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.

CHOSEN 1792x2688		76.24 54	74.03 73	82.88 1	83.09 109	62.84 2	85.73 110	85.39 94	69.79 19	64.87 129	83.49 176	41.52 146	93.69 26	70.55 82	81.82 67	92.12 32

MG-MVS		78.42 21	76.99 37	82.73 2	93.17 1	64.46 1	89.93 29	88.51 43	64.83 72	73.52 48	88.09 120	48.07 60	92.19 50	62.24 139	84.53 49	91.53 48

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 4	59.33 6	92.10 9	90.95 7	65.68 59	80.67 15	94.42 3	59.41 7	95.89 9	86.74 2	89.75 5	92.94 16

LFMVS		78.52 19	77.14 35	82.67 4	89.58 10	58.90 8	91.27 18	88.05 47	63.22 94	74.63 36	90.83 63	41.38 147	94.40 19	75.42 53	79.90 89	94.72 2

DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 180	59.50 5	92.24 8	90.72 8	69.37 22	83.22 6	94.47 2	63.81 3	93.18 30	74.02 63	93.25 2	94.80 1

CSCG		80.41 12	79.72 12	82.49 5	89.12 21	57.67 15	89.29 40	91.54 3	59.19 164	71.82 69	90.05 84	59.72 6	96.04 7	78.37 30	88.40 12	93.75 5

SED-MVS		81.92 6	81.75 7	82.44 7	89.48 14	56.89 26	92.48 3	88.94 27	57.50 204	84.61 3	94.09 4	58.81 9	96.37 5	82.28 11	87.60 16	94.06 3

DVP-MVS		81.30 8	81.00 10	82.20 8	89.40 17	57.45 18	92.34 5	89.99 14	57.71 198	81.91 9	93.64 10	55.17 17	96.44 2	81.68 13	87.13 19	92.72 21
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				82.20 8	89.50 12	57.73 13	92.34 5	88.88 29					96.39 4	81.68 13	87.13 19	92.47 24

MCST-MVS		83.01 2	83.30 3	82.15 10	92.84 2	57.58 16	93.77 1	91.10 6	75.95 2	77.10 24	93.09 17	54.15 23	95.57 10	85.80 3	85.87 34	93.31 9

DELS-MVS		82.32 4	82.50 4	81.79 11	86.80 41	56.89 26	92.77 2	86.30 79	77.83 1	77.88 21	92.13 32	60.24 4	94.78 18	78.97 25	89.61 6	93.69 6
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
OPU-MVS				81.71 12	92.05 3	55.97 42	92.48 3				94.01 6	67.21 2	95.10 13	89.82 1	92.55 3	94.06 3

PS-MVSNAJ		80.06 13	79.52 14	81.68 13	85.58 56	60.97 3	91.69 11	87.02 64	70.62 13	80.75 14	93.22 14	37.77 178	92.50 42	82.75 8	86.25 31	91.57 46

xiu_mvs_v2_base		79.86 14	79.31 15	81.53 14	85.03 72	60.73 4	91.65 12	86.86 67	70.30 17	80.77 13	93.07 18	37.63 183	92.28 48	82.73 9	85.71 35	91.57 46

CNVR-MVS		81.76 7	81.90 6	81.33 15	90.04 7	57.70 14	91.71 10	88.87 30	70.31 16	77.64 23	93.87 8	52.58 30	93.91 24	84.17 4	87.92 14	92.39 26

MVS		76.91 44	75.48 54	81.23 16	84.56 77	55.21 57	80.23 242	91.64 2	58.65 180	65.37 121	91.48 49	45.72 90	95.05 14	72.11 77	89.52 8	93.44 7

VDDNet		74.37 78	72.13 95	81.09 17	79.58 184	56.52 32	90.02 26	86.70 72	52.61 258	71.23 76	87.20 132	31.75 252	93.96 23	74.30 61	75.77 119	92.79 20

NCCC		79.57 16	79.23 16	80.59 18	89.50 12	56.99 24	91.38 15	88.17 46	67.71 37	73.81 44	92.75 21	46.88 73	93.28 28	78.79 28	84.07 53	91.50 50

API-MVS		74.17 81	72.07 98	80.49 19	90.02 8	58.55 9	87.30 73	84.27 129	57.51 203	65.77 118	87.77 127	41.61 144	95.97 8	51.71 223	82.63 59	86.94 147

3Dnovator	64.70 6	74.46 77	72.48 87	80.41 20	82.84 121	55.40 52	83.08 183	88.61 39	67.61 39	59.85 182	88.66 109	34.57 223	93.97 22	58.42 172	88.70 10	91.85 40

DPE-MVS		79.82 15	79.66 13	80.29 21	89.27 20	55.08 63	88.70 49	87.92 50	55.55 232	81.21 12	93.69 9	56.51 14	94.27 21	78.36 31	85.70 36	91.51 49
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DWT-MVSNet_test		75.47 67	73.87 75	80.29 21	87.33 37	57.05 23	82.86 189	87.96 49	72.59 6	67.29 99	87.79 125	51.61 35	91.52 64	54.75 203	72.63 147	92.29 28

MAR-MVS		76.76 48	75.60 53	80.21 23	90.87 5	54.68 76	89.14 41	89.11 22	62.95 97	70.54 81	92.33 29	41.05 148	94.95 15	57.90 181	86.55 28	91.00 63
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
SD-MVS		76.18 55	74.85 64	80.18 24	85.39 63	56.90 25	85.75 108	82.45 165	56.79 216	74.48 40	91.81 39	43.72 116	90.75 83	74.61 59	78.65 96	92.91 17
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
Effi-MVS+		75.24 68	73.61 77	80.16 25	81.92 138	57.42 20	85.21 121	76.71 268	60.68 137	73.32 50	89.34 97	47.30 68	91.63 61	68.28 95	79.72 90	91.42 51

SMA-MVS		79.10 17	78.76 17	80.12 26	84.42 79	55.87 43	87.58 67	86.76 70	61.48 123	80.26 16	93.10 15	46.53 78	92.41 45	79.97 21	88.77 9	92.08 33
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
MSLP-MVS++		74.21 80	72.25 92	80.11 27	81.45 155	56.47 33	86.32 95	79.65 209	58.19 186	66.36 109	92.29 31	36.11 209	90.66 85	67.39 99	82.49 60	93.18 13

CANet		80.90 9	81.17 9	80.09 28	87.62 34	54.21 85	91.60 13	86.47 75	73.13 5	79.89 18	93.10 15	49.88 51	92.98 31	84.09 5	84.75 47	93.08 14

IB-MVS	68.87 2	74.01 83	72.03 100	79.94 29	83.04 112	55.50 47	90.24 25	88.65 35	67.14 43	61.38 169	81.74 203	53.21 26	94.28 20	60.45 158	62.41 223	90.03 85
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
HPM-MVS++		80.50 11	80.71 11	79.88 30	87.34 36	55.20 58	89.93 29	87.55 59	66.04 58	79.46 19	93.00 19	53.10 27	91.76 59	80.40 20	89.56 7	92.68 22

xxxxxxxxxxxxxcwj		77.31 38	76.54 41	79.61 31	85.35 64	56.34 36	89.31 38	72.84 302	61.55 119	74.63 36	92.38 27	47.75 64	91.35 68	78.18 34	86.85 24	91.15 58

QAPM		71.88 115	69.33 135	79.52 32	82.20 134	54.30 83	86.30 96	88.77 33	56.61 220	59.72 184	87.48 130	33.90 230	95.36 11	47.48 248	81.49 70	88.90 110

VDD-MVS		76.08 57	74.97 62	79.44 33	84.27 84	53.33 111	91.13 19	85.88 86	65.33 66	72.37 63	89.34 97	32.52 242	92.76 37	77.90 37	75.96 116	92.22 31

MVS_111021_HR		76.39 53	75.38 57	79.42 34	85.33 66	56.47 33	88.15 55	84.97 112	65.15 70	66.06 113	89.88 87	43.79 113	92.16 51	75.03 56	80.03 87	89.64 93

SteuartSystems-ACMMP		77.08 41	76.33 46	79.34 35	80.98 162	55.31 53	89.76 33	86.91 66	62.94 98	71.65 70	91.56 47	42.33 131	92.56 41	77.14 42	83.69 55	90.15 83
Skip Steuart: Steuart Systems R&D Blog.
test12				79.24 36	86.89 40	56.08 41		85.16 107		72.27 65		47.15 70	91.10 74		85.93 33	90.54 74

APDe-MVS		78.44 20	78.20 21	79.19 37	88.56 22	54.55 80	89.76 33	87.77 54	55.91 227	78.56 20	92.49 25	48.20 59	92.65 40	79.49 22	83.04 57	90.39 76

lupinMVS		78.38 22	78.11 24	79.19 37	83.02 113	55.24 55	91.57 14	84.82 116	69.12 23	76.67 26	92.02 36	44.82 102	90.23 99	80.83 18	80.09 84	92.08 33

DeepC-MVS_fast	67.50 3	78.00 28	77.63 28	79.13 39	88.52 23	55.12 60	89.95 28	85.98 85	68.31 27	71.33 75	92.75 21	45.52 92	90.37 92	71.15 80	85.14 43	91.91 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
canonicalmvs		78.17 26	77.86 27	79.12 40	84.30 81	54.22 84	87.71 60	84.57 123	67.70 38	77.70 22	92.11 35	50.90 42	89.95 104	78.18 34	77.54 103	93.20 12

PHI-MVS		77.49 34	77.00 36	78.95 41	85.33 66	50.69 170	88.57 51	88.59 41	58.14 187	73.60 45	93.31 12	43.14 125	93.79 25	73.81 64	88.53 11	92.37 27

test_yl		75.85 61	74.83 65	78.91 42	88.08 30	51.94 144	91.30 16	89.28 18	57.91 192	71.19 77	89.20 100	42.03 138	92.77 35	69.41 87	75.07 127	92.01 36

DCV-MVSNet		75.85 61	74.83 65	78.91 42	88.08 30	51.94 144	91.30 16	89.28 18	57.91 192	71.19 77	89.20 100	42.03 138	92.77 35	69.41 87	75.07 127	92.01 36

Regformer-1		77.80 31	77.44 31	78.88 44	87.78 32	52.44 133	87.60 62	90.08 12	68.86 24	72.49 62	91.79 40	47.69 66	94.90 16	73.57 67	77.05 105	89.31 98

casdiffmvs		77.36 37	76.85 38	78.88 44	80.40 177	54.66 78	87.06 79	85.88 86	72.11 8	71.57 72	88.63 113	50.89 44	90.35 93	76.00 46	79.11 93	91.63 43

ETH3D cwj APD-0.16		78.36 23	78.19 22	78.86 46	84.21 85	52.68 127	86.70 88	89.02 25	59.13 170	75.37 31	92.49 25	49.06 56	93.20 29	80.67 19	87.08 21	90.71 69

PAPM		76.76 48	76.07 50	78.81 47	80.20 178	59.11 7	86.86 86	86.23 80	68.60 25	70.18 83	88.84 107	51.57 36	87.16 183	65.48 116	86.68 26	90.15 83

