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 8	92.27 2	88.34 5	80.80 2	88.00 9	86.99 13	91.09 4	95.16 1

HSP-MVS		87.45 2	90.22 2	84.22 6	90.00 17	91.80 2	90.59 3	75.80 3	89.93 3	78.35 14	92.54 1	89.18 2	80.89 1	87.99 10	86.29 24	89.70 34	93.85 7

HPM-MVS++		87.09 3	88.92 7	84.95 2	92.61 1	87.91 33	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 27	93.60 14

SD-MVS		86.96 4	89.45 3	84.05 9	90.13 14	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 14	86.79 16	90.67 11	93.76 10

TSAR-MVS + MP.		86.88 5	89.23 4	84.14 7	89.78 20	88.67 25	90.59 3	73.46 20	88.99 5	80.52 7	91.26 3	88.65 3	79.91 4	86.96 24	86.22 25	90.59 12	93.83 8

APD-MVS		86.84 6	88.91 8	84.41 3	90.66 7	90.10 6	90.78 2	75.64 4	87.38 11	78.72 12	90.68 5	86.82 9	80.15 3	87.13 19	86.45 22	90.51 14	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 11	90.28 13	90.09 7	90.32 6	74.05 14	88.32 8	79.74 9	87.04 10	85.59 16	76.97 23	89.35 1	88.44 3	90.35 23	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 12	78.97 11	81.19 22	86.76 10	78.74 6	89.30 2	88.58 1	90.45 20	94.33 4

HFP-MVS		86.15 9	87.95 12	84.06 8	90.80 5	89.20 17	89.62 13	74.26 10	87.52 9	80.63 6	86.82 11	84.19 22	78.22 9	87.58 13	87.19 11	90.81 6	93.13 18

SteuartSystems-ACMMP		85.99 10	88.31 10	83.27 15	90.73 6	89.84 8	90.27 7	74.31 9	84.56 24	75.88 23	87.32 9	85.04 17	77.31 18	89.01 3	88.46 2	91.14 3	93.96 6
Skip Steuart: Steuart Systems R&D Blog.
MPTG		85.71 11	86.88 17	84.34 4	90.54 11	87.11 37	89.77 11	74.17 12	88.54 7	83.08 2	78.60 26	86.10 12	78.11 10	87.80 12	87.46 9	90.35 23	92.56 20

ACMMPR		85.52 12	87.53 14	83.17 16	90.13 14	89.27 14	89.30 14	73.97 15	86.89 14	77.14 19	86.09 12	83.18 25	77.74 14	87.42 15	87.20 10	90.77 7	92.63 19

MP-MVS		85.50 13	87.40 15	83.28 14	90.65 8	89.51 13	89.16 17	74.11 13	83.70 27	78.06 16	85.54 14	84.89 20	77.31 18	87.40 16	87.14 12	90.41 21	93.65 13

NCCC		85.34 14	86.59 19	83.88 10	91.48 2	88.88 19	89.79 10	75.54 6	86.67 15	77.94 17	76.55 29	84.99 18	78.07 11	88.04 7	87.68 7	90.46 19	93.31 15

DeepPCF-MVS	79.04 1	85.30 15	88.93 6	81.06 25	88.77 28	90.48 4	85.46 39	73.08 21	90.97 2	73.77 30	84.81 16	85.95 13	77.43 17	88.22 6	87.73 6	87.85 65	94.34 3

CSCG		85.28 16	87.68 13	82.49 19	89.95 18	91.99 1	88.82 18	71.20 30	86.41 16	79.63 10	79.26 23	88.36 4	73.94 33	86.64 26	86.67 19	91.40 2	94.41 2

MCST-MVS		85.13 17	86.62 18	83.39 12	90.55 10	89.82 10	89.29 15	73.89 17	84.38 25	76.03 22	79.01 25	85.90 14	78.47 7	87.81 11	86.11 27	92.11 1	93.29 16

TSAR-MVS + ACMM		85.10 18	88.81 9	80.77 28	89.55 22	88.53 27	88.59 21	72.55 23	87.39 10	71.90 36	90.95 4	87.55 7	74.57 28	87.08 21	86.54 20	87.47 70	93.67 11

train_agg		84.86 19	87.21 16	82.11 21	90.59 9	85.47 48	89.81 9	73.55 19	83.95 26	73.30 31	89.84 7	87.23 8	75.61 26	86.47 28	85.46 32	89.78 30	92.06 26

DeepC-MVS	78.47 2	84.81 20	86.03 23	83.37 13	89.29 25	90.38 5	88.61 20	76.50 1	86.25 17	77.22 18	75.12 33	80.28 37	77.59 16	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 21	86.43 21	82.77 18	89.48 23	88.13 32	88.64 19	73.93 16	84.92 19	76.77 20	81.94 20	83.50 23	77.29 20	86.92 25	86.49 21	90.49 15	93.14 17

PGM-MVS		84.42 22	86.29 22	82.23 20	90.04 16	88.82 21	89.23 16	71.74 28	82.82 30	74.61 26	84.41 17	82.09 27	77.03 22	87.13 19	86.73 18	90.73 9	92.06 26

DeepC-MVS_fast	78.24 3	84.27 23	85.50 25	82.85 17	90.46 12	89.24 15	87.83 26	74.24 11	84.88 20	76.23 21	75.26 32	81.05 35	77.62 15	88.02 8	87.62 8	90.69 10	92.41 22
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 24	86.58 20	80.32 29	85.14 47	86.96 38	84.91 43	70.25 34	84.71 23	73.91 29	85.16 15	85.63 15	77.92 12	85.44 34	85.71 30	89.77 31	92.45 21

ACMMP		83.42 25	85.27 26	81.26 24	88.47 29	88.49 28	88.31 24	72.09 25	83.42 28	72.77 34	82.65 18	78.22 41	75.18 27	86.24 31	85.76 29	90.74 8	92.13 25
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 26	85.20 27	80.92 27	89.71 21	88.68 22	88.21 25	73.60 18	82.57 31	71.81 39	77.07 27	81.92 29	71.72 48	86.98 23	86.86 14	90.47 16	92.36 23

CDPH-MVS		82.64 27	85.03 28	79.86 32	89.41 24	88.31 29	88.32 23	71.84 27	80.11 38	67.47 54	82.09 19	81.44 33	71.85 46	85.89 33	86.15 26	90.24 25	91.25 32

3Dnovator+	75.73 4	82.40 28	82.76 33	81.97 22	88.02 30	89.67 11	86.60 30	71.48 29	81.28 36	78.18 15	64.78 68	77.96 43	77.13 21	87.32 17	86.83 15	90.41 21	91.48 30

PHI-MVS		82.36 29	85.89 24	78.24 42	86.40 40	89.52 12	85.52 37	69.52 41	82.38 33	65.67 59	81.35 21	82.36 26	73.07 38	87.31 18	86.76 17	89.24 41	91.56 29

MSLP-MVS++		82.09 30	82.66 34	81.42 23	87.03 36	87.22 36	85.82 35	70.04 35	80.30 37	78.66 13	68.67 55	81.04 36	77.81 13	85.19 37	84.88 37	89.19 43	91.31 31

CPTT-MVS		81.77 31	83.10 32	80.21 30	85.93 43	86.45 43	87.72 27	70.98 31	82.54 32	71.53 42	74.23 38	81.49 32	76.31 25	82.85 55	81.87 51	88.79 50	92.26 24

