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.

v1.0		80.27 26	76.62 54	84.53 1	92.88 2	93.82 1	88.95 1	76.05 4	92.95 4	80.32 3	93.12 3	86.87 1	80.88 5	85.54 10	84.01 18	88.09 34	0.00 246

ESAPD		87.60 1	90.44 1	84.29 3	92.09 6	93.44 2	88.69 2	75.11 5	93.06 3	80.80 2	94.23 2	86.70 2	81.44 4	84.84 15	83.52 24	87.64 40	97.28 2

APDe-MVS		86.37 3	88.41 4	84.00 5	91.43 10	91.83 11	88.34 3	74.67 6	91.19 5	81.76 1	91.13 4	81.94 13	80.07 6	83.38 23	82.58 31	87.69 38	96.78 7

MCST-MVS		85.75 5	86.99 9	84.31 2	94.07 1	92.80 4	88.15 4	79.10 1	85.66 19	70.72 26	76.50 29	80.45 15	82.17 2	88.35 1	87.49 2	91.63 2	97.65 1

CNVR-MVS		85.96 4	87.58 7	84.06 4	92.58 4	92.40 7	87.62 5	77.77 2	88.44 11	75.93 13	79.49 22	81.97 12	81.65 3	87.04 5	86.58 3	88.79 16	97.18 4

CSCG		82.90 15	84.52 18	81.02 14	91.85 7	93.43 3	87.14 6	74.01 10	81.96 29	76.14 11	70.84 33	82.49 9	69.71 55	82.32 35	85.18 11	87.26 48	95.40 18

SMA-MVS		85.24 7	88.27 5	81.72 11	91.74 8	90.71 16	86.71 7	73.16 15	90.56 8	74.33 16	83.07 15	85.88 3	77.16 14	86.28 7	85.58 6	87.23 49	95.77 11

NCCC		84.16 11	85.46 16	82.64 7	92.34 5	90.57 20	86.57 8	76.51 3	86.85 16	72.91 19	77.20 28	78.69 21	79.09 9	84.64 17	84.88 14	88.44 24	95.41 17

APD-MVS		84.83 8	87.00 8	82.30 9	89.61 21	89.21 32	86.51 9	73.64 12	90.98 6	77.99 8	89.89 6	80.04 18	79.18 8	82.00 39	81.37 44	86.88 55	95.49 16
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + ACMM		81.59 21	85.84 15	76.63 35	89.82 18	86.53 56	86.32 10	66.72 46	85.96 18	65.43 40	88.98 8	82.29 10	67.57 73	82.06 38	81.33 45	83.93 154	93.75 34

HPM-MVS++		85.64 6	88.43 3	82.39 8	92.65 3	90.24 23	85.83 11	74.21 7	90.68 7	75.63 14	86.77 10	84.15 5	78.68 10	86.33 6	85.26 9	87.32 46	95.60 14

SD-MVS		84.31 10	86.96 10	81.22 12	88.98 27	88.68 35	85.65 12	73.85 11	89.09 10	79.63 4	87.34 9	84.84 4	73.71 31	82.66 29	81.60 41	85.48 109	94.51 24

HSP-MVS		86.82 2	89.95 2	83.16 6	89.38 23	91.60 13	85.63 13	74.15 8	94.20 1	75.52 15	94.99 1	83.21 7	85.96 1	87.67 3	85.88 5	88.32 26	92.13 48

TSAR-MVS + MP.		84.39 9	86.58 12	81.83 10	88.09 35	86.47 57	85.63 13	73.62 13	90.13 9	79.24 5	89.67 7	82.99 8	77.72 12	81.22 46	80.92 52	86.68 59	94.66 23

train_agg		83.35 13	86.93 11	79.17 23	89.70 19	88.41 39	85.60 15	72.89 17	86.31 17	66.58 37	90.48 5	82.24 11	73.06 35	83.10 25	82.64 30	87.21 52	95.30 19

ACMMP_Plus		83.54 12	86.37 13	80.25 17	89.57 22	90.10 25	85.27 16	71.66 19	87.38 12	73.08 18	84.23 14	80.16 16	75.31 21	84.85 14	83.64 21	86.57 60	94.21 29

SteuartSystems-ACMMP		82.51 16	85.35 17	79.20 22	90.25 13	89.39 31	84.79 17	70.95 21	82.86 25	68.32 34	86.44 11	77.19 22	73.07 34	83.63 22	83.64 21	87.82 35	94.34 26
Skip Steuart: Steuart Systems R&D Blog.
MSLP-MVS++		78.57 34	77.33 45	80.02 18	88.39 30	84.79 69	84.62 18	66.17 50	75.96 48	78.40 6	61.59 54	71.47 40	73.54 33	78.43 68	78.88 64	88.97 14	90.18 72

HFP-MVS		82.48 17	84.12 19	80.56 15	90.15 14	87.55 49	84.28 19	69.67 30	85.22 20	77.95 9	84.69 13	75.94 25	75.04 23	81.85 40	81.17 47	86.30 66	92.40 45

CDPH-MVS		79.39 32	82.13 27	76.19 40	89.22 26	88.34 40	84.20 20	71.00 20	79.67 39	56.97 71	77.77 25	72.24 37	68.50 66	81.33 45	82.74 27	87.23 49	92.84 41

DELS-MVS		79.49 27	79.84 35	79.08 24	88.26 33	92.49 5	84.12 21	70.63 23	65.27 75	69.60 32	61.29 56	66.50 53	72.75 36	88.07 2	88.03 1	89.13 13	97.22 3
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
zzz-MVS		81.65 20	83.10 22	79.97 19	88.14 34	87.62 48	83.96 22	69.90 27	86.92 14	77.67 10	72.47 32	78.74 20	74.13 30	81.59 44	81.15 48	86.01 77	93.19 38

DeepC-MVS_fast	75.41 2	81.69 19	82.10 28	81.20 13	91.04 12	87.81 47	83.42 23	74.04 9	83.77 23	71.09 24	66.88 41	72.44 33	79.48 7	85.08 12	84.97 13	88.12 33	93.78 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS	74.46 3	80.30 25	81.05 31	79.42 20	87.42 37	88.50 37	83.23 24	73.27 14	82.78 26	71.01 25	62.86 50	69.93 46	74.80 25	84.30 18	84.20 17	86.79 58	94.77 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMPR		80.62 24	82.98 23	77.87 30	88.41 29	87.05 52	83.02 25	69.18 33	83.91 22	68.35 33	82.89 16	73.64 30	72.16 40	80.78 51	81.13 49	86.10 71	91.43 55

HQP-MVS		78.26 36	80.91 32	75.17 47	85.67 45	84.33 74	83.01 26	69.38 31	79.88 38	55.83 72	79.85 21	64.90 59	70.81 48	82.46 31	81.78 38	86.30 66	93.18 39

PGM-MVS		79.42 31	81.84 29	76.60 36	88.38 31	86.69 54	82.97 27	65.75 52	80.39 36	64.94 41	81.95 19	72.11 38	71.41 46	80.45 52	80.55 55	86.18 68	90.76 65

OPM-MVS		72.74 62	70.93 79	74.85 51	85.30 46	84.34 73	82.82 28	69.79 28	49.96 124	55.39 77	54.09 78	60.14 78	70.04 54	80.38 54	79.43 58	85.74 98	88.20 104

MP-MVS		80.94 22	83.49 21	77.96 28	88.48 28	88.16 43	82.82 28	69.34 32	80.79 35	69.67 30	82.35 17	77.13 23	71.60 45	80.97 50	80.96 51	85.87 90	94.06 30
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		79.44 28	81.51 30	77.02 34	86.95 39	85.96 63	82.00 30	68.44 38	81.82 30	67.39 35	77.43 26	73.68 29	71.62 44	79.56 59	79.58 57	85.73 99	92.51 44

