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.

HSP-MVS		94.69 2	95.39 2	93.88 4	96.78 14	98.11 5	94.75 7	90.91 9	96.89 2	89.12 10	96.98 1	89.47 9	94.76 1	95.24 2	93.29 10	96.98 7	97.73 30

CNVR-MVS		94.53 3	94.85 4	94.15 3	98.03 2	98.59 2	95.56 3	92.91 1	94.86 9	88.46 11	91.32 17	90.83 6	94.03 2	95.20 3	94.16 4	95.89 24	99.01 12

ESAPD		95.11 1	95.65 1	94.48 1	97.96 3	98.62 1	96.45 1	92.82 2	96.24 3	90.25 5	96.16 2	93.09 1	93.32 3	93.93 13	92.02 20	96.07 19	99.50 3

APDe-MVS		94.31 4	94.30 7	94.33 2	97.57 7	98.06 7	95.79 2	91.98 5	95.50 6	92.19 1	95.25 3	87.97 14	92.93 4	93.01 20	91.02 36	95.52 28	99.29 5

APD-MVS		93.47 8	93.44 13	93.50 5	97.06 10	97.09 22	95.27 6	91.47 6	95.71 5	89.57 7	93.66 6	86.28 19	92.81 5	92.06 28	90.70 38	94.83 43	98.60 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		93.59 7	94.00 10	93.10 7	97.90 5	97.93 9	95.40 5	92.39 4	94.47 12	84.94 18	91.21 18	89.32 10	92.53 6	93.90 14	92.98 12	95.44 30	98.22 24

DeepC-MVS_fast	86.59 2	91.69 16	91.39 22	92.05 14	97.43 8	96.92 27	94.05 15	90.23 11	93.31 20	83.19 25	77.91 39	84.23 28	92.42 7	94.62 8	94.83 2	95.00 38	97.88 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HPM-MVS++		94.04 6	94.96 3	92.96 8	97.93 4	97.71 13	94.65 9	91.01 8	95.91 4	87.43 13	93.52 8	92.63 2	92.29 8	94.22 12	92.34 17	94.47 46	98.37 22

MCST-MVS		94.10 5	94.77 5	93.31 6	98.31 1	98.34 3	95.43 4	92.54 3	94.41 13	83.05 27	91.38 15	90.97 5	92.24 9	95.05 5	94.02 5	98.31 1	99.20 7

TSAR-MVS + MP.		93.07 11	93.53 12	92.53 9	94.23 41	97.54 17	94.75 7	89.87 13	95.26 7	89.20 9	93.16 9	88.19 13	92.15 10	91.79 32	89.65 49	94.99 39	99.16 8

AdaColmap		88.46 35	85.75 54	91.62 16	96.25 24	95.35 54	90.71 39	91.08 7	90.22 35	86.17 15	74.33 48	73.67 69	92.00 11	86.31 95	85.82 86	93.52 81	94.53 94

SMA-MVS		93.47 8	94.29 8	92.52 10	97.72 6	97.77 12	94.46 12	90.19 12	94.96 8	87.15 14	90.15 21	90.99 4	91.49 12	94.31 10	93.33 9	94.10 52	98.53 20

HFP-MVS		92.02 14	92.13 19	91.89 15	97.16 9	96.46 35	93.57 18	87.60 21	93.79 15	88.17 12	93.15 10	83.94 32	91.19 13	90.81 41	89.83 44	93.66 71	96.94 54

ACMMP_Plus		92.16 13	92.91 17	91.28 17	96.95 11	97.36 18	93.66 17	89.23 17	93.33 17	83.71 22	90.53 19	86.84 16	90.39 14	93.30 18	91.56 29	93.74 66	97.43 37

TSAR-MVS + GP.		91.29 18	93.11 16	89.18 29	87.81 86	96.21 41	92.51 28	83.83 40	94.24 14	83.77 21	91.87 14	89.62 8	90.07 15	90.40 44	90.31 40	97.09 6	99.10 9

zzz-MVS		91.59 17	91.12 23	92.13 12	96.76 15	96.68 30	93.39 19	88.00 20	93.63 16	90.76 4	83.97 32	85.33 23	89.89 16	91.60 34	89.65 49	94.00 56	96.97 52

SD-MVS		93.36 10	94.33 6	92.22 11	94.68 38	97.89 11	94.56 10	90.89 10	94.80 10	90.04 6	93.53 7	90.14 7	89.78 17	92.74 22	92.17 18	93.35 98	99.07 10

MSLP-MVS++		90.33 24	88.82 34	92.10 13	96.52 21	95.93 42	94.35 13	86.26 28	88.37 47	89.24 8	75.94 44	82.60 35	89.71 18	89.45 55	92.17 18	96.51 14	97.24 42

ACMMPR		91.15 19	91.44 21	90.81 19	96.61 17	96.25 39	93.09 20	87.08 23	93.32 19	84.78 19	92.08 13	82.10 38	89.71 18	90.24 45	89.82 45	93.61 76	96.30 69

abl_6				89.54 27	95.55 33	97.59 15	89.01 50	85.00 34	94.67 11	83.04 28	84.70 31	91.47 3	89.46 20			95.20 35	98.63 16

3Dnovator+	81.14 5	88.59 33	87.49 40	89.88 25	95.83 29	96.45 37	91.94 33	82.41 52	87.09 52	85.94 17	62.80 90	85.37 22	89.46 20	91.51 35	91.89 26	93.72 68	97.30 40

CP-MVS		90.57 23	90.68 25	90.44 20	96.13 25	95.90 46	92.77 25	86.86 27	92.12 25	84.19 20	89.18 24	82.37 36	89.43 22	89.65 53	88.43 59	93.27 102	97.13 46

3Dnovator	80.58 8	88.20 37	86.53 47	90.15 21	96.86 13	96.46 35	91.97 32	83.06 46	85.16 57	83.66 23	62.28 93	82.15 37	88.98 23	90.99 39	92.65 15	96.38 18	96.03 72

SteuartSystems-ACMMP		92.31 12	93.31 14	91.15 18	96.88 12	97.36 18	93.95 16	89.44 15	92.62 22	83.20 24	94.34 5	85.55 21	88.95 24	93.07 19	91.90 24	94.51 45	98.30 23
Skip Steuart: Steuart Systems R&D Blog.
PGM-MVS		89.97 26	90.64 27	89.18 29	96.53 20	95.90 46	93.06 21	82.48 51	90.04 36	80.37 36	92.75 11	80.96 43	88.93 25	89.88 50	89.08 55	93.69 70	95.86 75

casdiffmvs		85.59 50	86.80 45	84.19 59	87.89 84	95.52 51	88.63 53	74.54 102	88.13 48	70.96 70	69.82 61	72.54 74	88.87 26	92.16 26	92.94 13	94.24 49	96.46 68

MAR-MVS		85.65 49	86.30 49	84.88 56	95.51 34	95.89 48	86.50 66	76.71 83	89.23 43	68.59 80	70.93 58	74.49 63	88.55 27	89.40 56	90.30 41	93.42 94	93.88 114
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
CPTT-MVS		88.17 38	87.84 39	88.55 33	93.33 44	93.75 65	92.33 30	84.75 35	89.87 38	81.72 35	83.93 33	81.12 42	88.45 28	85.42 105	84.07 107	90.72 179	96.72 62

CANet		89.98 25	90.42 29	89.47 28	94.13 42	98.05 8	91.76 34	83.27 43	90.87 31	81.90 34	72.32 51	84.82 26	88.42 29	94.52 9	93.78 7	97.34 4	98.58 19

PLC	81.02 6	84.81 56	81.81 77	88.31 35	93.77 43	90.35 103	88.80 51	84.47 38	86.76 53	82.17 32	66.56 69	71.01 80	88.41 30	85.48 103	84.28 105	92.26 147	88.21 172
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
train_agg		91.99 15	93.71 11	89.98 23	96.42 23	97.03 24	94.31 14	89.05 18	93.33 17	77.75 41	95.06 4	88.27 12	88.38 31	92.02 29	91.41 31	94.00 56	98.84 15

