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	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 63	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 6	91.38 2	88.42 16

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 72	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 19	90.87 5	88.23 22

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 25	63.71 12	89.23 20	81.51 2	88.44 27	88.09 27
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
DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 67	87.85 5	85.03 36	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 17	90.61 11	85.45 126
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
DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 45	83.27 13	91.83 10	64.96 7	90.47 11	76.41 32	89.67 18	86.84 66
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 21	62.49 63	82.20 15	92.28 1	56.53 37	89.70 17	79.85 5	91.48 1	88.19 24
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
MM		80.20 7	80.28 8	79.99 2	82.19 82	60.01 49	86.19 17	83.93 54	73.19 1	77.08 35	91.21 17	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 52	82.40 14	92.12 2	59.64 19	89.76 16	78.70 14	88.32 31	86.79 68
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 65	65.37 13	78.78 22	90.64 21	58.63 25	87.24 54	79.00 13	90.37 14	85.26 137

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 39	85.03 36	66.96 5	77.58 30	90.06 39	59.47 21	89.13 22	78.67 16	89.73 16	87.03 60

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 75	62.18 16	87.60 9	85.83 19	66.69 9	78.03 27	90.98 18	54.26 58	90.06 14	78.42 21	89.02 23	87.69 39
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 60	85.33 28	62.86 55	80.17 17	90.03 41	61.76 14	88.95 24	74.21 51	88.67 26	88.12 26

SF-MVS		78.82 13	79.22 12	77.60 46	82.88 77	57.83 84	84.99 31	88.13 2	61.86 76	79.16 20	90.75 20	57.96 26	87.09 63	77.08 28	90.18 15	87.87 32

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 44	75.08 51	90.47 28	53.96 63	88.68 27	76.48 31	89.63 20	87.16 58

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 52	85.16 31	62.88 54	78.10 25	91.26 16	52.51 82	88.39 30	79.34 8	90.52 13	86.78 69

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 30	84.42 45	66.73 8	74.67 64	89.38 52	55.30 47	89.18 21	74.19 52	87.34 44	86.38 82

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 65	61.62 23	84.17 45	86.85 6	63.23 47	73.84 75	90.25 35	57.68 29	89.96 15	74.62 49	89.03 22	87.89 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		78.45 18	78.28 19	78.98 26	80.73 107	57.91 83	84.68 35	81.64 107	68.35 2	75.77 41	90.38 29	53.98 61	90.26 13	81.30 3	87.68 42	88.77 11

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 48	83.82 63	59.34 129	79.37 19	89.76 48	59.84 16	87.62 51	76.69 29	86.74 53	87.68 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 56	82.93 62	85.39 27	62.15 68	76.41 39	91.51 11	52.47 84	86.78 70	80.66 4	89.64 19	87.80 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 69	63.89 37	73.60 77	90.60 22	54.85 53	86.72 71	77.20 27	88.06 36	85.74 114
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 30	63.56 41	74.29 70	90.03 41	52.56 81	88.53 29	74.79 48	88.34 29	86.63 76

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 46	60.61 94	79.05 21	90.30 33	55.54 46	88.32 32	73.48 59	87.03 46	84.83 151
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 47	62.82 56	73.96 73	90.50 26	53.20 75	88.35 31	74.02 54	87.05 45	86.13 97

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 48	62.82 56	73.55 78	90.56 24	49.80 119	88.24 33	74.02 54	87.03 46	86.32 90

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 37	83.87 60	60.37 101	79.89 18	89.38 52	54.97 51	85.58 100	76.12 35	84.94 64	86.33 88
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
region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 50	62.81 58	73.30 80	90.58 23	49.90 117	88.21 34	73.78 56	87.03 46	86.29 94

MCST-MVS		77.48 28	77.45 27	77.54 47	86.67 20	58.36 79	83.22 58	86.93 5	56.91 172	74.91 56	88.19 67	59.15 23	87.68 50	73.67 57	87.45 43	86.57 77

HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 63	85.08 33	62.57 61	73.09 89	89.97 44	50.90 110	87.48 52	75.30 42	86.85 51	87.33 55
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 34	84.85 40	61.98 75	73.06 90	88.88 59	53.72 68	89.06 23	68.27 90	88.04 37	87.42 49
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		77.17 31	76.56 36	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 104	90.01 43	47.95 140	88.01 40	71.55 76	86.74 53	86.37 84

CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 72	62.44 65	72.68 97	90.50 26	48.18 138	87.34 53	73.59 58	85.71 60	84.76 155

CSCG		76.92 33	76.75 31	77.41 49	83.96 64	59.60 54	82.95 61	86.50 13	60.78 91	75.27 46	84.83 146	60.76 15	86.56 76	67.86 95	87.87 41	86.06 99

reproduce-ours		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 108	77.85 28	91.42 13	50.67 111	87.69 48	72.46 65	84.53 68	85.46 124

our_new_method		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 108	77.85 28	91.42 13	50.67 111	87.69 48	72.46 65	84.53 68	85.46 124

MTAPA		76.90 34	76.42 38	78.35 35	86.08 37	63.57 2	74.92 218	80.97 132	65.13 15	75.77 41	90.88 19	48.63 133	86.66 73	77.23 26	88.17 33	84.81 152

PGM-MVS		76.77 37	76.06 42	78.88 28	86.14 35	62.73 9	82.55 70	83.74 64	61.71 77	72.45 103	90.34 32	48.48 136	88.13 37	72.32 67	86.85 51	85.78 108

balanced_conf03		76.58 38	76.55 37	76.68 59	81.73 88	52.90 171	80.94 91	85.70 23	61.12 86	74.90 57	87.17 91	56.46 38	88.14 36	72.87 62	88.03 38	89.00 8

mPP-MVS		76.54 39	75.93 44	78.34 36	86.47 26	63.50 3	85.74 25	82.28 97	62.90 53	71.77 108	90.26 34	46.61 165	86.55 77	71.71 74	85.66 61	84.97 148

CANet		76.46 40	75.93 44	78.06 39	81.29 97	57.53 88	82.35 72	83.31 80	67.78 3	70.09 124	86.34 116	54.92 52	88.90 25	72.68 64	84.55 67	87.76 38

reproduce_model		76.43 41	76.08 41	77.49 48	83.47 69	60.09 47	84.60 36	82.90 89	59.65 120	77.31 31	91.43 12	49.62 121	87.24 54	71.99 71	83.75 78	85.14 139

CDPH-MVS		76.31 42	75.67 48	78.22 37	85.35 48	59.14 65	81.31 88	84.02 51	56.32 187	74.05 71	88.98 57	53.34 74	87.92 43	69.23 88	88.42 28	87.59 44

train_agg		76.27 43	76.15 40	76.64 62	85.58 43	61.59 24	81.62 83	81.26 122	55.86 195	74.93 54	88.81 60	53.70 69	84.68 123	75.24 44	88.33 30	83.65 193

CS-MVS		76.25 44	75.98 43	77.06 53	80.15 121	55.63 123	84.51 38	83.90 57	63.24 46	73.30 80	87.27 89	55.06 49	86.30 86	71.78 73	84.58 66	89.25 5

casdiffmvs_mvg		76.14 45	76.30 39	75.66 77	76.46 228	51.83 195	79.67 111	85.08 33	65.02 19	75.84 40	88.58 65	59.42 22	85.08 111	72.75 63	83.93 76	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
SR-MVS		76.13 46	75.70 47	77.40 51	85.87 40	61.20 29	85.52 27	82.19 98	59.99 113	75.10 50	90.35 31	47.66 145	86.52 78	71.64 75	82.99 83	84.47 161

ACMMP		76.02 47	75.33 51	78.07 38	85.20 49	61.91 20	85.49 29	84.44 44	63.04 50	69.80 134	89.74 49	45.43 178	87.16 60	72.01 70	82.87 88	85.14 139
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
PHI-MVS		75.87 48	75.36 50	77.41 49	80.62 112	55.91 116	84.28 42	85.78 20	56.08 193	73.41 79	86.58 108	50.94 109	88.54 28	70.79 80	89.71 17	87.79 37

EC-MVSNet		75.84 49	75.87 46	75.74 75	78.86 149	52.65 176	83.73 53	86.08 17	63.47 43	72.77 96	87.25 90	53.13 76	87.93 42	71.97 72	85.57 62	86.66 74

3Dnovator+	66.72 4	75.84 49	74.57 59	79.66 9	82.40 79	59.92 51	85.83 22	86.32 16	66.92 7	67.80 171	89.24 54	42.03 212	89.38 19	64.07 128	86.50 57	89.69 3

MVSMamba_PlusPlus		75.75 51	75.44 49	76.67 60	80.84 105	53.06 168	78.62 126	85.13 32	59.65 120	71.53 112	87.47 83	56.92 34	88.17 35	72.18 69	86.63 56	88.80 10

SPE-MVS-test		75.62 52	75.31 52	76.56 64	80.63 111	55.13 133	83.88 51	85.22 29	62.05 72	71.49 113	86.03 126	53.83 65	86.36 84	67.74 96	86.91 50	88.19 24

DPM-MVS		75.47 53	75.00 54	76.88 54	81.38 96	59.16 62	79.94 104	85.71 22	56.59 181	72.46 101	86.76 98	56.89 35	87.86 45	66.36 109	88.91 25	83.64 194

APD-MVS_3200maxsize		74.96 54	74.39 61	76.67 60	82.20 81	58.24 80	83.67 54	83.29 81	58.41 146	73.71 76	90.14 36	45.62 171	85.99 90	69.64 84	82.85 89	85.78 108

TSAR-MVS + GP.		74.90 55	74.15 64	77.17 52	82.00 84	58.77 75	81.80 80	78.57 172	58.58 143	74.32 69	84.51 157	55.94 43	87.22 57	67.11 103	84.48 71	85.52 120

casdiffmvs		74.80 56	74.89 57	74.53 101	75.59 240	50.37 215	78.17 136	85.06 35	62.80 59	74.40 67	87.86 76	57.88 27	83.61 143	69.46 87	82.79 90	89.59 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DELS-MVS		74.76 57	74.46 60	75.65 78	77.84 185	52.25 186	75.59 201	84.17 49	63.76 38	73.15 85	82.79 186	59.58 20	86.80 69	67.24 102	86.04 59	87.89 30
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
OPM-MVS		74.73 58	74.25 63	76.19 68	80.81 106	59.01 70	82.60 69	83.64 66	63.74 39	72.52 100	87.49 82	47.18 156	85.88 93	69.47 86	80.78 107	83.66 192
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
sasdasda		74.67 59	74.98 55	73.71 126	78.94 147	50.56 212	80.23 98	83.87 60	60.30 105	77.15 33	86.56 109	59.65 17	82.00 179	66.01 113	82.12 94	88.58 14

canonicalmvs		74.67 59	74.98 55	73.71 126	78.94 147	50.56 212	80.23 98	83.87 60	60.30 105	77.15 33	86.56 109	59.65 17	82.00 179	66.01 113	82.12 94	88.58 14

baseline		74.61 61	74.70 58	74.34 105	75.70 236	49.99 223	77.54 154	84.63 42	62.73 60	73.98 72	87.79 79	57.67 30	83.82 139	69.49 85	82.74 91	89.20 7

SR-MVS-dyc-post		74.57 62	73.90 66	76.58 63	83.49 67	59.87 52	84.29 40	81.36 115	58.07 152	73.14 86	90.07 37	44.74 185	85.84 94	68.20 91	81.76 101	84.03 172

dcpmvs_2		74.55 63	75.23 53	72.48 161	82.34 80	53.34 161	77.87 143	81.46 111	57.80 162	75.49 43	86.81 97	62.22 13	77.75 260	71.09 79	82.02 97	86.34 86

ETV-MVS		74.46 64	73.84 68	76.33 67	79.27 138	55.24 132	79.22 117	85.00 38	64.97 21	72.65 98	79.46 262	53.65 72	87.87 44	67.45 101	82.91 86	85.89 105

HQP_MVS		74.31 65	73.73 69	76.06 69	81.41 94	56.31 105	84.22 43	84.01 52	64.52 25	69.27 142	86.10 123	45.26 182	87.21 58	68.16 93	80.58 112	84.65 156

HPM-MVS_fast		74.30 66	73.46 72	76.80 56	84.45 60	59.04 69	83.65 55	81.05 129	60.15 110	70.43 120	89.84 46	41.09 229	85.59 99	67.61 99	82.90 87	85.77 111

MVS_111021_HR		74.02 67	73.46 72	75.69 76	83.01 75	60.63 40	77.29 162	78.40 183	61.18 84	70.58 119	85.97 128	54.18 60	84.00 136	67.52 100	82.98 85	82.45 221

