High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 68	88.18 1	87.15 3	65.04 17	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 32

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 78	87.82 7	86.78 10	64.18 35	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 40

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 32	62.73 9	86.09 22	86.83 8	65.51 13	83.81 10	90.51 30	63.71 13	89.23 25	81.51 2	88.44 30	88.09 47
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DVP-MVS		80.84 4	81.64 3	78.42 38	87.75 7	59.07 73	87.85 5	85.03 42	64.26 32	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 167
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 31	86.42 15	63.28 52	83.27 15	91.83 10	64.96 7	90.47 11	76.41 40	89.67 18	86.84 97
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS		80.40 6	80.84 6	79.07 25	85.30 50	59.25 64	86.84 11	85.86 23	63.31 49	83.65 12	91.48 12	64.70 10	89.91 16	77.02 34	89.43 22	88.06 50

SMA-MVS		80.28 7	80.39 8	79.95 4	86.60 24	61.95 19	86.33 17	85.75 27	62.49 72	82.20 19	92.28 1	56.53 44	89.70 20	79.85 6	91.48 1	88.19 42
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MM		80.20 8	80.28 10	79.99 2	82.19 90	60.01 49	86.19 21	83.93 61	73.19 1	77.08 45	91.21 20	57.23 39	90.73 10	83.35 1	88.12 37	89.22 9

APDe-MVS		80.16 9	80.59 7	78.86 33	86.64 21	60.02 48	88.12 3	86.42 15	62.94 61	82.40 16	92.12 2	59.64 23	89.76 19	78.70 15	88.32 34	86.79 99
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ME-MVS		80.04 10	80.36 9	79.08 24	86.63 23	59.25 64	85.62 32	86.73 12	63.10 58	82.27 18	90.57 27	61.90 16	89.88 18	77.02 34	89.43 22	88.10 45

HPM-MVS++		79.88 11	80.14 11	79.10 21	88.17 1	64.80 1	86.59 16	83.70 80	65.37 14	78.78 29	90.64 24	58.63 31	87.24 60	79.00 14	90.37 14	85.26 179

CNVR-MVS		79.84 12	79.97 12	79.45 11	87.90 2	62.17 17	84.37 45	85.03 42	66.96 5	77.58 39	90.06 45	59.47 25	89.13 27	78.67 17	89.73 16	87.03 90

TestfortrainingZip a		79.61 13	79.84 13	78.92 30	85.30 50	59.08 72	86.84 11	86.01 20	63.31 49	82.37 17	91.48 12	60.88 18	89.61 21	76.25 43	86.13 65	88.06 50

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 81	62.18 16	87.60 9	85.83 25	66.69 10	78.03 36	90.98 21	54.26 76	90.06 14	78.42 23	89.02 26	87.69 62
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 60	60.32 46	83.03 68	85.33 34	62.86 64	80.17 21	90.03 47	61.76 17	88.95 29	74.21 62	88.67 29	88.12 44

SF-MVS		78.82 16	79.22 15	77.60 52	82.88 83	57.83 91	84.99 37	88.13 2	61.86 90	79.16 26	90.75 23	57.96 32	87.09 69	77.08 33	90.18 15	87.87 54

ZNCC-MVS		78.82 16	78.67 19	79.30 14	86.43 29	62.05 18	86.62 15	86.01 20	63.32 48	75.08 62	90.47 33	53.96 83	88.68 32	76.48 39	89.63 20	87.16 87

ACMMP_NAP		78.77 18	78.78 17	78.74 34	85.44 46	61.04 31	83.84 60	85.16 37	62.88 63	78.10 34	91.26 19	52.51 108	88.39 35	79.34 9	90.52 13	86.78 100

NCCC		78.58 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 52	66.73 8	74.67 76	89.38 58	55.30 65	89.18 26	74.19 63	87.34 49	86.38 117

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 71	61.62 23	84.17 53	86.85 6	63.23 54	73.84 94	90.25 40	57.68 35	89.96 15	74.62 60	89.03 25	87.89 52
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MGCNet		78.45 21	78.28 22	78.98 29	80.73 115	57.91 90	84.68 41	81.64 133	68.35 2	75.77 51	90.38 34	53.98 81	90.26 13	81.30 3	87.68 45	88.77 19

TSAR-MVS + MP.		78.44 22	78.28 22	78.90 31	84.96 56	61.41 26	84.03 56	83.82 75	59.34 155	79.37 25	89.76 54	59.84 20	87.62 57	76.69 37	86.74 58	87.68 63
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS-pluss		78.35 23	78.46 20	78.03 45	84.96 56	59.52 58	82.93 70	85.39 33	62.15 82	76.41 49	91.51 11	52.47 110	86.78 76	80.66 4	89.64 19	87.80 58
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		78.35 23	78.26 24	78.64 35	86.54 26	63.47 4	86.02 24	83.55 86	63.89 40	73.60 97	90.60 25	54.85 71	86.72 77	77.20 31	88.06 39	85.74 153
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		78.14 25	77.85 27	78.99 28	86.05 39	61.82 22	85.84 26	85.21 36	63.56 44	74.29 82	90.03 47	52.56 107	88.53 34	74.79 59	88.34 32	86.63 109

APD-MVS		78.02 26	78.04 26	77.98 46	86.44 28	60.81 38	85.52 33	84.36 53	60.61 116	79.05 27	90.30 38	55.54 64	88.32 37	73.48 70	87.03 51	84.83 194
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 27	77.65 29	79.10 21	86.71 19	62.81 8	86.29 18	84.32 54	62.82 65	73.96 87	90.50 31	53.20 98	88.35 36	74.02 65	87.05 50	86.13 133

lecture		77.75 28	77.84 28	77.50 54	82.75 85	57.62 94	85.92 25	86.20 18	60.53 118	78.99 28	91.45 14	51.51 129	87.78 52	75.65 49	87.55 46	87.10 89

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 55	62.82 65	73.55 99	90.56 29	49.80 154	88.24 38	74.02 65	87.03 51	86.32 126

SD-MVS		77.70 30	77.62 30	77.93 47	84.47 64	61.88 21	84.55 43	83.87 68	60.37 125	79.89 22	89.38 58	54.97 69	85.58 115	76.12 45	84.94 71	86.33 124
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
region2R		77.67 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 57	62.81 67	73.30 104	90.58 26	49.90 151	88.21 39	73.78 67	87.03 51	86.29 130

MCST-MVS		77.48 32	77.45 31	77.54 53	86.67 20	58.36 85	83.22 66	86.93 5	56.91 208	74.91 67	88.19 77	59.15 28	87.68 56	73.67 68	87.45 48	86.57 110

HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 55	60.81 38	82.91 71	85.08 39	62.57 70	73.09 115	89.97 50	50.90 140	87.48 58	75.30 53	86.85 56	87.33 82
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS_fast	68.24 3	77.25 34	76.63 37	79.12 20	86.15 35	60.86 36	84.71 40	84.85 46	61.98 89	73.06 116	88.88 67	53.72 89	89.06 28	68.27 106	88.04 40	87.42 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		77.17 35	76.56 40	79.00 26	86.32 30	62.62 11	85.83 27	83.92 62	64.55 26	72.17 133	90.01 49	47.95 179	88.01 45	71.55 89	86.74 58	86.37 119

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 89	62.44 74	72.68 125	90.50 31	48.18 177	87.34 59	73.59 69	85.71 67	84.76 198

CSCG		76.92 37	76.75 35	77.41 56	83.96 69	59.60 56	82.95 69	86.50 14	60.78 112	75.27 57	84.83 182	60.76 19	86.56 83	67.86 118	87.87 44	86.06 135

reproduce-ours		76.90 38	76.58 38	77.87 48	83.99 67	60.46 43	84.75 38	83.34 94	60.22 132	77.85 37	91.42 16	50.67 141	87.69 54	72.46 76	84.53 75	85.46 165

our_new_method		76.90 38	76.58 38	77.87 48	83.99 67	60.46 43	84.75 38	83.34 94	60.22 132	77.85 37	91.42 16	50.67 141	87.69 54	72.46 76	84.53 75	85.46 165

MTAPA		76.90 38	76.42 43	78.35 39	86.08 38	63.57 2	74.92 256	80.97 159	65.13 16	75.77 51	90.88 22	48.63 172	86.66 79	77.23 30	88.17 36	84.81 195

PGM-MVS		76.77 41	76.06 47	78.88 32	86.14 36	62.73 9	82.55 78	83.74 77	61.71 91	72.45 131	90.34 37	48.48 175	88.13 42	72.32 78	86.85 56	85.78 147

cashybrid2		76.62 42	76.52 42	76.90 62	77.91 197	53.66 165	80.76 103	84.47 49	66.73 8	75.75 53	88.63 74	59.17 27	86.66 79	72.28 79	83.01 91	90.39 1

BridgeMVS		76.58 43	76.55 41	76.68 68	81.73 96	52.90 188	80.94 99	85.70 29	61.12 105	74.90 68	87.17 112	56.46 45	88.14 41	72.87 73	88.03 41	89.00 12

mPP-MVS		76.54 44	75.93 49	78.34 40	86.47 27	63.50 3	85.74 30	82.28 123	62.90 62	71.77 138	90.26 39	46.61 203	86.55 86	71.71 87	85.66 68	84.97 190

CANet		76.46 45	75.93 49	78.06 43	81.29 105	57.53 96	82.35 80	83.31 97	67.78 3	70.09 162	86.34 143	54.92 70	88.90 30	72.68 75	84.55 74	87.76 60

reproduce_model		76.43 46	76.08 46	77.49 55	83.47 75	60.09 47	84.60 42	82.90 114	59.65 145	77.31 40	91.43 15	49.62 157	87.24 60	71.99 83	83.75 87	85.14 181

CDPH-MVS		76.31 47	75.67 54	78.22 41	85.35 49	59.14 70	81.31 96	84.02 58	56.32 225	74.05 85	88.98 63	53.34 95	87.92 48	69.23 102	88.42 31	87.59 68

train_agg		76.27 48	76.15 45	76.64 71	85.58 44	61.59 24	81.62 91	81.26 148	55.86 233	74.93 65	88.81 68	53.70 90	84.68 139	75.24 55	88.33 33	83.65 241

NormalMVS		76.26 49	75.74 52	77.83 50	82.75 85	59.89 52	84.36 46	83.21 102	64.69 23	74.21 83	87.40 97	49.48 158	86.17 98	68.04 115	87.55 46	87.42 74

CS-MVS		76.25 50	75.98 48	77.06 61	80.15 129	55.63 131	84.51 44	83.90 64	63.24 53	73.30 104	87.27 104	55.06 67	86.30 95	71.78 86	84.58 73	89.25 8

casdiffmvs_mvg		76.14 51	76.30 44	75.66 89	76.46 258	51.83 219	79.67 122	85.08 39	65.02 20	75.84 50	88.58 75	59.42 26	85.08 127	72.75 74	83.93 83	90.08 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
SR-MVS		76.13 52	75.70 53	77.40 58	85.87 41	61.20 29	85.52 33	82.19 124	59.99 138	75.10 61	90.35 36	47.66 184	86.52 87	71.64 88	82.99 93	84.47 207

ACMMP		76.02 53	75.33 57	78.07 42	85.20 53	61.91 20	85.49 35	84.44 51	63.04 59	69.80 172	89.74 55	45.43 217	87.16 66	72.01 82	82.87 99	85.14 181
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
PHI-MVS		75.87 54	75.36 56	77.41 56	80.62 120	55.91 124	84.28 50	85.78 26	56.08 231	73.41 100	86.58 134	50.94 139	88.54 33	70.79 94	89.71 17	87.79 59

EC-MVSNet		75.84 55	75.87 51	75.74 87	78.86 159	52.65 197	83.73 61	86.08 19	63.47 46	72.77 124	87.25 109	53.13 99	87.93 47	71.97 84	85.57 69	86.66 107

3Dnovator+	66.72 4	75.84 55	74.57 68	79.66 9	82.40 87	59.92 51	85.83 27	86.32 17	66.92 7	67.80 219	89.24 60	42.03 256	89.38 24	64.07 160	86.50 62	89.69 4

MVSMamba_PlusPlus		75.75 57	75.44 55	76.67 69	80.84 113	53.06 185	78.62 140	85.13 38	59.65 145	71.53 144	87.47 95	56.92 41	88.17 40	72.18 81	86.63 61	88.80 16

SPE-MVS-test		75.62 58	75.31 58	76.56 73	80.63 119	55.13 142	83.88 59	85.22 35	62.05 86	71.49 145	86.03 154	53.83 85	86.36 93	67.74 120	86.91 55	88.19 42

DPM-MVS		75.47 59	75.00 62	76.88 63	81.38 104	59.16 67	79.94 115	85.71 28	56.59 219	72.46 129	86.76 121	56.89 42	87.86 50	66.36 140	88.91 28	83.64 242

SymmetryMVS		75.28 60	74.60 67	77.30 59	83.85 70	59.89 52	84.36 46	75.51 284	64.69 23	74.21 83	87.40 97	49.48 158	86.17 98	68.04 115	83.88 84	85.85 144

fmvsm_s_conf0.5_n_9		75.16 61	75.22 60	75.01 102	78.34 180	55.37 139	77.30 188	73.95 316	61.40 97	79.46 23	90.14 41	57.07 40	81.15 231	80.00 5	79.31 154	88.51 31

APD-MVS_3200maxsize		74.96 62	74.39 70	76.67 69	82.20 89	58.24 86	83.67 62	83.29 98	58.41 173	73.71 95	90.14 41	45.62 210	85.99 105	69.64 98	82.85 100	85.78 147

TSAR-MVS + GP.		74.90 63	74.15 74	77.17 60	82.00 92	58.77 81	81.80 88	78.57 209	58.58 170	74.32 81	84.51 198	55.94 61	87.22 63	67.11 130	84.48 78	85.52 161

hybridcas		74.86 64	75.07 61	74.24 129	76.30 259	50.58 241	79.30 128	83.88 67	63.15 57	74.69 74	88.13 79	58.91 29	82.98 177	68.30 105	82.93 96	89.15 11

casdiffmvs		74.80 65	74.89 65	74.53 119	75.59 273	50.37 250	78.17 157	85.06 41	62.80 68	74.40 79	87.86 88	57.88 33	83.61 160	69.46 101	82.79 101	89.59 5
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DELS-MVS		74.76 66	74.46 69	75.65 90	77.84 200	52.25 208	75.59 238	84.17 56	63.76 41	73.15 110	82.79 234	59.58 24	86.80 75	67.24 128	86.04 66	87.89 52
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
OPM-MVS		74.73 67	74.25 73	76.19 78	80.81 114	59.01 76	82.60 77	83.64 83	63.74 42	72.52 128	87.49 94	47.18 194	85.88 108	69.47 100	80.78 121	83.66 240
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
sasdasda		74.67 68	74.98 63	73.71 158	78.94 157	50.56 244	80.23 108	83.87 68	60.30 129	77.15 42	86.56 135	59.65 21	82.00 211	66.01 144	82.12 105	88.58 29

canonicalmvs		74.67 68	74.98 63	73.71 158	78.94 157	50.56 244	80.23 108	83.87 68	60.30 129	77.15 42	86.56 135	59.65 21	82.00 211	66.01 144	82.12 105	88.58 29

baseline		74.61 70	74.70 66	74.34 124	75.70 268	49.99 260	77.54 178	84.63 48	62.73 69	73.98 86	87.79 91	57.67 36	83.82 156	69.49 99	82.74 102	89.20 10

SR-MVS-dyc-post		74.57 71	73.90 81	76.58 72	83.49 73	59.87 54	84.29 48	81.36 141	58.07 179	73.14 111	90.07 43	44.74 227	85.84 109	68.20 107	81.76 112	84.03 219

dcpmvs_2		74.55 72	75.23 59	72.48 197	82.34 88	53.34 177	77.87 166	81.46 137	57.80 190	75.49 54	86.81 120	62.22 14	77.75 319	71.09 92	82.02 108	86.34 121

ETV-MVS		74.46 73	73.84 83	76.33 76	79.27 147	55.24 141	79.22 129	85.00 44	64.97 22	72.65 126	79.46 317	53.65 93	87.87 49	67.45 127	82.91 97	85.89 141