MP-MVS		90.81 22	91.45 20	90.06 22	96.59 18	96.33 38	92.46 29	87.19 22	90.27 34	82.54 31	91.38 15	84.88 25	88.27 32	90.58 43	89.30 54	93.30 100	97.44 35
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS	84.14 3	88.80 31	88.03 38	89.71 26	94.83 36	96.56 31	92.57 27	89.38 16	89.25 42	79.59 38	70.02 60	77.05 55	88.24 33	92.44 24	92.79 14	93.65 74	98.10 25
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNLPA		84.72 57	82.14 73	87.73 38	92.85 47	93.83 64	84.70 86	85.07 33	90.90 30	83.16 26	56.28 117	71.53 76	88.14 34	84.19 113	84.00 111	92.48 142	94.26 100

canonicalmvs		85.93 48	86.26 50	85.54 53	88.94 68	95.44 52	89.56 46	76.01 89	87.83 49	77.70 42	76.43 43	68.66 89	87.80 35	87.02 84	91.51 30	93.25 106	96.95 53

DeepPCF-MVS	86.71 1	91.00 20	94.05 9	87.43 40	95.58 32	98.17 4	86.22 67	88.59 19	97.01 1	76.77 46	85.11 30	88.90 11	87.29 36	95.02 6	94.69 3	90.15 187	99.48 4

MVS_0304		88.43 36	89.46 31	87.21 41	91.85 54	97.60 14	92.62 26	81.10 58	87.16 51	73.80 55	72.19 53	83.36 34	87.03 37	94.64 7	93.67 8	96.88 8	97.64 32

QAPM		87.06 43	86.46 48	87.75 37	96.63 16	97.09 22	91.71 35	82.62 49	80.58 70	71.28 69	66.04 73	84.24 27	87.01 38	89.93 49	89.91 43	97.26 5	97.44 35

MVS_Test		84.60 58	85.13 56	83.99 60	88.17 80	95.27 55	88.21 55	73.15 111	84.31 59	70.55 74	68.67 64	68.78 88	86.99 39	91.71 33	91.90 24	96.84 9	95.27 86

TSAR-MVS + COLMAP		84.93 54	85.79 53	83.92 61	90.90 59	93.57 68	89.25 49	82.00 53	91.29 27	61.66 92	88.25 25	59.46 119	86.71 40	89.79 51	87.09 69	93.01 121	91.09 140

OMC-MVS		86.38 46	86.21 51	86.57 49	92.30 50	94.35 61	87.60 58	83.51 42	92.32 24	77.37 44	72.27 52	77.83 50	86.59 41	87.62 80	85.95 83	92.08 149	93.11 124

X-MVS		89.73 29	90.65 26	88.66 32	96.44 22	95.93 42	92.26 31	86.98 25	90.73 32	76.32 47	89.56 23	82.05 39	86.51 42	89.98 48	89.60 51	93.43 93	96.72 62

ACMMP		88.48 34	88.71 35	88.22 36	94.61 39	95.53 50	90.64 41	85.60 32	90.97 29	78.62 40	89.88 22	74.20 66	86.29 43	88.16 75	86.37 78	93.57 78	95.86 75
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
CLD-MVS		85.43 52	84.24 59	86.83 45	87.69 89	93.16 73	90.01 44	82.72 48	87.17 50	79.28 39	71.43 57	65.81 98	86.02 44	87.33 82	86.96 71	95.25 34	97.83 29
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CANet_DTU		83.33 59	86.59 46	79.53 90	88.88 69	94.87 58	86.63 63	68.85 148	85.45 56	50.54 158	77.86 40	69.94 84	85.62 45	92.63 23	90.88 37	96.63 11	94.46 95

PCF-MVS	82.38 4	85.52 51	84.41 58	86.81 46	91.51 56	96.23 40	90.27 42	89.81 14	77.87 78	70.67 72	69.20 63	77.86 49	85.55 46	85.92 101	86.38 77	93.03 120	97.43 37
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_LR		87.58 42	88.67 36	86.31 51	92.58 48	95.89 48	86.20 69	82.49 50	89.08 44	77.47 43	86.20 28	74.22 65	85.49 47	90.03 47	88.52 57	93.66 71	96.74 61

TAPA-MVS	80.99 7	84.83 55	84.42 57	85.31 54	91.89 53	93.73 66	88.53 54	82.80 47	89.99 37	69.78 77	71.53 56	75.03 62	85.47 48	86.26 96	84.54 102	93.39 96	89.90 147
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OPM-MVS		81.34 69	78.18 98	85.02 55	91.27 58	91.78 92	90.66 40	83.62 41	62.39 145	65.91 84	63.35 87	64.33 103	85.03 49	87.77 78	85.88 85	93.66 71	91.75 137

MVS_111021_HR		87.82 39	88.84 33	86.62 48	94.42 40	97.36 18	88.21 55	83.26 44	83.42 60	72.52 64	82.63 34	76.93 56	84.95 50	91.93 30	91.15 34	96.39 17	98.49 21

MVSTER		87.68 41	89.12 32	86.01 52	88.11 82	90.05 109	89.28 48	77.05 82	91.37 26	79.97 37	76.70 42	85.25 24	84.89 51	93.53 15	91.41 31	96.73 10	95.55 81

PHI-MVS		89.88 27	92.75 18	86.52 50	94.97 35	97.57 16	89.99 45	84.56 36	92.52 23	69.72 78	90.35 20	87.11 15	84.89 51	91.82 31	92.37 16	95.02 37	97.51 33

HQP-MVS		86.17 47	87.35 41	84.80 57	91.41 57	92.37 86	91.05 38	84.35 39	88.52 46	64.21 87	87.05 27	68.91 87	84.80 53	89.12 58	88.16 63	92.96 123	97.31 39

CSCG		89.81 28	89.69 30	89.96 24	96.55 19	97.90 10	92.89 23	87.06 24	88.74 45	86.17 15	78.24 38	86.53 18	84.75 54	87.82 77	90.59 39	92.32 145	98.01 26

PMMVS		82.26 63	85.48 55	78.51 99	85.92 111	91.92 90	78.30 147	70.77 134	86.30 55	61.11 97	82.46 35	70.88 81	84.70 55	88.05 76	84.78 98	90.24 186	93.98 106

Anonymous202405211			75.59 121		85.13 116	91.06 98	84.62 88	77.96 68	69.47 120		40.79 198	63.84 105	84.57 56	83.55 120	84.69 99	89.69 192	95.75 79

EPNet		89.30 30	90.89 24	87.44 39	95.67 31	96.81 28	91.13 37	83.12 45	91.14 28	76.31 51	87.60 26	80.40 46	84.45 57	92.13 27	91.12 35	93.96 59	97.01 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OpenMVS	77.91 11	85.09 53	83.42 62	87.03 42	96.12 26	96.55 33	89.36 47	81.59 56	79.19 73	75.20 52	55.84 121	79.04 48	84.45 57	88.47 67	89.35 53	95.48 29	95.48 82

TSAR-MVS + ACMM		90.98 21	93.18 15	88.42 34	95.69 30	96.73 29	94.52 11	86.97 26	92.99 21	76.32 47	92.31 12	86.64 17	84.40 59	92.97 21	92.02 20	92.62 139	98.59 18

Anonymous20231211		78.61 94	75.57 122	82.15 66	84.43 121	90.26 104	84.08 91	77.68 75	71.09 109	72.90 57	39.24 205	66.21 95	84.23 60	82.15 132	84.04 108	89.61 194	96.03 72

CDPH-MVS		88.76 32	90.43 28	86.81 46	96.04 27	96.53 34	92.95 22	85.95 30	90.36 33	67.93 83	85.80 29	80.69 44	83.82 61	90.81 41	91.85 27	94.18 50	96.99 51

