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 3	88.15 3	79.10 1	85.66 18	70.72 25	76.50 28	80.45 14	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 3	92.58 4	92.40 6	87.62 4	77.77 2	88.44 10	75.93 12	79.49 21	81.97 11	81.65 3	87.04 5	86.58 3	88.79 14	97.18 4

NCCC		84.16 11	85.46 16	82.64 6	92.34 5	90.57 18	86.57 6	76.51 3	86.85 15	72.91 17	77.20 27	78.69 20	79.09 8	84.64 16	84.88 14	88.44 22	95.41 17

ESAPD		87.78 1	90.56 1	84.53 1	92.88 2	93.82 1	88.95 1	76.05 4	92.95 3	80.32 2	93.12 2	86.87 1	80.88 4	85.54 10	84.01 18	88.09 32	97.62 2

APDe-MVS		86.37 3	88.41 4	84.00 4	91.43 9	91.83 10	88.34 2	74.67 5	91.19 4	81.76 1	91.13 3	81.94 12	80.07 5	83.38 22	82.58 30	87.69 36	96.78 7

HPM-MVS++		85.64 6	88.43 3	82.39 7	92.65 3	90.24 21	85.83 10	74.21 6	90.68 6	75.63 13	86.77 9	84.15 4	78.68 9	86.33 6	85.26 9	87.32 43	95.60 14

HSP-MVS		86.82 2	89.95 2	83.16 5	89.38 22	91.60 12	85.63 12	74.15 7	94.20 1	75.52 14	94.99 1	83.21 6	85.96 1	87.67 3	85.88 6	88.32 24	92.13 46

DeepC-MVS_fast	75.41 2	81.69 19	82.10 28	81.20 12	91.04 11	87.81 43	83.42 22	74.04 8	83.77 22	71.09 23	66.88 40	72.44 32	79.48 6	85.08 12	84.97 13	88.12 31	93.78 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CSCG		82.90 15	84.52 18	81.02 13	91.85 6	93.43 2	87.14 5	74.01 9	81.96 28	76.14 10	70.84 32	82.49 8	69.71 51	82.32 34	85.18 11	87.26 45	95.40 18

SD-MVS		84.31 10	86.96 10	81.22 11	88.98 26	88.68 32	85.65 11	73.85 10	89.09 9	79.63 3	87.34 8	84.84 3	73.71 28	82.66 28	81.60 39	85.48 106	94.51 24

APD-MVS		84.83 8	87.00 8	82.30 8	89.61 20	89.21 29	86.51 7	73.64 11	90.98 5	77.99 7	89.89 5	80.04 17	79.18 7	82.00 38	81.37 42	86.88 54	95.49 16
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		84.39 9	86.58 12	81.83 9	88.09 34	86.47 54	85.63 12	73.62 12	90.13 8	79.24 4	89.67 6	82.99 7	77.72 11	81.22 44	80.92 50	86.68 57	94.66 23

SMA-MVS		84.91 7	87.95 5	81.36 10	91.75 7	90.84 15	86.35 8	73.36 13	90.22 7	72.81 18	80.70 18	85.67 2	76.69 14	86.06 8	86.14 5	87.20 50	96.05 10

DeepC-MVS	74.46 3	80.30 25	81.05 31	79.42 19	87.42 36	88.50 34	83.23 23	73.27 14	82.78 25	71.01 24	62.86 48	69.93 46	74.80 23	84.30 17	84.20 17	86.79 56	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
AdaColmap		76.23 47	73.55 61	79.35 20	89.38 22	85.00 65	79.99 45	73.04 15	76.60 46	71.17 22	55.18 64	57.99 84	77.87 10	76.82 76	76.82 74	84.67 134	86.45 111

train_agg		83.35 13	86.93 11	79.17 22	89.70 18	88.41 35	85.60 14	72.89 16	86.31 16	66.58 36	90.48 4	82.24 10	73.06 33	83.10 24	82.64 29	87.21 49	95.30 19

DeepPCF-MVS	76.94 1	83.08 14	87.77 6	77.60 30	90.11 14	90.96 14	78.48 50	72.63 17	93.10 2	65.84 37	80.67 19	81.55 13	74.80 23	85.94 9	85.39 8	83.75 151	96.77 8

ACMMP_Plus		83.54 12	86.37 13	80.25 16	89.57 21	90.10 23	85.27 15	71.66 18	87.38 11	73.08 16	84.23 13	80.16 15	75.31 19	84.85 14	83.64 21	86.57 58	94.21 29

CDPH-MVS		79.39 31	82.13 27	76.19 38	89.22 25	88.34 36	84.20 19	71.00 19	79.67 38	56.97 68	77.77 24	72.24 36	68.50 61	81.33 43	82.74 27	87.23 47	92.84 40

SteuartSystems-ACMMP		82.51 16	85.35 17	79.20 21	90.25 12	89.39 28	84.79 16	70.95 20	82.86 24	68.32 33	86.44 10	77.19 21	73.07 32	83.63 21	83.64 21	87.82 33	94.34 26
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator	70.49 5	78.42 34	76.77 49	80.35 15	91.43 9	90.27 20	81.84 31	70.79 21	72.10 51	71.95 19	50.02 83	67.86 51	77.47 12	82.89 25	84.24 16	88.61 18	89.99 69

DELS-MVS		79.49 26	79.84 35	79.08 23	88.26 32	92.49 4	84.12 20	70.63 22	65.27 71	69.60 31	61.29 53	66.50 53	72.75 34	88.07 2	88.03 1	89.13 11	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
QAPM		77.50 40	77.43 43	77.59 31	91.52 8	92.00 9	81.41 35	70.63 22	66.22 64	58.05 65	54.70 66	71.79 38	74.49 26	82.46 30	82.04 32	89.46 9	92.79 42

PCF-MVS	70.85 4	75.73 48	76.55 52	74.78 49	83.67 48	88.04 42	81.47 33	70.62 24	69.24 61	57.52 66	60.59 56	69.18 47	70.65 46	77.11 73	77.65 71	84.75 132	94.01 31
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_111021_HR		77.42 41	78.40 40	76.28 36	86.95 38	90.68 16	77.41 57	70.56 25	66.21 65	62.48 46	66.17 42	63.98 60	72.08 39	82.87 26	83.15 24	88.24 27	95.71 12

zzz-MVS		81.65 20	83.10 22	79.97 18	88.14 33	87.62 45	83.96 21	69.90 26	86.92 13	77.67 9	72.47 31	78.74 19	74.13 27	81.59 42	81.15 46	86.01 74	93.19 37

OPM-MVS		72.74 59	70.93 74	74.85 48	85.30 45	84.34 68	82.82 27	69.79 27	49.96 116	55.39 73	54.09 71	60.14 75	70.04 50	80.38 53	79.43 56	85.74 95	88.20 100

abl_6				79.06 24	89.68 19	92.14 8	77.70 55	69.68 28	86.87 14	71.88 20	74.29 30	80.06 16	76.56 16			88.84 13	95.82 11

HFP-MVS		82.48 17	84.12 19	80.56 14	90.15 13	87.55 46	84.28 18	69.67 29	85.22 19	77.95 8	84.69 12	75.94 24	75.04 21	81.85 39	81.17 45	86.30 63	92.40 44

HQP-MVS		78.26 35	80.91 32	75.17 45	85.67 44	84.33 69	83.01 25	69.38 30	79.88 37	55.83 69	79.85 20	64.90 58	70.81 45	82.46 30	81.78 36	86.30 63	93.18 38

Anonymous20240521		72.27 61	70.27 77	74.61 50	82.92 49	87.72 44	79.91 46	69.34 31	58.86 84	55.09 74	42.31 127	48.00 115	73.17 31	80.77 50	82.92 26	87.26 45	91.30 55

