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.

MSP-MVS		99.38 6	99.78 5	98.91 12	99.61 13	99.96 7	99.85 2	96.94 8	99.96 19	97.38 14	99.60 23	100.00 1	99.70 21	99.96 1	98.96 35	100.00 1	100.00 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
CNVR-MVS		99.39 4	99.75 12	98.98 5	99.69 1	99.95 12	99.76 9	96.91 11	99.98 3	97.59 10	99.64 19	100.00 1	99.93 1	99.94 2	98.75 51	99.97 12	99.97 87

DeepPCF-MVS	97.16 4	97.58 48	99.72 18	95.07 66	98.45 51	99.96 7	93.83 145	95.93 38	100.00 1	90.79 60	98.38 67	99.85 47	95.28 136	99.94 2	99.97 1	96.15 212	99.97 87

DeepC-MVS_fast	98.03 2	99.05 23	99.78 5	98.21 26	99.47 24	99.97 3	99.75 17	96.80 19	99.97 8	93.58 39	98.68 64	99.94 43	99.69 22	99.93 4	99.95 3	99.96 13	99.98 76
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DPE-MVS		99.37 8	99.74 15	98.94 8	99.60 17	99.94 18	99.87 1	96.95 4	99.94 28	97.42 12	99.62 21	100.00 1	99.80 17	99.91 5	98.78 49	99.98 10	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + MP.		98.99 24	99.61 30	98.27 24	97.88 56	99.92 40	99.71 26	96.80 19	99.96 19	95.58 27	98.71 63	100.00 1	99.68 24	99.91 5	98.78 49	99.99 5	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MCST-MVS		99.08 21	99.72 18	98.33 23	99.59 20	99.97 3	99.78 7	96.96 3	99.95 25	93.72 37	99.67 11	100.00 1	99.90 5	99.91 5	98.55 58	100.00 1	100.00 1

DVP-MVS++		99.36 9	99.70 20	98.96 6	99.62 8	99.94 18	99.85 2	96.90 15	99.97 8	97.64 9	99.50 34	100.00 1	99.88 7	99.90 8	98.60 54	99.87 31	100.00 1

SED-MVS		99.44 1	99.69 21	99.15 1	99.61 13	99.95 12	99.81 5	96.94 8	99.97 8	98.73 2	99.53 30	100.00 1	99.91 4	99.90 8	98.52 60	99.87 31	100.00 1

SteuartSystems-ACMMP		98.95 26	99.80 3	97.95 29	99.43 29	99.96 7	99.76 9	96.45 35	99.82 57	93.63 38	99.64 19	100.00 1	98.56 79	99.90 8	99.31 24	99.84 37	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
DVP-MVS		99.38 6	99.57 36	99.15 1	99.62 8	99.94 18	99.72 24	96.99 2	99.98 3	98.85 1	98.21 76	100.00 1	99.88 7	99.88 11	98.96 35	99.85 34	100.00 1
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
SMA-MVS		99.34 10	99.79 4	98.81 14	99.69 1	99.94 18	99.75 17	96.91 11	99.98 3	96.76 16	99.37 41	100.00 1	99.90 5	99.88 11	99.46 17	99.84 37	99.92 126
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
APDe-MVS		99.40 3	99.81 2	98.92 9	99.62 8	99.96 7	99.76 9	96.87 16	99.95 25	97.66 8	99.57 28	100.00 1	99.63 29	99.88 11	99.28 26	100.00 1	100.00 1

train_agg		98.62 30	99.76 9	97.28 34	99.03 43	99.93 34	99.65 29	96.37 36	99.98 3	89.24 80	99.53 30	99.83 48	99.59 31	99.85 14	99.19 29	99.80 61	100.00 1

NCCC		99.24 12	99.75 12	98.65 16	99.63 7	99.96 7	99.76 9	96.91 11	99.97 8	95.86 25	99.67 11	100.00 1	99.75 18	99.85 14	98.80 47	99.98 10	99.97 87

ACMMP_NAP		98.68 29	99.58 33	97.62 32	99.62 8	99.92 40	99.72 24	96.78 22	99.71 71	90.13 70	99.66 15	99.99 33	99.64 28	99.78 16	98.14 70	99.82 48	99.89 135

CP-MVS		99.14 18	99.67 24	98.53 19	99.45 26	99.94 18	99.63 31	96.62 31	99.82 57	95.92 24	99.65 16	100.00 1	99.71 20	99.76 17	98.56 56	99.83 44	100.00 1

zzz-MVS		99.12 19	99.52 39	98.65 16	99.58 21	99.93 34	99.74 20	96.72 26	99.44 90	96.47 19	99.62 21	100.00 1	99.63 29	99.74 18	97.97 75	99.77 79	99.94 119

HPM-MVS++		98.98 25	99.62 29	98.22 25	99.62 8	99.94 18	99.74 20	96.95 4	99.87 48	93.76 36	99.49 36	100.00 1	99.39 41	99.73 19	98.35 63	99.89 27	99.96 107

TSAR-MVS + ACMM		98.30 35	99.64 26	96.74 42	99.08 42	99.94 18	99.67 28	96.73 25	99.97 8	86.30 106	98.30 68	99.99 33	98.78 73	99.73 19	99.57 14	99.88 30	99.98 76

APD-MVS		99.33 11	99.85 1	98.73 15	99.61 13	99.92 40	99.77 8	96.91 11	99.93 32	96.31 21	99.59 26	99.95 42	99.84 11	99.73 19	99.84 9	99.95 15	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MP-MVS		98.82 28	99.63 27	97.88 31	99.41 30	99.91 47	99.74 20	96.76 24	99.88 43	91.89 49	99.50 34	99.94 43	99.65 27	99.71 22	98.49 61	99.82 48	99.97 87
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
X-MVS		98.62 30	99.75 12	97.29 33	99.50 23	99.94 18	99.71 26	96.55 32	99.85 52	88.58 86	99.65 16	99.98 35	99.67 25	99.60 23	99.26 27	99.77 79	99.97 87

DPM-MVS		98.58 33	99.78 5	97.17 36	98.02 54	99.64 82	99.80 6	96.72 26	99.96 19	90.05 72	99.57 28	100.00 1	98.66 78	99.56 24	99.96 2	99.80 61	99.80 158

HFP-MVS		99.19 15	99.77 8	98.51 20	99.55 22	99.94 18	99.76 9	96.84 17	99.88 43	95.27 29	99.67 11	100.00 1	99.85 10	99.56 24	99.36 21	99.79 68	99.97 87

CPTT-MVS		99.08 21	99.53 38	98.57 18	99.44 28	99.93 34	99.60 32	95.92 39	99.77 66	97.01 15	99.67 11	100.00 1	99.72 19	99.56 24	97.76 84	99.70 116	99.98 76

ACMMPR		99.12 19	99.76 9	98.36 22	99.45 26	99.94 18	99.75 17	96.70 28	99.93 32	94.65 33	99.65 16	99.96 40	99.84 11	99.51 27	99.35 22	99.79 68	99.96 107

MVS_111021_LR		98.15 39	99.69 21	96.36 47	99.23 39	99.93 34	97.79 69	91.84 47	99.87 48	90.53 66	100.00 1	99.57 61	98.93 65	99.44 28	99.08 32	99.85 34	99.95 114

xxxxxxxxxxxxxcwj		97.57 49	96.26 121	99.10 3	99.65 5	99.94 18	99.76 9	96.95 4	99.88 43	98.39 5	99.60 23	76.05 175	99.82 13	99.43 29	98.93 37	99.99 5	100.00 1

