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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

SED-MVS		88.94 1	90.98 1	86.56 1	92.53 6	95.09 1	88.55 5	76.83 7	94.16 1	86.57 1	90.85 6	87.07 1	86.18 1	86.36 7	85.08 12	88.67 21	98.21 3

MSP-MVS		87.87 3	90.57 3	84.73 5	89.38 27	91.60 17	88.24 7	74.15 12	93.55 3	82.28 3	94.99 1	83.21 10	85.96 2	87.67 4	84.67 17	88.32 31	98.29 1

MCST-MVS		85.75 8	86.99 12	84.31 6	94.07 3	92.80 7	88.15 8	79.10 3	85.66 23	70.72 30	76.50 32	80.45 20	82.17 3	88.35 2	87.49 3	91.63 2	97.65 4

CNVR-MVS		85.96 7	87.58 10	84.06 8	92.58 5	92.40 10	87.62 10	77.77 5	88.44 14	75.93 17	79.49 25	81.97 16	81.65 4	87.04 6	86.58 4	88.79 17	97.18 7

DPE-MVS		87.60 4	90.44 4	84.29 7	92.09 8	93.44 5	88.69 4	75.11 9	93.06 5	80.80 5	94.23 2	86.70 3	81.44 5	84.84 17	83.52 26	87.64 46	97.28 5

APDe-MVS		86.37 6	88.41 7	84.00 9	91.43 14	91.83 15	88.34 6	74.67 10	91.19 6	81.76 4	91.13 5	81.94 17	80.07 6	83.38 27	82.58 34	87.69 44	96.78 10

xxxxxxxxxxxxxcwj		84.33 14	83.20 26	85.64 2	94.57 1	94.55 3	91.01 1	79.94 1	89.15 11	79.85 6	92.37 3	44.71 141	79.75 7	83.52 25	82.72 31	88.75 19	95.37 23

SF-MVS		87.30 5	88.71 5	85.64 2	94.57 1	94.55 3	91.01 1	79.94 1	89.15 11	79.85 6	92.37 3	83.29 9	79.75 7	83.52 25	82.72 31	88.75 19	95.37 23

ET-MVSNet_ETH3D		71.38 76	74.70 67	67.51 96	51.61 201	88.06 48	77.29 65	60.95 102	63.61 82	48.36 105	66.60 48	60.67 82	79.55 9	73.56 120	80.58 61	87.30 55	89.80 81

DeepC-MVS_fast	75.41 2	81.69 24	82.10 33	81.20 17	91.04 16	87.81 51	83.42 27	74.04 13	83.77 27	71.09 28	66.88 47	72.44 38	79.48 10	85.08 14	84.97 14	88.12 39	93.78 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DVP-MVS		88.07 2	90.73 2	84.97 4	91.98 9	95.01 2	87.86 9	76.88 6	93.90 2	85.15 2	90.11 8	86.90 2	79.46 11	86.26 10	84.67 17	88.50 28	98.25 2

DPM-MVS		85.41 10	86.72 15	83.89 10	91.66 12	91.92 14	90.49 3	78.09 4	86.90 18	73.95 20	74.52 34	82.01 15	79.29 12	90.24 1	90.65 1	89.86 6	90.78 70

APD-MVS		84.83 12	87.00 11	82.30 13	89.61 25	89.21 34	86.51 14	73.64 16	90.98 7	77.99 12	89.89 9	80.04 23	79.18 13	82.00 46	81.37 47	86.88 65	95.49 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		84.16 16	85.46 20	82.64 11	92.34 7	90.57 23	86.57 13	76.51 8	86.85 20	72.91 23	77.20 31	78.69 26	79.09 14	84.64 19	84.88 15	88.44 29	95.41 21

HPM-MVS++		85.64 9	88.43 6	82.39 12	92.65 4	90.24 26	85.83 16	74.21 11	90.68 8	75.63 18	86.77 13	84.15 7	78.68 15	86.33 8	85.26 9	87.32 53	95.60 18

AdaColmap		76.23 53	73.55 71	79.35 25	89.38 27	85.00 75	79.99 50	73.04 20	76.60 52	71.17 27	55.18 78	57.99 98	77.87 16	76.82 87	76.82 88	84.67 122	86.45 112

TSAR-MVS + MP.		84.39 13	86.58 16	81.83 14	88.09 39	86.47 64	85.63 18	73.62 17	90.13 10	79.24 9	89.67 10	82.99 11	77.72 17	81.22 52	80.92 58	86.68 68	94.66 30
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
3Dnovator	70.49 5	78.42 39	76.77 56	80.35 20	91.43 14	90.27 25	81.84 36	70.79 26	72.10 57	71.95 24	50.02 97	67.86 56	77.47 18	82.89 31	84.24 19	88.61 24	89.99 79

SMA-MVS		85.24 11	88.27 8	81.72 15	91.74 11	90.71 20	86.71 12	73.16 19	90.56 9	74.33 19	83.07 18	85.88 4	77.16 19	86.28 9	85.58 6	87.23 57	95.77 14
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
CANet		80.90 28	82.93 29	78.53 31	86.83 45	92.26 11	81.19 42	66.95 49	81.60 36	69.90 33	66.93 46	74.80 32	76.79 20	84.68 18	84.77 16	89.50 9	95.50 19

TSAR-MVS + GP.		82.27 23	85.98 18	77.94 33	80.72 71	88.25 44	81.12 43	67.71 45	87.10 16	73.31 21	85.23 15	83.68 8	76.64 21	80.43 60	81.47 46	88.15 37	95.66 17

abl_6				79.06 29	89.68 24	92.14 12	77.70 62	69.68 33	86.87 19	71.88 25	74.29 35	80.06 22	76.56 22			88.84 16	95.82 13

MVS_0304		79.43 33	82.20 31	76.20 42	84.22 53	91.79 16	81.82 37	63.81 70	76.83 51	61.71 56	66.37 49	75.52 31	76.38 23	85.54 13	85.03 13	89.28 11	94.32 34

3Dnovator+	70.16 6	77.87 42	77.29 52	78.55 30	89.25 29	88.32 43	80.09 48	67.95 44	74.89 56	71.83 26	52.05 91	70.68 48	76.27 24	82.27 42	82.04 36	85.92 83	90.77 71

ACMMP_NAP		83.54 17	86.37 17	80.25 21	89.57 26	90.10 28	85.27 20	71.66 23	87.38 15	73.08 22	84.23 17	80.16 21	75.31 25	84.85 16	83.64 23	86.57 69	94.21 37

MVS_Test		75.22 56	76.69 57	73.51 57	79.30 77	88.82 37	80.06 49	58.74 111	69.77 64	57.50 74	59.78 69	61.35 78	75.31 25	82.07 44	83.60 25	90.13 5	91.41 63

HFP-MVS		82.48 22	84.12 23	80.56 19	90.15 18	87.55 54	84.28 23	69.67 34	85.22 24	77.95 13	84.69 16	75.94 30	75.04 27	81.85 47	81.17 52	86.30 74	92.40 54

