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.

APDe-MVS		88.00 2	90.50 2	85.08 3	90.95 6	91.58 5	92.03 1	75.53 9	91.15 1	80.10 11	92.27 4	88.34 8	80.80 3	88.00 12	86.99 16	91.09 4	95.16 3

DeepPCF-MVS	79.04 1	85.30 17	88.93 8	81.06 28	88.77 31	90.48 7	85.46 44	73.08 24	90.97 2	73.77 33	84.81 19	85.95 16	77.43 20	88.22 9	87.73 7	87.85 70	94.34 5

SMA-MVS		87.56 3	90.17 4	84.52 5	91.71 3	90.57 6	90.77 5	75.19 10	90.67 3	80.50 9	86.59 14	88.86 5	78.09 13	89.92 1	89.41 1	90.84 8	95.19 2

ESAPD		88.63 1	91.29 1	85.53 1	90.87 7	92.20 1	91.98 2	76.00 4	90.55 4	82.09 4	93.85 1	90.75 1	81.25 1	88.62 6	87.59 11	90.96 7	95.48 1

v1.0		81.11 37	77.43 63	85.41 2	91.73 2	92.08 2	91.91 3	76.73 1	90.14 5	80.33 10	92.75 2	90.44 2	80.73 4	88.97 5	87.63 9	91.01 6	0.00 242

HSP-MVS		87.45 4	90.22 3	84.22 9	90.00 20	91.80 4	90.59 6	75.80 5	89.93 6	78.35 17	92.54 3	89.18 4	80.89 2	87.99 13	86.29 27	89.70 37	93.85 9

SD-MVS		86.96 6	89.45 5	84.05 12	90.13 17	89.23 19	89.77 14	74.59 11	89.17 7	80.70 6	89.93 8	89.67 3	78.47 9	87.57 17	86.79 19	90.67 14	93.76 12

TSAR-MVS + MP.		86.88 7	89.23 6	84.14 10	89.78 23	88.67 28	90.59 6	73.46 23	88.99 8	80.52 8	91.26 5	88.65 6	79.91 6	86.96 27	86.22 28	90.59 15	93.83 10

HPM-MVS++		87.09 5	88.92 9	84.95 4	92.61 1	87.91 36	90.23 11	76.06 3	88.85 9	81.20 5	87.33 10	87.93 9	79.47 7	88.59 7	88.23 5	90.15 30	93.60 16

zzz-MVS		85.71 13	86.88 19	84.34 7	90.54 14	87.11 40	89.77 14	74.17 15	88.54 10	83.08 2	78.60 29	86.10 15	78.11 12	87.80 15	87.46 12	90.35 26	92.56 22

ACMMP_Plus		86.52 9	89.01 7	83.62 14	90.28 16	90.09 10	90.32 9	74.05 17	88.32 11	79.74 12	87.04 12	85.59 19	76.97 26	89.35 2	88.44 4	90.35 26	94.27 7

HFP-MVS		86.15 11	87.95 14	84.06 11	90.80 8	89.20 20	89.62 16	74.26 13	87.52 12	80.63 7	86.82 13	84.19 25	78.22 11	87.58 16	87.19 14	90.81 9	93.13 20

TSAR-MVS + ACMM		85.10 20	88.81 11	80.77 31	89.55 25	88.53 30	88.59 24	72.55 26	87.39 13	71.90 40	90.95 6	87.55 10	74.57 31	87.08 24	86.54 23	87.47 75	93.67 13

APD-MVS		86.84 8	88.91 10	84.41 6	90.66 10	90.10 9	90.78 4	75.64 6	87.38 14	78.72 15	90.68 7	86.82 12	80.15 5	87.13 22	86.45 25	90.51 17	93.83 10
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		86.36 10	88.19 13	84.23 8	91.33 5	89.84 11	90.34 8	75.56 7	87.36 15	78.97 14	81.19 25	86.76 13	78.74 8	89.30 3	88.58 2	90.45 23	94.33 6

OMC-MVS		80.26 38	82.59 37	77.54 50	83.04 60	85.54 51	83.25 55	65.05 73	87.32 16	72.42 38	72.04 48	78.97 42	73.30 40	83.86 48	81.60 58	88.15 61	88.83 52

ACMMPR		85.52 14	87.53 16	83.17 19	90.13 17	89.27 17	89.30 17	73.97 18	86.89 17	77.14 22	86.09 15	83.18 28	77.74 17	87.42 18	87.20 13	90.77 10	92.63 21

NCCC		85.34 16	86.59 21	83.88 13	91.48 4	88.88 22	89.79 13	75.54 8	86.67 18	77.94 20	76.55 32	84.99 21	78.07 14	88.04 10	87.68 8	90.46 22	93.31 17

CSCG		85.28 18	87.68 15	82.49 22	89.95 21	91.99 3	88.82 21	71.20 33	86.41 19	79.63 13	79.26 26	88.36 7	73.94 37	86.64 29	86.67 22	91.40 2	94.41 4

DeepC-MVS	78.47 2	84.81 22	86.03 25	83.37 16	89.29 28	90.38 8	88.61 23	76.50 2	86.25 20	77.22 21	75.12 36	80.28 40	77.59 19	88.39 8	88.17 6	91.02 5	93.66 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNLPA		77.20 58	77.54 60	76.80 54	82.63 62	84.31 61	79.77 65	64.64 75	85.17 21	73.18 35	56.37 112	69.81 76	74.53 32	81.12 77	78.69 97	86.04 133	87.29 63

CP-MVS		84.74 23	86.43 23	82.77 21	89.48 26	88.13 35	88.64 22	73.93 19	84.92 22	76.77 23	81.94 23	83.50 26	77.29 23	86.92 28	86.49 24	90.49 18	93.14 19

DeepC-MVS_fast	78.24 3	84.27 25	85.50 27	82.85 20	90.46 15	89.24 18	87.83 29	74.24 14	84.88 23	76.23 24	75.26 35	81.05 38	77.62 18	88.02 11	87.62 10	90.69 13	92.41 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TAPA-MVS	71.42 9	77.69 57	80.05 52	74.94 61	80.68 73	84.52 60	81.36 57	63.14 86	84.77 24	64.82 68	68.72 58	75.91 53	71.86 50	81.62 64	79.55 90	87.80 71	85.24 82
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TSAR-MVS + COLMAP		78.34 55	81.64 40	74.48 65	80.13 79	85.01 57	81.73 56	65.93 67	84.75 25	61.68 75	85.79 16	66.27 92	71.39 55	82.91 58	80.78 66	86.01 134	85.98 69

TSAR-MVS + GP.		83.69 26	86.58 22	80.32 32	85.14 50	86.96 42	84.91 48	70.25 37	84.71 26	73.91 32	85.16 18	85.63 18	77.92 15	85.44 37	85.71 33	89.77 34	92.45 23

SteuartSystems-ACMMP		85.99 12	88.31 12	83.27 18	90.73 9	89.84 11	90.27 10	74.31 12	84.56 27	75.88 26	87.32 11	85.04 20	77.31 21	89.01 4	88.46 3	91.14 3	93.96 8
Skip Steuart: Steuart Systems R&D Blog.
MCST-MVS		85.13 19	86.62 20	83.39 15	90.55 13	89.82 13	89.29 18	73.89 20	84.38 28	76.03 25	79.01 28	85.90 17	78.47 9	87.81 14	86.11 30	92.11 1	93.29 18

train_agg		84.86 21	87.21 18	82.11 24	90.59 12	85.47 52	89.81 12	73.55 22	83.95 29	73.30 34	89.84 9	87.23 11	75.61 29	86.47 31	85.46 35	89.78 33	92.06 28