SF-MVS		99.41 2	99.68 23	99.10 3	99.65 5	99.94 18	99.76 9	96.95 4	99.88 43	98.39 5	99.60 23	100.00 1	99.82 13	99.43 29	98.93 37	99.99 5	100.00 1

PGM-MVS		98.47 34	99.73 16	97.00 39	99.68 3	99.94 18	99.76 9	91.74 48	99.84 55	91.17 57	100.00 1	99.69 56	99.81 15	99.38 31	99.30 25	99.82 48	99.95 114

CDPH-MVS		97.88 45	99.59 31	95.89 52	98.90 44	99.95 12	99.40 36	92.86 46	99.86 51	85.33 112	98.62 65	99.45 64	99.06 61	99.29 32	99.94 4	99.81 57	100.00 1

AdaColmap		99.21 14	99.45 40	98.92 9	99.67 4	99.95 12	99.65 29	96.77 23	99.97 8	97.67 7	100.00 1	99.69 56	99.93 1	99.26 33	97.25 99	99.85 34	100.00 1

GG-mvs-BLEND		69.85 212	99.39 41	35.39 221	3.67 228	99.94 18	99.10 44	1.69 225	99.85 52	3.19 229	98.13 77	99.46 63	4.92 224	99.23 34	99.14 30	99.80 61	100.00 1

SD-MVS		99.16 17	99.73 16	98.49 21	97.93 55	99.95 12	99.74 20	96.94 8	99.96 19	96.60 18	99.47 37	100.00 1	99.88 7	99.15 35	99.59 13	99.84 37	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
DeepC-MVS	96.33 6	97.05 53	97.59 89	96.42 46	97.37 59	99.92 40	99.10 44	96.54 33	99.34 101	86.64 103	91.93 134	93.15 119	99.11 59	99.11 36	99.68 12	99.73 109	99.97 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		99.39 4	99.76 9	98.95 7	99.60 17	99.99 1	99.83 4	96.82 18	99.92 36	97.58 11	99.58 27	100.00 1	99.93 1	98.98 37	99.86 8	99.96 13	100.00 1

UA-Net		94.95 101	98.66 60	90.63 118	94.60 84	98.94 124	96.03 106	85.28 106	98.01 153	78.92 131	97.42 84	99.96 40	89.09 192	98.95 38	98.80 47	99.82 48	98.57 193

EPNet		98.11 40	99.63 27	96.34 48	98.44 52	99.88 54	98.55 57	90.25 59	99.93 32	92.60 46	100.00 1	99.73 53	98.41 81	98.87 39	99.02 33	99.82 48	99.97 87
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		98.59 32	99.07 46	98.03 28	99.41 30	99.90 48	99.26 40	94.33 43	99.94 28	96.03 22	96.68 91	99.72 55	99.42 38	98.86 40	98.84 44	99.72 113	99.58 171

gm-plane-assit		84.93 198	91.61 165	77.14 208	84.14 199	91.29 214	66.18 222	69.70 197	85.22 218	47.95 219	78.58 194	89.24 133	94.90 140	98.82 41	98.12 71	99.99 5	100.00 1

CS-MVS		96.40 68	98.87 54	93.51 90	91.64 132	99.53 88	97.16 87	87.31 97	99.94 28	85.66 110	97.38 86	97.97 87	99.47 36	98.75 42	98.56 56	99.84 37	100.00 1

QAPM		97.90 44	98.89 52	96.74 42	99.35 33	99.80 66	98.84 50	90.20 60	99.94 28	92.85 41	94.17 116	99.78 51	99.42 38	98.71 43	99.87 7	99.79 68	99.98 76

ETV-MVS		96.79 63	99.19 44	94.00 83	91.78 123	99.63 84	97.15 88	88.00 87	99.95 25	88.34 90	99.32 43	98.71 74	98.82 71	98.69 44	98.01 73	99.90 25	100.00 1

MVS_111021_HR		97.94 43	99.59 31	96.02 51	99.27 38	99.97 3	97.03 92	90.44 56	99.89 40	90.75 61	100.00 1	99.73 53	98.68 77	98.67 45	98.89 41	99.95 15	99.97 87

MVSTER		97.00 58	98.85 55	94.83 73	92.71 103	97.43 154	99.03 46	85.52 104	99.82 57	92.74 44	99.15 48	99.94 43	99.19 55	98.66 46	96.99 113	99.79 68	99.98 76

CNLPA		99.24 12	99.58 33	98.85 13	99.34 34	99.95 12	99.32 37	96.65 29	99.96 19	98.44 4	98.97 56	100.00 1	99.57 32	98.66 46	99.56 15	99.76 86	99.97 87

gg-mvs-nofinetune		86.69 193	91.30 169	81.30 199	90.42 143	99.64 82	98.50 60	61.68 219	79.23 219	40.35 222	66.58 213	97.14 94	96.92 112	98.64 48	97.94 76	99.91 23	99.97 87

IS_MVSNet		96.66 64	98.62 61	94.38 77	92.41 109	99.70 78	97.19 85	87.67 90	99.05 114	91.27 56	95.09 106	98.46 82	97.95 97	98.64 48	99.37 19	99.79 68	100.00 1

MVS_0304		97.04 55	98.72 59	95.08 65	96.32 67	99.90 48	99.15 42	89.61 67	99.89 40	87.22 99	95.47 101	98.22 85	98.22 86	98.63 50	98.90 40	99.93 18	100.00 1

Vis-MVSNet (Re-imp)		95.60 87	98.52 67	92.19 107	92.37 110	99.56 87	96.37 102	87.41 96	98.95 117	84.77 117	94.88 111	98.48 81	92.44 165	98.63 50	99.37 19	99.76 86	99.77 161

MVS_Test		95.74 84	98.18 75	92.90 103	92.16 113	99.49 93	97.36 81	84.30 120	99.79 63	84.94 114	96.65 92	93.63 116	98.85 69	98.61 52	99.10 31	99.81 57	100.00 1

3Dnovator+	95.21 7	98.17 37	99.08 45	97.12 37	99.28 37	99.78 71	98.61 56	89.93 63	99.93 32	95.36 28	95.50 100	100.00 1	99.56 33	98.58 53	99.80 10	99.95 15	99.97 87

DELS-MVS		97.05 53	98.05 79	95.88 54	97.09 61	99.99 1	98.82 51	90.30 58	98.44 142	91.40 53	92.91 127	96.57 98	97.68 107	98.56 54	99.88 6	100.00 1	100.00 1
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
CHOSEN 280x420		97.16 52	99.58 33	94.35 80	96.95 63	99.97 3	97.19 85	81.55 148	99.92 36	91.75 50	100.00 1	100.00 1	98.84 70	98.55 55	98.65 52	99.79 68	99.97 87

3Dnovator	95.01 8	97.98 42	98.89 52	96.92 41	99.36 32	99.76 73	98.72 54	89.98 61	99.98 3	93.99 35	94.60 113	99.43 65	99.50 35	98.55 55	99.91 5	99.99 5	99.98 76