CLD-MVS		77.36 44	77.29 46	77.45 33	82.21 57	88.11 44	81.92 31	68.96 35	77.97 43	69.62 31	62.08 51	59.44 79	73.57 32	81.75 41	81.27 46	88.41 25	90.39 69
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
3Dnovator	70.49 5	78.42 35	76.77 51	80.35 16	91.43 10	90.27 22	81.84 32	70.79 22	72.10 53	71.95 20	50.02 90	67.86 51	77.47 13	82.89 26	84.24 16	88.61 20	89.99 73

MVS_0304		79.43 29	82.20 26	76.20 39	84.22 48	91.79 12	81.82 33	63.81 65	76.83 46	61.71 51	66.37 42	75.52 26	76.38 19	85.54 10	85.03 12	89.28 12	94.32 27

casdiffmvs1		77.74 39	78.92 38	76.36 37	82.58 51	90.61 18	81.58 34	61.31 94	75.68 49	66.24 38	64.21 45	65.17 56	76.54 18	80.07 55	82.68 29	89.88 7	94.00 32

PCF-MVS	70.85 4	75.73 51	76.55 55	74.78 52	83.67 49	88.04 46	81.47 35	70.62 25	69.24 64	57.52 69	60.59 59	69.18 47	70.65 49	77.11 77	77.65 75	84.75 135	94.01 31
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMMP		77.61 41	79.59 36	75.30 46	85.87 44	85.58 64	81.42 36	67.38 43	79.38 40	62.61 47	78.53 23	65.79 55	68.80 65	78.56 67	78.50 68	85.75 95	90.80 63
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
QAPM		77.50 42	77.43 44	77.59 32	91.52 9	92.00 10	81.41 37	70.63 23	66.22 68	58.05 68	54.70 71	71.79 39	74.49 29	82.46 31	82.04 34	89.46 11	92.79 43

canonicalmvs		77.65 40	79.59 36	75.39 44	81.52 61	89.83 30	81.32 38	60.74 103	80.05 37	66.72 36	68.43 37	65.09 57	74.72 27	78.87 64	82.73 28	87.32 46	92.16 47

CANet		80.90 23	82.93 24	78.53 27	86.83 41	92.26 8	81.19 39	66.95 44	81.60 32	69.90 29	66.93 40	74.80 27	76.79 15	84.68 16	84.77 15	89.50 10	95.50 15

TSAR-MVS + GP.		82.27 18	85.98 14	77.94 29	80.72 69	88.25 42	81.12 40	67.71 41	87.10 13	73.31 17	85.23 12	83.68 6	76.64 16	80.43 53	81.47 43	88.15 32	95.66 13

X-MVS		78.16 37	80.55 33	75.38 45	87.99 36	86.27 59	81.05 41	68.98 34	78.33 41	61.07 55	75.25 30	72.27 34	67.52 74	80.03 56	80.52 56	85.66 106	91.20 58

CPTT-MVS		75.43 52	77.13 48	73.44 55	81.43 62	82.55 85	80.96 42	64.35 60	77.95 44	61.39 52	69.20 36	70.94 42	69.38 61	73.89 118	73.32 145	83.14 169	92.06 50

PHI-MVS		79.43 29	84.06 20	74.04 53	86.15 43	91.57 14	80.85 43	68.90 36	82.22 28	51.81 84	78.10 24	74.28 28	70.39 52	84.01 21	84.00 19	86.14 70	94.24 28

DI_MVS_plusplus_trai		73.94 58	74.85 60	72.88 58	76.57 98	86.80 53	80.41 44	61.47 92	62.35 79	59.44 63	47.91 98	68.12 48	72.24 39	82.84 28	81.50 42	87.15 53	94.42 25

3Dnovator+	70.16 6	77.87 38	77.29 46	78.55 26	89.25 25	88.32 41	80.09 45	67.95 40	74.89 52	71.83 22	52.05 83	70.68 43	76.27 20	82.27 36	82.04 34	85.92 83	90.77 64

MVS_Test		75.22 53	76.69 52	73.51 54	79.30 74	88.82 34	80.06 46	58.74 114	69.77 61	57.50 70	59.78 62	61.35 73	75.31 21	82.07 37	83.60 23	90.13 5	91.41 56

AdaColmap		76.23 49	73.55 65	79.35 21	89.38 23	85.00 68	79.99 47	73.04 16	76.60 47	71.17 23	55.18 69	57.99 88	77.87 11	76.82 81	76.82 81	84.67 137	86.45 116

casdiffmvs		76.13 50	76.96 49	75.15 48	82.26 56	90.09 26	79.98 48	60.64 104	70.12 58	63.58 44	62.04 52	60.30 77	74.53 28	81.62 43	82.30 32	89.90 6	92.27 46

LGP-MVS_train		72.02 67	73.18 68	70.67 70	82.13 58	80.26 122	79.58 49	63.04 73	70.09 59	51.98 82	65.06 44	55.62 101	62.49 94	75.97 94	76.32 88	84.80 134	88.93 84

diffmvs1		74.20 56	76.05 56	72.03 62	77.16 92	88.46 38	79.55 50	58.73 115	72.02 55	58.23 66	60.24 60	62.08 66	72.03 42	78.95 63	79.16 60	86.50 63	91.45 54

EPNet		79.28 33	82.25 25	75.83 42	88.31 32	90.14 24	79.43 51	68.07 39	81.76 31	61.26 53	77.26 27	70.08 45	70.06 53	82.43 33	82.00 36	87.82 35	92.09 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MAR-MVS		77.19 45	78.37 42	75.81 43	89.87 17	90.58 19	79.33 52	65.56 54	77.62 45	58.33 65	59.24 63	67.98 49	74.83 24	82.37 34	83.12 26	86.95 54	87.67 108
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
DeepPCF-MVS	76.94 1	83.08 14	87.77 6	77.60 31	90.11 15	90.96 15	78.48 53	72.63 18	93.10 2	65.84 39	80.67 20	81.55 14	74.80 25	85.94 9	85.39 8	83.75 156	96.77 8

MVSTER		76.92 46	79.92 34	73.42 56	74.98 109	82.97 81	78.15 54	63.41 68	78.02 42	64.41 43	67.54 38	72.80 32	71.05 47	83.29 24	83.73 20	88.53 23	91.12 59

OpenMVS	67.62 8	74.92 54	73.91 62	76.09 41	90.10 16	90.38 21	78.01 55	66.35 48	66.09 70	62.80 46	46.33 115	64.55 60	71.77 43	79.92 57	80.88 53	87.52 42	89.20 80

diffmvs		72.46 64	73.75 64	70.95 67	76.33 100	87.21 50	77.96 56	58.43 118	66.25 67	55.75 73	59.11 64	56.77 93	70.42 50	77.35 76	78.90 62	86.80 57	90.64 67

ACMP	68.86 7	72.15 66	72.25 69	72.03 62	80.96 65	80.87 107	77.93 57	64.13 62	69.29 62	60.79 58	64.04 46	53.54 110	63.91 86	73.74 122	75.27 99	84.45 143	88.98 83
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MS-PatchMatch		70.34 75	69.00 89	71.91 65	85.20 47	85.35 65	77.84 58	61.77 90	58.01 93	55.40 76	41.26 139	58.34 85	61.69 97	81.70 42	78.29 69	89.56 9	80.02 170

abl_6				79.06 25	89.68 20	92.14 9	77.70 59	69.68 29	86.87 15	71.88 21	74.29 31	80.06 17	76.56 17			88.84 15	95.82 10

ACMM	66.70 10	70.42 71	68.49 93	72.67 59	82.85 50	77.76 147	77.70 59	64.76 59	64.61 76	60.74 59	49.29 92	53.97 108	65.86 78	74.97 103	75.57 97	84.13 152	83.29 144
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_111021_HR		77.42 43	78.40 41	76.28 38	86.95 39	90.68 17	77.41 61	70.56 26	66.21 69	62.48 49	66.17 43	63.98 61	72.08 41	82.87 27	83.15 25	88.24 29	95.71 12

GG-mvs-BLEND		54.54 198	77.58 43	27.67 234	0.03 248	90.09 26	77.20 62	0.02 245	66.83 66	0.05 249	59.90 61	73.33 31	0.04 245	78.40 69	79.30 59	88.65 18	95.20 20

