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
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
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
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
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

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

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

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

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	93.78 150	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
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

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
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

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

fmvsm_s_conf0.5_n_9		87.39 30	87.95 20	85.70 77	89.48 133	67.88 146	88.59 139	89.05 209	80.19 12	90.70 17	95.40 15	74.56 25	93.92 143	91.54 2	92.07 85	95.31 5

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-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
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.
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

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

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
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

fmvsm_s_conf0.5_n_3		86.36 49	87.46 29	83.09 185	87.08 238	65.21 209	89.09 116	90.21 163	79.67 19	89.98 19	95.02 20	73.17 39	91.71 246	91.30 3	91.60 92	92.34 148

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

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

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

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

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_8		86.56 44	87.17 35	84.73 111	87.76 213	65.62 200	89.20 107	92.21 90	79.94 17	89.74 22	94.86 22	68.63 98	94.20 128	90.83 5	91.39 97	94.38 45

MTAPA		87.23 33	87.00 36	87.90 22	94.18 35	74.25 5	86.58 211	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

balanced_conf03		86.78 39	86.99 37	86.15 66	91.24 86	67.61 154	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

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
CS-MVS		86.69 41	86.95 39	85.90 74	90.76 99	67.57 156	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

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

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

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
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

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
SPE-MVS-test		86.29 50	86.48 45	85.71 76	91.02 91	67.21 171	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

EC-MVSNet		86.01 53	86.38 46	84.91 105	89.31 143	66.27 184	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

fmvsm_l_conf0.5_n_3		86.02 52	86.32 47	85.14 92	87.20 232	68.54 126	89.57 93	90.44 152	75.31 111	87.49 48	94.39 37	72.86 43	92.72 203	89.04 24	90.56 111	94.16 54

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

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

fmvsm_s_conf0.5_n_6		85.55 66	86.20 50	83.60 164	87.32 229	65.13 212	88.86 123	91.63 117	75.41 107	88.23 34	93.45 74	68.56 99	92.47 214	89.52 16	92.78 75	93.20 111

train_agg		86.43 46	86.20 50	87.13 45	93.26 52	72.96 25	88.75 131	91.89 106	68.69 273	85.00 73	93.10 81	74.43 27	95.41 76	84.97 56	95.71 25	93.02 122

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

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

dcpmvs_2		85.63 64	86.15 54	84.06 145	91.71 80	64.94 219	86.47 214	91.87 108	73.63 157	86.60 60	93.02 86	76.57 15	91.87 240	83.36 77	92.15 83	95.35 3

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

casdiffmvs_mvg		85.99 54	86.09 56	85.70 77	87.65 217	67.22 170	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
test_fmvsmconf_n		85.92 57	86.04 57	85.57 82	85.03 290	69.51 96	89.62 92	90.58 147	73.42 165	87.75 44	94.02 54	72.85 44	93.24 176	90.37 7	90.75 108	93.96 64

MVSMamba_PlusPlus		85.99 54	85.96 58	86.05 69	91.09 88	67.64 153	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

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

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

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	93.91 144	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	93.91 144	81.50 97	88.80 141	94.77 25

MSLP-MVS++		85.43 69	85.76 63	84.45 119	91.93 77	70.24 81	90.71 62	92.86 59	77.46 55	84.22 93	92.81 92	67.16 115	92.94 197	80.36 111	94.35 59	90.16 228

fmvsm_s_conf0.5_n_4		85.39 71	85.75 64	84.30 126	86.70 247	65.83 193	88.77 129	89.78 175	75.46 106	88.35 30	93.73 67	69.19 89	93.06 192	91.30 3	88.44 150	94.02 62

test_fmvsmconf0.1_n		85.61 65	85.65 65	85.50 83	82.99 339	69.39 103	89.65 89	90.29 161	73.31 168	87.77 43	94.15 48	71.72 56	93.23 177	90.31 8	90.67 110	93.89 70

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

fmvsm_s_conf0.5_n_5		85.22 75	85.55 67	84.25 133	86.26 254	67.40 162	89.18 108	89.31 195	72.50 181	88.31 31	93.86 63	69.66 83	91.96 234	89.81 11	91.05 102	93.38 99

MGCFI-Net		85.06 79	85.51 68	83.70 162	89.42 135	63.01 265	89.43 97	92.62 74	76.43 84	87.53 47	91.34 129	72.82 45	93.42 170	81.28 100	88.74 144	94.66 32

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

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
test_fmvsm_n_1920		85.29 74	85.34 71	85.13 95	86.12 260	69.93 88	88.65 137	90.78 143	69.97 240	88.27 32	93.98 59	71.39 62	91.54 254	88.49 32	90.45 113	93.91 67

TSAR-MVS + GP.		85.71 63	85.33 72	86.84 52	91.34 84	72.50 36	89.07 117	87.28 257	76.41 85	85.80 64	90.22 160	74.15 32	95.37 81	81.82 95	91.88 87	92.65 135

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	93.78 150	81.51 96	88.95 138	94.63 33

DELS-MVS		85.41 70	85.30 74	85.77 75	88.49 175	67.93 145	85.52 246	93.44 28	78.70 34	83.63 108	89.03 191	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
CDPH-MVS		85.76 62	85.29 75	87.17 44	93.49 47	71.08 66	88.58 140	92.42 81	68.32 280	84.61 84	93.48 71	72.32 47	96.15 49	79.00 121	95.43 30	94.28 51

casdiffmvs		85.11 77	85.14 76	85.01 99	87.20 232	65.77 197	87.75 171	92.83 61	77.84 43	84.36 92	92.38 99	72.15 50	93.93 142	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
baseline		84.93 80	84.98 77	84.80 109	87.30 230	65.39 206	87.30 185	92.88 58	77.62 47	84.04 98	92.26 101	71.81 54	93.96 136	81.31 99	90.30 115	95.03 11

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	71.39 206	90.88 107	93.07 117

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

SymmetryMVS		85.38 72	84.81 80	87.07 46	91.47 83	72.47 38	91.65 43	88.06 238	79.31 24	84.39 89	92.18 102	64.64 142	95.53 67	80.70 108	90.91 106	93.21 109

MVS_111021_HR		85.14 76	84.75 81	86.32 61	91.65 81	72.70 30	85.98 228	90.33 158	76.11 94	82.08 125	91.61 121	71.36 63	94.17 131	81.02 102	92.58 78	92.08 161

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	85.71 285	69.32 87	95.38 78	80.82 105	91.37 98	92.72 130

fmvsm_l_conf0.5_n		84.47 84	84.54 83	84.27 130	85.42 277	68.81 112	88.49 142	87.26 259	68.08 282	88.03 38	93.49 70	72.04 52	91.77 242	88.90 26	89.14 137	92.24 155

patch_mono-2		83.65 96	84.54 83	80.99 247	90.06 116	65.83 193	84.21 278	88.74 225	71.60 198	85.01 72	92.44 98	74.51 26	83.50 373	82.15 93	92.15 83	93.64 89

test_fmvsmconf0.01_n		84.73 83	84.52 85	85.34 87	80.25 380	69.03 106	89.47 95	89.65 181	73.24 172	86.98 56	94.27 41	66.62 118	93.23 177	90.26 9	89.95 123	93.78 79

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	76.66 224	93.37 76	60.40 210	96.75 26	77.20 142	93.73 66	95.29 6

DPM-MVS		84.93 80	84.29 87	86.84 52	90.20 109	73.04 23	87.12 189	93.04 42	69.80 244	82.85 116	91.22 133	73.06 41	96.02 53	76.72 152	94.63 50	91.46 180

fmvsm_l_conf0.5_n_a		84.13 87	84.16 88	84.06 145	85.38 278	68.40 129	88.34 149	86.85 269	67.48 289	87.48 49	93.40 75	70.89 68	91.61 247	88.38 34	89.22 135	92.16 159

fmvsm_s_conf0.5_n_2		84.04 88	84.11 89	83.81 160	86.17 258	65.00 217	86.96 195	87.28 257	74.35 137	88.25 33	94.23 44	61.82 178	92.60 206	89.85 10	88.09 155	93.84 73

fmvsm_s_conf0.5_n_7		83.34 107	84.03 90	81.28 238	85.73 268	65.13 212	85.40 247	89.90 173	74.96 122	82.13 124	93.89 62	66.65 117	87.92 328	86.56 47	91.05 102	90.80 199

test_fmvsmvis_n_1920		84.02 89	83.87 91	84.49 118	84.12 308	69.37 104	88.15 157	87.96 240	70.01 238	83.95 100	93.23 79	68.80 97	91.51 257	88.61 29	89.96 122	92.57 136

EI-MVSNet-Vis-set		84.19 86	83.81 92	85.31 88	88.18 187	67.85 147	87.66 173	89.73 179	80.05 15	82.95 113	89.59 176	70.74 71	94.82 104	80.66 110	84.72 203	93.28 105

fmvsm_s_conf0.1_n_2		83.80 92	83.79 93	83.83 158	85.62 271	64.94 219	87.03 192	86.62 273	74.32 138	87.97 41	94.33 38	60.67 202	92.60 206	89.72 12	87.79 157	93.96 64

BP-MVS1		84.32 85	83.71 94	86.17 64	87.84 206	67.85 147	89.38 102	89.64 182	77.73 45	83.98 99	92.12 106	56.89 237	95.43 73	84.03 73	91.75 91	95.24 7

fmvsm_s_conf0.5_n		83.80 92	83.71 94	84.07 143	86.69 248	67.31 165	89.46 96	83.07 327	71.09 210	86.96 57	93.70 68	69.02 95	91.47 259	88.79 27	84.62 205	93.44 98

nrg030		83.88 90	83.53 96	84.96 101	86.77 245	69.28 105	90.46 70	92.67 68	74.79 127	82.95 113	91.33 130	72.70 46	93.09 190	80.79 107	79.28 285	92.50 141

MG-MVS		83.41 104	83.45 97	83.28 175	92.74 67	62.28 278	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

fmvsm_s_conf0.5_n_a		83.63 98	83.41 98	84.28 128	86.14 259	68.12 139	89.43 97	82.87 332	70.27 233	87.27 53	93.80 66	69.09 90	91.58 249	88.21 35	83.65 226	93.14 115

fmvsm_s_conf0.1_n		83.56 100	83.38 99	84.10 137	84.86 292	67.28 166	89.40 101	83.01 328	70.67 219	87.08 54	93.96 60	68.38 101	91.45 260	88.56 31	84.50 206	93.56 93

EI-MVSNet-UG-set		83.81 91	83.38 99	85.09 97	87.87 204	67.53 158	87.44 181	89.66 180	79.74 18	82.23 122	89.41 185	70.24 77	94.74 109	79.95 115	83.92 218	92.99 125

CPTT-MVS		83.73 94	83.33 101	84.92 104	93.28 49	70.86 74	92.09 37	90.38 154	68.75 272	79.57 162	92.83 90	60.60 206	93.04 195	80.92 104	91.56 95	90.86 198

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	78.92 171	91.00 144	60.42 208	95.38 78	78.71 125	86.32 181	91.33 181

Effi-MVS+		83.62 99	83.08 103	85.24 90	88.38 181	67.45 159	88.89 122	89.15 205	75.50 105	82.27 121	88.28 214	69.61 84	94.45 120	77.81 135	87.84 156	93.84 73

MVS_Test		83.15 111	83.06 104	83.41 172	86.86 241	63.21 261	86.11 226	92.00 100	74.31 139	82.87 115	89.44 184	70.03 78	93.21 179	77.39 141	88.50 149	93.81 75