MAR-MVS		77.19 48	78.37 48	75.81 46	89.87 21	90.58 22	79.33 53	65.56 60	77.62 49	58.33 69	59.24 70	67.98 54	74.83 28	82.37 40	83.12 28	86.95 63	87.67 104
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
DeepPCF-MVS	76.94 1	83.08 19	87.77 9	77.60 35	90.11 19	90.96 19	78.48 54	72.63 22	93.10 4	65.84 42	80.67 23	81.55 18	74.80 29	85.94 12	85.39 8	83.75 137	96.77 11

DeepC-MVS	74.46 3	80.30 30	81.05 36	79.42 24	87.42 41	88.50 40	83.23 28	73.27 18	82.78 30	71.01 29	62.86 57	69.93 51	74.80 29	84.30 20	84.20 20	86.79 67	94.77 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
canonicalmvs		77.65 43	79.59 42	75.39 47	81.52 64	89.83 32	81.32 41	60.74 103	80.05 41	66.72 40	68.43 42	65.09 63	74.72 31	78.87 70	82.73 30	87.32 53	92.16 55

QAPM		77.50 45	77.43 50	77.59 36	91.52 13	92.00 13	81.41 40	70.63 27	66.22 73	58.05 70	54.70 79	71.79 44	74.49 32	82.46 37	82.04 36	89.46 10	92.79 52

zzz-MVS		81.65 25	83.10 27	79.97 23	88.14 38	87.62 53	83.96 26	69.90 31	86.92 17	77.67 14	72.47 36	78.74 25	74.13 33	81.59 50	81.15 53	86.01 82	93.19 47

SD-MVS		84.31 15	86.96 13	81.22 16	88.98 31	88.68 38	85.65 17	73.85 15	89.09 13	79.63 8	87.34 12	84.84 5	73.71 34	82.66 34	81.60 44	85.48 100	94.51 31
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
casdiffmvs		75.20 57	75.69 63	74.63 55	79.26 78	89.07 35	78.47 55	63.59 73	67.05 71	63.79 47	55.72 76	60.32 84	73.58 35	82.16 43	81.78 40	89.08 13	93.72 43

CLD-MVS		77.36 47	77.29 52	77.45 37	82.21 60	88.11 46	81.92 35	68.96 39	77.97 47	69.62 35	62.08 58	59.44 89	73.57 36	81.75 48	81.27 50	88.41 30	90.39 75
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MSLP-MVS++		78.57 38	77.33 51	80.02 22	88.39 34	84.79 76	84.62 22	66.17 56	75.96 53	78.40 10	61.59 60	71.47 45	73.54 37	78.43 74	78.88 71	88.97 14	90.18 78

ETV-MVS		76.25 52	80.22 39	71.63 69	78.23 82	87.95 50	72.75 92	60.27 107	77.50 50	57.73 71	71.53 37	66.60 59	73.16 38	80.99 56	81.23 51	87.63 47	95.73 15

SteuartSystems-ACMMP		82.51 21	85.35 21	79.20 26	90.25 17	89.39 33	84.79 21	70.95 25	82.86 29	68.32 38	86.44 14	77.19 27	73.07 39	83.63 24	83.64 23	87.82 40	94.34 33
Skip Steuart: Steuart Systems R&D Blog.
train_agg		83.35 18	86.93 14	79.17 27	89.70 23	88.41 41	85.60 19	72.89 21	86.31 21	66.58 41	90.48 7	82.24 14	73.06 40	83.10 30	82.64 33	87.21 61	95.30 25

CS-MVS		75.18 58	78.59 44	71.20 70	77.74 86	87.69 52	73.93 89	58.81 110	69.17 69	55.73 77	67.86 43	66.89 57	72.87 41	82.50 35	81.29 49	88.15 37	94.71 29

DELS-MVS		79.49 31	79.84 41	79.08 28	88.26 37	92.49 8	84.12 25	70.63 27	65.27 80	69.60 36	61.29 62	66.50 60	72.75 42	88.07 3	88.03 2	89.13 12	97.22 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
CNLPA		71.37 77	70.27 91	72.66 64	80.79 70	81.33 103	71.07 110	65.75 58	82.36 31	64.80 45	42.46 131	56.49 103	72.70 43	73.00 127	70.52 162	80.84 170	85.76 120

CANet_DTU		72.84 67	76.63 58	68.43 90	76.81 97	86.62 62	75.54 74	54.71 156	72.06 58	43.54 123	67.11 45	58.46 93	72.40 44	81.13 55	80.82 60	87.57 48	90.21 77

DI_MVS_plusplus_trai		73.94 63	74.85 66	72.88 62	76.57 99	86.80 60	80.41 47	61.47 94	62.35 85	59.44 67	47.91 103	68.12 53	72.24 45	82.84 33	81.50 45	87.15 62	94.42 32

ACMMPR		80.62 29	82.98 28	77.87 34	88.41 33	87.05 59	83.02 29	69.18 37	83.91 26	68.35 37	82.89 19	73.64 35	72.16 46	80.78 58	81.13 54	86.10 79	91.43 61

MVS_111021_HR		77.42 46	78.40 47	76.28 41	86.95 43	90.68 21	77.41 64	70.56 30	66.21 74	62.48 52	66.17 50	63.98 67	72.08 47	82.87 32	83.15 27	88.24 34	95.71 16

diffmvs		74.32 60	75.42 64	73.04 61	75.60 107	87.27 56	78.20 56	62.96 78	68.66 70	61.89 54	59.79 68	59.84 87	71.80 48	78.30 77	79.87 64	87.80 42	94.23 36

OpenMVS	67.62 8	74.92 59	73.91 69	76.09 44	90.10 20	90.38 24	78.01 58	66.35 54	66.09 75	62.80 49	46.33 120	64.55 66	71.77 49	79.92 63	80.88 59	87.52 49	89.20 88

CP-MVS		79.44 32	81.51 35	77.02 38	86.95 43	85.96 70	82.00 34	68.44 42	81.82 34	67.39 39	77.43 29	73.68 34	71.62 50	79.56 66	79.58 65	85.73 90	92.51 53

MP-MVS		80.94 27	83.49 25	77.96 32	88.48 32	88.16 45	82.82 32	69.34 36	80.79 39	69.67 34	82.35 20	77.13 28	71.60 51	80.97 57	80.96 57	85.87 86	94.06 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		79.42 35	81.84 34	76.60 40	88.38 35	86.69 61	82.97 31	65.75 58	80.39 40	64.94 44	81.95 22	72.11 43	71.41 52	80.45 59	80.55 62	86.18 76	90.76 72

MVSTER		76.92 49	79.92 40	73.42 59	74.98 110	82.97 89	78.15 57	63.41 74	78.02 46	64.41 46	67.54 44	72.80 37	71.05 53	83.29 29	83.73 22	88.53 27	91.12 66

HQP-MVS		78.26 40	80.91 37	75.17 50	85.67 50	84.33 82	83.01 30	69.38 35	79.88 42	55.83 76	79.85 24	64.90 65	70.81 54	82.46 37	81.78 40	86.30 74	93.18 48

baseline		72.89 66	74.46 68	71.07 71	75.99 103	87.50 55	74.57 81	60.49 105	70.72 61	57.60 72	60.63 65	60.97 80	70.79 55	75.27 101	76.33 94	86.94 64	89.79 82