LS3D		78.72 92	75.79 118	82.15 66	91.91 52	89.39 118	83.66 94	85.88 31	76.81 86	59.22 110	57.67 107	58.53 123	83.72 62	82.07 134	81.63 143	88.50 201	84.39 188

DI_MVS_plusplus_trai		83.32 60	82.53 70	84.25 58	86.26 106	93.66 67	90.23 43	77.16 81	77.05 85	74.06 54	53.74 130	74.33 64	83.61 63	91.40 37	89.82 45	94.17 51	97.73 30

Effi-MVS+		79.80 82	80.04 84	79.52 91	85.53 112	93.31 71	85.28 76	70.68 136	74.15 96	58.79 111	62.03 95	60.51 115	83.37 64	88.41 68	86.09 82	93.49 89	95.80 77

diffmvs		82.25 64	82.33 72	82.15 66	86.10 109	94.52 60	86.22 67	73.32 110	82.19 66	70.14 76	67.88 66	62.49 108	83.02 65	85.97 100	88.53 56	94.10 52	94.77 89

Fast-Effi-MVS+		77.37 109	76.68 109	78.17 100	82.84 126	89.94 110	81.47 107	68.01 156	72.99 102	60.26 106	55.07 124	53.20 138	82.99 66	86.47 94	86.12 81	93.46 91	92.98 127

DELS-MVS		87.75 40	86.92 44	88.71 31	94.69 37	97.34 21	92.78 24	84.50 37	77.87 78	81.94 33	67.17 67	75.49 61	82.84 67	95.38 1	95.93 1	95.55 27	99.27 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
ACMM	78.09 10	80.91 71	78.39 96	83.86 62	89.61 66	87.71 126	85.16 79	80.67 59	79.04 74	74.18 53	63.82 85	60.84 113	82.59 68	84.33 112	83.59 114	90.96 174	89.39 155
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PatchMatch-RL		78.75 91	76.47 112	81.41 73	88.53 73	91.10 97	78.09 151	77.51 78	77.33 82	71.98 66	64.38 83	48.10 156	82.55 69	84.06 114	82.35 130	89.78 189	87.97 174

FMVSNet3		81.93 68	81.98 74	81.88 69	79.49 144	87.02 132	88.15 57	72.57 114	83.02 62	72.63 61	56.55 113	73.48 70	82.34 70	91.49 36	91.20 33	96.07 19	91.13 139

thres100view900		79.83 81	77.79 102	82.21 65	88.42 74	93.54 69	87.07 59	81.11 57	70.15 113	61.01 99	56.65 110	51.22 140	81.78 71	89.77 52	85.95 83	93.84 62	97.26 41

tfpn111		80.42 78	79.77 88	81.18 77	88.42 74	92.55 82	85.12 80	77.94 70	70.15 113	61.00 101	74.56 45	51.22 140	81.11 72	88.23 69	84.80 94	93.50 86	96.90 57

conf0.01		80.10 80	79.04 92	81.34 75	88.56 72	93.09 74	85.12 80	78.08 66	70.15 113	61.43 95	60.90 99	58.54 122	81.11 72	88.66 65	84.80 94	93.74 66	97.14 45

conf0.002		80.80 73	80.30 83	81.38 74	88.59 71	93.19 72	85.12 80	78.10 65	70.15 113	61.55 94	63.30 88	62.66 107	81.11 72	88.74 64	86.94 72	93.79 64	97.15 44

conf200view11		79.04 89	77.21 104	81.18 77	88.42 74	92.55 82	85.12 80	77.94 70	70.15 113	61.00 101	56.65 110	51.22 140	81.11 72	88.23 69	84.80 94	93.50 86	96.90 57

tfpn200view9		79.05 88	77.21 104	81.18 77	88.42 74	92.55 82	85.12 80	77.94 70	70.15 113	61.01 99	56.65 110	51.22 140	81.11 72	88.23 69	84.80 94	93.50 86	96.90 57

Anonymous20240521		79.76 83	79.17 89	80.44 86	84.65 118	84.51 171	84.20 90	72.36 119	75.17 91	70.81 71	66.21 72	66.56 93	80.99 77	82.89 124	84.56 101	89.65 193	94.30 99

CHOSEN 280x420		82.15 66	85.87 52	77.80 102	86.54 100	93.42 70	81.74 105	59.96 201	78.99 75	63.99 88	74.50 47	83.95 31	80.99 77	89.53 54	85.01 91	93.56 80	95.71 80

GBi-Net		80.72 74	80.49 81	81.00 81	78.18 148	86.19 150	86.73 60	72.57 114	83.02 62	72.63 61	56.55 113	73.48 70	80.99 77	86.57 89	86.83 73	94.89 40	90.77 141

test1		80.72 74	80.49 81	81.00 81	78.18 148	86.19 150	86.73 60	72.57 114	83.02 62	72.63 61	56.55 113	73.48 70	80.99 77	86.57 89	86.83 73	94.89 40	90.77 141

FMVSNet2		79.24 86	78.14 99	80.53 85	78.18 148	86.19 150	86.73 60	71.91 124	72.97 103	70.48 75	50.63 140	66.56 93	80.99 77	90.10 46	89.77 47	94.89 40	90.77 141

PVSNet_BlendedMVS		86.98 44	87.05 42	86.90 43	93.03 45	96.98 25	86.57 64	81.82 54	89.78 39	82.78 29	71.54 54	66.07 96	80.73 82	93.46 16	91.97 22	96.45 15	99.53 1

PVSNet_Blended		86.98 44	87.05 42	86.90 43	93.03 45	96.98 25	86.57 64	81.82 54	89.78 39	82.78 29	71.54 54	66.07 96	80.73 82	93.46 16	91.97 22	96.45 15	99.53 1

ACMP	79.58 9	82.23 65	81.82 76	82.71 64	88.15 81	90.95 100	85.23 78	78.52 63	81.70 67	72.52 64	78.41 37	60.63 114	80.48 84	82.88 125	83.44 116	91.37 167	94.70 91
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DWT-MVSNet_training		82.66 61	83.34 65	81.87 70	88.71 70	92.63 78	82.07 104	72.21 120	86.37 54	72.64 59	64.51 81	71.44 78	80.35 85	84.43 111	87.73 65	95.27 31	96.25 70

FC-MVSNet-train		79.54 84	78.20 97	81.09 80	86.55 99	88.63 122	79.96 116	78.53 62	70.90 111	68.24 81	65.87 74	56.45 131	80.29 86	86.20 98	84.08 106	92.97 122	95.31 85

CostFormer		80.72 74	81.81 77	79.44 92	86.50 101	91.65 94	84.31 89	59.84 202	80.86 69	72.69 58	62.46 92	73.74 67	79.93 87	82.58 128	84.50 103	93.37 97	96.90 57

tpmp4_e23		78.57 95	78.48 95	78.68 95	85.38 114	89.14 120	84.69 87	60.32 200	78.81 76	70.65 73	57.89 105	65.54 99	79.63 88	80.09 153	83.24 119	91.41 166	94.63 93

LGP-MVS_train		82.12 67	82.57 69	81.59 71	89.26 67	90.23 106	88.76 52	78.05 67	81.26 68	61.64 93	79.52 36	62.11 109	79.59 89	85.20 106	84.68 100	92.27 146	95.02 88

thres400		78.39 98	76.39 113	80.73 84	88.02 83	92.94 75	84.77 85	78.88 60	65.20 134	59.70 107	55.20 123	50.85 145	79.45 90	88.81 61	84.81 93	93.57 78	96.91 56

thres200		78.69 93	76.71 108	80.99 83	88.35 78	92.56 80	86.03 71	77.94 70	66.27 126	60.66 103	56.08 118	51.11 144	79.45 90	88.23 69	85.54 89	93.52 81	97.20 43