MSP-MVS		82.30 5	83.47 1	78.80 48	82.99 115	52.71 126	85.04 131	88.63 37	66.08 55	86.77 2	92.75 21	72.05 1	91.46 66	83.35 6	93.53 1	92.23 29
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
DeepC-MVS	67.15 4	76.90 46	76.27 47	78.80 48	80.70 170	55.02 64	86.39 93	86.71 71	66.96 45	67.91 95	89.97 86	48.03 61	91.41 67	75.60 50	84.14 52	89.96 86
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_part1		73.80 87	72.13 95	78.79 50	85.92 47	58.26 10	90.60 23	86.85 68	63.98 80	63.95 143	81.54 206	52.08 34	92.24 49	64.93 125	59.32 240	85.87 171

ETH3D-3000-0.1		78.73 18	78.71 18	78.78 51	85.58 56	52.40 134	88.42 53	89.03 24	60.01 144	76.06 29	92.80 20	48.34 57	92.88 33	81.66 15	86.48 29	91.04 61

ACMMP_NAP		76.43 52	75.66 52	78.73 52	81.92 138	54.67 77	84.06 155	85.35 96	61.10 128	72.99 52	91.50 48	40.25 156	91.00 76	76.84 43	86.98 22	90.51 75

baseline		76.86 47	76.24 48	78.71 53	80.47 176	54.20 87	83.90 160	84.88 115	71.38 11	71.51 73	89.15 102	50.51 45	90.55 89	75.71 48	78.65 96	91.39 52

jason		77.01 42	76.45 44	78.69 54	79.69 183	54.74 72	90.56 24	83.99 138	68.26 28	74.10 42	90.91 60	42.14 135	89.99 103	79.30 24	79.12 92	91.36 54
jason: jason.
ET-MVSNet_ETH3D		75.23 69	74.08 72	78.67 55	84.52 78	55.59 45	88.92 45	89.21 20	68.06 33	53.13 267	90.22 78	49.71 52	87.62 174	72.12 76	70.82 162	92.82 19

CostFormer		73.89 86	72.30 91	78.66 56	82.36 133	56.58 29	75.56 273	85.30 99	66.06 56	70.50 82	76.88 252	57.02 12	89.06 120	68.27 96	68.74 176	90.33 78

MVS_Test		75.85 61	74.93 63	78.62 57	84.08 87	55.20 58	83.99 158	85.17 106	68.07 32	73.38 49	82.76 186	50.44 46	89.00 126	65.90 112	80.61 77	91.64 42

CDPH-MVS		76.05 58	75.19 58	78.62 57	86.51 43	54.98 66	87.32 71	84.59 122	58.62 181	70.75 79	90.85 62	43.10 127	90.63 87	70.50 83	84.51 50	90.24 79

TSAR-MVS + GP.		77.82 30	77.59 29	78.49 59	85.25 68	50.27 187	90.02 26	90.57 9	56.58 221	74.26 41	91.60 46	54.26 21	92.16 51	75.87 47	79.91 88	93.05 15

ETV-MVS		77.17 39	76.74 40	78.48 60	81.80 140	54.55 80	86.13 99	85.33 97	68.20 29	73.10 51	90.52 71	45.23 95	90.66 85	79.37 23	80.95 72	90.22 80

TSAR-MVS + MP.		78.31 25	78.26 20	78.48 60	81.33 158	56.31 38	81.59 219	86.41 76	69.61 21	81.72 11	88.16 119	55.09 19	88.04 162	74.12 62	86.31 30	91.09 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
train_agg		76.91 44	76.40 45	78.45 62	85.68 51	55.42 49	87.59 65	84.00 136	57.84 195	72.99 52	90.98 56	44.99 97	88.58 139	78.19 32	85.32 41	91.34 56

PAPR		75.20 70	74.13 71	78.41 63	88.31 27	55.10 62	84.31 147	85.66 89	63.76 85	67.55 97	90.73 64	43.48 121	89.40 116	66.36 108	77.03 107	90.73 68

testtj		76.96 43	76.48 43	78.40 64	89.89 9	53.67 95	88.72 48	86.15 82	54.56 245	74.86 34	92.31 30	44.38 107	91.97 57	75.19 55	82.24 63	89.54 95

alignmvs		78.08 27	77.98 25	78.39 65	83.53 97	53.22 114	89.77 32	85.45 91	66.11 53	76.59 28	91.99 38	54.07 24	89.05 122	77.34 41	77.00 108	92.89 18

test_prior3		77.59 33	77.33 34	78.39 65	86.35 44	54.91 69	89.04 43	85.45 91	61.88 114	73.55 46	91.46 50	48.01 62	89.70 110	74.73 57	85.46 38	90.55 71

test_prior				78.39 65	86.35 44	54.91 69		85.45 91					89.70 110			90.55 71

SF-MVS		77.64 32	77.42 33	78.32 68	83.75 95	52.47 132	86.63 90	87.80 51	58.78 178	74.63 36	92.38 27	47.75 64	91.35 68	78.18 34	86.85 24	91.15 58

ZNCC-MVS		75.82 64	75.02 61	78.23 69	83.88 93	53.80 91	86.91 85	86.05 84	59.71 149	67.85 96	90.55 69	42.23 133	91.02 75	72.66 75	85.29 42	89.87 89

VNet		77.99 29	77.92 26	78.19 70	87.43 35	50.12 190	90.93 21	91.41 4	67.48 41	75.12 32	90.15 82	46.77 75	91.00 76	73.52 68	78.46 98	93.44 7

Regformer-2		77.15 40	76.82 39	78.14 71	87.78 32	51.84 148	87.60 62	89.12 21	67.23 42	71.93 68	91.79 40	46.03 84	93.53 27	72.85 73	77.05 105	89.05 107

EIA-MVS		75.92 60	75.18 59	78.13 72	85.14 69	51.60 154	87.17 77	85.32 98	64.69 73	68.56 89	90.53 70	45.79 89	91.58 62	67.21 101	82.18 65	91.20 57

HFP-MVS		74.37 78	73.13 81	78.10 73	84.30 81	53.68 93	85.58 113	84.36 126	56.82 214	65.78 116	90.56 67	40.70 153	90.90 79	69.18 90	80.88 73	89.71 90

#test#		74.86 76	73.78 76	78.10 73	84.30 81	53.68 93	86.95 82	84.36 126	59.00 174	65.78 116	90.56 67	40.70 153	90.90 79	71.48 78	80.88 73	89.71 90

agg_prior1		76.68 50	76.24 48	78.00 75	85.64 54	54.92 67	87.55 68	83.61 145	57.99 191	72.53 60	91.05 52	45.36 93	88.10 159	77.76 38	84.68 48	90.99 64

tpm2		70.82 129	68.44 144	77.98 76	80.78 168	56.11 40	74.21 283	81.28 184	60.24 142	68.04 94	75.27 270	52.26 32	88.50 144	55.82 197	68.03 180	89.33 97

thisisatest0515		73.64 91	72.20 93	77.97 77	81.63 144	53.01 121	86.69 89	88.81 32	62.53 103	64.06 140	85.65 152	52.15 33	92.50 42	58.43 170	69.84 169	88.39 123

EPNet		78.36 23	78.49 19	77.97 77	85.49 59	52.04 142	89.36 37	84.07 135	73.22 4	77.03 25	91.72 43	49.32 55	90.17 101	73.46 69	82.77 58	91.69 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	69.37 1	80.65 10	81.56 8	77.94 79	85.46 62	49.56 200	90.99 20	86.66 73	70.58 14	80.07 17	95.30 1	56.18 15	90.97 78	82.57 10	86.22 32	93.28 10

GST-MVS		74.87 75	73.90 74	77.77 80	83.30 103	53.45 104	85.75 108	85.29 100	59.22 163	66.50 108	89.85 88	40.94 149	90.76 82	70.94 81	83.35 56	89.10 106

GG-mvs-BLEND				77.77 80	86.68 42	50.61 171	68.67 315	88.45 44		68.73 88	87.45 131	59.15 8	90.67 84	54.83 200	87.67 15	92.03 35

Regformer-3		76.02 59	75.47 55	77.70 82	85.49 59	51.47 158	85.12 127	90.19 11	68.52 26	69.36 84	90.66 65	46.45 79	94.81 17	70.25 85	73.16 139	86.81 155

cascas		69.01 161	66.13 189	77.66 83	79.36 185	55.41 51	86.99 80	83.75 141	56.69 218	58.92 201	81.35 207	24.31 300	92.10 54	53.23 209	70.61 163	85.46 179

3Dnovator+	62.71 7	72.29 110	70.50 116	77.65 84	83.40 101	51.29 164	87.32 71	86.40 77	59.01 173	58.49 211	88.32 115	32.40 243	91.27 70	57.04 189	82.15 66	90.38 77

MVSFormer		73.53 92	72.19 94	77.57 85	83.02 113	55.24 55	81.63 216	81.44 179	50.28 272	76.67 26	90.91 60	44.82 102	86.11 211	60.83 150	80.09 84	91.36 54

APD-MVS		76.15 56	75.68 51	77.54 86	88.52 23	53.44 105	87.26 76	85.03 111	53.79 249	74.91 33	91.68 45	43.80 112	90.31 95	74.36 60	81.82 67	88.87 111
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Fast-Effi-MVS+		72.73 101	71.15 112	77.48 87	82.75 124	54.76 71	86.77 87	80.64 191	63.05 96	65.93 114	84.01 167	44.42 106	89.03 123	56.45 194	76.36 115	88.64 117

EPMVS		68.45 172	65.44 206	77.47 88	84.91 73	56.17 39	71.89 303	81.91 172	61.72 117	60.85 174	72.49 292	36.21 208	87.06 186	47.32 249	71.62 155	89.17 104

PatchmatchNet		67.07 204	63.63 224	77.40 89	83.10 107	58.03 11	72.11 301	77.77 247	58.85 177	59.37 191	70.83 305	37.84 177	84.93 241	42.96 271	69.83 170	89.26 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
RRT_test8_iter05		72.74 100	71.20 110	77.36 90	87.25 38	53.51 99	88.68 50	89.53 16	65.20 69	61.32 170	81.27 208	45.89 86	92.48 44	65.99 110	55.65 281	86.10 165

region2R		73.75 89	72.55 86	77.33 91	83.90 92	52.98 122	85.54 116	84.09 134	56.83 213	65.10 124	90.45 72	37.34 191	90.24 98	68.89 92	80.83 76	88.77 115

CS-MVS		77.40 36	77.43 32	77.32 92	81.95 137	52.05 141	89.14 41	85.29 100	67.60 40	72.89 55	90.99 54	47.02 72	91.99 56	77.61 39	84.37 51	87.90 131

WTY-MVS		77.47 35	77.52 30	77.30 93	88.33 26	46.25 262	88.46 52	90.32 10	71.40 10	72.32 64	91.72 43	53.44 25	92.37 46	66.28 109	75.42 122	93.28 10

OpenMVS	61.00 11	69.99 144	67.55 163	77.30 93	78.37 211	54.07 89	84.36 145	85.76 88	57.22 208	56.71 239	87.67 128	30.79 259	92.83 34	43.04 269	84.06 54	85.01 185

zzz-MVS		74.15 82	73.11 82	77.27 95	81.54 150	53.57 97	84.02 157	81.31 181	59.41 156	68.39 91	90.96 58	36.07 210	89.01 124	73.80 65	82.45 61	89.23 100