PCF-MVS	70.85 4	75.73 54	76.55 59	74.78 54	83.67 54	88.04 49	81.47 38	70.62 29	69.24 68	57.52 73	60.59 66	69.18 52	70.65 56	77.11 84	77.65 82	84.75 120	94.01 39
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	64.00 12	68.54 89	66.66 111	70.74 74	80.28 73	74.88 159	72.64 94	63.70 72	69.26 67	55.71 78	47.24 111	55.31 111	70.42 57	72.05 139	70.67 160	81.66 164	77.19 163
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PHI-MVS		79.43 33	84.06 24	74.04 56	86.15 48	91.57 18	80.85 46	68.90 40	82.22 32	51.81 90	78.10 27	74.28 33	70.39 58	84.01 23	84.00 21	86.14 78	94.24 35

EPNet		79.28 37	82.25 30	75.83 45	88.31 36	90.14 27	79.43 52	68.07 43	81.76 35	61.26 58	77.26 30	70.08 50	70.06 59	82.43 39	82.00 38	87.82 40	92.09 56
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		72.74 68	70.93 87	74.85 53	85.30 51	84.34 81	82.82 32	69.79 32	49.96 132	55.39 82	54.09 86	60.14 86	70.04 60	80.38 61	79.43 66	85.74 89	88.20 100

CSCG		82.90 20	84.52 22	81.02 18	91.85 10	93.43 6	87.14 11	74.01 14	81.96 33	76.14 15	70.84 38	82.49 12	69.71 61	82.32 41	85.18 11	87.26 56	95.40 22

Effi-MVS+		70.42 79	71.23 84	69.47 81	78.04 84	85.24 73	75.57 73	58.88 109	59.56 94	48.47 104	52.73 90	54.94 112	69.69 62	78.34 76	77.06 86	86.18 76	90.73 73

Anonymous202405211			66.35 115		78.00 85	84.41 80	74.85 79	63.18 76	51.00 128		31.37 183	53.73 119	69.67 63	76.28 90	76.84 87	83.21 146	90.85 68

EIA-MVS		73.48 64	76.05 60	70.47 76	78.12 83	87.21 57	71.78 98	60.63 104	69.66 65	55.56 80	64.86 52	60.69 81	69.53 64	77.35 83	78.59 72	87.22 59	94.01 39

PMMVS		70.37 82	75.06 65	64.90 108	71.46 126	81.88 95	64.10 147	55.64 143	71.31 59	46.69 109	70.69 39	58.56 90	69.53 64	79.03 69	75.63 102	81.96 161	88.32 99

MVS_111021_LR		74.26 61	75.95 61	72.27 65	79.43 76	85.04 74	72.71 93	65.27 63	70.92 60	63.58 48	69.32 40	60.31 85	69.43 66	77.01 85	77.15 85	83.22 144	91.93 59

OMC-MVS		74.03 62	75.82 62	71.95 67	79.56 74	80.98 107	75.35 77	63.21 75	84.48 25	61.83 55	61.54 61	66.89 57	69.41 67	76.60 88	74.07 121	82.34 158	86.15 115

CPTT-MVS		75.43 55	77.13 54	73.44 58	81.43 65	82.55 93	80.96 45	64.35 66	77.95 48	61.39 57	69.20 41	70.94 47	69.38 68	73.89 116	73.32 131	83.14 147	92.06 57

Anonymous20231211		68.44 90	66.37 114	70.86 72	77.58 90	83.49 87	75.15 78	61.89 89	52.54 125	58.50 68	28.89 188	56.78 102	69.29 69	74.96 105	76.61 89	82.73 150	91.36 64

Fast-Effi-MVS+		67.59 96	67.56 106	67.62 95	73.67 115	81.14 106	71.12 108	54.79 155	58.88 96	50.61 97	46.70 118	47.05 135	69.12 70	76.06 94	76.44 92	86.43 72	86.65 110

TSAR-MVS + COLMAP		73.09 65	76.86 55	68.71 87	74.97 111	82.49 94	74.51 86	61.83 90	83.16 28	49.31 102	82.22 21	51.62 125	68.94 71	78.76 72	75.52 105	82.67 152	84.23 130

ACMMP		77.61 44	79.59 42	75.30 49	85.87 49	85.58 71	81.42 39	67.38 48	79.38 44	62.61 50	78.53 26	65.79 62	68.80 72	78.56 73	78.50 75	85.75 87	90.80 69
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
CDPH-MVS		79.39 36	82.13 32	76.19 43	89.22 30	88.34 42	84.20 24	71.00 24	79.67 43	56.97 75	77.77 28	72.24 42	68.50 73	81.33 51	82.74 29	87.23 57	92.84 50

CostFormer		72.18 70	73.90 70	70.18 78	79.47 75	86.19 69	76.94 67	48.62 174	66.07 76	60.40 65	54.14 85	65.82 61	67.98 74	75.84 96	76.41 93	87.67 45	92.83 51

TAPA-MVS	67.10 9	71.45 75	73.47 73	69.10 85	77.04 95	80.78 110	73.81 90	62.10 86	80.80 38	51.28 91	60.91 63	63.80 69	67.98 74	74.59 107	72.42 143	82.37 157	80.97 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_BlendedMVS		76.84 50	78.47 45	74.95 51	82.37 58	89.90 30	75.45 75	65.45 61	74.99 54	70.66 31	63.07 55	58.27 96	67.60 76	84.24 21	81.70 42	88.18 35	97.10 8

PVSNet_Blended		76.84 50	78.47 45	74.95 51	82.37 58	89.90 30	75.45 75	65.45 61	74.99 54	70.66 31	63.07 55	58.27 96	67.60 76	84.24 21	81.70 42	88.18 35	97.10 8

TSAR-MVS + ACMM		81.59 26	85.84 19	76.63 39	89.82 22	86.53 63	86.32 15	66.72 52	85.96 22	65.43 43	88.98 11	82.29 13	67.57 78	82.06 45	81.33 48	83.93 135	93.75 42

X-MVS		78.16 41	80.55 38	75.38 48	87.99 40	86.27 66	81.05 44	68.98 38	78.33 45	61.07 60	75.25 33	72.27 39	67.52 79	80.03 62	80.52 63	85.66 97	91.20 65

FC-MVSNet-train		68.83 88	68.29 101	69.47 81	78.35 81	79.94 116	64.72 144	66.38 53	54.96 116	54.51 84	56.75 73	47.91 134	66.91 80	75.57 100	75.75 100	85.92 83	87.12 106

DCV-MVSNet		69.13 86	69.07 95	69.21 83	77.65 89	77.52 140	74.68 80	57.85 121	54.92 117	55.34 83	55.74 75	55.56 110	66.35 81	75.05 102	76.56 91	83.35 141	88.13 101

CHOSEN 1792x2688		72.55 69	71.98 78	73.22 60	86.57 46	92.41 9	75.63 71	66.77 51	62.08 86	52.32 87	30.27 186	50.74 128	66.14 82	86.22 11	85.41 7	91.90 1	96.75 12