MP-MVS		80.94 22	83.49 21	77.96 27	88.48 27	88.16 39	82.82 27	69.34 31	80.79 34	69.67 29	82.35 15	77.13 22	71.60 42	80.97 48	80.96 49	85.87 87	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 29	88.41 28	87.05 48	83.02 24	69.18 33	83.91 21	68.35 32	82.89 14	73.64 29	72.16 38	80.78 49	81.13 47	86.10 68	91.43 53

X-MVS		78.16 36	80.55 33	75.38 43	87.99 35	86.27 56	81.05 39	68.98 34	78.33 40	61.07 52	75.25 29	72.27 33	67.52 69	80.03 54	80.52 54	85.66 103	91.20 56

CLD-MVS		77.36 42	77.29 45	77.45 32	82.21 55	88.11 40	81.92 30	68.96 35	77.97 42	69.62 30	62.08 49	59.44 76	73.57 29	81.75 40	81.27 44	88.41 23	90.39 65
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PHI-MVS		79.43 28	84.06 20	74.04 51	86.15 42	91.57 13	80.85 41	68.90 36	82.22 27	51.81 80	78.10 23	74.28 27	70.39 48	84.01 20	84.00 19	86.14 67	94.24 28

MSDG		65.57 99	61.57 148	70.24 67	82.02 57	76.47 152	74.46 77	68.73 37	56.52 93	50.33 87	38.47 160	41.10 140	62.42 89	72.12 146	72.94 144	83.47 154	73.37 187

CP-MVS		79.44 27	81.51 30	77.02 33	86.95 38	85.96 60	82.00 29	68.44 38	81.82 29	67.39 34	77.43 25	73.68 28	71.62 41	79.56 57	79.58 55	85.73 96	92.51 43

EPNet		79.28 32	82.25 25	75.83 40	88.31 31	90.14 22	79.43 48	68.07 39	81.76 30	61.26 50	77.26 26	70.08 45	70.06 49	82.43 32	82.00 34	87.82 33	92.09 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	70.16 6	77.87 37	77.29 45	78.55 25	89.25 24	88.32 37	80.09 43	67.95 40	74.89 50	71.83 21	52.05 76	70.68 42	76.27 18	82.27 35	82.04 32	85.92 80	90.77 61

TSAR-MVS + GP.		82.27 18	85.98 14	77.94 28	80.72 67	88.25 38	81.12 38	67.71 41	87.10 12	73.31 15	85.23 11	83.68 5	76.64 15	80.43 52	81.47 41	88.15 30	95.66 13

ACMH	59.42 14	61.59 153	59.22 174	64.36 110	78.92 74	78.26 133	67.65 135	67.48 42	39.81 181	30.98 190	38.25 163	34.59 194	61.37 99	70.55 161	73.47 132	79.74 191	79.59 163
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMMP		77.61 39	79.59 36	75.30 44	85.87 43	85.58 61	81.42 34	67.38 43	79.38 39	62.61 44	78.53 22	65.79 55	68.80 59	78.56 64	78.50 64	85.75 92	90.80 60
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
CANet		80.90 23	82.93 24	78.53 26	86.83 40	92.26 7	81.19 37	66.95 44	81.60 31	69.90 28	66.93 39	74.80 26	76.79 13	84.68 15	84.77 15	89.50 8	95.50 15

CHOSEN 1792x2688		72.55 60	71.98 66	73.22 55	86.57 41	92.41 5	75.63 64	66.77 45	62.08 76	52.32 77	30.27 201	50.74 110	66.14 71	86.22 7	85.41 7	91.90 1	96.75 9

TSAR-MVS + ACMM		81.59 21	85.84 15	76.63 34	89.82 17	86.53 53	86.32 9	66.72 46	85.96 17	65.43 38	88.98 7	82.29 9	67.57 68	82.06 37	81.33 43	83.93 149	93.75 33

FC-MVSNet-train		68.83 77	68.29 88	69.47 70	78.35 75	79.94 116	64.72 151	66.38 47	54.96 103	54.51 75	56.75 62	47.91 117	66.91 70	75.57 93	75.75 85	85.92 80	87.12 106

OpenMVS	67.62 8	74.92 51	73.91 59	76.09 39	90.10 15	90.38 19	78.01 52	66.35 48	66.09 66	62.80 43	46.33 108	64.55 59	71.77 40	79.92 55	80.88 51	87.52 39	89.20 76

thres100view900		67.14 89	66.09 104	68.38 79	77.70 80	83.84 72	74.52 74	66.33 49	49.16 121	43.40 119	43.24 115	41.34 133	62.59 87	79.31 58	75.92 84	85.73 96	89.81 70

MSLP-MVS++		78.57 33	77.33 44	80.02 17	88.39 29	84.79 66	84.62 17	66.17 50	75.96 47	78.40 5	61.59 51	71.47 39	73.54 30	78.43 65	78.88 60	88.97 12	90.18 68

LS3D		64.54 108	62.14 141	67.34 85	80.85 64	75.79 158	69.99 121	65.87 51	60.77 79	44.35 111	42.43 126	45.95 121	65.01 74	69.88 169	68.69 179	77.97 200	71.43 197

PGM-MVS		79.42 30	81.84 29	76.60 35	88.38 30	86.69 51	82.97 26	65.75 52	80.39 35	64.94 39	81.95 17	72.11 37	71.41 43	80.45 51	80.55 53	86.18 65	90.76 62

CNLPA		71.37 67	70.27 77	72.66 58	80.79 66	81.33 94	71.07 112	65.75 52	82.36 26	64.80 40	42.46 125	56.49 87	72.70 35	73.00 122	70.52 171	80.84 182	85.76 120

MAR-MVS		77.19 43	78.37 41	75.81 41	89.87 16	90.58 17	79.33 49	65.56 54	77.62 44	58.33 63	59.24 59	67.98 49	74.83 22	82.37 33	83.12 25	86.95 53	87.67 103
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
PVSNet_BlendedMVS		76.84 45	78.47 38	74.95 46	82.37 53	89.90 25	75.45 68	65.45 55	74.99 48	70.66 26	63.07 46	58.27 82	67.60 66	84.24 18	81.70 37	88.18 28	97.10 5

PVSNet_Blended		76.84 45	78.47 38	74.95 46	82.37 53	89.90 25	75.45 68	65.45 55	74.99 48	70.66 26	63.07 46	58.27 82	67.60 66	84.24 18	81.70 37	88.18 28	97.10 5

MVS_111021_LR		74.26 52	75.95 53	72.27 59	79.43 71	85.04 64	72.71 80	65.27 57	70.92 55	63.58 42	69.32 34	60.31 74	69.43 54	77.01 74	77.15 72	83.22 158	91.93 51

IB-MVS	64.48 11	69.02 76	68.97 84	69.09 74	81.75 58	89.01 30	64.50 152	64.91 58	56.65 92	62.59 45	47.89 92	45.23 123	51.99 153	69.18 174	81.88 35	88.77 15	92.93 39
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
ACMM	66.70 10	70.42 68	68.49 87	72.67 57	82.85 50	77.76 140	77.70 55	64.76 59	64.61 72	60.74 56	49.29 85	53.97 100	65.86 72	74.97 96	75.57 89	84.13 147	83.29 137
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CPTT-MVS		75.43 49	77.13 47	73.44 53	81.43 60	82.55 78	80.96 40	64.35 60	77.95 43	61.39 49	69.20 35	70.94 41	69.38 56	73.89 110	73.32 136	83.14 162	92.06 48

thres400		65.18 103	64.44 114	66.04 92	76.40 93	82.63 76	71.52 103	64.27 61	44.93 150	40.69 144	41.86 130	40.79 147	58.12 134	77.67 68	74.64 97	85.26 109	88.56 93

