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	92.70 131
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	92.84 129

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	92.34 148

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	64.64 142	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	65.80 132	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	80.73 147	93.82 65	64.33 144	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	65.00 140	95.56 64	82.75 86	91.87 88	92.50 141

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	87.28 259	76.41 85	85.80 64	90.22 161	74.15 32	95.37 81	81.82 95	91.88 87	92.65 135

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	76.66 226	93.37 76	60.40 211	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	90.16 230

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	89.03 192	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	64.64 142	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	62.60 164	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	92.08 162

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	71.39 208	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	91.46 182

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	85.71 287	69.32 87	95.38 78	80.82 105	91.37 98	92.72 130

test_fmvsmconf0.01_n		84.73 83	84.52 85	85.34 87	80.25 382	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	87.26 261	68.08 284	88.03 38	93.49 70	72.04 52	91.77 244	88.90 26	89.14 137	92.24 155

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	56.89 239	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	89.59 177	70.74 71	94.82 104	80.66 110	84.72 205	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	86.85 271	67.48 291	87.48 49	93.40 75	70.89 68	91.61 249	88.38 34	89.22 135	92.16 160

fmvsm_s_conf0.5_n_2		84.04 88	84.11 89	83.81 161	86.17 260	65.00 218	86.96 196	87.28 259	74.35 137	88.25 33	94.23 44	61.82 179	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	87.96 242	70.01 240	83.95 100	93.23 79	68.80 97	91.51 259	88.61 29	89.96 122	92.57 136

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	79.28 287	92.50 141

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	89.41 186	70.24 77	94.74 109	79.95 115	83.92 220	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	86.62 275	74.32 138	87.97 41	94.33 38	60.67 203	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	83.07 329	71.09 210	86.96 57	93.70 68	69.02 95	91.47 261	88.79 27	84.62 207	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	79.57 162	92.83 90	60.60 207	93.04 196	80.92 104	91.56 95	90.86 200

EPNet		83.72 95	82.92 108	86.14 68	84.22 308	69.48 97	91.05 59	85.27 293	81.30 6	76.83 221	91.65 117	66.09 127	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	80.99 249	90.06 116	65.83 194	84.21 280	88.74 227	71.60 198	85.01 72	92.44 98	74.51 26	83.50 375	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	78.92 172	91.00 144	60.42 209	95.38 78	78.71 125	86.32 181	91.33 183

fmvsm_s_conf0.5_n_a		83.63 98	83.41 98	84.28 129	86.14 261	68.12 139	89.43 97	82.87 334	70.27 235	87.27 53	93.80 66	69.09 90	91.58 251	88.21 35	83.65 228	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	88.28 216	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	83.01 330	70.67 220	87.08 54	93.96 60	68.38 101	91.45 262	88.56 31	84.50 208	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	55.69 246	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	80.96 143	91.75 114	60.71 201	94.50 118	79.67 119	86.51 179	89.97 246
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	80.87 144	92.89 88	61.00 198	94.20 129	72.45 200	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	62.28 280	88.17 155	89.50 187	75.22 112	81.49 134	92.74 96	66.75 116	95.11 90	72.85 191	91.58 94	92.45 145

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	79.35 166	91.10 137	69.05 93	95.12 88	72.78 192	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	75.47 252	92.83 90	58.56 221	94.72 110	73.24 188	92.71 77	92.13 161

fmvsm_s_conf0.5_n_7		83.34 107	84.03 90	81.28 240	85.73 270	65.13 213	85.40 248	89.90 173	74.96 122	82.13 124	93.89 62	66.65 117	87.92 330	86.56 47	91.05 102	90.80 201

fmvsm_s_conf0.1_n_a		83.32 108	82.99 106	84.28 129	83.79 318	68.07 141	89.34 104	82.85 335	69.80 246	87.36 52	94.06 52	68.34 102	91.56 254	87.95 36	83.46 234	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	57.27 234	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	78.96 170	86.42 274	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	61.54 184	95.50 69	82.71 88	75.48 337	91.72 172

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	89.44 185	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	79.49 163	92.16 104	65.10 137	94.28 124	67.71 244	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	79.17 168	91.03 142	64.12 146	96.03 51	68.39 241	90.14 118	91.50 178

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	77.32 209	90.66 149	67.90 107	94.90 100	70.37 218	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	52.19 280	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	89.47 180	61.68 181	93.46 168	78.98 122	90.26 116	92.05 163

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	79.72 160	91.65 117	62.19 174	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	78.96 170	88.46 211	65.47 134	94.87 103	74.42 174	88.57 146	90.24 228

MVS_111021_LR		82.61 120	82.11 120	84.11 137	88.82 162	71.58 57	85.15 253	86.16 283	74.69 129	80.47 152	91.04 140	62.29 171	90.55 286	80.33 112	90.08 120	90.20 229

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	77.23 212	90.23 160	60.17 212	95.11 90	77.47 139	85.99 189	91.03 193

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	79.85 158	90.28 157	56.62 242	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	78.33 187	86.58 269	64.01 147	94.35 122	76.05 157	87.48 162	90.79 202
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	81.62 229	90.82 96	60.93 296	84.47 271	89.78 175	76.36 90	84.07 97	91.88 110	64.71 141	90.26 288	70.68 215	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	89.85 166	65.96 131	90.79 281	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	76.64 227	91.51 123	54.29 259	94.91 98	78.44 127	83.78 221	89.83 251

FIs		82.07 127	82.42 114	81.04 248	88.80 165	58.34 325	88.26 152	93.49 27	76.93 71	78.47 184	91.04 140	69.92 80	92.34 224	69.87 225	84.97 202	92.44 146

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	79.37 165	90.22 161	63.15 158	94.27 125	77.69 137	82.36 249	91.49 179

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	78.66 177	88.28 216	65.26 135	95.10 93	64.74 271	91.23 100	87.51 318

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	80.62 148	90.39 155	59.57 214	94.65 114	72.45 200	87.19 167	92.47 144

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	78.38 185	89.14 188	71.66 59	93.05 194	70.05 221	76.46 320	92.25 153

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	78.50 181	86.21 278	62.36 170	94.52 117	65.36 265	92.05 86	89.77 254
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	81.85 346	77.04 69	83.21 110	93.10 81	52.26 279	93.43 170	71.98 203	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	87.26 261	76.02 96	84.67 80	88.22 219	61.54 184	93.48 166	82.71 88	73.44 365	91.06 191

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	78.63 178	89.76 170	66.32 124	93.20 183	69.89 224	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	86.53 272	63.17 157	94.19 131	75.60 163	88.54 147	88.57 296

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	85.62 292	63.15 158	94.29 123	75.62 162	88.87 140	88.59 295

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	77.13 219	89.50 178	67.63 109	94.88 102	67.55 246	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	78.56 180	88.16 220	69.78 81	93.26 176	69.58 228	76.49 319	91.60 173

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	79.48 164	90.39 155	59.57 214	94.48 120	72.45 200	85.93 191	92.18 158

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	80.50 150	89.83 167	46.89 340	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	80.50 150	89.83 167	46.89 340	94.82 104	76.85 147	89.57 129	93.80 77

FC-MVSNet-test		81.52 143	82.02 124	80.03 271	88.42 181	55.97 364	87.95 163	93.42 30	77.10 67	77.38 207	90.98 146	69.96 79	91.79 243	68.46 240	84.50 208	92.33 149

VDDNet		81.52 143	80.67 143	84.05 149	90.44 104	64.13 238	89.73 87	85.91 286	71.11 209	83.18 111	93.48 71	50.54 306	93.49 165	73.40 185	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	75.04 274	90.41 154	53.82 265	94.54 115	77.56 138	82.91 241	89.86 250
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	86.77 272	63.24 342	81.07 142	89.47 180	61.08 197	92.15 230	78.33 130	90.07 121	92.05 163
jason: jason.
lupinMVS		81.39 146	80.27 154	84.76 111	87.35 224	70.21 82	85.55 243	86.41 277	62.85 349	81.32 136	88.61 206	61.68 181	92.24 228	78.41 129	90.26 116	91.83 166

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	89.61 175	57.50 231	93.58 159	70.75 213	86.90 171	92.52 139

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	89.61 175	57.50 231	93.58 159	70.75 213	86.90 171	92.52 139

guyue		81.13 150	80.64 144	82.60 213	86.52 253	63.92 243	86.69 209	87.73 250	73.97 147	80.83 146	89.69 171	56.70 240	91.33 267	78.26 134	85.40 199	92.54 138

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	78.38 185	89.07 190	65.02 138	93.05 194	70.05 221	76.46 320	92.20 156

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	78.11 192	85.05 308	66.02 129	94.27 125	71.52 205	89.50 131	89.01 276

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	79.61 161	87.57 236	58.35 223	94.72 110	71.29 209	86.25 183	92.56 137

QAPM		80.88 154	79.50 174	85.03 98	88.01 200	68.97 110	91.59 46	92.00 100	66.63 303	75.15 270	92.16 104	57.70 228	95.45 71	63.52 277	88.76 143	90.66 209

TranMVSNet+NR-MVSNet		80.84 155	80.31 152	82.42 216	87.85 206	62.33 278	87.74 172	91.33 127	80.55 9	77.99 196	89.86 165	65.23 136	92.62 206	67.05 253	75.24 347	92.30 151

UGNet		80.83 156	79.59 172	84.54 116	88.04 197	68.09 140	89.42 99	88.16 235	76.95 70	76.22 238	89.46 182	49.30 323	93.94 140	68.48 239	90.31 114	91.60 173
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	85.90 287	73.71 155	80.85 145	90.56 151	54.06 263	91.57 253	79.72 118	83.97 219	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	78.49 182	85.06 307	67.54 110	93.58 159	67.03 254	86.58 177	92.32 150