MP-MVS		85.50 15	87.40 17	83.28 17	90.65 11	89.51 16	89.16 20	74.11 16	83.70 30	78.06 19	85.54 17	84.89 23	77.31 21	87.40 19	87.14 15	90.41 24	93.65 15
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ACMMP		83.42 27	85.27 28	81.26 27	88.47 32	88.49 31	88.31 27	72.09 28	83.42 31	72.77 37	82.65 21	78.22 44	75.18 30	86.24 34	85.76 32	90.74 11	92.13 27
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
canonicalmvs		79.16 50	82.37 38	75.41 58	82.33 65	86.38 48	80.80 60	63.18 85	82.90 32	67.34 60	72.79 45	76.07 52	69.62 62	83.46 55	84.41 42	89.20 46	90.60 40

PGM-MVS		84.42 24	86.29 24	82.23 23	90.04 19	88.82 24	89.23 19	71.74 31	82.82 33	74.61 29	84.41 20	82.09 30	77.03 25	87.13 22	86.73 21	90.73 12	92.06 28

X-MVS		83.23 28	85.20 29	80.92 30	89.71 24	88.68 25	88.21 28	73.60 21	82.57 34	71.81 43	77.07 30	81.92 32	71.72 53	86.98 26	86.86 17	90.47 19	92.36 25

CPTT-MVS		81.77 33	83.10 34	80.21 33	85.93 46	86.45 47	87.72 30	70.98 34	82.54 35	71.53 46	74.23 41	81.49 35	76.31 28	82.85 59	81.87 55	88.79 54	92.26 26

PHI-MVS		82.36 31	85.89 26	78.24 45	86.40 43	89.52 15	85.52 41	69.52 44	82.38 36	65.67 64	81.35 24	82.36 29	73.07 43	87.31 21	86.76 20	89.24 45	91.56 31

abl_6				79.05 39	87.27 37	88.85 23	83.62 53	68.25 50	81.68 37	72.94 36	73.79 42	84.45 24	72.55 46			89.66 39	90.64 39

HQP-MVS		81.19 36	83.27 33	78.76 42	87.40 36	85.45 53	86.95 31	70.47 36	81.31 38	66.91 62	79.24 27	76.63 50	71.67 54	84.43 45	83.78 46	89.19 47	92.05 30

3Dnovator+	75.73 4	82.40 30	82.76 35	81.97 25	88.02 33	89.67 14	86.60 33	71.48 32	81.28 39	78.18 18	64.78 74	77.96 47	77.13 24	87.32 20	86.83 18	90.41 24	91.48 32

casdiffmvs		80.04 40	82.12 39	77.60 49	83.27 59	84.92 58	85.51 42	65.45 69	80.73 40	67.69 57	72.68 46	78.05 45	74.35 34	84.82 42	83.94 45	89.35 43	89.71 47

MSLP-MVS++		82.09 32	82.66 36	81.42 26	87.03 39	87.22 39	85.82 38	70.04 38	80.30 41	78.66 16	68.67 60	81.04 39	77.81 16	85.19 40	84.88 40	89.19 47	91.31 33

CDPH-MVS		82.64 29	85.03 30	79.86 35	89.41 27	88.31 32	88.32 26	71.84 30	80.11 42	67.47 59	82.09 22	81.44 36	71.85 51	85.89 36	86.15 29	90.24 28	91.25 34

NP-MVS									80.10 43

CLD-MVS		79.35 48	81.23 42	77.16 52	85.01 53	86.92 43	85.87 37	60.89 127	80.07 44	75.35 28	72.96 44	73.21 61	68.43 69	85.41 39	84.63 41	87.41 76	85.44 79
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
AdaColmap		79.74 44	78.62 55	81.05 29	89.23 29	86.06 49	84.95 47	71.96 29	79.39 45	75.51 27	63.16 78	68.84 85	76.51 27	83.55 52	82.85 51	88.13 62	86.46 67

casdiffmvs1		79.56 46	81.02 45	77.86 48	84.19 57	87.00 41	85.73 39	63.24 84	79.22 46	72.05 39	73.55 43	76.93 49	73.25 41	80.92 80	80.20 81	88.69 55	89.31 49

3Dnovator	73.76 5	79.75 43	80.52 48	78.84 41	84.94 55	87.35 37	84.43 50	65.54 68	78.29 47	73.97 31	63.00 80	75.62 54	74.07 36	85.00 41	85.34 36	90.11 31	89.04 50

LGP-MVS_train		79.83 41	81.22 43	78.22 46	86.28 44	85.36 55	86.76 32	69.59 42	77.34 48	65.14 66	75.68 34	70.79 69	71.37 56	84.60 43	84.01 43	90.18 29	90.74 38

ACMP	73.23 7	79.79 42	80.53 47	78.94 40	85.61 48	85.68 50	85.61 40	69.59 42	77.33 49	71.00 49	74.45 39	69.16 80	71.88 49	83.15 56	83.37 49	89.92 32	90.57 41
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	68.99 11	75.68 63	75.31 74	76.12 57	82.94 61	81.26 83	79.94 64	66.10 63	77.15 50	66.86 63	59.13 93	68.53 86	73.73 38	80.38 89	79.04 94	87.13 85	81.68 129
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet		81.62 35	83.41 32	79.53 37	87.06 38	88.59 29	85.47 43	67.96 54	76.59 51	74.05 30	74.69 37	81.98 31	72.98 44	86.14 35	85.47 34	89.68 38	90.42 42

MVS_111021_LR		78.13 56	79.85 53	76.13 56	81.12 69	81.50 80	80.28 62	65.25 71	76.09 52	71.32 48	76.49 33	72.87 62	72.21 47	82.79 60	81.29 60	86.59 117	87.91 56

MVS_0304		81.73 34	83.86 31	79.26 38	86.22 45	89.18 21	86.41 34	67.15 58	75.28 53	70.75 50	74.59 38	83.49 27	74.42 33	87.05 25	86.34 26	90.58 16	91.08 36

ACMM	72.26 8	78.86 53	78.13 56	79.71 36	86.89 40	83.40 67	86.02 36	70.50 35	75.28 53	71.49 47	63.01 79	69.26 79	73.57 39	84.11 47	83.98 44	89.76 35	87.84 57
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PCF-MVS	73.28 6	79.42 47	80.41 49	78.26 44	84.88 56	88.17 33	86.08 35	69.85 39	75.23 55	68.43 53	68.03 63	78.38 43	71.76 52	81.26 74	80.65 75	88.56 58	91.18 35
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPNet		79.08 52	80.62 46	77.28 51	88.90 30	83.17 70	83.65 52	72.41 27	74.41 56	67.15 61	76.78 31	74.37 57	64.43 104	83.70 51	83.69 47	87.15 81	88.19 54
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MAR-MVS		79.21 49	80.32 50	77.92 47	87.46 35	88.15 34	83.95 51	67.48 57	74.28 57	68.25 54	64.70 75	77.04 48	72.17 48	85.42 38	85.00 39	88.22 59	87.62 59
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
RPSCF		67.64 142	71.25 91	63.43 169	61.86 212	70.73 188	67.26 180	50.86 200	74.20 58	58.91 85	67.49 65	69.33 78	64.10 105	71.41 186	68.45 201	77.61 194	77.17 166

MVS_111021_HR		80.13 39	81.46 41	78.58 43	85.77 47	85.17 56	83.45 54	69.28 45	74.08 59	70.31 51	74.31 40	75.26 55	73.13 42	86.46 32	85.15 38	89.53 40	89.81 45

QAPM		78.47 54	80.22 51	76.43 55	85.03 52	86.75 45	80.62 61	66.00 65	73.77 60	65.35 65	65.54 71	78.02 46	72.69 45	83.71 50	83.36 50	88.87 53	90.41 43

LS3D		74.08 69	73.39 79	74.88 62	85.05 51	82.62 73	79.71 66	68.66 48	72.82 61	58.80 86	57.61 105	61.31 105	71.07 58	80.32 93	78.87 96	86.00 136	80.18 143

OpenMVS	70.44 10	76.15 62	76.82 69	75.37 59	85.01 53	84.79 59	78.99 75	62.07 113	71.27 62	67.88 56	57.91 104	72.36 63	70.15 60	82.23 62	81.41 59	88.12 63	87.78 58