LS3D		96.44 67	97.31 96	95.41 60	97.06 62	99.87 55	99.51 33	97.48 1	99.57 79	79.00 130	95.39 102	89.19 134	99.81 15	98.55 55	98.84 44	99.62 134	99.78 159

PLC	98.06 1	99.17 16	99.38 42	98.92 9	99.47 24	99.90 48	99.48 34	96.47 34	99.96 19	98.73 2	99.52 33	100.00 1	99.55 34	98.54 58	97.73 87	99.84 37	99.99 58
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet		97.62 46	98.94 50	96.08 50	97.19 60	99.93 34	99.29 39	90.38 57	99.87 48	91.00 59	95.79 99	99.51 62	98.72 76	98.53 59	99.00 34	99.90 25	99.99 58

PVSNet_BlendedMVS		96.01 73	96.48 116	95.46 57	96.47 65	99.89 52	95.64 113	91.23 52	99.75 68	91.59 51	96.80 88	82.44 155	98.05 92	98.53 59	97.92 78	99.80 61	100.00 1

PVSNet_Blended		96.01 73	96.48 116	95.46 57	96.47 65	99.89 52	95.64 113	91.23 52	99.75 68	91.59 51	96.80 88	82.44 155	98.05 92	98.53 59	97.92 78	99.80 61	100.00 1

TSAR-MVS + GP.		98.06 41	99.55 37	96.32 49	94.72 81	99.92 40	99.22 41	89.98 61	99.97 8	94.77 32	99.94 9	100.00 1	99.43 37	98.52 62	98.53 59	99.79 68	100.00 1

OpenMVS	94.03 11	96.87 60	98.10 77	95.44 59	99.29 36	99.78 71	98.46 62	89.92 64	99.47 88	85.78 109	91.05 137	98.50 78	99.30 46	98.49 63	99.41 18	99.89 27	99.98 76

CS-MVS-test		96.61 65	98.98 48	93.83 87	91.40 134	99.26 109	98.18 64	85.61 102	99.81 60	88.60 85	99.11 50	97.61 91	99.28 48	98.48 64	98.62 53	99.93 18	99.99 58

DROMVSNet		96.90 59	99.32 43	94.07 81	91.64 132	99.30 105	98.18 64	85.61 102	99.97 8	89.79 73	99.33 42	99.31 68	99.28 48	98.48 64	98.86 42	99.91 23	100.00 1

PHI-MVS		98.85 27	99.67 24	97.89 30	98.63 50	99.93 34	98.95 48	95.20 41	99.84 55	94.94 30	99.74 10	100.00 1	99.69 22	98.40 66	99.75 11	99.93 18	99.99 58

MDTV_nov1_ep13		94.32 109	98.77 58	89.14 135	91.70 128	99.52 89	95.21 124	72.09 196	99.80 61	78.91 132	96.32 95	99.62 58	97.71 106	98.39 67	97.71 88	99.22 194	100.00 1

EIA-MVS		96.34 69	98.55 62	93.76 88	91.93 119	99.66 80	97.14 89	88.33 85	99.51 83	85.98 108	98.82 62	96.08 103	99.33 44	98.38 68	97.40 96	99.81 57	100.00 1

FMVSNet3		95.59 88	97.51 92	93.34 94	89.48 150	96.57 167	97.67 71	84.17 121	99.48 85	89.76 74	95.09 106	94.35 110	99.14 58	98.37 69	98.86 42	99.82 48	99.89 135

baseline		95.85 79	98.13 76	93.20 96	92.29 112	99.58 86	97.49 76	84.33 119	99.44 90	87.28 97	97.00 87	94.04 113	97.93 98	98.36 70	98.47 62	99.87 31	99.99 58

CHOSEN 1792x2688		93.69 116	94.89 143	92.28 106	96.17 68	99.84 56	95.69 112	83.17 132	98.54 135	82.04 123	77.58 198	91.15 124	96.90 113	98.36 70	98.82 46	99.73 109	99.98 76

EPNet_dtu		95.10 99	98.81 57	90.78 116	98.38 53	98.47 133	96.54 101	89.36 71	99.78 65	65.65 184	99.31 44	98.24 84	94.79 141	98.28 72	99.35 22	99.93 18	98.27 195
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		95.37 90	97.44 94	92.95 102	95.20 74	99.80 66	92.68 153	88.41 84	99.12 109	87.64 92	88.31 151	99.10 71	94.07 152	98.27 73	97.51 93	99.73 109	100.00 1

HyFIR lowres test		93.13 126	94.48 147	91.56 112	96.12 70	99.68 79	93.52 147	79.98 156	97.24 159	81.73 126	72.66 207	95.74 106	98.29 84	98.27 73	97.79 82	99.70 116	100.00 1

baseline2		95.13 98	98.55 62	91.15 115	90.29 146	99.00 118	94.49 137	82.00 142	99.68 73	84.82 116	96.47 93	99.30 69	95.71 130	98.24 75	97.14 106	99.57 140	100.00 1

MSDG		97.29 51	97.55 90	97.00 39	98.66 49	99.71 77	99.03 46	96.15 37	99.59 78	89.67 78	92.77 130	94.86 108	98.75 74	98.22 76	97.94 76	99.72 113	99.76 162

SCA		93.53 118	98.90 51	87.27 153	92.01 117	99.30 105	93.43 150	65.72 207	99.80 61	75.20 146	97.66 82	99.74 52	97.44 109	98.21 77	97.62 90	99.84 37	100.00 1

baseline1		96.87 60	98.55 62	94.91 68	92.89 102	99.45 97	96.34 103	88.54 81	98.88 125	92.82 42	98.93 58	96.58 97	99.07 60	98.19 78	98.04 72	99.80 61	99.78 159

CR-MVSNet		92.32 142	97.97 82	85.74 170	90.63 141	98.95 122	95.46 119	65.50 208	99.09 111	67.51 177	94.20 115	98.18 86	95.59 134	98.16 79	97.20 103	99.74 99	100.00 1

PatchT		91.06 151	97.66 87	83.36 192	90.32 145	98.96 121	82.30 207	64.72 213	98.45 140	67.51 177	93.28 126	97.60 92	95.59 134	98.16 79	97.20 103	99.70 116	100.00 1

test2506		97.04 55	98.09 78	95.81 55	94.12 86	99.80 66	97.33 82	89.48 69	98.90 122	95.99 23	99.11 50	92.84 121	98.14 90	98.14 81	98.32 66	99.82 48	99.51 176

ECVR-MVS		95.46 89	95.58 133	95.31 62	94.12 86	99.80 66	97.33 82	89.48 69	98.90 122	92.99 40	87.97 152	86.41 141	98.14 90	98.14 81	98.32 66	99.82 48	99.52 175

test1111		95.15 96	95.18 138	95.12 63	94.07 88	99.80 66	97.20 84	89.53 68	98.80 126	92.22 47	85.44 163	86.24 142	97.89 99	98.12 83	98.34 65	99.80 61	99.51 176

EPMVS		94.08 114	98.54 66	88.87 136	92.51 106	99.47 94	94.18 141	66.53 203	99.68 73	82.40 121	95.24 103	99.40 66	97.86 100	98.12 83	97.99 74	99.75 95	99.88 140