ACMM	66.70 10	70.42 79	68.49 100	72.67 63	82.85 55	77.76 138	77.70 62	64.76 65	64.61 81	60.74 64	49.29 98	53.97 117	65.86 83	74.97 103	75.57 104	84.13 134	83.29 137
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		67.15 102	68.12 104	66.03 102	76.21 101	80.98 107	71.27 104	45.05 185	60.69 91	50.63 96	46.95 116	54.15 116	65.30 84	71.80 141	71.77 147	87.72 43	90.48 74

LS3D		64.54 119	62.14 143	67.34 98	80.85 68	75.79 153	69.99 115	65.87 57	60.77 90	44.35 120	42.43 132	45.95 138	65.01 85	69.88 159	68.69 169	77.97 185	71.43 184

HyFIR lowres test		68.39 91	68.28 102	68.52 89	80.85 68	88.11 46	71.08 109	58.09 116	54.87 119	47.80 108	27.55 192	55.80 107	64.97 86	79.11 68	79.14 69	88.31 32	93.35 44

FMVSNet3		70.41 81	71.89 80	68.68 88	70.89 132	79.42 122	75.63 71	60.97 99	65.32 77	51.06 92	47.37 108	62.05 72	64.90 87	82.49 36	82.27 35	88.64 23	84.34 129

PatchMatch-RL		62.22 139	60.69 153	64.01 116	68.74 142	75.75 154	59.27 171	60.35 106	56.09 109	53.80 85	47.06 114	36.45 172	64.80 88	68.22 166	67.22 173	77.10 187	74.02 172

tpm cat1		67.47 99	67.05 109	67.98 92	76.63 98	81.51 101	74.49 87	47.65 179	61.18 88	61.12 59	42.51 130	53.02 123	64.74 89	70.11 158	71.50 149	83.22 144	89.49 84

dps		64.08 121	63.22 131	65.08 106	75.27 109	79.65 119	66.68 138	46.63 183	56.94 102	55.67 79	43.96 122	43.63 144	64.00 90	69.50 163	69.82 164	82.25 159	79.02 159

ACMP	68.86 7	72.15 71	72.25 76	72.03 66	80.96 67	80.87 109	77.93 59	64.13 68	69.29 66	60.79 63	64.04 53	53.54 120	63.91 91	73.74 119	75.27 106	84.45 127	88.98 90
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Effi-MVS+-dtu		64.58 117	64.08 126	65.16 105	73.04 119	75.17 158	70.68 114	56.23 137	54.12 122	44.71 119	47.42 107	51.10 126	63.82 92	68.08 167	66.32 178	82.47 156	86.38 113

GBi-Net		69.21 84	70.40 89	67.81 93	69.49 137	78.65 127	74.54 82	60.97 99	65.32 77	51.06 92	47.37 108	62.05 72	63.43 93	77.49 79	78.22 77	87.37 50	83.73 132

test1		69.21 84	70.40 89	67.81 93	69.49 137	78.65 127	74.54 82	60.97 99	65.32 77	51.06 92	47.37 108	62.05 72	63.43 93	77.49 79	78.22 77	87.37 50	83.73 132

FMVSNet2		68.06 94	68.57 99	67.45 97	69.49 137	78.65 127	74.54 82	60.23 108	56.29 107	49.64 101	42.13 134	57.08 101	63.43 93	81.15 54	80.99 55	87.37 50	83.73 132

baseline2		71.22 78	73.01 75	69.13 84	75.76 105	86.34 65	71.23 105	62.78 84	62.62 83	52.85 86	57.32 72	54.31 114	63.27 96	79.74 64	79.31 67	88.89 15	91.43 61

EPMVS		66.21 105	67.49 107	64.73 109	75.81 104	84.20 84	68.94 122	44.37 189	61.55 87	48.07 107	49.21 100	54.87 113	62.88 97	71.82 140	71.40 153	88.28 33	79.37 158

CHOSEN 280x420		62.23 138	66.57 112	57.17 164	59.88 185	68.92 182	61.20 166	42.28 196	54.17 121	39.57 142	47.78 105	64.97 64	62.68 98	73.85 117	69.52 167	77.43 186	86.75 109

thres100view900		67.14 103	66.09 117	68.38 91	77.70 87	83.84 86	74.52 85	66.33 55	49.16 136	43.40 125	43.24 123	41.34 147	62.59 99	79.31 67	75.92 99	85.73 90	89.81 80

baseline1		71.47 74	72.02 77	70.82 73	80.56 72	84.51 78	76.61 68	66.93 50	56.22 108	48.66 103	55.40 77	60.43 83	62.55 100	83.35 28	80.99 55	89.60 7	83.28 138

LGP-MVS_train		72.02 72	73.18 74	70.67 75	82.13 61	80.26 115	79.58 51	63.04 77	70.09 62	51.98 88	65.06 51	55.62 109	62.49 101	75.97 95	76.32 95	84.80 119	88.93 91

MSDG		65.57 110	61.57 147	70.24 77	82.02 62	76.47 147	74.46 88	68.73 41	56.52 105	50.33 98	38.47 151	41.10 151	62.42 102	72.12 137	72.94 138	83.47 140	73.37 177

test_part1		66.32 104	63.35 130	69.77 79	77.40 93	78.35 131	77.85 60	56.25 136	44.52 155	62.15 53	33.05 177	53.91 118	62.38 103	72.19 136	74.65 111	82.59 153	86.81 108

IterMVS-LS		66.08 107	66.56 113	65.51 103	73.67 115	74.88 159	70.89 112	53.55 161	50.42 130	48.32 106	50.59 95	55.66 108	61.83 104	73.93 115	74.42 117	84.82 118	86.01 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MS-PatchMatch		70.34 83	69.00 96	71.91 68	85.20 52	85.35 72	77.84 61	61.77 92	58.01 100	55.40 81	41.26 138	58.34 95	61.69 105	81.70 49	78.29 76	89.56 8	80.02 155

v2v482		63.68 125	62.85 137	64.65 110	68.01 146	80.46 113	71.90 96	57.60 124	44.26 156	42.82 130	39.80 147	38.62 163	61.56 106	73.06 125	74.86 109	86.03 81	88.90 93

tfpn200view9		65.90 108	64.96 121	67.00 99	77.70 87	81.58 99	71.71 100	62.94 81	49.16 136	43.40 125	43.24 123	41.34 147	61.42 107	76.24 91	74.63 113	84.84 115	88.52 97

Fast-Effi-MVS+-dtu		63.05 129	64.72 124	61.11 138	71.21 130	76.81 146	70.72 113	43.13 194	52.51 126	35.34 166	46.55 119	46.36 136	61.40 108	71.57 144	71.44 151	84.84 115	87.79 103

