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.

ESAPD		95.53 1	96.13 1	94.82 1	96.81 21	98.05 1	97.42 1	93.09 2	94.31 5	91.49 4	97.12 1	95.03 2	93.27 2	95.55 4	94.58 8	96.86 2	98.25 1

APDe-MVS		95.23 2	95.69 2	94.70 2	97.12 11	97.81 4	97.19 2	92.83 3	95.06 2	90.98 6	96.47 2	92.77 9	93.38 1	95.34 7	94.21 13	96.68 5	98.17 2

HSP-MVS		94.83 3	95.37 3	94.21 6	96.82 20	97.94 3	96.69 4	92.37 8	93.97 9	90.29 11	96.16 4	93.71 4	92.70 6	94.80 14	93.13 33	96.37 9	97.90 6

SMA-MVS		94.70 4	95.35 4	93.93 9	97.57 2	97.57 6	95.98 10	91.91 10	94.50 3	90.35 9	93.46 14	92.72 10	91.89 15	95.89 1	95.22 1	95.88 21	98.10 3

HPM-MVS++		94.60 5	94.91 7	94.24 5	97.86 1	96.53 29	96.14 7	92.51 5	93.87 12	90.76 8	93.45 15	93.84 3	92.62 7	95.11 10	94.08 16	95.58 41	97.48 11

SD-MVS		94.53 6	95.22 5	93.73 12	95.69 32	97.03 11	95.77 18	91.95 9	94.41 4	91.35 5	94.97 5	93.34 6	91.80 17	94.72 17	93.99 17	95.82 28	98.07 4

TSAR-MVS + MP.		94.48 7	94.97 6	93.90 10	95.53 33	97.01 12	96.69 4	90.71 19	94.24 6	90.92 7	94.97 5	92.19 12	93.03 3	94.83 13	93.60 23	96.51 8	97.97 5

APD-MVS		94.37 8	94.47 12	94.26 4	97.18 9	96.99 13	96.53 6	92.68 4	92.45 21	89.96 14	94.53 8	91.63 16	92.89 4	94.58 19	93.82 20	96.31 12	97.26 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		94.37 8	94.65 8	94.04 8	97.29 7	97.11 9	96.00 9	92.43 7	93.45 13	89.85 16	90.92 22	93.04 7	92.59 8	95.77 2	94.82 4	96.11 16	97.42 13

SteuartSystems-ACMMP		94.06 10	94.65 8	93.38 16	96.97 16	97.36 7	96.12 8	91.78 11	92.05 25	87.34 27	94.42 9	90.87 20	91.87 16	95.47 6	94.59 7	96.21 14	97.77 8
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		94.02 11	94.22 14	93.78 11	97.25 8	96.85 17	95.81 16	90.94 18	94.12 7	90.29 11	94.09 11	89.98 26	92.52 9	93.94 27	93.49 28	95.87 23	97.10 19

ACMMP_Plus		93.94 12	94.49 11	93.30 17	97.03 14	97.31 8	95.96 11	91.30 15	93.41 15	88.55 21	93.00 16	90.33 23	91.43 23	95.53 5	94.41 11	95.53 43	97.47 12

MCST-MVS		93.81 13	94.06 15	93.53 14	96.79 22	96.85 17	95.95 12	91.69 13	92.20 23	87.17 29	90.83 24	93.41 5	91.96 13	94.49 21	93.50 26	97.61 1	97.12 18

zzz-MVS		93.80 14	93.45 22	94.20 7	97.53 3	96.43 33	95.88 15	91.12 17	94.09 8	92.74 3	87.68 30	90.77 21	92.04 12	94.74 16	93.56 25	95.91 20	96.85 23

ACMMPR		93.72 15	93.94 16	93.48 15	97.07 12	96.93 14	95.78 17	90.66 21	93.88 11	89.24 18	93.53 13	89.08 33	92.24 10	93.89 29	93.50 26	95.88 21	96.73 27

NCCC		93.69 16	93.66 19	93.72 13	97.37 5	96.66 26	95.93 14	92.50 6	93.40 16	88.35 22	87.36 32	92.33 11	92.18 11	94.89 12	94.09 15	96.00 17	96.91 22

MP-MVS		93.35 17	93.59 20	93.08 20	97.39 4	96.82 19	95.38 21	90.71 19	90.82 32	88.07 24	92.83 18	90.29 24	91.32 24	94.03 24	93.19 32	95.61 39	97.16 16
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		93.25 18	93.26 23	93.24 18	96.84 19	96.51 30	95.52 20	90.61 22	92.37 22	88.88 19	90.91 23	89.52 29	91.91 14	93.64 31	92.78 39	95.69 34	97.09 20

DeepC-MVS_fast	88.76 1	93.10 19	93.02 26	93.19 19	97.13 10	96.51 30	95.35 22	91.19 16	93.14 18	88.14 23	85.26 38	89.49 30	91.45 20	95.17 8	95.07 2	95.85 26	96.48 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + ACMM		92.97 20	94.51 10	91.16 33	95.88 30	96.59 27	95.09 25	90.45 25	93.42 14	83.01 51	94.68 7	90.74 22	88.74 36	94.75 15	93.78 21	93.82 142	97.63 9

train_agg		92.87 21	93.53 21	92.09 27	96.88 18	95.38 46	95.94 13	90.59 23	90.65 34	83.65 48	94.31 10	91.87 15	90.30 28	93.38 33	92.42 40	95.17 61	96.73 27

PGM-MVS		92.76 22	93.03 25	92.45 25	97.03 14	96.67 25	95.73 19	87.92 37	90.15 39	86.53 33	92.97 17	88.33 39	91.69 18	93.62 32	93.03 34	95.83 27	96.41 33

CSCG		92.76 22	93.16 24	92.29 26	96.30 24	97.74 5	94.67 29	88.98 31	92.46 20	89.73 17	86.67 34	92.15 13	88.69 37	92.26 47	92.92 37	95.40 47	97.89 7

TSAR-MVS + GP.		92.71 24	93.91 17	91.30 31	91.96 67	96.00 38	93.43 37	87.94 36	92.53 19	86.27 37	93.57 12	91.94 14	91.44 22	93.29 34	92.89 38	96.78 4	97.15 17

DeepPCF-MVS	88.51 2	92.64 25	94.42 13	90.56 37	94.84 39	96.92 15	91.31 59	89.61 27	95.16 1	84.55 43	89.91 26	91.45 17	90.15 30	95.12 9	94.81 5	92.90 168	97.58 10

X-MVS		92.36 26	92.75 27	91.90 29	96.89 17	96.70 22	95.25 23	90.48 24	91.50 30	83.95 45	88.20 28	88.82 35	89.11 33	93.75 30	93.43 29	95.75 33	96.83 25

DeepC-MVS	87.86 3	92.26 27	91.86 30	92.73 22	96.18 25	96.87 16	95.19 24	91.76 12	92.17 24	86.58 32	81.79 46	85.85 45	90.88 26	94.57 20	94.61 6	95.80 29	97.18 15
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PHI-MVS		92.05 28	93.74 18	90.08 40	94.96 36	97.06 10	93.11 41	87.71 39	90.71 33	80.78 61	92.40 19	91.03 18	87.68 48	94.32 23	94.48 10	96.21 14	96.16 36

ACMMP		92.03 29	92.16 28	91.87 30	95.88 30	96.55 28	94.47 31	89.49 28	91.71 28	85.26 39	91.52 21	84.48 50	90.21 29	92.82 42	91.63 46	95.92 19	96.42 32
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
MSLP-MVS++		92.02 30	91.40 32	92.75 21	96.01 28	95.88 41	93.73 36	89.00 29	89.89 40	90.31 10	81.28 51	88.85 34	91.45 20	92.88 41	94.24 12	96.00 17	96.76 26

3Dnovator+	86.06 4	91.60 31	90.86 37	92.47 24	96.00 29	96.50 32	94.70 28	87.83 38	90.49 35	89.92 15	74.68 79	89.35 31	90.66 27	94.02 25	94.14 14	95.67 36	96.85 23

