 197	49.06 279	91.11 233	60.91 248	78.52 231	90.09 185

tttt0517		79.40 160	77.91 172	83.90 131	88.10 173	63.84 207	88.37 127	84.05 267	71.45 165	76.78 176	89.12 150	49.93 270	94.89 98	70.18 169	83.18 182	92.96 91

v7n		78.97 172	77.58 185	83.14 154	83.45 259	65.51 175	88.32 128	91.21 121	73.69 129	72.41 247	86.32 232	57.93 194	93.81 139	69.18 180	75.65 267	90.11 183

COLMAP_ROB	66.92 17	73.01 260	70.41 271	80.81 217	87.13 205	65.63 173	88.30 129	84.19 266	62.96 282	63.80 325	87.69 187	38.04 335	92.56 190	46.66 330	74.91 283	84.24 311
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Regformer-3		85.23 62	85.07 63	85.70 73	88.95 143	69.01 107	88.29 130	89.91 156	80.95 8	85.01 49	90.01 125	72.45 45	94.19 121	82.50 66	87.57 126	93.90 47

Regformer-4		85.68 56	85.45 56	86.35 59	88.95 143	69.67 95	88.29 130	91.29 118	81.73 5	85.36 45	90.01 125	72.62 44	95.35 78	83.28 53	87.57 126	94.03 39

FIs		82.07 101	82.42 88	81.04 213	88.80 150	58.34 276	88.26 132	93.49 26	76.93 58	78.47 141	91.04 104	69.92 69	92.34 198	69.87 174	84.97 159	92.44 107

EIA-MVS		83.31 84	82.80 86	84.82 94	89.59 115	65.59 174	88.21 133	92.68 60	74.66 109	78.96 129	86.42 229	69.06 78	95.26 79	75.54 126	90.09 101	93.62 65

PLC	70.83 11	78.05 194	76.37 211	83.08 157	91.88 80	67.80 136	88.19 134	89.46 167	64.33 268	69.87 276	88.38 169	53.66 228	93.58 149	58.86 265	82.73 188	87.86 251
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MG-MVS		83.41 81	83.45 76	83.28 145	92.74 66	62.28 238	88.17 135	89.50 166	75.22 98	81.49 105	92.74 73	66.75 94	95.11 86	72.85 149	91.58 82	92.45 106

TAPA-MVS	73.13 9	79.15 166	77.94 171	82.79 173	89.59 115	62.99 231	88.16 136	91.51 111	65.77 250	77.14 171	91.09 102	60.91 174	93.21 165	50.26 313	87.05 136	92.17 117
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
hse-mvs3		83.15 85	82.19 93	86.02 70	90.56 97	70.85 73	88.15 137	89.16 178	76.02 81	84.67 59	91.39 95	61.54 159	95.50 65	82.71 62	75.48 271	91.72 128

PS-CasMVS		78.01 196	78.09 168	77.77 266	87.71 187	54.39 321	88.02 138	91.22 120	77.50 43	73.26 237	88.64 161	60.73 175	88.41 280	61.88 239	73.88 293	90.53 167

OMC-MVS		82.69 93	81.97 100	84.85 93	88.75 153	67.42 143	87.98 139	90.87 130	74.92 104	79.72 122	91.65 85	62.19 151	93.96 128	75.26 128	86.42 146	93.16 83

v8		79.97 149	79.02 149	82.80 171	84.09 248	64.50 195	87.96 140	90.29 147	74.13 122	75.24 216	86.81 210	62.88 139	93.89 137	74.39 132	75.40 275	90.00 191

FC-MVSNet-test		81.52 113	82.02 98	80.03 230	88.42 165	55.97 312	87.95 141	93.42 30	77.10 54	77.38 162	90.98 109	69.96 68	91.79 216	68.46 186	84.50 164	92.33 108

CP-MVSNet		78.22 187	78.34 163	77.84 264	87.83 182	54.54 319	87.94 142	91.17 123	77.65 35	73.48 235	88.49 166	62.24 150	88.43 279	62.19 235	74.07 289	90.55 166

PAPM_NR		83.02 89	82.41 89	84.82 94	92.47 72	66.37 160	87.93 143	91.80 101	73.82 127	77.32 164	90.66 112	67.90 85	94.90 97	70.37 167	89.48 108	93.19 82

PEN-MVS		77.73 201	77.69 182	77.84 264	87.07 206	53.91 324	87.91 144	91.18 122	77.56 40	73.14 239	88.82 157	61.23 168	89.17 267	59.95 254	72.37 304	90.43 170

v10		79.74 152	78.67 153	82.97 164	84.06 249	64.95 186	87.88 145	90.62 134	73.11 140	75.11 219	86.56 225	61.46 162	94.05 127	73.68 137	75.55 269	89.90 197

casdiffmvs		85.11 65	85.14 62	85.01 86	87.20 203	65.77 172	87.75 146	92.83 55	77.84 34	84.36 68	92.38 74	72.15 48	93.93 134	81.27 75	90.48 95	95.33 1

TranMVSNet+NR-MVSNet		80.84 124	80.31 123	82.42 181	87.85 181	62.33 236	87.74 147	91.33 117	80.55 11	77.99 152	89.86 127	65.23 112	92.62 187	67.05 200	75.24 281	92.30 111

EI-MVSNet-Vis-set		84.19 72	83.81 74	85.31 77	88.18 170	67.85 135	87.66 148	89.73 161	80.05 16	82.95 85	89.59 137	70.74 60	94.82 101	80.66 82	84.72 162	93.28 77

UniMVSNet (Re)		81.60 112	81.11 109	83.09 156	88.38 166	64.41 198	87.60 149	93.02 42	78.42 31	78.56 138	88.16 177	69.78 70	93.26 164	69.58 177	76.49 255	91.60 129

CNLPA		78.08 192	76.79 201	81.97 190	90.40 101	71.07 64	87.59 150	84.55 259	66.03 248	72.38 248	89.64 134	57.56 199	86.04 298	59.61 257	83.35 179	88.79 233

DTE-MVSNet		76.99 214	76.80 200	77.54 271	86.24 216	53.06 331	87.52 151	90.66 133	77.08 55	72.50 245	88.67 160	60.48 181	89.52 261	57.33 280	70.74 315	90.05 190

无先验							87.48 152	88.98 186	60.00 306				94.12 124	67.28 195		88.97 225

FMVSNet2		78.20 189	77.21 191	81.20 208	87.60 190	62.89 232	87.47 153	89.02 184	71.63 160	75.29 215	87.28 197	54.80 215	91.10 236	62.38 233	79.38 225	89.61 207

EI-MVSNet-UG-set		83.81 74	83.38 77	85.09 84	87.87 180	67.53 141	87.44 154	89.66 163	79.74 18	82.23 94	89.41 146	70.24 66	94.74 104	79.95 86	83.92 170	92.99 90

thisisatest0530		79.40 160	77.76 179	84.31 111	87.69 189	65.10 185	87.36 155	84.26 265	70.04 188	77.42 161	88.26 174	49.94 268	94.79 103	70.20 168	84.70 163	93.03 87

CANet_DTU		80.61 134	79.87 129	82.83 168	85.60 225	63.17 227	87.36 155	88.65 199	76.37 74	75.88 198	88.44 168	53.51 229	93.07 176	73.30 144	89.74 106	92.25 113

baseline		84.93 67	84.98 64	84.80 96	87.30 201	65.39 179	87.30 157	92.88 52	77.62 36	84.04 73	92.26 75	71.81 50	93.96 128	81.31 74	90.30 97	95.03 4

UniMVSNet_ETH3D		79.10 168	78.24 166	81.70 194	86.85 208	60.24 262	87.28 158	88.79 192	74.25 118	76.84 173	90.53 115	49.48 273	91.56 222	67.98 188	82.15 194	93.29 76

anonymousdsp		78.60 179	77.15 192	82.98 163	80.51 313	67.08 149	87.24 159	89.53 165	65.66 252	75.16 217	87.19 203	52.52 233	92.25 201	77.17 110	79.34 226	89.61 207