EPP-MVSNet		83.40 105	83.02 105	84.57 114	90.13 110	64.47 230	92.32 31	90.73 144	74.45 136	79.35 165	91.10 137	69.05 93	95.12 88	72.78 192	87.22 166	94.13 56

fmvsm_s_conf0.1_n_a		83.32 108	82.99 106	84.28 128	83.79 316	68.07 141	89.34 104	82.85 333	69.80 244	87.36 52	94.06 52	68.34 102	91.56 252	87.95 36	83.46 232	93.21 109

OPM-MVS		83.50 102	82.95 107	85.14 92	88.79 165	70.95 71	89.13 114	91.52 121	77.55 52	80.96 143	91.75 114	60.71 200	94.50 118	79.67 119	86.51 179	89.97 244
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EPNet		83.72 95	82.92 108	86.14 68	84.22 306	69.48 97	91.05 59	85.27 291	81.30 6	76.83 219	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
IS-MVSNet		83.15 111	82.81 109	84.18 135	89.94 119	63.30 259	91.59 46	88.46 231	79.04 30	79.49 163	92.16 104	65.10 137	94.28 123	67.71 242	91.86 90	94.95 12

EIA-MVS		83.31 109	82.80 110	84.82 107	89.59 126	65.59 201	88.21 153	92.68 67	74.66 131	78.96 169	86.42 272	69.06 92	95.26 83	75.54 164	90.09 119	93.62 90

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	61.00 197	94.20 128	72.45 199	90.97 104	93.35 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GDP-MVS		83.52 101	82.64 112	86.16 65	88.14 190	68.45 128	89.13 114	92.69 66	72.82 180	83.71 104	91.86 112	55.69 244	95.35 82	80.03 114	89.74 127	94.69 28

KinetiMVS		83.31 109	82.61 113	85.39 86	87.08 238	67.56 157	88.06 159	91.65 116	77.80 44	82.21 123	91.79 113	57.27 232	94.07 134	77.77 136	89.89 125	94.56 37

FIs		82.07 127	82.42 114	81.04 246	88.80 164	58.34 323	88.26 152	93.49 27	76.93 71	78.47 183	91.04 140	69.92 80	92.34 222	69.87 223	84.97 200	92.44 146

VNet		82.21 124	82.41 115	81.62 227	90.82 96	60.93 294	84.47 269	89.78 175	76.36 90	84.07 97	91.88 110	64.71 141	90.26 286	70.68 213	88.89 139	93.66 83

PAPM_NR		83.02 115	82.41 115	84.82 107	92.47 72	66.37 182	87.93 165	91.80 111	73.82 152	77.32 207	90.66 149	67.90 107	94.90 100	70.37 216	89.48 132	93.19 112

VDD-MVS		83.01 116	82.36 117	84.96 101	91.02 91	66.40 181	88.91 121	88.11 234	77.57 49	84.39 89	93.29 78	52.19 278	93.91 144	77.05 145	88.70 145	94.57 36

3Dnovator	76.31 5	83.38 106	82.31 118	86.59 57	87.94 201	72.94 28	90.64 63	92.14 97	77.21 62	75.47 250	92.83 90	58.56 219	94.72 110	73.24 188	92.71 77	92.13 160

h-mvs33		83.15 111	82.19 119	86.02 72	90.56 101	70.85 75	88.15 157	89.16 204	76.02 96	84.67 80	91.39 128	61.54 183	95.50 69	82.71 88	75.48 335	91.72 170

MVS_111021_LR		82.61 120	82.11 120	84.11 136	88.82 162	71.58 57	85.15 251	86.16 281	74.69 129	80.47 152	91.04 140	62.29 171	90.55 284	80.33 112	90.08 120	90.20 227

RRT-MVS		82.60 122	82.10 121	84.10 137	87.98 200	62.94 270	87.45 180	91.27 128	77.42 56	79.85 158	90.28 156	56.62 240	94.70 112	79.87 117	88.15 154	94.67 29

DP-MVS Recon		83.11 114	82.09 122	86.15 66	94.44 19	70.92 73	88.79 128	92.20 91	70.53 224	79.17 167	91.03 142	64.12 146	96.03 51	68.39 239	90.14 118	91.50 176

MVSFormer		82.85 117	82.05 123	85.24 90	87.35 223	70.21 82	90.50 67	90.38 154	68.55 275	81.32 136	89.47 179	61.68 180	93.46 167	78.98 122	90.26 116	92.05 162

FC-MVSNet-test		81.52 142	82.02 124	80.03 269	88.42 180	55.97 362	87.95 163	93.42 30	77.10 67	77.38 205	90.98 146	69.96 79	91.79 241	68.46 238	84.50 206	92.33 149

HQP-MVS		82.61 120	82.02 124	84.37 121	89.33 140	66.98 174	89.17 109	92.19 92	76.41 85	77.23 210	90.23 159	60.17 211	95.11 90	77.47 139	85.99 189	91.03 191

OMC-MVS		82.69 118	81.97 126	84.85 106	88.75 167	67.42 160	87.98 161	90.87 141	74.92 123	79.72 160	91.65 117	62.19 174	93.96 136	75.26 168	86.42 180	93.16 113

diffmvs		82.10 125	81.88 127	82.76 208	83.00 337	63.78 245	83.68 287	89.76 177	72.94 177	82.02 126	89.85 165	65.96 131	90.79 279	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
PVSNet_Blended_VisFu		82.62 119	81.83 128	84.96 101	90.80 97	69.76 93	88.74 133	91.70 115	69.39 252	78.96 169	88.46 209	65.47 134	94.87 103	74.42 174	88.57 146	90.24 226

CLD-MVS		82.31 123	81.65 129	84.29 127	88.47 176	67.73 151	85.81 236	92.35 83	75.78 99	78.33 186	86.58 267	64.01 147	94.35 121	76.05 157	87.48 162	90.79 200
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_NR-MVSNet		81.88 131	81.54 130	82.92 195	88.46 177	63.46 255	87.13 188	92.37 82	80.19 12	78.38 184	89.14 187	71.66 59	93.05 193	70.05 219	76.46 318	92.25 153

PS-MVSNAJss		82.07 127	81.31 131	84.34 124	86.51 252	67.27 167	89.27 105	91.51 122	71.75 193	79.37 164	90.22 160	63.15 158	94.27 124	77.69 137	82.36 247	91.49 177

LPG-MVS_test		82.08 126	81.27 132	84.50 116	89.23 148	68.76 115	90.22 76	91.94 104	75.37 109	76.64 225	91.51 123	54.29 257	94.91 98	78.44 127	83.78 219	89.83 249

LFMVS		81.82 133	81.23 133	83.57 167	91.89 78	63.43 257	89.84 81	81.85 344	77.04 69	83.21 110	93.10 81	52.26 277	93.43 169	71.98 201	89.95 123	93.85 71

API-MVS		81.99 129	81.23 133	84.26 132	90.94 93	70.18 87	91.10 58	89.32 194	71.51 200	78.66 176	88.28 214	65.26 135	95.10 93	64.74 269	91.23 100	87.51 316

UniMVSNet (Re)		81.60 139	81.11 135	83.09 185	88.38 181	64.41 232	87.60 174	93.02 46	78.42 37	78.56 179	88.16 218	69.78 81	93.26 175	69.58 226	76.49 317	91.60 171

xiu_mvs_v2_base		81.69 136	81.05 136	83.60 164	89.15 151	68.03 143	84.46 271	90.02 168	70.67 219	81.30 139	86.53 270	63.17 157	94.19 130	75.60 163	88.54 147	88.57 294

PS-MVSNAJ		81.69 136	81.02 137	83.70 162	89.51 130	68.21 138	84.28 277	90.09 167	70.79 216	81.26 140	85.62 290	63.15 158	94.29 122	75.62 162	88.87 140	88.59 293

GeoE		81.71 135	81.01 138	83.80 161	89.51 130	64.45 231	88.97 119	88.73 226	71.27 206	78.63 177	89.76 169	66.32 124	93.20 182	69.89 222	86.02 188	93.74 80

hse-mvs2		81.72 134	80.94 139	84.07 143	88.72 168	67.68 152	85.87 232	87.26 259	76.02 96	84.67 80	88.22 217	61.54 183	93.48 165	82.71 88	73.44 363	91.06 189

PAPR		81.66 138	80.89 140	83.99 153	90.27 107	64.00 238	86.76 206	91.77 114	68.84 271	77.13 217	89.50 177	67.63 109	94.88 102	67.55 244	88.52 148	93.09 116

mamba_0404		81.91 130	80.84 141	85.13 95	89.24 147	68.26 133	87.84 170	89.25 200	71.06 212	80.62 148	90.39 155	59.57 213	94.65 114	72.45 199	87.19 167	92.47 144

MAR-MVS		81.84 132	80.70 142	85.27 89	91.32 85	71.53 58	89.82 82	90.92 138	69.77 246	78.50 180	86.21 276	62.36 170	94.52 117	65.36 263	92.05 86	89.77 252
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
VDDNet		81.52 142	80.67 143	84.05 148	90.44 104	64.13 237	89.73 87	85.91 284	71.11 209	83.18 111	93.48 71	50.54 304	93.49 164	73.40 185	88.25 152	94.54 39

guyue		81.13 149	80.64 144	82.60 211	86.52 251	63.92 242	86.69 208	87.73 248	73.97 147	80.83 146	89.69 170	56.70 238	91.33 265	78.26 134	85.40 197	92.54 138

ACMP	74.13 6	81.51 144	80.57 145	84.36 122	89.42 135	68.69 122	89.97 80	91.50 125	74.46 135	75.04 272	90.41 154	53.82 263	94.54 115	77.56 138	82.91 239	89.86 248
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		80.60 167	80.55 146	80.76 253	88.07 195	60.80 297	86.86 200	91.58 120	75.67 103	80.24 154	89.45 183	63.34 151	90.25 287	70.51 215	79.22 286	91.23 184

DU-MVS		81.12 150	80.52 147	82.90 196	87.80 208	63.46 255	87.02 193	91.87 108	79.01 31	78.38 184	89.07 189	65.02 138	93.05 193	70.05 219	76.46 318	92.20 156

test_yl		81.17 147	80.47 148	83.24 178	89.13 152	63.62 246	86.21 223	89.95 171	72.43 185	81.78 131	89.61 174	57.50 229	93.58 158	70.75 211	86.90 171	92.52 139

DCV-MVSNet		81.17 147	80.47 148	83.24 178	89.13 152	63.62 246	86.21 223	89.95 171	72.43 185	81.78 131	89.61 174	57.50 229	93.58 158	70.75 211	86.90 171	92.52 139

PVSNet_Blended		80.98 151	80.34 150	82.90 196	88.85 159	65.40 204	84.43 273	92.00 100	67.62 286	78.11 191	85.05 306	66.02 129	94.27 124	71.52 203	89.50 131	89.01 274

TranMVSNet+NR-MVSNet		80.84 154	80.31 151	82.42 214	87.85 205	62.33 276	87.74 172	91.33 127	80.55 9	77.99 195	89.86 164	65.23 136	92.62 204	67.05 251	75.24 345	92.30 151

jason		81.39 145	80.29 152	84.70 112	86.63 250	69.90 90	85.95 229	86.77 270	63.24 340	81.07 142	89.47 179	61.08 196	92.15 228	78.33 130	90.07 121	92.05 162
jason: jason.
lupinMVS		81.39 145	80.27 153	84.76 110	87.35 223	70.21 82	85.55 242	86.41 275	62.85 347	81.32 136	88.61 204	61.68 180	92.24 226	78.41 129	90.26 116	91.83 165

SDMVSNet		80.38 173	80.18 154	80.99 247	89.03 157	64.94 219	80.45 338	89.40 189	75.19 115	76.61 227	89.98 162	60.61 205	87.69 332	76.83 150	83.55 228	90.33 222

