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

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

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

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

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

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

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

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

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

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
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

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

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

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

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

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
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

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

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
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
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

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

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.
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

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

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

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
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

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

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

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

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

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

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

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

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

NP-MVS									80.10 39

CLD-MVS		79.35 43	81.23 39	77.16 47	85.01 50	86.92 39	85.87 34	60.89 116	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
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

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

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
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
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_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

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

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
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
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
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
RPSCF		67.64 133	71.25 83	63.43 159	61.86 201	70.73 176	67.26 169	50.86 190	74.20 53	58.91 79	67.49 60	69.33 71	64.10 100	71.41 175	68.45 191	77.61 184	77.17 156

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

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

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

diffmvs		73.13 67	75.65 67	70.19 93	74.07 144	77.17 127	78.24 92	57.45 162	72.44 57	64.02 66	69.05 51	75.92 48	64.86 97	75.18 154	75.27 156	82.47 165	84.53 85

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

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
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

MVS_Test		75.37 59	77.13 62	73.31 64	79.07 76	81.32 74	79.98 58	60.12 135	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

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

CANet_DTU		73.29 66	76.96 63	69.00 105	77.04 99	82.06 70	79.49 63	56.30 169	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

USDC		67.36 137	67.90 135	66.74 137	71.72 164	75.23 157	71.58 149	60.28 128	67.45 65	50.54 136	60.93 76	45.20 201	62.08 109	76.56 144	74.50 161	84.25 156	75.38 170

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

EPNet_dtu		68.08 123	71.00 84	64.67 149	79.64 72	68.62 184	75.05 114	63.30 78	66.36 67	45.27 162	67.40 61	66.84 83	43.64 190	75.37 152	74.98 160	81.15 170	77.44 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
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	53.21 151	56.09 129	65.85 95	78.94 101	78.50 90	86.60 110	76.85 161

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

tpmp4_e23		68.32 119	67.08 144	69.76 99	77.86 85	75.22 159	78.37 89	56.17 171	66.06 70	64.27 64	57.15 103	54.89 137	63.40 103	70.97 182	68.29 192	78.46 182	77.00 160

CostFormer		68.92 113	69.58 101	68.15 112	75.98 111	76.17 148	78.22 93	51.86 185	65.80 71	61.56 71	63.57 71	62.83 93	61.85 113	70.40 189	68.67 187	79.42 178	79.62 141

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

Vis-MVSNet (Re-imp)		67.83 128	73.52 72	61.19 170	78.37 80	76.72 139	66.80 173	62.96 82	65.50 73	34.17 196	67.19 62	69.68 70	39.20 199	79.39 97	79.44 85	85.68 136	76.73 162

Fast-Effi-MVS+		73.11 68	73.66 71	72.48 66	77.72 92	80.88 81	78.55 84	58.83 154	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

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

UGNet		72.78 69	77.67 55	67.07 130	71.65 166	83.24 63	75.20 109	63.62 76	64.93 76	56.72 99	71.82 44	73.30 55	49.02 176	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
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

pmmvs4		67.89 126	67.39 141	68.48 110	71.60 168	73.57 168	74.45 118	60.98 115	64.65 78	57.97 87	54.95 122	51.73 175	61.88 112	73.78 161	75.11 158	83.99 159	77.91 151

MVSTER		72.06 72	74.24 70	69.51 101	70.39 174	75.97 149	76.91 102	57.36 164	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

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

GBi-Net		70.78 78	73.37 74	67.76 114	72.95 154	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	72.95 154	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

FMVSNet3		70.49 82	72.90 76	67.67 118	72.88 157	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

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	47.32 188	50.72 181	58.57 129	76.87 139	75.50 154	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
EPMVS		60.00 189	61.97 189	57.71 187	68.46 184	63.17 204	64.54 185	48.23 202	63.30 85	44.72 165	60.19 79	56.05 130	50.85 172	65.27 203	62.02 209	69.44 211	63.81 204

FMVSNet2		70.39 83	72.67 78	67.72 117	72.95 154	78.00 114	75.15 110	62.69 92	63.29 86	51.25 131	55.64 109	68.49 80	57.59 135	80.91 74	80.35 74	86.70 105	82.02 114

PatchmatchNet		64.21 163	64.65 171	63.69 155	71.29 172	68.66 183	69.63 155	51.70 187	63.04 87	53.77 116	59.83 84	58.34 106	60.23 125	68.54 196	66.06 199	75.56 193	68.08 197
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		65.41 147	63.81 177	67.28 126	75.61 115	72.88 169	75.32 107	52.85 179	62.97 88	63.66 67	53.24 150	53.29 158	61.83 114	65.54 201	64.14 204	74.43 198	74.60 173

PatchMatch-RL		67.78 129	66.65 149	69.10 104	73.01 153	72.69 170	68.49 161	61.85 106	62.93 89	60.20 76	56.83 105	50.42 182	69.52 58	75.62 151	74.46 162	81.51 168	73.62 181