CPTT-MVS		91.39 32	90.95 35	91.91 28	95.06 34	95.24 48	95.02 26	88.98 31	91.02 31	86.71 31	84.89 40	88.58 38	91.60 19	90.82 77	89.67 85	94.08 120	96.45 31

CANet		91.33 33	91.46 31	91.18 32	95.01 35	96.71 21	93.77 34	87.39 41	87.72 49	87.26 28	81.77 47	89.73 27	87.32 53	94.43 22	93.86 19	96.31 12	96.02 39

CDPH-MVS		91.14 34	92.01 29	90.11 39	96.18 25	96.18 36	94.89 27	88.80 33	88.76 45	77.88 78	89.18 27	87.71 42	87.29 54	93.13 36	93.31 31	95.62 38	95.84 41

MVS_0304		90.88 35	91.35 33	90.34 38	93.91 47	96.79 20	94.49 30	86.54 45	86.57 53	82.85 52	81.68 49	89.70 28	87.57 50	94.64 18	93.93 18	96.67 6	96.15 37

MVS_111021_HR		90.56 36	91.29 34	89.70 45	94.71 41	95.63 43	91.81 54	86.38 46	87.53 50	81.29 58	87.96 29	85.43 47	87.69 47	93.90 28	92.93 36	96.33 10	95.69 44

3Dnovator	85.17 5	90.48 37	89.90 41	91.16 33	94.88 38	95.74 42	93.82 33	85.36 52	89.28 41	87.81 25	74.34 81	87.40 43	88.56 38	93.07 37	93.74 22	96.53 7	95.71 43

AdaColmap		90.29 38	88.38 51	92.53 23	96.10 27	95.19 49	92.98 42	91.40 14	89.08 43	88.65 20	78.35 65	81.44 62	91.30 25	90.81 78	90.21 68	94.72 88	93.59 77

OMC-MVS		90.23 39	90.40 38	90.03 41	93.45 52	95.29 47	91.89 53	86.34 47	93.25 17	84.94 42	81.72 48	86.65 44	88.90 34	91.69 54	90.27 67	94.65 93	93.95 72

MVS_111021_LR		90.14 40	90.89 36	89.26 50	93.23 54	94.05 64	90.43 63	84.65 57	90.16 38	84.52 44	90.14 25	83.80 53	87.99 43	92.50 45	90.92 54	94.74 86	94.70 61

DELS-MVS		89.71 41	89.68 42	89.74 43	93.75 49	96.22 35	93.76 35	85.84 48	82.53 68	85.05 41	78.96 61	84.24 51	84.25 69	94.91 11	94.91 3	95.78 32	96.02 39
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
EPNet		89.60 42	89.91 40	89.24 51	96.45 23	93.61 70	92.95 43	88.03 35	85.74 56	83.36 49	87.29 33	83.05 56	80.98 86	92.22 48	91.85 44	93.69 149	95.58 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		89.49 43	89.58 43	89.38 48	94.73 40	95.94 39	92.35 46	85.00 55	85.69 57	80.03 64	76.97 70	87.81 41	87.87 44	92.18 51	92.10 42	96.33 10	96.40 34

canonicalmvs		89.36 44	89.92 39	88.70 55	91.38 69	95.92 40	91.81 54	82.61 89	90.37 36	82.73 54	82.09 44	79.28 77	88.30 41	91.17 61	93.59 24	95.36 51	97.04 21

casdiffmvs1		89.19 45	89.09 45	89.31 49	91.86 68	95.44 44	92.81 45	82.23 96	88.97 44	85.78 38	82.59 43	81.31 63	87.87 44	89.06 104	90.78 56	95.34 53	95.46 49

HQP-MVS		89.13 46	89.58 43	88.60 57	93.53 51	93.67 68	93.29 39	87.58 40	88.53 46	75.50 83	87.60 31	80.32 67	87.07 55	90.66 83	89.95 75	94.62 96	96.35 35

TAPA-MVS	84.37 7	88.91 47	88.93 47	88.89 52	93.00 58	94.85 55	92.00 50	84.84 56	91.68 29	80.05 63	79.77 56	84.56 49	88.17 42	90.11 88	89.00 104	95.30 56	92.57 101
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PCF-MVS	84.60 6	88.66 48	87.75 61	89.73 44	93.06 57	96.02 37	93.22 40	90.00 26	82.44 70	80.02 65	77.96 66	85.16 48	87.36 52	88.54 113	88.54 110	94.72 88	95.61 46
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		88.66 48	88.52 49	88.82 53	91.37 70	94.22 61	92.82 44	82.08 98	88.27 47	85.14 40	81.86 45	78.53 81	85.93 62	91.17 61	90.61 61	95.55 42	95.00 53
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PLC	83.76 9	88.61 50	86.83 67	90.70 35	94.22 44	92.63 93	91.50 56	87.19 42	89.16 42	86.87 30	75.51 76	80.87 64	89.98 31	90.01 89	89.20 98	94.41 110	90.45 154
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + COLMAP		88.40 51	89.09 45	87.60 64	92.72 62	93.92 66	92.21 47	85.57 51	91.73 27	73.72 91	91.75 20	73.22 105	87.64 49	91.49 55	89.71 84	93.73 148	91.82 123

CNLPA		88.40 51	87.00 65	90.03 41	93.73 50	94.28 60	89.56 73	85.81 49	91.87 26	87.55 26	69.53 113	81.49 61	89.23 32	89.45 99	88.59 109	94.31 114	93.82 75

MAR-MVS		88.39 53	88.44 50	88.33 60	94.90 37	95.06 52	90.51 62	83.59 68	85.27 58	79.07 68	77.13 68	82.89 57	87.70 46	92.19 50	92.32 41	94.23 115	94.20 70
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
ACMP	83.90 8	88.32 54	88.06 54	88.62 56	92.18 65	93.98 65	91.28 60	85.24 53	86.69 52	81.23 59	85.62 36	75.13 94	87.01 56	89.83 91	89.77 82	94.79 82	95.43 51
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		88.25 55	88.55 48	87.89 62	92.84 61	93.66 69	93.35 38	85.22 54	85.77 55	74.03 90	86.60 35	76.29 90	86.62 58	91.20 59	90.58 63	95.29 57	95.75 42

PVSNet_BlendedMVS		88.19 56	88.00 55	88.42 58	92.71 63	94.82 56	89.08 79	83.81 64	84.91 59	86.38 34	79.14 59	78.11 82	82.66 75	93.05 38	91.10 49	95.86 24	94.86 57

PVSNet_Blended		88.19 56	88.00 55	88.42 58	92.71 63	94.82 56	89.08 79	83.81 64	84.91 59	86.38 34	79.14 59	78.11 82	82.66 75	93.05 38	91.10 49	95.86 24	94.86 57

casdiffmvs		87.83 58	87.45 63	88.28 61	91.01 74	95.16 50	91.42 58	82.08 98	84.68 62	83.26 50	80.75 54	77.48 86	86.53 60	89.82 92	89.84 79	95.38 50	94.43 64

v1.0		87.80 59	81.94 95	94.63 3	97.35 6	97.95 2	97.09 3	93.48 1	93.91 10	90.13 13	96.41 3	95.14 1	92.88 5	95.64 3	94.53 9	96.86 2	0.00 243

OpenMVS	82.53 11	87.71 60	86.84 66	88.73 54	94.42 43	95.06 52	91.02 61	83.49 71	82.50 69	82.24 56	67.62 124	85.48 46	85.56 63	91.19 60	91.30 48	95.67 36	94.75 59

ACMM	83.27 10	87.68 61	86.09 73	89.54 47	93.26 53	92.19 96	91.43 57	86.74 44	86.02 54	82.85 52	75.63 75	75.14 93	88.41 39	90.68 82	89.99 72	94.59 97	92.97 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		87.56 62	85.80 74	89.62 46	93.90 48	94.09 63	94.12 32	88.18 34	75.40 120	77.30 82	76.41 71	77.93 84	88.79 35	92.20 49	90.82 55	95.40 47	93.72 76

