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.

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

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	0.00 245

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

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

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

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

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

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

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

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 89	67.86 51	77.47 13	82.89 26	84.24 16	88.61 20	89.99 73

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
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.
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

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	83.75 155	96.77 8

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	46.33 114	64.55 60	71.77 43	79.92 57	80.88 53	87.52 42	89.20 80

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
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	83.93 153	93.75 34

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

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

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
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

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

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 87	77.87 11	76.82 80	76.82 80	84.67 137	86.45 116

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 82	70.68 43	76.27 20	82.27 36	82.04 34	85.92 83	90.77 64

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

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

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.
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

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

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

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
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

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

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

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
mPP-MVS					86.96 38							70.61 44

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

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

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

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	30.27 208	50.74 118	66.14 77	86.22 8	85.41 7	91.90 1	96.75 9

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

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
HQP-MVS		78.26 36	80.91 32	75.17 47	85.67 45	84.33 73	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

OPM-MVS		72.74 62	70.93 78	74.85 51	85.30 46	84.34 72	82.82 28	69.79 28	49.96 123	55.39 77	54.09 77	60.14 78	70.04 54	80.38 54	79.43 58	85.74 98	88.20 104

MS-PatchMatch		70.34 75	69.00 88	71.91 65	85.20 47	85.35 65	77.84 58	61.77 90	58.01 92	55.40 76	41.26 138	58.34 84	61.69 97	81.70 42	78.29 69	89.56 9	80.02 169

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

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
ACMM	66.70 10	70.42 71	68.49 92	72.67 59	82.85 50	77.76 146	77.70 59	64.76 59	64.61 76	60.74 59	49.29 91	53.97 107	65.86 78	74.97 102	75.57 96	84.13 151	83.29 143
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
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

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

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 85	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 85	67.60 71	84.24 19	81.70 39	88.18 30	97.10 5

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

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
LGP-MVS_train		72.02 67	73.18 68	70.67 70	82.13 58	80.26 121	79.58 49	63.04 73	70.09 59	51.98 82	65.06 44	55.62 100	62.49 94	75.97 93	76.32 87	84.80 134	88.93 84

MSDG		65.57 105	61.57 155	70.24 71	82.02 59	76.47 159	74.46 83	68.73 37	56.52 97	50.33 91	38.47 166	41.10 147	62.42 95	72.12 153	72.94 152	83.47 158	73.37 194

IB-MVS	64.48 11	69.02 80	68.97 89	69.09 79	81.75 60	89.01 33	64.50 159	64.91 58	56.65 96	62.59 48	47.89 98	45.23 130	51.99 160	69.18 182	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
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

CPTT-MVS		75.43 52	77.13 48	73.44 55	81.43 62	82.55 84	80.96 42	64.35 60	77.95 44	61.39 52	69.20 36	70.94 42	69.38 61	73.89 117	73.32 144	83.14 168	92.06 50

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 79	78.63 65	89.59 8	90.86 61

EPNet_dtu		66.17 100	70.13 83	61.54 153	81.04 64	77.39 151	68.87 136	62.50 82	69.78 60	33.51 188	63.77 47	56.22 94	37.65 206	72.20 151	72.18 160	85.69 102	79.38 171
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMP	68.86 7	72.15 66	72.25 69	72.03 62	80.96 65	80.87 106	77.93 57	64.13 62	69.29 62	60.79 58	64.04 46	53.54 109	63.91 86	73.74 121	75.27 98	84.45 142	88.98 83
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HyFIR lowres test		68.39 84	68.28 94	68.52 82	80.85 66	88.11 44	71.08 117	58.09 121	54.87 110	47.80 99	27.55 214	55.80 97	64.97 81	79.11 61	79.14 61	88.31 27	93.35 35

LS3D		64.54 114	62.14 148	67.34 90	80.85 66	75.79 165	69.99 127	65.87 51	60.77 83	44.35 116	42.43 132	45.95 128	65.01 80	69.88 177	68.69 187	77.97 208	71.43 204

CNLPA		71.37 70	70.27 82	72.66 60	80.79 68	81.33 100	71.07 118	65.75 52	82.36 27	64.80 42	42.46 131	56.49 93	72.70 37	73.00 129	70.52 179	80.84 190	85.76 125

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

PLC	64.00 12	68.54 82	66.66 103	70.74 69	80.28 70	74.88 171	72.64 87	63.70 67	69.26 63	55.71 74	47.24 106	55.31 102	70.42 50	72.05 155	70.67 177	81.66 183	77.19 178
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OMC-MVS		74.03 57	75.82 58	71.95 64	79.56 71	80.98 104	75.35 73	63.21 69	84.48 21	61.83 50	61.54 55	66.89 52	69.41 60	76.60 82	74.07 132	82.34 178	86.15 120

CostFormer		72.18 65	73.90 63	70.18 72	79.47 72	86.19 62	76.94 63	48.62 198	66.07 71	60.40 60	54.14 76	65.82 54	67.98 68	75.84 94	76.41 86	87.67 39	92.83 42

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 77	83.22 163	91.93 52

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

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 98	59.66 62	53.88 78	62.48 64	59.08 136	81.17 47	78.90 62	86.53 62	94.74 22

ACMH	59.42 14	61.59 159	59.22 182	64.36 116	78.92 76	78.26 139	67.65 141	67.48 42	39.81 189	30.98 196	38.25 169	34.59 202	61.37 105	70.55 169	73.47 140	79.74 199	79.59 170
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		68.83 81	68.29 93	69.47 74	78.35 77	79.94 122	64.72 158	66.38 47	54.96 107	54.51 79	56.75 66	47.91 124	66.91 75	75.57 98	75.75 92	85.92 83	87.12 111

tpmp4_e23		69.38 76	69.47 85	69.28 76	78.20 78	82.35 86	75.92 64	49.20 197	64.15 77	59.96 61	47.93 96	55.77 98	68.06 67	73.05 128	74.53 111	84.34 144	88.50 102

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 86	48.47 96	52.73 81	54.94 103	69.69 56	78.34 70	77.06 78	86.18 68	90.73 66

