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

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

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
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

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

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

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

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

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

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

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

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

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
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

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.
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

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
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

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

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

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

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

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

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
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

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

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

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

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

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

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

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

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
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

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

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

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

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

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

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
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

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

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
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

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
CLD-MVS		79.35 43	81.23 39	77.16 47	85.01 50	86.92 39	85.87 34	60.89 115	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
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
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	86.04 123	87.29 58

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

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

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 80	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
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

OpenMVS	70.44 10	76.15 57	76.82 64	75.37 54	85.01 50	84.79 54	78.99 69	62.07 103	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

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

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
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 82	78.87 88	86.00 126	80.18 135

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

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	86.01 124	85.98 63

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 97	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

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	53.21 150	56.09 129	65.85 95	78.94 101	78.50 90	86.60 110	76.85 161

MVS_Test		75.37 59	77.13 62	73.31 64	79.07 76	81.32 74	79.98 58	60.12 134	69.72 62	64.11 65	70.53 47	73.22 56	68.90 60	80.14 86	79.48 84	87.67 67	85.50 70

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	51.50 169	54.14 142	68.56 63	78.85 102	79.50 83	86.82 97	83.94 91

Fast-Effi-MVS+		73.11 68	73.66 71	72.48 66	77.72 92	80.88 81	78.55 84	58.83 153	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

Effi-MVS+-dtu		71.82 73	71.86 81	71.78 67	78.77 77	80.47 90	78.55 84	61.67 109	60.68 103	55.49 106	58.48 91	65.48 86	68.85 61	76.92 137	75.55 152	87.35 72	85.46 71

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	56.19 149	46.06 158	54.30 131	51.20 178	68.68 62	80.66 75	80.72 63	86.07 119	84.45 87
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v7		70.33 87	69.87 95	70.88 69	74.79 127	81.04 77	79.22 65	60.57 119	57.70 128	56.65 101	54.23 136	55.29 134	66.95 69	78.28 108	77.47 109	87.12 83	85.05 79

v6		70.35 84	69.94 92	70.83 70	74.68 135	80.62 83	78.81 74	60.16 132	58.81 116	58.17 84	55.01 117	57.31 113	66.32 84	77.53 121	76.73 132	86.82 97	83.62 93

v1neww		70.34 85	69.93 93	70.82 71	74.68 135	80.61 84	78.80 75	60.17 129	58.74 118	58.10 85	55.00 118	57.28 114	66.33 82	77.53 121	76.74 128	86.82 97	83.61 94

v7new		70.34 85	69.93 93	70.82 71	74.68 135	80.61 84	78.80 75	60.17 129	58.74 118	58.10 85	55.00 118	57.28 114	66.33 82	77.53 121	76.74 128	86.82 97	83.61 94

v16		70.07 92	69.46 104	70.79 73	74.74 133	77.08 129	78.79 77	58.86 147	59.75 110	59.15 78	54.87 123	57.33 111	66.38 79	77.61 119	76.77 121	86.81 102	82.79 106

v18		70.10 91	69.52 102	70.77 74	74.66 138	77.06 130	78.84 72	58.84 152	60.01 109	59.23 77	55.06 116	57.47 109	66.34 81	77.50 125	76.75 126	86.71 104	82.77 108

v10		70.22 89	69.76 99	70.74 75	74.79 127	80.30 93	79.22 65	59.81 137	57.71 127	56.58 102	54.22 138	55.31 132	66.95 69	78.28 108	77.47 109	87.12 83	85.07 78

v2v482		70.05 93	69.46 104	70.74 75	74.62 139	80.32 92	79.00 68	60.62 118	57.41 129	56.89 94	55.43 111	55.14 135	66.39 78	77.25 133	77.14 116	86.90 90	83.57 99

v17		70.03 94	69.43 109	70.72 77	74.75 132	77.09 128	78.78 79	58.85 149	59.53 113	58.72 81	54.87 123	57.39 110	66.38 79	77.60 120	76.75 126	86.83 96	82.80 104

IB-MVS	66.94 12	71.21 77	71.66 82	70.68 78	79.18 75	82.83 67	72.61 141	61.77 107	59.66 111	63.44 68	53.26 148	59.65 102	59.16 128	76.78 140	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
v8		70.23 88	69.86 97	70.67 79	74.69 134	79.82 95	78.79 77	59.18 143	58.80 117	58.20 83	55.00 118	57.33 111	66.31 85	77.51 124	76.71 136	86.82 97	83.88 92

v1144		69.93 98	69.36 110	70.61 80	74.89 120	80.93 78	79.11 67	60.64 117	55.97 152	55.31 108	53.85 143	54.14 142	66.54 76	78.10 110	77.44 111	87.14 79	85.09 77

UA-Net		74.47 62	77.80 54	70.59 81	85.33 46	85.40 50	73.54 134	65.98 63	60.65 104	56.00 105	72.11 42	79.15 38	54.63 160	83.13 53	82.25 49	88.04 59	81.92 120

v1		69.97 95	69.45 106	70.59 81	74.78 129	80.51 87	78.84 72	60.30 124	56.98 132	56.81 96	54.69 126	56.29 124	65.91 94	77.37 128	76.71 136	86.89 92	83.59 96

v1141		69.96 97	69.44 107	70.58 83	74.78 129	80.50 88	78.85 70	60.30 124	56.95 134	56.74 98	54.68 127	56.26 126	65.93 92	77.38 127	76.72 133	86.88 93	83.57 99

divwei89l23v2f112		69.97 95	69.44 107	70.58 83	74.78 129	80.50 88	78.85 70	60.30 124	56.97 133	56.75 97	54.67 128	56.27 125	65.92 93	77.37 128	76.72 133	86.88 93	83.58 98

v15		69.61 100	68.88 115	70.46 85	74.81 126	77.03 133	78.75 80	58.83 153	57.06 131	57.18 90	54.55 129	56.37 120	66.13 89	77.70 116	76.76 123	87.03 87	82.69 111

