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		94.10 5	94.77 5	93.31 7	98.31 1	98.34 4	95.43 5	92.54 3	94.41 14	83.05 28	91.38 16	90.97 6	92.24 10	95.05 5	94.02 5	98.31 1	99.20 7

CNVR-MVS		94.53 3	94.85 4	94.15 4	98.03 2	98.59 3	95.56 4	92.91 1	94.86 10	88.46 12	91.32 18	90.83 7	94.03 2	95.20 3	94.16 4	95.89 25	99.01 12

v1.0		87.46 43	81.44 83	94.48 2	97.96 3	98.62 2	96.45 2	92.82 2	96.24 4	90.25 6	96.16 3	93.09 1	93.32 4	93.93 13	92.02 20	96.07 19	0.00 246

HPM-MVS++		94.04 6	94.96 3	92.96 9	97.93 4	97.71 14	94.65 10	91.01 9	95.91 5	87.43 14	93.52 9	92.63 2	92.29 9	94.22 12	92.34 16	94.47 49	98.37 22

NCCC		93.59 7	94.00 10	93.10 8	97.90 5	97.93 10	95.40 6	92.39 5	94.47 13	84.94 19	91.21 19	89.32 11	92.53 7	93.90 14	92.98 12	95.44 33	98.22 24

ESAPD		95.10 1	95.53 1	94.60 1	97.77 6	98.64 1	96.60 1	92.45 4	96.34 3	91.41 2	96.70 2	92.26 3	93.56 3	93.68 15	91.73 28	95.79 28	99.37 4

SMA-MVS		93.47 8	94.29 8	92.52 11	97.72 7	97.77 13	94.46 13	90.19 13	94.96 9	87.15 15	90.15 22	90.99 5	91.49 13	94.31 10	93.33 9	94.10 54	98.53 20

APDe-MVS		94.31 4	94.30 7	94.33 3	97.57 8	98.06 8	95.79 3	91.98 6	95.50 7	92.19 1	95.25 4	87.97 15	92.93 5	93.01 21	91.02 38	95.52 31	99.29 5

DeepC-MVS_fast	86.59 2	91.69 16	91.39 22	92.05 15	97.43 9	96.92 28	94.05 16	90.23 12	93.31 21	83.19 26	77.91 40	84.23 29	92.42 8	94.62 8	94.83 2	95.00 41	97.88 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HFP-MVS		92.02 14	92.13 19	91.89 16	97.16 10	96.46 36	93.57 19	87.60 22	93.79 16	88.17 13	93.15 11	83.94 33	91.19 14	90.81 42	89.83 46	93.66 74	96.94 54

APD-MVS		93.47 8	93.44 13	93.50 6	97.06 11	97.09 23	95.27 7	91.47 7	95.71 6	89.57 8	93.66 7	86.28 20	92.81 6	92.06 28	90.70 40	94.83 46	98.60 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		92.16 13	92.91 17	91.28 18	96.95 12	97.36 19	93.66 18	89.23 18	93.33 18	83.71 23	90.53 20	86.84 17	90.39 15	93.30 19	91.56 30	93.74 69	97.43 37

SteuartSystems-ACMMP		92.31 12	93.31 14	91.15 19	96.88 13	97.36 19	93.95 17	89.44 16	92.62 23	83.20 25	94.34 6	85.55 22	88.95 25	93.07 20	91.90 24	94.51 48	98.30 23
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator	80.58 8	88.20 37	86.53 46	90.15 22	96.86 14	96.46 36	91.97 33	83.06 47	85.16 59	83.66 24	62.28 98	82.15 38	88.98 24	90.99 40	92.65 14	96.38 18	96.03 73

HSP-MVS		94.69 2	95.39 2	93.88 5	96.78 15	98.11 6	94.75 8	90.91 10	96.89 2	89.12 11	96.98 1	89.47 10	94.76 1	95.24 2	93.29 10	96.98 7	97.73 30

zzz-MVS		91.59 17	91.12 23	92.13 13	96.76 16	96.68 31	93.39 20	88.00 21	93.63 17	90.76 5	83.97 33	85.33 24	89.89 17	91.60 34	89.65 51	94.00 58	96.97 52

QAPM		87.06 44	86.46 47	87.75 38	96.63 17	97.09 23	91.71 36	82.62 50	80.58 75	71.28 73	66.04 78	84.24 28	87.01 39	89.93 50	89.91 45	97.26 5	97.44 35

ACMMPR		91.15 19	91.44 21	90.81 20	96.61 18	96.25 40	93.09 21	87.08 24	93.32 20	84.78 20	92.08 14	82.10 39	89.71 19	90.24 46	89.82 47	93.61 80	96.30 69

MP-MVS		90.81 22	91.45 20	90.06 23	96.59 19	96.33 39	92.46 30	87.19 23	90.27 35	82.54 32	91.38 16	84.88 26	88.27 32	90.58 44	89.30 56	93.30 104	97.44 35
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CSCG		89.81 28	89.69 30	89.96 25	96.55 20	97.90 11	92.89 24	87.06 25	88.74 46	86.17 16	78.24 39	86.53 19	84.75 57	87.82 81	90.59 41	92.32 150	98.01 26

PGM-MVS		89.97 26	90.64 27	89.18 30	96.53 21	95.90 47	93.06 22	82.48 52	90.04 37	80.37 37	92.75 12	80.96 44	88.93 26	89.88 51	89.08 58	93.69 73	95.86 76

MSLP-MVS++		90.33 24	88.82 34	92.10 14	96.52 22	95.93 43	94.35 14	86.26 29	88.37 48	89.24 9	75.94 45	82.60 36	89.71 19	89.45 56	92.17 17	96.51 14	97.24 42

X-MVS		89.73 29	90.65 26	88.66 33	96.44 23	95.93 43	92.26 32	86.98 26	90.73 33	76.32 50	89.56 24	82.05 40	86.51 43	89.98 49	89.60 53	93.43 97	96.72 63

train_agg		91.99 15	93.71 11	89.98 24	96.42 24	97.03 25	94.31 15	89.05 19	93.33 18	77.75 43	95.06 5	88.27 13	88.38 31	92.02 29	91.41 32	94.00 58	98.84 15

AdaColmap		88.46 35	85.75 54	91.62 17	96.25 25	95.35 56	90.71 40	91.08 8	90.22 36	86.17 16	74.33 49	73.67 72	92.00 12	86.31 100	85.82 89	93.52 85	94.53 96

CP-MVS		90.57 23	90.68 25	90.44 21	96.13 26	95.90 47	92.77 26	86.86 28	92.12 26	84.19 21	89.18 25	82.37 37	89.43 23	89.65 54	88.43 62	93.27 106	97.13 46

OpenMVS	77.91 11	85.09 54	83.42 66	87.03 43	96.12 27	96.55 34	89.36 49	81.59 57	79.19 78	75.20 55	55.84 126	79.04 49	84.45 60	88.47 69	89.35 55	95.48 32	95.48 83

CDPH-MVS		88.76 32	90.43 28	86.81 47	96.04 28	96.53 35	92.95 23	85.95 31	90.36 34	67.93 86	85.80 30	80.69 45	83.82 64	90.81 42	91.85 27	94.18 52	96.99 51

mPP-MVS					95.90 29							80.22 48

3Dnovator+	81.14 5	88.59 33	87.49 40	89.88 26	95.83 30	96.45 38	91.94 34	82.41 53	87.09 52	85.94 18	62.80 95	85.37 23	89.46 21	91.51 35	91.89 26	93.72 71	97.30 40

TSAR-MVS + ACMM		90.98 21	93.18 15	88.42 35	95.69 31	96.73 30	94.52 12	86.97 27	92.99 22	76.32 50	92.31 13	86.64 18	84.40 62	92.97 22	92.02 20	92.62 144	98.59 18

