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		95.23 1	95.69 1	94.70 1	97.12 9	97.81 2	97.19 1	92.83 1	95.06 2	90.98 5	96.47 1	92.77 7	93.38 1	95.34 4	94.21 10	96.68 3	98.17 1

HSP-MVS		94.83 2	95.37 2	94.21 4	96.82 18	97.94 1	96.69 2	92.37 6	93.97 7	90.29 9	96.16 2	93.71 2	92.70 4	94.80 11	93.13 30	96.37 7	97.90 4

HPM-MVS++		94.60 3	94.91 5	94.24 3	97.86 1	96.53 26	96.14 5	92.51 3	93.87 9	90.76 7	93.45 12	93.84 1	92.62 5	95.11 7	94.08 13	95.58 38	97.48 9

SD-MVS		94.53 4	95.22 3	93.73 9	95.69 29	97.03 8	95.77 15	91.95 7	94.41 3	91.35 4	94.97 3	93.34 4	91.80 14	94.72 14	93.99 14	95.82 25	98.07 2

TSAR-MVS + MP.		94.48 5	94.97 4	93.90 7	95.53 30	97.01 9	96.69 2	90.71 16	94.24 4	90.92 6	94.97 3	92.19 9	93.03 2	94.83 10	93.60 20	96.51 6	97.97 3

APD-MVS		94.37 6	94.47 10	94.26 2	97.18 7	96.99 10	96.53 4	92.68 2	92.45 18	89.96 11	94.53 6	91.63 13	92.89 3	94.58 16	93.82 17	96.31 10	97.26 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		94.37 6	94.65 6	94.04 6	97.29 5	97.11 6	96.00 7	92.43 5	93.45 10	89.85 13	90.92 19	93.04 5	92.59 6	95.77 1	94.82 3	96.11 14	97.42 11

SteuartSystems-ACMMP		94.06 8	94.65 6	93.38 13	96.97 14	97.36 4	96.12 6	91.78 8	92.05 22	87.34 24	94.42 7	90.87 17	91.87 13	95.47 3	94.59 6	96.21 12	97.77 6
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		94.02 9	94.22 12	93.78 8	97.25 6	96.85 14	95.81 13	90.94 15	94.12 5	90.29 9	94.09 9	89.98 23	92.52 7	93.94 24	93.49 25	95.87 20	97.10 17

ACMMP_Plus		93.94 10	94.49 9	93.30 14	97.03 12	97.31 5	95.96 8	91.30 12	93.41 12	88.55 18	93.00 13	90.33 20	91.43 20	95.53 2	94.41 8	95.53 40	97.47 10

MCST-MVS		93.81 11	94.06 13	93.53 11	96.79 19	96.85 14	95.95 9	91.69 10	92.20 20	87.17 26	90.83 21	93.41 3	91.96 11	94.49 18	93.50 23	97.61 1	97.12 16

MPTG		93.80 12	93.45 20	94.20 5	97.53 2	96.43 30	95.88 12	91.12 14	94.09 6	92.74 3	87.68 27	90.77 18	92.04 10	94.74 13	93.56 22	95.91 18	96.85 21

ACMMPR		93.72 13	93.94 14	93.48 12	97.07 10	96.93 11	95.78 14	90.66 18	93.88 8	89.24 15	93.53 11	89.08 30	92.24 8	93.89 26	93.50 23	95.88 19	96.73 25

NCCC		93.69 14	93.66 17	93.72 10	97.37 4	96.66 23	95.93 11	92.50 4	93.40 13	88.35 19	87.36 29	92.33 8	92.18 9	94.89 9	94.09 12	96.00 15	96.91 20

MP-MVS		93.35 15	93.59 18	93.08 17	97.39 3	96.82 16	95.38 18	90.71 16	90.82 29	88.07 21	92.83 15	90.29 21	91.32 21	94.03 21	93.19 29	95.61 36	97.16 14

CP-MVS		93.25 16	93.26 21	93.24 15	96.84 17	96.51 27	95.52 17	90.61 19	92.37 19	88.88 16	90.91 20	89.52 26	91.91 12	93.64 28	92.78 36	95.69 31	97.09 18

DeepC-MVS_fast	88.76 1	93.10 17	93.02 24	93.19 16	97.13 8	96.51 27	95.35 19	91.19 13	93.14 15	88.14 20	85.26 35	89.49 27	91.45 17	95.17 5	95.07 1	95.85 23	96.48 28
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 18	94.51 8	91.16 30	95.88 27	96.59 24	95.09 22	90.45 22	93.42 11	83.01 46	94.68 5	90.74 19	88.74 33	94.75 12	93.78 18	93.82 133	97.63 7

train_agg		92.87 19	93.53 19	92.09 24	96.88 16	95.38 42	95.94 10	90.59 20	90.65 31	83.65 44	94.31 8	91.87 12	90.30 25	93.38 30	92.42 37	95.17 56	96.73 25

PGM-MVS		92.76 20	93.03 23	92.45 22	97.03 12	96.67 22	95.73 16	87.92 34	90.15 36	86.53 30	92.97 14	88.33 36	91.69 15	93.62 29	93.03 31	95.83 24	96.41 31

CSCG		92.76 20	93.16 22	92.29 23	96.30 21	97.74 3	94.67 26	88.98 28	92.46 17	89.73 14	86.67 31	92.15 10	88.69 34	92.26 44	92.92 34	95.40 44	97.89 5

TSAR-MVS + GP.		92.71 22	93.91 15	91.30 28	91.96 64	96.00 35	93.43 34	87.94 33	92.53 16	86.27 34	93.57 10	91.94 11	91.44 19	93.29 31	92.89 35	96.78 2	97.15 15

DeepPCF-MVS	88.51 2	92.64 23	94.42 11	90.56 34	94.84 36	96.92 12	91.31 54	89.61 24	95.16 1	84.55 39	89.91 23	91.45 14	90.15 27	95.12 6	94.81 4	92.90 154	97.58 8

X-MVS		92.36 24	92.75 25	91.90 26	96.89 15	96.70 19	95.25 20	90.48 21	91.50 27	83.95 41	88.20 25	88.82 32	89.11 30	93.75 27	93.43 26	95.75 30	96.83 23

DeepC-MVS	87.86 3	92.26 25	91.86 28	92.73 19	96.18 22	96.87 13	95.19 21	91.76 9	92.17 21	86.58 29	81.79 42	85.85 42	90.88 23	94.57 17	94.61 5	95.80 26	97.18 13
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 26	93.74 16	90.08 37	94.96 33	97.06 7	93.11 38	87.71 36	90.71 30	80.78 56	92.40 16	91.03 15	87.68 44	94.32 20	94.48 7	96.21 12	96.16 34

ACMMP		92.03 27	92.16 26	91.87 27	95.88 27	96.55 25	94.47 28	89.49 25	91.71 25	85.26 35	91.52 18	84.48 47	90.21 26	92.82 39	91.63 43	95.92 17	96.42 30
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 28	91.40 30	92.75 18	96.01 25	95.88 38	93.73 33	89.00 26	89.89 37	90.31 8	81.28 47	88.85 31	91.45 17	92.88 38	94.24 9	96.00 15	96.76 24

3Dnovator+	86.06 4	91.60 29	90.86 35	92.47 21	96.00 26	96.50 29	94.70 25	87.83 35	90.49 32	89.92 12	74.68 72	89.35 28	90.66 24	94.02 22	94.14 11	95.67 33	96.85 21