MVS_0304		81.73 32	83.86 29	79.26 35	86.22 42	89.18 18	86.41 31	67.15 55	75.28 48	70.75 46	74.59 35	83.49 24	74.42 30	87.05 22	86.34 23	90.58 13	91.08 34

CANet		81.62 33	83.41 30	79.53 34	87.06 35	88.59 26	85.47 38	67.96 51	76.59 46	74.05 27	74.69 34	81.98 28	72.98 39	86.14 32	85.47 31	89.68 35	90.42 40

HQP-MVS		81.19 34	83.27 31	78.76 39	87.40 33	85.45 49	86.95 28	70.47 33	81.31 35	66.91 57	79.24 24	76.63 45	71.67 49	84.43 41	83.78 42	89.19 43	92.05 28

OMC-MVS		80.26 35	82.59 35	77.54 45	83.04 55	85.54 47	83.25 50	65.05 68	87.32 13	72.42 35	72.04 43	78.97 39	73.30 36	83.86 44	81.60 54	88.15 56	88.83 48

MVS_111021_HR		80.13 36	81.46 38	78.58 40	85.77 44	85.17 52	83.45 49	69.28 42	74.08 54	70.31 47	74.31 37	75.26 51	73.13 37	86.46 29	85.15 35	89.53 37	89.81 43

LGP-MVS_train		79.83 37	81.22 40	78.22 43	86.28 41	85.36 51	86.76 29	69.59 39	77.34 43	65.14 61	75.68 31	70.79 64	71.37 51	84.60 39	84.01 40	90.18 26	90.74 36

ACMP	73.23 7	79.79 38	80.53 43	78.94 37	85.61 45	85.68 46	85.61 36	69.59 39	77.33 44	71.00 45	74.45 36	69.16 73	71.88 44	83.15 52	83.37 45	89.92 29	90.57 39
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator	73.76 5	79.75 39	80.52 44	78.84 38	84.94 52	87.35 34	84.43 45	65.54 65	78.29 42	73.97 28	63.00 74	75.62 50	74.07 32	85.00 38	85.34 33	90.11 28	89.04 46

AdaColmap		79.74 40	78.62 51	81.05 26	89.23 26	86.06 45	84.95 42	71.96 26	79.39 41	75.51 24	63.16 72	68.84 78	76.51 24	83.55 48	82.85 47	88.13 57	86.46 61

OPM-MVS		79.68 41	79.28 50	80.15 31	87.99 31	86.77 40	88.52 22	72.72 22	64.55 80	67.65 53	67.87 59	74.33 54	74.31 31	86.37 30	85.25 34	89.73 33	89.81 43

PCF-MVS	73.28 6	79.42 42	80.41 45	78.26 41	84.88 53	88.17 30	86.08 32	69.85 36	75.23 50	68.43 49	68.03 58	78.38 40	71.76 47	81.26 69	80.65 70	88.56 53	91.18 33
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		79.35 43	81.23 39	77.16 47	85.01 50	86.92 39	85.87 34	60.89 111	80.07 40	75.35 25	72.96 40	73.21 57	68.43 64	85.41 36	84.63 38	87.41 71	85.44 72
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 44	80.32 46	77.92 44	87.46 32	88.15 31	83.95 46	67.48 54	74.28 52	68.25 50	64.70 69	77.04 44	72.17 43	85.42 35	85.00 36	88.22 54	87.62 55
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 45	82.37 36	75.41 53	82.33 60	86.38 44	80.80 55	63.18 79	82.90 29	67.34 55	72.79 41	76.07 47	69.62 57	83.46 51	84.41 39	89.20 42	90.60 38

DELS-MVS		79.15 46	81.07 41	76.91 48	83.54 54	87.31 35	84.45 44	64.92 69	69.98 59	69.34 48	71.62 45	76.26 46	69.84 56	86.57 27	85.90 28	89.39 39	89.88 42
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 47	80.62 42	77.28 46	88.90 27	83.17 65	83.65 47	72.41 24	74.41 51	67.15 56	76.78 28	74.37 53	64.43 99	83.70 47	83.69 43	87.15 76	88.19 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMM	72.26 8	78.86 48	78.13 52	79.71 33	86.89 37	83.40 62	86.02 33	70.50 32	75.28 48	71.49 43	63.01 73	69.26 72	73.57 35	84.11 43	83.98 41	89.76 32	87.84 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
QAPM		78.47 49	80.22 47	76.43 50	85.03 49	86.75 41	80.62 56	66.00 62	73.77 55	65.35 60	65.54 66	78.02 42	72.69 40	83.71 46	83.36 46	88.87 49	90.41 41

TSAR-MVS + COLMAP		78.34 50	81.64 37	74.48 60	80.13 71	85.01 53	81.73 51	65.93 64	84.75 22	61.68 70	85.79 13	66.27 84	71.39 50	82.91 54	80.78 61	86.01 123	85.98 63

MVS_111021_LR		78.13 51	79.85 49	76.13 51	81.12 64	81.50 72	80.28 57	65.25 66	76.09 47	71.32 44	76.49 30	72.87 58	72.21 42	82.79 56	81.29 56	86.59 111	87.91 52

TAPA-MVS	71.42 9	77.69 52	80.05 48	74.94 56	80.68 67	84.52 55	81.36 52	63.14 80	84.77 21	64.82 63	68.72 53	75.91 49	71.86 45	81.62 60	79.55 82	87.80 66	85.24 75
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNLPA		77.20 53	77.54 56	76.80 49	82.63 57	84.31 56	79.77 60	64.64 70	85.17 18	73.18 32	56.37 106	69.81 69	74.53 29	81.12 71	78.69 89	86.04 122	87.29 58

PVSNet_Blended_VisFu		76.57 54	77.90 53	75.02 55	80.56 68	86.58 42	79.24 64	66.18 59	64.81 77	68.18 51	65.61 64	71.45 61	67.05 66	84.16 42	81.80 52	88.90 47	90.92 35

PVSNet_BlendedMVS		76.21 55	77.52 57	74.69 58	79.46 73	83.79 59	77.50 98	64.34 73	69.88 60	71.88 37	68.54 56	70.42 66	67.05 66	83.48 49	79.63 78	87.89 63	86.87 59

PVSNet_Blended		76.21 55	77.52 57	74.69 58	79.46 73	83.79 59	77.50 98	64.34 73	69.88 60	71.88 37	68.54 56	70.42 66	67.05 66	83.48 49	79.63 78	87.89 63	86.87 59

OpenMVS	70.44 10	76.15 57	76.82 64	75.37 54	85.01 50	84.79 54	78.99 69	62.07 102	71.27 58	67.88 52	57.91 98	72.36 59	70.15 55	82.23 58	81.41 55	88.12 58	87.78 54

PLC	68.99 11	75.68 58	75.31 68	76.12 52	82.94 56	81.26 75	79.94 59	66.10 60	77.15 45	66.86 58	59.13 87	68.53 79	73.73 34	80.38 79	79.04 86	87.13 80	81.68 122
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVS_Test		75.37 59	77.13 62	73.31 64	79.07 76	81.32 74	79.98 58	60.12 128	69.72 62	64.11 65	70.53 47	73.22 56	68.90 60	80.14 85	79.48 84	87.67 67	85.50 70