V9		69.58 102	68.83 117	70.46 85	74.85 124	77.04 131	78.65 82	58.85 149	56.83 137	57.12 91	54.26 134	56.31 122	66.14 88	77.83 114	76.76 123	87.13 80	82.79 106

V14		69.59 101	68.86 116	70.45 87	74.83 125	77.04 131	78.70 81	58.83 153	56.95 134	57.08 92	54.41 130	56.34 121	66.15 86	77.77 115	76.76 123	87.08 85	82.74 109

v13		69.52 105	68.76 121	70.41 88	74.88 121	77.02 135	78.52 88	58.86 147	56.61 143	56.91 93	54.00 141	56.17 128	66.11 90	77.93 111	76.74 128	87.21 74	82.83 103

v12		69.54 103	68.79 119	70.41 88	74.88 121	77.03 133	78.54 87	58.85 149	56.71 138	56.87 95	54.13 139	56.23 127	66.15 86	77.89 112	76.74 128	87.17 75	82.80 104

IterMVS-LS		71.69 74	72.82 77	70.37 90	77.54 94	76.34 145	75.13 113	60.46 122	61.53 99	57.57 88	64.89 67	67.33 81	66.04 91	77.09 136	77.37 113	85.48 139	85.18 76
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1192		69.50 106	68.83 117	70.29 91	74.49 140	80.92 80	78.55 84	60.54 120	55.04 161	54.21 111	52.79 158	52.33 166	66.92 71	77.88 113	77.35 114	87.04 86	85.51 69

v11		69.37 108	68.65 125	70.20 92	74.87 123	76.97 136	78.29 91	58.55 157	56.38 146	56.04 104	54.02 140	54.98 136	66.47 77	78.30 107	76.91 119	86.97 88	83.02 102

diffmvs		73.13 67	75.65 67	70.19 93	74.07 143	77.17 127	78.24 92	57.45 161	72.44 57	64.02 66	69.05 51	75.92 48	64.86 97	75.18 153	75.27 155	82.47 164	84.53 85

v144192		69.34 109	68.68 124	70.12 94	74.06 144	80.54 86	78.08 94	60.54 120	54.99 163	54.13 112	52.92 155	52.80 162	66.73 74	77.13 135	76.72 133	87.15 76	85.63 65

HyFIR lowres test		69.47 107	68.94 114	70.09 95	76.77 101	82.93 66	76.63 104	60.17 129	59.00 115	54.03 113	40.54 200	65.23 87	67.89 65	76.54 144	78.30 94	85.03 145	80.07 136

EPP-MVSNet		74.00 64	77.41 59	70.02 96	80.53 69	83.91 58	74.99 115	62.68 93	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

v1921920		69.03 112	68.32 130	69.86 97	74.03 145	80.37 91	77.55 96	60.25 128	54.62 164	53.59 117	52.36 165	51.50 177	66.75 73	77.17 134	76.69 138	86.96 89	85.56 66

MS-PatchMatch		70.17 90	70.49 88	69.79 98	80.98 66	77.97 119	77.51 97	58.95 145	62.33 91	55.22 109	53.14 151	65.90 85	62.03 110	79.08 100	77.11 117	84.08 156	77.91 151

tpmp4_e23		68.32 119	67.08 144	69.76 99	77.86 85	75.22 159	78.37 89	56.17 170	66.06 70	64.27 64	57.15 103	54.89 137	63.40 103	70.97 181	68.29 191	78.46 181	77.00 160

FC-MVSNet-train		72.60 71	75.07 69	69.71 100	81.10 65	78.79 106	73.74 132	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

v1240		68.64 117	67.89 136	69.51 101	73.89 147	80.26 94	76.73 103	59.97 136	53.43 173	53.08 120	51.82 168	50.84 180	66.62 75	76.79 139	76.77 121	86.78 103	85.34 73

MVSTER		72.06 72	74.24 70	69.51 101	70.39 173	75.97 149	76.91 102	57.36 163	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

CHOSEN 1792x2688		69.20 111	69.26 111	69.13 103	76.86 100	78.93 101	77.27 100	60.12 134	61.86 95	54.42 110	42.54 194	61.61 96	66.91 72	78.55 105	78.14 98	79.23 179	83.23 101

PatchMatch-RL		67.78 129	66.65 149	69.10 104	73.01 152	72.69 169	68.49 160	61.85 106	62.93 89	60.20 76	56.83 105	50.42 182	69.52 58	75.62 150	74.46 161	81.51 167	73.62 181

CANet_DTU		73.29 66	76.96 63	69.00 105	77.04 99	82.06 70	79.49 63	56.30 168	67.85 64	53.29 119	71.12 46	70.37 68	61.81 115	81.59 61	80.96 59	86.09 118	84.73 84

DWT-MVSNet_training		67.24 139	65.96 156	68.74 106	76.15 106	74.36 165	74.37 122	56.66 166	61.82 96	60.51 73	58.23 97	49.76 186	65.07 96	70.04 189	70.39 176	79.70 176	77.11 158