XVG-OURS-SEG-HR		80.81 157	79.76 167	83.96 156	85.60 274	68.78 114	83.54 296	90.50 150	70.66 223	76.71 225	91.66 116	60.69 202	91.26 268	76.94 146	81.58 257	91.83 166

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	78.49 182	89.03 192	63.26 154	93.27 175	72.56 197	85.56 196	91.74 169

xiu_mvs_v1_base_debu		80.80 160	79.72 168	84.03 151	87.35 224	70.19 84	85.56 240	88.77 223	69.06 267	81.83 127	88.16 220	50.91 300	92.85 201	78.29 131	87.56 159	89.06 271

xiu_mvs_v1_base		80.80 160	79.72 168	84.03 151	87.35 224	70.19 84	85.56 240	88.77 223	69.06 267	81.83 127	88.16 220	50.91 300	92.85 201	78.29 131	87.56 159	89.06 271

xiu_mvs_v1_base_debi		80.80 160	79.72 168	84.03 151	87.35 224	70.19 84	85.56 240	88.77 223	69.06 267	81.83 127	88.16 220	50.91 300	92.85 201	78.29 131	87.56 159	89.06 271

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	77.25 210	89.66 173	53.37 270	93.53 164	74.24 177	82.85 242	88.85 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LuminaMVS		80.68 165	79.62 171	83.83 159	85.07 291	68.01 144	86.99 195	88.83 220	70.36 230	81.38 135	87.99 227	50.11 311	92.51 215	79.02 120	86.89 173	90.97 196

114514_t		80.68 165	79.51 173	84.20 135	94.09 38	67.27 168	89.64 90	91.11 135	58.75 389	74.08 289	90.72 148	58.10 224	95.04 95	69.70 226	89.42 133	90.30 226

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	77.82 198	89.03 192	61.84 178	92.91 199	72.56 197	85.56 196	91.74 169

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	75.88 245	88.44 212	53.51 268	93.07 192	73.30 186	89.74 127	92.25 153

VPA-MVSNet		80.60 169	80.55 146	80.76 255	88.07 196	60.80 299	86.86 201	91.58 120	75.67 103	80.24 154	89.45 184	63.34 151	90.25 289	70.51 217	79.22 288	91.23 186

mvsmamba		80.60 169	79.38 176	84.27 131	89.74 124	67.24 170	87.47 178	86.95 267	70.02 239	75.38 258	88.93 196	51.24 297	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	78.11 192	86.09 282	66.02 129	94.27 125	71.52 205	82.06 252	87.39 320

AdaColmap		80.58 172	79.42 175	84.06 146	93.09 59	68.91 111	89.36 103	88.97 217	69.27 258	75.70 248	89.69 171	57.20 236	95.77 60	63.06 282	88.41 151	87.50 319

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	78.69 175	87.54 239	66.62 118	92.43 218	72.57 195	80.57 271	90.74 206

XVG-OURS		80.41 174	79.23 182	83.97 155	85.64 272	69.02 108	83.03 308	90.39 153	71.09 210	77.63 203	91.49 125	54.62 258	91.35 265	75.71 160	83.47 233	91.54 176

SDMVSNet		80.38 175	80.18 155	80.99 249	89.03 157	64.94 220	80.45 340	89.40 189	75.19 115	76.61 229	89.98 163	60.61 206	87.69 334	76.83 150	83.55 230	90.33 224

PCF-MVS	73.52 7	80.38 175	78.84 190	85.01 99	87.71 215	68.99 109	83.65 290	91.46 126	63.00 346	77.77 201	90.28 157	66.10 126	95.09 94	61.40 301	88.22 153	90.94 198
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		80.37 177	77.83 214	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 106	12.47 453	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

test_djsdf		80.30 178	79.32 179	83.27 177	83.98 314	65.37 208	90.50 67	90.38 154	68.55 277	76.19 239	88.70 202	56.44 243	93.46 168	78.98 122	80.14 277	90.97 196

v2v482		80.23 179	79.29 180	83.05 190	83.62 322	64.14 237	87.04 192	89.97 170	73.61 158	78.18 191	87.22 247	61.10 196	93.82 149	76.11 155	76.78 316	91.18 187

NR-MVSNet		80.23 179	79.38 176	82.78 207	87.80 209	63.34 260	86.31 221	91.09 136	79.01 31	72.17 315	89.07 190	67.20 114	92.81 204	66.08 260	75.65 333	92.20 156

Anonymous20240529		80.19 181	78.89 189	84.10 138	90.60 100	64.75 225	88.95 120	90.90 139	65.97 311	80.59 149	91.17 136	49.97 313	93.73 157	69.16 232	82.70 246	93.81 75

IterMVS-LS		80.06 182	79.38 176	82.11 220	85.89 266	63.20 264	86.79 204	89.34 191	74.19 143	75.45 255	86.72 259	66.62 118	92.39 220	72.58 194	76.86 313	90.75 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		80.03 183	78.57 194	84.42 121	85.13 289	68.74 117	88.77 129	88.10 237	74.99 119	74.97 276	83.49 343	57.27 234	93.36 172	73.53 182	80.88 265	91.18 187

v1144		80.03 183	79.03 186	83.01 192	83.78 319	64.51 228	87.11 191	90.57 149	71.96 192	78.08 194	86.20 279	61.41 188	93.94 140	74.93 170	77.23 307	90.60 212

v8		79.97 185	79.02 187	82.80 203	84.09 311	64.50 230	87.96 162	90.29 161	74.13 146	75.24 267	86.81 256	62.88 163	93.89 148	74.39 175	75.40 342	90.00 242

OpenMVS	72.83 10	79.77 186	78.33 201	84.09 142	85.17 285	69.91 89	90.57 64	90.97 137	66.70 297	72.17 315	91.91 108	54.70 256	93.96 137	61.81 298	90.95 105	88.41 300

v10		79.74 187	78.67 191	82.97 195	84.06 312	64.95 219	87.88 168	90.62 146	73.11 173	75.11 271	86.56 270	61.46 187	94.05 136	73.68 180	75.55 335	89.90 248

ECVR-MVS		79.61 188	79.26 181	80.67 257	90.08 112	54.69 379	87.89 167	77.44 392	74.88 124	80.27 153	92.79 93	48.96 329	92.45 217	68.55 238	92.50 80	94.86 19

BH-RMVSNet		79.61 188	78.44 197	83.14 184	89.38 139	65.93 191	84.95 259	87.15 264	73.56 160	78.19 190	89.79 169	56.67 241	93.36 172	59.53 317	86.74 175	90.13 232

v1192		79.59 190	78.43 198	83.07 189	83.55 324	64.52 227	86.93 199	90.58 147	70.83 216	77.78 200	85.90 283	59.15 218	93.94 140	73.96 179	77.19 309	90.76 204

ab-mvs		79.51 191	78.97 188	81.14 245	88.46 178	60.91 297	83.84 285	89.24 203	70.36 230	79.03 169	88.87 199	63.23 156	90.21 290	65.12 267	82.57 247	92.28 152

WR-MVS		79.49 192	79.22 183	80.27 266	88.79 166	58.35 324	85.06 256	88.61 231	78.56 35	77.65 202	88.34 214	63.81 150	90.66 285	64.98 269	77.22 308	91.80 168

v144192		79.47 193	78.37 199	82.78 207	83.35 327	63.96 240	86.96 196	90.36 157	69.99 241	77.50 204	85.67 290	60.66 204	93.77 153	74.27 176	76.58 317	90.62 210

BH-untuned		79.47 193	78.60 193	82.05 221	89.19 150	65.91 192	86.07 228	88.52 232	72.18 187	75.42 256	87.69 233	61.15 195	93.54 163	60.38 309	86.83 174	86.70 341

test1111		79.43 195	79.18 184	80.15 269	89.99 117	53.31 392	87.33 185	77.05 396	75.04 118	80.23 155	92.77 95	48.97 328	92.33 225	68.87 235	92.40 82	94.81 22

mvs_anonymous		79.42 196	79.11 185	80.34 264	84.45 305	57.97 331	82.59 310	87.62 252	67.40 292	76.17 242	88.56 209	68.47 100	89.59 301	70.65 216	86.05 187	93.47 97

thisisatest0530		79.40 197	77.76 219	84.31 126	87.69 217	65.10 216	87.36 183	84.26 309	70.04 238	77.42 206	88.26 218	49.94 314	94.79 108	70.20 219	84.70 206	93.03 121

tttt0517		79.40 197	77.91 210	83.90 158	88.10 194	63.84 244	88.37 148	84.05 311	71.45 201	76.78 223	89.12 189	49.93 316	94.89 101	70.18 220	83.18 239	92.96 126

V42		79.38 199	78.24 203	82.83 200	81.10 374	65.50 204	85.55 243	89.82 174	71.57 199	78.21 189	86.12 281	60.66 204	93.18 186	75.64 161	75.46 339	89.81 253

jajsoiax		79.29 200	77.96 208	83.27 177	84.68 299	66.57 181	89.25 106	90.16 165	69.20 263	75.46 254	89.49 179	45.75 356	93.13 189	76.84 149	80.80 267	90.11 234

v1921920		79.22 201	78.03 207	82.80 203	83.30 329	63.94 242	86.80 203	90.33 158	69.91 244	77.48 205	85.53 294	58.44 222	93.75 155	73.60 181	76.85 314	90.71 208

AUN-MVS		79.21 202	77.60 224	84.05 149	88.71 170	67.61 155	85.84 235	87.26 261	69.08 266	77.23 212	88.14 224	53.20 272	93.47 167	75.50 165	73.45 364	91.06 191