MG-MVS		73.96 68	73.89 67	74.16 111	85.65 42	49.69 228	81.59 85	81.29 121	61.45 79	71.05 116	88.11 68	51.77 97	87.73 47	61.05 157	83.09 81	85.05 144

alignmvs		73.86 69	73.99 65	73.45 140	78.20 170	50.50 214	78.57 128	82.43 95	59.40 127	76.57 37	86.71 102	56.42 40	81.23 196	65.84 116	81.79 100	88.62 12

MSLP-MVS++		73.77 70	73.47 71	74.66 94	83.02 74	59.29 61	82.30 77	81.88 102	59.34 129	71.59 111	86.83 96	45.94 169	83.65 142	65.09 121	85.22 63	81.06 249

fmvsm_s_conf0.5_n_3		73.55 71	74.39 61	71.03 203	74.09 272	51.86 194	77.77 148	75.60 222	61.18 84	78.67 23	88.98 57	55.88 44	77.73 261	78.69 15	78.68 144	83.50 197

HQP-MVS		73.45 72	72.80 78	75.40 82	80.66 108	54.94 135	82.31 74	83.90 57	62.10 69	67.85 166	85.54 140	45.46 176	86.93 66	67.04 104	80.35 116	84.32 163

BP-MVS1		73.41 73	72.25 85	76.88 54	76.68 221	53.70 152	79.15 118	81.07 128	60.66 93	71.81 107	87.39 85	40.93 230	87.24 54	71.23 78	81.29 106	89.71 2

CLD-MVS		73.33 74	72.68 80	75.29 86	78.82 151	53.33 162	78.23 133	84.79 41	61.30 82	70.41 121	81.04 229	52.41 85	87.12 61	64.61 127	82.49 93	85.41 130
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		73.31 75	72.54 82	75.62 79	77.87 183	53.64 154	79.62 113	79.61 151	61.63 78	72.02 106	82.61 191	56.44 39	85.97 91	63.99 131	79.07 137	87.25 57

fmvsm_l_conf0.5_n_3		73.23 76	73.13 75	73.55 136	74.40 263	55.13 133	78.97 120	74.96 240	56.64 175	74.76 62	88.75 63	55.02 50	78.77 246	76.33 33	78.31 151	86.74 70

UA-Net		73.13 77	72.93 77	73.76 121	83.58 66	51.66 196	78.75 122	77.66 193	67.75 4	72.61 99	89.42 50	49.82 118	83.29 148	53.61 215	83.14 80	86.32 90

EPNet		73.09 78	72.16 86	75.90 71	75.95 234	56.28 107	83.05 59	72.39 269	66.53 10	65.27 218	87.00 93	50.40 114	85.47 105	62.48 145	86.32 58	85.94 102
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		73.01 79	72.59 81	74.27 108	71.28 318	55.88 117	78.21 135	75.56 224	54.31 237	74.86 58	87.80 78	54.72 54	80.23 220	78.07 23	78.48 147	86.70 71

nrg030		72.96 80	73.01 76	72.84 153	75.41 243	50.24 216	80.02 102	82.89 91	58.36 148	74.44 66	86.73 100	58.90 24	80.83 206	65.84 116	74.46 193	87.44 48

test_fmvsmconf0.1_n		72.81 81	72.33 84	74.24 109	69.89 340	55.81 118	78.22 134	75.40 228	54.17 239	75.00 53	88.03 74	53.82 66	80.23 220	78.08 22	78.34 150	86.69 72

CPTT-MVS		72.78 82	72.08 88	74.87 90	84.88 57	61.41 26	84.15 46	77.86 189	55.27 211	67.51 177	88.08 70	41.93 215	81.85 182	69.04 89	80.01 120	81.35 242

LPG-MVS_test		72.74 83	71.74 91	75.76 73	80.22 116	57.51 89	82.55 70	83.40 74	61.32 80	66.67 192	87.33 87	39.15 247	86.59 74	67.70 97	77.30 167	83.19 205

h-mvs33		72.71 84	71.49 95	76.40 65	81.99 85	59.58 55	76.92 172	76.74 209	60.40 98	74.81 59	85.95 129	45.54 174	85.76 96	70.41 82	70.61 253	83.86 181

fmvsm_s_conf0.5_n_5		72.69 85	72.80 78	72.37 166	74.11 271	53.21 164	78.12 137	73.31 259	53.98 242	76.81 36	88.05 71	53.38 73	77.37 268	76.64 30	80.78 107	86.53 79

GDP-MVS		72.64 86	71.28 102	76.70 57	77.72 189	54.22 145	79.57 114	84.45 43	55.30 210	71.38 114	86.97 94	39.94 235	87.00 65	67.02 106	79.20 133	88.89 9

PAPM_NR		72.63 87	71.80 90	75.13 87	81.72 89	53.42 160	79.91 106	83.28 82	59.14 131	66.31 199	85.90 130	51.86 95	86.06 87	57.45 182	80.62 110	85.91 104

VDD-MVS		72.50 88	72.09 87	73.75 123	81.58 90	49.69 228	77.76 149	77.63 194	63.21 48	73.21 83	89.02 56	42.14 211	83.32 147	61.72 152	82.50 92	88.25 21

3Dnovator	64.47 5	72.49 89	71.39 98	75.79 72	77.70 190	58.99 71	80.66 96	83.15 85	62.24 67	65.46 214	86.59 107	42.38 210	85.52 101	59.59 170	84.72 65	82.85 214

MGCFI-Net		72.45 90	73.34 74	69.81 226	77.77 187	43.21 302	75.84 198	81.18 125	59.59 125	75.45 44	86.64 103	57.74 28	77.94 255	63.92 132	81.90 99	88.30 19

MVS_Test		72.45 90	72.46 83	72.42 165	74.88 249	48.50 246	76.28 186	83.14 86	59.40 127	72.46 101	84.68 149	55.66 45	81.12 197	65.98 115	79.66 124	87.63 42

EI-MVSNet-Vis-set		72.42 92	71.59 92	74.91 88	78.47 160	54.02 147	77.05 168	79.33 157	65.03 18	71.68 110	79.35 266	52.75 79	84.89 118	66.46 108	74.23 197	85.83 107

ACMP	63.53 6	72.30 93	71.20 104	75.59 81	80.28 114	57.54 87	82.74 66	82.84 92	60.58 95	65.24 222	86.18 120	39.25 245	86.03 89	66.95 107	76.79 174	83.22 203
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 94	71.21 103	75.31 84	78.50 158	55.93 115	81.63 82	82.12 99	56.24 190	70.02 128	85.68 136	47.05 158	84.34 129	65.27 120	74.41 196	85.67 115

Vis-MVSNet		72.18 95	71.37 99	74.61 97	81.29 97	55.41 129	80.90 92	78.28 185	60.73 92	69.23 145	88.09 69	44.36 191	82.65 167	57.68 180	81.75 103	85.77 111
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		72.17 96	71.50 94	74.16 111	67.96 358	55.58 126	78.06 140	74.67 243	54.19 238	74.54 65	88.23 66	50.35 116	80.24 219	78.07 23	77.46 163	86.65 75

API-MVS		72.17 96	71.41 97	74.45 103	81.95 86	57.22 92	84.03 48	80.38 142	59.89 118	68.40 154	82.33 200	49.64 120	87.83 46	51.87 229	84.16 75	78.30 287

EPP-MVSNet		72.16 98	71.31 101	74.71 91	78.68 155	49.70 226	82.10 78	81.65 106	60.40 98	65.94 204	85.84 132	51.74 98	86.37 83	55.93 191	79.55 127	88.07 29

DP-MVS Recon		72.15 99	70.73 112	76.40 65	86.57 24	57.99 82	81.15 90	82.96 87	57.03 169	66.78 188	85.56 137	44.50 189	88.11 38	51.77 231	80.23 119	83.10 209

fmvsm_s_conf0.5_n_4		72.04 100	71.85 89	72.58 158	73.74 274	52.49 182	76.69 177	72.42 268	56.42 185	75.32 45	87.04 92	52.13 91	78.01 254	79.29 11	73.65 206	87.26 56

EI-MVSNet-UG-set		71.92 101	71.06 107	74.52 102	77.98 181	53.56 156	76.62 178	79.16 158	64.40 27	71.18 115	78.95 271	52.19 89	84.66 125	65.47 119	73.57 209	85.32 133

VDDNet		71.81 102	71.33 100	73.26 147	82.80 78	47.60 258	78.74 123	75.27 230	59.59 125	72.94 92	89.40 51	41.51 223	83.91 137	58.75 175	82.99 83	88.26 20

EIA-MVS		71.78 103	70.60 114	75.30 85	79.85 125	53.54 157	77.27 163	83.26 83	57.92 158	66.49 194	79.39 264	52.07 92	86.69 72	60.05 164	79.14 136	85.66 116

LFMVS		71.78 103	71.59 92	72.32 167	83.40 70	46.38 267	79.75 109	71.08 278	64.18 32	72.80 95	88.64 64	42.58 207	83.72 140	57.41 183	84.49 70	86.86 65

test_fmvsm_n_1920		71.73 105	71.14 105	73.50 137	72.52 292	56.53 104	75.60 200	76.16 213	48.11 314	77.22 32	85.56 137	53.10 77	77.43 265	74.86 46	77.14 169	86.55 78

PAPR		71.72 106	70.82 110	74.41 104	81.20 101	51.17 198	79.55 115	83.33 79	55.81 198	66.93 187	84.61 153	50.95 108	86.06 87	55.79 194	79.20 133	86.00 100

IS-MVSNet		71.57 107	71.00 108	73.27 146	78.86 149	45.63 278	80.22 100	78.69 169	64.14 35	66.46 195	87.36 86	49.30 124	85.60 98	50.26 242	83.71 79	88.59 13

MAR-MVS		71.51 108	70.15 124	75.60 80	81.84 87	59.39 58	81.38 87	82.90 89	54.90 226	68.08 163	78.70 272	47.73 143	85.51 102	51.68 233	84.17 74	81.88 232
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
MVSFormer		71.50 109	70.38 119	74.88 89	78.76 152	57.15 97	82.79 64	78.48 176	51.26 273	69.49 137	83.22 181	43.99 195	83.24 149	66.06 111	79.37 128	84.23 166

RRT-MVS		71.46 110	70.70 113	73.74 124	77.76 188	49.30 234	76.60 179	80.45 140	61.25 83	68.17 159	84.78 148	44.64 187	84.90 117	64.79 123	77.88 157	87.03 60

PVSNet_Blended_VisFu		71.45 111	70.39 118	74.65 95	82.01 83	58.82 74	79.93 105	80.35 143	55.09 216	65.82 210	82.16 207	49.17 127	82.64 168	60.34 162	78.62 146	82.50 220

OMC-MVS		71.40 112	70.60 114	73.78 119	76.60 224	53.15 165	79.74 110	79.78 147	58.37 147	68.75 149	86.45 114	45.43 178	80.60 210	62.58 143	77.73 158	87.58 45

UniMVSNet_NR-MVSNet		71.11 113	71.00 108	71.44 188	79.20 140	44.13 291	76.02 194	82.60 94	66.48 11	68.20 157	84.60 154	56.82 36	82.82 163	54.62 205	70.43 255	87.36 54

hse-mvs2		71.04 114	69.86 127	74.60 98	79.58 130	57.12 99	73.96 235	75.25 231	60.40 98	74.81 59	81.95 212	45.54 174	82.90 156	70.41 82	66.83 307	83.77 186

GeoE		71.01 115	70.15 124	73.60 134	79.57 131	52.17 187	78.93 121	78.12 186	58.02 154	67.76 174	83.87 169	52.36 86	82.72 165	56.90 185	75.79 184	85.92 103

fmvsm_l_conf0.5_n		70.99 116	70.82 110	71.48 185	71.45 311	54.40 143	77.18 165	70.46 284	48.67 305	75.17 48	86.86 95	53.77 67	76.86 280	76.33 33	77.51 162	83.17 208

PCF-MVS	61.88 8	70.95 117	69.49 133	75.35 83	77.63 194	55.71 120	76.04 193	81.81 104	50.30 284	69.66 135	85.40 143	52.51 82	84.89 118	51.82 230	80.24 118	85.45 126
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_fmvsmvis_n_1920		70.84 118	70.38 119	72.22 169	71.16 319	55.39 130	75.86 196	72.21 271	49.03 301	73.28 82	86.17 121	51.83 96	77.29 270	75.80 36	78.05 154	83.98 175

114514_t		70.83 119	69.56 131	74.64 96	86.21 31	54.63 140	82.34 73	81.81 104	48.22 312	63.01 257	85.83 133	40.92 231	87.10 62	57.91 179	79.79 121	82.18 226

FIs		70.82 120	71.43 96	68.98 239	78.33 167	38.14 346	76.96 170	83.59 68	61.02 87	67.33 179	86.73 100	55.07 48	81.64 185	54.61 207	79.22 132	87.14 59