ACMH	59.42 14	61.59 144	59.22 163	64.36 114	78.92 80	78.26 132	67.65 129	67.48 47	39.81 173	30.98 179	38.25 153	34.59 183	61.37 109	70.55 153	73.47 127	79.74 177	79.59 156
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v10		63.00 130	62.22 142	63.90 119	67.88 148	77.78 137	71.59 101	54.34 157	45.37 152	42.76 131	38.53 150	38.93 161	61.05 110	74.39 110	74.52 116	85.75 87	86.04 116

tpm		64.85 115	66.02 118	63.48 121	74.52 112	78.38 130	70.98 111	44.99 187	51.61 127	43.28 127	47.66 106	53.18 121	60.57 111	70.58 152	71.30 156	86.54 70	89.45 86

v8		63.44 127	62.58 139	64.43 112	68.28 145	78.07 133	71.82 97	54.85 153	46.70 146	45.20 115	39.40 148	40.91 152	60.54 112	72.85 129	74.39 118	85.92 83	85.76 120

v1192		62.25 136	61.64 146	62.96 124	66.88 154	79.72 118	69.96 116	55.77 141	41.58 166	39.42 143	37.05 160	35.96 177	60.50 113	74.30 113	74.09 120	85.24 104	88.76 94

v1144		63.00 130	62.39 141	63.70 120	67.72 149	80.27 114	71.23 105	56.40 133	42.51 161	40.81 139	38.12 155	37.73 164	60.42 114	74.46 108	74.55 115	85.64 98	89.12 89

gm-plane-assit		54.99 173	57.99 169	51.49 181	69.27 141	54.42 205	32.32 208	42.59 195	21.18 208	13.71 204	23.61 196	43.84 143	60.21 115	87.09 5	86.55 5	90.81 4	89.28 87

PatchmatchNet		65.43 112	67.71 105	62.78 127	73.49 117	82.83 90	66.42 141	45.40 184	60.40 92	45.27 114	49.22 99	57.60 100	60.01 116	70.61 150	71.38 154	86.08 80	81.91 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH+	60.36 13	61.16 145	58.38 165	64.42 113	77.37 94	74.35 164	68.45 124	62.81 83	45.86 150	38.48 149	35.71 169	37.35 167	59.81 117	67.24 169	69.80 166	79.58 178	78.32 161

v144192		62.05 140	61.46 148	62.73 130	66.59 158	79.87 117	69.30 120	55.88 139	41.50 168	39.41 144	37.23 158	36.45 172	59.62 118	72.69 132	73.51 126	85.61 99	88.93 91

v1921920		61.66 143	61.10 151	62.31 132	66.32 159	79.57 120	68.41 125	55.49 146	41.03 169	38.69 148	36.64 166	35.27 180	59.60 119	73.23 123	73.41 128	85.37 101	88.51 98

GA-MVS		64.55 118	65.76 120	63.12 123	69.68 136	81.56 100	69.59 118	58.16 115	45.23 153	35.58 165	47.01 115	41.82 146	59.41 120	79.62 65	78.54 73	86.32 73	86.56 111

ADS-MVSNet		58.40 163	59.16 164	57.52 161	65.80 164	74.57 163	60.26 167	40.17 203	50.51 129	38.01 152	40.11 146	44.72 140	59.36 121	64.91 176	66.55 176	81.53 165	72.72 180

pmmvs4		63.14 128	62.46 140	63.94 118	66.03 161	76.40 148	66.82 137	57.60 124	56.74 103	50.26 99	40.81 142	37.51 166	59.26 122	71.75 142	71.48 150	83.68 139	82.53 143

v1240		61.09 146	60.55 155	61.72 136	65.92 163	79.28 123	67.16 135	54.91 152	39.79 174	38.10 151	36.08 168	34.64 182	59.15 123	72.86 128	73.36 130	85.10 106	87.84 102

PVSNet_Blended_VisFu		71.76 73	73.54 72	69.69 80	79.01 79	87.16 58	72.05 95	61.80 91	56.46 106	59.66 66	53.88 87	62.48 70	59.08 124	81.17 53	78.90 70	86.53 71	94.74 28

MDTV_nov1_ep13		65.21 113	67.28 108	62.79 126	70.91 131	81.72 96	69.28 121	49.50 173	58.08 99	43.94 122	50.50 96	56.02 105	58.86 125	70.72 149	73.37 129	84.24 130	80.52 154

FMVSNet1		63.48 126	63.07 133	63.97 117	65.31 165	76.37 149	71.77 99	57.90 120	43.32 160	45.66 112	35.06 174	49.43 130	58.57 126	77.49 79	78.22 77	84.59 124	81.60 151

USDC		59.69 154	60.03 159	59.28 151	64.04 170	71.84 172	63.15 159	55.36 148	54.90 118	35.02 167	48.34 101	29.79 197	58.16 127	70.60 151	71.33 155	79.99 175	73.42 176

thres400		65.18 114	64.44 125	66.04 101	76.40 100	82.63 91	71.52 102	64.27 67	44.93 154	40.69 140	41.86 135	40.79 153	58.12 128	77.67 78	74.64 112	85.26 103	88.56 96

thres200		65.58 109	64.74 123	66.56 100	77.52 92	81.61 97	73.44 91	62.95 79	46.23 148	42.45 132	42.76 125	41.18 149	58.12 128	76.24 91	75.59 103	84.89 113	89.58 83

thisisatest0530		68.38 92	70.98 86	65.35 104	72.61 120	84.42 79	68.21 126	57.98 117	59.77 93	50.80 95	54.63 80	58.48 92	57.92 130	76.99 86	77.47 83	84.60 123	85.07 123

tttt0517		67.99 95	70.61 88	64.94 107	71.94 125	83.96 85	67.62 130	57.98 117	59.30 95	49.90 100	54.50 83	57.98 99	57.92 130	76.48 89	77.47 83	84.24 130	84.58 126

SCA		63.90 123	66.67 110	60.66 140	73.75 113	71.78 174	59.87 170	43.66 190	61.13 89	45.03 116	51.64 92	59.45 88	57.92 130	70.96 147	70.80 158	83.71 138	80.92 153

test-LLR		68.23 93	71.61 82	64.28 115	71.37 127	81.32 104	63.98 150	61.03 97	58.62 97	42.96 128	52.74 88	61.65 76	57.74 133	75.64 98	78.09 80	88.61 24	93.21 45

TESTMET0.1,1		67.38 100	71.61 82	62.45 131	66.05 160	81.32 104	63.98 150	55.36 148	58.62 97	42.96 128	52.74 88	61.65 76	57.74 133	75.64 98	78.09 80	88.61 24	93.21 45

V42		62.86 132	62.97 134	62.74 129	60.84 182	78.99 125	71.46 103	57.13 131	46.85 144	44.28 121	38.87 149	40.73 155	57.63 135	72.60 133	74.14 119	85.09 108	88.63 95

CR-MVSNet		62.31 134	64.75 122	59.47 148	68.63 143	71.29 176	67.53 131	43.18 192	55.83 110	41.40 134	41.04 140	55.85 106	57.29 136	72.76 130	73.27 133	78.77 182	83.23 139

PatchT		60.46 150	63.85 127	56.51 167	65.95 162	75.68 155	47.34 191	41.39 199	53.89 123	41.40 134	37.84 156	50.30 129	57.29 136	72.76 130	73.27 133	85.67 94	83.23 139

TinyColmap		52.66 181	50.09 192	55.65 169	59.72 186	64.02 196	57.15 177	52.96 165	40.28 171	32.51 174	32.42 179	20.97 208	56.65 138	63.95 182	65.15 183	74.91 193	63.87 196