ACMP	68.86 7	72.15 63	72.25 65	72.03 60	80.96 63	80.87 100	77.93 53	64.13 62	69.29 59	60.79 55	64.04 44	53.54 101	63.91 80	73.74 114	75.27 91	84.45 139	88.98 79
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UniMVSNet_NR-MVSNet		62.30 138	63.51 119	60.89 150	69.48 137	77.83 138	64.07 156	63.94 63	50.03 115	31.17 188	44.82 110	41.12 139	51.37 155	71.02 155	74.81 96	85.30 108	84.95 124

thresconf0.02		63.92 115	65.18 108	62.46 138	75.91 96	80.65 109	67.51 138	63.86 64	45.00 149	33.32 183	51.38 78	51.68 106	48.34 165	75.49 94	75.13 92	85.84 91	76.91 173

MVS_0304		79.43 28	82.20 26	76.20 37	84.22 47	91.79 11	81.82 32	63.81 65	76.83 45	61.71 48	66.37 41	75.52 25	76.38 17	85.54 10	85.03 12	89.28 10	94.32 27

TranMVSNet+NR-MVSNet		60.38 160	61.30 150	59.30 159	68.34 144	75.57 161	63.38 165	63.78 66	46.74 136	27.73 198	42.56 123	36.84 179	47.66 168	70.36 164	74.59 102	84.91 121	82.46 146

PLC	64.00 12	68.54 78	66.66 98	70.74 65	80.28 68	74.88 163	72.64 81	63.70 67	69.26 60	55.71 70	47.24 100	55.31 95	70.42 47	72.05 148	70.67 169	81.66 176	77.19 171
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MVSTER		76.92 44	79.92 34	73.42 54	74.98 102	82.97 74	78.15 51	63.41 68	78.02 41	64.41 41	67.54 37	72.80 31	71.05 44	83.29 23	83.73 20	88.53 21	91.12 57

OMC-MVS		74.03 53	75.82 54	71.95 61	79.56 69	80.98 98	75.35 70	63.21 69	84.48 20	61.83 47	61.54 52	66.89 52	69.41 55	76.60 78	74.07 125	82.34 171	86.15 115

conf0.002		67.12 90	67.13 96	67.11 86	77.95 78	82.11 81	71.71 93	63.06 70	49.16 121	43.43 116	47.76 94	48.79 113	61.42 93	76.61 77	76.55 76	85.07 116	88.92 82

conf0.01		66.60 91	66.18 102	67.09 87	77.90 79	82.02 82	71.71 93	63.05 71	49.16 121	43.41 118	46.23 109	45.78 122	61.42 93	76.55 79	74.63 98	85.04 117	88.87 84

view600		63.91 116	63.27 123	64.66 104	75.57 99	81.73 84	69.71 124	63.04 72	43.97 153	39.18 150	41.09 134	40.24 155	55.38 144	76.28 80	72.04 153	85.08 115	87.52 104

LGP-MVS_train		72.02 64	73.18 64	70.67 66	82.13 56	80.26 115	79.58 47	63.04 72	70.09 56	51.98 78	65.06 43	55.62 94	62.49 88	75.97 88	76.32 79	84.80 131	88.93 80

thres600view7		63.77 117	63.14 125	64.51 106	75.49 100	81.61 86	69.59 125	62.95 74	43.96 154	38.90 152	41.09 134	40.24 155	55.25 146	76.24 81	71.54 156	84.89 122	87.30 105

thres200		65.58 98	64.74 112	66.56 91	77.52 85	81.61 86	73.44 79	62.95 74	46.23 142	42.45 136	42.76 119	41.18 138	58.12 134	76.24 81	75.59 88	84.89 122	89.58 71

tfpn111		66.52 92	66.12 103	66.98 89	77.70 80	81.58 88	71.71 93	62.94 76	49.16 121	43.28 121	51.38 78	41.34 133	61.42 93	76.24 81	74.63 98	84.84 125	88.52 94

conf200view11		65.89 97	64.96 109	66.98 89	77.70 80	81.58 88	71.71 93	62.94 76	49.16 121	43.28 121	43.24 115	41.34 133	61.42 93	76.24 81	74.63 98	84.84 125	88.52 94

tfpn200view9		65.90 96	64.96 109	67.00 88	77.70 80	81.58 88	71.71 93	62.94 76	49.16 121	43.40 119	43.24 115	41.34 133	61.42 93	76.24 81	74.63 98	84.84 125	88.52 94

Baseline_NR-MVSNet		59.47 164	60.28 160	58.54 164	66.69 168	73.90 169	61.63 172	62.90 79	49.15 127	26.87 199	35.18 186	37.62 174	48.20 166	69.67 171	73.61 130	84.92 119	82.82 143

ACMH+	60.36 13	61.16 154	58.38 176	64.42 109	77.37 86	74.35 168	68.45 131	62.81 80	45.86 144	38.48 155	35.71 182	37.35 176	59.81 119	67.24 180	69.80 175	79.58 192	78.32 169

EPNet_dtu		66.17 94	70.13 79	61.54 147	81.04 62	77.39 144	68.87 130	62.50 81	69.78 57	33.51 182	63.77 45	56.22 88	37.65 198	72.20 144	72.18 152	85.69 99	79.38 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAPA-MVS	67.10 9	71.45 66	73.47 63	69.10 73	77.04 87	80.78 101	73.81 78	62.10 82	80.80 33	51.28 81	60.91 54	63.80 62	67.98 63	74.59 99	72.42 150	82.37 170	80.97 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DU-MVS		60.87 157	61.82 144	59.76 156	66.69 168	75.87 156	64.07 156	61.96 83	49.31 119	31.17 188	42.76 119	36.95 178	51.37 155	69.67 171	73.20 142	83.30 157	84.95 124

NR-MVSNet		61.08 156	62.09 142	59.90 154	71.96 122	75.87 156	63.60 162	61.96 83	49.31 119	27.95 197	42.76 119	33.85 198	48.82 164	74.35 104	74.05 126	85.13 111	84.45 126

view800		63.02 129	62.69 136	63.39 126	74.79 105	80.76 105	67.83 134	61.93 85	43.16 164	37.78 161	40.43 139	39.73 162	53.16 151	75.01 95	73.32 136	84.87 124	86.43 112

TSAR-MVS + COLMAP		73.09 56	76.86 48	68.71 75	74.97 103	82.49 79	74.51 75	61.83 86	83.16 23	49.31 90	82.22 16	51.62 107	68.94 58	78.76 63	75.52 90	82.67 166	84.23 129

PVSNet_Blended_VisFu		71.76 65	73.54 62	69.69 69	79.01 73	87.16 47	72.05 84	61.80 87	56.46 94	59.66 60	53.88 72	62.48 63	59.08 130	81.17 45	78.90 59	86.53 60	94.74 22

MS-PatchMatch		70.34 72	69.00 83	71.91 62	85.20 46	85.35 62	77.84 54	61.77 88	58.01 88	55.40 72	41.26 133	58.34 81	61.69 91	81.70 41	78.29 65	89.56 7	80.02 162

tfpnnormal		58.97 166	56.48 184	61.89 144	71.27 126	76.21 155	66.65 144	61.76 89	32.90 207	36.41 165	27.83 205	29.14 210	50.64 160	73.06 119	73.05 143	84.58 137	83.15 142

DI_MVS_plusplus_trai		73.94 54	74.85 57	72.88 56	76.57 92	86.80 49	80.41 42	61.47 90	62.35 75	59.44 61	47.91 91	68.12 48	72.24 37	82.84 27	81.50 40	87.15 51	94.42 25

TransMVSNet (Re)		57.83 176	56.90 182	58.91 162	72.26 120	74.69 166	63.57 163	61.42 91	32.30 209	32.65 185	33.97 188	35.96 186	39.17 196	73.84 113	72.84 146	84.37 140	74.69 180

