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.

9.14			88.26 19		92.84 69		91.52 56	94.75 1	73.93 169	88.57 35	94.67 30	75.57 25	95.79 63	86.77 51	95.76 27

SF-MVS		88.46 15	88.74 15	87.64 38	92.78 70	71.95 51	92.40 29	94.74 2	75.71 112	89.16 29	95.10 18	75.65 24	96.19 51	87.07 49	96.01 17	94.79 23

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 126	92.25 9	95.03 20		97.39 11	88.15 39	95.96 19	94.75 30

MED-MVS		89.59 4	90.16 4	87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	76.30 98	92.25 9	95.03 20	81.59 7	97.39 11	88.15 39	95.96 19	94.75 30

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 77	93.28 12	94.36 3	75.24 126	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 54

ME-MVS		88.98 12	89.39 9	87.75 30	94.54 20	71.43 60	91.61 49	94.25 6	76.30 98	90.62 21	95.03 20	78.06 16	97.07 20	88.15 39	95.96 19	94.75 30

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 64	95.06 1	94.23 7	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

test_0728_SECOND				87.71 35	95.34 1	71.43 60	93.49 10	94.23 7					97.49 4	89.08 22	96.41 12	94.21 70

lecture		88.09 17	88.59 16	86.58 62	93.26 56	69.77 96	93.70 6	94.16 9	77.13 66	89.76 26	95.52 14	72.26 53	96.27 48	86.87 50	94.65 52	93.70 101

test_one_0601					95.07 7	71.46 59		94.14 10	78.27 41	92.05 14	95.74 6	80.83 13

test0726					95.27 5	71.25 64	93.60 7	94.11 11	77.33 58	92.81 3	95.79 3	80.98 11

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 11	80.27 10	91.35 17	94.16 54	78.35 15	96.77 28	89.59 17	94.22 66	94.67 38
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 107	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 85
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 204	85.22 78	91.90 122	69.47 95	96.42 44	83.28 86	95.94 23	94.35 63

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 71	93.57 8	94.06 15	77.24 61	93.10 1	95.72 8	82.99 1	97.44 7	89.07 25	96.63 4	94.88 16

test_241102_TWO								94.06 15	77.24 61	92.78 4	95.72 8	81.26 10	97.44 7	89.07 25	96.58 6	94.26 69

test_241102_ONE					95.30 2	70.98 71		94.06 15	77.17 64	93.10 1	95.39 16	82.99 1	97.27 15

APDe-MVS		89.15 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 114	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 57
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 91	72.32 45	90.31 79	93.94 19	77.12 67	82.82 135	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 106
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 153	91.30 18

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 196	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 60

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 28	73.33 19	93.03 19	93.81 22	76.81 75	85.24 77	94.32 44	71.76 60	96.93 23	85.53 61	95.79 26	94.32 66

SPE-MVS-test		86.29 54	86.48 51	85.71 80	91.02 95	67.21 180	92.36 34	93.78 23	78.97 33	83.51 120	91.20 153	70.65 78	95.15 91	81.96 102	94.89 46	94.77 25

3Dnovator+	77.84 4	85.48 73	84.47 92	88.51 7	91.08 93	73.49 16	93.18 16	93.78 23	80.79 8	76.66 254	93.37 83	60.40 236	96.75 30	77.20 162	93.73 70	95.29 6

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 78	72.96 25	93.73 5	93.67 25	80.19 12	88.10 42	94.80 27	73.76 37	97.11 18	87.51 46	95.82 25	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
SMA-MVS		89.08 10	89.23 10	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 147	92.29 7	95.97 2	74.28 33	97.24 16	88.58 33	96.91 1	94.87 18
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
EC-MVSNet		86.01 58	86.38 52	84.91 112	89.31 147	66.27 194	92.32 35	93.63 26	79.37 23	84.17 102	91.88 123	69.04 109	95.43 77	83.93 81	93.77 69	93.01 145

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 100	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 77

CSCG		86.41 51	86.19 58	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 167	83.16 127	91.07 158	75.94 21	95.19 89	79.94 124	94.38 62	93.55 114

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 90	93.50 30	75.17 134	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 47
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FIs		82.07 148	82.42 133	81.04 274	88.80 171	58.34 358	88.26 161	93.49 31	76.93 72	78.47 211	91.04 159	69.92 89	92.34 244	69.87 253	84.97 227	92.44 171

DELS-MVS		85.41 76	85.30 80	85.77 79	88.49 182	67.93 152	85.52 268	93.44 32	78.70 34	83.63 115	89.03 219	74.57 27	95.71 66	80.26 121	94.04 67	93.66 102
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
GST-MVS		87.42 31	87.26 34	87.89 24	94.12 40	72.97 24	92.39 31	93.43 33	76.89 73	84.68 86	93.99 65	70.67 77	96.82 26	84.18 79	95.01 41	93.90 88

FC-MVSNet-test		81.52 164	82.02 145	80.03 299	88.42 187	55.97 398	87.95 172	93.42 34	77.10 68	77.38 235	90.98 165	69.96 88	91.79 264	68.46 268	84.50 234	92.33 174

DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 59	92.24 77	69.03 110	89.57 99	93.39 35	77.53 53	89.79 25	94.12 56	78.98 14	96.58 39	85.66 58	95.72 28	94.58 47

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 104	83.81 110	93.95 68	69.77 92	96.01 58	85.15 62	94.66 51	94.32 66
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS		86.69 44	86.95 42	85.90 78	90.76 103	67.57 164	92.83 22	93.30 37	79.67 19	84.57 93	92.27 107	71.47 65	95.02 100	84.24 77	93.46 73	95.13 9

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 38	76.78 77	84.91 82	94.44 39	70.78 75	96.61 36	84.53 72	94.89 46	93.66 102

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 39	76.78 77	84.66 89	94.52 32	68.81 111	96.65 34	84.53 72	94.90 45	94.00 82

reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 148	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 133

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 138	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 140

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 138	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 140

SD-MVS		88.06 18	88.50 18	86.71 60	92.60 75	72.71 29	91.81 46	93.19 40	77.87 42	90.32 23	94.00 63	74.83 26	93.78 158	87.63 45	94.27 65	93.65 106
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
ACMMP		85.89 65	85.39 76	87.38 44	93.59 49	72.63 33	92.74 25	93.18 44	76.78 77	80.73 172	93.82 72	64.33 166	96.29 46	82.67 99	90.69 116	93.23 126
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
region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 45	76.73 80	84.45 94	94.52 32	69.09 105	96.70 31	84.37 74	94.83 49	94.03 80

DPM-MVS		84.93 86	84.29 93	86.84 56	90.20 113	73.04 23	87.12 204	93.04 46	69.80 273	82.85 134	91.22 152	73.06 44	96.02 57	76.72 174	94.63 54	91.46 210