Anonymous202405211			66.35 107		78.00 80	84.41 71	74.85 75	63.18 70	51.00 119		31.37 205	53.73 108	69.67 57	76.28 85	76.84 79	83.21 165	90.85 62

conf0.002		67.12 96	67.13 101	67.11 91	77.95 81	82.11 87	71.71 99	63.06 71	49.16 128	43.43 121	47.76 100	48.79 121	61.42 99	76.61 81	76.55 84	85.07 119	88.92 86

conf0.01		66.60 97	66.18 109	67.09 92	77.90 82	82.02 88	71.71 99	63.05 72	49.16 128	43.41 123	46.23 115	45.78 129	61.42 99	76.55 83	74.63 105	85.04 120	88.87 88

tfpn111		66.52 98	66.12 110	66.98 94	77.70 83	81.58 94	71.71 99	62.94 77	49.16 128	43.28 126	51.38 84	41.34 140	61.42 99	76.24 87	74.63 105	84.84 128	88.52 98

conf200view11		65.89 103	64.96 116	66.98 94	77.70 83	81.58 94	71.71 99	62.94 77	49.16 128	43.28 126	43.24 121	41.34 140	61.42 99	76.24 87	74.63 105	84.84 128	88.52 98

thres100view900		67.14 95	66.09 111	68.38 84	77.70 83	83.84 77	74.52 80	66.33 49	49.16 128	43.40 124	43.24 121	41.34 140	62.59 93	79.31 60	75.92 91	85.73 99	89.81 74

tfpn200view9		65.90 102	64.96 116	67.00 93	77.70 83	81.58 94	71.71 99	62.94 77	49.16 128	43.40 124	43.24 121	41.34 140	61.42 99	76.24 87	74.63 105	84.84 128	88.52 98

Anonymous20240521		69.13 79	69.07 87	69.21 77	77.65 87	77.52 148	74.68 76	57.85 125	54.92 108	55.34 78	55.74 68	55.56 101	66.35 76	75.05 100	76.56 83	83.35 160	88.13 105

Anonymous20231211		68.44 83	66.37 106	70.86 68	77.58 88	83.49 78	75.15 74	61.89 87	52.54 116	58.50 64	28.89 210	56.78 91	69.29 62	74.96 104	76.61 81	82.73 171	91.36 57

UA-Net		64.62 111	68.23 95	60.42 158	77.53 89	81.38 99	60.08 187	57.47 134	47.01 141	44.75 114	60.68 58	71.32 41	41.84 197	73.27 123	72.25 159	80.83 191	71.68 202

thres200		65.58 104	64.74 119	66.56 96	77.52 90	81.61 92	73.44 85	62.95 75	46.23 149	42.45 141	42.76 125	41.18 145	58.12 140	76.24 87	75.59 95	84.89 125	89.58 75

ACMH+	60.36 13	61.16 160	58.38 184	64.42 115	77.37 91	74.35 176	68.45 137	62.81 81	45.86 151	38.48 161	35.71 188	37.35 184	59.81 125	67.24 188	69.80 183	79.58 200	78.32 176

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

TAPA-MVS	67.10 9	71.45 69	73.47 67	69.10 78	77.04 93	80.78 107	73.81 84	62.10 83	80.80 34	51.28 85	60.91 57	63.80 63	67.98 68	74.59 106	72.42 158	82.37 177	80.97 164
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		67.29 93	71.98 70	61.82 151	76.92 94	84.32 74	65.90 156	58.22 119	55.75 103	39.22 155	54.51 73	62.47 65	45.99 186	78.83 65	78.52 67	84.70 136	89.47 77

tfpn_ndepth		62.95 139	63.75 124	62.02 149	76.89 95	79.48 129	64.09 162	60.98 98	49.48 125	38.73 159	49.92 90	44.79 131	47.37 177	71.91 156	71.66 163	84.07 152	79.00 174

CANet_DTU		72.84 60	76.63 53	68.43 83	76.81 96	86.62 55	75.54 70	54.71 165	72.06 54	43.54 119	67.11 39	58.46 82	72.40 38	81.13 49	80.82 54	87.57 41	90.21 71

tpm cat1		67.47 91	67.05 102	67.98 85	76.63 97	81.51 98	74.49 82	47.65 203	61.18 82	61.12 54	42.51 130	53.02 112	64.74 84	70.11 174	71.50 165	83.22 163	89.49 76

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 97	68.12 48	72.24 39	82.84 28	81.50 42	87.15 53	94.42 25

thres400		65.18 109	64.44 121	66.04 97	76.40 99	82.63 82	71.52 109	64.27 61	44.93 157	40.69 150	41.86 135	40.79 154	58.12 140	77.67 71	74.64 104	85.26 112	88.56 97

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 92	70.42 50	77.35 76	78.90 62	86.80 57	90.64 67

tpmrst		67.15 94	68.12 96	66.03 98	76.21 101	80.98 104	71.27 111	45.05 210	60.69 84	50.63 89	46.95 111	54.15 106	65.30 79	71.80 158	71.77 162	87.72 37	90.48 68

gg-mvs-nofinetune		62.34 142	66.19 108	57.86 176	76.15 102	88.61 36	71.18 114	41.24 227	25.74 229	13.16 231	22.91 224	63.97 62	54.52 155	85.06 13	85.25 10	90.92 3	91.78 53

thresconf0.02		63.92 121	65.18 115	62.46 144	75.91 103	80.65 115	67.51 145	63.86 64	45.00 156	33.32 189	51.38 84	51.68 114	48.34 172	75.49 99	75.13 99	85.84 94	76.91 180

EPMVS		66.21 99	67.49 99	64.73 108	75.81 104	84.20 75	68.94 135	44.37 214	61.55 81	48.07 98	49.21 93	54.87 104	62.88 91	71.82 157	71.40 169	88.28 28	79.37 172

EPP-MVSNet		67.58 89	71.10 77	63.48 130	75.71 105	83.35 79	66.85 148	57.83 126	53.02 115	41.15 147	55.82 67	67.89 50	56.01 149	74.40 108	72.92 153	83.33 161	90.30 70

view600		63.91 122	63.27 130	64.66 110	75.57 106	81.73 90	69.71 130	63.04 73	43.97 160	39.18 156	41.09 139	40.24 162	55.38 151	76.28 85	72.04 161	85.08 118	87.52 109

thres600view7		63.77 123	63.14 132	64.51 112	75.49 107	81.61 92	69.59 131	62.95 75	43.96 161	38.90 158	41.09 139	40.24 162	55.25 153	76.24 87	71.54 164	84.89 125	87.30 110

dps		64.08 117	63.22 131	65.08 101	75.27 108	79.65 126	66.68 150	46.63 208	56.94 94	55.67 75	43.96 117	43.63 136	64.00 85	69.50 181	69.82 182	82.25 179	79.02 173