TAPA-MVS	73.13 9	79.15 203	77.94 209	82.79 206	89.59 126	62.99 271	88.16 156	91.51 122	65.77 312	77.14 218	91.09 138	60.91 199	93.21 180	50.26 385	87.05 169	92.17 159
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 204	77.77 218	83.22 181	84.70 298	66.37 183	89.17 109	90.19 164	69.38 255	75.40 257	89.46 182	44.17 368	93.15 187	76.78 151	80.70 269	90.14 231

UniMVSNet_ETH3D		79.10 205	78.24 203	81.70 228	86.85 244	60.24 308	87.28 187	88.79 222	74.25 142	76.84 220	90.53 153	49.48 319	91.56 254	67.98 242	82.15 250	93.29 104

CDS-MVSNet		79.07 206	77.70 221	83.17 183	87.60 219	68.23 137	84.40 277	86.20 282	67.49 290	76.36 235	86.54 271	61.54 184	90.79 281	61.86 297	87.33 164	90.49 217
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 207	77.88 213	82.38 217	83.07 336	64.80 224	84.08 284	88.95 218	69.01 270	78.69 175	87.17 250	54.70 256	92.43 218	74.69 171	80.57 271	89.89 249

v1240		78.99 208	77.78 217	82.64 211	83.21 331	63.54 254	86.62 211	90.30 160	69.74 251	77.33 208	85.68 289	57.04 237	93.76 154	73.13 189	76.92 311	90.62 210

Anonymous20231211		78.97 209	77.69 222	82.81 202	90.54 102	64.29 235	90.11 78	91.51 122	65.01 323	76.16 243	88.13 225	50.56 305	93.03 197	69.68 227	77.56 306	91.11 189

v7n		78.97 209	77.58 225	83.14 184	83.45 326	65.51 203	88.32 150	91.21 130	73.69 156	72.41 311	86.32 277	57.93 225	93.81 150	69.18 231	75.65 333	90.11 234

TAMVS		78.89 211	77.51 226	83.03 191	87.80 209	67.79 151	84.72 263	85.05 298	67.63 287	76.75 224	87.70 232	62.25 172	90.82 280	58.53 328	87.13 168	90.49 217

c3_l		78.75 212	77.91 210	81.26 241	82.89 343	61.56 289	84.09 283	89.13 209	69.97 242	75.56 250	84.29 322	66.36 123	92.09 232	73.47 184	75.48 337	90.12 233

tt0805		78.73 213	77.83 214	81.43 234	85.17 285	60.30 307	89.41 100	90.90 139	71.21 207	77.17 217	88.73 201	46.38 345	93.21 180	72.57 195	78.96 289	90.79 202

v148		78.72 214	77.80 216	81.47 233	82.73 346	61.96 284	86.30 222	88.08 238	73.26 170	76.18 240	85.47 296	62.46 168	92.36 222	71.92 204	73.82 361	90.09 236

VPNet		78.69 215	78.66 192	78.76 295	88.31 184	55.72 368	84.45 274	86.63 274	76.79 75	78.26 188	90.55 152	59.30 217	89.70 300	66.63 255	77.05 310	90.88 199

ET-MVSNet_ETH3D		78.63 216	76.63 247	84.64 114	86.73 248	69.47 98	85.01 257	84.61 302	69.54 252	66.51 381	86.59 267	50.16 310	91.75 245	76.26 154	84.24 216	92.69 133

anonymousdsp		78.60 217	77.15 232	82.98 194	80.51 380	67.08 173	87.24 188	89.53 186	65.66 314	75.16 269	87.19 249	52.52 274	92.25 227	77.17 143	79.34 286	89.61 258

miper_ehance_all_eth		78.59 218	77.76 219	81.08 247	82.66 348	61.56 289	83.65 290	89.15 207	68.87 272	75.55 251	83.79 334	66.49 121	92.03 233	73.25 187	76.39 322	89.64 257

VortexMVS		78.57 219	77.89 212	80.59 258	85.89 266	62.76 274	85.61 238	89.62 183	72.06 190	74.99 275	85.38 298	55.94 245	90.77 283	74.99 169	76.58 317	88.23 302

WR-MVS_H		78.51 220	78.49 195	78.56 300	88.02 198	56.38 358	88.43 143	92.67 68	77.14 64	73.89 291	87.55 238	66.25 125	89.24 308	58.92 323	73.55 363	90.06 240

GBi-Net		78.40 221	77.40 227	81.40 236	87.60 219	63.01 267	88.39 145	89.28 197	71.63 195	75.34 260	87.28 243	54.80 252	91.11 271	62.72 284	79.57 281	90.09 236

test1		78.40 221	77.40 227	81.40 236	87.60 219	63.01 267	88.39 145	89.28 197	71.63 195	75.34 260	87.28 243	54.80 252	91.11 271	62.72 284	79.57 281	90.09 236

Vis-MVSNet (Re-imp)		78.36 223	78.45 196	78.07 311	88.64 172	51.78 402	86.70 208	79.63 374	74.14 145	75.11 271	90.83 147	61.29 192	89.75 298	58.10 333	91.60 92	92.69 133

Anonymous202405211		78.25 224	77.01 234	81.99 223	91.03 90	60.67 301	84.77 262	83.90 313	70.65 224	80.00 157	91.20 134	41.08 388	91.43 263	65.21 266	85.26 200	93.85 71

CP-MVSNet		78.22 225	78.34 200	77.84 315	87.83 208	54.54 381	87.94 164	91.17 132	77.65 46	73.48 297	88.49 210	62.24 173	88.43 324	62.19 292	74.07 356	90.55 214

BH-w/o		78.21 226	77.33 230	80.84 253	88.81 163	65.13 213	84.87 260	87.85 247	69.75 249	74.52 284	84.74 314	61.34 190	93.11 190	58.24 332	85.84 192	84.27 379

FMVSNet2		78.20 227	77.21 231	81.20 243	87.60 219	62.89 273	87.47 178	89.02 213	71.63 195	75.29 266	87.28 243	54.80 252	91.10 274	62.38 289	79.38 285	89.61 258

MVS		78.19 228	76.99 236	81.78 226	85.66 271	66.99 174	84.66 265	90.47 151	55.08 410	72.02 317	85.27 300	63.83 149	94.11 134	66.10 259	89.80 126	84.24 380

Baseline_NR-MVSNet		78.15 229	78.33 201	77.61 320	85.79 268	56.21 362	86.78 205	85.76 289	73.60 159	77.93 197	87.57 236	65.02 138	88.99 313	67.14 252	75.33 344	87.63 314

CNLPA		78.08 230	76.79 241	81.97 224	90.40 105	71.07 67	87.59 175	84.55 303	66.03 310	72.38 312	89.64 174	57.56 230	86.04 351	59.61 316	83.35 235	88.79 287

cl22		78.07 231	77.01 234	81.23 242	82.37 355	61.83 286	83.55 294	87.98 241	68.96 271	75.06 273	83.87 330	61.40 189	91.88 241	73.53 182	76.39 322	89.98 245

PLC	70.83 11	78.05 232	76.37 253	83.08 188	91.88 79	67.80 150	88.19 154	89.46 188	64.33 331	69.87 342	88.38 213	53.66 266	93.58 159	58.86 324	82.73 244	87.86 310
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 233	76.49 248	82.62 212	83.16 335	66.96 177	86.94 198	87.45 257	72.45 182	71.49 323	84.17 327	54.79 255	91.58 251	67.61 245	80.31 274	89.30 267

PS-CasMVS		78.01 234	78.09 206	77.77 317	87.71 215	54.39 383	88.02 160	91.22 129	77.50 54	73.26 299	88.64 205	60.73 200	88.41 325	61.88 296	73.88 360	90.53 215

HY-MVS	69.67 12	77.95 235	77.15 232	80.36 263	87.57 223	60.21 309	83.37 298	87.78 249	66.11 307	75.37 259	87.06 254	63.27 153	90.48 287	61.38 302	82.43 248	90.40 221

eth_miper_zixun_eth		77.92 236	76.69 245	81.61 231	83.00 339	61.98 283	83.15 302	89.20 205	69.52 253	74.86 278	84.35 321	61.76 180	92.56 211	71.50 207	72.89 369	90.28 227

FMVSNet3		77.88 237	76.85 239	80.97 251	86.84 245	62.36 277	86.52 214	88.77 223	71.13 208	75.34 260	86.66 265	54.07 262	91.10 274	62.72 284	79.57 281	89.45 262

miper_enhance_ethall		77.87 238	76.86 238	80.92 252	81.65 362	61.38 291	82.68 309	88.98 215	65.52 316	75.47 252	82.30 363	65.76 133	92.00 235	72.95 190	76.39 322	89.39 264

FE-MVS		77.78 239	75.68 259	84.08 143	88.09 195	66.00 189	83.13 303	87.79 248	68.42 281	78.01 195	85.23 302	45.50 359	95.12 88	59.11 321	85.83 193	91.11 189

PEN-MVS		77.73 240	77.69 222	77.84 315	87.07 242	53.91 386	87.91 166	91.18 131	77.56 51	73.14 301	88.82 200	61.23 193	89.17 310	59.95 312	72.37 371	90.43 219

cl____		77.72 241	76.76 242	80.58 259	82.49 352	60.48 304	83.09 304	87.87 245	69.22 261	74.38 287	85.22 303	62.10 175	91.53 257	71.09 210	75.41 341	89.73 256

DIV-MVS_self_test		77.72 241	76.76 242	80.58 259	82.48 353	60.48 304	83.09 304	87.86 246	69.22 261	74.38 287	85.24 301	62.10 175	91.53 257	71.09 210	75.40 342	89.74 255

sd_testset		77.70 243	77.40 227	78.60 298	89.03 157	60.02 310	79.00 360	85.83 288	75.19 115	76.61 229	89.98 163	54.81 251	85.46 359	62.63 288	83.55 230	90.33 224