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 117	83.86 108	94.42 40	67.87 126	96.64 35	82.70 98	94.57 56	93.66 102

casdiffmvs_mvg		85.99 59	86.09 62	85.70 81	87.65 229	67.22 179	88.69 142	93.04 46	79.64 21	85.33 76	92.54 104	73.30 39	94.50 124	83.49 83	91.14 108	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 50	72.37 43	91.26 59	93.04 46	76.62 83	84.22 100	93.36 84	71.44 66	96.76 29	80.82 113	95.33 37	94.16 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UniMVSNet (Re)		81.60 160	81.11 156	83.09 207	88.38 188	64.41 255	87.60 183	93.02 50	78.42 37	78.56 207	88.16 248	69.78 91	93.26 193	69.58 256	76.49 348	91.60 201

sasdasda		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 149	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

canonicalmvs		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 149	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

CNVR-MVS		88.93 13	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 51	80.90 7	88.06 43	94.06 59	76.43 19	96.84 25	88.48 36	95.99 18	94.34 64

MSC_two_6792asdad				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 58

No_MVS				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 58

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	94.17 53	67.45 129	96.60 37	83.06 87	94.50 57	94.07 78

X-MVStestdata		80.37 199	77.83 239	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 488	67.45 129	96.60 37	83.06 87	94.50 57	94.07 78

APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 180	85.94 69	94.51 35	65.80 154	95.61 67	83.04 89	92.51 83	93.53 116

test_prior				86.33 64	92.61 74	69.59 98		92.97 59					95.48 74			93.91 86

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 324	92.39 6				88.94 28	96.63 4	94.85 21

balanced_conf03		86.78 42	86.99 40	86.15 70	91.24 90	67.61 162	90.51 70	92.90 61	77.26 60	87.44 56	91.63 135	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

baseline		84.93 86	84.98 83	84.80 117	87.30 249	65.39 218	87.30 200	92.88 62	77.62 47	84.04 105	92.26 108	71.81 59	93.96 144	81.31 107	90.30 122	95.03 11

MSLP-MVS++		85.43 75	85.76 69	84.45 132	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 100	92.81 99	67.16 133	92.94 215	80.36 119	94.35 63	90.16 258

HPM-MVS++		89.02 11	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 64	80.26 11	87.78 48	94.27 47	75.89 22	96.81 27	87.45 47	96.44 9	93.05 142

casdiffmvs		85.11 83	85.14 82	85.01 105	87.20 251	65.77 209	87.75 180	92.83 65	77.84 43	84.36 99	92.38 106	72.15 55	93.93 150	81.27 109	90.48 119	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 198	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 156
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 65	81.50 5	85.79 72	93.47 80	73.02 45	97.00 22	84.90 64	94.94 44	94.10 76

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 68	76.62 83	83.68 112	94.46 36	67.93 124	95.95 62	84.20 78	94.39 61	93.23 126

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 64	93.49 10	92.73 69	77.33 58	92.12 12	95.78 4	80.98 11	97.40 9	89.08 22	96.41 12	93.33 123
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
GDP-MVS		83.52 118	82.64 130	86.16 69	88.14 197	68.45 132	89.13 121	92.69 70	72.82 202	83.71 111	91.86 125	55.69 274	95.35 86	80.03 122	89.74 134	94.69 33

EIA-MVS		83.31 127	82.80 127	84.82 115	89.59 130	65.59 213	88.21 162	92.68 71	74.66 150	78.96 197	86.42 302	69.06 107	95.26 87	75.54 188	90.09 126	93.62 109

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 239	87.75 50	94.07 58	74.01 36	96.70 31	84.66 70	94.84 48

nrg030		83.88 104	83.53 113	84.96 107	86.77 269	69.28 109	90.46 75	92.67 72	74.79 146	82.95 130	91.33 148	72.70 50	93.09 208	80.79 115	79.28 314	92.50 166

WR-MVS_H		78.51 246	78.49 220	78.56 332	88.02 204	56.38 392	88.43 151	92.67 72	77.14 65	73.89 319	87.55 266	66.25 145	89.24 339	58.92 357	73.55 392	90.06 268

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 73	91.09 92	67.64 161	89.63 97	92.65 75	72.89 201	84.64 90	91.71 130	71.85 58	96.03 55	84.77 69	94.45 60	94.49 56

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 75	77.57 49	83.84 109	94.40 41	72.24 54	96.28 47	85.65 59	95.30 39	93.62 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETV-MVS		84.90 88	84.67 88	85.59 86	89.39 142	68.66 127	88.74 140	92.64 77	79.97 16	84.10 103	85.71 315	69.32 98	95.38 82	80.82 113	91.37 105	92.72 155

MGCFI-Net		85.06 85	85.51 74	83.70 183	89.42 139	63.01 291	89.43 104	92.62 78	76.43 89	87.53 53	91.34 147	72.82 49	93.42 187	81.28 108	88.74 153	94.66 41

CANet		86.45 48	86.10 61	87.51 42	90.09 115	70.94 75	89.70 94	92.59 79	81.78 4	81.32 158	91.43 145	70.34 79	97.23 17	84.26 75	93.36 74	94.37 62

SR-MVS		86.73 43	86.67 48	86.91 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 152	86.84 64	94.65 31	67.31 131	95.77 64	84.80 68	92.85 78	92.84 154

alignmvs		85.48 73	85.32 79	85.96 77	89.51 134	69.47 102	89.74 92	92.47 81	76.17 102	87.73 52	91.46 144	70.32 80	93.78 158	81.51 104	88.95 147	94.63 44

原ACMM1				84.35 139	93.01 66	68.79 117		92.44 82	63.96 373	81.09 163	91.57 139	66.06 150	95.45 75	67.19 279	94.82 50	88.81 314

HQP_MVS		83.64 114	83.14 119	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 199	91.00 163	60.42 234	95.38 82	78.71 144	86.32 202	91.33 211

plane_prior5								92.44 82					95.38 82	78.71 144	86.32 202	91.33 211

CDPH-MVS		85.76 68	85.29 81	87.17 48	93.49 51	71.08 69	88.58 147	92.42 85	68.32 311	84.61 91	93.48 78	72.32 52	96.15 53	79.00 140	95.43 34	94.28 68

UniMVSNet_NR-MVSNet		81.88 152	81.54 151	82.92 218	88.46 184	63.46 281	87.13 203	92.37 86	80.19 12	78.38 212	89.14 215	71.66 64	93.05 211	70.05 249	76.46 349	92.25 178

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 87	74.62 151	88.90 32	93.85 71	75.75 23	96.00 59	87.80 43	94.63 54	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CLD-MVS		82.31 144	81.65 150	84.29 145	88.47 183	67.73 158	85.81 258	92.35 87	75.78 110	78.33 214	86.58 297	64.01 169	94.35 128	76.05 180	87.48 182	90.79 230
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
E5new		84.22 92	84.12 95	84.51 127	87.60 231	65.36 220	87.45 190	92.31 89	76.51 85	83.53 116	92.26 108	69.25 102	93.50 177	79.88 125	88.26 161	94.69 33

E5		84.22 92	84.12 95	84.51 127	87.60 231	65.36 220	87.45 190	92.31 89	76.51 85	83.53 116	92.26 108	69.25 102	93.50 177	79.88 125	88.26 161	94.69 33