CPTT-MVS		91.39 30	90.95 33	91.91 25	95.06 31	95.24 44	95.02 23	88.98 28	91.02 28	86.71 28	84.89 37	88.58 35	91.60 16	90.82 71	89.67 76	94.08 111	96.45 29

CANet		91.33 31	91.46 29	91.18 29	95.01 32	96.71 18	93.77 31	87.39 38	87.72 44	87.26 25	81.77 43	89.73 24	87.32 49	94.43 19	93.86 16	96.31 10	96.02 37

CDPH-MVS		91.14 32	92.01 27	90.11 36	96.18 22	96.18 33	94.89 24	88.80 30	88.76 41	77.88 71	89.18 24	87.71 39	87.29 50	93.13 33	93.31 28	95.62 35	95.84 39

MVS_0304		90.88 33	91.35 31	90.34 35	93.91 44	96.79 17	94.49 27	86.54 42	86.57 48	82.85 47	81.68 45	89.70 25	87.57 46	94.64 15	93.93 15	96.67 4	96.15 35

MVS_111021_HR		90.56 34	91.29 32	89.70 42	94.71 38	95.63 40	91.81 50	86.38 43	87.53 45	81.29 53	87.96 26	85.43 44	87.69 43	93.90 25	92.93 33	96.33 8	95.69 42

3Dnovator	85.17 5	90.48 35	89.90 39	91.16 30	94.88 35	95.74 39	93.82 30	85.36 49	89.28 38	87.81 22	74.34 74	87.40 40	88.56 35	93.07 34	93.74 19	96.53 5	95.71 41

AdaColmap		90.29 36	88.38 48	92.53 20	96.10 24	95.19 45	92.98 39	91.40 11	89.08 40	88.65 17	78.35 58	81.44 60	91.30 22	90.81 72	90.21 63	94.72 79	93.59 68

OMC-MVS		90.23 37	90.40 36	90.03 38	93.45 49	95.29 43	91.89 49	86.34 44	93.25 14	84.94 38	81.72 44	86.65 41	88.90 31	91.69 51	90.27 62	94.65 84	93.95 64

MVS_111021_LR		90.14 38	90.89 34	89.26 46	93.23 51	94.05 59	90.43 58	84.65 54	90.16 35	84.52 40	90.14 22	83.80 51	87.99 40	92.50 42	90.92 51	94.74 77	94.70 56

DELS-MVS		89.71 39	89.68 40	89.74 40	93.75 46	96.22 32	93.76 32	85.84 45	82.53 61	85.05 37	78.96 55	84.24 48	84.25 63	94.91 8	94.91 2	95.78 29	96.02 37
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 40	89.91 38	89.24 47	96.45 20	93.61 64	92.95 40	88.03 32	85.74 52	83.36 45	87.29 30	83.05 54	80.98 77	92.22 45	91.85 41	93.69 140	95.58 45
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		89.49 41	89.58 41	89.38 45	94.73 37	95.94 36	92.35 42	85.00 52	85.69 53	80.03 60	76.97 64	87.81 38	87.87 41	92.18 48	92.10 39	96.33 8	96.40 32

canonicalmvs		89.36 42	89.92 37	88.70 51	91.38 65	95.92 37	91.81 50	82.61 81	90.37 33	82.73 49	82.09 40	79.28 73	88.30 38	91.17 58	93.59 21	95.36 47	97.04 19

HQP-MVS		89.13 43	89.58 41	88.60 53	93.53 48	93.67 62	93.29 36	87.58 37	88.53 42	75.50 75	87.60 28	80.32 64	87.07 51	90.66 77	89.95 70	94.62 87	96.35 33

TAPA-MVS	84.37 7	88.91 44	88.93 44	88.89 48	93.00 55	94.85 50	92.00 46	84.84 53	91.68 26	80.05 59	79.77 51	84.56 46	88.17 39	90.11 82	89.00 92	95.30 51	92.57 93
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PCF-MVS	84.60 6	88.66 45	87.75 57	89.73 41	93.06 54	96.02 34	93.22 37	90.00 23	82.44 63	80.02 61	77.96 59	85.16 45	87.36 48	88.54 100	88.54 97	94.72 79	95.61 44
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		88.66 45	88.52 46	88.82 49	91.37 66	94.22 56	92.82 41	82.08 88	88.27 43	85.14 36	81.86 41	78.53 75	85.93 57	91.17 58	90.61 57	95.55 39	95.00 49
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 47	86.83 62	90.70 32	94.22 41	92.63 81	91.50 52	87.19 39	89.16 39	86.87 27	75.51 69	80.87 61	89.98 28	90.01 83	89.20 86	94.41 101	90.45 143
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TSAR-MVS + COLMAP		88.40 48	89.09 43	87.60 59	92.72 59	93.92 61	92.21 43	85.57 48	91.73 24	73.72 83	91.75 17	73.22 96	87.64 45	91.49 52	89.71 75	93.73 139	91.82 114

CNLPA		88.40 48	87.00 60	90.03 38	93.73 47	94.28 55	89.56 67	85.81 46	91.87 23	87.55 23	69.53 104	81.49 59	89.23 29	89.45 90	88.59 96	94.31 105	93.82 66

MAR-MVS		88.39 50	88.44 47	88.33 56	94.90 34	95.06 47	90.51 57	83.59 64	85.27 54	79.07 64	77.13 62	82.89 55	87.70 42	92.19 47	92.32 38	94.23 106	94.20 62
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 51	88.06 51	88.62 52	92.18 62	93.98 60	91.28 55	85.24 50	86.69 47	81.23 54	85.62 33	75.13 85	87.01 52	89.83 85	89.77 74	94.79 73	95.43 48
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		88.25 52	88.55 45	87.89 57	92.84 58	93.66 63	93.35 35	85.22 51	85.77 51	74.03 82	86.60 32	76.29 82	86.62 54	91.20 56	90.58 59	95.29 52	95.75 40

PVSNet_BlendedMVS		88.19 53	88.00 52	88.42 54	92.71 60	94.82 51	89.08 73	83.81 60	84.91 55	86.38 31	79.14 53	78.11 76	82.66 66	93.05 35	91.10 46	95.86 21	94.86 52

PVSNet_Blended		88.19 53	88.00 52	88.42 54	92.71 60	94.82 51	89.08 73	83.81 60	84.91 55	86.38 31	79.14 53	78.11 76	82.66 66	93.05 35	91.10 46	95.86 21	94.86 52

OpenMVS	82.53 11	87.71 55	86.84 61	88.73 50	94.42 40	95.06 47	91.02 56	83.49 67	82.50 62	82.24 51	67.62 114	85.48 43	85.56 58	91.19 57	91.30 45	95.67 33	94.75 54

ACMM	83.27 10	87.68 56	86.09 67	89.54 44	93.26 50	92.19 84	91.43 53	86.74 41	86.02 50	82.85 47	75.63 68	75.14 84	88.41 36	90.68 76	89.99 67	94.59 88	92.97 77
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		87.56 57	85.80 68	89.62 43	93.90 45	94.09 58	94.12 29	88.18 31	75.40 110	77.30 74	76.41 65	77.93 78	88.79 32	92.20 46	90.82 52	95.40 44	93.72 67

PVSNet_Blended_VisFu		87.40 58	87.80 54	86.92 62	92.86 56	95.40 41	88.56 82	83.45 70	79.55 86	82.26 50	74.49 73	84.03 49	79.24 124	92.97 37	91.53 44	95.15 58	96.65 27

MVS_Test		86.93 59	87.24 59	86.56 63	90.10 90	93.47 66	90.31 59	80.12 107	83.55 59	78.12 67	79.58 52	79.80 68	85.45 59	90.17 81	90.59 58	95.29 52	93.53 69