PAPM		77.68 244	76.40 252	81.51 232	87.29 232	61.85 285	83.78 286	89.59 184	64.74 325	71.23 325	88.70 202	62.59 165	93.66 158	52.66 369	87.03 170	89.01 276

CHOSEN 1792x2688		77.63 245	75.69 258	83.44 170	89.98 118	68.58 125	78.70 365	87.50 255	56.38 405	75.80 247	86.84 255	58.67 220	91.40 264	61.58 300	85.75 194	90.34 223

HyFIR lowres test		77.53 246	75.40 266	83.94 157	89.59 126	66.62 179	80.36 341	88.64 230	56.29 406	76.45 232	85.17 304	57.64 229	93.28 174	61.34 303	83.10 240	91.91 165

FMVSNet1		77.44 247	76.12 255	81.40 236	86.81 246	63.01 267	88.39 145	89.28 197	70.49 229	74.39 286	87.28 243	49.06 327	91.11 271	60.91 305	78.52 292	90.09 236

TR-MVS		77.44 247	76.18 254	81.20 243	88.24 186	63.24 262	84.61 268	86.40 278	67.55 289	77.81 199	86.48 273	54.10 261	93.15 187	57.75 336	82.72 245	87.20 326

1112_ss		77.40 249	76.43 250	80.32 265	89.11 156	60.41 306	83.65 290	87.72 251	62.13 359	73.05 302	86.72 259	62.58 166	89.97 294	62.11 295	80.80 267	90.59 213

thisisatest0515		77.33 250	75.38 267	83.18 182	85.27 284	63.80 245	82.11 315	83.27 323	65.06 321	75.91 244	83.84 332	49.54 318	94.27 125	67.24 250	86.19 184	91.48 180

test2506		77.30 251	76.49 248	79.74 277	90.08 112	52.02 396	87.86 169	63.10 439	74.88 124	80.16 156	92.79 93	38.29 403	92.35 223	68.74 237	92.50 80	94.86 19

pm-mvs1		77.25 252	76.68 246	78.93 293	84.22 308	58.62 322	86.41 217	88.36 234	71.37 202	73.31 298	88.01 226	61.22 194	89.15 311	64.24 275	73.01 368	89.03 275

ICG_test_0404		77.16 253	76.42 251	79.37 285	87.13 236	63.59 251	77.12 384	89.33 192	70.51 226	66.22 384	89.03 192	50.36 308	82.78 380	72.56 197	85.56 196	91.74 169

LCM-MVSNet-Re		77.05 254	76.94 237	77.36 324	87.20 233	51.60 403	80.06 345	80.46 362	75.20 114	67.69 361	86.72 259	62.48 167	88.98 314	63.44 279	89.25 134	91.51 177

DTE-MVSNet		76.99 255	76.80 240	77.54 323	86.24 257	53.06 395	87.52 176	90.66 145	77.08 68	72.50 309	88.67 204	60.48 208	89.52 302	57.33 340	70.74 383	90.05 241

baseline1		76.98 256	76.75 244	77.66 318	88.13 192	55.66 369	85.12 254	81.89 344	73.04 175	76.79 222	88.90 197	62.43 169	87.78 333	63.30 281	71.18 381	89.55 260

LS3D		76.95 257	74.82 275	83.37 174	90.45 103	67.36 165	89.15 113	86.94 268	61.87 362	69.52 345	90.61 150	51.71 293	94.53 116	46.38 407	86.71 176	88.21 304

GA-MVS		76.87 258	75.17 272	81.97 224	82.75 345	62.58 275	81.44 324	86.35 280	72.16 189	74.74 279	82.89 354	46.20 350	92.02 234	68.85 236	81.09 262	91.30 185

mamv4		76.81 259	78.23 205	72.54 376	86.12 262	65.75 199	78.76 364	82.07 343	64.12 333	72.97 303	91.02 143	67.97 105	68.08 441	83.04 82	78.02 299	83.80 387

DP-MVS		76.78 260	74.57 278	83.42 171	93.29 48	69.46 100	88.55 141	83.70 315	63.98 338	70.20 333	88.89 198	54.01 264	94.80 107	46.66 404	81.88 255	86.01 353

cascas		76.72 261	74.64 277	82.99 193	85.78 269	65.88 193	82.33 312	89.21 204	60.85 368	72.74 305	81.02 374	47.28 336	93.75 155	67.48 247	85.02 201	89.34 266

testing91		76.54 262	75.66 261	79.18 290	88.43 180	55.89 365	81.08 327	83.00 331	73.76 154	75.34 260	84.29 322	46.20 350	90.07 292	64.33 273	84.50 208	91.58 175

1314		76.53 263	75.30 270	80.21 268	83.93 315	62.32 279	84.66 265	88.81 221	60.23 373	70.16 336	84.07 329	55.30 249	90.73 284	67.37 248	83.21 238	87.59 317

thres100view900		76.50 264	75.55 263	79.33 286	89.52 129	56.99 347	85.83 236	83.23 324	73.94 149	76.32 236	87.12 251	51.89 289	91.95 237	48.33 395	83.75 224	89.07 269

thres600view7		76.50 264	75.44 264	79.68 279	89.40 137	57.16 344	85.53 245	83.23 324	73.79 153	76.26 237	87.09 252	51.89 289	91.89 240	48.05 400	83.72 227	90.00 242

thres400		76.50 264	75.37 268	79.86 274	89.13 152	57.65 338	85.17 251	83.60 316	73.41 166	76.45 232	86.39 275	52.12 281	91.95 237	48.33 395	83.75 224	90.00 242

MonoMVSNet		76.49 267	75.80 256	78.58 299	81.55 365	58.45 323	86.36 220	86.22 281	74.87 126	74.73 280	83.73 336	51.79 292	88.73 319	70.78 212	72.15 374	88.55 297

tfpn200view9		76.42 268	75.37 268	79.55 284	89.13 152	57.65 338	85.17 251	83.60 316	73.41 166	76.45 232	86.39 275	52.12 281	91.95 237	48.33 395	83.75 224	89.07 269

Test_1112_low_res		76.40 269	75.44 264	79.27 287	89.28 145	58.09 327	81.69 319	87.07 265	59.53 380	72.48 310	86.67 264	61.30 191	89.33 305	60.81 307	80.15 276	90.41 220

F-COLMAP		76.38 270	74.33 284	82.50 215	89.28 145	66.95 178	88.41 144	89.03 212	64.05 336	66.83 373	88.61 206	46.78 342	92.89 200	57.48 337	78.55 291	87.67 313

LTVRE_ROB	69.57 13	76.25 271	74.54 280	81.41 235	88.60 173	64.38 234	79.24 355	89.12 210	70.76 219	69.79 344	87.86 229	49.09 326	93.20 183	56.21 352	80.16 275	86.65 342
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		76.12 272	74.46 282	81.13 246	85.37 281	69.79 91	84.42 276	87.95 243	65.03 322	67.46 364	85.33 299	53.28 271	91.73 247	58.01 334	83.27 237	81.85 406
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 273	74.27 285	81.62 229	83.20 332	64.67 226	83.60 293	89.75 178	69.75 249	71.85 318	87.09 252	32.78 418	92.11 231	69.99 223	80.43 273	88.09 306

testing99		76.09 274	75.12 273	79.00 291	88.16 189	55.50 371	80.79 331	81.40 351	73.30 169	75.17 268	84.27 325	44.48 365	90.02 293	64.28 274	84.22 217	91.48 180

ACMH+	68.96 14	76.01 275	74.01 286	82.03 222	88.60 173	65.31 209	88.86 123	87.55 253	70.25 236	67.75 360	87.47 241	41.27 386	93.19 185	58.37 330	75.94 330	87.60 315

ACMH	67.68 16	75.89 276	73.93 288	81.77 227	88.71 170	66.61 180	88.62 138	89.01 214	69.81 245	66.78 374	86.70 263	41.95 384	91.51 259	55.64 353	78.14 298	87.17 327
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 277	73.36 297	83.31 175	84.76 297	66.03 187	83.38 297	85.06 297	70.21 237	69.40 346	81.05 373	45.76 355	94.66 113	65.10 268	75.49 336	89.25 268
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		75.70 278	73.83 291	81.30 239	83.26 330	61.79 287	82.57 311	80.65 358	66.81 294	66.88 372	83.42 344	57.86 227	92.19 229	63.47 278	79.57 281	89.91 247

WTY-MVS		75.65 279	75.68 259	75.57 340	86.40 255	56.82 349	77.92 378	82.40 339	65.10 320	76.18 240	87.72 231	63.13 161	80.90 392	60.31 310	81.96 253	89.00 278

thres200		75.55 280	74.47 281	78.82 294	87.78 212	57.85 334	83.07 306	83.51 319	72.44 184	75.84 246	84.42 317	52.08 284	91.75 245	47.41 402	83.64 229	86.86 337

test_vis1_n_1920		75.52 281	75.78 257	74.75 354	79.84 388	57.44 342	83.26 300	85.52 291	62.83 350	79.34 167	86.17 280	45.10 361	79.71 396	78.75 124	81.21 261	87.10 333

EPNet_dtu		75.46 282	74.86 274	77.23 327	82.57 350	54.60 380	86.89 200	83.09 328	71.64 194	66.25 383	85.86 285	55.99 244	88.04 329	54.92 357	86.55 178	89.05 274
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 283	73.87 290	80.11 270	82.69 347	64.85 223	81.57 321	83.47 320	69.16 264	70.49 330	84.15 328	51.95 287	88.15 327	69.23 230	72.14 375	87.34 322