MVSTER		76.92 46	79.92 34	73.42 56	74.98 109	82.97 80	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

TSAR-MVS + COLMAP		73.09 59	76.86 50	68.71 80	74.97 110	82.49 85	74.51 81	61.83 88	83.16 24	49.31 95	82.22 18	51.62 115	68.94 64	78.76 66	75.52 97	82.67 173	84.23 135

view800		63.02 135	62.69 143	63.39 132	74.79 111	80.76 111	67.83 140	61.93 86	43.16 171	37.78 167	40.43 144	39.73 169	53.16 158	75.01 101	73.32 144	84.87 127	86.43 117

tpm		64.85 110	66.02 112	63.48 130	74.52 112	78.38 138	70.98 119	44.99 212	51.61 118	43.28 126	47.66 101	53.18 110	60.57 111	70.58 168	71.30 174	86.54 61	89.45 78

tfpn		62.54 141	62.79 139	62.25 148	74.16 113	79.86 124	66.07 155	60.97 99	42.43 176	36.41 171	39.88 149	43.76 135	51.25 165	73.85 118	74.17 128	84.67 137	85.57 128

Vis-MVSNet (Re-imp)		62.25 145	68.74 90	54.68 192	73.70 114	78.74 134	56.51 199	57.49 133	55.22 105	26.86 206	54.56 72	61.35 73	31.06 209	73.10 125	74.90 101	82.49 175	83.31 142

Fast-Effi-MVS+		67.59 88	67.56 98	67.62 88	73.67 115	81.14 103	71.12 115	54.79 164	58.88 88	50.61 90	46.70 112	47.05 125	69.12 63	76.06 92	76.44 85	86.43 64	86.65 114

IterMVS-LS		66.08 101	66.56 105	65.51 99	73.67 115	74.88 171	70.89 121	53.55 173	50.42 121	48.32 97	50.59 87	55.66 99	61.83 96	73.93 116	74.42 118	84.82 133	86.01 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		65.43 107	67.71 97	62.78 140	73.49 117	82.83 81	66.42 153	45.40 209	60.40 85	45.27 108	49.22 92	57.60 89	60.01 120	70.61 166	71.38 172	86.08 73	81.91 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
COLMAP_ROB	51.17 15	55.13 190	52.90 204	57.73 178	73.47 118	67.21 207	62.13 176	55.82 150	47.83 139	34.39 184	31.60 204	34.24 203	44.90 191	63.88 203	62.52 213	75.67 214	63.02 220
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
conf0.05thres1000		60.33 167	59.42 179	61.40 154	73.15 119	78.25 140	65.29 157	60.30 107	36.61 201	35.75 177	33.25 195	39.23 174	50.35 168	72.18 152	72.67 156	83.57 157	83.74 137

Effi-MVS+-dtu		64.58 112	64.08 122	65.16 100	73.04 120	75.17 170	70.68 123	56.23 147	54.12 113	44.71 115	47.42 102	51.10 116	63.82 87	68.08 186	66.32 199	82.47 176	86.38 118

tfpnview11		58.92 174	59.60 177	58.13 171	72.99 121	77.11 155	60.48 182	60.37 105	42.10 179	29.10 200	43.45 118	40.72 157	41.67 198	70.53 170	70.43 180	84.17 150	72.85 196

tfpn_n400		58.64 178	59.27 180	57.89 174	72.83 122	77.26 153	60.35 183	60.29 108	39.77 191	29.10 200	43.45 118	40.72 157	41.61 199	70.06 175	71.39 170	83.17 166	72.26 199

tfpnconf		58.64 178	59.27 180	57.89 174	72.83 122	77.26 153	60.35 183	60.29 108	39.77 191	29.10 200	43.45 118	40.72 157	41.61 199	70.06 175	71.39 170	83.17 166	72.26 199

tfpn1000		58.35 182	59.96 174	56.47 185	72.78 124	77.51 149	56.66 198	59.16 112	43.74 162	29.76 199	42.79 124	42.49 137	37.04 207	68.92 183	68.98 185	83.45 159	75.25 184

EG-PatchMatch MVS		58.73 177	58.03 187	59.55 163	72.32 125	80.49 117	63.44 171	55.55 154	32.49 216	38.31 162	28.87 211	37.22 185	42.84 195	74.30 114	75.70 93	84.84 128	77.14 179

TransMVSNet (Re)		57.83 183	56.90 190	58.91 168	72.26 126	74.69 174	63.57 170	61.42 93	32.30 217	32.65 191	33.97 194	35.96 194	39.17 204	73.84 120	72.84 154	84.37 143	74.69 187

CMPMVS	43.63 17	57.67 185	55.43 193	60.28 159	72.01 127	79.00 132	62.77 175	53.23 178	41.77 181	45.42 107	30.74 207	39.03 175	53.01 159	64.81 196	64.65 205	75.26 216	68.03 210
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NR-MVSNet		61.08 162	62.09 149	59.90 160	71.96 128	75.87 163	63.60 169	61.96 84	49.31 126	27.95 203	42.76 125	33.85 206	48.82 171	74.35 111	74.05 133	85.13 114	84.45 132

tttt0517		67.99 87	70.61 79	64.94 102	71.94 129	83.96 76	67.62 142	57.98 122	59.30 87	49.90 93	54.50 74	57.98 88	57.92 142	76.48 84	77.47 76	84.24 146	84.58 131