EPP-MVSNet		86.55 60	87.76 56	85.15 67	90.52 78	94.41 54	87.24 101	82.32 86	81.79 66	73.60 84	78.57 57	82.41 56	82.07 70	91.23 54	90.39 61	95.14 59	95.48 46

DI_MVS_plusplus_trai		86.41 61	85.54 69	87.42 60	89.24 97	93.13 70	92.16 45	82.65 79	82.30 64	80.75 57	68.30 111	80.41 63	85.01 60	90.56 78	90.07 65	94.70 81	94.01 63

IS_MVSNet		86.18 62	88.18 50	83.85 86	91.02 69	94.72 53	87.48 93	82.46 83	81.05 73	70.28 94	76.98 63	82.20 58	76.65 139	93.97 23	93.38 27	95.18 55	94.97 50

UA-Net		86.07 63	87.78 55	84.06 82	92.85 57	95.11 46	87.73 89	84.38 55	73.22 133	73.18 86	79.99 50	89.22 29	71.47 171	93.22 32	93.03 31	94.76 76	90.69 138

MVSTER		86.03 64	86.12 66	85.93 64	88.62 100	89.93 120	89.33 69	79.91 110	81.87 65	81.35 52	81.07 48	74.91 86	80.66 83	92.13 49	90.10 64	95.68 32	92.80 83

LS3D		85.96 65	84.37 75	87.81 58	94.13 42	93.27 69	90.26 60	89.00 26	84.91 55	72.84 88	71.74 90	72.47 98	87.45 47	89.53 89	89.09 89	93.20 149	89.60 145

UGNet		85.90 66	88.23 49	83.18 94	88.96 98	94.10 57	87.52 92	83.60 63	81.66 67	77.90 70	80.76 49	83.19 53	66.70 190	91.13 66	90.71 56	94.39 102	96.06 36
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
diffmvs		85.70 67	86.35 65	84.95 69	87.75 105	90.96 96	89.09 72	78.56 130	86.50 49	80.44 58	77.86 60	83.93 50	81.64 72	85.52 146	86.79 119	92.21 161	92.87 80

CANet_DTU		85.43 68	87.72 58	82.76 98	90.95 72	93.01 75	89.99 61	75.46 166	82.67 60	64.91 142	83.14 38	80.09 65	80.68 82	92.03 50	91.03 48	94.57 90	92.08 106

Effi-MVS+		85.33 69	85.08 71	85.63 66	89.69 96	93.42 67	89.90 62	80.31 105	79.32 87	72.48 90	73.52 82	74.03 89	86.55 55	90.99 68	89.98 68	94.83 72	94.27 61

FC-MVSNet-train		85.18 70	85.31 70	85.03 68	90.67 73	91.62 90	87.66 90	83.61 62	79.75 83	74.37 81	78.69 56	71.21 102	78.91 126	91.23 54	89.96 69	94.96 64	94.69 57

GBi-Net		84.51 71	84.80 72	84.17 79	84.20 144	89.95 117	89.70 64	80.37 101	81.17 69	75.50 75	69.63 99	79.69 70	79.75 109	90.73 73	90.72 53	95.52 41	91.71 118

test1		84.51 71	84.80 72	84.17 79	84.20 144	89.95 117	89.70 64	80.37 101	81.17 69	75.50 75	69.63 99	79.69 70	79.75 109	90.73 73	90.72 53	95.52 41	91.71 118

FMVSNet3		84.44 73	84.64 74	84.21 78	84.32 143	90.13 115	89.85 63	80.37 101	81.17 69	75.50 75	69.63 99	79.69 70	79.62 112	89.72 87	90.52 60	95.59 37	91.58 124

Vis-MVSNet		84.38 74	86.68 64	81.70 114	87.65 109	94.89 49	88.14 84	80.90 99	74.48 117	68.23 110	77.53 61	80.72 62	69.98 176	92.68 40	91.90 40	95.33 50	94.58 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FMVSNet2		83.87 75	83.73 78	84.05 84	84.20 144	89.95 117	89.70 64	80.21 106	79.17 89	74.89 79	65.91 119	77.49 79	79.75 109	90.87 70	91.00 50	95.52 41	91.71 118

MSDG		83.87 75	81.02 99	87.19 61	92.17 63	89.80 123	89.15 70	85.72 47	80.61 78	79.24 63	66.66 117	68.75 110	82.69 65	87.95 106	87.44 109	94.19 107	85.92 179

Fast-Effi-MVS+		83.77 77	82.98 80	84.69 70	87.98 103	91.87 87	88.10 86	77.70 140	78.10 95	73.04 87	69.13 106	68.51 111	86.66 53	90.49 79	89.85 72	94.67 83	92.88 79

Vis-MVSNet (Re-imp)		83.65 78	86.81 63	79.96 148	90.46 81	92.71 80	84.84 147	82.00 89	80.93 75	62.44 159	76.29 66	82.32 57	65.54 193	92.29 43	91.66 42	94.49 96	91.47 125

RPSCF		83.46 79	83.36 79	83.59 90	87.75 105	87.35 156	84.82 148	79.46 121	83.84 58	78.12 67	82.69 39	79.87 66	82.60 68	82.47 184	81.13 188	88.78 190	86.13 177

PatchMatch-RL		83.34 80	81.36 93	85.65 65	90.33 84	89.52 130	84.36 151	81.82 91	80.87 77	79.29 62	74.04 75	62.85 137	86.05 56	88.40 102	87.04 116	92.04 163	86.77 170

IterMVS-LS		83.28 81	82.95 81	83.65 88	88.39 102	88.63 144	86.80 120	78.64 129	76.56 101	73.43 85	72.52 89	75.35 83	80.81 80	86.43 129	88.51 98	93.84 132	92.66 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		82.86 82	81.46 90	84.48 72	90.30 85	93.09 71	89.05 75	82.71 75	75.14 111	69.56 97	65.72 121	63.13 131	80.38 90	91.15 61	89.51 78	94.91 66	92.50 99

conf200view11		82.85 83	81.46 90	84.47 73	90.30 85	93.09 71	89.05 75	82.72 74	75.14 111	69.49 100	65.72 121	63.13 131	80.38 90	91.15 61	89.51 78	94.91 66	92.50 99

thres200		82.77 84	81.25 95	84.54 71	90.38 82	93.05 73	89.13 71	82.67 77	74.40 118	69.53 99	65.69 124	63.03 135	80.63 84	91.15 61	89.42 81	94.88 69	92.04 108

thres400		82.68 85	81.15 96	84.47 73	90.52 78	92.89 79	88.95 78	82.71 75	74.33 119	69.22 103	65.31 126	62.61 139	80.63 84	90.96 69	89.50 80	94.79 73	92.45 103

IB-MVS	79.09 12	82.60 86	82.19 84	83.07 95	91.08 68	93.55 65	80.90 179	81.35 95	76.56 101	80.87 55	64.81 135	69.97 105	68.87 180	85.64 139	90.06 66	95.36 47	94.74 55
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 87	81.01 101	84.34 75	90.30 85	92.27 82	89.04 77	82.77 73	75.14 111	69.56 97	65.72 121	63.13 131	79.62 112	89.97 84	89.26 84	94.73 78	91.61 123

thres600view7		82.53 88	81.02 99	84.28 76	90.61 74	93.05 73	88.57 80	82.67 77	74.12 123	68.56 108	65.09 130	62.13 150	80.40 89	91.15 61	89.02 91	94.88 69	92.59 90

view600		82.51 89	81.00 102	84.27 77	90.56 77	92.95 77	88.57 80	82.57 82	74.16 122	68.70 107	65.13 129	62.15 149	80.36 92	91.15 61	88.98 93	94.87 71	92.48 101

view800		82.38 90	80.93 103	84.06 82	90.59 76	92.96 76	88.11 85	82.44 84	73.92 124	68.10 111	65.07 131	61.64 151	80.10 98	91.17 58	89.24 85	95.01 62	92.56 94

