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++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 61	95.06 1	94.23 3	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 17	96.68 2	94.95 12

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 68	93.57 8	94.06 11	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 22	96.63 4	94.88 16

DVP-MVS		89.60 3	90.35 3	87.33 41	95.27 5	71.25 61	93.49 10	92.73 65	77.33 57	92.12 9	95.78 4	80.98 9	97.40 9	89.08 20	96.41 12	93.33 103
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
MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 17	94.11 7	80.27 10	91.35 14	94.16 47	78.35 13	96.77 24	89.59 15	94.22 62	94.67 29
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
DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 46	94.10 9	75.90 98	92.29 7	95.66 10	81.67 6	97.38 11	87.44 42	96.34 15	93.95 66
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MM		89.16 6	89.23 7	88.97 4	90.79 98	73.65 10	92.66 24	91.17 132	86.57 1	87.39 51	94.97 21	71.70 57	97.68 1	92.19 1	95.63 28	95.57 1

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 54	93.83 4	93.96 14	75.70 102	91.06 16	96.03 1	76.84 14	97.03 17	89.09 19	95.65 27	94.47 41
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 25	93.63 22	74.77 128	92.29 7	95.97 2	74.28 30	97.24 13	88.58 30	96.91 1	94.87 18
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
HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 28	92.85 60	80.26 11	87.78 42	94.27 41	75.89 19	96.81 23	87.45 41	96.44 9	93.05 120

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 65	93.00 47	80.90 7	88.06 37	94.06 52	76.43 16	96.84 21	88.48 33	95.99 18	94.34 48

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 74	72.96 25	93.73 5	93.67 21	80.19 12	88.10 36	94.80 23	73.76 34	97.11 15	87.51 40	95.82 21	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 66	71.95 51	92.40 25	94.74 2	75.71 100	89.16 23	95.10 18	75.65 21	96.19 47	87.07 43	96.01 17	94.79 23

DeepPCF-MVS	80.84 1	88.10 13	88.56 14	86.73 55	92.24 73	69.03 106	89.57 93	93.39 31	77.53 53	89.79 20	94.12 49	78.98 12	96.58 35	85.66 51	95.72 24	94.58 34

lecture		88.09 14	88.59 13	86.58 58	93.26 52	69.77 92	93.70 6	94.16 5	77.13 65	89.76 21	95.52 14	72.26 48	96.27 44	86.87 44	94.65 48	93.70 82

SD-MVS		88.06 15	88.50 15	86.71 56	92.60 71	72.71 29	91.81 42	93.19 36	77.87 42	90.32 18	94.00 56	74.83 23	93.78 151	87.63 39	94.27 61	93.65 87
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
NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 57	92.83 61	81.50 5	85.79 65	93.47 73	73.02 42	97.00 18	84.90 57	94.94 40	94.10 57

ACMMP_NAP		88.05 17	88.08 18	87.94 19	93.70 41	73.05 22	90.86 60	93.59 24	76.27 92	88.14 35	95.09 19	71.06 67	96.67 29	87.67 38	96.37 14	94.09 58

TSAR-MVS + MP.		88.02 18	88.11 17	87.72 30	93.68 43	72.13 48	91.41 53	92.35 83	74.62 132	88.90 26	93.85 64	75.75 20	96.00 55	87.80 37	94.63 50	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ZNCC-MVS		87.94 19	87.85 21	88.20 12	94.39 24	73.33 19	93.03 15	93.81 18	76.81 74	85.24 70	94.32 39	71.76 55	96.93 19	85.53 54	95.79 22	94.32 49

MP-MVS		87.71 20	87.64 23	87.93 21	94.36 26	73.88 6	92.71 23	92.65 71	77.57 49	83.84 102	94.40 36	72.24 49	96.28 43	85.65 52	95.30 35	93.62 90
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_0304		87.69 21	87.55 26	88.12 13	89.45 134	71.76 53	91.47 52	89.54 185	82.14 3	86.65 59	94.28 40	68.28 103	97.46 6	90.81 6	95.31 34	95.15 8

MP-MVS-pluss		87.67 22	87.72 22	87.54 36	93.64 44	72.04 50	89.80 84	93.50 26	75.17 117	86.34 61	95.29 17	70.86 69	96.00 55	88.78 28	96.04 16	94.58 34
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 23	87.47 28	87.94 19	94.58 16	73.54 15	93.04 13	93.24 34	76.78 76	84.91 75	94.44 34	70.78 70	96.61 32	84.53 65	94.89 42	93.66 83

reproduce-ours		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 120	88.96 24	95.54 12	71.20 65	96.54 36	86.28 48	93.49 67	93.06 118

our_new_method		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 120	88.96 24	95.54 12	71.20 65	96.54 36	86.28 48	93.49 67	93.06 118

ACMMPR		87.44 26	87.23 33	88.08 15	94.64 13	73.59 12	93.04 13	93.20 35	76.78 76	84.66 82	94.52 27	68.81 96	96.65 30	84.53 65	94.90 41	94.00 63

APD-MVS		87.44 26	87.52 27	87.19 43	94.24 32	72.39 41	91.86 41	92.83 61	73.01 176	88.58 28	94.52 27	73.36 35	96.49 38	84.26 68	95.01 37	92.70 132
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 28	87.26 31	87.89 24	94.12 36	72.97 24	92.39 27	93.43 29	76.89 72	84.68 79	93.99 58	70.67 72	96.82 22	84.18 72	95.01 37	93.90 69

region2R		87.42 28	87.20 34	88.09 14	94.63 14	73.55 13	93.03 15	93.12 41	76.73 79	84.45 87	94.52 27	69.09 90	96.70 27	84.37 67	94.83 45	94.03 61

fmvsm_s_conf0.5_n_9		87.39 30	87.95 20	85.70 77	89.48 133	67.88 147	88.59 139	89.05 211	80.19 12	90.70 17	95.40 15	74.56 25	93.92 144	91.54 2	92.07 85	95.31 5

MCST-MVS		87.37 31	87.25 32	87.73 28	94.53 17	72.46 40	89.82 82	93.82 17	73.07 174	84.86 78	92.89 88	76.22 17	96.33 41	84.89 59	95.13 36	94.40 44

reproduce_model		87.28 32	87.39 30	86.95 50	93.10 58	71.24 65	91.60 45	93.19 36	74.69 129	88.80 27	95.61 11	70.29 76	96.44 39	86.20 50	93.08 71	93.16 113

MTAPA		87.23 33	87.00 36	87.90 22	94.18 35	74.25 5	86.58 212	92.02 98	79.45 22	85.88 63	94.80 23	68.07 104	96.21 46	86.69 46	95.34 32	93.23 106

XVS		87.18 34	86.91 41	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 106	94.17 46	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

HPM-MVS		87.11 35	86.98 38	87.50 39	93.88 39	72.16 47	92.19 34	93.33 32	76.07 95	83.81 103	93.95 61	69.77 82	96.01 54	85.15 55	94.66 47	94.32 49
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 35	86.92 40	87.68 34	94.20 34	73.86 7	93.98 3	92.82 64	76.62 82	83.68 105	94.46 31	67.93 106	95.95 58	84.20 71	94.39 57	93.23 106

DeepC-MVS	79.81 2	87.08 37	86.88 42	87.69 33	91.16 87	72.32 45	90.31 74	93.94 15	77.12 66	82.82 117	94.23 44	72.13 51	97.09 16	84.83 60	95.37 31	93.65 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	79.65 3	86.91 38	86.62 44	87.76 27	93.52 46	72.37 43	91.26 54	93.04 42	76.62 82	84.22 93	93.36 77	71.44 61	96.76 25	80.82 105	95.33 33	94.16 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
balanced_conf03		86.78 39	86.99 37	86.15 66	91.24 86	67.61 155	90.51 65	92.90 57	77.26 59	87.44 50	91.63 119	71.27 64	96.06 50	85.62 53	95.01 37	94.78 24

SR-MVS		86.73 40	86.67 43	86.91 51	94.11 37	72.11 49	92.37 29	92.56 76	74.50 133	86.84 58	94.65 26	67.31 113	95.77 60	84.80 61	92.85 74	92.84 130

CS-MVS		86.69 41	86.95 39	85.90 74	90.76 99	67.57 157	92.83 18	93.30 33	79.67 19	84.57 86	92.27 100	71.47 60	95.02 96	84.24 70	93.46 69	95.13 9

PGM-MVS		86.68 42	86.27 49	87.90 22	94.22 33	73.38 18	90.22 76	93.04 42	75.53 104	83.86 101	94.42 35	67.87 108	96.64 31	82.70 90	94.57 52	93.66 83

mPP-MVS		86.67 43	86.32 47	87.72 30	94.41 22	73.55 13	92.74 21	92.22 89	76.87 73	82.81 118	94.25 43	66.44 122	96.24 45	82.88 85	94.28 60	93.38 99

fmvsm_s_conf0.5_n_8		86.56 44	87.17 35	84.73 112	87.76 214	65.62 201	89.20 107	92.21 90	79.94 17	89.74 22	94.86 22	68.63 98	94.20 129	90.83 5	91.39 97	94.38 45

CANet		86.45 45	86.10 55	87.51 38	90.09 111	70.94 72	89.70 88	92.59 75	81.78 4	81.32 136	91.43 127	70.34 74	97.23 14	84.26 68	93.36 70	94.37 46

train_agg		86.43 46	86.20 50	87.13 45	93.26 52	72.96 25	88.75 131	91.89 106	68.69 275	85.00 73	93.10 81	74.43 27	95.41 76	84.97 56	95.71 25	93.02 122

PHI-MVS		86.43 46	86.17 53	87.24 42	90.88 95	70.96 70	92.27 33	94.07 10	72.45 182	85.22 71	91.90 109	69.47 85	96.42 40	83.28 79	95.94 19	94.35 47

CSCG		86.41 48	86.19 52	87.07 46	92.91 63	72.48 37	90.81 61	93.56 25	73.95 148	83.16 112	91.07 139	75.94 18	95.19 85	79.94 116	94.38 58	93.55 94

fmvsm_s_conf0.5_n_3		86.36 49	87.46 29	83.09 186	87.08 240	65.21 210	89.09 116	90.21 163	79.67 19	89.98 19	95.02 20	73.17 39	91.71 248	91.30 3	91.60 92	92.34 149

NormalMVS		86.29 50	85.88 59	87.52 37	93.26 52	72.47 38	91.65 43	92.19 92	79.31 24	84.39 89	92.18 102	64.64 143	95.53 67	80.70 108	94.65 48	94.56 37

SPE-MVS-test		86.29 50	86.48 45	85.71 76	91.02 91	67.21 172	92.36 30	93.78 19	78.97 33	83.51 109	91.20 134	70.65 73	95.15 87	81.96 94	94.89 42	94.77 25

fmvsm_l_conf0.5_n_3		86.02 52	86.32 47	85.14 92	87.20 233	68.54 126	89.57 93	90.44 152	75.31 111	87.49 48	94.39 37	72.86 43	92.72 205	89.04 24	90.56 111	94.16 54

EC-MVSNet		86.01 53	86.38 46	84.91 105	89.31 143	66.27 185	92.32 31	93.63 22	79.37 23	84.17 95	91.88 110	69.04 94	95.43 73	83.93 74	93.77 65	93.01 123

MVSMamba_PlusPlus		85.99 54	85.96 58	86.05 69	91.09 88	67.64 154	89.63 91	92.65 71	72.89 179	84.64 83	91.71 115	71.85 53	96.03 51	84.77 62	94.45 56	94.49 40

casdiffmvs_mvg		85.99 54	86.09 56	85.70 77	87.65 218	67.22 171	88.69 135	93.04 42	79.64 21	85.33 69	92.54 97	73.30 36	94.50 118	83.49 76	91.14 101	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS_3200maxsize		85.97 56	85.88 59	86.22 63	92.69 68	69.53 95	91.93 38	92.99 50	73.54 161	85.94 62	94.51 30	65.80 133	95.61 63	83.04 82	92.51 79	93.53 96

test_fmvsmconf_n		85.92 57	86.04 57	85.57 82	85.03 292	69.51 96	89.62 92	90.58 147	73.42 165	87.75 44	94.02 54	72.85 44	93.24 177	90.37 7	90.75 108	93.96 64

sasdasda		85.91 58	85.87 61	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 39	91.23 131	73.28 37	93.91 145	81.50 97	88.80 141	94.77 25

canonicalmvs		85.91 58	85.87 61	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 39	91.23 131	73.28 37	93.91 145	81.50 97	88.80 141	94.77 25

ACMMP		85.89 60	85.39 70	87.38 40	93.59 45	72.63 33	92.74 21	93.18 40	76.78 76	80.73 148	93.82 65	64.33 145	96.29 42	82.67 91	90.69 109	93.23 106
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
SR-MVS-dyc-post		85.77 61	85.61 66	86.23 62	93.06 60	70.63 78	91.88 39	92.27 85	73.53 162	85.69 66	94.45 32	65.00 141	95.56 64	82.75 86	91.87 88	92.50 142

CDPH-MVS		85.76 62	85.29 75	87.17 44	93.49 47	71.08 66	88.58 140	92.42 81	68.32 282	84.61 84	93.48 71	72.32 47	96.15 49	79.00 121	95.43 30	94.28 51

