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 18	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 23	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 21	96.41 12	93.33 104
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 43	96.34 15	93.95 67
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 48	78.35 13	96.77 24	89.59 16	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 20	95.65 27	94.47 42
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	91.17 133	86.57 1	87.39 52	94.97 21	71.70 58	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	74.77 130	92.29 7	95.97 2	74.28 30	97.24 13	88.58 31	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 43	94.27 42	75.89 19	96.81 23	87.45 42	96.44 9	93.05 122

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 38	94.06 53	76.43 16	96.84 21	88.48 34	95.99 18	94.34 49

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 37	94.80 23	73.76 34	97.11 15	87.51 41	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 24	95.10 18	75.65 21	96.19 47	87.07 44	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 49	96.27 44	86.87 45	94.65 48	93.70 83

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 50	78.98 12	96.58 35	85.66 52	95.72 24	94.58 35

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 57	74.83 23	93.78 152	87.63 40	94.27 61	93.65 88
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
9.14			88.26 16		92.84 65		91.52 51	94.75 1	73.93 152	88.57 30	94.67 25	75.57 22	95.79 59	86.77 46	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	74.62 134	88.90 27	93.85 65	75.75 20	96.00 55	87.80 38	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 36	95.09 19	71.06 68	96.67 29	87.67 39	96.37 14	94.09 59

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 66	93.47 74	73.02 42	97.00 18	84.90 58	94.94 40	94.10 58

fmvsm_s_conf0.5_n_9		87.39 30	87.95 20	85.70 77	89.48 133	67.88 149	88.59 139	89.05 214	80.19 12	90.70 17	95.40 15	74.56 25	93.92 145	91.54 2	92.07 86	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 71	94.32 39	71.76 56	96.93 19	85.53 55	95.79 22	94.32 50

MP-MVS-pluss		87.67 22	87.72 22	87.54 36	93.64 44	72.04 50	89.80 84	93.50 26	75.17 118	86.34 62	95.29 17	70.86 70	96.00 55	88.78 29	96.04 16	94.58 35
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	83.84 103	94.40 36	72.24 50	96.28 43	85.65 53	95.30 35	93.62 91
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	74.98 121	88.96 25	95.54 12	71.20 66	96.54 36	86.28 49	93.49 67	93.06 120

our_new_method		87.47 24	87.61 24	87.07 46	93.27 50	71.60 55	91.56 49	93.19 36	74.98 121	88.96 25	95.54 12	71.20 66	96.54 36	86.28 49	93.49 67	93.06 120

MVS_0304		87.69 21	87.55 26	88.12 13	89.45 134	71.76 53	91.47 52	89.54 186	82.14 3	86.65 60	94.28 41	68.28 105	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	73.01 178	88.58 29	94.52 27	73.36 35	96.49 38	84.26 69	95.01 37	92.70 134
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 76	94.44 34	70.78 71	96.61 32	84.53 66	94.89 42	93.66 84

fmvsm_s_conf0.5_n_3		86.36 49	87.46 29	83.09 189	87.08 244	65.21 213	89.09 116	90.21 164	79.67 19	89.98 19	95.02 20	73.17 39	91.71 250	91.30 3	91.60 93	92.34 151

reproduce_model		87.28 32	87.39 30	86.95 50	93.10 58	71.24 65	91.60 45	93.19 36	74.69 131	88.80 28	95.61 11	70.29 77	96.44 39	86.20 51	93.08 71	93.16 114

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 80	93.99 59	70.67 73	96.82 22	84.18 73	95.01 37	93.90 70

MCST-MVS		87.37 31	87.25 32	87.73 28	94.53 17	72.46 40	89.82 82	93.82 17	73.07 176	84.86 79	92.89 89	76.22 17	96.33 41	84.89 60	95.13 36	94.40 45

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 83	94.52 27	68.81 97	96.65 30	84.53 66	94.90 41	94.00 64

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 88	94.52 27	69.09 91	96.70 27	84.37 68	94.83 45	94.03 62

fmvsm_s_conf0.5_n_8		86.56 44	87.17 35	84.73 113	87.76 216	65.62 204	89.20 107	92.21 90	79.94 17	89.74 22	94.86 22	68.63 100	94.20 130	90.83 5	91.39 98	94.38 46

MTAPA		87.23 33	87.00 36	87.90 22	94.18 35	74.25 5	86.58 214	92.02 98	79.45 22	85.88 64	94.80 23	68.07 106	96.21 46	86.69 47	95.34 32	93.23 107

balanced_conf03		86.78 39	86.99 37	86.15 66	91.24 86	67.61 157	90.51 65	92.90 57	77.26 59	87.44 51	91.63 121	71.27 65	96.06 50	85.62 54	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	83.81 104	93.95 62	69.77 83	96.01 54	85.15 56	94.66 47	94.32 50
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	67.57 159	92.83 18	93.30 33	79.67 19	84.57 87	92.27 101	71.47 61	95.02 96	84.24 71	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	83.68 106	94.46 31	67.93 108	95.95 58	84.20 72	94.39 57	93.23 107

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	83.67 107	94.17 47	67.45 113	96.60 33	83.06 81	94.50 53	94.07 60

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	82.82 119	94.23 45	72.13 52	97.09 16	84.83 61	95.37 31	93.65 88
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	74.50 135	86.84 59	94.65 26	67.31 115	95.77 60	84.80 62	92.85 74	92.84 132

fmvsm_l_conf0.5_n_9		85.84 61	86.63 44	83.46 172	87.12 243	66.01 191	88.56 141	89.43 190	75.59 104	89.32 23	94.32 39	72.89 43	91.21 273	90.11 10	92.33 83	93.16 114

DeepC-MVS_fast	79.65 3	86.91 38	86.62 45	87.76 27	93.52 46	72.37 43	91.26 54	93.04 42	76.62 82	84.22 94	93.36 78	71.44 62	96.76 25	80.82 106	95.33 33	94.16 55
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 46	85.71 76	91.02 91	67.21 174	92.36 30	93.78 19	78.97 33	83.51 110	91.20 136	70.65 74	95.15 87	81.96 95	94.89 42	94.77 25

EC-MVSNet		86.01 53	86.38 47	84.91 106	89.31 143	66.27 187	92.32 31	93.63 22	79.37 23	84.17 96	91.88 111	69.04 95	95.43 73	83.93 75	93.77 65	93.01 125

fmvsm_l_conf0.5_n_3		86.02 52	86.32 48	85.14 92	87.20 235	68.54 126	89.57 93	90.44 153	75.31 112	87.49 49	94.39 37	72.86 44	92.72 207	89.04 25	90.56 112	94.16 55

mPP-MVS		86.67 43	86.32 48	87.72 30	94.41 22	73.55 13	92.74 21	92.22 89	76.87 73	82.81 120	94.25 44	66.44 124	96.24 45	82.88 86	94.28 60	93.38 100

PGM-MVS		86.68 42	86.27 50	87.90 22	94.22 33	73.38 18	90.22 76	93.04 42	75.53 105	83.86 102	94.42 35	67.87 110	96.64 31	82.70 91	94.57 52	93.66 84

fmvsm_s_conf0.5_n_6		85.55 67	86.20 51	83.60 167	87.32 232	65.13 216	88.86 123	91.63 118	75.41 108	88.23 35	93.45 75	68.56 101	92.47 218	89.52 17	92.78 75	93.20 112

train_agg		86.43 46	86.20 51	87.13 45	93.26 52	72.96 25	88.75 131	91.89 106	68.69 280	85.00 74	93.10 82	74.43 27	95.41 76	84.97 57	95.71 25	93.02 124

CSCG		86.41 48	86.19 53	87.07 46	92.91 63	72.48 37	90.81 61	93.56 25	73.95 150	83.16 113	91.07 141	75.94 18	95.19 85	79.94 117	94.38 58	93.55 95

PHI-MVS		86.43 46	86.17 54	87.24 42	90.88 95	70.96 70	92.27 33	94.07 10	72.45 184	85.22 72	91.90 110	69.47 86	96.42 40	83.28 80	95.94 19	94.35 48

dcpmvs_2		85.63 65	86.15 55	84.06 147	91.71 80	64.94 223	86.47 217	91.87 108	73.63 159	86.60 61	93.02 87	76.57 15	91.87 244	83.36 78	92.15 84	95.35 3

CANet		86.45 45	86.10 56	87.51 38	90.09 111	70.94 72	89.70 88	92.59 75	81.78 4	81.32 138	91.43 129	70.34 75	97.23 14	84.26 69	93.36 70	94.37 47

casdiffmvs_mvg		85.99 54	86.09 57	85.70 77	87.65 220	67.22 173	88.69 135	93.04 42	79.64 21	85.33 70	92.54 98	73.30 36	94.50 119	83.49 77	91.14 102	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 58	85.57 82	85.03 297	69.51 96	89.62 92	90.58 148	73.42 167	87.75 45	94.02 55	72.85 45	93.24 178	90.37 7	90.75 109	93.96 65

MVSMamba_PlusPlus		85.99 54	85.96 59	86.05 69	91.09 88	67.64 156	89.63 91	92.65 71	72.89 181	84.64 84	91.71 116	71.85 54	96.03 51	84.77 63	94.45 56	94.49 41

NormalMVS		86.29 50	85.88 60	87.52 37	93.26 52	72.47 38	91.65 43	92.19 92	79.31 24	84.39 90	92.18 103	64.64 145	95.53 67	80.70 109	94.65 48	94.56 38

APD-MVS_3200maxsize		85.97 56	85.88 60	86.22 63	92.69 68	69.53 95	91.93 38	92.99 50	73.54 163	85.94 63	94.51 30	65.80 135	95.61 63	83.04 83	92.51 79	93.53 97

sasdasda		85.91 58	85.87 62	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 40	91.23 133	73.28 37	93.91 146	81.50 98	88.80 143	94.77 25

canonicalmvs		85.91 58	85.87 62	86.04 70	89.84 121	69.44 101	90.45 71	93.00 47	76.70 80	88.01 40	91.23 133	73.28 37	93.91 146	81.50 98	88.80 143	94.77 25

MSLP-MVS++		85.43 70	85.76 64	84.45 121	91.93 77	70.24 81	90.71 62	92.86 59	77.46 55	84.22 94	92.81 93	67.16 117	92.94 199	80.36 112	94.35 59	90.16 236

fmvsm_s_conf0.5_n_4		85.39 72	85.75 65	84.30 128	86.70 254	65.83 197	88.77 129	89.78 176	75.46 107	88.35 31	93.73 68	69.19 90	93.06 194	91.30 3	88.44 152	94.02 63

test_fmvsmconf0.1_n		85.61 66	85.65 66	85.50 83	82.99 346	69.39 103	89.65 89	90.29 162	73.31 170	87.77 44	94.15 49	71.72 57	93.23 179	90.31 8	90.67 111	93.89 71

SR-MVS-dyc-post		85.77 62	85.61 67	86.23 62	93.06 60	70.63 78	91.88 39	92.27 85	73.53 164	85.69 67	94.45 32	65.00 143	95.56 64	82.75 87	91.87 89	92.50 144

fmvsm_s_conf0.5_n_5		85.22 76	85.55 68	84.25 135	86.26 261	67.40 165	89.18 108	89.31 199	72.50 183	88.31 32	93.86 64	69.66 84	91.96 238	89.81 12	91.05 103	93.38 100

MGCFI-Net		85.06 80	85.51 69	83.70 165	89.42 135	63.01 272	89.43 97	92.62 74	76.43 84	87.53 48	91.34 131	72.82 46	93.42 172	81.28 101	88.74 146	94.66 32

RE-MVS-def			85.48 70		93.06 60	70.63 78	91.88 39	92.27 85	73.53 164	85.69 67	94.45 32	63.87 151		82.75 87	91.87 89	92.50 144

ACMMP		85.89 60	85.39 71	87.38 40	93.59 45	72.63 33	92.74 21	93.18 40	76.78 76	80.73 150	93.82 66	64.33 147	96.29 42	82.67 92	90.69 110	93.23 107
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 75	85.34 72	85.13 95	86.12 267	69.93 88	88.65 137	90.78 144	69.97 247	88.27 33	93.98 60	71.39 63	91.54 258	88.49 33	90.45 114	93.91 68

TSAR-MVS + GP.		85.71 64	85.33 73	86.84 52	91.34 84	72.50 36	89.07 117	87.28 265	76.41 85	85.80 65	90.22 164	74.15 32	95.37 81	81.82 96	91.88 88	92.65 138

alignmvs		85.48 68	85.32 74	85.96 73	89.51 130	69.47 98	89.74 86	92.47 77	76.17 93	87.73 47	91.46 128	70.32 76	93.78 152	81.51 97	88.95 140	94.63 33

DELS-MVS		85.41 71	85.30 75	85.77 75	88.49 178	67.93 148	85.52 249	93.44 28	78.70 34	83.63 109	89.03 197	74.57 24	95.71 62	80.26 114	94.04 63	93.66 84
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 63	85.29 76	87.17 44	93.49 47	71.08 66	88.58 140	92.42 81	68.32 287	84.61 85	93.48 72	72.32 48	96.15 49	79.00 123	95.43 30	94.28 52

casdiffmvs		85.11 78	85.14 77	85.01 99	87.20 235	65.77 201	87.75 172	92.83 61	77.84 43	84.36 93	92.38 100	72.15 51	93.93 144	81.27 102	90.48 113	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 81	84.98 78	84.80 111	87.30 233	65.39 210	87.30 187	92.88 58	77.62 47	84.04 99	92.26 102	71.81 55	93.96 138	81.31 100	90.30 116	95.03 11