PVSNet_Blended_VisFu		87.40 63	87.80 57	86.92 67	92.86 59	95.40 45	88.56 94	83.45 74	79.55 94	82.26 55	74.49 80	84.03 52	79.24 136	92.97 40	91.53 47	95.15 63	96.65 29

diffmvs1		87.00 64	87.76 59	86.12 69	88.93 110	93.78 67	90.40 64	80.42 113	88.25 48	78.77 70	79.52 58	79.73 73	85.07 65	89.30 101	89.49 92	93.11 164	94.28 67

MVS_Test		86.93 65	87.24 64	86.56 68	90.10 98	93.47 72	90.31 65	80.12 121	83.55 65	78.12 73	79.58 57	79.80 71	85.45 64	90.17 87	90.59 62	95.29 57	93.53 78

EPP-MVSNet		86.55 66	87.76 59	85.15 76	90.52 83	94.41 59	87.24 113	82.32 94	81.79 73	73.60 92	78.57 63	82.41 58	82.07 80	91.23 57	90.39 65	95.14 64	95.48 48

DI_MVS_plusplus_trai		86.41 67	85.54 75	87.42 65	89.24 107	93.13 79	92.16 49	82.65 87	82.30 71	80.75 62	68.30 120	80.41 66	85.01 66	90.56 84	90.07 70	94.70 90	94.01 71

IS_MVSNet		86.18 68	88.18 53	83.85 97	91.02 73	94.72 58	87.48 105	82.46 91	81.05 80	70.28 102	76.98 69	82.20 60	76.65 151	93.97 26	93.38 30	95.18 60	94.97 54

UA-Net		86.07 69	87.78 58	84.06 93	92.85 60	95.11 51	87.73 101	84.38 58	73.22 146	73.18 94	79.99 55	89.22 32	71.47 184	93.22 35	93.03 34	94.76 85	90.69 149

MVSTER		86.03 70	86.12 71	85.93 71	88.62 113	89.93 132	89.33 76	79.91 124	81.87 72	81.35 57	81.07 52	74.91 95	80.66 92	92.13 52	90.10 69	95.68 35	92.80 91

LS3D		85.96 71	84.37 81	87.81 63	94.13 45	93.27 76	90.26 66	89.00 29	84.91 59	72.84 96	71.74 99	72.47 107	87.45 51	89.53 98	89.09 101	93.20 162	89.60 156

UGNet		85.90 72	88.23 52	83.18 105	88.96 109	94.10 62	87.52 104	83.60 67	81.66 74	77.90 77	80.76 53	83.19 55	66.70 203	91.13 72	90.71 60	94.39 111	96.06 38
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
Anonymous20240521		85.88 73	86.17 70	85.54 74	89.10 108	89.85 134	89.34 75	80.70 111	83.04 66	78.08 75	76.19 73	79.00 78	82.42 78	89.67 95	90.30 66	93.63 152	95.12 52

diffmvs		85.72 74	86.10 72	85.27 75	88.43 115	93.34 75	88.98 88	80.17 120	84.21 63	77.41 81	78.53 64	76.01 91	83.28 72	88.09 117	88.61 108	93.34 158	93.92 73

CANet_DTU		85.43 75	87.72 62	82.76 109	90.95 77	93.01 85	89.99 67	75.46 179	82.67 67	64.91 153	83.14 41	80.09 68	80.68 91	92.03 53	91.03 51	94.57 99	92.08 115

Effi-MVS+		85.33 76	85.08 77	85.63 73	89.69 105	93.42 73	89.90 68	80.31 118	79.32 95	72.48 98	73.52 91	74.03 98	86.55 59	90.99 74	89.98 73	94.83 80	94.27 69

FC-MVSNet-train		85.18 77	85.31 76	85.03 77	90.67 78	91.62 102	87.66 102	83.61 66	79.75 91	74.37 89	78.69 62	71.21 111	78.91 138	91.23 57	89.96 74	94.96 71	94.69 62

GBi-Net		84.51 78	84.80 78	84.17 90	84.20 157	89.95 129	89.70 70	80.37 114	81.17 76	75.50 83	69.63 108	79.69 74	79.75 121	90.73 79	90.72 57	95.52 44	91.71 128

test1		84.51 78	84.80 78	84.17 90	84.20 157	89.95 129	89.70 70	80.37 114	81.17 76	75.50 83	69.63 108	79.69 74	79.75 121	90.73 79	90.72 57	95.52 44	91.71 128

FMVSNet3		84.44 80	84.64 80	84.21 89	84.32 156	90.13 127	89.85 69	80.37 114	81.17 76	75.50 83	69.63 108	79.69 74	79.62 124	89.72 94	90.52 64	95.59 40	91.58 134

Anonymous20231211		84.42 81	83.02 86	86.05 70	88.85 111	92.70 92	88.92 90	83.40 75	79.99 88	78.31 72	55.83 205	78.92 79	83.33 71	89.06 104	89.76 83	93.50 154	94.90 55

Vis-MVSNet		84.38 82	86.68 69	81.70 125	87.65 122	94.89 54	88.14 96	80.90 110	74.48 130	68.23 121	77.53 67	80.72 65	69.98 189	92.68 43	91.90 43	95.33 55	94.58 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FMVSNet2		83.87 83	83.73 84	84.05 95	84.20 157	89.95 129	89.70 70	80.21 119	79.17 97	74.89 87	65.91 130	77.49 85	79.75 121	90.87 76	91.00 53	95.52 44	91.71 128

MSDG		83.87 83	81.02 109	87.19 66	92.17 66	89.80 136	89.15 77	85.72 50	80.61 85	79.24 67	66.66 128	68.75 119	82.69 74	87.95 120	87.44 123	94.19 116	85.92 190

Fast-Effi-MVS+		83.77 85	82.98 87	84.69 78	87.98 117	91.87 99	88.10 98	77.70 153	78.10 104	73.04 95	69.13 115	68.51 120	86.66 57	90.49 85	89.85 78	94.67 92	92.88 88

Vis-MVSNet (Re-imp)		83.65 86	86.81 68	79.96 160	90.46 86	92.71 91	84.84 160	82.00 100	80.93 82	62.44 170	76.29 72	82.32 59	65.54 206	92.29 46	91.66 45	94.49 105	91.47 135

tfpn111		83.51 87	82.68 90	84.47 83	90.30 90	93.09 80	89.05 81	82.72 81	75.14 121	69.49 110	74.24 82	63.13 140	80.38 99	91.15 66	89.51 87	94.91 73	92.50 107

RPSCF		83.46 88	83.36 85	83.59 101	87.75 119	87.35 169	84.82 161	79.46 135	83.84 64	78.12 73	82.69 42	79.87 69	82.60 77	82.47 197	81.13 201	88.78 203	86.13 188

PatchMatch-RL		83.34 89	81.36 103	85.65 72	90.33 89	89.52 143	84.36 164	81.82 102	80.87 84	79.29 66	74.04 84	62.85 147	86.05 61	88.40 115	87.04 130	92.04 176	86.77 181