DELS-MVS		79.15 51	81.07 44	76.91 53	83.54 58	87.31 38	84.45 49	64.92 74	69.98 63	69.34 52	71.62 50	76.26 51	69.84 61	86.57 30	85.90 31	89.39 42	89.88 44
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
PVSNet_BlendedMVS		76.21 60	77.52 61	74.69 63	79.46 81	83.79 64	77.50 103	64.34 78	69.88 64	71.88 41	68.54 61	70.42 72	67.05 71	83.48 53	79.63 86	87.89 68	86.87 65

PVSNet_Blended		76.21 60	77.52 61	74.69 63	79.46 81	83.79 64	77.50 103	64.34 78	69.88 64	71.88 41	68.54 61	70.42 72	67.05 71	83.48 53	79.63 86	87.89 68	86.87 65

MVS_Test		75.37 64	77.13 67	73.31 69	79.07 84	81.32 82	79.98 63	60.12 147	69.72 66	64.11 70	70.53 52	73.22 60	68.90 65	80.14 97	79.48 92	87.67 72	85.50 77

diffmvs		74.38 68	76.65 70	71.74 73	77.05 110	81.86 76	79.30 69	60.54 132	69.54 67	62.16 73	69.70 55	70.74 70	66.73 79	79.18 111	78.14 109	84.63 164	87.42 60

DI_MVS_plusplus_trai		75.13 66	76.12 72	73.96 67	78.18 89	81.55 78	80.97 59	62.54 107	68.59 68	65.13 67	61.43 81	74.81 56	69.32 64	81.01 79	79.59 88	87.64 73	85.89 70

CANet_DTU		73.29 73	76.96 68	69.00 112	77.04 111	82.06 75	79.49 68	56.30 179	67.85 69	53.29 126	71.12 51	70.37 74	61.81 123	81.59 65	80.96 64	86.09 128	84.73 91

USDC		67.36 147	67.90 145	66.74 147	71.72 175	75.23 168	71.58 160	60.28 140	67.45 70	50.54 143	60.93 82	45.20 212	62.08 116	76.56 156	74.50 172	84.25 168	75.38 180

Anonymous20240521		73.65 71	75.78 73	71.16 76	80.19 77	79.27 108	77.45 105	61.68 119	66.73 71	58.72 87	65.31 72	69.96 75	62.19 115	81.29 73	80.97 63	86.74 109	86.91 64

Effi-MVS+		75.28 65	76.20 71	74.20 66	81.15 68	83.24 68	81.11 58	63.13 87	66.37 72	60.27 80	64.30 76	68.88 84	70.93 59	81.56 66	81.69 57	88.61 56	87.35 61

EPNet_dtu		68.08 131	71.00 92	64.67 159	79.64 80	68.62 196	75.05 122	63.30 83	66.36 73	45.27 172	67.40 66	66.84 91	43.64 201	75.37 164	74.98 171	81.15 181	77.44 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		71.52 82	69.87 103	73.44 68	82.21 66	79.35 107	79.52 67	64.59 76	66.15 74	61.87 74	53.21 160	56.09 137	65.85 101	78.94 113	78.50 98	86.60 116	76.85 171

FC-MVSNet-train		72.60 77	75.07 75	69.71 107	81.10 70	78.79 118	73.74 143	65.23 72	66.10 75	53.34 125	70.36 53	63.40 100	56.92 151	81.44 67	80.96 64	87.93 66	84.46 93

tpmp4_e23		68.32 127	67.08 155	69.76 106	77.86 93	75.22 170	78.37 95	56.17 181	66.06 76	64.27 69	57.15 109	54.89 145	63.40 108	70.97 193	68.29 202	78.46 192	77.00 170

CostFormer		68.92 120	69.58 109	68.15 119	75.98 123	76.17 159	78.22 98	51.86 195	65.80 77	61.56 76	63.57 77	62.83 101	61.85 121	70.40 200	68.67 197	79.42 188	79.62 150

IS_MVSNet		73.33 72	77.34 65	68.65 116	81.29 67	83.47 66	74.45 126	63.58 82	65.75 78	48.49 151	67.11 68	70.61 71	54.63 171	84.51 44	83.58 48	89.48 41	86.34 68

Vis-MVSNet (Re-imp)		67.83 136	73.52 78	61.19 180	78.37 88	76.72 150	66.80 185	62.96 88	65.50 79	34.17 206	67.19 67	69.68 77	39.20 210	79.39 108	79.44 93	85.68 147	76.73 172

Fast-Effi-MVS+		73.11 74	73.66 77	72.48 71	77.72 103	80.88 89	78.55 90	58.83 165	65.19 80	60.36 79	59.98 88	62.42 103	71.22 57	81.66 63	80.61 77	88.20 60	84.88 90

EPP-MVSNet		74.00 70	77.41 64	70.02 103	80.53 75	83.91 63	74.99 123	62.68 103	65.06 81	49.77 148	68.68 59	72.09 64	63.06 110	82.49 61	80.73 67	89.12 49	88.91 51

UGNet		72.78 75	77.67 59	67.07 140	71.65 177	83.24 68	75.20 117	63.62 81	64.93 82	56.72 106	71.82 49	73.30 59	49.02 187	81.02 78	80.70 73	86.22 121	88.67 53
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
PVSNet_Blended_VisFu		76.57 59	77.90 57	75.02 60	80.56 74	86.58 46	79.24 70	66.18 62	64.81 83	68.18 55	65.61 69	71.45 65	67.05 71	84.16 46	81.80 56	88.90 51	90.92 37

pmmvs4		67.89 134	67.39 152	68.48 117	71.60 179	73.57 179	74.45 126	60.98 126	64.65 84	57.97 94	54.95 129	51.73 184	61.88 120	73.78 172	75.11 169	83.99 171	77.91 161

MVSTER		72.06 78	74.24 76	69.51 108	70.39 185	75.97 160	76.91 109	57.36 174	64.64 85	61.39 77	68.86 57	63.76 98	63.46 107	81.44 67	79.70 85	87.56 74	85.31 81

OPM-MVS		79.68 45	79.28 54	80.15 34	87.99 34	86.77 44	88.52 25	72.72 25	64.55 86	67.65 58	67.87 64	74.33 58	74.31 35	86.37 33	85.25 37	89.73 36	89.81 45

Anonymous20231211		71.90 79	72.48 85	71.21 75	80.14 78	81.53 79	76.92 108	62.89 90	64.46 87	58.94 84	43.80 202	70.98 68	62.22 114	80.70 82	80.19 83	86.18 122	85.73 71

GBi-Net		70.78 85	73.37 80	67.76 121	72.95 165	78.00 126	75.15 118	62.72 98	64.13 88	51.44 134	58.37 98	69.02 81	57.59 143	81.33 70	80.72 68	86.70 111	82.02 121

test1		70.78 85	73.37 80	67.76 121	72.95 165	78.00 126	75.15 118	62.72 98	64.13 88	51.44 134	58.37 98	69.02 81	57.59 143	81.33 70	80.72 68	86.70 111	82.02 121

FMVSNet3		70.49 89	72.90 82	67.67 125	72.88 168	77.98 129	74.96 124	62.72 98	64.13 88	51.44 134	58.37 98	69.02 81	57.43 146	79.43 107	79.57 89	86.59 117	81.81 128

COLMAP_ROB	62.73 15	67.66 140	66.76 159	68.70 115	80.49 76	77.98 129	75.29 116	62.95 89	63.62 91	49.96 146	47.32 197	50.72 190	58.57 137	76.87 151	75.50 166	84.94 159	75.33 181
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EPMVS		60.00 199	61.97 200	57.71 197	68.46 195	63.17 215	64.54 197	48.23 212	63.30 92	44.72 175	60.19 85	56.05 138	50.85 183	65.27 213	62.02 219	69.44 221	63.81 213