TSAR-MVS + GP.		85.71 63	85.33 72	86.84 52	91.34 84	72.50 36	89.07 117	87.28 260	76.41 85	85.80 64	90.22 162	74.15 32	95.37 81	81.82 95	91.88 87	92.65 136

dcpmvs_2		85.63 64	86.15 54	84.06 146	91.71 80	64.94 220	86.47 215	91.87 108	73.63 157	86.60 60	93.02 86	76.57 15	91.87 242	83.36 77	92.15 83	95.35 3

test_fmvsmconf0.1_n		85.61 65	85.65 65	85.50 83	82.99 341	69.39 103	89.65 89	90.29 161	73.31 168	87.77 43	94.15 48	71.72 56	93.23 178	90.31 8	90.67 110	93.89 70

fmvsm_s_conf0.5_n_6		85.55 66	86.20 50	83.60 165	87.32 230	65.13 213	88.86 123	91.63 117	75.41 107	88.23 34	93.45 74	68.56 99	92.47 216	89.52 16	92.78 75	93.20 111

alignmvs		85.48 67	85.32 73	85.96 73	89.51 130	69.47 98	89.74 86	92.47 77	76.17 93	87.73 46	91.46 126	70.32 75	93.78 151	81.51 96	88.95 138	94.63 33

3Dnovator+	77.84 4	85.48 67	84.47 86	88.51 7	91.08 89	73.49 16	93.18 12	93.78 19	80.79 8	76.66 227	93.37 76	60.40 212	96.75 26	77.20 142	93.73 66	95.29 6

MSLP-MVS++		85.43 69	85.76 63	84.45 120	91.93 77	70.24 81	90.71 62	92.86 59	77.46 55	84.22 93	92.81 92	67.16 115	92.94 198	80.36 111	94.35 59	90.16 231

DELS-MVS		85.41 70	85.30 74	85.77 75	88.49 176	67.93 146	85.52 247	93.44 28	78.70 34	83.63 108	89.03 193	74.57 24	95.71 62	80.26 113	94.04 63	93.66 83
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
fmvsm_s_conf0.5_n_4		85.39 71	85.75 64	84.30 127	86.70 249	65.83 194	88.77 129	89.78 175	75.46 106	88.35 30	93.73 67	69.19 89	93.06 193	91.30 3	88.44 150	94.02 62

SymmetryMVS		85.38 72	84.81 80	87.07 46	91.47 83	72.47 38	91.65 43	88.06 240	79.31 24	84.39 89	92.18 102	64.64 143	95.53 67	80.70 108	90.91 106	93.21 109

HPM-MVS_fast		85.35 73	84.95 79	86.57 59	93.69 42	70.58 80	92.15 36	91.62 118	73.89 151	82.67 120	94.09 50	62.60 165	95.54 66	80.93 103	92.93 73	93.57 92

test_fmvsm_n_1920		85.29 74	85.34 71	85.13 95	86.12 262	69.93 88	88.65 137	90.78 143	69.97 242	88.27 32	93.98 59	71.39 62	91.54 256	88.49 32	90.45 113	93.91 67

fmvsm_s_conf0.5_n_5		85.22 75	85.55 67	84.25 134	86.26 256	67.40 163	89.18 108	89.31 196	72.50 181	88.31 31	93.86 63	69.66 83	91.96 236	89.81 11	91.05 102	93.38 99

MVS_111021_HR		85.14 76	84.75 81	86.32 61	91.65 81	72.70 30	85.98 229	90.33 158	76.11 94	82.08 125	91.61 121	71.36 63	94.17 132	81.02 102	92.58 78	92.08 163

casdiffmvs		85.11 77	85.14 76	85.01 99	87.20 233	65.77 198	87.75 171	92.83 61	77.84 43	84.36 92	92.38 99	72.15 50	93.93 143	81.27 101	90.48 112	95.33 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
UA-Net		85.08 78	84.96 78	85.45 84	92.07 75	68.07 141	89.78 85	90.86 142	82.48 2	84.60 85	93.20 80	69.35 86	95.22 84	71.39 209	90.88 107	93.07 117

MGCFI-Net		85.06 79	85.51 68	83.70 163	89.42 135	63.01 267	89.43 97	92.62 74	76.43 84	87.53 47	91.34 129	72.82 45	93.42 171	81.28 100	88.74 144	94.66 32

DPM-MVS		84.93 80	84.29 87	86.84 52	90.20 109	73.04 23	87.12 190	93.04 42	69.80 246	82.85 116	91.22 133	73.06 41	96.02 53	76.72 152	94.63 50	91.46 183

baseline		84.93 80	84.98 77	84.80 110	87.30 231	65.39 207	87.30 186	92.88 58	77.62 47	84.04 98	92.26 101	71.81 54	93.96 137	81.31 99	90.30 115	95.03 11

ETV-MVS		84.90 82	84.67 82	85.59 81	89.39 138	68.66 123	88.74 133	92.64 73	79.97 16	84.10 96	85.71 288	69.32 87	95.38 78	80.82 105	91.37 98	92.72 131

test_fmvsmconf0.01_n		84.73 83	84.52 85	85.34 87	80.25 383	69.03 106	89.47 95	89.65 181	73.24 172	86.98 56	94.27 41	66.62 118	93.23 178	90.26 9	89.95 123	93.78 79

fmvsm_l_conf0.5_n		84.47 84	84.54 83	84.27 131	85.42 279	68.81 112	88.49 142	87.26 262	68.08 284	88.03 38	93.49 70	72.04 52	91.77 244	88.90 26	89.14 137	92.24 156

BP-MVS1		84.32 85	83.71 94	86.17 64	87.84 207	67.85 148	89.38 102	89.64 182	77.73 45	83.98 99	92.12 106	56.89 240	95.43 73	84.03 73	91.75 91	95.24 7

EI-MVSNet-Vis-set		84.19 86	83.81 92	85.31 88	88.18 188	67.85 148	87.66 173	89.73 179	80.05 15	82.95 113	89.59 178	70.74 71	94.82 104	80.66 110	84.72 206	93.28 105

fmvsm_l_conf0.5_n_a		84.13 87	84.16 88	84.06 146	85.38 280	68.40 129	88.34 149	86.85 272	67.48 291	87.48 49	93.40 75	70.89 68	91.61 249	88.38 34	89.22 135	92.16 161

fmvsm_s_conf0.5_n_2		84.04 88	84.11 89	83.81 161	86.17 260	65.00 218	86.96 196	87.28 260	74.35 137	88.25 33	94.23 44	61.82 180	92.60 208	89.85 10	88.09 155	93.84 73

test_fmvsmvis_n_1920		84.02 89	83.87 91	84.49 119	84.12 310	69.37 104	88.15 157	87.96 243	70.01 240	83.95 100	93.23 79	68.80 97	91.51 259	88.61 29	89.96 122	92.57 137

nrg030		83.88 90	83.53 96	84.96 101	86.77 247	69.28 105	90.46 70	92.67 68	74.79 127	82.95 113	91.33 130	72.70 46	93.09 191	80.79 107	79.28 288	92.50 142

EI-MVSNet-UG-set		83.81 91	83.38 99	85.09 97	87.87 205	67.53 159	87.44 181	89.66 180	79.74 18	82.23 122	89.41 187	70.24 77	94.74 109	79.95 115	83.92 221	92.99 125

fmvsm_s_conf0.1_n_2		83.80 92	83.79 93	83.83 159	85.62 273	64.94 220	87.03 193	86.62 276	74.32 138	87.97 41	94.33 38	60.67 204	92.60 208	89.72 12	87.79 157	93.96 64

fmvsm_s_conf0.5_n		83.80 92	83.71 94	84.07 144	86.69 250	67.31 166	89.46 96	83.07 330	71.09 210	86.96 57	93.70 68	69.02 95	91.47 261	88.79 27	84.62 208	93.44 98

CPTT-MVS		83.73 94	83.33 101	84.92 104	93.28 49	70.86 74	92.09 37	90.38 154	68.75 274	79.57 163	92.83 90	60.60 208	93.04 196	80.92 104	91.56 95	90.86 201

EPNet		83.72 95	82.92 108	86.14 68	84.22 308	69.48 97	91.05 59	85.27 294	81.30 6	76.83 222	91.65 117	66.09 128	95.56 64	76.00 158	93.85 64	93.38 99
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
patch_mono-2		83.65 96	84.54 83	80.99 250	90.06 116	65.83 194	84.21 280	88.74 227	71.60 198	85.01 72	92.44 98	74.51 26	83.50 376	82.15 93	92.15 83	93.64 89

HQP_MVS		83.64 97	83.14 102	85.14 92	90.08 112	68.71 119	91.25 55	92.44 78	79.12 28	78.92 173	91.00 144	60.42 210	95.38 78	78.71 125	86.32 181	91.33 184

fmvsm_s_conf0.5_n_a		83.63 98	83.41 98	84.28 129	86.14 261	68.12 139	89.43 97	82.87 335	70.27 235	87.27 53	93.80 66	69.09 90	91.58 251	88.21 35	83.65 229	93.14 115

Effi-MVS+		83.62 99	83.08 103	85.24 90	88.38 182	67.45 160	88.89 122	89.15 207	75.50 105	82.27 121	88.28 217	69.61 84	94.45 121	77.81 135	87.84 156	93.84 73

fmvsm_s_conf0.1_n		83.56 100	83.38 99	84.10 138	84.86 294	67.28 167	89.40 101	83.01 331	70.67 220	87.08 54	93.96 60	68.38 101	91.45 262	88.56 31	84.50 209	93.56 93

GDP-MVS		83.52 101	82.64 112	86.16 65	88.14 191	68.45 128	89.13 114	92.69 66	72.82 180	83.71 104	91.86 112	55.69 247	95.35 82	80.03 114	89.74 127	94.69 28

OPM-MVS		83.50 102	82.95 107	85.14 92	88.79 166	70.95 71	89.13 114	91.52 121	77.55 52	80.96 144	91.75 114	60.71 202	94.50 118	79.67 119	86.51 179	89.97 247
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		83.46 103	82.80 110	85.43 85	90.25 108	68.74 117	90.30 75	90.13 166	76.33 91	80.87 145	92.89 88	61.00 199	94.20 129	72.45 201	90.97 104	93.35 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 104	83.45 97	83.28 176	92.74 67	62.28 281	88.17 155	89.50 187	75.22 112	81.49 134	92.74 96	66.75 116	95.11 90	72.85 192	91.58 94	92.45 146

EPP-MVSNet		83.40 105	83.02 105	84.57 115	90.13 110	64.47 231	92.32 31	90.73 144	74.45 136	79.35 167	91.10 137	69.05 93	95.12 88	72.78 193	87.22 166	94.13 56

3Dnovator	76.31 5	83.38 106	82.31 118	86.59 57	87.94 202	72.94 28	90.64 63	92.14 97	77.21 62	75.47 253	92.83 90	58.56 222	94.72 110	73.24 189	92.71 77	92.13 162

fmvsm_s_conf0.5_n_7		83.34 107	84.03 90	81.28 241	85.73 270	65.13 213	85.40 248	89.90 173	74.96 122	82.13 124	93.89 62	66.65 117	87.92 331	86.56 47	91.05 102	90.80 202

fmvsm_s_conf0.1_n_a		83.32 108	82.99 106	84.28 129	83.79 318	68.07 141	89.34 104	82.85 336	69.80 246	87.36 52	94.06 52	68.34 102	91.56 254	87.95 36	83.46 235	93.21 109

KinetiMVS		83.31 109	82.61 113	85.39 86	87.08 240	67.56 158	88.06 159	91.65 116	77.80 44	82.21 123	91.79 113	57.27 235	94.07 135	77.77 136	89.89 125	94.56 37

EIA-MVS		83.31 109	82.80 110	84.82 108	89.59 126	65.59 202	88.21 153	92.68 67	74.66 131	78.96 171	86.42 275	69.06 92	95.26 83	75.54 164	90.09 119	93.62 90

h-mvs33		83.15 111	82.19 119	86.02 72	90.56 101	70.85 75	88.15 157	89.16 206	76.02 96	84.67 80	91.39 128	61.54 185	95.50 69	82.71 88	75.48 338	91.72 173

MVS_Test		83.15 111	83.06 104	83.41 173	86.86 243	63.21 263	86.11 227	92.00 100	74.31 139	82.87 115	89.44 186	70.03 78	93.21 180	77.39 141	88.50 149	93.81 75

IS-MVSNet		83.15 111	82.81 109	84.18 136	89.94 119	63.30 261	91.59 46	88.46 233	79.04 30	79.49 164	92.16 104	65.10 138	94.28 124	67.71 245	91.86 90	94.95 12

DP-MVS Recon		83.11 114	82.09 122	86.15 66	94.44 19	70.92 73	88.79 128	92.20 91	70.53 225	79.17 169	91.03 142	64.12 147	96.03 51	68.39 242	90.14 118	91.50 179

PAPM_NR		83.02 115	82.41 115	84.82 108	92.47 72	66.37 183	87.93 165	91.80 111	73.82 152	77.32 210	90.66 149	67.90 107	94.90 100	70.37 219	89.48 132	93.19 112

VDD-MVS		83.01 116	82.36 117	84.96 101	91.02 91	66.40 182	88.91 121	88.11 236	77.57 49	84.39 89	93.29 78	52.19 281	93.91 145	77.05 145	88.70 145	94.57 36

MVSFormer		82.85 117	82.05 123	85.24 90	87.35 224	70.21 82	90.50 67	90.38 154	68.55 277	81.32 136	89.47 181	61.68 182	93.46 168	78.98 122	90.26 116	92.05 164

OMC-MVS		82.69 118	81.97 126	84.85 107	88.75 168	67.42 161	87.98 161	90.87 141	74.92 123	79.72 161	91.65 117	62.19 175	93.96 137	75.26 168	86.42 180	93.16 113

PVSNet_Blended_VisFu		82.62 119	81.83 128	84.96 101	90.80 97	69.76 93	88.74 133	91.70 115	69.39 254	78.96 171	88.46 212	65.47 135	94.87 103	74.42 175	88.57 146	90.24 229

MVS_111021_LR		82.61 120	82.11 120	84.11 137	88.82 162	71.58 57	85.15 253	86.16 284	74.69 129	80.47 153	91.04 140	62.29 172	90.55 287	80.33 112	90.08 120	90.20 230