UniMVSNet (Re)		60.62 158	62.93 129	57.92 167	67.64 163	77.90 137	61.75 171	61.24 92	49.83 117	29.80 192	42.57 122	40.62 153	43.36 185	70.49 163	73.27 139	83.76 150	85.81 119

test-LLR		68.23 80	71.61 70	64.28 115	71.37 124	81.32 95	63.98 158	61.03 93	58.62 85	42.96 126	52.74 73	61.65 69	57.74 136	75.64 91	78.09 69	88.61 18	93.21 35

test0.0.03 1		57.35 179	59.89 167	54.38 187	71.37 124	73.45 171	52.71 196	61.03 93	46.11 143	26.33 201	41.73 131	44.08 126	29.72 204	71.43 154	70.90 167	85.10 112	71.56 196

tfpn_ndepth		62.95 133	63.75 117	62.02 143	76.89 89	79.48 123	64.09 155	60.98 95	49.48 118	38.73 153	49.92 84	44.79 124	47.37 170	71.91 149	71.66 155	84.07 148	79.00 167

tfpn		62.54 135	62.79 132	62.25 142	74.16 107	79.86 118	66.07 148	60.97 96	42.43 169	36.41 165	39.88 143	43.76 128	51.25 158	73.85 111	74.17 121	84.67 134	85.57 123

GBi-Net		69.21 74	70.40 75	67.81 81	69.49 134	78.65 129	74.54 71	60.97 96	65.32 68	51.06 82	47.37 97	62.05 65	63.43 82	77.49 69	78.22 66	87.37 40	83.73 132

test1		69.21 74	70.40 75	67.81 81	69.49 134	78.65 129	74.54 71	60.97 96	65.32 68	51.06 82	47.37 97	62.05 65	63.43 82	77.49 69	78.22 66	87.37 40	83.73 132

FMVSNet3		70.41 70	71.89 68	68.68 76	70.89 129	79.42 124	75.63 64	60.97 96	65.32 68	51.06 82	47.37 97	62.05 65	64.90 76	82.49 29	82.27 31	88.64 17	84.34 128

canonicalmvs		77.65 38	79.59 36	75.39 42	81.52 59	89.83 27	81.32 36	60.74 100	80.05 36	66.72 35	68.43 36	65.09 56	74.72 25	78.87 61	82.73 28	87.32 43	92.16 45

tfpnview11		58.92 167	59.60 169	58.13 165	72.99 115	77.11 148	60.48 175	60.37 101	42.10 172	29.10 194	43.45 112	40.72 150	41.67 190	70.53 162	70.43 172	84.17 146	72.85 189

PatchMatch-RL		62.22 142	60.69 155	64.01 116	68.74 139	75.75 159	59.27 183	60.35 102	56.09 96	53.80 76	47.06 103	36.45 181	64.80 77	68.22 177	67.22 186	77.10 202	74.02 182

conf0.05thres1000		60.33 161	59.42 171	61.40 148	73.15 113	78.25 134	65.29 150	60.30 103	36.61 193	35.75 171	33.25 189	39.23 166	50.35 161	72.18 145	72.67 148	83.57 153	83.74 131

tfpn_n400		58.64 171	59.27 172	57.89 168	72.83 116	77.26 146	60.35 176	60.29 104	39.77 183	29.10 194	43.45 112	40.72 150	41.61 191	70.06 167	71.39 162	83.17 160	72.26 192

tfpnconf		58.64 171	59.27 172	57.89 168	72.83 116	77.26 146	60.35 176	60.29 104	39.77 183	29.10 194	43.45 112	40.72 150	41.61 191	70.06 167	71.39 162	83.17 160	72.26 192

FMVSNet2		68.06 81	68.57 86	67.45 84	69.49 134	78.65 129	74.54 71	60.23 106	56.29 95	49.64 89	42.13 129	57.08 86	63.43 82	81.15 46	80.99 48	87.37 40	83.73 132

DWT-MVSNet_training		72.81 58	73.98 58	71.45 63	81.26 61	86.37 55	72.08 83	59.82 107	69.13 62	58.15 64	54.71 65	61.33 73	67.81 65	76.86 75	78.63 61	89.59 6	90.86 59

tfpn1000		58.35 175	59.96 166	56.47 178	72.78 118	77.51 142	56.66 190	59.16 108	43.74 155	29.76 193	42.79 118	42.49 130	37.04 199	68.92 175	68.98 177	83.45 155	75.25 177

Effi-MVS+		70.42 68	71.23 72	69.47 70	78.04 77	85.24 63	75.57 66	58.88 109	59.56 82	48.47 91	52.73 75	54.94 96	69.69 52	78.34 67	77.06 73	86.18 65	90.73 63

MVS_Test		75.22 50	76.69 50	73.51 52	79.30 72	88.82 31	80.06 44	58.74 110	69.77 58	57.50 67	59.78 58	61.35 71	75.31 19	82.07 36	83.60 23	90.13 5	91.41 54

UGNet		67.57 84	71.69 69	62.76 135	69.88 132	82.58 77	66.43 145	58.64 111	54.71 106	51.87 79	61.74 50	62.01 68	45.46 181	74.78 98	74.99 93	84.24 143	91.02 58
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
Anonymous20231206		52.23 196	52.80 197	51.56 194	64.70 181	69.41 193	51.01 198	58.60 112	36.63 192	22.44 207	21.80 218	31.42 205	30.52 202	66.79 181	67.83 183	82.10 173	75.73 175

IS_MVSNet		67.29 87	71.98 66	61.82 145	76.92 88	84.32 70	65.90 149	58.22 113	55.75 99	39.22 149	54.51 68	62.47 64	45.99 178	78.83 62	78.52 63	84.70 133	89.47 73

GA-MVS		64.55 107	65.76 107	63.12 130	69.68 133	81.56 91	69.59 125	58.16 114	45.23 148	35.58 173	47.01 104	41.82 132	59.41 125	79.62 56	78.54 62	86.32 62	86.56 110

HyFIR lowres test		68.39 79	68.28 89	68.52 77	80.85 64	88.11 40	71.08 111	58.09 115	54.87 105	47.80 94	27.55 206	55.80 91	64.97 75	79.11 59	79.14 58	88.31 25	93.35 34

CDS-MVSNet		64.22 109	65.89 106	62.28 141	70.05 131	80.59 110	69.91 123	57.98 116	43.53 160	46.58 98	48.22 89	50.76 109	46.45 175	75.68 90	76.08 81	82.70 165	86.34 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FMVSNet1		63.48 121	63.07 126	63.97 117	65.31 178	76.37 154	71.77 92	57.90 117	43.32 162	45.66 101	35.06 187	49.43 112	58.57 132	77.49 69	78.22 66	84.59 136	81.60 155

EPP-MVSNet		67.58 83	71.10 73	63.48 124	75.71 98	83.35 73	66.85 141	57.83 118	53.02 110	41.15 141	55.82 63	67.89 50	56.01 142	74.40 101	72.92 145	83.33 156	90.30 66

v148		62.00 145	61.19 151	62.96 131	67.46 165	79.49 122	67.87 133	57.66 119	42.30 170	45.02 108	38.20 165	38.89 170	54.77 147	69.83 170	72.60 149	84.96 118	87.01 107

v1neww		64.08 111	63.38 121	64.89 99	68.27 147	80.77 103	71.84 88	57.65 120	46.66 139	45.10 106	39.40 146	40.86 145	60.57 105	72.86 127	74.40 112	85.92 80	88.71 90

v7new		64.08 111	63.38 121	64.89 99	68.27 147	80.77 103	71.84 88	57.65 120	46.66 139	45.10 106	39.40 146	40.86 145	60.57 105	72.86 127	74.40 112	85.92 80	88.71 90

v6		64.09 110	63.40 120	64.90 97	68.28 145	80.78 101	71.85 87	57.64 122	46.73 137	45.18 105	39.40 146	40.89 144	60.54 108	72.86 127	74.40 112	85.92 80	88.72 89

v2v482		63.68 118	62.85 130	64.65 105	68.01 155	80.46 112	71.90 85	57.60 123	44.26 151	42.82 134	39.80 144	38.62 171	61.56 92	73.06 119	74.86 95	86.03 73	88.90 83

pmmvs4		63.14 126	62.46 138	63.94 118	66.03 173	76.40 153	66.82 142	57.60 123	56.74 91	50.26 88	40.81 138	37.51 175	59.26 128	71.75 152	71.48 158	83.68 152	82.53 145