MTAPA		72.73 101	71.22 109	77.27 95	81.54 150	53.57 97	67.06 318	81.31 181	59.41 156	68.39 91	90.96 58	36.07 210	89.01 124	73.80 65	82.45 61	89.23 100

PAPM_NR		71.80 116	69.98 126	77.26 97	81.54 150	53.34 110	78.60 259	85.25 104	53.46 251	60.53 179	88.66 109	45.69 91	89.24 118	56.49 191	79.62 91	89.19 103

ACMMPR		73.76 88	72.61 84	77.24 98	83.92 91	52.96 123	85.58 113	84.29 128	56.82 214	65.12 123	90.45 72	37.24 193	90.18 100	69.18 90	80.84 75	88.58 119

hse-mvs3		73.95 84	72.89 83	77.15 99	80.17 179	50.37 180	84.68 139	83.33 148	68.08 30	71.97 66	88.65 112	42.50 129	91.15 73	78.82 26	57.78 261	89.91 88

MP-MVS-pluss		75.54 66	75.03 60	77.04 100	81.37 157	52.65 129	84.34 146	84.46 124	61.16 126	69.14 85	91.76 42	39.98 162	88.99 128	78.19 32	84.89 46	89.48 96
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HyFIR lowres test		69.94 146	67.58 160	77.04 100	77.11 231	57.29 21	81.49 224	79.11 223	58.27 185	58.86 203	80.41 215	42.33 131	86.96 189	61.91 142	68.68 177	86.87 149

DP-MVS Recon		71.99 114	70.31 119	77.01 102	90.65 6	53.44 105	89.37 36	82.97 159	56.33 224	63.56 151	89.47 94	34.02 227	92.15 53	54.05 206	72.41 149	85.43 180

Anonymous20240529		69.71 149	67.28 168	77.00 103	83.78 94	50.36 181	88.87 47	85.10 110	47.22 288	64.03 141	83.37 178	27.93 275	92.10 54	57.78 183	67.44 184	88.53 121

baseline2		75.15 71	74.54 69	76.98 104	81.67 143	51.74 151	83.84 161	91.94 1	69.97 18	58.98 198	86.02 147	59.73 5	91.73 60	68.37 94	70.40 166	87.48 139

MP-MVS		74.99 74	74.33 70	76.95 105	82.89 119	53.05 120	85.63 112	83.50 147	57.86 194	67.25 100	90.24 77	43.38 122	88.85 134	76.03 45	82.23 64	88.96 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs_anonymous		72.29 110	70.74 113	76.94 106	82.85 120	54.72 74	78.43 260	81.54 177	63.77 84	61.69 168	79.32 222	51.11 39	85.31 230	62.15 141	75.79 118	90.79 67

XVS		72.92 98	71.62 102	76.81 107	83.41 98	52.48 130	84.88 136	83.20 154	58.03 188	63.91 144	89.63 92	35.50 216	89.78 107	65.50 114	80.50 79	88.16 124

X-MVStestdata		65.85 221	62.20 230	76.81 107	83.41 98	52.48 130	84.88 136	83.20 154	58.03 188	63.91 144	4.82 364	35.50 216	89.78 107	65.50 114	80.50 79	88.16 124

PGM-MVS		72.60 103	71.20 110	76.80 109	82.95 116	52.82 125	83.07 184	82.14 166	56.51 222	63.18 153	89.81 89	35.68 215	89.76 109	67.30 100	80.19 83	87.83 133

Anonymous202405211		70.11 138	67.88 153	76.79 110	87.20 39	47.24 249	89.49 35	77.38 255	54.88 241	66.14 111	86.84 138	20.93 319	91.54 63	56.45 194	71.62 155	91.59 44

tpm cat1		66.28 215	62.78 226	76.77 111	81.40 156	57.14 22	70.03 310	77.19 257	53.00 255	58.76 206	70.73 308	46.17 80	86.73 196	43.27 268	64.46 203	86.44 160

PVSNet_Blended		76.53 51	76.54 41	76.50 112	85.91 48	51.83 149	88.89 46	84.24 132	67.82 35	69.09 86	89.33 99	46.70 76	88.13 157	75.43 51	81.48 71	89.55 94

Regformer-4		75.06 73	74.59 68	76.47 113	85.49 59	50.33 183	85.12 127	88.61 39	66.42 47	68.48 90	90.66 65	44.15 108	92.68 38	69.24 89	73.16 139	86.39 162

diffmvs		75.11 72	74.65 67	76.46 114	78.52 207	53.35 109	83.28 180	79.94 201	70.51 15	71.64 71	88.72 108	46.02 85	86.08 217	77.52 40	75.75 120	89.96 86

PVSNet_Blended_VisFu		73.40 94	72.44 88	76.30 115	81.32 159	54.70 75	85.81 104	78.82 227	63.70 86	64.53 133	85.38 156	47.11 71	87.38 180	67.75 98	77.55 102	86.81 155

BH-RMVSNet		70.08 140	68.01 150	76.27 116	84.21 85	51.22 166	87.29 74	79.33 220	58.96 176	63.63 150	86.77 139	33.29 236	90.30 97	44.63 263	73.96 133	87.30 144

CLD-MVS		75.60 65	75.39 56	76.24 117	80.69 171	52.40 134	90.69 22	86.20 81	74.40 3	65.01 127	88.93 104	42.05 137	90.58 88	76.57 44	73.96 133	85.73 173
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
GeoE		69.96 145	67.88 153	76.22 118	81.11 161	51.71 152	84.15 151	76.74 267	59.83 147	60.91 173	84.38 163	41.56 145	88.10 159	51.67 224	70.57 164	88.84 112

1314		71.11 124	69.41 132	76.22 118	79.32 187	50.49 175	80.23 242	85.14 109	59.44 155	58.93 200	88.89 106	33.83 232	89.60 114	61.49 145	77.42 104	88.57 120

thisisatest0530		70.47 136	68.56 142	76.20 120	79.78 182	51.52 157	83.49 172	88.58 42	57.62 201	58.60 207	82.79 185	51.03 41	91.48 65	52.84 214	62.36 225	85.59 178

HY-MVS	67.03 5	73.90 85	73.14 79	76.18 121	84.70 76	47.36 246	75.56 273	86.36 78	66.27 50	70.66 80	83.91 169	51.05 40	89.31 117	67.10 102	72.61 148	91.88 39

gg-mvs-nofinetune		67.43 193	64.53 217	76.13 122	85.95 46	47.79 241	64.38 322	88.28 45	39.34 326	66.62 104	41.27 348	58.69 11	89.00 126	49.64 235	86.62 27	91.59 44

原ACMM1				76.13 122	84.89 74	54.59 79		85.26 103	51.98 262	66.70 102	87.07 136	40.15 159	89.70 110	51.23 226	85.06 45	84.10 195

GA-MVS		69.04 159	66.70 178	76.06 124	75.11 253	52.36 136	83.12 182	80.23 197	63.32 92	60.65 177	79.22 224	30.98 258	88.37 147	61.25 146	66.41 190	87.46 140

mPP-MVS		71.79 117	70.38 118	76.04 125	82.65 128	52.06 140	84.45 143	81.78 174	55.59 231	62.05 166	89.68 91	33.48 234	88.28 154	65.45 119	78.24 100	87.77 135

MVSTER		73.25 95	72.33 89	76.01 126	85.54 58	53.76 92	83.52 166	87.16 62	67.06 44	63.88 146	81.66 204	52.77 28	90.44 90	64.66 126	64.69 201	83.84 206

CP-MVS		72.59 105	71.46 105	76.00 127	82.93 118	52.32 138	86.93 84	82.48 164	55.15 236	63.65 149	90.44 75	35.03 220	88.53 143	68.69 93	77.83 101	87.15 145

HPM-MVS		72.60 103	71.50 104	75.89 128	82.02 135	51.42 160	80.70 236	83.05 156	56.12 226	64.03 141	89.53 93	37.55 185	88.37 147	70.48 84	80.04 86	87.88 132
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
114514_t		69.87 147	67.88 153	75.85 129	88.38 25	52.35 137	86.94 83	83.68 142	53.70 250	55.68 249	85.60 153	30.07 264	91.20 71	55.84 196	71.02 160	83.99 199

PMMVS		72.98 97	72.05 99	75.78 130	83.57 96	48.60 220	84.08 153	82.85 161	61.62 118	68.24 93	90.33 76	28.35 270	87.78 170	72.71 74	76.69 110	90.95 65

MS-PatchMatch		72.34 108	71.26 108	75.61 131	82.38 132	55.55 46	88.00 56	89.95 15	65.38 64	56.51 243	80.74 214	32.28 245	92.89 32	57.95 180	88.10 13	78.39 279

xiu_mvs_v1_base_debu		71.60 118	70.29 120	75.55 132	77.26 226	53.15 115	85.34 117	79.37 214	55.83 228	72.54 57	90.19 79	22.38 310	86.66 198	73.28 70	76.39 112	86.85 151

xiu_mvs_v1_base		71.60 118	70.29 120	75.55 132	77.26 226	53.15 115	85.34 117	79.37 214	55.83 228	72.54 57	90.19 79	22.38 310	86.66 198	73.28 70	76.39 112	86.85 151

xiu_mvs_v1_base_debi		71.60 118	70.29 120	75.55 132	77.26 226	53.15 115	85.34 117	79.37 214	55.83 228	72.54 57	90.19 79	22.38 310	86.66 198	73.28 70	76.39 112	86.85 151

CANet_DTU		73.71 90	73.14 79	75.40 135	82.61 129	50.05 191	84.67 141	79.36 217	69.72 20	75.39 30	90.03 85	29.41 266	85.93 223	67.99 97	79.11 93	90.22 80

ACMMP		70.81 130	69.29 136	75.39 136	81.52 154	51.92 146	83.43 173	83.03 157	56.67 219	58.80 205	88.91 105	31.92 250	88.58 139	65.89 113	73.39 138	85.67 174
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
SCA		63.84 231	60.01 248	75.32 137	78.58 205	57.92 12	61.61 330	77.53 251	56.71 217	57.75 223	70.77 306	31.97 248	79.91 285	48.80 239	56.36 268	88.13 127

ab-mvs		70.65 132	69.11 138	75.29 138	80.87 167	46.23 263	73.48 287	85.24 105	59.99 145	66.65 103	80.94 211	43.13 126	88.69 135	63.58 131	68.07 179	90.95 65

TR-MVS		69.71 149	67.85 156	75.27 139	82.94 117	48.48 226	87.40 70	80.86 188	57.15 209	64.61 132	87.08 135	32.67 241	89.64 113	46.38 255	71.55 157	87.68 137

v2v482		69.55 155	67.64 159	75.26 140	72.32 285	53.83 90	84.93 135	81.94 169	65.37 65	60.80 175	79.25 223	41.62 143	88.98 129	63.03 134	59.51 237	82.98 222

PCF-MVS	61.03 10	70.10 139	68.40 145	75.22 141	77.15 230	51.99 143	79.30 255	82.12 167	56.47 223	61.88 167	86.48 145	43.98 109	87.24 182	55.37 198	72.79 146	86.43 161
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		72.34 108	71.44 106	75.03 142	79.02 193	51.56 155	88.00 56	83.68 142	65.45 60	64.48 134	85.13 157	37.35 189	88.62 137	66.70 104	73.12 141	84.91 187