UA-Net		85.08 79	84.96 79	85.45 84	92.07 75	68.07 141	89.78 85	90.86 143	82.48 2	84.60 86	93.20 81	69.35 87	95.22 84	71.39 212	90.88 108	93.07 119

HPM-MVS_fast		85.35 74	84.95 80	86.57 59	93.69 42	70.58 80	92.15 36	91.62 119	73.89 153	82.67 122	94.09 51	62.60 167	95.54 66	80.93 104	92.93 73	93.57 93

SymmetryMVS		85.38 73	84.81 81	87.07 46	91.47 83	72.47 38	91.65 43	88.06 245	79.31 24	84.39 90	92.18 103	64.64 145	95.53 67	80.70 109	90.91 107	93.21 110

MVS_111021_HR		85.14 77	84.75 82	86.32 61	91.65 81	72.70 30	85.98 231	90.33 159	76.11 94	82.08 127	91.61 123	71.36 64	94.17 133	81.02 103	92.58 78	92.08 167

ETV-MVS		84.90 83	84.67 83	85.59 81	89.39 138	68.66 123	88.74 133	92.64 73	79.97 16	84.10 97	85.71 293	69.32 88	95.38 78	80.82 106	91.37 99	92.72 133

fmvsm_l_conf0.5_n		84.47 85	84.54 84	84.27 132	85.42 284	68.81 112	88.49 143	87.26 267	68.08 289	88.03 39	93.49 71	72.04 53	91.77 246	88.90 27	89.14 139	92.24 158

patch_mono-2		83.65 98	84.54 84	80.99 253	90.06 116	65.83 197	84.21 282	88.74 230	71.60 200	85.01 73	92.44 99	74.51 26	83.50 379	82.15 94	92.15 84	93.64 90

test_fmvsmconf0.01_n		84.73 84	84.52 86	85.34 87	80.25 388	69.03 106	89.47 95	89.65 182	73.24 174	86.98 57	94.27 42	66.62 120	93.23 179	90.26 9	89.95 124	93.78 80

3Dnovator+	77.84 4	85.48 68	84.47 87	88.51 7	91.08 89	73.49 16	93.18 12	93.78 19	80.79 8	76.66 232	93.37 77	60.40 215	96.75 26	77.20 144	93.73 66	95.29 6

DPM-MVS		84.93 81	84.29 88	86.84 52	90.20 109	73.04 23	87.12 191	93.04 42	69.80 251	82.85 118	91.22 135	73.06 41	96.02 53	76.72 154	94.63 50	91.46 188

fmvsm_l_conf0.5_n_a		84.13 88	84.16 89	84.06 147	85.38 285	68.40 129	88.34 150	86.85 277	67.48 296	87.48 50	93.40 76	70.89 69	91.61 251	88.38 35	89.22 137	92.16 165

fmvsm_s_conf0.5_n_2		84.04 89	84.11 90	83.81 163	86.17 265	65.00 221	86.96 197	87.28 265	74.35 139	88.25 34	94.23 45	61.82 183	92.60 210	89.85 11	88.09 157	93.84 74

fmvsm_s_conf0.5_n_7		83.34 109	84.03 91	81.28 244	85.73 275	65.13 216	85.40 250	89.90 174	74.96 123	82.13 126	93.89 63	66.65 119	87.92 334	86.56 48	91.05 103	90.80 207

test_fmvsmvis_n_1920		84.02 90	83.87 92	84.49 120	84.12 315	69.37 104	88.15 158	87.96 248	70.01 245	83.95 101	93.23 80	68.80 98	91.51 261	88.61 30	89.96 123	92.57 139

EI-MVSNet-Vis-set		84.19 87	83.81 93	85.31 88	88.18 190	67.85 150	87.66 174	89.73 180	80.05 15	82.95 115	89.59 182	70.74 72	94.82 104	80.66 111	84.72 211	93.28 106

fmvsm_s_conf0.1_n_2		83.80 93	83.79 94	83.83 161	85.62 278	64.94 223	87.03 194	86.62 281	74.32 140	87.97 42	94.33 38	60.67 207	92.60 210	89.72 13	87.79 159	93.96 65

BP-MVS1		84.32 86	83.71 95	86.17 64	87.84 209	67.85 150	89.38 102	89.64 183	77.73 45	83.98 100	92.12 107	56.89 245	95.43 73	84.03 74	91.75 92	95.24 7

fmvsm_s_conf0.5_n		83.80 93	83.71 95	84.07 145	86.69 255	67.31 168	89.46 96	83.07 335	71.09 212	86.96 58	93.70 69	69.02 96	91.47 263	88.79 28	84.62 213	93.44 99

viewmanbaseed2359cas		83.66 97	83.55 97	84.00 155	86.81 250	64.53 230	86.65 211	91.75 115	74.89 125	83.15 114	91.68 117	68.74 99	92.83 205	79.02 121	89.24 136	94.63 33

nrg030		83.88 91	83.53 98	84.96 101	86.77 252	69.28 105	90.46 70	92.67 68	74.79 129	82.95 115	91.33 132	72.70 47	93.09 192	80.79 108	79.28 293	92.50 144

MG-MVS		83.41 106	83.45 99	83.28 179	92.74 67	62.28 286	88.17 156	89.50 188	75.22 113	81.49 136	92.74 97	66.75 118	95.11 90	72.85 194	91.58 95	92.45 148

fmvsm_s_conf0.5_n_a		83.63 100	83.41 100	84.28 130	86.14 266	68.12 139	89.43 97	82.87 340	70.27 240	87.27 54	93.80 67	69.09 91	91.58 253	88.21 36	83.65 234	93.14 117

fmvsm_s_conf0.1_n		83.56 102	83.38 101	84.10 139	84.86 299	67.28 169	89.40 101	83.01 336	70.67 224	87.08 55	93.96 61	68.38 103	91.45 264	88.56 32	84.50 214	93.56 94

EI-MVSNet-UG-set		83.81 92	83.38 101	85.09 97	87.87 207	67.53 161	87.44 182	89.66 181	79.74 18	82.23 124	89.41 191	70.24 78	94.74 109	79.95 116	83.92 226	92.99 127

CPTT-MVS		83.73 95	83.33 103	84.92 105	93.28 49	70.86 74	92.09 37	90.38 155	68.75 279	79.57 165	92.83 91	60.60 211	93.04 197	80.92 105	91.56 96	90.86 206

HQP_MVS		83.64 99	83.14 104	85.14 92	90.08 112	68.71 119	91.25 55	92.44 78	79.12 28	78.92 177	91.00 146	60.42 213	95.38 78	78.71 127	86.32 183	91.33 189

Effi-MVS+		83.62 101	83.08 105	85.24 90	88.38 184	67.45 162	88.89 122	89.15 210	75.50 106	82.27 123	88.28 222	69.61 85	94.45 122	77.81 137	87.84 158	93.84 74

MVS_Test		83.15 113	83.06 106	83.41 176	86.86 247	63.21 268	86.11 229	92.00 100	74.31 141	82.87 117	89.44 190	70.03 79	93.21 181	77.39 143	88.50 151	93.81 76

EPP-MVSNet		83.40 107	83.02 107	84.57 116	90.13 110	64.47 235	92.32 31	90.73 145	74.45 138	79.35 171	91.10 139	69.05 94	95.12 88	72.78 195	87.22 168	94.13 57

fmvsm_s_conf0.1_n_a		83.32 110	82.99 108	84.28 130	83.79 323	68.07 141	89.34 104	82.85 341	69.80 251	87.36 53	94.06 53	68.34 104	91.56 256	87.95 37	83.46 240	93.21 110

OPM-MVS		83.50 104	82.95 109	85.14 92	88.79 168	70.95 71	89.13 114	91.52 122	77.55 52	80.96 146	91.75 115	60.71 205	94.50 119	79.67 120	86.51 181	89.97 252
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EPNet		83.72 96	82.92 110	86.14 68	84.22 313	69.48 97	91.05 59	85.27 299	81.30 6	76.83 227	91.65 119	66.09 130	95.56 64	76.00 160	93.85 64	93.38 100
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		83.15 113	82.81 111	84.18 137	89.94 119	63.30 266	91.59 46	88.46 238	79.04 30	79.49 166	92.16 105	65.10 140	94.28 125	67.71 250	91.86 91	94.95 12

EIA-MVS		83.31 111	82.80 112	84.82 109	89.59 126	65.59 205	88.21 154	92.68 67	74.66 133	78.96 175	86.42 280	69.06 93	95.26 83	75.54 166	90.09 120	93.62 91

Vis-MVSNet		83.46 105	82.80 112	85.43 85	90.25 108	68.74 117	90.30 75	90.13 167	76.33 91	80.87 147	92.89 89	61.00 202	94.20 130	72.45 204	90.97 105	93.35 103
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GDP-MVS		83.52 103	82.64 114	86.16 65	88.14 193	68.45 128	89.13 114	92.69 66	72.82 182	83.71 105	91.86 113	55.69 252	95.35 82	80.03 115	89.74 128	94.69 28

KinetiMVS		83.31 111	82.61 115	85.39 86	87.08 244	67.56 160	88.06 160	91.65 117	77.80 44	82.21 125	91.79 114	57.27 240	94.07 136	77.77 138	89.89 126	94.56 38

FIs		82.07 129	82.42 116	81.04 252	88.80 167	58.34 331	88.26 153	93.49 27	76.93 71	78.47 189	91.04 142	69.92 81	92.34 226	69.87 231	84.97 207	92.44 149

VNet		82.21 126	82.41 117	81.62 233	90.82 96	60.93 302	84.47 273	89.78 176	76.36 90	84.07 98	91.88 111	64.71 144	90.26 292	70.68 219	88.89 141	93.66 84

PAPM_NR		83.02 117	82.41 117	84.82 109	92.47 72	66.37 185	87.93 166	91.80 111	73.82 154	77.32 215	90.66 151	67.90 109	94.90 100	70.37 222	89.48 133	93.19 113

VDD-MVS		83.01 118	82.36 119	84.96 101	91.02 91	66.40 184	88.91 121	88.11 241	77.57 49	84.39 90	93.29 79	52.19 286	93.91 146	77.05 147	88.70 147	94.57 37

3Dnovator	76.31 5	83.38 108	82.31 120	86.59 57	87.94 204	72.94 28	90.64 63	92.14 97	77.21 62	75.47 258	92.83 91	58.56 227	94.72 110	73.24 191	92.71 77	92.13 166

h-mvs33		83.15 113	82.19 121	86.02 72	90.56 101	70.85 75	88.15 158	89.16 209	76.02 96	84.67 81	91.39 130	61.54 188	95.50 69	82.71 89	75.48 343	91.72 178

MVS_111021_LR		82.61 122	82.11 122	84.11 138	88.82 162	71.58 57	85.15 255	86.16 289	74.69 131	80.47 155	91.04 142	62.29 174	90.55 290	80.33 113	90.08 121	90.20 235

RRT-MVS		82.60 124	82.10 123	84.10 139	87.98 203	62.94 277	87.45 181	91.27 129	77.42 56	79.85 161	90.28 160	56.62 248	94.70 112	79.87 118	88.15 156	94.67 29

DP-MVS Recon		83.11 116	82.09 124	86.15 66	94.44 19	70.92 73	88.79 128	92.20 91	70.53 229	79.17 173	91.03 144	64.12 149	96.03 51	68.39 247	90.14 119	91.50 184

MVSFormer		82.85 119	82.05 125	85.24 90	87.35 226	70.21 82	90.50 67	90.38 155	68.55 282	81.32 138	89.47 185	61.68 185	93.46 169	78.98 124	90.26 117	92.05 168

FC-MVSNet-test		81.52 145	82.02 126	80.03 275	88.42 183	55.97 370	87.95 164	93.42 30	77.10 67	77.38 213	90.98 148	69.96 80	91.79 245	68.46 246	84.50 214	92.33 152

HQP-MVS		82.61 122	82.02 126	84.37 123	89.33 140	66.98 177	89.17 109	92.19 92	76.41 85	77.23 218	90.23 163	60.17 216	95.11 90	77.47 141	85.99 191	91.03 199

OMC-MVS		82.69 120	81.97 128	84.85 108	88.75 170	67.42 163	87.98 162	90.87 142	74.92 124	79.72 163	91.65 119	62.19 177	93.96 138	75.26 170	86.42 182	93.16 114

diffmvs		82.10 127	81.88 129	82.76 212	83.00 344	63.78 250	83.68 291	89.76 178	72.94 179	82.02 128	89.85 169	65.96 134	90.79 285	82.38 93	87.30 167	93.71 82
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		82.62 121	81.83 130	84.96 101	90.80 97	69.76 93	88.74 133	91.70 116	69.39 259	78.96 175	88.46 217	65.47 137	94.87 103	74.42 177	88.57 148	90.24 234