PMMVS		70.37 74	75.06 59	64.90 103	71.46 130	81.88 89	64.10 161	55.64 153	71.31 56	46.69 102	70.69 34	58.56 80	69.53 58	79.03 62	75.63 94	81.96 181	88.32 103

test-LLR		68.23 85	71.61 74	64.28 121	71.37 131	81.32 101	63.98 165	61.03 96	58.62 89	42.96 131	52.74 79	61.65 71	57.74 143	75.64 96	78.09 73	88.61 20	93.21 36

test0.0.03 1		57.35 186	59.89 175	54.38 194	71.37 131	73.45 179	52.71 204	61.03 96	46.11 150	26.33 207	41.73 136	44.08 133	29.72 212	71.43 162	70.90 175	85.10 115	71.56 203

tfpnnormal		58.97 173	56.48 192	61.89 150	71.27 133	76.21 162	66.65 151	61.76 91	32.90 215	36.41 171	27.83 213	29.14 218	50.64 167	73.06 126	73.05 151	84.58 140	83.15 148

Fast-Effi-MVS+-dtu		63.05 134	64.72 120	61.11 155	71.21 134	76.81 158	70.72 122	43.13 218	52.51 117	35.34 180	46.55 113	46.36 126	61.40 104	71.57 161	71.44 167	84.84 128	87.79 107

MDTV_nov1_ep13		65.21 108	67.28 100	62.79 139	70.91 135	81.72 91	69.28 134	49.50 194	58.08 91	43.94 118	50.50 88	56.02 95	58.86 137	70.72 165	73.37 142	84.24 146	80.52 165

FMVSNet3		70.41 73	71.89 72	68.68 81	70.89 136	79.42 130	75.63 67	60.97 99	65.32 72	51.06 86	47.37 103	62.05 67	64.90 82	82.49 30	82.27 33	88.64 19	84.34 134

Vis-MVSNet		65.53 106	69.83 84	60.52 157	70.80 137	84.59 70	66.37 154	55.47 156	48.40 136	40.62 151	57.67 65	58.43 83	45.37 190	77.49 72	76.24 88	84.47 141	85.99 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CDS-MVSNet		64.22 115	65.89 113	62.28 147	70.05 138	80.59 116	69.91 129	57.98 122	43.53 167	46.58 103	48.22 95	50.76 117	46.45 183	75.68 95	76.08 89	82.70 172	86.34 119
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UGNet		67.57 90	71.69 73	62.76 141	69.88 139	82.58 83	66.43 152	58.64 116	54.71 111	51.87 83	61.74 53	62.01 70	45.46 189	74.78 105	74.99 100	84.24 146	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
GA-MVS		64.55 113	65.76 114	63.12 136	69.68 140	81.56 97	69.59 131	58.16 120	45.23 155	35.58 179	47.01 110	41.82 139	59.41 131	79.62 58	78.54 66	86.32 65	86.56 115

GBi-Net		69.21 77	70.40 80	67.81 86	69.49 141	78.65 135	74.54 77	60.97 99	65.32 72	51.06 86	47.37 103	62.05 67	63.43 88	77.49 72	78.22 70	87.37 43	83.73 138

test1		69.21 77	70.40 80	67.81 86	69.49 141	78.65 135	74.54 77	60.97 99	65.32 72	51.06 86	47.37 103	62.05 67	63.43 88	77.49 72	78.22 70	87.37 43	83.73 138

FMVSNet2		68.06 86	68.57 91	67.45 89	69.49 141	78.65 135	74.54 77	60.23 110	56.29 99	49.64 94	42.13 134	57.08 90	63.43 88	81.15 48	80.99 50	87.37 43	83.73 138

UniMVSNet_NR-MVSNet		62.30 144	63.51 126	60.89 156	69.48 144	77.83 144	64.07 163	63.94 63	50.03 122	31.17 194	44.82 116	41.12 146	51.37 162	71.02 163	74.81 103	85.30 111	84.95 129

gm-plane-assit		54.99 192	57.99 188	51.49 202	69.27 145	54.42 228	32.32 232	42.59 219	21.18 235	13.71 229	23.61 220	43.84 134	60.21 119	87.09 4	86.55 4	90.81 4	89.28 79

PatchMatch-RL		62.22 148	60.69 162	64.01 122	68.74 146	75.75 166	59.27 190	60.35 106	56.09 100	53.80 80	47.06 109	36.45 189	64.80 83	68.22 185	67.22 194	77.10 210	74.02 189

CR-MVSNet		62.31 143	64.75 118	59.47 164	68.63 147	71.29 197	67.53 143	43.18 216	55.83 101	41.40 144	41.04 141	55.85 96	57.29 146	72.76 141	73.27 147	78.77 205	83.23 146

v18		63.31 131	62.02 150	64.81 107	68.48 148	73.38 180	72.14 88	54.28 167	48.99 135	47.21 100	39.56 151	41.20 144	60.80 108	72.89 133	74.46 117	85.96 82	83.64 141

v16		63.12 133	61.78 152	64.68 109	68.45 149	73.29 181	71.86 92	54.12 168	48.36 137	47.00 101	39.30 156	41.01 148	60.67 109	72.83 139	74.40 119	86.01 77	83.24 145

v17		62.99 138	61.70 153	64.51 112	68.40 150	73.28 182	71.80 97	54.11 169	47.87 138	46.14 104	39.29 157	41.01 148	60.60 110	72.81 140	74.39 124	85.99 80	83.25 144

TranMVSNet+NR-MVSNet		60.38 166	61.30 157	59.30 165	68.34 151	75.57 169	63.38 172	63.78 66	46.74 143	27.73 204	42.56 129	36.84 187	47.66 175	70.36 172	74.59 109	84.91 124	82.46 152

v8		63.44 130	62.58 144	64.43 114	68.28 152	78.07 141	71.82 96	54.85 162	46.70 145	45.20 109	39.40 152	40.91 150	60.54 114	72.85 138	74.39 124	85.92 83	85.76 125

v6		64.09 116	63.40 127	64.90 103	68.28 152	80.78 107	71.85 93	57.64 130	46.73 144	45.18 110	39.40 152	40.89 151	60.54 114	72.86 134	74.40 119	85.92 83	88.72 93

v1neww		64.08 117	63.38 128	64.89 105	68.27 154	80.77 109	71.84 94	57.65 128	46.66 146	45.10 111	39.40 152	40.86 152	60.57 111	72.86 134	74.40 119	85.92 83	88.71 94