EPNet		89.30 30	90.89 24	87.44 40	95.67 32	96.81 29	91.13 38	83.12 46	91.14 29	76.31 54	87.60 27	80.40 47	84.45 60	92.13 27	91.12 37	93.96 61	97.01 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	86.71 1	91.00 20	94.05 9	87.43 41	95.58 33	98.17 5	86.22 70	88.59 20	97.01 1	76.77 48	85.11 31	88.90 12	87.29 37	95.02 6	94.69 3	90.15 193	99.48 3

abl_6				89.54 28	95.55 34	97.59 16	89.01 52	85.00 35	94.67 12	83.04 29	84.70 32	91.47 4	89.46 21			95.20 38	98.63 16

MAR-MVS		85.65 51	86.30 48	84.88 58	95.51 35	95.89 49	86.50 69	76.71 84	89.23 44	68.59 83	70.93 61	74.49 66	88.55 27	89.40 57	90.30 43	93.42 98	93.88 116
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
PHI-MVS		89.88 27	92.75 18	86.52 51	94.97 36	97.57 17	89.99 46	84.56 37	92.52 24	69.72 81	90.35 21	87.11 16	84.89 53	91.82 31	92.37 15	95.02 40	97.51 33

DeepC-MVS	84.14 3	88.80 31	88.03 38	89.71 27	94.83 37	96.56 32	92.57 28	89.38 17	89.25 43	79.59 40	70.02 63	77.05 58	88.24 33	92.44 25	92.79 13	93.65 77	98.10 25
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		87.75 40	86.92 44	88.71 32	94.69 38	97.34 22	92.78 25	84.50 38	77.87 83	81.94 34	67.17 72	75.49 64	82.84 70	95.38 1	95.93 1	95.55 30	99.27 6
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
SD-MVS		93.36 10	94.33 6	92.22 12	94.68 39	97.89 12	94.56 11	90.89 11	94.80 11	90.04 7	93.53 8	90.14 8	89.78 18	92.74 23	92.17 17	93.35 102	99.07 10

ACMMP		88.48 34	88.71 35	88.22 37	94.61 40	95.53 51	90.64 42	85.60 33	90.97 30	78.62 42	89.88 23	74.20 69	86.29 44	88.16 78	86.37 81	93.57 82	95.86 76
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
MVS_111021_HR		87.82 39	88.84 33	86.62 49	94.42 41	97.36 19	88.21 56	83.26 45	83.42 63	72.52 68	82.63 35	76.93 59	84.95 52	91.93 30	91.15 36	96.39 17	98.49 21

TSAR-MVS + MP.		93.07 11	93.53 12	92.53 10	94.23 42	97.54 18	94.75 8	89.87 14	95.26 8	89.20 10	93.16 10	88.19 14	92.15 11	91.79 32	89.65 51	94.99 42	99.16 8

CANet		89.98 25	90.42 29	89.47 29	94.13 43	98.05 9	91.76 35	83.27 44	90.87 32	81.90 35	72.32 53	84.82 27	88.42 29	94.52 9	93.78 7	97.34 4	98.58 19

PLC	81.02 6	84.81 58	81.81 81	88.31 36	93.77 44	90.35 108	88.80 53	84.47 39	86.76 53	82.17 33	66.56 74	71.01 82	88.41 30	85.48 108	84.28 110	92.26 152	88.21 177
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CPTT-MVS		88.17 38	87.84 39	88.55 34	93.33 45	93.75 69	92.33 31	84.75 36	89.87 39	81.72 36	83.93 34	81.12 43	88.45 28	85.42 110	84.07 112	90.72 185	96.72 63

PVSNet_BlendedMVS		86.98 45	87.05 42	86.90 44	93.03 46	96.98 26	86.57 67	81.82 55	89.78 40	82.78 30	71.54 57	66.07 100	80.73 85	93.46 17	91.97 22	96.45 15	99.53 1

PVSNet_Blended		86.98 45	87.05 42	86.90 44	93.03 46	96.98 26	86.57 67	81.82 55	89.78 40	82.78 30	71.54 57	66.07 100	80.73 85	93.46 17	91.97 22	96.45 15	99.53 1

CNLPA		84.72 59	82.14 77	87.73 39	92.85 48	93.83 68	84.70 89	85.07 34	90.90 31	83.16 27	56.28 122	71.53 78	88.14 34	84.19 118	84.00 116	92.48 147	94.26 102

MVS_111021_LR		87.58 42	88.67 36	86.31 52	92.58 49	95.89 49	86.20 72	82.49 51	89.08 45	77.47 45	86.20 29	74.22 68	85.49 49	90.03 48	88.52 60	93.66 74	96.74 62

EPNet_dtu		78.49 102	81.96 79	74.45 129	92.57 50	88.74 126	82.98 99	78.83 62	83.28 64	44.64 202	77.40 42	67.73 93	53.98 208	85.44 109	84.91 97	93.71 72	86.22 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		86.38 47	86.21 50	86.57 50	92.30 51	94.35 64	87.60 60	83.51 43	92.32 25	77.37 46	72.27 54	77.83 51	86.59 42	87.62 85	85.95 86	92.08 154	93.11 128

CHOSEN 1792x2688		80.23 84	79.16 95	81.48 75	91.97 52	96.56 32	86.18 73	75.40 98	76.17 93	61.32 101	37.43 216	61.08 117	76.52 106	92.35 26	91.64 29	97.46 3	98.86 13

LS3D		78.72 97	75.79 123	82.15 69	91.91 53	89.39 123	83.66 97	85.88 32	76.81 91	59.22 115	57.67 112	58.53 128	83.72 65	82.07 140	81.63 148	88.50 207	84.39 193

TAPA-MVS	80.99 7	84.83 57	84.42 58	85.31 56	91.89 54	93.73 71	88.53 55	82.80 48	89.99 38	69.78 80	71.53 59	75.03 65	85.47 50	86.26 101	84.54 107	93.39 100	89.90 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_0304		88.43 36	89.46 31	87.21 42	91.85 55	97.60 15	92.62 27	81.10 59	87.16 51	73.80 58	72.19 55	83.36 35	87.03 38	94.64 7	93.67 8	96.88 8	97.64 32

MSDG		78.11 107	73.17 143	83.86 64	91.78 56	86.83 142	85.25 80	86.02 30	72.84 109	69.69 82	51.43 143	54.00 142	77.61 98	81.95 144	82.27 137	92.83 140	82.91 203

PCF-MVS	82.38 4	85.52 52	84.41 59	86.81 47	91.51 57	96.23 41	90.27 43	89.81 15	77.87 83	70.67 75	69.20 65	77.86 50	85.55 48	85.92 106	86.38 80	93.03 125	97.43 37
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HQP-MVS		86.17 48	87.35 41	84.80 59	91.41 58	92.37 91	91.05 39	84.35 40	88.52 47	64.21 90	87.05 28	68.91 90	84.80 55	89.12 59	88.16 66	92.96 128	97.31 39

OPM-MVS		81.34 74	78.18 103	85.02 57	91.27 59	91.78 97	90.66 41	83.62 42	62.39 150	65.91 87	63.35 92	64.33 108	85.03 51	87.77 82	85.88 88	93.66 74	91.75 141

TSAR-MVS + COLMAP		84.93 55	85.79 53	83.92 63	90.90 60	93.57 73	89.25 51	82.00 54	91.29 28	61.66 97	88.25 26	59.46 124	86.71 41	89.79 52	87.09 72	93.01 126	91.09 144

HyFIR lowres test		78.08 108	76.81 111	79.56 92	90.77 61	94.64 62	82.97 100	69.85 146	69.81 124	59.53 113	33.52 221	64.66 105	78.97 95	88.77 64	88.38 63	95.27 34	97.86 28

IB-MVS	74.10 12	78.52 101	78.51 99	78.52 101	90.15 62	95.39 54	71.95 195	77.53 78	74.95 98	77.25 47	58.93 108	55.92 137	58.37 197	79.01 179	87.89 67	95.88 26	97.47 34
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
MS-PatchMatch		77.47 112	76.48 116	78.63 99	89.89 63	90.42 107	85.42 78	69.53 148	70.79 117	60.43 110	50.05 148	70.62 85	70.66 150	86.71 93	82.54 132	95.86 27	84.23 194