UniMVSNet_NR-MVSNet		81.88 104	81.54 104	82.92 165	88.46 163	63.46 217	87.13 160	92.37 73	80.19 14	78.38 142	89.14 148	71.66 53	93.05 177	70.05 170	76.46 256	92.25 113

DPM-MVS		84.93 67	84.29 73	86.84 50	90.20 104	73.04 22	87.12 161	93.04 39	69.80 194	82.85 88	91.22 98	73.06 41	96.02 48	76.72 116	94.63 49	91.46 136

v1144		80.03 147	79.03 148	83.01 161	83.78 254	64.51 193	87.11 162	90.57 136	71.96 156	78.08 151	86.20 234	61.41 163	93.94 131	74.93 129	77.23 243	90.60 164

v2v482		80.23 143	79.29 144	83.05 159	83.62 256	64.14 202	87.04 163	89.97 153	73.61 130	78.18 148	87.22 201	61.10 171	93.82 138	76.11 118	76.78 253	91.18 142

DU-MVS		81.12 120	80.52 119	82.90 166	87.80 183	63.46 217	87.02 164	91.87 99	79.01 26	78.38 142	89.07 151	65.02 114	93.05 177	70.05 170	76.46 256	92.20 115

v144192		79.47 157	78.37 162	82.78 174	83.35 260	63.96 205	86.96 165	90.36 143	69.99 189	77.50 159	85.67 243	60.66 178	93.77 142	74.27 133	76.58 254	90.62 162

Fast-Effi-MVS+-dtu		78.02 195	76.49 208	82.62 178	83.16 268	66.96 154	86.94 166	87.45 224	72.45 146	71.49 257	84.17 266	54.79 218	91.58 221	67.61 191	80.31 215	89.30 213

v1192		79.59 154	78.43 160	83.07 158	83.55 258	64.52 192	86.93 167	90.58 135	70.83 173	77.78 155	85.90 237	59.15 188	93.94 131	73.96 136	77.19 245	90.76 157

EPNet_dtu		75.46 236	74.86 227	77.23 276	82.57 283	54.60 318	86.89 168	83.09 285	71.64 159	66.25 309	85.86 239	55.99 210	88.04 284	54.92 291	86.55 144	89.05 220
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM2							86.86 169

VPA-MVSNet		80.60 135	80.55 118	80.76 218	88.07 174	60.80 255	86.86 169	91.58 109	75.67 89	80.24 119	89.45 144	63.34 128	90.25 250	70.51 166	79.22 229	91.23 141

v1921920		79.22 164	78.03 169	82.80 171	83.30 262	63.94 206	86.80 171	90.33 144	69.91 192	77.48 160	85.53 246	58.44 192	93.75 144	73.60 138	76.85 251	90.71 160

IterMVS-LS		80.06 146	79.38 141	82.11 185	85.89 220	63.20 225	86.79 172	89.34 169	74.19 119	75.45 207	86.72 213	66.62 95	92.39 195	72.58 151	76.86 250	90.75 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		75.39 239	74.56 231	77.86 263	85.50 227	57.10 295	86.78 173	86.09 245	72.17 153	71.53 256	87.34 196	63.01 138	89.31 265	56.84 284	61.83 337	87.17 268

Baseline_NR-MVSNet		78.15 191	78.33 164	77.61 269	85.79 221	56.21 310	86.78 173	85.76 247	73.60 131	77.93 153	87.57 190	65.02 114	88.99 270	67.14 199	75.33 277	87.63 255

PAPR		81.66 111	80.89 114	83.99 127	90.27 102	64.00 204	86.76 175	91.77 105	68.84 219	77.13 172	89.50 138	67.63 87	94.88 99	67.55 192	88.52 119	93.09 84

Vis-MVSNet (Re-imp)		78.36 185	78.45 158	78.07 262	88.64 157	51.78 334	86.70 176	79.63 318	74.14 121	75.11 219	90.83 110	61.29 167	89.75 257	58.10 273	91.60 81	92.69 98

pmmvs6		74.69 242	73.39 243	78.61 253	81.38 301	57.48 291	86.64 177	87.95 212	64.99 261	70.18 268	86.61 221	50.43 263	89.52 261	62.12 237	70.18 317	88.83 231

v1240		78.99 171	77.78 177	82.64 177	83.21 264	63.54 214	86.62 178	90.30 146	69.74 198	77.33 163	85.68 242	57.04 206	93.76 143	73.13 147	76.92 247	90.62 162

MTAPA		87.23 32	87.00 33	87.90 19	94.18 32	74.25 3	86.58 179	92.02 88	79.45 19	85.88 38	94.80 14	68.07 82	96.21 40	86.69 21	95.34 32	93.23 78

旧先验2							86.56 180		58.10 321	87.04 31			88.98 271	74.07 135

RRT_test8_iter05		78.38 184	77.40 187	81.34 204	86.00 219	58.86 271	86.55 181	91.26 119	72.13 155	75.91 196	87.42 195	44.97 303	93.73 146	77.02 112	75.30 278	91.45 137

FMVSNet3		77.88 199	76.85 199	80.97 214	86.84 209	62.36 235	86.52 182	88.77 193	71.13 168	75.34 211	86.66 220	54.07 225	91.10 236	62.72 229	79.57 221	89.45 210

pm-mvs1		77.25 211	76.68 206	78.93 249	84.22 246	58.62 274	86.41 183	88.36 204	71.37 166	73.31 236	88.01 183	61.22 169	89.15 268	64.24 220	73.01 301	89.03 221

EI-MVSNet		80.52 138	79.98 127	82.12 184	84.28 244	63.19 226	86.41 183	88.95 189	74.18 120	78.69 134	87.54 192	66.62 95	92.43 193	72.57 152	80.57 212	90.74 159

CVMVSNet		72.99 261	72.58 250	74.25 301	84.28 244	50.85 340	86.41 183	83.45 278	44.56 348	73.23 238	87.54 192	49.38 274	85.70 300	65.90 208	78.44 233	86.19 287

NR-MVSNet		80.23 143	79.38 141	82.78 174	87.80 183	63.34 220	86.31 186	91.09 126	79.01 26	72.17 250	89.07 151	67.20 92	92.81 186	66.08 207	75.65 267	92.20 115

v148		78.72 176	77.80 176	81.47 199	82.73 279	61.96 242	86.30 187	88.08 209	73.26 139	76.18 191	85.47 248	62.46 145	92.36 197	71.92 155	73.82 294	90.09 185

新几何2							86.29 188

test_yl		81.17 118	80.47 120	83.24 148	89.13 138	63.62 210	86.21 189	89.95 154	72.43 149	81.78 101	89.61 135	57.50 200	93.58 149	70.75 162	86.90 138	92.52 101

DCV-MVSNet		81.17 118	80.47 120	83.24 148	89.13 138	63.62 210	86.21 189	89.95 154	72.43 149	81.78 101	89.61 135	57.50 200	93.58 149	70.75 162	86.90 138	92.52 101

PVSNet_BlendedMVS		80.60 135	80.02 126	82.36 183	88.85 145	65.40 177	86.16 191	92.00 91	69.34 203	78.11 149	86.09 236	66.02 105	94.27 115	71.52 156	82.06 195	87.39 261

MVS_Test		83.15 85	83.06 81	83.41 142	86.86 207	63.21 224	86.11 192	92.00 91	74.31 115	82.87 87	89.44 145	70.03 67	93.21 165	77.39 108	88.50 120	93.81 53

BH-untuned		79.47 157	78.60 155	82.05 187	89.19 136	65.91 168	86.07 193	88.52 202	72.18 152	75.42 208	87.69 187	61.15 170	93.54 153	60.38 251	86.83 140	86.70 280