PMMVS		65.06 153	69.17 112	60.26 176	55.25 218	63.43 201	66.71 174	43.01 216	62.41 90	50.64 134	69.44 50	67.04 82	63.29 104	74.36 158	73.54 165	82.68 164	73.99 178

MS-PatchMatch		70.17 90	70.49 88	69.79 98	80.98 66	77.97 119	77.51 97	58.95 146	62.33 91	55.22 109	53.14 152	65.90 85	62.03 110	79.08 100	77.11 117	84.08 157	77.91 151

tpmrst		62.00 178	62.35 188	61.58 168	71.62 167	64.14 197	69.07 159	48.22 203	62.21 92	53.93 114	58.26 96	55.30 133	55.81 152	63.22 206	62.62 207	70.85 208	70.70 191

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

GG-mvs-BLEND		46.86 213	67.51 138	22.75 226	0.05 233	76.21 147	64.69 184	0.04 231	61.90 94	0.09 236	55.57 110	71.32 62	0.08 231	70.54 185	67.19 195	71.58 206	69.86 192

CHOSEN 1792x2688		69.20 111	69.26 111	69.13 103	76.86 100	78.93 101	77.27 100	60.12 135	61.86 95	54.42 110	42.54 195	61.61 96	66.91 72	78.55 105	78.14 98	79.23 180	83.23 101

DWT-MVSNet_training		67.24 139	65.96 156	68.74 106	76.15 107	74.36 166	74.37 122	56.66 167	61.82 96	60.51 73	58.23 97	49.76 186	65.07 96	70.04 190	70.39 177	79.70 177	77.11 158

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

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	51.50 170	54.14 142	68.56 63	78.85 102	79.50 83	86.82 97	83.94 91

IterMVS-LS		71.69 74	72.82 77	70.37 90	77.54 94	76.34 145	75.13 113	60.46 123	61.53 99	57.57 88	64.89 67	67.33 81	66.04 91	77.09 137	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.
Baseline_NR-MVSNet		67.53 136	68.77 120	66.09 140	75.99 109	74.75 163	72.43 143	68.41 46	61.33 100	38.33 185	51.31 171	54.13 144	56.03 149	79.22 98	78.19 96	85.37 140	82.45 112

DU-MVS		69.63 99	70.91 85	68.13 113	75.99 109	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

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

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	76.92 138	75.55 153	87.35 72	85.46 71

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

Vis-MVSNet		72.77 70	77.20 61	67.59 120	74.19 143	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

TDRefinement		66.09 145	65.03 169	67.31 124	69.73 178	76.75 138	75.33 106	64.55 72	60.28 107	49.72 142	45.63 190	42.83 204	60.46 123	75.75 148	75.95 148	84.08 157	78.04 150

MDTV_nov1_ep13		64.37 158	65.24 164	63.37 160	68.94 183	70.81 175	72.40 144	50.29 194	60.10 108	53.91 115	60.07 81	59.15 104	57.21 139	69.43 193	67.30 194	77.47 185	69.78 193

v18		70.10 91	69.52 102	70.77 74	74.66 139	77.06 130	78.84 72	58.84 153	60.01 109	59.23 77	55.06 117	57.47 109	66.34 81	77.50 125	76.75 126	86.71 104	82.77 108

v16		70.07 92	69.46 104	70.79 73	74.74 134	77.08 129	78.79 77	58.86 148	59.75 110	59.15 78	54.87 124	57.33 111	66.38 79	77.61 119	76.77 121	86.81 102	82.79 106

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	53.26 149	59.65 102	59.16 128	76.78 141	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
ADS-MVSNet		55.94 198	58.01 198	53.54 202	62.48 198	58.48 210	59.12 202	46.20 206	59.65 112	42.88 174	52.34 167	53.31 157	46.31 185	62.00 210	60.02 214	64.23 220	60.24 212

v17		70.03 94	69.43 109	70.72 77	74.75 133	77.09 128	78.78 79	58.85 150	59.53 113	58.72 81	54.87 124	57.39 110	66.38 79	77.60 120	76.75 126	86.83 96	82.80 104

FC-MVSNet-test		56.90 196	65.20 166	47.21 208	66.98 186	63.20 203	49.11 215	58.60 157	59.38 114	11.50 228	65.60 65	56.68 119	24.66 219	71.17 178	71.36 175	72.38 204	69.02 195

HyFIR lowres test		69.47 107	68.94 114	70.09 95	76.77 101	82.93 66	76.63 104	60.17 130	59.00 115	54.03 113	40.54 201	65.23 87	67.89 65	76.54 145	78.30 94	85.03 145	80.07 136

v6		70.35 84	69.94 92	70.83 70	74.68 136	80.62 83	78.81 74	60.16 133	58.81 116	58.17 84	55.01 118	57.31 113	66.32 84	77.53 121	76.73 132	86.82 97	83.62 93

v8		70.23 88	69.86 97	70.67 79	74.69 135	79.82 95	78.79 77	59.18 144	58.80 117	58.20 83	55.00 119	57.33 111	66.31 85	77.51 124	76.71 136	86.82 97	83.88 92

v1neww		70.34 85	69.93 93	70.82 71	74.68 136	80.61 84	78.80 75	60.17 130	58.74 118	58.10 85	55.00 119	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	74.68 136	80.61 84	78.80 75	60.17 130	58.74 118	58.10 85	55.00 119	57.28 114	66.33 82	77.53 121	76.74 128	86.82 97	83.61 94