HQP-MVS		82.61 120	82.02 124	84.37 122	89.33 140	66.98 175	89.17 109	92.19 92	76.41 85	77.23 213	90.23 161	60.17 213	95.11 90	77.47 139	85.99 189	91.03 194

RRT-MVS		82.60 122	82.10 121	84.10 138	87.98 201	62.94 272	87.45 180	91.27 128	77.42 56	79.85 159	90.28 158	56.62 243	94.70 112	79.87 117	88.15 154	94.67 29

CLD-MVS		82.31 123	81.65 129	84.29 128	88.47 177	67.73 152	85.81 237	92.35 83	75.78 99	78.33 188	86.58 270	64.01 148	94.35 122	76.05 157	87.48 162	90.79 203
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VNet		82.21 124	82.41 115	81.62 230	90.82 96	60.93 297	84.47 271	89.78 175	76.36 90	84.07 97	91.88 110	64.71 142	90.26 289	70.68 216	88.89 139	93.66 83

diffmvs		82.10 125	81.88 127	82.76 209	83.00 339	63.78 246	83.68 289	89.76 177	72.94 177	82.02 126	89.85 167	65.96 132	90.79 282	82.38 92	87.30 165	93.71 81
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LPG-MVS_test		82.08 126	81.27 132	84.50 117	89.23 148	68.76 115	90.22 76	91.94 104	75.37 109	76.64 228	91.51 123	54.29 260	94.91 98	78.44 127	83.78 222	89.83 252

FIs		82.07 127	82.42 114	81.04 249	88.80 165	58.34 326	88.26 152	93.49 27	76.93 71	78.47 185	91.04 140	69.92 80	92.34 224	69.87 226	84.97 202	92.44 147

PS-MVSNAJss		82.07 127	81.31 131	84.34 125	86.51 254	67.27 168	89.27 105	91.51 122	71.75 193	79.37 166	90.22 162	63.15 159	94.27 125	77.69 137	82.36 250	91.49 180

API-MVS		81.99 129	81.23 133	84.26 133	90.94 93	70.18 87	91.10 58	89.32 195	71.51 200	78.66 178	88.28 217	65.26 136	95.10 93	64.74 272	91.23 100	87.51 319

mamba_0404		81.91 130	80.84 141	85.13 95	89.24 147	68.26 133	87.84 170	89.25 201	71.06 212	80.62 149	90.39 155	59.57 215	94.65 114	72.45 201	87.19 167	92.47 145

UniMVSNet_NR-MVSNet		81.88 131	81.54 130	82.92 196	88.46 178	63.46 257	87.13 189	92.37 82	80.19 12	78.38 186	89.14 189	71.66 59	93.05 194	70.05 222	76.46 321	92.25 154

MAR-MVS		81.84 132	80.70 142	85.27 89	91.32 85	71.53 58	89.82 82	90.92 138	69.77 248	78.50 182	86.21 279	62.36 171	94.52 117	65.36 266	92.05 86	89.77 255
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
LFMVS		81.82 133	81.23 133	83.57 168	91.89 78	63.43 259	89.84 81	81.85 347	77.04 69	83.21 110	93.10 81	52.26 280	93.43 170	71.98 204	89.95 123	93.85 71

hse-mvs2		81.72 134	80.94 139	84.07 144	88.72 169	67.68 153	85.87 233	87.26 262	76.02 96	84.67 80	88.22 220	61.54 185	93.48 166	82.71 88	73.44 366	91.06 192

GeoE		81.71 135	81.01 138	83.80 162	89.51 130	64.45 232	88.97 119	88.73 228	71.27 206	78.63 179	89.76 171	66.32 124	93.20 183	69.89 225	86.02 188	93.74 80

xiu_mvs_v2_base		81.69 136	81.05 136	83.60 165	89.15 151	68.03 143	84.46 273	90.02 168	70.67 220	81.30 139	86.53 273	63.17 158	94.19 131	75.60 163	88.54 147	88.57 297

PS-MVSNAJ		81.69 136	81.02 137	83.70 163	89.51 130	68.21 138	84.28 279	90.09 167	70.79 217	81.26 140	85.62 293	63.15 159	94.29 123	75.62 162	88.87 140	88.59 296

PAPR		81.66 138	80.89 140	83.99 154	90.27 107	64.00 239	86.76 207	91.77 114	68.84 273	77.13 220	89.50 179	67.63 109	94.88 102	67.55 247	88.52 148	93.09 116

UniMVSNet (Re)		81.60 139	81.11 135	83.09 186	88.38 182	64.41 233	87.60 174	93.02 46	78.42 37	78.56 181	88.16 221	69.78 81	93.26 176	69.58 229	76.49 320	91.60 174

mamba_test_0407		81.58 140	80.48 148	84.87 106	88.81 163	67.96 145	87.37 182	89.25 201	71.06 212	79.48 165	90.39 155	59.57 215	94.48 120	72.45 201	85.93 191	92.18 159

Elysia		81.53 141	80.16 156	85.62 79	85.51 276	68.25 135	88.84 126	92.19 92	71.31 203	80.50 151	89.83 168	46.89 341	94.82 104	76.85 147	89.57 129	93.80 77

StellarMVS		81.53 141	80.16 156	85.62 79	85.51 276	68.25 135	88.84 126	92.19 92	71.31 203	80.50 151	89.83 168	46.89 341	94.82 104	76.85 147	89.57 129	93.80 77

FC-MVSNet-test		81.52 143	82.02 124	80.03 272	88.42 181	55.97 365	87.95 163	93.42 30	77.10 67	77.38 208	90.98 146	69.96 79	91.79 243	68.46 241	84.50 209	92.33 150

VDDNet		81.52 143	80.67 143	84.05 149	90.44 104	64.13 238	89.73 87	85.91 287	71.11 209	83.18 111	93.48 71	50.54 307	93.49 165	73.40 186	88.25 152	94.54 39

ACMP	74.13 6	81.51 145	80.57 145	84.36 123	89.42 135	68.69 122	89.97 80	91.50 125	74.46 135	75.04 275	90.41 154	53.82 266	94.54 115	77.56 138	82.91 242	89.86 251
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jason		81.39 146	80.29 153	84.70 113	86.63 252	69.90 90	85.95 230	86.77 273	63.24 343	81.07 142	89.47 181	61.08 198	92.15 230	78.33 130	90.07 121	92.05 164
jason: jason.
lupinMVS		81.39 146	80.27 154	84.76 111	87.35 224	70.21 82	85.55 243	86.41 278	62.85 350	81.32 136	88.61 207	61.68 182	92.24 228	78.41 129	90.26 116	91.83 167

test_yl		81.17 148	80.47 149	83.24 179	89.13 152	63.62 247	86.21 224	89.95 171	72.43 185	81.78 131	89.61 176	57.50 232	93.58 159	70.75 214	86.90 171	92.52 140

DCV-MVSNet		81.17 148	80.47 149	83.24 179	89.13 152	63.62 247	86.21 224	89.95 171	72.43 185	81.78 131	89.61 176	57.50 232	93.58 159	70.75 214	86.90 171	92.52 140

guyue		81.13 150	80.64 144	82.60 213	86.52 253	63.92 243	86.69 209	87.73 251	73.97 147	80.83 147	89.69 172	56.70 241	91.33 267	78.26 134	85.40 199	92.54 139

DU-MVS		81.12 151	80.52 147	82.90 197	87.80 209	63.46 257	87.02 194	91.87 108	79.01 31	78.38 186	89.07 191	65.02 139	93.05 194	70.05 222	76.46 321	92.20 157

PVSNet_Blended		80.98 152	80.34 151	82.90 197	88.85 159	65.40 205	84.43 275	92.00 100	67.62 288	78.11 193	85.05 309	66.02 130	94.27 125	71.52 206	89.50 131	89.01 277

FA-MVS(test-final)		80.96 153	79.91 163	84.10 138	88.30 185	65.01 217	84.55 270	90.01 169	73.25 171	79.61 162	87.57 237	58.35 224	94.72 110	71.29 210	86.25 183	92.56 138

QAPM		80.88 154	79.50 175	85.03 98	88.01 200	68.97 110	91.59 46	92.00 100	66.63 304	75.15 271	92.16 104	57.70 229	95.45 71	63.52 278	88.76 143	90.66 210

TranMVSNet+NR-MVSNet		80.84 155	80.31 152	82.42 216	87.85 206	62.33 279	87.74 172	91.33 127	80.55 9	77.99 197	89.86 166	65.23 137	92.62 206	67.05 254	75.24 348	92.30 152

UGNet		80.83 156	79.59 173	84.54 116	88.04 197	68.09 140	89.42 99	88.16 235	76.95 70	76.22 239	89.46 183	49.30 324	93.94 140	68.48 240	90.31 114	91.60 174
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
AstraMVS		80.81 157	80.14 158	82.80 203	86.05 265	63.96 240	86.46 216	85.90 288	73.71 155	80.85 146	90.56 151	54.06 264	91.57 253	79.72 118	83.97 220	92.86 128

Fast-Effi-MVS+		80.81 157	79.92 162	83.47 169	88.85 159	64.51 228	85.53 245	89.39 190	70.79 217	78.49 183	85.06 308	67.54 110	93.58 159	67.03 255	86.58 177	92.32 151

XVG-OURS-SEG-HR		80.81 157	79.76 168	83.96 156	85.60 274	68.78 114	83.54 296	90.50 150	70.66 223	76.71 226	91.66 116	60.69 203	91.26 268	76.94 146	81.58 258	91.83 167

icg_test_0403		80.80 160	80.12 159	82.87 199	87.13 236	63.59 251	85.19 250	89.33 192	70.51 226	78.49 183	89.03 193	63.26 155	93.27 175	72.56 198	85.56 196	91.74 170

xiu_mvs_v1_base_debu		80.80 160	79.72 169	84.03 151	87.35 224	70.19 84	85.56 240	88.77 223	69.06 267	81.83 127	88.16 221	50.91 301	92.85 201	78.29 131	87.56 159	89.06 272

xiu_mvs_v1_base		80.80 160	79.72 169	84.03 151	87.35 224	70.19 84	85.56 240	88.77 223	69.06 267	81.83 127	88.16 221	50.91 301	92.85 201	78.29 131	87.56 159	89.06 272

xiu_mvs_v1_base_debi		80.80 160	79.72 169	84.03 151	87.35 224	70.19 84	85.56 240	88.77 223	69.06 267	81.83 127	88.16 221	50.91 301	92.85 201	78.29 131	87.56 159	89.06 272

ACMM	73.20 8	80.78 164	79.84 166	83.58 167	89.31 143	68.37 130	89.99 79	91.60 119	70.28 234	77.25 211	89.66 174	53.37 271	93.53 164	74.24 178	82.85 243	88.85 285
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LuminaMVS		80.68 165	79.62 172	83.83 159	85.07 291	68.01 144	86.99 195	88.83 220	70.36 230	81.38 135	87.99 228	50.11 312	92.51 215	79.02 120	86.89 173	90.97 197

114514_t		80.68 165	79.51 174	84.20 135	94.09 38	67.27 168	89.64 90	91.11 135	58.75 390	74.08 290	90.72 148	58.10 225	95.04 95	69.70 227	89.42 133	90.30 227

icg_test_0407		80.61 167	79.90 164	82.75 210	87.13 236	63.59 251	85.33 249	89.33 192	70.51 226	77.82 199	89.03 193	61.84 179	92.91 199	72.56 198	85.56 196	91.74 170

CANet_DTU		80.61 167	79.87 165	82.83 200	85.60 274	63.17 266	87.36 183	88.65 229	76.37 89	75.88 246	88.44 213	53.51 269	93.07 192	73.30 187	89.74 127	92.25 154

VPA-MVSNet		80.60 169	80.55 146	80.76 256	88.07 196	60.80 300	86.86 201	91.58 120	75.67 103	80.24 155	89.45 185	63.34 152	90.25 290	70.51 218	79.22 289	91.23 187

mvsmamba		80.60 169	79.38 177	84.27 131	89.74 124	67.24 170	87.47 178	86.95 268	70.02 239	75.38 259	88.93 197	51.24 298	92.56 211	75.47 166	89.22 135	93.00 124

PVSNet_BlendedMVS		80.60 169	80.02 160	82.36 218	88.85 159	65.40 205	86.16 226	92.00 100	69.34 256	78.11 193	86.09 283	66.02 130	94.27 125	71.52 206	82.06 253	87.39 321

AdaColmap		80.58 172	79.42 176	84.06 146	93.09 59	68.91 111	89.36 103	88.97 217	69.27 258	75.70 249	89.69 172	57.20 237	95.77 60	63.06 283	88.41 151	87.50 320

EI-MVSNet		80.52 173	79.98 161	82.12 219	84.28 306	63.19 265	86.41 217	88.95 218	74.18 144	78.69 176	87.54 240	66.62 118	92.43 218	72.57 196	80.57 272	90.74 207

viewmamba		80.41 174	79.84 166	82.12 219	82.95 343	62.50 277	83.39 297	88.06 240	67.11 293	80.98 143	90.31 157	66.20 126	91.01 278	74.62 172	84.90 203	92.86 128

XVG-OURS		80.41 174	79.23 183	83.97 155	85.64 272	69.02 108	83.03 309	90.39 153	71.09 210	77.63 204	91.49 125	54.62 259	91.35 265	75.71 160	83.47 234	91.54 177

SDMVSNet		80.38 176	80.18 155	80.99 250	89.03 157	64.94 220	80.45 341	89.40 189	75.19 115	76.61 230	89.98 164	60.61 207	87.69 335	76.83 150	83.55 231	90.33 225