ACMM	61.98 7	70.80 121	69.73 129	74.02 113	80.59 113	58.59 77	82.68 67	82.02 101	55.46 207	67.18 182	84.39 159	38.51 252	83.17 151	60.65 160	76.10 181	80.30 262
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs		70.69 122	70.43 117	71.46 186	69.45 346	48.95 240	72.93 253	78.46 178	57.27 166	71.69 109	83.97 168	51.48 101	77.92 257	70.70 81	77.95 156	87.53 46
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UniMVSNet (Re)		70.63 123	70.20 122	71.89 172	78.55 157	45.29 281	75.94 195	82.92 88	63.68 40	68.16 160	83.59 175	53.89 64	83.49 146	53.97 211	71.12 248	86.89 64

xiu_mvs_v2_base		70.52 124	69.75 128	72.84 153	81.21 100	55.63 123	75.11 211	78.92 163	54.92 225	69.96 131	79.68 257	47.00 162	82.09 178	61.60 154	79.37 128	80.81 254

PS-MVSNAJ		70.51 125	69.70 130	72.93 151	81.52 91	55.79 119	74.92 218	79.00 161	55.04 222	69.88 132	78.66 274	47.05 158	82.19 176	61.61 153	79.58 125	80.83 253

fmvsm_l_conf0.5_n_a		70.50 126	70.27 121	71.18 198	71.30 317	54.09 146	76.89 173	69.87 288	47.90 318	74.37 68	86.49 112	53.07 78	76.69 285	75.41 41	77.11 170	82.76 215

v2v482		70.50 126	69.45 135	73.66 129	72.62 289	50.03 222	77.58 151	80.51 139	59.90 114	69.52 136	82.14 208	47.53 149	84.88 120	65.07 122	70.17 263	86.09 98

v1144		70.42 128	69.31 136	73.76 121	73.22 277	50.64 209	77.83 146	81.43 112	58.58 143	69.40 140	81.16 226	47.53 149	85.29 110	64.01 130	70.64 251	85.34 132

TranMVSNet+NR-MVSNet		70.36 129	70.10 126	71.17 199	78.64 156	42.97 305	76.53 181	81.16 127	66.95 6	68.53 153	85.42 142	51.61 100	83.07 152	52.32 223	69.70 275	87.46 47

v8		70.33 130	69.28 137	73.49 138	73.15 279	50.22 217	78.62 126	80.78 135	60.79 90	66.45 196	82.11 210	49.35 123	84.98 114	63.58 137	68.71 291	85.28 135

Fast-Effi-MVS+		70.28 131	69.12 141	73.73 125	78.50 158	51.50 197	75.01 214	79.46 155	56.16 192	68.59 150	79.55 260	53.97 62	84.05 132	53.34 217	77.53 161	85.65 117

X-MVStestdata		70.21 132	67.28 183	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 104	6.49 430	47.95 140	88.01 40	71.55 76	86.74 53	86.37 84

v10		70.21 132	69.02 142	73.81 118	73.51 276	50.92 204	78.74 123	81.39 113	60.05 112	66.39 197	81.83 215	47.58 147	85.41 108	62.80 142	68.86 290	85.09 143

QAPM		70.05 134	68.81 147	73.78 119	76.54 226	53.43 159	83.23 57	83.48 70	52.89 253	65.90 206	86.29 117	41.55 222	86.49 80	51.01 236	78.40 149	81.42 236

DU-MVS		70.01 135	69.53 132	71.44 188	78.05 178	44.13 291	75.01 214	81.51 110	64.37 28	68.20 157	84.52 155	49.12 130	82.82 163	54.62 205	70.43 255	87.37 52

AdaColmap		69.99 136	68.66 151	73.97 115	84.94 54	57.83 84	82.63 68	78.71 168	56.28 189	64.34 236	84.14 162	41.57 220	87.06 64	46.45 274	78.88 138	77.02 308

v1192		69.97 137	68.68 150	73.85 116	73.19 278	50.94 202	77.68 150	81.36 115	57.51 164	68.95 148	80.85 236	45.28 181	85.33 109	62.97 141	70.37 257	85.27 136

Anonymous20240529		69.91 138	69.02 142	72.56 159	80.19 119	47.65 256	77.56 153	80.99 131	55.45 208	69.88 132	86.76 98	39.24 246	82.18 177	54.04 210	77.10 171	87.85 33

patch_mono-2		69.85 139	71.09 106	66.16 275	79.11 144	54.80 139	71.97 269	74.31 248	53.50 248	70.90 117	84.17 161	57.63 31	63.31 361	66.17 110	82.02 97	80.38 261

fmvsm_s_conf0.5_n_2		69.82 140	69.27 138	71.46 186	72.00 303	51.08 199	73.30 247	67.79 307	55.06 221	75.24 47	87.51 81	44.02 194	77.00 276	75.67 38	72.86 224	86.31 93

FA-MVS(test-final)		69.82 140	68.48 154	73.84 117	78.44 161	50.04 221	75.58 203	78.99 162	58.16 150	67.59 175	82.14 208	42.66 205	85.63 97	56.60 186	76.19 180	85.84 106

FC-MVSNet-test		69.80 142	70.58 116	67.46 255	77.61 199	34.73 379	76.05 192	83.19 84	60.84 89	65.88 208	86.46 113	54.52 57	80.76 209	52.52 222	78.12 153	86.91 63

v144192		69.71 143	68.51 153	73.33 145	73.10 280	50.13 219	77.54 154	80.64 136	56.65 174	68.57 152	80.55 239	46.87 163	84.96 116	62.98 140	69.66 276	84.89 150

test_yl		69.69 144	69.13 139	71.36 192	78.37 165	45.74 274	74.71 222	80.20 144	57.91 159	70.01 129	83.83 170	42.44 208	82.87 159	54.97 201	79.72 122	85.48 122

DCV-MVSNet		69.69 144	69.13 139	71.36 192	78.37 165	45.74 274	74.71 222	80.20 144	57.91 159	70.01 129	83.83 170	42.44 208	82.87 159	54.97 201	79.72 122	85.48 122

VNet		69.68 146	70.19 123	68.16 249	79.73 127	41.63 318	70.53 289	77.38 199	60.37 101	70.69 118	86.63 105	51.08 106	77.09 273	53.61 215	81.69 105	85.75 113

jason		69.65 147	68.39 160	73.43 142	78.27 169	56.88 101	77.12 166	73.71 257	46.53 334	69.34 141	83.22 181	43.37 199	79.18 233	64.77 124	79.20 133	84.23 166
jason: jason.
fmvsm_s_conf0.1_n_2		69.64 148	69.01 144	71.52 184	71.66 308	51.04 200	73.39 246	67.14 313	55.02 223	75.11 49	87.64 80	42.94 204	77.01 275	75.55 39	72.63 230	86.52 80

Effi-MVS+-dtu		69.64 148	67.53 173	75.95 70	76.10 232	62.29 15	80.20 101	76.06 217	59.83 119	65.26 221	77.09 302	41.56 221	84.02 135	60.60 161	71.09 249	81.53 235

fmvsm_s_conf0.5_n		69.58 150	68.84 146	71.79 176	72.31 299	52.90 171	77.90 142	62.43 353	49.97 289	72.85 94	85.90 130	52.21 88	76.49 288	75.75 37	70.26 262	85.97 101

lupinMVS		69.57 151	68.28 161	73.44 141	78.76 152	57.15 97	76.57 180	73.29 261	46.19 337	69.49 137	82.18 204	43.99 195	79.23 232	64.66 125	79.37 128	83.93 176

fmvsm_s_conf0.5_n_a		69.54 152	68.74 149	71.93 171	72.47 294	53.82 150	78.25 132	62.26 355	49.78 291	73.12 88	86.21 119	52.66 80	76.79 282	75.02 45	68.88 288	85.18 138

NR-MVSNet		69.54 152	68.85 145	71.59 183	78.05 178	43.81 296	74.20 231	80.86 134	65.18 14	62.76 261	84.52 155	52.35 87	83.59 144	50.96 238	70.78 250	87.37 52

MVS_111021_LR		69.50 154	68.78 148	71.65 181	78.38 163	59.33 59	74.82 220	70.11 286	58.08 151	67.83 170	84.68 149	41.96 213	76.34 292	65.62 118	77.54 160	79.30 279

v1921920		69.47 155	68.17 162	73.36 144	73.06 281	50.10 220	77.39 157	80.56 137	56.58 182	68.59 150	80.37 241	44.72 186	84.98 114	62.47 146	69.82 271	85.00 145

test_djsdf		69.45 156	67.74 166	74.58 99	74.57 259	54.92 137	82.79 64	78.48 176	51.26 273	65.41 215	83.49 178	38.37 254	83.24 149	66.06 111	69.25 283	85.56 119

fmvsm_s_conf0.1_n		69.41 157	68.60 152	71.83 174	71.07 320	52.88 173	77.85 145	62.44 352	49.58 294	72.97 91	86.22 118	51.68 99	76.48 289	75.53 40	70.10 265	86.14 96

fmvsm_s_conf0.1_n_a		69.32 158	68.44 158	71.96 170	70.91 322	53.78 151	78.12 137	62.30 354	49.35 297	73.20 84	86.55 111	51.99 93	76.79 282	74.83 47	68.68 293	85.32 133

Anonymous20231211		69.28 159	68.47 156	71.73 178	80.28 114	47.18 262	79.98 103	82.37 96	54.61 230	67.24 180	84.01 166	39.43 242	82.41 174	55.45 199	72.83 225	85.62 118

EI-MVSNet		69.27 160	68.44 158	71.73 178	74.47 260	49.39 233	75.20 209	78.45 179	59.60 122	69.16 146	76.51 314	51.29 102	82.50 171	59.86 169	71.45 245	83.30 200

v1240		69.24 161	67.91 165	73.25 148	73.02 283	49.82 224	77.21 164	80.54 138	56.43 184	68.34 156	80.51 240	43.33 200	84.99 112	62.03 150	69.77 274	84.95 149

IterMVS-LS		69.22 162	68.48 154	71.43 190	74.44 262	49.40 232	76.23 187	77.55 195	59.60 122	65.85 209	81.59 221	51.28 103	81.58 188	59.87 168	69.90 270	83.30 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPA-MVSNet		69.02 163	69.47 134	67.69 253	77.42 204	41.00 323	74.04 233	79.68 149	60.06 111	69.26 144	84.81 147	51.06 107	77.58 263	54.44 208	74.43 195	84.48 160

v7n		69.01 164	67.36 180	73.98 114	72.51 293	52.65 176	78.54 130	81.30 120	60.26 107	62.67 263	81.62 218	43.61 197	84.49 126	57.01 184	68.70 292	84.79 153

OpenMVS	61.03 9	68.85 165	67.56 170	72.70 157	74.26 268	53.99 148	81.21 89	81.34 119	52.70 254	62.75 262	85.55 139	38.86 250	84.14 131	48.41 258	83.01 82	79.97 267

XVG-OURS-SEG-HR		68.81 166	67.47 176	72.82 155	74.40 263	56.87 102	70.59 288	79.04 160	54.77 228	66.99 185	86.01 127	39.57 241	78.21 251	62.54 144	73.33 216	83.37 199

BH-RMVSNet		68.81 166	67.42 177	72.97 150	80.11 122	52.53 180	74.26 230	76.29 212	58.48 145	68.38 155	84.20 160	42.59 206	83.83 138	46.53 273	75.91 182	82.56 216

UGNet		68.81 166	67.39 178	73.06 149	78.33 167	54.47 141	79.77 108	75.40 228	60.45 97	63.22 250	84.40 158	32.71 320	80.91 205	51.71 232	80.56 114	83.81 182
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
XVG-OURS		68.76 169	67.37 179	72.90 152	74.32 266	57.22 92	70.09 296	78.81 165	55.24 212	67.79 172	85.81 135	36.54 277	78.28 250	62.04 149	75.74 185	83.19 205

V42		68.65 170	67.35 181	72.56 159	68.93 352	50.18 218	72.90 254	79.47 154	56.92 171	69.45 139	80.26 245	46.29 167	82.99 153	64.07 128	67.82 299	84.53 158

PVSNet_Blended		68.59 171	67.72 167	71.19 197	77.03 215	50.57 210	72.51 261	81.52 108	51.91 262	64.22 242	77.77 294	49.13 128	82.87 159	55.82 192	79.58 125	80.14 265

xiu_mvs_v1_base_debu		68.58 172	67.28 183	72.48 161	78.19 171	57.19 94	75.28 206	75.09 236	51.61 264	70.04 125	81.41 223	32.79 316	79.02 240	63.81 134	77.31 164	81.22 244

xiu_mvs_v1_base		68.58 172	67.28 183	72.48 161	78.19 171	57.19 94	75.28 206	75.09 236	51.61 264	70.04 125	81.41 223	32.79 316	79.02 240	63.81 134	77.31 164	81.22 244

xiu_mvs_v1_base_debi		68.58 172	67.28 183	72.48 161	78.19 171	57.19 94	75.28 206	75.09 236	51.61 264	70.04 125	81.41 223	32.79 316	79.02 240	63.81 134	77.31 164	81.22 244