IterMVS-LS		83.28 90	82.95 88	83.65 99	88.39 116	88.63 157	86.80 132	78.64 143	76.56 111	73.43 93	72.52 98	75.35 92	80.81 89	86.43 143	88.51 111	93.84 141	92.66 95
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		82.86 91	81.46 100	84.48 82	90.30 90	93.09 80	89.05 81	82.71 83	75.14 121	69.56 107	65.72 132	63.13 140	80.38 99	91.15 66	89.51 87	94.91 73	92.50 107

conf200view11		82.85 92	81.46 100	84.47 83	90.30 90	93.09 80	89.05 81	82.72 81	75.14 121	69.49 110	65.72 132	63.13 140	80.38 99	91.15 66	89.51 87	94.91 73	92.50 107

thres200		82.77 93	81.25 105	84.54 79	90.38 87	93.05 83	89.13 78	82.67 85	74.40 131	69.53 109	65.69 135	63.03 145	80.63 93	91.15 66	89.42 93	94.88 77	92.04 117

thres400		82.68 94	81.15 106	84.47 83	90.52 83	92.89 90	88.95 89	82.71 83	74.33 132	69.22 114	65.31 137	62.61 149	80.63 93	90.96 75	89.50 91	94.79 82	92.45 112

conf0.01		82.64 95	81.02 109	84.53 81	90.30 90	93.22 78	89.05 81	82.75 79	75.14 121	69.69 106	67.15 126	59.19 189	80.38 99	91.16 64	89.51 87	95.00 69	91.76 126

IB-MVS	79.09 12	82.60 96	82.19 93	83.07 106	91.08 72	93.55 71	80.90 192	81.35 106	76.56 111	80.87 60	64.81 146	69.97 114	68.87 193	85.64 153	90.06 71	95.36 51	94.74 60
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
thres100view900		82.55 97	81.01 112	84.34 86	90.30 90	92.27 94	89.04 86	82.77 78	75.14 121	69.56 107	65.72 132	63.13 140	79.62 124	89.97 90	89.26 96	94.73 87	91.61 133

conf0.002		82.54 98	80.83 117	84.54 79	90.28 95	93.24 77	89.05 81	82.75 79	75.14 121	69.75 105	67.99 121	57.12 200	80.38 99	91.16 64	89.79 80	95.02 67	91.36 137

thres600view7		82.53 99	81.02 109	84.28 87	90.61 79	93.05 83	88.57 92	82.67 85	74.12 136	68.56 119	65.09 141	62.13 160	80.40 98	91.15 66	89.02 103	94.88 77	92.59 98

view600		82.51 100	81.00 113	84.27 88	90.56 82	92.95 88	88.57 92	82.57 90	74.16 135	68.70 118	65.13 140	62.15 159	80.36 104	91.15 66	88.98 105	94.87 79	92.48 110

view800		82.38 101	80.93 114	84.06 93	90.59 81	92.96 87	88.11 97	82.44 92	73.92 137	68.10 122	65.07 142	61.64 162	80.10 110	91.17 61	89.24 97	95.01 68	92.56 102

CHOSEN 1792x2688		82.16 102	80.91 116	83.61 100	91.14 71	92.01 98	89.55 74	79.15 139	79.87 90	70.29 101	52.51 214	72.56 106	81.39 82	88.87 108	88.17 115	90.15 195	92.37 113

Effi-MVS+-dtu		82.05 103	81.76 96	82.38 111	87.72 120	90.56 113	86.90 130	78.05 149	73.85 140	66.85 130	71.29 101	71.90 109	82.00 81	86.64 138	85.48 173	92.76 170	92.58 100

EPNet_dtu		81.98 104	83.82 83	79.83 162	94.10 46	85.97 187	87.29 110	84.08 63	80.61 85	59.96 191	81.62 50	77.19 87	62.91 210	87.21 124	86.38 143	90.66 191	87.77 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		81.87 105	81.33 104	82.50 110	85.31 144	91.30 105	85.70 146	84.25 59	75.89 115	64.21 156	66.95 127	64.65 133	80.22 106	87.07 127	89.18 99	95.27 59	94.29 65

ACMH	78.52 14	81.86 106	80.45 122	83.51 102	90.51 85	91.22 106	85.62 149	84.23 60	70.29 169	62.21 171	69.04 117	64.05 138	84.48 68	87.57 122	88.45 112	94.01 125	92.54 105
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	79.08 13	81.84 107	80.06 125	83.91 96	89.92 103	90.62 112	86.21 141	83.48 73	73.88 139	65.75 144	66.38 129	65.30 130	84.63 67	85.90 148	87.25 127	93.45 155	91.13 139

tfpn		81.79 108	80.06 125	83.82 98	90.61 79	92.91 89	87.62 103	82.34 93	73.66 143	67.46 125	64.99 143	55.50 208	79.77 120	91.12 73	89.62 86	95.14 64	92.59 98

MS-PatchMatch		81.79 108	81.44 102	82.19 114	90.35 88	89.29 147	88.08 99	75.36 180	77.60 106	69.00 115	64.37 149	78.87 80	77.14 150	88.03 119	85.70 169	93.19 163	86.24 187

tfpn_ndepth		81.77 110	82.29 92	81.15 140	89.79 104	91.71 101	85.49 152	81.63 105	79.17 97	64.76 154	73.04 93	68.14 124	70.62 187	88.72 109	87.88 119	94.63 95	87.38 174

PMMVS		81.65 111	84.05 82	78.86 167	78.56 215	82.63 209	83.10 172	67.22 210	81.39 75	70.11 104	84.91 39	79.74 72	82.12 79	87.31 123	85.70 169	92.03 177	86.67 184

FMVSNet1		81.64 112	80.61 121	82.84 108	82.36 198	89.20 149	88.67 91	79.58 133	70.79 163	72.63 97	58.95 192	72.26 108	79.34 135	90.73 79	90.72 57	94.47 106	91.62 132

CDS-MVSNet		81.63 113	82.09 94	81.09 142	87.21 127	90.28 123	87.46 107	80.33 117	69.06 182	70.66 99	71.30 100	73.87 99	67.99 196	89.58 96	89.87 77	92.87 169	90.69 149
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HyFIR lowres test		81.62 114	79.45 136	84.14 92	91.00 75	93.38 74	88.27 95	78.19 147	76.28 113	70.18 103	48.78 218	73.69 101	83.52 70	87.05 128	87.83 121	93.68 150	89.15 159

UniMVSNet (Re)		81.22 115	81.08 108	81.39 132	85.35 143	91.76 100	84.93 159	82.88 77	76.13 114	65.02 152	64.94 144	63.09 144	75.17 158	87.71 121	89.04 102	94.97 70	94.88 56

DU-MVS		81.20 116	80.30 123	82.25 112	84.98 151	90.94 110	85.70 146	83.58 69	75.74 117	64.21 156	65.30 138	59.60 186	80.22 106	86.89 131	89.31 94	94.77 84	94.29 65

thresconf0.02		81.14 117	80.93 114	81.39 132	90.01 102	91.31 104	86.79 133	82.28 95	76.97 110	61.46 182	74.24 82	62.08 161	72.98 176	88.70 110	87.90 117	94.81 81	85.28 193

tfpn1000		81.03 118	81.70 97	80.25 158	90.18 96	91.35 103	83.96 167	81.15 109	78.00 105	62.11 173	73.37 92	65.75 127	69.17 192	88.68 111	87.44 123	94.93 72	87.29 176

conf0.05thres1000		81.00 119	79.12 137	83.20 104	90.14 97	92.15 97	87.05 125	82.09 97	68.11 188	66.19 135	59.67 186	61.10 173	79.05 137	90.47 86	89.11 100	94.68 91	93.22 80

CostFormer		80.94 120	80.21 124	81.79 120	87.69 121	88.58 158	87.47 106	70.66 196	80.02 87	77.88 78	73.03 94	71.40 110	78.24 142	79.96 207	79.63 203	88.82 202	88.84 160

tfpnview11		80.84 121	81.10 107	80.54 152	90.10 98	90.96 109	85.44 153	81.84 101	75.77 116	59.27 195	73.54 88	64.40 134	71.69 181	89.16 102	87.97 116	94.91 73	85.92 190

USDC		80.69 122	79.89 129	81.62 128	86.48 132	89.11 152	86.53 137	78.86 140	81.15 79	63.48 163	72.98 95	59.12 192	81.16 84	87.10 126	85.01 177	93.23 161	84.77 197

tfpn_n400		80.63 123	80.79 118	80.43 155	90.02 100	91.08 107	85.34 155	81.79 103	72.93 149	59.27 195	73.54 88	64.40 134	71.61 182	89.05 106	88.21 113	94.56 100	86.32 185

tfpnconf		80.63 123	80.79 118	80.43 155	90.02 100	91.08 107	85.34 155	81.79 103	72.93 149	59.27 195	73.54 88	64.40 134	71.61 182	89.05 106	88.21 113	94.56 100	86.32 185