ACMMP		98.16 38	99.01 47	97.18 35	98.86 45	99.92 40	98.77 53	95.73 40	99.31 102	91.15 58	100.00 1	99.81 50	98.82 71	98.11 85	95.91 134	99.77 79	99.97 87
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
IterMVS-SCA-FT		91.75 147	96.87 107	85.78 168	90.34 144	95.93 184	95.06 127	73.85 191	98.91 120	61.01 197	89.21 144	98.87 73	94.66 145	98.09 86	97.12 107	99.76 86	99.99 58

RPMNet		92.64 137	97.88 84	86.53 161	90.79 139	98.95 122	95.13 125	64.44 214	99.09 111	72.36 154	93.58 124	99.01 72	96.74 119	98.05 87	96.45 120	99.71 115	100.00 1

PatchMatch-RL		96.84 62	98.03 80	95.47 56	98.84 46	99.81 64	95.61 116	89.20 73	99.65 75	91.28 55	99.39 38	93.46 117	98.18 87	98.05 87	96.28 123	99.69 121	99.55 173

IterMVS		91.65 148	96.62 110	85.85 167	90.27 147	95.80 186	95.32 123	74.15 187	98.91 120	60.95 198	88.79 150	97.76 89	94.69 144	98.04 89	97.07 109	99.73 109	100.00 1
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TAPA-MVS	96.62 5	97.60 47	98.46 68	96.60 45	98.73 48	99.90 48	99.30 38	94.96 42	99.46 89	87.57 93	96.05 98	98.53 77	99.26 51	98.04 89	97.33 98	99.77 79	99.88 140
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MAR-MVS		97.03 57	98.00 81	95.89 52	99.32 35	99.74 76	96.76 99	84.89 111	99.97 8	94.86 31	98.29 69	90.58 128	99.67 25	98.02 91	99.50 16	99.82 48	99.92 126
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
EPP-MVSNet		96.29 70	98.34 70	93.90 84	91.77 124	99.38 102	95.45 121	87.25 98	99.38 97	91.36 54	94.86 112	98.49 80	97.83 101	98.01 92	98.23 68	99.75 95	99.99 58

Fast-Effi-MVS+		92.11 144	94.33 149	89.52 130	89.06 154	99.00 118	95.13 125	76.72 179	98.59 134	78.21 136	89.99 141	77.35 166	98.34 83	97.97 93	97.44 95	99.67 123	99.96 107

casdiffmvs		94.54 105	95.56 134	93.36 93	91.84 121	99.46 96	95.92 108	87.54 94	98.45 140	86.57 105	90.51 139	84.72 149	98.49 80	97.97 93	97.80 81	99.77 79	100.00 1

FMVSNet2		94.48 107	95.95 126	92.77 104	89.11 151	96.47 170	96.90 93	83.38 129	99.11 110	88.64 84	87.50 160	92.26 122	98.87 66	97.91 95	98.60 54	99.74 99	99.81 155

PatchmatchNet		93.48 123	98.84 56	87.22 154	91.93 119	99.39 101	92.55 155	66.06 205	99.71 71	75.61 143	98.24 73	99.59 59	97.35 110	97.87 96	97.64 89	99.83 44	99.43 179
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PMMVS		96.45 66	98.24 73	94.36 79	92.58 104	99.01 117	97.08 91	87.42 95	99.88 43	90.06 71	99.39 38	94.63 109	99.33 44	97.85 97	96.99 113	99.70 116	99.96 107

DI_MVS_plusplus_trai		95.29 91	97.02 101	93.28 95	91.76 125	99.52 89	97.84 68	85.67 101	99.08 113	87.29 96	87.76 155	97.46 93	97.31 111	97.83 98	97.48 94	99.83 44	100.00 1

GeoE		92.88 131	95.20 137	90.18 124	90.59 142	99.18 112	96.31 104	78.36 170	97.52 158	78.53 134	87.11 161	88.01 136	97.63 108	97.79 99	96.76 116	99.66 124	100.00 1

Effi-MVS+		93.06 129	95.94 127	89.70 128	90.82 138	99.45 97	95.71 111	78.94 166	98.72 128	74.71 149	97.92 79	80.73 162	98.35 82	97.72 100	97.05 111	99.70 116	100.00 1

Fast-Effi-MVS+-dtu		92.73 134	97.62 88	87.02 155	88.91 155	98.83 127	95.79 109	73.98 190	99.89 40	68.62 173	97.73 81	93.30 118	95.21 137	97.67 101	95.96 133	99.59 136	100.00 1

Effi-MVS+-dtu		93.13 126	97.13 99	88.47 144	88.86 157	99.19 111	96.79 98	79.08 165	99.64 77	70.01 168	97.51 83	89.38 131	96.53 122	97.60 102	96.55 117	99.57 140	100.00 1

Vis-MVSNet		93.08 128	96.76 109	88.78 140	91.14 136	99.63 84	94.85 130	83.34 130	97.19 160	74.78 148	91.92 135	93.15 119	88.81 195	97.59 103	98.35 63	99.78 75	99.49 178
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
thres100view900		95.86 77	96.62 110	94.97 67	93.10 94	99.83 57	97.76 70	89.15 74	98.62 132	90.69 62	99.00 53	84.86 146	99.30 46	97.57 104	96.48 118	99.81 57	100.00 1

PCF-MVS	97.20 3	97.49 50	98.20 74	96.66 44	97.62 58	99.92 40	98.93 49	96.64 30	98.53 136	88.31 91	94.04 119	99.58 60	98.94 63	97.53 105	97.79 82	99.54 145	99.97 87
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CANet_DTU		94.90 102	98.98 48	90.13 125	94.74 80	99.81 64	98.53 59	82.23 141	99.97 8	66.76 181	100.00 1	98.50 78	98.74 75	97.52 106	97.19 105	99.76 86	99.88 140

thres400		95.72 85	96.48 116	94.84 72	93.00 99	99.83 57	97.55 75	88.93 75	98.49 137	90.61 65	98.86 59	84.63 150	99.20 53	97.45 107	96.10 127	99.77 79	99.99 58

diffmvs		94.60 104	95.63 132	93.41 92	91.98 118	99.30 105	96.86 96	87.62 91	99.30 103	86.07 107	94.12 118	81.63 160	98.16 88	97.43 108	97.60 91	99.76 86	100.00 1

test-LLR		93.71 115	97.23 97	89.60 129	91.69 129	99.10 114	94.68 135	83.60 126	99.36 98	71.94 158	93.82 121	96.51 99	95.96 128	97.42 109	94.37 156	99.74 99	99.99 58

TESTMET0.1,1		92.87 132	97.23 97	87.79 150	86.96 164	99.10 114	94.68 135	77.46 175	99.36 98	71.94 158	93.82 121	96.51 99	95.96 128	97.42 109	94.37 156	99.74 99	99.99 58

tfpn200view9		95.78 82	96.54 113	94.89 70	93.10 94	99.82 59	97.67 71	88.85 77	98.62 132	90.69 62	99.00 53	84.86 146	99.28 48	97.41 111	96.10 127	99.76 86	99.99 58

thres600view7		95.64 86	96.38 119	94.79 74	92.96 100	99.82 59	97.48 80	88.85 77	98.38 143	90.52 67	98.84 61	84.61 151	99.15 57	97.41 111	95.60 139	99.76 86	99.99 58

thres200		95.77 83	96.55 112	94.86 71	93.09 96	99.82 59	97.63 74	88.85 77	98.49 137	90.66 64	98.99 55	84.86 146	99.20 53	97.41 111	96.28 123	99.76 86	100.00 1

test-mter		92.67 136	97.13 99	87.47 152	86.72 166	99.07 116	94.28 140	76.90 177	99.21 105	71.53 162	93.63 123	96.32 102	95.67 131	97.32 114	94.36 158	99.74 99	99.99 58