Effi-MVS+		75.28 60	76.20 65	74.20 61	81.15 63	83.24 63	81.11 53	63.13 81	66.37 66	60.27 75	64.30 70	68.88 77	70.93 54	81.56 62	81.69 53	88.61 51	87.35 56

DI_MVS_plusplus_trai		75.13 61	76.12 66	73.96 62	78.18 81	81.55 71	80.97 54	62.54 96	68.59 63	65.13 62	61.43 75	74.81 52	69.32 59	81.01 73	79.59 80	87.64 68	85.89 64

UA-Net		74.47 62	77.80 54	70.59 81	85.33 46	85.40 50	73.54 133	65.98 63	60.65 104	56.00 105	72.11 42	79.15 38	54.63 159	83.13 53	82.25 49	88.04 59	81.92 120

LS3D		74.08 63	73.39 73	74.88 57	85.05 48	82.62 68	79.71 61	68.66 45	72.82 56	58.80 80	57.61 99	61.31 97	71.07 53	80.32 81	78.87 88	86.00 125	80.18 134

EPP-MVSNet		74.00 64	77.41 59	70.02 96	80.53 69	83.91 58	74.99 115	62.68 92	65.06 75	49.77 141	68.68 54	72.09 60	63.06 105	82.49 57	80.73 62	89.12 45	88.91 47

IS_MVSNet		73.33 65	77.34 60	68.65 109	81.29 62	83.47 61	74.45 118	63.58 77	65.75 72	48.49 144	67.11 63	70.61 65	54.63 159	84.51 40	83.58 44	89.48 38	86.34 62

CANet_DTU		73.29 66	76.96 63	69.00 105	77.04 98	82.06 70	79.49 63	56.30 162	67.85 64	53.29 119	71.12 46	70.37 68	61.81 115	81.59 61	80.96 59	86.09 117	84.73 84

diffmvs		73.13 67	75.65 67	70.19 93	74.07 137	77.17 126	78.24 92	57.45 155	72.44 57	64.02 66	69.05 51	75.92 48	64.86 97	75.18 147	75.27 149	82.47 158	84.53 85

Fast-Effi-MVS+		73.11 68	73.66 71	72.48 66	77.72 91	80.88 81	78.55 84	58.83 147	65.19 74	60.36 74	59.98 82	62.42 95	71.22 52	81.66 59	80.61 72	88.20 55	84.88 83

UGNet		72.78 69	77.67 55	67.07 129	71.65 159	83.24 63	75.20 109	63.62 76	64.93 76	56.72 99	71.82 44	73.30 55	49.02 174	81.02 72	80.70 68	86.22 115	88.67 49
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 70	77.20 61	67.59 120	74.19 136	84.01 57	76.61 105	61.69 107	60.62 105	50.61 135	70.25 49	71.31 63	55.57 154	83.85 45	82.28 48	86.90 90	88.08 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FC-MVSNet-train		72.60 71	75.07 69	69.71 100	81.10 65	78.79 105	73.74 131	65.23 67	66.10 69	53.34 118	70.36 48	63.40 92	56.92 143	81.44 63	80.96 59	87.93 61	84.46 86

MVSTER		72.06 72	74.24 70	69.51 101	70.39 167	75.97 147	76.91 102	57.36 157	64.64 79	61.39 72	68.86 52	63.76 90	63.46 102	81.44 63	79.70 77	87.56 69	85.31 74

Effi-MVS+-dtu		71.82 73	71.86 81	71.78 67	78.77 77	80.47 90	78.55 84	61.67 108	60.68 103	55.49 106	58.48 91	65.48 86	68.85 61	76.92 133	75.55 147	87.35 72	85.46 71

IterMVS-LS		71.69 74	72.82 77	70.37 90	77.54 93	76.34 144	75.13 113	60.46 118	61.53 99	57.57 88	64.89 67	67.33 81	66.04 91	77.09 132	77.37 112	85.48 138	85.18 76
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		71.52 75	69.87 95	73.44 63	82.21 61	79.35 99	79.52 62	64.59 71	66.15 68	61.87 69	53.21 145	56.09 129	65.85 95	78.94 100	78.50 90	86.60 110	76.85 160

ACMH+	66.54 13	71.36 76	70.09 90	72.85 65	82.59 58	81.13 76	78.56 83	68.04 49	61.55 98	52.52 125	51.50 163	54.14 141	68.56 63	78.85 101	79.50 83	86.82 97	83.94 91

IB-MVS	66.94 12	71.21 77	71.66 82	70.68 78	79.18 75	82.83 67	72.61 140	61.77 106	59.66 111	63.44 68	53.26 143	59.65 102	59.16 128	76.78 136	82.11 50	87.90 62	87.33 57
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 78	73.37 74	67.76 114	72.95 147	78.00 113	75.15 110	62.72 87	64.13 81	51.44 127	58.37 92	69.02 74	57.59 135	81.33 66	80.72 63	86.70 105	82.02 114

test1		70.78 78	73.37 74	67.76 114	72.95 147	78.00 113	75.15 110	62.72 87	64.13 81	51.44 127	58.37 92	69.02 74	57.59 135	81.33 66	80.72 63	86.70 105	82.02 114

ACMH	65.37 14	70.71 80	70.00 91	71.54 68	82.51 59	82.47 69	77.78 95	68.13 48	56.19 146	46.06 157	54.30 126	51.20 172	68.68 62	80.66 75	80.72 63	86.07 118	84.45 87
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_NR-MVSNet		70.59 81	72.19 79	68.72 107	77.72 91	80.72 82	73.81 129	69.65 38	61.99 93	43.23 168	60.54 78	57.50 108	58.57 129	79.56 93	81.07 58	89.34 40	83.97 89

FMVSNet3		70.49 82	72.90 76	67.67 118	72.88 150	77.98 116	74.96 116	62.72 87	64.13 81	51.44 127	58.37 92	69.02 74	57.43 138	79.43 95	79.57 81	86.59 111	81.81 121

FMVSNet2		70.39 83	72.67 78	67.72 117	72.95 147	78.00 113	75.15 110	62.69 91	63.29 86	51.25 131	55.64 108	68.49 80	57.59 135	80.91 74	80.35 74	86.70 105	82.02 114

v6		70.35 84	69.94 92	70.83 70	74.68 129	80.62 83	78.81 74	60.16 127	58.81 116	58.17 84	55.01 113	57.31 113	66.32 84	77.53 120	76.73 131	86.82 97	83.62 93

v1neww		70.34 85	69.93 93	70.82 71	74.68 129	80.61 84	78.80 75	60.17 124	58.74 118	58.10 85	55.00 114	57.28 114	66.33 82	77.53 120	76.74 127	86.82 97	83.61 94

v7new		70.34 85	69.93 93	70.82 71	74.68 129	80.61 84	78.80 75	60.17 124	58.74 118	58.10 85	55.00 114	57.28 114	66.33 82	77.53 120	76.74 127	86.82 97	83.61 94

v7		70.33 87	69.87 95	70.88 69	74.79 121	81.04 77	79.22 65	60.57 115	57.70 127	56.65 101	54.23 131	55.29 134	66.95 69	78.28 107	77.47 108	87.12 83	85.05 79

v8		70.23 88	69.86 97	70.67 79	74.69 128	79.82 95	78.79 77	59.18 137	58.80 117	58.20 83	55.00 114	57.33 111	66.31 85	77.51 123	76.71 135	86.82 97	83.88 92

v10		70.22 89	69.76 99	70.74 75	74.79 121	80.30 93	79.22 65	59.81 131	57.71 126	56.58 102	54.22 133	55.31 132	66.95 69	78.28 107	77.47 108	87.12 83	85.07 78