CLD-MVS		82.31 125	81.65 131	84.29 129	88.47 179	67.73 154	85.81 239	92.35 83	75.78 99	78.33 192	86.58 275	64.01 150	94.35 123	76.05 159	87.48 164	90.79 208
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		81.88 133	81.54 132	82.92 199	88.46 180	63.46 262	87.13 190	92.37 82	80.19 12	78.38 190	89.14 193	71.66 60	93.05 195	70.05 227	76.46 326	92.25 156

PS-MVSNAJss		82.07 129	81.31 133	84.34 126	86.51 259	67.27 170	89.27 105	91.51 123	71.75 195	79.37 170	90.22 164	63.15 161	94.27 126	77.69 139	82.36 255	91.49 185

LPG-MVS_test		82.08 128	81.27 134	84.50 118	89.23 148	68.76 115	90.22 76	91.94 104	75.37 110	76.64 233	91.51 125	54.29 265	94.91 98	78.44 129	83.78 227	89.83 257

LFMVS		81.82 135	81.23 135	83.57 170	91.89 78	63.43 264	89.84 81	81.85 352	77.04 69	83.21 111	93.10 82	52.26 285	93.43 171	71.98 207	89.95 124	93.85 72

API-MVS		81.99 131	81.23 135	84.26 134	90.94 93	70.18 87	91.10 58	89.32 198	71.51 202	78.66 182	88.28 222	65.26 138	95.10 93	64.74 277	91.23 101	87.51 324

UniMVSNet (Re)		81.60 141	81.11 137	83.09 189	88.38 184	64.41 237	87.60 175	93.02 46	78.42 37	78.56 185	88.16 226	69.78 82	93.26 177	69.58 234	76.49 325	91.60 179

xiu_mvs_v2_base		81.69 138	81.05 138	83.60 167	89.15 151	68.03 143	84.46 275	90.02 169	70.67 224	81.30 141	86.53 278	63.17 160	94.19 132	75.60 165	88.54 149	88.57 302

PS-MVSNAJ		81.69 138	81.02 139	83.70 165	89.51 130	68.21 138	84.28 281	90.09 168	70.79 221	81.26 142	85.62 298	63.15 161	94.29 124	75.62 164	88.87 142	88.59 301

GeoE		81.71 137	81.01 140	83.80 164	89.51 130	64.45 236	88.97 119	88.73 231	71.27 208	78.63 183	89.76 175	66.32 126	93.20 184	69.89 230	86.02 190	93.74 81

hse-mvs2		81.72 136	80.94 141	84.07 145	88.72 171	67.68 155	85.87 235	87.26 267	76.02 96	84.67 81	88.22 225	61.54 188	93.48 167	82.71 89	73.44 371	91.06 197

PAPR		81.66 140	80.89 142	83.99 156	90.27 107	64.00 243	86.76 208	91.77 114	68.84 278	77.13 225	89.50 183	67.63 111	94.88 102	67.55 252	88.52 150	93.09 118

mamba_0404		81.91 132	80.84 143	85.13 95	89.24 147	68.26 133	87.84 171	89.25 204	71.06 214	80.62 151	90.39 157	59.57 218	94.65 114	72.45 204	87.19 169	92.47 147

MAR-MVS		81.84 134	80.70 144	85.27 89	91.32 85	71.53 58	89.82 82	90.92 139	69.77 253	78.50 186	86.21 284	62.36 173	94.52 118	65.36 271	92.05 87	89.77 260
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		81.52 145	80.67 145	84.05 150	90.44 104	64.13 242	89.73 87	85.91 292	71.11 211	83.18 112	93.48 72	50.54 312	93.49 166	73.40 188	88.25 154	94.54 40

guyue		81.13 152	80.64 146	82.60 216	86.52 258	63.92 247	86.69 210	87.73 256	73.97 149	80.83 149	89.69 176	56.70 246	91.33 269	78.26 136	85.40 204	92.54 141

ACMP	74.13 6	81.51 147	80.57 147	84.36 124	89.42 135	68.69 122	89.97 80	91.50 126	74.46 137	75.04 280	90.41 156	53.82 271	94.54 116	77.56 140	82.91 247	89.86 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		80.60 171	80.55 148	80.76 259	88.07 198	60.80 305	86.86 202	91.58 121	75.67 103	80.24 157	89.45 189	63.34 154	90.25 293	70.51 221	79.22 294	91.23 192

DU-MVS		81.12 153	80.52 149	82.90 200	87.80 211	63.46 262	87.02 195	91.87 108	79.01 31	78.38 190	89.07 195	65.02 141	93.05 195	70.05 227	76.46 326	92.20 159

mamba_test_0407		81.58 142	80.48 150	84.87 107	88.81 163	67.96 145	87.37 183	89.25 204	71.06 214	79.48 167	90.39 157	59.57 218	94.48 121	72.45 204	85.93 193	92.18 161

test_yl		81.17 150	80.47 151	83.24 182	89.13 152	63.62 251	86.21 226	89.95 172	72.43 187	81.78 133	89.61 180	57.50 237	93.58 160	70.75 217	86.90 173	92.52 142

DCV-MVSNet		81.17 150	80.47 151	83.24 182	89.13 152	63.62 251	86.21 226	89.95 172	72.43 187	81.78 133	89.61 180	57.50 237	93.58 160	70.75 217	86.90 173	92.52 142

PVSNet_Blended		80.98 154	80.34 153	82.90 200	88.85 159	65.40 208	84.43 277	92.00 100	67.62 293	78.11 197	85.05 314	66.02 132	94.27 126	71.52 209	89.50 132	89.01 282

TranMVSNet+NR-MVSNet		80.84 157	80.31 154	82.42 219	87.85 208	62.33 284	87.74 173	91.33 128	80.55 9	77.99 201	89.86 168	65.23 139	92.62 208	67.05 259	75.24 353	92.30 154

jason		81.39 148	80.29 155	84.70 114	86.63 257	69.90 90	85.95 232	86.77 278	63.24 348	81.07 144	89.47 185	61.08 201	92.15 232	78.33 132	90.07 122	92.05 168
jason: jason.
lupinMVS		81.39 148	80.27 156	84.76 112	87.35 226	70.21 82	85.55 245	86.41 283	62.85 355	81.32 138	88.61 212	61.68 185	92.24 230	78.41 131	90.26 117	91.83 171

SDMVSNet		80.38 178	80.18 157	80.99 253	89.03 157	64.94 223	80.45 343	89.40 191	75.19 116	76.61 235	89.98 166	60.61 210	87.69 338	76.83 152	83.55 236	90.33 230

Elysia		81.53 143	80.16 158	85.62 79	85.51 281	68.25 135	88.84 126	92.19 92	71.31 205	80.50 153	89.83 170	46.89 346	94.82 104	76.85 149	89.57 130	93.80 78

StellarMVS		81.53 143	80.16 158	85.62 79	85.51 281	68.25 135	88.84 126	92.19 92	71.31 205	80.50 153	89.83 170	46.89 346	94.82 104	76.85 149	89.57 130	93.80 78

AstraMVS		80.81 159	80.14 160	82.80 206	86.05 270	63.96 244	86.46 218	85.90 293	73.71 157	80.85 148	90.56 153	54.06 269	91.57 255	79.72 119	83.97 225	92.86 130

icg_test_0403		80.80 162	80.12 161	82.87 202	87.13 238	63.59 255	85.19 252	89.33 194	70.51 230	78.49 187	89.03 197	63.26 157	93.27 176	72.56 200	85.56 200	91.74 174

PVSNet_BlendedMVS		80.60 171	80.02 162	82.36 221	88.85 159	65.40 208	86.16 228	92.00 100	69.34 261	78.11 197	86.09 288	66.02 132	94.27 126	71.52 209	82.06 258	87.39 326

EI-MVSNet		80.52 175	79.98 163	82.12 222	84.28 311	63.19 270	86.41 219	88.95 221	74.18 146	78.69 180	87.54 245	66.62 120	92.43 220	72.57 198	80.57 277	90.74 212

Fast-Effi-MVS+		80.81 159	79.92 164	83.47 171	88.85 159	64.51 232	85.53 247	89.39 192	70.79 221	78.49 187	85.06 313	67.54 112	93.58 160	67.03 260	86.58 179	92.32 153

FA-MVS(test-final)		80.96 155	79.91 165	84.10 139	88.30 187	65.01 220	84.55 272	90.01 170	73.25 173	79.61 164	87.57 242	58.35 229	94.72 110	71.29 213	86.25 185	92.56 140

icg_test_0407		80.61 169	79.90 166	82.75 213	87.13 238	63.59 255	85.33 251	89.33 194	70.51 230	77.82 203	89.03 197	61.84 181	92.91 200	72.56 200	85.56 200	91.74 174

CANet_DTU		80.61 169	79.87 167	82.83 203	85.60 279	63.17 271	87.36 184	88.65 234	76.37 89	75.88 251	88.44 218	53.51 274	93.07 193	73.30 189	89.74 128	92.25 156

viewmambaseed2359dif		80.41 176	79.84 168	82.12 222	82.95 348	62.50 282	83.39 299	88.06 245	67.11 298	80.98 145	90.31 159	66.20 128	91.01 281	74.62 174	84.90 208	92.86 130

ACMM	73.20 8	80.78 166	79.84 168	83.58 169	89.31 143	68.37 130	89.99 79	91.60 120	70.28 239	77.25 216	89.66 178	53.37 276	93.53 165	74.24 180	82.85 248	88.85 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-OURS-SEG-HR		80.81 159	79.76 170	83.96 158	85.60 279	68.78 114	83.54 298	90.50 151	70.66 227	76.71 231	91.66 118	60.69 206	91.26 270	76.94 148	81.58 263	91.83 171

xiu_mvs_v1_base_debu		80.80 162	79.72 171	84.03 152	87.35 226	70.19 84	85.56 242	88.77 226	69.06 272	81.83 129	88.16 226	50.91 306	92.85 202	78.29 133	87.56 161	89.06 277

xiu_mvs_v1_base		80.80 162	79.72 171	84.03 152	87.35 226	70.19 84	85.56 242	88.77 226	69.06 272	81.83 129	88.16 226	50.91 306	92.85 202	78.29 133	87.56 161	89.06 277

xiu_mvs_v1_base_debi		80.80 162	79.72 171	84.03 152	87.35 226	70.19 84	85.56 242	88.77 226	69.06 272	81.83 129	88.16 226	50.91 306	92.85 202	78.29 133	87.56 161	89.06 277

LuminaMVS		80.68 167	79.62 174	83.83 161	85.07 296	68.01 144	86.99 196	88.83 223	70.36 235	81.38 137	87.99 233	50.11 317	92.51 217	79.02 121	86.89 175	90.97 202

UGNet		80.83 158	79.59 175	84.54 117	88.04 199	68.09 140	89.42 99	88.16 240	76.95 70	76.22 244	89.46 187	49.30 329	93.94 141	68.48 245	90.31 115	91.60 179
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		80.68 167	79.51 176	84.20 136	94.09 38	67.27 170	89.64 90	91.11 136	58.75 395	74.08 295	90.72 150	58.10 230	95.04 95	69.70 232	89.42 134	90.30 232

QAPM		80.88 156	79.50 177	85.03 98	88.01 202	68.97 110	91.59 46	92.00 100	66.63 309	75.15 276	92.16 105	57.70 234	95.45 71	63.52 283	88.76 145	90.66 215

AdaColmap		80.58 174	79.42 178	84.06 147	93.09 59	68.91 111	89.36 103	88.97 220	69.27 263	75.70 254	89.69 176	57.20 242	95.77 60	63.06 288	88.41 153	87.50 325

NR-MVSNet		80.23 182	79.38 179	82.78 210	87.80 211	63.34 265	86.31 223	91.09 137	79.01 31	72.17 321	89.07 195	67.20 116	92.81 206	66.08 266	75.65 339	92.20 159

mvsmamba		80.60 171	79.38 179	84.27 132	89.74 124	67.24 172	87.47 179	86.95 273	70.02 244	75.38 264	88.93 202	51.24 303	92.56 213	75.47 168	89.22 137	93.00 126

IterMVS-LS		80.06 185	79.38 179	82.11 224	85.89 271	63.20 269	86.79 205	89.34 193	74.19 145	75.45 261	86.72 265	66.62 120	92.39 222	72.58 197	76.86 319	90.75 211
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_djsdf		80.30 181	79.32 182	83.27 180	83.98 319	65.37 211	90.50 67	90.38 155	68.55 282	76.19 245	88.70 208	56.44 249	93.46 169	78.98 124	80.14 283	90.97 202

v2v482		80.23 182	79.29 183	83.05 193	83.62 327	64.14 241	87.04 193	89.97 171	73.61 160	78.18 196	87.22 253	61.10 200	93.82 150	76.11 157	76.78 322	91.18 193

ECVR-MVS		79.61 191	79.26 184	80.67 261	90.08 112	54.69 385	87.89 168	77.44 398	74.88 126	80.27 156	92.79 94	48.96 335	92.45 219	68.55 244	92.50 80	94.86 19

XVG-OURS		80.41 176	79.23 185	83.97 157	85.64 277	69.02 108	83.03 311	90.39 154	71.09 212	77.63 209	91.49 127	54.62 264	91.35 267	75.71 162	83.47 239	91.54 182