HQP_MVS		74.31 74	73.73 85	76.06 79	81.41 102	56.31 113	84.22 51	84.01 59	64.52 28	69.27 181	86.10 151	45.26 221	87.21 64	68.16 111	80.58 127	84.65 199

fmvsm_s_conf0.5_n_8		74.30 75	74.39 70	74.01 143	75.33 280	52.89 190	78.24 149	77.32 242	61.65 92	78.13 33	88.90 66	52.82 104	81.54 221	78.46 22	78.67 176	87.60 67

HPM-MVS_fast		74.30 75	73.46 91	76.80 65	84.45 65	59.04 75	83.65 63	81.05 156	60.15 134	70.43 157	89.84 52	41.09 279	85.59 114	67.61 123	82.90 98	85.77 150

fmvsm_s_conf0.5_n_10		74.11 77	73.98 80	74.48 121	74.61 300	52.86 192	78.10 161	77.06 247	57.14 200	78.24 32	88.79 71	52.83 103	82.26 207	77.79 28	81.30 117	88.32 35

E5new		74.10 78	74.09 75	74.15 135	77.14 229	50.74 234	78.24 149	83.86 71	62.34 76	73.95 88	87.27 104	55.97 59	82.95 180	68.16 111	79.86 138	88.77 19

E6new		74.10 78	74.09 75	74.15 135	77.14 229	50.74 234	78.24 149	83.85 73	62.34 76	73.95 88	87.27 104	55.98 57	82.95 180	68.17 109	79.85 140	88.77 19

E6		74.10 78	74.09 75	74.15 135	77.14 229	50.74 234	78.24 149	83.85 73	62.34 76	73.95 88	87.27 104	55.98 57	82.95 180	68.17 109	79.85 140	88.77 19

E5		74.10 78	74.09 75	74.15 135	77.14 229	50.74 234	78.24 149	83.86 71	62.34 76	73.95 88	87.27 104	55.97 59	82.95 180	68.16 111	79.86 138	88.77 19

MVS_111021_HR		74.02 82	73.46 91	75.69 88	83.01 81	60.63 40	77.29 189	78.40 220	61.18 103	70.58 155	85.97 157	54.18 78	84.00 153	67.52 124	82.98 95	82.45 275

MG-MVS		73.96 83	73.89 82	74.16 133	85.65 43	49.69 269	81.59 93	81.29 147	61.45 96	71.05 149	88.11 80	51.77 124	87.73 53	61.05 200	83.09 90	85.05 186

E4		73.91 84	73.83 84	74.15 135	77.13 233	50.47 247	77.15 195	83.79 76	62.21 81	73.61 96	87.19 111	56.08 55	83.03 172	67.91 117	79.35 152	88.94 14

alignmvs		73.86 85	73.99 79	73.45 172	78.20 184	50.50 246	78.57 142	82.43 121	59.40 153	76.57 47	86.71 127	56.42 47	81.23 230	65.84 147	81.79 111	88.62 26

MSLP-MVS++		73.77 86	73.47 90	74.66 111	83.02 80	59.29 63	82.30 85	81.88 128	59.34 155	71.59 142	86.83 119	45.94 208	83.65 159	65.09 153	85.22 70	81.06 308

casdiffseed414692147		73.73 87	73.22 96	75.28 99	76.76 249	52.16 210	80.05 112	83.01 111	63.38 47	73.35 103	87.11 113	53.22 96	84.14 147	61.71 194	80.38 131	89.55 6

E2		73.72 88	73.60 88	74.06 140	77.16 227	50.40 248	76.97 200	83.74 77	61.64 93	73.36 101	86.75 124	56.14 51	82.99 174	67.50 125	79.18 162	88.80 16

E3		73.72 88	73.60 88	74.06 140	77.16 227	50.40 248	76.97 200	83.74 77	61.64 93	73.36 101	86.76 121	56.13 52	82.99 174	67.50 125	79.18 162	88.80 16

viewcassd2359sk11		73.56 90	73.41 93	74.00 144	77.13 233	50.35 251	76.86 207	83.69 81	61.23 102	73.14 111	86.38 142	56.09 54	82.96 178	67.15 129	79.01 167	88.70 25

fmvsm_s_conf0.5_n_3		73.55 91	74.39 70	71.03 249	74.09 318	51.86 218	77.77 172	75.60 280	61.18 103	78.67 30	88.98 63	55.88 62	77.73 320	78.69 16	78.68 175	83.50 245

HQP-MVS		73.45 92	72.80 104	75.40 94	80.66 116	54.94 144	82.31 82	83.90 64	62.10 83	67.85 213	85.54 173	45.46 215	86.93 72	67.04 132	80.35 132	84.32 209

viewdifsd2359ckpt09		73.42 93	72.45 111	76.30 77	77.25 225	53.27 179	80.36 107	82.48 120	57.96 184	72.24 132	85.73 167	53.22 96	86.27 96	63.79 170	79.06 166	89.36 7

E3new		73.41 94	73.22 96	73.95 147	77.06 238	50.31 252	76.78 210	83.66 82	60.90 108	72.93 119	86.02 155	55.99 56	82.95 180	66.89 137	78.77 172	88.61 27

BP-MVS1		73.41 94	72.25 113	76.88 63	76.68 251	53.70 163	79.15 130	81.07 155	60.66 115	71.81 137	87.39 99	40.93 280	87.24 60	71.23 91	81.29 118	89.71 3

CLD-MVS		73.33 96	72.68 106	75.29 98	78.82 161	53.33 178	78.23 154	84.79 47	61.30 100	70.41 159	81.04 283	52.41 111	87.12 67	64.61 159	82.49 104	85.41 171
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		73.31 97	72.54 109	75.62 91	77.87 198	53.64 166	79.62 124	79.61 181	61.63 95	72.02 136	82.61 239	56.44 46	85.97 106	63.99 163	79.07 165	87.25 84

fmvsm_l_conf0.5_n_9		73.27 98	73.66 87	72.09 206	73.82 319	52.72 196	77.45 182	74.28 309	56.61 218	77.10 44	88.16 78	56.17 50	77.09 335	78.27 24	81.13 119	86.48 114

fmvsm_l_conf0.5_n_3		73.23 99	73.13 99	73.55 168	74.40 307	55.13 142	78.97 132	74.96 299	56.64 212	74.76 73	88.75 72	55.02 68	78.77 299	76.33 41	78.31 186	86.74 102

fmvsm_s_conf0.5_n_11		73.16 100	73.35 94	72.58 192	75.48 275	52.41 207	78.84 134	76.85 252	58.64 168	73.58 98	87.25 109	54.09 80	79.47 270	76.19 44	79.27 155	85.86 143

viewmacassd2359aftdt		73.15 101	73.16 98	73.11 181	75.15 286	49.31 276	77.53 180	83.21 102	60.42 121	73.20 108	87.34 101	53.82 86	81.05 236	67.02 134	80.79 120	88.96 13

UA-Net		73.13 102	72.93 101	73.76 153	83.58 72	51.66 221	78.75 135	77.66 232	67.75 4	72.61 127	89.42 56	49.82 153	83.29 167	53.61 268	83.14 89	86.32 126

EPNet		73.09 103	72.16 114	75.90 81	75.95 265	56.28 115	83.05 67	72.39 335	66.53 11	65.27 271	87.00 115	50.40 145	85.47 120	62.48 186	86.32 64	85.94 138
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		73.01 104	72.59 107	74.27 127	71.28 373	55.88 125	78.21 156	75.56 282	54.31 281	74.86 69	87.80 90	54.72 72	80.23 258	78.07 26	78.48 181	86.70 103

balanced_ft_v1		72.98 105	72.55 108	74.27 127	79.52 141	50.64 239	77.78 171	83.29 98	56.76 209	67.88 212	85.95 158	49.42 161	85.29 125	68.64 104	83.76 86	86.87 95

nrg030		72.96 106	73.01 100	72.84 187	75.41 278	50.24 253	80.02 113	82.89 116	58.36 175	74.44 78	86.73 125	58.90 30	80.83 243	65.84 147	74.46 244	87.44 73

viewmanbaseed2359cas		72.92 107	72.89 102	73.00 183	75.16 284	49.25 279	77.25 192	83.11 110	59.52 152	72.93 119	86.63 130	54.11 79	80.98 237	66.63 138	80.67 124	88.76 24

test_fmvsmconf0.1_n		72.81 108	72.33 112	74.24 129	69.89 397	55.81 126	78.22 155	75.40 287	54.17 283	75.00 64	88.03 86	53.82 86	80.23 258	78.08 25	78.34 185	86.69 104

CPTT-MVS		72.78 109	72.08 116	74.87 105	84.88 61	61.41 26	84.15 54	77.86 228	55.27 251	67.51 225	88.08 82	41.93 259	81.85 214	69.04 103	80.01 137	81.35 298

LPG-MVS_test		72.74 110	71.74 121	75.76 85	80.22 124	57.51 97	82.55 78	83.40 91	61.32 98	66.67 243	87.33 102	39.15 300	86.59 81	67.70 121	77.30 204	83.19 253

h-mvs33		72.71 111	71.49 125	76.40 74	81.99 93	59.58 57	76.92 204	76.74 258	60.40 122	74.81 70	85.95 158	45.54 213	85.76 111	70.41 96	70.61 310	83.86 229

fmvsm_s_conf0.5_n_5		72.69 112	72.80 104	72.37 202	74.11 317	53.21 181	78.12 158	73.31 323	53.98 286	76.81 46	88.05 83	53.38 94	77.37 330	76.64 38	80.78 121	86.53 112

GDP-MVS		72.64 113	71.28 132	76.70 66	77.72 204	54.22 155	79.57 125	84.45 50	55.30 250	71.38 146	86.97 116	39.94 286	87.00 71	67.02 134	79.20 159	88.89 15

PAPM_NR		72.63 114	71.80 119	75.13 100	81.72 97	53.42 176	79.91 117	83.28 100	59.14 157	66.31 250	85.90 160	51.86 121	86.06 102	57.45 233	80.62 125	85.91 140

fmvsm_s_conf0.5_n_6		72.59 115	72.87 103	71.73 217	75.14 287	51.96 216	76.28 220	77.12 245	57.63 194	73.85 93	86.91 117	51.54 128	77.87 316	77.18 32	80.18 136	85.37 173

VDD-MVS		72.50 116	72.09 115	73.75 155	81.58 98	49.69 269	77.76 173	77.63 233	63.21 55	73.21 107	89.02 62	42.14 255	83.32 166	61.72 193	82.50 103	88.25 38

3Dnovator	64.47 5	72.49 117	71.39 128	75.79 84	77.70 205	58.99 77	80.66 105	83.15 107	62.24 80	65.46 267	86.59 133	42.38 254	85.52 116	59.59 213	84.72 72	82.85 263

MGCFI-Net		72.45 118	73.34 95	69.81 276	77.77 202	43.21 361	75.84 235	81.18 152	59.59 150	75.45 55	86.64 128	57.74 34	77.94 311	63.92 164	81.90 110	88.30 36

MVS_Test		72.45 118	72.46 110	72.42 201	74.88 289	48.50 294	76.28 220	83.14 108	59.40 153	72.46 129	84.68 187	55.66 63	81.12 232	65.98 146	79.66 145	87.63 65

EI-MVSNet-Vis-set		72.42 120	71.59 122	74.91 103	78.47 173	54.02 157	77.05 198	79.33 187	65.03 19	71.68 140	79.35 320	52.75 105	84.89 134	66.46 139	74.23 248	85.83 146

viewdifsd2359ckpt13		72.40 121	71.79 120	74.22 131	75.63 270	51.77 220	78.67 138	83.13 109	57.08 201	71.59 142	85.36 177	53.10 100	82.64 198	63.07 180	78.51 180	88.24 39

ACMP	63.53 6	72.30 122	71.20 134	75.59 93	80.28 122	57.54 95	82.74 74	82.84 117	60.58 117	65.24 275	86.18 148	39.25 298	86.03 104	66.95 136	76.79 212	83.22 251
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 123	71.21 133	75.31 96	78.50 171	55.93 123	81.63 90	82.12 125	56.24 228	70.02 166	85.68 169	47.05 196	84.34 145	65.27 152	74.41 247	85.67 156

Vis-MVSNet		72.18 124	71.37 129	74.61 114	81.29 105	55.41 137	80.90 100	78.28 223	60.73 113	69.23 184	88.09 81	44.36 233	82.65 197	57.68 231	81.75 114	85.77 150
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		72.17 125	71.50 124	74.16 133	67.96 428	55.58 134	78.06 162	74.67 302	54.19 282	74.54 77	88.23 76	50.35 147	80.24 257	78.07 26	77.46 199	86.65 108

API-MVS		72.17 125	71.41 127	74.45 122	81.95 94	57.22 100	84.03 56	80.38 170	59.89 143	68.40 195	82.33 252	49.64 156	87.83 51	51.87 282	84.16 82	78.30 359

EPP-MVSNet		72.16 127	71.31 131	74.71 108	78.68 165	49.70 267	82.10 86	81.65 132	60.40 122	65.94 257	85.84 162	51.74 125	86.37 92	55.93 244	79.55 148	88.07 49

DP-MVS Recon		72.15 128	70.73 143	76.40 74	86.57 25	57.99 89	81.15 98	82.96 112	57.03 204	66.78 238	85.56 170	44.50 231	88.11 43	51.77 284	80.23 135	83.10 258

fmvsm_s_conf0.5_n_4		72.04 129	71.85 118	72.58 192	73.74 322	52.49 203	76.69 211	72.42 334	56.42 223	75.32 56	87.04 114	52.13 117	78.01 310	79.29 12	73.65 258	87.26 83

EI-MVSNet-UG-set		71.92 130	71.06 137	74.52 120	77.98 195	53.56 169	76.62 212	79.16 188	64.40 30	71.18 147	78.95 325	52.19 115	84.66 141	65.47 150	73.57 261	85.32 175

viewdifsd2359ckpt07		71.90 131	71.97 117	71.69 220	74.81 293	48.08 303	75.30 243	80.49 167	60.00 137	71.63 141	86.33 144	56.34 48	79.25 275	65.40 151	77.41 200	87.76 60

VDDNet		71.81 132	71.33 130	73.26 179	82.80 84	47.60 312	78.74 136	75.27 289	59.59 150	72.94 118	89.40 57	41.51 272	83.91 154	58.75 225	82.99 93	88.26 37

EIA-MVS		71.78 133	70.60 145	75.30 97	79.85 133	53.54 170	77.27 191	83.26 101	57.92 186	66.49 245	79.39 318	52.07 118	86.69 78	60.05 207	79.14 164	85.66 157

LFMVS		71.78 133	71.59 122	72.32 203	83.40 76	46.38 321	79.75 120	71.08 344	64.18 35	72.80 123	88.64 73	42.58 251	83.72 157	57.41 234	84.49 77	86.86 96

test_fmvsm_n_1920		71.73 135	71.14 135	73.50 169	72.52 344	56.53 112	75.60 237	76.16 267	48.11 383	77.22 41	85.56 170	53.10 100	77.43 327	74.86 57	77.14 206	86.55 111

PAPR		71.72 136	70.82 141	74.41 123	81.20 109	51.17 224	79.55 126	83.33 96	55.81 236	66.93 237	84.61 192	50.95 138	86.06 102	55.79 247	79.20 159	86.00 136

IS-MVSNet		71.57 137	71.00 138	73.27 178	78.86 159	45.63 332	80.22 110	78.69 202	64.14 38	66.46 246	87.36 100	49.30 163	85.60 113	50.26 295	83.71 88	88.59 28

MAR-MVS		71.51 138	70.15 156	75.60 92	81.84 95	59.39 60	81.38 95	82.90 114	54.90 269	68.08 208	78.70 326	47.73 182	85.51 117	51.68 286	84.17 81	81.88 286
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
MVSFormer		71.50 139	70.38 150	74.88 104	78.76 162	57.15 105	82.79 72	78.48 213	51.26 337	69.49 175	83.22 229	43.99 237	83.24 168	66.06 142	79.37 149	84.23 213

RRT-MVS		71.46 140	70.70 144	73.74 156	77.76 203	49.30 277	76.60 213	80.45 168	61.25 101	68.17 200	84.78 184	44.64 229	84.90 133	64.79 155	77.88 192	87.03 90

PVSNet_Blended_VisFu		71.45 141	70.39 149	74.65 112	82.01 91	58.82 80	79.93 116	80.35 171	55.09 257	65.82 263	82.16 260	49.17 166	82.64 198	60.34 205	78.62 178	82.50 274