MS-PatchMatch		70.17 90	70.49 88	69.79 98	80.98 66	77.97 118	77.51 97	58.95 139	62.33 91	55.22 109	53.14 146	65.90 85	62.03 110	79.08 99	77.11 116	84.08 150	77.91 150

v18		70.10 91	69.52 102	70.77 74	74.66 132	77.06 129	78.84 72	58.84 146	60.01 109	59.23 77	55.06 112	57.47 109	66.34 81	77.50 124	76.75 125	86.71 104	82.77 108

v16		70.07 92	69.46 104	70.79 73	74.74 127	77.08 128	78.79 77	58.86 141	59.75 110	59.15 78	54.87 118	57.33 111	66.38 79	77.61 118	76.77 120	86.81 102	82.79 106

v2v482		70.05 93	69.46 104	70.74 75	74.62 133	80.32 92	79.00 68	60.62 114	57.41 128	56.89 94	55.43 111	55.14 135	66.39 78	77.25 129	77.14 115	86.90 90	83.57 99

v17		70.03 94	69.43 109	70.72 77	74.75 126	77.09 127	78.78 79	58.85 143	59.53 113	58.72 81	54.87 118	57.39 110	66.38 79	77.60 119	76.75 125	86.83 96	82.80 104

divwei89l23v2f112		69.97 95	69.44 107	70.58 83	74.78 123	80.50 88	78.85 70	60.30 119	56.97 132	56.75 97	54.67 123	56.27 125	65.92 93	77.37 127	76.72 132	86.88 93	83.58 98

v1		69.97 95	69.45 106	70.59 81	74.78 123	80.51 87	78.84 72	60.30 119	56.98 131	56.81 96	54.69 121	56.29 124	65.91 94	77.37 127	76.71 135	86.89 92	83.59 96

v1141		69.96 97	69.44 107	70.58 83	74.78 123	80.50 88	78.85 70	60.30 119	56.95 133	56.74 98	54.68 122	56.26 126	65.93 92	77.38 126	76.72 132	86.88 93	83.57 99

v1144		69.93 98	69.36 110	70.61 80	74.89 114	80.93 78	79.11 67	60.64 113	55.97 149	55.31 108	53.85 138	54.14 141	66.54 76	78.10 109	77.44 110	87.14 79	85.09 77

DU-MVS		69.63 99	70.91 85	68.13 113	75.99 104	79.54 96	73.81 129	69.20 43	61.20 101	43.23 168	58.52 89	53.50 148	58.57 129	79.22 97	80.45 73	87.97 60	83.97 89

v15		69.61 100	68.88 115	70.46 85	74.81 120	77.03 132	78.75 80	58.83 147	57.06 130	57.18 90	54.55 124	56.37 120	66.13 89	77.70 115	76.76 122	87.03 87	82.69 111

V14		69.59 101	68.86 116	70.45 87	74.83 119	77.04 130	78.70 81	58.83 147	56.95 133	57.08 92	54.41 125	56.34 121	66.15 86	77.77 114	76.76 122	87.08 85	82.74 109

V9		69.58 102	68.83 117	70.46 85	74.85 118	77.04 130	78.65 82	58.85 143	56.83 136	57.12 91	54.26 129	56.31 122	66.14 88	77.83 113	76.76 122	87.13 80	82.79 106

v12		69.54 103	68.79 119	70.41 88	74.88 115	77.03 132	78.54 87	58.85 143	56.71 137	56.87 95	54.13 134	56.23 127	66.15 86	77.89 111	76.74 127	87.17 75	82.80 104

UniMVSNet (Re)		69.53 104	71.90 80	66.76 135	76.42 101	80.93 78	72.59 141	68.03 50	61.75 97	41.68 176	58.34 95	57.23 116	53.27 166	79.53 94	80.62 71	88.57 52	84.90 82

v13		69.52 105	68.76 121	70.41 88	74.88 115	77.02 134	78.52 88	58.86 141	56.61 141	56.91 93	54.00 136	56.17 128	66.11 90	77.93 110	76.74 127	87.21 74	82.83 103

v1192		69.50 106	68.83 117	70.29 91	74.49 134	80.92 80	78.55 84	60.54 116	55.04 157	54.21 111	52.79 153	52.33 164	66.92 71	77.88 112	77.35 113	87.04 86	85.51 69

HyFIR lowres test		69.47 107	68.94 114	70.09 95	76.77 100	82.93 66	76.63 104	60.17 124	59.00 115	54.03 113	40.54 194	65.23 87	67.89 65	76.54 139	78.30 93	85.03 143	80.07 135

v11		69.37 108	68.65 124	70.20 92	74.87 117	76.97 135	78.29 91	58.55 151	56.38 143	56.04 104	54.02 135	54.98 136	66.47 77	78.30 106	76.91 118	86.97 88	83.02 102

v144192		69.34 109	68.68 123	70.12 94	74.06 138	80.54 86	78.08 94	60.54 116	54.99 159	54.13 112	52.92 150	52.80 160	66.73 74	77.13 131	76.72 132	87.15 76	85.63 65

TranMVSNet+NR-MVSNet		69.25 110	70.81 86	67.43 121	77.23 97	79.46 98	73.48 135	69.66 37	60.43 106	39.56 179	58.82 88	53.48 150	55.74 152	79.59 91	81.21 57	88.89 48	82.70 110

CHOSEN 1792x2688		69.20 111	69.26 111	69.13 103	76.86 99	78.93 101	77.27 100	60.12 128	61.86 95	54.42 110	42.54 188	61.61 96	66.91 72	78.55 104	78.14 97	79.23 173	83.23 101

v1921920		69.03 112	68.32 129	69.86 97	74.03 139	80.37 91	77.55 96	60.25 123	54.62 160	53.59 117	52.36 159	51.50 171	66.75 73	77.17 130	76.69 137	86.96 89	85.56 66

CostFormer		68.92 113	69.58 101	68.15 112	75.98 106	76.17 146	78.22 93	51.86 178	65.80 71	61.56 71	63.57 71	62.83 93	61.85 113	70.40 182	68.67 180	79.42 171	79.62 140

FMVSNet1		68.84 114	70.47 89	66.94 131	71.35 164	77.68 121	74.71 117	62.35 101	56.93 135	49.94 140	50.01 169	64.59 88	57.07 141	81.33 66	80.72 63	86.25 114	82.00 117

NR-MVSNet		68.79 115	70.56 87	66.71 137	77.48 94	79.54 96	73.52 134	69.20 43	61.20 101	39.76 178	58.52 89	50.11 178	51.37 170	80.26 83	80.71 67	88.97 46	83.59 96