PCF-MVS	73.52 7	80.38 176	78.84 191	85.01 99	87.71 215	68.99 109	83.65 290	91.46 126	63.00 347	77.77 202	90.28 158	66.10 127	95.09 94	61.40 302	88.22 153	90.94 199
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		80.37 178	77.83 215	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 106	12.47 454	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

test_djsdf		80.30 179	79.32 180	83.27 177	83.98 314	65.37 208	90.50 67	90.38 154	68.55 277	76.19 240	88.70 203	56.44 244	93.46 168	78.98 122	80.14 278	90.97 197

v2v482		80.23 180	79.29 181	83.05 190	83.62 322	64.14 237	87.04 192	89.97 170	73.61 158	78.18 192	87.22 248	61.10 197	93.82 149	76.11 155	76.78 317	91.18 188

NR-MVSNet		80.23 180	79.38 177	82.78 207	87.80 209	63.34 260	86.31 221	91.09 136	79.01 31	72.17 316	89.07 191	67.20 114	92.81 204	66.08 261	75.65 334	92.20 157

Anonymous20240529		80.19 182	78.89 190	84.10 138	90.60 100	64.75 225	88.95 120	90.90 139	65.97 312	80.59 150	91.17 136	49.97 314	93.73 157	69.16 233	82.70 247	93.81 75

IterMVS-LS		80.06 183	79.38 177	82.11 221	85.89 266	63.20 264	86.79 204	89.34 191	74.19 143	75.45 256	86.72 260	66.62 118	92.39 220	72.58 195	76.86 314	90.75 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		80.03 184	78.57 195	84.42 121	85.13 289	68.74 117	88.77 129	88.10 237	74.99 119	74.97 277	83.49 344	57.27 235	93.36 172	73.53 183	80.88 266	91.18 188

v1144		80.03 184	79.03 187	83.01 192	83.78 319	64.51 228	87.11 191	90.57 149	71.96 192	78.08 195	86.20 280	61.41 189	93.94 140	74.93 170	77.23 308	90.60 213

v8		79.97 186	79.02 188	82.80 203	84.09 311	64.50 230	87.96 162	90.29 161	74.13 146	75.24 268	86.81 257	62.88 164	93.89 148	74.39 176	75.40 343	90.00 243

OpenMVS	72.83 10	79.77 187	78.33 202	84.09 142	85.17 285	69.91 89	90.57 64	90.97 137	66.70 298	72.17 316	91.91 108	54.70 257	93.96 137	61.81 299	90.95 105	88.41 301

v10		79.74 188	78.67 192	82.97 195	84.06 312	64.95 219	87.88 168	90.62 146	73.11 173	75.11 272	86.56 271	61.46 188	94.05 136	73.68 181	75.55 336	89.90 249

ECVR-MVS		79.61 189	79.26 182	80.67 258	90.08 112	54.69 380	87.89 167	77.44 393	74.88 124	80.27 154	92.79 93	48.96 330	92.45 217	68.55 239	92.50 80	94.86 19

BH-RMVSNet		79.61 189	78.44 198	83.14 184	89.38 139	65.93 191	84.95 259	87.15 265	73.56 160	78.19 191	89.79 170	56.67 242	93.36 172	59.53 318	86.74 175	90.13 233

v1192		79.59 191	78.43 199	83.07 189	83.55 324	64.52 227	86.93 199	90.58 147	70.83 216	77.78 201	85.90 284	59.15 219	93.94 140	73.96 180	77.19 310	90.76 205

ab-mvs		79.51 192	78.97 189	81.14 246	88.46 178	60.91 298	83.84 285	89.24 203	70.36 230	79.03 170	88.87 200	63.23 157	90.21 291	65.12 268	82.57 248	92.28 153

WR-MVS		79.49 193	79.22 184	80.27 267	88.79 166	58.35 325	85.06 256	88.61 231	78.56 35	77.65 203	88.34 215	63.81 151	90.66 286	64.98 270	77.22 309	91.80 169

v144192		79.47 194	78.37 200	82.78 207	83.35 327	63.96 240	86.96 196	90.36 157	69.99 241	77.50 205	85.67 291	60.66 205	93.77 153	74.27 177	76.58 318	90.62 211

BH-untuned		79.47 194	78.60 194	82.05 222	89.19 150	65.91 192	86.07 228	88.52 232	72.18 187	75.42 257	87.69 234	61.15 196	93.54 163	60.38 310	86.83 174	86.70 342

test1111		79.43 196	79.18 185	80.15 270	89.99 117	53.31 393	87.33 185	77.05 397	75.04 118	80.23 156	92.77 95	48.97 329	92.33 225	68.87 236	92.40 82	94.81 22

mvs_anonymous		79.42 197	79.11 186	80.34 265	84.45 305	57.97 332	82.59 311	87.62 253	67.40 292	76.17 243	88.56 210	68.47 100	89.59 302	70.65 217	86.05 187	93.47 97

thisisatest0530		79.40 198	77.76 220	84.31 126	87.69 217	65.10 216	87.36 183	84.26 310	70.04 238	77.42 207	88.26 219	49.94 315	94.79 108	70.20 220	84.70 207	93.03 121

tttt0517		79.40 198	77.91 211	83.90 158	88.10 194	63.84 244	88.37 148	84.05 312	71.45 201	76.78 224	89.12 190	49.93 317	94.89 101	70.18 221	83.18 240	92.96 126

V42		79.38 200	78.24 204	82.83 200	81.10 375	65.50 204	85.55 243	89.82 174	71.57 199	78.21 190	86.12 282	60.66 205	93.18 186	75.64 161	75.46 340	89.81 254

jajsoiax		79.29 201	77.96 209	83.27 177	84.68 299	66.57 181	89.25 106	90.16 165	69.20 263	75.46 255	89.49 180	45.75 357	93.13 189	76.84 149	80.80 268	90.11 235

v1921920		79.22 202	78.03 208	82.80 203	83.30 329	63.94 242	86.80 203	90.33 158	69.91 244	77.48 206	85.53 295	58.44 223	93.75 155	73.60 182	76.85 315	90.71 209

AUN-MVS		79.21 203	77.60 225	84.05 149	88.71 170	67.61 155	85.84 235	87.26 262	69.08 266	77.23 213	88.14 225	53.20 273	93.47 167	75.50 165	73.45 365	91.06 192

TAPA-MVS	73.13 9	79.15 204	77.94 210	82.79 206	89.59 126	62.99 271	88.16 156	91.51 122	65.77 313	77.14 219	91.09 138	60.91 200	93.21 180	50.26 386	87.05 169	92.17 160
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 205	77.77 219	83.22 181	84.70 298	66.37 183	89.17 109	90.19 164	69.38 255	75.40 258	89.46 183	44.17 369	93.15 187	76.78 151	80.70 270	90.14 232

UniMVSNet_ETH3D		79.10 206	78.24 204	81.70 229	86.85 244	60.24 309	87.28 187	88.79 222	74.25 142	76.84 221	90.53 153	49.48 320	91.56 254	67.98 243	82.15 251	93.29 104

CDS-MVSNet		79.07 207	77.70 222	83.17 183	87.60 219	68.23 137	84.40 277	86.20 283	67.49 290	76.36 236	86.54 272	61.54 185	90.79 282	61.86 298	87.33 164	90.49 218
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 208	77.88 214	82.38 217	83.07 336	64.80 224	84.08 284	88.95 218	69.01 270	78.69 176	87.17 251	54.70 257	92.43 218	74.69 171	80.57 272	89.89 250

v1240		78.99 209	77.78 218	82.64 211	83.21 331	63.54 254	86.62 211	90.30 160	69.74 251	77.33 209	85.68 290	57.04 238	93.76 154	73.13 190	76.92 312	90.62 211

Anonymous20231211		78.97 210	77.69 223	82.81 202	90.54 102	64.29 235	90.11 78	91.51 122	65.01 324	76.16 244	88.13 226	50.56 306	93.03 197	69.68 228	77.56 307	91.11 190

v7n		78.97 210	77.58 226	83.14 184	83.45 326	65.51 203	88.32 150	91.21 130	73.69 156	72.41 312	86.32 278	57.93 226	93.81 150	69.18 232	75.65 334	90.11 235

TAMVS		78.89 212	77.51 227	83.03 191	87.80 209	67.79 151	84.72 263	85.05 299	67.63 287	76.75 225	87.70 233	62.25 173	90.82 281	58.53 329	87.13 168	90.49 218

c3_l		78.75 213	77.91 211	81.26 242	82.89 344	61.56 290	84.09 283	89.13 209	69.97 242	75.56 251	84.29 323	66.36 123	92.09 232	73.47 185	75.48 338	90.12 234

tt0805		78.73 214	77.83 215	81.43 235	85.17 285	60.30 308	89.41 100	90.90 139	71.21 207	77.17 218	88.73 202	46.38 346	93.21 180	72.57 196	78.96 290	90.79 203

v148		78.72 215	77.80 217	81.47 234	82.73 347	61.96 285	86.30 222	88.08 238	73.26 170	76.18 241	85.47 297	62.46 169	92.36 222	71.92 205	73.82 362	90.09 237

VPNet		78.69 216	78.66 193	78.76 296	88.31 184	55.72 369	84.45 274	86.63 275	76.79 75	78.26 189	90.55 152	59.30 218	89.70 301	66.63 256	77.05 311	90.88 200

ET-MVSNet_ETH3D		78.63 217	76.63 248	84.64 114	86.73 248	69.47 98	85.01 257	84.61 303	69.54 252	66.51 382	86.59 268	50.16 311	91.75 245	76.26 154	84.24 217	92.69 134

anonymousdsp		78.60 218	77.15 233	82.98 194	80.51 381	67.08 173	87.24 188	89.53 186	65.66 315	75.16 270	87.19 250	52.52 275	92.25 227	77.17 143	79.34 287	89.61 259

miper_ehance_all_eth		78.59 219	77.76 220	81.08 248	82.66 349	61.56 290	83.65 290	89.15 207	68.87 272	75.55 252	83.79 335	66.49 121	92.03 233	73.25 188	76.39 323	89.64 258

VortexMVS		78.57 220	77.89 213	80.59 259	85.89 266	62.76 274	85.61 238	89.62 183	72.06 190	74.99 276	85.38 299	55.94 246	90.77 284	74.99 169	76.58 318	88.23 303

WR-MVS_H		78.51 221	78.49 196	78.56 301	88.02 198	56.38 359	88.43 143	92.67 68	77.14 64	73.89 292	87.55 239	66.25 125	89.24 309	58.92 324	73.55 364	90.06 241

GBi-Net		78.40 222	77.40 228	81.40 237	87.60 219	63.01 267	88.39 145	89.28 197	71.63 195	75.34 261	87.28 244	54.80 253	91.11 271	62.72 285	79.57 282	90.09 237

test1		78.40 222	77.40 228	81.40 237	87.60 219	63.01 267	88.39 145	89.28 197	71.63 195	75.34 261	87.28 244	54.80 253	91.11 271	62.72 285	79.57 282	90.09 237

Vis-MVSNet (Re-imp)		78.36 224	78.45 197	78.07 312	88.64 172	51.78 403	86.70 208	79.63 375	74.14 145	75.11 272	90.83 147	61.29 193	89.75 299	58.10 334	91.60 92	92.69 134

Anonymous202405211		78.25 225	77.01 235	81.99 224	91.03 90	60.67 302	84.77 262	83.90 314	70.65 224	80.00 158	91.20 134	41.08 389	91.43 263	65.21 267	85.26 200	93.85 71

CP-MVSNet		78.22 226	78.34 201	77.84 316	87.83 208	54.54 382	87.94 164	91.17 132	77.65 46	73.48 298	88.49 211	62.24 174	88.43 325	62.19 293	74.07 357	90.55 215

BH-w/o		78.21 227	77.33 231	80.84 254	88.81 163	65.13 213	84.87 260	87.85 248	69.75 249	74.52 285	84.74 315	61.34 191	93.11 190	58.24 333	85.84 192	84.27 380

FMVSNet2		78.20 228	77.21 232	81.20 244	87.60 219	62.89 273	87.47 178	89.02 213	71.63 195	75.29 267	87.28 244	54.80 253	91.10 274	62.38 290	79.38 286	89.61 259

MVS		78.19 229	76.99 237	81.78 227	85.66 271	66.99 174	84.66 265	90.47 151	55.08 411	72.02 318	85.27 301	63.83 150	94.11 134	66.10 260	89.80 126	84.24 381

Baseline_NR-MVSNet		78.15 230	78.33 202	77.61 321	85.79 268	56.21 363	86.78 205	85.76 290	73.60 159	77.93 198	87.57 237	65.02 139	88.99 314	67.14 253	75.33 345	87.63 315

CNLPA		78.08 231	76.79 242	81.97 225	90.40 105	71.07 67	87.59 175	84.55 304	66.03 311	72.38 313	89.64 175	57.56 231	86.04 352	59.61 317	83.35 236	88.79 288

cl22		78.07 232	77.01 235	81.23 243	82.37 356	61.83 287	83.55 294	87.98 242	68.96 271	75.06 274	83.87 331	61.40 190	91.88 241	73.53 183	76.39 323	89.98 246

PLC	70.83 11	78.05 233	76.37 254	83.08 188	91.88 79	67.80 150	88.19 154	89.46 188	64.33 332	69.87 343	88.38 214	53.66 267	93.58 159	58.86 325	82.73 245	87.86 311
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 234	76.49 249	82.62 212	83.16 335	66.96 177	86.94 198	87.45 258	72.45 182	71.49 324	84.17 328	54.79 256	91.58 251	67.61 246	80.31 275	89.30 268

PS-CasMVS		78.01 235	78.09 207	77.77 318	87.71 215	54.39 384	88.02 160	91.22 129	77.50 54	73.26 300	88.64 206	60.73 201	88.41 326	61.88 297	73.88 361	90.53 216