CostFormer		72.18 65	73.90 63	70.18 72	79.47 72	86.19 62	76.94 63	48.62 199	66.07 71	60.40 60	54.14 77	65.82 54	67.98 68	75.84 95	76.41 87	87.67 39	92.83 42

tpmp4_e23		69.38 76	69.47 86	69.28 76	78.20 78	82.35 87	75.92 64	49.20 198	64.15 77	59.96 61	47.93 97	55.77 99	68.06 67	73.05 129	74.53 112	84.34 145	88.50 102

XVS					82.43 52	86.27 59	75.70 65			61.07 55		72.27 34				85.67 103

X-MVStestdata					82.43 52	86.27 59	75.70 65			61.07 55		72.27 34				85.67 103

CHOSEN 1792x2688		72.55 63	71.98 70	73.22 57	86.57 42	92.41 6	75.63 67	66.77 45	62.08 80	52.32 81	30.27 209	50.74 119	66.14 77	86.22 8	85.41 7	91.90 1	96.75 9

FMVSNet3		70.41 73	71.89 72	68.68 81	70.89 137	79.42 131	75.63 67	60.97 99	65.32 72	51.06 86	47.37 104	62.05 67	64.90 82	82.49 30	82.27 33	88.64 19	84.34 135

Effi-MVS+		70.42 71	71.23 76	69.47 74	78.04 79	85.24 66	75.57 69	58.88 113	59.56 87	48.47 97	52.73 82	54.94 104	69.69 56	78.34 70	77.06 79	86.18 68	90.73 66

CANet_DTU		72.84 60	76.63 53	68.43 83	76.81 96	86.62 55	75.54 70	54.71 166	72.06 54	43.54 120	67.11 39	58.46 83	72.40 38	81.13 49	80.82 54	87.57 41	90.21 71

PVSNet_BlendedMVS		76.84 47	78.47 39	74.95 49	82.37 54	89.90 28	75.45 71	65.45 55	74.99 50	70.66 27	63.07 48	58.27 86	67.60 71	84.24 19	81.70 39	88.18 30	97.10 5

PVSNet_Blended		76.84 47	78.47 39	74.95 49	82.37 54	89.90 28	75.45 71	65.45 55	74.99 50	70.66 27	63.07 48	58.27 86	67.60 71	84.24 19	81.70 39	88.18 30	97.10 5

OMC-MVS		74.03 57	75.82 58	71.95 64	79.56 71	80.98 105	75.35 73	63.21 69	84.48 21	61.83 50	61.54 55	66.89 52	69.41 60	76.60 83	74.07 133	82.34 179	86.15 120

Anonymous20231211		68.44 83	66.37 107	70.86 68	77.58 88	83.49 79	75.15 74	61.89 87	52.54 117	58.50 64	28.89 211	56.78 92	69.29 62	74.96 105	76.61 82	82.73 172	91.36 57

Anonymous202405211			66.35 108		78.00 80	84.41 72	74.85 75	63.18 70	51.00 120		31.37 206	53.73 109	69.67 57	76.28 86	76.84 80	83.21 166	90.85 62

Anonymous20240521		69.13 79	69.07 88	69.21 77	77.65 87	77.52 149	74.68 76	57.85 126	54.92 109	55.34 78	55.74 68	55.56 102	66.35 76	75.05 101	76.56 84	83.35 161	88.13 105

GBi-Net		69.21 77	70.40 81	67.81 86	69.49 142	78.65 136	74.54 77	60.97 99	65.32 72	51.06 86	47.37 104	62.05 67	63.43 88	77.49 72	78.22 70	87.37 43	83.73 139

test1		69.21 77	70.40 81	67.81 86	69.49 142	78.65 136	74.54 77	60.97 99	65.32 72	51.06 86	47.37 104	62.05 67	63.43 88	77.49 72	78.22 70	87.37 43	83.73 139

FMVSNet2		68.06 87	68.57 92	67.45 89	69.49 142	78.65 136	74.54 77	60.23 110	56.29 100	49.64 95	42.13 135	57.08 91	63.43 88	81.15 48	80.99 50	87.37 43	83.73 139

thres100view900		67.14 96	66.09 112	68.38 84	77.70 83	83.84 78	74.52 80	66.33 49	49.16 129	43.40 125	43.24 122	41.34 141	62.59 93	79.31 60	75.92 92	85.73 99	89.81 74

TSAR-MVS + COLMAP		73.09 59	76.86 50	68.71 80	74.97 110	82.49 86	74.51 81	61.83 88	83.16 24	49.31 96	82.22 18	51.62 116	68.94 64	78.76 66	75.52 98	82.67 174	84.23 136

tpm cat1		67.47 92	67.05 103	67.98 85	76.63 97	81.51 99	74.49 82	47.65 204	61.18 82	61.12 54	42.51 131	53.02 113	64.74 84	70.11 175	71.50 166	83.22 164	89.49 76

MSDG		65.57 106	61.57 156	70.24 71	82.02 59	76.47 160	74.46 83	68.73 37	56.52 98	50.33 92	38.47 167	41.10 148	62.42 95	72.12 154	72.94 153	83.47 159	73.37 195

TAPA-MVS	67.10 9	71.45 69	73.47 67	69.10 78	77.04 93	80.78 108	73.81 84	62.10 83	80.80 34	51.28 85	60.91 57	63.80 63	67.98 68	74.59 107	72.42 159	82.37 178	80.97 165
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
thres200		65.58 105	64.74 120	66.56 96	77.52 90	81.61 93	73.44 85	62.95 75	46.23 150	42.45 142	42.76 126	41.18 146	58.12 140	76.24 88	75.59 96	84.89 125	89.58 75

MVS_111021_LR		74.26 55	75.95 57	72.27 61	79.43 73	85.04 67	72.71 86	65.27 57	70.92 57	63.58 44	69.32 35	60.31 76	69.43 59	77.01 78	77.15 78	83.22 164	91.93 52

PLC	64.00 12	68.54 82	66.66 104	70.74 69	80.28 70	74.88 172	72.64 87	63.70 67	69.26 63	55.71 74	47.24 107	55.31 103	70.42 50	72.05 156	70.67 178	81.66 184	77.19 179
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v18		63.31 132	62.02 151	64.81 108	68.48 149	73.38 181	72.14 88	54.28 168	48.99 136	47.21 101	39.56 152	41.20 145	60.80 108	72.89 134	74.46 118	85.96 82	83.64 142

DWT-MVSNet_training		72.81 61	73.98 61	71.45 66	81.26 63	86.37 58	72.08 89	59.82 111	69.13 65	58.15 67	54.71 70	61.33 75	67.81 70	76.86 80	78.63 65	89.59 8	90.86 61

PVSNet_Blended_VisFu		71.76 68	73.54 66	69.69 73	79.01 75	87.16 51	72.05 90	61.80 89	56.46 99	59.66 62	53.88 79	62.48 64	59.08 136	81.17 47	78.90 62	86.53 62	94.74 22

v2v482		63.68 125	62.85 138	64.65 112	68.01 163	80.46 119	71.90 91	57.60 132	44.26 159	42.82 140	39.80 151	38.62 180	61.56 98	73.06 127	74.86 103	86.03 76	88.90 87

v16		63.12 134	61.78 153	64.68 110	68.45 150	73.29 182	71.86 92	54.12 169	48.36 138	47.00 102	39.30 157	41.01 149	60.67 109	72.83 140	74.40 120	86.01 77	83.24 146

v6		64.09 117	63.40 128	64.90 104	68.28 153	80.78 108	71.85 93	57.64 131	46.73 145	45.18 111	39.40 153	40.89 152	60.54 114	72.86 135	74.40 120	85.92 83	88.72 93

v1neww		64.08 118	63.38 129	64.89 106	68.27 155	80.77 110	71.84 94	57.65 129	46.66 147	45.10 112	39.40 153	40.86 153	60.57 111	72.86 135	74.40 120	85.92 83	88.71 94

v7new		64.08 118	63.38 129	64.89 106	68.27 155	80.77 110	71.84 94	57.65 129	46.66 147	45.10 112	39.40 153	40.86 153	60.57 111	72.86 135	74.40 120	85.92 83	88.71 94

v8		63.44 131	62.58 145	64.43 115	68.28 153	78.07 142	71.82 96	54.85 163	46.70 146	45.20 110	39.40 153	40.91 151	60.54 114	72.85 139	74.39 125	85.92 83	85.76 125

v17		62.99 139	61.70 154	64.51 113	68.40 151	73.28 183	71.80 97	54.11 170	47.87 139	46.14 105	39.29 158	41.01 149	60.60 110	72.81 141	74.39 125	85.99 80	83.25 145