UniMVSNet_NR-MVSNet		70.59 81	72.19 79	68.72 107	77.72 92	80.72 82	73.81 130	69.65 38	61.99 93	43.23 169	60.54 78	57.50 108	58.57 129	79.56 94	81.07 58	89.34 40	83.97 89

COLMAP_ROB	62.73 15	67.66 131	66.76 148	68.70 108	80.49 70	77.98 117	75.29 108	62.95 83	63.62 84	49.96 139	47.32 187	50.72 181	58.57 129	76.87 138	75.50 153	84.94 148	75.33 171
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
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	54.63 160	84.51 40	83.58 44	89.48 38	86.34 62

pmmvs4		67.89 126	67.39 141	68.48 110	71.60 167	73.57 167	74.45 118	60.98 114	64.65 78	57.97 87	54.95 121	51.73 175	61.88 112	73.78 160	75.11 157	83.99 158	77.91 151

v148		67.85 127	67.53 137	68.23 111	73.25 151	77.57 125	74.26 126	57.36 163	55.70 154	57.45 89	53.53 144	55.42 131	61.96 111	75.23 152	73.92 162	85.08 144	81.32 124

CostFormer		68.92 113	69.58 101	68.15 112	75.98 110	76.17 148	78.22 93	51.86 184	65.80 71	61.56 71	63.57 71	62.83 93	61.85 113	70.40 188	68.67 186	79.42 177	79.62 141

DU-MVS		69.63 99	70.91 85	68.13 113	75.99 108	79.54 96	73.81 130	69.20 43	61.20 101	43.23 169	58.52 89	53.50 149	58.57 129	79.22 98	80.45 73	87.97 60	83.97 89

GBi-Net		70.78 78	73.37 74	67.76 114	72.95 153	78.00 114	75.15 110	62.72 88	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	72.95 153	78.00 114	75.15 110	62.72 88	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

V42		68.76 116	69.63 100	67.74 116	64.93 194	78.01 113	78.30 90	56.48 167	58.65 120	56.30 103	54.26 134	57.03 117	64.85 98	77.47 126	77.01 118	85.60 137	84.96 81

FMVSNet2		70.39 83	72.67 78	67.72 117	72.95 153	78.00 114	75.15 110	62.69 92	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

FMVSNet3		70.49 82	72.90 76	67.67 118	72.88 156	77.98 117	74.96 116	62.72 88	64.13 81	51.44 127	58.37 92	69.02 74	57.43 138	79.43 96	79.57 81	86.59 111	81.81 121

EG-PatchMatch MVS		67.24 139	66.94 145	67.60 119	78.73 78	81.35 73	73.28 138	59.49 139	46.89 199	51.42 130	43.65 191	53.49 150	55.50 156	81.38 65	80.66 69	87.15 76	81.17 125

Vis-MVSNet		72.77 70	77.20 61	67.59 120	74.19 142	84.01 57	76.61 105	61.69 108	60.62 105	50.61 135	70.25 49	71.31 63	55.57 155	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
TranMVSNet+NR-MVSNet		69.25 110	70.81 86	67.43 121	77.23 98	79.46 98	73.48 136	69.66 37	60.43 106	39.56 181	58.82 88	53.48 151	55.74 153	79.59 92	81.21 57	88.89 48	82.70 110

tfpn200view9		68.11 121	68.72 122	67.40 122	77.83 87	78.93 101	74.28 123	62.81 84	56.64 140	46.82 152	52.65 160	53.47 152	56.59 144	80.41 76	78.43 91	86.11 116	80.52 132

conf200view11		68.11 121	68.72 122	67.39 123	77.83 87	78.93 101	74.28 123	62.81 84	56.64 140	46.70 154	52.65 160	53.47 152	56.59 144	80.41 76	78.43 91	86.11 116	80.53 131

TDRefinement		66.09 145	65.03 168	67.31 124	69.73 177	76.75 138	75.33 106	64.55 72	60.28 107	49.72 142	45.63 189	42.83 203	60.46 123	75.75 147	75.95 148	84.08 156	78.04 150

thres200		67.98 124	68.55 127	67.30 125	77.89 84	78.86 104	74.18 127	62.75 86	56.35 147	46.48 156	52.98 154	53.54 148	56.46 146	80.41 76	77.97 99	86.05 121	79.78 140

tpm cat1		65.41 147	63.81 176	67.28 126	75.61 114	72.88 168	75.32 107	52.85 178	62.97 88	63.66 67	53.24 149	53.29 158	61.83 114	65.54 200	64.14 203	74.43 197	74.60 173

v7n		67.05 141	66.94 145	67.17 127	72.35 158	78.97 100	73.26 139	58.88 146	51.16 184	50.90 132	48.21 180	50.11 184	60.96 118	77.70 116	77.38 112	86.68 108	85.05 79

thres400		67.95 125	68.62 126	67.17 127	77.90 82	78.59 109	74.27 125	62.72 88	56.34 148	45.77 160	53.00 153	53.35 156	56.46 146	80.21 85	78.43 91	85.91 130	80.43 133

thres100view900		67.60 135	68.02 133	67.12 129	77.83 87	77.75 121	73.90 128	62.52 98	56.64 140	46.82 152	52.65 160	53.47 152	55.92 150	78.77 103	77.62 106	85.72 135	79.23 144