PVSNet_Blended_VisFu		82.55 65	83.70 65	81.21 79	89.66 64	95.15 59	82.41 105	77.36 80	72.53 111	73.64 59	61.15 103	77.19 57	70.35 157	91.31 38	89.72 50	93.84 64	98.85 14

XVS					89.65 65	95.93 43	85.97 75			76.32 50		82.05 40				93.51 88

X-MVStestdata					89.65 65	95.93 43	85.97 75			76.32 50		82.05 40				93.51 88

ACMM	78.09 10	80.91 76	78.39 101	83.86 64	89.61 67	87.71 131	85.16 82	80.67 60	79.04 79	74.18 56	63.82 90	60.84 118	82.59 71	84.33 117	83.59 119	90.96 179	89.39 160
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LGP-MVS_train		82.12 70	82.57 73	81.59 74	89.26 68	90.23 111	88.76 54	78.05 68	81.26 72	61.64 98	79.52 37	62.11 114	79.59 92	85.20 111	84.68 105	92.27 151	95.02 90

casdiffmvs1		86.12 49	86.10 51	86.15 53	88.98 69	95.46 52	89.62 47	75.02 100	86.42 54	79.82 39	73.81 51	70.05 86	87.88 35	87.97 80	92.04 19	95.60 29	96.94 54

canonicalmvs		85.93 50	86.26 49	85.54 55	88.94 70	95.44 53	89.56 48	76.01 90	87.83 49	77.70 44	76.43 44	68.66 92	87.80 36	87.02 89	91.51 31	93.25 110	96.95 53

CANet_DTU		83.33 62	86.59 45	79.53 93	88.88 71	94.87 61	86.63 66	68.85 153	85.45 58	50.54 164	77.86 41	69.94 87	85.62 47	92.63 24	90.88 39	96.63 11	94.46 97

DWT-MVSNet_training		82.66 64	83.34 69	81.87 73	88.71 72	92.63 83	82.07 107	72.21 125	86.37 55	72.64 63	64.51 86	71.44 80	80.35 88	84.43 116	87.73 68	95.27 34	96.25 70

conf0.002		80.80 78	80.30 88	81.38 77	88.59 73	93.19 77	85.12 83	78.10 66	70.15 118	61.55 99	63.30 93	62.66 112	81.11 75	88.74 65	86.94 75	93.79 66	97.15 44

conf0.01		80.10 85	79.04 97	81.34 78	88.56 74	93.09 79	85.12 83	78.08 67	70.15 118	61.43 100	60.90 104	58.54 127	81.11 75	88.66 67	84.80 99	93.74 69	97.14 45

PatchMatch-RL		78.75 96	76.47 117	81.41 76	88.53 75	91.10 102	78.09 156	77.51 79	77.33 87	71.98 70	64.38 88	48.10 162	82.55 72	84.06 119	82.35 135	89.78 195	87.97 179

casdiffmvs		84.93 55	85.04 57	84.79 60	88.47 76	95.36 55	87.59 61	74.52 104	84.05 62	76.42 49	72.09 56	65.20 104	85.78 46	91.10 39	91.33 34	95.95 24	96.17 72

tfpn111		80.42 83	79.77 93	81.18 80	88.42 77	92.55 87	85.12 83	77.94 71	70.15 118	61.00 106	74.56 46	51.22 145	81.11 75	88.23 72	84.80 99	93.50 90	96.90 58

conf200view11		79.04 94	77.21 109	81.18 80	88.42 77	92.55 87	85.12 83	77.94 71	70.15 118	61.00 106	56.65 115	51.22 145	81.11 75	88.23 72	84.80 99	93.50 90	96.90 58

thres100view900		79.83 86	77.79 107	82.21 68	88.42 77	93.54 74	87.07 62	81.11 58	70.15 118	61.01 104	56.65 115	51.22 145	81.78 74	89.77 53	85.95 86	93.84 64	97.26 41

tfpn200view9		79.05 93	77.21 109	81.18 80	88.42 77	92.55 87	85.12 83	77.94 71	70.15 118	61.01 104	56.65 115	51.22 145	81.11 75	88.23 72	84.80 99	93.50 90	96.90 58

thres200		78.69 98	76.71 113	80.99 86	88.35 81	92.56 85	86.03 74	77.94 71	66.27 131	60.66 108	56.08 123	51.11 149	79.45 93	88.23 72	85.54 94	93.52 85	97.20 43

tfpn_ndepth		78.22 106	78.84 98	77.49 109	88.32 82	90.95 105	80.79 114	76.31 88	74.24 100	59.50 114	69.52 64	60.02 123	67.11 170	85.06 112	82.95 130	92.94 133	89.18 165

MVS_Test		84.60 60	85.13 56	83.99 62	88.17 83	95.27 57	88.21 56	73.15 116	84.31 61	70.55 77	68.67 68	68.78 91	86.99 40	91.71 33	91.90 24	96.84 9	95.27 88

ACMP	79.58 9	82.23 68	81.82 80	82.71 67	88.15 84	90.95 105	85.23 81	78.52 64	81.70 70	72.52 68	78.41 38	60.63 119	80.48 87	82.88 131	83.44 121	91.37 172	94.70 93
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSTER		87.68 41	89.12 32	86.01 54	88.11 85	90.05 114	89.28 50	77.05 83	91.37 27	79.97 38	76.70 43	85.25 25	84.89 53	93.53 16	91.41 32	96.73 10	95.55 82

thres400		78.39 103	76.39 118	80.73 87	88.02 86	92.94 80	84.77 88	78.88 61	65.20 139	59.70 112	55.20 128	50.85 150	79.45 93	88.81 62	84.81 98	93.57 82	96.91 57

thresconf0.02		78.87 95	80.50 85	76.96 113	87.88 87	91.71 98	82.90 104	78.51 65	67.91 128	50.85 157	74.56 46	69.93 88	67.32 169	86.86 92	85.65 92	94.32 51	86.89 185

TSAR-MVS + GP.		91.29 18	93.11 16	89.18 30	87.81 88	96.21 42	92.51 29	83.83 41	94.24 15	83.77 22	91.87 15	89.62 9	90.07 16	90.40 45	90.31 42	97.09 6	99.10 9

view600		77.68 110	75.68 124	80.01 90	87.72 89	92.57 84	83.79 95	77.95 70	64.41 142	58.72 117	54.32 133	50.54 151	78.25 96	88.23 72	83.13 126	93.64 78	96.59 67

thres600view7		77.66 111	75.67 125	79.98 91	87.71 90	92.56 85	83.79 95	77.94 71	64.41 142	58.69 118	54.32 133	50.54 151	78.23 97	88.23 72	83.06 128	93.52 85	96.55 68

CLD-MVS		85.43 53	84.24 61	86.83 46	87.69 91	93.16 78	90.01 45	82.72 49	87.17 50	79.28 41	71.43 60	65.81 102	86.02 45	87.33 87	86.96 74	95.25 37	97.83 29
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS_MVSNet		80.92 75	84.14 62	77.16 112	87.43 92	93.90 66	80.44 115	74.64 103	75.05 97	61.10 103	65.59 80	76.89 60	67.39 168	90.88 41	90.05 44	91.95 158	96.62 66

view800		77.22 116	75.35 128	79.41 96	87.42 93	92.21 93	82.94 102	77.19 81	63.67 146	57.78 119	53.68 136	50.19 154	77.32 99	87.70 84	83.84 117	93.79 66	96.19 71

UA-Net		78.30 104	80.92 84	75.25 121	87.42 93	92.48 90	79.54 135	75.49 97	60.47 156	60.52 109	68.44 69	84.08 31	57.54 199	88.54 68	88.45 61	90.96 179	83.97 198

tfpn		77.45 113	76.23 120	78.87 97	87.15 95	91.90 96	82.17 106	76.59 85	62.98 148	56.93 121	53.08 139	57.31 133	76.41 108	87.26 88	85.20 95	93.95 62	95.89 75