FMVSNet2		70.39 90	72.67 84	67.72 124	72.95 165	78.00 126	75.15 118	62.69 102	63.29 93	51.25 138	55.64 116	68.49 87	57.59 143	80.91 81	80.35 80	86.70 111	82.02 121

PatchmatchNet		64.21 173	64.65 182	63.69 165	71.29 183	68.66 195	69.63 166	51.70 197	63.04 94	53.77 123	59.83 90	58.34 114	60.23 133	68.54 207	66.06 209	75.56 203	68.08 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		65.41 157	63.81 188	67.28 136	75.61 127	72.88 180	75.32 115	52.85 189	62.97 95	63.66 71	53.24 159	53.29 167	61.83 122	65.54 211	64.14 214	74.43 208	74.60 183

PatchMatch-RL		67.78 137	66.65 160	69.10 111	73.01 164	72.69 181	68.49 172	61.85 116	62.93 96	60.20 81	56.83 111	50.42 191	69.52 63	75.62 163	74.46 173	81.51 179	73.62 190

Anonymous202405211			72.16 87		80.85 72	81.85 77	76.88 110	65.40 70	62.89 97		46.35 198	67.99 88	62.05 117	81.15 76	80.38 79	85.97 138	84.50 92

PMMVS		65.06 163	69.17 121	60.26 186	55.25 229	63.43 212	66.71 186	43.01 226	62.41 98	50.64 141	69.44 56	67.04 90	63.29 109	74.36 169	73.54 176	82.68 176	73.99 188

MS-PatchMatch		70.17 97	70.49 96	69.79 105	80.98 71	77.97 131	77.51 102	58.95 157	62.33 99	55.22 116	53.14 161	65.90 93	62.03 118	79.08 112	77.11 129	84.08 169	77.91 161

tpmrst		62.00 188	62.35 199	61.58 178	71.62 178	64.14 208	69.07 170	48.22 213	62.21 100	53.93 121	58.26 102	55.30 141	55.81 163	63.22 216	62.62 217	70.85 218	70.70 200

UniMVSNet_NR-MVSNet		70.59 88	72.19 86	68.72 114	77.72 103	80.72 90	73.81 141	69.65 41	61.99 101	43.23 179	60.54 84	57.50 116	58.57 137	79.56 105	81.07 62	89.34 44	83.97 96

GG-mvs-BLEND		46.86 222	67.51 149	22.75 235	0.05 244	76.21 158	64.69 196	0.04 241	61.90 102	0.09 245	55.57 117	71.32 66	0.08 241	70.54 196	67.19 205	71.58 216	69.86 201

CHOSEN 1792x2688		69.20 118	69.26 119	69.13 110	76.86 112	78.93 112	77.27 106	60.12 147	61.86 103	54.42 117	42.54 206	61.61 104	66.91 77	78.55 117	78.14 109	79.23 190	83.23 108

DWT-MVSNet_training		67.24 149	65.96 167	68.74 113	76.15 119	74.36 177	74.37 130	56.66 177	61.82 104	60.51 78	58.23 103	49.76 195	65.07 102	70.04 201	70.39 188	79.70 187	77.11 168

UniMVSNet (Re)		69.53 111	71.90 88	66.76 146	76.42 114	80.93 86	72.59 153	68.03 53	61.75 105	41.68 187	58.34 101	57.23 124	53.27 178	79.53 106	80.62 76	88.57 57	84.90 89

ACMH+	66.54 13	71.36 83	70.09 98	72.85 70	82.59 63	81.13 84	78.56 89	68.04 52	61.55 106	52.52 132	51.50 179	54.14 150	68.56 68	78.85 114	79.50 91	86.82 102	83.94 98

IterMVS-LS		71.69 81	72.82 83	70.37 98	77.54 105	76.34 156	75.13 121	60.46 135	61.53 107	57.57 95	64.89 73	67.33 89	66.04 97	77.09 149	77.37 125	85.48 150	85.18 83
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Baseline_NR-MVSNet		67.53 145	68.77 129	66.09 150	75.99 121	74.75 174	72.43 154	68.41 49	61.33 108	38.33 195	51.31 180	54.13 152	56.03 160	79.22 109	78.19 107	85.37 151	82.45 119

DU-MVS		69.63 106	70.91 93	68.13 120	75.99 121	79.54 104	73.81 141	69.20 46	61.20 109	43.23 179	58.52 95	53.50 157	58.57 137	79.22 109	80.45 78	87.97 65	83.97 96

NR-MVSNet		68.79 122	70.56 95	66.71 148	77.48 106	79.54 104	73.52 146	69.20 46	61.20 109	39.76 190	58.52 95	50.11 193	51.37 182	80.26 95	80.71 72	88.97 50	83.59 103

Effi-MVS+-dtu		71.82 80	71.86 89	71.78 72	78.77 85	80.47 98	78.55 90	61.67 120	60.68 111	55.49 113	58.48 97	65.48 94	68.85 66	76.92 150	75.55 165	87.35 77	85.46 78

UA-Net		74.47 67	77.80 58	70.59 89	85.33 49	85.40 54	73.54 145	65.98 66	60.65 112	56.00 112	72.11 47	79.15 41	54.63 171	83.13 57	82.25 53	88.04 64	81.92 127

Vis-MVSNet		72.77 76	77.20 66	67.59 127	74.19 155	84.01 62	76.61 113	61.69 118	60.62 113	50.61 142	70.25 54	71.31 67	55.57 166	83.85 49	82.28 52	86.90 95	88.08 55
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TranMVSNet+NR-MVSNet		69.25 117	70.81 94	67.43 128	77.23 109	79.46 106	73.48 147	69.66 40	60.43 114	39.56 191	58.82 94	53.48 159	55.74 164	79.59 103	81.21 61	88.89 52	82.70 117

TDRefinement		66.09 155	65.03 180	67.31 134	69.73 189	76.75 149	75.33 114	64.55 77	60.28 115	49.72 149	45.63 200	42.83 215	60.46 131	75.75 160	75.95 160	84.08 169	78.04 160

MDTV_nov1_ep13		64.37 168	65.24 175	63.37 170	68.94 194	70.81 187	72.40 155	50.29 204	60.10 116	53.91 122	60.07 87	59.15 112	57.21 147	69.43 204	67.30 204	77.47 195	69.78 202

v18		70.10 98	69.52 110	70.77 82	74.66 151	77.06 141	78.84 78	58.84 164	60.01 117	59.23 82	55.06 124	57.47 117	66.34 87	77.50 137	76.75 138	86.71 110	82.77 115

v16		70.07 99	69.46 112	70.79 81	74.74 146	77.08 140	78.79 83	58.86 159	59.75 118	59.15 83	54.87 131	57.33 119	66.38 85	77.61 131	76.77 133	86.81 107	82.79 113

IB-MVS	66.94 12	71.21 84	71.66 90	70.68 86	79.18 83	82.83 72	72.61 152	61.77 117	59.66 119	63.44 72	53.26 158	59.65 110	59.16 136	76.78 153	82.11 54	87.90 67	87.33 62
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
ADS-MVSNet		55.94 208	58.01 209	53.54 212	62.48 209	58.48 221	59.12 214	46.20 216	59.65 120	42.88 184	52.34 176	53.31 166	46.31 196	62.00 220	60.02 224	64.23 230	60.24 221

v17		70.03 101	69.43 117	70.72 85	74.75 145	77.09 139	78.78 85	58.85 161	59.53 121	58.72 87	54.87 131	57.39 118	66.38 85	77.60 132	76.75 138	86.83 101	82.80 111

FC-MVSNet-test		56.90 206	65.20 177	47.21 217	66.98 197	63.20 214	49.11 226	58.60 168	59.38 122	11.50 237	65.60 70	56.68 127	24.66 229	71.17 189	71.36 186	72.38 214	69.02 204