UGNet		72.78 69	77.67 55	67.07 130	71.65 165	83.24 63	75.20 109	63.62 76	64.93 76	56.72 99	71.82 44	73.30 55	49.02 175	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
Fast-Effi-MVS+-dtu		68.34 118	69.47 103	67.01 131	75.15 116	77.97 119	77.12 101	55.40 171	57.87 122	46.68 155	56.17 107	60.39 98	62.36 108	76.32 145	76.25 142	85.35 141	81.34 123

FMVSNet1		68.84 114	70.47 89	66.94 132	71.35 170	77.68 122	74.71 117	62.35 102	56.93 136	49.94 140	50.01 175	64.59 88	57.07 141	81.33 66	80.72 63	86.25 114	82.00 117

GA-MVS		68.14 120	69.17 112	66.93 133	73.77 148	78.50 110	74.45 118	58.28 158	55.11 160	48.44 145	60.08 80	53.99 145	61.50 116	78.43 106	77.57 107	85.13 143	80.54 130

thres600view7		67.68 130	68.43 128	66.80 134	77.90 82	78.86 104	73.84 129	62.75 86	56.07 150	44.70 166	52.85 157	52.81 161	55.58 154	80.41 76	77.77 102	86.05 121	80.28 134

view600		67.63 134	68.36 129	66.77 135	77.84 86	78.66 107	73.74 132	62.62 95	56.04 151	44.98 163	52.86 156	52.83 160	55.48 157	80.36 81	77.75 103	85.95 129	80.02 137

UniMVSNet (Re)		69.53 104	71.90 80	66.76 136	76.42 102	80.93 78	72.59 142	68.03 50	61.75 97	41.68 177	58.34 95	57.23 116	53.27 167	79.53 95	80.62 71	88.57 52	84.90 82

USDC		67.36 137	67.90 135	66.74 137	71.72 163	75.23 157	71.58 149	60.28 127	67.45 65	50.54 136	60.93 76	45.20 200	62.08 109	76.56 143	74.50 160	84.25 155	75.38 170

NR-MVSNet		68.79 115	70.56 87	66.71 138	77.48 95	79.54 96	73.52 135	69.20 43	61.20 101	39.76 180	58.52 89	50.11 184	51.37 171	80.26 84	80.71 67	88.97 46	83.59 96

view800		67.35 138	68.22 132	66.35 139	77.83 87	78.62 108	72.97 140	62.58 96	55.71 153	44.13 167	52.69 159	52.24 170	54.58 162	80.27 83	78.19 96	86.01 124	79.79 139

Baseline_NR-MVSNet		67.53 136	68.77 120	66.09 140	75.99 108	74.75 162	72.43 143	68.41 46	61.33 100	38.33 184	51.31 170	54.13 144	56.03 149	79.22 98	78.19 96	85.37 140	82.45 112

tfpn		66.58 142	67.18 142	65.88 141	77.82 91	78.45 111	72.07 145	62.52 98	55.35 157	43.21 171	52.54 164	46.12 197	53.68 163	80.02 87	78.23 95	85.99 127	79.55 142

conf0.05thres1000		66.26 144	66.77 147	65.66 142	77.45 96	78.10 112	71.85 148	62.44 101	51.47 183	43.00 172	47.92 182	51.66 176	53.40 165	79.71 90	77.97 99	85.82 131	80.56 129

dps		64.00 163	62.99 179	65.18 143	73.29 150	72.07 171	68.98 159	53.07 177	57.74 126	58.41 82	55.55 110	47.74 193	60.89 121	69.53 191	67.14 195	76.44 189	71.19 189

CDS-MVSNet		67.65 132	69.83 98	65.09 144	75.39 115	76.55 140	74.42 121	63.75 75	53.55 172	49.37 143	59.41 85	62.45 94	44.44 187	79.71 90	79.82 76	83.17 162	77.36 155
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpnnormal		64.27 159	63.64 177	65.02 145	75.84 111	75.61 151	71.24 151	62.52 98	47.79 196	42.97 173	42.65 193	44.49 201	52.66 169	78.77 103	76.86 120	84.88 149	79.29 143

v748		65.12 151	65.24 163	64.98 146	69.77 176	76.45 141	69.47 156	57.06 165	49.93 189	50.70 133	47.87 183	49.50 188	57.14 140	73.64 162	75.18 156	85.75 134	84.14 88

TinyColmap		62.84 168	61.03 192	64.96 147	69.61 178	71.69 172	68.48 161	59.76 138	55.41 156	47.69 150	47.33 186	34.20 213	62.76 107	74.52 155	72.59 169	81.44 168	71.47 188

pmmvs-eth3d		63.52 165	62.44 186	64.77 148	66.82 188	70.12 177	69.41 157	59.48 140	54.34 168	52.71 121	46.24 188	44.35 202	56.93 142	72.37 165	73.77 163	83.30 160	75.91 164

EPNet_dtu		68.08 123	71.00 84	64.67 149	79.64 72	68.62 183	75.05 114	63.30 78	66.36 67	45.27 162	67.40 61	66.84 83	43.64 189	75.37 151	74.98 159	81.15 169	77.44 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test-LLR		64.42 156	64.36 172	64.49 150	75.02 118	63.93 197	66.61 174	61.96 104	54.41 165	47.77 148	57.46 100	60.25 99	55.20 158	70.80 182	69.33 181	80.40 173	74.38 175