CHOSEN 1792x2688		82.16 91	80.91 104	83.61 89	91.14 67	92.01 86	89.55 68	79.15 125	79.87 82	70.29 93	52.51 201	72.56 97	81.39 73	88.87 96	88.17 102	90.15 182	92.37 104

Effi-MVS+-dtu		82.05 92	81.76 86	82.38 100	87.72 107	90.56 101	86.90 118	78.05 136	73.85 127	66.85 119	71.29 92	71.90 100	82.00 71	86.64 124	85.48 160	92.76 156	92.58 92

EPNet_dtu		81.98 93	83.82 77	79.83 150	94.10 43	85.97 174	87.29 98	84.08 59	80.61 78	59.96 179	81.62 46	77.19 80	62.91 197	87.21 110	86.38 130	90.66 178	87.77 161
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		81.87 94	81.33 94	82.50 99	85.31 131	91.30 92	85.70 133	84.25 56	75.89 105	64.21 145	66.95 116	64.65 124	80.22 94	87.07 113	89.18 87	95.27 54	94.29 59

ACMH	78.52 14	81.86 95	80.45 109	83.51 91	90.51 80	91.22 93	85.62 136	84.23 57	70.29 156	62.21 160	69.04 108	64.05 129	84.48 62	87.57 108	88.45 99	94.01 116	92.54 97
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	79.08 13	81.84 96	80.06 112	83.91 85	89.92 94	90.62 100	86.21 128	83.48 69	73.88 126	65.75 133	66.38 118	65.30 121	84.63 61	85.90 134	87.25 113	93.45 144	91.13 128

tfpn		81.79 97	80.06 112	83.82 87	90.61 74	92.91 78	87.62 91	82.34 85	73.66 130	67.46 114	64.99 132	55.50 195	79.77 108	91.12 67	89.62 77	95.14 59	92.59 90

MS-PatchMatch		81.79 97	81.44 92	82.19 103	90.35 83	89.29 134	88.08 87	75.36 167	77.60 97	69.00 104	64.37 138	78.87 74	77.14 138	88.03 105	85.70 156	93.19 150	86.24 176

tfpn_ndepth		81.77 99	82.29 83	81.15 128	89.79 95	91.71 89	85.49 139	81.63 94	79.17 89	64.76 143	73.04 84	68.14 115	70.62 174	88.72 97	87.88 105	94.63 86	87.38 163

PMMVS		81.65 100	84.05 76	78.86 155	78.56 201	82.63 195	83.10 159	67.22 198	81.39 68	70.11 96	84.91 36	79.74 69	82.12 69	87.31 109	85.70 156	92.03 164	86.67 173

FMVSNet1		81.64 101	80.61 108	82.84 97	82.36 185	89.20 136	88.67 79	79.58 119	70.79 150	72.63 89	58.95 181	72.26 99	79.34 123	90.73 73	90.72 53	94.47 97	91.62 122

CDS-MVSNet		81.63 102	82.09 85	81.09 130	87.21 114	90.28 111	87.46 95	80.33 104	69.06 169	70.66 91	71.30 91	73.87 90	67.99 183	89.58 88	89.87 71	92.87 155	90.69 138
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HyFIR lowres test		81.62 103	79.45 123	84.14 81	91.00 70	93.38 68	88.27 83	78.19 134	76.28 103	70.18 95	48.78 205	73.69 92	83.52 64	87.05 114	87.83 107	93.68 141	89.15 148

UniMVSNet (Re)		81.22 104	81.08 98	81.39 121	85.35 130	91.76 88	84.93 146	82.88 72	76.13 104	65.02 141	64.94 133	63.09 134	75.17 146	87.71 107	89.04 90	94.97 63	94.88 51

DU-MVS		81.20 105	80.30 110	82.25 101	84.98 138	90.94 98	85.70 133	83.58 65	75.74 107	64.21 145	65.30 127	59.60 175	80.22 94	86.89 117	89.31 82	94.77 75	94.29 59

tfpn1000		81.03 106	81.70 87	80.25 146	90.18 88	91.35 91	83.96 154	81.15 98	78.00 96	62.11 162	73.37 83	65.75 118	69.17 179	88.68 98	87.44 109	94.93 65	87.29 165

conf0.05thres1000		81.00 107	79.12 124	83.20 93	90.14 89	92.15 85	87.05 113	82.09 87	68.11 175	66.19 124	59.67 175	61.10 162	79.05 125	90.47 80	89.11 88	94.68 82	93.22 71

CostFormer		80.94 108	80.21 111	81.79 109	87.69 108	88.58 145	87.47 94	70.66 184	80.02 80	77.88 71	73.03 85	71.40 101	78.24 130	79.96 194	79.63 190	88.82 189	88.84 149

tfpnview11		80.84 109	81.10 97	80.54 140	90.10 90	90.96 96	85.44 140	81.84 90	75.77 106	59.27 183	73.54 79	64.40 125	71.69 168	89.16 92	87.97 103	94.91 66	85.92 179

USDC		80.69 110	79.89 116	81.62 117	86.48 119	89.11 139	86.53 124	78.86 126	81.15 72	63.48 152	72.98 86	59.12 180	81.16 75	87.10 112	85.01 164	93.23 148	84.77 186

tfpn_n400		80.63 111	80.79 105	80.43 143	90.02 92	91.08 94	85.34 142	81.79 92	72.93 136	59.27 183	73.54 79	64.40 125	71.61 169	89.05 94	88.21 100	94.56 91	86.32 174

tfpnconf		80.63 111	80.79 105	80.43 143	90.02 92	91.08 94	85.34 142	81.79 92	72.93 136	59.27 183	73.54 79	64.40 125	71.61 169	89.05 94	88.21 100	94.56 91	86.32 174

TranMVSNet+NR-MVSNet		80.52 113	79.84 117	81.33 123	84.92 140	90.39 105	85.53 138	84.22 58	74.27 120	60.68 177	64.93 134	59.96 170	77.48 135	86.75 122	89.28 83	95.12 61	93.29 70

DWT-MVSNet_training		80.51 114	78.05 143	83.39 92	88.64 99	88.33 150	86.11 130	76.33 151	79.65 84	78.64 66	69.62 102	58.89 182	80.82 78	80.50 191	82.03 186	89.77 185	87.36 164

COLMAP_ROB	76.78 15	80.50 115	78.49 129	82.85 96	90.96 71	89.65 128	86.20 129	83.40 71	77.15 99	66.54 120	62.27 143	65.62 120	77.89 133	85.23 158	84.70 168	92.11 162	84.83 185
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CHOSEN 280x420		80.28 116	81.66 88	78.67 157	82.92 178	79.24 207	85.36 141	66.79 200	78.11 94	70.32 92	75.03 71	79.87 66	81.09 76	89.07 93	83.16 177	85.54 206	87.17 167