MVS_111021_HR		85.14 64	84.75 69	86.32 62	91.65 81	72.70 29	85.98 194	90.33 144	76.11 79	82.08 95	91.61 88	71.36 55	94.17 123	81.02 76	92.58 74	92.08 119

jason		81.39 116	80.29 124	84.70 98	86.63 213	69.90 90	85.95 195	86.77 234	63.24 277	81.07 112	89.47 140	61.08 172	92.15 205	78.33 99	90.07 103	92.05 120
jason: jason.
test_0402		72.79 263	70.44 270	79.84 234	88.13 171	65.99 165	85.93 196	84.29 263	65.57 253	67.40 295	85.49 247	46.92 289	92.61 188	35.88 351	74.38 288	80.94 335

OurMVSNet-221017-0		74.26 245	72.42 252	79.80 235	83.76 255	59.59 267	85.92 197	86.64 235	66.39 243	66.96 298	87.58 189	39.46 329	91.60 220	65.76 210	69.27 319	88.22 244

hse-mvs2		81.72 107	80.94 113	84.07 119	88.72 154	67.68 139	85.87 198	87.26 227	76.02 81	84.67 59	88.22 175	61.54 159	93.48 156	82.71 62	73.44 298	91.06 146

EG-PatchMatch MVS		74.04 248	71.82 257	80.71 219	84.92 237	67.42 143	85.86 199	88.08 209	66.04 247	64.22 321	83.85 270	35.10 343	92.56 190	57.44 278	80.83 207	82.16 329

AUN-MVS		79.21 165	77.60 184	84.05 121	88.71 155	67.61 140	85.84 200	87.26 227	69.08 211	77.23 167	88.14 181	53.20 232	93.47 157	75.50 127	73.45 297	91.06 146

thres100view900		76.50 221	75.55 217	79.33 243	89.52 118	56.99 296	85.83 201	83.23 281	73.94 124	76.32 187	87.12 205	51.89 247	91.95 211	48.33 321	83.75 173	89.07 215

CLD-MVS		82.31 97	81.65 103	84.29 112	88.47 162	67.73 138	85.81 202	92.35 74	75.78 85	78.33 144	86.58 224	64.01 122	94.35 112	76.05 119	87.48 131	90.79 156
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SixPastTwentyTwo		73.37 254	71.26 264	79.70 236	85.08 236	57.89 284	85.57 203	83.56 274	71.03 171	65.66 311	85.88 238	42.10 320	92.57 189	59.11 262	63.34 336	88.65 237

xiu_mvs_v1_base_debu		80.80 129	79.72 133	84.03 124	87.35 196	70.19 85	85.56 204	88.77 193	69.06 212	81.83 97	88.16 177	50.91 256	92.85 183	78.29 100	87.56 128	89.06 217

xiu_mvs_v1_base		80.80 129	79.72 133	84.03 124	87.35 196	70.19 85	85.56 204	88.77 193	69.06 212	81.83 97	88.16 177	50.91 256	92.85 183	78.29 100	87.56 128	89.06 217

xiu_mvs_v1_base_debi		80.80 129	79.72 133	84.03 124	87.35 196	70.19 85	85.56 204	88.77 193	69.06 212	81.83 97	88.16 177	50.91 256	92.85 183	78.29 100	87.56 128	89.06 217

V42		79.38 162	78.24 166	82.83 168	81.10 307	65.50 176	85.55 207	89.82 157	71.57 163	78.21 146	86.12 235	60.66 178	93.18 170	75.64 123	75.46 273	89.81 202

lupinMVS		81.39 116	80.27 125	84.76 97	87.35 196	70.21 83	85.55 207	86.41 238	62.85 284	81.32 106	88.61 162	61.68 156	92.24 202	78.41 98	90.26 98	91.83 124

Fast-Effi-MVS+		80.81 127	79.92 128	83.47 138	88.85 145	64.51 193	85.53 209	89.39 168	70.79 175	78.49 140	85.06 257	67.54 88	93.58 149	67.03 201	86.58 143	92.32 109

thres600view7		76.50 221	75.44 219	79.68 237	89.40 123	57.16 293	85.53 209	83.23 281	73.79 128	76.26 188	87.09 206	51.89 247	91.89 214	48.05 326	83.72 176	90.00 191

DELS-MVS		85.41 60	85.30 60	85.77 72	88.49 161	67.93 134	85.52 211	93.44 28	78.70 28	83.63 80	89.03 153	74.57 25	95.71 58	80.26 85	94.04 61	93.66 58
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
tfpn200view9		76.42 224	75.37 223	79.55 242	89.13 138	57.65 288	85.17 212	83.60 272	73.41 137	76.45 182	86.39 230	52.12 240	91.95 211	48.33 321	83.75 173	89.07 215

thres400		76.50 221	75.37 223	79.86 233	89.13 138	57.65 288	85.17 212	83.60 272	73.41 137	76.45 182	86.39 230	52.12 240	91.95 211	48.33 321	83.75 173	90.00 191

MVS_111021_LR		82.61 95	82.11 94	84.11 116	88.82 148	71.58 56	85.15 214	86.16 243	74.69 108	80.47 118	91.04 104	62.29 148	90.55 247	80.33 84	90.08 102	90.20 178

baseline1		76.98 215	76.75 204	77.66 267	88.13 171	55.66 315	85.12 215	81.89 295	73.04 142	76.79 175	88.90 154	62.43 146	87.78 287	63.30 226	71.18 313	89.55 209

WR-MVS		79.49 156	79.22 146	80.27 227	88.79 151	58.35 275	85.06 216	88.61 201	78.56 29	77.65 157	88.34 170	63.81 125	90.66 246	64.98 216	77.22 244	91.80 127

ET-MVSNet_ETH3D		78.63 178	76.63 207	84.64 99	86.73 212	69.47 100	85.01 217	84.61 258	69.54 199	66.51 307	86.59 222	50.16 265	91.75 217	76.26 117	84.24 168	92.69 98

OpenMVS_ROB	64.09 19	70.56 278	68.19 283	77.65 268	80.26 314	59.41 269	85.01 217	82.96 287	58.76 317	65.43 313	82.33 290	37.63 337	91.23 232	45.34 338	76.03 263	82.32 327

BH-RMVSNet		79.61 153	78.44 159	83.14 154	89.38 125	65.93 167	84.95 219	87.15 229	73.56 132	78.19 147	89.79 129	56.67 208	93.36 161	59.53 258	86.74 141	90.13 181

BH-w/o		78.21 188	77.33 190	80.84 216	88.81 149	65.13 184	84.87 220	87.85 216	69.75 196	74.52 228	84.74 261	61.34 165	93.11 174	58.24 272	85.84 155	84.27 310

TDRefinement		67.49 297	64.34 306	76.92 278	73.47 350	61.07 251	84.86 221	82.98 286	59.77 308	58.30 341	85.13 255	26.06 353	87.89 285	47.92 327	60.59 341	81.81 331

Anonymous202405211		78.25 186	77.01 194	81.99 189	91.03 88	60.67 256	84.77 222	83.90 269	70.65 180	80.00 120	91.20 99	41.08 325	91.43 226	65.21 213	85.26 157	93.85 49

TAMVS		78.89 174	77.51 186	83.03 160	87.80 183	67.79 137	84.72 223	85.05 254	67.63 228	76.75 177	87.70 186	62.25 149	90.82 242	58.53 269	87.13 135	90.49 168

1314		76.53 220	75.30 225	80.21 228	83.93 252	62.32 237	84.66 224	88.81 191	60.23 304	70.16 270	84.07 268	55.30 213	90.73 245	67.37 194	83.21 181	87.59 258

1121		80.84 124	79.77 131	84.05 121	93.11 55	70.78 74	84.66 224	85.42 250	57.37 327	81.76 104	92.02 78	63.41 127	94.12 124	67.28 195	92.93 68	87.26 266