E6new		84.22 92	84.12 95	84.52 125	87.60 231	65.36 220	87.45 190	92.30 91	76.51 85	83.53 116	92.26 108	69.26 100	93.49 179	79.88 125	88.26 161	94.69 33

E6		84.22 92	84.12 95	84.52 125	87.60 231	65.36 220	87.45 190	92.30 91	76.51 85	83.53 116	92.26 108	69.26 100	93.49 179	79.88 125	88.26 161	94.69 33

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 66	93.06 64	70.63 82	91.88 43	92.27 93	73.53 181	85.69 73	94.45 37	65.00 162	95.56 68	82.75 94	91.87 95	92.50 166

RE-MVS-def			85.48 75		93.06 64	70.63 82	91.88 43	92.27 93	73.53 181	85.69 73	94.45 37	63.87 170		82.75 94	91.87 95	92.50 166

RPMNet		73.51 334	70.49 361	82.58 237	81.32 401	65.19 226	75.92 423	92.27 93	57.60 433	72.73 335	76.45 445	52.30 306	95.43 77	48.14 433	77.71 331	87.11 364

E4		84.10 98	83.99 101	84.45 132	87.58 239	64.99 234	86.54 230	92.25 96	76.38 94	83.37 121	92.09 119	69.88 90	93.58 166	79.78 130	88.03 171	94.77 25

E2		84.00 101	83.87 102	84.39 135	87.70 226	64.95 235	86.40 237	92.23 97	75.85 108	83.21 123	91.78 127	70.09 85	93.55 171	79.52 133	88.05 169	94.66 41

E3		84.00 101	83.87 102	84.39 135	87.70 226	64.95 235	86.40 237	92.23 97	75.85 108	83.21 123	91.78 127	70.09 85	93.55 171	79.52 133	88.05 169	94.66 41

test11								92.23 97

viewcassd2359sk11		83.89 103	83.74 107	84.34 140	87.76 221	64.91 241	86.30 241	92.22 100	75.47 119	83.04 129	91.52 140	70.15 83	93.53 174	79.26 135	87.96 172	94.57 49

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 100	76.87 74	82.81 136	94.25 49	66.44 142	96.24 49	82.88 92	94.28 64	93.38 119

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 120	87.76 221	65.62 212	89.20 114	92.21 102	79.94 17	89.74 27	94.86 26	68.63 114	94.20 136	90.83 5	91.39 104	94.38 61

E3new		83.78 108	83.60 111	84.31 142	87.76 221	64.89 242	86.24 244	92.20 103	75.15 135	82.87 132	91.23 149	70.11 84	93.52 176	79.05 136	87.79 175	94.51 55

DP-MVS Recon		83.11 132	82.09 143	86.15 70	94.44 23	70.92 76	88.79 135	92.20 103	70.53 251	79.17 195	91.03 161	64.12 168	96.03 55	68.39 269	90.14 125	91.50 206

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 105	79.31 24	84.39 96	92.18 113	64.64 164	95.53 71	80.70 116	94.65 52	94.56 51

Elysia		81.53 162	80.16 177	85.62 84	85.51 300	68.25 139	88.84 133	92.19 105	71.31 227	80.50 175	89.83 192	46.89 371	94.82 108	76.85 167	89.57 136	93.80 96

StellarMVS		81.53 162	80.16 177	85.62 84	85.51 300	68.25 139	88.84 133	92.19 105	71.31 227	80.50 175	89.83 192	46.89 371	94.82 108	76.85 167	89.57 136	93.80 96

HQP3-MVS								92.19 105							85.99 211

HQP-MVS		82.61 140	82.02 145	84.37 137	89.33 144	66.98 183	89.17 116	92.19 105	76.41 90	77.23 240	90.23 185	60.17 237	95.11 94	77.47 159	85.99 211	91.03 221

3Dnovator	76.31 5	83.38 123	82.31 137	86.59 61	87.94 208	72.94 28	90.64 68	92.14 110	77.21 63	75.47 280	92.83 97	58.56 248	94.72 115	73.24 213	92.71 81	92.13 188

MTGPA								92.02 111

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 228	92.02 111	79.45 22	85.88 70	94.80 27	68.07 122	96.21 50	86.69 52	95.34 36	93.23 126

MVS_Test		83.15 129	83.06 121	83.41 194	86.86 264	63.21 287	86.11 248	92.00 113	74.31 158	82.87 132	89.44 212	70.03 87	93.21 197	77.39 161	88.50 158	93.81 94

PVSNet_BlendedMVS		80.60 190	80.02 181	82.36 241	88.85 163	65.40 216	86.16 247	92.00 113	69.34 284	78.11 219	86.09 310	66.02 151	94.27 131	71.52 231	82.06 278	87.39 350

PVSNet_Blended		80.98 173	80.34 172	82.90 219	88.85 163	65.40 216	84.43 298	92.00 113	67.62 317	78.11 219	85.05 336	66.02 151	94.27 131	71.52 231	89.50 138	89.01 304

QAPM		80.88 175	79.50 198	85.03 104	88.01 206	68.97 114	91.59 51	92.00 113	66.63 333	75.15 298	92.16 115	57.70 255	95.45 75	63.52 305	88.76 152	90.66 237

LPG-MVS_test		82.08 147	81.27 153	84.50 129	89.23 152	68.76 119	90.22 81	91.94 117	75.37 123	76.64 255	91.51 141	54.29 287	94.91 102	78.44 146	83.78 247	89.83 279

LGP-MVS_train				84.50 129	89.23 152	68.76 119		91.94 117	75.37 123	76.64 255	91.51 141	54.29 287	94.91 102	78.44 146	83.78 247	89.83 279

TEST9					93.26 56	72.96 25	88.75 138	91.89 119	68.44 309	85.00 80	93.10 88	74.36 32	95.41 80

train_agg		86.43 49	86.20 56	87.13 49	93.26 56	72.96 25	88.75 138	91.89 119	68.69 304	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 144

dcpmvs_2		85.63 70	86.15 60	84.06 164	91.71 84	64.94 238	86.47 232	91.87 121	73.63 176	86.60 67	93.02 93	76.57 18	91.87 263	83.36 84	92.15 90	95.35 3

DU-MVS		81.12 172	80.52 168	82.90 219	87.80 215	63.46 281	87.02 208	91.87 121	79.01 31	78.38 212	89.07 217	65.02 160	93.05 211	70.05 249	76.46 349	92.20 181

test_8					93.13 60	72.57 35	88.68 143	91.84 123	68.69 304	84.87 84	93.10 88	74.43 30	95.16 90

viewmacassd2359aftdt		83.76 109	83.66 110	84.07 161	86.59 275	64.56 247	86.88 215	91.82 124	75.72 111	83.34 122	92.15 117	68.24 121	92.88 218	79.05 136	89.15 145	94.77 25

