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 1	90.50 1	85.08 1	90.95 4	91.58 3	92.03 1	75.53 7	91.15 1	80.10 7	92.27 2	88.34 5	80.80 2	88.00 9	86.99 12	91.09 4	95.16 1

HSP-MVS		87.45 2	90.22 2	84.22 6	90.00 16	91.80 2	90.59 3	75.80 3	89.93 3	78.35 13	92.54 1	89.18 2	80.89 1	87.99 10	86.29 23	89.70 33	93.85 7

HPM-MVS++		87.09 3	88.92 7	84.95 2	92.61 1	87.91 32	90.23 8	76.06 2	88.85 6	81.20 4	87.33 8	87.93 6	79.47 5	88.59 4	88.23 4	90.15 26	93.60 14

SD-MVS		86.96 4	89.45 3	84.05 8	90.13 13	89.23 16	89.77 11	74.59 8	89.17 4	80.70 5	89.93 6	89.67 1	78.47 7	87.57 13	86.79 15	90.67 10	93.76 10

TSAR-MVS + MP.		86.88 5	89.23 4	84.14 7	89.78 19	88.67 24	90.59 3	73.46 19	88.99 5	80.52 6	91.26 3	88.65 3	79.91 4	86.96 23	86.22 24	90.59 11	93.83 8

APD-MVS		86.84 6	88.91 8	84.41 3	90.66 6	90.10 6	90.78 2	75.64 4	87.38 10	78.72 11	90.68 5	86.82 9	80.15 3	87.13 18	86.45 21	90.51 13	93.83 8
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		86.52 7	89.01 5	83.62 10	90.28 12	90.09 7	90.32 6	74.05 13	88.32 8	79.74 8	87.04 10	85.59 16	76.97 22	89.35 1	88.44 3	90.35 22	94.27 5

CNVR-MVS		86.36 8	88.19 11	84.23 5	91.33 3	89.84 8	90.34 5	75.56 5	87.36 11	78.97 10	81.19 21	86.76 10	78.74 6	89.30 2	88.58 1	90.45 19	94.33 4

SteuartSystems-ACMMP		85.99 9	88.31 10	83.27 14	90.73 5	89.84 8	90.27 7	74.31 9	84.56 23	75.88 22	87.32 9	85.04 17	77.31 17	89.01 3	88.46 2	91.14 3	93.96 6
Skip Steuart: Steuart Systems R&D Blog.
MPTG		85.71 10	86.88 16	84.34 4	90.54 10	87.11 36	89.77 11	74.17 11	88.54 7	83.08 2	78.60 25	86.10 12	78.11 9	87.80 12	87.46 9	90.35 22	92.56 19

ACMMPR		85.52 11	87.53 13	83.17 15	90.13 13	89.27 14	89.30 13	73.97 14	86.89 13	77.14 18	86.09 11	83.18 24	77.74 13	87.42 14	87.20 10	90.77 6	92.63 18

MP-MVS		85.50 12	87.40 14	83.28 13	90.65 7	89.51 13	89.16 16	74.11 12	83.70 26	78.06 15	85.54 13	84.89 20	77.31 17	87.40 15	87.14 11	90.41 20	93.65 13

NCCC		85.34 13	86.59 18	83.88 9	91.48 2	88.88 18	89.79 10	75.54 6	86.67 14	77.94 16	76.55 28	84.99 18	78.07 10	88.04 7	87.68 7	90.46 18	93.31 15

DeepPCF-MVS	79.04 1	85.30 14	88.93 6	81.06 24	88.77 27	90.48 4	85.46 38	73.08 20	90.97 2	73.77 29	84.81 15	85.95 13	77.43 16	88.22 6	87.73 6	87.85 64	94.34 3

CSCG		85.28 15	87.68 12	82.49 18	89.95 17	91.99 1	88.82 17	71.20 29	86.41 15	79.63 9	79.26 22	88.36 4	73.94 32	86.64 25	86.67 18	91.40 2	94.41 2

MCST-MVS		85.13 16	86.62 17	83.39 11	90.55 9	89.82 10	89.29 14	73.89 16	84.38 24	76.03 21	79.01 24	85.90 14	78.47 7	87.81 11	86.11 26	92.11 1	93.29 16

TSAR-MVS + ACMM		85.10 17	88.81 9	80.77 27	89.55 21	88.53 26	88.59 20	72.55 22	87.39 9	71.90 35	90.95 4	87.55 7	74.57 27	87.08 20	86.54 19	87.47 69	93.67 11

train_agg		84.86 18	87.21 15	82.11 20	90.59 8	85.47 47	89.81 9	73.55 18	83.95 25	73.30 30	89.84 7	87.23 8	75.61 25	86.47 27	85.46 31	89.78 29	92.06 25

DeepC-MVS	78.47 2	84.81 19	86.03 22	83.37 12	89.29 24	90.38 5	88.61 19	76.50 1	86.25 16	77.22 17	75.12 32	80.28 36	77.59 15	88.39 5	88.17 5	91.02 5	93.66 12
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CP-MVS		84.74 20	86.43 20	82.77 17	89.48 22	88.13 31	88.64 18	73.93 15	84.92 18	76.77 19	81.94 19	83.50 22	77.29 19	86.92 24	86.49 20	90.49 14	93.14 17

PGM-MVS		84.42 21	86.29 21	82.23 19	90.04 15	88.82 20	89.23 15	71.74 27	82.82 29	74.61 25	84.41 16	82.09 26	77.03 21	87.13 18	86.73 17	90.73 8	92.06 25

DeepC-MVS_fast	78.24 3	84.27 22	85.50 24	82.85 16	90.46 11	89.24 15	87.83 25	74.24 10	84.88 19	76.23 20	75.26 31	81.05 34	77.62 14	88.02 8	87.62 8	90.69 9	92.41 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		83.69 23	86.58 19	80.32 28	85.14 46	86.96 37	84.91 42	70.25 33	84.71 22	73.91 28	85.16 14	85.63 15	77.92 11	85.44 33	85.71 29	89.77 30	92.45 20

ACMMP		83.42 24	85.27 25	81.26 23	88.47 28	88.49 27	88.31 23	72.09 24	83.42 27	72.77 33	82.65 17	78.22 40	75.18 26	86.24 30	85.76 28	90.74 7	92.13 24
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
X-MVS		83.23 25	85.20 26	80.92 26	89.71 20	88.68 21	88.21 24	73.60 17	82.57 30	71.81 38	77.07 26	81.92 28	71.72 47	86.98 22	86.86 13	90.47 15	92.36 22

CDPH-MVS		82.64 26	85.03 27	79.86 31	89.41 23	88.31 28	88.32 22	71.84 26	80.11 37	67.47 53	82.09 18	81.44 32	71.85 45	85.89 32	86.15 25	90.24 24	91.25 31

3Dnovator+	75.73 4	82.40 27	82.76 32	81.97 21	88.02 29	89.67 11	86.60 29	71.48 28	81.28 35	78.18 14	64.78 67	77.96 42	77.13 20	87.32 16	86.83 14	90.41 20	91.48 29

PHI-MVS		82.36 28	85.89 23	78.24 41	86.40 39	89.52 12	85.52 36	69.52 40	82.38 32	65.67 58	81.35 20	82.36 25	73.07 37	87.31 17	86.76 16	89.24 40	91.56 28