HyFIR lowres test		69.47 114	68.94 123	70.09 102	76.77 113	82.93 71	76.63 112	60.17 142	59.00 123	54.03 120	40.54 212	65.23 95	67.89 70	76.54 157	78.30 105	85.03 156	80.07 144

v6		70.35 91	69.94 100	70.83 78	74.68 148	80.62 91	78.81 80	60.16 145	58.81 124	58.17 91	55.01 125	57.31 121	66.32 90	77.53 133	76.73 144	86.82 102	83.62 100

v8		70.23 95	69.86 105	70.67 87	74.69 147	79.82 103	78.79 83	59.18 155	58.80 125	58.20 90	55.00 126	57.33 119	66.31 91	77.51 136	76.71 148	86.82 102	83.88 99

v1neww		70.34 92	69.93 101	70.82 79	74.68 148	80.61 92	78.80 81	60.17 142	58.74 126	58.10 92	55.00 126	57.28 122	66.33 88	77.53 133	76.74 140	86.82 102	83.61 101

v7new		70.34 92	69.93 101	70.82 79	74.68 148	80.61 92	78.80 81	60.17 142	58.74 126	58.10 92	55.00 126	57.28 122	66.33 88	77.53 133	76.74 140	86.82 102	83.61 101

V42		68.76 123	69.63 108	67.74 123	64.93 206	78.01 125	78.30 96	56.48 178	58.65 128	56.30 110	54.26 142	57.03 125	64.85 103	77.47 138	77.01 130	85.60 148	84.96 88

tfpn_ndepth		65.09 162	67.12 154	62.73 171	75.75 126	76.23 157	68.00 174	60.36 136	58.16 129	40.27 189	54.89 130	54.22 149	46.80 194	76.69 155	75.66 162	85.19 153	73.98 189

Fast-Effi-MVS+-dtu		68.34 126	69.47 111	67.01 141	75.15 129	77.97 131	77.12 107	55.40 182	57.87 130	46.68 165	56.17 115	60.39 106	62.36 113	76.32 158	76.25 154	85.35 152	81.34 130

tpm		62.41 184	63.15 190	61.55 179	72.24 171	63.79 211	71.31 161	46.12 217	57.82 131	55.33 114	59.90 89	54.74 146	53.63 175	67.24 210	64.29 212	70.65 219	74.25 187

CR-MVSNet		64.83 164	65.54 173	64.01 164	70.64 184	69.41 191	65.97 190	52.74 190	57.81 132	52.65 129	54.27 140	56.31 130	60.92 127	72.20 182	73.09 178	81.12 182	75.69 177

RPMNet		61.71 194	62.88 192	60.34 185	69.51 191	69.41 191	63.48 201	49.23 205	57.81 132	45.64 171	50.51 183	50.12 192	53.13 179	68.17 209	68.49 200	81.07 183	75.62 179

dps		64.00 174	62.99 191	65.18 153	73.29 162	72.07 183	68.98 171	53.07 188	57.74 134	58.41 89	55.55 118	47.74 204	60.89 129	69.53 203	67.14 206	76.44 200	71.19 199

v10		70.22 96	69.76 107	70.74 83	74.79 140	80.30 101	79.22 71	59.81 150	57.71 135	56.58 109	54.22 146	55.31 140	66.95 74	78.28 120	77.47 121	87.12 88	85.07 85

v7		70.33 94	69.87 103	70.88 77	74.79 140	81.04 85	79.22 71	60.57 131	57.70 136	56.65 108	54.23 144	55.29 142	66.95 74	78.28 120	77.47 121	87.12 88	85.05 86

v2v482		70.05 100	69.46 112	70.74 83	74.62 152	80.32 100	79.00 74	60.62 130	57.41 137	56.89 101	55.43 119	55.14 143	66.39 84	77.25 145	77.14 128	86.90 95	83.57 106

IterMVS		66.36 153	68.30 140	64.10 161	69.48 192	74.61 175	73.41 148	50.79 201	57.30 138	48.28 153	60.64 83	59.92 109	60.85 130	74.14 170	72.66 180	81.80 178	78.82 157
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v15		69.61 107	68.88 124	70.46 93	74.81 139	77.03 144	78.75 86	58.83 165	57.06 139	57.18 97	54.55 137	56.37 128	66.13 95	77.70 128	76.76 135	87.03 92	82.69 118

thresconf0.02		64.77 165	65.90 168	63.44 168	76.37 115	75.17 173	69.51 167	61.28 121	56.98 140	39.01 193	56.24 113	48.68 199	49.78 185	77.13 147	75.61 163	84.71 163	71.53 197

v1		69.97 102	69.45 114	70.59 89	74.78 142	80.51 95	78.84 78	60.30 137	56.98 140	56.81 103	54.69 134	56.29 132	65.91 100	77.37 140	76.71 148	86.89 97	83.59 103

divwei89l23v2f112		69.97 102	69.44 115	70.58 91	74.78 142	80.50 96	78.85 76	60.30 137	56.97 142	56.75 104	54.67 136	56.27 133	65.92 99	77.37 140	76.72 145	86.88 98	83.58 105

v1141		69.96 104	69.44 115	70.58 91	74.78 142	80.50 96	78.85 76	60.30 137	56.95 143	56.74 105	54.68 135	56.26 134	65.93 98	77.38 139	76.72 145	86.88 98	83.57 106

V14		69.59 108	68.86 125	70.45 95	74.83 138	77.04 142	78.70 87	58.83 165	56.95 143	57.08 99	54.41 138	56.34 129	66.15 92	77.77 127	76.76 135	87.08 90	82.74 116

FMVSNet1		68.84 121	70.47 97	66.94 142	71.35 182	77.68 134	74.71 125	62.35 112	56.93 145	49.94 147	50.01 185	64.59 96	57.07 149	81.33 70	80.72 68	86.25 120	82.00 124

V9		69.58 109	68.83 126	70.46 93	74.85 137	77.04 142	78.65 88	58.85 161	56.83 146	57.12 98	54.26 142	56.31 130	66.14 94	77.83 126	76.76 135	87.13 85	82.79 113

v12		69.54 110	68.79 128	70.41 96	74.88 134	77.03 144	78.54 93	58.85 161	56.71 147	56.87 102	54.13 147	56.23 135	66.15 92	77.89 124	76.74 140	87.17 80	82.80 111

PatchT		61.97 189	64.04 186	59.55 191	60.49 214	67.40 199	56.54 216	48.65 209	56.69 148	52.65 129	51.10 182	52.14 180	60.92 127	72.20 182	73.09 178	78.03 193	75.69 177

tfpn111		68.38 125	69.23 120	67.39 130	77.83 95	78.93 112	74.28 131	62.81 91	56.64 149	46.70 161	56.24 113	53.47 160	56.59 152	80.41 84	78.43 99	86.11 125	80.53 138

conf0.01		67.72 138	67.99 143	67.39 130	77.82 100	78.94 110	74.28 131	62.81 91	56.64 149	46.70 161	53.33 156	48.59 200	56.59 152	80.34 91	78.43 99	86.16 124	79.67 149

conf0.002		67.52 146	67.64 147	67.39 130	77.80 102	78.94 110	74.28 131	62.81 91	56.64 149	46.70 161	53.65 152	46.28 208	56.59 152	80.33 92	78.37 104	86.17 123	79.23 153

conf200view11		68.11 129	68.72 131	67.39 130	77.83 95	78.93 112	74.28 131	62.81 91	56.64 149	46.70 161	52.65 170	53.47 160	56.59 152	80.41 84	78.43 99	86.11 125	80.53 138

thres100view900		67.60 144	68.02 142	67.12 139	77.83 95	77.75 133	73.90 139	62.52 108	56.64 149	46.82 159	52.65 170	53.47 160	55.92 161	78.77 115	77.62 118	85.72 146	79.23 153

tfpn200view9		68.11 129	68.72 131	67.40 129	77.83 95	78.93 112	74.28 131	62.81 91	56.64 149	46.82 159	52.65 170	53.47 160	56.59 152	80.41 84	78.43 99	86.11 125	80.52 140

v13		69.52 112	68.76 130	70.41 96	74.88 134	77.02 146	78.52 94	58.86 159	56.61 155	56.91 100	54.00 149	56.17 136	66.11 96	77.93 123	76.74 140	87.21 79	82.83 110

tfpn1000		63.81 175	66.31 161	60.90 182	75.76 125	75.74 161	65.14 194	60.14 146	56.47 156	35.99 203	55.11 123	52.30 177	43.42 202	76.21 159	75.34 167	84.97 158	73.01 192