HyFIR lowres test		78.08 103	76.81 106	79.56 89	90.77 60	94.64 59	82.97 97	69.85 141	69.81 119	59.53 108	33.52 215	64.66 100	78.97 92	88.77 63	88.38 60	95.27 31	97.86 28

view600		77.68 105	75.68 119	80.01 87	87.72 87	92.57 79	83.79 92	77.95 69	64.41 137	58.72 112	54.32 128	50.54 146	78.25 93	88.23 69	83.13 121	93.64 75	96.59 66

thres600view7		77.66 106	75.67 120	79.98 88	87.71 88	92.56 80	83.79 92	77.94 70	64.41 137	58.69 113	54.32 128	50.54 146	78.23 94	88.23 69	83.06 123	93.52 81	96.55 67

MSDG		78.11 102	73.17 138	83.86 62	91.78 55	86.83 137	85.25 77	86.02 29	72.84 104	69.69 79	51.43 137	54.00 137	77.61 95	81.95 138	82.27 132	92.83 135	82.91 198

view800		77.22 111	75.35 123	79.41 93	87.42 91	92.21 88	82.94 99	77.19 80	63.67 141	57.78 114	53.68 131	50.19 148	77.32 96	87.70 79	83.84 112	93.79 64	96.19 71

tpmrst		76.27 118	77.65 103	74.66 120	86.13 108	89.53 117	79.31 135	54.91 213	77.19 84	56.27 119	55.87 120	64.58 101	77.25 97	80.85 147	80.21 175	94.07 54	95.32 84

ACMH	71.22 14	72.65 138	70.13 154	75.59 113	86.19 107	86.14 153	75.76 176	77.63 76	54.79 184	46.16 184	53.28 133	47.28 158	77.24 98	78.91 175	81.18 164	90.57 181	89.33 156
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		74.26 127	71.95 143	76.95 109	74.28 196	83.94 174	83.61 95	69.99 139	57.08 167	65.08 85	42.39 189	57.41 127	76.98 99	86.57 89	86.83 73	91.77 158	89.42 153

EPMVS		77.16 113	79.08 91	74.92 118	86.73 95	91.98 89	78.62 143	55.44 212	79.43 71	56.59 118	61.24 97	70.73 82	76.97 100	80.59 149	81.43 155	95.15 36	88.17 173

ACMH+	72.14 13	72.38 140	69.34 165	75.93 112	85.21 115	84.89 166	76.96 165	76.04 88	59.76 155	51.63 139	50.37 141	48.69 153	76.90 101	76.06 191	78.69 183	88.85 199	86.90 179

dps		75.76 119	75.02 126	76.63 110	84.51 120	88.12 124	77.51 158	58.33 205	75.91 89	71.98 66	57.37 108	57.85 125	76.81 102	77.89 183	78.40 187	90.63 180	89.63 150

CHOSEN 1792x2688		80.23 79	79.16 90	81.48 72	91.97 51	96.56 31	86.18 70	75.40 97	76.17 88	61.32 96	37.43 210	61.08 112	76.52 103	92.35 25	91.64 28	97.46 3	98.86 13

USDC		73.43 133	72.31 141	74.73 119	80.86 137	86.21 148	80.42 111	71.83 126	71.69 108	46.94 179	59.60 102	42.58 193	76.47 104	82.66 127	81.22 162	91.88 155	82.24 203

tfpn		77.45 108	76.23 115	78.87 94	87.15 93	91.90 91	82.17 103	76.59 84	62.98 143	56.93 116	53.08 134	57.31 128	76.41 105	87.26 83	85.20 90	93.95 60	95.89 74

gg-mvs-nofinetune		72.10 144	74.79 128	68.97 181	83.31 124	95.22 56	85.66 74	48.77 225	35.68 226	22.17 233	30.49 219	77.73 51	76.37 106	94.30 11	93.03 11	97.55 2	97.05 47

Effi-MVS+-dtu		74.57 123	74.60 130	74.53 122	81.38 133	86.74 139	80.39 112	67.70 160	67.36 125	53.06 129	59.86 101	57.50 126	75.84 107	80.19 151	78.62 185	88.79 200	91.95 136

tpm cat1		76.93 114	76.19 116	77.79 103	85.08 117	88.58 123	82.96 98	59.33 203	75.72 90	72.64 59	51.25 138	64.41 102	75.74 108	77.90 182	80.10 178	90.97 173	95.35 83

IterMVS-LS		76.80 116	76.33 114	77.35 106	84.07 123	84.11 172	81.54 106	68.52 150	66.17 127	61.74 91	57.84 106	64.31 104	74.88 109	83.48 122	86.21 80	93.34 99	92.16 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
conf0.05thres1000		74.20 128	71.44 146	77.43 105	86.09 110	89.85 113	80.82 108	75.79 92	53.51 191	54.71 122	44.37 166	49.78 149	74.67 110	85.02 108	83.47 115	92.49 141	94.10 103

PatchmatchNet		76.85 115	80.03 86	73.15 148	84.08 122	91.04 99	77.76 156	55.85 211	79.43 71	52.74 132	62.08 94	76.02 59	74.56 111	79.92 154	81.41 156	93.92 61	90.29 146
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TinyColmap		67.16 184	63.51 203	71.42 172	77.94 151	79.54 202	72.80 186	69.78 142	56.58 173	45.52 187	44.53 163	33.53 223	74.45 112	76.91 190	77.06 195	88.03 206	76.41 210

EPP-MVSNet		80.82 72	82.79 67	78.52 97	86.31 105	92.37 86	79.83 118	74.51 103	73.79 101	64.46 86	67.01 68	80.63 45	74.33 113	85.63 102	84.35 104	91.68 159	95.79 78

RPSCF		74.27 126	73.24 137	75.48 115	81.01 135	80.18 196	76.24 170	72.37 118	74.84 94	68.24 81	72.47 50	67.39 91	73.89 114	71.05 210	69.38 219	81.14 226	77.37 209

FMVSNet5		72.83 136	73.89 135	71.59 168	67.42 216	76.28 212	75.88 175	63.74 187	77.27 83	54.59 124	53.32 132	71.48 77	73.85 115	81.95 138	81.69 141	94.06 55	75.20 214

ADS-MVSNet		72.11 143	73.72 136	70.24 179	81.24 134	86.59 142	74.75 180	50.56 222	72.58 105	49.17 165	55.40 122	61.46 110	73.80 116	76.01 192	78.14 188	91.93 154	85.86 183

pmmvs4		73.38 134	71.53 145	75.55 114	75.95 176	85.24 163	77.25 161	71.59 131	71.03 110	63.10 89	49.09 148	44.22 181	73.73 117	82.04 135	80.18 176	91.68 159	88.89 166

Fast-Effi-MVS+-dtu		73.56 130	75.32 124	71.50 170	80.35 138	86.83 137	79.72 119	58.07 206	67.64 124	44.83 193	60.28 100	54.07 136	73.59 118	81.90 140	82.30 131	92.46 143	94.18 101

test-LLR		79.52 85	83.42 62	74.97 117	81.79 129	91.26 95	76.17 171	70.57 137	77.71 80	52.14 136	66.26 70	77.47 52	73.10 119	87.02 84	87.16 67	96.05 22	97.02 48

TESTMET0.1,1		79.15 87	83.42 62	74.18 127	79.81 142	91.26 95	76.17 171	67.83 159	77.71 80	52.14 136	66.26 70	77.47 52	73.10 119	87.02 84	87.16 67	96.05 22	97.02 48

v2v482		71.73 149	69.80 157	73.99 132	75.88 183	86.66 141	79.58 128	71.90 125	57.58 166	50.41 159	45.35 156	43.24 189	73.05 121	79.69 155	82.18 135	93.08 119	93.87 115