MSLP-MVS++		82.09 29	82.66 33	81.42 22	87.03 35	87.22 35	85.82 34	70.04 34	80.30 36	78.66 12	68.67 54	81.04 35	77.81 12	85.19 36	84.88 36	89.19 42	91.31 30

CPTT-MVS		81.77 30	83.10 31	80.21 29	85.93 42	86.45 42	87.72 26	70.98 30	82.54 31	71.53 41	74.23 37	81.49 31	76.31 24	82.85 54	81.87 50	88.79 49	92.26 23

MVS_0304		81.73 31	83.86 28	79.26 34	86.22 41	89.18 17	86.41 30	67.15 54	75.28 47	70.75 45	74.59 34	83.49 23	74.42 29	87.05 21	86.34 22	90.58 12	91.08 33

CANet		81.62 32	83.41 29	79.53 33	87.06 34	88.59 25	85.47 37	67.96 50	76.59 45	74.05 26	74.69 33	81.98 27	72.98 38	86.14 31	85.47 30	89.68 34	90.42 39

HQP-MVS		81.19 33	83.27 30	78.76 38	87.40 32	85.45 48	86.95 27	70.47 32	81.31 34	66.91 56	79.24 23	76.63 44	71.67 48	84.43 40	83.78 41	89.19 42	92.05 27

OMC-MVS		80.26 34	82.59 34	77.54 44	83.04 54	85.54 46	83.25 49	65.05 67	87.32 12	72.42 34	72.04 42	78.97 38	73.30 35	83.86 43	81.60 53	88.15 55	88.83 47

MVS_111021_HR		80.13 35	81.46 37	78.58 39	85.77 43	85.17 51	83.45 48	69.28 41	74.08 53	70.31 46	74.31 36	75.26 50	73.13 36	86.46 28	85.15 34	89.53 36	89.81 42

LGP-MVS_train		79.83 36	81.22 39	78.22 42	86.28 40	85.36 50	86.76 28	69.59 38	77.34 42	65.14 60	75.68 30	70.79 63	71.37 50	84.60 38	84.01 39	90.18 25	90.74 35

ACMP	73.23 7	79.79 37	80.53 42	78.94 36	85.61 44	85.68 45	85.61 35	69.59 38	77.33 43	71.00 44	74.45 35	69.16 72	71.88 43	83.15 51	83.37 44	89.92 28	90.57 38
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator	73.76 5	79.75 38	80.52 43	78.84 37	84.94 51	87.35 33	84.43 44	65.54 64	78.29 41	73.97 27	63.00 73	75.62 49	74.07 31	85.00 37	85.34 32	90.11 27	89.04 45

AdaColmap		79.74 39	78.62 50	81.05 25	89.23 25	86.06 44	84.95 41	71.96 25	79.39 40	75.51 23	63.16 71	68.84 77	76.51 23	83.55 47	82.85 46	88.13 56	86.46 60

OPM-MVS		79.68 40	79.28 49	80.15 30	87.99 30	86.77 39	88.52 21	72.72 21	64.55 79	67.65 52	67.87 58	74.33 53	74.31 30	86.37 29	85.25 33	89.73 32	89.81 42

PCF-MVS	73.28 6	79.42 41	80.41 44	78.26 40	84.88 52	88.17 29	86.08 31	69.85 35	75.23 49	68.43 48	68.03 57	78.38 39	71.76 46	81.26 68	80.65 69	88.56 52	91.18 32
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		79.35 42	81.23 38	77.16 46	85.01 49	86.92 38	85.87 33	60.89 110	80.07 39	75.35 24	72.96 39	73.21 56	68.43 63	85.41 35	84.63 37	87.41 70	85.44 71
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MAR-MVS		79.21 43	80.32 45	77.92 43	87.46 31	88.15 30	83.95 45	67.48 53	74.28 51	68.25 49	64.70 68	77.04 43	72.17 42	85.42 34	85.00 35	88.22 53	87.62 54
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
canonicalmvs		79.16 44	82.37 35	75.41 52	82.33 59	86.38 43	80.80 54	63.18 78	82.90 28	67.34 54	72.79 40	76.07 46	69.62 56	83.46 50	84.41 38	89.20 41	90.60 37

DELS-MVS		79.15 45	81.07 40	76.91 47	83.54 53	87.31 34	84.45 43	64.92 68	69.98 58	69.34 47	71.62 44	76.26 45	69.84 55	86.57 26	85.90 27	89.39 38	89.88 41
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		79.08 46	80.62 41	77.28 45	88.90 26	83.17 64	83.65 46	72.41 23	74.41 50	67.15 55	76.78 27	74.37 52	64.43 98	83.70 46	83.69 42	87.15 75	88.19 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMM	72.26 8	78.86 47	78.13 51	79.71 32	86.89 36	83.40 61	86.02 32	70.50 31	75.28 47	71.49 42	63.01 72	69.26 71	73.57 34	84.11 42	83.98 40	89.76 31	87.84 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
QAPM		78.47 48	80.22 46	76.43 49	85.03 48	86.75 40	80.62 55	66.00 61	73.77 54	65.35 59	65.54 65	78.02 41	72.69 39	83.71 45	83.36 45	88.87 48	90.41 40

TSAR-MVS + COLMAP		78.34 49	81.64 36	74.48 59	80.13 70	85.01 52	81.73 50	65.93 63	84.75 21	61.68 69	85.79 12	66.27 83	71.39 49	82.91 53	80.78 60	86.01 122	85.98 62

MVS_111021_LR		78.13 50	79.85 48	76.13 50	81.12 63	81.50 71	80.28 56	65.25 65	76.09 46	71.32 43	76.49 29	72.87 57	72.21 41	82.79 55	81.29 55	86.59 110	87.91 51

TAPA-MVS	71.42 9	77.69 51	80.05 47	74.94 55	80.68 66	84.52 54	81.36 51	63.14 79	84.77 20	64.82 62	68.72 52	75.91 48	71.86 44	81.62 59	79.55 81	87.80 65	85.24 74
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNLPA		77.20 52	77.54 55	76.80 48	82.63 56	84.31 55	79.77 59	64.64 69	85.17 17	73.18 31	56.37 105	69.81 68	74.53 28	81.12 70	78.69 88	86.04 121	87.29 57

PVSNet_Blended_VisFu		76.57 53	77.90 52	75.02 54	80.56 67	86.58 41	79.24 63	66.18 58	64.81 76	68.18 50	65.61 63	71.45 60	67.05 65	84.16 41	81.80 51	88.90 46	90.92 34

PVSNet_BlendedMVS		76.21 54	77.52 56	74.69 57	79.46 72	83.79 58	77.50 97	64.34 72	69.88 59	71.88 36	68.54 55	70.42 65	67.05 65	83.48 48	79.63 77	87.89 62	86.87 58

PVSNet_Blended		76.21 54	77.52 56	74.69 57	79.46 72	83.79 58	77.50 97	64.34 72	69.88 59	71.88 36	68.54 55	70.42 65	67.05 65	83.48 48	79.63 77	87.89 62	86.87 58

OpenMVS	70.44 10	76.15 56	76.82 63	75.37 53	85.01 49	84.79 53	78.99 68	62.07 101	71.27 57	67.88 51	57.91 97	72.36 58	70.15 54	82.23 57	81.41 54	88.12 57	87.78 53