MVS-HIRNet		88.27 169	94.05 152	81.51 198	88.90 156	98.93 125	83.38 205	60.52 221	98.06 151	63.78 190	80.67 181	90.36 129	92.94 160	97.29 115	96.41 121	99.56 142	96.66 207

TSAR-MVS + COLMAP		95.20 92	95.03 140	95.41 60	96.17 68	98.69 130	99.11 43	93.40 45	99.97 8	84.89 115	98.23 74	75.01 177	99.34 43	97.27 116	96.37 122	99.58 138	99.64 169

thisisatest0530		95.89 75	98.32 71	93.06 100	91.76 125	99.75 74	94.94 128	87.60 92	99.91 38	86.66 102	98.28 70	99.98 35	97.72 103	97.10 117	93.24 169	99.65 126	99.95 114

tttt0517		95.88 76	98.31 72	93.04 101	91.75 127	99.75 74	94.90 129	87.60 92	99.91 38	86.63 104	98.28 70	99.98 35	97.72 103	97.10 117	93.24 169	99.65 126	99.95 114

IterMVS-LS		93.50 120	96.22 122	90.33 123	90.93 137	95.50 195	94.83 131	80.54 152	98.92 119	79.11 129	90.64 138	93.70 115	96.79 116	96.93 119	97.85 80	99.78 75	99.99 58
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
HQP-MVS		94.48 107	95.39 136	93.42 91	95.10 75	98.35 136	98.19 63	91.41 50	99.77 66	79.79 127	99.30 45	77.08 167	96.25 124	96.93 119	96.28 123	99.76 86	99.99 58

test_method		84.44 200	89.04 189	79.08 203	81.15 210	92.82 211	82.06 209	61.92 217	96.17 167	59.38 203	74.47 206	67.52 204	91.96 170	96.92 121	95.53 140	97.98 209	99.85 150

CDS-MVSNet		94.32 109	97.00 103	91.19 114	89.82 149	98.71 129	95.51 118	85.14 110	96.85 161	82.33 122	92.48 131	96.40 101	94.71 142	96.86 122	97.76 84	99.63 132	99.92 126
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MIMVSNet		91.01 152	96.22 122	84.93 178	85.24 178	98.09 143	90.40 173	64.96 212	97.55 157	72.65 150	96.23 96	90.81 126	96.79 116	96.69 123	97.06 110	99.52 149	97.09 205

GA-MVS		90.38 155	94.59 146	85.46 174	88.30 160	98.44 134	92.18 157	83.30 131	97.89 155	58.05 206	92.86 128	84.25 153	91.27 178	96.65 124	92.61 180	99.66 124	99.43 179

test0.0.03 1		95.15 96	97.87 85	91.99 110	91.69 129	98.82 128	93.04 151	83.60 126	99.65 75	88.80 83	94.15 117	97.67 90	94.97 138	96.62 125	98.16 69	99.83 44	100.00 1

DCV-MVSNet		95.85 79	97.53 91	93.89 85	93.20 93	97.01 160	97.14 89	84.77 112	99.16 106	90.38 69	98.96 57	93.73 114	98.23 85	96.57 126	97.37 97	99.64 130	99.93 122

GBi-Net		95.19 94	96.99 104	93.09 98	89.11 151	96.47 170	96.90 93	84.17 121	99.48 85	89.76 74	95.09 106	94.35 110	98.87 66	96.50 127	97.21 100	99.74 99	99.81 155

test1		95.19 94	96.99 104	93.09 98	89.11 151	96.47 170	96.90 93	84.17 121	99.48 85	89.76 74	95.09 106	94.35 110	98.87 66	96.50 127	97.21 100	99.74 99	99.81 155

FMVSNet1		92.55 138	93.66 153	91.26 113	87.91 162	96.12 177	94.75 132	81.69 147	97.67 156	85.63 111	80.56 182	87.88 138	98.15 89	96.50 127	97.21 100	99.41 177	99.71 165

UGNet		96.05 71	98.55 62	93.13 97	94.64 82	99.65 81	94.70 133	87.78 88	99.40 96	89.69 77	98.25 72	99.25 70	92.12 168	96.50 127	97.08 108	99.84 37	99.72 164
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
Anonymous202405211			95.78 129		93.26 92	99.52 89	96.70 100	88.55 80	97.93 154		88.99 147	90.68 127	98.99 62	96.46 131	97.02 112	99.64 130	99.89 135

COLMAP_ROB	93.56 12	96.03 72	96.83 108	95.11 64	97.87 57	99.52 89	98.81 52	91.40 51	99.42 93	84.97 113	90.46 140	96.82 96	98.05 92	96.46 131	96.19 126	99.54 145	98.92 191
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MS-PatchMatch		93.46 124	95.91 128	90.61 119	95.48 72	99.31 104	95.62 115	77.23 176	99.42 93	81.88 125	88.92 148	96.06 104	93.80 154	96.45 133	93.11 173	99.65 126	98.10 199

ACMM	94.44 10	94.26 112	94.62 145	93.84 86	94.86 79	97.73 149	93.48 148	90.76 55	99.27 104	87.46 94	99.04 52	76.60 169	96.76 118	96.37 134	93.76 164	99.74 99	99.55 173
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CSCG		98.22 36	98.37 69	98.04 27	99.60 17	99.82 59	99.45 35	93.59 44	99.16 106	96.46 20	98.22 75	95.86 105	99.41 40	96.33 135	99.22 28	99.75 95	99.94 119

LGP-MVS_train		93.60 117	95.05 139	91.90 111	94.90 78	98.29 139	97.93 67	88.06 86	99.14 108	74.83 147	99.26 47	76.50 170	96.07 127	96.31 136	95.90 136	99.59 136	99.97 87

TAMVS		92.43 141	94.21 151	90.35 122	88.68 158	98.85 126	94.15 142	81.53 149	95.58 171	83.61 119	87.05 162	86.45 140	94.71 142	96.27 137	95.91 134	99.42 175	99.38 181

canonicalmvs		95.80 81	97.02 101	94.37 78	92.96 100	99.47 94	97.49 76	84.58 114	99.44 90	92.05 48	98.54 66	86.65 139	99.37 42	96.18 138	98.93 37	99.77 79	99.92 126

CVMVSNet		92.13 143	95.40 135	88.32 147	91.29 135	97.29 156	91.85 160	86.42 100	96.71 163	71.84 160	89.56 143	91.18 123	88.98 194	96.17 139	97.76 84	99.51 153	99.14 187

OPM-MVS		93.50 120	93.00 158	94.07 81	95.82 71	98.26 140	98.49 61	91.62 49	94.69 182	81.93 124	92.82 129	76.18 174	96.82 115	96.12 140	94.57 150	99.74 99	98.39 194
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
FC-MVSNet-test		92.78 133	96.19 124	88.80 139	88.00 161	97.54 151	93.60 146	82.36 140	98.16 147	79.71 128	91.55 136	95.41 107	89.65 187	96.09 141	95.23 143	99.49 156	99.31 182