AdaColmap		67.86 183	65.48 203	75.00 143	88.15 29	54.99 65	86.10 100	76.63 270	49.30 278	57.80 220	86.65 142	29.39 267	88.94 132	45.10 261	70.21 167	81.06 251

EI-MVSNet-Vis-set		73.19 96	72.60 85	74.99 144	82.56 130	49.80 196	82.55 196	89.00 26	66.17 52	65.89 115	88.98 103	43.83 111	92.29 47	65.38 123	69.01 174	82.87 224

tpmrst		71.04 125	69.77 128	74.86 145	83.19 106	55.86 44	75.64 272	78.73 230	67.88 34	64.99 128	73.73 278	49.96 50	79.56 288	65.92 111	67.85 183	89.14 105

1121		68.79 167	66.77 175	74.82 146	83.08 110	53.46 101	80.23 242	71.53 312	45.47 303	66.31 110	87.19 133	34.02 227	85.13 237	52.78 216	80.36 81	85.87 171

v1144		68.81 165	66.82 173	74.80 147	72.34 284	53.46 101	84.68 139	81.77 175	64.25 77	60.28 180	77.91 234	40.23 157	88.95 130	60.37 159	59.52 236	81.97 230

v1192		67.96 182	65.74 198	74.63 148	71.79 287	53.43 107	84.06 155	80.99 187	63.19 95	59.56 188	77.46 241	37.50 188	88.65 136	58.20 175	58.93 243	81.79 233

BH-w/o		70.02 142	68.51 143	74.56 149	82.77 122	50.39 179	86.60 91	78.14 241	59.77 148	59.65 185	85.57 154	39.27 167	87.30 181	49.86 233	74.94 129	85.99 166

SR-MVS		70.92 128	69.73 129	74.50 150	83.38 102	50.48 176	84.27 148	79.35 218	48.96 281	66.57 107	90.45 72	33.65 233	87.11 184	66.42 106	74.56 130	85.91 169

tttt0517		68.33 175	66.29 184	74.46 151	78.08 213	49.06 208	80.88 233	89.08 23	54.40 247	54.75 253	80.77 213	51.31 38	90.33 94	49.35 237	58.01 255	83.99 199

TESTMET0.1,1		72.86 99	72.33 89	74.46 151	81.98 136	50.77 168	85.13 124	85.47 90	66.09 54	67.30 98	83.69 174	37.27 192	83.57 254	65.06 124	78.97 95	89.05 107

nrg030		72.27 112	71.56 103	74.42 153	75.93 245	50.60 172	86.97 81	83.21 153	62.75 100	67.15 101	84.38 163	50.07 48	86.66 198	71.19 79	62.37 224	85.99 166

RPMNet		59.29 260	54.25 283	74.42 153	73.97 268	56.57 30	60.52 333	76.98 261	35.72 338	57.49 229	58.87 341	37.73 181	85.26 233	27.01 332	59.93 234	81.42 241

Vis-MVSNet		70.61 133	69.34 134	74.42 153	80.95 166	48.49 225	86.03 102	77.51 252	58.74 179	65.55 120	87.78 126	34.37 224	85.95 222	52.53 221	80.61 77	88.80 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		71.14 122	70.07 125	74.33 156	79.18 190	46.52 256	83.81 162	86.49 74	56.32 225	57.95 217	84.90 161	54.23 22	89.14 119	58.14 176	69.65 171	87.33 142

EI-MVSNet-UG-set		72.37 107	71.73 101	74.29 157	81.60 146	49.29 206	81.85 211	88.64 36	65.29 68	65.05 125	88.29 116	43.18 123	91.83 58	63.74 130	67.97 181	81.75 234

OPM-MVS		70.75 131	69.58 130	74.26 158	75.55 250	51.34 162	86.05 101	83.29 152	61.94 113	62.95 156	85.77 151	34.15 226	88.44 145	65.44 120	71.07 159	82.99 221

v144192		67.86 183	65.76 197	74.16 159	71.68 289	53.09 118	84.14 152	80.83 189	62.85 99	59.21 195	77.28 244	39.30 166	88.00 163	58.67 169	57.88 259	81.40 243

HQP_MVS		70.96 127	69.91 127	74.12 160	77.95 215	49.57 198	85.76 106	82.59 162	63.60 89	62.15 163	83.28 180	36.04 212	88.30 152	65.46 117	72.34 150	84.49 190

v1921920		67.45 192	65.23 210	74.10 161	71.51 292	52.90 124	83.75 164	80.44 194	62.48 105	59.12 197	77.13 245	36.98 196	87.90 164	57.53 185	58.14 253	81.49 238

v8		67.25 198	64.99 213	74.04 162	72.89 278	53.31 112	82.37 201	80.11 199	61.54 121	54.29 258	76.02 266	42.89 128	88.41 146	58.43 170	56.36 268	80.39 260

VPNet		72.07 113	71.42 107	74.04 162	78.64 204	47.17 250	89.91 31	87.97 48	72.56 7	64.66 130	85.04 159	41.83 142	88.33 150	61.17 147	60.97 230	86.62 157

v1240		66.99 205	64.68 215	73.93 164	71.38 295	52.66 128	83.39 177	79.98 200	61.97 112	58.44 214	77.11 246	35.25 218	87.81 166	56.46 193	58.15 251	81.33 246

BH-untuned		68.28 176	66.40 181	73.91 165	81.62 145	50.01 192	85.56 115	77.39 254	57.63 200	57.47 231	83.69 174	36.36 207	87.08 185	44.81 262	73.08 144	84.65 189

v148		68.24 178	66.35 182	73.88 166	71.76 288	51.47 158	84.23 149	81.90 173	63.69 87	58.94 199	76.44 257	43.72 116	87.78 170	60.63 152	55.86 278	82.39 227

V42		67.66 187	65.60 202	73.86 167	70.69 300	53.63 96	81.50 222	78.61 233	63.85 83	59.49 190	77.49 240	37.98 175	87.65 173	62.33 137	58.43 248	80.29 261

Fast-Effi-MVS+-dtu		66.53 212	64.10 221	73.84 168	72.41 283	52.30 139	84.73 138	75.66 277	59.51 153	56.34 244	79.11 226	28.11 273	85.85 224	57.74 184	63.29 213	83.35 211

v10		66.61 211	64.20 220	73.83 169	72.59 281	53.37 108	81.88 210	79.91 203	61.11 127	54.09 260	75.60 268	40.06 161	88.26 155	56.47 192	56.10 274	79.86 267

APD-MVS_3200maxsize		69.62 154	68.23 148	73.80 170	81.58 148	48.22 232	81.91 209	79.50 212	48.21 283	64.24 139	89.75 90	31.91 251	87.55 176	63.08 133	73.85 135	85.64 176

AUN-MVS		68.20 179	66.35 182	73.76 171	76.37 235	47.45 244	79.52 252	79.52 211	60.98 131	62.34 161	86.02 147	36.59 206	86.94 190	62.32 138	53.47 296	86.89 148

PVSNet_BlendedMVS		73.42 93	73.30 78	73.76 171	85.91 48	51.83 149	86.18 98	84.24 132	65.40 63	69.09 86	80.86 212	46.70 76	88.13 157	75.43 51	65.92 196	81.33 246

hse-mvs2		71.44 121	70.68 114	73.73 173	76.34 236	47.44 245	79.45 253	79.47 213	68.08 30	71.97 66	86.01 149	42.50 129	86.93 191	78.82 26	53.46 297	86.83 154

baseline1		72.51 106	72.12 97	73.69 174	85.05 70	44.46 279	83.51 170	86.13 83	71.61 9	64.64 131	87.97 123	55.00 20	89.48 115	59.07 165	56.05 275	87.13 146

abl_6		68.03 180	66.15 188	73.66 175	78.54 206	48.48 226	79.77 247	78.04 242	47.39 287	63.70 148	88.25 117	28.21 271	89.06 120	60.17 162	71.25 158	83.45 210

CDS-MVSNet		70.48 135	69.43 131	73.64 176	77.56 221	48.83 217	83.51 170	77.45 253	63.27 93	62.33 162	85.54 155	43.85 110	83.29 258	57.38 188	74.00 132	88.79 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet	62.49 8	69.27 157	67.81 157	73.64 176	84.41 80	51.85 147	84.63 142	77.80 246	66.42 47	59.80 183	84.95 160	22.14 314	80.44 277	55.03 199	75.11 126	88.62 118

PS-MVSNAJss		68.78 168	67.17 170	73.62 178	73.01 275	48.33 231	84.95 134	84.81 117	59.30 162	58.91 202	79.84 218	37.77 178	88.86 133	62.83 135	63.12 218	83.67 208

TAMVS		69.51 156	68.16 149	73.56 179	76.30 239	48.71 219	82.57 194	77.17 258	62.10 109	61.32 170	84.23 165	41.90 140	83.46 256	54.80 202	73.09 143	88.50 122

UGNet		68.71 169	67.11 171	73.50 180	80.55 175	47.61 242	84.08 153	78.51 235	59.45 154	65.68 119	82.73 189	23.78 302	85.08 239	52.80 215	76.40 111	87.80 134
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
mvs-test1		69.04 159	67.57 162	73.44 181	75.17 251	51.68 153	86.57 92	74.48 286	62.15 107	62.07 165	85.79 150	30.59 260	87.48 177	65.40 121	65.94 195	81.18 250

test1172		69.64 153	68.38 146	73.41 182	82.77 122	48.84 216	82.79 191	78.34 239	47.02 291	65.27 122	90.07 83	31.17 256	86.09 215	64.51 127	73.49 137	85.31 181

Anonymous20231211		66.08 219	63.67 223	73.31 183	83.07 111	48.75 218	86.01 103	84.67 121	45.27 304	56.54 241	76.67 255	28.06 274	88.95 130	52.78 216	59.95 233	82.23 228

新几何1				73.30 184	83.10 107	53.48 100		71.43 313	45.55 301	66.14 111	87.17 134	33.88 231	80.54 275	48.50 242	80.33 82	85.88 170

FMVSNet3		68.84 163	67.40 166	73.19 185	85.05 70	48.53 223	85.71 111	85.36 95	60.90 133	57.58 226	79.15 225	42.16 134	86.77 194	47.25 250	63.40 209	84.27 194

thres200		68.71 169	67.27 169	73.02 186	84.73 75	46.76 253	85.03 132	87.73 55	62.34 106	59.87 181	83.45 177	43.15 124	88.32 151	31.25 316	67.91 182	83.98 201

PVSNet_0	57.04 13	61.19 252	57.24 263	73.02 186	77.45 223	50.31 185	79.43 254	77.36 256	63.96 82	47.51 299	72.45 294	25.03 297	83.78 251	52.76 219	19.22 355	84.96 186

dp		64.41 227	61.58 234	72.90 188	82.40 131	54.09 88	72.53 293	76.59 271	60.39 140	55.68 249	70.39 309	35.18 219	76.90 310	39.34 279	61.71 227	87.73 136

FMVSNet2		67.57 189	65.79 196	72.90 188	82.71 125	47.97 240	85.15 123	84.93 113	58.55 182	56.71 239	78.26 232	36.72 203	86.67 197	46.15 257	62.94 220	84.07 196