NR-MVSNet		80.25 117	79.98 115	80.56 139	85.20 133	90.94 98	85.65 135	83.58 65	75.74 107	61.36 172	65.30 127	56.75 189	72.38 164	88.46 101	88.80 94	95.16 57	93.87 65

v6		80.11 118	78.47 130	82.01 104	83.97 151	90.49 102	87.19 106	79.67 115	71.59 143	67.51 113	61.26 147	62.46 144	79.81 107	85.49 148	86.18 143	93.89 126	91.86 111

v1neww		80.09 119	78.45 132	82.00 105	83.97 151	90.49 102	87.18 107	79.67 115	71.49 144	67.44 115	61.24 149	62.41 145	79.83 104	85.49 148	86.19 140	93.88 128	91.86 111

v7new		80.09 119	78.45 132	82.00 105	83.97 151	90.49 102	87.18 107	79.67 115	71.49 144	67.44 115	61.24 149	62.41 145	79.83 104	85.49 148	86.19 140	93.88 128	91.86 111

pmmvs4		79.99 121	78.08 139	82.22 102	83.04 175	87.16 159	84.95 145	78.80 128	78.64 92	74.53 80	64.61 136	59.41 176	79.45 122	84.13 170	84.54 170	92.53 158	88.08 156

Fast-Effi-MVS+-dtu		79.95 122	80.69 107	79.08 153	86.36 120	89.14 138	85.85 131	72.28 178	72.85 138	59.32 182	70.43 97	68.42 112	77.57 134	86.14 131	86.44 129	93.11 151	91.39 126

v8		79.90 123	78.39 135	81.66 116	83.97 151	89.81 122	87.16 110	77.40 142	71.49 144	67.71 112	61.24 149	62.49 142	79.83 104	85.48 152	86.17 144	93.89 126	92.02 110

v2v482		79.84 124	78.07 140	81.90 108	83.75 164	90.21 114	87.17 109	79.85 114	70.65 151	65.93 132	61.93 144	60.07 169	80.82 78	85.25 157	86.71 120	93.88 128	91.70 121

Baseline_NR-MVSNet		79.84 124	78.37 136	81.55 119	84.98 138	86.66 163	85.06 144	83.49 67	75.57 109	63.31 153	58.22 185	60.97 163	78.00 132	86.89 117	87.13 114	94.47 97	93.15 73

tpmp4_e23		79.82 126	77.96 148	82.00 105	87.59 110	86.93 160	87.81 88	72.21 179	79.99 81	78.02 69	67.83 113	64.77 122	78.74 127	79.99 193	78.90 193	87.65 195	87.29 165

v7		79.79 127	78.28 137	81.54 120	83.73 165	90.34 110	87.27 99	78.27 133	70.50 153	65.59 134	60.59 164	60.47 165	80.46 87	86.90 116	86.63 123	93.92 122	92.56 94

v1		79.76 128	78.06 142	81.74 112	83.89 158	90.38 106	87.20 103	79.88 113	70.23 157	66.17 129	60.92 157	61.56 152	79.50 120	85.37 153	86.17 144	93.81 134	91.77 115

v1141		79.75 129	78.04 144	81.75 110	83.89 158	90.37 107	87.20 103	79.89 112	70.23 157	66.18 126	60.92 157	61.48 156	79.54 116	85.36 154	86.17 144	93.81 134	91.76 117

divwei89l23v2f112		79.75 129	78.04 144	81.75 110	83.90 155	90.37 107	87.21 102	79.90 111	70.20 159	66.18 126	60.92 157	61.48 156	79.52 119	85.36 154	86.17 144	93.81 134	91.77 115

v18		79.71 131	77.98 147	81.73 113	84.02 150	86.67 162	87.37 96	76.35 150	72.61 139	68.86 105	61.35 146	62.65 138	79.94 100	85.49 148	86.21 135	93.85 131	90.92 131

v16		79.65 132	77.91 149	81.69 115	84.04 148	86.65 165	87.20 103	76.32 152	72.41 140	68.71 106	61.13 154	62.52 141	79.93 101	85.55 144	86.22 133	93.92 122	90.91 132

v10		79.62 133	78.19 138	81.28 124	83.73 165	89.69 127	87.27 99	76.86 146	70.50 153	65.46 135	60.58 166	60.47 165	80.44 88	86.91 115	86.63 123	93.93 120	92.55 96

v17		79.59 134	77.88 150	81.60 118	84.03 149	86.66 163	87.13 112	76.31 153	72.09 141	68.29 109	61.15 153	62.57 140	79.90 102	85.55 144	86.20 138	93.93 120	90.93 130

V42		79.59 134	78.43 134	80.94 134	82.79 181	89.71 126	86.66 121	76.73 148	71.38 147	67.42 117	61.01 155	62.30 147	78.39 129	85.56 143	86.48 127	93.65 142	92.60 89

GA-MVS		79.52 136	79.71 120	79.30 152	85.68 126	90.36 109	84.55 149	78.44 131	70.47 155	57.87 190	68.52 110	61.38 160	76.21 141	89.40 91	87.89 104	93.04 153	89.96 144

test-LLR		79.47 137	79.84 117	79.03 154	87.47 111	82.40 198	81.24 174	78.05 136	73.72 128	62.69 156	73.76 76	74.42 87	73.49 159	84.61 166	82.99 179	91.25 172	87.01 168

v1144		79.38 138	77.83 151	81.18 126	83.62 167	90.23 112	87.15 111	78.35 132	69.13 168	64.02 149	60.20 172	59.41 176	80.14 97	86.78 120	86.57 125	93.81 134	92.53 98

MDTV_nov1_ep13		79.14 139	79.49 122	78.74 156	85.40 129	86.89 161	84.32 153	70.29 186	78.85 91	69.42 101	75.37 70	73.29 95	75.64 144	80.61 190	79.48 192	87.36 196	81.91 194

v15		79.13 140	77.37 155	81.19 125	83.90 155	86.56 167	87.01 114	76.15 157	70.20 159	66.48 121	60.71 162	61.55 153	79.60 114	85.59 142	86.19 140	93.98 118	90.80 137

V14		79.11 141	77.35 157	81.16 127	83.90 155	86.54 168	86.94 115	76.10 159	70.14 161	66.41 123	60.59 164	61.54 154	79.59 115	85.64 139	86.20 138	94.04 114	90.82 135

V9		79.08 142	77.32 159	81.14 129	83.89 158	86.52 169	86.85 119	76.06 160	70.02 162	66.42 122	60.44 167	61.52 155	79.54 116	85.68 138	86.21 135	94.08 111	90.83 134

TDRefinement		79.05 143	77.05 165	81.39 121	88.45 101	89.00 141	86.92 116	82.65 79	74.21 121	64.41 144	59.17 178	59.16 178	74.52 152	85.23 158	85.09 163	91.37 170	87.51 162

v12		79.03 144	77.28 160	81.06 131	83.88 162	86.49 170	86.62 122	76.02 161	69.99 163	66.18 126	60.34 170	61.44 158	79.54 116	85.70 137	86.21 135	94.11 110	90.82 135

v11		79.02 145	77.36 156	80.95 133	83.89 158	86.48 171	86.53 124	75.77 165	69.69 165	65.21 140	60.36 169	60.24 168	80.32 93	87.20 111	86.54 126	93.96 119	91.02 129

v13		78.99 146	77.25 162	81.02 132	83.87 163	86.47 172	86.60 123	75.96 163	69.87 164	66.07 130	60.25 171	61.41 159	79.49 121	85.72 136	86.22 133	94.14 109	90.84 133

v1192		78.94 147	77.33 158	80.82 135	83.25 171	89.90 121	86.91 117	77.72 139	68.63 172	62.61 158	59.17 178	57.53 186	80.62 86	86.89 117	86.47 128	93.79 138	92.75 86

v144192		78.81 148	77.22 163	80.67 137	82.95 176	89.79 124	86.40 126	77.42 141	68.26 174	63.13 154	59.50 176	58.13 184	80.08 99	85.93 133	86.08 149	94.06 113	92.83 82