Vis-MVSNet (Re-imp)		62.25 139	68.74 85	54.68 185	73.70 108	78.74 128	56.51 191	57.49 125	55.22 101	26.86 200	54.56 67	61.35 71	31.06 201	73.10 118	74.90 94	82.49 168	83.31 136

UA-Net		64.62 105	68.23 90	60.42 152	77.53 84	81.38 93	60.08 180	57.47 126	47.01 134	44.75 109	60.68 55	71.32 40	41.84 189	73.27 116	72.25 151	80.83 183	71.68 195

v1141		63.48 121	62.75 135	64.32 111	68.13 149	80.69 107	71.69 100	57.43 127	43.66 159	42.83 133	39.02 153	39.74 161	59.95 115	72.94 123	74.49 107	85.86 88	88.75 87

divwei89l23v2f112		63.48 121	62.76 134	64.32 111	68.13 149	80.68 108	71.71 93	57.43 127	43.69 157	42.84 131	39.01 154	39.75 160	59.94 116	72.93 124	74.49 107	85.86 88	88.75 87

v1		63.49 120	62.77 133	64.32 111	68.13 149	80.70 106	71.70 99	57.43 127	43.69 157	42.89 130	39.03 152	39.77 159	59.93 117	72.93 124	74.48 109	85.86 88	88.77 85

diffmvs		73.50 55	75.66 55	70.97 64	74.96 104	86.71 50	77.16 59	57.42 130	71.12 54	60.43 57	57.20 61	70.40 44	68.79 60	76.11 86	76.05 82	87.10 52	92.06 48

DTE-MVSNet		49.82 202	51.92 201	47.37 206	61.75 191	64.38 207	45.89 211	57.33 131	36.11 196	12.79 226	36.87 175	31.93 204	25.73 213	58.01 203	65.22 195	80.75 184	70.93 199

pm-mvs1		59.21 165	59.58 170	58.77 163	67.97 157	77.07 150	64.12 153	57.20 132	34.73 201	36.86 163	35.34 184	40.54 154	43.34 186	74.32 106	73.30 138	83.13 163	81.77 153

PEN-MVS		51.04 197	52.94 195	48.82 201	61.45 192	66.00 202	48.68 202	57.20 132	36.87 191	15.36 219	36.98 174	32.72 201	28.77 208	57.63 206	66.37 190	81.44 179	74.00 183

V42		62.86 134	62.97 128	62.74 136	60.84 194	78.99 127	71.46 104	57.13 134	46.85 135	44.28 112	38.87 155	40.73 149	57.63 138	72.60 139	74.14 122	85.09 114	88.63 92

test20.03		47.23 210	48.69 208	45.53 211	63.28 185	64.39 206	41.01 218	56.93 135	29.16 214	15.21 220	23.90 211	30.76 208	17.51 227	64.63 189	65.26 194	79.21 196	62.71 215

v7		63.61 119	63.02 127	64.29 114	67.88 159	80.32 113	71.60 101	56.63 136	45.37 146	42.84 131	38.54 158	38.91 169	61.05 100	74.39 102	74.52 105	85.75 92	89.10 78

v1144		63.00 130	62.39 139	63.70 120	67.72 162	80.27 114	71.23 107	56.40 137	42.51 168	40.81 143	38.12 167	37.73 173	60.42 111	74.46 100	74.55 103	85.64 104	89.12 77

WR-MVS		51.02 198	54.56 190	46.90 207	63.84 183	69.23 194	44.78 212	56.38 138	38.19 188	14.19 221	37.38 170	36.82 180	22.39 218	60.14 201	66.20 193	79.81 190	73.95 184

CP-MVSNet		50.57 199	52.60 199	48.21 204	58.77 203	65.82 203	48.17 203	56.29 139	37.41 189	16.59 216	37.14 172	31.95 203	29.21 205	56.60 209	63.71 200	80.22 187	75.56 176

Effi-MVS+-dtu		64.58 106	64.08 115	65.16 95	73.04 114	75.17 162	70.68 117	56.23 140	54.12 108	44.71 110	47.42 96	51.10 108	63.82 81	68.08 178	66.32 191	82.47 169	86.38 113

PS-CasMVS		50.17 200	52.02 200	48.02 205	58.60 204	65.54 204	48.04 204	56.19 141	36.42 195	16.42 218	35.68 183	31.33 206	28.85 207	56.42 211	63.54 201	80.01 188	75.18 178

v144192		62.05 144	61.46 149	62.73 137	66.59 170	79.87 117	69.30 127	55.88 142	41.50 176	39.41 148	37.23 171	36.45 181	59.62 121	72.69 137	73.51 131	85.61 105	88.93 80

COLMAP_ROB	51.17 15	55.13 183	52.90 196	57.73 171	73.47 112	67.21 199	62.13 169	55.82 143	47.83 132	34.39 178	31.60 198	34.24 195	44.90 183	63.88 195	62.52 205	75.67 206	63.02 214
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1192		62.25 139	61.64 147	62.96 131	66.88 167	79.72 119	69.96 122	55.77 144	41.58 175	39.42 147	37.05 173	35.96 186	60.50 110	74.30 107	74.09 124	85.24 110	88.76 86

MDA-MVSNet-bldmvs		44.15 214	42.27 219	46.34 208	38.34 230	62.31 212	46.28 208	55.74 145	29.83 213	20.98 209	27.11 208	16.45 231	41.98 188	41.11 229	57.47 214	74.72 210	61.65 218

PMMVS		70.37 71	75.06 56	64.90 97	71.46 123	81.88 83	64.10 154	55.64 146	71.31 53	46.69 97	70.69 33	58.56 77	69.53 53	79.03 60	75.63 87	81.96 174	88.32 99

EG-PatchMatch MVS		58.73 170	58.03 179	59.55 157	72.32 119	80.49 111	63.44 164	55.55 147	32.49 208	38.31 156	28.87 203	37.22 177	42.84 187	74.30 107	75.70 86	84.84 125	77.14 172

v1921920		61.66 151	61.10 152	62.31 140	66.32 171	79.57 121	68.41 132	55.49 148	41.03 177	38.69 154	36.64 179	35.27 192	59.60 122	73.23 117	73.41 133	85.37 107	88.51 97

Vis-MVSNet		65.53 100	69.83 80	60.52 151	70.80 130	84.59 67	66.37 147	55.47 149	48.40 129	40.62 145	57.67 60	58.43 80	45.37 182	77.49 69	76.24 80	84.47 138	85.99 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TESTMET0.1,1		67.38 86	71.61 70	62.45 139	66.05 172	81.32 95	63.98 158	55.36 150	58.62 85	42.96 126	52.74 73	61.65 69	57.74 136	75.64 91	78.09 69	88.61 18	93.21 35

USDC		59.69 163	60.03 165	59.28 160	64.04 182	71.84 187	63.15 167	55.36 150	54.90 104	35.02 177	48.34 88	29.79 209	58.16 133	70.60 159	71.33 165	79.99 189	73.42 186