PLC	68.99 11	75.68 57	75.31 67	76.12 51	82.94 55	81.26 74	79.94 58	66.10 59	77.15 44	66.86 57	59.13 86	68.53 78	73.73 33	80.38 78	79.04 85	87.13 79	81.68 121
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_Test		75.37 58	77.13 61	73.31 63	79.07 75	81.32 73	79.98 57	60.12 127	69.72 61	64.11 64	70.53 46	73.22 55	68.90 59	80.14 84	79.48 83	87.67 66	85.50 69

Effi-MVS+		75.28 59	76.20 64	74.20 60	81.15 62	83.24 62	81.11 52	63.13 80	66.37 65	60.27 74	64.30 69	68.88 76	70.93 53	81.56 61	81.69 52	88.61 50	87.35 55

DI_MVS_plusplus_trai		75.13 60	76.12 65	73.96 61	78.18 80	81.55 70	80.97 53	62.54 95	68.59 62	65.13 61	61.43 74	74.81 51	69.32 58	81.01 72	79.59 79	87.64 67	85.89 63

UA-Net		74.47 61	77.80 53	70.59 80	85.33 45	85.40 49	73.54 132	65.98 62	60.65 103	56.00 104	72.11 41	79.15 37	54.63 158	83.13 52	82.25 48	88.04 58	81.92 119

LS3D		74.08 62	73.39 72	74.88 56	85.05 47	82.62 67	79.71 60	68.66 44	72.82 55	58.80 79	57.61 98	61.31 96	71.07 52	80.32 80	78.87 87	86.00 124	80.18 133

EPP-MVSNet		74.00 63	77.41 58	70.02 95	80.53 68	83.91 57	74.99 114	62.68 91	65.06 74	49.77 140	68.68 53	72.09 59	63.06 104	82.49 56	80.73 61	89.12 44	88.91 46

IS_MVSNet		73.33 64	77.34 59	68.65 108	81.29 61	83.47 60	74.45 117	63.58 76	65.75 71	48.49 143	67.11 62	70.61 64	54.63 158	84.51 39	83.58 43	89.48 37	86.34 61

CANet_DTU		73.29 65	76.96 62	69.00 104	77.04 97	82.06 69	79.49 62	56.30 161	67.85 63	53.29 118	71.12 45	70.37 67	61.81 114	81.59 60	80.96 58	86.09 116	84.73 83

diffmvs		73.13 66	75.65 66	70.19 92	74.07 136	77.17 125	78.24 91	57.45 154	72.44 56	64.02 65	69.05 50	75.92 47	64.86 96	75.18 146	75.27 148	82.47 157	84.53 84

Fast-Effi-MVS+		73.11 67	73.66 70	72.48 65	77.72 90	80.88 80	78.55 83	58.83 146	65.19 73	60.36 73	59.98 81	62.42 94	71.22 51	81.66 58	80.61 71	88.20 54	84.88 82

UGNet		72.78 68	77.67 54	67.07 128	71.65 158	83.24 62	75.20 108	63.62 75	64.93 75	56.72 98	71.82 43	73.30 54	49.02 173	81.02 71	80.70 67	86.22 114	88.67 48
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Vis-MVSNet		72.77 69	77.20 60	67.59 119	74.19 135	84.01 56	76.61 104	61.69 106	60.62 104	50.61 134	70.25 48	71.31 62	55.57 153	83.85 44	82.28 47	86.90 89	88.08 50
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FC-MVSNet-train		72.60 70	75.07 68	69.71 99	81.10 64	78.79 104	73.74 130	65.23 66	66.10 68	53.34 117	70.36 47	63.40 91	56.92 142	81.44 62	80.96 58	87.93 60	84.46 85

MVSTER		72.06 71	74.24 69	69.51 100	70.39 166	75.97 146	76.91 101	57.36 156	64.64 78	61.39 71	68.86 51	63.76 89	63.46 101	81.44 62	79.70 76	87.56 68	85.31 73

Effi-MVS+-dtu		71.82 72	71.86 80	71.78 66	78.77 76	80.47 89	78.55 83	61.67 107	60.68 102	55.49 105	58.48 90	65.48 85	68.85 60	76.92 132	75.55 146	87.35 71	85.46 70

IterMVS-LS		71.69 73	72.82 76	70.37 89	77.54 92	76.34 143	75.13 112	60.46 117	61.53 98	57.57 87	64.89 66	67.33 80	66.04 90	77.09 131	77.37 111	85.48 137	85.18 75
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		71.52 74	69.87 94	73.44 62	82.21 60	79.35 98	79.52 61	64.59 70	66.15 67	61.87 68	53.21 144	56.09 128	65.85 94	78.94 99	78.50 89	86.60 109	76.85 159

ACMH+	66.54 13	71.36 75	70.09 89	72.85 64	82.59 57	81.13 75	78.56 82	68.04 48	61.55 97	52.52 124	51.50 162	54.14 140	68.56 62	78.85 100	79.50 82	86.82 96	83.94 90

IB-MVS	66.94 12	71.21 76	71.66 81	70.68 77	79.18 74	82.83 66	72.61 139	61.77 105	59.66 110	63.44 67	53.26 142	59.65 101	59.16 127	76.78 135	82.11 49	87.90 61	87.33 56
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
GBi-Net		70.78 77	73.37 73	67.76 113	72.95 146	78.00 112	75.15 109	62.72 86	64.13 80	51.44 126	58.37 91	69.02 73	57.59 134	81.33 65	80.72 62	86.70 104	82.02 113

test1		70.78 77	73.37 73	67.76 113	72.95 146	78.00 112	75.15 109	62.72 86	64.13 80	51.44 126	58.37 91	69.02 73	57.59 134	81.33 65	80.72 62	86.70 104	82.02 113

ACMH	65.37 14	70.71 79	70.00 90	71.54 67	82.51 58	82.47 68	77.78 94	68.13 47	56.19 145	46.06 156	54.30 125	51.20 171	68.68 61	80.66 74	80.72 62	86.07 117	84.45 86
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_NR-MVSNet		70.59 80	72.19 78	68.72 106	77.72 90	80.72 81	73.81 128	69.65 37	61.99 92	43.23 167	60.54 77	57.50 107	58.57 128	79.56 92	81.07 57	89.34 39	83.97 88

FMVSNet3		70.49 81	72.90 75	67.67 117	72.88 149	77.98 115	74.96 115	62.72 86	64.13 80	51.44 126	58.37 91	69.02 73	57.43 137	79.43 94	79.57 80	86.59 110	81.81 120

FMVSNet2		70.39 82	72.67 77	67.72 116	72.95 146	78.00 112	75.15 109	62.69 90	63.29 85	51.25 130	55.64 107	68.49 79	57.59 134	80.91 73	80.35 73	86.70 104	82.02 113

v6		70.35 83	69.94 91	70.83 69	74.68 128	80.62 82	78.81 73	60.16 126	58.81 115	58.17 83	55.01 112	57.31 112	66.32 83	77.53 119	76.73 130	86.82 96	83.62 92

v1neww		70.34 84	69.93 92	70.82 70	74.68 128	80.61 83	78.80 74	60.17 123	58.74 117	58.10 84	55.00 113	57.28 113	66.33 81	77.53 119	76.74 126	86.82 96	83.61 93

v7new		70.34 84	69.93 92	70.82 70	74.68 128	80.61 83	78.80 74	60.17 123	58.74 117	58.10 84	55.00 113	57.28 113	66.33 81	77.53 119	76.74 126	86.82 96	83.61 93

v7		70.33 86	69.87 94	70.88 68	74.79 120	81.04 76	79.22 64	60.57 114	57.70 126	56.65 100	54.23 130	55.29 133	66.95 68	78.28 106	77.47 107	87.12 82	85.05 78

v8		70.23 87	69.86 96	70.67 78	74.69 127	79.82 94	78.79 76	59.18 136	58.80 116	58.20 82	55.00 113	57.33 110	66.31 84	77.51 122	76.71 134	86.82 96	83.88 91

v10		70.22 88	69.76 98	70.74 74	74.79 120	80.30 92	79.22 64	59.81 130	57.71 125	56.58 101	54.22 132	55.31 131	66.95 68	78.28 106	77.47 107	87.12 82	85.07 77