MVS		78.19 190	76.99 196	81.78 192	85.66 223	66.99 150	84.66 224	90.47 138	55.08 337	72.02 252	85.27 251	63.83 124	94.11 126	66.10 206	89.80 105	84.24 311

tfpnnormal		74.39 243	73.16 246	78.08 261	86.10 218	58.05 279	84.65 227	87.53 221	70.32 184	71.22 259	85.63 244	54.97 214	89.86 255	43.03 342	75.02 282	86.32 284

TR-MVS		77.44 207	76.18 212	81.20 208	88.24 169	63.24 223	84.61 228	86.40 239	67.55 230	77.81 154	86.48 228	54.10 224	93.15 171	57.75 276	82.72 189	87.20 267

AllTest		70.96 274	68.09 286	79.58 240	85.15 232	63.62 210	84.58 229	79.83 316	62.31 290	60.32 335	86.73 211	32.02 348	88.96 273	50.28 311	71.57 311	86.15 288

EU-MVSNet		68.53 294	67.61 294	71.31 318	78.51 331	47.01 349	84.47 230	84.27 264	42.27 349	66.44 308	84.79 260	40.44 327	83.76 313	58.76 267	68.54 324	83.17 320

VNet		82.21 98	82.41 89	81.62 195	90.82 93	60.93 252	84.47 230	89.78 158	76.36 75	84.07 72	91.88 82	64.71 118	90.26 249	70.68 164	88.89 112	93.66 58

xiu_mvs_v2_base		81.69 109	81.05 110	83.60 134	89.15 137	68.03 133	84.46 232	90.02 152	70.67 178	81.30 109	86.53 227	63.17 133	94.19 121	75.60 125	88.54 118	88.57 239

VPNet		78.69 177	78.66 154	78.76 251	88.31 168	55.72 314	84.45 233	86.63 236	76.79 62	78.26 145	90.55 114	59.30 187	89.70 259	66.63 202	77.05 246	90.88 153

PVSNet_Blended		80.98 121	80.34 122	82.90 166	88.85 145	65.40 177	84.43 234	92.00 91	67.62 229	78.11 149	85.05 258	66.02 105	94.27 115	71.52 156	89.50 107	89.01 222

MVP-Stereo		76.12 228	74.46 234	81.13 211	85.37 229	69.79 92	84.42 235	87.95 212	65.03 259	67.46 293	85.33 250	53.28 231	91.73 219	58.01 274	83.27 180	81.85 330
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CDS-MVSNet		79.07 169	77.70 181	83.17 153	87.60 190	68.23 129	84.40 236	86.20 242	67.49 231	76.36 186	86.54 226	61.54 159	90.79 243	61.86 240	87.33 132	90.49 168
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
K. test v3		71.19 272	68.51 280	79.21 246	83.04 271	57.78 287	84.35 237	76.91 331	72.90 145	62.99 328	82.86 284	39.27 330	91.09 238	61.65 242	52.66 349	88.75 234

PS-MVSNAJ		81.69 109	81.02 111	83.70 133	89.51 119	68.21 130	84.28 238	90.09 151	70.79 175	81.26 110	85.62 245	63.15 134	94.29 113	75.62 124	88.87 113	88.59 238

test222					91.50 83	68.26 128	84.16 239	83.20 283	54.63 338	79.74 121	91.63 87	58.97 189			91.42 84	86.77 278

testdata1							84.14 240		75.71 86

MVS_0304		72.48 264	70.89 267	77.24 275	82.20 289	59.68 265	84.11 241	83.49 276	67.10 233	66.87 300	80.59 307	35.00 344	87.40 289	59.07 263	79.58 220	84.63 308

cl_fuxian		78.75 175	77.91 172	81.26 206	82.89 276	61.56 247	84.09 242	89.13 181	69.97 190	75.56 202	84.29 265	66.36 99	92.09 207	73.47 142	75.48 271	90.12 182

MVSTER		79.01 170	77.88 174	82.38 182	83.07 269	64.80 188	84.08 243	88.95 189	69.01 215	78.69 134	87.17 204	54.70 219	92.43 193	74.69 130	80.57 212	89.89 198

ab-mvs		79.51 155	78.97 150	81.14 210	88.46 163	60.91 253	83.84 244	89.24 175	70.36 183	79.03 128	88.87 156	63.23 132	90.21 251	65.12 214	82.57 191	92.28 112

PAPM		77.68 204	76.40 210	81.51 198	87.29 202	61.85 243	83.78 245	89.59 164	64.74 262	71.23 258	88.70 158	62.59 142	93.66 148	52.66 300	87.03 137	89.01 222

diffmvs		82.10 99	81.88 101	82.76 176	83.00 272	63.78 209	83.68 246	89.76 159	72.94 144	82.02 96	89.85 128	65.96 107	90.79 243	82.38 68	87.30 133	93.71 57

miper_ehance_all_eth		78.59 180	77.76 179	81.08 212	82.66 281	61.56 247	83.65 247	89.15 179	68.87 218	75.55 203	83.79 273	66.49 97	92.03 208	73.25 145	76.39 258	89.64 206

1112_ss		77.40 209	76.43 209	80.32 226	89.11 142	60.41 261	83.65 247	87.72 218	62.13 292	73.05 240	86.72 213	62.58 143	89.97 254	62.11 238	80.80 208	90.59 165

PCF-MVS	73.52 7	80.38 140	78.84 152	85.01 86	87.71 187	68.99 108	83.65 247	91.46 115	63.00 281	77.77 156	90.28 117	66.10 102	95.09 90	61.40 244	88.22 123	90.94 152
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
XVG-ACMP-BASELINE		76.11 229	74.27 236	81.62 195	83.20 265	64.67 190	83.60 250	89.75 160	69.75 196	71.85 253	87.09 206	32.78 347	92.11 206	69.99 172	80.43 214	88.09 246

cl-mvsnet2		78.07 193	77.01 194	81.23 207	82.37 288	61.83 244	83.55 251	87.98 211	68.96 216	75.06 221	83.87 269	61.40 164	91.88 215	73.53 139	76.39 258	89.98 194

XVG-OURS-SEG-HR		80.81 127	79.76 132	83.96 129	85.60 225	68.78 112	83.54 252	90.50 137	70.66 179	76.71 178	91.66 84	60.69 177	91.26 230	76.94 113	81.58 200	91.83 124

IB-MVS	68.01 15	75.85 232	73.36 244	83.31 144	84.76 238	66.03 163	83.38 253	85.06 253	70.21 187	69.40 280	81.05 301	45.76 299	94.66 106	65.10 215	75.49 270	89.25 214
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
HY-MVS	69.67 12	77.95 197	77.15 192	80.36 224	87.57 194	60.21 263	83.37 254	87.78 217	66.11 245	75.37 210	87.06 208	63.27 130	90.48 248	61.38 245	82.43 192	90.40 172

Anonymous20240521		68.80 291	67.22 297	73.55 304	74.33 345	54.11 322	83.18 255	85.61 248	58.15 320	61.68 331	80.94 304	30.71 351	81.27 325	57.00 283	73.34 300	85.28 299

eth_miper_zixun_eth		77.92 198	76.69 205	81.61 197	83.00 272	61.98 241	83.15 256	89.20 177	69.52 200	74.86 225	84.35 264	61.76 155	92.56 190	71.50 158	72.89 302	90.28 176

cl-mvsnet____		77.72 202	76.76 202	80.58 220	82.49 285	60.48 259	83.09 257	87.87 214	69.22 206	74.38 230	85.22 253	62.10 152	91.53 223	71.09 160	75.41 274	89.73 205

cl-mvsnet1		77.72 202	76.76 202	80.58 220	82.48 286	60.48 259	83.09 257	87.86 215	69.22 206	74.38 230	85.24 252	62.10 152	91.53 223	71.09 160	75.40 275	89.74 204

thres200		75.55 235	74.47 233	78.82 250	87.78 186	57.85 285	83.07 259	83.51 275	72.44 148	75.84 199	84.42 263	52.08 242	91.75 217	47.41 328	83.64 177	86.86 276