TranMVSNet+NR-MVSNet		80.52 125	79.84 130	81.33 135	84.92 153	90.39 117	85.53 151	84.22 61	74.27 133	60.68 189	64.93 145	59.96 181	77.48 147	86.75 136	89.28 95	95.12 66	93.29 79

DWT-MVSNet_training		80.51 126	78.05 156	83.39 103	88.64 112	88.33 163	86.11 143	76.33 164	79.65 92	78.64 71	69.62 111	58.89 194	80.82 87	80.50 204	82.03 199	89.77 198	87.36 175

COLMAP_ROB	76.78 15	80.50 127	78.49 142	82.85 107	90.96 76	89.65 141	86.20 142	83.40 75	77.15 108	66.54 131	62.27 154	65.62 129	77.89 145	85.23 171	84.70 181	92.11 175	84.83 196
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CHOSEN 280x420		80.28 128	81.66 98	78.67 169	82.92 191	79.24 221	85.36 154	66.79 212	78.11 103	70.32 100	75.03 78	79.87 69	81.09 85	89.07 103	83.16 190	85.54 218	87.17 178

NR-MVSNet		80.25 129	79.98 128	80.56 151	85.20 146	90.94 110	85.65 148	83.58 69	75.74 117	61.36 184	65.30 138	56.75 202	72.38 177	88.46 114	88.80 106	95.16 62	93.87 74

v6		80.11 130	78.47 143	82.01 115	83.97 164	90.49 114	87.19 118	79.67 129	71.59 156	67.51 124	61.26 158	62.46 154	79.81 119	85.49 161	86.18 156	93.89 135	91.86 120

v1neww		80.09 131	78.45 145	82.00 116	83.97 164	90.49 114	87.18 119	79.67 129	71.49 157	67.44 126	61.24 160	62.41 155	79.83 116	85.49 161	86.19 153	93.88 137	91.86 120

v7new		80.09 131	78.45 145	82.00 116	83.97 164	90.49 114	87.18 119	79.67 129	71.49 157	67.44 126	61.24 160	62.41 155	79.83 116	85.49 161	86.19 153	93.88 137	91.86 120

pmmvs4		79.99 133	78.08 152	82.22 113	83.04 188	87.16 172	84.95 158	78.80 142	78.64 101	74.53 88	64.61 147	59.41 187	79.45 134	84.13 183	84.54 183	92.53 172	88.08 167

Fast-Effi-MVS+-dtu		79.95 134	80.69 120	79.08 165	86.36 133	89.14 151	85.85 144	72.28 190	72.85 151	59.32 194	70.43 106	68.42 121	77.57 146	86.14 145	86.44 142	93.11 164	91.39 136

v8		79.90 135	78.39 148	81.66 127	83.97 164	89.81 135	87.16 122	77.40 155	71.49 157	67.71 123	61.24 160	62.49 152	79.83 116	85.48 165	86.17 157	93.89 135	92.02 119

v2v482		79.84 136	78.07 153	81.90 119	83.75 177	90.21 126	87.17 121	79.85 128	70.65 164	65.93 143	61.93 155	60.07 180	80.82 87	85.25 170	86.71 133	93.88 137	91.70 131

Baseline_NR-MVSNet		79.84 136	78.37 149	81.55 130	84.98 151	86.66 176	85.06 157	83.49 71	75.57 119	63.31 164	58.22 196	60.97 174	78.00 144	86.89 131	87.13 128	94.47 106	93.15 82

tpmp4_e23		79.82 138	77.96 161	82.00 116	87.59 123	86.93 173	87.81 100	72.21 191	79.99 88	78.02 76	67.83 123	64.77 131	78.74 139	79.99 206	78.90 206	87.65 208	87.29 176

v7		79.79 139	78.28 150	81.54 131	83.73 178	90.34 122	87.27 111	78.27 146	70.50 166	65.59 145	60.59 175	60.47 176	80.46 96	86.90 130	86.63 136	93.92 131	92.56 102

v1		79.76 140	78.06 155	81.74 123	83.89 171	90.38 118	87.20 115	79.88 127	70.23 170	66.17 140	60.92 168	61.56 163	79.50 132	85.37 166	86.17 157	93.81 143	91.77 124

v1141		79.75 141	78.04 157	81.75 121	83.89 171	90.37 119	87.20 115	79.89 126	70.23 170	66.18 137	60.92 168	61.48 167	79.54 128	85.36 167	86.17 157	93.81 143	91.76 126

divwei89l23v2f112		79.75 141	78.04 157	81.75 121	83.90 168	90.37 119	87.21 114	79.90 125	70.20 172	66.18 137	60.92 168	61.48 167	79.52 131	85.36 167	86.17 157	93.81 143	91.77 124

v18		79.71 143	77.98 160	81.73 124	84.02 163	86.67 175	87.37 108	76.35 163	72.61 152	68.86 116	61.35 157	62.65 148	79.94 112	85.49 161	86.21 148	93.85 140	90.92 142

v16		79.65 144	77.91 162	81.69 126	84.04 161	86.65 178	87.20 115	76.32 165	72.41 153	68.71 117	61.13 165	62.52 151	79.93 113	85.55 158	86.22 146	93.92 131	90.91 143

v10		79.62 145	78.19 151	81.28 136	83.73 178	89.69 140	87.27 111	76.86 159	70.50 166	65.46 146	60.58 177	60.47 176	80.44 97	86.91 129	86.63 136	93.93 129	92.55 104

v17		79.59 146	77.88 163	81.60 129	84.03 162	86.66 176	87.13 124	76.31 166	72.09 154	68.29 120	61.15 164	62.57 150	79.90 114	85.55 158	86.20 151	93.93 129	90.93 141

V42		79.59 146	78.43 147	80.94 146	82.79 194	89.71 139	86.66 134	76.73 161	71.38 160	67.42 128	61.01 166	62.30 157	78.39 141	85.56 157	86.48 140	93.65 151	92.60 97

GA-MVS		79.52 148	79.71 133	79.30 164	85.68 139	90.36 121	84.55 162	78.44 144	70.47 168	57.87 202	68.52 119	61.38 171	76.21 153	89.40 100	87.89 118	93.04 167	89.96 155

test-LLR		79.47 149	79.84 130	79.03 166	87.47 124	82.40 212	81.24 187	78.05 149	73.72 141	62.69 167	73.76 85	74.42 96	73.49 171	84.61 179	82.99 192	91.25 185	87.01 179

v1144		79.38 150	77.83 164	81.18 138	83.62 180	90.23 124	87.15 123	78.35 145	69.13 181	64.02 160	60.20 183	59.41 187	80.14 109	86.78 134	86.57 138	93.81 143	92.53 106

MDTV_nov1_ep13		79.14 151	79.49 135	78.74 168	85.40 142	86.89 174	84.32 166	70.29 198	78.85 99	69.42 112	75.37 77	73.29 104	75.64 156	80.61 203	79.48 205	87.36 209	81.91 205

v15		79.13 152	77.37 168	81.19 137	83.90 168	86.56 180	87.01 126	76.15 170	70.20 172	66.48 132	60.71 173	61.55 164	79.60 126	85.59 156	86.19 153	93.98 127	90.80 148

V14		79.11 153	77.35 170	81.16 139	83.90 168	86.54 181	86.94 127	76.10 172	70.14 174	66.41 134	60.59 175	61.54 165	79.59 127	85.64 153	86.20 151	94.04 123	90.82 146

V9		79.08 154	77.32 172	81.14 141	83.89 171	86.52 182	86.85 131	76.06 173	70.02 175	66.42 133	60.44 178	61.52 166	79.54 128	85.68 152	86.21 148	94.08 120	90.83 145