IterMVS		78.79 149	79.71 120	77.71 162	85.26 132	85.91 175	84.54 150	69.84 190	73.38 132	61.25 173	70.53 96	70.35 103	74.43 153	85.21 160	83.80 174	90.95 176	88.77 150
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		78.71 150	78.86 126	78.55 158	85.85 125	85.15 183	82.30 166	68.23 193	74.71 115	65.37 137	64.39 137	69.59 107	77.18 136	85.10 162	84.87 165	92.34 160	88.21 154

PatchmatchNet		78.67 151	78.85 127	78.46 159	86.85 118	86.03 173	83.77 156	68.11 195	80.88 76	66.19 124	72.90 87	73.40 94	78.06 131	79.25 198	77.71 200	87.75 194	81.75 195
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		78.59 152	76.84 168	80.62 138	83.61 168	89.16 137	83.65 157	79.24 124	69.38 167	69.34 102	59.88 174	60.41 167	75.19 145	83.81 172	84.63 169	92.70 157	90.63 140

v1921920		78.57 153	76.99 166	80.41 145	82.93 177	89.63 129	86.38 127	77.14 144	68.31 173	61.80 166	58.89 182	56.79 188	80.19 96	86.50 128	86.05 151	94.02 115	92.76 85

pm-mvs1		78.51 154	77.75 153	79.40 151	84.83 141	89.30 133	83.55 158	79.38 122	62.64 197	63.68 151	58.73 183	64.68 123	70.78 173	89.79 86	87.84 106	94.17 108	91.28 127

v1240		78.15 155	76.53 169	80.04 147	82.85 180	89.48 132	85.61 137	76.77 147	67.05 177	61.18 175	58.37 184	56.16 193	79.89 103	86.11 132	86.08 149	93.92 122	92.47 102

dps		78.02 156	75.94 177	80.44 142	86.06 122	86.62 166	82.58 161	69.98 188	75.14 111	77.76 73	69.08 107	59.93 171	78.47 128	79.47 196	77.96 197	87.78 193	83.40 190

anonymousdsp		77.94 157	79.00 125	76.71 173	79.03 199	87.83 153	79.58 184	72.87 177	65.80 186	58.86 189	65.82 120	62.48 143	75.99 142	86.77 121	88.66 95	93.92 122	95.68 43

test-mter		77.79 158	80.02 114	75.18 184	81.18 193	82.85 193	80.52 182	62.03 215	73.62 131	62.16 161	73.55 78	73.83 91	73.81 157	84.67 165	83.34 176	91.37 170	88.31 153

TESTMET0.1,1		77.78 159	79.84 117	75.38 183	80.86 194	82.40 198	81.24 174	62.72 214	73.72 128	62.69 156	73.76 76	74.42 87	73.49 159	84.61 166	82.99 179	91.25 172	87.01 168

tpm cat1		77.78 159	75.28 186	80.70 136	87.14 115	85.84 176	85.81 132	70.40 185	77.44 98	78.80 65	63.72 139	64.01 130	76.55 140	75.60 208	75.21 208	85.51 207	85.12 183

EPMVS		77.53 161	78.07 140	76.90 172	86.89 117	84.91 186	82.18 169	66.64 201	81.00 74	64.11 148	72.75 88	69.68 106	74.42 154	79.36 197	78.13 196	87.14 199	80.68 200

tfpnnormal		77.46 162	74.86 189	80.49 141	86.34 121	88.92 142	84.33 152	81.26 96	61.39 201	61.70 168	51.99 202	53.66 202	74.84 149	88.63 99	87.38 112	94.50 95	92.08 106

v7n		77.22 163	76.23 172	78.38 160	81.89 188	89.10 140	82.24 168	76.36 149	65.96 185	61.21 174	56.56 188	55.79 194	75.07 148	86.55 125	86.68 121	93.52 143	92.95 78

RPMNet		77.07 164	77.63 154	76.42 175	85.56 128	85.15 183	81.37 171	65.27 207	74.71 115	60.29 178	63.71 140	66.59 117	73.64 158	82.71 181	82.12 184	92.38 159	88.39 152

pmmvs5		76.93 165	76.33 171	77.62 163	81.97 187	88.40 149	81.32 173	74.35 171	65.42 191	61.42 171	63.07 141	57.95 185	73.23 162	85.60 141	85.35 162	93.41 145	88.55 151

TinyColmap		76.73 166	73.95 192	79.96 148	85.16 135	85.64 179	82.34 165	78.19 134	70.63 152	62.06 163	60.69 163	49.61 209	80.81 80	85.12 161	83.69 175	91.22 174	82.27 193

CVMVSNet		76.70 167	78.46 131	74.64 189	83.34 170	84.48 187	81.83 170	74.58 168	68.88 170	51.23 202	69.77 98	70.05 104	67.49 186	84.27 169	83.81 173	89.38 187	87.96 158

WR-MVS		76.63 168	78.02 146	75.02 185	84.14 147	89.76 125	78.34 192	80.64 100	69.56 166	52.32 198	61.26 147	61.24 161	60.66 198	84.45 168	87.07 115	93.99 117	92.77 84

TransMVSNet (Re)		76.57 169	75.16 187	78.22 161	85.60 127	87.24 157	82.46 162	81.23 97	59.80 205	59.05 188	57.07 187	59.14 179	66.60 191	88.09 104	86.82 117	94.37 103	87.95 159

v52		76.55 170	75.89 178	77.31 167	79.94 198	88.49 147	81.07 177	73.62 174	65.49 189	61.66 169	56.29 191	58.90 181	74.30 155	83.47 176	85.62 158	93.28 146	92.99 75

V4		76.55 170	75.89 178	77.32 166	79.95 197	88.50 146	81.07 177	73.62 174	65.47 190	61.71 167	56.31 190	58.87 183	74.28 156	83.48 175	85.62 158	93.28 146	92.98 76

tpmrst		76.55 170	75.99 176	77.20 168	87.32 113	83.05 191	82.86 160	65.62 205	78.61 93	67.22 118	69.19 105	65.71 119	75.87 143	76.75 205	75.33 207	84.31 211	83.28 191

FC-MVSNet-test		76.53 173	81.62 89	70.58 198	84.99 137	85.73 177	74.81 199	78.85 127	77.00 100	39.13 222	75.90 67	73.50 93	54.08 205	86.54 126	85.99 152	91.65 166	86.68 171

PatchT		76.42 174	77.81 152	74.80 187	78.46 202	84.30 188	71.82 205	65.03 209	73.89 125	65.37 137	61.58 145	66.70 116	77.18 136	85.10 162	84.87 165	90.94 177	88.21 154

TAMVS		76.42 174	77.16 164	75.56 181	83.05 174	85.55 180	80.58 181	71.43 181	65.40 192	61.04 176	67.27 115	69.22 109	67.99 183	84.88 164	84.78 167	89.28 188	83.01 192

EG-PatchMatch MVS		76.40 176	75.47 184	77.48 164	85.86 124	90.22 113	82.45 163	73.96 173	59.64 206	59.60 181	52.75 200	62.20 148	68.44 182	88.23 103	87.50 108	94.55 93	87.78 160