V42		68.76 116	69.63 100	67.74 116	64.93 195	78.01 113	78.30 90	56.48 168	58.65 120	56.30 103	54.26 135	57.03 117	64.85 98	77.47 126	77.01 118	85.60 137	84.96 81

tfpn_ndepth		65.09 152	67.12 143	62.73 161	75.75 114	76.23 146	68.00 163	60.36 124	58.16 121	40.27 179	54.89 123	54.22 141	46.80 183	76.69 143	75.66 150	85.19 142	73.98 179

Fast-Effi-MVS+-dtu		68.34 118	69.47 103	67.01 131	75.15 117	77.97 119	77.12 101	55.40 172	57.87 122	46.68 155	56.17 108	60.39 98	62.36 108	76.32 146	76.25 142	85.35 141	81.34 123

tpm		62.41 174	63.15 179	61.55 169	72.24 160	63.79 200	71.31 150	46.12 207	57.82 123	55.33 107	59.90 83	54.74 138	53.63 164	67.24 199	64.29 202	70.65 209	74.25 177

CR-MVSNet		64.83 154	65.54 162	64.01 154	70.64 173	69.41 179	65.97 178	52.74 180	57.81 124	52.65 122	54.27 133	56.31 122	60.92 119	72.20 171	73.09 167	81.12 171	75.69 167

RPMNet		61.71 184	62.88 181	60.34 175	69.51 180	69.41 179	63.48 189	49.23 195	57.81 124	45.64 161	50.51 174	50.12 183	53.13 168	68.17 198	68.49 190	81.07 172	75.62 169

dps		64.00 164	62.99 180	65.18 143	73.29 151	72.07 172	68.98 160	53.07 178	57.74 126	58.41 82	55.55 111	47.74 194	60.89 121	69.53 192	67.14 196	76.44 190	71.19 190

v10		70.22 89	69.76 99	70.74 75	74.79 128	80.30 93	79.22 65	59.81 138	57.71 127	56.58 102	54.22 139	55.31 132	66.95 69	78.28 108	77.47 109	87.12 83	85.07 78

v7		70.33 87	69.87 95	70.88 69	74.79 128	81.04 77	79.22 65	60.57 120	57.70 128	56.65 101	54.23 137	55.29 134	66.95 69	78.28 108	77.47 109	87.12 83	85.05 79

v2v482		70.05 93	69.46 104	70.74 75	74.62 140	80.32 92	79.00 68	60.62 119	57.41 129	56.89 94	55.43 112	55.14 135	66.39 78	77.25 133	77.14 116	86.90 90	83.57 99

IterMVS		66.36 143	68.30 131	64.10 151	69.48 181	74.61 164	73.41 137	50.79 191	57.30 130	48.28 146	60.64 77	59.92 101	60.85 122	74.14 159	72.66 169	81.80 167	78.82 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v15		69.61 100	68.88 115	70.46 85	74.81 127	77.03 133	78.75 80	58.83 154	57.06 131	57.18 90	54.55 130	56.37 120	66.13 89	77.70 116	76.76 123	87.03 87	82.69 111

thresconf0.02		64.77 155	65.90 157	63.44 158	76.37 103	75.17 162	69.51 156	61.28 110	56.98 132	39.01 183	56.24 107	48.68 190	49.78 174	77.13 135	75.61 151	84.71 152	71.53 188

v1		69.97 95	69.45 106	70.59 81	74.78 130	80.51 87	78.84 72	60.30 125	56.98 132	56.81 96	54.69 127	56.29 124	65.91 94	77.37 128	76.71 136	86.89 92	83.59 96

divwei89l23v2f112		69.97 95	69.44 107	70.58 83	74.78 130	80.50 88	78.85 70	60.30 125	56.97 134	56.75 97	54.67 129	56.27 125	65.92 93	77.37 128	76.72 133	86.88 93	83.58 98

v1141		69.96 97	69.44 107	70.58 83	74.78 130	80.50 88	78.85 70	60.30 125	56.95 135	56.74 98	54.68 128	56.26 126	65.93 92	77.38 127	76.72 133	86.88 93	83.57 99

V14		69.59 101	68.86 116	70.45 87	74.83 126	77.04 131	78.70 81	58.83 154	56.95 135	57.08 92	54.41 131	56.34 121	66.15 86	77.77 115	76.76 123	87.08 85	82.74 109

FMVSNet1		68.84 114	70.47 89	66.94 132	71.35 171	77.68 122	74.71 117	62.35 102	56.93 137	49.94 140	50.01 176	64.59 88	57.07 141	81.33 66	80.72 63	86.25 114	82.00 117