OMC-MVS		71.40 142	70.60 145	73.78 151	76.60 254	53.15 182	79.74 121	79.78 177	58.37 174	68.75 189	86.45 140	45.43 217	80.60 247	62.58 184	77.73 193	87.58 69

KinetiMVS		71.26 143	70.16 155	74.57 117	74.59 301	52.77 195	75.91 232	81.20 151	60.72 114	69.10 187	85.71 168	41.67 267	83.53 162	63.91 166	78.62 178	87.42 74

UniMVSNet_NR-MVSNet		71.11 144	71.00 138	71.44 230	79.20 149	44.13 347	76.02 230	82.60 119	66.48 12	68.20 198	84.60 195	56.82 43	82.82 193	54.62 258	70.43 312	87.36 81

hse-mvs2		71.04 145	69.86 159	74.60 115	79.58 138	57.12 107	73.96 275	75.25 290	60.40 122	74.81 70	81.95 265	45.54 213	82.90 186	70.41 96	66.83 366	83.77 234

diffmvs_AUTHOR		71.02 146	70.87 140	71.45 229	69.89 397	48.97 285	73.16 297	78.33 222	57.79 191	72.11 135	85.26 178	51.84 122	77.89 315	71.00 93	78.47 183	87.49 71

GeoE		71.01 147	70.15 156	73.60 166	79.57 139	52.17 209	78.93 133	78.12 225	58.02 181	67.76 222	83.87 212	52.36 112	82.72 195	56.90 236	75.79 228	85.92 139

fmvsm_l_conf0.5_n		70.99 148	70.82 141	71.48 226	71.45 366	54.40 151	77.18 194	70.46 353	48.67 372	75.17 59	86.86 118	53.77 88	76.86 343	76.33 41	77.51 198	83.17 257

PCF-MVS	61.88 8	70.95 149	69.49 166	75.35 95	77.63 209	55.71 128	76.04 229	81.81 130	50.30 350	69.66 173	85.40 176	52.51 108	84.89 134	51.82 283	80.24 134	85.45 167
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
SSM_0404		70.84 150	69.41 170	75.12 101	79.20 149	53.86 159	77.89 165	80.00 175	53.88 288	69.40 178	84.61 192	43.21 243	86.56 83	58.80 223	77.68 195	84.95 191

test_fmvsmvis_n_1920		70.84 150	70.38 150	72.22 205	71.16 374	55.39 138	75.86 233	72.21 337	49.03 367	73.28 106	86.17 149	51.83 123	77.29 332	75.80 46	78.05 189	83.98 222

114514_t		70.83 152	69.56 164	74.64 113	86.21 32	54.63 149	82.34 81	81.81 130	48.22 381	63.01 311	85.83 163	40.92 281	87.10 68	57.91 230	79.79 142	82.18 280

FIs		70.82 153	71.43 126	68.98 291	78.33 181	38.14 418	76.96 202	83.59 85	61.02 106	67.33 227	86.73 125	55.07 66	81.64 217	54.61 260	79.22 158	87.14 88

ACMM	61.98 7	70.80 154	69.73 161	74.02 142	80.59 121	58.59 83	82.68 75	82.02 127	55.46 246	67.18 232	84.39 201	38.51 309	83.17 170	60.65 203	76.10 224	80.30 328
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs		70.69 155	70.43 148	71.46 227	69.45 404	48.95 286	72.93 300	78.46 215	57.27 198	71.69 139	83.97 211	51.48 130	77.92 314	70.70 95	77.95 191	87.53 70
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UniMVSNet (Re)		70.63 156	70.20 153	71.89 210	78.55 170	45.29 335	75.94 231	82.92 113	63.68 43	68.16 201	83.59 220	53.89 84	83.49 164	53.97 264	71.12 303	86.89 94

xiu_mvs_v2_base		70.52 157	69.75 160	72.84 187	81.21 108	55.63 131	75.11 249	78.92 195	54.92 268	69.96 169	79.68 312	47.00 200	82.09 210	61.60 196	79.37 149	80.81 313

PS-MVSNAJ		70.51 158	69.70 162	72.93 185	81.52 99	55.79 127	74.92 256	79.00 193	55.04 263	69.88 170	78.66 328	47.05 196	82.19 208	61.61 195	79.58 146	80.83 312

fmvsm_l_conf0.5_n_a		70.50 159	70.27 152	71.18 242	71.30 372	54.09 156	76.89 205	69.87 357	47.90 387	74.37 80	86.49 138	53.07 102	76.69 349	75.41 52	77.11 207	82.76 264

v2v482		70.50 159	69.45 168	73.66 161	72.62 341	50.03 259	77.58 175	80.51 166	59.90 139	69.52 174	82.14 261	47.53 187	84.88 136	65.07 154	70.17 320	86.09 134

v1144		70.42 161	69.31 171	73.76 153	73.22 329	50.64 239	77.83 169	81.43 138	58.58 170	69.40 178	81.16 280	47.53 187	85.29 125	64.01 162	70.64 308	85.34 174

SSM_0407		70.41 162	68.96 180	74.75 107	78.65 166	53.46 172	77.28 190	80.00 175	53.88 288	68.14 202	84.61 192	43.21 243	86.26 97	58.80 223	76.11 221	84.54 201

TranMVSNet+NR-MVSNet		70.36 163	70.10 158	71.17 243	78.64 169	42.97 368	76.53 215	81.16 154	66.95 6	68.53 193	85.42 175	51.61 127	83.07 171	52.32 276	69.70 333	87.46 72

v8		70.33 164	69.28 172	73.49 170	73.15 331	50.22 254	78.62 140	80.78 162	60.79 111	66.45 247	82.11 263	49.35 162	84.98 130	63.58 173	68.71 349	85.28 177

Fast-Effi-MVS+		70.28 165	69.12 176	73.73 157	78.50 171	51.50 222	75.01 252	79.46 185	56.16 230	68.59 190	79.55 315	53.97 82	84.05 149	53.34 270	77.53 197	85.65 158

X-MVStestdata		70.21 166	67.28 228	79.00 26	86.32 30	62.62 11	85.83 27	83.92 62	64.55 26	72.17 133	6.49 517	47.95 179	88.01 45	71.55 89	86.74 58	86.37 119

v10		70.21 166	69.02 177	73.81 150	73.51 325	50.92 230	78.74 136	81.39 139	60.05 136	66.39 248	81.83 268	47.58 186	85.41 123	62.80 183	68.86 348	85.09 185

Elysia		70.19 168	68.29 200	75.88 82	74.15 314	54.33 153	78.26 146	83.21 102	55.04 263	67.28 228	83.59 220	30.16 405	86.11 100	63.67 171	79.26 156	87.20 85

StellarMVS		70.19 168	68.29 200	75.88 82	74.15 314	54.33 153	78.26 146	83.21 102	55.04 263	67.28 228	83.59 220	30.16 405	86.11 100	63.67 171	79.26 156	87.20 85

QAPM		70.05 170	68.81 184	73.78 151	76.54 256	53.43 175	83.23 65	83.48 87	52.89 305	65.90 259	86.29 145	41.55 271	86.49 89	51.01 289	78.40 184	81.42 292

DU-MVS		70.01 171	69.53 165	71.44 230	78.05 192	44.13 347	75.01 252	81.51 136	64.37 31	68.20 198	84.52 196	49.12 169	82.82 193	54.62 258	70.43 312	87.37 79

AdaColmap		69.99 172	68.66 188	73.97 146	84.94 58	57.83 91	82.63 76	78.71 201	56.28 227	64.34 290	84.14 205	41.57 269	87.06 70	46.45 334	78.88 168	77.02 380

v1192		69.97 173	68.68 187	73.85 148	73.19 330	50.94 228	77.68 174	81.36 141	57.51 196	68.95 188	80.85 290	45.28 220	85.33 124	62.97 182	70.37 314	85.27 178

Anonymous20240529		69.91 174	69.02 177	72.56 194	80.19 127	47.65 310	77.56 177	80.99 158	55.45 247	69.88 170	86.76 121	39.24 299	82.18 209	54.04 263	77.10 208	87.85 55

hybridnocas07		69.86 175	69.44 169	71.14 245	68.10 426	48.28 297	72.52 310	77.08 246	56.94 206	70.50 156	84.91 181	50.48 144	78.37 303	67.84 119	76.55 216	86.76 101

patch_mono-2		69.85 176	71.09 136	66.16 339	79.11 154	54.80 148	71.97 321	74.31 307	53.50 297	70.90 151	84.17 204	57.63 37	63.31 435	66.17 141	82.02 108	80.38 323

fmvsm_s_conf0.5_n_2		69.82 177	69.27 173	71.46 227	72.00 356	51.08 225	73.30 290	67.79 376	55.06 262	75.24 58	87.51 93	44.02 236	77.00 339	75.67 48	72.86 276	86.31 129

FA-MVS(test-final)		69.82 177	68.48 191	73.84 149	78.44 174	50.04 258	75.58 240	78.99 194	58.16 177	67.59 223	82.14 261	42.66 249	85.63 112	56.60 237	76.19 220	85.84 145

FC-MVSNet-test		69.80 179	70.58 147	67.46 314	77.61 214	34.73 452	76.05 228	83.19 106	60.84 110	65.88 261	86.46 139	54.52 75	80.76 246	52.52 275	78.12 188	86.91 93

v144192		69.71 180	68.51 190	73.33 177	73.10 332	50.13 256	77.54 178	80.64 163	56.65 211	68.57 192	80.55 293	46.87 201	84.96 132	62.98 181	69.66 334	84.89 193

test_yl		69.69 181	69.13 174	71.36 236	78.37 178	45.74 328	74.71 260	80.20 172	57.91 187	70.01 167	83.83 213	42.44 252	82.87 189	54.97 254	79.72 143	85.48 163

DCV-MVSNet		69.69 181	69.13 174	71.36 236	78.37 178	45.74 328	74.71 260	80.20 172	57.91 187	70.01 167	83.83 213	42.44 252	82.87 189	54.97 254	79.72 143	85.48 163

VNet		69.68 183	70.19 154	68.16 304	79.73 135	41.63 383	70.53 346	77.38 239	60.37 125	70.69 152	86.63 130	51.08 136	77.09 335	53.61 268	81.69 116	85.75 152

jason		69.65 184	68.39 197	73.43 174	78.27 183	56.88 109	77.12 196	73.71 319	46.53 407	69.34 180	83.22 229	43.37 241	79.18 277	64.77 156	79.20 159	84.23 213
jason: jason.
fmvsm_s_conf0.1_n_2		69.64 185	69.01 179	71.52 225	71.66 361	51.04 226	73.39 289	67.14 382	55.02 266	75.11 60	87.64 92	42.94 248	77.01 338	75.55 50	72.63 282	86.52 113

Effi-MVS+-dtu		69.64 185	67.53 218	75.95 80	76.10 263	62.29 15	80.20 111	76.06 271	59.83 144	65.26 274	77.09 361	41.56 270	84.02 152	60.60 204	71.09 306	81.53 291

fmvsm_s_conf0.5_n		69.58 187	68.84 183	71.79 215	72.31 352	52.90 188	77.90 164	62.43 428	49.97 355	72.85 122	85.90 160	52.21 114	76.49 352	75.75 47	70.26 319	85.97 137

lupinMVS		69.57 188	68.28 202	73.44 173	78.76 162	57.15 105	76.57 214	73.29 325	46.19 410	69.49 175	82.18 257	43.99 237	79.23 276	64.66 157	79.37 149	83.93 224

fmvsm_s_conf0.5_n_7		69.54 189	69.67 163	69.15 290	73.47 327	51.41 223	70.35 350	73.34 322	57.05 203	68.41 194	85.83 163	49.86 152	72.84 373	71.86 85	76.83 211	83.19 253

fmvsm_s_conf0.5_n_a		69.54 189	68.74 186	71.93 209	72.47 346	53.82 161	78.25 148	62.26 430	49.78 357	73.12 114	86.21 147	52.66 106	76.79 345	75.02 56	68.88 346	85.18 180

NR-MVSNet		69.54 189	68.85 182	71.59 224	78.05 192	43.81 352	74.20 271	80.86 161	65.18 15	62.76 315	84.52 196	52.35 113	83.59 161	50.96 291	70.78 307	87.37 79

MVS_111021_LR		69.50 192	68.78 185	71.65 222	78.38 176	59.33 61	74.82 258	70.11 355	58.08 178	67.83 218	84.68 187	41.96 257	76.34 356	65.62 149	77.54 196	79.30 347

v1921920		69.47 193	68.17 204	73.36 176	73.06 333	50.10 257	77.39 183	80.56 164	56.58 220	68.59 190	80.37 295	44.72 228	84.98 130	62.47 187	69.82 328	85.00 187

test_djsdf		69.45 194	67.74 211	74.58 116	74.57 303	54.92 146	82.79 72	78.48 213	51.26 337	65.41 268	83.49 225	38.37 311	83.24 168	66.06 142	69.25 341	85.56 160

fmvsm_s_conf0.1_n		69.41 195	68.60 189	71.83 212	71.07 375	52.88 191	77.85 168	62.44 427	49.58 360	72.97 117	86.22 146	51.68 126	76.48 353	75.53 51	70.10 322	86.14 132

hybrid		69.38 196	68.93 181	70.75 255	67.86 430	48.20 299	72.49 312	76.90 250	55.23 253	70.42 158	84.34 202	49.76 155	77.62 324	67.11 130	76.20 219	86.42 116

fmvsm_s_conf0.1_n_a		69.32 197	68.44 195	71.96 207	70.91 377	53.78 162	78.12 158	62.30 429	49.35 363	73.20 108	86.55 137	51.99 119	76.79 345	74.83 58	68.68 351	85.32 175

Anonymous20231211		69.28 198	68.47 193	71.73 217	80.28 122	47.18 316	79.98 114	82.37 122	54.61 274	67.24 230	84.01 209	39.43 293	82.41 205	55.45 252	72.83 277	85.62 159

EI-MVSNet		69.27 199	68.44 195	71.73 217	74.47 304	49.39 274	75.20 247	78.45 216	59.60 147	69.16 185	76.51 374	51.29 132	82.50 202	59.86 212	71.45 300	83.30 248

v1240		69.24 200	67.91 209	73.25 180	73.02 335	49.82 261	77.21 193	80.54 165	56.43 222	68.34 197	80.51 294	43.33 242	84.99 128	62.03 191	69.77 331	84.95 191

IterMVS-LS		69.22 201	68.48 191	71.43 232	74.44 306	49.40 273	76.23 222	77.55 234	59.60 147	65.85 262	81.59 275	51.28 133	81.58 220	59.87 211	69.90 327	83.30 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
viewdifsd2359ckpt11		69.13 202	68.38 198	71.38 234	71.57 363	48.61 291	73.22 295	73.18 326	57.65 192	70.67 153	84.73 185	50.03 149	79.80 262	63.25 176	71.10 304	85.74 153

viewmsd2359difaftdt		69.13 202	68.38 198	71.38 234	71.57 363	48.61 291	73.22 295	73.18 326	57.65 192	70.67 153	84.73 185	50.03 149	79.80 262	63.25 176	71.10 304	85.74 153

IMVS_0403		69.09 204	68.14 205	71.95 208	77.06 238	49.73 263	74.51 264	78.60 205	52.70 307	66.69 241	82.58 240	46.43 204	83.38 165	59.20 218	75.46 234	82.74 265

VPA-MVSNet		69.02 205	69.47 167	67.69 310	77.42 219	41.00 390	74.04 273	79.68 179	60.06 135	69.26 183	84.81 183	51.06 137	77.58 325	54.44 261	74.43 246	84.48 206

v7n		69.01 206	67.36 225	73.98 145	72.51 345	52.65 197	78.54 144	81.30 146	60.26 131	62.67 317	81.62 272	43.61 239	84.49 142	57.01 235	68.70 350	84.79 196

viewmambaseed2359dif		68.91 207	68.18 203	71.11 246	70.21 389	48.05 306	72.28 316	75.90 273	51.96 321	70.93 150	84.47 199	51.37 131	78.59 301	61.55 198	74.97 239	86.68 105

IMVS_0407		68.90 208	67.93 208	71.82 213	77.06 238	49.73 263	74.40 269	78.60 205	52.70 307	66.19 251	82.58 240	45.17 223	83.00 173	59.20 218	75.46 234	82.74 265

OpenMVS	61.03 9	68.85 209	67.56 215	72.70 191	74.26 312	53.99 158	81.21 97	81.34 145	52.70 307	62.75 316	85.55 172	38.86 304	84.14 147	48.41 311	83.01 91	79.97 334