V42		68.76 116	69.63 100	67.74 116	64.93 188	78.01 112	78.30 90	56.48 161	58.65 120	56.30 103	54.26 129	57.03 117	64.85 98	77.47 125	77.01 117	85.60 136	84.96 81

v1240		68.64 117	67.89 135	69.51 101	73.89 141	80.26 94	76.73 103	59.97 130	53.43 167	53.08 120	51.82 162	50.84 174	66.62 75	76.79 135	76.77 120	86.78 103	85.34 73

Fast-Effi-MVS+-dtu		68.34 118	69.47 103	67.01 130	75.15 110	77.97 118	77.12 101	55.40 165	57.87 121	46.68 154	56.17 107	60.39 98	62.36 108	76.32 140	76.25 141	85.35 140	81.34 123

tpmp4_e23		68.32 119	67.08 142	69.76 99	77.86 85	75.22 153	78.37 89	56.17 164	66.06 70	64.27 64	57.15 103	54.89 137	63.40 103	70.97 175	68.29 185	78.46 175	77.00 159

GA-MVS		68.14 120	69.17 112	66.93 132	73.77 142	78.50 109	74.45 118	58.28 152	55.11 156	48.44 145	60.08 80	53.99 144	61.50 116	78.43 105	77.57 106	85.13 141	80.54 130

tfpn200view9		68.11 121	68.72 122	67.40 122	77.83 87	78.93 101	74.28 123	62.81 84	56.64 139	46.82 152	52.65 155	53.47 151	56.59 144	80.41 76	78.43 91	86.11 116	80.52 131

EPNet_dtu		68.08 122	71.00 84	64.67 148	79.64 72	68.62 177	75.05 114	63.30 78	66.36 67	45.27 161	67.40 61	66.84 83	43.64 184	75.37 145	74.98 153	81.15 163	77.44 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres200		67.98 123	68.55 126	67.30 124	77.89 84	78.86 103	74.18 126	62.75 85	56.35 144	46.48 155	52.98 149	53.54 147	56.46 145	80.41 76	77.97 98	86.05 120	79.78 139

thres400		67.95 124	68.62 125	67.17 126	77.90 82	78.59 108	74.27 124	62.72 87	56.34 145	45.77 159	53.00 148	53.35 154	56.46 145	80.21 84	78.43 91	85.91 129	80.43 132

pmmvs4		67.89 125	67.39 140	68.48 110	71.60 161	73.57 161	74.45 118	60.98 110	64.65 78	57.97 87	54.95 117	51.73 169	61.88 112	73.78 154	75.11 151	83.99 152	77.91 150

v148		67.85 126	67.53 136	68.23 111	73.25 145	77.57 124	74.26 125	57.36 157	55.70 151	57.45 89	53.53 139	55.42 131	61.96 111	75.23 146	73.92 156	85.08 142	81.32 124

Vis-MVSNet (Re-imp)		67.83 127	73.52 72	61.19 164	78.37 80	76.72 138	66.80 167	62.96 82	65.50 73	34.17 189	67.19 62	69.68 70	39.20 192	79.39 96	79.44 85	85.68 135	76.73 161

PatchMatch-RL		67.78 128	66.65 147	69.10 104	73.01 146	72.69 163	68.49 159	61.85 105	62.93 89	60.20 76	56.83 105	50.42 176	69.52 58	75.62 144	74.46 155	81.51 161	73.62 179

thres600view7		67.68 129	68.43 127	66.80 133	77.90 82	78.86 103	73.84 128	62.75 85	56.07 147	44.70 165	52.85 152	52.81 159	55.58 153	80.41 76	77.77 101	86.05 120	80.28 133

COLMAP_ROB	62.73 15	67.66 130	66.76 146	68.70 108	80.49 70	77.98 116	75.29 108	62.95 83	63.62 84	49.96 139	47.32 181	50.72 175	58.57 129	76.87 134	75.50 148	84.94 145	75.33 170
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CDS-MVSNet		67.65 131	69.83 98	65.09 143	75.39 109	76.55 139	74.42 121	63.75 75	53.55 166	49.37 143	59.41 85	62.45 94	44.44 182	79.71 89	79.82 76	83.17 156	77.36 154
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RPSCF		67.64 132	71.25 83	63.43 157	61.86 194	70.73 169	67.26 163	50.86 183	74.20 53	58.91 79	67.49 60	69.33 71	64.10 100	71.41 168	68.45 184	77.61 177	77.17 155

view600		67.63 133	68.36 128	66.77 134	77.84 86	78.66 106	73.74 131	62.62 94	56.04 148	44.98 162	52.86 151	52.83 158	55.48 156	80.36 80	77.75 102	85.95 128	80.02 136

thres100view900		67.60 134	68.02 132	67.12 128	77.83 87	77.75 120	73.90 127	62.52 97	56.64 139	46.82 152	52.65 155	53.47 151	55.92 149	78.77 102	77.62 105	85.72 134	79.23 143

Baseline_NR-MVSNet		67.53 135	68.77 120	66.09 139	75.99 104	74.75 156	72.43 142	68.41 46	61.33 100	38.33 182	51.31 164	54.13 143	56.03 148	79.22 97	78.19 95	85.37 139	82.45 112

USDC		67.36 136	67.90 134	66.74 136	71.72 157	75.23 152	71.58 148	60.28 122	67.45 65	50.54 136	60.93 76	45.20 194	62.08 109	76.56 138	74.50 154	84.25 149	75.38 169

view800		67.35 137	68.22 131	66.35 138	77.83 87	78.62 107	72.97 139	62.58 95	55.71 150	44.13 166	52.69 154	52.24 167	54.58 161	80.27 82	78.19 95	86.01 123	79.79 138

DWT-MVSNet_training		67.24 138	65.96 150	68.74 106	76.15 102	74.36 159	74.37 122	56.66 160	61.82 96	60.51 73	58.23 97	49.76 180	65.07 96	70.04 183	70.39 170	79.70 170	77.11 157

EG-PatchMatch MVS		67.24 138	66.94 143	67.60 119	78.73 78	81.35 73	73.28 137	59.49 133	46.89 193	51.42 130	43.65 185	53.49 149	55.50 155	81.38 65	80.66 69	87.15 76	81.17 125

v7n		67.05 140	66.94 143	67.17 126	72.35 152	78.97 100	73.26 138	58.88 140	51.16 178	50.90 132	48.21 174	50.11 178	60.96 118	77.70 115	77.38 111	86.68 108	85.05 79

tfpn		66.58 141	67.18 141	65.88 140	77.82 90	78.45 110	72.07 144	62.52 97	55.35 153	43.21 170	52.54 158	46.12 191	53.68 162	80.02 86	78.23 94	85.99 126	79.55 141

IterMVS		66.36 142	68.30 130	64.10 150	69.48 174	74.61 157	73.41 136	50.79 184	57.30 129	48.28 146	60.64 77	59.92 101	60.85 122	74.14 152	72.66 162	81.80 160	78.82 146
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
conf0.05thres1000		66.26 143	66.77 145	65.66 141	77.45 95	78.10 111	71.85 147	62.44 100	51.47 177	43.00 171	47.92 176	51.66 170	53.40 164	79.71 89	77.97 98	85.82 130	80.56 129