HY-MVS	69.67 12	77.95 236	77.15 233	80.36 264	87.57 223	60.21 310	83.37 299	87.78 250	66.11 308	75.37 260	87.06 255	63.27 154	90.48 288	61.38 303	82.43 249	90.40 222

eth_miper_zixun_eth		77.92 237	76.69 246	81.61 232	83.00 339	61.98 284	83.15 303	89.20 205	69.52 253	74.86 279	84.35 322	61.76 181	92.56 211	71.50 208	72.89 370	90.28 228

FMVSNet3		77.88 238	76.85 240	80.97 252	86.84 245	62.36 278	86.52 214	88.77 223	71.13 208	75.34 261	86.66 266	54.07 263	91.10 274	62.72 285	79.57 282	89.45 263

miper_enhance_ethall		77.87 239	76.86 239	80.92 253	81.65 363	61.38 292	82.68 310	88.98 215	65.52 317	75.47 253	82.30 364	65.76 134	92.00 235	72.95 191	76.39 323	89.39 265

FE-MVS		77.78 240	75.68 260	84.08 143	88.09 195	66.00 189	83.13 304	87.79 249	68.42 281	78.01 196	85.23 303	45.50 360	95.12 88	59.11 322	85.83 193	91.11 190

PEN-MVS		77.73 241	77.69 223	77.84 316	87.07 242	53.91 387	87.91 166	91.18 131	77.56 51	73.14 302	88.82 201	61.23 194	89.17 311	59.95 313	72.37 372	90.43 220

cl____		77.72 242	76.76 243	80.58 260	82.49 353	60.48 305	83.09 305	87.87 246	69.22 261	74.38 288	85.22 304	62.10 176	91.53 257	71.09 211	75.41 342	89.73 257

DIV-MVS_self_test		77.72 242	76.76 243	80.58 260	82.48 354	60.48 305	83.09 305	87.86 247	69.22 261	74.38 288	85.24 302	62.10 176	91.53 257	71.09 211	75.40 343	89.74 256

sd_testset		77.70 244	77.40 228	78.60 299	89.03 157	60.02 311	79.00 361	85.83 289	75.19 115	76.61 230	89.98 164	54.81 252	85.46 360	62.63 289	83.55 231	90.33 225

PAPM		77.68 245	76.40 253	81.51 233	87.29 232	61.85 286	83.78 286	89.59 184	64.74 326	71.23 326	88.70 203	62.59 166	93.66 158	52.66 370	87.03 170	89.01 277

CHOSEN 1792x2688		77.63 246	75.69 259	83.44 170	89.98 118	68.58 125	78.70 366	87.50 256	56.38 406	75.80 248	86.84 256	58.67 221	91.40 264	61.58 301	85.75 194	90.34 224

HyFIR lowres test		77.53 247	75.40 267	83.94 157	89.59 126	66.62 179	80.36 342	88.64 230	56.29 407	76.45 233	85.17 305	57.64 230	93.28 174	61.34 304	83.10 241	91.91 166

FMVSNet1		77.44 248	76.12 256	81.40 237	86.81 246	63.01 267	88.39 145	89.28 197	70.49 229	74.39 287	87.28 244	49.06 328	91.11 271	60.91 306	78.52 293	90.09 237

TR-MVS		77.44 248	76.18 255	81.20 244	88.24 186	63.24 262	84.61 268	86.40 279	67.55 289	77.81 200	86.48 274	54.10 262	93.15 187	57.75 337	82.72 246	87.20 327

1112_ss		77.40 250	76.43 251	80.32 266	89.11 156	60.41 307	83.65 290	87.72 252	62.13 360	73.05 303	86.72 260	62.58 167	89.97 295	62.11 296	80.80 268	90.59 214

thisisatest0515		77.33 251	75.38 268	83.18 182	85.27 284	63.80 245	82.11 316	83.27 324	65.06 322	75.91 245	83.84 333	49.54 319	94.27 125	67.24 251	86.19 184	91.48 181

test2506		77.30 252	76.49 249	79.74 278	90.08 112	52.02 397	87.86 169	63.10 440	74.88 124	80.16 157	92.79 93	38.29 404	92.35 223	68.74 238	92.50 80	94.86 19

pm-mvs1		77.25 253	76.68 247	78.93 294	84.22 308	58.62 323	86.41 217	88.36 234	71.37 202	73.31 299	88.01 227	61.22 195	89.15 312	64.24 276	73.01 369	89.03 276

ICG_test_0404		77.16 254	76.42 252	79.37 286	87.13 236	63.59 251	77.12 385	89.33 192	70.51 226	66.22 385	89.03 193	50.36 309	82.78 381	72.56 198	85.56 196	91.74 170

LCM-MVSNet-Re		77.05 255	76.94 238	77.36 325	87.20 233	51.60 404	80.06 346	80.46 363	75.20 114	67.69 362	86.72 260	62.48 168	88.98 315	63.44 280	89.25 134	91.51 178

DTE-MVSNet		76.99 256	76.80 241	77.54 324	86.24 257	53.06 396	87.52 176	90.66 145	77.08 68	72.50 310	88.67 205	60.48 209	89.52 303	57.33 341	70.74 384	90.05 242

baseline1		76.98 257	76.75 245	77.66 319	88.13 192	55.66 370	85.12 254	81.89 345	73.04 175	76.79 223	88.90 198	62.43 170	87.78 334	63.30 282	71.18 382	89.55 261

LS3D		76.95 258	74.82 276	83.37 174	90.45 103	67.36 165	89.15 113	86.94 269	61.87 363	69.52 346	90.61 150	51.71 294	94.53 116	46.38 408	86.71 176	88.21 305

GA-MVS		76.87 259	75.17 273	81.97 225	82.75 346	62.58 275	81.44 325	86.35 281	72.16 189	74.74 280	82.89 355	46.20 351	92.02 234	68.85 237	81.09 263	91.30 186

mamv4		76.81 260	78.23 206	72.54 377	86.12 262	65.75 199	78.76 365	82.07 344	64.12 334	72.97 304	91.02 143	67.97 105	68.08 442	83.04 82	78.02 300	83.80 388

DP-MVS		76.78 261	74.57 279	83.42 171	93.29 48	69.46 100	88.55 141	83.70 316	63.98 339	70.20 334	88.89 199	54.01 265	94.80 107	46.66 405	81.88 256	86.01 354

cascas		76.72 262	74.64 278	82.99 193	85.78 269	65.88 193	82.33 313	89.21 204	60.85 369	72.74 306	81.02 375	47.28 337	93.75 155	67.48 248	85.02 201	89.34 267

testing91		76.54 263	75.66 262	79.18 291	88.43 180	55.89 366	81.08 328	83.00 332	73.76 154	75.34 261	84.29 323	46.20 351	90.07 293	64.33 274	84.50 209	91.58 176

1314		76.53 264	75.30 271	80.21 269	83.93 315	62.32 280	84.66 265	88.81 221	60.23 374	70.16 337	84.07 330	55.30 250	90.73 285	67.37 249	83.21 239	87.59 318

thres100view900		76.50 265	75.55 264	79.33 287	89.52 129	56.99 348	85.83 236	83.23 325	73.94 149	76.32 237	87.12 252	51.89 290	91.95 237	48.33 396	83.75 225	89.07 270

thres600view7		76.50 265	75.44 265	79.68 280	89.40 137	57.16 345	85.53 245	83.23 325	73.79 153	76.26 238	87.09 253	51.89 290	91.89 240	48.05 401	83.72 228	90.00 243

thres400		76.50 265	75.37 269	79.86 275	89.13 152	57.65 339	85.17 251	83.60 317	73.41 166	76.45 233	86.39 276	52.12 282	91.95 237	48.33 396	83.75 225	90.00 243

MonoMVSNet		76.49 268	75.80 257	78.58 300	81.55 366	58.45 324	86.36 220	86.22 282	74.87 126	74.73 281	83.73 337	51.79 293	88.73 320	70.78 213	72.15 375	88.55 298

tfpn200view9		76.42 269	75.37 269	79.55 285	89.13 152	57.65 339	85.17 251	83.60 317	73.41 166	76.45 233	86.39 276	52.12 282	91.95 237	48.33 396	83.75 225	89.07 270

Test_1112_low_res		76.40 270	75.44 265	79.27 288	89.28 145	58.09 328	81.69 320	87.07 266	59.53 381	72.48 311	86.67 265	61.30 192	89.33 306	60.81 308	80.15 277	90.41 221

F-COLMAP		76.38 271	74.33 285	82.50 215	89.28 145	66.95 178	88.41 144	89.03 212	64.05 337	66.83 374	88.61 207	46.78 343	92.89 200	57.48 338	78.55 292	87.67 314

LTVRE_ROB	69.57 13	76.25 272	74.54 281	81.41 236	88.60 173	64.38 234	79.24 356	89.12 210	70.76 219	69.79 345	87.86 230	49.09 327	93.20 183	56.21 353	80.16 276	86.65 343
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
MVP-Stereo		76.12 273	74.46 283	81.13 247	85.37 281	69.79 91	84.42 276	87.95 244	65.03 323	67.46 365	85.33 300	53.28 272	91.73 247	58.01 335	83.27 238	81.85 407
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 274	74.27 286	81.62 230	83.20 332	64.67 226	83.60 293	89.75 178	69.75 249	71.85 319	87.09 253	32.78 419	92.11 231	69.99 224	80.43 274	88.09 307

testing99		76.09 275	75.12 274	79.00 292	88.16 189	55.50 372	80.79 332	81.40 352	73.30 169	75.17 269	84.27 326	44.48 366	90.02 294	64.28 275	84.22 218	91.48 181

ACMH+	68.96 14	76.01 276	74.01 287	82.03 223	88.60 173	65.31 209	88.86 123	87.55 254	70.25 236	67.75 361	87.47 242	41.27 387	93.19 185	58.37 331	75.94 331	87.60 316

ACMH	67.68 16	75.89 277	73.93 289	81.77 228	88.71 170	66.61 180	88.62 138	89.01 214	69.81 245	66.78 375	86.70 264	41.95 385	91.51 259	55.64 354	78.14 299	87.17 328
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 278	73.36 298	83.31 175	84.76 297	66.03 187	83.38 298	85.06 298	70.21 237	69.40 347	81.05 374	45.76 356	94.66 113	65.10 269	75.49 337	89.25 269
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
baseline2		75.70 279	73.83 292	81.30 240	83.26 330	61.79 288	82.57 312	80.65 359	66.81 295	66.88 373	83.42 345	57.86 228	92.19 229	63.47 279	79.57 282	89.91 248

WTY-MVS		75.65 280	75.68 260	75.57 341	86.40 255	56.82 350	77.92 379	82.40 340	65.10 321	76.18 241	87.72 232	63.13 162	80.90 393	60.31 311	81.96 254	89.00 279

thres200		75.55 281	74.47 282	78.82 295	87.78 212	57.85 335	83.07 307	83.51 320	72.44 184	75.84 247	84.42 318	52.08 285	91.75 245	47.41 403	83.64 230	86.86 338

test_vis1_n_1920		75.52 282	75.78 258	74.75 355	79.84 389	57.44 343	83.26 301	85.52 292	62.83 351	79.34 168	86.17 281	45.10 362	79.71 397	78.75 124	81.21 262	87.10 334

EPNet_dtu		75.46 283	74.86 275	77.23 328	82.57 351	54.60 381	86.89 200	83.09 329	71.64 194	66.25 384	85.86 286	55.99 245	88.04 330	54.92 358	86.55 178	89.05 275
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 284	73.87 291	80.11 271	82.69 348	64.85 223	81.57 322	83.47 321	69.16 264	70.49 331	84.15 329	51.95 288	88.15 328	69.23 231	72.14 376	87.34 323

XXY-MVS		75.41 285	75.56 263	74.96 350	83.59 323	57.82 336	80.59 338	83.87 315	66.54 305	74.93 278	88.31 216	63.24 156	80.09 396	62.16 294	76.85 315	86.97 336

reproduce_monomvs		75.40 286	74.38 284	78.46 306	83.92 316	57.80 337	83.78 286	86.94 269	73.47 164	72.25 315	84.47 317	38.74 400	89.27 308	75.32 167	70.53 385	88.31 302

TransMVSNet (Re)		75.39 287	74.56 280	77.86 315	85.50 278	57.10 347	86.78 205	86.09 286	72.17 188	71.53 323	87.34 243	63.01 163	89.31 307	56.84 347	61.83 413	87.17 328

CostFormer		75.24 288	73.90 290	79.27 288	82.65 350	58.27 327	80.80 331	82.73 338	61.57 364	75.33 265	83.13 350	55.52 248	91.07 277	64.98 270	78.34 298	88.45 299

testing11		75.14 289	74.01 287	78.53 303	88.16 189	56.38 359	80.74 335	80.42 365	70.67 220	72.69 309	83.72 338	43.61 373	89.86 296	62.29 292	83.76 224	89.36 266

testing3-2		75.12 290	75.19 272	74.91 351	90.40 105	45.09 433	80.29 344	78.42 385	78.37 40	76.54 232	87.75 231	44.36 367	87.28 340	57.04 344	83.49 233	92.37 148

D2MVS		74.82 291	73.21 299	79.64 282	79.81 390	62.56 276	80.34 343	87.35 259	64.37 331	68.86 352	82.66 359	46.37 347	90.10 292	67.91 244	81.24 261	86.25 347

pmmvs6		74.69 292	73.39 296	78.61 298	81.38 370	57.48 342	86.64 210	87.95 244	64.99 325	70.18 335	86.61 267	50.43 308	89.52 303	62.12 295	70.18 387	88.83 286