WR-MVS		79.49 195	79.22 186	80.27 270	88.79 168	58.35 330	85.06 258	88.61 236	78.56 35	77.65 208	88.34 220	63.81 153	90.66 289	64.98 275	77.22 314	91.80 173

test1111		79.43 198	79.18 187	80.15 273	89.99 117	53.31 398	87.33 186	77.05 402	75.04 119	80.23 158	92.77 96	48.97 334	92.33 227	68.87 241	92.40 82	94.81 22

mvs_anonymous		79.42 199	79.11 188	80.34 268	84.45 310	57.97 337	82.59 313	87.62 258	67.40 297	76.17 248	88.56 215	68.47 102	89.59 305	70.65 220	86.05 189	93.47 98

v1144		80.03 186	79.03 189	83.01 195	83.78 324	64.51 232	87.11 192	90.57 150	71.96 194	78.08 199	86.20 285	61.41 192	93.94 141	74.93 172	77.23 313	90.60 218

v8		79.97 188	79.02 190	82.80 206	84.09 316	64.50 234	87.96 163	90.29 162	74.13 148	75.24 273	86.81 262	62.88 166	93.89 149	74.39 178	75.40 348	90.00 248

ab-mvs		79.51 194	78.97 191	81.14 249	88.46 180	60.91 303	83.84 287	89.24 206	70.36 235	79.03 174	88.87 205	63.23 159	90.21 294	65.12 273	82.57 253	92.28 155

icg_test_0407_2		78.92 215	78.93 192	78.90 298	87.13 238	63.59 255	76.58 389	89.33 194	70.51 230	77.82 203	89.03 197	61.84 181	81.38 394	72.56 200	85.56 200	91.74 174

Anonymous20240529		80.19 184	78.89 193	84.10 139	90.60 100	64.75 228	88.95 120	90.90 140	65.97 317	80.59 152	91.17 138	49.97 319	93.73 158	69.16 238	82.70 252	93.81 76

PCF-MVS	73.52 7	80.38 178	78.84 194	85.01 99	87.71 217	68.99 109	83.65 292	91.46 127	63.00 352	77.77 207	90.28 160	66.10 129	95.09 94	61.40 307	88.22 155	90.94 204
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v10		79.74 190	78.67 195	82.97 198	84.06 317	64.95 222	87.88 169	90.62 147	73.11 175	75.11 277	86.56 276	61.46 191	94.05 137	73.68 183	75.55 341	89.90 254

VPNet		78.69 220	78.66 196	78.76 300	88.31 186	55.72 374	84.45 276	86.63 280	76.79 75	78.26 193	90.55 154	59.30 221	89.70 304	66.63 261	77.05 316	90.88 205

BH-untuned		79.47 196	78.60 197	82.05 225	89.19 150	65.91 195	86.07 230	88.52 237	72.18 189	75.42 262	87.69 239	61.15 199	93.54 164	60.38 315	86.83 176	86.70 347

Effi-MVS+-dtu		80.03 186	78.57 198	84.42 122	85.13 294	68.74 117	88.77 129	88.10 242	74.99 120	74.97 282	83.49 349	57.27 240	93.36 173	73.53 185	80.88 271	91.18 193

WR-MVS_H		78.51 225	78.49 199	78.56 305	88.02 200	56.38 364	88.43 144	92.67 68	77.14 64	73.89 297	87.55 244	66.25 127	89.24 312	58.92 329	73.55 369	90.06 246

Vis-MVSNet (Re-imp)		78.36 228	78.45 200	78.07 317	88.64 174	51.78 408	86.70 209	79.63 380	74.14 147	75.11 277	90.83 149	61.29 196	89.75 302	58.10 339	91.60 93	92.69 136

BH-RMVSNet		79.61 191	78.44 201	83.14 187	89.38 139	65.93 194	84.95 261	87.15 270	73.56 162	78.19 195	89.79 174	56.67 247	93.36 173	59.53 323	86.74 177	90.13 238

v1192		79.59 193	78.43 202	83.07 192	83.55 329	64.52 231	86.93 200	90.58 148	70.83 220	77.78 206	85.90 289	59.15 222	93.94 141	73.96 182	77.19 315	90.76 210

v144192		79.47 196	78.37 203	82.78 210	83.35 332	63.96 244	86.96 197	90.36 158	69.99 246	77.50 210	85.67 296	60.66 208	93.77 154	74.27 179	76.58 323	90.62 216

CP-MVSNet		78.22 230	78.34 204	77.84 321	87.83 210	54.54 387	87.94 165	91.17 133	77.65 46	73.48 303	88.49 216	62.24 176	88.43 328	62.19 298	74.07 362	90.55 220

Baseline_NR-MVSNet		78.15 234	78.33 205	77.61 326	85.79 273	56.21 368	86.78 206	85.76 295	73.60 161	77.93 202	87.57 242	65.02 141	88.99 317	67.14 258	75.33 350	87.63 320

OpenMVS	72.83 10	79.77 189	78.33 205	84.09 143	85.17 290	69.91 89	90.57 64	90.97 138	66.70 303	72.17 321	91.91 109	54.70 262	93.96 138	61.81 304	90.95 106	88.41 306

UniMVSNet_ETH3D		79.10 209	78.24 207	81.70 232	86.85 248	60.24 314	87.28 188	88.79 225	74.25 144	76.84 226	90.53 155	49.48 325	91.56 256	67.98 248	82.15 256	93.29 105

V42		79.38 202	78.24 207	82.83 203	81.10 380	65.50 207	85.55 245	89.82 175	71.57 201	78.21 194	86.12 287	60.66 208	93.18 187	75.64 163	75.46 345	89.81 259

mamv4		76.81 265	78.23 209	72.54 382	86.12 267	65.75 202	78.76 367	82.07 349	64.12 339	72.97 309	91.02 145	67.97 107	68.08 447	83.04 83	78.02 305	83.80 393

PS-CasMVS		78.01 239	78.09 210	77.77 323	87.71 217	54.39 389	88.02 161	91.22 130	77.50 54	73.26 305	88.64 211	60.73 204	88.41 329	61.88 302	73.88 366	90.53 221

v1921920		79.22 205	78.03 211	82.80 206	83.30 334	63.94 246	86.80 204	90.33 159	69.91 249	77.48 211	85.53 300	58.44 228	93.75 156	73.60 184	76.85 320	90.71 214

jajsoiax		79.29 204	77.96 212	83.27 180	84.68 304	66.57 183	89.25 106	90.16 166	69.20 268	75.46 260	89.49 184	45.75 362	93.13 190	76.84 151	80.80 273	90.11 240

TAPA-MVS	73.13 9	79.15 207	77.94 213	82.79 209	89.59 126	62.99 276	88.16 157	91.51 123	65.77 318	77.14 224	91.09 140	60.91 203	93.21 181	50.26 391	87.05 171	92.17 164
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tttt0517		79.40 200	77.91 214	83.90 160	88.10 196	63.84 248	88.37 149	84.05 317	71.45 203	76.78 229	89.12 194	49.93 322	94.89 101	70.18 226	83.18 245	92.96 128

c3_l		78.75 217	77.91 214	81.26 245	82.89 349	61.56 295	84.09 285	89.13 212	69.97 247	75.56 256	84.29 328	66.36 125	92.09 234	73.47 187	75.48 343	90.12 239

VortexMVS		78.57 224	77.89 216	80.59 262	85.89 271	62.76 279	85.61 240	89.62 184	72.06 192	74.99 281	85.38 304	55.94 251	90.77 287	74.99 171	76.58 323	88.23 308

MVSTER		79.01 211	77.88 217	82.38 220	83.07 341	64.80 227	84.08 286	88.95 221	69.01 275	78.69 180	87.17 256	54.70 262	92.43 220	74.69 173	80.57 277	89.89 255

tt0805		78.73 218	77.83 218	81.43 238	85.17 290	60.30 313	89.41 100	90.90 140	71.21 209	77.17 223	88.73 207	46.38 351	93.21 181	72.57 198	78.96 295	90.79 208

X-MVStestdata		80.37 180	77.83 218	88.00 17	94.42 20	73.33 19	92.78 19	92.99 50	79.14 26	83.67 107	12.47 459	67.45 113	96.60 33	83.06 81	94.50 53	94.07 60

v148		78.72 219	77.80 220	81.47 237	82.73 352	61.96 290	86.30 224	88.08 243	73.26 172	76.18 246	85.47 302	62.46 171	92.36 224	71.92 208	73.82 367	90.09 242

v1240		78.99 212	77.78 221	82.64 214	83.21 336	63.54 259	86.62 213	90.30 161	69.74 256	77.33 214	85.68 295	57.04 243	93.76 155	73.13 192	76.92 317	90.62 216

mvs_tets		79.13 208	77.77 222	83.22 184	84.70 303	66.37 185	89.17 109	90.19 165	69.38 260	75.40 263	89.46 187	44.17 374	93.15 188	76.78 153	80.70 275	90.14 237

miper_ehance_all_eth		78.59 223	77.76 223	81.08 251	82.66 354	61.56 295	83.65 292	89.15 210	68.87 277	75.55 257	83.79 340	66.49 123	92.03 235	73.25 190	76.39 328	89.64 263

thisisatest0530		79.40 200	77.76 223	84.31 127	87.69 219	65.10 219	87.36 184	84.26 315	70.04 243	77.42 212	88.26 224	49.94 320	94.79 108	70.20 225	84.70 212	93.03 123

CDS-MVSNet		79.07 210	77.70 225	83.17 186	87.60 221	68.23 137	84.40 279	86.20 288	67.49 295	76.36 241	86.54 277	61.54 188	90.79 285	61.86 303	87.33 166	90.49 223
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Anonymous20231211		78.97 213	77.69 226	82.81 205	90.54 102	64.29 239	90.11 78	91.51 123	65.01 329	76.16 249	88.13 231	50.56 311	93.03 198	69.68 233	77.56 312	91.11 195

PEN-MVS		77.73 245	77.69 226	77.84 321	87.07 246	53.91 392	87.91 167	91.18 132	77.56 51	73.14 307	88.82 206	61.23 197	89.17 314	59.95 318	72.37 377	90.43 225

AUN-MVS		79.21 206	77.60 228	84.05 150	88.71 172	67.61 157	85.84 237	87.26 267	69.08 271	77.23 218	88.14 230	53.20 278	93.47 168	75.50 167	73.45 370	91.06 197

v7n		78.97 213	77.58 229	83.14 187	83.45 331	65.51 206	88.32 151	91.21 131	73.69 158	72.41 317	86.32 283	57.93 231	93.81 151	69.18 237	75.65 339	90.11 240

mamba_0408		79.37 203	77.52 230	84.93 104	88.81 163	67.96 145	65.03 442	88.66 232	70.96 218	79.48 167	89.80 172	58.69 224	94.65 114	70.35 223	85.93 193	92.18 161

mamba_test_0407_2		77.67 250	77.52 230	78.12 315	88.81 163	67.96 145	65.03 442	88.66 232	70.96 218	79.48 167	89.80 172	58.69 224	74.23 435	70.35 223	85.93 193	92.18 161

TAMVS		78.89 216	77.51 232	83.03 194	87.80 211	67.79 153	84.72 265	85.05 304	67.63 292	76.75 230	87.70 238	62.25 175	90.82 284	58.53 334	87.13 170	90.49 223

sd_testset		77.70 248	77.40 233	78.60 303	89.03 157	60.02 316	79.00 363	85.83 294	75.19 116	76.61 235	89.98 166	54.81 257	85.46 363	62.63 294	83.55 236	90.33 230

GBi-Net		78.40 226	77.40 233	81.40 240	87.60 221	63.01 272	88.39 146	89.28 200	71.63 197	75.34 266	87.28 249	54.80 258	91.11 274	62.72 290	79.57 287	90.09 242

test1		78.40 226	77.40 233	81.40 240	87.60 221	63.01 272	88.39 146	89.28 200	71.63 197	75.34 266	87.28 249	54.80 258	91.11 274	62.72 290	79.57 287	90.09 242

BH-w/o		78.21 231	77.33 236	80.84 257	88.81 163	65.13 216	84.87 262	87.85 253	69.75 254	74.52 290	84.74 320	61.34 194	93.11 191	58.24 338	85.84 196	84.27 385

FMVSNet2		78.20 232	77.21 237	81.20 247	87.60 221	62.89 278	87.47 179	89.02 216	71.63 197	75.29 272	87.28 249	54.80 258	91.10 277	62.38 295	79.38 291	89.61 264

anonymousdsp		78.60 222	77.15 238	82.98 197	80.51 386	67.08 175	87.24 189	89.53 187	65.66 320	75.16 275	87.19 255	52.52 280	92.25 229	77.17 145	79.34 292	89.61 264

HY-MVS	69.67 12	77.95 240	77.15 238	80.36 267	87.57 225	60.21 315	83.37 301	87.78 255	66.11 313	75.37 265	87.06 260	63.27 156	90.48 291	61.38 308	82.43 254	90.40 227

cl22		78.07 236	77.01 240	81.23 246	82.37 361	61.83 292	83.55 296	87.98 247	68.96 276	75.06 279	83.87 336	61.40 193	91.88 243	73.53 185	76.39 328	89.98 251

Anonymous202405211		78.25 229	77.01 240	81.99 227	91.03 90	60.67 307	84.77 264	83.90 319	70.65 228	80.00 160	91.20 136	41.08 394	91.43 265	65.21 272	85.26 205	93.85 72