Elysia		81.53 140	80.16 155	85.62 79	85.51 274	68.25 135	88.84 126	92.19 92	71.31 203	80.50 150	89.83 166	46.89 338	94.82 104	76.85 147	89.57 129	93.80 77

StellarMVS		81.53 140	80.16 155	85.62 79	85.51 274	68.25 135	88.84 126	92.19 92	71.31 203	80.50 150	89.83 166	46.89 338	94.82 104	76.85 147	89.57 129	93.80 77

AstraMVS		80.81 156	80.14 157	82.80 202	86.05 263	63.96 239	86.46 215	85.90 285	73.71 155	80.85 145	90.56 151	54.06 261	91.57 251	79.72 118	83.97 217	92.86 128

icg_test_0403		80.80 159	80.12 158	82.87 198	87.13 235	63.59 250	85.19 248	89.33 192	70.51 225	78.49 181	89.03 191	63.26 154	93.27 174	72.56 197	85.56 195	91.74 168

PVSNet_BlendedMVS		80.60 167	80.02 159	82.36 216	88.85 159	65.40 204	86.16 225	92.00 100	69.34 254	78.11 191	86.09 280	66.02 129	94.27 124	71.52 203	82.06 250	87.39 318

EI-MVSNet		80.52 171	79.98 160	82.12 217	84.28 304	63.19 263	86.41 216	88.95 216	74.18 144	78.69 174	87.54 237	66.62 118	92.43 216	72.57 195	80.57 269	90.74 204

Fast-Effi-MVS+		80.81 156	79.92 161	83.47 168	88.85 159	64.51 227	85.53 244	89.39 190	70.79 216	78.49 181	85.06 305	67.54 110	93.58 158	67.03 252	86.58 177	92.32 150

FA-MVS(test-final)		80.96 152	79.91 162	84.10 137	88.30 184	65.01 216	84.55 268	90.01 169	73.25 171	79.61 161	87.57 234	58.35 221	94.72 110	71.29 207	86.25 183	92.56 137

CANet_DTU		80.61 166	79.87 163	82.83 199	85.60 272	63.17 264	87.36 182	88.65 227	76.37 89	75.88 243	88.44 210	53.51 266	93.07 191	73.30 186	89.74 127	92.25 153

ACMM	73.20 8	80.78 163	79.84 164	83.58 166	89.31 143	68.37 130	89.99 79	91.60 119	70.28 232	77.25 208	89.66 172	53.37 268	93.53 163	74.24 177	82.85 240	88.85 282
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-OURS-SEG-HR		80.81 156	79.76 165	83.96 155	85.60 272	68.78 114	83.54 294	90.50 150	70.66 222	76.71 223	91.66 116	60.69 201	91.26 266	76.94 146	81.58 255	91.83 165

xiu_mvs_v1_base_debu		80.80 159	79.72 166	84.03 150	87.35 223	70.19 84	85.56 239	88.77 221	69.06 265	81.83 127	88.16 218	50.91 298	92.85 199	78.29 131	87.56 159	89.06 269

xiu_mvs_v1_base		80.80 159	79.72 166	84.03 150	87.35 223	70.19 84	85.56 239	88.77 221	69.06 265	81.83 127	88.16 218	50.91 298	92.85 199	78.29 131	87.56 159	89.06 269

xiu_mvs_v1_base_debi		80.80 159	79.72 166	84.03 150	87.35 223	70.19 84	85.56 239	88.77 221	69.06 265	81.83 127	88.16 218	50.91 298	92.85 199	78.29 131	87.56 159	89.06 269

LuminaMVS		80.68 164	79.62 169	83.83 158	85.07 289	68.01 144	86.99 194	88.83 218	70.36 228	81.38 135	87.99 225	50.11 309	92.51 213	79.02 120	86.89 173	90.97 194

UGNet		80.83 155	79.59 170	84.54 115	88.04 196	68.09 140	89.42 99	88.16 233	76.95 70	76.22 236	89.46 181	49.30 321	93.94 139	68.48 237	90.31 114	91.60 171
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
114514_t		80.68 164	79.51 171	84.20 134	94.09 38	67.27 167	89.64 90	91.11 135	58.75 387	74.08 287	90.72 148	58.10 222	95.04 95	69.70 224	89.42 133	90.30 224

QAPM		80.88 153	79.50 172	85.03 98	88.01 199	68.97 110	91.59 46	92.00 100	66.63 301	75.15 268	92.16 104	57.70 226	95.45 71	63.52 275	88.76 143	90.66 207

AdaColmap		80.58 170	79.42 173	84.06 145	93.09 59	68.91 111	89.36 103	88.97 215	69.27 256	75.70 246	89.69 170	57.20 234	95.77 60	63.06 280	88.41 151	87.50 317

NR-MVSNet		80.23 177	79.38 174	82.78 206	87.80 208	63.34 258	86.31 220	91.09 136	79.01 31	72.17 313	89.07 189	67.20 114	92.81 202	66.08 258	75.65 331	92.20 156

mvsmamba		80.60 167	79.38 174	84.27 130	89.74 124	67.24 169	87.47 178	86.95 265	70.02 237	75.38 256	88.93 194	51.24 295	92.56 209	75.47 166	89.22 135	93.00 124

IterMVS-LS		80.06 180	79.38 174	82.11 218	85.89 264	63.20 262	86.79 203	89.34 191	74.19 143	75.45 253	86.72 257	66.62 118	92.39 218	72.58 194	76.86 311	90.75 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_djsdf		80.30 176	79.32 177	83.27 176	83.98 312	65.37 207	90.50 67	90.38 154	68.55 275	76.19 237	88.70 200	56.44 241	93.46 167	78.98 122	80.14 275	90.97 194

v2v482		80.23 177	79.29 178	83.05 189	83.62 320	64.14 236	87.04 191	89.97 170	73.61 158	78.18 190	87.22 245	61.10 195	93.82 148	76.11 155	76.78 314	91.18 185

ECVR-MVS		79.61 186	79.26 179	80.67 255	90.08 112	54.69 377	87.89 167	77.44 390	74.88 124	80.27 153	92.79 93	48.96 327	92.45 215	68.55 236	92.50 80	94.86 19

XVG-OURS		80.41 172	79.23 180	83.97 154	85.64 270	69.02 108	83.03 306	90.39 153	71.09 210	77.63 201	91.49 125	54.62 256	91.35 263	75.71 160	83.47 231	91.54 174

WR-MVS		79.49 190	79.22 181	80.27 264	88.79 165	58.35 322	85.06 254	88.61 229	78.56 35	77.65 200	88.34 212	63.81 150	90.66 283	64.98 267	77.22 306	91.80 167

test1111		79.43 193	79.18 182	80.15 267	89.99 117	53.31 390	87.33 184	77.05 394	75.04 118	80.23 155	92.77 95	48.97 326	92.33 223	68.87 233	92.40 82	94.81 22

mvs_anonymous		79.42 194	79.11 183	80.34 262	84.45 303	57.97 329	82.59 308	87.62 250	67.40 290	76.17 240	88.56 207	68.47 100	89.59 299	70.65 214	86.05 187	93.47 97

v1144		80.03 181	79.03 184	83.01 191	83.78 317	64.51 227	87.11 190	90.57 149	71.96 192	78.08 193	86.20 277	61.41 187	93.94 139	74.93 170	77.23 305	90.60 210

v8		79.97 183	79.02 185	82.80 202	84.09 309	64.50 229	87.96 162	90.29 161	74.13 146	75.24 265	86.81 254	62.88 163	93.89 147	74.39 175	75.40 340	90.00 240

ab-mvs		79.51 189	78.97 186	81.14 243	88.46 177	60.91 295	83.84 283	89.24 201	70.36 228	79.03 168	88.87 197	63.23 156	90.21 288	65.12 265	82.57 245	92.28 152

Anonymous20240529		80.19 179	78.89 187	84.10 137	90.60 100	64.75 224	88.95 120	90.90 139	65.97 309	80.59 149	91.17 136	49.97 311	93.73 156	69.16 230	82.70 244	93.81 75

PCF-MVS	73.52 7	80.38 173	78.84 188	85.01 99	87.71 214	68.99 109	83.65 288	91.46 126	63.00 344	77.77 199	90.28 156	66.10 126	95.09 94	61.40 299	88.22 153	90.94 196
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v10		79.74 185	78.67 189	82.97 194	84.06 310	64.95 218	87.88 168	90.62 146	73.11 173	75.11 269	86.56 268	61.46 186	94.05 135	73.68 180	75.55 333	89.90 246

VPNet		78.69 213	78.66 190	78.76 293	88.31 183	55.72 366	84.45 272	86.63 272	76.79 75	78.26 187	90.55 152	59.30 215	89.70 298	66.63 253	77.05 308	90.88 197

BH-untuned		79.47 191	78.60 191	82.05 219	89.19 150	65.91 191	86.07 227	88.52 230	72.18 187	75.42 254	87.69 231	61.15 194	93.54 162	60.38 307	86.83 174	86.70 339

Effi-MVS+-dtu		80.03 181	78.57 192	84.42 120	85.13 287	68.74 117	88.77 129	88.10 235	74.99 119	74.97 274	83.49 341	57.27 232	93.36 171	73.53 182	80.88 263	91.18 185

WR-MVS_H		78.51 218	78.49 193	78.56 298	88.02 197	56.38 356	88.43 143	92.67 68	77.14 64	73.89 289	87.55 236	66.25 125	89.24 306	58.92 321	73.55 361	90.06 238

Vis-MVSNet (Re-imp)		78.36 221	78.45 194	78.07 309	88.64 171	51.78 400	86.70 207	79.63 372	74.14 145	75.11 269	90.83 147	61.29 191	89.75 296	58.10 331	91.60 92	92.69 133

BH-RMVSNet		79.61 186	78.44 195	83.14 183	89.38 139	65.93 190	84.95 257	87.15 262	73.56 160	78.19 189	89.79 168	56.67 239	93.36 171	59.53 315	86.74 175	90.13 230

v1192		79.59 188	78.43 196	83.07 188	83.55 322	64.52 226	86.93 198	90.58 147	70.83 215	77.78 198	85.90 281	59.15 216	93.94 139	73.96 179	77.19 307	90.76 202

v144192		79.47 191	78.37 197	82.78 206	83.35 325	63.96 239	86.96 195	90.36 157	69.99 239	77.50 202	85.67 288	60.66 203	93.77 152	74.27 176	76.58 315	90.62 208

CP-MVSNet		78.22 223	78.34 198	77.84 313	87.83 207	54.54 379	87.94 164	91.17 132	77.65 46	73.48 295	88.49 208	62.24 173	88.43 322	62.19 290	74.07 354	90.55 212

Baseline_NR-MVSNet		78.15 227	78.33 199	77.61 318	85.79 266	56.21 360	86.78 204	85.76 287	73.60 159	77.93 196	87.57 234	65.02 138	88.99 311	67.14 250	75.33 342	87.63 312

OpenMVS	72.83 10	79.77 184	78.33 199	84.09 141	85.17 283	69.91 89	90.57 64	90.97 137	66.70 295	72.17 313	91.91 108	54.70 254	93.96 136	61.81 296	90.95 105	88.41 298

UniMVSNet_ETH3D		79.10 203	78.24 201	81.70 226	86.85 242	60.24 306	87.28 186	88.79 220	74.25 142	76.84 218	90.53 153	49.48 317	91.56 252	67.98 240	82.15 248	93.29 104

V42		79.38 197	78.24 201	82.83 199	81.10 372	65.50 203	85.55 242	89.82 174	71.57 199	78.21 188	86.12 279	60.66 203	93.18 185	75.64 161	75.46 337	89.81 251

mamv4		76.81 257	78.23 203	72.54 374	86.12 260	65.75 198	78.76 362	82.07 341	64.12 331	72.97 301	91.02 143	67.97 105	68.08 439	83.04 82	78.02 297	83.80 385