MDTV_nov1_ep13		77.20 112	80.04 84	73.90 133	82.22 127	90.14 107	79.25 136	61.52 195	78.63 77	56.98 115	65.52 77	72.80 73	73.05 121	80.93 146	83.20 120	90.36 183	89.05 162

tpm		73.50 131	74.85 127	71.93 164	83.19 125	86.84 136	78.61 144	55.91 210	65.64 129	48.90 167	56.30 116	61.09 111	72.31 123	79.10 172	80.61 174	92.68 137	94.35 98

gm-plane-assit		64.86 194	68.15 176	61.02 210	76.44 168	68.29 222	41.60 230	53.37 218	34.68 228	26.19 231	33.22 216	57.09 129	71.97 124	95.12 4	93.97 6	96.54 13	94.66 92

v7		71.49 155	69.98 156	73.25 145	75.89 181	86.45 144	79.44 133	69.29 146	58.07 164	50.08 161	43.87 177	43.67 183	71.94 125	82.03 137	81.70 139	92.88 132	94.04 104

v10		70.97 160	69.44 162	72.75 149	75.90 180	84.58 170	79.43 134	66.45 168	58.07 164	49.93 162	43.87 177	43.68 182	71.91 126	82.04 135	81.70 139	92.89 129	92.11 134

v1192		70.32 166	68.77 170	72.12 163	74.76 192	85.62 158	78.73 141	68.53 149	55.08 183	46.34 183	42.39 189	40.67 201	71.90 127	82.27 130	81.53 144	92.43 144	93.86 116

v11		69.84 173	67.85 178	72.17 160	75.78 187	79.15 206	78.20 150	64.76 185	56.10 178	49.50 163	43.54 181	43.36 187	71.62 128	82.21 131	81.52 146	93.17 110	89.05 162

UniMVSNet_NR-MVSNet		73.11 135	72.59 139	73.71 135	76.90 157	86.58 143	77.01 162	75.82 90	65.59 130	48.82 168	50.97 139	48.42 154	71.61 129	79.19 170	83.03 124	92.11 148	94.37 96

DU-MVS		72.19 141	71.35 147	73.17 146	75.95 176	86.02 155	77.01 162	74.42 105	65.39 132	48.82 168	49.10 146	42.81 191	71.61 129	78.67 176	83.10 122	91.22 169	94.37 96

v1144		70.93 161	69.42 164	72.70 150	75.48 190	86.26 146	79.22 137	69.39 145	55.61 181	48.05 173	43.47 183	42.55 194	71.51 131	82.11 133	81.74 138	92.56 140	94.17 102

test-mter		77.90 104	82.44 71	72.60 152	78.52 146	90.24 105	73.85 183	65.31 177	76.37 87	51.29 140	65.58 76	75.94 60	71.36 132	85.98 99	86.26 79	95.26 33	96.71 64

v18		71.13 158	68.98 167	73.63 139	76.66 161	79.78 198	79.95 117	65.98 171	61.34 147	54.71 122	44.75 158	46.06 160	71.27 133	79.59 159	81.51 149	93.21 108	89.81 148

v16		70.93 161	68.76 171	73.47 141	76.60 162	79.66 200	79.57 129	65.81 174	60.85 148	54.44 125	44.50 165	45.90 162	71.15 134	79.50 164	81.39 157	93.27 102	89.51 152

v1921920		69.85 172	68.38 175	71.58 169	74.35 194	85.39 161	77.78 155	67.88 158	54.64 186	45.39 188	42.11 192	39.97 204	71.10 135	81.68 141	81.17 166	92.96 123	93.69 123

v17		70.82 163	68.69 172	73.31 143	76.53 163	79.67 199	79.45 132	65.80 175	60.32 152	53.75 126	44.51 164	45.92 161	71.09 136	79.49 165	81.38 158	93.26 105	89.54 151

v1neww		72.02 147	70.23 152	74.10 129	76.45 165	87.06 129	79.59 125	71.75 127	59.35 158	52.60 134	44.59 161	45.74 165	71.06 137	79.57 160	81.46 151	93.16 112	93.84 118

v7new		72.02 147	70.23 152	74.10 129	76.45 165	87.06 129	79.59 125	71.75 127	59.35 158	52.60 134	44.59 161	45.74 165	71.06 137	79.57 160	81.46 151	93.16 112	93.84 118

v8		71.42 157	69.69 159	73.43 142	76.45 165	85.12 165	79.53 131	67.47 163	59.34 160	52.90 130	44.60 160	45.82 163	71.05 139	79.56 163	81.45 153	93.17 110	91.96 135

v6		72.04 146	70.26 150	74.11 128	76.46 164	87.06 129	79.60 122	71.75 127	59.48 157	52.69 133	44.61 159	45.79 164	71.01 140	79.57 160	81.45 153	93.16 112	93.85 117

v144192		70.10 167	68.55 173	71.90 166	74.55 193	85.67 157	77.81 154	68.22 154	54.65 185	46.91 180	42.76 187	41.27 200	70.95 141	80.48 150	81.11 169	92.96 123	93.90 113

CR-MVSNet		74.84 122	77.91 100	71.26 174	81.77 131	85.52 159	78.32 145	54.14 215	74.05 97	51.09 144	50.00 143	71.38 79	70.77 142	86.48 92	84.03 109	91.46 165	93.92 110

PatchT		72.66 137	76.58 110	68.09 184	79.02 145	86.09 154	59.81 214	51.78 221	72.00 107	51.09 144	46.84 153	66.70 92	70.77 142	86.48 92	84.03 109	96.07 19	93.92 110

v1141		71.53 153	69.69 159	73.68 136	76.08 170	86.86 134	79.59 125	72.07 122	57.01 168	50.78 153	44.23 170	44.70 173	70.68 144	79.61 158	81.52 146	92.89 129	93.92 110

MS-PatchMatch		77.47 107	76.48 111	78.63 96	89.89 62	90.42 102	85.42 75	69.53 143	70.79 112	60.43 105	50.05 142	70.62 83	70.66 145	86.71 88	82.54 127	95.86 26	84.23 189

divwei89l23v2f112		71.53 153	69.69 159	73.68 136	76.09 169	86.86 134	79.60 122	72.08 121	56.96 170	50.78 153	44.24 169	44.70 173	70.65 146	79.62 156	81.53 144	92.89 129	93.93 108

v1		71.54 152	69.71 158	73.66 138	76.08 170	86.88 133	79.60 122	72.06 123	57.00 169	50.75 155	44.23 170	44.79 170	70.61 147	79.62 156	81.52 146	92.88 132	93.93 108

LTVRE_ROB	63.07 16	64.49 198	63.16 206	66.04 194	77.47 154	82.64 181	70.98 192	65.02 181	34.01 229	29.61 221	49.12 145	35.58 219	70.57 148	75.10 195	78.45 186	82.60 221	87.24 177
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
v15		70.00 169	67.82 179	72.55 154	76.06 172	79.37 203	79.10 139	65.30 178	56.89 171	51.18 142	43.96 176	44.76 171	70.52 149	79.40 167	81.22 162	93.13 117	89.14 161

v1240		69.28 179	67.82 179	71.00 176	74.09 197	85.13 164	76.54 169	67.28 165	53.17 192	44.70 194	41.55 196	39.38 206	70.51 150	81.29 144	81.18 164	92.88 132	93.02 126

V14		69.91 170	67.71 181	72.47 156	76.01 174	79.30 204	78.92 140	65.17 179	56.74 172	51.08 147	43.82 179	44.73 172	70.44 151	79.31 168	81.14 167	93.20 109	88.91 165

PVSNet_Blended_VisFu		82.55 62	83.70 61	81.21 76	89.66 63	95.15 57	82.41 102	77.36 79	72.53 106	73.64 56	61.15 98	77.19 54	70.35 152	91.31 38	89.72 48	93.84 62	98.85 14