MVS		78.19 233	76.99 242	81.78 230	85.66 276	66.99 176	84.66 267	90.47 152	55.08 416	72.02 323	85.27 306	63.83 152	94.11 135	66.10 265	89.80 127	84.24 386

LCM-MVSNet-Re		77.05 260	76.94 243	77.36 330	87.20 235	51.60 409	80.06 348	80.46 368	75.20 115	67.69 367	86.72 265	62.48 170	88.98 318	63.44 285	89.25 135	91.51 183

miper_enhance_ethall		77.87 243	76.86 244	80.92 256	81.65 368	61.38 297	82.68 312	88.98 218	65.52 322	75.47 258	82.30 369	65.76 136	92.00 237	72.95 193	76.39 328	89.39 270

FMVSNet3		77.88 242	76.85 245	80.97 255	86.84 249	62.36 283	86.52 216	88.77 226	71.13 210	75.34 266	86.66 271	54.07 268	91.10 277	62.72 290	79.57 287	89.45 268

DTE-MVSNet		76.99 261	76.80 246	77.54 329	86.24 262	53.06 401	87.52 177	90.66 146	77.08 68	72.50 315	88.67 210	60.48 212	89.52 306	57.33 346	70.74 389	90.05 247

CNLPA		78.08 235	76.79 247	81.97 228	90.40 105	71.07 67	87.59 176	84.55 309	66.03 316	72.38 318	89.64 179	57.56 236	86.04 355	59.61 322	83.35 241	88.79 293

cl____		77.72 246	76.76 248	80.58 263	82.49 358	60.48 310	83.09 307	87.87 251	69.22 266	74.38 293	85.22 309	62.10 178	91.53 259	71.09 214	75.41 347	89.73 262

DIV-MVS_self_test		77.72 246	76.76 248	80.58 263	82.48 359	60.48 310	83.09 307	87.86 252	69.22 266	74.38 293	85.24 307	62.10 178	91.53 259	71.09 214	75.40 348	89.74 261

baseline1		76.98 262	76.75 250	77.66 324	88.13 194	55.66 375	85.12 256	81.89 350	73.04 177	76.79 228	88.90 203	62.43 172	87.78 337	63.30 287	71.18 387	89.55 266

eth_miper_zixun_eth		77.92 241	76.69 251	81.61 235	83.00 344	61.98 289	83.15 305	89.20 208	69.52 258	74.86 284	84.35 327	61.76 184	92.56 213	71.50 211	72.89 375	90.28 233

pm-mvs1		77.25 258	76.68 252	78.93 297	84.22 313	58.62 328	86.41 219	88.36 239	71.37 204	73.31 304	88.01 232	61.22 198	89.15 315	64.24 281	73.01 374	89.03 281

ET-MVSNet_ETH3D		78.63 221	76.63 253	84.64 115	86.73 253	69.47 98	85.01 259	84.61 308	69.54 257	66.51 387	86.59 273	50.16 316	91.75 247	76.26 156	84.24 222	92.69 136

test2506		77.30 257	76.49 254	79.74 281	90.08 112	52.02 402	87.86 170	63.10 445	74.88 126	80.16 159	92.79 94	38.29 409	92.35 225	68.74 243	92.50 80	94.86 19

Fast-Effi-MVS+-dtu		78.02 238	76.49 254	82.62 215	83.16 340	66.96 179	86.94 199	87.45 263	72.45 184	71.49 329	84.17 333	54.79 261	91.58 253	67.61 251	80.31 280	89.30 273

1112_ss		77.40 255	76.43 256	80.32 269	89.11 156	60.41 312	83.65 292	87.72 257	62.13 365	73.05 308	86.72 265	62.58 169	89.97 298	62.11 301	80.80 273	90.59 219

ICG_test_0404		77.16 259	76.42 257	79.37 289	87.13 238	63.59 255	77.12 387	89.33 194	70.51 230	66.22 390	89.03 197	50.36 314	82.78 384	72.56 200	85.56 200	91.74 174

PAPM		77.68 249	76.40 258	81.51 236	87.29 234	61.85 291	83.78 288	89.59 185	64.74 331	71.23 331	88.70 208	62.59 168	93.66 159	52.66 375	87.03 172	89.01 282

PLC	70.83 11	78.05 237	76.37 259	83.08 191	91.88 79	67.80 152	88.19 155	89.46 189	64.33 337	69.87 348	88.38 219	53.66 272	93.58 160	58.86 330	82.73 250	87.86 316
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 253	76.18 260	81.20 247	88.24 188	63.24 267	84.61 270	86.40 284	67.55 294	77.81 205	86.48 279	54.10 267	93.15 188	57.75 342	82.72 251	87.20 332

FMVSNet1		77.44 253	76.12 261	81.40 240	86.81 250	63.01 272	88.39 146	89.28 200	70.49 234	74.39 292	87.28 249	49.06 333	91.11 274	60.91 311	78.52 298	90.09 242

MonoMVSNet		76.49 273	75.80 262	78.58 304	81.55 371	58.45 329	86.36 222	86.22 287	74.87 128	74.73 286	83.73 342	51.79 298	88.73 323	70.78 216	72.15 380	88.55 303

test_vis1_n_1920		75.52 287	75.78 263	74.75 360	79.84 394	57.44 348	83.26 303	85.52 297	62.83 356	79.34 172	86.17 286	45.10 367	79.71 401	78.75 126	81.21 267	87.10 339

CHOSEN 1792x2688		77.63 251	75.69 264	83.44 173	89.98 118	68.58 125	78.70 368	87.50 261	56.38 411	75.80 253	86.84 261	58.67 226	91.40 266	61.58 306	85.75 198	90.34 229

FE-MVS		77.78 244	75.68 265	84.08 144	88.09 197	66.00 192	83.13 306	87.79 254	68.42 286	78.01 200	85.23 308	45.50 365	95.12 88	59.11 327	85.83 197	91.11 195

WTY-MVS		75.65 285	75.68 265	75.57 346	86.40 260	56.82 355	77.92 381	82.40 345	65.10 326	76.18 246	87.72 237	63.13 164	80.90 397	60.31 316	81.96 259	89.00 284

testing91		76.54 268	75.66 267	79.18 294	88.43 182	55.89 371	81.08 330	83.00 337	73.76 156	75.34 266	84.29 328	46.20 356	90.07 296	64.33 279	84.50 214	91.58 181

XXY-MVS		75.41 290	75.56 268	74.96 355	83.59 328	57.82 341	80.59 340	83.87 320	66.54 310	74.93 283	88.31 221	63.24 158	80.09 400	62.16 299	76.85 320	86.97 341

thres100view900		76.50 270	75.55 269	79.33 290	89.52 129	56.99 353	85.83 238	83.23 330	73.94 151	76.32 242	87.12 257	51.89 295	91.95 239	48.33 401	83.75 230	89.07 275

thres600view7		76.50 270	75.44 270	79.68 283	89.40 137	57.16 350	85.53 247	83.23 330	73.79 155	76.26 243	87.09 258	51.89 295	91.89 242	48.05 406	83.72 233	90.00 248

Test_1112_low_res		76.40 275	75.44 270	79.27 291	89.28 145	58.09 333	81.69 322	87.07 271	59.53 386	72.48 316	86.67 270	61.30 195	89.33 309	60.81 313	80.15 282	90.41 226

HyFIR lowres test		77.53 252	75.40 272	83.94 159	89.59 126	66.62 181	80.36 344	88.64 235	56.29 412	76.45 238	85.17 310	57.64 235	93.28 175	61.34 309	83.10 246	91.91 170

thisisatest0515		77.33 256	75.38 273	83.18 185	85.27 289	63.80 249	82.11 318	83.27 329	65.06 327	75.91 250	83.84 338	49.54 324	94.27 126	67.24 256	86.19 186	91.48 186

tfpn200view9		76.42 274	75.37 274	79.55 288	89.13 152	57.65 344	85.17 253	83.60 322	73.41 168	76.45 238	86.39 281	52.12 287	91.95 239	48.33 401	83.75 230	89.07 275

thres400		76.50 270	75.37 274	79.86 278	89.13 152	57.65 344	85.17 253	83.60 322	73.41 168	76.45 238	86.39 281	52.12 287	91.95 239	48.33 401	83.75 230	90.00 248

1314		76.53 269	75.30 276	80.21 272	83.93 320	62.32 285	84.66 267	88.81 224	60.23 379	70.16 342	84.07 335	55.30 255	90.73 288	67.37 254	83.21 244	87.59 323

testing3-2		75.12 295	75.19 277	74.91 356	90.40 105	45.09 438	80.29 346	78.42 390	78.37 40	76.54 237	87.75 236	44.36 372	87.28 343	57.04 349	83.49 238	92.37 150

GA-MVS		76.87 264	75.17 278	81.97 228	82.75 351	62.58 280	81.44 327	86.35 286	72.16 191	74.74 285	82.89 360	46.20 356	92.02 236	68.85 242	81.09 268	91.30 191

testing99		76.09 280	75.12 279	79.00 295	88.16 191	55.50 377	80.79 334	81.40 357	73.30 171	75.17 274	84.27 331	44.48 371	90.02 297	64.28 280	84.22 223	91.48 186

EPNet_dtu		75.46 288	74.86 280	77.23 333	82.57 356	54.60 386	86.89 201	83.09 334	71.64 196	66.25 389	85.86 291	55.99 250	88.04 333	54.92 363	86.55 180	89.05 280
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		76.95 263	74.82 281	83.37 177	90.45 103	67.36 167	89.15 113	86.94 274	61.87 368	69.52 351	90.61 152	51.71 299	94.53 117	46.38 413	86.71 178	88.21 310

SD_0403		74.65 298	74.77 282	74.29 364	86.20 264	47.42 427	83.71 290	85.12 301	69.30 262	68.50 362	87.95 234	59.40 220	86.05 354	49.38 395	83.35 241	89.40 269

cascas		76.72 267	74.64 283	82.99 196	85.78 274	65.88 196	82.33 315	89.21 207	60.85 374	72.74 311	81.02 380	47.28 342	93.75 156	67.48 253	85.02 206	89.34 272

DP-MVS		76.78 266	74.57 284	83.42 174	93.29 48	69.46 100	88.55 142	83.70 321	63.98 344	70.20 339	88.89 204	54.01 270	94.80 107	46.66 410	81.88 261	86.01 359

TransMVSNet (Re)		75.39 292	74.56 285	77.86 320	85.50 283	57.10 352	86.78 206	86.09 291	72.17 190	71.53 328	87.34 248	63.01 165	89.31 310	56.84 352	61.83 418	87.17 333

LTVRE_ROB	69.57 13	76.25 277	74.54 286	81.41 239	88.60 175	64.38 238	79.24 358	89.12 213	70.76 223	69.79 350	87.86 235	49.09 332	93.20 184	56.21 358	80.16 281	86.65 348
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		75.55 286	74.47 287	78.82 299	87.78 214	57.85 340	83.07 309	83.51 325	72.44 186	75.84 252	84.42 323	52.08 290	91.75 247	47.41 408	83.64 235	86.86 343

MVP-Stereo		76.12 278	74.46 288	81.13 250	85.37 286	69.79 91	84.42 278	87.95 249	65.03 328	67.46 370	85.33 305	53.28 277	91.73 249	58.01 340	83.27 243	81.85 412
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
reproduce_monomvs		75.40 291	74.38 289	78.46 310	83.92 321	57.80 342	83.78 288	86.94 274	73.47 166	72.25 320	84.47 322	38.74 405	89.27 311	75.32 169	70.53 390	88.31 307

F-COLMAP		76.38 276	74.33 290	82.50 218	89.28 145	66.95 180	88.41 145	89.03 215	64.05 342	66.83 379	88.61 212	46.78 348	92.89 201	57.48 343	78.55 297	87.67 319

XVG-ACMP-BASELINE		76.11 279	74.27 291	81.62 233	83.20 337	64.67 229	83.60 295	89.75 179	69.75 254	71.85 324	87.09 258	32.78 424	92.11 233	69.99 229	80.43 279	88.09 312

testing11		75.14 294	74.01 292	78.53 307	88.16 191	56.38 364	80.74 337	80.42 370	70.67 224	72.69 314	83.72 343	43.61 378	89.86 299	62.29 297	83.76 229	89.36 271

ACMH+	68.96 14	76.01 281	74.01 292	82.03 226	88.60 175	65.31 212	88.86 123	87.55 259	70.25 241	67.75 366	87.47 247	41.27 392	93.19 186	58.37 336	75.94 336	87.60 321

ACMH	67.68 16	75.89 282	73.93 294	81.77 231	88.71 172	66.61 182	88.62 138	89.01 217	69.81 250	66.78 380	86.70 269	41.95 390	91.51 261	55.64 359	78.14 304	87.17 333
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		75.24 293	73.90 295	79.27 291	82.65 355	58.27 332	80.80 333	82.73 343	61.57 369	75.33 270	83.13 355	55.52 253	91.07 280	64.98 275	78.34 303	88.45 304