ADS-MVSNet		92.91 130	97.97 82	87.01 156	92.07 115	99.27 108	92.70 152	65.39 210	99.85 52	75.40 144	94.93 110	98.26 83	96.86 114	96.09 141	97.52 92	99.65 126	99.84 151

ACMP	94.49 9	94.19 113	94.74 144	93.56 89	94.25 85	98.32 138	96.02 107	89.35 72	98.90 122	87.28 97	99.14 49	76.41 172	94.94 139	96.07 143	94.35 159	99.49 156	99.99 58
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-train		94.61 103	96.27 120	92.68 105	92.35 111	97.14 158	93.45 149	87.73 89	98.93 118	87.31 95	96.42 94	89.35 132	95.67 131	96.06 144	96.01 131	99.56 142	99.98 76

anonymousdsp		87.98 174	92.38 159	82.85 194	83.68 204	96.79 162	90.78 168	74.06 189	95.29 176	57.91 207	83.33 171	83.12 154	91.15 181	95.96 145	92.37 182	99.52 149	99.76 162

Anonymous20231211		94.96 100	94.99 141	94.91 68	93.01 98	99.44 100	96.85 97	88.49 82	98.78 127	92.61 45	83.94 169	90.25 130	98.94 63	95.87 146	96.77 115	99.58 138	99.89 135

USDC		90.36 156	91.68 164	88.82 138	92.58 104	98.02 144	96.27 105	79.83 157	98.37 145	70.61 167	89.05 146	67.50 205	94.17 150	95.77 147	94.43 154	99.46 164	98.62 192

testgi		92.47 140	95.68 131	88.73 141	90.68 140	98.35 136	91.67 163	79.50 161	98.96 116	77.12 139	95.17 105	85.84 144	93.95 153	95.75 148	96.47 119	99.45 167	99.21 185

TinyColmap		89.94 158	90.88 171	88.84 137	92.43 108	97.91 147	95.59 117	80.10 155	98.12 149	71.33 164	84.56 165	67.46 206	94.15 151	95.57 149	94.27 160	99.43 172	98.26 196

CLD-MVS		94.53 106	94.45 148	94.61 75	93.85 90	98.36 135	98.12 66	89.68 65	99.35 100	89.62 79	95.19 104	77.08 167	96.66 120	95.51 150	95.67 137	99.74 99	100.00 1
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pmmvs4		91.41 150	93.05 155	89.49 132	85.85 174	96.52 168	91.70 162	82.49 138	98.14 148	83.17 120	87.57 157	81.76 159	94.39 147	95.47 151	92.62 179	99.33 184	99.29 183

pmmvs5		87.33 183	90.01 177	84.20 185	84.31 197	96.04 180	87.63 193	76.59 180	93.17 202	65.35 187	84.30 168	71.68 191	91.91 171	95.41 152	91.37 196	99.39 179	98.13 197

pm-mvs1		89.68 159	92.00 162	86.96 157	86.23 170	96.62 166	90.36 174	83.05 135	93.97 190	72.15 157	81.77 177	82.10 157	90.69 184	95.38 153	94.50 152	99.29 190	99.65 167

ACMH	92.34 14	91.59 149	93.02 157	89.92 127	93.97 89	97.98 146	90.10 178	84.70 113	98.46 139	76.80 140	93.38 125	71.94 189	94.39 147	95.34 154	94.04 161	99.54 145	100.00 1
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tfpnnormal		89.09 164	89.71 179	88.38 145	87.37 163	96.78 163	91.46 164	85.20 108	90.33 210	72.35 155	83.45 170	69.30 200	94.45 146	95.29 155	92.86 176	99.44 171	99.93 122

LTVRE_ROB	88.65 16	87.87 175	91.11 170	84.10 187	86.64 168	97.47 153	94.40 138	78.41 169	96.13 168	52.02 215	87.95 153	65.92 211	93.59 157	95.29 155	95.09 146	99.52 149	99.95 114
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
thisisatest0515		90.28 157	94.32 150	85.57 173	85.23 179	97.23 157	85.44 199	83.09 134	96.80 162	72.41 153	89.82 142	90.87 125	87.93 200	95.27 157	90.39 202	99.33 184	99.88 140

tpmrst		92.52 139	97.45 93	86.77 159	92.15 114	99.36 103	92.53 156	65.95 206	99.53 81	72.50 152	92.22 132	99.83 48	97.81 102	95.18 158	96.05 130	99.69 121	100.00 1

CostFormer		93.50 120	96.50 115	90.00 126	91.69 129	98.65 132	93.88 144	67.64 200	98.97 115	89.16 81	97.79 80	88.92 135	97.97 96	95.14 159	96.06 129	99.63 132	100.00 1

Anonymous20231206		84.28 201	89.53 185	78.17 205	82.31 209	94.16 206	82.57 206	76.51 181	93.38 200	52.98 213	79.47 189	73.74 184	75.45 213	95.07 160	94.41 155	99.18 197	96.46 209

TransMVSNet (Re)		88.33 168	89.55 184	86.91 158	86.65 167	95.56 192	90.48 171	84.44 118	92.02 209	71.07 166	80.13 185	72.48 187	89.41 189	95.05 161	94.44 153	99.39 179	97.14 204

FMVSNet5		93.53 118	96.09 125	90.56 120	86.74 165	92.84 210	92.64 154	77.50 174	99.41 95	88.97 82	98.02 78	97.81 88	98.00 95	94.85 162	95.43 141	99.50 155	94.25 212

EG-PatchMatch MVS		86.96 188	89.56 183	83.93 188	86.29 169	97.61 150	90.75 169	73.31 194	95.43 175	66.08 183	75.88 204	71.31 192	87.55 202	94.79 163	92.74 177	99.61 135	99.13 188

N_pmnet		87.31 184	91.51 167	82.41 197	85.13 181	95.57 191	80.59 212	81.79 145	96.20 166	58.52 205	78.62 193	85.66 145	89.36 190	94.64 164	92.14 184	99.08 200	97.72 203

ACMH+	92.61 13	91.80 146	93.03 156	90.37 121	93.03 97	98.17 141	94.00 143	84.13 124	98.12 149	77.39 138	91.95 133	74.62 178	94.36 149	94.62 165	93.82 163	99.32 186	99.87 145

ET-MVSNet_ETH3D		95.20 92	97.82 86	92.15 109	80.77 211	98.13 142	97.65 73	86.93 99	99.72 70	88.56 89	99.29 46	97.01 95	99.24 52	94.58 166	95.98 132	99.75 95	99.99 58

NR-MVSNet		89.52 161	90.71 172	88.14 149	86.19 171	96.20 175	92.07 158	84.58 114	95.54 172	75.27 145	87.52 158	67.96 203	91.24 179	94.33 167	93.45 167	99.49 156	99.97 87

tpm cat1		93.29 125	96.53 114	89.50 131	91.84 121	99.18 112	94.70 133	67.70 199	98.38 143	86.67 101	89.16 145	99.38 67	96.66 120	94.33 167	95.30 142	99.43 172	100.00 1

v7n		85.39 197	87.70 201	82.70 195	82.77 207	95.64 188	88.27 192	74.83 185	92.30 207	62.58 192	76.37 202	64.80 214	88.38 198	94.29 169	90.61 201	99.34 183	99.87 145