PVSNet_BlendedMVS		68.56 175	67.72 167	71.07 202	77.03 215	50.57 210	74.50 226	81.52 108	53.66 247	64.22 242	79.72 256	49.13 128	82.87 159	55.82 192	73.92 201	79.77 274

WR-MVS		68.47 176	68.47 156	68.44 246	80.20 118	39.84 330	73.75 243	76.07 216	64.68 22	68.11 162	83.63 174	50.39 115	79.14 238	49.78 243	69.66 276	86.34 86

mvsmamba		68.47 176	66.56 194	74.21 110	79.60 129	52.95 169	74.94 217	75.48 226	52.09 261	60.10 294	83.27 180	36.54 277	84.70 122	59.32 174	77.69 159	84.99 147

AUN-MVS		68.45 178	66.41 201	74.57 100	79.53 132	57.08 100	73.93 238	75.23 232	54.44 235	66.69 191	81.85 214	37.10 272	82.89 157	62.07 148	66.84 306	83.75 187

c3_l		68.33 179	67.56 170	70.62 210	70.87 323	46.21 270	74.47 227	78.80 166	56.22 191	66.19 200	78.53 279	51.88 94	81.40 190	62.08 147	69.04 286	84.25 165

BH-untuned		68.27 180	67.29 182	71.21 196	79.74 126	53.22 163	76.06 191	77.46 198	57.19 167	66.10 201	81.61 219	45.37 180	83.50 145	45.42 289	76.68 176	76.91 312

jajsoiax		68.25 181	66.45 197	73.66 129	75.62 238	55.49 128	80.82 93	78.51 175	52.33 258	64.33 237	84.11 163	28.28 356	81.81 184	63.48 138	70.62 252	83.67 190

v148		68.24 182	67.19 189	71.40 191	70.43 330	47.77 255	75.76 199	77.03 204	58.91 135	67.36 178	80.10 248	48.60 135	81.89 181	60.01 165	66.52 310	84.53 158

CANet_DTU		68.18 183	67.71 169	69.59 229	74.83 251	46.24 269	78.66 125	76.85 206	59.60 122	63.45 248	82.09 211	35.25 286	77.41 266	59.88 167	78.76 142	85.14 139

mvs_tets		68.18 183	66.36 203	73.63 132	75.61 239	55.35 131	80.77 94	78.56 173	52.48 257	64.27 239	84.10 164	27.45 363	81.84 183	63.45 139	70.56 254	83.69 189

SDMVSNet		68.03 185	68.10 164	67.84 251	77.13 211	48.72 244	65.32 334	79.10 159	58.02 154	65.08 225	82.55 193	47.83 142	73.40 305	63.92 132	73.92 201	81.41 237

miper_ehance_all_eth		68.03 185	67.24 187	70.40 214	70.54 327	46.21 270	73.98 234	78.68 170	55.07 219	66.05 202	77.80 291	52.16 90	81.31 193	61.53 156	69.32 280	83.67 190

mvs_anonymous		68.03 185	67.51 174	69.59 229	72.08 301	44.57 288	71.99 268	75.23 232	51.67 263	67.06 184	82.57 192	54.68 55	77.94 255	56.56 187	75.71 186	86.26 95

ET-MVSNet_ETH3D		67.96 188	65.72 215	74.68 93	76.67 222	55.62 125	75.11 211	74.74 241	52.91 252	60.03 296	80.12 247	33.68 305	82.64 168	61.86 151	76.34 178	85.78 108

thisisatest0530		67.92 189	65.78 214	74.33 106	76.29 229	51.03 201	76.89 173	74.25 250	53.67 246	65.59 212	81.76 216	35.15 287	85.50 103	55.94 190	72.47 231	86.47 81

PAPM		67.92 189	66.69 193	71.63 182	78.09 176	49.02 237	77.09 167	81.24 124	51.04 276	60.91 288	83.98 167	47.71 144	84.99 112	40.81 323	79.32 131	80.90 252

tttt0517		67.83 191	65.66 216	74.33 106	76.69 220	50.82 206	77.86 144	73.99 254	54.54 233	64.64 234	82.53 196	35.06 288	85.50 103	55.71 195	69.91 269	86.67 73

tt0805		67.77 192	67.24 187	69.34 234	74.87 250	40.08 327	77.36 158	81.37 114	55.31 209	66.33 198	84.65 151	37.35 266	82.55 170	55.65 197	72.28 236	85.39 131

ECVR-MVS		67.72 193	67.51 174	68.35 247	79.46 133	36.29 369	74.79 221	66.93 315	58.72 138	67.19 181	88.05 71	36.10 279	81.38 191	52.07 226	84.25 72	87.39 50

eth_miper_zixun_eth		67.63 194	66.28 207	71.67 180	71.60 309	48.33 248	73.68 244	77.88 188	55.80 199	65.91 205	78.62 277	47.35 155	82.88 158	59.45 171	66.25 311	83.81 182

UniMVSNet_ETH3D		67.60 195	67.07 191	69.18 238	77.39 205	42.29 309	74.18 232	75.59 223	60.37 101	66.77 189	86.06 125	37.64 262	78.93 245	52.16 225	73.49 211	86.32 90

VPNet		67.52 196	68.11 163	65.74 284	79.18 141	36.80 361	72.17 266	72.83 265	62.04 73	67.79 172	85.83 133	48.88 132	76.60 287	51.30 234	72.97 223	83.81 182

cl22		67.47 197	66.45 197	70.54 212	69.85 341	46.49 266	73.85 241	77.35 200	55.07 219	65.51 213	77.92 287	47.64 146	81.10 198	61.58 155	69.32 280	84.01 174

Fast-Effi-MVS+-dtu		67.37 198	65.33 221	73.48 139	72.94 284	57.78 86	77.47 156	76.88 205	57.60 163	61.97 275	76.85 306	39.31 243	80.49 214	54.72 204	70.28 261	82.17 228

MVS		67.37 198	66.33 204	70.51 213	75.46 242	50.94 202	73.95 236	81.85 103	41.57 374	62.54 267	78.57 278	47.98 139	85.47 105	52.97 220	82.05 96	75.14 328

test1111		67.21 200	67.14 190	67.42 256	79.24 139	34.76 378	73.89 240	65.65 324	58.71 140	66.96 186	87.95 75	36.09 280	80.53 211	52.03 227	83.79 77	86.97 62

GBi-Net		67.21 200	66.55 195	69.19 235	77.63 194	43.33 299	77.31 159	77.83 190	56.62 178	65.04 227	82.70 187	41.85 216	80.33 216	47.18 268	72.76 226	83.92 177

test1		67.21 200	66.55 195	69.19 235	77.63 194	43.33 299	77.31 159	77.83 190	56.62 178	65.04 227	82.70 187	41.85 216	80.33 216	47.18 268	72.76 226	83.92 177

cl____		67.18 203	66.26 208	69.94 221	70.20 333	45.74 274	73.30 247	76.83 207	55.10 214	65.27 218	79.57 259	47.39 153	80.53 211	59.41 173	69.22 284	83.53 196

DIV-MVS_self_test		67.18 203	66.26 208	69.94 221	70.20 333	45.74 274	73.29 249	76.83 207	55.10 214	65.27 218	79.58 258	47.38 154	80.53 211	59.43 172	69.22 284	83.54 195

MVSTER		67.16 205	65.58 218	71.88 173	70.37 332	49.70 226	70.25 294	78.45 179	51.52 267	69.16 146	80.37 241	38.45 253	82.50 171	60.19 163	71.46 244	83.44 198

miper_enhance_ethall		67.11 206	66.09 210	70.17 218	69.21 349	45.98 272	72.85 255	78.41 182	51.38 270	65.65 211	75.98 324	51.17 105	81.25 194	60.82 159	69.32 280	83.29 202

Baseline_NR-MVSNet		67.05 207	67.56 170	65.50 287	75.65 237	37.70 352	75.42 204	74.65 244	59.90 114	68.14 161	83.15 184	49.12 130	77.20 271	52.23 224	69.78 272	81.60 234

WR-MVS_H		67.02 208	66.92 192	67.33 259	77.95 182	37.75 350	77.57 152	82.11 100	62.03 74	62.65 264	82.48 197	50.57 113	79.46 228	42.91 310	64.01 328	84.79 153

anonymousdsp		67.00 209	64.82 226	73.57 135	70.09 336	56.13 110	76.35 184	77.35 200	48.43 310	64.99 230	80.84 237	33.01 313	80.34 215	64.66 125	67.64 301	84.23 166

FMVSNet2		66.93 210	66.31 206	68.79 242	77.63 194	42.98 304	76.11 189	77.47 196	56.62 178	65.22 224	82.17 206	41.85 216	80.18 222	47.05 271	72.72 229	83.20 204

BH-w/o		66.85 211	65.83 213	69.90 224	79.29 135	52.46 183	74.66 224	76.65 210	54.51 234	64.85 231	78.12 281	45.59 173	82.95 155	43.26 306	75.54 188	74.27 342

Anonymous202405211		66.84 212	65.99 211	69.40 233	80.19 119	42.21 311	71.11 282	71.31 277	58.80 137	67.90 164	86.39 115	29.83 344	79.65 225	49.60 249	78.78 141	86.33 88

CDS-MVSNet		66.80 213	65.37 219	71.10 201	78.98 146	53.13 167	73.27 250	71.07 279	52.15 260	64.72 232	80.23 246	43.56 198	77.10 272	45.48 287	78.88 138	83.05 210
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 214	65.27 222	71.33 195	79.16 143	53.67 153	73.84 242	69.59 292	52.32 259	65.28 217	81.72 217	44.49 190	77.40 267	42.32 314	78.66 145	82.92 211

FMVSNet1		66.70 215	65.87 212	69.19 235	77.49 202	43.33 299	77.31 159	77.83 190	56.45 183	64.60 235	82.70 187	38.08 260	80.33 216	46.08 277	72.31 235	83.92 177

ab-mvs		66.65 216	66.42 200	67.37 257	76.17 231	41.73 315	70.41 292	76.14 215	53.99 241	65.98 203	83.51 177	49.48 122	76.24 293	48.60 256	73.46 213	84.14 170

PEN-MVS		66.60 217	66.45 197	67.04 260	77.11 213	36.56 363	77.03 169	80.42 141	62.95 51	62.51 269	84.03 165	46.69 164	79.07 239	44.22 293	63.08 338	85.51 121

TAPA-MVS	59.36 10	66.60 217	65.20 223	70.81 206	76.63 223	48.75 242	76.52 182	80.04 146	50.64 281	65.24 222	84.93 145	39.15 247	78.54 247	36.77 348	76.88 173	85.14 139
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 219	65.07 224	71.17 199	79.18 141	49.63 230	73.48 245	75.20 234	52.95 251	67.90 164	80.33 244	39.81 239	83.68 141	43.20 307	73.56 210	80.20 263

CP-MVSNet		66.49 220	66.41 201	66.72 262	77.67 192	36.33 366	76.83 176	79.52 153	62.45 64	62.54 267	83.47 179	46.32 166	78.37 248	45.47 288	63.43 335	85.45 126

PS-CasMVS		66.42 221	66.32 205	66.70 264	77.60 200	36.30 368	76.94 171	79.61 151	62.36 66	62.43 272	83.66 173	45.69 170	78.37 248	45.35 290	63.26 336	85.42 129

FMVSNet3		66.32 222	65.61 217	68.46 245	76.48 227	42.34 308	74.98 216	77.15 203	55.83 197	65.04 227	81.16 226	39.91 236	80.14 223	47.18 268	72.76 226	82.90 213

ACMH+	57.40 11	66.12 223	64.06 230	72.30 168	77.79 186	52.83 174	80.39 97	78.03 187	57.30 165	57.47 325	82.55 193	27.68 361	84.17 130	45.54 284	69.78 272	79.90 269

cascas		65.98 224	63.42 241	73.64 131	77.26 209	52.58 179	72.26 265	77.21 202	48.56 306	61.21 285	74.60 339	32.57 326	85.82 95	50.38 241	76.75 175	82.52 219

FE-MVS		65.91 225	63.33 243	73.63 132	77.36 206	51.95 193	72.62 258	75.81 218	53.70 245	65.31 216	78.96 270	28.81 353	86.39 82	43.93 298	73.48 212	82.55 217

thisisatest0515		65.83 226	63.50 240	72.82 155	73.75 273	49.50 231	71.32 276	73.12 264	49.39 296	63.82 244	76.50 316	34.95 290	84.84 121	53.20 219	75.49 189	84.13 171