MIMVSNet		58.52 203	61.34 203	55.22 205	60.76 213	67.01 201	66.81 184	49.02 207	56.43 157	38.90 194	40.59 211	54.54 148	40.57 209	73.16 175	71.65 183	75.30 206	66.00 209

v11		69.37 115	68.65 134	70.20 100	74.87 136	76.97 147	78.29 97	58.55 169	56.38 158	56.04 111	54.02 148	54.98 144	66.47 83	78.30 119	76.91 131	86.97 93	83.02 109

thres200		67.98 132	68.55 136	67.30 135	77.89 92	78.86 116	74.18 138	62.75 96	56.35 159	46.48 166	52.98 164	53.54 156	56.46 157	80.41 84	77.97 111	86.05 131	79.78 148

thres400		67.95 133	68.62 135	67.17 137	77.90 90	78.59 121	74.27 136	62.72 98	56.34 160	45.77 170	53.00 163	53.35 165	56.46 157	80.21 96	78.43 99	85.91 141	80.43 141

ACMH	65.37 14	70.71 87	70.00 99	71.54 74	82.51 64	82.47 74	77.78 100	68.13 51	56.19 161	46.06 168	54.30 139	51.20 187	68.68 67	80.66 83	80.72 68	86.07 129	84.45 94
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres600view7		67.68 139	68.43 137	66.80 144	77.90 90	78.86 116	73.84 140	62.75 96	56.07 162	44.70 176	52.85 167	52.81 170	55.58 165	80.41 84	77.77 114	86.05 131	80.28 142

view600		67.63 143	68.36 138	66.77 145	77.84 94	78.66 119	73.74 143	62.62 105	56.04 163	44.98 173	52.86 166	52.83 169	55.48 168	80.36 90	77.75 115	85.95 140	80.02 145

v1144		69.93 105	69.36 118	70.61 88	74.89 133	80.93 86	79.11 73	60.64 129	55.97 164	55.31 115	53.85 151	54.14 150	66.54 82	78.10 122	77.44 123	87.14 84	85.09 84

view800		67.35 148	68.22 141	66.35 149	77.83 95	78.62 120	72.97 151	62.58 106	55.71 165	44.13 177	52.69 169	52.24 179	54.58 173	80.27 94	78.19 107	86.01 134	79.79 147

v148		67.85 135	67.53 148	68.23 118	73.25 163	77.57 137	74.26 137	57.36 174	55.70 166	57.45 96	53.53 153	55.42 139	61.96 119	75.23 165	73.92 174	85.08 155	81.32 131

tfpnview11		64.33 169	66.17 164	62.18 173	76.25 116	75.23 168	67.45 177	61.16 122	55.50 167	36.38 200	55.35 120	51.89 181	46.96 190	77.28 144	76.10 159	84.86 161	71.85 196

TinyColmap		62.84 179	61.03 204	64.96 157	69.61 190	71.69 184	68.48 173	59.76 151	55.41 168	47.69 157	47.33 196	34.20 225	62.76 112	74.52 167	72.59 181	81.44 180	71.47 198

tfpn		66.58 152	67.18 153	65.88 151	77.82 100	78.45 123	72.07 156	62.52 108	55.35 169	43.21 181	52.54 174	46.12 209	53.68 174	80.02 98	78.23 106	85.99 137	79.55 151

CHOSEN 280x420		58.70 202	61.88 201	54.98 206	55.45 228	50.55 232	64.92 195	40.36 228	55.21 170	38.13 196	48.31 189	63.76 98	63.03 111	73.73 173	68.58 199	68.00 224	73.04 191

FMVSNet5		57.24 204	60.02 207	53.99 209	56.45 224	62.74 216	65.27 193	47.03 214	55.14 171	39.55 192	40.88 209	53.42 164	41.83 203	72.35 178	71.10 187	73.79 210	64.50 212

GA-MVS		68.14 128	69.17 121	66.93 143	73.77 160	78.50 122	74.45 126	58.28 170	55.11 172	48.44 152	60.08 86	53.99 153	61.50 124	78.43 118	77.57 119	85.13 154	80.54 137

v1192		69.50 113	68.83 126	70.29 99	74.49 153	80.92 88	78.55 90	60.54 132	55.04 173	54.21 118	52.79 168	52.33 175	66.92 76	77.88 125	77.35 126	87.04 91	85.51 76

PM-MVS		60.48 197	60.94 205	59.94 187	58.85 219	66.83 202	64.27 199	51.39 198	55.03 174	48.03 154	50.00 187	40.79 219	58.26 140	69.20 205	67.13 207	78.84 191	77.60 163

v144192		69.34 116	68.68 133	70.12 101	74.06 156	80.54 94	78.08 99	60.54 132	54.99 175	54.13 119	52.92 165	52.80 171	66.73 79	77.13 147	76.72 145	87.15 81	85.63 72

v1921920		69.03 119	68.32 139	69.86 104	74.03 157	80.37 99	77.55 101	60.25 141	54.62 176	53.59 124	52.36 175	51.50 186	66.75 78	77.17 146	76.69 150	86.96 94	85.56 73

test-LLR		64.42 167	64.36 184	64.49 160	75.02 131	63.93 209	66.61 187	61.96 114	54.41 177	47.77 155	57.46 106	60.25 107	55.20 169	70.80 194	69.33 192	80.40 185	74.38 185

TESTMET0.1,1		61.10 195	64.36 184	57.29 198	57.53 222	63.93 209	66.61 187	36.22 232	54.41 177	47.77 155	57.46 106	60.25 107	55.20 169	70.80 194	69.33 192	80.40 185	74.38 185

test-mter		60.84 196	64.62 183	56.42 201	55.99 227	64.18 207	65.39 192	34.23 234	54.39 179	46.21 167	57.40 108	59.49 111	55.86 162	71.02 192	69.65 191	80.87 184	76.20 173

pmmvs-eth3d		63.52 176	62.44 198	64.77 158	66.82 200	70.12 190	69.41 169	59.48 153	54.34 180	52.71 128	46.24 199	44.35 214	56.93 150	72.37 177	73.77 175	83.30 173	75.91 174

WR-MVS		63.03 177	67.40 151	57.92 196	75.14 130	77.60 136	60.56 209	66.10 63	54.11 181	23.88 218	53.94 150	53.58 155	34.50 214	73.93 171	77.71 116	87.35 77	80.94 133

tfpn_n400		64.23 171	66.05 165	62.12 175	76.20 117	75.24 166	67.43 178	61.15 123	54.04 182	36.38 200	55.35 120	51.89 181	46.94 191	77.31 142	76.15 157	84.59 165	72.36 193

tfpnconf		64.23 171	66.05 165	62.12 175	76.20 117	75.24 166	67.43 178	61.15 123	54.04 182	36.38 200	55.35 120	51.89 181	46.94 191	77.31 142	76.15 157	84.59 165	72.36 193

CDS-MVSNet		67.65 141	69.83 106	65.09 154	75.39 128	76.55 151	74.42 129	63.75 80	53.55 184	49.37 150	59.41 91	62.45 102	44.44 199	79.71 101	79.82 84	83.17 175	77.36 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v1240		68.64 124	67.89 146	69.51 108	73.89 159	80.26 102	76.73 111	59.97 149	53.43 185	53.08 127	51.82 178	50.84 189	66.62 81	76.79 152	76.77 133	86.78 108	85.34 80

test0.0.03 1		58.80 201	61.58 202	55.56 204	75.02 131	68.45 197	59.58 213	61.96 114	52.74 186	29.57 210	49.75 188	54.56 147	31.46 217	71.19 188	69.77 190	75.75 201	64.57 211