V9		69.79 174	67.57 182	72.38 158	75.95 176	79.21 205	78.72 142	65.06 180	56.51 174	51.06 148	43.66 180	44.70 173	70.28 153	79.22 169	81.06 170	93.24 107	88.67 169

v12		69.66 175	67.45 183	72.23 159	75.89 181	79.13 207	78.29 148	64.96 183	56.40 175	50.75 155	43.53 182	44.60 176	70.21 154	79.11 171	80.99 171	93.27 102	88.41 170

GA-MVS		73.62 129	74.52 131	72.58 153	79.93 140	89.29 119	78.02 153	71.67 130	60.79 150	42.68 200	54.41 127	49.07 152	70.07 155	89.39 57	86.55 76	93.13 117	92.12 133

v13		69.55 177	67.33 184	72.14 162	75.83 184	79.04 208	78.22 149	64.85 184	56.16 177	50.60 157	43.43 184	44.56 177	70.05 156	79.01 173	80.92 173	93.28 101	88.22 171

V42		71.58 151	70.11 155	73.30 144	75.66 189	86.68 140	79.17 138	69.92 140	59.29 161	52.80 131	44.36 167	45.66 167	68.83 157	79.48 166	81.49 150	93.44 92	93.82 120

Baseline_NR-MVSNet		70.61 164	68.87 169	72.65 151	75.95 176	80.49 194	75.92 174	74.75 100	65.10 135	48.78 170	41.28 197	44.28 180	68.45 158	78.67 176	79.64 179	92.04 150	92.62 128

tfpnnormal		69.29 178	65.58 187	73.62 140	79.87 141	84.82 167	76.97 164	75.12 98	45.29 216	49.03 166	35.57 213	37.20 214	68.02 159	82.70 126	81.24 160	92.69 136	92.20 131

pmmvs5		70.01 168	69.31 166	70.82 177	75.80 186	86.26 146	72.94 185	67.91 157	53.84 189	47.22 178	47.31 152	41.47 199	67.61 160	83.93 116	81.93 137	93.42 94	90.42 145

TranMVSNet+NR-MVSNet		71.12 159	70.24 151	72.15 161	76.01 174	84.80 168	76.55 168	75.65 95	61.99 146	45.29 189	48.42 150	43.07 190	67.55 161	78.28 179	82.83 126	91.85 156	92.29 130

MIMVSNet		68.66 181	69.43 163	67.76 185	64.92 221	84.68 169	74.16 181	54.10 217	60.85 148	51.27 141	39.47 202	49.48 150	67.48 162	84.86 109	85.57 88	94.63 44	81.10 204

IS_MVSNet		80.92 70	84.14 60	77.16 107	87.43 90	93.90 63	80.44 110	74.64 101	75.05 92	61.10 98	65.59 75	76.89 57	67.39 163	90.88 40	90.05 42	91.95 153	96.62 65

thresconf0.02		78.87 90	80.50 80	76.96 108	87.88 85	91.71 93	82.90 101	78.51 64	67.91 123	50.85 151	74.56 45	69.93 85	67.32 164	86.86 87	85.65 87	94.32 48	86.89 180

tfpn_ndepth		78.22 101	78.84 93	77.49 104	88.32 79	90.95 100	80.79 109	76.31 87	74.24 95	59.50 109	69.52 62	60.02 118	67.11 165	85.06 107	82.95 125	92.94 128	89.18 160

MVS-HIRNet		64.63 197	64.03 201	65.33 196	75.01 191	82.84 178	58.54 218	52.10 220	55.42 182	49.29 164	29.83 222	43.48 185	66.97 166	78.28 179	78.81 182	90.07 188	79.52 206

CDS-MVSNet		76.57 117	76.78 107	76.32 111	80.94 136	89.75 114	82.94 99	72.64 113	59.01 162	62.95 90	58.60 104	62.67 106	66.91 167	86.26 96	87.20 66	91.57 161	93.97 107
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		72.06 145	71.76 144	72.41 157	76.68 160	88.12 124	74.82 179	68.09 155	53.52 190	56.91 117	52.94 136	56.93 130	66.91 167	81.37 143	82.44 129	91.07 171	86.99 178

RPMNet		73.46 132	77.85 101	68.34 182	81.71 132	85.52 159	73.83 184	50.54 223	74.05 97	46.10 185	53.03 135	71.91 75	66.31 169	83.55 120	82.18 135	91.55 163	94.71 90

COLMAP_ROB	66.31 15	69.91 170	66.61 185	73.76 134	86.44 102	82.76 179	76.59 167	76.46 86	63.82 140	50.92 150	45.60 155	49.13 151	65.87 170	74.96 196	74.45 207	86.30 213	75.57 212
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
IterMVS		72.43 139	74.05 134	70.55 178	80.34 139	81.17 191	77.44 159	61.00 197	63.57 142	46.82 181	55.88 119	59.09 121	65.03 171	83.15 123	83.83 113	92.67 138	91.65 138
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		70.34 165	68.46 174	72.54 155	76.04 173	86.38 145	74.83 178	72.73 112	55.88 180	55.26 120	43.32 186	43.49 184	64.52 172	76.93 189	80.11 177	91.85 156	93.11 124

NR-MVSNet		71.47 156	71.11 148	71.90 166	77.73 153	86.02 155	76.88 166	74.42 105	65.39 132	46.09 186	49.10 146	39.87 205	64.27 173	81.40 142	82.24 133	91.99 152	93.75 121

tfpnview11		74.85 121	75.06 125	74.61 121	86.58 98	89.54 116	79.98 115	75.81 91	64.95 136	47.47 175	64.85 78	54.72 133	63.86 174	84.54 110	82.20 134	93.97 58	84.64 185

UniMVSNet (Re)		72.12 142	72.28 142	71.93 164	76.77 158	87.38 128	75.73 177	73.51 109	65.76 128	50.24 160	48.65 149	46.49 159	63.85 175	80.10 152	82.47 128	91.49 164	95.13 87

CMPMVS	50.59 17	66.74 187	62.72 207	71.42 172	85.40 113	89.72 115	72.69 187	70.72 135	51.24 197	51.75 138	38.91 206	44.40 178	63.74 176	70.84 211	71.52 211	84.19 218	72.45 219
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tfpn_n400		74.36 124	74.39 132	74.32 125	86.37 103	89.86 111	79.71 120	75.69 93	60.00 153	47.47 175	64.85 78	54.72 133	63.70 177	83.80 117	83.35 117	92.96 123	84.16 190

tfpnconf		74.36 124	74.39 132	74.32 125	86.37 103	89.86 111	79.71 120	75.69 93	60.00 153	47.47 175	64.85 78	54.72 133	63.70 177	83.80 117	83.35 117	92.96 123	84.16 190

PM-MVS		63.52 205	62.51 208	64.70 197	64.79 223	76.08 213	65.07 205	62.08 193	58.13 163	46.56 182	44.98 157	31.31 226	62.89 179	72.58 203	69.93 218	86.81 211	84.55 186

anonymousdsp		67.61 183	68.94 168	66.04 194	71.44 210	83.97 173	66.45 201	63.53 189	50.54 201	42.42 201	49.39 144	45.63 168	62.84 180	77.99 181	81.34 159	89.59 195	93.75 121

v7n		66.43 188	65.51 188	67.51 186	71.63 209	83.10 177	70.89 193	65.02 181	50.13 204	44.68 195	39.59 201	38.77 208	62.57 181	77.59 186	78.91 181	90.29 185	90.44 144

pmmvs-eth3d		64.24 200	61.96 209	66.90 189	66.35 218	76.04 214	66.09 203	66.31 169	52.59 193	50.94 149	37.61 208	32.79 225	62.43 182	75.78 193	75.48 203	89.27 197	83.39 195

pm-mvs1		69.62 176	68.07 177	71.44 171	77.21 155	85.32 162	76.11 173	71.05 132	46.55 214	51.17 143	41.83 194	48.20 155	61.81 183	84.00 115	81.14 167	91.28 168	89.42 153