XXY-MVS		75.41 284	75.56 262	74.96 349	83.59 323	57.82 335	80.59 337	83.87 314	66.54 304	74.93 277	88.31 215	63.24 155	80.09 395	62.16 293	76.85 314	86.97 335

reproduce_monomvs		75.40 285	74.38 283	78.46 305	83.92 316	57.80 336	83.78 286	86.94 268	73.47 164	72.25 314	84.47 316	38.74 399	89.27 307	75.32 167	70.53 384	88.31 301

TransMVSNet (Re)		75.39 286	74.56 279	77.86 314	85.50 278	57.10 346	86.78 205	86.09 285	72.17 188	71.53 322	87.34 242	63.01 162	89.31 306	56.84 346	61.83 412	87.17 327

CostFormer		75.24 287	73.90 289	79.27 287	82.65 349	58.27 326	80.80 330	82.73 337	61.57 363	75.33 264	83.13 349	55.52 247	91.07 277	64.98 269	78.34 297	88.45 298

testing11		75.14 288	74.01 286	78.53 302	88.16 189	56.38 358	80.74 334	80.42 364	70.67 220	72.69 308	83.72 337	43.61 372	89.86 295	62.29 291	83.76 223	89.36 265

testing3-2		75.12 289	75.19 271	74.91 350	90.40 105	45.09 432	80.29 343	78.42 384	78.37 40	76.54 231	87.75 230	44.36 366	87.28 339	57.04 343	83.49 232	92.37 147

D2MVS		74.82 290	73.21 298	79.64 281	79.81 389	62.56 276	80.34 342	87.35 258	64.37 330	68.86 351	82.66 358	46.37 346	90.10 291	67.91 243	81.24 260	86.25 346

pmmvs6		74.69 291	73.39 295	78.61 297	81.38 369	57.48 341	86.64 210	87.95 243	64.99 324	70.18 334	86.61 266	50.43 307	89.52 302	62.12 294	70.18 386	88.83 285

SD_0403		74.65 292	74.77 276	74.29 358	86.20 259	47.42 421	83.71 288	85.12 295	69.30 257	68.50 356	87.95 228	59.40 216	86.05 350	49.38 389	83.35 235	89.40 263

tfpnnormal		74.39 293	73.16 299	78.08 310	86.10 264	58.05 328	84.65 267	87.53 254	70.32 233	71.22 326	85.63 291	54.97 250	89.86 295	43.03 418	75.02 349	86.32 345

IterMVS		74.29 294	72.94 302	78.35 306	81.53 366	63.49 256	81.58 320	82.49 338	68.06 285	69.99 339	83.69 338	51.66 294	85.54 357	65.85 262	71.64 378	86.01 353
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 295	72.42 308	79.80 276	83.76 320	59.59 315	85.92 232	86.64 273	66.39 305	66.96 371	87.58 235	39.46 394	91.60 250	65.76 263	69.27 389	88.22 303

SCA		74.22 296	72.33 309	79.91 273	84.05 313	62.17 281	79.96 348	79.29 378	66.30 306	72.38 312	80.13 386	51.95 287	88.60 322	59.25 319	77.67 305	88.96 280

mmtdpeth		74.16 297	73.01 301	77.60 322	83.72 321	61.13 292	85.10 255	85.10 296	72.06 190	77.21 216	80.33 383	43.84 370	85.75 353	77.14 144	52.61 431	85.91 356

miper_lstm_enhance		74.11 298	73.11 300	77.13 328	80.11 384	59.62 314	72.23 409	86.92 270	66.76 296	70.40 331	82.92 353	56.93 238	82.92 379	69.06 233	72.63 370	88.87 283

testing222		74.04 299	72.66 305	78.19 308	87.89 204	55.36 372	81.06 328	79.20 379	71.30 205	74.65 282	83.57 342	39.11 398	88.67 321	51.43 377	85.75 194	90.53 215

EG-PatchMatch MVS		74.04 299	71.82 313	80.71 256	84.92 293	67.42 161	85.86 234	88.08 238	66.04 309	64.22 396	83.85 331	35.10 414	92.56 211	57.44 338	80.83 266	82.16 405

pmmvs4		74.03 301	71.91 312	80.39 262	81.96 358	68.32 131	81.45 323	82.14 341	59.32 381	69.87 342	85.13 305	52.40 277	88.13 328	60.21 311	74.74 352	84.73 376

MS-PatchMatch		73.83 302	72.67 304	77.30 326	83.87 317	66.02 188	81.82 316	84.66 301	61.37 366	68.61 354	82.82 356	47.29 335	88.21 326	59.27 318	84.32 215	77.68 421

test_cas_vis1_n_1920		73.76 303	73.74 292	73.81 364	75.90 410	59.77 312	80.51 338	82.40 339	58.30 391	81.62 133	85.69 288	44.35 367	76.41 414	76.29 153	78.61 290	85.23 366

myMVS_eth3d28		73.62 304	73.53 294	73.90 363	88.20 187	47.41 422	78.06 375	79.37 376	74.29 141	73.98 290	84.29 322	44.67 362	83.54 374	51.47 375	87.39 163	90.74 206

sss		73.60 305	73.64 293	73.51 366	82.80 344	55.01 377	76.12 387	81.69 347	62.47 355	74.68 281	85.85 286	57.32 233	78.11 403	60.86 306	80.93 263	87.39 320

RPMNet		73.51 306	70.49 329	82.58 214	81.32 372	65.19 211	75.92 389	92.27 85	57.60 398	72.73 306	76.45 413	52.30 278	95.43 73	48.14 399	77.71 302	87.11 331

WBMVS		73.43 307	72.81 303	75.28 346	87.91 203	50.99 409	78.59 368	81.31 353	65.51 318	74.47 285	84.83 311	46.39 344	86.68 343	58.41 329	77.86 300	88.17 305

SixPastTwentyTwo		73.37 308	71.26 322	79.70 278	85.08 290	57.89 333	85.57 239	83.56 318	71.03 214	65.66 386	85.88 284	42.10 382	92.57 210	59.11 321	63.34 408	88.65 293

CR-MVSNet		73.37 308	71.27 321	79.67 280	81.32 372	65.19 211	75.92 389	80.30 366	59.92 376	72.73 306	81.19 371	52.50 275	86.69 342	59.84 313	77.71 302	87.11 331

MSDG		73.36 310	70.99 324	80.49 261	84.51 304	65.80 196	80.71 335	86.13 284	65.70 313	65.46 387	83.74 335	44.60 363	90.91 279	51.13 378	76.89 312	84.74 375

SSC-MVS3.2		73.35 311	73.39 295	73.23 367	85.30 283	49.01 417	74.58 402	81.57 348	75.21 113	73.68 294	85.58 293	52.53 273	82.05 385	54.33 361	77.69 304	88.63 294

tpm2		73.26 312	71.46 317	78.63 296	83.34 328	56.71 352	80.65 336	80.40 365	56.63 404	73.55 296	82.02 368	51.80 291	91.24 269	56.35 351	78.42 295	87.95 307

RPSCF		73.23 313	71.46 317	78.54 301	82.50 351	59.85 311	82.18 314	82.84 336	58.96 385	71.15 327	89.41 186	45.48 360	84.77 366	58.82 325	71.83 377	91.02 195

PatchmatchNet		73.12 314	71.33 320	78.49 304	83.18 333	60.85 298	79.63 350	78.57 383	64.13 332	71.73 319	79.81 391	51.20 298	85.97 352	57.40 339	76.36 327	88.66 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
UBG		73.08 315	72.27 310	75.51 342	88.02 198	51.29 407	78.35 372	77.38 393	65.52 316	73.87 292	82.36 361	45.55 357	86.48 346	55.02 356	84.39 214	88.75 289

COLMAP_ROB	66.92 17	73.01 316	70.41 331	80.81 254	87.13 236	65.63 200	88.30 151	84.19 310	62.96 347	63.80 401	87.69 233	38.04 404	92.56 211	46.66 404	74.91 350	84.24 380
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 317	72.58 306	74.25 359	84.28 306	50.85 410	86.41 217	83.45 321	44.56 430	73.23 300	87.54 239	49.38 321	85.70 354	65.90 261	78.44 294	86.19 348

test-LLR		72.94 318	72.43 307	74.48 355	81.35 370	58.04 329	78.38 369	77.46 390	66.66 298	69.95 340	79.00 397	48.06 332	79.24 397	66.13 257	84.83 203	86.15 349

test_0402		72.79 319	70.44 330	79.84 275	88.13 192	65.99 190	85.93 231	84.29 307	65.57 315	67.40 367	85.49 295	46.92 339	92.61 207	35.88 432	74.38 355	80.94 411

tpmrst		72.39 320	72.13 311	73.18 371	80.54 379	49.91 414	79.91 349	79.08 380	63.11 344	71.69 320	79.95 388	55.32 248	82.77 381	65.66 264	73.89 359	86.87 336

PatchMatch-RL		72.38 321	70.90 325	76.80 331	88.60 173	67.38 164	79.53 351	76.17 402	62.75 352	69.36 347	82.00 369	45.51 358	84.89 365	53.62 364	80.58 270	78.12 420

CL-MVSNet_self_test		72.37 322	71.46 317	75.09 348	79.49 395	53.53 388	80.76 333	85.01 299	69.12 265	70.51 329	82.05 367	57.92 226	84.13 369	52.27 371	66.00 402	87.60 315

tpm		72.37 322	71.71 314	74.35 357	82.19 356	52.00 397	79.22 356	77.29 394	64.56 327	72.95 304	83.68 339	51.35 295	83.26 378	58.33 331	75.80 331	87.81 311

ETVMVS		72.25 324	71.05 323	75.84 336	87.77 213	51.91 399	79.39 353	74.98 405	69.26 259	73.71 293	82.95 352	40.82 390	86.14 349	46.17 408	84.43 213	89.47 261

sc_t1		72.19 325	69.51 336	80.23 267	84.81 295	61.09 294	84.68 264	80.22 368	60.70 369	71.27 324	83.58 341	36.59 409	89.24 308	60.41 308	63.31 409	90.37 222