XVG-OURS		80.41 139	79.23 145	83.97 128	85.64 224	69.02 106	83.03 260	90.39 139	71.09 170	77.63 158	91.49 92	54.62 221	91.35 228	75.71 122	83.47 178	91.54 131

miper_enhance_ethall		77.87 200	76.86 198	80.92 215	81.65 295	61.38 249	82.68 261	88.98 186	65.52 254	75.47 204	82.30 291	65.76 109	92.00 210	72.95 148	76.39 258	89.39 211

mvs_anonymous		79.42 159	79.11 147	80.34 225	84.45 243	57.97 282	82.59 262	87.62 219	67.40 232	76.17 193	88.56 165	68.47 81	89.59 260	70.65 165	86.05 151	93.47 71

baseline2		75.70 233	73.83 241	81.30 205	83.26 263	61.79 245	82.57 263	80.65 306	66.81 234	66.88 299	83.42 278	57.86 196	92.19 203	63.47 223	79.57 221	89.91 196

DWT-MVSNet_test		73.70 251	71.86 256	79.21 246	82.91 275	58.94 270	82.34 264	82.17 292	65.21 255	71.05 261	78.31 325	44.21 307	90.17 252	63.29 227	77.28 242	88.53 240

cascas		76.72 219	74.64 229	82.99 162	85.78 222	65.88 169	82.33 265	89.21 176	60.85 300	72.74 242	81.02 302	47.28 287	93.75 144	67.48 193	85.02 158	89.34 212

RPSCF		73.23 258	71.46 259	78.54 255	82.50 284	59.85 264	82.18 266	82.84 288	58.96 315	71.15 260	89.41 146	45.48 302	84.77 308	58.82 266	71.83 309	91.02 150

thisisatest0515		77.33 210	75.38 222	83.18 152	85.27 230	63.80 208	82.11 267	83.27 280	65.06 258	75.91 196	83.84 271	49.54 272	94.27 115	67.24 197	86.19 149	91.48 135

pmmvs-eth3d		70.50 279	67.83 290	78.52 256	77.37 335	66.18 162	81.82 268	81.51 299	58.90 316	63.90 324	80.42 309	42.69 315	86.28 297	58.56 268	65.30 332	83.11 322

MS-PatchMatch		73.83 250	72.67 249	77.30 274	83.87 253	66.02 164	81.82 268	84.66 257	61.37 298	68.61 286	82.82 285	47.29 286	88.21 281	59.27 259	84.32 167	77.68 344

pmmvs5		71.55 270	70.20 273	75.61 287	77.83 332	56.39 306	81.74 270	80.89 302	57.76 323	67.46 293	84.49 262	49.26 277	85.32 304	57.08 282	75.29 279	85.11 303

Test_1112_low_res		76.40 225	75.44 219	79.27 244	89.28 132	58.09 278	81.69 271	87.07 230	59.53 311	72.48 246	86.67 219	61.30 166	89.33 264	60.81 250	80.15 217	90.41 171

IterMVS		74.29 244	72.94 248	78.35 258	81.53 298	63.49 216	81.58 272	82.49 290	68.06 227	69.99 273	83.69 275	51.66 251	85.54 301	65.85 209	71.64 310	86.01 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		75.43 237	73.87 240	80.11 229	82.69 280	64.85 187	81.57 273	83.47 277	69.16 209	70.49 264	84.15 267	51.95 245	88.15 282	69.23 179	72.14 307	87.34 263

pmmvs4		74.03 249	71.91 255	80.39 223	81.96 292	68.32 126	81.45 274	82.14 293	59.32 312	69.87 276	85.13 255	52.40 236	88.13 283	60.21 253	74.74 285	84.73 307

GA-MVS		76.87 217	75.17 226	81.97 190	82.75 278	62.58 233	81.44 275	86.35 241	72.16 154	74.74 226	82.89 283	46.20 295	92.02 209	68.85 184	81.09 204	91.30 140

bset_n11_16_dypcd		77.12 212	75.47 218	82.06 186	81.12 306	65.99 165	81.37 276	83.20 283	69.94 191	76.09 195	83.38 279	47.75 284	92.26 200	78.51 94	77.91 237	87.95 247

CostFormer		75.24 240	73.90 239	79.27 244	82.65 282	58.27 277	80.80 277	82.73 289	61.57 295	75.33 214	83.13 281	55.52 211	91.07 239	64.98 216	78.34 235	88.45 241

MIMVSNet1		68.58 293	66.78 300	73.98 303	80.07 317	51.82 333	80.77 278	84.37 260	64.40 266	59.75 338	82.16 294	36.47 339	83.63 315	42.73 343	70.33 316	86.48 283

CL-MVSNet_2432*1600		72.37 267	71.46 259	75.09 293	79.49 326	53.53 326	80.76 279	85.01 255	69.12 210	70.51 263	82.05 295	57.92 195	84.13 311	52.27 301	66.00 330	87.60 256

MSDG		73.36 256	70.99 265	80.49 222	84.51 242	65.80 170	80.71 280	86.13 244	65.70 251	65.46 312	83.74 274	44.60 304	90.91 241	51.13 306	76.89 249	84.74 306

tpm2		73.26 257	71.46 259	78.63 252	83.34 261	56.71 301	80.65 281	80.40 311	56.63 331	73.55 234	82.02 296	51.80 249	91.24 231	56.35 287	78.42 234	87.95 247

XXY-MVS		75.41 238	75.56 216	74.96 294	83.59 257	57.82 286	80.59 282	83.87 270	66.54 242	74.93 224	88.31 171	63.24 131	80.09 329	62.16 236	76.85 251	86.97 274

HyFIR lowres test		77.53 206	75.40 221	83.94 130	89.59 115	66.62 156	80.36 283	88.64 200	56.29 333	76.45 182	85.17 254	57.64 198	93.28 163	61.34 246	83.10 184	91.91 122

D2MVS		74.82 241	73.21 245	79.64 239	79.81 320	62.56 234	80.34 284	87.35 225	64.37 267	68.86 283	82.66 287	46.37 292	90.10 253	67.91 189	81.24 203	86.25 285

TinyColmap		67.30 300	64.81 304	74.76 297	81.92 293	56.68 302	80.29 285	81.49 300	60.33 302	56.27 347	83.22 280	24.77 354	87.66 288	45.52 336	69.47 318	79.95 339

LCM-MVSNet-Re		77.05 213	76.94 197	77.36 272	87.20 203	51.60 335	80.06 286	80.46 310	75.20 99	67.69 291	86.72 213	62.48 144	88.98 271	63.44 224	89.25 110	91.51 132

FMVSNet5		69.50 286	67.96 287	74.15 302	82.97 274	55.35 316	80.01 287	82.12 294	62.56 288	63.02 326	81.53 298	36.92 338	81.92 322	48.42 320	74.06 290	85.17 302

SCA		74.22 246	72.33 253	79.91 232	84.05 250	62.17 239	79.96 288	79.29 320	66.30 244	72.38 248	80.13 311	51.95 245	88.60 277	59.25 260	77.67 240	88.96 226

tpmrst		72.39 265	72.13 254	73.18 309	80.54 312	49.91 343	79.91 289	79.08 321	63.11 279	71.69 255	79.95 313	55.32 212	82.77 320	65.66 211	73.89 292	86.87 275

PatchmatchNet		73.12 259	71.33 262	78.49 257	83.18 266	60.85 254	79.63 290	78.57 322	64.13 269	71.73 254	79.81 316	51.20 254	85.97 299	57.40 279	76.36 261	88.66 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.38 266	70.90 266	76.80 280	88.60 158	67.38 145	79.53 291	76.17 333	62.75 286	69.36 281	82.00 297	45.51 301	84.89 307	53.62 296	80.58 211	78.12 343