SixPastTwentyTwo		63.75 204	63.42 204	64.13 202	72.91 199	80.34 195	61.29 211	63.90 186	49.58 207	40.42 204	54.99 125	37.13 215	60.90 184	68.46 215	70.80 214	85.37 216	82.65 202

TDRefinement		67.82 182	64.91 193	71.22 175	82.08 128	81.45 187	77.42 160	73.79 108	59.62 156	48.35 172	42.35 191	42.40 195	60.87 185	74.69 197	74.64 206	84.83 217	79.20 207

tfpn1000		75.39 120	76.18 117	74.47 123	86.71 96	90.10 108	77.57 157	74.78 99	68.76 122	53.33 128	63.57 86	58.37 124	60.84 186	83.80 117	81.24 160	93.58 77	87.42 176

v52		65.34 190	64.59 195	66.21 192	69.63 214	82.41 183	69.22 194	62.80 191	49.63 205	45.15 192	39.31 204	41.85 197	60.68 187	72.61 201	77.02 197	89.75 191	89.33 156

V4		65.34 190	64.59 195	66.21 192	69.64 213	82.42 182	69.22 194	62.80 191	49.60 206	45.21 190	39.33 203	41.82 198	60.66 188	72.61 201	77.03 196	89.76 190	89.32 158

MDTV_nov1_ep13_2view		64.72 196	64.94 192	64.46 199	71.14 211	81.94 186	67.53 197	54.54 214	55.92 179	43.29 199	44.02 174	43.27 188	59.87 189	71.85 207	74.77 205	90.36 183	82.82 199

UGNet		80.71 77	83.09 66	77.93 101	87.02 94	92.71 76	80.28 114	76.53 85	73.83 100	71.35 68	70.07 59	73.71 68	58.93 190	87.39 81	86.97 70	93.48 90	96.94 54
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
pmmvs6		64.24 200	61.77 211	67.12 188	72.39 203	81.39 189	71.33 191	65.95 173	36.05 225	48.48 171	30.55 218	43.45 186	58.75 191	77.88 184	76.36 201	85.83 214	86.70 181

IB-MVS	74.10 12	78.52 96	78.51 94	78.52 97	90.15 61	95.39 53	71.95 190	77.53 77	74.95 93	77.25 45	58.93 103	55.92 132	58.37 192	79.01 173	87.89 64	95.88 25	97.47 34
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
UA-Net		78.30 99	80.92 79	75.25 116	87.42 91	92.48 85	79.54 130	75.49 96	60.47 151	60.52 104	68.44 65	84.08 30	57.54 193	88.54 66	88.45 58	90.96 174	83.97 193

Vis-MVSNet		77.24 110	79.99 87	74.02 131	84.62 119	93.92 62	80.33 113	72.55 117	62.58 144	55.25 121	64.45 82	69.49 86	57.00 194	88.78 62	88.21 62	94.36 47	92.54 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v748		65.00 192	63.86 202	66.33 191	71.85 207	82.15 185	66.80 199	65.64 176	48.50 210	47.98 174	39.62 200	39.20 207	56.44 195	71.25 208	77.53 192	89.29 196	88.74 168

TransMVSNet (Re)		66.87 186	64.30 198	69.88 180	78.32 147	81.35 190	73.88 182	74.34 107	43.19 220	45.20 191	40.12 199	42.37 196	55.97 196	80.85 147	79.15 180	91.56 162	83.06 197

LP		59.72 211	58.23 216	61.44 208	75.67 188	74.97 216	61.05 212	48.34 226	54.02 188	40.82 203	31.61 217	36.92 217	54.69 197	67.52 217	71.18 213	88.08 205	71.42 222

Vis-MVSNet (Re-imp)		78.28 100	82.68 68	73.16 147	86.64 97	92.68 77	78.07 152	74.48 104	74.05 97	53.47 127	64.22 84	76.52 58	54.28 198	88.96 60	88.29 61	92.03 151	94.00 105

MDA-MVSNet-bldmvs		54.99 217	52.66 220	57.71 212	52.74 233	74.87 217	55.61 219	68.41 151	43.65 219	32.54 217	37.93 207	22.11 233	54.11 199	48.85 231	67.34 221	82.85 220	73.88 218

EG-PatchMatch MVS		66.23 189	65.20 190	67.43 187	77.74 152	86.20 149	72.51 188	63.68 188	43.95 218	43.44 198	36.22 212	45.43 169	54.04 200	81.00 145	80.95 172	93.15 116	82.67 201

CVMVSNet		68.95 180	70.79 149	66.79 190	79.69 143	83.75 176	72.05 189	70.90 133	56.20 176	36.30 211	54.94 126	59.22 120	54.03 201	78.33 178	78.65 184	87.77 207	84.44 187

EPNet_dtu		78.49 97	81.96 75	74.45 124	92.57 49	88.74 121	82.98 96	78.83 61	83.28 61	44.64 196	77.40 41	67.73 90	53.98 202	85.44 104	84.91 92	93.71 69	86.22 182
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVSNet		64.84 195	64.97 191	64.69 198	72.09 204	81.04 192	66.66 200	67.53 162	52.45 194	37.40 207	44.00 175	38.37 210	53.54 203	72.26 205	76.93 198	90.94 176	89.75 149

pmmvs3		52.59 220	52.43 221	52.78 220	54.53 231	64.49 227	50.07 225	46.89 229	35.31 227	30.19 220	27.27 225	26.96 230	53.02 204	67.28 218	70.54 215	81.96 222	75.20 214

PS-CasMVS		64.22 202	64.19 200	64.25 201	71.86 206	80.67 193	66.42 202	67.43 164	50.64 200	36.48 209	42.60 188	37.46 213	52.56 205	71.98 206	76.69 200	90.76 177	89.29 159

PEN-MVS		64.35 199	64.29 199	64.42 200	72.67 200	79.83 197	66.97 198	68.24 153	51.21 198	35.29 214	44.09 172	38.51 209	52.36 206	71.06 209	77.65 191	90.99 172	87.68 175

N_pmnet		60.52 210	58.83 215	62.50 206	68.97 215	75.61 215	59.72 216	66.47 167	51.90 195	41.26 202	35.42 214	35.63 218	52.25 207	67.07 219	70.08 217	86.35 212	76.10 211

test0.0.03 1		71.70 150	74.68 129	68.23 183	81.79 129	83.81 175	68.64 196	70.57 137	68.81 121	43.47 197	62.77 91	60.09 117	51.77 208	82.48 129	81.67 142	93.16 112	83.13 196

DTE-MVSNet		63.26 206	63.41 205	63.08 204	72.59 201	78.56 209	65.03 206	68.28 152	50.53 202	32.38 218	44.03 173	37.79 212	49.48 209	70.83 212	76.73 199	90.73 178	85.42 184

new_pmnet		50.32 221	51.36 222	49.11 222	49.19 234	64.89 226	48.66 228	47.99 228	47.55 212	26.27 230	29.51 224	28.66 227	44.89 210	61.12 226	62.74 227	77.66 227	65.03 226

WR-MVS		64.98 193	66.59 186	63.09 203	74.34 195	82.68 180	64.98 207	69.17 147	54.42 187	36.18 212	44.32 168	44.35 179	44.65 211	73.60 198	77.83 189	89.21 198	88.96 164

test2356		58.43 214	59.52 213	57.16 213	66.71 217	68.00 223	54.69 220	60.91 199	49.22 208	28.63 224	41.86 193	33.68 222	44.36 212	72.98 199	75.47 204	87.69 208	75.40 213

WR-MVS_H		64.14 203	65.36 189	62.71 205	72.47 202	82.33 184	65.13 204	66.99 166	51.81 196	36.47 210	43.33 185	42.77 192	43.99 213	72.41 204	75.99 202	91.20 170	88.86 167