PAPM_NR		83.02 133	82.41 134	84.82 115	92.47 76	66.37 192	87.93 174	91.80 125	73.82 171	77.32 237	90.66 171	67.90 125	94.90 104	70.37 244	89.48 139	93.19 132

test12				86.80 58	92.63 73	70.70 81		91.79 126		82.71 137		71.67 63	96.16 52		94.50 57	93.54 115

agg_prior					92.85 68	71.94 52		91.78 127		84.41 95			94.93 101

PAPR		81.66 159	80.89 161	83.99 174	90.27 111	64.00 261	86.76 222	91.77 128	68.84 302	77.13 247	89.50 205	67.63 127	94.88 106	67.55 274	88.52 157	93.09 138

viewmanbaseed2359cas		83.66 112	83.55 112	84.00 172	86.81 267	64.53 248	86.65 225	91.75 129	74.89 142	83.15 128	91.68 131	68.74 113	92.83 222	79.02 138	89.24 142	94.63 44

PVSNet_Blended_VisFu		82.62 139	81.83 149	84.96 107	90.80 101	69.76 97	88.74 140	91.70 130	69.39 282	78.96 197	88.46 239	65.47 156	94.87 107	74.42 199	88.57 155	90.24 256

viewdifsd2359ckpt09		83.34 124	82.55 132	85.70 81	87.64 230	67.72 159	88.43 151	91.68 131	71.91 216	81.65 154	90.68 170	67.10 134	94.75 113	76.17 177	87.70 178	94.62 46

KinetiMVS		83.31 127	82.61 131	85.39 91	87.08 260	67.56 165	88.06 168	91.65 132	77.80 44	82.21 143	91.79 126	57.27 261	94.07 142	77.77 155	89.89 132	94.56 51

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 185	87.32 248	65.13 228	88.86 130	91.63 133	75.41 121	88.23 40	93.45 81	68.56 115	92.47 236	89.52 18	92.78 79	93.20 131

viewdifsd2359ckpt13		82.91 135	82.29 138	84.77 118	86.96 263	66.90 187	87.47 187	91.62 134	72.19 209	81.68 153	90.71 169	66.92 135	93.28 190	75.90 182	87.15 188	94.12 75

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 134	73.89 170	82.67 138	94.09 57	62.60 188	95.54 70	80.93 111	92.93 77	93.57 112

ACMM	73.20 8	80.78 185	79.84 187	83.58 187	89.31 147	68.37 134	89.99 84	91.60 136	70.28 261	77.25 238	89.66 200	53.37 298	93.53 174	74.24 202	82.85 268	88.85 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
VPA-MVSNet		80.60 190	80.55 167	80.76 281	88.07 202	60.80 331	86.86 216	91.58 137	75.67 115	80.24 179	89.45 211	63.34 173	90.25 320	70.51 243	79.22 315	91.23 214

OPM-MVS		83.50 119	82.95 124	85.14 98	88.79 172	70.95 74	89.13 121	91.52 138	77.55 52	80.96 166	91.75 129	60.71 226	94.50 124	79.67 132	86.51 200	89.97 274
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Anonymous20231211		78.97 234	77.69 247	82.81 224	90.54 106	64.29 257	90.11 83	91.51 139	65.01 356	76.16 271	88.13 253	50.56 336	93.03 214	69.68 255	77.56 335	91.11 217

PS-MVSNAJss		82.07 148	81.31 152	84.34 140	86.51 277	67.27 176	89.27 112	91.51 139	71.75 217	79.37 192	90.22 186	63.15 180	94.27 131	77.69 157	82.36 275	91.49 207

TAPA-MVS	73.13 9	79.15 228	77.94 234	82.79 228	89.59 130	62.99 295	88.16 165	91.51 139	65.77 342	77.14 246	91.09 157	60.91 224	93.21 197	50.26 419	87.05 190	92.17 186
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ACMP	74.13 6	81.51 166	80.57 166	84.36 138	89.42 139	68.69 126	89.97 85	91.50 142	74.46 154	75.04 302	90.41 178	53.82 293	94.54 121	77.56 158	82.91 267	89.86 278
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	73.52 7	80.38 197	78.84 215	85.01 105	87.71 224	68.99 113	83.65 317	91.46 143	63.00 381	77.77 229	90.28 182	66.10 148	95.09 98	61.40 334	88.22 166	90.94 226
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TranMVSNet+NR-MVSNet		80.84 176	80.31 173	82.42 239	87.85 212	62.33 306	87.74 181	91.33 144	80.55 9	77.99 223	89.86 190	65.23 158	92.62 226	67.05 281	75.24 376	92.30 176

RRT-MVS		82.60 142	82.10 142	84.10 155	87.98 207	62.94 296	87.45 190	91.27 145	77.42 56	79.85 183	90.28 182	56.62 269	94.70 117	79.87 129	88.15 167	94.67 38

PS-CasMVS		78.01 260	78.09 231	77.77 350	87.71 224	54.39 417	88.02 169	91.22 146	77.50 54	73.26 327	88.64 233	60.73 225	88.41 357	61.88 329	73.88 389	90.53 243

v7n		78.97 234	77.58 250	83.14 205	83.45 352	65.51 214	88.32 159	91.21 147	73.69 175	72.41 340	86.32 305	57.93 252	93.81 157	69.18 259	75.65 362	90.11 262

PEN-MVS		77.73 266	77.69 247	77.84 348	87.07 262	53.91 420	87.91 175	91.18 148	77.56 51	73.14 329	88.82 228	61.23 218	89.17 341	59.95 345	72.37 400	90.43 247

MM		89.16 8	89.23 10	88.97 4	90.79 102	73.65 10	92.66 28	91.17 149	86.57 1	87.39 57	94.97 25	71.70 62	97.68 1	92.19 1	95.63 32	95.57 1

save fliter					93.80 44	72.35 44	90.47 74	91.17 149	74.31 158

CP-MVSNet		78.22 251	78.34 225	77.84 348	87.83 214	54.54 415	87.94 173	91.17 149	77.65 46	73.48 325	88.49 238	62.24 197	88.43 356	62.19 325	74.07 385	90.55 242

114514_t		80.68 186	79.51 197	84.20 152	94.09 42	67.27 176	89.64 96	91.11 152	58.75 424	74.08 317	90.72 168	58.10 251	95.04 99	69.70 254	89.42 140	90.30 254

NR-MVSNet		80.23 203	79.38 200	82.78 229	87.80 215	63.34 284	86.31 240	91.09 153	79.01 31	72.17 344	89.07 217	67.20 132	92.81 223	66.08 288	75.65 362	92.20 181

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 246	67.30 174	89.50 101	90.98 154	76.25 101	90.56 22	94.75 29	68.38 117	94.24 135	90.80 7	92.32 89	94.19 71

OpenMVS	72.83 10	79.77 210	78.33 226	84.09 159	85.17 309	69.91 93	90.57 69	90.97 155	66.70 327	72.17 344	91.91 121	54.70 284	93.96 144	61.81 331	90.95 112	88.41 328