TDRefinement		66.09 144	65.03 162	67.31 123	69.73 171	76.75 137	75.33 106	64.55 72	60.28 107	49.72 142	45.63 183	42.83 197	60.46 123	75.75 141	75.95 144	84.08 150	78.04 149

pm-mvs1		65.62 145	67.42 138	63.53 156	73.66 143	76.39 143	69.66 153	60.87 112	49.73 185	43.97 167	51.24 165	57.00 118	48.16 175	79.89 87	77.84 100	84.85 147	79.82 137

tpm cat1		65.41 146	63.81 170	67.28 125	75.61 108	72.88 162	75.32 107	52.85 172	62.97 88	63.66 67	53.24 144	53.29 156	61.83 114	65.54 194	64.14 197	74.43 191	74.60 172

anonymousdsp		65.28 147	67.98 133	62.13 160	58.73 202	73.98 160	67.10 165	50.69 185	48.41 188	47.66 151	54.27 127	52.75 161	61.45 117	76.71 137	80.20 75	87.13 80	89.53 45

v52		65.23 148	66.24 148	64.06 151	61.94 192	76.42 141	72.06 145	54.30 167	49.94 182	50.04 138	47.41 179	52.42 162	60.23 125	75.71 142	76.22 142	85.78 131	85.56 66

V4		65.23 148	66.23 149	64.06 151	61.94 192	76.42 141	72.05 146	54.31 166	49.91 184	50.06 137	47.42 178	52.40 163	60.24 124	75.71 142	76.22 142	85.78 131	85.56 66

v748		65.12 150	65.24 157	64.98 145	69.77 170	76.45 140	69.47 155	57.06 159	49.93 183	50.70 133	47.87 177	49.50 182	57.14 140	73.64 156	75.18 150	85.75 133	84.14 88

PMMVS		65.06 151	69.17 112	60.26 169	55.25 211	63.43 194	66.71 168	43.01 209	62.41 90	50.64 134	69.44 50	67.04 82	63.29 104	74.36 151	73.54 158	82.68 157	73.99 177

CR-MVSNet		64.83 152	65.54 155	64.01 153	70.64 166	69.41 172	65.97 172	52.74 173	57.81 123	52.65 122	54.27 127	56.31 122	60.92 119	72.20 164	73.09 160	81.12 164	75.69 166

TransMVSNet (Re)		64.74 153	65.66 154	63.66 155	77.40 96	75.33 151	69.86 152	62.67 93	47.63 191	41.21 177	50.01 169	52.33 164	45.31 181	79.57 92	77.69 104	85.49 137	77.07 158

test-LLR		64.42 154	64.36 166	64.49 149	75.02 112	63.93 191	66.61 169	61.96 103	54.41 161	47.77 148	57.46 100	60.25 99	55.20 157	70.80 176	69.33 175	80.40 167	74.38 174

MDTV_nov1_ep13		64.37 155	65.24 157	63.37 158	68.94 176	70.81 168	72.40 143	50.29 187	60.10 108	53.91 115	60.07 81	59.15 104	57.21 139	69.43 186	67.30 187	77.47 178	69.78 186

tfpnnormal		64.27 156	63.64 171	65.02 144	75.84 107	75.61 148	71.24 150	62.52 97	47.79 190	42.97 172	42.65 187	44.49 195	52.66 168	78.77 102	76.86 119	84.88 146	79.29 142

PatchmatchNet		64.21 157	64.65 164	63.69 154	71.29 165	68.66 176	69.63 154	51.70 180	63.04 87	53.77 116	59.83 84	58.34 106	60.23 125	68.54 189	66.06 192	75.56 186	68.08 190
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dps		64.00 158	62.99 173	65.18 142	73.29 144	72.07 165	68.98 158	53.07 171	57.74 125	58.41 82	55.55 110	47.74 187	60.89 121	69.53 185	67.14 189	76.44 183	71.19 183

pmmvs-eth3d		63.52 159	62.44 180	64.77 147	66.82 182	70.12 171	69.41 156	59.48 134	54.34 164	52.71 121	46.24 182	44.35 196	56.93 142	72.37 159	73.77 157	83.30 154	75.91 163

WR-MVS		63.03 160	67.40 139	57.92 179	75.14 111	77.60 123	60.56 190	66.10 60	54.11 165	23.88 201	53.94 137	53.58 146	34.50 197	73.93 153	77.71 103	87.35 72	80.94 126

PEN-MVS		62.96 161	65.77 153	59.70 172	73.98 140	75.45 149	63.39 183	67.61 53	52.49 170	25.49 200	53.39 140	49.12 183	40.85 190	71.94 166	77.26 114	86.86 95	80.72 128

TinyColmap		62.84 162	61.03 186	64.96 146	69.61 172	71.69 166	68.48 160	59.76 132	55.41 152	47.69 150	47.33 180	34.20 207	62.76 107	74.52 149	72.59 163	81.44 162	71.47 182

CP-MVSNet		62.68 163	65.49 156	59.40 175	71.84 155	75.34 150	62.87 185	67.04 56	52.64 169	27.19 198	53.38 141	48.15 185	41.40 188	71.26 169	75.68 145	86.07 118	82.00 117

gg-mvs-nofinetune		62.55 164	65.05 161	59.62 173	78.72 79	77.61 122	70.83 151	53.63 168	39.71 205	22.04 210	36.36 198	64.32 89	47.53 176	81.16 70	79.03 87	85.00 144	77.17 155

CVMVSNet		62.55 164	65.89 151	58.64 177	66.95 180	69.15 174	66.49 171	56.29 163	52.46 171	32.70 190	59.27 86	58.21 107	50.09 172	71.77 167	71.39 167	79.31 172	78.99 145

CMPMVS	47.78 17	62.49 166	62.52 178	62.46 159	70.01 169	70.66 170	62.97 184	51.84 179	51.98 173	56.71 100	42.87 186	53.62 145	57.80 134	72.23 162	70.37 171	75.45 188	75.91 163
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		62.41 167	62.88 174	61.87 161	71.38 163	75.18 155	67.76 162	59.45 135	41.64 201	42.52 175	37.33 196	52.91 157	46.87 177	77.67 117	76.26 140	83.23 155	79.18 144

tpm		62.41 167	63.15 172	61.55 163	72.24 153	63.79 193	71.31 149	46.12 200	57.82 122	55.33 107	59.90 83	54.74 138	53.63 163	67.24 192	64.29 195	70.65 202	74.25 176

PS-CasMVS		62.38 169	65.06 160	59.25 176	71.73 156	75.21 154	62.77 186	66.99 57	51.94 175	26.96 199	52.00 161	47.52 188	41.06 189	71.16 172	75.60 146	85.97 127	81.97 119

pmmvs5		62.37 170	64.04 168	60.42 167	65.03 186	71.67 167	67.17 164	52.70 175	50.30 179	44.80 163	54.23 131	51.19 173	49.37 173	72.88 158	73.48 159	83.45 153	74.55 173

tpmrst		62.00 171	62.35 181	61.58 162	71.62 160	64.14 190	69.07 157	48.22 196	62.21 92	53.93 114	58.26 96	55.30 133	55.81 151	63.22 199	62.62 200	70.85 201	70.70 184

PatchT		61.97 172	64.04 168	59.55 174	60.49 196	67.40 180	56.54 197	48.65 192	56.69 138	52.65 122	51.10 166	52.14 168	60.92 119	72.20 164	73.09 160	78.03 176	75.69 166

DTE-MVSNet		61.85 173	64.96 163	58.22 178	74.32 135	74.39 158	61.01 189	67.85 52	51.76 176	21.91 211	53.28 142	48.17 184	37.74 193	72.22 163	76.44 138	86.52 113	78.49 147