XVG-OURS-SEG-HR		68.81 210	67.47 221	72.82 189	74.40 307	56.87 110	70.59 345	79.04 192	54.77 271	66.99 235	86.01 156	39.57 292	78.21 307	62.54 185	73.33 268	83.37 247

BH-RMVSNet		68.81 210	67.42 222	72.97 184	80.11 130	52.53 201	74.26 270	76.29 266	58.48 172	68.38 196	84.20 203	42.59 250	83.83 155	46.53 333	75.91 226	82.56 269

UGNet		68.81 210	67.39 223	73.06 182	78.33 181	54.47 150	79.77 119	75.40 287	60.45 120	63.22 304	84.40 200	32.71 382	80.91 242	51.71 285	80.56 129	83.81 230
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
XVG-OURS		68.76 213	67.37 224	72.90 186	74.32 310	57.22 100	70.09 354	78.81 198	55.24 252	67.79 220	85.81 166	36.54 335	78.28 306	62.04 190	75.74 229	83.19 253

V42		68.65 214	67.35 226	72.56 194	68.93 414	50.18 255	72.90 302	79.47 184	56.92 207	69.45 177	80.26 299	46.29 206	82.99 174	64.07 160	67.82 357	84.53 204

PVSNet_Blended		68.59 215	67.72 212	71.19 241	77.03 244	50.57 242	72.51 311	81.52 134	51.91 322	64.22 296	77.77 352	49.13 167	82.87 189	55.82 245	79.58 146	80.14 332

xiu_mvs_v1_base_debu		68.58 216	67.28 228	72.48 197	78.19 185	57.19 102	75.28 244	75.09 295	51.61 326	70.04 163	81.41 277	32.79 378	79.02 290	63.81 167	77.31 201	81.22 301

xiu_mvs_v1_base		68.58 216	67.28 228	72.48 197	78.19 185	57.19 102	75.28 244	75.09 295	51.61 326	70.04 163	81.41 277	32.79 378	79.02 290	63.81 167	77.31 201	81.22 301

xiu_mvs_v1_base_debi		68.58 216	67.28 228	72.48 197	78.19 185	57.19 102	75.28 244	75.09 295	51.61 326	70.04 163	81.41 277	32.79 378	79.02 290	63.81 167	77.31 201	81.22 301

PVSNet_BlendedMVS		68.56 219	67.72 212	71.07 248	77.03 244	50.57 242	74.50 265	81.52 134	53.66 296	64.22 296	79.72 311	49.13 167	82.87 189	55.82 245	73.92 252	79.77 342

dtuplus		68.48 220	67.76 210	70.63 259	70.33 388	48.09 302	72.62 306	75.88 275	52.33 315	71.09 148	84.66 189	50.09 148	77.93 313	58.02 229	74.82 242	85.87 142

WR-MVS		68.47 221	68.47 193	68.44 299	80.20 126	39.84 400	73.75 283	76.07 270	64.68 25	68.11 206	83.63 219	50.39 146	79.14 282	49.78 296	69.66 334	86.34 121

mvsmamba		68.47 221	66.56 243	74.21 132	79.60 137	52.95 186	74.94 255	75.48 285	52.09 320	60.10 350	83.27 228	36.54 335	84.70 138	59.32 217	77.69 194	84.99 189

AUN-MVS		68.45 223	66.41 250	74.57 117	79.53 140	57.08 108	73.93 278	75.23 291	54.44 279	66.69 241	81.85 267	37.10 329	82.89 187	62.07 189	66.84 365	83.75 235

c3_l		68.33 224	67.56 215	70.62 260	70.87 378	46.21 324	74.47 266	78.80 199	56.22 229	66.19 251	78.53 333	51.88 120	81.40 224	62.08 188	69.04 344	84.25 212

BH-untuned		68.27 225	67.29 227	71.21 240	79.74 134	53.22 180	76.06 227	77.46 237	57.19 199	66.10 254	81.61 273	45.37 219	83.50 163	45.42 352	76.68 214	76.91 384

jajsoiax		68.25 226	66.45 246	73.66 161	75.62 271	55.49 136	80.82 101	78.51 212	52.33 315	64.33 291	84.11 206	28.28 426	81.81 216	63.48 174	70.62 309	83.67 238

LuminaMVS		68.24 227	66.82 240	72.51 196	73.46 328	53.60 168	76.23 222	78.88 196	52.78 306	68.08 208	80.13 301	32.70 383	81.41 223	63.16 179	75.97 225	82.53 271

v148		68.24 227	67.19 235	71.40 233	70.43 385	47.77 309	75.76 236	77.03 248	58.91 161	67.36 226	80.10 303	48.60 174	81.89 213	60.01 208	66.52 369	84.53 204

CANet_DTU		68.18 229	67.71 214	69.59 279	74.83 292	46.24 323	78.66 139	76.85 252	59.60 147	63.45 302	82.09 264	35.25 346	77.41 328	59.88 210	78.76 173	85.14 181

mvs_tets		68.18 229	66.36 252	73.63 164	75.61 272	55.35 140	80.77 102	78.56 210	52.48 314	64.27 293	84.10 207	27.45 435	81.84 215	63.45 175	70.56 311	83.69 237

guyue		68.10 231	67.23 234	70.71 258	73.67 324	49.27 278	73.65 285	76.04 272	55.62 243	67.84 217	82.26 255	41.24 277	78.91 297	61.01 201	73.72 256	83.94 223

SDMVSNet		68.03 232	68.10 207	67.84 306	77.13 233	48.72 290	65.32 403	79.10 189	58.02 181	65.08 278	82.55 245	47.83 181	73.40 370	63.92 164	73.92 252	81.41 293

miper_ehance_all_eth		68.03 232	67.24 232	70.40 264	70.54 382	46.21 324	73.98 274	78.68 203	55.07 260	66.05 255	77.80 349	52.16 116	81.31 227	61.53 199	69.32 338	83.67 238

mvs_anonymous		68.03 232	67.51 219	69.59 279	72.08 354	44.57 344	71.99 320	75.23 291	51.67 324	67.06 234	82.57 244	54.68 73	77.94 311	56.56 240	75.71 230	86.26 131

ET-MVSNet_ETH3D		67.96 235	65.72 264	74.68 110	76.67 252	55.62 133	75.11 249	74.74 300	52.91 304	60.03 352	80.12 302	33.68 367	82.64 198	61.86 192	76.34 217	85.78 147

thisisatest0530		67.92 236	65.78 263	74.33 125	76.29 260	51.03 227	76.89 205	74.25 310	53.67 295	65.59 265	81.76 270	35.15 347	85.50 118	55.94 243	72.47 283	86.47 115

PAPM		67.92 236	66.69 242	71.63 223	78.09 190	49.02 282	77.09 197	81.24 150	51.04 342	60.91 344	83.98 210	47.71 183	84.99 128	40.81 392	79.32 153	80.90 311

AstraMVS		67.86 238	66.83 239	70.93 251	73.50 326	49.34 275	73.28 293	74.01 314	55.45 247	68.10 207	83.28 227	38.93 303	79.14 282	63.22 178	71.74 295	84.30 211

tttt0517		67.83 239	65.66 265	74.33 125	76.69 250	50.82 232	77.86 167	73.99 315	54.54 277	64.64 288	82.53 248	35.06 348	85.50 118	55.71 248	69.91 326	86.67 106

mamba_0408		67.78 240	65.42 269	74.85 106	78.65 166	53.46 172	50.83 477	79.09 190	53.75 291	68.14 202	83.83 213	41.79 265	86.56 83	56.58 238	76.11 221	84.54 201

tt0805		67.77 241	67.24 232	69.34 284	74.87 290	40.08 397	77.36 184	81.37 140	55.31 249	66.33 249	84.65 190	37.35 323	82.55 201	55.65 250	72.28 288	85.39 172

ECVR-MVS		67.72 242	67.51 219	68.35 300	79.46 142	36.29 441	74.79 259	66.93 384	58.72 164	67.19 231	88.05 83	36.10 338	81.38 225	52.07 279	84.25 79	87.39 77

eth_miper_zixun_eth		67.63 243	66.28 256	71.67 221	71.60 362	48.33 296	73.68 284	77.88 227	55.80 237	65.91 258	78.62 331	47.35 193	82.88 188	59.45 214	66.25 370	83.81 230

UniMVSNet_ETH3D		67.60 244	67.07 237	69.18 288	77.39 220	42.29 374	74.18 272	75.59 281	60.37 125	66.77 239	86.06 153	37.64 319	78.93 295	52.16 278	73.49 263	86.32 126

VPNet		67.52 245	68.11 206	65.74 349	79.18 151	36.80 433	72.17 318	72.83 331	62.04 87	67.79 220	85.83 163	48.88 171	76.60 351	51.30 287	72.97 275	83.81 230

cl22		67.47 246	66.45 246	70.54 262	69.85 399	46.49 320	73.85 281	77.35 240	55.07 260	65.51 266	77.92 342	47.64 185	81.10 233	61.58 197	69.32 338	84.01 221

Fast-Effi-MVS+-dtu		67.37 247	65.33 273	73.48 171	72.94 336	57.78 93	77.47 181	76.88 251	57.60 195	61.97 329	76.85 365	39.31 296	80.49 252	54.72 257	70.28 318	82.17 282

MVS		67.37 247	66.33 253	70.51 263	75.46 276	50.94 228	73.95 276	81.85 129	41.57 448	62.54 321	78.57 332	47.98 178	85.47 120	52.97 273	82.05 107	75.14 401

test1111		67.21 249	67.14 236	67.42 315	79.24 148	34.76 451	73.89 280	65.65 394	58.71 166	66.96 236	87.95 87	36.09 339	80.53 249	52.03 280	83.79 85	86.97 92

GBi-Net		67.21 249	66.55 244	69.19 285	77.63 209	43.33 358	77.31 185	77.83 229	56.62 215	65.04 280	82.70 235	41.85 262	80.33 254	47.18 325	72.76 278	83.92 225

test1		67.21 249	66.55 244	69.19 285	77.63 209	43.33 358	77.31 185	77.83 229	56.62 215	65.04 280	82.70 235	41.85 262	80.33 254	47.18 325	72.76 278	83.92 225

cl____		67.18 252	66.26 257	69.94 271	70.20 390	45.74 328	73.30 290	76.83 254	55.10 255	65.27 271	79.57 314	47.39 191	80.53 249	59.41 216	69.22 342	83.53 244

DIV-MVS_self_test		67.18 252	66.26 257	69.94 271	70.20 390	45.74 328	73.29 292	76.83 254	55.10 255	65.27 271	79.58 313	47.38 192	80.53 249	59.43 215	69.22 342	83.54 243

MVSTER		67.16 254	65.58 267	71.88 211	70.37 387	49.70 267	70.25 352	78.45 216	51.52 329	69.16 185	80.37 295	38.45 310	82.50 202	60.19 206	71.46 299	83.44 246

miper_enhance_ethall		67.11 255	66.09 259	70.17 268	69.21 408	45.98 326	72.85 303	78.41 219	51.38 334	65.65 264	75.98 384	51.17 135	81.25 228	60.82 202	69.32 338	83.29 250

Baseline_NR-MVSNet		67.05 256	67.56 215	65.50 353	75.65 269	37.70 424	75.42 241	74.65 303	59.90 139	68.14 202	83.15 232	49.12 169	77.20 333	52.23 277	69.78 329	81.60 288

WR-MVS_H		67.02 257	66.92 238	67.33 318	77.95 196	37.75 422	77.57 176	82.11 126	62.03 88	62.65 318	82.48 249	50.57 143	79.46 271	42.91 378	64.01 387	84.79 196

anonymousdsp		67.00 258	64.82 278	73.57 167	70.09 393	56.13 118	76.35 218	77.35 240	48.43 378	64.99 283	80.84 291	33.01 375	80.34 253	64.66 157	67.64 359	84.23 213

FMVSNet2		66.93 259	66.31 255	68.79 294	77.63 209	42.98 367	76.11 225	77.47 235	56.62 215	65.22 277	82.17 259	41.85 262	80.18 260	47.05 331	72.72 281	83.20 252

BH-w/o		66.85 260	65.83 262	69.90 274	79.29 144	52.46 204	74.66 262	76.65 259	54.51 278	64.85 285	78.12 336	45.59 212	82.95 180	43.26 374	75.54 232	74.27 416

Anonymous202405211		66.84 261	65.99 260	69.40 283	80.19 127	42.21 376	71.11 336	71.31 343	58.80 163	67.90 210	86.39 141	29.83 410	79.65 265	49.60 302	78.78 171	86.33 124

CDS-MVSNet		66.80 262	65.37 271	71.10 247	78.98 156	53.13 184	73.27 294	71.07 345	52.15 318	64.72 286	80.23 300	43.56 240	77.10 334	45.48 350	78.88 168	83.05 259
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 263	65.27 274	71.33 239	79.16 153	53.67 164	73.84 282	69.59 361	52.32 317	65.28 270	81.72 271	44.49 232	77.40 329	42.32 382	78.66 177	82.92 260

FMVSNet1		66.70 264	65.87 261	69.19 285	77.49 217	43.33 358	77.31 185	77.83 229	56.45 221	64.60 289	82.70 235	38.08 317	80.33 254	46.08 339	72.31 287	83.92 225

ab-mvs		66.65 265	66.42 249	67.37 316	76.17 262	41.73 380	70.41 349	76.14 269	53.99 285	65.98 256	83.51 224	49.48 158	76.24 357	48.60 309	73.46 265	84.14 217

PEN-MVS		66.60 266	66.45 246	67.04 320	77.11 237	36.56 435	77.03 199	80.42 169	62.95 60	62.51 323	84.03 208	46.69 202	79.07 285	44.22 360	63.08 400	85.51 162

TAPA-MVS	59.36 10	66.60 266	65.20 275	70.81 253	76.63 253	48.75 288	76.52 216	80.04 174	50.64 347	65.24 275	84.93 180	39.15 300	78.54 302	36.77 420	76.88 210	85.14 181
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 268	65.07 276	71.17 243	79.18 151	49.63 271	73.48 286	75.20 293	52.95 303	67.90 210	80.33 298	39.81 290	83.68 158	43.20 375	73.56 262	80.20 330

CP-MVSNet		66.49 269	66.41 250	66.72 323	77.67 207	36.33 438	76.83 209	79.52 183	62.45 73	62.54 321	83.47 226	46.32 205	78.37 303	45.47 351	63.43 396	85.45 167

PS-CasMVS		66.42 270	66.32 254	66.70 325	77.60 215	36.30 440	76.94 203	79.61 181	62.36 75	62.43 326	83.66 218	45.69 209	78.37 303	45.35 353	63.26 398	85.42 170

icg_test_0407_2		66.41 271	66.75 241	65.37 357	77.06 238	49.73 263	63.79 419	78.60 205	52.70 307	66.19 251	82.58 240	45.17 223	63.65 434	59.20 218	75.46 234	82.74 265

VortexMVS		66.41 271	65.50 268	69.16 289	73.75 320	48.14 300	73.41 288	78.28 223	53.73 293	64.98 284	78.33 334	40.62 282	79.07 285	58.88 222	67.50 360	80.26 329

FMVSNet3		66.32 273	65.61 266	68.46 298	76.48 257	42.34 373	74.98 254	77.15 244	55.83 235	65.04 280	81.16 280	39.91 287	80.14 261	47.18 325	72.76 278	82.90 262

ACMH+	57.40 11	66.12 274	64.06 283	72.30 204	77.79 201	52.83 193	80.39 106	78.03 226	57.30 197	57.47 387	82.55 245	27.68 433	84.17 146	45.54 346	69.78 329	79.90 336

cascas		65.98 275	63.42 297	73.64 163	77.26 224	52.58 200	72.26 317	77.21 243	48.56 374	61.21 341	74.60 399	32.57 389	85.82 110	50.38 294	76.75 213	82.52 273

FE-MVS		65.91 276	63.33 299	73.63 164	77.36 221	51.95 217	72.62 306	75.81 276	53.70 294	65.31 269	78.96 324	28.81 420	86.39 91	43.93 365	73.48 264	82.55 270

thisisatest0515		65.83 277	63.50 295	72.82 189	73.75 320	49.50 272	71.32 330	73.12 330	49.39 362	63.82 298	76.50 376	34.95 350	84.84 137	53.20 272	75.49 233	84.13 218