PS-CasMVS		78.01 232	78.09 204	77.77 315	87.71 214	54.39 381	88.02 160	91.22 129	77.50 54	73.26 297	88.64 203	60.73 199	88.41 323	61.88 294	73.88 358	90.53 213

v1921920		79.22 199	78.03 205	82.80 202	83.30 327	63.94 241	86.80 202	90.33 158	69.91 242	77.48 203	85.53 292	58.44 220	93.75 154	73.60 181	76.85 312	90.71 206

jajsoiax		79.29 198	77.96 206	83.27 176	84.68 297	66.57 180	89.25 106	90.16 165	69.20 261	75.46 252	89.49 178	45.75 354	93.13 188	76.84 149	80.80 265	90.11 232

TAPA-MVS	73.13 9	79.15 201	77.94 207	82.79 205	89.59 126	62.99 269	88.16 156	91.51 122	65.77 310	77.14 216	91.09 138	60.91 198	93.21 179	50.26 383	87.05 169	92.17 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tttt0517		79.40 195	77.91 208	83.90 157	88.10 193	63.84 243	88.37 148	84.05 309	71.45 201	76.78 221	89.12 188	49.93 314	94.89 101	70.18 218	83.18 237	92.96 126

c3_l		78.75 210	77.91 208	81.26 239	82.89 341	61.56 287	84.09 281	89.13 207	69.97 240	75.56 248	84.29 320	66.36 123	92.09 230	73.47 184	75.48 335	90.12 231

VortexMVS		78.57 217	77.89 210	80.59 256	85.89 264	62.76 272	85.61 237	89.62 183	72.06 190	74.99 273	85.38 296	55.94 243	90.77 281	74.99 169	76.58 315	88.23 300

MVSTER		79.01 205	77.88 211	82.38 215	83.07 334	64.80 223	84.08 282	88.95 216	69.01 268	78.69 174	87.17 248	54.70 254	92.43 216	74.69 171	80.57 269	89.89 247

tt0805		78.73 211	77.83 212	81.43 232	85.17 283	60.30 305	89.41 100	90.90 139	71.21 207	77.17 215	88.73 199	46.38 343	93.21 179	72.57 195	78.96 287	90.79 200

X-MVStestdata		80.37 175	77.83 212	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 106	12.47 451	67.45 111	96.60 33	83.06 80	94.50 53	94.07 59

v148		78.72 212	77.80 214	81.47 231	82.73 344	61.96 282	86.30 221	88.08 236	73.26 170	76.18 238	85.47 294	62.46 168	92.36 220	71.92 202	73.82 359	90.09 234

v1240		78.99 206	77.78 215	82.64 209	83.21 329	63.54 252	86.62 210	90.30 160	69.74 249	77.33 206	85.68 287	57.04 235	93.76 153	73.13 189	76.92 309	90.62 208

mvs_tets		79.13 202	77.77 216	83.22 180	84.70 296	66.37 182	89.17 109	90.19 164	69.38 253	75.40 255	89.46 181	44.17 366	93.15 186	76.78 151	80.70 267	90.14 229

miper_ehance_all_eth		78.59 216	77.76 217	81.08 245	82.66 346	61.56 287	83.65 288	89.15 205	68.87 270	75.55 249	83.79 332	66.49 121	92.03 231	73.25 187	76.39 320	89.64 255

thisisatest0530		79.40 195	77.76 217	84.31 125	87.69 216	65.10 215	87.36 182	84.26 307	70.04 236	77.42 204	88.26 216	49.94 312	94.79 108	70.20 217	84.70 204	93.03 121

CDS-MVSNet		79.07 204	77.70 219	83.17 182	87.60 218	68.23 137	84.40 275	86.20 280	67.49 288	76.36 233	86.54 269	61.54 183	90.79 279	61.86 295	87.33 164	90.49 215
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Anonymous20231211		78.97 207	77.69 220	82.81 201	90.54 102	64.29 234	90.11 78	91.51 122	65.01 321	76.16 241	88.13 223	50.56 303	93.03 196	69.68 225	77.56 304	91.11 187

PEN-MVS		77.73 238	77.69 220	77.84 313	87.07 240	53.91 384	87.91 166	91.18 131	77.56 51	73.14 299	88.82 198	61.23 192	89.17 308	59.95 310	72.37 369	90.43 217

AUN-MVS		79.21 200	77.60 222	84.05 148	88.71 169	67.61 154	85.84 234	87.26 259	69.08 264	77.23 210	88.14 222	53.20 270	93.47 166	75.50 165	73.45 362	91.06 189

v7n		78.97 207	77.58 223	83.14 183	83.45 324	65.51 202	88.32 150	91.21 130	73.69 156	72.41 309	86.32 275	57.93 223	93.81 149	69.18 229	75.65 331	90.11 232

TAMVS		78.89 209	77.51 224	83.03 190	87.80 208	67.79 150	84.72 261	85.05 296	67.63 285	76.75 222	87.70 230	62.25 172	90.82 278	58.53 326	87.13 168	90.49 215

sd_testset		77.70 241	77.40 225	78.60 296	89.03 157	60.02 308	79.00 358	85.83 286	75.19 115	76.61 227	89.98 162	54.81 249	85.46 357	62.63 286	83.55 228	90.33 222

GBi-Net		78.40 219	77.40 225	81.40 234	87.60 218	63.01 265	88.39 145	89.28 196	71.63 195	75.34 258	87.28 241	54.80 250	91.11 269	62.72 282	79.57 279	90.09 234

test1		78.40 219	77.40 225	81.40 234	87.60 218	63.01 265	88.39 145	89.28 196	71.63 195	75.34 258	87.28 241	54.80 250	91.11 269	62.72 282	79.57 279	90.09 234

BH-w/o		78.21 224	77.33 228	80.84 251	88.81 163	65.13 212	84.87 258	87.85 245	69.75 247	74.52 282	84.74 312	61.34 189	93.11 189	58.24 330	85.84 191	84.27 377

FMVSNet2		78.20 225	77.21 229	81.20 241	87.60 218	62.89 271	87.47 178	89.02 211	71.63 195	75.29 264	87.28 241	54.80 250	91.10 272	62.38 287	79.38 283	89.61 256

anonymousdsp		78.60 215	77.15 230	82.98 193	80.51 378	67.08 172	87.24 187	89.53 186	65.66 312	75.16 267	87.19 247	52.52 272	92.25 225	77.17 143	79.34 284	89.61 256

HY-MVS	69.67 12	77.95 233	77.15 230	80.36 261	87.57 222	60.21 307	83.37 296	87.78 247	66.11 305	75.37 257	87.06 252	63.27 153	90.48 285	61.38 300	82.43 246	90.40 219

cl22		78.07 229	77.01 232	81.23 240	82.37 353	61.83 284	83.55 292	87.98 239	68.96 269	75.06 271	83.87 328	61.40 188	91.88 239	73.53 182	76.39 320	89.98 243

Anonymous202405211		78.25 222	77.01 232	81.99 221	91.03 90	60.67 299	84.77 260	83.90 311	70.65 223	80.00 157	91.20 134	41.08 386	91.43 261	65.21 264	85.26 198	93.85 71

MVS		78.19 226	76.99 234	81.78 224	85.66 269	66.99 173	84.66 263	90.47 151	55.08 408	72.02 315	85.27 298	63.83 149	94.11 133	66.10 257	89.80 126	84.24 378

LCM-MVSNet-Re		77.05 252	76.94 235	77.36 322	87.20 232	51.60 401	80.06 343	80.46 360	75.20 114	67.69 359	86.72 257	62.48 167	88.98 312	63.44 277	89.25 134	91.51 175

miper_enhance_ethall		77.87 236	76.86 236	80.92 250	81.65 360	61.38 289	82.68 307	88.98 213	65.52 314	75.47 250	82.30 361	65.76 133	92.00 233	72.95 190	76.39 320	89.39 262

FMVSNet3		77.88 235	76.85 237	80.97 249	86.84 243	62.36 275	86.52 213	88.77 221	71.13 208	75.34 258	86.66 263	54.07 260	91.10 272	62.72 282	79.57 279	89.45 260

DTE-MVSNet		76.99 253	76.80 238	77.54 321	86.24 255	53.06 393	87.52 176	90.66 145	77.08 68	72.50 307	88.67 202	60.48 207	89.52 300	57.33 338	70.74 381	90.05 239

CNLPA		78.08 228	76.79 239	81.97 222	90.40 105	71.07 67	87.59 175	84.55 301	66.03 308	72.38 310	89.64 173	57.56 228	86.04 349	59.61 314	83.35 233	88.79 285

cl____		77.72 239	76.76 240	80.58 257	82.49 350	60.48 302	83.09 302	87.87 243	69.22 259	74.38 285	85.22 301	62.10 175	91.53 255	71.09 208	75.41 339	89.73 254

DIV-MVS_self_test		77.72 239	76.76 240	80.58 257	82.48 351	60.48 302	83.09 302	87.86 244	69.22 259	74.38 285	85.24 299	62.10 175	91.53 255	71.09 208	75.40 340	89.74 253

baseline1		76.98 254	76.75 242	77.66 316	88.13 191	55.66 367	85.12 252	81.89 342	73.04 175	76.79 220	88.90 195	62.43 169	87.78 331	63.30 279	71.18 379	89.55 258

eth_miper_zixun_eth		77.92 234	76.69 243	81.61 229	83.00 337	61.98 281	83.15 300	89.20 203	69.52 251	74.86 276	84.35 319	61.76 179	92.56 209	71.50 205	72.89 367	90.28 225

pm-mvs1		77.25 250	76.68 244	78.93 291	84.22 306	58.62 320	86.41 216	88.36 232	71.37 202	73.31 296	88.01 224	61.22 193	89.15 309	64.24 273	73.01 366	89.03 273

ET-MVSNet_ETH3D		78.63 214	76.63 245	84.64 113	86.73 246	69.47 98	85.01 255	84.61 300	69.54 250	66.51 379	86.59 265	50.16 308	91.75 243	76.26 154	84.24 214	92.69 133

test2506		77.30 249	76.49 246	79.74 275	90.08 112	52.02 394	87.86 169	63.10 437	74.88 124	80.16 156	92.79 93	38.29 401	92.35 221	68.74 235	92.50 80	94.86 19

Fast-Effi-MVS+-dtu		78.02 231	76.49 246	82.62 210	83.16 333	66.96 176	86.94 197	87.45 255	72.45 182	71.49 321	84.17 325	54.79 253	91.58 249	67.61 243	80.31 272	89.30 265

1112_ss		77.40 247	76.43 248	80.32 263	89.11 156	60.41 304	83.65 288	87.72 249	62.13 357	73.05 300	86.72 257	62.58 166	89.97 292	62.11 293	80.80 265	90.59 211

ICG_test_0404		77.16 251	76.42 249	79.37 283	87.13 235	63.59 250	77.12 382	89.33 192	70.51 225	66.22 382	89.03 191	50.36 306	82.78 378	72.56 197	85.56 195	91.74 168

PAPM		77.68 242	76.40 250	81.51 230	87.29 231	61.85 283	83.78 284	89.59 184	64.74 323	71.23 323	88.70 200	62.59 165	93.66 157	52.66 367	87.03 170	89.01 274

PLC	70.83 11	78.05 230	76.37 251	83.08 187	91.88 79	67.80 149	88.19 154	89.46 188	64.33 329	69.87 340	88.38 211	53.66 264	93.58 158	58.86 322	82.73 242	87.86 308
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 245	76.18 252	81.20 241	88.24 185	63.24 260	84.61 266	86.40 276	67.55 287	77.81 197	86.48 271	54.10 259	93.15 186	57.75 334	82.72 243	87.20 324