WR-MVS_H		49.62 203	52.63 198	46.11 210	58.80 202	67.58 198	46.14 210	54.94 152	36.51 194	13.63 224	36.75 177	35.67 190	22.10 219	56.43 210	62.76 203	81.06 181	72.73 190

TDRefinement		52.70 194	51.02 202	54.66 186	57.41 206	65.06 205	61.47 173	54.94 152	44.03 152	33.93 180	30.13 202	27.57 212	46.17 177	61.86 197	62.48 206	74.01 212	66.06 207

v1240		61.09 155	60.55 157	61.72 146	65.92 175	79.28 125	67.16 140	54.91 154	39.79 182	38.10 157	36.08 181	34.64 193	59.15 129	72.86 127	73.36 135	85.10 112	87.84 101

v8		63.44 124	62.58 137	64.43 108	68.28 145	78.07 135	71.82 90	54.85 155	46.70 138	45.20 104	39.40 146	40.91 143	60.54 108	72.85 131	74.39 117	85.92 80	85.76 120

testgi		48.51 206	50.53 203	46.16 209	64.78 179	67.15 200	41.54 216	54.81 156	29.12 215	17.03 214	32.07 195	31.98 202	20.15 222	65.26 185	67.00 188	78.67 198	61.10 219

Fast-Effi-MVS+		67.59 82	67.56 93	67.62 83	73.67 109	81.14 97	71.12 109	54.79 157	58.88 83	50.61 86	46.70 106	47.05 118	69.12 57	76.06 87	76.44 77	86.43 61	86.65 109

CANet_DTU		72.84 57	76.63 51	68.43 78	76.81 90	86.62 52	75.54 67	54.71 158	72.06 52	43.54 114	67.11 38	58.46 79	72.40 36	81.13 47	80.82 52	87.57 38	90.21 67

v10		63.00 130	62.22 140	63.90 119	67.88 159	77.78 139	71.59 102	54.34 159	45.37 146	42.76 135	38.53 159	38.93 168	61.05 100	74.39 102	74.52 105	85.75 92	86.04 116

v18		63.31 125	62.02 143	64.81 101	68.48 141	73.38 172	72.14 82	54.28 160	48.99 128	47.21 95	39.56 145	41.20 137	60.80 102	72.89 126	74.46 110	85.96 79	83.64 135

v16		63.12 127	61.78 145	64.68 103	68.45 142	73.29 173	71.86 86	54.12 161	48.36 130	47.00 96	39.30 150	41.01 141	60.67 103	72.83 132	74.40 112	86.01 74	83.24 139

v17		62.99 132	61.70 146	64.51 106	68.40 143	73.28 174	71.80 91	54.11 162	47.87 131	46.14 99	39.29 151	41.01 141	60.60 104	72.81 133	74.39 117	85.99 77	83.25 138

pmmvs5		59.72 162	60.24 162	59.11 161	62.77 188	77.33 145	63.17 166	54.00 163	40.21 180	37.23 162	40.41 140	35.99 185	51.75 154	72.55 140	72.74 147	85.72 98	82.45 147

pmmvs-eth3d		55.20 181	53.95 192	56.65 176	57.34 207	67.77 197	57.54 188	53.74 164	40.93 178	41.09 142	31.19 199	29.10 211	49.07 163	65.54 183	67.28 185	81.14 180	75.81 174

IterMVS-LS		66.08 95	66.56 100	65.51 94	73.67 109	74.88 163	70.89 115	53.55 165	50.42 114	48.32 92	50.59 81	55.66 93	61.83 90	73.93 109	74.42 111	84.82 130	86.01 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v15		62.07 143	60.70 154	63.67 121	68.09 152	73.00 175	71.27 105	53.41 166	43.70 156	43.43 116	38.77 156	39.83 157	59.87 118	72.74 136	74.25 119	85.98 78	82.61 144

FC-MVSNet-test		47.24 209	54.37 191	38.93 219	59.49 200	58.25 217	34.48 224	53.36 167	45.66 145	6.66 234	50.62 80	42.02 131	16.62 228	58.39 202	61.21 208	62.99 224	64.40 210

V14		61.96 146	60.56 156	63.59 122	68.06 153	72.93 178	71.10 110	53.33 168	43.47 161	43.28 121	38.59 157	39.78 158	59.76 120	72.65 138	74.19 120	86.01 74	82.32 149

V9		61.85 148	60.42 159	63.51 123	68.02 154	72.85 179	70.91 114	53.24 169	43.25 163	43.27 125	38.41 161	39.73 162	59.60 122	72.55 140	74.13 123	86.04 72	82.04 151

CMPMVS	43.63 17	57.67 178	55.43 185	60.28 153	72.01 121	79.00 126	62.77 168	53.23 170	41.77 174	45.42 102	30.74 200	39.03 167	53.01 152	64.81 188	64.65 197	75.26 208	68.03 203
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v12		61.70 150	60.27 161	63.38 127	68.00 156	72.76 180	70.63 118	53.14 171	43.01 165	42.95 129	38.25 163	39.64 164	59.48 124	72.47 142	74.05 126	86.06 71	81.71 154

v13		61.60 152	60.13 164	63.31 128	67.95 158	72.67 182	70.51 119	53.05 172	42.80 166	42.96 126	38.10 168	39.57 165	59.31 127	72.36 143	73.98 128	86.10 68	81.40 156

TAMVS		58.86 168	60.91 153	56.47 178	62.38 190	77.57 141	58.97 185	52.98 173	38.76 187	36.17 168	42.26 128	47.94 116	46.45 175	70.23 165	70.79 168	81.86 175	78.82 168

TinyColmap		52.66 195	50.09 205	55.65 181	59.72 199	64.02 209	57.15 189	52.96 174	40.28 179	32.51 186	32.42 192	20.97 224	56.65 141	63.95 192	65.15 196	74.91 209	63.87 211

v11		61.74 149	60.47 158	63.22 129	67.83 161	72.72 181	70.31 120	52.95 175	42.75 167	41.89 137	38.16 166	38.49 172	60.40 112	74.35 104	74.40 112	85.92 80	82.39 148

FMVSNet5		58.86 168	60.24 162	57.25 174	52.66 218	66.25 201	63.77 161	52.86 176	57.85 89	37.92 159	36.12 180	52.22 105	51.37 155	70.88 156	71.43 160	84.92 119	66.91 205

CVMVSNet		54.92 187	58.16 177	51.13 196	62.61 189	68.44 196	55.45 193	52.38 177	42.28 171	21.45 208	47.10 101	46.10 120	37.96 197	64.42 191	63.81 199	76.92 204	75.01 179

pmmvs6		54.20 192	53.54 193	54.97 183	63.22 186	72.98 176	60.17 179	52.32 178	26.77 220	34.30 179	23.29 215	36.23 183	40.33 194	68.77 176	68.76 178	79.47 194	78.00 170

v7n		57.04 180	56.64 183	57.52 172	62.85 187	74.75 165	61.76 170	51.80 179	35.58 200	36.02 170	32.33 193	33.61 199	50.16 162	67.73 179	70.34 173	82.51 167	82.12 150

v748		55.19 182	54.63 188	55.85 180	61.44 193	72.97 177	58.72 186	51.62 180	34.48 203	36.39 167	32.09 194	33.05 200	45.48 180	61.85 198	67.87 182	81.45 178	80.08 161

N_pmnet		47.67 208	47.00 212	48.45 203	54.72 210	62.78 211	46.95 207	51.25 181	36.01 197	26.09 202	26.59 209	25.93 219	35.50 200	55.67 213	59.01 210	76.22 205	63.04 213