SD_0403		74.65 293	74.77 277	74.29 359	86.20 259	47.42 422	83.71 288	85.12 296	69.30 257	68.50 357	87.95 229	59.40 217	86.05 351	49.38 390	83.35 236	89.40 264

tfpnnormal		74.39 294	73.16 300	78.08 311	86.10 264	58.05 329	84.65 267	87.53 255	70.32 233	71.22 327	85.63 292	54.97 251	89.86 296	43.03 419	75.02 350	86.32 346

IterMVS		74.29 295	72.94 303	78.35 307	81.53 367	63.49 256	81.58 321	82.49 339	68.06 285	69.99 340	83.69 339	51.66 295	85.54 358	65.85 263	71.64 379	86.01 354
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 296	72.42 309	79.80 277	83.76 320	59.59 316	85.92 232	86.64 274	66.39 306	66.96 372	87.58 236	39.46 395	91.60 250	65.76 264	69.27 390	88.22 304

SCA		74.22 297	72.33 310	79.91 274	84.05 313	62.17 282	79.96 349	79.29 379	66.30 307	72.38 313	80.13 387	51.95 288	88.60 323	59.25 320	77.67 306	88.96 281

mmtdpeth		74.16 298	73.01 302	77.60 323	83.72 321	61.13 293	85.10 255	85.10 297	72.06 190	77.21 217	80.33 384	43.84 371	85.75 354	77.14 144	52.61 432	85.91 357

miper_lstm_enhance		74.11 299	73.11 301	77.13 329	80.11 385	59.62 315	72.23 410	86.92 271	66.76 297	70.40 332	82.92 354	56.93 239	82.92 380	69.06 234	72.63 371	88.87 284

testing222		74.04 300	72.66 306	78.19 309	87.89 204	55.36 373	81.06 329	79.20 380	71.30 205	74.65 283	83.57 343	39.11 399	88.67 322	51.43 378	85.75 194	90.53 216

EG-PatchMatch MVS		74.04 300	71.82 314	80.71 257	84.92 293	67.42 161	85.86 234	88.08 238	66.04 310	64.22 397	83.85 332	35.10 415	92.56 211	57.44 339	80.83 267	82.16 406

pmmvs4		74.03 302	71.91 313	80.39 263	81.96 359	68.32 131	81.45 324	82.14 342	59.32 382	69.87 343	85.13 306	52.40 278	88.13 329	60.21 312	74.74 353	84.73 377

MS-PatchMatch		73.83 303	72.67 305	77.30 327	83.87 317	66.02 188	81.82 317	84.66 302	61.37 367	68.61 355	82.82 357	47.29 336	88.21 327	59.27 319	84.32 216	77.68 422

test_cas_vis1_n_1920		73.76 304	73.74 293	73.81 365	75.90 411	59.77 313	80.51 339	82.40 340	58.30 392	81.62 133	85.69 289	44.35 368	76.41 415	76.29 153	78.61 291	85.23 367

myMVS_eth3d28		73.62 305	73.53 295	73.90 364	88.20 187	47.41 423	78.06 376	79.37 377	74.29 141	73.98 291	84.29 323	44.67 363	83.54 375	51.47 376	87.39 163	90.74 207

sss		73.60 306	73.64 294	73.51 367	82.80 345	55.01 378	76.12 388	81.69 348	62.47 356	74.68 282	85.85 287	57.32 234	78.11 404	60.86 307	80.93 264	87.39 321

RPMNet		73.51 307	70.49 330	82.58 214	81.32 373	65.19 211	75.92 390	92.27 85	57.60 399	72.73 307	76.45 414	52.30 279	95.43 73	48.14 400	77.71 303	87.11 332

WBMVS		73.43 308	72.81 304	75.28 347	87.91 203	50.99 410	78.59 369	81.31 354	65.51 319	74.47 286	84.83 312	46.39 345	86.68 344	58.41 330	77.86 301	88.17 306

SixPastTwentyTwo		73.37 309	71.26 323	79.70 279	85.08 290	57.89 334	85.57 239	83.56 319	71.03 214	65.66 387	85.88 285	42.10 383	92.57 210	59.11 322	63.34 409	88.65 294

CR-MVSNet		73.37 309	71.27 322	79.67 281	81.32 373	65.19 211	75.92 390	80.30 367	59.92 377	72.73 307	81.19 372	52.50 276	86.69 343	59.84 314	77.71 303	87.11 332

MSDG		73.36 311	70.99 325	80.49 262	84.51 304	65.80 196	80.71 336	86.13 285	65.70 314	65.46 388	83.74 336	44.60 364	90.91 280	51.13 379	76.89 313	84.74 376

SSC-MVS3.2		73.35 312	73.39 296	73.23 368	85.30 283	49.01 418	74.58 403	81.57 349	75.21 113	73.68 295	85.58 294	52.53 274	82.05 386	54.33 362	77.69 305	88.63 295

tpm2		73.26 313	71.46 318	78.63 297	83.34 328	56.71 353	80.65 337	80.40 366	56.63 405	73.55 297	82.02 369	51.80 292	91.24 269	56.35 352	78.42 296	87.95 308

RPSCF		73.23 314	71.46 318	78.54 302	82.50 352	59.85 312	82.18 315	82.84 337	58.96 386	71.15 328	89.41 187	45.48 361	84.77 367	58.82 326	71.83 378	91.02 196

PatchmatchNet		73.12 315	71.33 321	78.49 305	83.18 333	60.85 299	79.63 351	78.57 384	64.13 333	71.73 320	79.81 392	51.20 299	85.97 353	57.40 340	76.36 328	88.66 293
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
UBG		73.08 316	72.27 311	75.51 343	88.02 198	51.29 408	78.35 373	77.38 394	65.52 317	73.87 293	82.36 362	45.55 358	86.48 347	55.02 357	84.39 215	88.75 290

COLMAP_ROB	66.92 17	73.01 317	70.41 332	80.81 255	87.13 236	65.63 200	88.30 151	84.19 311	62.96 348	63.80 402	87.69 234	38.04 405	92.56 211	46.66 405	74.91 351	84.24 381
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 318	72.58 307	74.25 360	84.28 306	50.85 411	86.41 217	83.45 322	44.56 431	73.23 301	87.54 240	49.38 322	85.70 355	65.90 262	78.44 295	86.19 349

test-LLR		72.94 319	72.43 308	74.48 356	81.35 371	58.04 330	78.38 370	77.46 391	66.66 299	69.95 341	79.00 398	48.06 333	79.24 398	66.13 258	84.83 204	86.15 350

test_0402		72.79 320	70.44 331	79.84 276	88.13 192	65.99 190	85.93 231	84.29 308	65.57 316	67.40 368	85.49 296	46.92 340	92.61 207	35.88 433	74.38 356	80.94 412

tpmrst		72.39 321	72.13 312	73.18 372	80.54 380	49.91 415	79.91 350	79.08 381	63.11 345	71.69 321	79.95 389	55.32 249	82.77 382	65.66 265	73.89 360	86.87 337

PatchMatch-RL		72.38 322	70.90 326	76.80 332	88.60 173	67.38 164	79.53 352	76.17 403	62.75 353	69.36 348	82.00 370	45.51 359	84.89 366	53.62 365	80.58 271	78.12 421

CL-MVSNet_self_test		72.37 323	71.46 318	75.09 349	79.49 396	53.53 389	80.76 334	85.01 300	69.12 265	70.51 330	82.05 368	57.92 227	84.13 370	52.27 372	66.00 403	87.60 316

tpm		72.37 323	71.71 315	74.35 358	82.19 357	52.00 398	79.22 357	77.29 395	64.56 328	72.95 305	83.68 340	51.35 296	83.26 379	58.33 332	75.80 332	87.81 312

ETVMVS		72.25 325	71.05 324	75.84 337	87.77 213	51.91 400	79.39 354	74.98 406	69.26 259	73.71 294	82.95 353	40.82 391	86.14 350	46.17 409	84.43 214	89.47 262

sc_t1		72.19 326	69.51 337	80.23 268	84.81 295	61.09 295	84.68 264	80.22 369	60.70 370	71.27 325	83.58 342	36.59 410	89.24 309	60.41 309	63.31 410	90.37 223

UWE-MVS		72.13 327	71.49 317	74.03 362	86.66 251	47.70 420	81.40 326	76.89 399	63.60 342	75.59 250	84.22 327	39.94 394	85.62 357	48.98 393	86.13 186	88.77 289

PVSNet	64.34 18	72.08 328	70.87 327	75.69 339	86.21 258	56.44 357	74.37 404	80.73 358	62.06 361	70.17 336	82.23 366	42.86 377	83.31 378	54.77 359	84.45 213	87.32 324

WB-MVSnew		71.96 329	71.65 316	72.89 373	84.67 302	51.88 401	82.29 314	77.57 390	62.31 357	73.67 296	83.00 352	53.49 270	81.10 392	45.75 412	82.13 252	85.70 360

pmmvs5		71.55 330	70.20 335	75.61 340	77.83 404	56.39 358	81.74 319	80.89 355	57.76 397	67.46 365	84.49 316	49.26 325	85.32 362	57.08 343	75.29 346	85.11 371

test-mter		71.41 331	70.39 333	74.48 356	81.35 371	58.04 330	78.38 370	77.46 391	60.32 373	69.95 341	79.00 398	36.08 413	79.24 398	66.13 258	84.83 204	86.15 350

K. test v3		71.19 332	68.51 344	79.21 290	83.04 338	57.78 338	84.35 278	76.91 398	72.90 178	62.99 405	82.86 356	39.27 396	91.09 276	61.65 300	52.66 431	88.75 290

dmvs_re		71.14 333	70.58 328	72.80 374	81.96 359	59.68 314	75.60 394	79.34 378	68.55 277	69.27 350	80.72 380	49.42 321	76.54 412	52.56 371	77.79 302	82.19 405

tpmvs		71.09 334	69.29 339	76.49 333	82.04 358	56.04 364	78.92 363	81.37 353	64.05 337	67.18 370	78.28 404	49.74 318	89.77 298	49.67 389	72.37 372	83.67 389

AllTest		70.96 335	68.09 350	79.58 283	85.15 287	63.62 247	84.58 269	79.83 372	62.31 357	60.32 414	86.73 258	32.02 420	88.96 317	50.28 384	71.57 380	86.15 350

test_fmvs1		70.93 336	70.52 329	72.16 379	73.71 422	55.05 377	80.82 330	78.77 383	51.21 423	78.58 180	84.41 319	31.20 424	76.94 410	75.88 159	80.12 279	84.47 379

test_fmvs1_n		70.86 337	70.24 334	72.73 375	72.51 433	55.28 375	81.27 327	79.71 374	51.49 422	78.73 175	84.87 311	27.54 429	77.02 409	76.06 156	79.97 280	85.88 358

Patchmtry		70.74 338	69.16 341	75.49 344	80.72 377	54.07 386	74.94 401	80.30 367	58.34 391	70.01 338	81.19 372	52.50 276	86.54 345	53.37 367	71.09 383	85.87 359

MIMVSNet		70.69 339	69.30 338	74.88 352	84.52 303	56.35 361	75.87 392	79.42 376	64.59 327	67.76 360	82.41 361	41.10 388	81.54 389	46.64 407	81.34 259	86.75 341

tpm cat1		70.57 340	68.31 346	77.35 326	82.41 355	57.95 333	78.08 375	80.22 369	52.04 418	68.54 356	77.66 409	52.00 287	87.84 333	51.77 373	72.07 377	86.25 347

OpenMVS_ROB	64.09 19	70.56 341	68.19 347	77.65 320	80.26 382	59.41 319	85.01 257	82.96 334	58.76 389	65.43 389	82.33 363	37.63 407	91.23 270	45.34 415	76.03 330	82.32 403

pmmvs-eth3d		70.50 342	67.83 356	78.52 304	77.37 407	66.18 186	81.82 317	81.51 350	58.90 387	63.90 401	80.42 382	42.69 378	86.28 349	58.56 328	65.30 405	83.11 395

tt0320		70.49 343	68.03 351	77.89 314	84.78 296	59.12 320	83.55 294	80.44 364	58.13 394	67.43 367	80.41 383	39.26 397	87.54 337	55.12 356	63.18 411	86.99 335

USDC		70.33 344	68.37 345	76.21 335	80.60 379	56.23 362	79.19 358	86.49 277	60.89 368	61.29 410	85.47 297	31.78 422	89.47 305	53.37 367	76.21 329	82.94 399

Patchmatch-RL test		70.24 345	67.78 358	77.61 321	77.43 406	59.57 317	71.16 414	70.33 420	62.94 349	68.65 354	72.77 426	50.62 305	85.49 359	69.58 229	66.58 400	87.77 313

CMPMVS	51.72 21	70.19 346	68.16 348	76.28 334	73.15 429	57.55 341	79.47 353	83.92 313	48.02 427	56.48 427	84.81 313	43.13 375	86.42 348	62.67 288	81.81 257	84.89 374
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tt0320-xc		70.11 347	67.45 364	78.07 312	85.33 282	59.51 318	83.28 300	78.96 382	58.77 388	67.10 371	80.28 385	36.73 409	87.42 338	56.83 348	59.77 420	87.29 325

ppachtmachnet_test		70.04 348	67.34 366	78.14 310	79.80 391	61.13 293	79.19 358	80.59 360	59.16 384	65.27 390	79.29 395	46.75 344	87.29 339	49.33 391	66.72 398	86.00 356

gg-mvs-nofinetune		69.95 349	67.96 352	75.94 336	83.07 336	54.51 383	77.23 384	70.29 421	63.11 345	70.32 333	62.33 435	43.62 372	88.69 321	53.88 364	87.76 158	84.62 378