FMVSNet1		77.44 245	76.12 253	81.40 234	86.81 244	63.01 265	88.39 145	89.28 196	70.49 227	74.39 284	87.28 241	49.06 325	91.11 269	60.91 303	78.52 290	90.09 234

MonoMVSNet		76.49 265	75.80 254	78.58 297	81.55 363	58.45 321	86.36 219	86.22 279	74.87 126	74.73 278	83.73 334	51.79 290	88.73 317	70.78 210	72.15 372	88.55 295

test_vis1_n_1920		75.52 279	75.78 255	74.75 352	79.84 386	57.44 340	83.26 298	85.52 289	62.83 348	79.34 166	86.17 278	45.10 359	79.71 394	78.75 124	81.21 259	87.10 331

CHOSEN 1792x2688		77.63 243	75.69 256	83.44 169	89.98 118	68.58 125	78.70 363	87.50 253	56.38 403	75.80 245	86.84 253	58.67 218	91.40 262	61.58 298	85.75 193	90.34 221

FE-MVS		77.78 237	75.68 257	84.08 142	88.09 194	66.00 188	83.13 301	87.79 246	68.42 279	78.01 194	85.23 300	45.50 357	95.12 88	59.11 319	85.83 192	91.11 187

WTY-MVS		75.65 277	75.68 257	75.57 338	86.40 253	56.82 347	77.92 376	82.40 337	65.10 318	76.18 238	87.72 229	63.13 161	80.90 390	60.31 308	81.96 251	89.00 276

testing91		76.54 260	75.66 259	79.18 288	88.43 179	55.89 363	81.08 325	83.00 329	73.76 154	75.34 258	84.29 320	46.20 348	90.07 290	64.33 271	84.50 206	91.58 173

XXY-MVS		75.41 282	75.56 260	74.96 347	83.59 321	57.82 333	80.59 335	83.87 312	66.54 302	74.93 275	88.31 213	63.24 155	80.09 393	62.16 291	76.85 312	86.97 333

thres100view900		76.50 262	75.55 261	79.33 284	89.52 129	56.99 345	85.83 235	83.23 322	73.94 149	76.32 234	87.12 249	51.89 287	91.95 235	48.33 393	83.75 222	89.07 267

thres600view7		76.50 262	75.44 262	79.68 277	89.40 137	57.16 342	85.53 244	83.23 322	73.79 153	76.26 235	87.09 250	51.89 287	91.89 238	48.05 398	83.72 225	90.00 240

Test_1112_low_res		76.40 267	75.44 262	79.27 285	89.28 145	58.09 325	81.69 317	87.07 263	59.53 378	72.48 308	86.67 262	61.30 190	89.33 303	60.81 305	80.15 274	90.41 218

HyFIR lowres test		77.53 244	75.40 264	83.94 156	89.59 126	66.62 178	80.36 339	88.64 228	56.29 404	76.45 230	85.17 302	57.64 227	93.28 173	61.34 301	83.10 238	91.91 164

thisisatest0515		77.33 248	75.38 265	83.18 181	85.27 282	63.80 244	82.11 313	83.27 321	65.06 319	75.91 242	83.84 330	49.54 316	94.27 124	67.24 248	86.19 184	91.48 178

tfpn200view9		76.42 266	75.37 266	79.55 282	89.13 152	57.65 336	85.17 249	83.60 314	73.41 166	76.45 230	86.39 273	52.12 279	91.95 235	48.33 393	83.75 222	89.07 267

thres400		76.50 262	75.37 266	79.86 272	89.13 152	57.65 336	85.17 249	83.60 314	73.41 166	76.45 230	86.39 273	52.12 279	91.95 235	48.33 393	83.75 222	90.00 240

1314		76.53 261	75.30 268	80.21 266	83.93 313	62.32 277	84.66 263	88.81 219	60.23 371	70.16 334	84.07 327	55.30 247	90.73 282	67.37 246	83.21 236	87.59 315

testing3-2		75.12 287	75.19 269	74.91 348	90.40 105	45.09 430	80.29 341	78.42 382	78.37 40	76.54 229	87.75 228	44.36 364	87.28 337	57.04 341	83.49 230	92.37 147

GA-MVS		76.87 256	75.17 270	81.97 222	82.75 343	62.58 273	81.44 322	86.35 278	72.16 189	74.74 277	82.89 352	46.20 348	92.02 232	68.85 234	81.09 260	91.30 183

testing99		76.09 272	75.12 271	79.00 289	88.16 188	55.50 369	80.79 329	81.40 349	73.30 169	75.17 266	84.27 323	44.48 363	90.02 291	64.28 272	84.22 215	91.48 178

EPNet_dtu		75.46 280	74.86 272	77.23 325	82.57 348	54.60 378	86.89 199	83.09 326	71.64 194	66.25 381	85.86 283	55.99 242	88.04 327	54.92 355	86.55 178	89.05 272
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		76.95 255	74.82 273	83.37 173	90.45 103	67.36 164	89.15 113	86.94 266	61.87 360	69.52 343	90.61 150	51.71 291	94.53 116	46.38 405	86.71 176	88.21 302

SD_0403		74.65 290	74.77 274	74.29 356	86.20 257	47.42 419	83.71 286	85.12 293	69.30 255	68.50 354	87.95 226	59.40 214	86.05 348	49.38 387	83.35 233	89.40 261

cascas		76.72 259	74.64 275	82.99 192	85.78 267	65.88 192	82.33 310	89.21 202	60.85 366	72.74 303	81.02 372	47.28 334	93.75 154	67.48 245	85.02 199	89.34 264

DP-MVS		76.78 258	74.57 276	83.42 170	93.29 48	69.46 100	88.55 141	83.70 313	63.98 336	70.20 331	88.89 196	54.01 262	94.80 107	46.66 402	81.88 253	86.01 351

TransMVSNet (Re)		75.39 284	74.56 277	77.86 312	85.50 276	57.10 344	86.78 204	86.09 283	72.17 188	71.53 320	87.34 240	63.01 162	89.31 304	56.84 344	61.83 410	87.17 325

LTVRE_ROB	69.57 13	76.25 269	74.54 278	81.41 233	88.60 172	64.38 233	79.24 353	89.12 208	70.76 218	69.79 342	87.86 227	49.09 324	93.20 182	56.21 350	80.16 273	86.65 340
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
thres200		75.55 278	74.47 279	78.82 292	87.78 211	57.85 332	83.07 304	83.51 317	72.44 184	75.84 244	84.42 315	52.08 282	91.75 243	47.41 400	83.64 227	86.86 335

MVP-Stereo		76.12 270	74.46 280	81.13 244	85.37 279	69.79 91	84.42 274	87.95 241	65.03 320	67.46 362	85.33 297	53.28 269	91.73 245	58.01 332	83.27 235	81.85 404
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
reproduce_monomvs		75.40 283	74.38 281	78.46 303	83.92 314	57.80 334	83.78 284	86.94 266	73.47 164	72.25 312	84.47 314	38.74 397	89.27 305	75.32 167	70.53 382	88.31 299

F-COLMAP		76.38 268	74.33 282	82.50 213	89.28 145	66.95 177	88.41 144	89.03 210	64.05 334	66.83 371	88.61 204	46.78 340	92.89 198	57.48 335	78.55 289	87.67 311

XVG-ACMP-BASELINE		76.11 271	74.27 283	81.62 227	83.20 330	64.67 225	83.60 291	89.75 178	69.75 247	71.85 316	87.09 250	32.78 416	92.11 229	69.99 221	80.43 271	88.09 304

testing11		75.14 286	74.01 284	78.53 300	88.16 188	56.38 356	80.74 332	80.42 362	70.67 219	72.69 306	83.72 335	43.61 370	89.86 293	62.29 289	83.76 221	89.36 263

ACMH+	68.96 14	76.01 273	74.01 284	82.03 220	88.60 172	65.31 208	88.86 123	87.55 251	70.25 234	67.75 358	87.47 239	41.27 384	93.19 184	58.37 328	75.94 328	87.60 313

ACMH	67.68 16	75.89 274	73.93 286	81.77 225	88.71 169	66.61 179	88.62 138	89.01 212	69.81 243	66.78 372	86.70 261	41.95 382	91.51 257	55.64 351	78.14 296	87.17 325
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		75.24 285	73.90 287	79.27 285	82.65 347	58.27 324	80.80 328	82.73 335	61.57 361	75.33 262	83.13 347	55.52 245	91.07 275	64.98 267	78.34 295	88.45 296

IterMVS-SCA-FT		75.43 281	73.87 288	80.11 268	82.69 345	64.85 222	81.57 319	83.47 318	69.16 262	70.49 328	84.15 326	51.95 285	88.15 325	69.23 228	72.14 373	87.34 320

baseline2		75.70 276	73.83 289	81.30 237	83.26 328	61.79 285	82.57 309	80.65 356	66.81 292	66.88 370	83.42 342	57.86 225	92.19 227	63.47 276	79.57 279	89.91 245

test_cas_vis1_n_1920		73.76 301	73.74 290	73.81 362	75.90 408	59.77 310	80.51 336	82.40 337	58.30 389	81.62 133	85.69 286	44.35 365	76.41 412	76.29 153	78.61 288	85.23 364

sss		73.60 303	73.64 291	73.51 364	82.80 342	55.01 375	76.12 385	81.69 345	62.47 353	74.68 279	85.85 284	57.32 231	78.11 401	60.86 304	80.93 261	87.39 318

myMVS_eth3d28		73.62 302	73.53 292	73.90 361	88.20 186	47.41 420	78.06 373	79.37 374	74.29 141	73.98 288	84.29 320	44.67 360	83.54 372	51.47 373	87.39 163	90.74 204

SSC-MVS3.2		73.35 309	73.39 293	73.23 365	85.30 281	49.01 415	74.58 400	81.57 346	75.21 113	73.68 292	85.58 291	52.53 271	82.05 383	54.33 359	77.69 302	88.63 292

pmmvs6		74.69 289	73.39 293	78.61 295	81.38 367	57.48 339	86.64 209	87.95 241	64.99 322	70.18 332	86.61 264	50.43 305	89.52 300	62.12 292	70.18 384	88.83 283

IB-MVS	68.01 15	75.85 275	73.36 295	83.31 174	84.76 295	66.03 186	83.38 295	85.06 295	70.21 235	69.40 344	81.05 371	45.76 353	94.66 113	65.10 266	75.49 334	89.25 266
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
D2MVS		74.82 288	73.21 296	79.64 279	79.81 387	62.56 274	80.34 340	87.35 256	64.37 328	68.86 349	82.66 356	46.37 344	90.10 289	67.91 241	81.24 258	86.25 344

tfpnnormal		74.39 291	73.16 297	78.08 308	86.10 262	58.05 326	84.65 265	87.53 252	70.32 231	71.22 324	85.63 289	54.97 248	89.86 293	43.03 416	75.02 347	86.32 343

miper_lstm_enhance		74.11 296	73.11 298	77.13 326	80.11 382	59.62 312	72.23 407	86.92 268	66.76 294	70.40 329	82.92 351	56.93 236	82.92 377	69.06 231	72.63 368	88.87 281

mmtdpeth		74.16 295	73.01 299	77.60 320	83.72 319	61.13 290	85.10 253	85.10 294	72.06 190	77.21 214	80.33 381	43.84 368	85.75 351	77.14 144	52.61 429	85.91 354

IterMVS		74.29 292	72.94 300	78.35 304	81.53 364	63.49 254	81.58 318	82.49 336	68.06 283	69.99 337	83.69 336	51.66 292	85.54 355	65.85 260	71.64 376	86.01 351
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WBMVS		73.43 305	72.81 301	75.28 344	87.91 202	50.99 407	78.59 366	81.31 351	65.51 316	74.47 283	84.83 309	46.39 342	86.68 341	58.41 327	77.86 298	88.17 303