FMVSNet1		63.48 128	63.07 134	63.97 124	65.31 186	76.37 162	71.77 98	57.90 125	43.32 170	45.66 107	35.06 194	49.43 121	58.57 138	77.49 72	78.22 70	84.59 140	81.60 163

tfpn111		66.52 99	66.12 111	66.98 94	77.70 83	81.58 95	71.71 99	62.94 77	49.16 129	43.28 127	51.38 85	41.34 141	61.42 99	76.24 88	74.63 106	84.84 128	88.52 98

conf0.01		66.60 98	66.18 110	67.09 92	77.90 82	82.02 89	71.71 99	63.05 72	49.16 129	43.41 124	46.23 116	45.78 130	61.42 99	76.55 84	74.63 106	85.04 120	88.87 88

conf0.002		67.12 97	67.13 102	67.11 91	77.95 81	82.11 88	71.71 99	63.06 71	49.16 129	43.43 122	47.76 101	48.79 122	61.42 99	76.61 82	76.55 85	85.07 119	88.92 86

conf200view11		65.89 104	64.96 117	66.98 94	77.70 83	81.58 95	71.71 99	62.94 77	49.16 129	43.28 127	43.24 122	41.34 141	61.42 99	76.24 88	74.63 106	84.84 128	88.52 98

tfpn200view9		65.90 103	64.96 117	67.00 93	77.70 83	81.58 95	71.71 99	62.94 77	49.16 129	43.40 125	43.24 122	41.34 141	61.42 99	76.24 88	74.63 106	84.84 128	88.52 98

divwei89l23v2f112		63.48 128	62.76 142	64.32 118	68.13 157	80.68 115	71.71 99	57.43 136	43.69 165	42.84 137	39.01 161	39.75 168	59.94 122	72.93 132	74.49 115	85.86 91	88.75 91

v1		63.49 127	62.77 141	64.32 118	68.13 157	80.70 113	71.70 105	57.43 136	43.69 165	42.89 136	39.03 159	39.77 167	59.93 123	72.93 132	74.48 117	85.86 91	88.77 89

v1141		63.48 128	62.75 143	64.32 118	68.13 157	80.69 114	71.69 106	57.43 136	43.66 167	42.83 139	39.02 160	39.74 169	59.95 121	72.94 131	74.49 115	85.86 91	88.75 91

v7		63.61 126	63.02 135	64.29 121	67.88 167	80.32 120	71.60 107	56.63 144	45.37 154	42.84 137	38.54 165	38.91 178	61.05 106	74.39 110	74.52 113	85.75 95	89.10 82

v10		63.00 137	62.22 148	63.90 126	67.88 167	77.78 146	71.59 108	54.34 167	45.37 154	42.76 141	38.53 166	38.93 177	61.05 106	74.39 110	74.52 113	85.75 95	86.04 121

thres400		65.18 110	64.44 122	66.04 97	76.40 99	82.63 83	71.52 109	64.27 61	44.93 158	40.69 151	41.86 136	40.79 155	58.12 140	77.67 71	74.64 105	85.26 112	88.56 97

V42		62.86 141	62.97 136	62.74 143	60.84 203	78.99 134	71.46 110	57.13 142	46.85 143	44.28 118	38.87 162	40.73 157	57.63 146	72.60 147	74.14 130	85.09 117	88.63 96

v15		62.07 150	60.70 162	63.67 128	68.09 160	73.00 184	71.27 111	53.41 175	43.70 164	43.43 122	38.77 163	39.83 165	59.87 124	72.74 144	74.25 127	85.98 81	82.61 151

tpmrst		67.15 95	68.12 97	66.03 98	76.21 101	80.98 105	71.27 111	45.05 211	60.69 84	50.63 90	46.95 112	54.15 107	65.30 79	71.80 159	71.77 163	87.72 37	90.48 68

v1144		63.00 137	62.39 147	63.70 127	67.72 170	80.27 121	71.23 113	56.40 145	42.51 176	40.81 150	38.12 174	37.73 182	60.42 117	74.46 108	74.55 111	85.64 107	89.12 81

gg-mvs-nofinetune		62.34 143	66.19 109	57.86 177	76.15 102	88.61 36	71.18 114	41.24 228	25.74 230	13.16 232	22.91 225	63.97 62	54.52 156	85.06 13	85.25 10	90.92 3	91.78 53

Fast-Effi-MVS+		67.59 89	67.56 99	67.62 88	73.67 115	81.14 104	71.12 115	54.79 165	58.88 89	50.61 91	46.70 113	47.05 126	69.12 63	76.06 93	76.44 86	86.43 64	86.65 114

V14		61.96 153	60.56 165	63.59 129	68.06 161	72.93 187	71.10 116	53.33 177	43.47 169	43.28 127	38.59 164	39.78 166	59.76 126	72.65 146	74.19 128	86.01 77	82.32 156

HyFIR lowres test		68.39 84	68.28 95	68.52 82	80.85 66	88.11 44	71.08 117	58.09 121	54.87 111	47.80 100	27.55 215	55.80 98	64.97 81	79.11 61	79.14 61	88.31 27	93.35 35

CNLPA		71.37 70	70.27 83	72.66 60	80.79 68	81.33 101	71.07 118	65.75 52	82.36 27	64.80 42	42.46 132	56.49 94	72.70 37	73.00 130	70.52 180	80.84 191	85.76 125

tpm		64.85 111	66.02 113	63.48 131	74.52 112	78.38 139	70.98 119	44.99 213	51.61 119	43.28 127	47.66 102	53.18 111	60.57 111	70.58 169	71.30 175	86.54 61	89.45 78

V9		61.85 155	60.42 168	63.51 130	68.02 162	72.85 188	70.91 120	53.24 178	43.25 171	43.27 131	38.41 168	39.73 170	59.60 128	72.55 148	74.13 131	86.04 75	82.04 159

IterMVS-LS		66.08 102	66.56 106	65.51 99	73.67 115	74.88 172	70.89 121	53.55 174	50.42 122	48.32 98	50.59 88	55.66 100	61.83 96	73.93 117	74.42 119	84.82 133	86.01 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+-dtu		63.05 135	64.72 121	61.11 156	71.21 135	76.81 159	70.72 122	43.13 219	52.51 118	35.34 181	46.55 114	46.36 127	61.40 104	71.57 162	71.44 168	84.84 128	87.79 107

Effi-MVS+-dtu		64.58 113	64.08 123	65.16 101	73.04 120	75.17 171	70.68 123	56.23 148	54.12 114	44.71 116	47.42 103	51.10 117	63.82 87	68.08 187	66.32 200	82.47 177	86.38 118

v12		61.70 157	60.27 170	63.38 134	68.00 164	72.76 189	70.63 124	53.14 180	43.01 173	42.95 135	38.25 170	39.64 172	59.48 130	72.47 150	74.05 134	86.06 74	81.71 162

v13		61.60 159	60.13 173	63.31 135	67.95 166	72.67 191	70.51 125	53.05 181	42.80 174	42.96 132	38.10 175	39.57 174	59.31 133	72.36 151	73.98 136	86.10 71	81.40 164

v11		61.74 156	60.47 167	63.22 136	67.83 169	72.72 190	70.31 126	52.95 184	42.75 175	41.89 144	38.16 173	38.49 181	60.40 118	74.35 112	74.40 120	85.92 83	82.39 155