MAR-MVS		81.84 153	80.70 163	85.27 94	91.32 89	71.53 58	89.82 88	90.92 156	69.77 275	78.50 208	86.21 306	62.36 194	94.52 123	65.36 293	92.05 93	89.77 282
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
tt0805		78.73 239	77.83 239	81.43 260	85.17 309	60.30 340	89.41 107	90.90 157	71.21 231	77.17 245	88.73 229	46.38 377	93.21 197	72.57 220	78.96 316	90.79 230

Anonymous20240529		80.19 205	78.89 214	84.10 155	90.60 104	64.75 245	88.95 127	90.90 157	65.97 341	80.59 174	91.17 155	49.97 344	93.73 164	69.16 260	82.70 272	93.81 94

OMC-MVS		82.69 138	81.97 147	84.85 114	88.75 174	67.42 168	87.98 170	90.87 159	74.92 141	79.72 185	91.65 133	62.19 198	93.96 144	75.26 192	86.42 201	93.16 133

UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 160	82.48 2	84.60 92	93.20 87	69.35 97	95.22 88	71.39 234	90.88 114	93.07 139

viewdifsd2359ckpt07		82.83 137	82.78 129	82.99 214	86.51 277	62.58 299	85.09 277	90.83 161	75.22 128	82.28 140	91.63 135	69.43 96	92.03 253	77.71 156	86.32 202	94.34 64

fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 172	87.78 218	66.09 196	89.96 86	90.80 162	77.37 57	86.72 65	94.20 52	72.51 51	92.78 224	89.08 22	92.33 87	93.13 137

test_fmvsm_n_1920		85.29 80	85.34 77	85.13 101	86.12 286	69.93 92	88.65 144	90.78 163	69.97 269	88.27 38	93.98 66	71.39 67	91.54 279	88.49 35	90.45 120	93.91 86

EPP-MVSNet		83.40 122	83.02 122	84.57 123	90.13 114	64.47 253	92.32 35	90.73 164	74.45 155	79.35 193	91.10 156	69.05 108	95.12 92	72.78 217	87.22 186	94.13 74

DTE-MVSNet		76.99 282	76.80 267	77.54 357	86.24 281	53.06 429	87.52 185	90.66 165	77.08 69	72.50 338	88.67 232	60.48 233	89.52 333	57.33 374	70.74 412	90.05 269

v10		79.74 211	78.67 216	82.97 217	84.06 336	64.95 235	87.88 177	90.62 166	73.11 195	75.11 299	86.56 298	61.46 212	94.05 143	73.68 205	75.55 364	89.90 276

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 316	69.51 100	89.62 98	90.58 167	73.42 184	87.75 50	94.02 61	72.85 48	93.24 194	90.37 8	90.75 115	93.96 83

v1192		79.59 214	78.43 223	83.07 210	83.55 350	64.52 249	86.93 213	90.58 167	70.83 242	77.78 228	85.90 311	59.15 243	93.94 147	73.96 204	77.19 338	90.76 232

v1144		80.03 207	79.03 210	83.01 213	83.78 343	64.51 250	87.11 205	90.57 169	71.96 215	78.08 221	86.20 307	61.41 213	93.94 147	74.93 194	77.23 336	90.60 240

XVG-OURS-SEG-HR		80.81 178	79.76 189	83.96 176	85.60 298	68.78 118	83.54 323	90.50 170	70.66 249	76.71 253	91.66 132	60.69 227	91.26 291	76.94 166	81.58 283	91.83 193

MVS		78.19 254	76.99 263	81.78 252	85.66 295	66.99 182	84.66 287	90.47 171	55.08 445	72.02 346	85.27 328	63.83 171	94.11 141	66.10 287	89.80 133	84.24 414

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 98	87.20 251	68.54 130	89.57 99	90.44 172	75.31 125	87.49 54	94.39 42	72.86 47	92.72 225	89.04 27	90.56 118	94.16 72

XVG-OURS		80.41 195	79.23 206	83.97 175	85.64 296	69.02 112	83.03 336	90.39 173	71.09 234	77.63 231	91.49 143	54.62 286	91.35 288	75.71 184	83.47 259	91.54 204

MVSFormer		82.85 136	82.05 144	85.24 95	87.35 241	70.21 86	90.50 72	90.38 174	68.55 306	81.32 158	89.47 207	61.68 206	93.46 184	78.98 141	90.26 123	92.05 190

test_djsdf		80.30 202	79.32 203	83.27 198	83.98 338	65.37 219	90.50 72	90.38 174	68.55 306	76.19 267	88.70 230	56.44 270	93.46 184	78.98 141	80.14 303	90.97 224

CPTT-MVS		83.73 110	83.33 118	84.92 111	93.28 53	70.86 78	92.09 41	90.38 174	68.75 303	79.57 187	92.83 97	60.60 232	93.04 213	80.92 112	91.56 102	90.86 228

v144192		79.47 217	78.37 224	82.78 229	83.35 353	63.96 262	86.96 210	90.36 177	69.99 268	77.50 232	85.67 318	60.66 229	93.77 160	74.27 201	76.58 346	90.62 238

v1921920		79.22 226	78.03 232	82.80 225	83.30 355	63.94 264	86.80 218	90.33 178	69.91 271	77.48 233	85.53 322	58.44 249	93.75 162	73.60 206	76.85 343	90.71 236

MVS_111021_HR		85.14 82	84.75 87	86.32 65	91.65 85	72.70 30	85.98 250	90.33 178	76.11 103	82.08 145	91.61 138	71.36 68	94.17 139	81.02 110	92.58 82	92.08 189

v1240		78.99 233	77.78 242	82.64 234	83.21 358	63.54 278	86.62 227	90.30 180	69.74 278	77.33 236	85.68 317	57.04 264	93.76 161	73.13 214	76.92 340	90.62 238

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 88	82.99 369	69.39 107	89.65 95	90.29 181	73.31 188	87.77 49	94.15 55	71.72 61	93.23 195	90.31 9	90.67 117	93.89 89

v8		79.97 209	79.02 211	82.80 225	84.09 335	64.50 252	87.96 171	90.29 181	74.13 165	75.24 295	86.81 284	62.88 187	93.89 155	74.39 200	75.40 371	90.00 270

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 207	87.08 260	65.21 225	89.09 123	90.21 183	79.67 19	89.98 24	95.02 24	73.17 42	91.71 269	91.30 3	91.60 99	92.34 173

mvs_tets		79.13 229	77.77 243	83.22 202	84.70 322	66.37 192	89.17 116	90.19 184	69.38 283	75.40 285	89.46 209	44.17 400	93.15 204	76.78 173	80.70 295	90.14 259

jajsoiax		79.29 225	77.96 233	83.27 198	84.68 323	66.57 190	89.25 113	90.16 185	69.20 291	75.46 282	89.49 206	45.75 388	93.13 206	76.84 169	80.80 293	90.11 262