DP-MVS		65.68 227	63.66 238	71.75 177	84.93 55	56.87 102	80.74 95	73.16 262	53.06 250	59.09 310	82.35 199	36.79 276	85.94 92	32.82 372	69.96 268	72.45 356

HyFIR lowres test		65.67 228	63.01 248	73.67 128	79.97 124	55.65 122	69.07 305	75.52 225	42.68 368	63.53 247	77.95 285	40.43 233	81.64 185	46.01 278	71.91 239	83.73 188

DTE-MVSNet		65.58 229	65.34 220	66.31 271	76.06 233	34.79 376	76.43 183	79.38 156	62.55 62	61.66 280	83.83 170	45.60 172	79.15 237	41.64 322	60.88 353	85.00 145

GA-MVS		65.53 230	63.70 237	71.02 204	70.87 323	48.10 250	70.48 290	74.40 246	56.69 173	64.70 233	76.77 307	33.66 306	81.10 198	55.42 200	70.32 260	83.87 180

CNLPA		65.43 231	64.02 231	69.68 227	78.73 154	58.07 81	77.82 147	70.71 282	51.49 268	61.57 282	83.58 176	38.23 258	70.82 318	43.90 299	70.10 265	80.16 264

MVP-Stereo		65.41 232	63.80 235	70.22 215	77.62 198	55.53 127	76.30 185	78.53 174	50.59 282	56.47 335	78.65 275	39.84 238	82.68 166	44.10 297	72.12 238	72.44 357
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 233	62.73 252	73.40 143	74.89 248	52.78 175	73.09 252	75.13 235	55.69 201	58.48 318	73.73 345	32.86 315	86.32 85	50.63 239	70.11 264	81.10 248
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
test2506		65.33 234	64.61 227	67.50 254	79.46 133	34.19 384	74.43 229	51.92 393	58.72 138	66.75 190	88.05 71	25.99 375	80.92 204	51.94 228	84.25 72	87.39 50

pm-mvs1		65.24 235	64.97 225	66.04 279	72.38 296	39.40 336	72.62 258	75.63 221	55.53 205	62.35 274	83.18 183	47.45 151	76.47 290	49.06 253	66.54 309	82.24 225

ACMH	55.70 15	65.20 236	63.57 239	70.07 219	78.07 177	52.01 192	79.48 116	79.69 148	55.75 200	56.59 332	80.98 231	27.12 366	80.94 202	42.90 311	71.58 243	77.25 306
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 237	63.21 246	70.72 209	81.04 103	54.87 138	78.57 128	77.47 196	48.51 308	55.71 338	81.89 213	33.71 304	79.71 224	41.66 320	70.37 257	77.58 299
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 238	62.84 250	71.82 175	81.49 93	56.26 108	66.32 322	74.20 252	40.53 380	63.16 253	78.65 275	41.30 224	77.80 259	45.80 280	74.09 198	81.40 239

TransMVSNet (Re)		64.72 239	64.33 229	65.87 283	75.22 245	38.56 342	74.66 224	75.08 239	58.90 136	61.79 278	82.63 190	51.18 104	78.07 253	43.63 303	55.87 376	80.99 251

EG-PatchMatch MVS		64.71 240	62.87 249	70.22 215	77.68 191	53.48 158	77.99 141	78.82 164	53.37 249	56.03 337	77.41 299	24.75 383	84.04 133	46.37 275	73.42 215	73.14 348

LS3D		64.71 240	62.50 254	71.34 194	79.72 128	55.71 120	79.82 107	74.72 242	48.50 309	56.62 331	84.62 152	33.59 307	82.34 175	29.65 393	75.23 190	75.97 318

1314		64.61 242	63.21 246	68.80 241	71.87 306	47.46 259	73.95 236	78.39 184	42.88 367	59.97 297	76.60 313	38.11 259	79.39 230	54.84 203	72.32 234	79.55 275

HY-MVS	56.14 13	64.55 243	63.89 232	66.55 267	74.73 254	41.02 320	69.96 297	74.43 245	49.29 298	61.66 280	80.92 233	47.43 152	76.68 286	44.91 292	71.69 241	81.94 230

testing91		64.46 244	63.80 235	66.47 268	78.43 162	40.06 328	67.63 313	69.59 292	59.06 132	63.18 252	78.05 283	34.05 298	76.99 277	48.30 259	75.87 183	82.37 223

sd_testset		64.46 244	64.45 228	64.51 297	77.13 211	42.25 310	62.67 353	72.11 272	58.02 154	65.08 225	82.55 193	41.22 228	69.88 326	47.32 266	73.92 201	81.41 237

XVG-ACMP-BASELINE		64.36 246	62.23 258	70.74 208	72.35 297	52.45 184	70.80 286	78.45 179	53.84 244	59.87 299	81.10 228	16.24 402	79.32 231	55.64 198	71.76 240	80.47 258

MonoMVSNet		64.15 247	63.31 244	66.69 265	70.51 328	44.12 293	74.47 227	74.21 251	57.81 161	63.03 255	76.62 310	38.33 255	77.31 269	54.22 209	60.59 358	78.64 285

testing99		64.05 248	63.29 245	66.34 270	78.17 174	39.76 332	67.33 318	68.00 306	58.60 142	63.03 255	78.10 282	32.57 326	76.94 279	48.22 260	75.58 187	82.34 224

CostFormer		64.04 249	62.51 253	68.61 244	71.88 305	45.77 273	71.30 277	70.60 283	47.55 322	64.31 238	76.61 312	41.63 219	79.62 227	49.74 245	69.00 287	80.42 259

1112_ss		64.00 250	63.36 242	65.93 281	79.28 137	42.58 307	71.35 275	72.36 270	46.41 335	60.55 291	77.89 289	46.27 168	73.28 306	46.18 276	69.97 267	81.92 231

baseline1		63.81 251	63.87 234	63.62 302	76.29 229	36.36 364	71.78 272	67.29 311	56.05 194	64.23 241	82.95 185	47.11 157	74.41 302	47.30 267	61.85 347	80.10 266

pmmvs6		63.69 252	62.82 251	66.27 273	70.63 325	39.27 337	73.13 251	75.47 227	52.69 255	59.75 303	82.30 201	39.71 240	77.03 274	47.40 265	64.35 327	82.53 218

Vis-MVSNet (Re-imp)		63.69 252	63.88 233	63.14 307	74.75 253	31.04 400	71.16 280	63.64 342	56.32 187	59.80 301	84.99 144	44.51 188	75.46 297	39.12 334	80.62 110	82.92 211

baseline2		63.42 254	61.26 272	69.89 225	72.55 291	47.62 257	71.54 273	68.38 303	50.11 286	54.82 349	75.55 329	43.06 202	80.96 201	48.13 261	67.16 305	81.11 247

thres400		63.31 255	62.18 259	66.72 262	76.85 218	39.62 333	71.96 270	69.44 295	56.63 176	62.61 265	79.83 251	37.18 268	79.17 234	31.84 378	73.25 218	81.36 240

thres600view7		63.30 256	62.27 257	66.41 269	77.18 210	38.87 339	72.35 263	69.11 299	56.98 170	62.37 273	80.96 232	37.01 274	79.00 243	31.43 385	73.05 222	81.36 240

thres100view900		63.28 257	62.41 255	65.89 282	77.31 208	38.66 341	72.65 256	69.11 299	57.07 168	62.45 270	81.03 230	37.01 274	79.17 234	31.84 378	73.25 218	79.83 271

test_0402		63.25 258	61.01 277	69.96 220	80.00 123	54.37 144	76.86 175	72.02 273	54.58 232	58.71 313	80.79 238	35.00 289	84.36 128	26.41 405	64.71 322	71.15 375

tfpn200view9		63.18 259	62.18 259	66.21 274	76.85 218	39.62 333	71.96 270	69.44 295	56.63 176	62.61 265	79.83 251	37.18 268	79.17 234	31.84 378	73.25 218	79.83 271

LTVRE_ROB	55.42 16	63.15 260	61.23 273	68.92 240	76.57 225	47.80 253	59.92 369	76.39 211	54.35 236	58.67 314	82.46 198	29.44 348	81.49 189	42.12 315	71.14 247	77.46 300
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
F-COLMAP		63.05 261	60.87 280	69.58 231	76.99 217	53.63 155	78.12 137	76.16 213	47.97 317	52.41 369	81.61 219	27.87 358	78.11 252	40.07 326	66.66 308	77.00 309

testing11		62.81 262	61.90 262	65.54 286	78.38 163	40.76 325	67.59 315	66.78 317	55.48 206	60.13 293	77.11 301	31.67 333	76.79 282	45.53 285	74.45 194	79.06 280

IterMVS		62.79 263	61.27 271	67.35 258	69.37 347	52.04 191	71.17 279	68.24 305	52.63 256	59.82 300	76.91 305	37.32 267	72.36 309	52.80 221	63.19 337	77.66 298
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
reproduce_monomvs		62.56 264	61.20 274	66.62 266	70.62 326	44.30 290	70.13 295	73.13 263	54.78 227	61.13 286	76.37 317	25.63 378	75.63 296	58.75 175	60.29 359	79.93 268

IterMVS-SCA-FT		62.49 265	61.52 266	65.40 289	71.99 304	50.80 207	71.15 281	69.63 291	45.71 343	60.61 290	77.93 286	37.45 264	65.99 352	55.67 196	63.50 334	79.42 277

tfpnnormal		62.47 266	61.63 265	64.99 294	74.81 252	39.01 338	71.22 278	73.72 256	55.22 213	60.21 292	80.09 249	41.26 227	76.98 278	30.02 391	68.09 297	78.97 283

MS-PatchMatch		62.42 267	61.46 267	65.31 291	75.21 246	52.10 188	72.05 267	74.05 253	46.41 335	57.42 327	74.36 340	34.35 296	77.57 264	45.62 283	73.67 205	66.26 394

Test_1112_low_res		62.32 268	61.77 263	64.00 301	79.08 145	39.53 335	68.17 309	70.17 285	43.25 363	59.03 311	79.90 250	44.08 192	71.24 317	43.79 301	68.42 294	81.25 243

D2MVS		62.30 269	60.29 283	68.34 248	66.46 370	48.42 247	65.70 326	73.42 258	47.71 320	58.16 320	75.02 335	30.51 337	77.71 262	53.96 212	71.68 242	78.90 284

testing222		62.29 270	61.31 270	65.25 292	77.87 183	38.53 343	68.34 308	66.31 321	56.37 186	63.15 254	77.58 297	28.47 354	76.18 295	37.04 346	76.65 177	81.05 250

thres200		62.20 271	61.16 275	65.34 290	75.38 244	39.99 329	69.60 300	69.29 297	55.64 204	61.87 277	76.99 303	37.07 273	78.96 244	31.28 386	73.28 217	77.06 307

tpm2		62.07 272	60.10 284	67.99 250	72.79 286	43.86 295	71.05 284	66.85 316	43.14 365	62.77 260	75.39 333	38.32 256	80.80 207	41.69 319	68.88 288	79.32 278

testing3-2		62.06 273	62.36 256	61.17 322	79.29 135	30.31 402	64.09 346	63.49 343	63.50 42	62.84 258	82.22 203	32.35 330	69.02 330	40.01 329	73.43 214	84.17 169

miper_lstm_enhance		62.03 274	60.88 279	65.49 288	66.71 367	46.25 268	56.29 388	75.70 220	50.68 279	61.27 284	75.48 331	40.21 234	68.03 336	56.31 189	65.25 318	82.18 226

EPNet_dtu		61.90 275	61.97 261	61.68 315	72.89 285	39.78 331	75.85 197	65.62 325	55.09 216	54.56 353	79.36 265	37.59 263	67.02 345	39.80 330	76.95 172	78.25 288
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 276	61.35 269	63.46 303	74.58 258	31.48 399	61.42 360	58.14 371	58.71 140	53.02 367	79.55 260	43.07 201	76.80 281	45.69 281	77.96 155	82.11 229

MSDG		61.81 277	59.23 289	69.55 232	72.64 288	52.63 178	70.45 291	75.81 218	51.38 270	53.70 360	76.11 319	29.52 346	81.08 200	37.70 341	65.79 315	74.93 333

SixPastTwentyTwo		61.65 278	58.80 295	70.20 217	75.80 235	47.22 261	75.59 201	69.68 290	54.61 230	54.11 357	79.26 267	27.07 367	82.96 154	43.27 305	49.79 394	80.41 260

CL-MVSNet_self_test		61.53 279	60.94 278	63.30 305	68.95 351	36.93 360	67.60 314	72.80 266	55.67 202	59.95 298	76.63 309	45.01 184	72.22 312	39.74 331	62.09 346	80.74 256