DP-MVS		65.68 278	63.66 291	71.75 216	84.93 59	56.87 110	80.74 104	73.16 328	53.06 302	59.09 366	82.35 251	36.79 334	85.94 107	32.82 444	69.96 325	72.45 431

HyFIR lowres test		65.67 279	63.01 304	73.67 160	79.97 132	55.65 130	69.07 369	75.52 283	42.68 442	63.53 301	77.95 340	40.43 284	81.64 217	46.01 340	71.91 293	83.73 236

DTE-MVSNet		65.58 280	65.34 272	66.31 335	76.06 264	34.79 449	76.43 217	79.38 186	62.55 71	61.66 336	83.83 213	45.60 211	79.15 281	41.64 390	60.88 422	85.00 187

GA-MVS		65.53 281	63.70 290	71.02 250	70.87 378	48.10 301	70.48 347	74.40 305	56.69 210	64.70 287	76.77 366	33.66 368	81.10 233	55.42 253	70.32 317	83.87 228

CNLPA		65.43 282	64.02 284	69.68 277	78.73 164	58.07 88	77.82 170	70.71 351	51.49 331	61.57 338	83.58 223	38.23 315	70.82 388	43.90 366	70.10 322	80.16 331

MVP-Stereo		65.41 283	63.80 288	70.22 265	77.62 213	55.53 135	76.30 219	78.53 211	50.59 348	56.47 399	78.65 329	39.84 289	82.68 196	44.10 364	72.12 292	72.44 432
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 284	62.73 308	73.40 175	74.89 288	52.78 194	73.09 299	75.13 294	55.69 239	58.48 375	73.73 407	32.86 377	86.32 94	50.63 292	70.11 321	81.10 306
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
test2506		65.33 285	64.61 279	67.50 311	79.46 142	34.19 457	74.43 268	51.92 468	58.72 164	66.75 240	88.05 83	25.99 448	80.92 241	51.94 281	84.25 79	87.39 77

pm-mvs1		65.24 286	64.97 277	66.04 343	72.38 349	39.40 407	72.62 306	75.63 279	55.53 244	62.35 328	83.18 231	47.45 189	76.47 354	49.06 306	66.54 368	82.24 279

ACMH	55.70 15	65.20 287	63.57 292	70.07 269	78.07 191	52.01 215	79.48 127	79.69 178	55.75 238	56.59 396	80.98 285	27.12 438	80.94 239	42.90 379	71.58 298	77.25 378
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 288	63.21 302	70.72 257	81.04 111	54.87 147	78.57 142	77.47 235	48.51 376	55.71 404	81.89 266	33.71 366	79.71 264	41.66 388	70.37 314	77.58 371
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 289	62.84 306	71.82 213	81.49 101	56.26 116	66.32 391	74.20 312	40.53 454	63.16 307	78.65 329	41.30 273	77.80 318	45.80 342	74.09 249	81.40 295

SSM_04072		64.98 290	65.42 269	63.68 372	78.65 166	53.46 172	50.83 477	79.09 190	53.75 291	68.14 202	83.83 213	41.79 265	53.03 479	56.58 238	76.11 221	84.54 201

TransMVSNet (Re)		64.72 291	64.33 281	65.87 348	75.22 281	38.56 413	74.66 262	75.08 298	58.90 162	61.79 332	82.63 238	51.18 134	78.07 309	43.63 371	55.87 447	80.99 310

EG-PatchMatch MVS		64.71 292	62.87 305	70.22 265	77.68 206	53.48 171	77.99 163	78.82 197	53.37 298	56.03 403	77.41 357	24.75 456	84.04 150	46.37 335	73.42 267	73.14 422

LS3D		64.71 292	62.50 310	71.34 238	79.72 136	55.71 128	79.82 118	74.72 301	48.50 377	56.62 395	84.62 191	33.59 369	82.34 206	29.65 466	75.23 238	75.97 391

IMVS_0404		64.63 294	64.22 282	65.88 347	77.06 238	49.73 263	64.40 412	78.60 205	52.70 307	53.16 438	82.58 240	34.82 351	65.16 428	59.20 218	75.46 234	82.74 265

1314		64.61 295	63.21 302	68.80 293	71.87 359	47.46 313	73.95 276	78.39 221	42.88 441	59.97 353	76.60 373	38.11 316	79.39 273	54.84 256	72.32 286	79.55 343

HY-MVS	56.14 13	64.55 296	63.89 285	66.55 331	74.73 296	41.02 387	69.96 355	74.43 304	49.29 364	61.66 336	80.92 287	47.43 190	76.68 350	44.91 357	71.69 296	81.94 284

testing91		64.46 297	63.80 288	66.47 332	78.43 175	40.06 398	67.63 380	69.59 361	59.06 158	63.18 306	78.05 338	34.05 360	76.99 340	48.30 312	75.87 227	82.37 277

sd_testset		64.46 297	64.45 280	64.51 365	77.13 233	42.25 375	62.67 426	72.11 338	58.02 181	65.08 278	82.55 245	41.22 278	69.88 396	47.32 323	73.92 252	81.41 293

XVG-ACMP-BASELINE		64.36 299	62.23 314	70.74 256	72.35 350	52.45 205	70.80 343	78.45 216	53.84 290	59.87 355	81.10 282	16.24 476	79.32 274	55.64 251	71.76 294	80.47 319

usedtu_dtu_shiyan1		64.34 300	63.57 292	66.66 327	72.44 347	40.74 393	69.60 361	76.80 256	53.21 300	61.73 334	77.92 342	41.92 260	77.68 322	46.23 336	72.25 289	81.57 289

FE-MVSNET3		64.34 300	63.57 292	66.66 327	72.44 347	40.74 393	69.60 361	76.80 256	53.21 300	61.73 334	77.92 342	41.92 260	77.68 322	46.23 336	72.25 289	81.57 289

MonoMVSNet		64.15 302	63.31 300	66.69 326	70.51 383	44.12 349	74.47 266	74.21 311	57.81 189	63.03 309	76.62 370	38.33 312	77.31 331	54.22 262	60.59 428	78.64 356

testing99		64.05 303	63.29 301	66.34 334	78.17 188	39.76 402	67.33 385	68.00 375	58.60 169	63.03 309	78.10 337	32.57 389	76.94 342	48.22 313	75.58 231	82.34 278

CostFormer		64.04 304	62.51 309	68.61 296	71.88 358	45.77 327	71.30 331	70.60 352	47.55 394	64.31 292	76.61 372	41.63 268	79.62 267	49.74 298	69.00 345	80.42 321

1112_ss		64.00 305	63.36 298	65.93 345	79.28 146	42.58 372	71.35 329	72.36 336	46.41 408	60.55 347	77.89 346	46.27 207	73.28 371	46.18 338	69.97 324	81.92 285

baseline1		63.81 306	63.87 287	63.62 373	76.29 260	36.36 436	71.78 325	67.29 380	56.05 232	64.23 295	82.95 233	47.11 195	74.41 366	47.30 324	61.85 416	80.10 333

pmmvs6		63.69 307	62.82 307	66.27 337	70.63 380	39.27 408	73.13 298	75.47 286	52.69 312	59.75 359	82.30 253	39.71 291	77.03 337	47.40 320	64.35 386	82.53 271

Vis-MVSNet (Re-imp)		63.69 307	63.88 286	63.14 378	74.75 295	31.04 475	71.16 334	63.64 415	56.32 225	59.80 357	84.99 179	44.51 230	75.46 361	39.12 405	80.62 125	82.92 260

baseline2		63.42 309	61.26 328	69.89 275	72.55 343	47.62 311	71.54 327	68.38 372	50.11 352	54.82 417	75.55 389	43.06 246	80.96 238	48.13 314	67.16 364	81.11 305

thres400		63.31 310	62.18 315	66.72 323	76.85 247	39.62 404	71.96 322	69.44 364	56.63 213	62.61 319	79.83 306	37.18 325	79.17 278	31.84 450	73.25 270	81.36 296

thres600view7		63.30 311	62.27 313	66.41 333	77.18 226	38.87 410	72.35 314	69.11 368	56.98 205	62.37 327	80.96 286	37.01 331	79.00 293	31.43 457	73.05 274	81.36 296

thres100view900		63.28 312	62.41 311	65.89 346	77.31 223	38.66 412	72.65 304	69.11 368	57.07 202	62.45 324	81.03 284	37.01 331	79.17 278	31.84 450	73.25 270	79.83 339

test_0402		63.25 313	61.01 333	69.96 270	80.00 131	54.37 152	76.86 207	72.02 339	54.58 276	58.71 369	80.79 292	35.00 349	84.36 144	26.41 479	64.71 381	71.15 450

tfpn200view9		63.18 314	62.18 315	66.21 338	76.85 247	39.62 404	71.96 322	69.44 364	56.63 213	62.61 319	79.83 306	37.18 325	79.17 278	31.84 450	73.25 270	79.83 339

LTVRE_ROB	55.42 16	63.15 315	61.23 329	68.92 292	76.57 255	47.80 307	59.92 443	76.39 263	54.35 280	58.67 371	82.46 250	29.44 414	81.49 222	42.12 383	71.14 302	77.46 372
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
SD_0403		63.07 316	63.49 296	61.82 386	75.16 284	31.14 474	71.89 324	73.47 320	53.34 299	58.22 378	81.81 269	45.17 223	73.86 369	37.43 414	74.87 241	80.45 320

F-COLMAP		63.05 317	60.87 337	69.58 281	76.99 246	53.63 167	78.12 158	76.16 267	47.97 386	52.41 442	81.61 273	27.87 430	78.11 308	40.07 396	66.66 367	77.00 381

testing11		62.81 318	61.90 318	65.54 351	78.38 176	40.76 392	67.59 382	66.78 386	55.48 245	60.13 349	77.11 360	31.67 396	76.79 345	45.53 347	74.45 245	79.06 350

IterMVS		62.79 319	61.27 327	67.35 317	69.37 405	52.04 214	71.17 333	68.24 374	52.63 313	59.82 356	76.91 364	37.32 324	72.36 376	52.80 274	63.19 399	77.66 370
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
usedtu_blend_shiyan5		62.63 320	60.77 338	68.20 302	68.53 419	44.64 341	73.47 287	77.00 249	51.91 322	57.10 390	69.95 439	38.83 305	79.61 268	47.44 317	62.67 403	80.37 324

reproduce_monomvs		62.56 321	61.20 330	66.62 330	70.62 381	44.30 346	70.13 353	73.13 329	54.78 270	61.13 342	76.37 377	25.63 451	75.63 360	58.75 225	60.29 429	79.93 335

IterMVS-SCA-FT		62.49 322	61.52 322	65.40 356	71.99 357	50.80 233	71.15 335	69.63 360	45.71 416	60.61 346	77.93 341	37.45 321	65.99 424	55.67 249	63.50 395	79.42 345

tfpnnormal		62.47 323	61.63 321	64.99 362	74.81 293	39.01 409	71.22 332	73.72 318	55.22 254	60.21 348	80.09 304	41.26 276	76.98 341	30.02 464	68.09 355	78.97 353

blended_shiyan8		62.46 324	60.71 339	67.71 308	69.15 410	43.43 356	70.83 340	76.52 260	51.49 331	57.67 383	71.36 427	39.38 294	79.07 285	47.37 321	62.67 403	80.62 317

blended_shiyan6		62.46 324	60.71 339	67.71 308	69.14 411	43.42 357	70.82 341	76.52 260	51.50 330	57.64 384	71.37 426	39.38 294	79.08 284	47.36 322	62.67 403	80.65 316

gbinet_0.2-2-1-0.02		62.43 326	60.41 342	68.49 297	68.91 415	43.71 353	71.73 326	75.89 274	52.10 319	58.33 376	69.67 446	36.86 333	80.59 248	47.18 325	63.05 401	81.16 304

MS-PatchMatch		62.42 327	61.46 323	65.31 359	75.21 282	52.10 211	72.05 319	74.05 313	46.41 408	57.42 389	74.36 400	34.35 357	77.57 326	45.62 345	73.67 257	66.26 469

Test_1112_low_res		62.32 328	61.77 319	64.00 370	79.08 155	39.53 406	68.17 376	70.17 354	43.25 436	59.03 367	79.90 305	44.08 234	71.24 386	43.79 368	68.42 352	81.25 300

D2MVS		62.30 329	60.29 344	68.34 301	66.46 442	48.42 295	65.70 395	73.42 321	47.71 391	58.16 379	75.02 395	30.51 400	77.71 321	53.96 265	71.68 297	78.90 354

testing222		62.29 330	61.31 326	65.25 360	77.87 198	38.53 414	68.34 374	66.31 390	56.37 224	63.15 308	77.58 355	28.47 422	76.18 359	37.04 418	76.65 215	81.05 309

thres200		62.20 331	61.16 331	65.34 358	75.38 279	39.99 399	69.60 361	69.29 366	55.64 242	61.87 331	76.99 362	37.07 330	78.96 294	31.28 458	73.28 269	77.06 379

tpm2		62.07 332	60.10 347	67.99 305	72.79 338	43.86 351	71.05 338	66.85 385	43.14 438	62.77 314	75.39 393	38.32 313	80.80 244	41.69 387	68.88 346	79.32 346

testing3-2		62.06 333	62.36 312	61.17 394	79.29 144	30.31 477	64.09 418	63.49 416	63.50 45	62.84 312	82.22 256	32.35 393	69.02 400	40.01 399	73.43 266	84.17 216

miper_lstm_enhance		62.03 334	60.88 335	65.49 354	66.71 439	46.25 322	56.29 461	75.70 278	50.68 345	61.27 340	75.48 391	40.21 285	68.03 406	56.31 242	65.25 377	82.18 280

FE-MVSNET2		62.01 335	60.88 335	65.42 355	68.74 416	38.43 416	72.92 301	77.39 238	54.74 273	55.40 409	76.71 367	35.46 344	76.72 348	44.25 359	62.31 412	81.10 306

wanda-best-256-512		62.00 336	60.17 345	67.49 312	68.53 419	43.07 365	69.65 358	76.38 264	51.26 337	57.10 390	69.95 439	38.83 305	79.04 288	47.14 329	62.67 403	80.37 324

FE-blended-shiyan7		62.00 336	60.17 345	67.49 312	68.53 419	43.07 365	69.65 358	76.38 264	51.26 337	57.10 390	69.95 439	38.83 305	79.04 288	47.14 329	62.67 403	80.37 324

EPNet_dtu		61.90 338	61.97 317	61.68 387	72.89 337	39.78 401	75.85 234	65.62 395	55.09 257	54.56 422	79.36 319	37.59 320	67.02 415	39.80 401	76.95 209	78.25 360
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 339	61.35 325	63.46 374	74.58 302	31.48 473	61.42 434	58.14 446	58.71 166	53.02 440	79.55 315	43.07 245	76.80 344	45.69 343	77.96 190	82.11 283

MSDG		61.81 340	59.23 352	69.55 282	72.64 340	52.63 199	70.45 348	75.81 276	51.38 334	53.70 429	76.11 379	29.52 412	81.08 235	37.70 412	65.79 374	74.93 406

SixPastTwentyTwo		61.65 341	58.80 359	70.20 267	75.80 266	47.22 315	75.59 238	69.68 359	54.61 274	54.11 426	79.26 321	27.07 439	82.96 178	43.27 373	49.79 469	80.41 322

CL-MVSNet_self_test		61.53 342	60.94 334	63.30 376	68.95 412	36.93 432	67.60 381	72.80 332	55.67 240	59.95 354	76.63 369	45.01 226	72.22 380	39.74 402	62.09 415	80.74 315

RPMNet		61.53 342	58.42 362	70.86 252	69.96 395	52.07 212	65.31 404	81.36 141	43.20 437	59.36 362	70.15 437	35.37 345	85.47 120	36.42 427	64.65 382	75.06 402

pmmvs4		61.48 344	59.39 351	67.76 307	71.57 363	53.86 159	71.42 328	65.34 397	44.20 427	59.46 361	77.92 342	35.90 340	74.71 364	43.87 367	64.87 380	74.71 411

blend_shiyan4		61.38 345	59.10 355	68.20 302	68.94 413	44.64 341	70.81 342	76.52 260	51.63 325	57.56 386	69.94 442	28.30 425	79.61 268	47.44 317	60.78 424	80.36 327