V9		69.58 102	68.83 117	70.46 85	74.85 125	77.04 131	78.65 82	58.85 150	56.83 138	57.12 91	54.26 135	56.31 122	66.14 88	77.83 114	76.76 123	87.13 80	82.79 106

v12		69.54 103	68.79 119	70.41 88	74.88 122	77.03 133	78.54 87	58.85 150	56.71 139	56.87 95	54.13 140	56.23 127	66.15 86	77.89 112	76.74 128	87.17 75	82.80 104

PatchT		61.97 179	64.04 175	59.55 181	60.49 203	67.40 187	56.54 204	48.65 199	56.69 140	52.65 122	51.10 173	52.14 171	60.92 119	72.20 171	73.09 167	78.03 183	75.69 167

conf200view11		68.11 121	68.72 122	67.39 123	77.83 87	78.93 101	74.28 123	62.81 84	56.64 141	46.70 154	52.65 161	53.47 152	56.59 144	80.41 76	78.43 91	86.11 116	80.53 131

thres100view900		67.60 135	68.02 133	67.12 129	77.83 87	77.75 121	73.90 128	62.52 98	56.64 141	46.82 152	52.65 161	53.47 152	55.92 150	78.77 103	77.62 106	85.72 135	79.23 144

tfpn200view9		68.11 121	68.72 122	67.40 122	77.83 87	78.93 101	74.28 123	62.81 84	56.64 141	46.82 152	52.65 161	53.47 152	56.59 144	80.41 76	78.43 91	86.11 116	80.52 132

v13		69.52 105	68.76 121	70.41 88	74.88 122	77.02 135	78.52 88	58.86 148	56.61 144	56.91 93	54.00 142	56.17 128	66.11 90	77.93 111	76.74 128	87.21 74	82.83 103

tfpn1000		63.81 165	66.31 150	60.90 172	75.76 113	75.74 150	65.14 182	60.14 134	56.47 145	35.99 193	55.11 116	52.30 168	43.42 191	76.21 147	75.34 155	84.97 147	73.01 183

MIMVSNet		58.52 193	61.34 192	55.22 195	60.76 202	67.01 189	66.81 172	49.02 197	56.43 146	38.90 184	40.59 200	54.54 140	40.57 198	73.16 164	71.65 172	75.30 196	66.00 200

v11		69.37 108	68.65 125	70.20 92	74.87 124	76.97 136	78.29 91	58.55 158	56.38 147	56.04 104	54.02 141	54.98 136	66.47 77	78.30 107	76.91 119	86.97 88	83.02 102

thres200		67.98 124	68.55 127	67.30 125	77.89 84	78.86 104	74.18 127	62.75 86	56.35 148	46.48 156	52.98 155	53.54 148	56.46 146	80.41 76	77.97 99	86.05 121	79.78 140

thres400		67.95 125	68.62 126	67.17 127	77.90 82	78.59 109	74.27 125	62.72 88	56.34 149	45.77 160	53.00 154	53.35 156	56.46 146	80.21 85	78.43 91	85.91 130	80.43 133

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	56.19 150	46.06 158	54.30 132	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
thres600view7		67.68 130	68.43 128	66.80 134	77.90 82	78.86 104	73.84 129	62.75 86	56.07 151	44.70 166	52.85 158	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	56.04 152	44.98 163	52.86 157	52.83 160	55.48 157	80.36 81	77.75 103	85.95 129	80.02 137

v1144		69.93 98	69.36 110	70.61 80	74.89 121	80.93 78	79.11 67	60.64 118	55.97 153	55.31 108	53.85 144	54.14 142	66.54 76	78.10 110	77.44 111	87.14 79	85.09 77

view800		67.35 138	68.22 132	66.35 139	77.83 87	78.62 108	72.97 140	62.58 96	55.71 154	44.13 167	52.69 160	52.24 170	54.58 162	80.27 83	78.19 96	86.01 124	79.79 139

v148		67.85 127	67.53 137	68.23 111	73.25 152	77.57 125	74.26 126	57.36 164	55.70 155	57.45 89	53.53 145	55.42 131	61.96 111	75.23 153	73.92 163	85.08 144	81.32 124

tfpnview11		64.33 159	66.17 153	62.18 163	76.25 104	75.23 157	67.45 166	61.16 111	55.50 156	36.38 190	55.35 113	51.89 172	46.96 179	77.28 132	76.10 147	84.86 150	71.85 187

TinyColmap		62.84 169	61.03 193	64.96 147	69.61 179	71.69 173	68.48 162	59.76 139	55.41 157	47.69 150	47.33 187	34.20 214	62.76 107	74.52 156	72.59 170	81.44 169	71.47 189

tfpn		66.58 142	67.18 142	65.88 141	77.82 91	78.45 111	72.07 145	62.52 98	55.35 158	43.21 171	52.54 165	46.12 198	53.68 163	80.02 87	78.23 95	85.99 127	79.55 142