MS-PatchMatch		70.17 89	70.49 87	69.79 97	80.98 65	77.97 117	77.51 96	58.95 138	62.33 90	55.22 108	53.14 145	65.90 84	62.03 109	79.08 98	77.11 115	84.08 149	77.91 149

v18		70.10 90	69.52 101	70.77 73	74.66 131	77.06 128	78.84 71	58.84 145	60.01 108	59.23 76	55.06 111	57.47 108	66.34 80	77.50 123	76.75 124	86.71 103	82.77 107

v16		70.07 91	69.46 103	70.79 72	74.74 126	77.08 127	78.79 76	58.86 140	59.75 109	59.15 77	54.87 117	57.33 110	66.38 78	77.61 117	76.77 119	86.81 101	82.79 105

v2v482		70.05 92	69.46 103	70.74 74	74.62 132	80.32 91	79.00 67	60.62 113	57.41 127	56.89 93	55.43 110	55.14 134	66.39 77	77.25 128	77.14 114	86.90 89	83.57 98

v17		70.03 93	69.43 108	70.72 76	74.75 125	77.09 126	78.78 78	58.85 142	59.53 112	58.72 80	54.87 117	57.39 109	66.38 78	77.60 118	76.75 124	86.83 95	82.80 103

divwei89l23v2f112		69.97 94	69.44 106	70.58 82	74.78 122	80.50 87	78.85 69	60.30 118	56.97 131	56.75 96	54.67 122	56.27 124	65.92 92	77.37 126	76.72 131	86.88 92	83.58 97

v1		69.97 94	69.45 105	70.59 80	74.78 122	80.51 86	78.84 71	60.30 118	56.98 130	56.81 95	54.69 120	56.29 123	65.91 93	77.37 126	76.71 134	86.89 91	83.59 95

v1141		69.96 96	69.44 106	70.58 82	74.78 122	80.50 87	78.85 69	60.30 118	56.95 132	56.74 97	54.68 121	56.26 125	65.93 91	77.38 125	76.72 131	86.88 92	83.57 98

v1144		69.93 97	69.36 109	70.61 79	74.89 113	80.93 77	79.11 66	60.64 112	55.97 148	55.31 107	53.85 137	54.14 140	66.54 75	78.10 108	77.44 109	87.14 78	85.09 76

DU-MVS		69.63 98	70.91 84	68.13 112	75.99 103	79.54 95	73.81 128	69.20 42	61.20 100	43.23 167	58.52 88	53.50 147	58.57 128	79.22 96	80.45 72	87.97 59	83.97 88

v15		69.61 99	68.88 114	70.46 84	74.81 119	77.03 131	78.75 79	58.83 146	57.06 129	57.18 89	54.55 123	56.37 119	66.13 88	77.70 114	76.76 121	87.03 86	82.69 110

V14		69.59 100	68.86 115	70.45 86	74.83 118	77.04 129	78.70 80	58.83 146	56.95 132	57.08 91	54.41 124	56.34 120	66.15 85	77.77 113	76.76 121	87.08 84	82.74 108

V9		69.58 101	68.83 116	70.46 84	74.85 117	77.04 129	78.65 81	58.85 142	56.83 135	57.12 90	54.26 128	56.31 121	66.14 87	77.83 112	76.76 121	87.13 79	82.79 105

v12		69.54 102	68.79 118	70.41 87	74.88 114	77.03 131	78.54 86	58.85 142	56.71 136	56.87 94	54.13 133	56.23 126	66.15 85	77.89 110	76.74 126	87.17 74	82.80 103

UniMVSNet (Re)		69.53 103	71.90 79	66.76 134	76.42 100	80.93 77	72.59 140	68.03 49	61.75 96	41.68 175	58.34 94	57.23 115	53.27 165	79.53 93	80.62 70	88.57 51	84.90 81

v13		69.52 104	68.76 120	70.41 87	74.88 114	77.02 133	78.52 87	58.86 140	56.61 140	56.91 92	54.00 135	56.17 127	66.11 89	77.93 109	76.74 126	87.21 73	82.83 102

v1192		69.50 105	68.83 116	70.29 90	74.49 133	80.92 79	78.55 83	60.54 115	55.04 156	54.21 110	52.79 152	52.33 163	66.92 70	77.88 111	77.35 112	87.04 85	85.51 68

HyFIR lowres test		69.47 106	68.94 113	70.09 94	76.77 99	82.93 65	76.63 103	60.17 123	59.00 114	54.03 112	40.54 193	65.23 86	67.89 64	76.54 138	78.30 92	85.03 142	80.07 134

v11		69.37 107	68.65 123	70.20 91	74.87 116	76.97 134	78.29 90	58.55 150	56.38 142	56.04 103	54.02 134	54.98 135	66.47 76	78.30 105	76.91 117	86.97 87	83.02 101

v144192		69.34 108	68.68 122	70.12 93	74.06 137	80.54 85	78.08 93	60.54 115	54.99 158	54.13 111	52.92 149	52.80 159	66.73 73	77.13 130	76.72 131	87.15 75	85.63 64

TranMVSNet+NR-MVSNet		69.25 109	70.81 85	67.43 120	77.23 96	79.46 97	73.48 134	69.66 36	60.43 105	39.56 178	58.82 87	53.48 149	55.74 151	79.59 90	81.21 56	88.89 47	82.70 109

CHOSEN 1792x2688		69.20 110	69.26 110	69.13 102	76.86 98	78.93 100	77.27 99	60.12 127	61.86 94	54.42 109	42.54 187	61.61 95	66.91 71	78.55 103	78.14 96	79.23 172	83.23 100

v1921920		69.03 111	68.32 128	69.86 96	74.03 138	80.37 90	77.55 95	60.25 122	54.62 159	53.59 116	52.36 158	51.50 170	66.75 72	77.17 129	76.69 136	86.96 88	85.56 65

CostFormer		68.92 112	69.58 100	68.15 111	75.98 105	76.17 145	78.22 92	51.86 177	65.80 70	61.56 70	63.57 70	62.83 92	61.85 112	70.40 181	68.67 179	79.42 170	79.62 139

FMVSNet1		68.84 113	70.47 88	66.94 130	71.35 163	77.68 120	74.71 116	62.35 100	56.93 134	49.94 139	50.01 168	64.59 87	57.07 140	81.33 65	80.72 62	86.25 113	82.00 116

NR-MVSNet		68.79 114	70.56 86	66.71 136	77.48 93	79.54 95	73.52 133	69.20 42	61.20 100	39.76 177	58.52 88	50.11 177	51.37 169	80.26 82	80.71 66	88.97 45	83.59 95