IterMVS		66.36 143	68.30 131	64.10 151	69.48 180	74.61 163	73.41 137	50.79 190	57.30 130	48.28 146	60.64 77	59.92 101	60.85 122	74.14 158	72.66 168	81.80 166	78.82 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v52		65.23 149	66.24 151	64.06 152	61.94 198	76.42 142	72.06 146	54.30 173	49.94 188	50.04 138	47.41 185	52.42 164	60.23 125	75.71 148	76.22 143	85.78 132	85.56 66

V4		65.23 149	66.23 152	64.06 152	61.94 198	76.42 142	72.05 147	54.31 172	49.91 190	50.06 137	47.42 184	52.40 165	60.24 124	75.71 148	76.22 143	85.78 132	85.56 66

CR-MVSNet		64.83 154	65.54 161	64.01 154	70.64 172	69.41 178	65.97 177	52.74 179	57.81 124	52.65 122	54.27 132	56.31 122	60.92 119	72.20 170	73.09 166	81.12 170	75.69 167

PatchmatchNet		64.21 162	64.65 170	63.69 155	71.29 171	68.66 182	69.63 155	51.70 186	63.04 87	53.77 116	59.83 84	58.34 106	60.23 125	68.54 195	66.06 198	75.56 192	68.08 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TransMVSNet (Re)		64.74 155	65.66 160	63.66 156	77.40 97	75.33 154	69.86 153	62.67 94	47.63 197	41.21 178	50.01 175	52.33 166	45.31 186	79.57 93	77.69 105	85.49 138	77.07 159

pm-mvs1		65.62 146	67.42 139	63.53 157	73.66 149	76.39 144	69.66 154	60.87 116	49.73 191	43.97 168	51.24 171	57.00 118	48.16 176	79.89 88	77.84 101	84.85 151	79.82 138

RPSCF		67.64 133	71.25 83	63.43 158	61.86 200	70.73 175	67.26 168	50.86 189	74.20 53	58.91 79	67.49 60	69.33 71	64.10 100	71.41 174	68.45 190	77.61 183	77.17 156

MDTV_nov1_ep13		64.37 157	65.24 163	63.37 159	68.94 182	70.81 174	72.40 144	50.29 193	60.10 108	53.91 115	60.07 81	59.15 104	57.21 139	69.43 192	67.30 193	77.47 184	69.78 192

tfpn_ndepth		65.09 152	67.12 143	62.73 160	75.75 113	76.23 146	68.00 162	60.36 123	58.16 121	40.27 179	54.89 122	54.22 141	46.80 182	76.69 142	75.66 150	85.19 142	73.98 179

CMPMVS	47.78 17	62.49 172	62.52 184	62.46 161	70.01 175	70.66 176	62.97 190	51.84 185	51.98 179	56.71 100	42.87 192	53.62 146	57.80 134	72.23 168	70.37 177	75.45 194	75.91 164
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tfpnview11		64.33 158	66.17 153	62.18 162	76.25 103	75.23 157	67.45 165	61.16 110	55.50 155	36.38 189	55.35 112	51.89 172	46.96 178	77.28 132	76.10 147	84.86 150	71.85 187

anonymousdsp		65.28 148	67.98 134	62.13 163	58.73 208	73.98 166	67.10 170	50.69 191	48.41 194	47.66 151	54.27 132	52.75 163	61.45 117	76.71 141	80.20 75	87.13 80	89.53 45

tfpn_n400		64.23 160	66.05 154	62.12 164	76.20 104	75.24 155	67.43 166	61.15 111	54.04 170	36.38 189	55.35 112	51.89 172	46.94 179	77.31 130	76.15 145	84.59 152	72.36 184

tfpnconf		64.23 160	66.05 154	62.12 164	76.20 104	75.24 155	67.43 166	61.15 111	54.04 170	36.38 189	55.35 112	51.89 172	46.94 179	77.31 130	76.15 145	84.59 152	72.36 184

pmmvs6		62.41 173	62.88 180	61.87 166	71.38 169	75.18 161	67.76 164	59.45 141	41.64 207	42.52 176	37.33 202	52.91 159	46.87 181	77.67 118	76.26 141	83.23 161	79.18 145

tpmrst		62.00 177	62.35 187	61.58 167	71.62 166	64.14 196	69.07 158	48.22 202	62.21 92	53.93 114	58.26 96	55.30 133	55.81 152	63.22 205	62.62 206	70.85 207	70.70 190

tpm		62.41 173	63.15 178	61.55 168	72.24 159	63.79 199	71.31 150	46.12 206	57.82 123	55.33 107	59.90 83	54.74 138	53.63 164	67.24 198	64.29 201	70.65 208	74.25 177

Vis-MVSNet (Re-imp)		67.83 128	73.52 72	61.19 169	78.37 80	76.72 139	66.80 172	62.96 82	65.50 73	34.17 195	67.19 62	69.68 70	39.20 198	79.39 97	79.44 85	85.68 136	76.73 162

SixPastTwentyTwo		61.84 180	62.45 185	61.12 170	69.20 181	72.20 170	62.03 193	57.40 162	46.54 200	38.03 186	57.14 104	41.72 205	58.12 133	69.67 190	71.58 172	81.94 165	78.30 149

tfpn1000		63.81 164	66.31 150	60.90 171	75.76 112	75.74 150	65.14 181	60.14 133	56.47 144	35.99 192	55.11 115	52.30 168	43.42 190	76.21 146	75.34 154	84.97 147	73.01 183