Vis-MVSNet		83.46 120	82.80 127	85.43 90	90.25 112	68.74 121	90.30 80	90.13 186	76.33 97	80.87 169	92.89 95	61.00 223	94.20 136	72.45 226	90.97 111	93.35 122
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PS-MVSNAJ		81.69 157	81.02 158	83.70 183	89.51 134	68.21 142	84.28 303	90.09 187	70.79 243	81.26 162	85.62 320	63.15 180	94.29 129	75.62 186	88.87 149	88.59 323

xiu_mvs_v2_base		81.69 157	81.05 157	83.60 185	89.15 155	68.03 147	84.46 295	90.02 188	70.67 246	81.30 161	86.53 300	63.17 179	94.19 138	75.60 187	88.54 156	88.57 324

FA-MVS(test-final)		80.96 174	79.91 184	84.10 155	88.30 191	65.01 232	84.55 292	90.01 189	73.25 191	79.61 186	87.57 264	58.35 250	94.72 115	71.29 235	86.25 205	92.56 162

v2v482		80.23 203	79.29 204	83.05 211	83.62 348	64.14 259	87.04 206	89.97 190	73.61 177	78.18 218	87.22 275	61.10 221	93.82 156	76.11 178	76.78 345	91.18 215

test_yl		81.17 169	80.47 170	83.24 200	89.13 156	63.62 270	86.21 245	89.95 191	72.43 207	81.78 151	89.61 202	57.50 258	93.58 166	70.75 239	86.90 192	92.52 164

DCV-MVSNet		81.17 169	80.47 170	83.24 200	89.13 156	63.62 270	86.21 245	89.95 191	72.43 207	81.78 151	89.61 202	57.50 258	93.58 166	70.75 239	86.90 192	92.52 164

fmvsm_s_conf0.5_n_7		83.34 124	84.03 100	81.28 266	85.73 294	65.13 228	85.40 269	89.90 193	74.96 140	82.13 144	93.89 69	66.65 137	87.92 362	86.56 53	91.05 109	90.80 229

V42		79.38 223	78.24 228	82.83 222	81.10 403	65.50 215	85.55 264	89.82 194	71.57 223	78.21 216	86.12 309	60.66 229	93.18 203	75.64 185	75.46 368	89.81 281

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 144	86.70 271	65.83 205	88.77 136	89.78 195	75.46 120	88.35 36	93.73 74	69.19 104	93.06 210	91.30 3	88.44 159	94.02 81

VNet		82.21 145	82.41 134	81.62 255	90.82 100	60.93 328	84.47 293	89.78 195	76.36 96	84.07 104	91.88 123	64.71 163	90.26 319	70.68 241	88.89 148	93.66 102

diffmvs_AUTHOR		82.38 143	82.27 139	82.73 233	83.26 356	63.80 267	83.89 311	89.76 197	73.35 187	82.37 139	90.84 166	66.25 145	90.79 310	82.77 93	87.93 173	93.59 111

diffmvs		82.10 146	81.88 148	82.76 231	83.00 366	63.78 269	83.68 316	89.76 197	72.94 199	82.02 146	89.85 191	65.96 153	90.79 310	82.38 100	87.30 185	93.71 100
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
XVG-ACMP-BASELINE		76.11 301	74.27 313	81.62 255	83.20 359	64.67 246	83.60 320	89.75 199	69.75 276	71.85 347	87.09 280	32.78 452	92.11 251	69.99 251	80.43 299	88.09 335

EI-MVSNet-Vis-set		84.19 96	83.81 105	85.31 93	88.18 194	67.85 154	87.66 182	89.73 200	80.05 15	82.95 130	89.59 204	70.74 76	94.82 108	80.66 118	84.72 231	93.28 125

EI-MVSNet-UG-set		83.81 105	83.38 116	85.09 103	87.87 211	67.53 166	87.44 195	89.66 201	79.74 18	82.23 142	89.41 213	70.24 82	94.74 114	79.95 123	83.92 246	92.99 147

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 92	80.25 411	69.03 110	89.47 102	89.65 202	73.24 192	86.98 62	94.27 47	66.62 138	93.23 195	90.26 10	89.95 130	93.78 98

BP-MVS1		84.32 91	83.71 108	86.17 68	87.84 213	67.85 154	89.38 109	89.64 203	77.73 45	83.98 106	92.12 118	56.89 266	95.43 77	84.03 80	91.75 98	95.24 7

VortexMVS		78.57 245	77.89 237	80.59 284	85.89 290	62.76 298	85.61 259	89.62 204	72.06 213	74.99 303	85.38 326	55.94 273	90.77 313	74.99 193	76.58 346	88.23 331

PAPM		77.68 270	76.40 279	81.51 258	87.29 250	61.85 315	83.78 313	89.59 205	64.74 358	71.23 354	88.70 230	62.59 189	93.66 165	52.66 403	87.03 191	89.01 304

MGCNet		87.69 24	87.55 29	88.12 13	89.45 138	71.76 53	91.47 57	89.54 206	82.14 3	86.65 66	94.28 46	68.28 120	97.46 6	90.81 6	95.31 38	95.15 8

anonymousdsp		78.60 243	77.15 259	82.98 216	80.51 409	67.08 181	87.24 202	89.53 207	65.66 344	75.16 297	87.19 277	52.52 302	92.25 247	77.17 163	79.34 313	89.61 286

MG-MVS		83.41 121	83.45 114	83.28 197	92.74 71	62.28 308	88.17 164	89.50 208	75.22 128	81.49 156	92.74 103	66.75 136	95.11 94	72.85 216	91.58 101	92.45 170

PLC	70.83 11	78.05 258	76.37 280	83.08 209	91.88 83	67.80 156	88.19 163	89.46 209	64.33 365	69.87 371	88.38 241	53.66 294	93.58 166	58.86 358	82.73 270	87.86 339
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 190	87.12 259	66.01 199	88.56 148	89.43 210	75.59 116	89.32 28	94.32 44	72.89 46	91.21 296	90.11 11	92.33 87	93.16 133

SDMVSNet		80.38 197	80.18 176	80.99 275	89.03 161	64.94 238	80.45 372	89.40 211	75.19 132	76.61 257	89.98 188	60.61 231	87.69 366	76.83 170	83.55 256	90.33 252

Fast-Effi-MVS+		80.81 178	79.92 183	83.47 189	88.85 163	64.51 250	85.53 266	89.39 212	70.79 243	78.49 209	85.06 335	67.54 128	93.58 166	67.03 282	86.58 198	92.32 175

IterMVS-LS		80.06 206	79.38 200	82.11 246	85.89 290	63.20 288	86.79 219	89.34 213	74.19 162	75.45 283	86.72 287	66.62 138	92.39 240	72.58 219	76.86 342	90.75 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
icg_test_0407_2		78.92 236	78.93 213	78.90 325	87.13 254	63.59 274	76.58 419	89.33 214	70.51 252	77.82 225	89.03 219	61.84 202	81.38 423	72.56 222	85.56 220	91.74 196

IMVS_0407		80.61 188	79.90 185	82.75 232	87.13 254	63.59 274	85.33 270	89.33 214	70.51 252	77.82 225	89.03 219	61.84 202	92.91 216	72.56 222	85.56 220	91.74 196