EPP-MVSNet		67.58 97	71.10 85	63.48 121	75.71 106	83.35 88	66.85 136	57.83 122	53.02 124	41.15 137	55.82 74	67.89 55	56.01 139	74.40 109	72.92 139	83.33 142	90.30 76

MVS-HIRNet		53.86 179	53.02 181	54.85 172	60.30 184	72.36 170	44.63 199	42.20 197	39.45 175	43.47 124	21.66 202	34.00 186	55.47 140	65.42 174	67.16 174	83.02 149	71.08 185

test-mter		64.06 122	69.24 94	58.01 156	59.07 188	77.40 141	59.13 172	48.11 177	55.64 113	39.18 146	51.56 93	58.54 91	55.38 141	73.52 121	76.00 98	87.22 59	92.05 58

thres600view7		63.77 124	63.14 132	64.51 111	75.49 108	81.61 97	69.59 118	62.95 79	43.96 158	38.90 147	41.09 139	40.24 158	55.25 142	76.24 91	71.54 148	84.89 113	87.30 105

v148		62.00 141	61.19 150	62.96 124	67.46 152	79.49 121	67.87 127	57.66 123	42.30 162	45.02 117	38.20 154	38.89 162	54.77 143	69.83 160	72.60 142	84.96 109	87.01 107

gg-mvs-nofinetune		62.34 133	66.19 116	57.86 158	76.15 102	88.61 39	71.18 107	41.24 202	25.74 204	13.16 206	22.91 199	63.97 68	54.52 144	85.06 15	85.25 10	90.92 3	91.78 60

IterMVS		61.87 142	63.55 128	59.90 144	67.29 153	72.20 171	67.34 134	48.56 175	47.48 142	37.86 154	47.07 113	48.27 131	54.08 145	72.12 137	73.71 124	84.30 129	83.99 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPSCF		55.07 172	58.06 167	51.57 179	48.87 204	58.95 201	53.68 182	41.26 201	62.42 84	45.88 111	54.38 84	54.26 115	53.75 146	57.15 192	53.53 202	66.01 202	65.75 193

CMPMVS	43.63 17	57.67 167	55.43 175	60.28 143	72.01 123	79.00 124	62.77 160	53.23 163	41.77 165	45.42 113	30.74 185	39.03 160	53.01 147	64.81 178	64.65 184	75.26 192	68.03 189
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
IterMVS-SCA-FT		60.21 152	62.97 134	57.00 165	66.64 157	71.84 172	67.53 131	46.93 182	47.56 141	36.77 159	46.85 117	48.21 132	52.51 148	70.36 155	72.40 144	71.63 200	83.53 135

IB-MVS	64.48 11	69.02 87	68.97 97	69.09 86	81.75 63	89.01 36	64.50 145	64.91 64	56.65 104	62.59 51	47.89 104	45.23 139	51.99 149	69.18 164	81.88 39	88.77 18	92.93 49
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
pmmvs5		59.72 153	60.24 157	59.11 152	62.77 177	77.33 143	63.17 158	54.00 158	40.21 172	37.23 155	40.41 143	35.99 176	51.75 150	72.55 134	72.74 141	85.72 92	82.45 145

UniMVSNet_ETH3D		57.83 164	56.46 174	59.43 149	63.24 174	73.22 168	67.70 128	55.58 144	36.17 184	36.84 157	32.64 178	35.14 181	51.50 151	65.81 172	69.81 165	81.73 163	82.44 146

UniMVSNet_NR-MVSNet		62.30 135	63.51 129	60.89 139	69.48 140	77.83 136	64.07 148	63.94 69	50.03 131	31.17 177	44.82 121	41.12 150	51.37 152	71.02 146	74.81 110	85.30 102	84.95 124

DU-MVS		60.87 148	61.82 145	59.76 146	66.69 155	75.87 151	64.07 148	61.96 87	49.31 134	31.17 177	42.76 125	36.95 169	51.37 152	69.67 161	73.20 136	83.30 143	84.95 124

FMVSNet5		58.86 159	60.24 157	57.25 163	52.66 200	66.25 188	63.77 153	52.86 166	57.85 101	37.92 153	36.12 167	52.22 124	51.37 152	70.88 148	71.43 152	84.92 110	66.91 191

LTVRE_ROB	47.26 16	49.41 190	49.91 193	48.82 185	64.76 167	69.79 179	49.05 187	47.12 181	20.36 210	16.52 198	36.65 165	26.96 201	50.76 155	60.47 186	63.16 188	64.73 203	72.00 181
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
tfpnnormal		58.97 158	56.48 173	61.89 134	71.27 129	76.21 150	66.65 139	61.76 93	32.90 192	36.41 160	27.83 191	29.14 198	50.64 156	73.06 125	73.05 137	84.58 125	83.15 141

v7n		57.04 169	56.64 172	57.52 161	62.85 176	74.75 161	61.76 162	51.80 169	35.58 188	36.02 163	32.33 180	33.61 188	50.16 157	67.73 168	70.34 163	82.51 154	82.12 147

pmmvs-eth3d		55.20 170	53.95 179	56.65 166	57.34 194	67.77 184	57.54 176	53.74 160	40.93 170	41.09 138	31.19 184	29.10 199	49.07 158	65.54 173	67.28 172	81.14 167	75.81 165

NR-MVSNet		61.08 147	62.09 144	59.90 144	71.96 124	75.87 151	63.60 154	61.96 87	49.31 134	27.95 182	42.76 125	33.85 187	48.82 159	74.35 111	74.05 122	85.13 105	84.45 127

Baseline_NR-MVSNet		59.47 155	60.28 156	58.54 155	66.69 155	73.90 165	61.63 164	62.90 82	49.15 138	26.87 184	35.18 173	37.62 165	48.20 160	69.67 161	73.61 125	84.92 110	82.82 142

RPMNet		58.63 162	62.80 138	53.76 177	67.59 151	71.29 176	54.60 181	38.13 204	55.83 110	35.70 164	41.58 137	53.04 122	47.89 161	66.10 171	67.38 171	78.65 184	84.40 128

TranMVSNet+NR-MVSNet		60.38 151	61.30 149	59.30 150	68.34 144	75.57 157	63.38 157	63.78 71	46.74 145	27.73 183	42.56 129	36.84 170	47.66 162	70.36 155	74.59 114	84.91 112	82.46 144

anonymousdsp		54.99 173	57.24 170	52.36 178	53.82 198	71.75 175	51.49 184	48.14 176	33.74 190	33.66 171	38.34 152	36.13 175	47.54 163	64.53 180	70.60 161	79.53 179	85.59 122

MDTV_nov1_ep13_2view		54.47 177	54.61 176	54.30 176	60.50 183	73.82 166	57.92 175	43.38 191	39.43 176	32.51 174	33.23 176	34.05 185	47.26 164	62.36 184	66.21 179	84.24 130	73.19 178

thisisatest0515		59.37 156	60.68 154	57.84 159	64.39 169	75.65 156	58.56 174	53.86 159	41.55 167	42.12 133	40.40 144	39.59 159	47.09 165	71.69 143	73.79 123	81.02 169	82.08 148