LTVRE_ROB	59.44 16	61.82 182	62.64 183	60.87 172	72.83 157	77.19 126	64.37 185	58.97 144	33.56 220	28.00 203	52.59 163	42.21 204	63.93 101	74.52 155	76.28 140	77.15 186	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
pmmvs5		62.37 176	64.04 174	60.42 173	65.03 192	71.67 173	67.17 169	52.70 181	50.30 185	44.80 164	54.23 136	51.19 179	49.37 174	72.88 164	73.48 165	83.45 159	74.55 174

RPMNet		61.71 183	62.88 180	60.34 174	69.51 179	69.41 178	63.48 188	49.23 194	57.81 124	45.64 161	50.51 173	50.12 183	53.13 168	68.17 197	68.49 189	81.07 171	75.62 169

PMMVS		65.06 153	69.17 112	60.26 175	55.25 217	63.43 200	66.71 173	43.01 215	62.41 90	50.64 134	69.44 50	67.04 82	63.29 104	74.36 157	73.54 164	82.68 163	73.99 178

PM-MVS		60.48 186	60.94 193	59.94 176	58.85 207	66.83 189	64.27 186	51.39 187	55.03 162	48.03 147	50.00 177	40.79 207	58.26 132	69.20 193	67.13 196	78.84 180	77.60 153

MDTV_nov1_ep13_2view		60.16 187	60.51 194	59.75 177	65.39 191	69.05 181	68.00 162	48.29 200	51.99 178	45.95 159	48.01 181	49.64 187	53.39 166	68.83 194	66.52 197	77.47 184	69.55 193

PEN-MVS		62.96 167	65.77 159	59.70 178	73.98 146	75.45 152	63.39 189	67.61 53	52.49 176	25.49 206	53.39 145	49.12 189	40.85 196	71.94 172	77.26 115	86.86 95	80.72 128

gg-mvs-nofinetune		62.55 170	65.05 167	59.62 179	78.72 79	77.61 123	70.83 152	53.63 174	39.71 211	22.04 216	36.36 204	64.32 89	47.53 177	81.16 70	79.03 87	85.00 146	77.17 156

PatchT		61.97 178	64.04 174	59.55 180	60.49 202	67.40 186	56.54 203	48.65 198	56.69 139	52.65 122	51.10 172	52.14 171	60.92 119	72.20 170	73.09 166	78.03 182	75.69 167

CP-MVSNet		62.68 169	65.49 162	59.40 181	71.84 161	75.34 153	62.87 191	67.04 56	52.64 175	27.19 204	53.38 146	48.15 191	41.40 194	71.26 175	75.68 149	86.07 119	82.00 117

PS-CasMVS		62.38 175	65.06 166	59.25 182	71.73 162	75.21 160	62.77 192	66.99 57	51.94 181	26.96 205	52.00 167	47.52 194	41.06 195	71.16 178	75.60 151	85.97 128	81.97 119

CVMVSNet		62.55 170	65.89 157	58.64 183	66.95 186	69.15 180	66.49 176	56.29 169	52.46 177	32.70 196	59.27 86	58.21 107	50.09 173	71.77 173	71.39 173	79.31 178	78.99 146

DTE-MVSNet		61.85 179	64.96 169	58.22 184	74.32 141	74.39 164	61.01 195	67.85 52	51.76 182	21.91 217	53.28 147	48.17 190	37.74 199	72.22 169	76.44 139	86.52 113	78.49 148

WR-MVS		63.03 166	67.40 140	57.92 185	75.14 117	77.60 124	60.56 196	66.10 60	54.11 169	23.88 207	53.94 142	53.58 147	34.50 203	73.93 159	77.71 104	87.35 72	80.94 126

EPMVS		60.00 188	61.97 188	57.71 186	68.46 183	63.17 203	64.54 184	48.23 201	63.30 85	44.72 165	60.19 79	56.05 130	50.85 172	65.27 202	62.02 208	69.44 210	63.81 203

TESTMET0.1,1		61.10 184	64.36 172	57.29 187	57.53 210	63.93 197	66.61 174	36.22 221	54.41 165	47.77 148	57.46 100	60.25 99	55.20 158	70.80 182	69.33 181	80.40 173	74.38 175

WR-MVS_H		61.83 181	65.87 158	57.12 188	71.72 163	76.87 137	61.45 194	66.19 58	51.97 180	22.92 214	53.13 152	52.30 168	33.80 204	71.03 179	75.00 158	86.65 109	80.78 127

MVS-HIRNet		54.41 199	52.10 206	57.11 189	58.99 206	56.10 212	49.68 213	49.10 195	46.18 201	52.15 126	33.18 211	46.11 198	56.10 148	63.19 206	59.70 214	76.64 188	60.25 210

test-mter		60.84 185	64.62 171	56.42 190	55.99 215	64.18 195	65.39 179	34.23 223	54.39 167	46.21 157	57.40 102	59.49 103	55.86 151	71.02 180	69.65 179	80.87 172	76.20 163

gm-plane-assit		57.00 194	57.62 200	56.28 191	76.10 107	62.43 207	47.62 216	46.57 204	33.84 219	23.24 210	37.52 201	40.19 208	59.61 127	79.81 89	77.55 108	84.55 154	72.03 186

TAMVS		59.58 189	62.81 182	55.81 192	66.03 190	65.64 194	63.86 187	48.74 197	49.95 187	37.07 188	54.77 125	58.54 105	44.44 187	72.29 167	71.79 170	74.70 196	66.66 198

test0.0.03 1		58.80 190	61.58 190	55.56 193	75.02 118	68.45 184	59.58 200	61.96 104	52.74 174	29.57 199	49.75 178	54.56 139	31.46 206	71.19 176	69.77 178	75.75 190	64.57 201