PM-MVS		82.79 205	84.51 207	80.77 201	77.22 213	92.13 212	83.61 204	73.31 194	93.50 195	61.06 196	77.15 200	46.52 223	90.55 186	94.14 170	89.05 208	98.85 204	99.12 189

SixPastTwentyTwo		88.35 167	91.51 167	84.66 180	85.39 177	96.96 161	86.57 195	79.62 160	96.57 164	63.73 191	87.86 154	75.18 176	93.43 158	94.03 171	90.37 203	99.24 193	99.58 171

MDTV_nov1_ep13_2view		87.75 178	93.32 154	81.26 200	83.74 203	96.64 165	85.66 198	66.20 204	98.36 146	61.61 195	84.34 167	87.95 137	91.12 182	94.01 172	92.66 178	99.22 194	99.27 184

ambc			74.33 213		66.84 221	84.26 220	84.17 200		93.39 199	58.99 204	45.93 220	18.06 230	70.61 216	93.94 173	86.62 211	92.61 218	98.13 197

pmmvs-eth3d		82.92 204	83.31 209	82.47 196	76.97 214	91.76 213	83.79 202	76.10 182	90.33 210	69.95 169	71.04 210	48.09 220	89.02 193	93.85 174	89.14 206	99.02 202	98.96 190

v1192		86.93 189	89.01 190	84.50 181	84.46 194	95.51 194	89.93 183	78.65 168	93.75 192	62.29 193	77.19 199	70.88 195	92.28 167	93.84 175	91.96 186	99.38 181	99.90 132

pmmvs3		80.91 206	85.62 205	75.42 210	75.01 216	89.09 217	75.31 216	68.70 198	86.99 216	46.74 221	81.18 180	62.91 216	87.95 199	93.84 175	89.06 207	98.80 205	96.23 210

v1240		86.24 195	88.56 196	83.54 189	84.05 201	95.21 200	89.27 189	76.76 178	93.42 197	60.68 201	75.99 203	69.80 199	91.21 180	93.83 177	91.76 190	99.29 190	99.91 131

v1144		87.49 180	89.64 180	84.97 177	84.73 187	95.84 185	90.17 177	79.30 162	93.96 191	64.65 188	78.83 192	73.38 186	91.51 175	93.77 178	91.77 189	99.45 167	99.93 122

RPSCF		95.86 77	96.94 106	94.61 75	96.52 64	98.67 131	98.54 58	88.43 83	99.56 80	90.51 68	99.39 38	98.70 75	97.72 103	93.77 178	92.00 185	95.93 213	96.50 208

dps		94.29 111	97.33 95	90.75 117	92.02 116	99.21 110	94.31 139	66.97 202	99.50 84	95.61 26	96.22 97	98.64 76	96.08 126	93.71 180	94.03 162	99.52 149	99.98 76

v1921920		86.81 190	88.93 191	84.33 184	84.23 198	95.41 198	90.09 179	78.10 171	93.74 193	62.17 194	76.98 201	71.14 193	92.05 169	93.69 181	91.69 192	99.32 186	99.88 140

v10		87.40 182	89.62 181	84.80 179	84.93 185	95.07 201	90.44 172	75.63 183	94.51 184	66.52 182	78.87 191	73.47 185	91.86 172	93.69 181	91.87 188	99.45 167	99.86 148

pmnet_mix02		88.07 172	92.32 160	83.10 193	86.14 172	96.23 174	81.90 210	83.05 135	98.04 152	57.59 208	84.93 164	82.02 158	90.87 183	93.54 183	91.53 194	99.06 201	99.97 87

WR-MVS_H		88.47 166	90.55 174	86.04 163	85.13 181	96.07 179	89.86 185	79.80 158	94.37 187	72.32 156	83.12 172	74.44 181	89.60 188	93.52 184	92.40 181	99.51 153	99.96 107

MIMVSNet1		80.64 207	83.97 208	76.76 209	68.91 219	91.15 216	78.32 215	75.47 184	89.58 214	56.64 211	65.10 214	65.17 213	82.14 208	93.51 185	91.64 193	99.10 199	91.66 214

pmmvs6		85.75 196	86.97 204	84.34 183	84.88 186	95.59 190	87.41 194	79.19 164	87.81 215	67.56 176	63.05 216	77.76 165	89.15 191	93.45 186	91.90 187	97.83 210	99.21 185

UniMVSNet (Re)		90.41 154	91.96 163	88.59 143	85.71 175	96.73 164	90.82 167	84.11 125	95.23 177	78.54 133	88.91 149	76.41 172	92.84 162	93.40 187	93.05 174	99.55 144	100.00 1

CP-MVSNet		88.09 171	89.57 182	86.36 162	84.63 191	95.46 197	89.48 187	80.53 153	93.42 197	71.26 165	81.25 179	69.90 198	92.78 163	93.30 188	93.69 165	99.47 162	99.96 107

UniMVSNet_NR-MVSNet		90.50 153	92.31 161	88.38 145	85.04 184	96.34 173	90.94 165	85.32 105	95.87 170	75.69 141	87.68 156	78.49 163	93.78 155	93.21 189	94.60 149	99.53 148	99.97 87

TDRefinement		87.79 177	88.76 194	86.66 160	93.54 91	98.02 144	95.76 110	85.18 109	96.57 164	67.90 174	80.51 183	66.51 210	78.37 211	93.20 190	89.73 204	99.22 194	96.75 206

PS-CasMVS		87.24 185	88.52 197	85.73 171	84.58 192	95.35 199	89.03 190	80.17 154	93.11 203	68.86 172	77.71 197	66.89 207	92.30 166	93.13 191	93.50 166	99.46 164	99.96 107

test_part1		91.82 145	91.53 166	92.16 108	92.47 107	96.48 169	95.41 122	83.13 133	95.13 179	87.04 100	80.43 184	80.96 161	96.28 123	93.11 192	95.01 147	98.63 206	99.61 170

new_pmnet		84.12 202	87.89 199	79.72 202	80.43 212	94.14 207	80.26 213	74.14 188	96.01 169	56.30 212	74.94 205	76.45 171	88.59 197	93.11 192	89.31 205	98.59 207	91.27 215

v144192		86.80 191	88.90 192	84.35 182	84.33 196	95.56 192	89.34 188	77.74 173	93.60 194	64.03 189	77.82 196	70.76 196	91.28 177	92.91 194	91.74 191	99.37 182	99.90 132

IB-MVS	90.59 15	92.70 135	95.70 130	89.21 134	94.62 83	99.45 97	83.77 203	88.92 76	99.53 81	92.82 42	98.86 59	86.08 143	75.24 214	92.81 195	93.17 171	99.89 27	100.00 1
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
WR-MVS		88.23 170	90.15 176	86.00 165	84.39 195	95.64 188	89.96 182	81.80 144	94.46 185	71.60 161	82.10 175	74.36 182	88.76 196	92.48 196	92.20 183	99.46 164	99.83 153

test20.03		83.86 203	88.73 195	78.16 206	82.60 208	93.00 209	81.61 211	74.68 186	92.36 206	57.50 209	83.01 174	74.48 180	73.30 215	92.40 197	91.14 199	99.29 190	94.75 211