V42		68.76 115	69.63 99	67.74 115	64.93 187	78.01 111	78.30 89	56.48 160	58.65 119	56.30 102	54.26 128	57.03 116	64.85 97	77.47 124	77.01 116	85.60 135	84.96 80

v1240		68.64 116	67.89 134	69.51 100	73.89 140	80.26 93	76.73 102	59.97 129	53.43 166	53.08 119	51.82 161	50.84 173	66.62 74	76.79 134	76.77 119	86.78 102	85.34 72

Fast-Effi-MVS+-dtu		68.34 117	69.47 102	67.01 129	75.15 109	77.97 117	77.12 100	55.40 164	57.87 120	46.68 153	56.17 106	60.39 97	62.36 107	76.32 139	76.25 140	85.35 139	81.34 122

tpmp4_e23		68.32 118	67.08 141	69.76 98	77.86 84	75.22 152	78.37 88	56.17 163	66.06 69	64.27 63	57.15 102	54.89 136	63.40 102	70.97 174	68.29 184	78.46 174	77.00 158

GA-MVS		68.14 119	69.17 111	66.93 131	73.77 141	78.50 108	74.45 117	58.28 151	55.11 155	48.44 144	60.08 79	53.99 143	61.50 115	78.43 104	77.57 105	85.13 140	80.54 129

tfpn200view9		68.11 120	68.72 121	67.40 121	77.83 86	78.93 100	74.28 122	62.81 83	56.64 138	46.82 151	52.65 154	53.47 150	56.59 143	80.41 75	78.43 90	86.11 115	80.52 130

EPNet_dtu		68.08 121	71.00 83	64.67 147	79.64 71	68.62 176	75.05 113	63.30 77	66.36 66	45.27 160	67.40 60	66.84 82	43.64 183	75.37 144	74.98 152	81.15 162	77.44 152
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		67.98 122	68.55 125	67.30 123	77.89 83	78.86 102	74.18 125	62.75 84	56.35 143	46.48 154	52.98 148	53.54 146	56.46 144	80.41 75	77.97 97	86.05 119	79.78 138

thres400		67.95 123	68.62 124	67.17 125	77.90 81	78.59 107	74.27 123	62.72 86	56.34 144	45.77 158	53.00 147	53.35 153	56.46 144	80.21 83	78.43 90	85.91 128	80.43 131

pmmvs4		67.89 124	67.39 139	68.48 109	71.60 160	73.57 160	74.45 117	60.98 109	64.65 77	57.97 86	54.95 116	51.73 168	61.88 111	73.78 153	75.11 150	83.99 151	77.91 149

v148		67.85 125	67.53 135	68.23 110	73.25 144	77.57 123	74.26 124	57.36 156	55.70 150	57.45 88	53.53 138	55.42 130	61.96 110	75.23 145	73.92 155	85.08 141	81.32 123

Vis-MVSNet (Re-imp)		67.83 126	73.52 71	61.19 163	78.37 79	76.72 137	66.80 166	62.96 81	65.50 72	34.17 188	67.19 61	69.68 69	39.20 191	79.39 95	79.44 84	85.68 134	76.73 160

PatchMatch-RL		67.78 127	66.65 146	69.10 103	73.01 145	72.69 162	68.49 158	61.85 104	62.93 88	60.20 75	56.83 104	50.42 175	69.52 57	75.62 143	74.46 154	81.51 160	73.62 178

thres600view7		67.68 128	68.43 126	66.80 132	77.90 81	78.86 102	73.84 127	62.75 84	56.07 146	44.70 164	52.85 151	52.81 158	55.58 152	80.41 75	77.77 100	86.05 119	80.28 132

COLMAP_ROB	62.73 15	67.66 129	66.76 145	68.70 107	80.49 69	77.98 115	75.29 107	62.95 82	63.62 83	49.96 138	47.32 180	50.72 174	58.57 128	76.87 133	75.50 147	84.94 144	75.33 169
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CDS-MVSNet		67.65 130	69.83 97	65.09 142	75.39 108	76.55 138	74.42 120	63.75 74	53.55 165	49.37 142	59.41 84	62.45 93	44.44 181	79.71 88	79.82 75	83.17 155	77.36 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RPSCF		67.64 131	71.25 82	63.43 156	61.86 193	70.73 168	67.26 162	50.86 182	74.20 52	58.91 78	67.49 59	69.33 70	64.10 99	71.41 167	68.45 183	77.61 176	77.17 154

view600		67.63 132	68.36 127	66.77 133	77.84 85	78.66 105	73.74 130	62.62 93	56.04 147	44.98 161	52.86 150	52.83 157	55.48 155	80.36 79	77.75 101	85.95 127	80.02 135

thres100view900		67.60 133	68.02 131	67.12 127	77.83 86	77.75 119	73.90 126	62.52 96	56.64 138	46.82 151	52.65 154	53.47 150	55.92 148	78.77 101	77.62 104	85.72 133	79.23 142

Baseline_NR-MVSNet		67.53 134	68.77 119	66.09 138	75.99 103	74.75 155	72.43 141	68.41 45	61.33 99	38.33 181	51.31 163	54.13 142	56.03 147	79.22 96	78.19 94	85.37 138	82.45 111

USDC		67.36 135	67.90 133	66.74 135	71.72 156	75.23 151	71.58 147	60.28 121	67.45 64	50.54 135	60.93 75	45.20 193	62.08 108	76.56 137	74.50 153	84.25 148	75.38 168

view800		67.35 136	68.22 130	66.35 137	77.83 86	78.62 106	72.97 138	62.58 94	55.71 149	44.13 165	52.69 153	52.24 166	54.58 160	80.27 81	78.19 94	86.01 122	79.79 137

DWT-MVSNet_training		67.24 137	65.96 149	68.74 105	76.15 101	74.36 158	74.37 121	56.66 159	61.82 95	60.51 72	58.23 96	49.76 179	65.07 95	70.04 182	70.39 169	79.70 169	77.11 156

EG-PatchMatch MVS		67.24 137	66.94 142	67.60 118	78.73 77	81.35 72	73.28 136	59.49 132	46.89 192	51.42 129	43.65 184	53.49 148	55.50 154	81.38 64	80.66 68	87.15 75	81.17 124

v7n		67.05 139	66.94 142	67.17 125	72.35 151	78.97 99	73.26 137	58.88 139	51.16 177	50.90 131	48.21 173	50.11 177	60.96 117	77.70 114	77.38 110	86.68 107	85.05 78

tfpn		66.58 140	67.18 140	65.88 139	77.82 89	78.45 109	72.07 143	62.52 96	55.35 152	43.21 169	52.54 157	46.12 190	53.68 161	80.02 85	78.23 93	85.99 125	79.55 140

IterMVS		66.36 141	68.30 129	64.10 149	69.48 173	74.61 156	73.41 135	50.79 183	57.30 128	48.28 145	60.64 76	59.92 100	60.85 121	74.14 151	72.66 161	81.80 159	78.82 145
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
conf0.05thres1000		66.26 142	66.77 144	65.66 140	77.45 94	78.10 110	71.85 146	62.44 99	51.47 176	43.00 170	47.92 175	51.66 169	53.40 163	79.71 88	77.97 97	85.82 129	80.56 128