MS-PatchMatch		73.83 300	72.67 302	77.30 324	83.87 315	66.02 187	81.82 314	84.66 299	61.37 364	68.61 352	82.82 354	47.29 333	88.21 324	59.27 316	84.32 213	77.68 419

testing222		74.04 297	72.66 303	78.19 306	87.89 203	55.36 370	81.06 326	79.20 377	71.30 205	74.65 280	83.57 340	39.11 396	88.67 319	51.43 375	85.75 193	90.53 213

CVMVSNet		72.99 315	72.58 304	74.25 357	84.28 304	50.85 408	86.41 216	83.45 319	44.56 428	73.23 298	87.54 237	49.38 319	85.70 352	65.90 259	78.44 292	86.19 346

test-LLR		72.94 316	72.43 305	74.48 353	81.35 368	58.04 327	78.38 367	77.46 388	66.66 296	69.95 338	79.00 395	48.06 330	79.24 395	66.13 255	84.83 201	86.15 347

OurMVSNet-221017-0		74.26 293	72.42 306	79.80 274	83.76 318	59.59 313	85.92 231	86.64 271	66.39 303	66.96 369	87.58 233	39.46 392	91.60 248	65.76 261	69.27 387	88.22 301

SCA		74.22 294	72.33 307	79.91 271	84.05 311	62.17 279	79.96 346	79.29 376	66.30 304	72.38 310	80.13 384	51.95 285	88.60 320	59.25 317	77.67 303	88.96 278

UBG		73.08 313	72.27 308	75.51 340	88.02 197	51.29 405	78.35 370	77.38 391	65.52 314	73.87 290	82.36 359	45.55 355	86.48 344	55.02 354	84.39 212	88.75 287

tpmrst		72.39 318	72.13 309	73.18 369	80.54 377	49.91 412	79.91 347	79.08 378	63.11 342	71.69 318	79.95 386	55.32 246	82.77 379	65.66 262	73.89 357	86.87 334

pmmvs4		74.03 299	71.91 310	80.39 260	81.96 356	68.32 131	81.45 321	82.14 339	59.32 379	69.87 340	85.13 303	52.40 275	88.13 326	60.21 309	74.74 350	84.73 374

EG-PatchMatch MVS		74.04 297	71.82 311	80.71 254	84.92 291	67.42 160	85.86 233	88.08 236	66.04 307	64.22 394	83.85 329	35.10 412	92.56 209	57.44 336	80.83 264	82.16 403

tpm		72.37 320	71.71 312	74.35 355	82.19 354	52.00 395	79.22 354	77.29 392	64.56 325	72.95 302	83.68 337	51.35 293	83.26 376	58.33 329	75.80 329	87.81 309

WB-MVSnew		71.96 326	71.65 313	72.89 370	84.67 300	51.88 398	82.29 311	77.57 387	62.31 354	73.67 293	83.00 349	53.49 267	81.10 389	45.75 409	82.13 249	85.70 357

UWE-MVS		72.13 324	71.49 314	74.03 359	86.66 249	47.70 417	81.40 323	76.89 396	63.60 339	75.59 247	84.22 324	39.94 391	85.62 354	48.98 390	86.13 186	88.77 286

CL-MVSNet_self_test		72.37 320	71.46 315	75.09 346	79.49 393	53.53 386	80.76 331	85.01 297	69.12 263	70.51 327	82.05 365	57.92 224	84.13 367	52.27 369	66.00 400	87.60 313

tpm2		73.26 310	71.46 315	78.63 294	83.34 326	56.71 350	80.65 334	80.40 363	56.63 402	73.55 294	82.02 366	51.80 289	91.24 267	56.35 349	78.42 293	87.95 305

RPSCF		73.23 311	71.46 315	78.54 299	82.50 349	59.85 309	82.18 312	82.84 334	58.96 383	71.15 325	89.41 185	45.48 358	84.77 364	58.82 323	71.83 375	91.02 193

PatchmatchNet		73.12 312	71.33 318	78.49 302	83.18 331	60.85 296	79.63 348	78.57 381	64.13 330	71.73 317	79.81 389	51.20 296	85.97 350	57.40 337	76.36 325	88.66 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		73.37 306	71.27 319	79.67 278	81.32 370	65.19 210	75.92 387	80.30 364	59.92 374	72.73 304	81.19 369	52.50 273	86.69 340	59.84 311	77.71 300	87.11 329

SixPastTwentyTwo		73.37 306	71.26 320	79.70 276	85.08 288	57.89 331	85.57 238	83.56 316	71.03 213	65.66 384	85.88 282	42.10 380	92.57 208	59.11 319	63.34 406	88.65 291

ETVMVS		72.25 322	71.05 321	75.84 334	87.77 212	51.91 397	79.39 351	74.98 403	69.26 257	73.71 291	82.95 350	40.82 388	86.14 347	46.17 406	84.43 211	89.47 259

MSDG		73.36 308	70.99 322	80.49 259	84.51 302	65.80 195	80.71 333	86.13 282	65.70 311	65.46 385	83.74 333	44.60 361	90.91 277	51.13 376	76.89 310	84.74 373

PatchMatch-RL		72.38 319	70.90 323	76.80 329	88.60 172	67.38 163	79.53 349	76.17 400	62.75 350	69.36 345	82.00 367	45.51 356	84.89 363	53.62 362	80.58 268	78.12 418

PVSNet	64.34 18	72.08 325	70.87 324	75.69 336	86.21 256	56.44 354	74.37 401	80.73 355	62.06 358	70.17 333	82.23 363	42.86 374	83.31 375	54.77 356	84.45 210	87.32 321

dmvs_re		71.14 330	70.58 325	72.80 371	81.96 356	59.68 311	75.60 391	79.34 375	68.55 275	69.27 347	80.72 377	49.42 318	76.54 409	52.56 368	77.79 299	82.19 402

test_fmvs1		70.93 333	70.52 326	72.16 376	73.71 419	55.05 374	80.82 327	78.77 380	51.21 420	78.58 178	84.41 316	31.20 421	76.94 407	75.88 159	80.12 276	84.47 376

RPMNet		73.51 304	70.49 327	82.58 212	81.32 370	65.19 210	75.92 387	92.27 85	57.60 396	72.73 304	76.45 411	52.30 276	95.43 73	48.14 397	77.71 300	87.11 329

test_0402		72.79 317	70.44 328	79.84 273	88.13 191	65.99 189	85.93 230	84.29 305	65.57 313	67.40 365	85.49 293	46.92 337	92.61 205	35.88 430	74.38 353	80.94 409

COLMAP_ROB	66.92 17	73.01 314	70.41 329	80.81 252	87.13 235	65.63 199	88.30 151	84.19 308	62.96 345	63.80 399	87.69 231	38.04 402	92.56 209	46.66 402	74.91 348	84.24 378
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test-mter		71.41 328	70.39 330	74.48 353	81.35 368	58.04 327	78.38 367	77.46 388	60.32 370	69.95 338	79.00 395	36.08 410	79.24 395	66.13 255	84.83 201	86.15 347

test_fmvs1_n		70.86 334	70.24 331	72.73 372	72.51 430	55.28 372	81.27 324	79.71 371	51.49 419	78.73 173	84.87 308	27.54 426	77.02 406	76.06 156	79.97 277	85.88 355

pmmvs5		71.55 327	70.20 332	75.61 337	77.83 401	56.39 355	81.74 316	80.89 352	57.76 394	67.46 362	84.49 313	49.26 322	85.32 359	57.08 340	75.29 343	85.11 368

MDTV_nov1_ep13			69.97 333		83.18 331	53.48 387	77.10 383	80.18 368	60.45 368	69.33 346	80.44 378	48.89 328	86.90 339	51.60 372	78.51 291

sc_t1		72.19 323	69.51 334	80.23 265	84.81 293	61.09 292	84.68 262	80.22 366	60.70 367	71.27 322	83.58 339	36.59 407	89.24 306	60.41 306	63.31 407	90.37 220

MIMVSNet		70.69 336	69.30 335	74.88 349	84.52 301	56.35 358	75.87 389	79.42 373	64.59 324	67.76 357	82.41 358	41.10 385	81.54 386	46.64 404	81.34 256	86.75 338

tpmvs		71.09 331	69.29 336	76.49 330	82.04 355	56.04 361	78.92 360	81.37 350	64.05 334	67.18 367	78.28 401	49.74 315	89.77 295	49.67 386	72.37 369	83.67 386

test_vis1_n		69.85 348	69.21 337	71.77 378	72.66 429	55.27 373	81.48 320	76.21 399	52.03 416	75.30 263	83.20 346	28.97 424	76.22 414	74.60 172	78.41 294	83.81 384

Patchmtry		70.74 335	69.16 338	75.49 341	80.72 374	54.07 383	74.94 398	80.30 364	58.34 388	70.01 335	81.19 369	52.50 273	86.54 342	53.37 364	71.09 380	85.87 356

TESTMET0.1,1		69.89 347	69.00 339	72.55 373	79.27 396	56.85 346	78.38 367	74.71 407	57.64 395	68.09 356	77.19 408	37.75 403	76.70 408	63.92 274	84.09 216	84.10 381

PMMVS		69.34 351	68.67 340	71.35 383	75.67 410	62.03 280	75.17 393	73.46 410	50.00 421	68.68 350	79.05 393	52.07 283	78.13 400	61.16 302	82.77 241	73.90 425

K. test v3		71.19 329	68.51 341	79.21 287	83.04 336	57.78 335	84.35 276	76.91 395	72.90 178	62.99 402	82.86 353	39.27 393	91.09 274	61.65 297	52.66 428	88.75 287

USDC		70.33 341	68.37 342	76.21 332	80.60 376	56.23 359	79.19 355	86.49 274	60.89 365	61.29 407	85.47 294	31.78 419	89.47 302	53.37 364	76.21 326	82.94 396

tpm cat1		70.57 337	68.31 343	77.35 323	82.41 352	57.95 330	78.08 372	80.22 366	52.04 415	68.54 353	77.66 406	52.00 284	87.84 330	51.77 370	72.07 374	86.25 344

OpenMVS_ROB	64.09 19	70.56 338	68.19 344	77.65 317	80.26 379	59.41 316	85.01 255	82.96 331	58.76 386	65.43 386	82.33 360	37.63 404	91.23 268	45.34 412	76.03 327	82.32 400

EPMVS		69.02 353	68.16 345	71.59 379	79.61 391	49.80 414	77.40 379	66.93 428	62.82 349	70.01 335	79.05 393	45.79 352	77.86 403	56.58 347	75.26 344	87.13 328

CMPMVS	51.72 21	70.19 343	68.16 345	76.28 331	73.15 426	57.55 338	79.47 350	83.92 310	48.02 424	56.48 424	84.81 310	43.13 372	86.42 345	62.67 285	81.81 254	84.89 371
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
AllTest		70.96 332	68.09 347	79.58 280	85.15 285	63.62 246	84.58 267	79.83 369	62.31 354	60.32 411	86.73 255	32.02 417	88.96 314	50.28 381	71.57 377	86.15 347

tt0320		70.49 340	68.03 348	77.89 311	84.78 294	59.12 317	83.55 292	80.44 361	58.13 391	67.43 364	80.41 380	39.26 394	87.54 334	55.12 353	63.18 408	86.99 332

gg-mvs-nofinetune		69.95 346	67.96 349	75.94 333	83.07 334	54.51 380	77.23 381	70.29 418	63.11 342	70.32 330	62.33 432	43.62 369	88.69 318	53.88 361	87.76 158	84.62 375