OurMVSNet-221017-0		61.37 346	58.63 361	69.61 278	72.05 355	48.06 304	73.93 278	72.51 333	47.23 400	54.74 418	80.92 287	21.49 466	81.24 229	48.57 310	56.22 446	79.53 344

COLMAP_ROB	52.97 17	61.27 347	58.81 357	68.64 295	74.63 299	52.51 202	78.42 145	73.30 324	49.92 356	50.96 447	81.51 276	23.06 459	79.40 272	31.63 454	65.85 372	74.01 419
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 348	61.67 320	57.70 423	70.43 385	38.45 415	64.19 415	66.47 387	48.05 385	63.22 304	80.86 289	49.28 164	60.47 444	45.25 354	67.28 363	74.19 417

myMVS_eth3d28		60.66 349	61.04 332	59.51 402	77.32 222	31.58 472	63.11 423	63.87 412	59.00 159	60.90 345	78.26 335	32.69 384	66.15 423	36.10 429	78.13 187	80.81 313

SSC-MVS3.2		60.57 350	61.39 324	58.12 419	74.29 311	32.63 467	59.52 444	65.53 396	59.90 139	62.45 324	79.75 310	41.96 257	63.90 433	39.47 403	69.65 336	77.84 368

WBMVS		60.54 351	60.61 341	60.34 399	78.00 194	35.95 444	64.55 411	64.89 400	49.63 358	63.39 303	78.70 326	33.85 365	67.65 409	42.10 384	70.35 316	77.43 373

SCA		60.49 352	58.38 363	66.80 322	74.14 316	48.06 304	63.35 422	63.23 419	49.13 366	59.33 365	72.10 418	37.45 321	74.27 367	44.17 361	62.57 409	78.05 363

K. test v3		60.47 353	57.11 371	70.56 261	73.74 322	48.22 298	75.10 251	62.55 425	58.27 176	53.62 432	76.31 378	27.81 431	81.59 219	47.42 319	39.18 484	81.88 286

mmtdpeth		60.40 354	59.12 354	64.27 368	69.59 401	48.99 283	70.67 344	70.06 356	54.96 267	62.78 313	73.26 412	27.00 440	67.66 408	58.44 228	45.29 476	76.16 390

UWE-MVS		60.18 355	59.78 348	61.39 392	77.67 207	33.92 460	69.04 370	63.82 413	48.56 374	64.27 293	77.64 354	27.20 437	70.40 393	33.56 441	76.24 218	79.83 339

OpenMVS_ROB	52.78 18	60.03 356	58.14 366	65.69 350	70.47 384	44.82 337	75.33 242	70.86 350	45.04 419	56.06 402	76.00 381	26.89 442	79.65 265	35.36 433	67.29 362	72.60 427

CR-MVSNet		59.91 357	57.90 368	65.96 344	69.96 395	52.07 212	65.31 404	63.15 420	42.48 443	59.36 362	74.84 396	35.83 341	70.75 389	45.50 348	64.65 382	75.06 402

PatchmatchNet		59.84 358	58.24 364	64.65 364	73.05 334	46.70 319	69.42 365	62.18 431	47.55 394	58.88 368	71.96 420	34.49 355	69.16 398	42.99 377	63.60 393	78.07 362
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sc_t1		59.76 359	57.84 369	65.54 351	74.87 290	42.95 369	69.61 360	64.16 410	48.90 369	58.68 370	77.12 359	28.19 428	72.35 377	43.75 370	55.28 449	81.31 299

WTY-MVS		59.75 360	60.39 343	57.85 421	72.32 351	37.83 421	61.05 439	64.18 408	45.95 415	61.91 330	79.11 323	47.01 199	60.88 443	42.50 381	69.49 337	74.83 407

WB-MVSnew		59.66 361	59.69 349	59.56 401	75.19 283	35.78 446	69.34 366	64.28 407	46.88 404	61.76 333	75.79 385	40.61 283	65.20 427	32.16 446	71.21 301	77.70 369

CVMVSNet		59.63 362	59.14 353	61.08 396	74.47 304	38.84 411	75.20 247	68.74 370	31.15 475	58.24 377	76.51 374	32.39 391	68.58 402	49.77 297	65.84 373	75.81 393

UBG		59.62 363	59.53 350	59.89 400	78.12 189	35.92 445	64.11 417	60.81 438	49.45 361	61.34 339	75.55 389	33.05 373	67.39 413	38.68 407	74.62 243	76.35 389

ETVMVS		59.51 364	58.81 357	61.58 389	77.46 218	34.87 448	64.94 409	59.35 441	54.06 284	61.08 343	76.67 368	29.54 411	71.87 382	32.16 446	74.07 250	78.01 367

0.4-1-1-0.1		59.29 365	56.70 379	67.07 319	69.35 406	43.16 362	66.59 387	70.87 349	48.59 373	55.11 413	62.25 475	28.22 427	78.92 296	45.49 349	63.79 390	79.14 348

tpm cat1		59.25 366	56.95 374	66.15 340	72.19 353	46.96 317	68.09 377	65.76 393	40.03 458	57.81 382	70.56 432	38.32 313	74.51 365	38.26 410	61.50 419	77.00 381

test_vis1_n_1920		58.86 367	59.06 356	58.25 415	63.76 455	43.14 363	67.49 383	66.36 389	40.22 456	65.89 260	71.95 421	31.04 397	59.75 449	59.94 209	64.90 379	71.85 440

pmmvs-eth3d		58.81 368	56.31 384	66.30 336	67.61 431	52.42 206	72.30 315	64.76 402	43.55 433	54.94 416	74.19 402	28.95 417	72.60 374	43.31 372	57.21 441	73.88 420

tt0320		58.59 369	56.81 377	63.92 371	75.46 276	41.32 385	68.63 372	64.06 411	47.05 402	56.19 401	74.19 402	30.34 402	71.36 384	39.92 400	55.45 448	79.09 349

tpmvs		58.47 370	56.95 374	63.03 380	70.20 390	41.21 386	67.90 379	67.23 381	49.62 359	54.73 419	70.84 430	34.14 359	76.24 357	36.64 424	61.29 420	71.64 442

0.3-1-1-0.015		58.40 371	55.56 390	66.91 321	68.08 427	43.09 364	65.25 406	70.96 348	47.89 389	53.10 439	59.82 478	26.48 443	78.79 298	45.07 356	63.43 396	78.84 355

PVSNet	50.76 19	58.40 371	57.39 370	61.42 390	75.53 274	44.04 350	61.43 433	63.45 417	47.04 403	56.91 393	73.61 408	27.00 440	64.76 429	39.12 405	72.40 284	75.47 398

tt0320-xc		58.33 373	56.41 383	64.08 369	75.79 267	41.34 384	68.30 375	62.72 424	47.90 387	56.29 400	74.16 404	28.53 421	71.04 387	41.50 391	52.50 461	79.88 337

0.4-1-1-0.2		58.31 374	55.53 391	66.64 329	67.46 433	42.78 371	64.38 413	70.97 347	47.65 392	53.38 437	59.02 479	28.39 424	78.72 300	44.86 358	63.63 392	78.42 358

tpmrst		58.24 375	58.70 360	56.84 425	66.97 436	34.32 455	69.57 364	61.14 436	47.17 401	58.58 374	71.60 423	41.28 275	60.41 445	49.20 304	62.84 402	75.78 394

Patchmatch-RL test		58.16 376	55.49 392	66.15 340	67.92 429	48.89 287	60.66 441	51.07 472	47.86 390	59.36 362	62.71 474	34.02 362	72.27 379	56.41 241	59.40 432	77.30 375

test-LLR		58.15 377	58.13 367	58.22 416	68.57 417	44.80 338	65.46 400	57.92 447	50.08 353	55.44 407	69.82 443	32.62 386	57.44 461	49.66 300	73.62 259	72.41 433

ppachtmachnet_test		58.06 378	55.38 393	66.10 342	69.51 402	48.99 283	68.01 378	66.13 392	44.50 424	54.05 427	70.74 431	32.09 394	72.34 378	36.68 423	56.71 445	76.99 383

gg-mvs-nofinetune		57.86 379	56.43 382	62.18 384	72.62 341	35.35 447	66.57 388	56.33 456	50.65 346	57.64 384	57.10 483	30.65 399	76.36 355	37.38 415	78.88 168	74.82 408

CMPMVS	42.80 21	57.81 380	55.97 386	63.32 375	60.98 472	47.38 314	64.66 410	69.50 363	32.06 473	46.83 465	77.80 349	29.50 413	71.36 384	48.68 308	73.75 255	71.21 449
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 381	57.07 372	58.22 416	74.21 313	37.18 427	62.46 427	60.88 437	48.88 370	55.29 411	75.99 383	31.68 395	62.04 440	31.87 449	72.35 285	75.43 399

tpm		57.34 382	58.16 365	54.86 435	71.80 360	34.77 450	67.47 384	56.04 460	48.20 382	60.10 350	76.92 363	37.17 327	53.41 478	40.76 393	65.01 378	76.40 388

Patchmtry		57.16 383	56.47 381	59.23 406	69.17 409	34.58 453	62.98 424	63.15 420	44.53 423	56.83 394	74.84 396	35.83 341	68.71 401	40.03 397	60.91 421	74.39 415

AllTest		57.08 384	54.65 399	64.39 366	71.44 367	49.03 280	69.92 356	67.30 378	45.97 413	47.16 463	79.77 308	17.47 470	67.56 411	33.65 438	59.16 433	76.57 386

test_cas_vis1_n_1920		56.91 385	56.71 378	57.51 424	59.13 478	45.40 334	63.58 420	61.29 435	36.24 467	67.14 233	71.85 422	29.89 409	56.69 465	57.65 232	63.58 394	70.46 455

dmvs_re		56.77 386	56.83 376	56.61 426	69.23 407	41.02 387	58.37 449	64.18 408	50.59 348	57.45 388	71.42 424	35.54 343	58.94 454	37.23 416	67.45 361	69.87 460

testing3		56.54 387	55.92 387	58.41 414	77.52 216	27.93 485	69.72 357	56.36 455	54.75 272	58.63 373	77.80 349	20.88 467	71.75 383	25.31 481	62.25 413	75.53 397

our_test_3		56.49 388	54.42 402	62.68 382	69.51 402	45.48 333	66.08 392	61.49 434	44.11 430	50.73 451	69.60 447	33.05 373	68.15 403	38.38 409	56.86 442	74.40 414

pmmvs5		56.47 389	55.68 389	58.86 411	61.41 468	36.71 434	66.37 390	62.75 423	40.38 455	53.70 429	76.62 370	34.56 353	67.05 414	40.02 398	65.27 376	72.83 425

test-mter		56.42 390	55.82 388	58.22 416	68.57 417	44.80 338	65.46 400	57.92 447	39.94 460	55.44 407	69.82 443	21.92 462	57.44 461	49.66 300	73.62 259	72.41 433

USDC		56.35 391	54.24 406	62.69 381	64.74 451	40.31 396	65.05 407	73.83 317	43.93 431	47.58 461	77.71 353	15.36 479	75.05 363	38.19 411	61.81 417	72.70 426

PatchMatch-RL		56.25 392	54.55 401	61.32 393	77.06 238	56.07 120	65.57 397	54.10 465	44.13 429	53.49 436	71.27 429	25.20 453	66.78 416	36.52 426	63.66 391	61.12 473

sss		56.17 393	56.57 380	54.96 434	66.93 437	36.32 439	57.94 452	61.69 433	41.67 446	58.64 372	75.32 394	38.72 308	56.25 468	42.04 385	66.19 371	72.31 436

Syy-MVS		56.00 394	56.23 385	55.32 432	74.69 297	26.44 491	65.52 398	57.49 450	50.97 343	56.52 397	72.18 416	39.89 288	68.09 404	24.20 482	64.59 384	71.44 446

dtuonlycased		55.96 395	54.88 398	59.22 407	68.38 424	40.38 395	69.17 368	63.12 422	40.00 459	53.62 432	68.84 451	36.27 337	66.23 422	40.57 394	53.92 456	71.06 452

FMVSNet5		55.86 396	54.93 396	58.66 413	71.05 376	36.35 437	64.18 416	62.48 426	46.76 406	50.66 452	74.73 398	25.80 449	64.04 431	33.11 442	65.57 375	75.59 396

RPSCF		55.80 397	54.22 407	60.53 398	65.13 450	42.91 370	64.30 414	57.62 449	36.84 466	58.05 381	82.28 254	28.01 429	56.24 469	37.14 417	58.61 436	82.44 276

mvs5depth		55.64 398	53.81 410	61.11 395	59.39 477	40.98 391	65.89 393	68.28 373	50.21 351	58.11 380	75.42 392	17.03 472	67.63 410	43.79 368	46.21 473	74.73 410

EU-MVSNet		55.61 399	54.41 403	59.19 409	65.41 448	33.42 462	72.44 313	71.91 340	28.81 477	51.27 445	73.87 406	24.76 455	69.08 399	43.04 376	58.20 437	75.06 402

Anonymous20240521		55.30 400	54.41 403	57.96 420	60.92 474	41.73 380	71.09 337	71.06 346	41.18 449	48.65 459	73.31 410	16.93 473	59.25 451	42.54 380	64.01 387	72.90 424

TESTMET0.1,1		55.28 401	54.90 397	56.42 427	66.56 440	43.67 354	65.46 400	56.27 458	39.18 462	53.83 428	67.44 458	24.21 457	55.46 472	48.04 315	73.11 273	70.13 458

KD-MVS_self_test		55.22 402	53.89 409	59.21 408	57.80 482	27.47 487	57.75 455	74.32 306	47.38 396	50.90 448	70.00 438	28.45 423	70.30 394	40.44 395	57.92 438	79.87 338

MIMVSNet1		55.17 403	54.31 405	57.77 422	70.03 394	32.01 470	65.68 396	64.81 401	49.19 365	46.75 466	76.00 381	25.53 452	64.04 431	28.65 469	62.13 414	77.26 377

FE-MVSNET		55.16 404	53.75 411	59.41 403	65.29 449	33.20 464	67.21 386	66.21 391	48.39 380	49.56 457	73.53 409	29.03 416	72.51 375	30.38 462	54.10 455	72.52 429

Anonymous20231206		55.10 405	55.30 394	54.48 437	69.81 400	33.94 459	62.91 425	62.13 432	41.08 450	55.18 412	75.65 387	32.75 381	56.59 467	30.32 463	67.86 356	72.91 423

dtuonly		54.95 406	55.26 395	54.01 440	59.03 479	35.99 442	61.92 431	56.33 456	38.48 463	54.61 421	77.85 348	34.27 358	51.60 485	45.10 355	69.74 332	74.43 413

myMVS_eth3d		54.86 407	54.61 400	55.61 431	74.69 297	27.31 488	65.52 398	57.49 450	50.97 343	56.52 397	72.18 416	21.87 465	68.09 404	27.70 473	64.59 384	71.44 446

TinyColmap		54.14 408	51.72 420	61.40 391	66.84 438	41.97 377	66.52 389	68.51 371	44.81 420	42.69 478	75.77 386	11.66 486	72.94 372	31.96 448	56.77 444	69.27 464

EPMVS		53.96 409	53.69 412	54.79 436	66.12 445	31.96 471	62.34 429	49.05 476	44.42 426	55.54 405	71.33 428	30.22 404	56.70 464	41.65 389	62.54 410	75.71 395

PMMVS		53.96 409	53.26 415	56.04 428	62.60 462	50.92 230	61.17 437	56.09 459	32.81 472	53.51 435	66.84 463	34.04 361	59.93 448	44.14 363	68.18 354	57.27 481

test20.03		53.87 411	54.02 408	53.41 446	61.47 467	28.11 484	61.30 435	59.21 442	51.34 336	52.09 443	77.43 356	33.29 372	58.55 456	29.76 465	60.27 430	73.58 421

MDA-MVSNet-bldmvs		53.87 411	50.81 424	63.05 379	66.25 443	48.58 293	56.93 459	63.82 413	48.09 384	41.22 479	70.48 435	30.34 402	68.00 407	34.24 436	45.92 475	72.57 428

KD-MVS_2432*1600		53.45 413	51.50 422	59.30 404	62.82 459	37.14 428	55.33 462	71.79 341	47.34 398	55.09 414	70.52 433	21.91 463	70.45 391	35.72 431	42.97 479	70.31 456

miper_refine_blended		53.45 413	51.50 422	59.30 404	62.82 459	37.14 428	55.33 462	71.79 341	47.34 398	55.09 414	70.52 433	21.91 463	70.45 391	35.72 431	42.97 479	70.31 456