MIMVSNet		58.52 192	61.34 191	55.22 194	60.76 201	67.01 188	66.81 171	49.02 196	56.43 145	38.90 183	40.59 199	54.54 140	40.57 197	73.16 163	71.65 171	75.30 195	66.00 199

CHOSEN 280x420		58.70 191	61.88 189	54.98 195	55.45 216	50.55 220	64.92 182	40.36 217	55.21 158	38.13 185	48.31 179	63.76 90	63.03 106	73.73 161	68.58 188	68.00 213	73.04 182

Anonymous20231206		56.36 196	57.80 199	54.67 196	70.08 174	66.39 191	60.46 197	57.54 160	49.50 193	29.30 200	33.86 210	46.64 195	35.18 202	70.44 186	68.88 185	75.47 193	68.88 195

FPMVS		51.87 204	50.00 209	54.07 197	66.83 187	57.25 210	60.25 198	50.91 188	50.25 186	34.36 194	36.04 207	32.02 215	41.49 193	58.98 217	56.07 217	70.56 209	59.36 212

FMVSNet5		57.24 193	60.02 195	53.99 198	56.45 212	62.74 204	65.27 180	47.03 203	55.14 159	39.55 182	40.88 197	53.42 155	41.83 191	72.35 166	71.10 175	73.79 199	64.50 202

LP		53.62 202	53.43 202	53.83 199	58.51 209	62.59 206	57.31 202	46.04 207	47.86 195	42.69 175	36.08 206	36.86 211	46.53 183	64.38 203	64.25 202	71.92 204	62.00 208

MDA-MVSNet-bldmvs		53.37 203	53.01 205	53.79 200	43.67 225	67.95 185	59.69 199	57.92 159	43.69 203	32.41 197	41.47 195	27.89 223	52.38 170	56.97 219	65.99 199	76.68 187	67.13 197

ADS-MVSNet		55.94 197	58.01 197	53.54 201	62.48 197	58.48 209	59.12 201	46.20 205	59.65 112	42.88 174	52.34 166	53.31 157	46.31 184	62.00 209	60.02 213	64.23 219	60.24 211

Anonymous20231211		51.46 205	50.59 207	52.46 202	67.30 184	66.70 190	55.00 205	59.22 142	29.96 222	17.62 222	19.11 224	28.74 222	35.72 201	66.42 199	69.52 180	79.92 175	73.71 180

test20.03		53.93 201	56.28 201	51.19 203	72.19 160	65.83 192	53.20 208	61.08 113	42.74 205	22.08 215	37.07 203	45.76 199	24.29 219	70.44 186	69.04 183	74.31 198	63.05 205

testgi		54.39 200	57.86 198	50.35 204	71.59 168	67.24 187	54.95 206	53.25 176	43.36 204	23.78 208	44.64 190	47.87 192	24.96 215	70.45 185	68.66 187	73.60 200	62.78 206

EU-MVSNet		54.63 198	58.69 196	49.90 205	56.99 211	62.70 205	56.41 204	50.64 192	45.95 202	23.14 211	50.42 174	46.51 196	36.63 200	65.51 201	64.85 200	75.57 191	74.91 172

PMVS	39.38 18	46.06 213	43.30 218	49.28 206	62.93 195	38.75 226	41.88 219	53.50 175	33.33 221	35.46 193	28.90 215	31.01 218	33.04 205	58.61 218	54.63 220	68.86 211	57.88 215
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FC-MVSNet-test		56.90 195	65.20 165	47.21 207	66.98 185	63.20 202	49.11 214	58.60 156	59.38 114	11.50 227	65.60 65	56.68 119	24.66 218	71.17 177	71.36 174	72.38 203	69.02 194

testpf		47.41 208	48.47 214	46.18 208	66.30 189	50.67 219	48.15 215	42.60 216	37.10 215	28.75 201	40.97 196	39.01 210	30.82 207	52.95 222	53.74 221	60.46 220	64.87 200

pmmvs3		47.65 207	49.08 211	45.99 209	44.61 222	54.79 215	50.04 211	31.95 226	33.91 218	29.90 198	30.37 212	33.53 214	46.31 184	63.50 204	63.67 204	73.14 202	63.77 204

test2356		47.20 210	48.62 213	45.54 210	56.38 213	54.89 214	50.62 210	45.08 210	38.65 212	23.40 209	36.23 205	31.10 217	29.31 209	62.76 207	62.49 207	68.48 212	54.23 218

MIMVSNet1		49.27 206	53.25 204	44.62 211	44.61 222	61.52 208	53.61 207	52.18 182	41.62 208	18.68 219	28.14 218	41.58 206	25.50 213	68.46 196	69.04 183	73.15 201	62.37 207

N_pmnet		47.35 209	50.13 208	44.11 212	59.98 203	51.64 218	51.86 209	44.80 211	49.58 192	20.76 218	40.65 198	40.05 209	29.64 208	59.84 215	55.15 218	57.63 221	54.00 219

new-patchmatchnet		46.97 211	49.47 210	44.05 213	62.82 196	56.55 211	45.35 217	52.01 183	42.47 206	17.04 223	35.73 208	35.21 212	21.84 224	61.27 210	54.83 219	65.26 218	60.26 209