CHOSEN 280x420		58.70 192	61.88 190	54.98 196	55.45 217	50.55 221	64.92 183	40.36 218	55.21 159	38.13 186	48.31 180	63.76 90	63.03 106	73.73 162	68.58 189	68.00 214	73.04 182

FMVSNet5		57.24 194	60.02 196	53.99 199	56.45 213	62.74 205	65.27 181	47.03 204	55.14 160	39.55 182	40.88 198	53.42 155	41.83 192	72.35 167	71.10 176	73.79 200	64.50 203

GA-MVS		68.14 120	69.17 112	66.93 133	73.77 149	78.50 110	74.45 118	58.28 159	55.11 161	48.44 145	60.08 80	53.99 145	61.50 116	78.43 106	77.57 107	85.13 143	80.54 130

v1192		69.50 106	68.83 117	70.29 91	74.49 141	80.92 80	78.55 84	60.54 121	55.04 162	54.21 111	52.79 159	52.33 166	66.92 71	77.88 113	77.35 114	87.04 86	85.51 69

PM-MVS		60.48 187	60.94 194	59.94 177	58.85 208	66.83 190	64.27 187	51.39 188	55.03 163	48.03 147	50.00 178	40.79 208	58.26 132	69.20 194	67.13 197	78.84 181	77.60 153

v144192		69.34 109	68.68 124	70.12 94	74.06 145	80.54 86	78.08 94	60.54 121	54.99 164	54.13 112	52.92 156	52.80 162	66.73 74	77.13 135	76.72 133	87.15 76	85.63 65

v1921920		69.03 112	68.32 130	69.86 97	74.03 146	80.37 91	77.55 96	60.25 129	54.62 165	53.59 117	52.36 166	51.50 177	66.75 73	77.17 134	76.69 138	86.96 89	85.56 66

test-LLR		64.42 157	64.36 173	64.49 150	75.02 119	63.93 198	66.61 175	61.96 104	54.41 166	47.77 148	57.46 100	60.25 99	55.20 158	70.80 183	69.33 182	80.40 174	74.38 175

TESTMET0.1,1		61.10 185	64.36 173	57.29 188	57.53 211	63.93 198	66.61 175	36.22 222	54.41 166	47.77 148	57.46 100	60.25 99	55.20 158	70.80 183	69.33 182	80.40 174	74.38 175

test-mter		60.84 186	64.62 172	56.42 191	55.99 216	64.18 196	65.39 180	34.23 224	54.39 168	46.21 157	57.40 102	59.49 103	55.86 151	71.02 181	69.65 180	80.87 173	76.20 163

pmmvs-eth3d		63.52 166	62.44 187	64.77 148	66.82 189	70.12 178	69.41 158	59.48 141	54.34 169	52.71 121	46.24 189	44.35 203	56.93 142	72.37 166	73.77 164	83.30 161	75.91 164

WR-MVS		63.03 167	67.40 140	57.92 186	75.14 118	77.60 124	60.56 197	66.10 60	54.11 170	23.88 208	53.94 143	53.58 147	34.50 204	73.93 160	77.71 104	87.35 72	80.94 126

tfpn_n400		64.23 161	66.05 154	62.12 165	76.20 105	75.24 155	67.43 167	61.15 112	54.04 171	36.38 190	55.35 113	51.89 172	46.94 180	77.31 130	76.15 145	84.59 153	72.36 184

tfpnconf		64.23 161	66.05 154	62.12 165	76.20 105	75.24 155	67.43 167	61.15 112	54.04 171	36.38 190	55.35 113	51.89 172	46.94 180	77.31 130	76.15 145	84.59 153	72.36 184

CDS-MVSNet		67.65 132	69.83 98	65.09 144	75.39 116	76.55 140	74.42 121	63.75 75	53.55 173	49.37 143	59.41 85	62.45 94	44.44 188	79.71 90	79.82 76	83.17 163	77.36 155
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v1240		68.64 117	67.89 136	69.51 101	73.89 148	80.26 94	76.73 103	59.97 137	53.43 174	53.08 120	51.82 169	50.84 180	66.62 75	76.79 140	76.77 121	86.78 103	85.34 73

test0.0.03 1		58.80 191	61.58 191	55.56 194	75.02 119	68.45 185	59.58 201	61.96 104	52.74 175	29.57 200	49.75 179	54.56 139	31.46 207	71.19 177	69.77 179	75.75 191	64.57 202

CP-MVSNet		62.68 170	65.49 163	59.40 182	71.84 162	75.34 153	62.87 192	67.04 56	52.64 176	27.19 205	53.38 147	48.15 192	41.40 195	71.26 176	75.68 149	86.07 119	82.00 117

PEN-MVS		62.96 168	65.77 160	59.70 179	73.98 147	75.45 152	63.39 190	67.61 53	52.49 177	25.49 207	53.39 146	49.12 189	40.85 197	71.94 173	77.26 115	86.86 95	80.72 128