RPMNet		61.53 279	58.42 298	70.86 205	69.96 338	52.07 189	65.31 335	81.36 115	43.20 364	59.36 306	70.15 372	35.37 285	85.47 105	36.42 355	64.65 323	75.06 329

pmmvs4		61.48 281	59.39 288	67.76 252	71.57 310	53.86 149	71.42 274	65.34 327	44.20 354	59.46 305	77.92 287	35.90 281	74.71 300	43.87 300	64.87 321	74.71 338

OurMVSNet-221017-0		61.37 282	58.63 297	69.61 228	72.05 302	48.06 251	73.93 238	72.51 267	47.23 328	54.74 350	80.92 233	21.49 393	81.24 195	48.57 257	56.22 375	79.53 276

COLMAP_ROB	52.97 17	61.27 283	58.81 293	68.64 243	74.63 257	52.51 181	78.42 131	73.30 260	49.92 290	50.96 374	81.51 222	23.06 386	79.40 229	31.63 382	65.85 313	74.01 345
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 284	61.67 264	57.70 348	70.43 330	38.45 344	64.19 343	66.47 318	48.05 316	63.22 250	80.86 235	49.28 125	60.47 370	45.25 291	67.28 304	74.19 343

myMVS_eth3d28		60.66 285	61.04 276	59.51 329	77.32 207	31.58 398	63.11 350	63.87 339	59.00 133	60.90 289	78.26 280	32.69 321	66.15 351	36.10 357	78.13 152	80.81 254

SSC-MVS3.2		60.57 286	61.39 268	58.12 344	74.29 267	32.63 393	59.52 370	65.53 326	59.90 114	62.45 270	79.75 255	41.96 213	63.90 360	39.47 332	69.65 278	77.84 296

WBMVS		60.54 287	60.61 281	60.34 326	78.00 180	35.95 371	64.55 341	64.89 330	49.63 292	63.39 249	78.70 272	33.85 303	67.65 339	42.10 316	70.35 259	77.43 301

SCA		60.49 288	58.38 299	66.80 261	74.14 270	48.06 251	63.35 349	63.23 346	49.13 300	59.33 309	72.10 355	37.45 264	74.27 303	44.17 294	62.57 341	78.05 291

K. test v3		60.47 289	57.11 307	70.56 211	73.74 274	48.22 249	75.10 213	62.55 350	58.27 149	53.62 363	76.31 318	27.81 359	81.59 187	47.42 264	39.18 409	81.88 232

mmtdpeth		60.40 290	59.12 291	64.27 300	69.59 343	48.99 238	70.67 287	70.06 287	54.96 224	62.78 259	73.26 349	27.00 368	67.66 338	58.44 178	45.29 401	76.16 317

UWE-MVS		60.18 291	59.78 285	61.39 320	77.67 192	33.92 387	69.04 306	63.82 340	48.56 306	64.27 239	77.64 296	27.20 365	70.40 323	33.56 369	76.24 179	79.83 271

OpenMVS_ROB	52.78 18	60.03 292	58.14 302	65.69 285	70.47 329	44.82 283	75.33 205	70.86 281	45.04 346	56.06 336	76.00 321	26.89 370	79.65 225	35.36 361	67.29 303	72.60 353

CR-MVSNet		59.91 293	57.90 305	65.96 280	69.96 338	52.07 189	65.31 335	63.15 347	42.48 369	59.36 306	74.84 336	35.83 282	70.75 319	45.50 286	64.65 323	75.06 329

PatchmatchNet		59.84 294	58.24 300	64.65 296	73.05 282	46.70 265	69.42 302	62.18 356	47.55 322	58.88 312	71.96 357	34.49 294	69.16 328	42.99 309	63.60 332	78.07 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WTY-MVS		59.75 295	60.39 282	57.85 346	72.32 298	37.83 349	61.05 365	64.18 337	45.95 342	61.91 276	79.11 269	47.01 161	60.88 369	42.50 313	69.49 279	74.83 334

WB-MVSnew		59.66 296	59.69 286	59.56 328	75.19 247	35.78 373	69.34 303	64.28 336	46.88 331	61.76 279	75.79 325	40.61 232	65.20 355	32.16 374	71.21 246	77.70 297

CVMVSNet		59.63 297	59.14 290	61.08 324	74.47 260	38.84 340	75.20 209	68.74 301	31.15 400	58.24 319	76.51 314	32.39 328	68.58 332	49.77 244	65.84 314	75.81 320

UBG		59.62 298	59.53 287	59.89 327	78.12 175	35.92 372	64.11 345	60.81 363	49.45 295	61.34 283	75.55 329	33.05 311	67.39 343	38.68 336	74.62 192	76.35 316

ETVMVS		59.51 299	58.81 293	61.58 317	77.46 203	34.87 375	64.94 339	59.35 366	54.06 240	61.08 287	76.67 308	29.54 345	71.87 314	32.16 374	74.07 199	78.01 295

tpm cat1		59.25 300	56.95 310	66.15 276	72.19 300	46.96 263	68.09 310	65.76 323	40.03 384	57.81 323	70.56 367	38.32 256	74.51 301	38.26 339	61.50 350	77.00 309

test_vis1_n_1920		58.86 301	59.06 292	58.25 340	63.76 382	43.14 303	67.49 316	66.36 320	40.22 382	65.89 207	71.95 358	31.04 334	59.75 375	59.94 166	64.90 320	71.85 365

pmmvs-eth3d		58.81 302	56.31 317	66.30 272	67.61 360	52.42 185	72.30 264	64.76 332	43.55 360	54.94 348	74.19 342	28.95 350	72.60 308	43.31 304	57.21 370	73.88 346

tpmvs		58.47 303	56.95 310	63.03 309	70.20 333	41.21 319	67.90 312	67.23 312	49.62 293	54.73 351	70.84 365	34.14 297	76.24 293	36.64 352	61.29 351	71.64 367

PVSNet	50.76 19	58.40 304	57.39 306	61.42 318	75.53 241	44.04 294	61.43 359	63.45 344	47.04 330	56.91 329	73.61 346	27.00 368	64.76 356	39.12 334	72.40 232	75.47 325

tpmrst		58.24 305	58.70 296	56.84 350	66.97 364	34.32 382	69.57 301	61.14 361	47.17 329	58.58 317	71.60 360	41.28 226	60.41 371	49.20 251	62.84 339	75.78 321

Patchmatch-RL test		58.16 306	55.49 323	66.15 276	67.92 359	48.89 241	60.66 367	51.07 397	47.86 319	59.36 306	62.71 402	34.02 300	72.27 311	56.41 188	59.40 362	77.30 303

test-LLR		58.15 307	58.13 303	58.22 341	68.57 353	44.80 284	65.46 331	57.92 372	50.08 287	55.44 341	69.82 374	32.62 323	57.44 387	49.66 247	73.62 207	72.41 358

ppachtmachnet_test		58.06 308	55.38 324	66.10 278	69.51 344	48.99 238	68.01 311	66.13 322	44.50 351	54.05 358	70.74 366	32.09 331	72.34 310	36.68 351	56.71 374	76.99 311

gg-mvs-nofinetune		57.86 309	56.43 316	62.18 313	72.62 289	35.35 374	66.57 319	56.33 381	50.65 280	57.64 324	57.10 408	30.65 336	76.36 291	37.38 343	78.88 138	74.82 335

CMPMVS	42.80 21	57.81 310	55.97 319	63.32 304	60.98 398	47.38 260	64.66 340	69.50 294	32.06 398	46.83 391	77.80 291	29.50 347	71.36 316	48.68 255	73.75 204	71.21 374
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 311	57.07 308	58.22 341	74.21 269	37.18 355	62.46 354	60.88 362	48.88 303	55.29 344	75.99 323	31.68 332	62.04 366	31.87 377	72.35 233	75.43 326

tpm		57.34 312	58.16 301	54.86 360	71.80 307	34.77 377	67.47 317	56.04 384	48.20 313	60.10 294	76.92 304	37.17 270	53.41 404	40.76 324	65.01 319	76.40 315

Patchmtry		57.16 313	56.47 315	59.23 332	69.17 350	34.58 380	62.98 351	63.15 347	44.53 350	56.83 330	74.84 336	35.83 282	68.71 331	40.03 327	60.91 352	74.39 341

AllTest		57.08 314	54.65 328	64.39 298	71.44 312	49.03 235	69.92 298	67.30 309	45.97 340	47.16 389	79.77 253	17.47 396	67.56 341	33.65 366	59.16 363	76.57 313

test_cas_vis1_n_1920		56.91 315	56.71 313	57.51 349	59.13 404	45.40 280	63.58 347	61.29 360	36.24 392	67.14 183	71.85 359	29.89 343	56.69 391	57.65 181	63.58 333	70.46 379

mamv4		56.85 316	58.00 304	53.43 370	72.46 295	54.47 141	57.56 383	54.74 385	38.81 388	57.42 327	79.45 263	47.57 148	38.70 423	60.88 158	53.07 384	67.11 393

dmvs_re		56.77 317	56.83 312	56.61 351	69.23 348	41.02 320	58.37 375	64.18 337	50.59 282	57.45 326	71.42 361	35.54 284	58.94 380	37.23 344	67.45 302	69.87 384

testing3		56.54 318	55.92 320	58.41 339	77.52 201	27.93 410	69.72 299	56.36 380	54.75 229	58.63 316	77.80 291	20.88 394	71.75 315	25.31 407	62.25 344	75.53 324

our_test_3		56.49 319	54.42 331	62.68 311	69.51 344	45.48 279	66.08 323	61.49 359	44.11 357	50.73 378	69.60 377	33.05 311	68.15 333	38.38 338	56.86 371	74.40 340

pmmvs5		56.47 320	55.68 322	58.86 336	61.41 394	36.71 362	66.37 321	62.75 349	40.38 381	53.70 360	76.62 310	34.56 292	67.05 344	40.02 328	65.27 317	72.83 351

test-mter		56.42 321	55.82 321	58.22 341	68.57 353	44.80 284	65.46 331	57.92 372	39.94 385	55.44 341	69.82 374	21.92 389	57.44 387	49.66 247	73.62 207	72.41 358

USDC		56.35 322	54.24 335	62.69 310	64.74 378	40.31 326	65.05 337	73.83 255	43.93 358	47.58 387	77.71 295	15.36 405	75.05 299	38.19 340	61.81 348	72.70 352

PatchMatch-RL		56.25 323	54.55 330	61.32 321	77.06 214	56.07 112	65.57 328	54.10 390	44.13 356	53.49 366	71.27 364	25.20 380	66.78 346	36.52 354	63.66 331	61.12 398

sss		56.17 324	56.57 314	54.96 359	66.93 365	36.32 367	57.94 378	61.69 358	41.67 372	58.64 315	75.32 334	38.72 251	56.25 394	42.04 317	66.19 312	72.31 361

Syy-MVS		56.00 325	56.23 318	55.32 357	74.69 255	26.44 416	65.52 329	57.49 375	50.97 277	56.52 333	72.18 353	39.89 237	68.09 334	24.20 408	64.59 325	71.44 371

FMVSNet5		55.86 326	54.93 326	58.66 338	71.05 321	36.35 365	64.18 344	62.48 351	46.76 333	50.66 379	74.73 338	25.80 376	64.04 358	33.11 370	65.57 316	75.59 323

RPSCF		55.80 327	54.22 336	60.53 325	65.13 377	42.91 306	64.30 342	57.62 374	36.84 391	58.05 322	82.28 202	28.01 357	56.24 395	37.14 345	58.61 365	82.44 222

mvs5depth		55.64 328	53.81 339	61.11 323	59.39 403	40.98 324	65.89 324	68.28 304	50.21 285	58.11 321	75.42 332	17.03 398	67.63 340	43.79 301	46.21 398	74.73 337

EU-MVSNet		55.61 329	54.41 332	59.19 334	65.41 376	33.42 389	72.44 262	71.91 274	28.81 402	51.27 372	73.87 344	24.76 382	69.08 329	43.04 308	58.20 366	75.06 329

Anonymous20240521		55.30 330	54.41 332	57.96 345	60.92 400	41.73 315	71.09 283	71.06 280	41.18 375	48.65 385	73.31 347	16.93 399	59.25 377	42.54 312	64.01 328	72.90 350

TESTMET0.1,1		55.28 331	54.90 327	56.42 352	66.56 368	43.67 297	65.46 331	56.27 382	39.18 387	53.83 359	67.44 386	24.21 384	55.46 398	48.04 262	73.11 221	70.13 382

KD-MVS_self_test		55.22 332	53.89 338	59.21 333	57.80 407	27.47 412	57.75 381	74.32 247	47.38 324	50.90 375	70.00 373	28.45 355	70.30 324	40.44 325	57.92 367	79.87 270