TDRefinement		53.44 415	50.72 426	61.60 388	64.31 454	46.96 317	70.89 339	65.27 399	41.78 444	44.61 473	77.98 339	11.52 488	66.36 420	28.57 470	51.59 463	71.49 445

usedtu_dtu_shiyan2		53.34 416	50.78 425	61.00 397	61.86 466	39.63 403	68.47 373	64.58 404	42.94 439	45.22 470	67.61 457	19.25 469	66.71 417	28.08 471	59.05 435	76.66 385

test0.0.03 1		53.32 417	53.59 413	52.50 452	62.81 461	29.45 479	59.51 445	54.11 464	50.08 353	54.40 424	74.31 401	32.62 386	55.92 470	30.50 461	63.95 389	72.15 438

PatchT		53.17 418	53.44 414	52.33 453	68.29 425	25.34 495	58.21 450	54.41 463	44.46 425	54.56 422	69.05 450	33.32 371	60.94 442	36.93 419	61.76 418	70.73 454

UnsupCasMVSNet_eth		53.16 419	52.47 416	55.23 433	59.45 476	33.39 463	59.43 446	69.13 367	45.98 412	50.35 454	72.32 415	29.30 415	58.26 458	42.02 386	44.30 477	74.05 418

PM-MVS		52.33 420	50.19 429	58.75 412	62.10 464	45.14 336	65.75 394	40.38 494	43.60 432	53.52 434	72.65 413	9.16 494	65.87 425	50.41 293	54.18 454	65.24 471

UWE-MVS-28		52.25 421	52.35 418	51.93 456	66.99 435	22.79 499	63.48 421	48.31 480	46.78 405	52.73 441	76.11 379	27.78 432	57.82 460	20.58 489	68.41 353	75.17 400

testgi		51.90 422	52.37 417	50.51 459	60.39 475	23.55 498	58.42 448	58.15 445	49.03 367	51.83 444	79.21 322	22.39 460	55.59 471	29.24 468	62.64 408	72.40 435

dp		51.89 423	51.60 421	52.77 450	68.44 423	32.45 469	62.36 428	54.57 462	44.16 428	49.31 458	67.91 453	28.87 419	56.61 466	33.89 437	54.89 451	69.24 465

JIA-IIPM		51.56 424	47.68 438	63.21 377	64.61 452	50.73 238	47.71 483	58.77 444	42.90 440	48.46 460	51.72 487	24.97 454	70.24 395	36.06 430	53.89 457	68.64 466

test_fmvs1_n		51.37 425	50.35 428	54.42 439	52.85 486	37.71 423	61.16 438	51.93 467	28.15 479	63.81 299	69.73 445	13.72 480	53.95 476	51.16 288	60.65 426	71.59 443

ADS-MVSNet2		51.33 426	48.76 433	59.07 410	66.02 446	44.60 343	50.90 475	59.76 440	36.90 464	50.74 449	66.18 466	26.38 444	63.11 436	27.17 475	54.76 452	69.50 462

test_fmvs1		51.32 427	50.48 427	53.81 442	53.57 484	37.51 425	60.63 442	51.16 470	28.02 481	63.62 300	69.23 449	16.41 475	53.93 477	51.01 289	60.70 425	69.99 459

YYNet1		50.73 428	48.96 430	56.03 429	61.10 470	41.78 379	51.94 472	56.44 454	40.94 452	44.84 471	67.80 455	30.08 407	55.08 474	36.77 420	50.71 465	71.22 448

MDA-MVSNet_test_wron		50.71 429	48.95 431	56.00 430	61.17 469	41.84 378	51.90 473	56.45 453	40.96 451	44.79 472	67.84 454	30.04 408	55.07 475	36.71 422	50.69 466	71.11 451

dmvs_testset		50.16 430	51.90 419	44.94 467	66.49 441	11.78 508	61.01 440	51.50 469	51.17 341	50.30 455	67.44 458	39.28 297	60.29 446	22.38 485	57.49 440	62.76 472

UnsupCasMVSNet_bld		50.07 431	48.87 432	53.66 443	60.97 473	33.67 461	57.62 456	64.56 405	39.47 461	47.38 462	64.02 472	27.47 434	59.32 450	34.69 435	43.68 478	67.98 468

test_vis1_n		49.89 432	48.69 434	53.50 445	53.97 483	37.38 426	61.53 432	47.33 484	28.54 478	59.62 360	67.10 462	13.52 481	52.27 482	49.07 305	57.52 439	70.84 453

Patchmatch-test		49.08 433	48.28 435	51.50 457	64.40 453	30.85 476	45.68 487	48.46 479	35.60 468	46.10 469	72.10 418	34.47 356	46.37 491	27.08 477	60.65 426	77.27 376

test_fmvs2		48.69 434	47.49 439	52.29 454	48.63 493	33.06 466	57.76 454	48.05 482	25.71 485	59.76 358	69.60 447	11.57 487	52.23 483	49.45 303	56.86 442	71.58 444

ADS-MVSNet		48.48 435	47.77 436	50.63 458	66.02 446	29.92 478	50.90 475	50.87 474	36.90 464	50.74 449	66.18 466	26.38 444	52.47 481	27.17 475	54.76 452	69.50 462

CHOSEN 280x420		47.83 436	46.36 440	52.24 455	67.37 434	49.78 262	38.91 495	43.11 492	35.00 469	43.27 477	63.30 473	28.95 417	49.19 487	36.53 425	60.80 423	57.76 480

new-patchmatchnet		47.56 437	47.73 437	47.06 462	58.81 480	9.37 511	48.78 481	59.21 442	43.28 435	44.22 474	68.66 452	25.67 450	57.20 463	31.57 456	49.35 470	74.62 412

PVSNet_0	43.31 20	47.46 438	45.64 441	52.92 449	67.60 432	44.65 340	54.06 467	54.64 461	41.59 447	46.15 468	58.75 480	30.99 398	58.66 455	32.18 445	24.81 495	55.46 483

ttmdpeth		45.56 439	42.95 444	53.39 447	52.33 489	29.15 480	57.77 453	48.20 481	31.81 474	49.86 456	77.21 358	8.69 495	59.16 452	27.31 474	33.40 491	71.84 441

MVS-HIRNet		45.52 440	44.48 442	48.65 461	68.49 422	34.05 458	59.41 447	44.50 489	27.03 482	37.96 489	50.47 492	26.16 447	64.10 430	26.74 478	59.52 431	47.82 490

pmmvs3		44.92 441	41.95 448	53.86 441	52.58 488	43.55 355	62.11 430	46.90 486	26.05 484	40.63 480	60.19 477	11.08 491	57.91 459	31.83 453	46.15 474	60.11 474

test_fmvs3		44.30 442	42.55 445	49.55 460	42.83 498	27.15 490	53.03 469	44.93 488	22.03 493	53.69 431	64.94 469	4.21 502	49.63 486	47.47 316	49.82 468	71.88 439

WB-MVS		43.26 443	43.41 443	42.83 471	63.32 458	10.32 510	58.17 451	45.20 487	45.42 417	40.44 482	67.26 461	34.01 363	58.98 453	11.96 500	24.88 494	59.20 475

LF4IMVS		42.95 444	42.26 446	45.04 465	48.30 494	32.50 468	54.80 464	48.49 478	28.03 480	40.51 481	70.16 436	9.24 493	43.89 494	31.63 454	49.18 471	58.72 477

MVStest1		42.65 445	39.29 452	52.71 451	47.26 496	34.58 453	54.41 466	50.84 475	23.35 487	39.31 487	74.08 405	12.57 483	55.09 473	23.32 483	28.47 493	68.47 467

EGC-MVSNET		42.47 446	38.48 454	54.46 438	74.33 309	48.73 289	70.33 351	51.10 471	0.03 543	0.18 541	67.78 456	13.28 482	66.49 419	18.91 491	50.36 467	48.15 488

FPMVS		42.18 447	41.11 449	45.39 464	58.03 481	41.01 389	49.50 479	53.81 466	30.07 476	33.71 491	64.03 470	11.69 485	52.08 484	14.01 495	55.11 450	43.09 492

SSC-MVS		41.96 448	41.99 447	41.90 472	62.46 463	9.28 512	57.41 457	44.32 490	43.38 434	38.30 488	66.45 464	32.67 385	58.42 457	10.98 502	21.91 497	57.99 479

ANet_high		41.38 449	37.47 456	53.11 448	39.73 504	24.45 496	56.94 458	69.69 358	47.65 392	26.04 496	52.32 486	12.44 484	62.38 439	21.80 486	10.61 505	72.49 430

test_vis1_rt		41.35 450	39.45 451	47.03 463	46.65 497	37.86 420	47.76 482	38.65 495	23.10 489	44.21 475	51.22 490	11.20 490	44.08 493	39.27 404	53.02 459	59.14 476

LCM-MVSNet		40.30 451	35.88 457	53.57 444	42.24 499	29.15 480	45.21 489	60.53 439	22.23 492	28.02 494	50.98 491	3.72 504	61.78 441	31.22 459	38.76 485	69.78 461

mvsany_test1		39.38 452	38.16 455	43.02 470	49.05 491	34.28 456	44.16 491	25.94 505	22.74 491	46.57 467	62.21 476	23.85 458	41.16 498	33.01 443	35.91 487	53.63 484

N_pmnet		39.35 453	40.28 450	36.54 478	63.76 455	1.62 526	49.37 480	0.76 527	34.62 470	43.61 476	66.38 465	26.25 446	42.57 495	26.02 480	51.77 462	65.44 470

DSMNet-mixed		39.30 454	38.72 453	41.03 473	51.22 490	19.66 502	45.53 488	31.35 501	15.83 500	39.80 484	67.42 460	22.19 461	45.13 492	22.43 484	52.69 460	58.31 478

APD_test1		37.39 455	34.94 458	44.72 468	48.88 492	33.19 465	52.95 470	44.00 491	19.49 494	27.28 495	58.59 481	3.18 506	52.84 480	18.92 490	41.17 482	48.14 489

PMVS	28.69 22	36.22 456	33.29 461	45.02 466	36.82 506	35.98 443	54.68 465	48.74 477	26.31 483	21.02 500	51.61 489	2.88 507	60.10 447	9.99 506	47.58 472	38.99 498
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.77 457	31.91 462	43.33 469	62.05 465	37.87 419	20.39 501	67.03 383	23.23 488	18.41 502	25.84 507	4.24 501	62.73 437	14.71 494	51.32 464	29.38 500
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		34.52 458	34.94 458	33.26 481	61.06 471	16.00 506	52.79 471	23.78 507	40.71 453	39.33 486	48.65 496	16.91 474	48.34 488	12.18 499	19.05 499	35.44 499

new_pmnet		34.13 459	34.29 460	33.64 480	52.63 487	18.23 504	44.43 490	33.90 500	22.81 490	30.89 493	53.18 485	10.48 492	35.72 502	20.77 488	39.51 483	46.98 491

mvsany_test3		32.62 460	30.57 465	38.77 476	36.16 507	24.20 497	38.10 496	20.63 509	19.14 495	40.36 483	57.43 482	5.06 499	36.63 501	29.59 467	28.66 492	55.49 482

test_vis3_rt		32.09 461	30.20 466	37.76 477	35.36 508	27.48 486	40.60 494	28.29 504	16.69 498	32.52 492	40.53 500	1.96 508	37.40 500	33.64 440	42.21 481	48.39 487

test_f		31.86 462	31.05 463	34.28 479	32.33 510	21.86 500	32.34 497	30.46 502	16.02 499	39.78 485	55.45 484	4.80 500	32.36 504	30.61 460	37.66 486	48.64 486

testf1		31.46 463	28.89 467	39.16 474	41.99 501	28.78 482	46.45 485	37.56 496	14.28 501	21.10 498	48.96 493	1.48 510	47.11 489	13.63 496	34.56 488	41.60 494

APD_test2		31.46 463	28.89 467	39.16 474	41.99 501	28.78 482	46.45 485	37.56 496	14.28 501	21.10 498	48.96 493	1.48 510	47.11 489	13.63 496	34.56 488	41.60 494

kuosan		29.62 465	30.82 464	26.02 486	52.99 485	16.22 505	51.09 474	22.71 508	33.91 471	33.99 490	40.85 498	15.89 477	33.11 503	7.59 511	18.37 500	28.72 501

PMMVS2		27.40 466	25.91 469	31.87 483	39.46 505	6.57 515	31.17 498	28.52 503	23.96 486	20.45 501	48.94 495	4.20 503	37.94 499	16.51 492	19.97 498	51.09 485

E-PMN		23.77 467	22.73 471	26.90 484	42.02 500	20.67 501	42.66 492	35.70 498	17.43 496	10.28 511	25.05 508	6.42 497	42.39 496	10.28 505	14.71 502	17.63 506

EMVS		22.97 468	21.84 472	26.36 485	40.20 503	19.53 503	41.95 493	34.64 499	17.09 497	9.73 512	22.83 510	7.29 496	42.22 497	9.18 508	13.66 503	17.32 507

MVE	17.77 23	21.41 469	17.77 475	32.34 482	34.34 509	25.44 494	16.11 503	24.11 506	11.19 503	13.22 505	31.92 503	1.58 509	30.95 505	10.47 504	17.03 501	40.62 497
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ArgMatch-SfM		20.82 470	19.10 473	25.97 487	21.54 511	13.77 507	29.84 500	6.08 513	9.69 504	22.36 497	51.71 488	0.53 512	21.69 507	20.98 487	9.18 508	42.43 493

test_method		19.68 471	18.10 474	24.41 488	13.68 515	3.11 521	12.06 508	42.37 493	2.00 512	11.97 507	36.38 501	5.77 498	29.35 506	15.06 493	23.65 496	40.76 496

cdsmvs_eth3d_5k		17.50 472	23.34 470	0.00 528	0.00 551	0.00 552	0.00 539	78.63 204	0.00 546	0.00 547	82.18 257	49.25 165	0.00 545	0.00 545	0.00 543	0.00 543

DenseAffine		14.16 473	13.16 476	17.15 489	17.01 513	8.89 513	19.68 502	2.17 516	7.89 505	15.00 504	40.64 499	0.19 515	15.28 509	11.16 501	4.69 511	27.27 502

wuyk23d		13.32 474	12.52 477	15.71 490	47.54 495	26.27 492	31.06 499	1.98 517	4.93 509	5.18 516	1.94 529	0.45 513	18.54 508	6.81 512	12.83 504	2.33 516

RoMa-SfM		11.96 475	11.39 478	13.68 491	10.24 517	6.80 514	15.83 504	1.33 520	6.34 507	13.06 506	41.41 497	0.16 516	12.72 510	10.58 503	3.56 513	21.52 503

DKM		10.33 476	10.10 480	11.02 493	10.54 516	5.43 516	14.18 505	1.03 523	4.97 508	11.74 508	36.09 502	0.11 519	9.09 513	9.38 507	2.85 514	18.53 505

LoFTR		9.45 477	9.00 481	10.79 494	10.22 518	4.31 518	11.11 509	4.11 514	2.40 511	10.53 510	30.89 504	0.13 517	10.75 512	3.12 514	8.52 509	17.31 508

tmp_tt		9.43 478	11.14 479	4.30 500	2.38 527	4.40 517	13.62 506	16.08 511	0.39 518	15.89 503	13.06 515	15.80 478	5.54 516	12.63 498	10.46 506	2.95 515

PDCNetPlus		9.23 479	8.89 482	10.23 495	13.70 514	3.70 519	12.27 507	1.51 519	3.98 510	6.73 514	29.50 505	0.24 514	8.07 515	7.83 510	4.30 512	18.93 504

MatchFormer		7.03 480	6.96 484	7.26 496	7.64 519	3.36 520	10.21 510	3.04 515	1.31 513	9.02 513	22.94 509	0.08 526	8.15 514	1.46 517	6.91 510	10.26 511

ab-mvs-re		6.49 481	8.65 483	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	77.89 346	0.00 549	0.00 545	0.00 545	0.00 543	0.00 543

test123		4.73 482	6.30 485	0.02 526	0.01 549	0.01 551	56.36 460	0.00 550	0.01 544	0.04 545	0.21 545	0.01 544	0.00 545	0.03 537	0.00 543	0.04 541

testmvs		4.52 483	6.03 486	0.01 527	0.01 549	0.00 552	53.86 468	0.00 550	0.01 544	0.04 545	0.27 544	0.00 549	0.00 545	0.04 529	0.00 543	0.03 542