TDRefinement		66.09 143	65.03 161	67.31 122	69.73 170	76.75 136	75.33 105	64.55 71	60.28 106	49.72 141	45.63 182	42.83 196	60.46 122	75.75 140	75.95 143	84.08 149	78.04 148

pm-mvs1		65.62 144	67.42 137	63.53 155	73.66 142	76.39 142	69.66 152	60.87 111	49.73 184	43.97 166	51.24 164	57.00 117	48.16 174	79.89 86	77.84 99	84.85 146	79.82 136

tpm cat1		65.41 145	63.81 169	67.28 124	75.61 107	72.88 161	75.32 106	52.85 171	62.97 87	63.66 66	53.24 143	53.29 155	61.83 113	65.54 193	64.14 196	74.43 190	74.60 171

anonymousdsp		65.28 146	67.98 132	62.13 159	58.73 201	73.98 159	67.10 164	50.69 184	48.41 187	47.66 150	54.27 126	52.75 160	61.45 116	76.71 136	80.20 74	87.13 79	89.53 44

v52		65.23 147	66.24 147	64.06 150	61.94 191	76.42 140	72.06 144	54.30 166	49.94 181	50.04 137	47.41 178	52.42 161	60.23 124	75.71 141	76.22 141	85.78 130	85.56 65

V4		65.23 147	66.23 148	64.06 150	61.94 191	76.42 140	72.05 145	54.31 165	49.91 183	50.06 136	47.42 177	52.40 162	60.24 123	75.71 141	76.22 141	85.78 130	85.56 65

v748		65.12 149	65.24 156	64.98 144	69.77 169	76.45 139	69.47 154	57.06 158	49.93 182	50.70 132	47.87 176	49.50 181	57.14 139	73.64 155	75.18 149	85.75 132	84.14 87

PMMVS		65.06 150	69.17 111	60.26 168	55.25 210	63.43 193	66.71 167	43.01 208	62.41 89	50.64 133	69.44 49	67.04 81	63.29 103	74.36 150	73.54 157	82.68 156	73.99 176

CR-MVSNet		64.83 151	65.54 154	64.01 152	70.64 165	69.41 171	65.97 171	52.74 172	57.81 122	52.65 121	54.27 126	56.31 121	60.92 118	72.20 163	73.09 159	81.12 163	75.69 165

TransMVSNet (Re)		64.74 152	65.66 153	63.66 154	77.40 95	75.33 150	69.86 151	62.67 92	47.63 190	41.21 176	50.01 168	52.33 163	45.31 180	79.57 91	77.69 103	85.49 136	77.07 157

test-LLR		64.42 153	64.36 165	64.49 148	75.02 111	63.93 190	66.61 168	61.96 102	54.41 160	47.77 147	57.46 99	60.25 98	55.20 156	70.80 175	69.33 174	80.40 166	74.38 173

MDTV_nov1_ep13		64.37 154	65.24 156	63.37 157	68.94 175	70.81 167	72.40 142	50.29 186	60.10 107	53.91 114	60.07 80	59.15 103	57.21 138	69.43 185	67.30 186	77.47 177	69.78 185

tfpnnormal		64.27 155	63.64 170	65.02 143	75.84 106	75.61 147	71.24 149	62.52 96	47.79 189	42.97 171	42.65 186	44.49 194	52.66 167	78.77 101	76.86 118	84.88 145	79.29 141

PatchmatchNet		64.21 156	64.65 163	63.69 153	71.29 164	68.66 175	69.63 153	51.70 179	63.04 86	53.77 115	59.83 83	58.34 105	60.23 124	68.54 188	66.06 191	75.56 185	68.08 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		64.00 157	62.99 172	65.18 141	73.29 143	72.07 164	68.98 157	53.07 170	57.74 124	58.41 81	55.55 109	47.74 186	60.89 120	69.53 184	67.14 188	76.44 182	71.19 182

pmmvs-eth3d		63.52 158	62.44 179	64.77 146	66.82 181	70.12 170	69.41 155	59.48 133	54.34 163	52.71 120	46.24 181	44.35 195	56.93 141	72.37 158	73.77 156	83.30 153	75.91 162

WR-MVS		63.03 159	67.40 138	57.92 178	75.14 110	77.60 122	60.56 189	66.10 59	54.11 164	23.88 200	53.94 136	53.58 145	34.50 196	73.93 152	77.71 102	87.35 71	80.94 125

PEN-MVS		62.96 160	65.77 152	59.70 171	73.98 139	75.45 148	63.39 182	67.61 52	52.49 169	25.49 199	53.39 139	49.12 182	40.85 189	71.94 165	77.26 113	86.86 94	80.72 127

TinyColmap		62.84 161	61.03 185	64.96 145	69.61 171	71.69 165	68.48 159	59.76 131	55.41 151	47.69 149	47.33 179	34.20 206	62.76 106	74.52 148	72.59 162	81.44 161	71.47 181

CP-MVSNet		62.68 162	65.49 155	59.40 174	71.84 154	75.34 149	62.87 184	67.04 55	52.64 168	27.19 197	53.38 140	48.15 184	41.40 187	71.26 168	75.68 144	86.07 117	82.00 116

gg-mvs-nofinetune		62.55 163	65.05 160	59.62 172	78.72 78	77.61 121	70.83 150	53.63 167	39.71 204	22.04 209	36.36 197	64.32 88	47.53 175	81.16 69	79.03 86	85.00 143	77.17 154

CVMVSNet		62.55 163	65.89 150	58.64 176	66.95 179	69.15 173	66.49 170	56.29 162	52.46 170	32.70 189	59.27 85	58.21 106	50.09 171	71.77 166	71.39 166	79.31 171	78.99 144

CMPMVS	47.78 17	62.49 165	62.52 177	62.46 158	70.01 168	70.66 169	62.97 183	51.84 178	51.98 172	56.71 99	42.87 185	53.62 144	57.80 133	72.23 161	70.37 170	75.45 187	75.91 162
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		62.41 166	62.88 173	61.87 160	71.38 162	75.18 154	67.76 161	59.45 134	41.64 200	42.52 174	37.33 195	52.91 156	46.87 176	77.67 116	76.26 139	83.23 154	79.18 143

tpm		62.41 166	63.15 171	61.55 162	72.24 152	63.79 192	71.31 148	46.12 199	57.82 121	55.33 106	59.90 82	54.74 137	53.63 162	67.24 191	64.29 194	70.65 201	74.25 175

PS-CasMVS		62.38 168	65.06 159	59.25 175	71.73 155	75.21 153	62.77 185	66.99 56	51.94 174	26.96 198	52.00 160	47.52 187	41.06 188	71.16 171	75.60 145	85.97 126	81.97 118

pmmvs5		62.37 169	64.04 167	60.42 166	65.03 185	71.67 166	67.17 163	52.70 174	50.30 178	44.80 162	54.23 130	51.19 172	49.37 172	72.88 157	73.48 158	83.45 152	74.55 172

tpmrst		62.00 170	62.35 180	61.58 161	71.62 159	64.14 189	69.07 156	48.22 195	62.21 91	53.93 113	58.26 95	55.30 132	55.81 150	63.22 198	62.62 199	70.85 200	70.70 183

PatchT		61.97 171	64.04 167	59.55 173	60.49 195	67.40 179	56.54 196	48.65 191	56.69 137	52.65 121	51.10 165	52.14 167	60.92 118	72.20 163	73.09 159	78.03 175	75.69 165

DTE-MVSNet		61.85 172	64.96 162	58.22 177	74.32 134	74.39 157	61.01 188	67.85 51	51.76 175	21.91 210	53.28 141	48.17 183	37.74 192	72.22 162	76.44 137	86.52 112	78.49 146