IMVS_0404		77.16 280	76.42 278	79.37 316	87.13 254	63.59 274	77.12 417	89.33 214	70.51 252	66.22 417	89.03 219	50.36 339	82.78 413	72.56 222	85.56 220	91.74 196

IMVS_0403		80.80 181	80.12 180	82.87 221	87.13 254	63.59 274	85.19 271	89.33 214	70.51 252	78.49 209	89.03 219	63.26 176	93.27 192	72.56 222	85.56 220	91.74 196

API-MVS		81.99 150	81.23 154	84.26 150	90.94 97	70.18 91	91.10 63	89.32 218	71.51 224	78.66 204	88.28 244	65.26 157	95.10 97	64.74 299	91.23 107	87.51 348

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 151	86.26 280	67.40 170	89.18 115	89.31 219	72.50 203	88.31 37	93.86 70	69.66 93	91.96 257	89.81 13	91.05 109	93.38 119

GBi-Net		78.40 247	77.40 254	81.40 262	87.60 231	63.01 291	88.39 154	89.28 220	71.63 219	75.34 288	87.28 271	54.80 280	91.11 297	62.72 316	79.57 307	90.09 264

test1		78.40 247	77.40 254	81.40 262	87.60 231	63.01 291	88.39 154	89.28 220	71.63 219	75.34 288	87.28 271	54.80 280	91.11 297	62.72 316	79.57 307	90.09 264

FMVSNet1		77.44 274	76.12 282	81.40 262	86.81 267	63.01 291	88.39 154	89.28 220	70.49 256	74.39 314	87.28 271	49.06 358	91.11 297	60.91 338	78.52 319	90.09 264

cdsmvs_eth3d_5k		19.96 455	26.61 457	0.00 476	0.00 499	0.00 501	0.00 488	89.26 223	0.00 494	0.00 495	88.61 234	61.62 208	0.00 495	0.00 494	0.00 493	0.00 491

SSM_0407		81.58 161	80.48 169	84.87 113	88.81 167	67.96 149	87.37 196	89.25 224	71.06 236	79.48 189	90.39 179	59.57 239	94.48 126	72.45 226	85.93 213	92.18 183

SSM_0404		81.91 151	80.84 162	85.13 101	89.24 151	68.26 137	87.84 179	89.25 224	71.06 236	80.62 173	90.39 179	59.57 239	94.65 119	72.45 226	87.19 187	92.47 169

ab-mvs		79.51 215	78.97 212	81.14 271	88.46 184	60.91 329	83.84 312	89.24 226	70.36 257	79.03 196	88.87 227	63.23 178	90.21 321	65.12 295	82.57 273	92.28 177

cascas		76.72 288	74.64 305	82.99 214	85.78 293	65.88 204	82.33 341	89.21 227	60.85 403	72.74 334	81.02 404	47.28 367	93.75 162	67.48 275	85.02 226	89.34 294

eth_miper_zixun_eth		77.92 262	76.69 272	81.61 257	83.00 366	61.98 313	83.15 330	89.20 228	69.52 281	74.86 306	84.35 349	61.76 205	92.56 231	71.50 233	72.89 398	90.28 255

h-mvs33		83.15 129	82.19 140	86.02 76	90.56 105	70.85 79	88.15 166	89.16 229	76.02 105	84.67 87	91.39 146	61.54 209	95.50 73	82.71 96	75.48 366	91.72 200

miper_ehance_all_eth		78.59 244	77.76 244	81.08 273	82.66 377	61.56 319	83.65 317	89.15 230	68.87 301	75.55 279	83.79 363	66.49 141	92.03 253	73.25 212	76.39 351	89.64 285

Effi-MVS+		83.62 116	83.08 120	85.24 95	88.38 188	67.45 167	88.89 129	89.15 230	75.50 118	82.27 141	88.28 244	69.61 94	94.45 127	77.81 154	87.84 174	93.84 92

c3_l		78.75 238	77.91 235	81.26 267	82.89 372	61.56 319	84.09 309	89.13 232	69.97 269	75.56 278	84.29 350	66.36 143	92.09 252	73.47 209	75.48 366	90.12 261

LTVRE_ROB	69.57 13	76.25 299	74.54 308	81.41 261	88.60 179	64.38 256	79.24 388	89.12 233	70.76 245	69.79 373	87.86 257	49.09 357	93.20 200	56.21 386	80.16 301	86.65 376
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
fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 81	89.48 137	67.88 153	88.59 146	89.05 234	80.19 12	90.70 20	95.40 15	74.56 28	93.92 151	91.54 2	92.07 92	95.31 5

F-COLMAP		76.38 298	74.33 312	82.50 238	89.28 149	66.95 186	88.41 153	89.03 235	64.05 370	66.83 406	88.61 234	46.78 373	92.89 217	57.48 371	78.55 318	87.67 342

FMVSNet2		78.20 253	77.21 258	81.20 269	87.60 231	62.89 297	87.47 187	89.02 236	71.63 219	75.29 294	87.28 271	54.80 280	91.10 300	62.38 322	79.38 312	89.61 286

ACMH	67.68 16	75.89 304	73.93 316	81.77 253	88.71 176	66.61 189	88.62 145	89.01 237	69.81 272	66.78 407	86.70 291	41.95 416	91.51 282	55.64 387	78.14 327	87.17 360
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
miper_enhance_ethall		77.87 264	76.86 265	80.92 278	81.65 391	61.38 322	82.68 337	88.98 238	65.52 346	75.47 280	82.30 392	65.76 155	92.00 256	72.95 215	76.39 351	89.39 292

无先验							87.48 186	88.98 238	60.00 410				94.12 140	67.28 277		88.97 307

AdaColmap		80.58 193	79.42 199	84.06 164	93.09 63	68.91 115	89.36 110	88.97 240	69.27 286	75.70 276	89.69 198	57.20 263	95.77 64	63.06 313	88.41 160	87.50 349

EI-MVSNet		80.52 194	79.98 182	82.12 244	84.28 330	63.19 289	86.41 234	88.95 241	74.18 163	78.69 202	87.54 267	66.62 138	92.43 238	72.57 220	80.57 297	90.74 234

MVSTER		79.01 232	77.88 238	82.38 240	83.07 363	64.80 244	84.08 310	88.95 241	69.01 298	78.69 202	87.17 278	54.70 284	92.43 238	74.69 195	80.57 297	89.89 277

FE-MVSNET2		72.88 350	71.28 349	77.67 351	78.30 433	57.78 370	84.43 298	88.92 243	69.56 279	64.61 427	81.67 399	46.73 375	88.54 355	59.33 351	67.99 424	86.69 375

LuminaMVS		80.68 186	79.62 195	83.83 179	85.07 315	68.01 148	86.99 209	88.83 244	70.36 257	81.38 157	87.99 255	50.11 342	92.51 235	79.02 138	86.89 194	90.97 224

1314		76.53 290	75.30 298	80.21 295	83.93 339	62.32 307	84.66 287	88.81 245	60.23 408	70.16 365	84.07 358	55.30 277	90.73 314	67.37 276	83.21 264	87.59 346