XXY-MVS		70.18 137	69.28 137	72.89 190	77.64 219	42.88 294	85.06 130	87.50 60	62.58 102	62.66 160	82.34 196	43.64 118	89.83 106	58.42 172	63.70 208	85.96 168

CR-MVSNet		62.47 245	59.04 255	72.77 191	73.97 268	56.57 30	60.52 333	71.72 308	60.04 143	57.49 229	65.86 323	38.94 168	80.31 278	42.86 272	59.93 234	81.42 241

EI-MVSNet		69.70 151	68.70 141	72.68 192	75.00 256	48.90 214	79.54 250	87.16 62	61.05 129	63.88 146	83.74 172	45.87 87	90.44 90	57.42 187	64.68 202	78.70 272

HPM-MVS_fast		67.86 183	66.28 185	72.61 193	80.67 172	48.34 230	81.18 228	75.95 276	50.81 271	59.55 189	88.05 122	27.86 276	85.98 219	58.83 167	73.58 136	83.51 209

MVP-Stereo		70.97 126	70.44 117	72.59 194	76.03 244	51.36 161	85.02 133	86.99 65	60.31 141	56.53 242	78.92 227	40.11 160	90.00 102	60.00 163	90.01 4	76.41 301
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS_111021_LR		69.07 158	67.91 151	72.54 195	77.27 225	49.56 200	79.77 247	73.96 292	59.33 161	60.73 176	87.82 124	30.19 263	81.53 266	69.94 86	72.19 152	86.53 158

IS-MVSNet		68.80 166	67.55 163	72.54 195	78.50 208	43.43 289	81.03 230	79.35 218	59.12 171	57.27 234	86.71 140	46.05 83	87.70 172	44.32 264	75.60 121	86.49 159

VPA-MVSNet		71.12 123	70.66 115	72.49 197	78.75 199	44.43 281	87.64 61	90.02 13	63.97 81	65.02 126	81.58 205	42.14 135	87.42 179	63.42 132	63.38 212	85.63 177

SR-MVS-dyc-post		68.27 177	66.87 172	72.48 198	80.96 163	48.14 235	81.54 220	76.98 261	46.42 296	62.75 158	89.42 95	31.17 256	86.09 215	60.52 156	72.06 153	83.19 217

bset_n11_16_dypcd		65.51 223	63.21 225	72.41 199	68.84 309	50.15 188	81.25 226	72.40 304	59.17 168	59.20 196	78.66 229	25.69 293	85.27 232	66.80 103	56.88 266	81.80 232

miper_enhance_ethall		69.77 148	68.90 140	72.38 200	78.93 196	49.91 194	83.29 179	78.85 225	64.90 71	59.37 191	79.46 220	52.77 28	85.16 236	63.78 129	58.72 244	82.08 229

cl-mvsnet2		68.85 162	67.69 158	72.35 201	78.07 214	49.98 193	82.45 199	78.48 236	62.50 104	58.46 212	77.95 233	49.99 49	85.17 235	62.55 136	58.72 244	81.90 231

MSDG		59.44 259	55.14 279	72.32 202	74.69 259	50.71 169	74.39 282	73.58 295	44.44 310	43.40 313	77.52 239	19.45 323	90.87 81	31.31 315	57.49 263	75.38 307

v7n		62.50 244	59.27 253	72.20 203	67.25 321	49.83 195	77.87 262	80.12 198	52.50 259	48.80 290	73.07 286	32.10 246	87.90 164	46.83 253	54.92 284	78.86 270

1112_ss		70.05 141	69.37 133	72.10 204	80.77 169	42.78 295	85.12 127	76.75 266	59.69 150	61.19 172	92.12 33	47.48 67	83.84 249	53.04 212	68.21 178	89.66 92

miper_ehance_all_eth		68.70 171	67.58 160	72.08 205	76.91 232	49.48 203	82.47 198	78.45 237	62.68 101	58.28 216	77.88 235	50.90 42	85.01 240	61.91 142	58.72 244	81.75 234

eth_miper_zixun_eth		66.98 206	65.28 209	72.06 206	75.61 249	50.40 178	81.00 231	76.97 264	62.00 110	56.99 236	76.97 248	44.84 101	85.58 225	58.75 168	54.42 288	80.21 262

LPG-MVS_test		66.44 214	64.58 216	72.02 207	74.42 262	48.60 220	83.07 184	80.64 191	54.69 243	53.75 263	83.83 170	25.73 291	86.98 187	60.33 160	64.71 199	80.48 258

LGP-MVS_train				72.02 207	74.42 262	48.60 220		80.64 191	54.69 243	53.75 263	83.83 170	25.73 291	86.98 187	60.33 160	64.71 199	80.48 258

ACMP	61.11 9	66.24 217	64.33 218	72.00 209	74.89 258	49.12 207	83.18 181	79.83 204	55.41 234	52.29 272	82.68 190	25.83 289	86.10 213	60.89 149	63.94 206	80.78 254
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
GBi-Net		67.09 202	65.47 204	71.96 210	82.71 125	46.36 258	83.52 166	83.31 149	58.55 182	57.58 226	76.23 261	36.72 203	86.20 207	47.25 250	63.40 209	83.32 212

test1		67.09 202	65.47 204	71.96 210	82.71 125	46.36 258	83.52 166	83.31 149	58.55 182	57.58 226	76.23 261	36.72 203	86.20 207	47.25 250	63.40 209	83.32 212

FMVSNet1		64.57 226	62.11 231	71.96 210	77.32 224	46.36 258	83.52 166	83.31 149	52.43 260	54.42 256	76.23 261	27.80 277	86.20 207	42.59 273	61.34 229	83.32 212

cl-mvsnet____		67.43 193	65.93 193	71.95 213	76.33 237	48.02 238	82.58 193	79.12 222	61.30 125	56.72 238	76.92 250	46.12 81	86.44 205	57.98 178	56.31 270	81.38 245

cl-mvsnet1		67.43 193	65.93 193	71.94 214	76.33 237	48.01 239	82.57 194	79.11 223	61.31 124	56.73 237	76.92 250	46.09 82	86.43 206	57.98 178	56.31 270	81.39 244

Patchmatch-RL test		58.72 270	54.32 282	71.92 215	63.91 334	44.25 283	61.73 329	55.19 342	57.38 206	49.31 288	54.24 345	37.60 184	80.89 271	62.19 140	47.28 315	90.63 70

cl_fuxian		67.97 181	66.66 179	71.91 216	76.20 241	49.31 205	82.13 205	78.00 244	61.99 111	57.64 225	76.94 249	49.41 53	84.93 241	60.62 153	57.01 265	81.49 238

tfpn200view9		67.57 189	66.13 189	71.89 217	84.05 88	45.07 274	83.40 175	87.71 57	60.79 134	57.79 221	82.76 186	43.53 119	87.80 167	28.80 322	66.36 191	82.78 225

MIMVSNet		63.12 237	60.29 246	71.61 218	75.92 246	46.65 254	65.15 319	81.94 169	59.14 169	54.65 254	69.47 312	25.74 290	80.63 274	41.03 276	69.56 173	87.55 138

test-LLR		69.65 152	69.01 139	71.60 219	78.67 201	48.17 233	85.13 124	79.72 206	59.18 166	63.13 154	82.58 191	36.91 198	80.24 279	60.56 154	75.17 124	86.39 162

test-mter		68.36 173	67.29 167	71.60 219	78.67 201	48.17 233	85.13 124	79.72 206	53.38 252	63.13 154	82.58 191	27.23 281	80.24 279	60.56 154	75.17 124	86.39 162

sss		70.49 134	70.13 124	71.58 221	81.59 147	39.02 316	80.78 235	84.71 120	59.34 159	66.61 105	88.09 120	37.17 194	85.52 226	61.82 144	71.02 160	90.20 82

tpmvs		62.45 246	59.42 251	71.53 222	83.93 90	54.32 82	70.03 310	77.61 250	51.91 263	53.48 266	68.29 316	37.91 176	86.66 198	33.36 306	58.27 249	73.62 319

ACMM	58.35 12	64.35 228	62.01 232	71.38 223	74.21 265	48.51 224	82.25 203	79.66 208	47.61 285	54.54 255	80.11 216	25.26 294	86.00 218	51.26 225	63.16 216	79.64 268
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH	53.70 16	59.78 257	55.94 274	71.28 224	76.59 234	48.35 229	80.15 245	76.11 274	49.74 276	41.91 319	73.45 285	16.50 337	90.31 95	31.42 314	57.63 262	75.17 309
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ppachtmachnet_test		58.56 272	54.34 281	71.24 225	71.42 293	54.74 72	81.84 212	72.27 305	49.02 280	45.86 307	68.99 315	26.27 286	83.30 257	30.12 318	43.23 328	75.69 304

thres100view900		66.87 208	65.42 207	71.24 225	83.29 104	43.15 291	81.67 215	87.78 52	59.04 172	55.92 247	82.18 198	43.73 114	87.80 167	28.80 322	66.36 191	82.78 225

thres400		67.40 196	66.13 189	71.19 227	84.05 88	45.07 274	83.40 175	87.71 57	60.79 134	57.79 221	82.76 186	43.53 119	87.80 167	28.80 322	66.36 191	80.71 256

our_test_3		59.11 264	55.08 280	71.18 228	71.42 293	53.29 113	81.96 207	74.52 285	48.32 282	42.08 317	69.28 314	28.14 272	82.15 262	34.35 303	45.68 323	78.11 284

CPTT-MVS		67.15 201	65.84 195	71.07 229	80.96 163	50.32 184	81.94 208	74.10 289	46.18 299	57.91 218	87.64 129	29.57 265	81.31 268	64.10 128	70.18 168	81.56 237

NR-MVSNet		67.25 198	65.99 192	71.04 230	73.27 273	43.91 285	85.32 120	84.75 119	66.05 57	53.65 265	82.11 199	45.05 96	85.97 221	47.55 247	56.18 273	83.24 215

tpm		68.36 173	67.48 165	70.97 231	79.93 181	51.34 162	76.58 269	78.75 229	67.73 36	63.54 152	74.86 272	48.33 58	72.36 331	53.93 207	63.71 207	89.21 102

TranMVSNet+NR-MVSNet		66.94 207	65.61 201	70.93 232	73.45 270	43.38 290	83.02 186	84.25 130	65.31 67	58.33 215	81.90 202	39.92 163	85.52 226	49.43 236	54.89 285	83.89 205

EG-PatchMatch MVS		62.40 247	59.59 249	70.81 233	73.29 272	49.05 209	85.81 104	84.78 118	51.85 265	44.19 308	73.48 284	15.52 340	89.85 105	40.16 277	67.24 185	73.54 320

test_djsdf		63.84 231	61.56 235	70.70 234	68.78 310	44.69 278	81.63 216	81.44 179	50.28 272	52.27 273	76.26 260	26.72 284	86.11 211	60.83 150	55.84 279	81.29 249

UA-Net		67.32 197	66.23 186	70.59 235	78.85 197	41.23 308	73.60 285	75.45 280	61.54 121	66.61 105	84.53 162	38.73 171	86.57 203	42.48 274	74.24 131	83.98 201

thres600view7		66.46 213	65.12 211	70.47 236	83.41 98	43.80 287	82.15 204	87.78 52	59.37 158	56.02 246	82.21 197	43.73 114	86.90 192	26.51 333	64.94 198	80.71 256