CMPMVS	51.72 21	70.19 282	68.16 284	76.28 282	73.15 352	57.55 290	79.47 292	83.92 268	48.02 347	56.48 346	84.81 259	43.13 312	86.42 296	62.67 232	81.81 199	84.89 304
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
GG-mvs-BLEND				75.38 291	81.59 297	55.80 313	79.32 293	69.63 348		67.19 296	73.67 342	43.24 311	88.90 275	50.41 308	84.50 164	81.45 332

LTVRE_ROB	69.57 13	76.25 227	74.54 232	81.41 200	88.60 158	64.38 199	79.24 294	89.12 182	70.76 177	69.79 278	87.86 184	49.09 278	93.20 167	56.21 288	80.16 216	86.65 281
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
tpm		72.37 267	71.71 258	74.35 300	82.19 290	52.00 332	79.22 295	77.29 329	64.56 264	72.95 241	83.68 276	51.35 252	83.26 318	58.33 271	75.80 265	87.81 252

ppachtmachnet_test		70.04 283	67.34 296	78.14 260	79.80 321	61.13 250	79.19 296	80.59 307	59.16 314	65.27 314	79.29 317	46.75 291	87.29 290	49.33 317	66.72 326	86.00 294

USDC		70.33 280	68.37 281	76.21 283	80.60 311	56.23 309	79.19 296	86.49 237	60.89 299	61.29 332	85.47 248	31.78 350	89.47 263	53.37 297	76.21 262	82.94 326

PM-MVS		66.41 305	64.14 307	73.20 308	73.92 347	56.45 304	78.97 298	64.96 357	63.88 276	64.72 318	80.24 310	19.84 358	83.44 316	66.24 203	64.52 334	79.71 340

tpmvs		71.09 273	69.29 276	76.49 281	82.04 291	56.04 311	78.92 299	81.37 301	64.05 272	67.18 297	78.28 326	49.74 271	89.77 256	49.67 316	72.37 304	83.67 316

test_post1							78.90 300				5.43 365	48.81 281	85.44 303	59.25 260

CHOSEN 1792x2688		77.63 205	75.69 214	83.44 139	89.98 109	68.58 123	78.70 301	87.50 222	56.38 332	75.80 200	86.84 209	58.67 190	91.40 227	61.58 243	85.75 156	90.34 174

test-LLR		72.94 262	72.43 251	74.48 298	81.35 302	58.04 280	78.38 302	77.46 327	66.66 238	69.95 274	79.00 320	48.06 282	79.24 330	66.13 204	84.83 160	86.15 288

TESTMET0.1,1		69.89 285	69.00 278	72.55 310	79.27 329	56.85 297	78.38 302	74.71 339	57.64 324	68.09 288	77.19 333	37.75 336	76.70 341	63.92 221	84.09 169	84.10 314

test-mter		71.41 271	70.39 272	74.48 298	81.35 302	58.04 280	78.38 302	77.46 327	60.32 303	69.95 274	79.00 320	36.08 341	79.24 330	66.13 204	84.83 160	86.15 288

Anonymous20231206		68.60 292	67.80 291	71.02 319	80.23 315	50.75 341	78.30 305	80.47 309	56.79 330	66.11 310	82.63 288	46.35 293	78.95 332	43.62 341	75.70 266	83.36 319

tpm cat1		70.57 277	68.31 282	77.35 273	82.41 287	57.95 283	78.08 306	80.22 314	52.04 343	68.54 287	77.66 331	52.00 244	87.84 286	51.77 302	72.07 308	86.25 285

our_test_3		69.14 288	67.00 298	75.57 288	79.80 321	58.80 272	77.96 307	77.81 325	59.55 310	62.90 329	78.25 327	47.43 285	83.97 312	51.71 303	67.58 325	83.93 315

DIV-MVS_2432*1600		68.81 290	67.59 295	72.46 311	74.29 346	45.45 350	77.93 308	87.00 231	63.12 278	63.99 323	78.99 322	42.32 317	84.77 308	56.55 286	64.09 335	87.16 270

WTY-MVS		75.65 234	75.68 215	75.57 288	86.40 215	56.82 298	77.92 309	82.40 291	65.10 257	76.18 191	87.72 185	63.13 137	80.90 326	60.31 252	81.96 196	89.00 224

test20.03		67.45 298	66.95 299	68.94 325	75.48 342	44.84 352	77.50 310	77.67 326	66.66 238	63.01 327	83.80 272	47.02 288	78.40 334	42.53 344	68.86 323	83.58 317

EPMVS		69.02 289	68.16 284	71.59 313	79.61 324	49.80 345	77.40 311	66.93 353	62.82 285	70.01 271	79.05 318	45.79 298	77.86 338	56.58 285	75.26 280	87.13 271

gg-mvs-nofinetune		69.95 284	67.96 287	75.94 284	83.07 269	54.51 320	77.23 312	70.29 346	63.11 279	70.32 266	62.33 349	43.62 310	88.69 276	53.88 295	87.76 125	84.62 309

MDTV_nov1_ep13			69.97 274		83.18 266	53.48 327	77.10 313	80.18 315	60.45 301	69.33 282	80.44 308	48.89 280	86.90 292	51.60 304	78.51 232

LF4IMVS		64.02 314	62.19 317	69.50 324	70.90 354	53.29 330	76.13 314	77.18 330	52.65 342	58.59 339	80.98 303	23.55 355	76.52 342	53.06 299	66.66 327	78.68 342

sss		73.60 252	73.64 242	73.51 305	82.80 277	55.01 317	76.12 315	81.69 298	62.47 289	74.68 227	85.85 240	57.32 202	78.11 336	60.86 249	80.93 205	87.39 261

testgi		66.67 303	66.53 301	67.08 331	75.62 341	41.69 356	75.93 316	76.50 332	66.11 245	65.20 317	86.59 222	35.72 342	74.71 349	43.71 340	73.38 299	84.84 305

CR-MVSNet		73.37 254	71.27 263	79.67 238	81.32 304	65.19 182	75.92 317	80.30 312	59.92 307	72.73 243	81.19 299	52.50 234	86.69 293	59.84 255	77.71 238	87.11 272

RPMNet		73.51 253	70.49 269	82.58 179	81.32 304	65.19 182	75.92 317	92.27 77	57.60 325	72.73 243	76.45 336	52.30 237	95.43 70	48.14 325	77.71 238	87.11 272

MIMVSNet		70.69 276	69.30 275	74.88 295	84.52 241	56.35 308	75.87 319	79.42 319	64.59 263	67.76 289	82.41 289	41.10 324	81.54 324	46.64 332	81.34 201	86.75 279

test0.0.03 1		68.00 296	67.69 293	68.90 326	77.55 333	47.43 347	75.70 320	72.95 343	66.66 238	66.56 303	82.29 292	48.06 282	75.87 345	44.97 339	74.51 287	83.41 318

PMMVS		69.34 287	68.67 279	71.35 317	75.67 340	62.03 240	75.17 321	73.46 341	50.00 346	68.68 284	79.05 318	52.07 243	78.13 335	61.16 247	82.77 187	73.90 347

UnsupCasMVSNet_eth		67.33 299	65.99 302	71.37 315	73.48 349	51.47 337	75.16 322	85.19 252	65.20 256	60.78 334	80.93 306	42.35 316	77.20 340	57.12 281	53.69 348	85.44 297

MDTV_nov1_ep13_2view						37.79 358	75.16 322		55.10 336	66.53 304		49.34 275		53.98 294		87.94 249

pmmvs3		57.79 319	54.26 323	68.37 329	64.02 358	56.72 300	75.12 324	65.17 355	40.20 351	52.93 350	69.86 347	20.36 357	75.48 347	45.45 337	55.25 347	72.90 348

dp		66.80 301	65.43 303	70.90 320	79.74 323	48.82 346	75.12 324	74.77 337	59.61 309	64.08 322	77.23 332	42.89 313	80.72 327	48.86 319	66.58 328	83.16 321