UWE-MVS		72.13 326	71.49 316	74.03 361	86.66 251	47.70 419	81.40 325	76.89 398	63.60 341	75.59 249	84.22 326	39.94 393	85.62 356	48.98 392	86.13 186	88.77 288

PVSNet	64.34 18	72.08 327	70.87 326	75.69 338	86.21 258	56.44 356	74.37 403	80.73 357	62.06 360	70.17 335	82.23 365	42.86 376	83.31 377	54.77 358	84.45 212	87.32 323

WB-MVSnew		71.96 328	71.65 315	72.89 372	84.67 302	51.88 400	82.29 313	77.57 389	62.31 356	73.67 295	83.00 351	53.49 269	81.10 391	45.75 411	82.13 251	85.70 359

pmmvs5		71.55 329	70.20 334	75.61 339	77.83 403	56.39 357	81.74 318	80.89 354	57.76 396	67.46 364	84.49 315	49.26 324	85.32 361	57.08 342	75.29 345	85.11 370

test-mter		71.41 330	70.39 332	74.48 355	81.35 370	58.04 329	78.38 369	77.46 390	60.32 372	69.95 340	79.00 397	36.08 412	79.24 397	66.13 257	84.83 203	86.15 349

K. test v3		71.19 331	68.51 343	79.21 289	83.04 338	57.78 337	84.35 278	76.91 397	72.90 178	62.99 404	82.86 355	39.27 395	91.09 276	61.65 299	52.66 430	88.75 289

dmvs_re		71.14 332	70.58 327	72.80 373	81.96 358	59.68 313	75.60 393	79.34 377	68.55 277	69.27 349	80.72 379	49.42 320	76.54 411	52.56 370	77.79 301	82.19 404

tpmvs		71.09 333	69.29 338	76.49 332	82.04 357	56.04 363	78.92 362	81.37 352	64.05 336	67.18 369	78.28 403	49.74 317	89.77 297	49.67 388	72.37 371	83.67 388

AllTest		70.96 334	68.09 349	79.58 282	85.15 287	63.62 247	84.58 269	79.83 371	62.31 356	60.32 413	86.73 257	32.02 419	88.96 316	50.28 383	71.57 379	86.15 349

test_fmvs1		70.93 335	70.52 328	72.16 378	73.71 421	55.05 376	80.82 329	78.77 382	51.21 422	78.58 179	84.41 318	31.20 423	76.94 409	75.88 159	80.12 278	84.47 378

test_fmvs1_n		70.86 336	70.24 333	72.73 374	72.51 432	55.28 374	81.27 326	79.71 373	51.49 421	78.73 174	84.87 310	27.54 428	77.02 408	76.06 156	79.97 279	85.88 357

Patchmtry		70.74 337	69.16 340	75.49 343	80.72 376	54.07 385	74.94 400	80.30 366	58.34 390	70.01 337	81.19 371	52.50 275	86.54 344	53.37 366	71.09 382	85.87 358

MIMVSNet		70.69 338	69.30 337	74.88 351	84.52 303	56.35 360	75.87 391	79.42 375	64.59 326	67.76 359	82.41 360	41.10 387	81.54 388	46.64 406	81.34 258	86.75 340

tpm cat1		70.57 339	68.31 345	77.35 325	82.41 354	57.95 332	78.08 374	80.22 368	52.04 417	68.54 355	77.66 408	52.00 286	87.84 332	51.77 372	72.07 376	86.25 346

OpenMVS_ROB	64.09 19	70.56 340	68.19 346	77.65 319	80.26 381	59.41 318	85.01 257	82.96 333	58.76 388	65.43 388	82.33 362	37.63 406	91.23 270	45.34 414	76.03 329	82.32 402

pmmvs-eth3d		70.50 341	67.83 355	78.52 303	77.37 406	66.18 186	81.82 316	81.51 349	58.90 386	63.90 400	80.42 381	42.69 377	86.28 348	58.56 327	65.30 404	83.11 394

tt0320		70.49 342	68.03 350	77.89 313	84.78 296	59.12 319	83.55 294	80.44 363	58.13 393	67.43 366	80.41 382	39.26 396	87.54 336	55.12 355	63.18 410	86.99 334

USDC		70.33 343	68.37 344	76.21 334	80.60 378	56.23 361	79.19 357	86.49 276	60.89 367	61.29 409	85.47 296	31.78 421	89.47 304	53.37 366	76.21 328	82.94 398

Patchmatch-RL test		70.24 344	67.78 357	77.61 320	77.43 405	59.57 316	71.16 413	70.33 419	62.94 348	68.65 353	72.77 425	50.62 304	85.49 358	69.58 228	66.58 399	87.77 312

CMPMVS	51.72 21	70.19 345	68.16 347	76.28 333	73.15 428	57.55 340	79.47 352	83.92 312	48.02 426	56.48 426	84.81 312	43.13 374	86.42 347	62.67 287	81.81 256	84.89 373
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tt0320-xc		70.11 346	67.45 363	78.07 311	85.33 282	59.51 317	83.28 299	78.96 381	58.77 387	67.10 370	80.28 384	36.73 408	87.42 337	56.83 347	59.77 419	87.29 324

ppachtmachnet_test		70.04 347	67.34 365	78.14 309	79.80 390	61.13 292	79.19 357	80.59 359	59.16 383	65.27 389	79.29 394	46.75 343	87.29 338	49.33 390	66.72 397	86.00 355

gg-mvs-nofinetune		69.95 348	67.96 351	75.94 335	83.07 336	54.51 382	77.23 383	70.29 420	63.11 344	70.32 332	62.33 434	43.62 371	88.69 320	53.88 363	87.76 158	84.62 377

TESTMET0.1,1		69.89 349	69.00 341	72.55 375	79.27 398	56.85 348	78.38 369	74.71 409	57.64 397	68.09 358	77.19 410	37.75 405	76.70 410	63.92 276	84.09 218	84.10 383

test_vis1_n		69.85 350	69.21 339	71.77 380	72.66 431	55.27 375	81.48 322	76.21 401	52.03 418	75.30 265	83.20 348	28.97 426	76.22 416	74.60 172	78.41 296	83.81 386

FMVSNet5		69.50 351	67.96 351	74.15 360	82.97 342	55.35 373	80.01 347	82.12 342	62.56 354	63.02 402	81.53 370	36.92 407	81.92 386	48.42 394	74.06 357	85.17 369

mvs5depth		69.45 352	67.45 363	75.46 344	73.93 419	55.83 366	79.19 357	83.23 324	66.89 293	71.63 321	83.32 345	33.69 417	85.09 362	59.81 314	55.34 427	85.46 362

PMMVS		69.34 353	68.67 342	71.35 385	75.67 412	62.03 282	75.17 395	73.46 412	50.00 423	68.68 352	79.05 395	52.07 285	78.13 402	61.16 304	82.77 243	73.90 427

our_test_3		69.14 354	67.00 367	75.57 340	79.80 390	58.80 320	77.96 376	77.81 387	59.55 379	62.90 405	78.25 404	47.43 334	83.97 370	51.71 373	67.58 396	83.93 385

EPMVS		69.02 355	68.16 347	71.59 381	79.61 393	49.80 416	77.40 381	66.93 430	62.82 351	70.01 337	79.05 395	45.79 354	77.86 405	56.58 349	75.26 346	87.13 330

KD-MVS_self_test		68.81 356	67.59 361	72.46 377	74.29 418	45.45 427	77.93 377	87.00 266	63.12 343	63.99 399	78.99 399	42.32 379	84.77 366	56.55 350	64.09 407	87.16 329

Anonymous20240521		68.80 357	67.22 366	73.55 365	74.33 417	54.11 384	83.18 301	85.61 290	58.15 392	61.68 408	80.94 376	30.71 424	81.27 390	57.00 344	73.34 367	85.28 365

Anonymous20231206		68.60 358	67.80 356	71.02 388	80.23 383	50.75 411	78.30 373	80.47 361	56.79 403	66.11 385	82.63 359	46.35 347	78.95 399	43.62 417	75.70 332	83.36 391

MIMVSNet1		68.58 359	66.78 369	73.98 362	80.07 385	51.82 401	80.77 332	84.37 304	64.40 329	59.75 416	82.16 366	36.47 410	83.63 373	42.73 419	70.33 385	86.48 344

testing3		68.56 360	67.67 359	71.22 387	87.33 229	42.87 437	83.06 307	71.54 417	70.36 230	69.08 350	84.38 319	30.33 425	85.69 355	37.50 430	75.45 340	85.09 371

EU-MVSNet		68.53 361	67.61 360	71.31 386	78.51 402	47.01 424	84.47 271	84.27 308	42.27 433	66.44 382	84.79 313	40.44 391	83.76 371	58.76 326	68.54 394	83.17 392

PatchT		68.46 362	67.85 353	70.29 391	80.70 377	43.93 435	72.47 408	74.88 406	60.15 374	70.55 328	76.57 412	49.94 314	81.59 387	50.58 379	74.83 351	85.34 364

test_fmvs2		68.35 363	67.48 362	70.98 389	69.50 435	51.95 398	80.05 346	76.38 400	49.33 424	74.65 282	84.38 319	23.30 437	75.40 425	74.51 173	75.17 348	85.60 360

Syy-MVS		68.05 364	67.85 353	68.67 400	84.68 299	40.97 443	78.62 366	73.08 414	66.65 301	66.74 375	79.46 392	52.11 283	82.30 383	32.89 435	76.38 325	82.75 399

test0.0.03 1		68.00 365	67.69 358	68.90 397	77.55 404	47.43 420	75.70 392	72.95 416	66.66 298	66.56 377	82.29 364	48.06 332	75.87 420	44.97 415	74.51 354	83.41 390