MIMVSNet1		40.84 219	43.46 215	37.79 221	32.14 232	58.92 216	39.24 220	50.83 182	27.00 219	11.29 229	16.76 229	26.53 215	17.75 226	57.14 208	61.12 209	75.46 207	56.78 223

test2356		46.29 211	47.37 210	45.03 212	54.38 211	57.99 218	42.03 215	50.32 183	30.78 212	16.65 215	27.40 207	23.70 221	29.86 203	61.20 199	64.31 198	76.93 203	66.22 206

testus		42.30 216	43.69 214	40.67 217	53.21 216	53.50 222	31.81 226	49.96 184	27.06 218	11.55 228	25.67 210	19.00 227	25.20 214	55.34 214	62.59 204	72.31 215	62.69 216

new-patchmatchnet		42.21 217	42.97 216	41.33 215	53.05 217	59.89 214	39.38 219	49.61 185	28.26 217	12.10 227	22.17 217	21.54 223	19.22 223	50.96 222	56.04 220	74.61 211	61.92 217

MDTV_nov1_ep13		65.21 102	67.28 95	62.79 133	70.91 128	81.72 85	69.28 128	49.50 186	58.08 87	43.94 113	50.50 82	56.02 89	58.86 131	70.72 157	73.37 134	84.24 143	80.52 158

v52		54.79 188	55.15 186	54.36 189	54.07 213	72.13 185	59.84 181	49.39 187	34.50 202	35.08 176	31.63 197	35.74 188	47.21 173	63.90 193	67.92 180	80.59 185	80.23 159

V4		54.78 189	55.14 187	54.37 188	54.07 213	72.13 185	59.83 182	49.39 187	34.46 204	35.11 175	31.64 196	35.72 189	47.22 172	63.90 193	67.92 180	80.59 185	80.23 159

tpmp4_e23		69.38 73	69.47 81	69.28 72	78.20 76	82.35 80	75.92 61	49.20 189	64.15 73	59.96 59	47.93 90	55.77 92	68.06 62	73.05 121	74.53 104	84.34 141	88.50 98

Anonymous20231211		44.16 213	39.25 220	49.89 198	54.29 212	57.29 219	41.10 217	49.06 190	17.67 232	58.47 62	10.63 231	16.79 230	25.15 215	52.14 219	56.70 217	71.30 217	63.51 212

CostFormer		72.18 62	73.90 60	70.18 68	79.47 70	86.19 59	76.94 60	48.62 191	66.07 67	60.40 58	54.14 70	65.82 54	67.98 63	75.84 89	76.41 78	87.67 37	92.83 41

IterMVS		61.87 147	63.55 118	59.90 154	67.29 166	72.20 184	67.34 139	48.56 192	47.48 133	37.86 160	47.07 102	48.27 114	54.08 149	72.12 146	73.71 129	84.30 142	83.99 130
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
anonymousdsp		54.99 185	57.24 181	52.36 192	53.82 215	71.75 188	51.49 197	48.14 193	33.74 205	33.66 181	38.34 162	36.13 184	47.54 169	64.53 190	70.60 170	79.53 193	85.59 122

test-mter		64.06 114	69.24 82	58.01 166	59.07 201	77.40 143	59.13 184	48.11 194	55.64 100	39.18 150	51.56 77	58.54 78	55.38 144	73.52 115	76.00 83	87.22 48	92.05 50

SixPastTwentyTwo		49.11 205	49.22 207	48.99 200	58.54 205	64.14 208	47.18 206	47.75 195	31.15 211	24.42 203	41.01 137	26.55 214	44.04 184	54.76 217	58.70 212	71.99 216	68.21 201

tpm cat1		67.47 85	67.05 97	67.98 80	76.63 91	81.51 92	74.49 76	47.65 196	61.18 78	61.12 51	42.51 124	53.02 104	64.74 78	70.11 166	71.50 157	83.22 158	89.49 72

EU-MVSNet		44.84 212	47.85 209	41.32 216	49.26 222	56.59 220	43.07 214	47.64 197	33.03 206	13.82 222	36.78 176	30.99 207	24.37 216	53.80 218	55.57 221	69.78 218	68.21 201

LTVRE_ROB	47.26 16	49.41 204	49.91 206	48.82 201	64.76 180	69.79 192	49.05 200	47.12 198	20.36 229	16.52 217	36.65 178	26.96 213	50.76 159	60.47 200	63.16 202	64.73 223	72.00 194
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
testmv		37.40 222	37.95 222	36.76 222	48.97 223	49.33 227	28.65 230	46.74 199	18.34 230	7.68 232	16.80 227	14.47 232	19.18 224	51.72 220	56.93 215	69.36 219	58.09 221

test1235678		37.40 222	37.94 223	36.76 222	48.97 223	49.30 228	28.65 230	46.73 200	18.33 231	7.68 232	16.79 228	14.46 233	19.18 224	51.72 220	56.92 216	69.36 219	58.07 222

dps		64.08 111	63.22 124	65.08 96	75.27 101	79.65 120	66.68 143	46.63 201	56.94 90	55.67 71	43.96 111	43.63 129	64.00 79	69.50 173	69.82 174	82.25 172	79.02 166

PatchmatchNet		65.43 101	67.71 92	62.78 134	73.49 111	82.83 75	66.42 146	45.40 202	60.40 81	45.27 103	49.22 86	57.60 85	60.01 114	70.61 158	71.38 164	86.08 70	81.91 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		67.15 88	68.12 91	66.03 93	76.21 94	80.98 98	71.27 105	45.05 203	60.69 80	50.63 85	46.95 105	54.15 99	65.30 73	71.80 151	71.77 154	87.72 35	90.48 64

MIMVSNet		57.78 177	59.71 168	55.53 182	54.79 209	77.10 149	63.89 160	45.02 204	46.59 141	36.79 164	28.36 204	40.77 148	45.84 179	74.97 96	76.58 75	86.87 55	73.60 185

tpm		64.85 104	66.02 105	63.48 124	74.52 106	78.38 132	70.98 113	44.99 205	51.61 112	43.28 121	47.66 95	53.18 102	60.57 105	70.58 160	71.30 166	86.54 59	89.45 74

PM-MVS		50.11 201	50.38 204	49.80 199	47.23 227	62.08 213	50.91 199	44.84 206	41.90 173	36.10 169	35.22 185	26.05 218	46.83 174	57.64 205	55.42 222	72.90 213	74.32 181

EPMVS		66.21 93	67.49 94	64.73 102	75.81 97	84.20 71	68.94 129	44.37 207	61.55 77	48.07 93	49.21 87	54.87 97	62.88 85	71.82 150	71.40 161	88.28 26	79.37 165

MDTV_nov1_ep13_2view		54.47 191	54.61 189	54.30 190	60.50 195	73.82 170	57.92 187	43.38 208	39.43 186	32.51 186	33.23 190	34.05 196	47.26 171	62.36 196	66.21 192	84.24 143	73.19 188

CR-MVSNet		62.31 137	64.75 111	59.47 158	68.63 140	71.29 189	67.53 136	43.18 209	55.83 97	41.40 138	41.04 136	55.85 90	57.29 139	72.76 134	73.27 139	78.77 197	83.23 140

Patchmtry						78.06 136	67.53 136	43.18 209		41.40 138

Fast-Effi-MVS+-dtu		63.05 128	64.72 113	61.11 149	71.21 127	76.81 151	70.72 116	43.13 211	52.51 111	35.34 174	46.55 107	46.36 119	61.40 98	71.57 153	71.44 159	84.84 125	87.79 102

gm-plane-assit		54.99 185	57.99 180	51.49 195	69.27 138	54.42 221	32.32 225	42.59 212	21.18 227	13.71 223	23.61 212	43.84 127	60.21 113	87.09 4	86.55 4	90.81 4	89.28 75