Patchmtry		70.74 275	69.16 277	75.49 290	80.72 309	54.07 323	74.94 326	80.30 312	58.34 319	70.01 271	81.19 299	52.50 234	86.54 294	53.37 297	71.09 314	85.87 295

PVSNet	64.34 18	72.08 269	70.87 268	75.69 286	86.21 217	56.44 305	74.37 327	80.73 305	62.06 293	70.17 269	82.23 293	42.86 314	83.31 317	54.77 292	84.45 166	87.32 264

MDA-MVSNet-bldmvs		66.68 302	63.66 310	75.75 285	79.28 328	60.56 258	73.92 328	78.35 323	64.43 265	50.13 352	79.87 315	44.02 309	83.67 314	46.10 334	56.86 344	83.03 324

UnsupCasMVSNet_bld		63.70 315	61.53 319	70.21 322	73.69 348	51.39 338	72.82 329	81.89 295	55.63 335	57.81 342	71.80 345	38.67 332	78.61 333	49.26 318	52.21 350	80.63 336

PatchT		68.46 295	67.85 289	70.29 321	80.70 310	43.93 353	72.47 330	74.88 336	60.15 305	70.55 262	76.57 335	49.94 268	81.59 323	50.58 307	74.83 284	85.34 298

miper_lstm_enhance		74.11 247	73.11 247	77.13 277	80.11 316	59.62 266	72.23 331	86.92 233	66.76 236	70.40 265	82.92 282	56.93 207	82.92 319	69.06 182	72.63 303	88.87 229

MVS-HIRNet		59.14 318	57.67 321	63.57 333	81.65 295	43.50 354	71.73 332	65.06 356	39.59 353	51.43 351	57.73 353	38.34 334	82.58 321	39.53 348	73.95 291	64.62 352

Patchmatch-RL test		70.24 281	67.78 292	77.61 269	77.43 334	59.57 268	71.16 333	70.33 345	62.94 283	68.65 285	72.77 343	50.62 260	85.49 302	69.58 177	66.58 328	87.77 253

test123		6.12 337	8.11 340	0.14 349	0.06 371	0.09 371	71.05 334	0.03 372	0.04 367	0.25 368	1.30 368	0.05 372	0.03 368	0.21 366	0.01 366	0.29 363

ANet_high		50.57 325	46.10 328	63.99 332	48.67 364	39.13 357	70.99 335	80.85 303	61.39 297	31.18 357	57.70 354	17.02 360	73.65 353	31.22 353	15.89 362	79.18 341

KD-MVS_2432*1600		66.22 307	63.89 308	73.21 306	75.47 343	53.42 328	70.76 336	84.35 261	64.10 270	66.52 305	78.52 323	34.55 345	84.98 305	50.40 309	50.33 352	81.23 333

miper_refine_blended		66.22 307	63.89 308	73.21 306	75.47 343	53.42 328	70.76 336	84.35 261	64.10 270	66.52 305	78.52 323	34.55 345	84.98 305	50.40 309	50.33 352	81.23 333

testmvs		6.04 338	8.02 341	0.10 350	0.08 370	0.03 372	69.74 338	0.04 371	0.05 366	0.31 367	1.68 367	0.02 373	0.04 367	0.24 365	0.02 365	0.25 364

N_pmnet		52.79 323	53.26 324	51.40 340	78.99 330	7.68 369	69.52 339	3.89 369	51.63 345	57.01 344	74.98 340	40.83 326	65.96 357	37.78 350	64.67 333	80.56 338

FPMVS		53.68 322	51.64 325	59.81 336	65.08 357	51.03 339	69.48 340	69.58 349	41.46 350	40.67 354	72.32 344	16.46 361	70.00 355	24.24 357	65.42 331	58.40 354

DSMNet-mixed		57.77 320	56.90 322	60.38 335	67.70 356	35.61 359	69.18 341	53.97 361	32.30 358	57.49 343	79.88 314	40.39 328	68.57 356	38.78 349	72.37 304	76.97 345

new-patchmatchnet		61.73 316	61.73 318	61.70 334	72.74 353	24.50 366	69.16 342	78.03 324	61.40 296	56.72 345	75.53 339	38.42 333	76.48 343	45.95 335	57.67 343	84.13 313

YYNet1		65.03 310	62.91 314	71.38 314	75.85 339	56.60 303	69.12 343	74.66 340	57.28 328	54.12 348	77.87 329	45.85 297	74.48 350	49.95 314	61.52 339	83.05 323

MDA-MVSNet_test_wron		65.03 310	62.92 313	71.37 315	75.93 338	56.73 299	69.09 344	74.73 338	57.28 328	54.03 349	77.89 328	45.88 296	74.39 351	49.89 315	61.55 338	82.99 325

PVSNet_0	57.27 20	61.67 317	59.27 320	68.85 327	79.61 324	57.44 292	68.01 345	73.44 342	55.93 334	58.54 340	70.41 346	44.58 305	77.55 339	47.01 329	35.91 355	71.55 349

ADS-MVSNet2		66.20 309	63.33 311	74.82 296	79.92 318	58.75 273	67.55 346	75.19 335	53.37 340	65.25 315	75.86 337	42.32 317	80.53 328	41.57 345	68.91 321	85.18 300

ADS-MVSNet		64.36 313	62.88 315	68.78 328	79.92 318	47.17 348	67.55 346	71.18 344	53.37 340	65.25 315	75.86 337	42.32 317	73.99 352	41.57 345	68.91 321	85.18 300

LCM-MVSNet		54.25 321	49.68 327	67.97 330	53.73 361	45.28 351	66.85 348	80.78 304	35.96 355	39.45 355	62.23 351	8.70 367	78.06 337	48.24 324	51.20 351	80.57 337

JIA-IIPM		66.32 306	62.82 316	76.82 279	77.09 336	61.72 246	65.34 349	75.38 334	58.04 322	64.51 319	62.32 350	42.05 321	86.51 295	51.45 305	69.22 320	82.21 328

PMVS	37.38 22	44.16 327	40.28 330	55.82 337	40.82 366	42.54 355	65.12 350	63.99 358	34.43 356	24.48 359	57.12 355	3.92 369	76.17 344	17.10 360	55.52 346	48.75 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		50.91 324	50.29 326	52.78 339	68.58 355	34.94 361	63.71 351	56.63 360	39.73 352	44.95 353	65.47 348	21.93 356	58.48 358	34.98 352	56.62 345	64.92 351

Patchmatch-test		64.82 312	63.24 312	69.57 323	79.42 327	49.82 344	63.49 352	69.05 351	51.98 344	59.95 337	80.13 311	50.91 256	70.98 354	40.66 347	73.57 295	87.90 250

ambc				75.24 292	73.16 351	50.51 342	63.05 353	87.47 223		64.28 320	77.81 330	17.80 359	89.73 258	57.88 275	60.64 340	85.49 296

CHOSEN 280x420		66.51 304	64.71 305	71.90 312	81.45 299	63.52 215	57.98 354	68.95 352	53.57 339	62.59 330	76.70 334	46.22 294	75.29 348	55.25 290	79.68 219	76.88 346

E-PMN		31.77 329	30.64 332	35.15 344	52.87 362	27.67 363	57.09 355	47.86 363	24.64 359	16.40 364	33.05 360	11.23 364	54.90 360	14.46 362	18.15 360	22.87 359

EMVS		30.81 331	29.65 333	34.27 345	50.96 363	25.95 365	56.58 356	46.80 364	24.01 360	15.53 365	30.68 361	12.47 363	54.43 361	12.81 363	17.05 361	22.43 360