UGNet		80.71 82	83.09 70	77.93 106	87.02 96	92.71 81	80.28 119	76.53 86	73.83 105	71.35 72	70.07 62	73.71 71	58.93 195	87.39 86	86.97 73	93.48 94	96.94 54
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
diffmvs1		83.68 61	84.07 64	83.22 66	86.76 97	95.08 60	88.02 59	73.67 113	85.48 57	72.93 60	68.37 70	67.43 94	84.78 56	87.74 83	89.10 57	93.14 121	95.31 86

EPMVS		77.16 118	79.08 96	74.92 123	86.73 98	91.98 94	78.62 148	55.44 218	79.43 76	56.59 123	61.24 102	70.73 84	76.97 103	80.59 155	81.43 161	95.15 39	88.17 178

tfpn1000		75.39 125	76.18 122	74.47 128	86.71 99	90.10 113	77.57 162	74.78 101	68.76 127	53.33 133	63.57 91	58.37 129	60.84 191	83.80 122	81.24 166	93.58 81	87.42 181

Vis-MVSNet (Re-imp)		78.28 105	82.68 72	73.16 152	86.64 100	92.68 82	78.07 157	74.48 106	74.05 102	53.47 132	64.22 89	76.52 61	54.28 204	88.96 61	88.29 64	92.03 156	94.00 107

tfpnview11		74.85 126	75.06 130	74.61 126	86.58 101	89.54 121	79.98 120	75.81 92	64.95 141	47.47 181	64.85 83	54.72 138	63.86 179	84.54 115	82.20 139	93.97 60	84.64 190

FC-MVSNet-train		79.54 89	78.20 102	81.09 83	86.55 102	88.63 127	79.96 121	78.53 63	70.90 116	68.24 84	65.87 79	56.45 136	80.29 89	86.20 103	84.08 111	92.97 127	95.31 86

CHOSEN 280x420		82.15 69	85.87 52	77.80 107	86.54 103	93.42 75	81.74 109	59.96 207	78.99 80	63.99 91	74.50 48	83.95 32	80.99 80	89.53 55	85.01 96	93.56 84	95.71 81

CostFormer		80.72 79	81.81 81	79.44 95	86.50 104	91.65 99	84.31 92	59.84 208	80.86 74	72.69 62	62.46 97	73.74 70	79.93 90	82.58 134	84.50 108	93.37 101	96.90 58

COLMAP_ROB	66.31 15	69.91 176	66.61 191	73.76 139	86.44 105	82.76 185	76.59 172	76.46 87	63.82 145	50.92 156	45.60 161	49.13 157	65.87 175	74.96 202	74.45 213	86.30 219	75.57 217
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpn_n400		74.36 129	74.39 137	74.32 130	86.37 106	89.86 116	79.71 125	75.69 94	60.00 158	47.47 181	64.85 83	54.72 138	63.70 182	83.80 122	83.35 122	92.96 128	84.16 195

tfpnconf		74.36 129	74.39 137	74.32 130	86.37 106	89.86 116	79.71 125	75.69 94	60.00 158	47.47 181	64.85 83	54.72 138	63.70 182	83.80 122	83.35 122	92.96 128	84.16 195

EPP-MVSNet		80.82 77	82.79 71	78.52 101	86.31 108	92.37 91	79.83 123	74.51 105	73.79 106	64.46 89	67.01 73	80.63 46	74.33 116	85.63 107	84.35 109	91.68 164	95.79 79

DI_MVS_plusplus_trai		83.32 63	82.53 74	84.25 61	86.26 109	93.66 72	90.23 44	77.16 82	77.05 90	74.06 57	53.74 135	74.33 67	83.61 66	91.40 37	89.82 47	94.17 53	97.73 30

ACMH	71.22 14	72.65 143	70.13 160	75.59 118	86.19 110	86.14 158	75.76 181	77.63 77	54.79 190	46.16 190	53.28 138	47.28 164	77.24 101	78.91 181	81.18 170	90.57 187	89.33 161
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		76.27 123	77.65 108	74.66 125	86.13 111	89.53 122	79.31 140	54.91 219	77.19 89	56.27 124	55.87 125	64.58 106	77.25 100	80.85 153	80.21 181	94.07 56	95.32 85

diffmvs		82.25 67	82.33 76	82.15 69	86.10 112	94.52 63	86.22 70	73.32 115	82.19 69	70.14 79	67.88 71	62.49 113	83.02 68	85.97 105	88.53 59	94.10 54	94.77 91

conf0.05thres1000		74.20 133	71.44 152	77.43 110	86.09 113	89.85 118	80.82 113	75.79 93	53.51 197	54.71 127	44.37 172	49.78 155	74.67 113	85.02 113	83.47 120	92.49 146	94.10 105

PMMVS		82.26 66	85.48 55	78.51 103	85.92 114	91.92 95	78.30 152	70.77 139	86.30 56	61.11 102	82.46 36	70.88 83	84.70 58	88.05 79	84.78 103	90.24 192	93.98 108

thisisatest0530		81.67 72	84.27 60	78.63 99	85.53 115	93.88 67	81.77 108	73.84 110	81.35 71	63.85 92	68.79 66	77.64 53	73.02 126	88.73 66	85.73 90	93.76 68	93.80 123

Effi-MVS+		79.80 87	80.04 89	79.52 94	85.53 115	93.31 76	85.28 79	70.68 141	74.15 101	58.79 116	62.03 100	60.51 120	83.37 67	88.41 71	86.09 85	93.49 93	95.80 78

CMPMVS	50.59 17	66.74 193	62.72 213	71.42 177	85.40 117	89.72 120	72.69 192	70.72 140	51.24 203	51.75 144	38.91 212	44.40 184	63.74 181	70.84 217	71.52 217	84.19 224	72.45 224
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tpmp4_e23		78.57 100	78.48 100	78.68 98	85.38 118	89.14 125	84.69 90	60.32 206	78.81 81	70.65 76	57.89 110	65.54 103	79.63 91	80.09 159	83.24 124	91.41 171	94.63 95

tttt0517		81.51 73	84.12 63	78.47 104	85.33 119	93.74 70	81.42 112	73.84 110	81.21 73	63.59 93	68.73 67	77.46 56	73.02 126	88.47 69	85.73 90	93.63 79	93.49 127

ACMH+	72.14 13	72.38 145	69.34 171	75.93 117	85.21 120	84.89 172	76.96 170	76.04 89	59.76 160	51.63 145	50.37 147	48.69 159	76.90 104	76.06 197	78.69 189	88.85 205	86.90 184

Anonymous202405211			75.59 126		85.13 121	91.06 103	84.62 91	77.96 69	69.47 125		40.79 204	63.84 110	84.57 59	83.55 125	84.69 104	89.69 198	95.75 80

tpm cat1		76.93 119	76.19 121	77.79 108	85.08 122	88.58 128	82.96 101	59.33 209	75.72 95	72.64 63	51.25 144	64.41 107	75.74 111	77.90 188	80.10 184	90.97 178	95.35 84

Anonymous20240521		79.76 88	79.17 94	80.44 89	84.65 123	84.51 177	84.20 93	72.36 124	75.17 96	70.81 74	66.21 77	66.56 97	80.99 80	82.89 130	84.56 106	89.65 199	94.30 101

Vis-MVSNet		77.24 115	79.99 92	74.02 136	84.62 124	93.92 65	80.33 118	72.55 122	62.58 149	55.25 126	64.45 87	69.49 89	57.00 200	88.78 63	88.21 65	94.36 50	92.54 133
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dps		75.76 124	75.02 131	76.63 115	84.51 125	88.12 129	77.51 163	58.33 211	75.91 94	71.98 70	57.37 113	57.85 130	76.81 105	77.89 189	78.40 193	90.63 186	89.63 155

Anonymous20231211		78.61 99	75.57 127	82.15 69	84.43 126	90.26 109	84.08 94	77.68 76	71.09 114	72.90 61	39.24 211	66.21 99	84.23 63	82.15 138	84.04 113	89.61 200	96.03 73