v7new		64.08 117	63.38 128	64.89 105	68.27 154	80.77 109	71.84 94	57.65 128	46.66 146	45.10 111	39.40 152	40.86 152	60.57 111	72.86 134	74.40 119	85.92 83	88.71 94

v1141		63.48 127	62.75 142	64.32 117	68.13 156	80.69 113	71.69 106	57.43 135	43.66 166	42.83 138	39.02 159	39.74 168	59.95 121	72.94 130	74.49 114	85.86 91	88.75 91

divwei89l23v2f112		63.48 127	62.76 141	64.32 117	68.13 156	80.68 114	71.71 99	57.43 135	43.69 164	42.84 136	39.01 160	39.75 167	59.94 122	72.93 131	74.49 114	85.86 91	88.75 91

v1		63.49 126	62.77 140	64.32 117	68.13 156	80.70 112	71.70 105	57.43 135	43.69 164	42.89 135	39.03 158	39.77 166	59.93 123	72.93 131	74.48 116	85.86 91	88.77 89

v15		62.07 149	60.70 161	63.67 127	68.09 159	73.00 183	71.27 111	53.41 174	43.70 163	43.43 121	38.77 162	39.83 164	59.87 124	72.74 143	74.25 126	85.98 81	82.61 150

V14		61.96 152	60.56 164	63.59 128	68.06 160	72.93 186	71.10 116	53.33 176	43.47 168	43.28 126	38.59 163	39.78 165	59.76 126	72.65 145	74.19 127	86.01 77	82.32 155

V9		61.85 154	60.42 167	63.51 129	68.02 161	72.85 187	70.91 120	53.24 177	43.25 170	43.27 130	38.41 167	39.73 169	59.60 128	72.55 147	74.13 130	86.04 75	82.04 158

v2v482		63.68 124	62.85 137	64.65 111	68.01 162	80.46 118	71.90 91	57.60 131	44.26 158	42.82 139	39.80 150	38.62 179	61.56 98	73.06 126	74.86 102	86.03 76	88.90 87

v12		61.70 156	60.27 169	63.38 133	68.00 163	72.76 188	70.63 124	53.14 179	43.01 172	42.95 134	38.25 169	39.64 171	59.48 130	72.47 149	74.05 133	86.06 74	81.71 161

pm-mvs1		59.21 172	59.58 178	58.77 169	67.97 164	77.07 157	64.12 160	57.20 139	34.73 209	36.86 169	35.34 190	40.54 161	43.34 194	74.32 113	73.30 146	83.13 169	81.77 160

v13		61.60 158	60.13 172	63.31 134	67.95 165	72.67 190	70.51 125	53.05 180	42.80 173	42.96 131	38.10 174	39.57 173	59.31 133	72.36 150	73.98 135	86.10 71	81.40 163

v7		63.61 125	63.02 134	64.29 120	67.88 166	80.32 119	71.60 107	56.63 143	45.37 153	42.84 136	38.54 164	38.91 177	61.05 106	74.39 109	74.52 112	85.75 95	89.10 82

v10		63.00 136	62.22 147	63.90 125	67.88 166	77.78 145	71.59 108	54.34 166	45.37 153	42.76 140	38.53 165	38.93 176	61.05 106	74.39 109	74.52 112	85.75 95	86.04 121

v11		61.74 155	60.47 166	63.22 135	67.83 168	72.72 189	70.31 126	52.95 183	42.75 174	41.89 143	38.16 172	38.49 180	60.40 118	74.35 111	74.40 119	85.92 83	82.39 154

v1144		63.00 136	62.39 146	63.70 126	67.72 169	80.27 120	71.23 113	56.40 144	42.51 175	40.81 149	38.12 173	37.73 181	60.42 117	74.46 107	74.55 110	85.64 107	89.12 81

UniMVSNet (Re)		60.62 164	62.93 136	57.92 173	67.64 170	77.90 143	61.75 178	61.24 95	49.83 124	29.80 198	42.57 128	40.62 160	43.36 193	70.49 171	73.27 147	83.76 154	85.81 124

RPMNet		58.63 180	62.80 138	53.76 198	67.59 171	71.29 197	54.60 202	38.13 231	55.83 101	35.70 178	41.58 137	53.04 111	47.89 174	66.10 190	67.38 192	78.65 207	84.40 133

v148		62.00 151	61.19 158	62.96 137	67.46 172	79.49 128	67.87 139	57.66 127	42.30 177	45.02 113	38.20 171	38.89 178	54.77 154	69.83 178	72.60 157	84.96 121	87.01 112

IterMVS		61.87 153	63.55 125	59.90 160	67.29 173	72.20 192	67.34 146	48.56 199	47.48 140	37.86 166	47.07 108	48.27 122	54.08 156	72.12 153	73.71 137	84.30 145	83.99 136
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1192		62.25 145	61.64 154	62.96 137	66.88 174	79.72 125	69.96 128	55.77 151	41.58 182	39.42 153	37.05 179	35.96 194	60.50 116	74.30 114	74.09 131	85.24 113	88.76 90

DU-MVS		60.87 163	61.82 151	59.76 162	66.69 175	75.87 163	64.07 163	61.96 84	49.31 126	31.17 194	42.76 125	36.95 186	51.37 162	69.67 179	73.20 150	83.30 162	84.95 129

Baseline_NR-MVSNet		59.47 170	60.28 168	58.54 170	66.69 175	73.90 177	61.63 179	62.90 80	49.15 134	26.87 205	35.18 192	37.62 182	48.20 173	69.67 179	73.61 138	84.92 122	82.82 149

v144192		62.05 150	61.46 156	62.73 143	66.59 177	79.87 123	69.30 133	55.88 149	41.50 184	39.41 154	37.23 177	36.45 189	59.62 127	72.69 144	73.51 139	85.61 108	88.93 84

v1921920		61.66 157	61.10 159	62.31 146	66.32 178	79.57 127	68.41 138	55.49 155	41.03 185	38.69 160	36.64 185	35.27 200	59.60 128	73.23 124	73.41 141	85.37 110	88.51 101

TESTMET0.1,1		67.38 92	71.61 74	62.45 145	66.05 179	81.32 101	63.98 165	55.36 157	58.62 89	42.96 131	52.74 79	61.65 71	57.74 143	75.64 96	78.09 73	88.61 20	93.21 36

pmmvs4		63.14 132	62.46 145	63.94 124	66.03 180	76.40 160	66.82 149	57.60 131	56.74 95	50.26 92	40.81 143	37.51 183	59.26 134	71.75 159	71.48 166	83.68 156	82.53 151