LS3D		64.54 115	62.14 149	67.34 90	80.85 66	75.79 166	69.99 127	65.87 51	60.77 83	44.35 117	42.43 133	45.95 129	65.01 80	69.88 178	68.69 188	77.97 209	71.43 205

v1192		62.25 146	61.64 155	62.96 138	66.88 175	79.72 126	69.96 128	55.77 152	41.58 183	39.42 154	37.05 180	35.96 195	60.50 116	74.30 115	74.09 132	85.24 113	88.76 90

CDS-MVSNet		64.22 116	65.89 114	62.28 148	70.05 139	80.59 117	69.91 129	57.98 122	43.53 168	46.58 104	48.22 96	50.76 118	46.45 184	75.68 96	76.08 90	82.70 173	86.34 119
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
view600		63.91 123	63.27 131	64.66 111	75.57 106	81.73 91	69.71 130	63.04 73	43.97 161	39.18 157	41.09 140	40.24 163	55.38 152	76.28 86	72.04 162	85.08 118	87.52 109

GA-MVS		64.55 114	65.76 115	63.12 137	69.68 141	81.56 98	69.59 131	58.16 120	45.23 156	35.58 180	47.01 111	41.82 140	59.41 131	79.62 58	78.54 66	86.32 65	86.56 115

thres600view7		63.77 124	63.14 133	64.51 113	75.49 107	81.61 93	69.59 131	62.95 75	43.96 162	38.90 159	41.09 140	40.24 163	55.25 154	76.24 88	71.54 165	84.89 125	87.30 110

v144192		62.05 151	61.46 157	62.73 144	66.59 178	79.87 124	69.30 133	55.88 150	41.50 185	39.41 155	37.23 178	36.45 190	59.62 127	72.69 145	73.51 140	85.61 108	88.93 84

MDTV_nov1_ep13		65.21 109	67.28 101	62.79 140	70.91 136	81.72 92	69.28 134	49.50 195	58.08 92	43.94 119	50.50 89	56.02 96	58.86 137	70.72 166	73.37 143	84.24 147	80.52 166

EPMVS		66.21 100	67.49 100	64.73 109	75.81 104	84.20 76	68.94 135	44.37 215	61.55 81	48.07 99	49.21 94	54.87 105	62.88 91	71.82 158	71.40 170	88.28 28	79.37 173

EPNet_dtu		66.17 101	70.13 84	61.54 154	81.04 64	77.39 152	68.87 136	62.50 82	69.78 60	33.51 189	63.77 47	56.22 95	37.65 207	72.20 152	72.18 161	85.69 102	79.38 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMH+	60.36 13	61.16 161	58.38 185	64.42 116	77.37 91	74.35 177	68.45 137	62.81 81	45.86 152	38.48 162	35.71 189	37.35 185	59.81 125	67.24 189	69.80 184	79.58 201	78.32 177

v1921920		61.66 158	61.10 160	62.31 147	66.32 179	79.57 128	68.41 138	55.49 156	41.03 186	38.69 161	36.64 186	35.27 201	59.60 128	73.23 125	73.41 142	85.37 110	88.51 101

thisisatest0530		68.38 85	70.98 78	65.35 100	72.61 125	84.42 71	68.21 139	57.98 122	59.77 86	50.80 89	54.63 72	58.48 82	57.92 142	76.99 79	77.47 76	84.60 139	85.07 129

v148		62.00 152	61.19 159	62.96 138	67.46 173	79.49 129	67.87 140	57.66 128	42.30 178	45.02 114	38.20 172	38.89 179	54.77 155	69.83 179	72.60 158	84.96 121	87.01 112

view800		63.02 136	62.69 144	63.39 133	74.79 111	80.76 112	67.83 141	61.93 86	43.16 172	37.78 168	40.43 145	39.73 170	53.16 159	75.01 102	73.32 145	84.87 127	86.43 117

ACMH	59.42 14	61.59 160	59.22 183	64.36 117	78.92 76	78.26 140	67.65 142	67.48 42	39.81 190	30.98 197	38.25 170	34.59 203	61.37 105	70.55 170	73.47 141	79.74 200	79.59 171
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		67.99 88	70.61 80	64.94 103	71.94 130	83.96 77	67.62 143	57.98 122	59.30 88	49.90 94	54.50 75	57.98 89	57.92 142	76.48 85	77.47 76	84.24 147	84.58 132

CR-MVSNet		62.31 144	64.75 119	59.47 165	68.63 148	71.29 198	67.53 144	43.18 217	55.83 102	41.40 145	41.04 142	55.85 97	57.29 147	72.76 142	73.27 148	78.77 206	83.23 147

Patchmtry						78.06 143	67.53 144	43.18 217		41.40 145

thresconf0.02		63.92 122	65.18 116	62.46 145	75.91 103	80.65 116	67.51 146	63.86 64	45.00 157	33.32 190	51.38 85	51.68 115	48.34 173	75.49 100	75.13 100	85.84 94	76.91 181

IterMVS		61.87 154	63.55 126	59.90 161	67.29 174	72.20 193	67.34 147	48.56 200	47.48 141	37.86 167	47.07 109	48.27 123	54.08 157	72.12 154	73.71 138	84.30 146	83.99 137
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1240		61.09 162	60.55 166	61.72 153	65.92 183	79.28 132	67.16 148	54.91 162	39.79 191	38.10 164	36.08 188	34.64 202	59.15 135	72.86 135	73.36 144	85.10 115	87.84 106

EPP-MVSNet		67.58 90	71.10 77	63.48 131	75.71 105	83.35 80	66.85 149	57.83 127	53.02 116	41.15 148	55.82 67	67.89 50	56.01 150	74.40 109	72.92 154	83.33 162	90.30 70

pmmvs4		63.14 133	62.46 146	63.94 125	66.03 181	76.40 161	66.82 150	57.60 132	56.74 96	50.26 93	40.81 144	37.51 184	59.26 134	71.75 160	71.48 167	83.68 157	82.53 152

dps		64.08 118	63.22 132	65.08 102	75.27 108	79.65 127	66.68 151	46.63 209	56.94 95	55.67 75	43.96 118	43.63 137	64.00 85	69.50 182	69.82 183	82.25 180	79.02 174

tfpnnormal		58.97 174	56.48 193	61.89 151	71.27 134	76.21 163	66.65 152	61.76 91	32.90 216	36.41 172	27.83 214	29.14 219	50.64 168	73.06 127	73.05 152	84.58 141	83.15 149

UGNet		67.57 91	71.69 73	62.76 142	69.88 140	82.58 84	66.43 153	58.64 116	54.71 112	51.87 83	61.74 53	62.01 70	45.46 190	74.78 106	74.99 101	84.24 147	91.02 60
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
PatchmatchNet		65.43 108	67.71 98	62.78 141	73.49 117	82.83 82	66.42 154	45.40 210	60.40 85	45.27 109	49.22 93	57.60 90	60.01 120	70.61 167	71.38 173	86.08 73	81.91 160
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Vis-MVSNet		65.53 107	69.83 85	60.52 158	70.80 138	84.59 70	66.37 155	55.47 157	48.40 137	40.62 152	57.67 65	58.43 84	45.37 191	77.49 72	76.24 89	84.47 142	85.99 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tfpn		62.54 142	62.79 140	62.25 149	74.16 113	79.86 125	66.07 156	60.97 99	42.43 177	36.41 172	39.88 150	43.76 136	51.25 166	73.85 119	74.17 129	84.67 137	85.57 128

IS_MVSNet		67.29 94	71.98 70	61.82 152	76.92 94	84.32 75	65.90 157	58.22 119	55.75 104	39.22 156	54.51 74	62.47 65	45.99 187	78.83 65	78.52 67	84.70 136	89.47 77

conf0.05thres1000		60.33 168	59.42 180	61.40 155	73.15 119	78.25 141	65.29 158	60.30 107	36.61 202	35.75 178	33.25 196	39.23 175	50.35 169	72.18 153	72.67 157	83.57 158	83.74 138

FC-MVSNet-train		68.83 81	68.29 94	69.47 74	78.35 77	79.94 123	64.72 159	66.38 47	54.96 108	54.51 79	56.75 66	47.91 125	66.91 75	75.57 99	75.75 93	85.92 83	87.12 111