SixPastTwentyTwo		61.84 173	62.45 178	61.12 164	69.20 174	72.20 163	62.03 186	57.40 155	46.54 193	38.03 183	57.14 103	41.72 198	58.12 132	69.67 183	71.58 165	81.94 158	78.30 147

WR-MVS_H		61.83 174	65.87 151	57.12 181	71.72 156	76.87 135	61.45 187	66.19 57	51.97 173	22.92 207	53.13 146	52.30 165	33.80 197	71.03 172	75.00 151	86.65 108	80.78 126

LTVRE_ROB	59.44 16	61.82 175	62.64 176	60.87 165	72.83 150	77.19 124	64.37 178	58.97 137	33.56 213	28.00 196	52.59 156	42.21 197	63.93 100	74.52 148	76.28 138	77.15 179	82.13 112
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
RPMNet		61.71 176	62.88 173	60.34 167	69.51 172	69.41 171	63.48 181	49.23 187	57.81 122	45.64 159	50.51 166	50.12 176	53.13 166	68.17 190	68.49 182	81.07 164	75.62 167

TESTMET0.1,1		61.10 177	64.36 165	57.29 180	57.53 203	63.93 190	66.61 168	36.22 214	54.41 160	47.77 147	57.46 99	60.25 98	55.20 156	70.80 175	69.33 174	80.40 166	74.38 173

test-mter		60.84 178	64.62 164	56.42 183	55.99 208	64.18 188	65.39 173	34.23 216	54.39 162	46.21 155	57.40 101	59.49 102	55.86 149	71.02 173	69.65 172	80.87 165	76.20 161

PM-MVS		60.48 179	60.94 186	59.94 169	58.85 200	66.83 182	64.27 179	51.39 180	55.03 157	48.03 146	50.00 170	40.79 200	58.26 131	69.20 186	67.13 189	78.84 173	77.60 151

MDTV_nov1_ep13_2view		60.16 180	60.51 187	59.75 170	65.39 184	69.05 174	68.00 160	48.29 193	51.99 171	45.95 157	48.01 174	49.64 180	53.39 164	68.83 187	66.52 190	77.47 177	69.55 186

EPMVS		60.00 181	61.97 181	57.71 179	68.46 176	63.17 196	64.54 177	48.23 194	63.30 84	44.72 163	60.19 78	56.05 129	50.85 170	65.27 195	62.02 201	69.44 203	63.81 196

TAMVS		59.58 182	62.81 175	55.81 185	66.03 183	65.64 187	63.86 180	48.74 190	49.95 180	37.07 185	54.77 119	58.54 104	44.44 181	72.29 160	71.79 163	74.70 189	66.66 191

test0.0.03 1		58.80 183	61.58 183	55.56 186	75.02 111	68.45 177	59.58 193	61.96 102	52.74 167	29.57 192	49.75 171	54.56 138	31.46 199	71.19 169	69.77 171	75.75 183	64.57 194

CHOSEN 280x420		58.70 184	61.88 182	54.98 188	55.45 209	50.55 213	64.92 175	40.36 210	55.21 153	38.13 182	48.31 172	63.76 89	63.03 105	73.73 154	68.58 181	68.00 206	73.04 179

MIMVSNet		58.52 185	61.34 184	55.22 187	60.76 194	67.01 181	66.81 165	49.02 189	56.43 141	38.90 180	40.59 192	54.54 139	40.57 190	73.16 156	71.65 164	75.30 188	66.00 192

FMVSNet5		57.24 186	60.02 188	53.99 191	56.45 205	62.74 197	65.27 174	47.03 196	55.14 154	39.55 179	40.88 190	53.42 152	41.83 184	72.35 159	71.10 168	73.79 192	64.50 195

gm-plane-assit		57.00 187	57.62 193	56.28 184	76.10 102	62.43 200	47.62 209	46.57 197	33.84 212	23.24 203	37.52 194	40.19 201	59.61 126	79.81 87	77.55 106	84.55 147	72.03 180

FC-MVSNet-test		56.90 188	65.20 158	47.21 200	66.98 178	63.20 195	49.11 207	58.60 149	59.38 113	11.50 220	65.60 64	56.68 118	24.66 211	71.17 170	71.36 167	72.38 196	69.02 187

Anonymous20231206		56.36 189	57.80 192	54.67 189	70.08 167	66.39 184	60.46 190	57.54 153	49.50 186	29.30 193	33.86 203	46.64 188	35.18 195	70.44 179	68.88 178	75.47 186	68.88 188

ADS-MVSNet		55.94 190	58.01 190	53.54 194	62.48 190	58.48 202	59.12 194	46.20 198	59.65 111	42.88 172	52.34 159	53.31 154	46.31 178	62.00 202	60.02 206	64.23 212	60.24 204

EU-MVSNet		54.63 191	58.69 189	49.90 198	56.99 204	62.70 198	56.41 197	50.64 185	45.95 195	23.14 204	50.42 167	46.51 189	36.63 193	65.51 194	64.85 193	75.57 184	74.91 170

MVS-HIRNet		54.41 192	52.10 199	57.11 182	58.99 199	56.10 205	49.68 206	49.10 188	46.18 194	52.15 125	33.18 204	46.11 191	56.10 146	63.19 199	59.70 207	76.64 181	60.25 203

testgi		54.39 193	57.86 191	50.35 197	71.59 161	67.24 180	54.95 199	53.25 169	43.36 197	23.78 201	44.64 183	47.87 185	24.96 208	70.45 178	68.66 180	73.60 193	62.78 199

test20.03		53.93 194	56.28 194	51.19 196	72.19 153	65.83 185	53.20 201	61.08 108	42.74 198	22.08 208	37.07 196	45.76 192	24.29 212	70.44 179	69.04 176	74.31 191	63.05 198

LP		53.62 195	53.43 195	53.83 192	58.51 202	62.59 199	57.31 195	46.04 200	47.86 188	42.69 173	36.08 199	36.86 204	46.53 177	64.38 196	64.25 195	71.92 197	62.00 201

MDA-MVSNet-bldmvs		53.37 196	53.01 198	53.79 193	43.67 218	67.95 178	59.69 192	57.92 152	43.69 196	32.41 190	41.47 188	27.89 216	52.38 168	56.97 212	65.99 192	76.68 180	67.13 190

FPMVS		51.87 197	50.00 202	54.07 190	66.83 180	57.25 203	60.25 191	50.91 181	50.25 179	34.36 187	36.04 200	32.02 208	41.49 186	58.98 210	56.07 210	70.56 202	59.36 205

Anonymous20231211		51.46 198	50.59 200	52.46 195	67.30 177	66.70 183	55.00 198	59.22 135	29.96 215	17.62 215	19.11 217	28.74 215	35.72 194	66.42 192	69.52 173	79.92 168	73.71 177

MIMVSNet1		49.27 199	53.25 197	44.62 204	44.61 215	61.52 201	53.61 200	52.18 175	41.62 201	18.68 212	28.14 211	41.58 199	25.50 206	68.46 189	69.04 176	73.15 194	62.37 200

pmmvs3		47.65 200	49.08 204	45.99 202	44.61 215	54.79 208	50.04 204	31.95 219	33.91 211	29.90 191	30.37 205	33.53 207	46.31 178	63.50 197	63.67 197	73.14 195	63.77 197

testpf		47.41 201	48.47 207	46.18 201	66.30 182	50.67 212	48.15 208	42.60 209	37.10 208	28.75 194	40.97 189	39.01 203	30.82 200	52.95 215	53.74 214	60.46 213	64.87 193