CHOSEN 280x420		62.23 141	66.57 99	57.17 175	59.88 198	68.92 195	61.20 174	42.28 213	54.17 107	39.57 146	47.78 93	64.97 57	62.68 86	73.85 111	69.52 176	77.43 201	86.75 108

MVS-HIRNet		53.86 193	53.02 194	54.85 184	60.30 197	72.36 183	44.63 213	42.20 214	39.45 185	43.47 115	21.66 219	34.00 197	55.47 143	65.42 184	67.16 187	83.02 164	71.08 198

FPMVS		39.11 220	36.39 224	42.28 213	55.97 208	45.94 230	46.23 209	41.57 215	35.73 198	22.61 205	23.46 213	19.82 226	28.32 211	43.57 225	40.67 228	58.96 226	45.54 225

PatchT		60.46 159	63.85 116	56.51 177	65.95 174	75.68 160	47.34 205	41.39 216	53.89 109	41.40 138	37.84 169	50.30 111	57.29 139	72.76 134	73.27 139	85.67 100	83.23 140

PMVS	27.44 18	32.08 225	29.07 227	35.60 224	48.33 226	24.79 235	26.97 232	41.34 217	20.45 228	22.50 206	17.11 226	18.64 228	20.44 221	41.99 228	38.06 229	54.02 231	42.44 229
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
no-one		26.96 227	26.51 228	27.49 228	37.87 231	39.14 232	17.12 235	41.31 218	12.02 235	3.68 238	8.04 233	8.42 237	10.67 232	28.11 231	45.96 227	54.27 230	43.89 227

RPSCF		55.07 184	58.06 178	51.57 193	48.87 225	58.95 215	53.68 195	41.26 219	62.42 74	45.88 100	54.38 69	54.26 98	53.75 150	57.15 207	53.53 223	66.01 222	65.75 208

gg-mvs-nofinetune		62.34 136	66.19 101	57.86 170	76.15 95	88.61 33	71.18 108	41.24 220	25.74 221	13.16 225	22.91 216	63.97 61	54.52 148	85.06 13	85.25 10	90.92 3	91.78 52

ADS-MVSNet		58.40 174	59.16 175	57.52 172	65.80 176	74.57 167	60.26 178	40.17 221	50.51 113	38.01 158	40.11 142	44.72 125	59.36 126	64.91 186	66.55 189	81.53 177	72.72 191

test12356		29.92 226	31.49 226	28.08 226	38.46 229	37.74 233	21.36 233	40.17 221	16.83 233	5.61 236	15.66 230	11.48 234	6.60 234	42.01 227	51.23 224	56.29 229	45.52 226

LP		48.21 207	46.65 213	50.03 197	60.39 196	63.86 210	48.73 201	38.71 223	35.60 199	32.99 184	23.31 214	24.95 220	40.07 195	57.73 204	61.56 207	79.29 195	59.51 220

RPMNet		58.63 173	62.80 131	53.76 191	67.59 164	71.29 189	54.60 194	38.13 224	55.83 97	35.70 172	41.58 132	53.04 103	47.89 167	66.10 182	67.38 184	78.65 199	84.40 127

1111		38.93 221	38.98 221	38.86 220	50.10 220	50.42 224	29.52 227	38.00 225	22.67 225	17.99 212	17.40 222	26.26 216	28.72 209	54.86 215	58.20 213	68.82 221	43.08 228

.test1245		25.86 228	24.56 230	27.39 229	50.10 220	50.42 224	29.52 227	38.00 225	22.67 225	17.99 212	17.40 222	26.26 216	28.72 209	54.86 215	0.05 235	0.01 239	0.24 237

Gipuma		24.91 229	24.61 229	25.26 230	31.47 233	21.59 236	18.06 234	37.53 227	25.43 222	10.03 230	4.18 237	4.25 239	14.85 229	43.20 226	47.03 225	39.62 233	26.55 233
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		14.40 232	10.71 234	18.70 232	28.15 235	12.09 240	7.06 239	36.89 228	11.00 236	3.56 240	4.95 235	2.27 241	13.91 230	10.13 236	16.06 234	22.63 236	18.51 235

E-PMN		15.08 231	11.65 233	19.08 231	28.73 234	12.31 239	6.95 240	36.87 229	10.71 237	3.63 239	5.13 234	2.22 242	13.81 231	11.34 235	18.50 233	24.49 235	21.32 234

pmmvs3		41.86 218	42.29 218	41.36 214	39.80 228	52.66 223	38.93 221	35.85 230	23.40 224	20.22 210	19.30 220	20.84 225	40.56 193	55.98 212	58.79 211	72.80 214	65.03 209

testpf		43.39 215	47.17 211	38.98 218	65.58 177	47.38 229	36.09 222	31.67 231	36.97 190	19.47 211	33.01 191	35.62 191	23.61 217	50.86 223	56.08 219	57.48 228	70.27 200

new_pmnet		33.19 224	35.52 225	30.47 225	27.55 236	45.31 231	29.29 229	30.92 232	29.00 216	9.88 231	18.77 221	17.64 229	26.77 212	44.07 224	45.98 226	58.41 227	47.87 224

MVE	15.98 19	14.37 233	16.36 232	12.04 235	7.72 239	20.24 237	5.90 241	29.05 233	8.28 238	3.92 237	4.72 236	2.42 240	9.57 233	18.89 234	31.46 231	16.07 238	28.53 232
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		20.45 230	22.31 231	18.27 233	20.52 237	26.73 234	14.85 237	28.43 234	13.69 234	0.79 241	10.35 232	9.10 235	3.83 236	27.64 232	32.87 230	41.17 232	35.81 230

DeepMVS_CX						19.81 238	17.01 236	10.02 235	23.61 223	5.85 235	17.21 225	8.03 238	21.13 220	22.60 233		21.42 237	30.01 231

tmp_tt				16.09 234	13.07 238	8.12 241	13.61 238	2.08 236	55.09 102	30.10 191	40.26 141	22.83 222	5.35 235	29.91 230	25.25 232	32.33 234

GG-mvs-BLEND		54.54 190	77.58 42	27.67 227	0.03 240	90.09 24	77.20 58	0.02 237	66.83 63	0.05 242	59.90 57	73.33 30	0.04 237	78.40 66	79.30 57	88.65 16	95.20 20

testmvs		0.05 234	0.08 235	0.01 236	0.00 241	0.01 242	0.03 243	0.01 238	0.05 239	0.00 243	0.14 239	0.01 243	0.03 239	0.05 237	0.05 235	0.01 239	0.24 237

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 241	0.00 244	0.00 245	0.00 239	0.00 241	0.00 243	0.00 240	0.00 244	0.00 240	0.00 239	0.00 238	0.00 241	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 241	0.00 244	0.00 245	0.00 239	0.00 241	0.00 243	0.00 240	0.00 244	0.00 240	0.00 239	0.00 238	0.00 241	0.00 239

test123		0.05 234	0.08 235	0.01 236	0.00 241	0.01 242	0.01 244	0.00 239	0.05 239	0.00 243	0.16 238	0.00 244	0.04 237	0.02 238	0.05 235	0.00 241	0.26 236

our_test_3					63.32 184	71.07 191	55.90 192

ambc			42.30 217		50.36 219	49.51 226	35.47 223		32.04 210	23.53 204	17.36 224	8.95 236	29.06 206	64.88 187	56.26 218	61.29 225	67.12 204

MTAPA										78.32 6		79.42 18

MTMP										76.04 11		76.65 23

Patchmatch-RL test							2.17 242

XVS					82.43 51	86.27 56	75.70 62			61.07 52		72.27 33				85.67 100

X-MVStestdata					82.43 51	86.27 56	75.70 62			61.07 52		72.27 33				85.67 100

mPP-MVS					86.96 37							70.61 43

NP-MVS									81.60 31

High-resolution multi-view benchmark