IB-MVS	64.48 11	69.02 80	68.97 90	69.09 79	81.75 60	89.01 33	64.50 160	64.91 58	56.65 97	62.59 48	47.89 99	45.23 131	51.99 161	69.18 183	81.88 37	88.77 17	92.93 40
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
pm-mvs1		59.21 173	59.58 179	58.77 170	67.97 165	77.07 158	64.12 161	57.20 140	34.73 210	36.86 170	35.34 191	40.54 162	43.34 195	74.32 114	73.30 147	83.13 170	81.77 161

PMMVS		70.37 74	75.06 59	64.90 104	71.46 131	81.88 90	64.10 162	55.64 154	71.31 56	46.69 103	70.69 34	58.56 80	69.53 58	79.03 62	75.63 95	81.96 182	88.32 103

tfpn_ndepth		62.95 140	63.75 125	62.02 150	76.89 95	79.48 130	64.09 163	60.98 98	49.48 126	38.73 160	49.92 91	44.79 132	47.37 178	71.91 157	71.66 164	84.07 153	79.00 175

UniMVSNet_NR-MVSNet		62.30 145	63.51 127	60.89 157	69.48 145	77.83 145	64.07 164	63.94 63	50.03 123	31.17 195	44.82 117	41.12 147	51.37 163	71.02 164	74.81 104	85.30 111	84.95 130

DU-MVS		60.87 164	61.82 152	59.76 163	66.69 176	75.87 164	64.07 164	61.96 84	49.31 127	31.17 195	42.76 126	36.95 187	51.37 163	69.67 180	73.20 151	83.30 163	84.95 130

test-LLR		68.23 86	71.61 74	64.28 122	71.37 132	81.32 102	63.98 166	61.03 96	58.62 90	42.96 132	52.74 80	61.65 71	57.74 144	75.64 97	78.09 73	88.61 20	93.21 36

TESTMET0.1,1		67.38 93	71.61 74	62.45 146	66.05 180	81.32 102	63.98 166	55.36 158	58.62 90	42.96 132	52.74 80	61.65 71	57.74 144	75.64 97	78.09 73	88.61 20	93.21 36

MIMVSNet		57.78 185	59.71 177	55.53 190	54.79 218	77.10 157	63.89 168	45.02 212	46.59 149	36.79 171	28.36 213	40.77 156	45.84 188	74.97 103	76.58 83	86.87 56	73.60 193

FMVSNet5		58.86 176	60.24 171	57.25 182	52.66 226	66.25 210	63.77 169	52.86 185	57.85 94	37.92 166	36.12 187	52.22 114	51.37 163	70.88 165	71.43 169	84.92 122	66.91 213

NR-MVSNet		61.08 163	62.09 150	59.90 161	71.96 129	75.87 164	63.60 170	61.96 84	49.31 127	27.95 204	42.76 126	33.85 207	48.82 172	74.35 112	74.05 134	85.13 114	84.45 133

TransMVSNet (Re)		57.83 184	56.90 191	58.91 169	72.26 127	74.69 175	63.57 171	61.42 93	32.30 218	32.65 192	33.97 195	35.96 195	39.17 205	73.84 121	72.84 155	84.37 144	74.69 188

EG-PatchMatch MVS		58.73 178	58.03 188	59.55 164	72.32 126	80.49 118	63.44 172	55.55 155	32.49 217	38.31 163	28.87 212	37.22 186	42.84 196	74.30 115	75.70 94	84.84 128	77.14 180

TranMVSNet+NR-MVSNet		60.38 167	61.30 158	59.30 166	68.34 152	75.57 170	63.38 173	63.78 66	46.74 144	27.73 205	42.56 130	36.84 188	47.66 176	70.36 173	74.59 110	84.91 124	82.46 153

pmmvs5		59.72 169	60.24 171	59.11 168	62.77 197	77.33 153	63.17 174	54.00 171	40.21 189	37.23 169	40.41 146	35.99 194	51.75 162	72.55 148	72.74 156	85.72 101	82.45 154

USDC		59.69 170	60.03 174	59.28 167	64.04 191	71.84 196	63.15 175	55.36 158	54.90 110	35.02 184	48.34 95	29.79 218	58.16 139	70.60 168	71.33 174	79.99 198	73.42 194

CMPMVS	43.63 17	57.67 186	55.43 194	60.28 160	72.01 128	79.00 133	62.77 176	53.23 179	41.77 182	45.42 108	30.74 208	39.03 176	53.01 160	64.81 197	64.65 206	75.26 217	68.03 211
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
COLMAP_ROB	51.17 15	55.13 191	52.90 205	57.73 179	73.47 118	67.21 208	62.13 177	55.82 151	47.83 140	34.39 185	31.60 205	34.24 204	44.90 192	63.88 204	62.52 214	75.67 215	63.02 221
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v7n		57.04 188	56.64 192	57.52 180	62.85 196	74.75 174	61.76 178	51.80 188	35.58 209	36.02 177	32.33 200	33.61 208	50.16 170	67.73 188	70.34 182	82.51 175	82.12 157

UniMVSNet (Re)		60.62 165	62.93 137	57.92 174	67.64 171	77.90 144	61.75 179	61.24 95	49.83 125	29.80 199	42.57 129	40.62 161	43.36 194	70.49 172	73.27 148	83.76 155	85.81 124

Baseline_NR-MVSNet		59.47 171	60.28 169	58.54 171	66.69 176	73.90 178	61.63 180	62.90 80	49.15 135	26.87 206	35.18 193	37.62 183	48.20 174	69.67 180	73.61 139	84.92 122	82.82 150

TDRefinement		52.70 202	51.02 211	54.66 194	57.41 215	65.06 214	61.47 181	54.94 160	44.03 160	33.93 187	30.13 210	27.57 221	46.17 186	61.86 206	62.48 215	74.01 221	66.06 215

CHOSEN 280x420		62.23 148	66.57 105	57.17 183	59.88 207	68.92 204	61.20 182	42.28 221	54.17 113	39.57 153	47.78 100	64.97 58	62.68 92	73.85 119	69.52 185	77.43 210	86.75 113

tfpnview11		58.92 175	59.60 178	58.13 172	72.99 121	77.11 156	60.48 183	60.37 105	42.10 180	29.10 201	43.45 119	40.72 158	41.67 199	70.53 171	70.43 181	84.17 151	72.85 197

tfpn_n400		58.64 179	59.27 181	57.89 175	72.83 122	77.26 154	60.35 184	60.29 108	39.77 192	29.10 201	43.45 119	40.72 158	41.61 200	70.06 176	71.39 171	83.17 167	72.26 200

tfpnconf		58.64 179	59.27 181	57.89 175	72.83 122	77.26 154	60.35 184	60.29 108	39.77 192	29.10 201	43.45 119	40.72 158	41.61 200	70.06 176	71.39 171	83.17 167	72.26 200

ADS-MVSNet		58.40 182	59.16 184	57.52 180	65.80 184	74.57 176	60.26 186	40.17 229	50.51 121	38.01 165	40.11 149	44.72 133	59.36 132	64.91 195	66.55 198	81.53 185	72.72 199

pmmvs6		54.20 200	53.54 202	54.97 191	63.22 195	72.98 185	60.17 187	52.32 187	26.77 229	34.30 186	23.29 224	36.23 192	40.33 203	68.77 185	68.76 187	79.47 203	78.00 178

UA-Net		64.62 112	68.23 96	60.42 159	77.53 89	81.38 100	60.08 188	57.47 135	47.01 142	44.75 115	60.68 58	71.32 41	41.84 198	73.27 124	72.25 160	80.83 192	71.68 203

v52		54.79 196	55.15 195	54.36 197	54.07 221	72.13 194	59.84 189	49.39 196	34.50 211	35.08 183	31.63 204	35.74 197	47.21 181	63.90 202	67.92 189	80.59 194	80.23 167

V4		54.78 197	55.14 196	54.37 196	54.07 221	72.13 194	59.83 190	49.39 196	34.46 213	35.11 182	31.64 203	35.72 198	47.22 180	63.90 202	67.92 189	80.59 194	80.23 167