TDRefinement		79.05 155	77.05 178	81.39 132	88.45 114	89.00 154	86.92 128	82.65 87	74.21 134	64.41 155	59.17 189	59.16 190	74.52 164	85.23 171	85.09 176	91.37 183	87.51 173

v12		79.03 156	77.28 173	81.06 143	83.88 175	86.49 183	86.62 135	76.02 174	69.99 176	66.18 137	60.34 181	61.44 169	79.54 128	85.70 151	86.21 148	94.11 119	90.82 146

v11		79.02 157	77.36 169	80.95 145	83.89 171	86.48 184	86.53 137	75.77 178	69.69 178	65.21 151	60.36 180	60.24 179	80.32 105	87.20 125	86.54 139	93.96 128	91.02 140

v13		78.99 158	77.25 175	81.02 144	83.87 176	86.47 185	86.60 136	75.96 176	69.87 177	66.07 141	60.25 182	61.41 170	79.49 133	85.72 150	86.22 146	94.14 118	90.84 144

v1192		78.94 159	77.33 171	80.82 147	83.25 184	89.90 133	86.91 129	77.72 152	68.63 185	62.61 169	59.17 189	57.53 198	80.62 95	86.89 131	86.47 141	93.79 147	92.75 94

v144192		78.81 160	77.22 176	80.67 149	82.95 189	89.79 137	86.40 139	77.42 154	68.26 187	63.13 165	59.50 187	58.13 196	80.08 111	85.93 147	86.08 162	94.06 122	92.83 90

IterMVS		78.79 161	79.71 133	77.71 174	85.26 145	85.91 188	84.54 163	69.84 202	73.38 145	61.25 185	70.53 105	70.35 112	74.43 165	85.21 173	83.80 187	90.95 189	88.77 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		78.71 162	78.86 139	78.55 170	85.85 138	85.15 196	82.30 179	68.23 205	74.71 128	65.37 148	64.39 148	69.59 116	77.18 148	85.10 175	84.87 178	92.34 174	88.21 165

PatchmatchNet		78.67 163	78.85 140	78.46 171	86.85 131	86.03 186	83.77 169	68.11 207	80.88 83	66.19 135	72.90 96	73.40 103	78.06 143	79.25 211	77.71 212	87.75 207	81.75 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		78.59 164	76.84 181	80.62 150	83.61 181	89.16 150	83.65 170	79.24 138	69.38 180	69.34 113	59.88 185	60.41 178	75.19 157	83.81 185	84.63 182	92.70 171	90.63 151

v1921920		78.57 165	76.99 179	80.41 157	82.93 190	89.63 142	86.38 140	77.14 157	68.31 186	61.80 177	58.89 193	56.79 201	80.19 108	86.50 142	86.05 164	94.02 124	92.76 93

pm-mvs1		78.51 166	77.75 166	79.40 163	84.83 154	89.30 146	83.55 171	79.38 136	62.64 210	63.68 162	58.73 194	64.68 132	70.78 186	89.79 93	87.84 120	94.17 117	91.28 138

v1240		78.15 167	76.53 182	80.04 159	82.85 193	89.48 145	85.61 150	76.77 160	67.05 190	61.18 187	58.37 195	56.16 206	79.89 115	86.11 146	86.08 162	93.92 131	92.47 111

dps		78.02 168	75.94 190	80.44 154	86.06 135	86.62 179	82.58 174	69.98 200	75.14 121	77.76 80	69.08 116	59.93 182	78.47 140	79.47 209	77.96 210	87.78 206	83.40 201

anonymousdsp		77.94 169	79.00 138	76.71 185	79.03 213	87.83 166	79.58 197	72.87 189	65.80 199	58.86 201	65.82 131	62.48 153	75.99 154	86.77 135	88.66 107	93.92 131	95.68 45

test-mter		77.79 170	80.02 127	75.18 196	81.18 207	82.85 207	80.52 195	62.03 227	73.62 144	62.16 172	73.55 87	73.83 100	73.81 169	84.67 178	83.34 189	91.37 183	88.31 164

TESTMET0.1,1		77.78 171	79.84 130	75.38 195	80.86 208	82.40 212	81.24 187	62.72 226	73.72 141	62.69 167	73.76 85	74.42 96	73.49 171	84.61 179	82.99 192	91.25 185	87.01 179

tpm cat1		77.78 171	75.28 199	80.70 148	87.14 128	85.84 189	85.81 145	70.40 197	77.44 107	78.80 69	63.72 150	64.01 139	76.55 152	75.60 221	75.21 220	85.51 219	85.12 194

EPMVS		77.53 173	78.07 153	76.90 184	86.89 130	84.91 199	82.18 182	66.64 213	81.00 81	64.11 159	72.75 97	69.68 115	74.42 166	79.36 210	78.13 209	87.14 211	80.68 211

tfpnnormal		77.46 174	74.86 202	80.49 153	86.34 134	88.92 155	84.33 165	81.26 107	61.39 214	61.70 179	51.99 215	53.66 215	74.84 161	88.63 112	87.38 126	94.50 104	92.08 115

v7n		77.22 175	76.23 185	78.38 172	81.89 201	89.10 153	82.24 181	76.36 162	65.96 198	61.21 186	56.56 200	55.79 207	75.07 160	86.55 139	86.68 134	93.52 153	92.95 87

RPMNet		77.07 176	77.63 167	76.42 187	85.56 141	85.15 196	81.37 184	65.27 219	74.71 128	60.29 190	63.71 151	66.59 126	73.64 170	82.71 194	82.12 197	92.38 173	88.39 163

pmmvs5		76.93 177	76.33 184	77.62 175	81.97 200	88.40 162	81.32 186	74.35 183	65.42 204	61.42 183	63.07 152	57.95 197	73.23 174	85.60 155	85.35 175	93.41 156	88.55 162

TinyColmap		76.73 178	73.95 205	79.96 160	85.16 148	85.64 192	82.34 178	78.19 147	70.63 165	62.06 174	60.69 174	49.61 222	80.81 89	85.12 174	83.69 188	91.22 187	82.27 204

CVMVSNet		76.70 179	78.46 144	74.64 201	83.34 183	84.48 200	81.83 183	74.58 181	68.88 183	51.23 214	69.77 107	70.05 113	67.49 199	84.27 182	83.81 186	89.38 200	87.96 169

WR-MVS		76.63 180	78.02 159	75.02 197	84.14 160	89.76 138	78.34 205	80.64 112	69.56 179	52.32 210	61.26 158	61.24 172	60.66 211	84.45 181	87.07 129	93.99 126	92.77 92

TransMVSNet (Re)		76.57 181	75.16 200	78.22 173	85.60 140	87.24 170	82.46 175	81.23 108	59.80 218	59.05 200	57.07 199	59.14 191	66.60 204	88.09 117	86.82 131	94.37 112	87.95 170

v52		76.55 182	75.89 191	77.31 179	79.94 212	88.49 160	81.07 190	73.62 186	65.49 202	61.66 180	56.29 203	58.90 193	74.30 167	83.47 189	85.62 171	93.28 159	92.99 84

V4		76.55 182	75.89 191	77.32 178	79.95 211	88.50 159	81.07 190	73.62 186	65.47 203	61.71 178	56.31 202	58.87 195	74.28 168	83.48 188	85.62 171	93.28 159	92.98 85

tpmrst		76.55 182	75.99 189	77.20 180	87.32 126	83.05 205	82.86 173	65.62 217	78.61 102	67.22 129	69.19 114	65.71 128	75.87 155	76.75 218	75.33 219	84.31 223	83.28 202

FC-MVSNet-test		76.53 185	81.62 99	70.58 210	84.99 150	85.73 190	74.81 213	78.85 141	77.00 109	39.13 233	75.90 74	73.50 102	54.08 218	86.54 140	85.99 165	91.65 179	86.68 182