PM-MVS		50.11 187	50.38 191	49.80 183	47.23 206	62.08 199	50.91 186	44.84 188	41.90 164	36.10 162	35.22 172	26.05 204	46.83 166	57.64 190	55.42 201	72.90 197	74.32 171

CDS-MVSNet		64.22 120	65.89 119	62.28 133	70.05 134	80.59 111	69.91 117	57.98 117	43.53 159	46.58 110	48.22 102	50.76 127	46.45 167	75.68 97	76.08 97	82.70 151	86.34 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		58.86 159	60.91 152	56.47 168	62.38 179	77.57 139	58.97 173	52.98 164	38.76 177	36.17 161	42.26 133	47.94 133	46.45 167	70.23 157	70.79 159	81.86 162	78.82 160

TDRefinement		52.70 180	51.02 189	54.66 174	57.41 193	65.06 192	61.47 165	54.94 150	44.03 157	33.93 170	30.13 187	27.57 200	46.17 169	61.86 185	62.48 191	74.01 196	66.06 192

IS_MVSNet		67.29 101	71.98 78	61.82 135	76.92 96	84.32 83	65.90 143	58.22 114	55.75 112	39.22 145	54.51 82	62.47 71	45.99 170	78.83 71	78.52 74	84.70 121	89.47 85

MIMVSNet		57.78 166	59.71 161	55.53 170	54.79 196	77.10 144	63.89 152	45.02 186	46.59 147	36.79 158	28.36 190	40.77 154	45.84 171	74.97 103	76.58 90	86.87 66	73.60 175

UGNet		67.57 98	71.69 81	62.76 128	69.88 135	82.58 92	66.43 140	58.64 112	54.71 120	51.87 89	61.74 59	62.01 75	45.46 172	74.78 106	74.99 107	84.24 130	91.02 67
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
Vis-MVSNet		65.53 111	69.83 93	60.52 141	70.80 133	84.59 77	66.37 142	55.47 147	48.40 139	40.62 141	57.67 71	58.43 94	45.37 173	77.49 79	76.24 96	84.47 126	85.99 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
COLMAP_ROB	51.17 15	55.13 171	52.90 183	57.73 160	73.47 118	67.21 186	62.13 161	55.82 140	47.83 140	34.39 168	31.60 182	34.24 184	44.90 174	63.88 183	62.52 190	75.67 190	63.02 198
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SixPastTwentyTwo		49.11 191	49.22 194	48.99 184	58.54 192	64.14 195	47.18 192	47.75 178	31.15 196	24.42 188	41.01 141	26.55 202	44.04 175	54.76 199	58.70 196	71.99 199	68.21 187

UniMVSNet (Re)		60.62 149	62.93 136	57.92 157	67.64 150	77.90 135	61.75 163	61.24 96	49.83 133	29.80 181	42.57 128	40.62 156	43.36 176	70.49 154	73.27 133	83.76 136	85.81 119

pm-mvs1		59.21 157	59.58 162	58.77 154	67.97 147	77.07 145	64.12 146	57.20 129	34.73 189	36.86 156	35.34 171	40.54 157	43.34 177	74.32 112	73.30 132	83.13 148	81.77 150

EG-PatchMatch MVS		58.73 161	58.03 168	59.55 147	72.32 121	80.49 112	63.44 156	55.55 145	32.49 193	38.31 150	28.87 189	37.22 168	42.84 178	74.30 113	75.70 101	84.84 115	77.14 164

MDA-MVSNet-bldmvs		44.15 197	42.27 202	46.34 192	38.34 208	62.31 198	46.28 194	55.74 142	29.83 197	20.98 194	27.11 193	16.45 213	41.98 179	41.11 206	57.47 197	74.72 194	61.65 201

UA-Net		64.62 116	68.23 103	60.42 142	77.53 91	81.38 102	60.08 169	57.47 127	47.01 143	44.75 118	60.68 64	71.32 46	41.84 180	73.27 122	72.25 145	80.83 171	71.68 182

pmmvs3		41.86 199	42.29 201	41.36 197	39.80 207	52.66 206	38.93 205	35.85 208	23.40 207	20.22 195	19.30 203	20.84 209	40.56 181	55.98 197	58.79 195	72.80 198	65.03 194

pmmvs6		54.20 178	53.54 180	54.97 171	63.22 175	72.98 169	60.17 168	52.32 168	26.77 203	34.30 169	23.29 198	36.23 174	40.33 182	68.77 165	68.76 168	79.47 180	78.00 162

TransMVSNet (Re)		57.83 164	56.90 171	58.91 153	72.26 122	74.69 162	63.57 155	61.42 95	32.30 194	32.65 173	33.97 175	35.96 177	39.17 183	73.84 118	72.84 140	84.37 128	74.69 170

CVMVSNet		54.92 175	58.16 166	51.13 182	62.61 178	68.44 183	55.45 180	52.38 167	42.28 163	21.45 193	47.10 112	46.10 137	37.96 184	64.42 181	63.81 185	76.92 188	75.01 169

EPNet_dtu		66.17 106	70.13 92	61.54 137	81.04 66	77.39 142	68.87 123	62.50 85	69.78 63	33.51 172	63.77 54	56.22 104	37.65 185	72.20 135	72.18 146	85.69 93	79.38 157
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
N_pmnet		47.67 193	47.00 197	48.45 187	54.72 197	62.78 197	46.95 193	51.25 170	36.01 186	26.09 187	26.59 194	25.93 205	35.50 186	55.67 198	59.01 194	76.22 189	63.04 197

Vis-MVSNet (Re-imp)		62.25 136	68.74 98	54.68 173	73.70 114	78.74 126	56.51 178	57.49 126	55.22 114	26.86 185	54.56 81	61.35 78	31.06 187	73.10 124	74.90 108	82.49 155	83.31 136

Anonymous20231206		52.23 182	52.80 184	51.56 180	64.70 168	69.41 180	51.01 185	58.60 113	36.63 181	22.44 192	21.80 201	31.42 193	30.52 188	66.79 170	67.83 170	82.10 160	75.73 166

test0.0.03 1		57.35 168	59.89 160	54.38 175	71.37 127	73.45 167	52.71 183	61.03 97	46.11 149	26.33 186	41.73 136	44.08 142	29.72 189	71.43 145	70.90 157	85.10 106	71.56 183

CP-MVSNet		50.57 185	52.60 186	48.21 188	58.77 190	65.82 190	48.17 189	56.29 135	37.41 179	16.59 197	37.14 159	31.95 191	29.21 190	56.60 194	63.71 186	80.22 173	75.56 167

ambc			42.30 200		50.36 202	49.51 207	35.47 206		32.04 195	23.53 189	17.36 205	8.95 215	29.06 191	64.88 177	56.26 198	61.29 205	67.12 190

PS-CasMVS		50.17 186	52.02 187	48.02 189	58.60 191	65.54 191	48.04 190	56.19 138	36.42 183	16.42 199	35.68 170	31.33 194	28.85 192	56.42 196	63.54 187	80.01 174	75.18 168

PEN-MVS		51.04 183	52.94 182	48.82 185	61.45 181	66.00 189	48.68 188	57.20 129	36.87 180	15.36 200	36.98 161	32.72 189	28.77 193	57.63 191	66.37 177	81.44 166	74.00 173