CP-MVSNet		62.68 180	65.49 174	59.40 192	71.84 173	75.34 164	62.87 204	67.04 59	52.64 187	27.19 215	53.38 155	48.15 202	41.40 206	71.26 187	75.68 161	86.07 129	82.00 124

PEN-MVS		62.96 178	65.77 171	59.70 189	73.98 158	75.45 163	63.39 202	67.61 56	52.49 188	25.49 217	53.39 154	49.12 198	40.85 208	71.94 184	77.26 127	86.86 100	80.72 135

CVMVSNet		62.55 181	65.89 169	58.64 194	66.95 198	69.15 193	66.49 189	56.29 180	52.46 189	32.70 207	59.27 92	58.21 115	50.09 184	71.77 185	71.39 185	79.31 189	78.99 156

MDTV_nov1_ep13_2view		60.16 198	60.51 206	59.75 188	65.39 203	69.05 194	68.00 174	48.29 211	51.99 190	45.95 169	48.01 191	49.64 196	53.39 177	68.83 206	66.52 208	77.47 195	69.55 203

CMPMVS	47.78 17	62.49 183	62.52 196	62.46 172	70.01 187	70.66 189	62.97 203	51.84 196	51.98 191	56.71 107	42.87 204	53.62 154	57.80 142	72.23 180	70.37 189	75.45 205	75.91 174
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WR-MVS_H		61.83 192	65.87 170	57.12 199	71.72 175	76.87 148	61.45 207	66.19 61	51.97 192	22.92 225	53.13 162	52.30 177	33.80 215	71.03 191	75.00 170	86.65 115	80.78 134

PS-CasMVS		62.38 186	65.06 178	59.25 193	71.73 174	75.21 171	62.77 205	66.99 60	51.94 193	26.96 216	52.00 177	47.52 205	41.06 207	71.16 190	75.60 164	85.97 138	81.97 126

DTE-MVSNet		61.85 190	64.96 181	58.22 195	74.32 154	74.39 176	61.01 208	67.85 55	51.76 194	21.91 228	53.28 157	48.17 201	37.74 211	72.22 181	76.44 151	86.52 119	78.49 158

conf0.05thres1000		66.26 154	66.77 158	65.66 152	77.45 107	78.10 124	71.85 159	62.44 111	51.47 195	43.00 182	47.92 192	51.66 185	53.40 176	79.71 101	77.97 111	85.82 142	80.56 136

v7n		67.05 151	66.94 156	67.17 137	72.35 170	78.97 109	73.26 150	58.88 158	51.16 196	50.90 139	48.21 190	50.11 193	60.96 126	77.70 128	77.38 124	86.68 114	85.05 86

pmmvs5		62.37 187	64.04 186	60.42 184	65.03 204	71.67 185	67.17 181	52.70 192	50.30 197	44.80 174	54.23 144	51.19 188	49.37 186	72.88 176	73.48 177	83.45 172	74.55 184

FPMVS		51.87 215	50.00 220	54.07 208	66.83 199	57.25 222	60.25 211	50.91 199	50.25 198	34.36 205	36.04 219	32.02 227	41.49 205	58.98 228	56.07 228	70.56 220	59.36 222

TAMVS		59.58 200	62.81 194	55.81 203	66.03 202	65.64 206	63.86 200	48.74 208	49.95 199	37.07 199	54.77 133	58.54 113	44.44 199	72.29 179	71.79 182	74.70 207	66.66 208

v52		65.23 159	66.24 162	64.06 162	61.94 210	76.42 153	72.06 157	54.30 184	49.94 200	50.04 145	47.41 195	52.42 173	60.23 133	75.71 161	76.22 155	85.78 143	85.56 73

v748		65.12 161	65.24 175	64.98 156	69.77 188	76.45 152	69.47 168	57.06 176	49.93 201	50.70 140	47.87 193	49.50 197	57.14 148	73.64 174	75.18 168	85.75 145	84.14 95

V4		65.23 159	66.23 163	64.06 162	61.94 210	76.42 153	72.05 158	54.31 183	49.91 202	50.06 144	47.42 194	52.40 174	60.24 132	75.71 161	76.22 155	85.78 143	85.56 73

pm-mvs1		65.62 156	67.42 150	63.53 167	73.66 161	76.39 155	69.66 165	60.87 128	49.73 203	43.97 178	51.24 181	57.00 126	48.16 188	79.89 99	77.84 113	84.85 162	79.82 146

N_pmnet		47.35 219	50.13 219	44.11 222	59.98 215	51.64 230	51.86 221	44.80 222	49.58 204	20.76 229	40.65 210	40.05 221	29.64 219	59.84 226	55.15 229	57.63 232	54.00 229

Anonymous20231206		56.36 207	57.80 211	54.67 207	70.08 186	66.39 203	60.46 210	57.54 172	49.50 205	29.30 211	33.86 222	46.64 206	35.18 213	70.44 198	68.88 196	75.47 204	68.88 205

anonymousdsp		65.28 158	67.98 144	62.13 174	58.73 220	73.98 178	67.10 182	50.69 202	48.41 206	47.66 158	54.27 140	52.75 172	61.45 125	76.71 154	80.20 81	87.13 85	89.53 48

LP		53.62 213	53.43 214	53.83 210	58.51 221	62.59 218	57.31 215	46.04 218	47.86 207	42.69 185	36.08 218	36.86 223	46.53 195	64.38 214	64.25 213	71.92 215	62.00 218

tfpnnormal		64.27 170	63.64 189	65.02 155	75.84 124	75.61 162	71.24 162	62.52 108	47.79 208	42.97 183	42.65 205	44.49 213	52.66 180	78.77 115	76.86 132	84.88 160	79.29 152

TransMVSNet (Re)		64.74 166	65.66 172	63.66 166	77.40 108	75.33 165	69.86 164	62.67 104	47.63 209	41.21 188	50.01 185	52.33 175	45.31 198	79.57 104	77.69 117	85.49 149	77.07 169

ambc			53.42 215		64.99 205	63.36 213	49.96 224		47.07 210	37.12 198	28.97 226	16.36 240	41.82 204	75.10 166	67.34 203	71.55 217	75.72 176

EG-PatchMatch MVS		67.24 149	66.94 156	67.60 126	78.73 86	81.35 81	73.28 149	59.49 152	46.89 211	51.42 137	43.65 203	53.49 158	55.50 167	81.38 69	80.66 74	87.15 81	81.17 132

SixPastTwentyTwo		61.84 191	62.45 197	61.12 181	69.20 193	72.20 182	62.03 206	57.40 173	46.54 212	38.03 197	57.14 110	41.72 217	58.12 141	69.67 202	71.58 184	81.94 177	78.30 159

MVS-HIRNet		54.41 210	52.10 218	57.11 200	58.99 218	56.10 224	49.68 225	49.10 206	46.18 213	52.15 133	33.18 223	46.11 210	56.10 159	63.19 217	59.70 225	76.64 199	60.25 220

EU-MVSNet		54.63 209	58.69 208	49.90 215	56.99 223	62.70 217	56.41 217	50.64 203	45.95 214	23.14 222	50.42 184	46.51 207	36.63 212	65.51 212	64.85 211	75.57 202	74.91 182