TESTMET0.1,1		69.89 350	69.00 342	72.55 376	79.27 399	56.85 349	78.38 370	74.71 410	57.64 398	68.09 359	77.19 411	37.75 406	76.70 411	63.92 277	84.09 219	84.10 384

test_vis1_n		69.85 351	69.21 340	71.77 381	72.66 432	55.27 376	81.48 323	76.21 402	52.03 419	75.30 266	83.20 349	28.97 427	76.22 417	74.60 173	78.41 297	83.81 387

FMVSNet5		69.50 352	67.96 352	74.15 361	82.97 342	55.35 374	80.01 348	82.12 343	62.56 355	63.02 403	81.53 371	36.92 408	81.92 387	48.42 395	74.06 358	85.17 370

mvs5depth		69.45 353	67.45 364	75.46 345	73.93 420	55.83 367	79.19 358	83.23 325	66.89 294	71.63 322	83.32 346	33.69 418	85.09 363	59.81 315	55.34 428	85.46 363

PMMVS		69.34 354	68.67 343	71.35 386	75.67 413	62.03 283	75.17 396	73.46 413	50.00 424	68.68 353	79.05 396	52.07 286	78.13 403	61.16 305	82.77 244	73.90 428

our_test_3		69.14 355	67.00 368	75.57 341	79.80 391	58.80 321	77.96 377	77.81 388	59.55 380	62.90 406	78.25 405	47.43 335	83.97 371	51.71 374	67.58 397	83.93 386

EPMVS		69.02 356	68.16 348	71.59 382	79.61 394	49.80 417	77.40 382	66.93 431	62.82 352	70.01 338	79.05 396	45.79 355	77.86 406	56.58 350	75.26 347	87.13 331

KD-MVS_self_test		68.81 357	67.59 362	72.46 378	74.29 419	45.45 428	77.93 378	87.00 267	63.12 344	63.99 400	78.99 400	42.32 380	84.77 367	56.55 351	64.09 408	87.16 330

Anonymous20240521		68.80 358	67.22 367	73.55 366	74.33 418	54.11 385	83.18 302	85.61 291	58.15 393	61.68 409	80.94 377	30.71 425	81.27 391	57.00 345	73.34 368	85.28 366

Anonymous20231206		68.60 359	67.80 357	71.02 389	80.23 384	50.75 412	78.30 374	80.47 362	56.79 404	66.11 386	82.63 360	46.35 348	78.95 400	43.62 418	75.70 333	83.36 392

MIMVSNet1		68.58 360	66.78 370	73.98 363	80.07 386	51.82 402	80.77 333	84.37 305	64.40 330	59.75 417	82.16 367	36.47 411	83.63 374	42.73 420	70.33 386	86.48 345

testing3		68.56 361	67.67 360	71.22 388	87.33 229	42.87 438	83.06 308	71.54 418	70.36 230	69.08 351	84.38 320	30.33 426	85.69 356	37.50 431	75.45 341	85.09 372

EU-MVSNet		68.53 362	67.61 361	71.31 387	78.51 403	47.01 425	84.47 271	84.27 309	42.27 434	66.44 383	84.79 314	40.44 392	83.76 372	58.76 327	68.54 395	83.17 393

PatchT		68.46 363	67.85 354	70.29 392	80.70 378	43.93 436	72.47 409	74.88 407	60.15 375	70.55 329	76.57 413	49.94 315	81.59 388	50.58 380	74.83 352	85.34 365

test_fmvs2		68.35 364	67.48 363	70.98 390	69.50 436	51.95 399	80.05 347	76.38 401	49.33 425	74.65 283	84.38 320	23.30 438	75.40 426	74.51 174	75.17 349	85.60 361

Syy-MVS		68.05 365	67.85 354	68.67 401	84.68 299	40.97 444	78.62 367	73.08 415	66.65 302	66.74 376	79.46 393	52.11 284	82.30 384	32.89 436	76.38 326	82.75 400

test0.0.03 1		68.00 366	67.69 359	68.90 398	77.55 405	47.43 421	75.70 393	72.95 417	66.66 299	66.56 378	82.29 365	48.06 333	75.87 421	44.97 416	74.51 355	83.41 391

TDRefinement		67.49 367	64.34 378	76.92 330	73.47 426	61.07 296	84.86 261	82.98 333	59.77 378	58.30 421	85.13 306	26.06 430	87.89 332	47.92 402	60.59 418	81.81 408

test20.03		67.45 368	66.95 369	68.94 397	75.48 415	44.84 434	77.50 381	77.67 389	66.66 299	63.01 404	83.80 334	47.02 339	78.40 402	42.53 422	68.86 394	83.58 390

UnsupCasMVSNet_eth		67.33 369	65.99 373	71.37 384	73.48 425	51.47 406	75.16 397	85.19 295	65.20 320	60.78 412	80.93 379	42.35 379	77.20 408	57.12 342	53.69 430	85.44 364

TinyColmap		67.30 370	64.81 376	74.76 354	81.92 361	56.68 354	80.29 344	81.49 351	60.33 372	56.27 428	83.22 347	24.77 434	87.66 336	45.52 413	69.47 389	79.95 417

myMVS_eth3d		67.02 371	66.29 372	69.21 396	84.68 299	42.58 439	78.62 367	73.08 415	66.65 302	66.74 376	79.46 393	31.53 423	82.30 384	39.43 428	76.38 326	82.75 400

dp		66.80 372	65.43 374	70.90 391	79.74 393	48.82 419	75.12 399	74.77 408	59.61 379	64.08 399	77.23 410	42.89 376	80.72 394	48.86 394	66.58 400	83.16 394

MDA-MVSNet-bldmvs		66.68 373	63.66 383	75.75 338	79.28 398	60.56 304	73.92 406	78.35 386	64.43 329	50.13 436	79.87 391	44.02 370	83.67 373	46.10 410	56.86 422	83.03 397

testgi		66.67 374	66.53 371	67.08 408	75.62 414	41.69 443	75.93 389	76.50 400	66.11 308	65.20 393	86.59 268	35.72 414	74.71 428	43.71 417	73.38 367	84.84 375

CHOSEN 280x420		66.51 375	64.71 377	71.90 380	81.45 368	63.52 255	57.98 444	68.95 427	53.57 414	62.59 407	76.70 412	46.22 350	75.29 427	55.25 355	79.68 281	76.88 424

PM-MVS		66.41 376	64.14 379	73.20 371	73.92 421	56.45 356	78.97 362	64.96 437	63.88 341	64.72 394	80.24 386	19.84 442	83.44 377	66.24 257	64.52 407	79.71 418

JIA-IIPM		66.32 377	62.82 389	76.82 331	77.09 408	61.72 289	65.34 437	75.38 404	58.04 396	64.51 395	62.32 436	42.05 384	86.51 346	51.45 377	69.22 391	82.21 404

KD-MVS_2432*1600		66.22 378	63.89 381	73.21 369	75.47 416	53.42 391	70.76 417	84.35 306	64.10 335	66.52 380	78.52 402	34.55 416	84.98 364	50.40 382	50.33 435	81.23 410

miper_refine_blended		66.22 378	63.89 381	73.21 369	75.47 416	53.42 391	70.76 417	84.35 306	64.10 335	66.52 380	78.52 402	34.55 416	84.98 364	50.40 382	50.33 435	81.23 410

ADS-MVSNet2		66.20 380	63.33 384	74.82 353	79.92 387	58.75 322	67.55 429	75.19 405	53.37 415	65.25 391	75.86 417	42.32 380	80.53 395	41.57 423	68.91 392	85.18 368

UWE-MVS-28		65.32 381	64.93 375	66.49 409	78.70 401	38.55 446	77.86 380	64.39 438	62.00 362	64.13 398	83.60 341	41.44 386	76.00 419	31.39 438	80.89 265	84.92 373

YYNet1		65.03 382	62.91 387	71.38 383	75.85 412	56.60 355	69.12 425	74.66 411	57.28 402	54.12 430	77.87 407	45.85 354	74.48 429	49.95 387	61.52 415	83.05 396

MDA-MVSNet_test_wron		65.03 382	62.92 386	71.37 384	75.93 410	56.73 351	69.09 426	74.73 409	57.28 402	54.03 431	77.89 406	45.88 353	74.39 430	49.89 388	61.55 414	82.99 398

Patchmatch-test		64.82 384	63.24 385	69.57 394	79.42 397	49.82 416	63.49 441	69.05 426	51.98 420	59.95 416	80.13 387	50.91 301	70.98 435	40.66 425	73.57 363	87.90 310

ADS-MVSNet		64.36 385	62.88 388	68.78 400	79.92 387	47.17 424	67.55 429	71.18 419	53.37 415	65.25 391	75.86 417	42.32 380	73.99 431	41.57 423	68.91 392	85.18 368

LF4IMVS		64.02 386	62.19 390	69.50 395	70.90 434	53.29 394	76.13 387	77.18 396	52.65 417	58.59 419	80.98 376	23.55 437	76.52 413	53.06 369	66.66 399	78.68 420

UnsupCasMVSNet_bld		63.70 387	61.53 393	70.21 393	73.69 423	51.39 407	72.82 408	81.89 345	55.63 409	57.81 423	71.80 428	38.67 401	78.61 401	49.26 392	52.21 433	80.63 414

test_fmvs3		63.36 388	61.82 391	67.98 405	62.51 445	46.96 426	77.37 383	74.03 412	45.24 430	67.50 364	78.79 401	12.16 450	72.98 434	72.77 194	66.02 402	83.99 385

dmvs_testset		62.63 389	64.11 380	58.19 419	78.55 402	24.76 457	75.28 395	65.94 434	67.91 286	60.34 413	76.01 416	53.56 268	73.94 432	31.79 437	67.65 396	75.88 426

mvsany_test1		62.30 390	61.26 394	65.41 411	69.52 435	54.86 379	66.86 431	49.78 451	46.65 428	68.50 357	83.21 348	49.15 326	66.28 443	56.93 346	60.77 416	75.11 427

new-patchmatchnet		61.73 391	61.73 392	61.70 415	72.74 431	24.50 458	69.16 424	78.03 387	61.40 365	56.72 426	75.53 420	38.42 402	76.48 414	45.95 411	57.67 421	84.13 383

PVSNet_0	57.27 20	61.67 392	59.27 395	68.85 399	79.61 394	57.44 343	68.01 427	73.44 414	55.93 408	58.54 420	70.41 431	44.58 365	77.55 407	47.01 404	35.91 443	71.55 431

test_vis1_rt		60.28 393	58.42 396	65.84 410	67.25 439	55.60 371	70.44 419	60.94 443	44.33 432	59.00 418	66.64 433	24.91 433	68.67 440	62.80 284	69.48 388	73.25 429

ttmdpeth		59.91 394	57.10 398	68.34 403	67.13 440	46.65 427	74.64 402	67.41 430	48.30 426	62.52 408	85.04 310	20.40 440	75.93 420	42.55 421	45.90 441	82.44 402

MVS-HIRNet		59.14 395	57.67 397	63.57 413	81.65 363	43.50 437	71.73 411	65.06 436	39.59 438	51.43 433	57.73 441	38.34 403	82.58 383	39.53 426	73.95 359	64.62 437

pmmvs3		57.79 396	54.26 401	68.37 402	64.02 444	56.72 352	75.12 399	65.17 435	40.20 436	52.93 432	69.86 432	20.36 441	75.48 424	45.45 414	55.25 429	72.90 430

DSMNet-mixed		57.77 397	56.90 399	60.38 417	67.70 438	35.61 448	69.18 423	53.97 449	32.30 447	57.49 424	79.88 390	40.39 393	68.57 441	38.78 429	72.37 372	76.97 423

MVStest1		56.63 398	52.76 404	68.25 404	61.67 446	53.25 395	71.67 412	68.90 428	38.59 439	50.59 435	83.05 351	25.08 432	70.66 436	36.76 432	38.56 442	80.83 413

WB-MVS		54.94 399	54.72 400	55.60 425	73.50 424	20.90 459	74.27 405	61.19 442	59.16 384	50.61 434	74.15 422	47.19 338	75.78 422	17.31 450	35.07 444	70.12 432

LCM-MVSNet		54.25 400	49.68 410	67.97 406	53.73 454	45.28 431	66.85 432	80.78 357	35.96 443	39.45 444	62.23 437	8.70 454	78.06 405	48.24 399	51.20 434	80.57 415

mvsany_test3		53.99 401	51.45 406	61.61 416	55.51 450	44.74 435	63.52 440	45.41 455	43.69 433	58.11 422	76.45 414	17.99 443	63.76 446	54.77 359	47.59 437	76.34 425

SSC-MVS		53.88 402	53.59 402	54.75 427	72.87 430	19.59 460	73.84 407	60.53 444	57.58 400	49.18 438	73.45 425	46.34 349	75.47 425	16.20 453	32.28 446	69.20 433

FPMVS		53.68 403	51.64 405	59.81 418	65.08 442	51.03 409	69.48 422	69.58 424	41.46 435	40.67 442	72.32 427	16.46 446	70.00 439	24.24 446	65.42 404	58.40 442

APD_test1		53.31 404	49.93 409	63.42 414	65.68 441	50.13 414	71.59 413	66.90 432	34.43 444	40.58 443	71.56 429	8.65 455	76.27 416	34.64 435	55.36 427	63.86 438

N_pmnet		52.79 405	53.26 403	51.40 429	78.99 400	7.68 463	69.52 421	3.89 462	51.63 421	57.01 425	74.98 421	40.83 390	65.96 444	37.78 430	64.67 406	80.56 416

test_f		52.09 406	50.82 407	55.90 423	53.82 453	42.31 442	59.42 443	58.31 447	36.45 442	56.12 429	70.96 430	12.18 449	57.79 449	53.51 366	56.57 424	67.60 434