FMVSNet5		69.50 349	67.96 349	74.15 358	82.97 340	55.35 371	80.01 345	82.12 340	62.56 352	63.02 400	81.53 368	36.92 405	81.92 384	48.42 392	74.06 355	85.17 367

Syy-MVS		68.05 362	67.85 351	68.67 398	84.68 297	40.97 441	78.62 364	73.08 412	66.65 299	66.74 373	79.46 390	52.11 281	82.30 381	32.89 433	76.38 323	82.75 397

PatchT		68.46 360	67.85 351	70.29 389	80.70 375	43.93 433	72.47 406	74.88 404	60.15 372	70.55 326	76.57 410	49.94 312	81.59 385	50.58 377	74.83 349	85.34 362

pmmvs-eth3d		70.50 339	67.83 353	78.52 301	77.37 404	66.18 185	81.82 314	81.51 347	58.90 384	63.90 398	80.42 379	42.69 375	86.28 346	58.56 325	65.30 402	83.11 392

Anonymous20231206		68.60 356	67.80 354	71.02 386	80.23 381	50.75 409	78.30 371	80.47 359	56.79 401	66.11 383	82.63 357	46.35 345	78.95 397	43.62 415	75.70 330	83.36 389

Patchmatch-RL test		70.24 342	67.78 355	77.61 318	77.43 403	59.57 314	71.16 411	70.33 417	62.94 346	68.65 351	72.77 423	50.62 302	85.49 356	69.58 226	66.58 397	87.77 310

test0.0.03 1		68.00 363	67.69 356	68.90 395	77.55 402	47.43 418	75.70 390	72.95 414	66.66 296	66.56 375	82.29 362	48.06 330	75.87 418	44.97 413	74.51 352	83.41 388

testing3		68.56 358	67.67 357	71.22 385	87.33 228	42.87 435	83.06 305	71.54 415	70.36 228	69.08 348	84.38 317	30.33 423	85.69 353	37.50 428	75.45 338	85.09 369

EU-MVSNet		68.53 359	67.61 358	71.31 384	78.51 400	47.01 422	84.47 269	84.27 306	42.27 431	66.44 380	84.79 311	40.44 389	83.76 369	58.76 324	68.54 392	83.17 390

KD-MVS_self_test		68.81 354	67.59 359	72.46 375	74.29 416	45.45 425	77.93 375	87.00 264	63.12 341	63.99 397	78.99 397	42.32 377	84.77 364	56.55 348	64.09 405	87.16 327

test_fmvs2		68.35 361	67.48 360	70.98 387	69.50 433	51.95 396	80.05 344	76.38 398	49.33 422	74.65 280	84.38 317	23.30 435	75.40 423	74.51 173	75.17 346	85.60 358

tt0320-xc		70.11 344	67.45 361	78.07 309	85.33 280	59.51 315	83.28 297	78.96 379	58.77 385	67.10 368	80.28 382	36.73 406	87.42 335	56.83 345	59.77 417	87.29 322

mvs5depth		69.45 350	67.45 361	75.46 342	73.93 417	55.83 364	79.19 355	83.23 322	66.89 291	71.63 319	83.32 343	33.69 415	85.09 360	59.81 312	55.34 425	85.46 360

ppachtmachnet_test		70.04 345	67.34 363	78.14 307	79.80 388	61.13 290	79.19 355	80.59 357	59.16 381	65.27 387	79.29 392	46.75 341	87.29 336	49.33 388	66.72 395	86.00 353

Anonymous20240521		68.80 355	67.22 364	73.55 363	74.33 415	54.11 382	83.18 299	85.61 288	58.15 390	61.68 406	80.94 374	30.71 422	81.27 388	57.00 342	73.34 365	85.28 363

our_test_3		69.14 352	67.00 365	75.57 338	79.80 388	58.80 318	77.96 374	77.81 385	59.55 377	62.90 403	78.25 402	47.43 332	83.97 368	51.71 371	67.58 394	83.93 383

test20.03		67.45 365	66.95 366	68.94 394	75.48 412	44.84 431	77.50 378	77.67 386	66.66 296	63.01 401	83.80 331	47.02 336	78.40 399	42.53 419	68.86 391	83.58 387

MIMVSNet1		68.58 357	66.78 367	73.98 360	80.07 383	51.82 399	80.77 330	84.37 302	64.40 327	59.75 414	82.16 364	36.47 408	83.63 371	42.73 417	70.33 383	86.48 342

testgi		66.67 371	66.53 368	67.08 405	75.62 411	41.69 440	75.93 386	76.50 397	66.11 305	65.20 390	86.59 265	35.72 411	74.71 425	43.71 414	73.38 364	84.84 372

myMVS_eth3d		67.02 368	66.29 369	69.21 393	84.68 297	42.58 436	78.62 364	73.08 412	66.65 299	66.74 373	79.46 390	31.53 420	82.30 381	39.43 425	76.38 323	82.75 397

UnsupCasMVSNet_eth		67.33 366	65.99 370	71.37 381	73.48 422	51.47 403	75.16 394	85.19 292	65.20 317	60.78 409	80.93 376	42.35 376	77.20 405	57.12 339	53.69 427	85.44 361

dp		66.80 369	65.43 371	70.90 388	79.74 390	48.82 416	75.12 396	74.77 405	59.61 376	64.08 396	77.23 407	42.89 373	80.72 391	48.86 391	66.58 397	83.16 391

UWE-MVS-28		65.32 378	64.93 372	66.49 406	78.70 398	38.55 443	77.86 377	64.39 435	62.00 359	64.13 395	83.60 338	41.44 383	76.00 416	31.39 435	80.89 262	84.92 370

TinyColmap		67.30 367	64.81 373	74.76 351	81.92 358	56.68 351	80.29 341	81.49 348	60.33 369	56.27 425	83.22 344	24.77 431	87.66 333	45.52 410	69.47 386	79.95 414

CHOSEN 280x420		66.51 372	64.71 374	71.90 377	81.45 365	63.52 253	57.98 441	68.95 424	53.57 411	62.59 404	76.70 409	46.22 347	75.29 424	55.25 352	79.68 278	76.88 421

TDRefinement		67.49 364	64.34 375	76.92 327	73.47 423	61.07 293	84.86 259	82.98 330	59.77 375	58.30 418	85.13 303	26.06 427	87.89 329	47.92 399	60.59 415	81.81 405

PM-MVS		66.41 373	64.14 376	73.20 368	73.92 418	56.45 353	78.97 359	64.96 434	63.88 338	64.72 391	80.24 383	19.84 439	83.44 374	66.24 254	64.52 404	79.71 415

dmvs_testset		62.63 386	64.11 377	58.19 416	78.55 399	24.76 454	75.28 392	65.94 431	67.91 284	60.34 410	76.01 413	53.56 265	73.94 429	31.79 434	67.65 393	75.88 423

KD-MVS_2432*1600		66.22 375	63.89 378	73.21 366	75.47 413	53.42 388	70.76 414	84.35 303	64.10 332	66.52 377	78.52 399	34.55 413	84.98 361	50.40 379	50.33 432	81.23 407

miper_refine_blended		66.22 375	63.89 378	73.21 366	75.47 413	53.42 388	70.76 414	84.35 303	64.10 332	66.52 377	78.52 399	34.55 413	84.98 361	50.40 379	50.33 432	81.23 407

MDA-MVSNet-bldmvs		66.68 370	63.66 380	75.75 335	79.28 395	60.56 301	73.92 403	78.35 383	64.43 326	50.13 433	79.87 388	44.02 367	83.67 370	46.10 407	56.86 419	83.03 394

ADS-MVSNet2		66.20 377	63.33 381	74.82 350	79.92 384	58.75 319	67.55 426	75.19 402	53.37 412	65.25 388	75.86 414	42.32 377	80.53 392	41.57 420	68.91 389	85.18 365

Patchmatch-test		64.82 381	63.24 382	69.57 391	79.42 394	49.82 413	63.49 438	69.05 423	51.98 417	59.95 413	80.13 384	50.91 298	70.98 432	40.66 422	73.57 360	87.90 307

MDA-MVSNet_test_wron		65.03 379	62.92 383	71.37 381	75.93 407	56.73 348	69.09 423	74.73 406	57.28 399	54.03 428	77.89 403	45.88 350	74.39 427	49.89 385	61.55 411	82.99 395

YYNet1		65.03 379	62.91 384	71.38 380	75.85 409	56.60 352	69.12 422	74.66 408	57.28 399	54.12 427	77.87 404	45.85 351	74.48 426	49.95 384	61.52 412	83.05 393

ADS-MVSNet		64.36 382	62.88 385	68.78 397	79.92 384	47.17 421	67.55 426	71.18 416	53.37 412	65.25 388	75.86 414	42.32 377	73.99 428	41.57 420	68.91 389	85.18 365

JIA-IIPM		66.32 374	62.82 386	76.82 328	77.09 405	61.72 286	65.34 434	75.38 401	58.04 393	64.51 392	62.32 433	42.05 381	86.51 343	51.45 374	69.22 388	82.21 401

LF4IMVS		64.02 383	62.19 387	69.50 392	70.90 431	53.29 391	76.13 384	77.18 393	52.65 414	58.59 416	80.98 373	23.55 434	76.52 410	53.06 366	66.66 396	78.68 417

test_fmvs3		63.36 385	61.82 388	67.98 402	62.51 442	46.96 423	77.37 380	74.03 409	45.24 427	67.50 361	78.79 398	12.16 447	72.98 431	72.77 193	66.02 399	83.99 382

new-patchmatchnet		61.73 388	61.73 389	61.70 412	72.74 428	24.50 455	69.16 421	78.03 384	61.40 362	56.72 423	75.53 417	38.42 399	76.48 411	45.95 408	57.67 418	84.13 380

UnsupCasMVSNet_bld		63.70 384	61.53 390	70.21 390	73.69 420	51.39 404	72.82 405	81.89 342	55.63 406	57.81 420	71.80 425	38.67 398	78.61 398	49.26 389	52.21 430	80.63 411

mvsany_test1		62.30 387	61.26 391	65.41 408	69.52 432	54.86 376	66.86 428	49.78 448	46.65 425	68.50 354	83.21 345	49.15 323	66.28 440	56.93 343	60.77 413	75.11 424

PVSNet_0	57.27 20	61.67 389	59.27 392	68.85 396	79.61 391	57.44 340	68.01 424	73.44 411	55.93 405	58.54 417	70.41 428	44.58 362	77.55 404	47.01 401	35.91 440	71.55 428

test_vis1_rt		60.28 390	58.42 393	65.84 407	67.25 436	55.60 368	70.44 416	60.94 440	44.33 429	59.00 415	66.64 430	24.91 430	68.67 437	62.80 281	69.48 385	73.25 426

MVS-HIRNet		59.14 392	57.67 394	63.57 410	81.65 360	43.50 434	71.73 408	65.06 433	39.59 435	51.43 430	57.73 438	38.34 400	82.58 380	39.53 423	73.95 356	64.62 434

ttmdpeth		59.91 391	57.10 395	68.34 400	67.13 437	46.65 424	74.64 399	67.41 427	48.30 423	62.52 405	85.04 307	20.40 437	75.93 417	42.55 418	45.90 438	82.44 399

DSMNet-mixed		57.77 394	56.90 396	60.38 414	67.70 435	35.61 445	69.18 420	53.97 446	32.30 444	57.49 421	79.88 387	40.39 390	68.57 438	38.78 426	72.37 369	76.97 420

WB-MVS		54.94 396	54.72 397	55.60 422	73.50 421	20.90 456	74.27 402	61.19 439	59.16 381	50.61 431	74.15 419	47.19 335	75.78 419	17.31 447	35.07 441	70.12 429

pmmvs3		57.79 393	54.26 398	68.37 399	64.02 441	56.72 349	75.12 396	65.17 432	40.20 433	52.93 429	69.86 429	20.36 438	75.48 421	45.45 411	55.25 426	72.90 427