CVMVSNet		62.55 171	65.89 158	58.64 184	66.95 187	69.15 181	66.49 177	56.29 170	52.46 178	32.70 197	59.27 86	58.21 107	50.09 173	71.77 174	71.39 174	79.31 179	78.99 146

MDTV_nov1_ep13_2view		60.16 188	60.51 195	59.75 178	65.39 192	69.05 182	68.00 163	48.29 201	51.99 179	45.95 159	48.01 182	49.64 187	53.39 166	68.83 195	66.52 198	77.47 185	69.55 194

CMPMVS	47.78 17	62.49 173	62.52 185	62.46 162	70.01 176	70.66 177	62.97 191	51.84 186	51.98 180	56.71 100	42.87 193	53.62 146	57.80 134	72.23 169	70.37 178	75.45 195	75.91 164
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WR-MVS_H		61.83 182	65.87 159	57.12 189	71.72 164	76.87 137	61.45 195	66.19 58	51.97 181	22.92 215	53.13 153	52.30 168	33.80 205	71.03 180	75.00 159	86.65 109	80.78 127

PS-CasMVS		62.38 176	65.06 167	59.25 183	71.73 163	75.21 160	62.77 193	66.99 57	51.94 182	26.96 206	52.00 168	47.52 195	41.06 196	71.16 179	75.60 152	85.97 128	81.97 119

DTE-MVSNet		61.85 180	64.96 170	58.22 185	74.32 142	74.39 165	61.01 196	67.85 52	51.76 183	21.91 218	53.28 148	48.17 191	37.74 200	72.22 170	76.44 139	86.52 113	78.49 148

conf0.05thres1000		66.26 144	66.77 147	65.66 142	77.45 96	78.10 112	71.85 148	62.44 101	51.47 184	43.00 172	47.92 183	51.66 176	53.40 165	79.71 90	77.97 99	85.82 131	80.56 129

v7n		67.05 141	66.94 145	67.17 127	72.35 159	78.97 100	73.26 139	58.88 147	51.16 185	50.90 132	48.21 181	50.11 184	60.96 118	77.70 116	77.38 112	86.68 108	85.05 79

pmmvs5		62.37 177	64.04 175	60.42 174	65.03 193	71.67 174	67.17 170	52.70 182	50.30 186	44.80 164	54.23 137	51.19 179	49.37 175	72.88 165	73.48 166	83.45 160	74.55 174

FPMVS		51.87 205	50.00 210	54.07 198	66.83 188	57.25 211	60.25 199	50.91 189	50.25 187	34.36 195	36.04 208	32.02 216	41.49 194	58.98 218	56.07 218	70.56 210	59.36 213

TAMVS		59.58 190	62.81 183	55.81 193	66.03 191	65.64 195	63.86 188	48.74 198	49.95 188	37.07 189	54.77 126	58.54 105	44.44 188	72.29 168	71.79 171	74.70 197	66.66 199

v52		65.23 149	66.24 151	64.06 152	61.94 199	76.42 142	72.06 146	54.30 174	49.94 189	50.04 138	47.41 186	52.42 164	60.23 125	75.71 149	76.22 143	85.78 132	85.56 66

v748		65.12 151	65.24 164	64.98 146	69.77 177	76.45 141	69.47 157	57.06 166	49.93 190	50.70 133	47.87 184	49.50 188	57.14 140	73.64 163	75.18 157	85.75 134	84.14 88

V4		65.23 149	66.23 152	64.06 152	61.94 199	76.42 142	72.05 147	54.31 173	49.91 191	50.06 137	47.42 185	52.40 165	60.24 124	75.71 149	76.22 143	85.78 132	85.56 66

pm-mvs1		65.62 146	67.42 139	63.53 157	73.66 150	76.39 144	69.66 154	60.87 117	49.73 192	43.97 168	51.24 172	57.00 118	48.16 177	79.89 88	77.84 101	84.85 151	79.82 138

N_pmnet		47.35 210	50.13 209	44.11 213	59.98 204	51.64 219	51.86 210	44.80 212	49.58 193	20.76 219	40.65 199	40.05 210	29.64 209	59.84 216	55.15 219	57.63 222	54.00 220

Anonymous20231206		56.36 197	57.80 200	54.67 197	70.08 175	66.39 192	60.46 198	57.54 161	49.50 194	29.30 201	33.86 211	46.64 196	35.18 203	70.44 187	68.88 186	75.47 194	68.88 196

anonymousdsp		65.28 148	67.98 134	62.13 164	58.73 209	73.98 167	67.10 171	50.69 192	48.41 195	47.66 151	54.27 133	52.75 163	61.45 117	76.71 142	80.20 75	87.13 80	89.53 45

LP		53.62 203	53.43 203	53.83 200	58.51 210	62.59 207	57.31 203	46.04 208	47.86 196	42.69 175	36.08 207	36.86 212	46.53 184	64.38 204	64.25 203	71.92 205	62.00 209

tfpnnormal		64.27 160	63.64 178	65.02 145	75.84 112	75.61 151	71.24 151	62.52 98	47.79 197	42.97 173	42.65 194	44.49 202	52.66 169	78.77 103	76.86 120	84.88 149	79.29 143