SixPastTwentyTwo		61.84 174	62.45 179	61.12 165	69.20 175	72.20 164	62.03 187	57.40 156	46.54 194	38.03 184	57.14 104	41.72 199	58.12 133	69.67 184	71.58 166	81.94 159	78.30 148

WR-MVS_H		61.83 175	65.87 152	57.12 182	71.72 157	76.87 136	61.45 188	66.19 58	51.97 174	22.92 208	53.13 147	52.30 166	33.80 198	71.03 173	75.00 152	86.65 109	80.78 127

LTVRE_ROB	59.44 16	61.82 176	62.64 177	60.87 166	72.83 151	77.19 125	64.37 179	58.97 138	33.56 214	28.00 197	52.59 157	42.21 198	63.93 101	74.52 149	76.28 139	77.15 180	82.13 113
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		61.71 177	62.88 174	60.34 168	69.51 173	69.41 172	63.48 182	49.23 188	57.81 123	45.64 160	50.51 167	50.12 177	53.13 167	68.17 191	68.49 183	81.07 165	75.62 168

TESTMET0.1,1		61.10 178	64.36 166	57.29 181	57.53 204	63.93 191	66.61 169	36.22 215	54.41 161	47.77 148	57.46 100	60.25 99	55.20 157	70.80 176	69.33 175	80.40 167	74.38 174

test-mter		60.84 179	64.62 165	56.42 184	55.99 209	64.18 189	65.39 174	34.23 217	54.39 163	46.21 156	57.40 102	59.49 103	55.86 150	71.02 174	69.65 173	80.87 166	76.20 162

PM-MVS		60.48 180	60.94 187	59.94 170	58.85 201	66.83 183	64.27 180	51.39 181	55.03 158	48.03 147	50.00 171	40.79 201	58.26 132	69.20 187	67.13 190	78.84 174	77.60 152

MDTV_nov1_ep13_2view		60.16 181	60.51 188	59.75 171	65.39 185	69.05 175	68.00 161	48.29 194	51.99 172	45.95 158	48.01 175	49.64 181	53.39 165	68.83 188	66.52 191	77.47 178	69.55 187

EPMVS		60.00 182	61.97 182	57.71 180	68.46 177	63.17 197	64.54 178	48.23 195	63.30 85	44.72 164	60.19 79	56.05 130	50.85 171	65.27 196	62.02 202	69.44 204	63.81 197

TAMVS		59.58 183	62.81 176	55.81 186	66.03 184	65.64 188	63.86 181	48.74 191	49.95 181	37.07 186	54.77 120	58.54 105	44.44 182	72.29 161	71.79 164	74.70 190	66.66 192

test0.0.03 1		58.80 184	61.58 184	55.56 187	75.02 112	68.45 178	59.58 194	61.96 103	52.74 168	29.57 193	49.75 172	54.56 139	31.46 200	71.19 170	69.77 172	75.75 184	64.57 195

CHOSEN 280x420		58.70 185	61.88 183	54.98 189	55.45 210	50.55 214	64.92 176	40.36 211	55.21 154	38.13 183	48.31 173	63.76 90	63.03 106	73.73 155	68.58 182	68.00 207	73.04 180

MIMVSNet		58.52 186	61.34 185	55.22 188	60.76 195	67.01 182	66.81 166	49.02 190	56.43 142	38.90 181	40.59 193	54.54 140	40.57 191	73.16 157	71.65 165	75.30 189	66.00 193

FMVSNet5		57.24 187	60.02 189	53.99 192	56.45 206	62.74 198	65.27 175	47.03 197	55.14 155	39.55 180	40.88 191	53.42 153	41.83 185	72.35 160	71.10 169	73.79 193	64.50 196

gm-plane-assit		57.00 188	57.62 194	56.28 185	76.10 103	62.43 201	47.62 210	46.57 198	33.84 213	23.24 204	37.52 195	40.19 202	59.61 127	79.81 88	77.55 107	84.55 148	72.03 181

FC-MVSNet-test		56.90 189	65.20 159	47.21 201	66.98 179	63.20 196	49.11 208	58.60 150	59.38 114	11.50 221	65.60 65	56.68 119	24.66 212	71.17 171	71.36 168	72.38 197	69.02 188

Anonymous20231206		56.36 190	57.80 193	54.67 190	70.08 168	66.39 185	60.46 191	57.54 154	49.50 187	29.30 194	33.86 204	46.64 189	35.18 196	70.44 180	68.88 179	75.47 187	68.88 189

ADS-MVSNet		55.94 191	58.01 191	53.54 195	62.48 191	58.48 203	59.12 195	46.20 199	59.65 112	42.88 173	52.34 160	53.31 155	46.31 179	62.00 203	60.02 207	64.23 213	60.24 205

EU-MVSNet		54.63 192	58.69 190	49.90 199	56.99 205	62.70 199	56.41 198	50.64 186	45.95 196	23.14 205	50.42 168	46.51 190	36.63 194	65.51 195	64.85 194	75.57 185	74.91 171

MVS-HIRNet		54.41 193	52.10 200	57.11 183	58.99 200	56.10 206	49.68 207	49.10 189	46.18 195	52.15 126	33.18 205	46.11 192	56.10 147	63.19 200	59.70 208	76.64 182	60.25 204

testgi		54.39 194	57.86 192	50.35 198	71.59 162	67.24 181	54.95 200	53.25 170	43.36 198	23.78 202	44.64 184	47.87 186	24.96 209	70.45 179	68.66 181	73.60 194	62.78 200

test20.03		53.93 195	56.28 195	51.19 197	72.19 154	65.83 186	53.20 202	61.08 109	42.74 199	22.08 209	37.07 197	45.76 193	24.29 213	70.44 180	69.04 177	74.31 192	63.05 199

LP		53.62 196	53.43 196	53.83 193	58.51 203	62.59 200	57.31 196	46.04 201	47.86 189	42.69 174	36.08 200	36.86 205	46.53 178	64.38 197	64.25 196	71.92 198	62.00 202

MDA-MVSNet-bldmvs		53.37 197	53.01 199	53.79 194	43.67 219	67.95 179	59.69 193	57.92 153	43.69 197	32.41 191	41.47 189	27.89 217	52.38 169	56.97 213	65.99 193	76.68 181	67.13 191

FPMVS		51.87 198	50.00 203	54.07 191	66.83 181	57.25 204	60.25 192	50.91 182	50.25 180	34.36 188	36.04 201	32.02 209	41.49 187	58.98 211	56.07 211	70.56 203	59.36 206

Anonymous20231211		51.46 199	50.59 201	52.46 196	67.30 178	66.70 184	55.00 199	59.22 136	29.96 216	17.62 216	19.11 218	28.74 216	35.72 195	66.42 193	69.52 174	79.92 169	73.71 178

MIMVSNet1		49.27 200	53.25 198	44.62 205	44.61 216	61.52 202	53.61 201	52.18 176	41.62 202	18.68 213	28.14 212	41.58 200	25.50 207	68.46 190	69.04 177	73.15 195	62.37 201

pmmvs3		47.65 201	49.08 205	45.99 203	44.61 216	54.79 209	50.04 205	31.95 220	33.91 212	29.90 192	30.37 206	33.53 208	46.31 179	63.50 198	63.67 198	73.14 196	63.77 198

testpf		47.41 202	48.47 208	46.18 202	66.30 183	50.67 213	48.15 209	42.60 210	37.10 209	28.75 195	40.97 190	39.01 204	30.82 201	52.95 216	53.74 215	60.46 214	64.87 194