EGC-MVSNET		52.07 407	47.05 411	67.14 407	83.51 325	60.71 301	80.50 340	67.75 429	0.07 457	0.43 458	75.85 419	24.26 435	81.54 389	28.82 440	62.25 412	59.16 440

new_pmnet		50.91 408	50.29 408	52.78 428	68.58 437	34.94 450	63.71 439	56.63 448	39.73 437	44.95 439	65.47 434	21.93 439	58.48 448	34.98 434	56.62 423	64.92 436

ANet_high		50.57 409	46.10 413	63.99 412	48.67 457	39.13 445	70.99 416	80.85 356	61.39 366	31.18 446	57.70 442	17.02 445	73.65 433	31.22 439	15.89 454	79.18 419

test_vis3_rt		49.26 410	47.02 412	56.00 422	54.30 451	45.27 432	66.76 433	48.08 452	36.83 441	44.38 440	53.20 445	7.17 457	64.07 445	56.77 349	55.66 425	58.65 441

testf1		45.72 411	41.96 415	57.00 420	56.90 448	45.32 429	66.14 434	59.26 445	26.19 448	30.89 447	60.96 439	4.14 458	70.64 437	26.39 444	46.73 439	55.04 443

APD_test2		45.72 411	41.96 415	57.00 420	56.90 448	45.32 429	66.14 434	59.26 445	26.19 448	30.89 447	60.96 439	4.14 458	70.64 437	26.39 444	46.73 439	55.04 443

dongtai		45.42 413	45.38 414	45.55 431	73.36 427	26.85 455	67.72 428	34.19 457	54.15 413	49.65 437	56.41 444	25.43 431	62.94 447	19.45 448	28.09 448	46.86 447

Gipuma		45.18 414	41.86 417	55.16 426	77.03 409	51.52 405	32.50 450	80.52 361	32.46 446	27.12 449	35.02 450	9.52 453	75.50 423	22.31 447	60.21 419	38.45 449
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 415	40.28 419	55.82 424	40.82 459	42.54 441	65.12 438	63.99 439	34.43 444	24.48 450	57.12 443	3.92 460	76.17 418	17.10 451	55.52 426	48.75 445
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 416	38.86 420	46.69 430	53.84 452	16.45 461	48.61 447	49.92 450	37.49 440	31.67 445	60.97 438	8.14 456	56.42 450	28.42 441	30.72 447	67.19 435

kuosan		39.70 417	40.40 418	37.58 434	64.52 443	26.98 453	65.62 436	33.02 458	46.12 429	42.79 441	48.99 447	24.10 436	46.56 455	12.16 456	26.30 449	39.20 448

E-PMN		31.77 418	30.64 421	35.15 435	52.87 455	27.67 452	57.09 445	47.86 453	24.64 450	16.40 455	33.05 451	11.23 451	54.90 451	14.46 454	18.15 452	22.87 451

test_method		31.52 419	29.28 423	38.23 433	27.03 461	6.50 464	20.94 452	62.21 441	4.05 455	22.35 453	52.50 446	13.33 447	47.58 453	27.04 443	34.04 445	60.62 439

EMVS		30.81 420	29.65 422	34.27 436	50.96 456	25.95 456	56.58 446	46.80 454	24.01 451	15.53 456	30.68 452	12.47 448	54.43 452	12.81 455	17.05 453	22.43 452

MVE	26.22 23	30.37 421	25.89 425	43.81 432	44.55 458	35.46 449	28.87 451	39.07 456	18.20 452	18.58 454	40.18 449	2.68 461	47.37 454	17.07 452	23.78 451	48.60 446
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 422	26.61 424	0.00 442	0.00 465	0.00 467	0.00 453	89.26 200	0.00 460	0.00 461	88.61 207	61.62 184	0.00 461	0.00 460	0.00 459	0.00 457

tmp_tt		18.61 423	21.40 426	10.23 439	4.82 462	10.11 462	34.70 449	30.74 460	1.48 456	23.91 452	26.07 453	28.42 428	13.41 458	27.12 442	15.35 455	7.17 453

wuyk23d		16.82 424	15.94 427	19.46 438	58.74 447	31.45 451	39.22 448	3.74 463	6.84 454	6.04 457	2.70 457	1.27 462	24.29 457	10.54 457	14.40 456	2.63 454

ab-mvs-re		7.23 425	9.64 428	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	86.72 260	0.00 465	0.00 461	0.00 460	0.00 459	0.00 457

test123		6.12 426	8.11 429	0.14 440	0.06 464	0.09 465	71.05 415	0.03 465	0.04 459	0.25 460	1.30 459	0.05 463	0.03 460	0.21 459	0.01 458	0.29 455

testmvs		6.04 427	8.02 430	0.10 441	0.08 463	0.03 466	69.74 420	0.04 464	0.05 458	0.31 459	1.68 458	0.02 464	0.04 459	0.24 458	0.02 457	0.25 456

pcd_1.5k_mvsjas		5.26 428	7.02 431	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	0.00 460	63.15 159	0.00 461	0.00 460	0.00 459	0.00 457

mmdepth		0.00 429	0.00 432	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	0.00 460	0.00 465	0.00 461	0.00 460	0.00 459	0.00 457

monomultidepth		0.00 429	0.00 432	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	0.00 460	0.00 465	0.00 461	0.00 460	0.00 459	0.00 457

test_blank		0.00 429	0.00 432	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	0.00 460	0.00 465	0.00 461	0.00 460	0.00 459	0.00 457

uanet_test		0.00 429	0.00 432	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	0.00 460	0.00 465	0.00 461	0.00 460	0.00 459	0.00 457

DCPMVS		0.00 429	0.00 432	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	0.00 460	0.00 465	0.00 461	0.00 460	0.00 459	0.00 457

sosnet-low-res		0.00 429	0.00 432	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	0.00 460	0.00 465	0.00 461	0.00 460	0.00 459	0.00 457

sosnet		0.00 429	0.00 432	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	0.00 460	0.00 465	0.00 461	0.00 460	0.00 459	0.00 457

uncertanet		0.00 429	0.00 432	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	0.00 460	0.00 465	0.00 461	0.00 460	0.00 459	0.00 457

Regformer		0.00 429	0.00 432	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	0.00 460	0.00 465	0.00 461	0.00 460	0.00 459	0.00 457

uanet		0.00 429	0.00 432	0.00 442	0.00 465	0.00 467	0.00 453	0.00 466	0.00 460	0.00 461	0.00 460	0.00 465	0.00 461	0.00 460	0.00 459	0.00 457

WAC-MVS						42.58 439								39.46 427

FOURS1					95.00 10	72.39 41	95.06 1	93.84 16	74.49 134	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 13	96.44 9	94.41 42

PC_three_1452									68.21 283	92.02 12	94.00 56	82.09 5	95.98 57	84.58 64	96.68 2	94.95 12

No_MVS				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 13	96.44 9	94.41 42

test_one_0601					95.07 7	71.46 59		94.14 6	78.27 41	92.05 11	95.74 6	80.83 11

eth-test2					0.00 465

eth-test					0.00 465

ZD-MVS					94.38 25	72.22 46		92.67 68	70.98 215	87.75 44	94.07 51	74.01 33	96.70 27	84.66 63	94.84 44

RE-MVS-def			85.48 69		93.06 60	70.63 78	91.88 39	92.27 85	73.53 162	85.69 66	94.45 32	63.87 149		82.75 86	91.87 88	92.50 142

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 295	92.39 6				88.94 25	96.63 4	94.85 21

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 54	82.45 3	96.87 20	83.77 75	96.48 8	94.88 16

test_241102_TWO								94.06 11	77.24 60	92.78 4	95.72 8	81.26 8	97.44 7	89.07 22	96.58 6	94.26 52

test_241102_ONE					95.30 2	70.98 68		94.06 11	77.17 63	93.10 1	95.39 16	82.99 1	97.27 12

9.14			88.26 16		92.84 65		91.52 51	94.75 1	73.93 150	88.57 29	94.67 25	75.57 22	95.79 59	86.77 45	95.76 23

save fliter					93.80 40	72.35 44	90.47 69	91.17 132	74.31 139

test_0728_THIRD									78.38 38	92.12 9	95.78 4	81.46 7	97.40 9	89.42 17	96.57 7	94.67 29

test_0728_SECOND				87.71 32	95.34 1	71.43 60	93.49 10	94.23 3					97.49 4	89.08 20	96.41 12	94.21 53

test0726					95.27 5	71.25 61	93.60 7	94.11 7	77.33 57	92.81 3	95.79 3	80.98 9

GSMVS																88.96 281

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 297				88.96 281

sam_mvs												50.01 313

ambc				75.24 348	73.16 428	50.51 413	63.05 442	87.47 257		64.28 396	77.81 408	17.80 444	89.73 300	57.88 336	60.64 417	85.49 362

MTGPA								92.02 98

test_post1							78.90 364				5.43 456	48.81 332	85.44 361	59.25 320

test_post											5.46 455	50.36 309	84.24 369

patchmatchnet-post											74.00 423	51.12 300	88.60 323

GG-mvs-BLEND				75.38 346	81.59 365	55.80 368	79.32 355	69.63 423		67.19 369	73.67 424	43.24 374	88.90 319	50.41 381	84.50 209	81.45 409

MTMP							92.18 35	32.83 459

gm-plane-assit					81.40 369	53.83 388			62.72 354		80.94 377		92.39 220	63.40 281

test9_res														84.90 57	95.70 26	92.87 127

TEST9					93.26 52	72.96 25	88.75 131	91.89 106	68.44 280	85.00 73	93.10 81	74.36 29	95.41 76

test_8					93.13 56	72.57 35	88.68 136	91.84 110	68.69 275	84.87 77	93.10 81	74.43 27	95.16 86

agg_prior2														82.91 84	95.45 29	92.70 132

agg_prior					92.85 64	71.94 52		91.78 113		84.41 88			94.93 97

TestCases				79.58 283	85.15 287	63.62 247		79.83 372	62.31 357	60.32 414	86.73 258	32.02 420	88.96 317	50.28 384	71.57 380	86.15 350

test_prior4						72.60 34	89.01 118

test_prior2							88.85 125		75.41 107	84.91 75	93.54 69	74.28 30		83.31 78	95.86 20

test_prior				86.33 60	92.61 70	69.59 94		92.97 55					95.48 70			93.91 67

旧先验2							86.56 213		58.10 395	87.04 55			88.98 315	74.07 179

新几何2							86.29 223

新几何1				83.42 171	93.13 56	70.71 76		85.48 293	57.43 401	81.80 130	91.98 107	63.28 153	92.27 226	64.60 273	92.99 72	87.27 326

旧先验1					91.96 76	65.79 197		86.37 280			93.08 85	69.31 88			92.74 76	88.74 292

无先验							87.48 177	88.98 215	60.00 376				94.12 133	67.28 250		88.97 280

原ACMM2							86.86 201

原ACMM1				84.35 124	93.01 62	68.79 113		92.44 78	63.96 340	81.09 141	91.57 122	66.06 129	95.45 71	67.19 252	94.82 46	88.81 287

test222					91.50 82	68.26 133	84.16 281	83.20 328	54.63 412	79.74 160	91.63 119	58.97 220			91.42 96	86.77 340

testdata2													91.01 278	62.37 291

segment_acmp												73.08 40

testdata				79.97 273	90.90 94	64.21 236		84.71 301	59.27 383	85.40 68	92.91 87	62.02 178	89.08 313	68.95 235	91.37 98	86.63 344

testdata1							84.14 282		75.71 100

test12				86.80 54	92.63 69	70.70 77		91.79 112		82.71 119		71.67 58	96.16 48		94.50 53	93.54 95

plane_prior7					90.08 112	68.51 127

plane_prior6					89.84 121	68.70 121						60.42 210

plane_prior5								92.44 78					95.38 78	78.71 125	86.32 181	91.33 184

plane_prior4											91.00 144

plane_prior3						68.60 124			78.44 36	78.92 173

plane_prior2							91.25 55		79.12 28

plane_prior1					89.90 120

plane_prior						68.71 119	90.38 73		77.62 47						86.16 185

n2								0.00 466

nn								0.00 466

door-mid								69.98 422

lessismore_v0				78.97 293	81.01 376	57.15 346		65.99 433		61.16 411	82.82 357	39.12 398	91.34 266	59.67 316	46.92 438	88.43 300

LGP-MVS_train				84.50 117	89.23 148	68.76 115		91.94 104	75.37 109	76.64 228	91.51 123	54.29 260	94.91 98	78.44 127	83.78 222	89.83 252

test11								92.23 88

door								69.44 425

HQP5-MVS						66.98 175

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 213

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 213

BP-MVS														77.47 139

HQP4-MVS										77.24 212			95.11 90			91.03 194

HQP3-MVS								92.19 92							85.99 189

HQP2-MVS												60.17 213

NP-MVS					89.62 125	68.32 131					90.24 160

MDTV_nov1_ep13_2view						37.79 447	75.16 397		55.10 410	66.53 379		49.34 323		53.98 363		87.94 309

MDTV_nov1_ep13			69.97 336		83.18 333	53.48 390	77.10 386	80.18 371	60.45 371	69.33 349	80.44 381	48.89 331	86.90 342	51.60 375	78.51 294

ACMMP++_ref															81.95 255

ACMMP++															81.25 260

Test By Simon												64.33 145

ITE_SJBPF				78.22 308	81.77 362	60.57 303		83.30 323	69.25 260	67.54 363	87.20 249	36.33 412	87.28 340	54.34 361	74.62 354	86.80 339

DeepMVS_CX				27.40 437	40.17 460	26.90 454		24.59 461	17.44 453	23.95 451	48.61 448	9.77 452	26.48 456	18.06 449	24.47 450	28.83 450

High-resolution multi-view benchmark