UniMVSNet (Re)		67.71 186	66.80 174	70.45 237	74.44 261	42.93 293	82.42 200	84.90 114	63.69 87	59.63 186	80.99 210	47.18 69	85.23 234	51.17 227	56.75 267	83.19 217

IterMVS-LS		66.63 210	65.36 208	70.42 238	75.10 254	48.90 214	81.45 225	76.69 269	61.05 129	55.71 248	77.10 247	45.86 88	83.65 253	57.44 186	57.88 259	78.70 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		68.82 164	68.29 147	70.40 239	75.71 248	42.59 297	84.23 149	86.78 69	66.31 49	58.51 208	82.45 193	51.57 36	84.64 245	53.11 210	55.96 276	83.96 203

jajsoiax		63.21 236	60.84 241	70.32 240	68.33 315	44.45 280	81.23 227	81.05 186	53.37 253	50.96 282	77.81 237	17.49 332	85.49 228	59.31 164	58.05 254	81.02 252

mvs_tets		62.96 239	60.55 243	70.19 241	68.22 318	44.24 284	80.90 232	80.74 190	52.99 256	50.82 284	77.56 238	16.74 335	85.44 229	59.04 166	57.94 256	80.89 253

pmmvs4		63.34 235	61.07 240	70.16 242	70.14 302	50.53 174	79.97 246	71.41 314	55.08 237	54.12 259	78.58 230	32.79 240	82.09 264	50.33 230	57.22 264	77.86 285

DU-MVS		66.84 209	65.74 198	70.16 242	73.27 273	42.59 297	81.50 222	82.92 160	63.53 91	58.51 208	82.11 199	40.75 150	84.64 245	53.11 210	55.96 276	83.24 215

Effi-MVS+-dtu		66.24 217	64.96 214	70.08 244	75.17 251	49.64 197	82.01 206	74.48 286	62.15 107	57.83 219	76.08 265	30.59 260	83.79 250	65.40 121	60.93 231	76.81 294

IterMVS		63.77 233	61.67 233	70.08 244	72.68 280	51.24 165	80.44 238	75.51 278	60.51 139	51.41 277	73.70 281	32.08 247	78.91 290	54.30 205	54.35 289	80.08 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WR-MVS		67.58 188	66.76 176	70.04 246	75.92 246	45.06 277	86.23 97	85.28 102	64.31 76	58.50 210	81.00 209	44.80 104	82.00 265	49.21 238	55.57 282	83.06 220

Test_1112_low_res		67.18 200	66.23 186	70.02 247	78.75 199	41.02 309	83.43 173	73.69 294	57.29 207	58.45 213	82.39 195	45.30 94	80.88 272	50.50 229	66.26 194	88.16 124

D2MVS		63.49 234	61.39 237	69.77 248	69.29 307	48.93 213	78.89 257	77.71 249	60.64 138	49.70 286	72.10 300	27.08 282	83.48 255	54.48 204	62.65 221	76.90 293

RRT_MVS		65.43 225	64.01 222	69.68 249	81.54 150	50.15 188	82.31 202	76.78 265	55.25 235	60.64 178	82.00 201	25.18 295	79.00 289	60.96 148	51.45 303	79.89 266

XVG-OURS		61.88 249	59.34 252	69.49 250	65.37 326	46.27 261	64.80 321	73.49 297	47.04 290	57.41 233	82.85 184	25.15 296	78.18 294	53.00 213	64.98 197	84.01 198

XVG-OURS-SEG-HR		62.02 248	59.54 250	69.46 251	65.30 327	45.88 265	65.06 320	73.57 296	46.45 295	57.42 232	83.35 179	26.95 283	78.09 296	53.77 208	64.03 204	84.42 192

FIs		70.00 143	70.24 123	69.30 252	77.93 217	38.55 318	83.99 158	87.72 56	66.86 46	57.66 224	84.17 166	52.28 31	85.31 230	52.72 220	68.80 175	84.02 197

Baseline_NR-MVSNet		65.49 224	64.27 219	69.13 253	74.37 264	41.65 304	83.39 177	78.85 225	59.56 152	59.62 187	76.88 252	40.75 150	87.44 178	49.99 231	55.05 283	78.28 281

TransMVSNet (Re)		62.82 240	60.76 242	69.02 254	73.98 267	41.61 305	86.36 94	79.30 221	56.90 211	52.53 270	76.44 257	41.85 141	87.60 175	38.83 280	40.61 333	77.86 285

anonymousdsp		60.46 256	57.65 260	68.88 255	63.63 335	45.09 273	72.93 291	78.63 232	46.52 294	51.12 279	72.80 290	21.46 317	83.07 259	57.79 182	53.97 290	78.47 276

ADS-MVSNet		56.17 286	51.95 295	68.84 256	80.60 173	53.07 119	55.03 340	70.02 320	44.72 307	51.00 280	61.19 334	22.83 306	78.88 291	28.54 325	53.63 292	74.57 313

OpenMVS_ROB	53.19 17	59.20 262	56.00 273	68.83 257	71.13 297	44.30 282	83.64 165	75.02 283	46.42 296	46.48 304	73.03 287	18.69 327	88.14 156	27.74 329	61.80 226	74.05 316

Patchmatch-test		53.33 300	48.17 307	68.81 258	73.31 271	42.38 301	42.98 349	58.23 339	32.53 343	38.79 330	70.77 306	39.66 164	73.51 325	25.18 336	52.06 301	90.55 71

pm-mvs1		64.12 229	62.56 227	68.78 259	71.68 289	38.87 317	82.89 188	81.57 176	55.54 233	53.89 262	77.82 236	37.73 181	86.74 195	48.46 243	53.49 295	80.72 255

miper_lstm_enhance		63.91 230	62.30 229	68.75 260	75.06 255	46.78 252	69.02 314	81.14 185	59.68 151	52.76 269	72.39 295	40.71 152	77.99 300	56.81 190	53.09 298	81.48 240

OMC-MVS		65.97 220	65.06 212	68.71 261	72.97 276	42.58 299	78.61 258	75.35 281	54.72 242	59.31 193	86.25 146	33.30 235	77.88 302	57.99 177	67.05 186	85.66 175

DP-MVS		59.24 261	56.12 272	68.63 262	88.24 28	50.35 182	82.51 197	64.43 331	41.10 324	46.70 302	78.77 228	24.75 299	88.57 142	22.26 341	56.29 272	66.96 339

tfpnnormal		61.47 251	59.09 254	68.62 263	76.29 240	41.69 303	81.14 229	85.16 107	54.48 246	51.32 278	73.63 282	32.32 244	86.89 193	21.78 343	55.71 280	77.29 291

UniMVSNet_ETH3D		62.51 243	60.49 244	68.57 264	68.30 316	40.88 311	73.89 284	79.93 202	51.81 266	54.77 252	79.61 219	24.80 298	81.10 269	49.93 232	61.35 228	83.73 207

CL-MVSNet_2432*1600		62.98 238	61.14 239	68.50 265	65.86 324	42.96 292	84.37 144	82.98 158	60.98 131	53.95 261	72.70 291	40.43 155	83.71 252	41.10 275	47.93 311	78.83 271

ACMH+	54.58 15	58.55 273	55.24 276	68.50 265	74.68 260	45.80 268	80.27 240	70.21 319	47.15 289	42.77 316	75.48 269	16.73 336	85.98 219	35.10 301	54.78 286	73.72 318

lessismore_v0				67.98 267	64.76 331	41.25 307		45.75 351		36.03 336	65.63 325	19.29 325	84.11 247	35.67 293	21.24 354	78.59 275

K. test v3		54.04 296	49.42 304	67.92 268	68.55 312	42.57 300	75.51 275	63.07 334	52.07 261	39.21 327	64.59 327	19.34 324	82.21 261	37.11 286	25.31 351	78.97 269

pmmvs5		62.80 241	61.18 238	67.66 269	69.53 306	42.37 302	82.65 192	75.19 282	54.30 248	52.03 275	78.51 231	31.64 253	80.67 273	48.60 241	58.15 251	79.95 265

PatchT		56.60 282	52.97 289	67.48 270	72.94 277	46.16 264	57.30 338	73.78 293	38.77 328	54.37 257	57.26 344	37.52 186	78.06 297	32.02 311	52.79 299	78.23 283

Patchmtry		56.56 283	52.95 290	67.42 271	72.53 282	50.59 173	59.05 335	71.72 308	37.86 332	46.92 300	65.86 323	38.94 168	80.06 282	36.94 289	46.72 320	71.60 330

SixPastTwentyTwo		54.37 293	50.10 300	67.21 272	70.70 299	41.46 306	74.73 280	64.69 330	47.56 286	39.12 328	69.49 311	18.49 329	84.69 244	31.87 312	34.20 344	75.48 306

pmmvs6		59.64 258	57.15 264	67.09 273	66.01 322	36.86 325	80.50 237	78.64 231	45.05 306	49.05 289	73.94 277	27.28 280	86.10 213	43.96 266	49.94 306	78.31 280

testdata				67.08 274	77.59 220	45.46 271		69.20 322	44.47 309	71.50 74	88.34 114	31.21 255	70.76 336	52.20 222	75.88 117	85.03 184

CNLPA		60.59 255	58.44 258	67.05 275	79.21 189	47.26 248	79.75 249	64.34 332	42.46 322	51.90 276	83.94 168	27.79 278	75.41 315	37.12 285	59.49 238	78.47 276

KD-MVS_2432*1600		59.04 266	56.44 269	66.86 276	79.07 191	45.87 266	72.13 299	80.42 195	55.03 238	48.15 292	71.01 303	36.73 201	78.05 298	35.21 297	30.18 349	76.67 295

miper_refine_blended		59.04 266	56.44 269	66.86 276	79.07 191	45.87 266	72.13 299	80.42 195	55.03 238	48.15 292	71.01 303	36.73 201	78.05 298	35.21 297	30.18 349	76.67 295

TAPA-MVS	56.12 14	61.82 250	60.18 247	66.71 278	78.48 209	37.97 321	75.19 278	76.41 273	46.82 292	57.04 235	86.52 144	27.67 279	77.03 308	26.50 334	67.02 187	85.14 183
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_0402		56.45 284	53.03 288	66.69 279	76.78 233	50.31 185	81.76 213	69.61 321	42.79 320	43.88 309	72.13 298	22.82 308	86.46 204	16.57 352	50.94 304	63.31 345

PLC	52.38 18	60.89 253	58.97 256	66.68 280	81.77 141	45.70 269	78.96 256	74.04 291	43.66 316	47.63 296	83.19 182	23.52 305	77.78 305	37.47 282	60.46 232	76.55 300
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ADS-MVSNet2		55.21 292	51.44 296	66.51 281	80.60 173	49.56 200	55.03 340	65.44 328	44.72 307	51.00 280	61.19 334	22.83 306	75.41 315	28.54 325	53.63 292	74.57 313

FC-MVSNet-test		67.49 191	67.91 151	66.21 282	76.06 242	33.06 335	80.82 234	87.18 61	64.44 75	54.81 251	82.87 183	50.40 47	82.60 260	48.05 245	66.55 189	82.98 222