TDRefinement		67.49 366	64.34 377	76.92 329	73.47 425	61.07 295	84.86 261	82.98 332	59.77 377	58.30 420	85.13 305	26.06 429	87.89 331	47.92 401	60.59 417	81.81 407

test20.03		67.45 367	66.95 368	68.94 396	75.48 414	44.84 433	77.50 380	77.67 388	66.66 298	63.01 403	83.80 333	47.02 338	78.40 401	42.53 421	68.86 393	83.58 389

UnsupCasMVSNet_eth		67.33 368	65.99 372	71.37 383	73.48 424	51.47 405	75.16 396	85.19 294	65.20 319	60.78 411	80.93 378	42.35 378	77.20 407	57.12 341	53.69 429	85.44 363

TinyColmap		67.30 369	64.81 375	74.76 353	81.92 360	56.68 353	80.29 343	81.49 350	60.33 371	56.27 427	83.22 346	24.77 433	87.66 335	45.52 412	69.47 388	79.95 416

myMVS_eth3d		67.02 370	66.29 371	69.21 395	84.68 299	42.58 438	78.62 366	73.08 414	66.65 301	66.74 375	79.46 392	31.53 422	82.30 383	39.43 427	76.38 325	82.75 399

dp		66.80 371	65.43 373	70.90 390	79.74 392	48.82 418	75.12 398	74.77 407	59.61 378	64.08 398	77.23 409	42.89 375	80.72 393	48.86 393	66.58 399	83.16 393

MDA-MVSNet-bldmvs		66.68 372	63.66 382	75.75 337	79.28 397	60.56 303	73.92 405	78.35 385	64.43 328	50.13 435	79.87 390	44.02 369	83.67 372	46.10 409	56.86 421	83.03 396

testgi		66.67 373	66.53 370	67.08 407	75.62 413	41.69 442	75.93 388	76.50 399	66.11 307	65.20 392	86.59 267	35.72 413	74.71 427	43.71 416	73.38 366	84.84 374

CHOSEN 280x420		66.51 374	64.71 376	71.90 379	81.45 367	63.52 255	57.98 443	68.95 426	53.57 413	62.59 406	76.70 411	46.22 349	75.29 426	55.25 354	79.68 280	76.88 423

PM-MVS		66.41 375	64.14 378	73.20 370	73.92 420	56.45 355	78.97 361	64.96 436	63.88 340	64.72 393	80.24 385	19.84 441	83.44 376	66.24 256	64.52 406	79.71 417

JIA-IIPM		66.32 376	62.82 388	76.82 330	77.09 407	61.72 288	65.34 436	75.38 403	58.04 395	64.51 394	62.32 435	42.05 383	86.51 345	51.45 376	69.22 390	82.21 403

KD-MVS_2432*1600		66.22 377	63.89 380	73.21 368	75.47 415	53.42 390	70.76 416	84.35 305	64.10 334	66.52 379	78.52 401	34.55 415	84.98 363	50.40 381	50.33 434	81.23 409

miper_refine_blended		66.22 377	63.89 380	73.21 368	75.47 415	53.42 390	70.76 416	84.35 305	64.10 334	66.52 379	78.52 401	34.55 415	84.98 363	50.40 381	50.33 434	81.23 409

ADS-MVSNet2		66.20 379	63.33 383	74.82 352	79.92 386	58.75 321	67.55 428	75.19 404	53.37 414	65.25 390	75.86 416	42.32 379	80.53 394	41.57 422	68.91 391	85.18 367

UWE-MVS-28		65.32 380	64.93 374	66.49 408	78.70 400	38.55 445	77.86 379	64.39 437	62.00 361	64.13 397	83.60 340	41.44 385	76.00 418	31.39 437	80.89 264	84.92 372

YYNet1		65.03 381	62.91 386	71.38 382	75.85 411	56.60 354	69.12 424	74.66 410	57.28 401	54.12 429	77.87 406	45.85 353	74.48 428	49.95 386	61.52 414	83.05 395

MDA-MVSNet_test_wron		65.03 381	62.92 385	71.37 383	75.93 409	56.73 350	69.09 425	74.73 408	57.28 401	54.03 430	77.89 405	45.88 352	74.39 429	49.89 387	61.55 413	82.99 397

Patchmatch-test		64.82 383	63.24 384	69.57 393	79.42 396	49.82 415	63.49 440	69.05 425	51.98 419	59.95 415	80.13 386	50.91 300	70.98 434	40.66 424	73.57 362	87.90 309

ADS-MVSNet		64.36 384	62.88 387	68.78 399	79.92 386	47.17 423	67.55 428	71.18 418	53.37 414	65.25 390	75.86 416	42.32 379	73.99 430	41.57 422	68.91 391	85.18 367

LF4IMVS		64.02 385	62.19 389	69.50 394	70.90 433	53.29 393	76.13 386	77.18 395	52.65 416	58.59 418	80.98 375	23.55 436	76.52 412	53.06 368	66.66 398	78.68 419

UnsupCasMVSNet_bld		63.70 386	61.53 392	70.21 392	73.69 422	51.39 406	72.82 407	81.89 344	55.63 408	57.81 422	71.80 427	38.67 400	78.61 400	49.26 391	52.21 432	80.63 413

test_fmvs3		63.36 387	61.82 390	67.98 404	62.51 444	46.96 425	77.37 382	74.03 411	45.24 429	67.50 363	78.79 400	12.16 449	72.98 433	72.77 193	66.02 401	83.99 384

dmvs_testset		62.63 388	64.11 379	58.19 418	78.55 401	24.76 456	75.28 394	65.94 433	67.91 286	60.34 412	76.01 415	53.56 267	73.94 431	31.79 436	67.65 395	75.88 425

mvsany_test1		62.30 389	61.26 393	65.41 410	69.52 434	54.86 378	66.86 430	49.78 450	46.65 427	68.50 356	83.21 347	49.15 325	66.28 442	56.93 345	60.77 415	75.11 426

new-patchmatchnet		61.73 390	61.73 391	61.70 414	72.74 430	24.50 457	69.16 423	78.03 386	61.40 364	56.72 425	75.53 419	38.42 401	76.48 413	45.95 410	57.67 420	84.13 382

PVSNet_0	57.27 20	61.67 391	59.27 394	68.85 398	79.61 393	57.44 342	68.01 426	73.44 413	55.93 407	58.54 419	70.41 430	44.58 364	77.55 406	47.01 403	35.91 442	71.55 430

test_vis1_rt		60.28 392	58.42 395	65.84 409	67.25 438	55.60 370	70.44 418	60.94 442	44.33 431	59.00 417	66.64 432	24.91 432	68.67 439	62.80 283	69.48 387	73.25 428

ttmdpeth		59.91 393	57.10 397	68.34 402	67.13 439	46.65 426	74.64 401	67.41 429	48.30 425	62.52 407	85.04 309	20.40 439	75.93 419	42.55 420	45.90 440	82.44 401

MVS-HIRNet		59.14 394	57.67 396	63.57 412	81.65 362	43.50 436	71.73 410	65.06 435	39.59 437	51.43 432	57.73 440	38.34 402	82.58 382	39.53 425	73.95 358	64.62 436

pmmvs3		57.79 395	54.26 400	68.37 401	64.02 443	56.72 351	75.12 398	65.17 434	40.20 435	52.93 431	69.86 431	20.36 440	75.48 423	45.45 413	55.25 428	72.90 429

DSMNet-mixed		57.77 396	56.90 398	60.38 416	67.70 437	35.61 447	69.18 422	53.97 448	32.30 446	57.49 423	79.88 389	40.39 392	68.57 440	38.78 428	72.37 371	76.97 422

MVStest1		56.63 397	52.76 403	68.25 403	61.67 445	53.25 394	71.67 411	68.90 427	38.59 438	50.59 434	83.05 350	25.08 431	70.66 435	36.76 431	38.56 441	80.83 412

WB-MVS		54.94 398	54.72 399	55.60 424	73.50 423	20.90 458	74.27 404	61.19 441	59.16 383	50.61 433	74.15 421	47.19 337	75.78 421	17.31 449	35.07 443	70.12 431

LCM-MVSNet		54.25 399	49.68 409	67.97 405	53.73 453	45.28 430	66.85 431	80.78 356	35.96 442	39.45 443	62.23 436	8.70 453	78.06 404	48.24 398	51.20 433	80.57 414

mvsany_test3		53.99 400	51.45 405	61.61 415	55.51 449	44.74 434	63.52 439	45.41 454	43.69 432	58.11 421	76.45 413	17.99 442	63.76 445	54.77 358	47.59 436	76.34 424

SSC-MVS		53.88 401	53.59 401	54.75 426	72.87 429	19.59 459	73.84 406	60.53 443	57.58 399	49.18 437	73.45 424	46.34 348	75.47 424	16.20 452	32.28 445	69.20 432

FPMVS		53.68 402	51.64 404	59.81 417	65.08 441	51.03 408	69.48 421	69.58 423	41.46 434	40.67 441	72.32 426	16.46 445	70.00 438	24.24 445	65.42 403	58.40 441

APD_test1		53.31 403	49.93 408	63.42 413	65.68 440	50.13 413	71.59 412	66.90 431	34.43 443	40.58 442	71.56 428	8.65 454	76.27 415	34.64 434	55.36 426	63.86 437

N_pmnet		52.79 404	53.26 402	51.40 428	78.99 399	7.68 462	69.52 420	3.89 461	51.63 420	57.01 424	74.98 420	40.83 389	65.96 443	37.78 429	64.67 405	80.56 415

test_f		52.09 405	50.82 406	55.90 422	53.82 452	42.31 441	59.42 442	58.31 446	36.45 441	56.12 428	70.96 429	12.18 448	57.79 448	53.51 365	56.57 423	67.60 433