TransMVSNet (Re)		64.74 156	65.66 161	63.66 156	77.40 97	75.33 154	69.86 153	62.67 94	47.63 198	41.21 178	50.01 176	52.33 166	45.31 187	79.57 93	77.69 105	85.49 138	77.07 159

ambc			53.42 204		64.99 194	63.36 202	49.96 213		47.07 199	37.12 188	28.97 215	16.36 230	41.82 193	75.10 155	67.34 193	71.55 207	75.72 166

EG-PatchMatch MVS		67.24 139	66.94 145	67.60 119	78.73 78	81.35 73	73.28 138	59.49 140	46.89 200	51.42 130	43.65 192	53.49 150	55.50 156	81.38 65	80.66 69	87.15 76	81.17 125

SixPastTwentyTwo		61.84 181	62.45 186	61.12 171	69.20 182	72.20 171	62.03 194	57.40 163	46.54 201	38.03 187	57.14 104	41.72 206	58.12 133	69.67 191	71.58 173	81.94 166	78.30 149

MVS-HIRNet		54.41 200	52.10 207	57.11 190	58.99 207	56.10 213	49.68 214	49.10 196	46.18 202	52.15 126	33.18 212	46.11 199	56.10 148	63.19 207	59.70 215	76.64 189	60.25 211

EU-MVSNet		54.63 199	58.69 197	49.90 206	56.99 212	62.70 206	56.41 205	50.64 193	45.95 203	23.14 212	50.42 175	46.51 197	36.63 201	65.51 202	64.85 201	75.57 192	74.91 172

MDA-MVSNet-bldmvs		53.37 204	53.01 206	53.79 201	43.67 226	67.95 186	59.69 200	57.92 160	43.69 204	32.41 198	41.47 196	27.89 224	52.38 170	56.97 220	65.99 200	76.68 188	67.13 198

testgi		54.39 201	57.86 199	50.35 205	71.59 169	67.24 188	54.95 207	53.25 177	43.36 205	23.78 209	44.64 191	47.87 193	24.96 216	70.45 186	68.66 188	73.60 201	62.78 207

test20.03		53.93 202	56.28 202	51.19 204	72.19 161	65.83 193	53.20 209	61.08 114	42.74 206	22.08 216	37.07 204	45.76 200	24.29 220	70.44 187	69.04 184	74.31 199	63.05 206

new-patchmatchnet		46.97 212	49.47 211	44.05 214	62.82 197	56.55 212	45.35 218	52.01 184	42.47 207	17.04 224	35.73 209	35.21 213	21.84 225	61.27 211	54.83 220	65.26 219	60.26 210

pmmvs6		62.41 174	62.88 181	61.87 167	71.38 170	75.18 161	67.76 165	59.45 142	41.64 208	42.52 176	37.33 203	52.91 159	46.87 182	77.67 118	76.26 141	83.23 162	79.18 145

MIMVSNet1		49.27 207	53.25 205	44.62 212	44.61 223	61.52 209	53.61 208	52.18 183	41.62 209	18.68 220	28.14 219	41.58 207	25.50 214	68.46 197	69.04 184	73.15 202	62.37 208

new_pmnet		38.40 219	42.64 220	33.44 221	37.54 229	45.00 225	36.60 225	32.72 226	40.27 210	12.72 227	29.89 214	28.90 222	24.78 217	53.17 222	52.90 223	56.31 223	48.34 221

Gipuma		36.38 220	35.80 223	37.07 219	45.76 222	33.90 228	29.81 227	48.47 200	39.91 211	18.02 222	8.00 231	8.14 233	25.14 215	59.29 217	61.02 212	55.19 226	40.31 224
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
gg-mvs-nofinetune		62.55 171	65.05 168	59.62 180	78.72 79	77.61 123	70.83 152	53.63 175	39.71 212	22.04 217	36.36 205	64.32 89	47.53 178	81.16 70	79.03 87	85.00 146	77.17 156

test2356		47.20 211	48.62 214	45.54 211	56.38 214	54.89 215	50.62 211	45.08 211	38.65 213	23.40 210	36.23 206	31.10 218	29.31 210	62.76 208	62.49 208	68.48 213	54.23 219

testus		45.61 215	49.06 213	41.59 216	56.13 215	55.28 214	43.51 219	39.64 220	37.74 214	18.23 221	35.52 210	31.28 217	24.69 218	62.46 209	62.90 206	67.33 215	58.26 215

tmp_tt				14.50 229	14.68 231	7.17 234	10.46 234	2.21 230	37.73 215	28.71 203	25.26 222	16.98 228	4.37 230	31.49 226	29.77 226	26.56 230

testpf		47.41 209	48.47 215	46.18 209	66.30 190	50.67 220	48.15 216	42.60 217	37.10 216	28.75 202	40.97 197	39.01 211	30.82 208	52.95 223	53.74 222	60.46 221	64.87 201