N_pmnet		47.35 202	50.13 201	44.11 205	59.98 196	51.64 211	51.86 202	44.80 204	49.58 185	20.76 211	40.65 191	40.05 202	29.64 201	59.84 208	55.15 211	57.63 214	54.00 212

test2356		47.20 203	48.62 206	45.54 203	56.38 206	54.89 207	50.62 203	45.08 203	38.65 205	23.40 202	36.23 198	31.10 210	29.31 202	62.76 200	62.49 200	68.48 205	54.23 211

new-patchmatchnet		46.97 204	49.47 203	44.05 206	62.82 189	56.55 204	45.35 210	52.01 176	42.47 199	17.04 216	35.73 201	35.21 205	21.84 217	61.27 203	54.83 212	65.26 211	60.26 202

GG-mvs-BLEND		46.86 205	67.51 136	22.75 218	0.05 225	76.21 144	64.69 176	0.04 223	61.90 93	0.09 228	55.57 108	71.32 61	0.08 223	70.54 177	67.19 187	71.58 198	69.86 184

PMVS	39.38 18	46.06 206	43.30 211	49.28 199	62.93 188	38.75 219	41.88 212	53.50 168	33.33 214	35.46 186	28.90 208	31.01 211	33.04 198	58.61 211	54.63 213	68.86 204	57.88 208
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testus		45.61 207	49.06 205	41.59 208	56.13 207	55.28 206	43.51 211	39.64 212	37.74 206	18.23 213	35.52 202	31.28 209	24.69 210	62.46 201	62.90 198	67.33 207	58.26 207

1111		43.08 208	44.02 210	41.98 207	59.22 197	49.27 215	41.48 213	45.63 201	35.01 209	23.06 205	28.60 209	30.15 212	27.22 203	60.42 206	57.97 208	55.27 217	46.74 214

testmv		42.58 209	44.36 208	40.49 209	54.63 211	52.76 209	41.21 215	44.37 205	28.83 216	12.87 217	27.16 212	25.03 217	23.01 213	60.83 204	61.13 202	66.88 208	54.81 209

test1235678		42.57 210	44.36 208	40.49 209	54.63 211	52.75 210	41.21 215	44.37 205	28.82 217	12.87 217	27.15 213	25.01 218	23.01 213	60.83 204	61.13 202	66.88 208	54.81 209

new_pmnet		38.40 211	42.64 212	33.44 213	37.54 221	45.00 217	36.60 217	32.72 218	40.27 202	12.72 219	29.89 206	28.90 214	24.78 209	53.17 214	52.90 215	56.31 215	48.34 213

Gipuma		36.38 212	35.80 215	37.07 211	45.76 214	33.90 220	29.81 219	48.47 192	39.91 203	18.02 214	8.00 223	8.14 225	25.14 207	59.29 209	61.02 204	55.19 218	40.31 216
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		36.35 213	37.59 214	34.91 212	46.13 213	49.89 214	27.99 220	43.56 207	20.91 221	7.03 223	14.64 219	15.50 223	18.92 218	42.95 216	60.20 205	65.84 210	59.03 206

test12356		35.10 214	38.50 213	31.13 215	44.14 217	43.70 218	32.27 218	34.42 215	26.51 219	9.47 221	25.22 215	20.34 219	10.86 220	53.47 213	56.15 209	55.59 216	44.11 215

.test1245		30.81 215	29.14 217	32.77 214	59.22 197	49.27 215	41.48 213	45.63 201	35.01 209	23.06 205	28.60 209	30.15 212	27.22 203	60.42 206	0.10 221	0.01 225	0.43 223

PMMVS2		25.60 216	29.75 216	20.76 219	28.00 222	30.93 221	23.10 221	29.18 220	23.14 220	1.46 227	18.23 218	16.54 221	5.08 221	40.22 217	41.40 217	37.76 219	37.79 218

E-PMN		21.77 217	18.24 219	25.89 216	40.22 219	19.58 223	12.46 224	39.87 211	18.68 223	6.71 224	9.57 220	4.31 228	22.36 216	19.89 221	27.28 219	33.73 220	28.34 220

EMVS		20.98 218	17.15 220	25.44 217	39.51 220	19.37 224	12.66 223	39.59 213	19.10 222	6.62 225	9.27 221	4.40 227	22.43 215	17.99 222	24.40 220	31.81 221	25.53 221

MVE	19.12 19	20.47 219	23.27 218	17.20 220	12.66 224	25.41 222	10.52 225	34.14 217	14.79 224	6.53 226	8.79 222	4.68 226	16.64 219	29.49 219	41.63 216	22.73 223	38.11 217
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.09 220	0.15 221	0.02 222	0.01 226	0.02 227	0.05 228	0.01 224	0.11 225	0.01 229	0.26 225	0.01 229	0.06 225	0.10 223	0.10 221	0.01 225	0.43 223

test123		0.09 220	0.14 222	0.02 222	0.00 227	0.02 227	0.02 229	0.01 224	0.09 226	0.00 230	0.30 224	0.00 230	0.08 223	0.03 224	0.09 223	0.01 225	0.45 222

ESAPD		0.00 222	0.00 223	0.00 224	0.00 227	0.00 229	0.00 230	0.00 226	0.00 227	0.00 230	0.00 226	0.00 230	0.00 226	0.00 225	0.00 224	0.00 228	0.00 225

sosnet-low-res		0.00 222	0.00 223	0.00 224	0.00 227	0.00 229	0.00 230	0.00 226	0.00 227	0.00 230	0.00 226	0.00 230	0.00 226	0.00 225	0.00 224	0.00 228	0.00 225

sosnet		0.00 222	0.00 223	0.00 224	0.00 227	0.00 229	0.00 230	0.00 226	0.00 227	0.00 230	0.00 226	0.00 230	0.00 226	0.00 225	0.00 224	0.00 228	0.00 225

ambc			53.42 196		64.99 186	63.36 194	49.96 205		47.07 191	37.12 184	28.97 207	16.36 222	41.82 185	75.10 147	67.34 185	71.55 199	75.72 164

MTAPA										83.48 1		86.45 11

MTMP										82.66 3		84.91 19

Patchmatch-RL test							2.85 227

tmp_tt				14.50 221	14.68 223	7.17 226	10.46 226	2.21 222	37.73 207	28.71 195	25.26 214	16.98 220	4.37 222	31.49 218	29.77 218	26.56 222

XVS					86.63 37	88.68 21	85.00 39			71.81 38		81.92 28				90.47 15

X-MVStestdata					86.63 37	88.68 21	85.00 39			71.81 38		81.92 28				90.47 15

abl_6				79.05 35	87.27 33	88.85 19	83.62 47	68.25 46	81.68 33	72.94 32	73.79 38	84.45 21	72.55 40			89.66 35	90.64 36

mPP-MVS					89.90 18							81.29 33

NP-MVS									80.10 38

Patchmtry						65.80 186	65.97 171	52.74 172		52.65 121

DeepMVS_CX						18.74 225	18.55 222	8.02 221	26.96 218	7.33 222	23.81 216	13.05 224	25.99 205	25.17 220		22.45 224	36.25 219

High-resolution multi-view benchmark