SSC-MVS		53.88 399	53.59 399	54.75 424	72.87 427	19.59 457	73.84 404	60.53 441	57.58 397	49.18 435	73.45 422	46.34 346	75.47 422	16.20 450	32.28 443	69.20 430

N_pmnet		52.79 402	53.26 400	51.40 426	78.99 397	7.68 460	69.52 418	3.89 459	51.63 418	57.01 422	74.98 418	40.83 387	65.96 441	37.78 427	64.67 403	80.56 413

MVStest1		56.63 395	52.76 401	68.25 401	61.67 443	53.25 392	71.67 409	68.90 425	38.59 436	50.59 432	83.05 348	25.08 429	70.66 433	36.76 429	38.56 439	80.83 410

FPMVS		53.68 400	51.64 402	59.81 415	65.08 439	51.03 406	69.48 419	69.58 421	41.46 432	40.67 439	72.32 424	16.46 443	70.00 436	24.24 443	65.42 401	58.40 439

mvsany_test3		53.99 398	51.45 403	61.61 413	55.51 447	44.74 432	63.52 437	45.41 452	43.69 430	58.11 419	76.45 411	17.99 440	63.76 443	54.77 356	47.59 434	76.34 422

test_f		52.09 403	50.82 404	55.90 420	53.82 450	42.31 439	59.42 440	58.31 444	36.45 439	56.12 426	70.96 427	12.18 446	57.79 446	53.51 363	56.57 421	67.60 431

new_pmnet		50.91 405	50.29 405	52.78 425	68.58 434	34.94 447	63.71 436	56.63 445	39.73 434	44.95 436	65.47 431	21.93 436	58.48 445	34.98 431	56.62 420	64.92 433

APD_test1		53.31 401	49.93 406	63.42 411	65.68 438	50.13 411	71.59 410	66.90 429	34.43 441	40.58 440	71.56 426	8.65 452	76.27 413	34.64 432	55.36 424	63.86 435

LCM-MVSNet		54.25 397	49.68 407	67.97 403	53.73 451	45.28 428	66.85 429	80.78 354	35.96 440	39.45 441	62.23 434	8.70 451	78.06 402	48.24 396	51.20 431	80.57 412

EGC-MVSNET		52.07 404	47.05 408	67.14 404	83.51 323	60.71 298	80.50 337	67.75 426	0.07 454	0.43 455	75.85 416	24.26 432	81.54 386	28.82 437	62.25 409	59.16 437

test_vis3_rt		49.26 407	47.02 409	56.00 419	54.30 448	45.27 429	66.76 430	48.08 449	36.83 438	44.38 437	53.20 442	7.17 454	64.07 442	56.77 346	55.66 422	58.65 438

ANet_high		50.57 406	46.10 410	63.99 409	48.67 454	39.13 442	70.99 413	80.85 353	61.39 363	31.18 443	57.70 439	17.02 442	73.65 430	31.22 436	15.89 451	79.18 416

dongtai		45.42 410	45.38 411	45.55 428	73.36 424	26.85 452	67.72 425	34.19 454	54.15 410	49.65 434	56.41 441	25.43 428	62.94 444	19.45 445	28.09 445	46.86 444

testf1		45.72 408	41.96 412	57.00 417	56.90 445	45.32 426	66.14 431	59.26 442	26.19 445	30.89 444	60.96 436	4.14 455	70.64 434	26.39 441	46.73 436	55.04 440

APD_test2		45.72 408	41.96 412	57.00 417	56.90 445	45.32 426	66.14 431	59.26 442	26.19 445	30.89 444	60.96 436	4.14 455	70.64 434	26.39 441	46.73 436	55.04 440

Gipuma		45.18 411	41.86 414	55.16 423	77.03 406	51.52 402	32.50 447	80.52 358	32.46 443	27.12 446	35.02 447	9.52 450	75.50 420	22.31 444	60.21 416	38.45 446
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		39.70 414	40.40 415	37.58 431	64.52 440	26.98 450	65.62 433	33.02 455	46.12 426	42.79 438	48.99 444	24.10 433	46.56 452	12.16 453	26.30 446	39.20 445

PMVS	37.38 22	44.16 412	40.28 416	55.82 421	40.82 456	42.54 438	65.12 435	63.99 436	34.43 441	24.48 447	57.12 440	3.92 457	76.17 415	17.10 448	55.52 423	48.75 442
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 413	38.86 417	46.69 427	53.84 449	16.45 458	48.61 444	49.92 447	37.49 437	31.67 442	60.97 435	8.14 453	56.42 447	28.42 438	30.72 444	67.19 432

E-PMN		31.77 415	30.64 418	35.15 432	52.87 452	27.67 449	57.09 442	47.86 450	24.64 447	16.40 452	33.05 448	11.23 448	54.90 448	14.46 451	18.15 449	22.87 448

EMVS		30.81 417	29.65 419	34.27 433	50.96 453	25.95 453	56.58 443	46.80 451	24.01 448	15.53 453	30.68 449	12.47 445	54.43 449	12.81 452	17.05 450	22.43 449

test_method		31.52 416	29.28 420	38.23 430	27.03 458	6.50 461	20.94 449	62.21 438	4.05 452	22.35 450	52.50 443	13.33 444	47.58 450	27.04 440	34.04 442	60.62 436

cdsmvs_eth3d_5k		19.96 419	26.61 421	0.00 439	0.00 462	0.00 464	0.00 450	89.26 199	0.00 457	0.00 458	88.61 204	61.62 182	0.00 458	0.00 457	0.00 456	0.00 454

MVE	26.22 23	30.37 418	25.89 422	43.81 429	44.55 455	35.46 446	28.87 448	39.07 453	18.20 449	18.58 451	40.18 446	2.68 458	47.37 451	17.07 449	23.78 448	48.60 443
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		18.61 420	21.40 423	10.23 436	4.82 459	10.11 459	34.70 446	30.74 457	1.48 453	23.91 449	26.07 450	28.42 425	13.41 455	27.12 439	15.35 452	7.17 450

wuyk23d		16.82 421	15.94 424	19.46 435	58.74 444	31.45 448	39.22 445	3.74 460	6.84 451	6.04 454	2.70 454	1.27 459	24.29 454	10.54 454	14.40 453	2.63 451

ab-mvs-re		7.23 422	9.64 425	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	86.72 257	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

test123		6.12 423	8.11 426	0.14 437	0.06 461	0.09 462	71.05 412	0.03 462	0.04 456	0.25 457	1.30 456	0.05 460	0.03 457	0.21 456	0.01 455	0.29 452

testmvs		6.04 424	8.02 427	0.10 438	0.08 460	0.03 463	69.74 417	0.04 461	0.05 455	0.31 456	1.68 455	0.02 461	0.04 456	0.24 455	0.02 454	0.25 453

pcd_1.5k_mvsjas		5.26 425	7.02 428	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	63.15 158	0.00 458	0.00 457	0.00 456	0.00 454

mmdepth		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

monomultidepth		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

test_blank		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uanet_test		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

DCPMVS		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

sosnet-low-res		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

sosnet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uncertanet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

Regformer		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

uanet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 464	0.00 450	0.00 463	0.00 457	0.00 458	0.00 457	0.00 462	0.00 458	0.00 457	0.00 456	0.00 454

WAC-MVS						42.58 436								39.46 424

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									68.21 281	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					0.00 462

eth-test					0.00 462

ZD-MVS					94.38 25	72.22 46		92.67 68	70.98 214	87.75 44	94.07 51	74.01 33	96.70 27	84.66 63	94.84 44

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 292	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

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																88.96 278

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 294				88.96 278

sam_mvs												50.01 310

ambc				75.24 345	73.16 425	50.51 410	63.05 439	87.47 254		64.28 393	77.81 405	17.80 441	89.73 297	57.88 333	60.64 414	85.49 359

MTGPA								92.02 98

test_post1							78.90 361				5.43 453	48.81 329	85.44 358	59.25 317

test_post											5.46 452	50.36 306	84.24 366

patchmatchnet-post											74.00 420	51.12 297	88.60 320

GG-mvs-BLEND				75.38 343	81.59 362	55.80 365	79.32 352	69.63 420		67.19 366	73.67 421	43.24 371	88.90 316	50.41 378	84.50 206	81.45 406

MTMP							92.18 35	32.83 456

gm-plane-assit					81.40 366	53.83 385			62.72 351		80.94 374		92.39 218	63.40 278

test9_res														84.90 57	95.70 26	92.87 127

TEST9					93.26 52	72.96 25	88.75 131	91.89 106	68.44 278	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	68.69 273	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				79.58 280	85.15 285	63.62 246		79.83 369	62.31 354	60.32 411	86.73 255	32.02 417	88.96 314	50.28 381	71.57 377	86.15 347

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							86.56 212		58.10 392	87.04 55			88.98 312	74.07 178

新几何2							86.29 222

新几何1				83.42 170	93.13 56	70.71 76		85.48 290	57.43 398	81.80 130	91.98 107	63.28 152	92.27 224	64.60 270	92.99 72	87.27 323

旧先验1					91.96 76	65.79 196		86.37 277			93.08 85	69.31 88			92.74 76	88.74 289

无先验							87.48 177	88.98 213	60.00 373				94.12 132	67.28 247		88.97 277

原ACMM2							86.86 200

原ACMM1				84.35 123	93.01 62	68.79 113		92.44 78	63.96 337	81.09 141	91.57 122	66.06 128	95.45 71	67.19 249	94.82 46	88.81 284

test222					91.50 82	68.26 133	84.16 279	83.20 325	54.63 409	79.74 159	91.63 119	58.97 217			91.42 96	86.77 337

testdata2													91.01 276	62.37 288

segment_acmp												73.08 40

testdata				79.97 270	90.90 94	64.21 235		84.71 298	59.27 380	85.40 68	92.91 87	62.02 177	89.08 310	68.95 232	91.37 98	86.63 341

testdata1							84.14 280		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						60.42 208

plane_prior5								92.44 78					95.38 78	78.71 125	86.32 181	91.33 181

plane_prior4											91.00 144

plane_prior3						68.60 124			78.44 36	78.92 171

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								0.00 463

nn								0.00 463

door-mid								69.98 419

lessismore_v0				78.97 290	81.01 373	57.15 343		65.99 430		61.16 408	82.82 354	39.12 395	91.34 264	59.67 313	46.92 435	88.43 297

LGP-MVS_train				84.50 116	89.23 148	68.76 115		91.94 104	75.37 109	76.64 225	91.51 123	54.29 257	94.91 98	78.44 127	83.78 219	89.83 249

test11								92.23 88

door								69.44 422

HQP5-MVS						66.98 174

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 210

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 210

BP-MVS														77.47 139

HQP4-MVS										77.24 209			95.11 90			91.03 191

HQP3-MVS								92.19 92							85.99 189

HQP2-MVS												60.17 211

NP-MVS					89.62 125	68.32 131					90.24 158

MDTV_nov1_ep13_2view						37.79 444	75.16 394		55.10 407	66.53 376		49.34 320		53.98 360		87.94 306

ACMMP++_ref															81.95 252

ACMMP++															81.25 257

Test By Simon												64.33 144

ITE_SJBPF				78.22 305	81.77 359	60.57 300		83.30 320	69.25 258	67.54 360	87.20 246	36.33 409	87.28 337	54.34 358	74.62 351	86.80 336

DeepMVS_CX				27.40 434	40.17 457	26.90 451		24.59 458	17.44 450	23.95 448	48.61 445	9.77 449	26.48 453	18.06 446	24.47 447	28.83 447

High-resolution multi-view benchmark