PatchMatch-RL		62.22 149	60.69 163	64.01 123	68.74 147	75.75 167	59.27 191	60.35 106	56.09 101	53.80 80	47.06 110	36.45 190	64.80 83	68.22 186	67.22 195	77.10 211	74.02 190

test-mter		64.06 121	69.24 87	58.01 173	59.07 210	77.40 151	59.13 192	48.11 202	55.64 105	39.18 157	51.56 84	58.54 81	55.38 152	73.52 123	76.00 91	87.22 51	92.05 51

TAMVS		58.86 176	60.91 161	56.47 186	62.38 199	77.57 148	58.97 193	52.98 182	38.76 196	36.17 175	42.26 134	47.94 124	46.45 184	70.23 174	70.79 177	81.86 183	78.82 176

v748		55.19 190	54.63 197	55.85 188	61.44 202	72.97 186	58.72 194	51.62 189	34.48 212	36.39 174	32.09 201	33.05 209	45.48 189	61.85 207	67.87 191	81.45 186	80.08 169

thisisatest0515		59.37 172	60.68 164	57.84 178	64.39 190	75.65 169	58.56 195	53.86 172	41.55 184	42.12 143	40.40 147	39.59 173	47.09 182	71.69 161	73.79 137	81.02 190	82.08 158

MDTV_nov1_ep13_2view		54.47 199	54.61 198	54.30 198	60.50 204	73.82 179	57.92 196	43.38 216	39.43 195	32.51 193	33.23 197	34.05 205	47.26 179	62.36 205	66.21 201	84.24 147	73.19 196

pmmvs-eth3d		55.20 189	53.95 201	56.65 184	57.34 216	67.77 206	57.54 197	53.74 173	40.93 187	41.09 149	31.19 207	29.10 220	49.07 171	65.54 192	67.28 194	81.14 188	75.81 182

TinyColmap		52.66 203	50.09 214	55.65 189	59.72 208	64.02 218	57.15 198	52.96 183	40.28 188	32.51 193	32.42 199	20.97 233	56.65 149	63.95 201	65.15 205	74.91 218	63.87 219

tfpn1000		58.35 183	59.96 175	56.47 186	72.78 124	77.51 150	56.66 199	59.16 112	43.74 163	29.76 200	42.79 125	42.49 138	37.04 208	68.92 184	68.98 186	83.45 160	75.25 185

Vis-MVSNet (Re-imp)		62.25 146	68.74 91	54.68 193	73.70 114	78.74 135	56.51 200	57.49 134	55.22 106	26.86 207	54.56 73	61.35 73	31.06 210	73.10 126	74.90 102	82.49 176	83.31 143

our_test_3					63.32 193	71.07 200	55.90 201

CVMVSNet		54.92 195	58.16 186	51.13 204	62.61 198	68.44 205	55.45 202	52.38 186	42.28 179	21.45 215	47.10 108	46.10 128	37.96 206	64.42 200	63.81 208	76.92 213	75.01 187

RPMNet		58.63 181	62.80 139	53.76 199	67.59 172	71.29 198	54.60 203	38.13 232	55.83 102	35.70 179	41.58 138	53.04 112	47.89 175	66.10 191	67.38 193	78.65 208	84.40 134

RPSCF		55.07 192	58.06 187	51.57 201	48.87 233	58.95 224	53.68 204	41.26 227	62.42 78	45.88 106	54.38 76	54.26 106	53.75 158	57.15 216	53.53 231	66.01 230	65.75 216

test0.0.03 1		57.35 187	59.89 176	54.38 195	71.37 132	73.45 180	52.71 205	61.03 96	46.11 151	26.33 208	41.73 137	44.08 134	29.72 213	71.43 163	70.90 176	85.10 115	71.56 204

anonymousdsp		54.99 193	57.24 190	52.36 200	53.82 223	71.75 197	51.49 206	48.14 201	33.74 214	33.66 188	38.34 169	36.13 193	47.54 177	64.53 199	70.60 179	79.53 202	85.59 127

Anonymous20231206		52.23 204	52.80 206	51.56 202	64.70 189	69.41 202	51.01 207	58.60 117	36.63 201	22.44 214	21.80 227	31.42 214	30.52 211	66.79 190	67.83 192	82.10 181	75.73 183

PM-MVS		50.11 209	50.38 213	49.80 206	47.23 235	62.08 222	50.91 208	44.84 214	41.90 181	36.10 176	35.22 192	26.05 227	46.83 183	57.64 214	55.42 230	72.90 222	74.32 189

LTVRE_ROB	47.26 16	49.41 212	49.91 215	48.82 208	64.76 188	69.79 201	49.05 209	47.12 206	20.36 238	16.52 224	36.65 185	26.96 222	50.76 167	60.47 209	63.16 211	64.73 231	72.00 202
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
LP		48.21 215	46.65 222	50.03 205	60.39 205	63.86 219	48.73 210	38.71 231	35.60 208	32.99 191	23.31 223	24.95 229	40.07 204	57.73 213	61.56 216	79.29 204	59.51 227

PEN-MVS		51.04 205	52.94 204	48.82 208	61.45 201	66.00 211	48.68 211	57.20 140	36.87 200	15.36 226	36.98 181	32.72 210	28.77 217	57.63 215	66.37 199	81.44 187	74.00 191

CP-MVSNet		50.57 207	52.60 208	48.21 211	58.77 212	65.82 212	48.17 212	56.29 147	37.41 198	16.59 223	37.14 179	31.95 212	29.21 214	56.60 218	63.71 209	80.22 196	75.56 184

PS-CasMVS		50.17 208	52.02 209	48.02 212	58.60 213	65.54 213	48.04 213	56.19 149	36.42 204	16.42 225	35.68 190	31.33 215	28.85 216	56.42 220	63.54 210	80.01 197	75.18 186

PatchT		60.46 166	63.85 124	56.51 185	65.95 182	75.68 168	47.34 214	41.39 224	53.89 115	41.40 145	37.84 176	50.30 120	57.29 147	72.76 142	73.27 148	85.67 103	83.23 147

SixPastTwentyTwo		49.11 213	49.22 216	48.99 207	58.54 214	64.14 217	47.18 215	47.75 203	31.15 220	24.42 210	41.01 143	26.55 223	44.04 193	54.76 226	58.70 221	71.99 225	68.21 209

N_pmnet		47.67 216	47.00 221	48.45 210	54.72 219	62.78 220	46.95 216	51.25 190	36.01 206	26.09 209	26.59 218	25.93 228	35.50 209	55.67 222	59.01 219	76.22 214	63.04 220

MDA-MVSNet-bldmvs		44.15 221	42.27 228	46.34 215	38.34 238	62.31 221	46.28 217	55.74 153	29.83 222	20.98 216	27.11 217	16.45 239	41.98 197	41.11 237	57.47 223	74.72 219	61.65 225

FPMVS		39.11 227	36.39 232	42.28 220	55.97 217	45.94 238	46.23 218	41.57 223	35.73 207	22.61 212	23.46 222	19.82 235	28.32 220	43.57 233	40.67 236	58.96 234	45.54 232

WR-MVS_H		49.62 211	52.63 207	46.11 217	58.80 211	67.58 207	46.14 219	54.94 160	36.51 203	13.63 231	36.75 184	35.67 199	22.10 227	56.43 219	62.76 212	81.06 189	72.73 198

DTE-MVSNet		49.82 210	51.92 210	47.37 213	61.75 200	64.38 216	45.89 220	57.33 139	36.11 205	12.79 233	36.87 182	31.93 213	25.73 222	58.01 212	65.22 204	80.75 193	70.93 207

WR-MVS		51.02 206	54.56 199	46.90 214	63.84 192	69.23 203	44.78 221	56.38 146	38.19 197	14.19 228	37.38 177	36.82 189	22.39 226	60.14 210	66.20 202	79.81 199	73.95 192

MVS-HIRNet		53.86 201	53.02 203	54.85 192	60.30 206	72.36 192	44.63 222	42.20 222	39.45 194	43.47 121	21.66 228	34.00 206	55.47 151	65.42 193	67.16 196	83.02 171	71.08 206