PatchT		60.46 165	63.85 123	56.51 184	65.95 181	75.68 167	47.34 213	41.39 223	53.89 114	41.40 144	37.84 175	50.30 119	57.29 146	72.76 141	73.27 147	85.67 103	83.23 146

v1240		61.09 161	60.55 165	61.72 152	65.92 182	79.28 131	67.16 147	54.91 161	39.79 190	38.10 163	36.08 187	34.64 201	59.15 135	72.86 134	73.36 143	85.10 115	87.84 106

ADS-MVSNet		58.40 181	59.16 183	57.52 179	65.80 183	74.57 175	60.26 185	40.17 228	50.51 120	38.01 164	40.11 148	44.72 132	59.36 132	64.91 194	66.55 197	81.53 184	72.72 198

testpf		43.39 221	47.17 219	38.98 224	65.58 184	47.38 236	36.09 229	31.67 238	36.97 198	19.47 217	33.01 197	35.62 199	23.61 224	50.86 230	56.08 226	57.48 235	70.27 207

FMVSNet1		63.48 127	63.07 133	63.97 123	65.31 185	76.37 161	71.77 98	57.90 124	43.32 169	45.66 106	35.06 193	49.43 120	58.57 138	77.49 72	78.22 70	84.59 139	81.60 162

testgi		48.51 213	50.53 211	46.16 215	64.78 186	67.15 208	41.54 224	54.81 163	29.12 223	17.03 220	32.07 201	31.98 210	20.15 229	65.26 193	67.00 196	78.67 206	61.10 225

LTVRE_ROB	47.26 16	49.41 211	49.91 214	48.82 207	64.76 187	69.79 200	49.05 208	47.12 205	20.36 237	16.52 223	36.65 184	26.96 221	50.76 166	60.47 208	63.16 210	64.73 230	72.00 201
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
Anonymous20231206		52.23 203	52.80 205	51.56 201	64.70 188	69.41 201	51.01 206	58.60 117	36.63 200	22.44 213	21.80 226	31.42 213	30.52 210	66.79 189	67.83 191	82.10 180	75.73 182

thisisatest0515		59.37 171	60.68 163	57.84 177	64.39 189	75.65 168	58.56 194	53.86 171	41.55 183	42.12 142	40.40 146	39.59 172	47.09 181	71.69 160	73.79 136	81.02 189	82.08 157

USDC		59.69 169	60.03 173	59.28 166	64.04 190	71.84 195	63.15 174	55.36 157	54.90 109	35.02 183	48.34 94	29.79 217	58.16 139	70.60 167	71.33 173	79.99 197	73.42 193

WR-MVS		51.02 205	54.56 198	46.90 213	63.84 191	69.23 202	44.78 220	56.38 145	38.19 196	14.19 227	37.38 176	36.82 188	22.39 225	60.14 209	66.20 201	79.81 198	73.95 191

our_test_3					63.32 192	71.07 199	55.90 200

test20.03		47.23 217	48.69 216	45.53 217	63.28 193	64.39 214	41.01 225	56.93 142	29.16 222	15.21 226	23.90 219	30.76 216	17.51 234	64.63 197	65.26 202	79.21 204	62.71 221

pmmvs6		54.20 199	53.54 201	54.97 190	63.22 194	72.98 184	60.17 186	52.32 186	26.77 228	34.30 185	23.29 223	36.23 191	40.33 202	68.77 184	68.76 186	79.47 202	78.00 177

v7n		57.04 187	56.64 191	57.52 179	62.85 195	74.75 173	61.76 177	51.80 187	35.58 208	36.02 176	32.33 199	33.61 207	50.16 169	67.73 187	70.34 181	82.51 174	82.12 156

pmmvs5		59.72 168	60.24 170	59.11 167	62.77 196	77.33 152	63.17 173	54.00 170	40.21 188	37.23 168	40.41 145	35.99 193	51.75 161	72.55 147	72.74 155	85.72 101	82.45 153

CVMVSNet		54.92 194	58.16 185	51.13 203	62.61 197	68.44 204	55.45 201	52.38 185	42.28 178	21.45 214	47.10 107	46.10 127	37.96 205	64.42 199	63.81 207	76.92 212	75.01 186

TAMVS		58.86 175	60.91 160	56.47 185	62.38 198	77.57 147	58.97 192	52.98 181	38.76 195	36.17 174	42.26 133	47.94 123	46.45 183	70.23 173	70.79 176	81.86 182	78.82 175

DTE-MVSNet		49.82 209	51.92 209	47.37 212	61.75 199	64.38 215	45.89 219	57.33 138	36.11 204	12.79 232	36.87 181	31.93 212	25.73 221	58.01 211	65.22 203	80.75 192	70.93 206

PEN-MVS		51.04 204	52.94 203	48.82 207	61.45 200	66.00 210	48.68 210	57.20 139	36.87 199	15.36 225	36.98 180	32.72 209	28.77 216	57.63 214	66.37 198	81.44 186	74.00 190

v748		55.19 189	54.63 196	55.85 187	61.44 201	72.97 185	58.72 193	51.62 188	34.48 211	36.39 173	32.09 200	33.05 208	45.48 188	61.85 206	67.87 190	81.45 185	80.08 168

V42		62.86 140	62.97 135	62.74 142	60.84 202	78.99 133	71.46 110	57.13 141	46.85 142	44.28 117	38.87 161	40.73 156	57.63 145	72.60 146	74.14 129	85.09 117	88.63 96

MDTV_nov1_ep13_2view		54.47 198	54.61 197	54.30 197	60.50 203	73.82 178	57.92 195	43.38 215	39.43 194	32.51 192	33.23 196	34.05 204	47.26 178	62.36 204	66.21 200	84.24 146	73.19 195

LP		48.21 214	46.65 221	50.03 204	60.39 204	63.86 218	48.73 209	38.71 230	35.60 207	32.99 190	23.31 222	24.95 228	40.07 203	57.73 212	61.56 215	79.29 203	59.51 226