1111		43.08 216	44.02 218	41.98 215	59.22 205	49.27 223	41.48 221	45.63 209	35.01 217	23.06 213	28.60 217	30.15 220	27.22 211	60.42 214	57.97 216	55.27 225	46.74 222

.test1245		30.81 223	29.14 225	32.77 222	59.22 205	49.27 223	41.48 221	45.63 209	35.01 217	23.06 213	28.60 217	30.15 220	27.22 211	60.42 214	0.10 229	0.01 233	0.43 231

pmmvs3		47.65 208	49.08 212	45.99 210	44.61 223	54.79 216	50.04 212	31.95 227	33.91 219	29.90 199	30.37 213	33.53 215	46.31 185	63.50 205	63.67 205	73.14 203	63.77 205

gm-plane-assit		57.00 195	57.62 201	56.28 192	76.10 108	62.43 208	47.62 217	46.57 205	33.84 220	23.24 211	37.52 202	40.19 209	59.61 127	79.81 89	77.55 108	84.55 155	72.03 186

LTVRE_ROB	59.44 16	61.82 183	62.64 184	60.87 173	72.83 158	77.19 126	64.37 186	58.97 145	33.56 221	28.00 204	52.59 164	42.21 205	63.93 101	74.52 156	76.28 140	77.15 187	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
PMVS	39.38 18	46.06 214	43.30 219	49.28 207	62.93 196	38.75 227	41.88 220	53.50 176	33.33 222	35.46 194	28.90 216	31.01 219	33.04 206	58.61 219	54.63 221	68.86 212	57.88 216
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Anonymous20231211		51.46 206	50.59 208	52.46 203	67.30 185	66.70 191	55.00 206	59.22 143	29.96 223	17.62 223	19.11 225	28.74 223	35.72 202	66.42 200	69.52 181	79.92 176	73.71 180

testmv		42.58 217	44.36 216	40.49 217	54.63 219	52.76 217	41.21 223	44.37 213	28.83 224	12.87 225	27.16 220	25.03 225	23.01 221	60.83 212	61.13 210	66.88 216	54.81 217

test1235678		42.57 218	44.36 216	40.49 217	54.63 219	52.75 218	41.21 223	44.37 213	28.82 225	12.87 225	27.15 221	25.01 226	23.01 221	60.83 212	61.13 210	66.88 216	54.81 217

DeepMVS_CX						18.74 233	18.55 230	8.02 229	26.96 226	7.33 230	23.81 224	13.05 232	25.99 213	25.17 228		22.45 232	36.25 227

test12356		35.10 222	38.50 221	31.13 223	44.14 225	43.70 226	32.27 226	34.42 223	26.51 227	9.47 229	25.22 223	20.34 227	10.86 228	53.47 221	56.15 217	55.59 224	44.11 223

PMMVS2		25.60 224	29.75 224	20.76 227	28.00 230	30.93 229	23.10 229	29.18 228	23.14 228	1.46 235	18.23 226	16.54 229	5.08 229	40.22 225	41.40 225	37.76 227	37.79 226

no-one		36.35 221	37.59 222	34.91 220	46.13 221	49.89 222	27.99 228	43.56 215	20.91 229	7.03 231	14.64 227	15.50 231	18.92 226	42.95 224	60.20 213	65.84 218	59.03 214

EMVS		20.98 226	17.15 228	25.44 225	39.51 228	19.37 232	12.66 231	39.59 221	19.10 230	6.62 233	9.27 229	4.40 235	22.43 223	17.99 230	24.40 228	31.81 229	25.53 229

E-PMN		21.77 225	18.24 227	25.89 224	40.22 227	19.58 231	12.46 232	39.87 219	18.68 231	6.71 232	9.57 228	4.31 236	22.36 224	19.89 229	27.28 227	33.73 228	28.34 228

MVE	19.12 19	20.47 227	23.27 226	17.20 228	12.66 232	25.41 230	10.52 233	34.14 225	14.79 232	6.53 234	8.79 230	4.68 234	16.64 227	29.49 227	41.63 224	22.73 231	38.11 225
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.09 228	0.15 229	0.02 230	0.01 234	0.02 235	0.05 236	0.01 232	0.11 233	0.01 237	0.26 233	0.01 237	0.06 233	0.10 231	0.10 229	0.01 233	0.43 231

test123		0.09 228	0.14 230	0.02 230	0.00 235	0.02 235	0.02 237	0.01 232	0.09 234	0.00 238	0.30 232	0.00 238	0.08 231	0.03 232	0.09 231	0.01 233	0.45 230

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

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

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

MTAPA										83.48 1		86.45 11

MTMP										82.66 3		84.91 19

Patchmatch-RL test							2.85 235

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

Patchmtry						65.80 194	65.97 178	52.74 180		52.65 122

High-resolution multi-view benchmark