EGC-MVSNET		52.07 406	47.05 410	67.14 406	83.51 325	60.71 300	80.50 339	67.75 428	0.07 456	0.43 457	75.85 418	24.26 434	81.54 388	28.82 439	62.25 411	59.16 439

new_pmnet		50.91 407	50.29 407	52.78 427	68.58 436	34.94 449	63.71 438	56.63 447	39.73 436	44.95 438	65.47 433	21.93 438	58.48 447	34.98 433	56.62 422	64.92 435

ANet_high		50.57 408	46.10 412	63.99 411	48.67 456	39.13 444	70.99 415	80.85 355	61.39 365	31.18 445	57.70 441	17.02 444	73.65 432	31.22 438	15.89 453	79.18 418

test_vis3_rt		49.26 409	47.02 411	56.00 421	54.30 450	45.27 431	66.76 432	48.08 451	36.83 440	44.38 439	53.20 444	7.17 456	64.07 444	56.77 348	55.66 424	58.65 440

testf1		45.72 410	41.96 414	57.00 419	56.90 447	45.32 428	66.14 433	59.26 444	26.19 447	30.89 446	60.96 438	4.14 457	70.64 436	26.39 443	46.73 438	55.04 442

APD_test2		45.72 410	41.96 414	57.00 419	56.90 447	45.32 428	66.14 433	59.26 444	26.19 447	30.89 446	60.96 438	4.14 457	70.64 436	26.39 443	46.73 438	55.04 442

dongtai		45.42 412	45.38 413	45.55 430	73.36 426	26.85 454	67.72 427	34.19 456	54.15 412	49.65 436	56.41 443	25.43 430	62.94 446	19.45 447	28.09 447	46.86 446

Gipuma		45.18 413	41.86 416	55.16 425	77.03 408	51.52 404	32.50 449	80.52 360	32.46 445	27.12 448	35.02 449	9.52 452	75.50 422	22.31 446	60.21 418	38.45 448
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 414	40.28 418	55.82 423	40.82 458	42.54 440	65.12 437	63.99 438	34.43 443	24.48 449	57.12 442	3.92 459	76.17 417	17.10 450	55.52 425	48.75 444
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 415	38.86 419	46.69 429	53.84 451	16.45 460	48.61 446	49.92 449	37.49 439	31.67 444	60.97 437	8.14 455	56.42 449	28.42 440	30.72 446	67.19 434

kuosan		39.70 416	40.40 417	37.58 433	64.52 442	26.98 452	65.62 435	33.02 457	46.12 428	42.79 440	48.99 446	24.10 435	46.56 454	12.16 455	26.30 448	39.20 447

E-PMN		31.77 417	30.64 420	35.15 434	52.87 454	27.67 451	57.09 444	47.86 452	24.64 449	16.40 454	33.05 450	11.23 450	54.90 450	14.46 453	18.15 451	22.87 450

test_method		31.52 418	29.28 422	38.23 432	27.03 460	6.50 463	20.94 451	62.21 440	4.05 454	22.35 452	52.50 445	13.33 446	47.58 452	27.04 442	34.04 444	60.62 438

EMVS		30.81 419	29.65 421	34.27 435	50.96 455	25.95 455	56.58 445	46.80 453	24.01 450	15.53 455	30.68 451	12.47 447	54.43 451	12.81 454	17.05 452	22.43 451

MVE	26.22 23	30.37 420	25.89 424	43.81 431	44.55 457	35.46 448	28.87 450	39.07 455	18.20 451	18.58 453	40.18 448	2.68 460	47.37 453	17.07 451	23.78 450	48.60 445
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 421	26.61 423	0.00 441	0.00 464	0.00 466	0.00 452	89.26 200	0.00 459	0.00 460	88.61 206	61.62 183	0.00 460	0.00 459	0.00 458	0.00 456

tmp_tt		18.61 422	21.40 425	10.23 438	4.82 461	10.11 461	34.70 448	30.74 459	1.48 455	23.91 451	26.07 452	28.42 427	13.41 457	27.12 441	15.35 454	7.17 452

wuyk23d		16.82 423	15.94 426	19.46 437	58.74 446	31.45 450	39.22 447	3.74 462	6.84 453	6.04 456	2.70 456	1.27 461	24.29 456	10.54 456	14.40 455	2.63 453

ab-mvs-re		7.23 424	9.64 427	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	86.72 259	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

test123		6.12 425	8.11 428	0.14 439	0.06 463	0.09 464	71.05 414	0.03 464	0.04 458	0.25 459	1.30 458	0.05 462	0.03 459	0.21 458	0.01 457	0.29 454

testmvs		6.04 426	8.02 429	0.10 440	0.08 462	0.03 465	69.74 419	0.04 463	0.05 457	0.31 458	1.68 457	0.02 463	0.04 458	0.24 457	0.02 456	0.25 455

pcd_1.5k_mvsjas		5.26 427	7.02 430	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	63.15 158	0.00 460	0.00 459	0.00 458	0.00 456

mmdepth		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

monomultidepth		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

test_blank		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

uanet_test		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

DCPMVS		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

sosnet-low-res		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

sosnet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

uncertanet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

Regformer		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

uanet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 466	0.00 452	0.00 465	0.00 459	0.00 460	0.00 459	0.00 464	0.00 460	0.00 459	0.00 458	0.00 456

WAC-MVS						42.58 438								39.46 426

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					0.00 464

eth-test					0.00 464

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	63.87 148		82.75 86	91.87 88	92.50 141

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 294	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																88.96 280

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 296				88.96 280

sam_mvs												50.01 312

ambc				75.24 347	73.16 427	50.51 412	63.05 441	87.47 256		64.28 395	77.81 407	17.80 443	89.73 299	57.88 335	60.64 416	85.49 361

MTGPA								92.02 98

test_post1							78.90 363				5.43 455	48.81 331	85.44 360	59.25 319

test_post											5.46 454	50.36 308	84.24 368

patchmatchnet-post											74.00 422	51.12 299	88.60 322

GG-mvs-BLEND				75.38 345	81.59 364	55.80 367	79.32 354	69.63 422		67.19 368	73.67 423	43.24 373	88.90 318	50.41 380	84.50 208	81.45 408

MTMP							92.18 35	32.83 458

gm-plane-assit					81.40 368	53.83 387			62.72 353		80.94 376		92.39 220	63.40 280

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	92.70 131

agg_prior					92.85 64	71.94 52		91.78 113		84.41 88			94.93 97

TestCases				79.58 282	85.15 287	63.62 247		79.83 371	62.31 356	60.32 413	86.73 257	32.02 419	88.96 316	50.28 383	71.57 379	86.15 349

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		58.10 394	87.04 55			88.98 314	74.07 178

新几何2							86.29 223

新几何1				83.42 171	93.13 56	70.71 76		85.48 292	57.43 400	81.80 130	91.98 107	63.28 152	92.27 226	64.60 272	92.99 72	87.27 325

旧先验1					91.96 76	65.79 197		86.37 279			93.08 85	69.31 88			92.74 76	88.74 291

无先验							87.48 177	88.98 215	60.00 375				94.12 133	67.28 249		88.97 279

原ACMM2							86.86 201

原ACMM1				84.35 124	93.01 62	68.79 113		92.44 78	63.96 339	81.09 141	91.57 122	66.06 128	95.45 71	67.19 251	94.82 46	88.81 286

test222					91.50 82	68.26 133	84.16 281	83.20 327	54.63 411	79.74 159	91.63 119	58.97 219			91.42 96	86.77 339

testdata2													91.01 278	62.37 290

segment_acmp												73.08 40

testdata				79.97 272	90.90 94	64.21 236		84.71 300	59.27 382	85.40 68	92.91 87	62.02 177	89.08 312	68.95 234	91.37 98	86.63 343

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						60.42 209

plane_prior5								92.44 78					95.38 78	78.71 125	86.32 181	91.33 183

plane_prior4											91.00 144

plane_prior3						68.60 124			78.44 36	78.92 172

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								0.00 465

nn								0.00 465

door-mid								69.98 421

lessismore_v0				78.97 292	81.01 375	57.15 345		65.99 432		61.16 410	82.82 356	39.12 397	91.34 266	59.67 315	46.92 437	88.43 299

LGP-MVS_train				84.50 117	89.23 148	68.76 115		91.94 104	75.37 109	76.64 227	91.51 123	54.29 259	94.91 98	78.44 127	83.78 221	89.83 251

test11								92.23 88

door								69.44 424

HQP5-MVS						66.98 175

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 212

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 212

BP-MVS														77.47 139

HQP4-MVS										77.24 211			95.11 90			91.03 193

HQP3-MVS								92.19 92							85.99 189

HQP2-MVS												60.17 212

NP-MVS					89.62 125	68.32 131					90.24 159

MDTV_nov1_ep13_2view						37.79 446	75.16 396		55.10 409	66.53 378		49.34 322		53.98 362		87.94 308

MDTV_nov1_ep13			69.97 335		83.18 333	53.48 389	77.10 385	80.18 370	60.45 370	69.33 348	80.44 380	48.89 330	86.90 341	51.60 374	78.51 293

ACMMP++_ref															81.95 254

ACMMP++															81.25 259

Test By Simon												64.33 144

ITE_SJBPF				78.22 307	81.77 361	60.57 302		83.30 322	69.25 260	67.54 362	87.20 248	36.33 411	87.28 339	54.34 360	74.62 353	86.80 338

DeepMVS_CX				27.40 436	40.17 459	26.90 453		24.59 460	17.44 452	23.95 450	48.61 447	9.77 451	26.48 455	18.06 448	24.47 449	28.83 449

High-resolution multi-view benchmark