CP-MVSNet		76.36 177	76.41 170	76.32 177	82.73 182	88.64 143	79.39 185	79.62 118	67.21 176	53.70 194	60.72 161	55.22 197	67.91 185	83.52 174	86.34 131	94.55 93	93.19 72

tpm		76.30 178	76.05 175	76.59 174	86.97 116	83.01 192	83.83 155	67.06 199	71.83 142	63.87 150	69.56 103	62.88 136	73.41 161	79.79 195	78.59 194	84.41 210	86.68 171

v748		76.17 179	75.10 188	77.43 165	81.60 189	88.01 151	79.02 189	76.28 154	64.47 193	64.14 147	56.55 189	56.26 192	70.40 175	82.50 183	85.77 154	93.11 151	92.15 105

test0.0.03 1		76.03 180	78.51 128	73.12 195	87.47 111	85.13 185	76.32 196	78.05 136	73.19 135	50.98 203	70.64 94	69.28 108	55.53 201	85.33 156	84.38 171	90.39 180	81.63 196

PEN-MVS		76.02 181	76.07 173	75.95 180	83.17 173	87.97 152	79.65 183	80.07 109	66.57 181	51.45 200	60.94 156	55.47 196	66.81 189	82.72 180	86.80 118	94.59 88	92.03 109

SixPastTwentyTwo		76.02 181	75.72 181	76.36 176	83.38 169	87.54 154	75.50 198	76.22 155	65.50 188	57.05 191	70.64 94	53.97 201	74.54 151	80.96 189	82.12 184	91.44 168	89.35 147

PS-CasMVS		75.90 183	75.86 180	75.96 179	82.59 183	88.46 148	79.23 188	79.56 120	66.00 184	52.77 196	59.48 177	54.35 200	67.14 188	83.37 177	86.23 132	94.47 97	93.10 74

WR-MVS_H		75.84 184	76.93 167	74.57 190	82.86 179	89.50 131	78.34 192	79.36 123	66.90 179	52.51 197	60.20 172	59.71 172	59.73 199	83.61 173	85.77 154	94.65 84	92.84 81

LTVRE_ROB	74.41 16	75.78 185	74.72 190	77.02 171	85.88 123	89.22 135	82.44 164	77.17 143	50.57 217	45.45 210	65.44 125	52.29 205	81.25 74	85.50 147	87.42 111	89.94 184	92.62 88
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		75.64 186	77.26 161	73.76 191	87.92 104	92.20 83	87.32 97	64.67 210	51.92 216	35.35 223	46.44 209	77.05 81	71.97 165	92.64 41	91.02 49	95.34 49	89.53 146

FMVSNet5		75.50 187	76.07 173	74.83 186	76.16 207	81.19 201	81.34 172	70.21 187	73.20 134	61.59 170	58.97 180	68.33 113	68.50 181	85.87 135	85.85 153	91.18 175	79.11 204

DTE-MVSNet		75.14 188	75.44 185	74.80 187	83.18 172	87.19 158	78.25 194	80.11 108	66.05 183	48.31 206	60.88 160	54.67 198	64.54 195	82.57 182	86.17 144	94.43 100	90.53 142

pmmvs6		74.83 189	72.89 195	77.09 169	82.11 186	87.50 155	80.88 180	76.97 145	52.79 215	61.91 165	46.66 208	60.49 164	69.28 178	86.74 123	85.46 161	91.39 169	90.56 141

MIMVSNet		74.69 190	75.60 183	73.62 192	76.02 209	85.31 182	81.21 176	67.43 196	71.02 149	59.07 187	54.48 193	64.07 128	66.14 192	86.52 127	86.64 122	91.83 165	81.17 198

ADS-MVSNet		74.53 191	75.69 182	73.17 194	81.57 191	80.71 203	79.27 187	63.03 213	79.27 88	59.94 180	67.86 112	68.32 114	71.08 172	77.33 202	76.83 203	84.12 213	79.53 201

pmmvs-eth3d		74.32 192	71.96 197	77.08 170	77.33 205	82.71 194	78.41 191	76.02 161	66.65 180	65.98 131	54.23 196	49.02 211	73.14 163	82.37 185	82.69 181	91.61 167	86.05 178

PM-MVS		74.17 193	73.10 193	75.41 182	76.07 208	82.53 196	77.56 195	71.69 180	71.04 148	61.92 164	61.23 152	47.30 212	74.82 150	81.78 187	79.80 189	90.42 179	88.05 157

CMPMVS	56.49 17	73.84 194	71.73 198	76.31 178	85.20 133	85.67 178	75.80 197	73.23 176	62.26 198	65.40 136	53.40 199	59.70 173	71.77 167	80.25 192	79.56 191	86.45 202	81.28 197
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		73.21 195	72.91 194	73.56 193	80.01 195	84.28 189	78.62 190	66.43 202	68.64 171	59.12 186	60.39 168	59.69 174	69.81 177	78.82 200	77.43 202	87.36 196	81.11 199

testgi		71.92 196	74.20 191	69.27 201	84.58 142	83.06 190	73.40 201	74.39 170	64.04 195	46.17 209	68.90 109	57.15 187	48.89 212	84.07 171	83.08 178	88.18 192	79.09 205

Anonymous20231206		70.80 197	70.59 200	71.04 197	81.60 189	82.49 197	74.64 200	75.87 164	64.17 194	49.27 204	44.85 212	53.59 203	54.68 204	83.07 178	82.34 183	90.17 181	83.65 189

gm-plane-assit		70.29 198	70.65 199	69.88 199	85.03 136	78.50 208	58.41 219	65.47 206	50.39 218	40.88 215	49.60 204	50.11 208	75.14 147	91.43 53	89.78 73	94.32 104	84.73 187

EU-MVSNet		69.98 199	72.30 196	67.28 204	75.67 210	79.39 205	73.12 202	69.94 189	63.59 196	42.80 213	62.93 142	56.71 190	55.07 203	79.13 199	78.55 195	87.06 200	85.82 181

MVS-HIRNet		68.83 200	66.39 204	71.68 196	77.58 203	75.52 211	66.45 211	65.05 208	62.16 199	62.84 155	44.76 213	56.60 191	71.96 166	78.04 201	75.06 209	86.18 204	72.56 213

LP		68.35 201	67.23 202	69.67 200	77.49 204	79.38 206	72.84 204	61.37 216	66.94 178	55.08 192	47.00 207	50.35 207	65.16 194	75.61 207	76.03 204	86.08 205	75.28 210

test20.03		68.31 202	70.05 201	66.28 206	82.41 184	80.84 202	67.35 210	76.11 158	58.44 208	40.80 216	53.77 197	54.54 199	42.28 218	83.07 178	81.96 187	88.73 191	77.76 207

N_pmnet		66.85 203	66.63 203	67.11 205	78.73 200	74.66 212	70.53 206	71.07 182	66.46 182	46.54 208	51.68 203	51.91 206	55.48 202	74.68 210	72.38 214	80.29 218	74.65 211

MDA-MVSNet-bldmvs		66.22 204	64.49 207	68.24 202	61.67 222	82.11 200	70.07 207	76.16 156	59.14 207	47.94 207	54.35 195	35.82 224	67.33 187	64.94 222	75.68 206	86.30 203	79.36 202