IterMVS-SCA-FT		75.43 289	73.87 296	80.11 274	82.69 353	64.85 226	81.57 324	83.47 326	69.16 269	70.49 336	84.15 334	51.95 293	88.15 331	69.23 236	72.14 381	87.34 328

baseline2		75.70 284	73.83 297	81.30 243	83.26 335	61.79 293	82.57 314	80.65 364	66.81 300	66.88 378	83.42 350	57.86 233	92.19 231	63.47 284	79.57 287	89.91 253

test_cas_vis1_n_1920		73.76 309	73.74 298	73.81 370	75.90 416	59.77 318	80.51 341	82.40 345	58.30 397	81.62 135	85.69 294	44.35 373	76.41 419	76.29 155	78.61 296	85.23 372

sss		73.60 311	73.64 299	73.51 372	82.80 350	55.01 383	76.12 391	81.69 353	62.47 361	74.68 287	85.85 292	57.32 239	78.11 408	60.86 312	80.93 269	87.39 326

myMVS_eth3d28		73.62 310	73.53 300	73.90 369	88.20 189	47.41 428	78.06 378	79.37 382	74.29 143	73.98 296	84.29 328	44.67 368	83.54 378	51.47 381	87.39 165	90.74 212

SSC-MVS3.2		73.35 317	73.39 301	73.23 373	85.30 288	49.01 423	74.58 406	81.57 354	75.21 114	73.68 300	85.58 299	52.53 279	82.05 389	54.33 367	77.69 310	88.63 300

pmmvs6		74.69 297	73.39 301	78.61 302	81.38 375	57.48 347	86.64 212	87.95 249	64.99 330	70.18 340	86.61 272	50.43 313	89.52 306	62.12 300	70.18 392	88.83 291

IB-MVS	68.01 15	75.85 283	73.36 303	83.31 178	84.76 302	66.03 189	83.38 300	85.06 303	70.21 242	69.40 352	81.05 379	45.76 361	94.66 113	65.10 274	75.49 342	89.25 274
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		74.82 296	73.21 304	79.64 285	79.81 395	62.56 281	80.34 345	87.35 264	64.37 336	68.86 357	82.66 364	46.37 352	90.10 295	67.91 249	81.24 266	86.25 352

tfpnnormal		74.39 299	73.16 305	78.08 316	86.10 269	58.05 334	84.65 269	87.53 260	70.32 238	71.22 332	85.63 297	54.97 256	89.86 299	43.03 424	75.02 355	86.32 351

miper_lstm_enhance		74.11 304	73.11 306	77.13 334	80.11 390	59.62 320	72.23 413	86.92 276	66.76 302	70.40 337	82.92 359	56.93 244	82.92 383	69.06 239	72.63 376	88.87 289

mmtdpeth		74.16 303	73.01 307	77.60 328	83.72 326	61.13 298	85.10 257	85.10 302	72.06 192	77.21 222	80.33 389	43.84 376	85.75 357	77.14 146	52.61 437	85.91 362

IterMVS		74.29 300	72.94 308	78.35 311	81.53 372	63.49 261	81.58 323	82.49 344	68.06 290	69.99 345	83.69 344	51.66 300	85.54 361	65.85 268	71.64 384	86.01 359
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WBMVS		73.43 313	72.81 309	75.28 352	87.91 205	50.99 415	78.59 371	81.31 359	65.51 324	74.47 291	84.83 317	46.39 350	86.68 347	58.41 335	77.86 306	88.17 311

MS-PatchMatch		73.83 308	72.67 310	77.30 332	83.87 322	66.02 190	81.82 319	84.66 307	61.37 372	68.61 360	82.82 362	47.29 341	88.21 330	59.27 324	84.32 221	77.68 427

testing222		74.04 305	72.66 311	78.19 313	87.89 206	55.36 378	81.06 331	79.20 385	71.30 207	74.65 288	83.57 348	39.11 404	88.67 325	51.43 383	85.75 198	90.53 221

CVMVSNet		72.99 323	72.58 312	74.25 365	84.28 311	50.85 416	86.41 219	83.45 327	44.56 436	73.23 306	87.54 245	49.38 327	85.70 358	65.90 267	78.44 300	86.19 354

test-LLR		72.94 324	72.43 313	74.48 361	81.35 376	58.04 335	78.38 372	77.46 396	66.66 304	69.95 346	79.00 403	48.06 338	79.24 402	66.13 263	84.83 209	86.15 355

OurMVSNet-221017-0		74.26 301	72.42 314	79.80 280	83.76 325	59.59 321	85.92 234	86.64 279	66.39 311	66.96 377	87.58 241	39.46 400	91.60 252	65.76 269	69.27 395	88.22 309

SCA		74.22 302	72.33 315	79.91 277	84.05 318	62.17 287	79.96 351	79.29 384	66.30 312	72.38 318	80.13 392	51.95 293	88.60 326	59.25 325	77.67 311	88.96 286

UBG		73.08 321	72.27 316	75.51 348	88.02 200	51.29 413	78.35 375	77.38 399	65.52 322	73.87 298	82.36 367	45.55 363	86.48 350	55.02 362	84.39 220	88.75 295

tpmrst		72.39 326	72.13 317	73.18 377	80.54 385	49.91 420	79.91 352	79.08 386	63.11 350	71.69 326	79.95 394	55.32 254	82.77 385	65.66 270	73.89 365	86.87 342

pmmvs4		74.03 307	71.91 318	80.39 266	81.96 364	68.32 131	81.45 326	82.14 347	59.32 387	69.87 348	85.13 311	52.40 283	88.13 332	60.21 317	74.74 358	84.73 382

EG-PatchMatch MVS		74.04 305	71.82 319	80.71 260	84.92 298	67.42 163	85.86 236	88.08 243	66.04 315	64.22 402	83.85 337	35.10 420	92.56 213	57.44 344	80.83 272	82.16 411

tpm		72.37 328	71.71 320	74.35 363	82.19 362	52.00 403	79.22 359	77.29 400	64.56 333	72.95 310	83.68 345	51.35 301	83.26 382	58.33 337	75.80 337	87.81 317

WB-MVSnew		71.96 334	71.65 321	72.89 378	84.67 307	51.88 406	82.29 316	77.57 395	62.31 362	73.67 301	83.00 357	53.49 275	81.10 396	45.75 417	82.13 257	85.70 365

UWE-MVS		72.13 332	71.49 322	74.03 367	86.66 256	47.70 425	81.40 328	76.89 404	63.60 347	75.59 255	84.22 332	39.94 399	85.62 360	48.98 398	86.13 188	88.77 294

CL-MVSNet_self_test		72.37 328	71.46 323	75.09 354	79.49 401	53.53 394	80.76 336	85.01 305	69.12 270	70.51 335	82.05 373	57.92 232	84.13 373	52.27 377	66.00 408	87.60 321

tpm2		73.26 318	71.46 323	78.63 301	83.34 333	56.71 358	80.65 339	80.40 371	56.63 410	73.55 302	82.02 374	51.80 297	91.24 271	56.35 357	78.42 301	87.95 313

RPSCF		73.23 319	71.46 323	78.54 306	82.50 357	59.85 317	82.18 317	82.84 342	58.96 391	71.15 333	89.41 191	45.48 366	84.77 370	58.82 331	71.83 383	91.02 201

PatchmatchNet		73.12 320	71.33 326	78.49 309	83.18 338	60.85 304	79.63 353	78.57 389	64.13 338	71.73 325	79.81 397	51.20 304	85.97 356	57.40 345	76.36 333	88.66 298
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		73.37 314	71.27 327	79.67 284	81.32 378	65.19 214	75.92 393	80.30 372	59.92 382	72.73 312	81.19 377	52.50 281	86.69 346	59.84 319	77.71 308	87.11 337

SixPastTwentyTwo		73.37 314	71.26 328	79.70 282	85.08 295	57.89 339	85.57 241	83.56 324	71.03 216	65.66 392	85.88 290	42.10 388	92.57 212	59.11 327	63.34 414	88.65 299

ETVMVS		72.25 330	71.05 329	75.84 342	87.77 215	51.91 405	79.39 356	74.98 411	69.26 264	73.71 299	82.95 358	40.82 396	86.14 353	46.17 414	84.43 219	89.47 267

MSDG		73.36 316	70.99 330	80.49 265	84.51 309	65.80 199	80.71 338	86.13 290	65.70 319	65.46 393	83.74 341	44.60 369	90.91 283	51.13 384	76.89 318	84.74 381

PatchMatch-RL		72.38 327	70.90 331	76.80 337	88.60 175	67.38 166	79.53 354	76.17 408	62.75 358	69.36 353	82.00 375	45.51 364	84.89 369	53.62 370	80.58 276	78.12 426

PVSNet	64.34 18	72.08 333	70.87 332	75.69 344	86.21 263	56.44 362	74.37 407	80.73 363	62.06 366	70.17 341	82.23 371	42.86 382	83.31 381	54.77 364	84.45 218	87.32 329

dmvs_re		71.14 338	70.58 333	72.80 379	81.96 364	59.68 319	75.60 397	79.34 383	68.55 282	69.27 355	80.72 385	49.42 326	76.54 416	52.56 376	77.79 307	82.19 410

test_fmvs1		70.93 341	70.52 334	72.16 384	73.71 427	55.05 382	80.82 332	78.77 388	51.21 428	78.58 184	84.41 324	31.20 429	76.94 414	75.88 161	80.12 284	84.47 384

RPMNet		73.51 312	70.49 335	82.58 217	81.32 378	65.19 214	75.92 393	92.27 85	57.60 404	72.73 312	76.45 419	52.30 284	95.43 73	48.14 405	77.71 308	87.11 337

test_0402		72.79 325	70.44 336	79.84 279	88.13 194	65.99 193	85.93 233	84.29 313	65.57 321	67.40 373	85.49 301	46.92 345	92.61 209	35.88 438	74.38 361	80.94 417

COLMAP_ROB	66.92 17	73.01 322	70.41 337	80.81 258	87.13 238	65.63 203	88.30 152	84.19 316	62.96 353	63.80 407	87.69 239	38.04 410	92.56 213	46.66 410	74.91 356	84.24 386
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test-mter		71.41 336	70.39 338	74.48 361	81.35 376	58.04 335	78.38 372	77.46 396	60.32 378	69.95 346	79.00 403	36.08 418	79.24 402	66.13 263	84.83 209	86.15 355

test_fmvs1_n		70.86 342	70.24 339	72.73 380	72.51 438	55.28 380	81.27 329	79.71 379	51.49 427	78.73 179	84.87 316	27.54 434	77.02 413	76.06 158	79.97 285	85.88 363

pmmvs5		71.55 335	70.20 340	75.61 345	77.83 409	56.39 363	81.74 321	80.89 360	57.76 402	67.46 370	84.49 321	49.26 330	85.32 365	57.08 348	75.29 351	85.11 376

MDTV_nov1_ep13			69.97 341		83.18 338	53.48 395	77.10 388	80.18 376	60.45 376	69.33 354	80.44 386	48.89 336	86.90 345	51.60 380	78.51 299

sc_t1		72.19 331	69.51 342	80.23 271	84.81 300	61.09 300	84.68 266	80.22 374	60.70 375	71.27 330	83.58 347	36.59 415	89.24 312	60.41 314	63.31 415	90.37 228

MIMVSNet		70.69 344	69.30 343	74.88 357	84.52 308	56.35 366	75.87 395	79.42 381	64.59 332	67.76 365	82.41 366	41.10 393	81.54 392	46.64 412	81.34 264	86.75 346

tpmvs		71.09 339	69.29 344	76.49 338	82.04 363	56.04 369	78.92 365	81.37 358	64.05 342	67.18 375	78.28 409	49.74 323	89.77 301	49.67 394	72.37 377	83.67 394

test_vis1_n		69.85 356	69.21 345	71.77 386	72.66 437	55.27 381	81.48 325	76.21 407	52.03 424	75.30 271	83.20 354	28.97 432	76.22 421	74.60 175	78.41 302	83.81 392

Patchmtry		70.74 343	69.16 346	75.49 349	80.72 382	54.07 391	74.94 404	80.30 372	58.34 396	70.01 343	81.19 377	52.50 281	86.54 348	53.37 372	71.09 388	85.87 364

TESTMET0.1,1		69.89 355	69.00 347	72.55 381	79.27 404	56.85 354	78.38 372	74.71 415	57.64 403	68.09 364	77.19 416	37.75 411	76.70 415	63.92 282	84.09 224	84.10 389

PMMVS		69.34 359	68.67 348	71.35 391	75.67 418	62.03 288	75.17 399	73.46 418	50.00 429	68.68 358	79.05 401	52.07 291	78.13 407	61.16 310	82.77 249	73.90 433

K. test v3		71.19 337	68.51 349	79.21 293	83.04 343	57.78 343	84.35 280	76.91 403	72.90 180	62.99 410	82.86 361	39.27 401	91.09 279	61.65 305	52.66 436	88.75 295

USDC		70.33 349	68.37 350	76.21 340	80.60 384	56.23 367	79.19 360	86.49 282	60.89 373	61.29 415	85.47 302	31.78 427	89.47 308	53.37 372	76.21 334	82.94 404

tpm cat1		70.57 345	68.31 351	77.35 331	82.41 360	57.95 338	78.08 377	80.22 374	52.04 423	68.54 361	77.66 414	52.00 292	87.84 336	51.77 378	72.07 382	86.25 352

OpenMVS_ROB	64.09 19	70.56 346	68.19 352	77.65 325	80.26 387	59.41 324	85.01 259	82.96 339	58.76 394	65.43 394	82.33 368	37.63 412	91.23 272	45.34 420	76.03 335	82.32 408