MDA-MVSNet-bldmvs		53.37 214	53.01 217	53.79 211	43.67 237	67.95 198	59.69 212	57.92 171	43.69 215	32.41 208	41.47 207	27.89 234	52.38 181	56.97 230	65.99 210	76.68 198	67.13 207

testgi		54.39 211	57.86 210	50.35 214	71.59 180	67.24 200	54.95 218	53.25 187	43.36 216	23.78 219	44.64 201	47.87 203	24.96 226	70.45 197	68.66 198	73.60 211	62.78 216

test20.03		53.93 212	56.28 213	51.19 213	72.19 172	65.83 204	53.20 220	61.08 125	42.74 217	22.08 226	37.07 215	45.76 211	24.29 230	70.44 198	69.04 194	74.31 209	63.05 215

new-patchmatchnet		46.97 221	49.47 221	44.05 223	62.82 208	56.55 223	45.35 229	52.01 194	42.47 218	17.04 233	35.73 220	35.21 224	21.84 235	61.27 221	54.83 230	65.26 229	60.26 219

pmmvs6		62.41 184	62.88 192	61.87 177	71.38 181	75.18 172	67.76 176	59.45 154	41.64 219	42.52 186	37.33 214	52.91 168	46.87 193	77.67 130	76.26 153	83.23 174	79.18 155

MIMVSNet1		49.27 216	53.25 216	44.62 221	44.61 234	61.52 220	53.61 219	52.18 193	41.62 220	18.68 230	28.14 230	41.58 218	25.50 224	68.46 208	69.04 194	73.15 212	62.37 217

new_pmnet		38.40 228	42.64 230	33.44 230	37.54 240	45.00 236	36.60 236	32.72 236	40.27 221	12.72 236	29.89 225	28.90 233	24.78 227	53.17 232	52.90 233	56.31 233	48.34 230

Gipuma		36.38 229	35.80 233	37.07 228	45.76 233	33.90 239	29.81 238	48.47 210	39.91 222	18.02 232	8.00 241	8.14 243	25.14 225	59.29 227	61.02 222	55.19 236	40.31 233
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
gg-mvs-nofinetune		62.55 181	65.05 179	59.62 190	78.72 87	77.61 135	70.83 163	53.63 185	39.71 223	22.04 227	36.36 216	64.32 97	47.53 189	81.16 75	79.03 95	85.00 157	77.17 166

test2356		47.20 220	48.62 224	45.54 220	56.38 225	54.89 226	50.62 222	45.08 221	38.65 224	23.40 220	36.23 217	31.10 229	29.31 220	62.76 218	62.49 218	68.48 223	54.23 228

testus		45.61 224	49.06 223	41.59 225	56.13 226	55.28 225	43.51 230	39.64 230	37.74 225	18.23 231	35.52 221	31.28 228	24.69 228	62.46 219	62.90 216	67.33 225	58.26 224

tmp_tt				14.50 238	14.68 242	7.17 245	10.46 245	2.21 240	37.73 226	28.71 213	25.26 233	16.98 238	4.37 240	31.49 236	29.77 236	26.56 240

testpf		47.41 218	48.47 225	46.18 218	66.30 201	50.67 231	48.15 227	42.60 227	37.10 227	28.75 212	40.97 208	39.01 222	30.82 218	52.95 233	53.74 232	60.46 231	64.87 210

1111		43.08 225	44.02 228	41.98 224	59.22 216	49.27 234	41.48 232	45.63 219	35.01 228	23.06 223	28.60 228	30.15 231	27.22 221	60.42 224	57.97 226	55.27 235	46.74 231

.test1245		30.81 232	29.14 235	32.77 231	59.22 216	49.27 234	41.48 232	45.63 219	35.01 228	23.06 223	28.60 228	30.15 231	27.22 221	60.42 224	0.10 239	0.01 243	0.43 240

pmmvs3		47.65 217	49.08 222	45.99 219	44.61 234	54.79 227	50.04 223	31.95 237	33.91 230	29.90 209	30.37 224	33.53 226	46.31 196	63.50 215	63.67 215	73.14 213	63.77 214

gm-plane-assit		57.00 205	57.62 212	56.28 202	76.10 120	62.43 219	47.62 228	46.57 215	33.84 231	23.24 221	37.52 213	40.19 220	59.61 135	79.81 100	77.55 120	84.55 167	72.03 195

LTVRE_ROB	59.44 16	61.82 193	62.64 195	60.87 183	72.83 169	77.19 138	64.37 198	58.97 156	33.56 232	28.00 214	52.59 173	42.21 216	63.93 106	74.52 167	76.28 152	77.15 197	82.13 120
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
PMVS	39.38 18	46.06 223	43.30 229	49.28 216	62.93 207	38.75 238	41.88 231	53.50 186	33.33 233	35.46 204	28.90 227	31.01 230	33.04 216	58.61 229	54.63 231	68.86 222	57.88 225
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmv		42.58 226	44.36 226	40.49 226	54.63 230	52.76 228	41.21 234	44.37 223	28.83 234	12.87 234	27.16 231	25.03 235	23.01 231	60.83 222	61.13 220	66.88 226	54.81 226

test1235678		42.57 227	44.36 226	40.49 226	54.63 230	52.75 229	41.21 234	44.37 223	28.82 235	12.87 234	27.15 232	25.01 236	23.01 231	60.83 222	61.13 220	66.88 226	54.81 226

DeepMVS_CX						18.74 244	18.55 241	8.02 239	26.96 236	7.33 239	23.81 235	13.05 242	25.99 223	25.17 238		22.45 242	36.25 236

test12356		35.10 231	38.50 231	31.13 232	44.14 236	43.70 237	32.27 237	34.42 233	26.51 237	9.47 238	25.22 234	20.34 237	10.86 238	53.47 231	56.15 227	55.59 234	44.11 232

PMMVS2		25.60 233	29.75 234	20.76 236	28.00 241	30.93 240	23.10 240	29.18 238	23.14 238	1.46 244	18.23 236	16.54 239	5.08 239	40.22 235	41.40 235	37.76 237	37.79 235

no-one		36.35 230	37.59 232	34.91 229	46.13 232	49.89 233	27.99 239	43.56 225	20.91 239	7.03 240	14.64 237	15.50 241	18.92 236	42.95 234	60.20 223	65.84 228	59.03 223

EMVS		20.98 235	17.15 238	25.44 234	39.51 239	19.37 243	12.66 242	39.59 231	19.10 240	6.62 242	9.27 239	4.40 245	22.43 233	17.99 240	24.40 238	31.81 239	25.53 238

E-PMN		21.77 234	18.24 237	25.89 233	40.22 238	19.58 242	12.46 243	39.87 229	18.68 241	6.71 241	9.57 238	4.31 246	22.36 234	19.89 239	27.28 237	33.73 238	28.34 237

MVE	19.12 19	20.47 236	23.27 236	17.20 237	12.66 243	25.41 241	10.52 244	34.14 235	14.79 242	6.53 243	8.79 240	4.68 244	16.64 237	29.49 237	41.63 234	22.73 241	38.11 234
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.09 237	0.15 239	0.02 239	0.01 245	0.02 246	0.05 247	0.01 242	0.11 243	0.01 246	0.26 243	0.01 247	0.06 243	0.10 241	0.10 239	0.01 243	0.43 240

test123		0.09 237	0.14 240	0.02 239	0.00 246	0.02 246	0.02 248	0.01 242	0.09 244	0.00 247	0.30 242	0.00 248	0.08 241	0.03 242	0.09 241	0.01 243	0.45 239

sosnet-low-res		0.00 239	0.00 241	0.00 241	0.00 246	0.00 248	0.00 249	0.00 244	0.00 245	0.00 247	0.00 244	0.00 248	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

sosnet		0.00 239	0.00 241	0.00 241	0.00 246	0.00 248	0.00 249	0.00 244	0.00 245	0.00 247	0.00 244	0.00 248	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

our_test_3					67.93 196	70.99 186	66.89 183

MTAPA										83.48 1		86.45 14

MTMP										82.66 3		84.91 22

Patchmatch-RL test							2.85 246

XVS					86.63 41	88.68 25	85.00 45			71.81 43		81.92 32				90.47 19

X-MVStestdata					86.63 41	88.68 25	85.00 45			71.81 43		81.92 32				90.47 19

mPP-MVS					89.90 22							81.29 37

Patchmtry						65.80 205	65.97 190	52.74 190		52.65 129

High-resolution multi-view benchmark