PMatch-SfM		4.42 484	4.43 488	4.39 499	2.90 524	1.50 527	4.85 511	0.36 530	1.17 514	4.73 518	20.99 511	0.01 544	3.26 519	3.74 513	1.10 525	8.40 513

GLUNet-SfM		4.33 485	3.64 490	6.41 497	3.38 523	1.65 524	3.23 516	1.54 518	0.66 517	6.36 515	15.13 514	0.08 526	5.54 516	0.94 518	1.44 521	12.05 510

ELoFTR		4.04 486	3.55 491	5.50 498	2.33 528	1.25 528	3.58 513	1.18 521	0.90 515	4.23 519	16.28 513	0.03 532	5.46 518	1.95 516	1.42 522	9.81 512

pcd_1.5k_mvsjas		3.92 487	5.23 487	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	0.00 546	47.05 196	0.00 545	0.00 545	0.00 543	0.00 543

MASt3R-SfM		3.33 488	3.70 489	2.21 501	2.02 531	1.04 529	3.52 515	1.05 522	0.67 516	4.93 517	16.68 512	0.10 521	1.50 522	2.06 515	2.29 518	4.09 514

ALIKED-LG		2.35 489	2.54 492	1.78 502	5.54 520	1.79 523	3.81 512	0.96 524	0.33 519	1.86 520	7.18 516	0.13 517	1.60 520	0.20 526	2.81 515	1.94 517

ALIKED-MNN		2.09 490	2.23 493	1.67 503	5.15 521	1.82 522	3.53 514	0.77 525	0.25 520	1.45 522	6.03 518	0.09 524	1.52 521	0.17 527	2.64 516	1.66 518

ALIKED-NN		1.96 491	2.12 494	1.48 504	4.72 522	1.65 524	3.19 517	0.77 525	0.23 521	1.43 523	5.87 519	0.10 521	1.37 523	0.16 528	2.61 517	1.42 524

XFeat-MNN		1.07 492	1.17 495	0.77 506	0.52 547	0.31 544	1.15 523	0.41 528	0.15 525	1.62 521	4.35 520	0.07 530	0.77 524	0.38 520	1.88 519	1.22 525

SP-DiffGlue		0.98 493	1.05 496	0.75 509	0.81 546	0.40 536	1.24 522	0.37 529	0.19 522	1.26 525	3.80 521	0.11 519	0.34 530	0.51 519	1.18 523	1.52 522

SP-LightGlue		0.94 494	0.99 497	0.78 505	2.60 525	0.38 537	1.71 518	0.34 531	0.17 523	0.50 527	2.14 525	0.09 524	0.38 527	0.26 522	1.13 524	1.59 519

SP-SuperGlue		0.93 495	0.98 498	0.77 506	2.54 526	0.38 537	1.70 519	0.34 531	0.17 523	0.52 526	2.13 526	0.10 521	0.36 529	0.26 522	1.10 525	1.57 521

SP-MNN		0.89 496	0.93 500	0.77 506	2.32 529	0.34 541	1.68 520	0.33 533	0.13 527	0.49 528	2.07 527	0.08 526	0.39 526	0.25 524	1.07 527	1.58 520

XFeat-NN		0.87 497	0.97 499	0.59 511	0.48 548	0.24 547	0.94 524	0.29 535	0.12 528	1.41 524	3.45 524	0.06 531	0.56 525	0.29 521	1.65 520	0.95 526

SP-NN		0.85 498	0.90 501	0.73 510	2.22 530	0.33 543	1.63 521	0.31 534	0.14 526	0.47 529	1.97 528	0.08 526	0.38 527	0.25 524	1.01 528	1.47 523

SIFT-NN		0.60 499	0.65 502	0.45 512	1.90 532	0.55 530	0.90 525	0.16 536	0.10 529	0.34 530	1.43 530	0.02 533	0.28 531	0.04 529	0.95 529	0.50 527

SIFT-MNN		0.56 500	0.61 503	0.43 513	1.75 533	0.50 531	0.82 526	0.16 536	0.10 529	0.30 531	1.38 531	0.02 533	0.28 531	0.04 529	0.92 531	0.50 527

SIFT-NN-NCMNet		0.53 501	0.58 504	0.40 514	1.60 535	0.49 532	0.80 527	0.15 538	0.09 532	0.28 533	1.29 532	0.02 533	0.27 533	0.04 529	0.94 530	0.44 531

SIFT-NCM-Cal		0.51 502	0.55 505	0.38 515	1.66 534	0.45 533	0.75 528	0.12 539	0.09 532	0.21 538	1.18 537	0.02 533	0.27 533	0.03 537	0.89 532	0.43 533

SIFT-NN-CMatch		0.49 503	0.53 506	0.38 515	1.35 539	0.41 535	0.70 530	0.12 539	0.09 532	0.30 531	1.28 534	0.02 533	0.26 535	0.04 529	0.83 534	0.47 529

SIFT-NN-UMatch		0.48 504	0.52 507	0.36 517	1.27 541	0.36 539	0.75 528	0.12 539	0.10 529	0.25 535	1.29 532	0.02 533	0.26 535	0.04 529	0.85 533	0.44 531

SIFT-ConvMatch		0.48 504	0.52 507	0.35 518	1.51 536	0.42 534	0.64 532	0.11 542	0.09 532	0.26 534	1.24 535	0.02 533	0.25 537	0.04 529	0.76 536	0.38 534

SIFT-UMatch		0.45 506	0.50 509	0.32 520	1.46 537	0.34 541	0.66 531	0.10 544	0.09 532	0.22 537	1.19 536	0.02 533	0.25 537	0.04 529	0.73 537	0.36 536

SIFT-NN-PointCN		0.44 507	0.47 510	0.33 519	1.17 542	0.29 545	0.64 532	0.11 542	0.09 532	0.25 535	1.14 538	0.02 533	0.25 537	0.03 537	0.78 535	0.46 530

SIFT-CM-Cal		0.42 508	0.46 511	0.31 521	1.40 538	0.35 540	0.56 535	0.09 545	0.09 532	0.20 539	1.09 540	0.02 533	0.23 540	0.03 537	0.66 539	0.34 537

SIFT-UM-Cal		0.41 509	0.46 511	0.28 522	1.35 539	0.29 545	0.57 534	0.08 546	0.09 532	0.20 539	1.10 539	0.02 533	0.23 540	0.03 537	0.68 538	0.30 539

SIFT-PCN-Cal		0.36 510	0.39 513	0.26 523	1.16 543	0.21 548	0.46 537	0.07 548	0.08 540	0.17 542	0.92 541	0.01 544	0.20 543	0.03 537	0.59 541	0.37 535

SIFT-PointCN		0.36 510	0.39 513	0.25 524	1.14 544	0.21 548	0.50 536	0.08 546	0.08 540	0.17 542	0.89 542	0.01 544	0.21 542	0.03 537	0.60 540	0.34 537

SIFT-NCMNet		0.30 512	0.33 515	0.19 525	1.04 545	0.18 550	0.39 538	0.05 549	0.08 540	0.14 544	0.77 543	0.01 544	0.16 544	0.02 544	0.49 542	0.22 540

mmdepth		0.00 513	0.00 516	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	0.00 546	0.00 549	0.00 545	0.00 545	0.00 543	0.00 543

monomultidepth		0.00 513	0.00 516	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	0.00 546	0.00 549	0.00 545	0.00 545	0.00 543	0.00 543

test_blank		0.00 513	0.00 516	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	0.00 546	0.00 549	0.00 545	0.00 545	0.00 543	0.00 543

uanet_test		0.00 513	0.00 516	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	0.00 546	0.00 549	0.00 545	0.00 545	0.00 543	0.00 543

DCPMVS		0.00 513	0.00 516	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	0.00 546	0.00 549	0.00 545	0.00 545	0.00 543	0.00 543

sosnet-low-res		0.00 513	0.00 516	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	0.00 546	0.00 549	0.00 545	0.00 545	0.00 543	0.00 543

sosnet		0.00 513	0.00 516	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	0.00 546	0.00 549	0.00 545	0.00 545	0.00 543	0.00 543

uncertanet		0.00 513	0.00 516	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	0.00 546	0.00 549	0.00 545	0.00 545	0.00 543	0.00 543

Regformer		0.00 513	0.00 516	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	0.00 546	0.00 549	0.00 545	0.00 545	0.00 543	0.00 543

uanet		0.00 513	0.00 516	0.00 528	0.00 551	0.00 552	0.00 539	0.00 550	0.00 546	0.00 547	0.00 546	0.00 549	0.00 545	0.00 545	0.00 543	0.00 543

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 23	60.95 107	83.65 12	90.57 27		89.91 16	77.02 34	89.43 22	88.10 45

TestfortrainingZip				78.05 44	84.66 62	58.22 87	86.84 11	85.98 22	63.31 49	79.39 24	88.94 65	62.01 15	89.61 21		86.45 63	86.34 121

WAC-MVS						27.31 488								27.77 472

FOURS1					86.12 37	60.82 37	88.18 1	83.61 84	60.87 109	81.50 20

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 30					90.96 1	79.31 10	90.65 8	87.85 55

PC_three_1452									55.09 257	84.46 4	89.84 52	66.68 5	89.41 23	74.24 61	91.38 2	88.42 32

No_MVS				79.95 4	87.24 14	61.04 31		85.62 30					90.96 1	79.31 10	90.65 8	87.85 55

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 21	83.80 11	91.86 6	64.03 12

eth-test2					0.00 551

eth-test					0.00 551

ZD-MVS					86.64 21	60.38 45		82.70 118	57.95 185	78.10 34	90.06 45	56.12 53	88.84 31	74.05 64	87.00 54

RE-MVS-def			73.71 86		83.49 73	59.87 54	84.29 48	81.36 141	58.07 179	73.14 111	90.07 43	43.06 246		68.20 107	81.76 112	84.03 219

IU-MVS					87.77 4	59.15 68		85.53 32	53.93 287	84.64 3				79.07 13	90.87 5	88.37 34

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 51	67.01 1	90.33 12	73.16 71	91.15 4	88.23 40

test_241102_TWO								86.73 12	64.18 35	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 64

test_241102_ONE					87.77 4	58.90 78		86.78 10	64.20 34	85.97 1	91.34 18	66.87 3	90.78 7

9.14			78.75 18		83.10 78		84.15 54	88.26 1	59.90 139	78.57 31	90.36 35	57.51 38	86.86 74	77.39 29	89.52 21

save fliter					86.17 34	61.30 28	83.98 58	79.66 180	59.00 159

test_0728_THIRD									65.04 17	83.82 8	92.00 3	64.69 11	90.75 8	79.48 7	90.63 10	88.09 47

test_0728_SECOND				79.19 16	87.82 3	59.11 71	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 66

test0726					87.75 7	59.07 73	87.86 4	86.83 8	64.26 32	84.19 7	91.92 5	64.82 8

GSMVS																78.05 363

test_part2					87.58 9	60.47 42				83.42 14

sam_mvs1												34.74 352				78.05 363

sam_mvs												33.43 370

ambc				65.13 361	63.72 457	37.07 430	47.66 484	78.78 200		54.37 425	71.42 424	11.24 489	80.94 239	45.64 344	53.85 458	77.38 374

MTGPA								80.97 159

test_post1							68.67 371				3.64 522	32.39 391	69.49 397	44.17 361

test_post											3.55 523	33.90 364	66.52 418

patchmatchnet-post											64.03 470	34.50 354	74.27 367

GG-mvs-BLEND				62.34 383	71.36 371	37.04 431	69.20 367	57.33 452		54.73 419	65.48 468	30.37 401	77.82 317	34.82 434	74.93 240	72.17 437

MTMP							86.03 23	17.08 510

gm-plane-assit					71.40 370	41.72 382			48.85 371		73.31 410		82.48 204	48.90 307

test9_res														75.28 54	88.31 35	83.81 230

TEST9					85.58 44	61.59 24	81.62 91	81.26 148	55.65 241	74.93 65	88.81 68	53.70 90	84.68 139

test_8					85.40 47	60.96 34	81.54 94	81.18 152	55.86 233	74.81 70	88.80 70	53.70 90	84.45 143

agg_prior2														73.09 72	87.93 43	84.33 208

agg_prior					85.04 54	59.96 50		81.04 157		74.68 75			84.04 150

TestCases				64.39 366	71.44 367	49.03 280		67.30 378	45.97 413	47.16 463	79.77 308	17.47 470	67.56 411	33.65 438	59.16 433	76.57 386

test_prior4						62.51 14	82.08 87

test_prior2							81.75 89		60.37 125	75.01 63	89.06 61	56.22 49		72.19 80	88.96 27

test_prior				76.69 67	84.20 66	57.27 99		84.88 45					86.43 90			86.38 117

旧先验2							76.08 226		45.32 418	76.55 48			65.56 426	58.75 225

新几何2							76.12 224

新几何1				70.76 254	85.66 42	61.13 30		66.43 388	44.68 422	70.29 160	86.64 128	41.29 274	75.23 362	49.72 299	81.75 114	75.93 392

旧先验1					83.04 79	53.15 182		67.52 377			87.85 89	44.08 234			80.76 123	78.03 366

无先验							79.66 123	74.30 308	48.40 379				80.78 245	53.62 267		79.03 352

原ACMM2							79.02 131

原ACMM1				74.69 109	85.39 48	59.40 59		83.42 90	51.47 333	70.27 161	86.61 132	48.61 173	86.51 88	53.85 266	87.96 42	78.16 361

test222					83.14 77	58.68 82	72.57 309	63.45 417	41.78 444	67.56 224	86.12 150	37.13 328			78.73 174	74.98 405

testdata2													72.18 381	46.95 332

segment_acmp												54.23 77

testdata				64.66 363	81.52 99	52.93 187		65.29 398	46.09 411	73.88 92	87.46 96	38.08 317	66.26 421	53.31 271	78.48 181	74.78 409

testdata1							72.65 304		60.50 119

test12				77.76 51	84.52 63	58.41 84		83.36 93		72.93 119		54.61 74	88.05 44		88.12 37	86.81 98

plane_prior7					81.41 102	55.96 122

plane_prior6					81.20 109	56.24 117						45.26 221

plane_prior5								84.01 59					87.21 64	68.16 111	80.58 127	84.65 199

plane_prior4											86.10 151

plane_prior3						56.09 119			63.92 39	69.27 181

plane_prior2							84.22 51		64.52 28

plane_prior1					81.27 107

plane_prior						56.31 113	83.58 64		63.19 56						80.48 130

n2								0.00 550

nn								0.00 550

door-mid								47.19 485

lessismore_v0				69.91 273	71.42 369	47.80 307		50.90 473		50.39 453	75.56 388	27.43 436	81.33 226	45.91 341	34.10 490	80.59 318

LGP-MVS_train				75.76 85	80.22 124	57.51 97		83.40 91	61.32 98	66.67 243	87.33 102	39.15 300	86.59 81	67.70 121	77.30 204	83.19 253

test11								83.47 88

door								47.60 483

HQP5-MVS						54.94 144

HQP-NCC					80.66 116		82.31 82		62.10 83	67.85 213

ACMP_Plane					80.66 116		82.31 82		62.10 83	67.85 213

BP-MVS														67.04 132

HQP4-MVS										67.85 213			86.93 72			84.32 209

HQP3-MVS								83.90 64							80.35 132

HQP2-MVS												45.46 215

NP-MVS					80.98 112	56.05 121					85.54 173

MDTV_nov1_ep13_2view						25.89 493	61.22 436		40.10 457	51.10 446		32.97 376		38.49 408		78.61 357

MDTV_nov1_ep13			57.00 373		72.73 339	38.26 417	65.02 408	64.73 403	44.74 421	55.46 406	72.48 414	32.61 388	70.47 390	37.47 413	67.75 358

ACMMP++_ref															74.07 250

ACMMP++															72.16 291

Test By Simon												48.33 176

ITE_SJBPF				62.09 385	66.16 444	44.55 345		64.32 406	47.36 397	55.31 410	80.34 297	19.27 468	62.68 438	36.29 428	62.39 411	79.04 351

DeepMVS_CX				12.03 492	17.97 512	10.91 509		10.60 512	7.46 506	11.07 509	28.36 506	3.28 505	11.29 511	8.01 509	9.74 507	13.89 509

High-resolution multi-view benchmark