N_pmnet		47.35 203	50.13 202	44.11 206	59.98 197	51.64 212	51.86 203	44.80 205	49.58 186	20.76 212	40.65 192	40.05 203	29.64 202	59.84 209	55.15 212	57.63 215	54.00 213

test2356		47.20 204	48.62 207	45.54 204	56.38 207	54.89 208	50.62 204	45.08 204	38.65 206	23.40 203	36.23 199	31.10 211	29.31 203	62.76 201	62.49 201	68.48 206	54.23 212

new-patchmatchnet		46.97 205	49.47 204	44.05 207	62.82 190	56.55 205	45.35 211	52.01 177	42.47 200	17.04 217	35.73 202	35.21 206	21.84 218	61.27 204	54.83 213	65.26 212	60.26 203

GG-mvs-BLEND		46.86 206	67.51 137	22.75 219	0.05 226	76.21 145	64.69 177	0.04 224	61.90 94	0.09 229	55.57 109	71.32 62	0.08 224	70.54 178	67.19 188	71.58 199	69.86 185

PMVS	39.38 18	46.06 207	43.30 212	49.28 200	62.93 189	38.75 220	41.88 213	53.50 169	33.33 215	35.46 187	28.90 209	31.01 212	33.04 199	58.61 212	54.63 214	68.86 205	57.88 209
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testus		45.61 208	49.06 206	41.59 209	56.13 208	55.28 207	43.51 212	39.64 213	37.74 207	18.23 214	35.52 203	31.28 210	24.69 211	62.46 202	62.90 199	67.33 208	58.26 208

1111		43.08 209	44.02 211	41.98 208	59.22 198	49.27 216	41.48 214	45.63 202	35.01 210	23.06 206	28.60 210	30.15 213	27.22 204	60.42 207	57.97 209	55.27 218	46.74 215

testmv		42.58 210	44.36 209	40.49 210	54.63 212	52.76 210	41.21 216	44.37 206	28.83 217	12.87 218	27.16 213	25.03 218	23.01 214	60.83 205	61.13 203	66.88 209	54.81 210

test1235678		42.57 211	44.36 209	40.49 210	54.63 212	52.75 211	41.21 216	44.37 206	28.82 218	12.87 218	27.15 214	25.01 219	23.01 214	60.83 205	61.13 203	66.88 209	54.81 210

new_pmnet		38.40 212	42.64 213	33.44 214	37.54 222	45.00 218	36.60 218	32.72 219	40.27 203	12.72 220	29.89 207	28.90 215	24.78 210	53.17 215	52.90 216	56.31 216	48.34 214

Gipuma		36.38 213	35.80 216	37.07 212	45.76 215	33.90 221	29.81 220	48.47 193	39.91 204	18.02 215	8.00 224	8.14 226	25.14 208	59.29 210	61.02 205	55.19 219	40.31 217
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		36.35 214	37.59 215	34.91 213	46.13 214	49.89 215	27.99 221	43.56 208	20.91 222	7.03 224	14.64 220	15.50 224	18.92 219	42.95 217	60.20 206	65.84 211	59.03 207

test12356		35.10 215	38.50 214	31.13 216	44.14 218	43.70 219	32.27 219	34.42 216	26.51 220	9.47 222	25.22 216	20.34 220	10.86 221	53.47 214	56.15 210	55.59 217	44.11 216

.test1245		30.81 216	29.14 218	32.77 215	59.22 198	49.27 216	41.48 214	45.63 202	35.01 210	23.06 206	28.60 210	30.15 213	27.22 204	60.42 207	0.10 222	0.01 226	0.43 224

PMMVS2		25.60 217	29.75 217	20.76 220	28.00 223	30.93 222	23.10 222	29.18 221	23.14 221	1.46 228	18.23 219	16.54 222	5.08 222	40.22 218	41.40 218	37.76 220	37.79 219

E-PMN		21.77 218	18.24 220	25.89 217	40.22 220	19.58 224	12.46 225	39.87 212	18.68 224	6.71 225	9.57 221	4.31 229	22.36 217	19.89 222	27.28 220	33.73 221	28.34 221

EMVS		20.98 219	17.15 221	25.44 218	39.51 221	19.37 225	12.66 224	39.59 214	19.10 223	6.62 226	9.27 222	4.40 228	22.43 216	17.99 223	24.40 221	31.81 222	25.53 222

MVE	19.12 19	20.47 220	23.27 219	17.20 221	12.66 225	25.41 223	10.52 226	34.14 218	14.79 225	6.53 227	8.79 223	4.68 227	16.64 220	29.49 220	41.63 217	22.73 224	38.11 218
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.09 221	0.15 222	0.02 223	0.01 227	0.02 228	0.05 229	0.01 225	0.11 226	0.01 230	0.26 226	0.01 230	0.06 226	0.10 224	0.10 222	0.01 226	0.43 224

test123		0.09 221	0.14 223	0.02 223	0.00 228	0.02 228	0.02 230	0.01 225	0.09 227	0.00 231	0.30 225	0.00 231	0.08 224	0.03 225	0.09 224	0.01 226	0.45 223

ESAPD		0.00 223	0.00 224	0.00 225	0.00 228	0.00 230	0.00 231	0.00 227	0.00 228	0.00 231	0.00 227	0.00 231	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

sosnet-low-res		0.00 223	0.00 224	0.00 225	0.00 228	0.00 230	0.00 231	0.00 227	0.00 228	0.00 231	0.00 227	0.00 231	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

sosnet		0.00 223	0.00 224	0.00 225	0.00 228	0.00 230	0.00 231	0.00 227	0.00 228	0.00 231	0.00 227	0.00 231	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

ambc			53.42 197		64.99 187	63.36 195	49.96 206		47.07 192	37.12 185	28.97 208	16.36 223	41.82 186	75.10 148	67.34 186	71.55 200	75.72 165

MTAPA										83.48 1		86.45 11

MTMP										82.66 3		84.91 19

Patchmatch-RL test							2.85 228

tmp_tt				14.50 222	14.68 224	7.17 227	10.46 227	2.21 223	37.73 208	28.71 196	25.26 215	16.98 221	4.37 223	31.49 219	29.77 219	26.56 223

XVS					86.63 38	88.68 22	85.00 40			71.81 39		81.92 29				90.47 16

X-MVStestdata					86.63 38	88.68 22	85.00 40			71.81 39		81.92 29				90.47 16

abl_6				79.05 36	87.27 34	88.85 20	83.62 48	68.25 47	81.68 34	72.94 33	73.79 39	84.45 21	72.55 41			89.66 36	90.64 37

mPP-MVS					89.90 19							81.29 34

NP-MVS									80.10 39

Patchmtry						65.80 187	65.97 172	52.74 173		52.65 122

DeepMVS_CX						18.74 226	18.55 223	8.02 222	26.96 219	7.33 223	23.81 217	13.05 225	25.99 206	25.17 221		22.45 225	36.25 220

High-resolution multi-view benchmark