1111		43.08 215	44.02 217	41.98 214	59.22 204	49.27 222	41.48 220	45.63 208	35.01 216	23.06 212	28.60 216	30.15 219	27.22 210	60.42 213	57.97 215	55.27 224	46.74 221

testus		45.61 214	49.06 212	41.59 215	56.13 214	55.28 213	43.51 218	39.64 219	37.74 213	18.23 220	35.52 209	31.28 216	24.69 217	62.46 208	62.90 205	67.33 214	58.26 214

testmv		42.58 216	44.36 215	40.49 216	54.63 218	52.76 216	41.21 222	44.37 212	28.83 223	12.87 224	27.16 219	25.03 224	23.01 220	60.83 211	61.13 209	66.88 215	54.81 216

test1235678		42.57 217	44.36 215	40.49 216	54.63 218	52.75 217	41.21 222	44.37 212	28.82 224	12.87 224	27.15 220	25.01 225	23.01 220	60.83 211	61.13 209	66.88 215	54.81 216

Gipuma		36.38 219	35.80 222	37.07 218	45.76 221	33.90 227	29.81 226	48.47 199	39.91 210	18.02 221	8.00 230	8.14 232	25.14 214	59.29 216	61.02 211	55.19 225	40.31 223
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		36.35 220	37.59 221	34.91 219	46.13 220	49.89 221	27.99 227	43.56 214	20.91 228	7.03 230	14.64 226	15.50 230	18.92 225	42.95 223	60.20 212	65.84 217	59.03 213

new_pmnet		38.40 218	42.64 219	33.44 220	37.54 228	45.00 224	36.60 224	32.72 225	40.27 209	12.72 226	29.89 213	28.90 221	24.78 216	53.17 221	52.90 222	56.31 222	48.34 220

.test1245		30.81 222	29.14 224	32.77 221	59.22 204	49.27 222	41.48 220	45.63 208	35.01 216	23.06 212	28.60 216	30.15 219	27.22 210	60.42 213	0.10 228	0.01 232	0.43 230

test12356		35.10 221	38.50 220	31.13 222	44.14 224	43.70 225	32.27 225	34.42 222	26.51 226	9.47 228	25.22 222	20.34 226	10.86 227	53.47 220	56.15 216	55.59 223	44.11 222

E-PMN		21.77 224	18.24 226	25.89 223	40.22 226	19.58 230	12.46 231	39.87 218	18.68 230	6.71 231	9.57 227	4.31 235	22.36 223	19.89 228	27.28 226	33.73 227	28.34 227

EMVS		20.98 225	17.15 227	25.44 224	39.51 227	19.37 231	12.66 230	39.59 220	19.10 229	6.62 232	9.27 228	4.40 234	22.43 222	17.99 229	24.40 227	31.81 228	25.53 228

GG-mvs-BLEND		46.86 212	67.51 138	22.75 225	0.05 232	76.21 147	64.69 183	0.04 230	61.90 94	0.09 235	55.57 109	71.32 62	0.08 230	70.54 184	67.19 194	71.58 205	69.86 191

PMMVS2		25.60 223	29.75 223	20.76 226	28.00 229	30.93 228	23.10 228	29.18 227	23.14 227	1.46 234	18.23 225	16.54 228	5.08 228	40.22 224	41.40 224	37.76 226	37.79 225

MVE	19.12 19	20.47 226	23.27 225	17.20 227	12.66 231	25.41 229	10.52 232	34.14 224	14.79 231	6.53 233	8.79 229	4.68 233	16.64 226	29.49 226	41.63 223	22.73 230	38.11 224
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				14.50 228	14.68 230	7.17 233	10.46 233	2.21 229	37.73 214	28.71 202	25.26 221	16.98 227	4.37 229	31.49 225	29.77 225	26.56 229

testmvs		0.09 227	0.15 228	0.02 229	0.01 233	0.02 234	0.05 235	0.01 231	0.11 232	0.01 236	0.26 232	0.01 236	0.06 232	0.10 230	0.10 228	0.01 232	0.43 230

test123		0.09 227	0.14 229	0.02 229	0.00 234	0.02 234	0.02 236	0.01 231	0.09 233	0.00 237	0.30 231	0.00 237	0.08 230	0.03 231	0.09 230	0.01 232	0.45 229

test_full		0.00 229	0.00 230	0.00 231	0.00 234	0.00 236	0.00 237	0.00 233	0.00 234	0.00 237	0.00 233	0.00 237	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	0.00 234	0.00 236	0.00 237	0.00 233	0.00 234	0.00 237	0.00 233	0.00 237	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	0.00 234	0.00 236	0.00 237	0.00 233	0.00 234	0.00 237	0.00 233	0.00 237	0.00 233	0.00 232	0.00 231	0.00 235	0.00 232

ambc			53.42 203		64.99 193	63.36 201	49.96 212		47.07 198	37.12 187	28.97 214	16.36 229	41.82 192	75.10 154	67.34 192	71.55 206	75.72 166

MTAPA										83.48 1		86.45 11

MTMP										82.66 3		84.91 19

Patchmatch-RL test							2.85 234

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

mPP-MVS					89.90 19							81.29 34

NP-MVS									80.10 39

Patchmtry						65.80 193	65.97 177	52.74 179		52.65 122

DeepMVS_CX						18.74 232	18.55 229	8.02 228	26.96 225	7.33 229	23.81 223	13.05 231	25.99 212	25.17 227		22.45 231	36.25 226

High-resolution multi-view benchmark