MIMVSNet1		65.00 205	66.24 205	63.55 210	58.41 226	80.01 204	69.00 208	74.03 172	55.81 213	41.88 214	36.81 221	49.48 210	47.89 213	81.32 188	82.40 182	90.08 183	77.88 206

test2356		63.96 206	64.10 209	63.78 209	74.71 211	71.55 215	65.83 212	67.38 197	57.11 210	40.41 217	53.58 198	41.13 218	49.35 211	77.00 204	77.57 201	85.01 209	70.79 214

testpf		63.91 207	65.23 206	62.38 211	81.32 192	69.95 218	62.71 217	54.16 223	61.29 202	48.73 205	57.31 186	52.50 204	50.97 208	67.50 218	68.86 219	76.36 221	79.21 203

new-patchmatchnet		63.80 208	63.31 210	64.37 208	76.49 206	75.99 210	63.73 214	70.99 183	57.27 209	43.08 212	45.86 210	43.80 213	45.13 217	73.20 213	70.68 218	86.80 201	76.34 209

FPMVS		63.63 209	60.08 215	67.78 203	80.01 195	71.50 216	72.88 203	69.41 192	61.82 200	53.11 195	45.12 211	42.11 216	50.86 209	66.69 219	63.84 221	80.41 217	69.46 218

testus		63.31 210	64.48 208	61.94 213	73.99 213	71.99 214	63.56 216	63.25 212	57.01 211	39.41 221	54.38 194	38.73 222	46.24 216	77.01 203	77.93 198	85.20 208	74.29 212

Anonymous20231211		62.95 211	60.42 214	65.89 207	74.22 212	78.37 209	67.66 209	74.47 169	40.37 225	39.59 220	27.51 224	38.26 223	52.13 206	75.39 209	77.89 199	87.28 198	85.16 182

pmmvs3		61.89 212	61.74 212	62.06 212	64.30 220	70.83 217	64.22 213	52.14 225	48.78 219	44.47 211	41.67 215	41.70 217	63.03 196	76.06 206	76.02 205	84.18 212	77.14 208

new_pmnet		59.28 213	61.47 213	56.73 218	61.66 223	68.29 219	59.57 218	54.91 221	60.83 203	34.38 224	44.66 214	43.65 214	49.90 210	71.66 216	71.56 217	79.94 219	69.67 217

GG-mvs-BLEND		57.56 214	82.61 82	28.34 227	0.22 232	90.10 116	79.37 186	0.14 231	79.56 85	0.40 235	71.25 93	83.40 52	0.30 232	86.27 130	83.87 172	89.59 186	83.83 188

1111		57.32 215	57.20 216	57.46 215	71.89 217	67.50 222	52.34 220	58.78 218	46.57 220	39.69 218	37.38 219	38.78 220	46.37 214	74.15 211	74.36 213	75.70 222	61.66 221

testmv		56.62 216	56.41 217	56.86 216	71.92 215	67.58 220	52.17 222	65.69 203	40.60 223	28.53 226	37.90 217	31.52 225	40.10 220	72.64 214	74.73 211	82.78 215	69.91 215

test1235678		56.61 217	56.40 218	56.86 216	71.92 215	67.58 220	52.17 222	65.69 203	40.58 224	28.52 227	37.89 218	31.49 226	40.10 220	72.64 214	74.72 212	82.78 215	69.90 216

PMVS	50.48 18	55.81 218	51.93 219	60.33 214	72.90 214	49.34 227	48.78 224	69.51 191	43.49 222	54.25 193	36.26 222	41.04 219	39.71 222	65.07 221	60.70 222	76.85 220	67.58 219
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		50.02 219	51.22 220	48.61 220	63.00 221	60.15 225	47.60 226	56.49 220	38.02 226	24.74 229	36.14 223	25.93 228	24.79 225	66.19 220	71.68 216	75.07 223	60.44 223

Gipuma		49.17 220	47.05 221	51.65 219	59.67 225	48.39 228	41.98 227	63.47 211	55.64 214	33.33 225	14.90 227	13.78 232	41.34 219	69.31 217	72.30 215	70.11 225	55.00 225
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		44.14 221	43.91 223	44.40 222	59.91 224	61.10 224	34.07 229	60.09 217	27.71 228	14.44 231	19.11 226	19.28 230	23.90 227	47.36 226	66.69 220	73.98 224	66.11 220

PMMVS2		41.68 222	44.74 222	38.10 223	46.97 229	52.32 226	40.63 228	48.08 226	35.51 227	7.36 234	26.86 225	24.64 229	16.72 228	55.24 224	59.03 223	68.85 226	59.59 224

.test1245		41.43 223	38.48 224	44.88 221	71.89 217	67.50 222	52.34 220	58.78 218	46.57 220	39.69 218	37.38 219	38.78 220	46.37 214	74.15 211	1.18 228	0.20 232	3.76 230

E-PMN		31.40 224	26.80 226	36.78 224	51.39 228	29.96 231	20.20 231	54.17 222	25.93 230	12.75 232	14.73 228	8.58 234	34.10 224	27.36 228	37.83 226	48.07 229	43.18 227

MVE	30.17 19	30.88 225	33.52 225	27.80 228	23.78 231	39.16 230	18.69 233	46.90 227	21.88 231	15.39 230	14.37 229	7.31 235	24.41 226	41.63 227	56.22 224	37.64 231	54.07 226
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		30.49 226	25.44 227	36.39 225	51.47 227	29.89 232	20.17 232	54.00 224	26.49 229	12.02 233	13.94 230	8.84 233	34.37 223	25.04 229	34.37 227	46.29 230	39.53 228

testmvs		1.03 227	1.63 228	0.34 229	0.09 233	0.35 234	0.61 235	0.16 230	1.49 232	0.10 236	3.15 231	0.15 236	0.86 231	1.32 230	1.18 228	0.20 232	3.76 230

test123		0.87 228	1.40 229	0.25 230	0.03 234	0.25 235	0.35 236	0.08 232	1.21 233	0.05 237	2.84 232	0.03 237	0.89 230	0.43 231	1.16 230	0.13 234	3.87 229

test_all		0.00 229	0.00 230	0.00 231	0.00 235	0.00 236	0.00 237	0.00 233	0.00 234	0.00 238	0.00 233	0.00 238	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet-low-res		0.00 229	0.00 230	0.00 231	0.00 235	0.00 236	0.00 237	0.00 233	0.00 234	0.00 238	0.00 233	0.00 238	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

sosnet		0.00 229	0.00 230	0.00 231	0.00 235	0.00 236	0.00 237	0.00 233	0.00 234	0.00 238	0.00 233	0.00 238	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

ambc			61.92 211		70.98 219	73.54 213	63.64 215		60.06 204	52.23 199	38.44 216	19.17 231	57.12 200	82.33 186	75.03 210	83.21 214	84.89 184

MTAPA										92.97 2		91.03 15

MTMP										93.14 1		90.21 22

Patchmatch-RL test							8.55 234

tmp_tt				32.73 226	43.96 230	21.15 233	26.71 230	8.99 229	65.67 187	51.39 201	56.01 192	42.64 215	11.76 229	56.60 223	50.81 225	53.55 228

XVS					93.11 52	96.70 19	91.91 47			83.95 41		88.82 32				95.79 27

X-MVStestdata					93.11 52	96.70 19	91.91 47			83.95 41		88.82 32				95.79 27

abl_6				90.66 33	94.65 39	96.27 31	92.21 43	86.94 40	90.23 34	86.38 31	85.50 34	92.96 6	88.37 37			95.40 44	95.46 47

mPP-MVS					97.06 11							88.08 37

NP-MVS									87.47 46

Patchmtry						85.54 181	82.30 166	68.23 193		65.37 137

DeepMVS_CX						48.31 229	48.03 225	26.08 228	56.42 212	25.77 228	47.51 206	31.31 227	51.30 207	48.49 225		53.61 227	61.52 222

High-resolution multi-view benchmark