JIA-IIPM		52.33 304	47.77 310	66.03 283	71.20 296	46.92 251	40.00 353	76.48 272	37.10 333	46.73 301	37.02 350	32.96 237	77.88 302	35.97 292	52.45 300	73.29 322

LCM-MVSNet-Re		58.82 269	56.54 267	65.68 284	79.31 188	29.09 347	61.39 332	45.79 350	60.73 136	37.65 332	72.47 293	31.42 254	81.08 270	49.66 234	70.41 165	86.87 149

XVG-ACMP-BASELINE		56.03 287	52.85 291	65.58 285	61.91 340	40.95 310	63.36 323	72.43 303	45.20 305	46.02 305	74.09 275	9.20 351	78.12 295	45.13 260	58.27 249	77.66 288

pmmvs-eth3d		55.97 288	52.78 292	65.54 286	61.02 342	46.44 257	75.36 277	67.72 325	49.61 277	43.65 311	67.58 319	21.63 316	77.04 307	44.11 265	44.33 325	73.15 324

MDA-MVSNet_test_wron		53.82 298	49.95 302	65.43 287	70.13 303	49.05 209	72.30 296	71.65 311	44.23 313	31.85 347	63.13 330	23.68 304	74.01 320	33.25 308	39.35 336	73.23 323

YYNet1		53.82 298	49.96 301	65.41 288	70.09 304	48.95 211	72.30 296	71.66 310	44.25 312	31.89 346	63.07 331	23.73 303	73.95 321	33.26 307	39.40 335	73.34 321

PatchMatch-RL		56.66 281	53.75 286	65.37 289	77.91 218	45.28 272	69.78 312	60.38 337	41.35 323	47.57 297	73.73 278	16.83 334	76.91 309	36.99 288	59.21 241	73.92 317

Vis-MVSNet (Re-imp)		65.52 222	65.63 200	65.17 290	77.49 222	30.54 342	75.49 276	77.73 248	59.34 159	52.26 274	86.69 141	49.38 54	80.53 276	37.07 287	75.28 123	84.42 192

FMVSNet5		58.61 271	56.45 268	65.10 291	77.20 229	39.74 313	74.77 279	77.12 259	50.27 274	43.28 314	67.71 318	26.15 288	76.90 310	36.78 290	54.78 286	78.65 274

EPNet_dtu		66.25 216	66.71 177	64.87 292	78.66 203	34.12 330	82.80 190	75.51 278	61.75 116	64.47 137	86.90 137	37.06 195	72.46 330	43.65 267	69.63 172	88.02 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UnsupCasMVSNet_eth		57.56 277	55.15 278	64.79 293	64.57 332	33.12 334	73.17 290	83.87 140	58.98 175	41.75 320	70.03 310	22.54 309	79.92 283	46.12 258	35.31 339	81.32 248

LS3D		56.40 285	53.82 285	64.12 294	81.12 160	45.69 270	73.42 288	66.14 327	35.30 342	43.24 315	79.88 217	22.18 313	79.62 287	19.10 349	64.00 205	67.05 338

UnsupCasMVSNet_bld		53.86 297	50.53 299	63.84 295	63.52 336	34.75 328	71.38 304	81.92 171	46.53 293	38.95 329	57.93 342	20.55 320	80.20 281	39.91 278	34.09 345	76.57 299

USDC		54.36 294	51.23 297	63.76 296	64.29 333	37.71 322	62.84 328	73.48 299	56.85 212	35.47 337	71.94 301	9.23 350	78.43 292	38.43 281	48.57 308	75.13 310

Anonymous20231206		59.08 265	57.59 261	63.55 297	68.77 311	32.14 340	80.26 241	79.78 205	50.00 275	49.39 287	72.39 295	26.64 285	78.36 293	33.12 309	57.94 256	80.14 263

CMPMVS	40.41 21	55.34 290	52.64 293	63.46 298	60.88 343	43.84 286	61.58 331	71.06 315	30.43 345	36.33 334	74.63 274	24.14 301	75.44 314	48.05 245	66.62 188	71.12 333
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OurMVSNet-221017-0		52.39 303	48.73 305	63.35 299	65.21 328	38.42 319	68.54 316	64.95 329	38.19 329	39.57 326	71.43 302	13.23 344	79.92 283	37.16 284	40.32 334	71.72 329

MDA-MVSNet-bldmvs		51.56 306	47.75 311	63.00 300	71.60 291	47.32 247	69.70 313	72.12 306	43.81 315	27.65 351	63.38 329	21.97 315	75.96 312	27.30 331	32.19 346	65.70 342

F-COLMAP		55.96 289	53.65 287	62.87 301	72.76 279	42.77 296	74.70 281	70.37 318	40.03 325	41.11 323	79.36 221	17.77 331	73.70 324	32.80 310	53.96 291	72.15 326

test0.0.03 1		62.54 242	62.44 228	62.86 302	72.28 286	29.51 344	82.93 187	78.78 228	59.18 166	53.07 268	82.41 194	36.91 198	77.39 306	37.45 283	58.96 242	81.66 236

CVMVSNet		60.85 254	60.44 245	62.07 303	75.00 256	32.73 337	79.54 250	73.49 297	36.98 334	56.28 245	83.74 172	29.28 268	69.53 339	46.48 254	63.23 214	83.94 204

ambc				62.06 304	53.98 350	29.38 345	35.08 355	79.65 209		41.37 321	59.96 337	6.27 357	82.15 262	35.34 296	38.22 337	74.65 312

PEN-MVS		58.35 275	57.15 264	61.94 305	67.55 320	34.39 329	77.01 265	78.35 238	51.87 264	47.72 295	76.73 254	33.91 229	73.75 323	34.03 304	47.17 316	77.68 287

MVS-HIRNet		49.01 310	44.71 314	61.92 306	76.06 242	46.61 255	63.23 325	54.90 343	24.77 349	33.56 342	36.60 352	21.28 318	75.88 313	29.49 319	62.54 222	63.26 346

LTVRE_ROB	45.45 19	52.73 301	49.74 303	61.69 307	69.78 305	34.99 327	44.52 347	67.60 326	43.11 319	43.79 310	74.03 276	18.54 328	81.45 267	28.39 327	57.94 256	68.62 336
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
WR-MVS_H		58.91 268	58.04 259	61.54 308	69.07 308	33.83 332	76.91 266	81.99 168	51.40 268	48.17 291	74.67 273	40.23 157	74.15 319	31.78 313	48.10 309	76.64 298

CP-MVSNet		58.54 274	57.57 262	61.46 309	68.50 313	33.96 331	76.90 267	78.60 234	51.67 267	47.83 294	76.60 256	34.99 221	72.79 328	35.45 294	47.58 312	77.64 289

MVS_0304		56.72 280	55.17 277	61.37 310	70.71 298	36.80 326	75.74 270	68.75 323	44.11 314	52.53 270	68.20 317	15.05 341	74.53 318	42.98 270	58.44 247	72.79 325

PS-CasMVS		58.12 276	57.03 266	61.37 310	68.24 317	33.80 333	76.73 268	78.01 243	51.20 269	47.54 298	76.20 264	32.85 238	72.76 329	35.17 299	47.37 314	77.55 290

Anonymous20240521		51.65 305	48.42 306	61.34 312	56.43 348	39.65 315	73.57 286	73.47 300	36.64 336	36.59 333	63.98 328	10.75 347	72.25 332	35.35 295	49.01 307	72.11 327

CHOSEN 280x420		57.53 278	56.38 271	60.97 313	74.01 266	48.10 237	46.30 346	54.31 344	48.18 284	50.88 283	77.43 242	38.37 174	59.16 347	54.83 200	63.14 217	75.66 305

DTE-MVSNet		57.03 279	55.73 275	60.95 314	65.94 323	32.57 338	75.71 271	77.09 260	51.16 270	46.65 303	76.34 259	32.84 239	73.22 327	30.94 317	44.87 324	77.06 292

IterMVS-SCA-FT		59.12 263	58.81 257	60.08 315	70.68 301	45.07 274	80.42 239	74.25 288	43.54 317	50.02 285	73.73 278	31.97 248	56.74 348	51.06 228	53.60 294	78.42 278

COLMAP_ROB	43.60 20	50.90 307	48.05 308	59.47 316	67.81 319	40.57 312	71.25 305	62.72 336	36.49 337	36.19 335	73.51 283	13.48 343	73.92 322	20.71 345	50.26 305	63.92 344
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testgi		54.25 295	52.57 294	59.29 317	62.76 338	21.65 356	72.21 298	70.47 317	53.25 254	41.94 318	77.33 243	14.28 342	77.95 301	29.18 321	51.72 302	78.28 281

TinyColmap		48.15 312	44.49 316	59.13 318	65.73 325	38.04 320	63.34 324	62.86 335	38.78 327	29.48 349	67.23 321	6.46 356	73.30 326	24.59 337	41.90 331	66.04 340

test20.03		55.22 291	54.07 284	58.68 319	63.14 337	25.00 351	77.69 263	74.78 284	52.64 257	43.43 312	72.39 295	26.21 287	74.76 317	29.31 320	47.05 318	76.28 302

EU-MVSNet		52.63 302	50.72 298	58.37 320	62.69 339	28.13 349	72.60 292	75.97 275	30.94 344	40.76 325	72.11 299	20.16 321	70.80 335	35.11 300	46.11 321	76.19 303

MIMVSNet1		50.35 308	47.81 309	57.96 321	61.53 341	27.80 350	67.40 317	74.06 290	43.25 318	33.31 345	65.38 326	16.03 338	71.34 333	21.80 342	47.55 313	74.75 311

pmmvs3		45.53 316	41.55 319	57.44 322	48.97 354	39.68 314	70.06 309	57.66 340	28.32 347	34.06 340	57.29 343	8.50 352	66.85 341	34.86 302	34.26 343	65.80 341

DIV-MVS_2432*1600		49.24 309	46.85 312	56.44 323	54.32 349	22.87 353	57.39 337	73.36 301	44.36 311	37.98 331	59.30 340	18.97 326	71.17 334	33.48 305	42.44 329	75.26 308

PM-MVS		46.92 314	43.76 318	56.41 324	52.18 351	32.26 339	63.21 326	38.18 355	37.99 331	40.78 324	66.20 322	5.09 359	65.42 342	48.19 244	41.99 330	71.54 331

AllTest		47.32 313	44.66 315	55.32 325	65.08 329	37.50 323	62.96 327	54.25 345	35.45 340	33.42 343	72.82 288	9.98 348	59.33 345	24.13 338	43.84 326	69.13 334

TestCases				55.32 325	65.08 329	37.50 323		54.25 345	35.45 340	33.42 343	72.82 288	9.98 348	59.33 345	24.13 338	43.84 326	69.13 334

new-patchmatchnet		48.21 311	46.55 313	53.18 327	57.73 346	18.19 360	70.24 308	71.02 316	45.70 300	33.70 341	60.23 336	18.00 330	69.86 338	27.97 328	34.35 342	71.49 332

ITE_SJBPF				51.84 328	58.03 345	31.94 341		53.57 347	36.67 335	41.32 322	75.23 271	11.17 346	51.57 352	25.81 335	48.04 310	72.02 328

RPSCF		45.77 315	44.13 317	50.68 329	57.67 347	29.66 343	54.92 342	45.25 352	26.69 348	45.92 306	75.92 267	17.43 333	45.70 356	27.44 330	45.95 322	76.67 295