PatchmatchNet		76.85 120	80.03 91	73.15 153	84.08 127	91.04 104	77.76 161	55.85 217	79.43 76	52.74 138	62.08 99	76.02 62	74.56 114	79.92 160	81.41 162	93.92 63	90.29 151
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		76.80 121	76.33 119	77.35 111	84.07 128	84.11 178	81.54 110	68.52 155	66.17 132	61.74 96	57.84 111	64.31 109	74.88 112	83.48 127	86.21 83	93.34 103	92.16 136
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
gg-mvs-nofinetune		72.10 149	74.79 133	68.97 187	83.31 129	95.22 58	85.66 77	48.77 231	35.68 232	22.17 239	30.49 225	77.73 52	76.37 109	94.30 11	93.03 11	97.55 2	97.05 47

tpm		73.50 136	74.85 132	71.93 169	83.19 130	86.84 141	78.61 149	55.91 216	65.64 134	48.90 173	56.30 121	61.09 116	72.31 128	79.10 178	80.61 180	92.68 142	94.35 100

Fast-Effi-MVS+		77.37 114	76.68 114	78.17 105	82.84 131	89.94 115	81.47 111	68.01 162	72.99 107	60.26 111	55.07 129	53.20 143	82.99 69	86.47 99	86.12 84	93.46 95	92.98 131

MDTV_nov1_ep13		77.20 117	80.04 89	73.90 138	82.22 132	90.14 112	79.25 141	61.52 201	78.63 82	56.98 120	65.52 82	72.80 76	73.05 124	80.93 152	83.20 125	90.36 189	89.05 167

TDRefinement		67.82 188	64.91 199	71.22 180	82.08 133	81.45 193	77.42 165	73.79 112	59.62 162	48.35 178	42.35 197	42.40 201	60.87 190	74.69 203	74.64 212	84.83 223	79.20 212

test-LLR		79.52 90	83.42 66	74.97 122	81.79 134	91.26 100	76.17 176	70.57 142	77.71 85	52.14 142	66.26 75	77.47 54	73.10 122	87.02 89	87.16 70	96.05 22	97.02 48

test0.0.03 1		71.70 155	74.68 134	68.23 189	81.79 134	83.81 181	68.64 201	70.57 142	68.81 126	43.47 203	62.77 96	60.09 122	51.77 214	82.48 135	81.67 147	93.16 116	83.13 201

CR-MVSNet		74.84 127	77.91 105	71.26 179	81.77 136	85.52 165	78.32 150	54.14 221	74.05 102	51.09 150	50.00 149	71.38 81	70.77 147	86.48 97	84.03 114	91.46 170	93.92 112

RPMNet		73.46 137	77.85 106	68.34 188	81.71 137	85.52 165	73.83 189	50.54 229	74.05 102	46.10 191	53.03 140	71.91 77	66.31 174	83.55 125	82.18 140	91.55 168	94.71 92

Effi-MVS+-dtu		74.57 128	74.60 135	74.53 127	81.38 138	86.74 144	80.39 117	67.70 166	67.36 130	53.06 134	59.86 106	57.50 131	75.84 110	80.19 157	78.62 191	88.79 206	91.95 140

ADS-MVSNet		72.11 148	73.72 141	70.24 184	81.24 139	86.59 147	74.75 185	50.56 228	72.58 110	49.17 171	55.40 127	61.46 115	73.80 119	76.01 198	78.14 194	91.93 159	85.86 188

RPSCF		74.27 131	73.24 142	75.48 120	81.01 140	80.18 202	76.24 175	72.37 123	74.84 99	68.24 84	72.47 52	67.39 95	73.89 117	71.05 216	69.38 225	81.14 232	77.37 214

CDS-MVSNet		76.57 122	76.78 112	76.32 116	80.94 141	89.75 119	82.94 102	72.64 118	59.01 168	62.95 95	58.60 109	62.67 111	66.91 172	86.26 101	87.20 69	91.57 166	93.97 109
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
USDC		73.43 138	72.31 146	74.73 124	80.86 142	86.21 153	80.42 116	71.83 131	71.69 113	46.94 185	59.60 107	42.58 199	76.47 107	82.66 133	81.22 168	91.88 160	82.24 208

Fast-Effi-MVS+-dtu		73.56 135	75.32 129	71.50 175	80.35 143	86.83 142	79.72 124	58.07 212	67.64 129	44.83 199	60.28 105	54.07 141	73.59 121	81.90 146	82.30 136	92.46 148	94.18 103

IterMVS		72.43 144	74.05 139	70.55 183	80.34 144	81.17 197	77.44 164	61.00 203	63.57 147	46.82 187	55.88 124	59.09 126	65.03 176	83.15 128	83.83 118	92.67 143	91.65 142
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GA-MVS		73.62 134	74.52 136	72.58 158	79.93 145	89.29 124	78.02 158	71.67 135	60.79 155	42.68 206	54.41 132	49.07 158	70.07 160	89.39 58	86.55 79	93.13 122	92.12 137

tfpnnormal		69.29 184	65.58 193	73.62 145	79.87 146	84.82 173	76.97 169	75.12 99	45.29 222	49.03 172	35.57 219	37.20 220	68.02 164	82.70 132	81.24 166	92.69 141	92.20 135

TESTMET0.1,1		79.15 92	83.42 66	74.18 132	79.81 147	91.26 100	76.17 176	67.83 165	77.71 85	52.14 142	66.26 75	77.47 54	73.10 122	87.02 89	87.16 70	96.05 22	97.02 48

CVMVSNet		68.95 186	70.79 155	66.79 196	79.69 148	83.75 182	72.05 194	70.90 138	56.20 182	36.30 217	54.94 131	59.22 125	54.03 207	78.33 184	78.65 190	87.77 213	84.44 192

FMVSNet3		81.93 71	81.98 78	81.88 72	79.49 149	87.02 137	88.15 58	72.57 119	83.02 65	72.63 65	56.55 118	73.48 73	82.34 73	91.49 36	91.20 35	96.07 19	91.13 143

PatchT		72.66 142	76.58 115	68.09 190	79.02 150	86.09 159	59.81 220	51.78 227	72.00 112	51.09 150	46.84 159	66.70 96	70.77 147	86.48 97	84.03 114	96.07 19	93.92 112

test-mter		77.90 109	82.44 75	72.60 157	78.52 151	90.24 110	73.85 188	65.31 183	76.37 92	51.29 146	65.58 81	75.94 63	71.36 137	85.98 104	86.26 82	95.26 36	96.71 65

TransMVSNet (Re)		66.87 192	64.30 204	69.88 185	78.32 152	81.35 196	73.88 187	74.34 109	43.19 226	45.20 197	40.12 205	42.37 202	55.97 202	80.85 153	79.15 186	91.56 167	83.06 202

GBi-Net		80.72 79	80.49 86	81.00 84	78.18 153	86.19 155	86.73 63	72.57 119	83.02 65	72.63 65	56.55 118	73.48 73	80.99 80	86.57 94	86.83 76	94.89 43	90.77 146

test1		80.72 79	80.49 86	81.00 84	78.18 153	86.19 155	86.73 63	72.57 119	83.02 65	72.63 65	56.55 118	73.48 73	80.99 80	86.57 94	86.83 76	94.89 43	90.77 146

FMVSNet2		79.24 91	78.14 104	80.53 88	78.18 153	86.19 155	86.73 63	71.91 129	72.97 108	70.48 78	50.63 146	66.56 97	80.99 80	90.10 47	89.77 49	94.89 43	90.77 146

TinyColmap		67.16 190	63.51 209	71.42 177	77.94 156	79.54 208	72.80 191	69.78 147	56.58 179	45.52 193	44.53 169	33.53 229	74.45 115	76.91 196	77.06 201	88.03 212	76.41 215

EG-PatchMatch MVS		66.23 195	65.20 196	67.43 193	77.74 157	86.20 154	72.51 193	63.68 194	43.95 224	43.44 204	36.22 218	45.43 175	54.04 206	81.00 151	80.95 178	93.15 120	82.67 206