EPMVS		69.02 361	68.16 353	71.59 387	79.61 399	49.80 422	77.40 384	66.93 436	62.82 357	70.01 343	79.05 401	45.79 360	77.86 410	56.58 355	75.26 352	87.13 336

CMPMVS	51.72 21	70.19 351	68.16 353	76.28 339	73.15 434	57.55 346	79.47 355	83.92 318	48.02 432	56.48 432	84.81 318	43.13 380	86.42 351	62.67 293	81.81 262	84.89 379
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
AllTest		70.96 340	68.09 355	79.58 286	85.15 292	63.62 251	84.58 271	79.83 377	62.31 362	60.32 419	86.73 263	32.02 425	88.96 320	50.28 389	71.57 385	86.15 355

tt0320		70.49 348	68.03 356	77.89 319	84.78 301	59.12 325	83.55 296	80.44 369	58.13 399	67.43 372	80.41 388	39.26 402	87.54 340	55.12 361	63.18 416	86.99 340

gg-mvs-nofinetune		69.95 354	67.96 357	75.94 341	83.07 341	54.51 388	77.23 386	70.29 426	63.11 350	70.32 338	62.33 440	43.62 377	88.69 324	53.88 369	87.76 160	84.62 383

FMVSNet5		69.50 357	67.96 357	74.15 366	82.97 347	55.35 379	80.01 350	82.12 348	62.56 360	63.02 408	81.53 376	36.92 413	81.92 390	48.42 400	74.06 363	85.17 375

Syy-MVS		68.05 370	67.85 359	68.67 406	84.68 304	40.97 449	78.62 369	73.08 420	66.65 307	66.74 381	79.46 398	52.11 289	82.30 387	32.89 441	76.38 331	82.75 405

PatchT		68.46 368	67.85 359	70.29 397	80.70 383	43.93 441	72.47 412	74.88 412	60.15 380	70.55 334	76.57 418	49.94 320	81.59 391	50.58 385	74.83 357	85.34 370

pmmvs-eth3d		70.50 347	67.83 361	78.52 308	77.37 412	66.18 188	81.82 319	81.51 355	58.90 392	63.90 406	80.42 387	42.69 383	86.28 352	58.56 333	65.30 410	83.11 400

Anonymous20231206		68.60 364	67.80 362	71.02 394	80.23 389	50.75 417	78.30 376	80.47 367	56.79 409	66.11 391	82.63 365	46.35 353	78.95 404	43.62 423	75.70 338	83.36 397

Patchmatch-RL test		70.24 350	67.78 363	77.61 326	77.43 411	59.57 322	71.16 417	70.33 425	62.94 354	68.65 359	72.77 431	50.62 310	85.49 362	69.58 234	66.58 405	87.77 318

test0.0.03 1		68.00 371	67.69 364	68.90 403	77.55 410	47.43 426	75.70 396	72.95 422	66.66 304	66.56 383	82.29 370	48.06 338	75.87 425	44.97 421	74.51 360	83.41 396

testing3		68.56 366	67.67 365	71.22 393	87.33 231	42.87 443	83.06 310	71.54 423	70.36 235	69.08 356	84.38 325	30.33 431	85.69 359	37.50 436	75.45 346	85.09 377

EU-MVSNet		68.53 367	67.61 366	71.31 392	78.51 408	47.01 430	84.47 273	84.27 314	42.27 439	66.44 388	84.79 319	40.44 397	83.76 375	58.76 332	68.54 400	83.17 398

KD-MVS_self_test		68.81 362	67.59 367	72.46 383	74.29 424	45.45 433	77.93 380	87.00 272	63.12 349	63.99 405	78.99 405	42.32 385	84.77 370	56.55 356	64.09 413	87.16 335

test_fmvs2		68.35 369	67.48 368	70.98 395	69.50 441	51.95 404	80.05 349	76.38 406	49.33 430	74.65 288	84.38 325	23.30 443	75.40 430	74.51 176	75.17 354	85.60 366

tt0320-xc		70.11 352	67.45 369	78.07 317	85.33 287	59.51 323	83.28 302	78.96 387	58.77 393	67.10 376	80.28 390	36.73 414	87.42 341	56.83 353	59.77 425	87.29 330

mvs5depth		69.45 358	67.45 369	75.46 350	73.93 425	55.83 372	79.19 360	83.23 330	66.89 299	71.63 327	83.32 351	33.69 423	85.09 366	59.81 320	55.34 433	85.46 368

ppachtmachnet_test		70.04 353	67.34 371	78.14 314	79.80 396	61.13 298	79.19 360	80.59 365	59.16 389	65.27 395	79.29 400	46.75 349	87.29 342	49.33 396	66.72 403	86.00 361

Anonymous20240521		68.80 363	67.22 372	73.55 371	74.33 423	54.11 390	83.18 304	85.61 296	58.15 398	61.68 414	80.94 382	30.71 430	81.27 395	57.00 350	73.34 373	85.28 371

our_test_3		69.14 360	67.00 373	75.57 346	79.80 396	58.80 326	77.96 379	77.81 393	59.55 385	62.90 411	78.25 410	47.43 340	83.97 374	51.71 379	67.58 402	83.93 391

test20.03		67.45 373	66.95 374	68.94 402	75.48 420	44.84 439	77.50 383	77.67 394	66.66 304	63.01 409	83.80 339	47.02 344	78.40 406	42.53 427	68.86 399	83.58 395

MIMVSNet1		68.58 365	66.78 375	73.98 368	80.07 391	51.82 407	80.77 335	84.37 310	64.40 335	59.75 422	82.16 372	36.47 416	83.63 377	42.73 425	70.33 391	86.48 350

testgi		66.67 379	66.53 376	67.08 413	75.62 419	41.69 448	75.93 392	76.50 405	66.11 313	65.20 398	86.59 273	35.72 419	74.71 432	43.71 422	73.38 372	84.84 380

myMVS_eth3d		67.02 376	66.29 377	69.21 401	84.68 304	42.58 444	78.62 369	73.08 420	66.65 307	66.74 381	79.46 398	31.53 428	82.30 387	39.43 433	76.38 331	82.75 405

UnsupCasMVSNet_eth		67.33 374	65.99 378	71.37 389	73.48 430	51.47 411	75.16 400	85.19 300	65.20 325	60.78 417	80.93 384	42.35 384	77.20 412	57.12 347	53.69 435	85.44 369

dp		66.80 377	65.43 379	70.90 396	79.74 398	48.82 424	75.12 402	74.77 413	59.61 384	64.08 404	77.23 415	42.89 381	80.72 398	48.86 399	66.58 405	83.16 399

UWE-MVS-28		65.32 386	64.93 380	66.49 414	78.70 406	38.55 451	77.86 382	64.39 443	62.00 367	64.13 403	83.60 346	41.44 391	76.00 423	31.39 443	80.89 270	84.92 378

TinyColmap		67.30 375	64.81 381	74.76 359	81.92 366	56.68 359	80.29 346	81.49 356	60.33 377	56.27 433	83.22 352	24.77 439	87.66 339	45.52 418	69.47 394	79.95 422

CHOSEN 280x420		66.51 380	64.71 382	71.90 385	81.45 373	63.52 260	57.98 449	68.95 432	53.57 419	62.59 412	76.70 417	46.22 355	75.29 431	55.25 360	79.68 286	76.88 429

TDRefinement		67.49 372	64.34 383	76.92 335	73.47 431	61.07 301	84.86 263	82.98 338	59.77 383	58.30 426	85.13 311	26.06 435	87.89 335	47.92 407	60.59 423	81.81 413

PM-MVS		66.41 381	64.14 384	73.20 376	73.92 426	56.45 361	78.97 364	64.96 442	63.88 346	64.72 399	80.24 391	19.84 447	83.44 380	66.24 262	64.52 412	79.71 423

dmvs_testset		62.63 394	64.11 385	58.19 424	78.55 407	24.76 462	75.28 398	65.94 439	67.91 291	60.34 418	76.01 421	53.56 273	73.94 437	31.79 442	67.65 401	75.88 431

KD-MVS_2432*1600		66.22 383	63.89 386	73.21 374	75.47 421	53.42 396	70.76 420	84.35 311	64.10 340	66.52 385	78.52 407	34.55 421	84.98 367	50.40 387	50.33 440	81.23 415

miper_refine_blended		66.22 383	63.89 386	73.21 374	75.47 421	53.42 396	70.76 420	84.35 311	64.10 340	66.52 385	78.52 407	34.55 421	84.98 367	50.40 387	50.33 440	81.23 415

MDA-MVSNet-bldmvs		66.68 378	63.66 388	75.75 343	79.28 403	60.56 309	73.92 409	78.35 391	64.43 334	50.13 441	79.87 396	44.02 375	83.67 376	46.10 415	56.86 427	83.03 402

ADS-MVSNet2		66.20 385	63.33 389	74.82 358	79.92 392	58.75 327	67.55 432	75.19 410	53.37 420	65.25 396	75.86 422	42.32 385	80.53 399	41.57 428	68.91 397	85.18 373

Patchmatch-test		64.82 389	63.24 390	69.57 399	79.42 402	49.82 421	63.49 446	69.05 431	51.98 425	59.95 421	80.13 392	50.91 306	70.98 440	40.66 430	73.57 368	87.90 315

MDA-MVSNet_test_wron		65.03 387	62.92 391	71.37 389	75.93 415	56.73 356	69.09 429	74.73 414	57.28 407	54.03 436	77.89 411	45.88 358	74.39 434	49.89 393	61.55 419	82.99 403

YYNet1		65.03 387	62.91 392	71.38 388	75.85 417	56.60 360	69.12 428	74.66 416	57.28 407	54.12 435	77.87 412	45.85 359	74.48 433	49.95 392	61.52 420	83.05 401

ADS-MVSNet		64.36 390	62.88 393	68.78 405	79.92 392	47.17 429	67.55 432	71.18 424	53.37 420	65.25 396	75.86 422	42.32 385	73.99 436	41.57 428	68.91 397	85.18 373

JIA-IIPM		66.32 382	62.82 394	76.82 336	77.09 413	61.72 294	65.34 440	75.38 409	58.04 401	64.51 400	62.32 441	42.05 389	86.51 349	51.45 382	69.22 396	82.21 409

LF4IMVS		64.02 391	62.19 395	69.50 400	70.90 439	53.29 399	76.13 390	77.18 401	52.65 422	58.59 424	80.98 381	23.55 442	76.52 417	53.06 374	66.66 404	78.68 425

test_fmvs3		63.36 393	61.82 396	67.98 410	62.51 450	46.96 431	77.37 385	74.03 417	45.24 435	67.50 369	78.79 406	12.16 455	72.98 439	72.77 196	66.02 407	83.99 390

new-patchmatchnet		61.73 396	61.73 397	61.70 420	72.74 436	24.50 463	69.16 427	78.03 392	61.40 370	56.72 431	75.53 425	38.42 407	76.48 418	45.95 416	57.67 426	84.13 388

UnsupCasMVSNet_bld		63.70 392	61.53 398	70.21 398	73.69 428	51.39 412	72.82 411	81.89 350	55.63 414	57.81 428	71.80 433	38.67 406	78.61 405	49.26 397	52.21 438	80.63 419

mvsany_test1		62.30 395	61.26 399	65.41 416	69.52 440	54.86 384	66.86 434	49.78 456	46.65 433	68.50 362	83.21 353	49.15 331	66.28 448	56.93 351	60.77 421	75.11 432

PVSNet_0	57.27 20	61.67 397	59.27 400	68.85 404	79.61 399	57.44 348	68.01 430	73.44 419	55.93 413	58.54 425	70.41 436	44.58 370	77.55 411	47.01 409	35.91 448	71.55 436

test_vis1_rt		60.28 398	58.42 401	65.84 415	67.25 444	55.60 376	70.44 422	60.94 448	44.33 437	59.00 423	66.64 438	24.91 438	68.67 445	62.80 289	69.48 393	73.25 434

MVS-HIRNet		59.14 400	57.67 402	63.57 418	81.65 368	43.50 442	71.73 414	65.06 441	39.59 443	51.43 438	57.73 446	38.34 408	82.58 386	39.53 431	73.95 364	64.62 442

ttmdpeth		59.91 399	57.10 403	68.34 408	67.13 445	46.65 432	74.64 405	67.41 435	48.30 431	62.52 413	85.04 315	20.40 445	75.93 424	42.55 426	45.90 446	82.44 407

DSMNet-mixed		57.77 402	56.90 404	60.38 422	67.70 443	35.61 453	69.18 426	53.97 454	32.30 452	57.49 429	79.88 395	40.39 398	68.57 446	38.78 434	72.37 377	76.97 428

WB-MVS		54.94 404	54.72 405	55.60 430	73.50 429	20.90 464	74.27 408	61.19 447	59.16 389	50.61 439	74.15 427	47.19 343	75.78 426	17.31 455	35.07 449	70.12 437

pmmvs3		57.79 401	54.26 406	68.37 407	64.02 449	56.72 357	75.12 402	65.17 440	40.20 441	52.93 437	69.86 437	20.36 446	75.48 428	45.45 419	55.25 434	72.90 435