MIMVSNet1		55.17 333	54.31 334	57.77 347	70.03 337	32.01 396	65.68 327	64.81 331	49.19 299	46.75 392	76.00 321	25.53 379	64.04 358	28.65 396	62.13 345	77.26 305

Anonymous20231206		55.10 334	55.30 325	54.48 362	69.81 342	33.94 386	62.91 352	62.13 357	41.08 376	55.18 345	75.65 327	32.75 319	56.59 393	30.32 390	67.86 298	72.91 349

myMVS_eth3d		54.86 335	54.61 329	55.61 356	74.69 255	27.31 413	65.52 329	57.49 375	50.97 277	56.52 333	72.18 353	21.87 392	68.09 334	27.70 399	64.59 325	71.44 371

TinyColmap		54.14 336	51.72 348	61.40 319	66.84 366	41.97 312	66.52 320	68.51 302	44.81 347	42.69 403	75.77 326	11.66 412	72.94 307	31.96 376	56.77 373	69.27 388

EPMVS		53.96 337	53.69 340	54.79 361	66.12 373	31.96 397	62.34 356	49.05 401	44.42 353	55.54 339	71.33 363	30.22 340	56.70 390	41.65 321	62.54 342	75.71 322

PMMVS		53.96 337	53.26 343	56.04 353	62.60 389	50.92 204	61.17 363	56.09 383	32.81 397	53.51 365	66.84 391	34.04 299	59.93 374	44.14 296	68.18 296	57.27 406

test20.03		53.87 339	54.02 337	53.41 371	61.47 393	28.11 409	61.30 361	59.21 367	51.34 272	52.09 370	77.43 298	33.29 310	58.55 382	29.76 392	60.27 360	73.58 347

MDA-MVSNet-bldmvs		53.87 339	50.81 352	63.05 308	66.25 371	48.58 245	56.93 386	63.82 340	48.09 315	41.22 404	70.48 370	30.34 339	68.00 337	34.24 364	45.92 400	72.57 354

KD-MVS_2432*1600		53.45 341	51.50 350	59.30 330	62.82 386	37.14 356	55.33 389	71.79 275	47.34 326	55.09 346	70.52 368	21.91 390	70.45 321	35.72 359	42.97 404	70.31 380

miper_refine_blended		53.45 341	51.50 350	59.30 330	62.82 386	37.14 356	55.33 389	71.79 275	47.34 326	55.09 346	70.52 368	21.91 390	70.45 321	35.72 359	42.97 404	70.31 380

TDRefinement		53.44 343	50.72 353	61.60 316	64.31 381	46.96 263	70.89 285	65.27 329	41.78 370	44.61 398	77.98 284	11.52 414	66.36 349	28.57 397	51.59 388	71.49 370

test0.0.03 1		53.32 344	53.59 341	52.50 377	62.81 388	29.45 404	59.51 371	54.11 389	50.08 287	54.40 355	74.31 341	32.62 323	55.92 396	30.50 389	63.95 330	72.15 363

PatchT		53.17 345	53.44 342	52.33 378	68.29 357	25.34 420	58.21 376	54.41 388	44.46 352	54.56 353	69.05 380	33.32 309	60.94 368	36.93 347	61.76 349	70.73 378

UnsupCasMVSNet_eth		53.16 346	52.47 344	55.23 358	59.45 402	33.39 390	59.43 372	69.13 298	45.98 339	50.35 381	72.32 352	29.30 349	58.26 384	42.02 318	44.30 402	74.05 344

PM-MVS		52.33 347	50.19 356	58.75 337	62.10 391	45.14 282	65.75 325	40.38 419	43.60 359	53.52 364	72.65 350	9.16 420	65.87 353	50.41 240	54.18 381	65.24 396

UWE-MVS-28		52.25 348	52.35 346	51.93 381	66.99 363	22.79 424	63.48 348	48.31 405	46.78 332	52.73 368	76.11 319	27.78 360	57.82 386	20.58 414	68.41 295	75.17 327

testgi		51.90 349	52.37 345	50.51 384	60.39 401	23.55 423	58.42 374	58.15 370	49.03 301	51.83 371	79.21 268	22.39 387	55.59 397	29.24 395	62.64 340	72.40 360

dp		51.89 350	51.60 349	52.77 375	68.44 356	32.45 395	62.36 355	54.57 387	44.16 355	49.31 384	67.91 382	28.87 352	56.61 392	33.89 365	54.89 378	69.24 389

JIA-IIPM		51.56 351	47.68 365	63.21 306	64.61 379	50.73 208	47.71 408	58.77 369	42.90 366	48.46 386	51.72 412	24.97 381	70.24 325	36.06 358	53.89 382	68.64 390

test_fmvs1_n		51.37 352	50.35 355	54.42 364	52.85 411	37.71 351	61.16 364	51.93 392	28.15 404	63.81 245	69.73 376	13.72 406	53.95 402	51.16 235	60.65 356	71.59 368

ADS-MVSNet2		51.33 353	48.76 360	59.07 335	66.02 374	44.60 287	50.90 402	59.76 365	36.90 389	50.74 376	66.18 394	26.38 371	63.11 362	27.17 401	54.76 379	69.50 386

test_fmvs1		51.32 354	50.48 354	53.81 366	53.57 409	37.51 353	60.63 368	51.16 395	28.02 406	63.62 246	69.23 379	16.41 401	53.93 403	51.01 236	60.70 355	69.99 383

YYNet1		50.73 355	48.96 357	56.03 354	61.10 396	41.78 314	51.94 399	56.44 379	40.94 378	44.84 396	67.80 384	30.08 341	55.08 400	36.77 348	50.71 390	71.22 373

MDA-MVSNet_test_wron		50.71 356	48.95 358	56.00 355	61.17 395	41.84 313	51.90 400	56.45 378	40.96 377	44.79 397	67.84 383	30.04 342	55.07 401	36.71 350	50.69 391	71.11 376

dmvs_testset		50.16 357	51.90 347	44.94 392	66.49 369	11.78 432	61.01 366	51.50 394	51.17 275	50.30 382	67.44 386	39.28 244	60.29 372	22.38 411	57.49 369	62.76 397

UnsupCasMVSNet_bld		50.07 358	48.87 359	53.66 367	60.97 399	33.67 388	57.62 382	64.56 334	39.47 386	47.38 388	64.02 400	27.47 362	59.32 376	34.69 363	43.68 403	67.98 392

test_vis1_n		49.89 359	48.69 361	53.50 369	53.97 408	37.38 354	61.53 358	47.33 409	28.54 403	59.62 304	67.10 390	13.52 407	52.27 407	49.07 252	57.52 368	70.84 377

Patchmatch-test		49.08 360	48.28 362	51.50 382	64.40 380	30.85 401	45.68 412	48.46 404	35.60 393	46.10 395	72.10 355	34.47 295	46.37 415	27.08 403	60.65 356	77.27 304

test_fmvs2		48.69 361	47.49 366	52.29 379	48.63 418	33.06 392	57.76 380	48.05 407	25.71 410	59.76 302	69.60 377	11.57 413	52.23 408	49.45 250	56.86 371	71.58 369

ADS-MVSNet		48.48 362	47.77 363	50.63 383	66.02 374	29.92 403	50.90 402	50.87 399	36.90 389	50.74 376	66.18 394	26.38 371	52.47 406	27.17 401	54.76 379	69.50 386

CHOSEN 280x420		47.83 363	46.36 367	52.24 380	67.37 362	49.78 225	38.91 420	43.11 417	35.00 394	43.27 402	63.30 401	28.95 350	49.19 411	36.53 353	60.80 354	57.76 405

new-patchmatchnet		47.56 364	47.73 364	47.06 387	58.81 405	9.37 435	48.78 406	59.21 367	43.28 362	44.22 399	68.66 381	25.67 377	57.20 389	31.57 384	49.35 395	74.62 339

PVSNet_0	43.31 20	47.46 365	45.64 368	52.92 374	67.60 361	44.65 286	54.06 394	54.64 386	41.59 373	46.15 394	58.75 405	30.99 335	58.66 381	32.18 373	24.81 420	55.46 408

ttmdpeth		45.56 366	42.95 371	53.39 372	52.33 414	29.15 405	57.77 379	48.20 406	31.81 399	49.86 383	77.21 300	8.69 421	59.16 378	27.31 400	33.40 416	71.84 366

MVS-HIRNet		45.52 367	44.48 369	48.65 386	68.49 355	34.05 385	59.41 373	44.50 414	27.03 407	37.96 414	50.47 416	26.16 374	64.10 357	26.74 404	59.52 361	47.82 415

pmmvs3		44.92 368	41.95 375	53.86 365	52.58 413	43.55 298	62.11 357	46.90 411	26.05 409	40.63 405	60.19 404	11.08 417	57.91 385	31.83 381	46.15 399	60.11 399

test_fmvs3		44.30 369	42.55 372	49.55 385	42.83 423	27.15 415	53.03 396	44.93 413	22.03 418	53.69 362	64.94 397	4.21 428	49.63 410	47.47 263	49.82 393	71.88 364

WB-MVS		43.26 370	43.41 370	42.83 396	63.32 385	10.32 434	58.17 377	45.20 412	45.42 344	40.44 407	67.26 389	34.01 301	58.98 379	11.96 425	24.88 419	59.20 400

LF4IMVS		42.95 371	42.26 373	45.04 390	48.30 419	32.50 394	54.80 391	48.49 403	28.03 405	40.51 406	70.16 371	9.24 419	43.89 418	31.63 382	49.18 396	58.72 402

MVStest1		42.65 372	39.29 379	52.71 376	47.26 421	34.58 380	54.41 393	50.84 400	23.35 412	39.31 412	74.08 343	12.57 409	55.09 399	23.32 409	28.47 418	68.47 391

EGC-MVSNET		42.47 373	38.48 381	54.46 363	74.33 265	48.73 243	70.33 293	51.10 396	0.03 433	0.18 434	67.78 385	13.28 408	66.49 348	18.91 416	50.36 392	48.15 413

FPMVS		42.18 374	41.11 376	45.39 389	58.03 406	41.01 322	49.50 404	53.81 391	30.07 401	33.71 416	64.03 398	11.69 411	52.08 409	14.01 420	55.11 377	43.09 417

SSC-MVS		41.96 375	41.99 374	41.90 397	62.46 390	9.28 436	57.41 384	44.32 415	43.38 361	38.30 413	66.45 392	32.67 322	58.42 383	10.98 426	21.91 422	57.99 404

ANet_high		41.38 376	37.47 383	53.11 373	39.73 429	24.45 421	56.94 385	69.69 289	47.65 321	26.04 421	52.32 411	12.44 410	62.38 365	21.80 412	10.61 430	72.49 355

test_vis1_rt		41.35 377	39.45 378	47.03 388	46.65 422	37.86 348	47.76 407	38.65 420	23.10 414	44.21 400	51.22 414	11.20 416	44.08 417	39.27 333	53.02 385	59.14 401

LCM-MVSNet		40.30 378	35.88 384	53.57 368	42.24 424	29.15 405	45.21 414	60.53 364	22.23 417	28.02 419	50.98 415	3.72 430	61.78 367	31.22 387	38.76 410	69.78 385

mvsany_test1		39.38 379	38.16 382	43.02 395	49.05 416	34.28 383	44.16 416	25.94 430	22.74 416	46.57 393	62.21 403	23.85 385	41.16 422	33.01 371	35.91 412	53.63 409

N_pmnet		39.35 380	40.28 377	36.54 403	63.76 382	1.62 440	49.37 405	0.76 439	34.62 395	43.61 401	66.38 393	26.25 373	42.57 419	26.02 406	51.77 387	65.44 395

DSMNet-mixed		39.30 381	38.72 380	41.03 398	51.22 415	19.66 427	45.53 413	31.35 426	15.83 425	39.80 409	67.42 388	22.19 388	45.13 416	22.43 410	52.69 386	58.31 403

APD_test1		37.39 382	34.94 385	44.72 393	48.88 417	33.19 391	52.95 397	44.00 416	19.49 419	27.28 420	58.59 406	3.18 432	52.84 405	18.92 415	41.17 407	48.14 414