NR-MVSNet		71.47 161	71.11 154	71.90 171	77.73 158	86.02 160	76.88 171	74.42 107	65.39 137	46.09 192	49.10 152	39.87 211	64.27 178	81.40 148	82.24 138	91.99 157	93.75 124

LTVRE_ROB	63.07 16	64.49 204	63.16 212	66.04 200	77.47 159	82.64 187	70.98 197	65.02 187	34.01 235	29.61 227	49.12 151	35.58 225	70.57 153	75.10 201	78.45 192	82.60 227	87.24 182
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
pm-mvs1		69.62 182	68.07 183	71.44 176	77.21 160	85.32 168	76.11 178	71.05 137	46.55 220	51.17 149	41.83 200	48.20 161	61.81 188	84.00 120	81.14 173	91.28 173	89.42 158

FC-MVSNet-test		67.04 191	72.47 145	60.70 217	76.92 161	81.41 194	61.52 216	69.45 149	65.58 136	26.74 235	61.79 101	60.40 121	41.17 227	77.60 191	77.78 196	88.41 208	82.70 205

UniMVSNet_NR-MVSNet		73.11 140	72.59 144	73.71 140	76.90 162	86.58 148	77.01 167	75.82 91	65.59 135	48.82 174	50.97 145	48.42 160	71.61 134	79.19 176	83.03 129	92.11 153	94.37 98

UniMVSNet (Re)		72.12 147	72.28 147	71.93 169	76.77 163	87.38 133	75.73 182	73.51 114	65.76 133	50.24 166	48.65 155	46.49 165	63.85 180	80.10 158	82.47 133	91.49 169	95.13 89

testpf		59.38 218	64.51 203	53.40 225	76.71 164	66.40 230	50.18 230	38.98 242	64.13 144	35.10 221	47.91 157	51.41 144	43.16 221	66.37 226	71.23 218	76.25 234	84.14 197

TAMVS		72.06 150	71.76 150	72.41 162	76.68 165	88.12 129	74.82 184	68.09 160	53.52 196	56.91 122	52.94 141	56.93 135	66.91 172	81.37 149	82.44 134	91.07 176	86.99 183

v18		71.13 163	68.98 173	73.63 144	76.66 166	79.78 204	79.95 122	65.98 177	61.34 152	54.71 127	44.75 164	46.06 166	71.27 138	79.59 165	81.51 155	93.21 112	89.81 153

v16		70.93 166	68.76 177	73.47 146	76.60 167	79.66 206	79.57 134	65.81 180	60.85 153	54.44 130	44.50 171	45.90 168	71.15 139	79.50 170	81.39 163	93.27 106	89.51 157

v17		70.82 168	68.69 178	73.31 148	76.53 168	79.67 205	79.45 137	65.80 181	60.32 157	53.75 131	44.51 170	45.92 167	71.09 141	79.49 171	81.38 164	93.26 109	89.54 156

thisisatest0515		70.62 169	71.94 149	69.07 186	76.48 169	85.59 164	68.03 202	68.02 161	59.70 161	52.94 135	52.19 142	50.36 153	58.10 198	83.15 128	81.63 148	90.87 182	90.99 145

v6		72.04 151	70.26 156	74.11 133	76.46 170	87.06 134	79.60 127	71.75 132	59.48 163	52.69 139	44.61 165	45.79 170	71.01 145	79.57 166	81.45 159	93.16 116	93.85 119

v1neww		72.02 152	70.23 158	74.10 134	76.45 171	87.06 134	79.59 130	71.75 132	59.35 164	52.60 140	44.59 167	45.74 171	71.06 142	79.57 166	81.46 157	93.16 116	93.84 120

v7new		72.02 152	70.23 158	74.10 134	76.45 171	87.06 134	79.59 130	71.75 132	59.35 164	52.60 140	44.59 167	45.74 171	71.06 142	79.57 166	81.46 157	93.16 116	93.84 120

v8		71.42 162	69.69 165	73.43 147	76.45 171	85.12 171	79.53 136	67.47 169	59.34 166	52.90 136	44.60 166	45.82 169	71.05 144	79.56 169	81.45 159	93.17 114	91.96 139

gm-plane-assit		64.86 200	68.15 182	61.02 216	76.44 174	68.29 228	41.60 236	53.37 224	34.68 234	26.19 237	33.22 222	57.09 134	71.97 129	95.12 4	93.97 6	96.54 13	94.66 94

divwei89l23v2f112		71.53 158	69.69 165	73.68 141	76.09 175	86.86 139	79.60 127	72.08 126	56.96 176	50.78 159	44.24 175	44.70 179	70.65 151	79.62 162	81.53 150	92.89 134	93.93 110

v1141		71.53 158	69.69 165	73.68 141	76.08 176	86.86 139	79.59 130	72.07 127	57.01 174	50.78 159	44.23 176	44.70 179	70.68 149	79.61 164	81.52 152	92.89 134	93.92 112

v1		71.54 157	69.71 164	73.66 143	76.08 176	86.88 138	79.60 127	72.06 128	57.00 175	50.75 161	44.23 176	44.79 176	70.61 152	79.62 162	81.52 152	92.88 137	93.93 110

v15		70.00 175	67.82 185	72.55 159	76.06 178	79.37 209	79.10 144	65.30 184	56.89 177	51.18 148	43.96 182	44.76 177	70.52 154	79.40 173	81.22 168	93.13 122	89.14 166

v148		70.34 171	68.46 180	72.54 160	76.04 179	86.38 150	74.83 183	72.73 117	55.88 186	55.26 125	43.32 192	43.49 190	64.52 177	76.93 195	80.11 183	91.85 161	93.11 128

V14		69.91 176	67.71 187	72.47 161	76.01 180	79.30 210	78.92 145	65.17 185	56.74 178	51.08 153	43.82 185	44.73 178	70.44 156	79.31 174	81.14 173	93.20 113	88.91 170

TranMVSNet+NR-MVSNet		71.12 164	70.24 157	72.15 166	76.01 180	84.80 174	76.55 173	75.65 96	61.99 151	45.29 195	48.42 156	43.07 196	67.55 166	78.28 185	82.83 131	91.85 161	92.29 134

V9		69.79 180	67.57 188	72.38 163	75.95 182	79.21 211	78.72 147	65.06 186	56.51 180	51.06 154	43.66 186	44.70 179	70.28 158	79.22 175	81.06 176	93.24 111	88.67 174

pmmvs4		73.38 139	71.53 151	75.55 119	75.95 182	85.24 169	77.25 166	71.59 136	71.03 115	63.10 94	49.09 154	44.22 187	73.73 120	82.04 141	80.18 182	91.68 164	88.89 171

DU-MVS		72.19 146	71.35 153	73.17 151	75.95 182	86.02 160	77.01 167	74.42 107	65.39 137	48.82 174	49.10 152	42.81 197	71.61 134	78.67 182	83.10 127	91.22 174	94.37 98

Baseline_NR-MVSNet		70.61 170	68.87 175	72.65 156	75.95 182	80.49 200	75.92 179	74.75 102	65.10 140	48.78 176	41.28 203	44.28 186	68.45 163	78.67 182	79.64 185	92.04 155	92.62 132

v10		70.97 165	69.44 168	72.75 154	75.90 186	84.58 176	79.43 139	66.45 174	58.07 170	49.93 168	43.87 183	43.68 188	71.91 131	82.04 141	81.70 144	92.89 134	92.11 138

v12		69.66 181	67.45 189	72.23 164	75.89 187	79.13 213	78.29 153	64.96 189	56.40 181	50.75 161	43.53 188	44.60 182	70.21 159	79.11 177	80.99 177	93.27 106	88.41 175

v7		71.49 160	69.98 162	73.25 150	75.89 187	86.45 149	79.44 138	69.29 151	58.07 170	50.08 167	43.87 183	43.67 189	71.94 130	82.03 143	81.70 144	92.88 137	94.04 106

v2v482		71.73 154	69.80 163	73.99 137	75.88 189	86.66 146	79.58 133	71.90 130	57.58 172	50.41 165	45.35 162	43.24 195	73.05 124	79.69 161	82.18 140	93.08 124	93.87 117

v13		69.55 183	67.33 190	72.14 167	75.83 190	79.04 214	78.22 154	64.85 190	56.16 183	50.60 163	43.43 190	44.56 183	70.05 161	79.01 179	80.92 179	93.28 105	88.22 176

testgi		63.11 213	64.88 200	61.05 215	75.83 190	78.51 216	60.42 219	66.20 176	48.77 215	34.56 222	56.96 114	40.35 208	40.95 228	77.46 193	77.22 200	88.37 210	74.86 221

pmmvs5		70.01 174	69.31 172	70.82 182	75.80 192	86.26 151	72.94 190	67.91 163	53.84 195	47.22 184	47.31 158	41.47 205	67.61 165	83.93 121	81.93 142	93.42 98	90.42 150

v11		69.84 179	67.85 184	72.17 165	75.78 193	79.15 212	78.20 155	64.76 191	56.10 184	49.50 169	43.54 187	43.36 193	71.62 133	82.21 137	81.52 152	93.17 114	89.05 167