SSC-MVS		53.88 407	53.59 407	54.75 432	72.87 435	19.59 465	73.84 410	60.53 449	57.58 405	49.18 443	73.45 430	46.34 354	75.47 429	16.20 458	32.28 451	69.20 438

N_pmnet		52.79 410	53.26 408	51.40 434	78.99 405	7.68 468	69.52 424	3.89 467	51.63 426	57.01 430	74.98 426	40.83 395	65.96 449	37.78 435	64.67 411	80.56 421

MVStest1		56.63 403	52.76 409	68.25 409	61.67 451	53.25 400	71.67 415	68.90 433	38.59 444	50.59 440	83.05 356	25.08 437	70.66 441	36.76 437	38.56 447	80.83 418

FPMVS		53.68 408	51.64 410	59.81 423	65.08 447	51.03 414	69.48 425	69.58 429	41.46 440	40.67 447	72.32 432	16.46 451	70.00 444	24.24 451	65.42 409	58.40 447

mvsany_test3		53.99 406	51.45 411	61.61 421	55.51 455	44.74 440	63.52 445	45.41 460	43.69 438	58.11 427	76.45 419	17.99 448	63.76 451	54.77 364	47.59 442	76.34 430

test_f		52.09 411	50.82 412	55.90 428	53.82 458	42.31 447	59.42 448	58.31 452	36.45 447	56.12 434	70.96 435	12.18 454	57.79 454	53.51 371	56.57 429	67.60 439

new_pmnet		50.91 413	50.29 413	52.78 433	68.58 442	34.94 455	63.71 444	56.63 453	39.73 442	44.95 444	65.47 439	21.93 444	58.48 453	34.98 439	56.62 428	64.92 441

APD_test1		53.31 409	49.93 414	63.42 419	65.68 446	50.13 419	71.59 416	66.90 437	34.43 449	40.58 448	71.56 434	8.65 460	76.27 420	34.64 440	55.36 432	63.86 443

LCM-MVSNet		54.25 405	49.68 415	67.97 411	53.73 459	45.28 436	66.85 435	80.78 362	35.96 448	39.45 449	62.23 442	8.70 459	78.06 409	48.24 404	51.20 439	80.57 420

EGC-MVSNET		52.07 412	47.05 416	67.14 412	83.51 330	60.71 306	80.50 342	67.75 434	0.07 462	0.43 463	75.85 424	24.26 440	81.54 392	28.82 445	62.25 417	59.16 445

test_vis3_rt		49.26 415	47.02 417	56.00 427	54.30 456	45.27 437	66.76 436	48.08 457	36.83 446	44.38 445	53.20 450	7.17 462	64.07 450	56.77 354	55.66 430	58.65 446

ANet_high		50.57 414	46.10 418	63.99 417	48.67 462	39.13 450	70.99 419	80.85 361	61.39 371	31.18 451	57.70 447	17.02 450	73.65 438	31.22 444	15.89 459	79.18 424

dongtai		45.42 418	45.38 419	45.55 436	73.36 432	26.85 460	67.72 431	34.19 462	54.15 418	49.65 442	56.41 449	25.43 436	62.94 452	19.45 453	28.09 453	46.86 452

testf1		45.72 416	41.96 420	57.00 425	56.90 453	45.32 434	66.14 437	59.26 450	26.19 453	30.89 452	60.96 444	4.14 463	70.64 442	26.39 449	46.73 444	55.04 448

APD_test2		45.72 416	41.96 420	57.00 425	56.90 453	45.32 434	66.14 437	59.26 450	26.19 453	30.89 452	60.96 444	4.14 463	70.64 442	26.39 449	46.73 444	55.04 448

Gipuma		45.18 419	41.86 422	55.16 431	77.03 414	51.52 410	32.50 455	80.52 366	32.46 451	27.12 454	35.02 455	9.52 458	75.50 427	22.31 452	60.21 424	38.45 454
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		39.70 422	40.40 423	37.58 439	64.52 448	26.98 458	65.62 439	33.02 463	46.12 434	42.79 446	48.99 452	24.10 441	46.56 460	12.16 461	26.30 454	39.20 453

PMVS	37.38 22	44.16 420	40.28 424	55.82 429	40.82 464	42.54 446	65.12 441	63.99 444	34.43 449	24.48 455	57.12 448	3.92 465	76.17 422	17.10 456	55.52 431	48.75 450
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 421	38.86 425	46.69 435	53.84 457	16.45 466	48.61 452	49.92 455	37.49 445	31.67 450	60.97 443	8.14 461	56.42 455	28.42 446	30.72 452	67.19 440

E-PMN		31.77 423	30.64 426	35.15 440	52.87 460	27.67 457	57.09 450	47.86 458	24.64 455	16.40 460	33.05 456	11.23 456	54.90 456	14.46 459	18.15 457	22.87 456

EMVS		30.81 425	29.65 427	34.27 441	50.96 461	25.95 461	56.58 451	46.80 459	24.01 456	15.53 461	30.68 457	12.47 453	54.43 457	12.81 460	17.05 458	22.43 457

test_method		31.52 424	29.28 428	38.23 438	27.03 466	6.50 469	20.94 457	62.21 446	4.05 460	22.35 458	52.50 451	13.33 452	47.58 458	27.04 448	34.04 450	60.62 444

cdsmvs_eth3d_5k		19.96 427	26.61 429	0.00 447	0.00 470	0.00 472	0.00 458	89.26 203	0.00 465	0.00 466	88.61 212	61.62 187	0.00 466	0.00 465	0.00 464	0.00 462

MVE	26.22 23	30.37 426	25.89 430	43.81 437	44.55 463	35.46 454	28.87 456	39.07 461	18.20 457	18.58 459	40.18 454	2.68 466	47.37 459	17.07 457	23.78 456	48.60 451
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		18.61 428	21.40 431	10.23 444	4.82 467	10.11 467	34.70 454	30.74 465	1.48 461	23.91 457	26.07 458	28.42 433	13.41 463	27.12 447	15.35 460	7.17 458

wuyk23d		16.82 429	15.94 432	19.46 443	58.74 452	31.45 456	39.22 453	3.74 468	6.84 459	6.04 462	2.70 462	1.27 467	24.29 462	10.54 462	14.40 461	2.63 459

ab-mvs-re		7.23 430	9.64 433	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	86.72 265	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

test123		6.12 431	8.11 434	0.14 445	0.06 469	0.09 470	71.05 418	0.03 470	0.04 464	0.25 465	1.30 464	0.05 468	0.03 465	0.21 464	0.01 463	0.29 460

testmvs		6.04 432	8.02 435	0.10 446	0.08 468	0.03 471	69.74 423	0.04 469	0.05 463	0.31 464	1.68 463	0.02 469	0.04 464	0.24 463	0.02 462	0.25 461

pcd_1.5k_mvsjas		5.26 433	7.02 436	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	63.15 161	0.00 466	0.00 465	0.00 464	0.00 462

mmdepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

monomultidepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

test_blank		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uanet_test		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

DCPMVS		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

sosnet-low-res		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

sosnet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uncertanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

Regformer		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

uanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 472	0.00 458	0.00 471	0.00 465	0.00 466	0.00 465	0.00 470	0.00 466	0.00 465	0.00 464	0.00 462

WAC-MVS						42.58 444								39.46 432

FOURS1					95.00 10	72.39 41	95.06 1	93.84 16	74.49 136	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 50					97.53 2	89.67 14	96.44 9	94.41 43

PC_three_1452									68.21 288	92.02 12	94.00 57	82.09 5	95.98 57	84.58 65	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 14	96.44 9	94.41 43

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					0.00 470

eth-test					0.00 470

ZD-MVS					94.38 25	72.22 46		92.67 68	70.98 217	87.75 45	94.07 52	74.01 33	96.70 27	84.66 64	94.84 44

IU-MVS					95.30 2	71.25 61		92.95 56	66.81 300	92.39 6				88.94 26	96.63 4	94.85 21

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 55	82.45 3	96.87 20	83.77 76	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 23	96.58 6	94.26 53

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	91.17 133	74.31 141

test_0728_THIRD									78.38 38	92.12 9	95.78 4	81.46 7	97.40 9	89.42 18	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 21	96.41 12	94.21 54

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																88.96 286

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 302				88.96 286

sam_mvs												50.01 318

ambc				75.24 353	73.16 433	50.51 418	63.05 447	87.47 262		64.28 401	77.81 413	17.80 449	89.73 303	57.88 341	60.64 422	85.49 367

MTGPA								92.02 98

test_post1							78.90 366				5.43 461	48.81 337	85.44 364	59.25 325

test_post											5.46 460	50.36 314	84.24 372

patchmatchnet-post											74.00 428	51.12 305	88.60 326

GG-mvs-BLEND				75.38 351	81.59 370	55.80 373	79.32 357	69.63 428		67.19 374	73.67 429	43.24 379	88.90 322	50.41 386	84.50 214	81.45 414

MTMP							92.18 35	32.83 464

gm-plane-assit					81.40 374	53.83 393			62.72 359		80.94 382		92.39 222	63.40 286

test9_res														84.90 58	95.70 26	92.87 129

TEST9					93.26 52	72.96 25	88.75 131	91.89 106	68.44 285	85.00 74	93.10 82	74.36 29	95.41 76

test_8					93.13 56	72.57 35	88.68 136	91.84 110	68.69 280	84.87 78	93.10 82	74.43 27	95.16 86

agg_prior2														82.91 85	95.45 29	92.70 134

agg_prior					92.85 64	71.94 52		91.78 113		84.41 89			94.93 97

TestCases				79.58 286	85.15 292	63.62 251		79.83 377	62.31 362	60.32 419	86.73 263	32.02 425	88.96 320	50.28 389	71.57 385	86.15 355

test_prior4						72.60 34	89.01 118

test_prior2							88.85 125		75.41 108	84.91 76	93.54 70	74.28 30		83.31 79	95.86 20

test_prior				86.33 60	92.61 70	69.59 94		92.97 55					95.48 70			93.91 68

旧先验2							86.56 215		58.10 400	87.04 56			88.98 318	74.07 181

新几何2							86.29 225

新几何1				83.42 174	93.13 56	70.71 76		85.48 298	57.43 406	81.80 132	91.98 108	63.28 155	92.27 228	64.60 278	92.99 72	87.27 331

旧先验1					91.96 76	65.79 200		86.37 285			93.08 86	69.31 89			92.74 76	88.74 297

无先验							87.48 178	88.98 218	60.00 381				94.12 134	67.28 255		88.97 285

原ACMM2							86.86 202

原ACMM1				84.35 125	93.01 62	68.79 113		92.44 78	63.96 345	81.09 143	91.57 124	66.06 131	95.45 71	67.19 257	94.82 46	88.81 292

test222					91.50 82	68.26 133	84.16 283	83.20 333	54.63 417	79.74 162	91.63 121	58.97 223			91.42 97	86.77 345

testdata2													91.01 281	62.37 296

segment_acmp												73.08 40

testdata				79.97 276	90.90 94	64.21 240		84.71 306	59.27 388	85.40 69	92.91 88	62.02 180	89.08 316	68.95 240	91.37 99	86.63 349

testdata1							84.14 284		75.71 100

test12				86.80 54	92.63 69	70.70 77		91.79 112		82.71 121		71.67 59	96.16 48		94.50 53	93.54 96

plane_prior7					90.08 112	68.51 127

plane_prior6					89.84 121	68.70 121						60.42 213

plane_prior5								92.44 78					95.38 78	78.71 127	86.32 183	91.33 189

plane_prior4											91.00 146

plane_prior3						68.60 124			78.44 36	78.92 177

plane_prior2							91.25 55		79.12 28

plane_prior1					89.90 120

plane_prior						68.71 119	90.38 73		77.62 47						86.16 187

n2								0.00 471

nn								0.00 471

door-mid								69.98 427

lessismore_v0				78.97 296	81.01 381	57.15 351		65.99 438		61.16 416	82.82 362	39.12 403	91.34 268	59.67 321	46.92 443	88.43 305

LGP-MVS_train				84.50 118	89.23 148	68.76 115		91.94 104	75.37 110	76.64 233	91.51 125	54.29 265	94.91 98	78.44 129	83.78 227	89.83 257

test11								92.23 88

door								69.44 430

HQP5-MVS						66.98 177

HQP-NCC					89.33 140		89.17 109		76.41 85	77.23 218

ACMP_Plane					89.33 140		89.17 109		76.41 85	77.23 218

BP-MVS														77.47 141

HQP4-MVS										77.24 217			95.11 90			91.03 199

HQP3-MVS								92.19 92							85.99 191

HQP2-MVS												60.17 216

NP-MVS					89.62 125	68.32 131					90.24 162

MDTV_nov1_ep13_2view						37.79 452	75.16 400		55.10 415	66.53 384		49.34 328		53.98 368		87.94 314

ACMMP++_ref															81.95 260

ACMMP++															81.25 265

Test By Simon												64.33 147

ITE_SJBPF				78.22 312	81.77 367	60.57 308		83.30 328	69.25 265	67.54 368	87.20 254	36.33 417	87.28 343	54.34 366	74.62 359	86.80 344

DeepMVS_CX				27.40 442	40.17 465	26.90 459		24.59 466	17.44 458	23.95 456	48.61 453	9.77 457	26.48 461	18.06 454	24.47 455	28.83 455

High-resolution multi-view benchmark