EU-MVSNet		44.84 220	47.85 218	41.32 223	49.26 230	56.59 228	43.07 223	47.64 205	33.03 215	13.82 229	36.78 183	30.99 216	24.37 224	53.80 227	55.57 229	69.78 226	68.21 209

test2356		46.29 219	47.37 219	45.03 219	54.38 220	57.99 227	42.03 224	50.32 192	30.78 221	16.65 222	27.40 216	23.70 230	29.86 212	61.20 208	64.31 207	76.93 212	66.22 214

testgi		48.51 214	50.53 212	46.16 216	64.78 187	67.15 209	41.54 225	54.81 164	29.12 224	17.03 221	32.07 202	31.98 211	20.15 230	65.26 194	67.00 197	78.67 207	61.10 226

test20.03		47.23 218	48.69 217	45.53 218	63.28 194	64.39 215	41.01 226	56.93 143	29.16 223	15.21 227	23.90 220	30.76 217	17.51 235	64.63 198	65.26 203	79.21 205	62.71 222

new-patchmatchnet		42.21 224	42.97 225	41.33 222	53.05 225	59.89 223	39.38 227	49.61 194	28.26 226	12.10 234	22.17 226	21.54 232	19.22 231	50.96 230	56.04 228	74.61 220	61.92 224

MIMVSNet1		40.84 226	43.46 224	37.79 228	32.14 240	58.92 225	39.24 228	50.83 191	27.00 228	11.29 236	16.76 238	26.53 224	17.75 234	57.14 217	61.12 218	75.46 216	56.78 230

pmmvs3		41.86 225	42.29 227	41.36 221	39.80 236	52.66 231	38.93 229	35.85 238	23.40 233	20.22 217	19.30 229	20.84 234	40.56 202	55.98 221	58.79 220	72.80 223	65.03 217

testpf		43.39 222	47.17 220	38.98 225	65.58 185	47.38 237	36.09 230	31.67 239	36.97 199	19.47 218	33.01 198	35.62 200	23.61 225	50.86 231	56.08 227	57.48 236	70.27 208

ambc			42.30 226		50.36 227	49.51 234	35.47 231		32.04 219	23.53 211	17.36 233	8.95 244	29.06 215	64.88 196	56.26 226	61.29 233	67.12 212

FC-MVSNet-test		47.24 217	54.37 200	38.93 226	59.49 209	58.25 226	34.48 232	53.36 176	45.66 153	6.66 241	50.62 87	42.02 139	16.62 236	58.39 211	61.21 217	62.99 232	64.40 218

gm-plane-assit		54.99 193	57.99 189	51.49 203	69.27 146	54.42 229	32.32 233	42.59 220	21.18 236	13.71 230	23.61 221	43.84 135	60.21 119	87.09 4	86.55 4	90.81 4	89.28 79

testus		42.30 223	43.69 223	40.67 224	53.21 224	53.50 230	31.81 234	49.96 193	27.06 227	11.55 235	25.67 219	19.00 236	25.20 223	55.34 223	62.59 213	72.31 224	62.69 223

1111		38.93 228	38.98 229	38.86 227	50.10 228	50.42 232	29.52 235	38.00 233	22.67 234	17.99 219	17.40 231	26.26 225	28.72 218	54.86 224	58.20 222	68.82 229	43.08 235

.test1245		25.86 235	24.56 238	27.39 236	50.10 228	50.42 232	29.52 235	38.00 233	22.67 234	17.99 219	17.40 231	26.26 225	28.72 218	54.86 224	0.05 243	0.01 247	0.24 244

new_pmnet		33.19 231	35.52 233	30.47 232	27.55 244	45.31 239	29.29 237	30.92 240	29.00 225	9.88 238	18.77 230	17.64 238	26.77 221	44.07 232	45.98 234	58.41 235	47.87 231

testmv		37.40 229	37.95 230	36.76 229	48.97 231	49.33 235	28.65 238	46.74 207	18.34 239	7.68 239	16.80 236	14.47 240	19.18 232	51.72 228	56.93 224	69.36 227	58.09 228

test1235678		37.40 229	37.94 231	36.76 229	48.97 231	49.30 236	28.65 238	46.73 208	18.33 240	7.68 239	16.79 237	14.46 241	19.18 232	51.72 228	56.92 225	69.36 227	58.07 229

PMVS	27.44 18	32.08 232	29.07 235	35.60 231	48.33 234	24.79 243	26.97 240	41.34 225	20.45 237	22.50 213	17.11 235	18.64 237	20.44 229	41.99 236	38.06 237	54.02 239	42.44 236
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		29.92 233	31.49 234	28.08 233	38.46 237	37.74 241	21.36 241	40.17 229	16.83 241	5.61 243	15.66 239	11.48 242	6.60 242	42.01 235	51.23 232	56.29 237	45.52 233

Gipuma		24.91 236	24.61 237	25.26 237	31.47 241	21.59 244	18.06 242	37.53 235	25.43 231	10.03 237	4.18 245	4.25 247	14.85 237	43.20 234	47.03 233	39.62 241	26.55 240
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		26.96 234	26.51 236	27.49 235	37.87 239	39.14 240	17.12 243	41.31 226	12.02 243	3.68 245	8.04 241	8.42 245	10.67 240	28.11 239	45.96 235	54.27 238	43.89 234

DeepMVS_CX						19.81 246	17.01 244	10.02 243	23.61 232	5.85 242	17.21 234	8.03 246	21.13 228	22.60 241		21.42 245	30.01 238

PMMVS2		20.45 237	22.31 239	18.27 240	20.52 245	26.73 242	14.85 245	28.43 242	13.69 242	0.79 248	10.35 240	9.10 243	3.83 244	27.64 240	32.87 238	41.17 240	35.81 237

tmp_tt				16.09 241	13.07 246	8.12 249	13.61 246	2.08 244	55.09 107	30.10 198	40.26 148	22.83 231	5.35 243	29.91 238	25.25 240	32.33 242

EMVS		14.40 239	10.71 242	18.70 239	28.15 243	12.09 248	7.06 247	36.89 236	11.00 244	3.56 247	4.95 243	2.27 249	13.91 238	10.13 244	16.06 242	22.63 244	18.51 242

E-PMN		15.08 238	11.65 241	19.08 238	28.73 242	12.31 247	6.95 248	36.87 237	10.71 245	3.63 246	5.13 242	2.22 250	13.81 239	11.34 243	18.50 241	24.49 243	21.32 241

MVE	15.98 19	14.37 240	16.36 240	12.04 242	7.72 247	20.24 245	5.90 249	29.05 241	8.28 246	3.92 244	4.72 244	2.42 248	9.57 241	18.89 242	31.46 239	16.07 246	28.53 239
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Patchmatch-RL test							2.17 250

testmvs		0.05 241	0.08 243	0.01 243	0.00 249	0.01 250	0.03 251	0.01 246	0.05 247	0.00 250	0.14 247	0.01 251	0.03 247	0.05 245	0.05 243	0.01 247	0.24 244

test123		0.05 241	0.08 243	0.01 243	0.00 249	0.01 250	0.01 252	0.00 247	0.05 247	0.00 250	0.16 246	0.00 252	0.04 245	0.02 246	0.05 243	0.00 249	0.26 243

sosnet-low-res		0.00 243	0.00 245	0.00 245	0.00 249	0.00 252	0.00 253	0.00 247	0.00 249	0.00 250	0.00 248	0.00 252	0.00 248	0.00 247	0.00 246	0.00 249	0.00 246

sosnet		0.00 243	0.00 245	0.00 245	0.00 249	0.00 252	0.00 253	0.00 247	0.00 249	0.00 250	0.00 248	0.00 252	0.00 248	0.00 247	0.00 246	0.00 249	0.00 246

MTAPA										78.32 7		79.42 19

MTMP										76.04 12		76.65 24

mPP-MVS					86.96 38							70.61 44

NP-MVS									81.60 32

High-resolution multi-view benchmark