FPMVS		39.11 201	36.39 203	42.28 196	55.97 195	45.94 208	46.23 195	41.57 198	35.73 187	22.61 190	23.46 197	19.82 210	28.32 194	43.57 203	40.67 205	58.96 206	45.54 205

new_pmnet		33.19 202	35.52 204	30.47 203	27.55 213	45.31 209	29.29 209	30.92 209	29.00 200	9.88 211	18.77 204	17.64 212	26.77 195	44.07 202	45.98 204	58.41 207	47.87 204

DTE-MVSNet		49.82 188	51.92 188	47.37 190	61.75 180	64.38 194	45.89 197	57.33 128	36.11 185	12.79 207	36.87 162	31.93 192	25.73 196	58.01 189	65.22 182	80.75 172	70.93 186

EU-MVSNet		44.84 196	47.85 196	41.32 199	49.26 203	56.59 204	43.07 200	47.64 180	33.03 191	13.82 203	36.78 163	30.99 195	24.37 197	53.80 200	55.57 200	69.78 201	68.21 187

WR-MVS		51.02 184	54.56 177	46.90 191	63.84 171	69.23 181	44.78 198	56.38 134	38.19 178	14.19 202	37.38 157	36.82 171	22.39 198	60.14 187	66.20 180	79.81 176	73.95 174

WR-MVS_H		49.62 189	52.63 185	46.11 194	58.80 189	67.58 185	46.14 196	54.94 150	36.51 182	13.63 205	36.75 164	35.67 179	22.10 199	56.43 195	62.76 189	81.06 168	72.73 179

DeepMVS_CX						19.81 214	17.01 212	10.02 212	23.61 206	5.85 213	17.21 206	8.03 216	21.13 200	22.60 209		21.42 214	30.01 208

PMVS	27.44 18	32.08 203	29.07 205	35.60 202	48.33 205	24.79 211	26.97 210	41.34 200	20.45 209	22.50 191	17.11 207	18.64 211	20.44 201	41.99 205	38.06 206	54.02 208	42.44 206
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testgi		48.51 192	50.53 190	46.16 193	64.78 166	67.15 187	41.54 201	54.81 154	29.12 199	17.03 196	32.07 181	31.98 190	20.15 202	65.26 175	67.00 175	78.67 183	61.10 202

new-patchmatchnet		42.21 198	42.97 199	41.33 198	53.05 199	59.89 200	39.38 203	49.61 172	28.26 201	12.10 208	22.17 200	21.54 207	19.22 203	50.96 201	56.04 199	74.61 195	61.92 200

MIMVSNet1		40.84 200	43.46 198	37.79 201	32.14 209	58.92 202	39.24 204	50.83 171	27.00 202	11.29 209	16.76 208	26.53 203	17.75 204	57.14 193	61.12 193	75.46 191	56.78 203

test20.03		47.23 195	48.69 195	45.53 195	63.28 173	64.39 193	41.01 202	56.93 132	29.16 198	15.21 201	23.90 195	30.76 196	17.51 205	64.63 179	65.26 181	79.21 181	62.71 199

FC-MVSNet-test		47.24 194	54.37 178	38.93 200	59.49 187	58.25 203	34.48 207	53.36 162	45.66 151	6.66 212	50.62 94	42.02 145	16.62 206	58.39 188	61.21 192	62.99 204	64.40 195

Gipuma		24.91 204	24.61 206	25.26 205	31.47 210	21.59 212	18.06 211	37.53 205	25.43 205	10.03 210	4.18 213	4.25 217	14.85 207	43.20 204	47.03 203	39.62 210	26.55 210
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		14.40 207	10.71 210	18.70 207	28.15 212	12.09 216	7.06 215	36.89 206	11.00 212	3.56 216	4.95 211	2.27 219	13.91 208	10.13 212	16.06 211	22.63 213	18.51 212

E-PMN		15.08 206	11.65 209	19.08 206	28.73 211	12.31 215	6.95 216	36.87 207	10.71 213	3.63 215	5.13 210	2.22 220	13.81 209	11.34 211	18.50 210	24.49 212	21.32 211

MVE	15.98 19	14.37 208	16.36 208	12.04 210	7.72 216	20.24 213	5.90 217	29.05 210	8.28 214	3.92 214	4.72 212	2.42 218	9.57 210	18.89 210	31.46 208	16.07 215	28.53 209
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				16.09 209	13.07 215	8.12 217	13.61 214	2.08 213	55.09 115	30.10 180	40.26 145	22.83 206	5.35 211	29.91 207	25.25 209	32.33 211

PMMVS2		20.45 205	22.31 207	18.27 208	20.52 214	26.73 210	14.85 213	28.43 211	13.69 211	0.79 217	10.35 209	9.10 214	3.83 212	27.64 208	32.87 207	41.17 209	35.81 207

GG-mvs-BLEND		54.54 176	77.58 49	27.67 204	0.03 217	90.09 29	77.20 66	0.02 214	66.83 72	0.05 218	59.90 67	73.33 36	0.04 213	78.40 75	79.30 68	88.65 22	95.20 26

test123		0.05 209	0.08 211	0.01 211	0.00 218	0.01 218	0.01 220	0.00 216	0.05 215	0.00 219	0.16 214	0.00 222	0.04 213	0.02 214	0.05 212	0.00 217	0.26 213

testmvs		0.05 209	0.08 211	0.01 211	0.00 218	0.01 218	0.03 219	0.01 215	0.05 215	0.00 219	0.14 215	0.01 221	0.03 215	0.05 213	0.05 212	0.01 216	0.24 214

uanet_test		0.00 211	0.00 213	0.00 213	0.00 218	0.00 220	0.00 221	0.00 216	0.00 217	0.00 219	0.00 216	0.00 222	0.00 216	0.00 215	0.00 214	0.00 217	0.00 215

sosnet-low-res		0.00 211	0.00 213	0.00 213	0.00 218	0.00 220	0.00 221	0.00 216	0.00 217	0.00 219	0.00 216	0.00 222	0.00 216	0.00 215	0.00 214	0.00 217	0.00 215

sosnet		0.00 211	0.00 213	0.00 213	0.00 218	0.00 220	0.00 221	0.00 216	0.00 217	0.00 219	0.00 216	0.00 222	0.00 216	0.00 215	0.00 214	0.00 217	0.00 215

RE-MVS-def										31.47 176

9.14												84.47 6

SR-MVS					86.33 47			67.54 46				80.78 19

our_test_3					63.32 172	71.07 178	55.90 179

MTAPA										78.32 11		79.42 24

MTMP										76.04 16		76.65 29

Patchmatch-RL test							2.17 218

XVS					82.43 56	86.27 66	75.70 69			61.07 60		72.27 39				85.67 94

X-MVStestdata					82.43 56	86.27 66	75.70 69			61.07 60		72.27 39				85.67 94

mPP-MVS					86.96 42							70.61 49

NP-MVS									81.60 36

Patchmtry						78.06 134	67.53 131	43.18 192		41.40 134

High-resolution multi-view benchmark