MVS-HIRNet		53.86 200	53.02 202	54.85 191	60.30 205	72.36 191	44.63 221	42.20 221	39.45 193	43.47 120	21.66 227	34.00 205	55.47 150	65.42 192	67.16 195	83.02 170	71.08 205

CHOSEN 280x420		62.23 147	66.57 104	57.17 182	59.88 206	68.92 203	61.20 181	42.28 220	54.17 112	39.57 152	47.78 99	64.97 58	62.68 92	73.85 118	69.52 184	77.43 209	86.75 113

TinyColmap		52.66 202	50.09 213	55.65 188	59.72 207	64.02 217	57.15 197	52.96 182	40.28 187	32.51 192	32.42 198	20.97 232	56.65 148	63.95 200	65.15 204	74.91 217	63.87 218

FC-MVSNet-test		47.24 216	54.37 199	38.93 225	59.49 208	58.25 225	34.48 231	53.36 175	45.66 152	6.66 240	50.62 86	42.02 138	16.62 235	58.39 210	61.21 216	62.99 231	64.40 217

test-mter		64.06 120	69.24 86	58.01 172	59.07 209	77.40 150	59.13 191	48.11 201	55.64 104	39.18 156	51.56 83	58.54 81	55.38 151	73.52 122	76.00 90	87.22 51	92.05 51

WR-MVS_H		49.62 210	52.63 206	46.11 216	58.80 210	67.58 206	46.14 218	54.94 159	36.51 202	13.63 230	36.75 183	35.67 198	22.10 226	56.43 218	62.76 211	81.06 188	72.73 197

CP-MVSNet		50.57 206	52.60 207	48.21 210	58.77 211	65.82 211	48.17 211	56.29 146	37.41 197	16.59 222	37.14 178	31.95 211	29.21 213	56.60 217	63.71 208	80.22 195	75.56 183

PS-CasMVS		50.17 207	52.02 208	48.02 211	58.60 212	65.54 212	48.04 212	56.19 148	36.42 203	16.42 224	35.68 189	31.33 214	28.85 215	56.42 219	63.54 209	80.01 196	75.18 185

SixPastTwentyTwo		49.11 212	49.22 215	48.99 206	58.54 213	64.14 216	47.18 214	47.75 202	31.15 219	24.42 209	41.01 142	26.55 222	44.04 192	54.76 225	58.70 220	71.99 224	68.21 208

TDRefinement		52.70 201	51.02 210	54.66 193	57.41 214	65.06 213	61.47 180	54.94 159	44.03 159	33.93 186	30.13 209	27.57 220	46.17 185	61.86 205	62.48 214	74.01 220	66.06 214

pmmvs-eth3d		55.20 188	53.95 200	56.65 183	57.34 215	67.77 205	57.54 196	53.74 172	40.93 186	41.09 148	31.19 206	29.10 219	49.07 170	65.54 191	67.28 193	81.14 187	75.81 181

FPMVS		39.11 226	36.39 231	42.28 219	55.97 216	45.94 237	46.23 217	41.57 222	35.73 206	22.61 211	23.46 221	19.82 234	28.32 219	43.57 232	40.67 235	58.96 233	45.54 231

MIMVSNet		57.78 184	59.71 176	55.53 189	54.79 217	77.10 156	63.89 167	45.02 211	46.59 148	36.79 170	28.36 212	40.77 155	45.84 187	74.97 102	76.58 82	86.87 56	73.60 192

N_pmnet		47.67 215	47.00 220	48.45 209	54.72 218	62.78 219	46.95 215	51.25 189	36.01 205	26.09 208	26.59 217	25.93 227	35.50 208	55.67 221	59.01 218	76.22 213	63.04 219

test2356		46.29 218	47.37 218	45.03 218	54.38 219	57.99 226	42.03 223	50.32 191	30.78 220	16.65 221	27.40 215	23.70 229	29.86 211	61.20 207	64.31 206	76.93 211	66.22 213

v52		54.79 195	55.15 194	54.36 196	54.07 220	72.13 193	59.84 188	49.39 195	34.50 210	35.08 182	31.63 203	35.74 196	47.21 180	63.90 201	67.92 188	80.59 193	80.23 166

V4		54.78 196	55.14 195	54.37 195	54.07 220	72.13 193	59.83 189	49.39 195	34.46 212	35.11 181	31.64 202	35.72 197	47.22 179	63.90 201	67.92 188	80.59 193	80.23 166

anonymousdsp		54.99 192	57.24 189	52.36 199	53.82 222	71.75 196	51.49 205	48.14 200	33.74 213	33.66 187	38.34 168	36.13 192	47.54 176	64.53 198	70.60 178	79.53 201	85.59 127

testus		42.30 222	43.69 222	40.67 223	53.21 223	53.50 229	31.81 233	49.96 192	27.06 226	11.55 234	25.67 218	19.00 235	25.20 222	55.34 222	62.59 212	72.31 223	62.69 222

new-patchmatchnet		42.21 223	42.97 224	41.33 221	53.05 224	59.89 222	39.38 226	49.61 193	28.26 225	12.10 233	22.17 225	21.54 231	19.22 230	50.96 229	56.04 227	74.61 219	61.92 223

FMVSNet5		58.86 175	60.24 170	57.25 181	52.66 225	66.25 209	63.77 168	52.86 184	57.85 93	37.92 165	36.12 186	52.22 113	51.37 162	70.88 164	71.43 168	84.92 122	66.91 212

ambc			42.30 225		50.36 226	49.51 233	35.47 230		32.04 218	23.53 210	17.36 232	8.95 243	29.06 214	64.88 195	56.26 225	61.29 232	67.12 211

1111		38.93 227	38.98 228	38.86 226	50.10 227	50.42 231	29.52 234	38.00 232	22.67 233	17.99 218	17.40 230	26.26 224	28.72 217	54.86 223	58.20 221	68.82 228	43.08 234

.test1245		25.86 234	24.56 237	27.39 235	50.10 227	50.42 231	29.52 234	38.00 232	22.67 233	17.99 218	17.40 230	26.26 224	28.72 217	54.86 223	0.05 242	0.01 246	0.24 243