EU-MVSNet		87.20 186	90.47 175	83.38 191	85.11 183	93.85 208	86.10 197	79.76 159	93.30 201	65.39 186	84.41 166	78.43 164	85.04 207	92.20 198	93.03 175	98.86 203	98.05 200

tpm		89.60 160	94.93 142	83.39 190	89.94 148	97.11 159	90.09 179	65.28 211	98.67 130	60.03 202	96.79 90	84.38 152	95.66 133	91.90 199	95.65 138	99.32 186	99.98 76

v2v482		87.46 181	88.90 192	85.78 168	84.58 192	95.95 183	89.90 184	82.43 139	94.19 189	65.65 184	79.80 187	69.12 201	92.67 164	91.88 200	91.46 195	99.45 167	99.93 122

v8		87.54 179	89.33 188	85.45 175	85.41 176	95.50 195	90.32 176	78.94 166	94.35 188	66.93 180	81.90 176	70.99 194	91.62 174	91.49 201	91.22 197	99.48 160	99.87 145

V42		87.84 176	89.42 187	85.99 166	85.16 180	96.01 181	90.52 170	81.78 146	94.43 186	67.59 175	81.32 178	71.87 190	91.48 176	91.25 202	91.16 198	99.43 172	99.92 126

DU-MVS		89.49 162	90.60 173	88.19 148	84.71 188	96.20 175	90.94 165	84.58 114	95.54 172	75.69 141	87.52 158	68.74 202	93.78 155	91.10 203	95.13 145	99.47 162	99.97 87

Baseline_NR-MVSNet		89.13 163	89.53 185	88.66 142	84.71 188	94.43 203	91.79 161	84.49 117	95.54 172	78.28 135	78.52 195	72.46 188	93.29 159	91.10 203	94.82 148	99.42 175	99.86 148

PEN-MVS		87.20 186	88.22 198	86.01 164	84.01 202	94.93 202	90.00 181	81.52 151	93.46 196	69.29 170	79.69 188	65.51 212	91.72 173	91.01 205	93.12 172	99.49 156	99.84 151

TranMVSNet+NR-MVSNet		88.88 165	89.90 178	87.69 151	84.06 200	95.68 187	91.88 159	85.23 107	95.16 178	72.54 151	83.06 173	70.14 197	92.93 161	90.81 206	94.53 151	99.48 160	99.89 135

UniMVSNet_ETH3D		88.05 173	87.01 203	89.27 133	88.53 159	97.49 152	90.35 175	83.48 128	94.57 183	77.87 137	70.08 211	61.75 217	96.22 125	90.17 207	95.21 144	99.16 198	99.82 154

DTE-MVSNet		86.70 192	87.66 202	85.58 172	83.30 205	94.29 204	89.74 186	81.53 149	92.77 205	68.93 171	80.13 185	64.00 215	90.62 185	89.45 208	93.34 168	99.32 186	99.67 166

v148		86.63 194	87.79 200	85.28 176	84.65 190	95.97 182	86.46 196	82.84 137	92.91 204	71.52 163	78.99 190	66.74 209	86.83 204	89.28 209	90.69 200	99.41 177	99.94 119

new-patchmatchnet		78.17 209	80.82 211	75.07 211	76.93 215	91.20 215	71.90 218	73.32 193	86.59 217	48.91 216	67.11 212	47.85 222	81.19 209	88.18 210	87.02 209	98.19 208	97.79 202

CMPMVS	65.66 17	84.62 199	85.02 206	84.15 186	95.40 73	97.79 148	88.35 191	79.22 163	89.66 213	60.71 200	72.20 208	73.94 183	87.32 203	86.73 211	84.55 213	93.90 215	90.31 216
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Gipuma		71.02 211	72.60 215	69.19 213	71.31 217	75.11 222	66.36 221	61.65 220	94.93 180	47.29 220	38.74 221	38.52 224	75.52 212	86.09 212	85.92 212	93.01 216	88.87 218
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DeepMVS_CX						97.31 155	79.48 214	89.65 66	98.66 131	60.89 199	94.40 114	66.89 207	87.65 201	81.69 213		92.76 217	94.24 213

tmp_tt				78.81 204	98.80 47	85.73 218	70.08 219	77.87 172	98.68 129	83.71 118	99.53 30	74.55 179	54.97 220	78.28 214	72.43 217	87.45 219

testmvs		61.76 215	72.90 214	48.76 218	21.21 226	68.61 223	66.11 223	37.38 223	94.83 181	33.06 224	64.31 215	29.72 226	86.08 206	74.44 215	78.71 215	48.74 225	99.65 167

PMMVS2		65.18 213	68.25 216	61.59 214	61.37 222	79.72 221	59.18 225	61.80 218	64.72 222	37.33 223	53.82 218	35.59 225	54.46 222	73.94 216	80.52 214	95.40 214	89.43 217

FPMVS		73.80 210	74.62 212	72.84 212	83.09 206	84.44 219	83.89 201	73.64 192	92.20 208	48.50 217	72.19 209	59.51 218	63.16 217	69.13 217	66.26 221	84.74 220	78.59 222

MDA-MVSNet-bldmvs		80.30 208	82.83 210	77.34 207	69.16 218	94.29 204	72.16 217	81.97 143	90.14 212	57.32 210	94.01 120	47.97 221	86.81 205	68.74 218	86.82 210	96.63 211	97.86 201

PMVS	60.14 18	62.67 214	64.05 217	61.06 215	68.32 220	53.27 228	52.23 226	67.63 201	75.07 221	48.30 218	58.27 217	57.43 219	49.99 223	67.20 219	62.42 222	79.87 223	74.68 223
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	58.81 19	52.07 218	55.15 221	48.48 219	42.45 225	62.35 224	36.41 229	54.70 222	49.88 225	27.65 225	29.98 224	18.08 229	54.87 221	65.93 220	77.26 216	74.79 224	82.59 219
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		55.33 216	55.79 219	54.81 217	59.81 224	57.23 226	38.83 227	63.59 215	64.06 224	24.66 226	35.33 223	26.40 228	58.69 219	55.41 221	70.54 218	83.26 221	81.56 221

EMVS		55.14 217	55.29 220	54.97 216	60.87 223	57.52 225	38.58 228	63.57 216	64.54 223	23.36 227	36.96 222	27.99 227	60.69 218	51.17 222	66.61 220	82.73 222	82.25 220

test123		48.14 219	58.11 218	36.51 220	8.71 227	56.81 227	59.55 224	24.08 224	77.50 220	14.41 228	49.20 219	11.94 231	80.98 210	41.62 223	69.81 219	31.32 226	99.90 132

uanet_test		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

RE-MVS-def										52.74 214

9.14												100.00 1

SR-MVS					99.61 13			96.80 19				100.00 1

our_test_3					85.89 173	96.09 178	82.15 208

MTAPA										96.61 17		100.00 1

MTMP										97.42 12		100.00 1

Patchmatch-RL test							68.01 220

XVS					95.09 76	99.94 18	97.49 76			88.58 86		99.98 35				99.78 75

X-MVStestdata					95.09 76	99.94 18	97.49 76			88.58 86		99.98 35				99.78 75

abl_6				97.06 38	99.17 41	99.82 59	98.68 55	90.86 54	100.00 1	94.53 34	97.40 85	100.00 1	99.17 56			99.93 18	99.99 58

mPP-MVS					99.23 39							99.87 46

NP-MVS									99.79 63

Patchmtry						99.00 118	95.46 119	65.50 208		67.51 177

High-resolution multi-view benchmark