LP		59.72 217	58.23 222	61.44 214	75.67 194	74.97 222	61.05 218	48.34 232	54.02 194	40.82 209	31.61 223	36.92 223	54.69 203	67.52 223	71.18 219	88.08 211	71.42 227

V42		71.58 156	70.11 161	73.30 149	75.66 195	86.68 145	79.17 143	69.92 145	59.29 167	52.80 137	44.36 173	45.66 173	68.83 162	79.48 172	81.49 156	93.44 96	93.82 122

v1144		70.93 166	69.42 170	72.70 155	75.48 196	86.26 151	79.22 142	69.39 150	55.61 187	48.05 179	43.47 189	42.55 200	71.51 136	82.11 139	81.74 143	92.56 145	94.17 104

MVS-HIRNet		64.63 203	64.03 207	65.33 202	75.01 197	82.84 184	58.54 224	52.10 226	55.42 188	49.29 170	29.83 228	43.48 191	66.97 171	78.28 185	78.81 188	90.07 194	79.52 211

v1192		70.32 172	68.77 176	72.12 168	74.76 198	85.62 163	78.73 146	68.53 154	55.08 189	46.34 189	42.39 195	40.67 207	71.90 132	82.27 136	81.53 150	92.43 149	93.86 118

v144192		70.10 173	68.55 179	71.90 171	74.55 199	85.67 162	77.81 159	68.22 159	54.65 191	46.91 186	42.76 193	41.27 206	70.95 146	80.48 156	81.11 175	92.96 128	93.90 115

v1921920		69.85 178	68.38 181	71.58 174	74.35 200	85.39 167	77.78 160	67.88 164	54.64 192	45.39 194	42.11 198	39.97 210	71.10 140	81.68 147	81.17 172	92.96 128	93.69 126

WR-MVS		64.98 199	66.59 192	63.09 209	74.34 201	82.68 186	64.98 213	69.17 152	54.42 193	36.18 218	44.32 174	44.35 185	44.65 217	73.60 204	77.83 195	89.21 204	88.96 169

FMVSNet1		74.26 132	71.95 148	76.95 114	74.28 202	83.94 180	83.61 98	69.99 144	57.08 173	65.08 88	42.39 195	57.41 132	76.98 102	86.57 94	86.83 76	91.77 163	89.42 158

v1240		69.28 185	67.82 185	71.00 181	74.09 203	85.13 170	76.54 174	67.28 171	53.17 198	44.70 200	41.55 202	39.38 212	70.51 155	81.29 150	81.18 170	92.88 137	93.02 130

our_test_3					73.80 204	79.57 207	64.47 214

SixPastTwentyTwo		63.75 210	63.42 210	64.13 208	72.91 205	80.34 201	61.29 217	63.90 192	49.58 213	40.42 210	54.99 130	37.13 221	60.90 189	68.46 221	70.80 220	85.37 222	82.65 207

PEN-MVS		64.35 205	64.29 205	64.42 206	72.67 206	79.83 203	66.97 204	68.24 158	51.21 204	35.29 220	44.09 178	38.51 215	52.36 212	71.06 215	77.65 197	90.99 177	87.68 180

DTE-MVSNet		63.26 212	63.41 211	63.08 210	72.59 207	78.56 215	65.03 212	68.28 157	50.53 208	32.38 224	44.03 179	37.79 218	49.48 215	70.83 218	76.73 205	90.73 184	85.42 189

WR-MVS_H		64.14 209	65.36 195	62.71 211	72.47 208	82.33 190	65.13 210	66.99 172	51.81 202	36.47 216	43.33 191	42.77 198	43.99 219	72.41 210	75.99 208	91.20 175	88.86 172

pmmvs6		64.24 206	61.77 217	67.12 194	72.39 209	81.39 195	71.33 196	65.95 179	36.05 231	48.48 177	30.55 224	43.45 192	58.75 196	77.88 190	76.36 207	85.83 220	86.70 186

Anonymous20231206		62.05 215	61.83 216	62.30 213	72.09 210	77.84 217	63.10 215	67.62 167	50.20 209	36.68 214	29.59 229	37.05 222	43.90 220	77.33 194	77.31 199	90.41 188	83.49 199

CP-MVSNet		64.84 201	64.97 197	64.69 204	72.09 210	81.04 198	66.66 206	67.53 168	52.45 200	37.40 213	44.00 181	38.37 216	53.54 209	72.26 211	76.93 204	90.94 181	89.75 154

PS-CasMVS		64.22 208	64.19 206	64.25 207	71.86 212	80.67 199	66.42 208	67.43 170	50.64 206	36.48 215	42.60 194	37.46 219	52.56 211	71.98 212	76.69 206	90.76 183	89.29 164

v748		65.00 198	63.86 208	66.33 197	71.85 213	82.15 191	66.80 205	65.64 182	48.50 216	47.98 180	39.62 206	39.20 213	56.44 201	71.25 214	77.53 198	89.29 202	88.74 173

test20.03		57.93 221	59.22 220	56.44 220	71.84 214	73.78 224	53.55 228	65.96 178	43.02 227	28.46 231	37.50 215	38.17 217	30.41 235	75.25 200	74.42 214	88.41 208	72.37 225

v7n		66.43 194	65.51 194	67.51 192	71.63 215	83.10 183	70.89 198	65.02 187	50.13 210	44.68 201	39.59 207	38.77 214	62.57 186	77.59 192	78.91 187	90.29 191	90.44 149

anonymousdsp		67.61 189	68.94 174	66.04 200	71.44 216	83.97 179	66.45 207	63.53 195	50.54 207	42.42 207	49.39 150	45.63 174	62.84 185	77.99 187	81.34 165	89.59 201	93.75 124

MDTV_nov1_ep13_2view		64.72 202	64.94 198	64.46 205	71.14 217	81.94 192	67.53 203	54.54 220	55.92 185	43.29 205	44.02 180	43.27 194	59.87 194	71.85 213	74.77 211	90.36 189	82.82 204

FPMVS		50.25 228	45.67 233	55.58 222	70.48 218	60.12 234	59.78 221	59.33 209	46.66 219	37.94 211	30.22 226	27.51 235	35.94 231	50.98 236	47.90 236	70.02 237	56.31 233

V4		65.34 196	64.59 201	66.21 198	69.64 219	82.42 188	69.22 199	62.80 197	49.60 212	45.21 196	39.33 209	41.82 204	60.66 193	72.61 207	77.03 202	89.76 196	89.32 163

v52		65.34 196	64.59 201	66.21 198	69.63 220	82.41 189	69.22 199	62.80 197	49.63 211	45.15 198	39.31 210	41.85 203	60.68 192	72.61 207	77.02 203	89.75 197	89.33 161

N_pmnet		60.52 216	58.83 221	62.50 212	68.97 221	75.61 221	59.72 222	66.47 173	51.90 201	41.26 208	35.42 220	35.63 224	52.25 213	67.07 225	70.08 223	86.35 218	76.10 216