EU-MVSNet		44.84 219	47.85 217	41.32 222	49.26 229	56.59 227	43.07 222	47.64 204	33.03 214	13.82 228	36.78 182	30.99 215	24.37 223	53.80 226	55.57 228	69.78 225	68.21 208

testmv		37.40 228	37.95 229	36.76 228	48.97 230	49.33 234	28.65 237	46.74 206	18.34 238	7.68 238	16.80 235	14.47 239	19.18 231	51.72 227	56.93 223	69.36 226	58.09 227

test1235678		37.40 228	37.94 230	36.76 228	48.97 230	49.30 235	28.65 237	46.73 207	18.33 239	7.68 238	16.79 236	14.46 240	19.18 231	51.72 227	56.92 224	69.36 226	58.07 228

RPSCF		55.07 191	58.06 186	51.57 200	48.87 232	58.95 223	53.68 203	41.26 226	62.42 78	45.88 105	54.38 75	54.26 105	53.75 157	57.15 215	53.53 230	66.01 229	65.75 215

PMVS	27.44 18	32.08 231	29.07 234	35.60 230	48.33 233	24.79 242	26.97 239	41.34 224	20.45 236	22.50 212	17.11 234	18.64 236	20.44 228	41.99 235	38.06 236	54.02 238	42.44 235
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PM-MVS		50.11 208	50.38 212	49.80 205	47.23 234	62.08 221	50.91 207	44.84 213	41.90 180	36.10 175	35.22 191	26.05 226	46.83 182	57.64 213	55.42 229	72.90 221	74.32 188

pmmvs3		41.86 224	42.29 226	41.36 220	39.80 235	52.66 230	38.93 228	35.85 237	23.40 232	20.22 216	19.30 228	20.84 233	40.56 201	55.98 220	58.79 219	72.80 222	65.03 216

test12356		29.92 232	31.49 233	28.08 232	38.46 236	37.74 240	21.36 240	40.17 228	16.83 240	5.61 242	15.66 238	11.48 241	6.60 241	42.01 234	51.23 231	56.29 236	45.52 232

MDA-MVSNet-bldmvs		44.15 220	42.27 227	46.34 214	38.34 237	62.31 220	46.28 216	55.74 152	29.83 221	20.98 215	27.11 216	16.45 238	41.98 196	41.11 236	57.47 222	74.72 218	61.65 224

no-one		26.96 233	26.51 235	27.49 234	37.87 238	39.14 239	17.12 242	41.31 225	12.02 242	3.68 244	8.04 240	8.42 244	10.67 239	28.11 238	45.96 234	54.27 237	43.89 233

MIMVSNet1		40.84 225	43.46 223	37.79 227	32.14 239	58.92 224	39.24 227	50.83 190	27.00 227	11.29 235	16.76 237	26.53 223	17.75 233	57.14 216	61.12 217	75.46 215	56.78 229

Gipuma		24.91 235	24.61 236	25.26 236	31.47 240	21.59 243	18.06 241	37.53 234	25.43 230	10.03 236	4.18 244	4.25 246	14.85 236	43.20 233	47.03 232	39.62 240	26.55 239
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		15.08 237	11.65 240	19.08 237	28.73 241	12.31 246	6.95 247	36.87 236	10.71 244	3.63 245	5.13 241	2.22 249	13.81 238	11.34 242	18.50 240	24.49 242	21.32 240

EMVS		14.40 238	10.71 241	18.70 238	28.15 242	12.09 247	7.06 246	36.89 235	11.00 243	3.56 246	4.95 242	2.27 248	13.91 237	10.13 243	16.06 241	22.63 243	18.51 241

new_pmnet		33.19 230	35.52 232	30.47 231	27.55 243	45.31 238	29.29 236	30.92 239	29.00 224	9.88 237	18.77 229	17.64 237	26.77 220	44.07 231	45.98 233	58.41 234	47.87 230

PMMVS2		20.45 236	22.31 238	18.27 239	20.52 244	26.73 241	14.85 244	28.43 241	13.69 241	0.79 247	10.35 239	9.10 242	3.83 243	27.64 239	32.87 237	41.17 239	35.81 236

tmp_tt				16.09 240	13.07 245	8.12 248	13.61 245	2.08 243	55.09 106	30.10 197	40.26 147	22.83 230	5.35 242	29.91 237	25.25 239	32.33 241

MVE	15.98 19	14.37 239	16.36 239	12.04 241	7.72 246	20.24 244	5.90 248	29.05 240	8.28 245	3.92 243	4.72 243	2.42 247	9.57 240	18.89 241	31.46 238	16.07 245	28.53 238
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		54.54 197	77.58 43	27.67 233	0.03 247	90.09 26	77.20 62	0.02 244	66.83 66	0.05 248	59.90 61	73.33 31	0.04 244	78.40 69	79.30 59	88.65 18	95.20 20

sosnet-low-res		0.00 242	0.00 244	0.00 244	0.00 248	0.00 251	0.00 252	0.00 246	0.00 248	0.00 249	0.00 247	0.00 251	0.00 247	0.00 246	0.00 245	0.00 248	0.00 245

sosnet		0.00 242	0.00 244	0.00 244	0.00 248	0.00 251	0.00 252	0.00 246	0.00 248	0.00 249	0.00 247	0.00 251	0.00 247	0.00 246	0.00 245	0.00 248	0.00 245

testmvs		0.05 240	0.08 242	0.01 242	0.00 248	0.01 249	0.03 250	0.01 245	0.05 246	0.00 249	0.14 246	0.01 250	0.03 246	0.05 244	0.05 242	0.01 246	0.24 243

test123		0.05 240	0.08 242	0.01 242	0.00 248	0.01 249	0.01 251	0.00 246	0.05 246	0.00 249	0.16 245	0.00 251	0.04 244	0.02 245	0.05 242	0.00 248	0.26 242

MTAPA										78.32 7		79.42 19

MTMP										76.04 12		76.65 24

Patchmatch-RL test							2.17 249

NP-MVS									81.60 32

Patchmtry						78.06 142	67.53 143	43.18 216		41.40 144

DeepMVS_CX						19.81 245	17.01 243	10.02 242	23.61 231	5.85 241	17.21 233	8.03 245	21.13 227	22.60 240		21.42 244	30.01 237

High-resolution multi-view benchmark