PatchT		76.42 186	77.81 165	74.80 199	78.46 216	84.30 201	71.82 219	65.03 221	73.89 138	65.37 148	61.58 156	66.70 125	77.18 148	85.10 175	84.87 178	90.94 190	88.21 165

TAMVS		76.42 186	77.16 177	75.56 193	83.05 187	85.55 193	80.58 194	71.43 193	65.40 205	61.04 188	67.27 125	69.22 118	67.99 196	84.88 177	84.78 180	89.28 201	83.01 203

EG-PatchMatch MVS		76.40 188	75.47 197	77.48 176	85.86 137	90.22 125	82.45 176	73.96 185	59.64 219	59.60 193	52.75 213	62.20 158	68.44 195	88.23 116	87.50 122	94.55 102	87.78 171

CP-MVSNet		76.36 189	76.41 183	76.32 189	82.73 195	88.64 156	79.39 198	79.62 132	67.21 189	53.70 206	60.72 172	55.22 210	67.91 198	83.52 187	86.34 144	94.55 102	93.19 81

tpm		76.30 190	76.05 188	76.59 186	86.97 129	83.01 206	83.83 168	67.06 211	71.83 155	63.87 161	69.56 112	62.88 146	73.41 173	79.79 208	78.59 207	84.41 222	86.68 182

v748		76.17 191	75.10 201	77.43 177	81.60 203	88.01 164	79.02 202	76.28 167	64.47 206	64.14 158	56.55 201	56.26 205	70.40 188	82.50 196	85.77 167	93.11 164	92.15 114

test0.0.03 1		76.03 192	78.51 141	73.12 207	87.47 124	85.13 198	76.32 210	78.05 149	73.19 148	50.98 215	70.64 103	69.28 117	55.53 214	85.33 169	84.38 184	90.39 193	81.63 207

PEN-MVS		76.02 193	76.07 186	75.95 192	83.17 186	87.97 165	79.65 196	80.07 123	66.57 194	51.45 212	60.94 167	55.47 209	66.81 202	82.72 193	86.80 132	94.59 97	92.03 118

SixPastTwentyTwo		76.02 193	75.72 194	76.36 188	83.38 182	87.54 167	75.50 212	76.22 168	65.50 201	57.05 203	70.64 103	53.97 214	74.54 163	80.96 202	82.12 197	91.44 181	89.35 158

PS-CasMVS		75.90 195	75.86 193	75.96 191	82.59 196	88.46 161	79.23 201	79.56 134	66.00 197	52.77 208	59.48 188	54.35 213	67.14 201	83.37 190	86.23 145	94.47 106	93.10 83

WR-MVS_H		75.84 196	76.93 180	74.57 202	82.86 192	89.50 144	78.34 205	79.36 137	66.90 192	52.51 209	60.20 183	59.71 183	59.73 212	83.61 186	85.77 167	94.65 93	92.84 89

LTVRE_ROB	74.41 16	75.78 197	74.72 203	77.02 183	85.88 136	89.22 148	82.44 177	77.17 156	50.57 230	45.45 222	65.44 136	52.29 218	81.25 83	85.50 160	87.42 125	89.94 197	92.62 96
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
gg-mvs-nofinetune		75.64 198	77.26 174	73.76 203	87.92 118	92.20 95	87.32 109	64.67 222	51.92 229	35.35 234	46.44 222	77.05 88	71.97 178	92.64 44	91.02 52	95.34 53	89.53 157

FMVSNet5		75.50 199	76.07 186	74.83 198	76.16 221	81.19 215	81.34 185	70.21 199	73.20 147	61.59 181	58.97 191	68.33 122	68.50 194	85.87 149	85.85 166	91.18 188	79.11 215

DTE-MVSNet		75.14 200	75.44 198	74.80 199	83.18 185	87.19 171	78.25 207	80.11 122	66.05 196	48.31 218	60.88 171	54.67 211	64.54 208	82.57 195	86.17 157	94.43 109	90.53 153

pmmvs6		74.83 201	72.89 208	77.09 181	82.11 199	87.50 168	80.88 193	76.97 158	52.79 228	61.91 176	46.66 221	60.49 175	69.28 191	86.74 137	85.46 174	91.39 182	90.56 152

MIMVSNet		74.69 202	75.60 196	73.62 204	76.02 223	85.31 195	81.21 189	67.43 208	71.02 162	59.07 199	54.48 206	64.07 137	66.14 205	86.52 141	86.64 135	91.83 178	81.17 209

ADS-MVSNet		74.53 203	75.69 195	73.17 206	81.57 205	80.71 217	79.27 200	63.03 225	79.27 96	59.94 192	67.86 122	68.32 123	71.08 185	77.33 215	76.83 215	84.12 225	79.53 212

pmmvs-eth3d		74.32 204	71.96 210	77.08 182	77.33 219	82.71 208	78.41 204	76.02 174	66.65 193	65.98 142	54.23 209	49.02 224	73.14 175	82.37 198	82.69 194	91.61 180	86.05 189

PM-MVS		74.17 205	73.10 206	75.41 194	76.07 222	82.53 210	77.56 208	71.69 192	71.04 161	61.92 175	61.23 163	47.30 225	74.82 162	81.78 200	79.80 202	90.42 192	88.05 168

CMPMVS	56.49 17	73.84 206	71.73 211	76.31 190	85.20 146	85.67 191	75.80 211	73.23 188	62.26 211	65.40 147	53.40 212	59.70 184	71.77 180	80.25 205	79.56 204	86.45 214	81.28 208
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		73.21 207	72.91 207	73.56 205	80.01 209	84.28 202	78.62 203	66.43 214	68.64 184	59.12 198	60.39 179	59.69 185	69.81 190	78.82 213	77.43 214	87.36 209	81.11 210

testgi		71.92 208	74.20 204	69.27 213	84.58 155	83.06 204	73.40 215	74.39 182	64.04 208	46.17 221	68.90 118	57.15 199	48.89 224	84.07 184	83.08 191	88.18 205	79.09 216

Anonymous20231206		70.80 209	70.59 213	71.04 209	81.60 203	82.49 211	74.64 214	75.87 177	64.17 207	49.27 216	44.85 225	53.59 216	54.68 217	83.07 191	82.34 196	90.17 194	83.65 200

gm-plane-assit		70.29 210	70.65 212	69.88 211	85.03 149	78.50 222	58.41 232	65.47 218	50.39 231	40.88 227	49.60 217	50.11 221	75.14 159	91.43 56	89.78 81	94.32 113	84.73 198

EU-MVSNet		69.98 211	72.30 209	67.28 216	75.67 224	79.39 219	73.12 216	69.94 201	63.59 209	42.80 225	62.93 153	56.71 203	55.07 216	79.13 212	78.55 208	87.06 212	85.82 192

MVS-HIRNet		68.83 212	66.39 217	71.68 208	77.58 217	75.52 224	66.45 224	65.05 220	62.16 212	62.84 166	44.76 226	56.60 204	71.96 179	78.04 214	75.06 221	86.18 216	72.56 224

LP		68.35 213	67.23 215	69.67 212	77.49 218	79.38 220	72.84 218	61.37 228	66.94 191	55.08 204	47.00 220	50.35 220	65.16 207	75.61 220	76.03 216	86.08 217	75.28 221

test20.03		68.31 214	70.05 214	66.28 218	82.41 197	80.84 216	67.35 223	76.11 171	58.44 221	40.80 228	53.77 210	54.54 212	42.28 230	83.07 191	81.96 200	88.73 204	77.76 218

N_pmnet		66.85 215	66.63 216	67.11 217	78.73 214	74.66 225	70.53 220	71.07 194	66.46 195	46.54 220	51.68 216	51.91 219	55.48 215	74.68 222	72.38 226	80.29 230	74.65 222

MDA-MVSNet-bldmvs		66.22 216	64.49 220	68.24 214	61.67 235	82.11 214	70.07 221	76.16 169	59.14 220	47.94 219	54.35 208	35.82 236	67.33 200	64.94 234	75.68 218	86.30 215	79.36 213