Anonymous20231206		62.05 209	61.83 210	62.30 207	72.09 204	77.84 211	63.10 209	67.62 161	50.20 203	36.68 208	29.59 223	37.05 216	43.90 214	77.33 188	77.31 193	90.41 182	83.49 194

testpf		59.38 212	64.51 197	53.40 219	76.71 159	66.40 224	50.18 224	38.98 236	64.13 139	35.10 215	47.91 151	51.41 139	43.16 215	66.37 220	71.23 212	76.25 228	84.14 192

1111		48.34 223	47.93 224	48.83 223	58.14 227	59.33 230	37.54 231	43.85 230	31.76 230	29.36 222	23.26 229	34.58 220	42.20 216	65.15 221	68.72 220	81.86 223	52.66 231

.test1245		33.05 229	31.21 232	35.20 230	58.14 227	59.33 230	37.54 231	43.85 230	31.76 230	29.36 222	23.26 229	34.58 220	42.20 216	65.15 221	0.77 237	0.11 241	3.62 239

testus		55.91 216	56.38 217	55.37 217	65.15 220	65.88 225	50.07 225	60.92 198	45.62 215	26.99 228	41.74 195	24.43 232	42.08 218	69.50 214	73.60 209	86.97 210	73.91 217

DeepMVS_CX						48.96 235	43.77 229	40.58 234	50.93 199	24.67 232	36.95 211	20.18 234	41.60 219	38.92 234		52.37 237	53.31 230

ambc			50.35 223		55.61 230	59.93 229	48.73 227		44.08 217	35.81 213	24.01 226	10.64 240	41.57 220	72.83 200	63.35 226	74.99 229	77.61 208

FC-MVSNet-test		67.04 185	72.47 140	60.70 211	76.92 156	81.41 188	61.52 210	69.45 144	65.58 131	26.74 229	61.79 96	60.40 116	41.17 221	77.60 185	77.78 190	88.41 202	82.70 200

testgi		63.11 207	64.88 194	61.05 209	75.83 184	78.51 210	60.42 213	66.20 170	48.77 209	34.56 216	56.96 109	40.35 202	40.95 222	77.46 187	77.22 194	88.37 204	74.86 216

EU-MVSNet		58.73 213	60.92 212	56.17 215	66.17 219	72.39 219	58.85 217	61.24 196	48.47 211	27.91 226	46.70 154	40.06 203	39.07 223	68.27 216	70.34 216	83.77 219	80.23 205

new-patchmatchnet		53.91 218	52.69 219	55.33 218	64.83 222	70.90 220	52.24 223	61.75 194	41.09 222	30.82 219	29.90 221	28.22 228	36.69 224	61.52 225	65.08 224	85.64 215	72.14 221

FPMVS		50.25 222	45.67 227	55.58 216	70.48 212	60.12 228	59.78 215	59.33 203	46.66 213	37.94 205	30.22 220	27.51 229	35.94 225	50.98 230	47.90 230	70.02 231	56.31 228

MIMVSNet1		52.76 219	53.95 218	51.38 221	41.96 237	70.79 221	53.56 221	63.03 190	39.36 223	27.83 227	22.73 232	33.07 224	34.47 226	70.49 213	72.69 210	87.41 209	68.51 223

testmv		46.89 224	46.37 225	47.48 224	60.96 224	58.36 232	36.71 233	56.94 207	27.16 233	17.93 235	23.94 227	18.84 235	31.06 227	61.55 223	66.72 222	81.28 224	68.05 224

test1235678		46.88 225	46.36 226	47.48 224	60.96 224	58.35 233	36.71 233	56.94 207	27.15 234	17.93 235	23.93 228	18.82 236	31.06 227	61.55 223	66.71 223	81.27 225	68.04 225

test20.03		57.93 215	59.22 214	56.44 214	71.84 208	73.78 218	53.55 222	65.96 172	43.02 221	28.46 225	37.50 209	38.17 211	30.41 229	75.25 194	74.42 208	88.41 202	72.37 220

Gipuma		35.20 228	33.96 230	36.65 229	43.30 236	32.51 239	26.96 239	48.31 227	38.87 224	20.08 234	8.08 236	7.41 241	26.44 230	53.60 227	58.43 228	54.81 236	38.79 235
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	36.83 18	40.62 227	36.39 229	45.56 226	58.40 226	33.20 238	32.62 237	56.02 209	28.25 232	37.92 206	22.29 233	26.15 231	25.29 231	48.49 232	43.82 233	63.13 234	52.53 232
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		20.61 234	16.32 236	25.62 235	36.41 238	18.93 243	11.51 242	43.75 232	15.65 238	6.53 241	7.56 239	4.68 242	22.03 232	14.56 238	23.10 236	33.51 239	29.77 237

E-PMN		21.42 232	17.56 235	25.94 234	36.25 239	19.02 242	11.56 241	43.72 233	15.25 239	6.99 240	8.04 237	4.53 243	21.77 233	16.13 237	26.16 235	35.34 238	33.77 236

test12356		41.15 226	41.46 228	40.78 227	53.10 232	49.87 234	33.37 236	52.25 219	25.12 235	15.64 237	22.76 231	15.01 237	15.81 234	52.97 228	64.54 225	74.50 230	59.96 227

tmp_tt				39.78 228	56.31 229	31.71 240	35.84 235	15.08 238	82.57 65	50.83 152	63.07 89	47.51 157	15.28 235	52.23 229	44.24 232	65.35 233

MVE	25.07 19	21.25 233	23.51 234	18.62 236	15.07 241	29.77 241	10.67 243	34.60 237	12.51 240	9.46 238	7.84 238	3.82 244	14.38 236	27.45 236	42.42 234	27.56 240	40.74 234
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
no-one		32.08 231	31.09 233	33.23 231	46.10 235	46.90 236	20.80 240	49.13 224	16.27 237	7.85 239	10.62 235	10.68 239	13.65 237	31.50 235	51.31 229	61.83 235	50.38 233

PMMVS2		32.52 230	33.92 231	30.88 233	34.15 240	44.70 237	27.79 238	39.69 235	22.21 236	4.31 242	15.73 234	14.13 238	12.45 238	40.11 233	47.00 231	66.88 232	53.54 229

test123		0.67 236	1.11 238	0.16 238	0.01 244	0.14 245	0.20 246	0.04 241	0.77 242	0.02 245	2.15 240	0.02 246	0.61 239	0.23 240	0.72 239	0.07 243	3.76 238

testmvs		0.76 235	1.23 237	0.21 237	0.05 243	0.21 244	0.38 245	0.09 239	0.94 241	0.05 244	2.13 241	0.08 245	0.60 240	0.82 239	0.77 237	0.11 241	3.62 239

GG-mvs-BLEND		62.08 208	88.31 37	31.46 232	0.16 242	98.10 6	91.57 36	0.09 239	85.07 58	0.21 243	73.90 49	83.74 33	0.19 241	88.98 59	89.39 52	96.58 12	99.02 11

sosnet-low-res		0.00 237	0.00 239	0.00 239	0.00 245	0.00 246	0.00 247	0.00 242	0.00 243	0.00 246	0.00 242	0.00 247	0.00 242	0.00 241	0.00 240	0.00 244	0.00 241

sosnet		0.00 237	0.00 239	0.00 239	0.00 245	0.00 246	0.00 247	0.00 242	0.00 243	0.00 246	0.00 242	0.00 247	0.00 242	0.00 241	0.00 240	0.00 244	0.00 241

our_test_3					73.80 198	79.57 201	64.47 208

MTAPA										91.14 2		85.84 20

MTMP										90.95 3		84.13 29

Patchmatch-RL test							8.17 244

XVS					89.65 64	95.93 42	85.97 72			76.32 47		82.05 39				93.51 84

X-MVStestdata					89.65 64	95.93 42	85.97 72			76.32 47		82.05 39				93.51 84

mPP-MVS					95.90 28							80.22 47

NP-MVS									89.55 41

Patchmtry						87.41 127	78.32 145	54.14 215		51.09 144

High-resolution multi-view benchmark