ANet_high		34.39 321	29.59 326	48.78 330	30.34 362	22.28 354	55.53 339	63.79 333	38.11 330	15.47 355	36.56 353	6.94 353	59.98 344	13.93 354	5.64 363	64.08 343

TDRefinement		40.91 318	38.37 321	48.55 331	50.45 352	33.03 336	58.98 336	50.97 348	28.50 346	29.89 348	67.39 320	6.21 358	54.51 349	17.67 351	35.25 340	58.11 347

DSMNet-mixed		38.35 319	35.36 322	47.33 332	48.11 355	14.91 362	37.87 354	36.60 357	19.18 353	34.37 339	59.56 339	15.53 339	53.01 351	20.14 347	46.89 319	74.07 315

N_pmnet		41.25 317	39.77 320	45.66 333	68.50 313	0.82 369	72.51 294	0.38 369	35.61 339	35.26 338	61.51 333	20.07 322	67.74 340	23.51 340	40.63 332	68.42 337

LCM-MVSNet		28.07 324	23.85 329	40.71 334	27.46 365	18.93 359	30.82 356	46.19 349	12.76 357	16.40 354	34.70 355	1.90 366	48.69 355	20.25 346	24.22 352	54.51 349

FPMVS		35.40 320	33.67 323	40.57 335	46.34 356	28.74 348	41.05 351	57.05 341	20.37 352	22.27 353	53.38 346	6.87 354	44.94 357	8.62 356	47.11 317	48.01 352

new_pmnet		33.56 322	31.89 325	38.59 336	49.01 353	20.42 357	51.01 343	37.92 356	20.58 350	23.45 352	46.79 347	6.66 355	49.28 354	20.00 348	31.57 348	46.09 353

PMMVS2		26.71 326	22.98 330	37.87 337	36.89 359	8.51 366	42.51 350	29.32 363	19.09 354	13.01 357	37.54 349	2.23 364	53.11 350	14.54 353	11.71 356	51.99 350

Gipuma		27.47 325	24.26 328	37.12 338	60.55 344	29.17 346	11.68 360	60.00 338	14.18 356	10.52 360	15.12 361	2.20 365	63.01 343	8.39 357	35.65 338	19.18 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LF4IMVS		33.04 323	32.55 324	34.52 339	40.96 357	22.03 355	44.45 348	35.62 358	20.42 351	28.12 350	62.35 332	5.03 360	31.88 362	21.61 344	34.42 341	49.63 351

test_method		24.09 328	21.07 332	33.16 340	27.67 364	8.35 367	26.63 357	35.11 360	3.40 362	14.35 356	36.98 351	3.46 363	35.31 361	19.08 350	22.95 353	55.81 348

PMVS	19.57 22	25.07 327	22.43 331	32.99 341	23.12 366	22.98 352	40.98 352	35.19 359	15.99 355	11.95 359	35.87 354	1.47 368	49.29 353	5.41 361	31.90 347	26.70 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	16.60 23	17.34 332	13.39 335	29.16 342	28.43 363	19.72 358	13.73 359	23.63 364	7.23 361	7.96 361	21.41 357	0.80 369	36.08 360	6.97 358	10.39 357	31.69 354
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		19.16 329	18.40 333	21.44 343	36.19 360	13.63 363	47.59 344	30.89 361	10.73 358	5.91 363	16.59 359	3.66 362	39.77 358	5.95 360	8.14 358	10.92 358

EMVS		18.42 330	17.66 334	20.71 344	34.13 361	12.64 364	46.94 345	29.94 362	10.46 360	5.58 364	14.93 362	4.23 361	38.83 359	5.24 362	7.51 360	10.67 359

DeepMVS_CX				13.10 345	21.34 367	8.99 365		10.02 367	10.59 359	7.53 362	30.55 356	1.82 367	14.55 363	6.83 359	7.52 359	15.75 357

wuyk23d		9.11 334	8.77 338	10.15 346	40.18 358	16.76 361	20.28 358	1.01 368	2.58 363	2.66 365	0.98 365	0.23 370	12.49 364	4.08 363	6.90 361	1.19 361

tmp_tt		9.44 333	10.68 336	5.73 347	2.49 368	4.21 368	10.48 361	18.04 365	0.34 364	12.59 358	20.49 358	11.39 345	7.03 365	13.84 355	6.46 362	5.95 360

testmvs		6.14 336	8.18 339	0.01 348	0.01 369	0.00 371	73.40 289	0.00 370	0.00 365	0.02 366	0.15 366	0.00 371	0.00 366	0.02 364	0.00 364	0.02 362

test123		6.01 337	8.01 340	0.01 348	0.00 370	0.01 370	71.93 302	0.00 370	0.00 365	0.02 366	0.11 367	0.00 371	0.00 366	0.02 364	0.00 364	0.02 362

uanet_test		0.00 339	0.00 342	0.00 350	0.00 370	0.00 371	0.00 362	0.00 370	0.00 365	0.00 368	0.00 368	0.00 371	0.00 366	0.00 366	0.00 364	0.00 364

cdsmvs_eth3d_5k		18.33 331	24.44 327	0.00 350	0.00 370	0.00 371	0.00 362	89.40 17	0.00 365	0.00 368	92.02 36	38.55 172	0.00 366	0.00 366	0.00 364	0.00 364

pcd_1.5k_mvsjas		3.15 338	4.20 341	0.00 350	0.00 370	0.00 371	0.00 362	0.00 370	0.00 365	0.00 368	0.00 368	37.77 178	0.00 366	0.00 366	0.00 364	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 370	0.00 371	0.00 362	0.00 370	0.00 365	0.00 368	0.00 368	0.00 371	0.00 366	0.00 366	0.00 364	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 370	0.00 371	0.00 362	0.00 370	0.00 365	0.00 368	0.00 368	0.00 371	0.00 366	0.00 366	0.00 364	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 370	0.00 371	0.00 362	0.00 370	0.00 365	0.00 368	0.00 368	0.00 371	0.00 366	0.00 366	0.00 364	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 370	0.00 371	0.00 362	0.00 370	0.00 365	0.00 368	0.00 368	0.00 371	0.00 366	0.00 366	0.00 364	0.00 364

ab-mvs-re		7.68 335	10.24 337	0.00 350	0.00 370	0.00 371	0.00 362	0.00 370	0.00 365	0.00 368	92.12 33	0.00 371	0.00 366	0.00 366	0.00 364	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 370	0.00 371	0.00 362	0.00 370	0.00 365	0.00 368	0.00 368	0.00 371	0.00 366	0.00 366	0.00 364	0.00 364

ZD-MVS					89.55 11	53.46 101		84.38 125	57.02 210	73.97 43	91.03 53	44.57 105	91.17 72	75.41 54	81.78 69

RE-MVS-def			66.66 179		80.96 163	48.14 235	81.54 220	76.98 261	46.42 296	62.75 158	89.42 95	29.28 268		60.52 156	72.06 153	83.19 217

IU-MVS					89.48 14	57.49 17		91.38 5	66.22 51	88.26 1				82.83 7	87.60 16	92.44 25

test_241102_TWO								88.76 34	57.50 204	83.60 5	94.09 4	56.14 16	96.37 5	82.28 11	87.43 18	92.55 23

test_241102_ONE					89.48 14	56.89 26		88.94 27	57.53 202	84.61 3	93.29 13	58.81 9	96.45 1

9.14			78.19 22		85.67 53		88.32 54	88.84 31	59.89 146	74.58 39	92.62 24	46.80 74	92.66 39	81.40 17	85.62 37

save fliter					85.35 64	56.34 36	89.31 38	81.46 178	61.55 119

test_0728_THIRD									58.00 190	81.91 9	93.64 10	56.54 13	96.44 2	81.64 16	86.86 23	92.23 29

test0726					89.40 17	57.45 18	92.32 7	88.63 37	57.71 198	83.14 7	93.96 7	55.17 17

GSMVS																88.13 127

test_part2					89.33 19	55.48 48				82.27 8

sam_mvs1												38.86 170				88.13 127

sam_mvs												35.99 214

MTGPA								81.31 181

test_post1							70.84 307				14.72 363	34.33 225	83.86 248	48.80 239

test_post											16.22 360	37.52 186	84.72 243

patchmatchnet-post											59.74 338	38.41 173	79.91 285

MTMP							87.27 75	15.34 366

gm-plane-assit					83.24 105	54.21 85			70.91 12		88.23 118		95.25 12	66.37 107

test9_res														78.72 29	85.44 40	91.39 52

TEST9					85.68 51	55.42 49	87.59 65	84.00 136	57.72 197	72.99 52	90.98 56	44.87 100	88.58 139

test_8					85.72 50	55.31 53	87.60 62	83.88 139	57.84 195	72.84 56	90.99 54	44.99 97	88.34 149

agg_prior2														75.65 49	85.11 44	91.01 62

agg_prior					85.64 54	54.92 67		83.61 145		72.53 60			88.10 159

test_prior4						56.39 35	87.15 78

test_prior2							89.04 43		61.88 114	73.55 46	91.46 50	48.01 62		74.73 57	85.46 38

旧先验2							81.73 214		45.53 302	74.66 35			70.48 337	58.31 174

新几何2							81.61 218

旧先验1					81.57 149	47.48 243		71.83 307			88.66 109	36.94 197			78.34 99	88.67 116

无先验							85.19 122	78.00 244	49.08 279				85.13 237	52.78 216		87.45 141

原ACMM2							83.77 163

test222					79.36 185	50.97 167	77.99 261	67.84 324	42.54 321	62.84 157	86.53 143	30.26 262			76.91 109	85.23 182

testdata2													77.81 304	45.64 259

segment_acmp												44.97 99

testdata1							77.55 264		64.14 78

plane_prior7					77.95 215	48.46 228

plane_prior6					78.42 210	49.39 204						36.04 212

plane_prior5								82.59 162					88.30 152	65.46 117	72.34 150	84.49 190

plane_prior4											83.28 180

plane_prior3						48.95 211			64.01 79	62.15 163

plane_prior2							85.76 106		63.60 89

plane_prior1					78.31 212

plane_prior						49.57 198	87.43 69		64.57 74						72.84 145

n2								0.00 370

nn								0.00 370

door-mid								41.31 354

test11								84.25 130

door								43.27 353

HQP5-MVS						51.56 155

HQP-NCC					79.02 193		88.00 56		65.45 60	64.48 134

ACMP_Plane					79.02 193		88.00 56		65.45 60	64.48 134

BP-MVS														66.70 104

HQP4-MVS										64.47 137			88.61 138			84.91 187

HQP3-MVS								83.68 142							73.12 141

HQP2-MVS												37.35 189

NP-MVS					78.76 198	50.43 177					85.12 158

MDTV_nov1_ep13_2view						43.62 288	71.13 306		54.95 240	59.29 194		36.76 200		46.33 256		87.32 143

MDTV_nov1_ep13			61.56 235		81.68 142	55.12 60	72.41 295	78.18 240	59.19 164	58.85 204	69.29 313	34.69 222	86.16 210	36.76 291	62.96 219

ACMMP++_ref															63.20 215

ACMMP++															59.38 239

Test By Simon												39.38 165

High-resolution multi-view benchmark