PMVS	28.69 22	36.22 383	33.29 388	45.02 391	36.82 431	35.98 370	54.68 392	48.74 402	26.31 408	21.02 424	51.61 413	2.88 433	60.10 373	9.99 429	47.58 397	38.99 422
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.77 384	31.91 389	43.33 394	62.05 392	37.87 347	20.39 425	67.03 314	23.23 413	18.41 426	25.84 426	4.24 427	62.73 363	14.71 419	51.32 389	29.38 424
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		34.52 385	34.94 385	33.26 406	61.06 397	16.00 431	52.79 398	23.78 432	40.71 379	39.33 411	48.65 420	16.91 400	48.34 412	12.18 424	19.05 424	35.44 423

new_pmnet		34.13 386	34.29 387	33.64 405	52.63 412	18.23 429	44.43 415	33.90 425	22.81 415	30.89 418	53.18 410	10.48 418	35.72 427	20.77 413	39.51 408	46.98 416

mvsany_test3		32.62 387	30.57 392	38.77 401	36.16 432	24.20 422	38.10 421	20.63 434	19.14 420	40.36 408	57.43 407	5.06 425	36.63 426	29.59 394	28.66 417	55.49 407

test_vis3_rt		32.09 388	30.20 393	37.76 402	35.36 433	27.48 411	40.60 419	28.29 429	16.69 423	32.52 417	40.53 422	1.96 434	37.40 425	33.64 368	42.21 406	48.39 412

test_f		31.86 389	31.05 390	34.28 404	32.33 435	21.86 425	32.34 422	30.46 427	16.02 424	39.78 410	55.45 409	4.80 426	32.36 429	30.61 388	37.66 411	48.64 411

testf1		31.46 390	28.89 394	39.16 399	41.99 426	28.78 407	46.45 410	37.56 421	14.28 426	21.10 422	48.96 417	1.48 436	47.11 413	13.63 421	34.56 413	41.60 418

APD_test2		31.46 390	28.89 394	39.16 399	41.99 426	28.78 407	46.45 410	37.56 421	14.28 426	21.10 422	48.96 417	1.48 436	47.11 413	13.63 421	34.56 413	41.60 418

kuosan		29.62 392	30.82 391	26.02 411	52.99 410	16.22 430	51.09 401	22.71 433	33.91 396	33.99 415	40.85 421	15.89 403	33.11 428	7.59 432	18.37 425	28.72 425

PMMVS2		27.40 393	25.91 396	31.87 408	39.46 430	6.57 437	31.17 423	28.52 428	23.96 411	20.45 425	48.94 419	4.20 429	37.94 424	16.51 417	19.97 423	51.09 410

E-PMN		23.77 394	22.73 398	26.90 409	42.02 425	20.67 426	42.66 417	35.70 423	17.43 421	10.28 431	25.05 427	6.42 423	42.39 420	10.28 428	14.71 427	17.63 426

EMVS		22.97 395	21.84 399	26.36 410	40.20 428	19.53 428	41.95 418	34.64 424	17.09 422	9.73 432	22.83 428	7.29 422	42.22 421	9.18 430	13.66 428	17.32 427

MVE	17.77 23	21.41 396	17.77 401	32.34 407	34.34 434	25.44 419	16.11 426	24.11 431	11.19 428	13.22 428	31.92 424	1.58 435	30.95 430	10.47 427	17.03 426	40.62 421
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.68 397	18.10 400	24.41 412	13.68 437	3.11 439	12.06 428	42.37 418	2.00 431	11.97 429	36.38 423	5.77 424	29.35 431	15.06 418	23.65 421	40.76 420

cdsmvs_eth3d_5k		17.50 398	23.34 397	0.00 418	0.00 441	0.00 442	0.00 429	78.63 171	0.00 436	0.00 437	82.18 204	49.25 126	0.00 435	0.00 436	0.00 433	0.00 433

wuyk23d		13.32 399	12.52 402	15.71 413	47.54 420	26.27 417	31.06 424	1.98 438	4.93 430	5.18 433	1.94 433	0.45 438	18.54 432	6.81 433	12.83 429	2.33 430

tmp_tt		9.43 400	11.14 403	4.30 415	2.38 438	4.40 438	13.62 427	16.08 436	0.39 432	15.89 427	13.06 429	15.80 404	5.54 434	12.63 423	10.46 431	2.95 429

ab-mvs-re		6.49 401	8.65 404	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	77.89 289	0.00 440	0.00 435	0.00 436	0.00 433	0.00 433

test123		4.73 402	6.30 405	0.02 416	0.01 439	0.01 441	56.36 387	0.00 440	0.01 434	0.04 435	0.21 435	0.01 439	0.00 435	0.03 435	0.00 433	0.04 431

testmvs		4.52 403	6.03 406	0.01 417	0.01 439	0.00 442	53.86 395	0.00 440	0.01 434	0.04 435	0.27 434	0.00 440	0.00 435	0.04 434	0.00 433	0.03 432

pcd_1.5k_mvsjas		3.92 404	5.23 407	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	0.00 436	47.05 158	0.00 435	0.00 436	0.00 433	0.00 433

mmdepth		0.00 405	0.00 408	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	0.00 436	0.00 440	0.00 435	0.00 436	0.00 433	0.00 433

monomultidepth		0.00 405	0.00 408	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	0.00 436	0.00 440	0.00 435	0.00 436	0.00 433	0.00 433

test_blank		0.00 405	0.00 408	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	0.00 436	0.00 440	0.00 435	0.00 436	0.00 433	0.00 433

uanet_test		0.00 405	0.00 408	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	0.00 436	0.00 440	0.00 435	0.00 436	0.00 433	0.00 433

DCPMVS		0.00 405	0.00 408	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	0.00 436	0.00 440	0.00 435	0.00 436	0.00 433	0.00 433

sosnet-low-res		0.00 405	0.00 408	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	0.00 436	0.00 440	0.00 435	0.00 436	0.00 433	0.00 433

sosnet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	0.00 436	0.00 440	0.00 435	0.00 436	0.00 433	0.00 433

uncertanet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	0.00 436	0.00 440	0.00 435	0.00 436	0.00 433	0.00 433

Regformer		0.00 405	0.00 408	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	0.00 436	0.00 440	0.00 435	0.00 436	0.00 433	0.00 433

uanet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 442	0.00 429	0.00 440	0.00 436	0.00 437	0.00 436	0.00 440	0.00 435	0.00 436	0.00 433	0.00 433

WAC-MVS						27.31 413								27.77 398

FOURS1					86.12 36	60.82 37	88.18 1	83.61 67	60.87 88	81.50 16

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

PC_three_1452									55.09 216	84.46 4	89.84 46	66.68 5	89.41 18	74.24 50	91.38 2	88.42 16

No_MVS				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

test_one_0601					87.58 9	59.30 60		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

eth-test2					0.00 441

eth-test					0.00 441

ZD-MVS					86.64 21	60.38 45		82.70 93	57.95 157	78.10 25	90.06 39	56.12 42	88.84 26	74.05 53	87.00 49

RE-MVS-def			73.71 70		83.49 67	59.87 52	84.29 40	81.36 115	58.07 152	73.14 86	90.07 37	43.06 202		68.20 91	81.76 101	84.03 172

IU-MVS					87.77 4	59.15 63		85.53 26	53.93 243	84.64 3				79.07 12	90.87 5	88.37 18

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 45	67.01 1	90.33 12	73.16 60	91.15 4	88.23 22

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 19	90.70 7	87.65 41

test_241102_ONE					87.77 4	58.90 72		86.78 10	64.20 31	85.97 1	91.34 15	66.87 3	90.78 7

9.14			78.75 15		83.10 72		84.15 46	88.26 1	59.90 114	78.57 24	90.36 30	57.51 32	86.86 68	77.39 25	89.52 21

save fliter					86.17 33	61.30 28	83.98 50	79.66 150	59.00 133

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 27

test_0728_SECOND				79.19 16	87.82 3	59.11 66	87.85 5	87.15 3					90.84 3	78.66 17	90.61 11	87.62 43

test0726					87.75 7	59.07 67	87.86 4	86.83 8	64.26 29	84.19 7	91.92 5	64.82 8

GSMVS																78.05 291

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 291				78.05 291

sam_mvs												33.43 308

ambc				65.13 293	63.72 384	37.07 358	47.66 409	78.78 167		54.37 356	71.42 361	11.24 415	80.94 202	45.64 282	53.85 383	77.38 302

MTGPA								80.97 132

test_post1							68.67 307				3.64 431	32.39 328	69.49 327	44.17 294

test_post											3.55 432	33.90 302	66.52 347

patchmatchnet-post											64.03 398	34.50 293	74.27 303

GG-mvs-BLEND				62.34 312	71.36 316	37.04 359	69.20 304	57.33 377		54.73 351	65.48 396	30.37 338	77.82 258	34.82 362	74.93 191	72.17 362

MTMP							86.03 19	17.08 435

gm-plane-assit					71.40 315	41.72 317			48.85 304		73.31 347		82.48 173	48.90 254

test9_res														75.28 43	88.31 32	83.81 182

TEST9					85.58 43	61.59 24	81.62 83	81.26 122	55.65 203	74.93 54	88.81 60	53.70 69	84.68 123

test_8					85.40 46	60.96 34	81.54 86	81.18 125	55.86 195	74.81 59	88.80 62	53.70 69	84.45 127

agg_prior2														73.09 61	87.93 40	84.33 162

agg_prior					85.04 50	59.96 50		81.04 130		74.68 63			84.04 133

TestCases				64.39 298	71.44 312	49.03 235		67.30 309	45.97 340	47.16 389	79.77 253	17.47 396	67.56 341	33.65 366	59.16 363	76.57 313

test_prior4						62.51 14	82.08 79

test_prior2							81.75 81		60.37 101	75.01 52	89.06 55	56.22 41		72.19 68	88.96 24

test_prior				76.69 58	84.20 61	57.27 91		84.88 39					86.43 81			86.38 82

旧先验2							76.08 190		45.32 345	76.55 38			65.56 354	58.75 175

新几何2							76.12 188

新几何1				70.76 207	85.66 41	61.13 30		66.43 319	44.68 349	70.29 122	86.64 103	41.29 225	75.23 298	49.72 246	81.75 103	75.93 319

旧先验1					83.04 73	53.15 165		67.52 308			87.85 77	44.08 192			80.76 109	78.03 294

无先验							79.66 112	74.30 249	48.40 311				80.78 208	53.62 214		79.03 282

原ACMM2							79.02 119

原ACMM1				74.69 92	85.39 47	59.40 57		83.42 73	51.47 269	70.27 123	86.61 106	48.61 134	86.51 79	53.85 213	87.96 39	78.16 289

test222					83.14 71	58.68 76	72.57 260	63.45 344	41.78 370	67.56 176	86.12 122	37.13 271			78.73 143	74.98 332

testdata2													72.18 313	46.95 272

segment_acmp												54.23 59

testdata				64.66 295	81.52 91	52.93 170		65.29 328	46.09 338	73.88 74	87.46 84	38.08 260	66.26 350	53.31 218	78.48 147	74.78 336

testdata1							72.65 256		60.50 96

test12				77.76 45	84.52 58	58.41 78		83.36 76		72.93 93		54.61 56	88.05 39		88.12 34	86.81 67

plane_prior7					81.41 94	55.96 114

plane_prior6					81.20 101	56.24 109						45.26 182

plane_prior5								84.01 52					87.21 58	68.16 93	80.58 112	84.65 156

plane_prior4											86.10 123

plane_prior3						56.09 111			63.92 36	69.27 142

plane_prior2							84.22 43		64.52 25

plane_prior1					81.27 99

plane_prior						56.31 105	83.58 56		63.19 49						80.48 115

n2								0.00 440

nn								0.00 440

door-mid								47.19 410

lessismore_v0				69.91 223	71.42 314	47.80 253		50.90 398		50.39 380	75.56 328	27.43 364	81.33 192	45.91 279	34.10 415	80.59 257

LGP-MVS_train				75.76 73	80.22 116	57.51 89		83.40 74	61.32 80	66.67 192	87.33 87	39.15 247	86.59 74	67.70 97	77.30 167	83.19 205

test11								83.47 71

door								47.60 408

HQP5-MVS						54.94 135

HQP-NCC					80.66 108		82.31 74		62.10 69	67.85 166

ACMP_Plane					80.66 108		82.31 74		62.10 69	67.85 166

BP-MVS														67.04 104

HQP4-MVS										67.85 166			86.93 66			84.32 163

HQP3-MVS								83.90 57							80.35 116

HQP2-MVS												45.46 176

NP-MVS					80.98 104	56.05 113					85.54 140

MDTV_nov1_ep13_2view						25.89 418	61.22 362		40.10 383	51.10 373		32.97 314		38.49 337		78.61 286

MDTV_nov1_ep13			57.00 309		72.73 287	38.26 345	65.02 338	64.73 333	44.74 348	55.46 340	72.48 351	32.61 325	70.47 320	37.47 342	67.75 300

ACMMP++_ref															74.07 199

ACMMP++															72.16 237

Test By Simon												48.33 137

ITE_SJBPF				62.09 314	66.16 372	44.55 289		64.32 335	47.36 325	55.31 343	80.34 243	19.27 395	62.68 364	36.29 356	62.39 343	79.04 281

DeepMVS_CX				12.03 414	17.97 436	10.91 433		10.60 437	7.46 429	11.07 430	28.36 425	3.28 431	11.29 433	8.01 431	9.74 432	13.89 428

High-resolution multi-view benchmark