FMVSNet5		72.83 141	73.89 140	71.59 173	67.42 222	76.28 218	75.88 180	63.74 193	77.27 88	54.59 129	53.32 137	71.48 79	73.85 118	81.95 144	81.69 146	94.06 57	75.20 219

test2356		58.43 220	59.52 219	57.16 219	66.71 223	68.00 229	54.69 226	60.91 205	49.22 214	28.63 230	41.86 199	33.68 228	44.36 218	72.98 205	75.47 210	87.69 214	75.40 218

pmmvs-eth3d		64.24 206	61.96 215	66.90 195	66.35 224	76.04 220	66.09 209	66.31 175	52.59 199	50.94 155	37.61 214	32.79 231	62.43 187	75.78 199	75.48 209	89.27 203	83.39 200

EU-MVSNet		58.73 219	60.92 218	56.17 221	66.17 225	72.39 225	58.85 223	61.24 202	48.47 217	27.91 232	46.70 160	40.06 209	39.07 229	68.27 222	70.34 222	83.77 225	80.23 210

testus		55.91 222	56.38 223	55.37 223	65.15 226	65.88 231	50.07 231	60.92 204	45.62 221	26.99 234	41.74 201	24.43 238	42.08 224	69.50 220	73.60 215	86.97 216	73.91 222

MIMVSNet		68.66 187	69.43 169	67.76 191	64.92 227	84.68 175	74.16 186	54.10 223	60.85 153	51.27 147	39.47 208	49.48 156	67.48 167	84.86 114	85.57 93	94.63 47	81.10 209

new-patchmatchnet		53.91 224	52.69 225	55.33 224	64.83 228	70.90 226	52.24 229	61.75 200	41.09 228	30.82 225	29.90 227	28.22 234	36.69 230	61.52 231	65.08 230	85.64 221	72.14 226

PM-MVS		63.52 211	62.51 214	64.70 203	64.79 229	76.08 219	65.07 211	62.08 199	58.13 169	46.56 188	44.98 163	31.31 232	62.89 184	72.58 209	69.93 224	86.81 217	84.55 191

testmv		46.89 230	46.37 231	47.48 230	60.96 230	58.36 238	36.71 239	56.94 213	27.16 239	17.93 241	23.94 233	18.84 241	31.06 233	61.55 229	66.72 228	81.28 230	68.05 229

test1235678		46.88 231	46.36 232	47.48 230	60.96 230	58.35 239	36.71 239	56.94 213	27.15 240	17.93 241	23.93 234	18.82 242	31.06 233	61.55 229	66.71 229	81.27 231	68.04 230

PMVS	36.83 18	40.62 233	36.39 235	45.56 232	58.40 232	33.20 244	32.62 243	56.02 215	28.25 238	37.92 212	22.29 239	26.15 237	25.29 237	48.49 238	43.82 239	63.13 240	52.53 237
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1111		48.34 229	47.93 230	48.83 229	58.14 233	59.33 236	37.54 237	43.85 236	31.76 236	29.36 228	23.26 235	34.58 226	42.20 222	65.15 227	68.72 226	81.86 229	52.66 236

.test1245		33.05 235	31.21 238	35.20 236	58.14 233	59.33 236	37.54 237	43.85 236	31.76 236	29.36 228	23.26 235	34.58 226	42.20 222	65.15 227	0.77 243	0.11 247	3.62 244

tmp_tt				39.78 234	56.31 235	31.71 246	35.84 241	15.08 244	82.57 68	50.83 158	63.07 94	47.51 163	15.28 241	52.23 235	44.24 238	65.35 239

ambc			50.35 229		55.61 236	59.93 235	48.73 233		44.08 223	35.81 219	24.01 232	10.64 246	41.57 226	72.83 206	63.35 232	74.99 235	77.61 213

pmmvs3		52.59 226	52.43 227	52.78 226	54.53 237	64.49 233	50.07 231	46.89 235	35.31 233	30.19 226	27.27 231	26.96 236	53.02 210	67.28 224	70.54 221	81.96 228	75.20 219

test12356		41.15 232	41.46 234	40.78 233	53.10 238	49.87 240	33.37 242	52.25 225	25.12 241	15.64 243	22.76 237	15.01 243	15.81 240	52.97 234	64.54 231	74.50 236	59.96 232

MDA-MVSNet-bldmvs		54.99 223	52.66 226	57.71 218	52.74 239	74.87 223	55.61 225	68.41 156	43.65 225	32.54 223	37.93 213	22.11 239	54.11 205	48.85 237	67.34 227	82.85 226	73.88 223

new_pmnet		50.32 227	51.36 228	49.11 228	49.19 240	64.89 232	48.66 234	47.99 234	47.55 218	26.27 236	29.51 230	28.66 233	44.89 216	61.12 232	62.74 233	77.66 233	65.03 231

no-one		32.08 237	31.09 239	33.23 237	46.10 241	46.90 242	20.80 246	49.13 230	16.27 243	7.85 245	10.62 241	10.68 245	13.65 243	31.50 241	51.31 235	61.83 241	50.38 238

Gipuma		35.20 234	33.96 236	36.65 235	43.30 242	32.51 245	26.96 245	48.31 233	38.87 230	20.08 240	8.08 242	7.41 247	26.44 236	53.60 233	58.43 234	54.81 242	38.79 240
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		52.76 225	53.95 224	51.38 227	41.96 243	70.79 227	53.56 227	63.03 196	39.36 229	27.83 233	22.73 238	33.07 230	34.47 232	70.49 219	72.69 216	87.41 215	68.51 228

EMVS		20.61 240	16.32 242	25.62 241	36.41 244	18.93 249	11.51 248	43.75 238	15.65 244	6.53 247	7.56 245	4.68 248	22.03 238	14.56 244	23.10 242	33.51 245	29.77 242

E-PMN		21.42 238	17.56 241	25.94 240	36.25 245	19.02 248	11.56 247	43.72 239	15.25 245	6.99 246	8.04 243	4.53 249	21.77 239	16.13 243	26.16 241	35.34 244	33.77 241

PMMVS2		32.52 236	33.92 237	30.88 239	34.15 246	44.70 243	27.79 244	39.69 241	22.21 242	4.31 248	15.73 240	14.13 244	12.45 244	40.11 239	47.00 237	66.88 238	53.54 234

MVE	25.07 19	21.25 239	23.51 240	18.62 242	15.07 247	29.77 247	10.67 249	34.60 243	12.51 246	9.46 244	7.84 244	3.82 250	14.38 242	27.45 242	42.42 240	27.56 246	40.74 239
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		62.08 214	88.31 37	31.46 238	0.16 248	98.10 7	91.57 37	0.09 245	85.07 60	0.21 249	73.90 50	83.74 34	0.19 247	88.98 60	89.39 54	96.58 12	99.02 11

testmvs		0.76 241	1.23 243	0.21 243	0.05 249	0.21 250	0.38 251	0.09 245	0.94 247	0.05 250	2.13 247	0.08 251	0.60 246	0.82 245	0.77 243	0.11 247	3.62 244

test123		0.67 242	1.11 244	0.16 244	0.01 250	0.14 251	0.20 252	0.04 247	0.77 248	0.02 251	2.15 246	0.02 252	0.61 245	0.23 246	0.72 245	0.07 249	3.76 243

sosnet-low-res		0.00 243	0.00 245	0.00 245	0.00 251	0.00 252	0.00 253	0.00 248	0.00 249	0.00 252	0.00 248	0.00 253	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

sosnet		0.00 243	0.00 245	0.00 245	0.00 251	0.00 252	0.00 253	0.00 248	0.00 249	0.00 252	0.00 248	0.00 253	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

MTAPA										91.14 3		85.84 21

MTMP										90.95 4		84.13 30

Patchmatch-RL test							8.17 250

NP-MVS									89.55 42

Patchmtry						87.41 132	78.32 150	54.14 221		51.09 150

DeepMVS_CX						48.96 241	43.77 235	40.58 240	50.93 205	24.67 238	36.95 217	20.18 240	41.60 225	38.92 240		52.37 243	53.31 235

High-resolution multi-view benchmark