PMMVS2		40.82 328	38.86 331	46.69 341	53.84 360	16.45 367	48.61 357	49.92 362	37.49 354	31.67 356	60.97 352	8.14 368	56.42 359	28.42 354	30.72 357	67.19 350

wuyk23d		16.82 335	15.94 338	19.46 347	58.74 359	31.45 362	39.22 358	3.74 370	6.84 363	6.04 366	2.70 366	1.27 371	24.29 365	10.54 364	14.40 364	2.63 362

tmp_tt		18.61 334	21.40 337	10.23 348	4.82 369	10.11 368	34.70 359	30.74 367	1.48 365	23.91 361	26.07 362	28.42 352	13.41 366	27.12 355	15.35 363	7.17 361

Gipuma		45.18 326	41.86 329	55.16 338	77.03 337	51.52 336	32.50 360	80.52 308	32.46 357	27.12 358	35.02 359	9.52 366	75.50 346	22.31 358	60.21 342	38.45 357
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	26.22 23	30.37 332	25.89 336	43.81 342	44.55 365	35.46 360	28.87 361	39.07 365	18.20 361	18.58 363	40.18 358	2.68 370	47.37 363	17.07 361	23.78 359	48.60 356
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		31.52 330	29.28 334	38.23 343	27.03 368	6.50 370	20.94 362	62.21 359	4.05 364	22.35 362	52.50 356	13.33 362	47.58 362	27.04 356	34.04 356	60.62 353

uanet_test		0.00 340	0.00 343	0.00 351	0.00 372	0.00 373	0.00 363	0.00 373	0.00 368	0.00 369	0.00 369	0.00 374	0.00 369	0.00 367	0.00 367	0.00 365

cdsmvs_eth3d_5k		19.96 333	26.61 335	0.00 351	0.00 372	0.00 373	0.00 363	89.26 174	0.00 368	0.00 369	88.61 162	61.62 158	0.00 369	0.00 367	0.00 367	0.00 365

pcd_1.5k_mvsjas		5.26 339	7.02 342	0.00 351	0.00 372	0.00 373	0.00 363	0.00 373	0.00 368	0.00 369	0.00 369	63.15 134	0.00 369	0.00 367	0.00 367	0.00 365

sosnet-low-res		0.00 340	0.00 343	0.00 351	0.00 372	0.00 373	0.00 363	0.00 373	0.00 368	0.00 369	0.00 369	0.00 374	0.00 369	0.00 367	0.00 367	0.00 365

sosnet		0.00 340	0.00 343	0.00 351	0.00 372	0.00 373	0.00 363	0.00 373	0.00 368	0.00 369	0.00 369	0.00 374	0.00 369	0.00 367	0.00 367	0.00 365

uncertanet		0.00 340	0.00 343	0.00 351	0.00 372	0.00 373	0.00 363	0.00 373	0.00 368	0.00 369	0.00 369	0.00 374	0.00 369	0.00 367	0.00 367	0.00 365

Regformer		0.00 340	0.00 343	0.00 351	0.00 372	0.00 373	0.00 363	0.00 373	0.00 368	0.00 369	0.00 369	0.00 374	0.00 369	0.00 367	0.00 367	0.00 365

ab-mvs-re		7.23 336	9.64 339	0.00 351	0.00 372	0.00 373	0.00 363	0.00 373	0.00 368	0.00 369	86.72 213	0.00 374	0.00 369	0.00 367	0.00 367	0.00 365

uanet		0.00 340	0.00 343	0.00 351	0.00 372	0.00 373	0.00 363	0.00 373	0.00 368	0.00 369	0.00 369	0.00 374	0.00 369	0.00 367	0.00 367	0.00 365

eth-test2					0.00 372

eth-test					0.00 372

ZD-MVS					94.38 25	72.22 45		92.67 61	70.98 172	87.75 27	94.07 43	74.01 35	96.70 23	84.66 36	94.84 44

IU-MVS					95.30 2	71.25 59		92.95 50	66.81 234	92.39 5				88.94 8	96.63 2	94.85 10

test_241102_TWO								94.06 10	77.24 47	92.78 4	95.72 6	81.26 6	97.44 2	89.07 6	96.58 4	94.26 31

test_241102_ONE					95.30 2	70.98 65		94.06 10	77.17 51	93.10 1	95.39 9	82.99 1	97.27 7

test_0728_THIRD									78.38 32	92.12 8	95.78 4	81.46 5	97.40 4	89.42 2	96.57 5	94.67 16

GSMVS																88.96 226

test_part2					95.06 7	72.65 31				91.80 10

sam_mvs1												51.32 253				88.96 226

sam_mvs												50.01 266

MTGPA								92.02 88

test_post											5.46 364	50.36 264	84.24 310

patchmatchnet-post											74.00 341	51.12 255	88.60 277

gm-plane-assit					81.40 300	53.83 325			62.72 287		80.94 304		92.39 195	63.40 225

test9_res														84.90 30	95.70 27	92.87 93

agg_prior2														82.91 58	95.45 29	92.70 96

agg_prior					92.85 61	71.94 51		91.78 103		84.41 65			94.93 93

TestCases				79.58 240	85.15 232	63.62 210		79.83 316	62.31 290	60.32 335	86.73 211	32.02 348	88.96 273	50.28 311	71.57 311	86.15 288

test_prior				86.33 60	92.61 69	69.59 96		92.97 48					95.48 66			93.91 45

新几何1				83.42 140	93.13 53	70.71 75		85.48 249	57.43 326	81.80 100	91.98 79	63.28 129	92.27 199	64.60 219	92.99 67	87.27 265

旧先验1					91.96 77	65.79 171		86.37 240			93.08 66	69.31 76			92.74 71	88.74 235

原ACMM1				84.35 109	93.01 59	68.79 111		92.44 69	63.96 275	81.09 111	91.57 89	66.06 104	95.45 68	67.19 198	94.82 47	88.81 232

testdata2													91.01 240	62.37 234

segment_acmp												73.08 40

testdata				79.97 231	90.90 91	64.21 201		84.71 256	59.27 313	85.40 44	92.91 67	62.02 154	89.08 269	68.95 183	91.37 85	86.63 282

test12				86.80 52	92.63 68	70.70 76		91.79 102		82.71 91		71.67 52	96.16 44		94.50 52	93.54 69

plane_prior7					90.08 107	68.51 124

plane_prior6					89.84 112	68.70 119						60.42 182

plane_prior5								92.44 69					95.38 74	78.71 92	86.32 147	91.33 138

plane_prior4											91.00 107

plane_prior3						68.60 122			78.44 30	78.92 131

plane_prior1					89.90 111

n2								0.00 373

nn								0.00 373

door-mid								69.98 347

lessismore_v0				78.97 248	81.01 308	57.15 294		65.99 354		61.16 333	82.82 285	39.12 331	91.34 229	59.67 256	46.92 354	88.43 242

LGP-MVS_train				84.50 102	89.23 134	68.76 113		91.94 95	75.37 94	76.64 180	91.51 90	54.29 222	94.91 95	78.44 96	83.78 171	89.83 200

test11								92.23 80

door								69.44 350

HQP5-MVS						66.98 151

BP-MVS														77.47 106

HQP4-MVS										77.24 166			95.11 86			91.03 148

HQP3-MVS								92.19 83							85.99 153

HQP2-MVS												60.17 185

NP-MVS					89.62 114	68.32 126					90.24 118

ACMMP++_ref															81.95 197

ACMMP++															81.25 202

Test By Simon												64.33 119

ITE_SJBPF				78.22 259	81.77 294	60.57 257		83.30 279	69.25 205	67.54 292	87.20 202	36.33 340	87.28 291	54.34 293	74.62 286	86.80 277

DeepMVS_CX				27.40 346	40.17 367	26.90 364		24.59 368	17.44 362	23.95 360	48.61 357	9.77 365	26.48 364	18.06 359	24.47 358	28.83 358

High-resolution multi-view benchmark