MIMVSNet1		65.00 217	66.24 218	63.55 221	58.41 239	80.01 218	69.00 222	74.03 184	55.81 226	41.88 226	36.81 234	49.48 223	47.89 225	81.32 201	82.40 195	90.08 196	77.88 217

test2356		63.96 218	64.10 222	63.78 220	74.71 225	71.55 228	65.83 225	67.38 209	57.11 223	40.41 229	53.58 211	41.13 231	49.35 223	77.00 217	77.57 213	85.01 221	70.79 225

testpf		63.91 219	65.23 219	62.38 222	81.32 206	69.95 231	62.71 230	54.16 235	61.29 215	48.73 217	57.31 197	52.50 217	50.97 220	67.50 230	68.86 231	76.36 233	79.21 214

new-patchmatchnet		63.80 220	63.31 223	64.37 219	76.49 220	75.99 223	63.73 227	70.99 195	57.27 222	43.08 224	45.86 223	43.80 226	45.13 229	73.20 225	70.68 230	86.80 213	76.34 220

FPMVS		63.63 221	60.08 227	67.78 215	80.01 209	71.50 229	72.88 217	69.41 204	61.82 213	53.11 207	45.12 224	42.11 229	50.86 221	66.69 231	63.84 233	80.41 229	69.46 229

testus		63.31 222	64.48 221	61.94 224	73.99 226	71.99 227	63.56 229	63.25 224	57.01 224	39.41 232	54.38 207	38.73 235	46.24 228	77.01 216	77.93 211	85.20 220	74.29 223

pmmvs3		61.89 223	61.74 225	62.06 223	64.30 233	70.83 230	64.22 226	52.14 237	48.78 232	44.47 223	41.67 228	41.70 230	63.03 209	76.06 219	76.02 217	84.18 224	77.14 219

new_pmnet		59.28 224	61.47 226	56.73 229	61.66 236	68.29 232	59.57 231	54.91 233	60.83 216	34.38 235	44.66 227	43.65 227	49.90 222	71.66 228	71.56 229	79.94 231	69.67 228

GG-mvs-BLEND		57.56 225	82.61 91	28.34 238	0.22 245	90.10 128	79.37 199	0.14 243	79.56 93	0.40 246	71.25 102	83.40 54	0.30 244	86.27 144	83.87 185	89.59 199	83.83 199

1111		57.32 226	57.20 228	57.46 226	71.89 230	67.50 235	52.34 233	58.78 230	46.57 233	39.69 230	37.38 232	38.78 233	46.37 226	74.15 223	74.36 225	75.70 234	61.66 232

testmv		56.62 227	56.41 229	56.86 227	71.92 228	67.58 233	52.17 235	65.69 215	40.60 236	28.53 237	37.90 230	31.52 237	40.10 232	72.64 226	74.73 223	82.78 227	69.91 226

test1235678		56.61 228	56.40 230	56.86 227	71.92 228	67.58 233	52.17 235	65.69 215	40.58 237	28.52 238	37.89 231	31.49 238	40.10 232	72.64 226	74.72 224	82.78 227	69.90 227

PMVS	50.48 18	55.81 229	51.93 231	60.33 225	72.90 227	49.34 240	48.78 237	69.51 203	43.49 235	54.25 205	36.26 235	41.04 232	39.71 234	65.07 233	60.70 234	76.85 232	67.58 230
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		50.02 230	51.22 232	48.61 231	63.00 234	60.15 238	47.60 239	56.49 232	38.02 238	24.74 240	36.14 236	25.93 240	24.79 237	66.19 232	71.68 228	75.07 235	60.44 234

Gipuma		49.17 231	47.05 233	51.65 230	59.67 238	48.39 241	41.98 240	63.47 223	55.64 227	33.33 236	14.90 239	13.78 244	41.34 231	69.31 229	72.30 227	70.11 237	55.00 236
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		44.14 232	43.91 235	44.40 233	59.91 237	61.10 237	34.07 242	60.09 229	27.71 240	14.44 242	19.11 238	19.28 242	23.90 239	47.36 238	66.69 232	73.98 236	66.11 231

PMMVS2		41.68 233	44.74 234	38.10 234	46.97 242	52.32 239	40.63 241	48.08 238	35.51 239	7.36 245	26.86 237	24.64 241	16.72 240	55.24 236	59.03 235	68.85 238	59.59 235

.test1245		41.43 234	38.48 236	44.88 232	71.89 230	67.50 235	52.34 233	58.78 230	46.57 233	39.69 230	37.38 232	38.78 233	46.37 226	74.15 223	1.18 240	0.20 244	3.76 241

E-PMN		31.40 235	26.80 238	36.78 235	51.39 241	29.96 244	20.20 244	54.17 234	25.93 242	12.75 243	14.73 240	8.58 246	34.10 236	27.36 240	37.83 238	48.07 241	43.18 238

MVE	30.17 19	30.88 236	33.52 237	27.80 239	23.78 244	39.16 243	18.69 246	46.90 239	21.88 243	15.39 241	14.37 241	7.31 247	24.41 238	41.63 239	56.22 236	37.64 243	54.07 237
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		30.49 237	25.44 239	36.39 236	51.47 240	29.89 245	20.17 245	54.00 236	26.49 241	12.02 244	13.94 242	8.84 245	34.37 235	25.04 241	34.37 239	46.29 242	39.53 239

testmvs		1.03 238	1.63 240	0.34 240	0.09 246	0.35 247	0.61 248	0.16 242	1.49 244	0.10 247	3.15 243	0.15 248	0.86 243	1.32 242	1.18 240	0.20 244	3.76 241

test123		0.87 239	1.40 241	0.25 241	0.03 247	0.25 248	0.35 249	0.08 244	1.21 245	0.05 248	2.84 244	0.03 249	0.89 242	0.43 243	1.16 242	0.13 246	3.87 240

sosnet-low-res		0.00 240	0.00 242	0.00 242	0.00 248	0.00 249	0.00 250	0.00 245	0.00 246	0.00 249	0.00 245	0.00 250	0.00 245	0.00 244	0.00 243	0.00 247	0.00 243

sosnet		0.00 240	0.00 242	0.00 242	0.00 248	0.00 249	0.00 250	0.00 245	0.00 246	0.00 249	0.00 245	0.00 250	0.00 245	0.00 244	0.00 243	0.00 247	0.00 243

Anonymous202405211			82.75 89		89.58 106	92.97 86	89.04 86	84.13 62	78.72 100		57.18 198	76.64 89	83.13 73	89.55 97	89.92 76	93.38 157	94.28 67

our_test_3					81.81 202	83.96 203	76.61 209

ambc			61.92 224		70.98 232	73.54 226	63.64 228		60.06 217	52.23 211	38.44 229	19.17 243	57.12 213	82.33 199	75.03 222	83.21 226	84.89 195

MTAPA										92.97 2		91.03 18

MTMP										93.14 1		90.21 25

Patchmatch-RL test							8.55 247

tmp_tt				32.73 237	43.96 243	21.15 246	26.71 243	8.99 241	65.67 200	51.39 213	56.01 204	42.64 228	11.76 241	56.60 235	50.81 237	53.55 240

XVS					93.11 55	96.70 22	91.91 51			83.95 45		88.82 35				95.79 30

X-MVStestdata					93.11 55	96.70 22	91.91 51			83.95 45		88.82 35				95.79 30

abl_6				90.66 36	94.65 42	96.27 34	92.21 47	86.94 43	90.23 37	86.38 34	85.50 37	92.96 8	88.37 40			95.40 47	95.46 49

mPP-MVS					97.06 13							88.08 40

NP-MVS									87.47 51

Patchmtry						85.54 194	82.30 179	68.23 205		65.37 148

DeepMVS_CX						48.31 242	48.03 238	26.08 240	56.42 225	25.77 239	47.51 219	31.31 239	51.30 219	48.49 237		53.61 239	61.52 233

High-resolution multi-view benchmark