UniMVSNet_ETH3D		79.10 230	78.24 228	81.70 254	86.85 265	60.24 341	87.28 201	88.79 246	74.25 161	76.84 248	90.53 177	49.48 350	91.56 275	67.98 270	82.15 276	93.29 124

xiu_mvs_v1_base_debu		80.80 181	79.72 192	84.03 169	87.35 241	70.19 88	85.56 261	88.77 247	69.06 295	81.83 147	88.16 248	50.91 331	92.85 219	78.29 150	87.56 179	89.06 299

xiu_mvs_v1_base		80.80 181	79.72 192	84.03 169	87.35 241	70.19 88	85.56 261	88.77 247	69.06 295	81.83 147	88.16 248	50.91 331	92.85 219	78.29 150	87.56 179	89.06 299

xiu_mvs_v1_base_debi		80.80 181	79.72 192	84.03 169	87.35 241	70.19 88	85.56 261	88.77 247	69.06 295	81.83 147	88.16 248	50.91 331	92.85 219	78.29 150	87.56 179	89.06 299

FMVSNet3		77.88 263	76.85 266	80.97 277	86.84 266	62.36 305	86.52 231	88.77 247	71.13 232	75.34 288	86.66 293	54.07 290	91.10 300	62.72 316	79.57 307	89.45 290

FE-MVSNET3		76.43 295	75.32 297	79.76 307	83.00 366	60.72 332	81.74 348	88.76 251	68.99 299	72.98 331	84.19 355	56.41 271	90.27 318	62.39 321	79.40 311	88.31 329

patch_mono-2		83.65 113	84.54 89	80.99 275	90.06 120	65.83 205	84.21 304	88.74 252	71.60 222	85.01 79	92.44 105	74.51 29	83.50 408	82.15 101	92.15 90	93.64 108

GeoE		81.71 156	81.01 159	83.80 182	89.51 134	64.45 254	88.97 126	88.73 253	71.27 230	78.63 205	89.76 197	66.32 144	93.20 200	69.89 252	86.02 210	93.74 99

mamba_0408		79.37 224	77.52 251	84.93 110	88.81 167	67.96 149	65.03 472	88.66 254	70.96 240	79.48 189	89.80 194	58.69 245	94.65 119	70.35 245	85.93 213	92.18 183

SSM_04072		77.67 271	77.52 251	78.12 342	88.81 167	67.96 149	65.03 472	88.66 254	70.96 240	79.48 189	89.80 194	58.69 245	74.23 464	70.35 245	85.93 213	92.18 183

CANet_DTU		80.61 188	79.87 186	82.83 222	85.60 298	63.17 290	87.36 197	88.65 256	76.37 95	75.88 273	88.44 240	53.51 296	93.07 209	73.30 211	89.74 134	92.25 178

HyFIR lowres test		77.53 273	75.40 293	83.94 177	89.59 130	66.62 188	80.36 373	88.64 257	56.29 441	76.45 260	85.17 332	57.64 256	93.28 190	61.34 336	83.10 266	91.91 192

WR-MVS		79.49 216	79.22 207	80.27 292	88.79 172	58.35 357	85.06 278	88.61 258	78.56 35	77.65 230	88.34 242	63.81 172	90.66 315	64.98 297	77.22 337	91.80 195

BH-untuned		79.47 217	78.60 218	82.05 247	89.19 154	65.91 203	86.07 249	88.52 259	72.18 210	75.42 284	87.69 261	61.15 220	93.54 173	60.38 342	86.83 195	86.70 374

IS-MVSNet		83.15 129	82.81 126	84.18 153	89.94 123	63.30 285	91.59 51	88.46 260	79.04 30	79.49 188	92.16 115	65.10 159	94.28 130	67.71 272	91.86 97	94.95 12

pm-mvs1		77.25 279	76.68 273	78.93 324	84.22 332	58.62 355	86.41 234	88.36 261	71.37 226	73.31 326	88.01 254	61.22 219	89.15 342	64.24 303	73.01 397	89.03 303

UGNet		80.83 177	79.59 196	84.54 124	88.04 203	68.09 144	89.42 106	88.16 262	76.95 71	76.22 266	89.46 209	49.30 354	93.94 147	68.48 267	90.31 121	91.60 201
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
VDD-MVS		83.01 134	82.36 136	84.96 107	91.02 95	66.40 191	88.91 128	88.11 263	77.57 49	84.39 96	93.29 85	52.19 308	93.91 152	77.05 165	88.70 154	94.57 49

Effi-MVS+-dtu		80.03 207	78.57 219	84.42 134	85.13 313	68.74 121	88.77 136	88.10 264	74.99 137	74.97 304	83.49 372	57.27 261	93.36 188	73.53 207	80.88 291	91.18 215

v148		78.72 240	77.80 241	81.47 259	82.73 375	61.96 314	86.30 241	88.08 265	73.26 190	76.18 268	85.47 324	62.46 192	92.36 242	71.92 230	73.82 390	90.09 264

EG-PatchMatch MVS		74.04 327	71.82 341	80.71 282	84.92 317	67.42 168	85.86 255	88.08 265	66.04 339	64.22 430	83.85 360	35.10 448	92.56 231	57.44 372	80.83 292	82.16 439

viewmambaseed2359dif		80.41 195	79.84 187	82.12 244	82.95 371	62.50 302	83.39 324	88.06 267	67.11 322	80.98 165	90.31 181	66.20 147	91.01 304	74.62 196	84.90 228	92.86 152

SymmetryMVS		85.38 78	84.81 86	87.07 50	91.47 87	72.47 38	91.65 47	88.06 267	79.31 24	84.39 96	92.18 113	64.64 164	95.53 71	80.70 116	90.91 113	93.21 129

cl22		78.07 257	77.01 261	81.23 268	82.37 384	61.83 316	83.55 321	87.98 269	68.96 300	75.06 301	83.87 359	61.40 214	91.88 262	73.53 207	76.39 351	89.98 273

test_fmvsmvis_n_1920		84.02 100	83.87 102	84.49 131	84.12 334	69.37 108	88.15 166	87.96 270	70.01 267	83.95 107	93.23 86	68.80 112	91.51 282	88.61 32	89.96 129	92.57 161

pmmvs6		74.69 319	73.39 323	78.61 329	81.38 398	57.48 375	86.64 226	87.95 271	64.99 357	70.18 363	86.61 294	50.43 338	89.52 333	62.12 327	70.18 415	88.83 313

MVP-Stereo		76.12 300	74.46 310	81.13 272	85.37 305	69.79 95	84.42 300	87.95 271	65.03 355	67.46 397	85.33 327	53.28 299	91.73 268	58.01 368	83.27 263	81.85 441
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
cl____		77.72 267	76.76 269	80.58 285	82.49 381	60.48 337	83.09 332	87.87 273	69.22 289	74.38 315	85.22 331	62.10 199	91.53 280	71.09 236	75.41 370	89.73 284

DIV-MVS_self_test		77.72 267	76.76 269	80.58 285	82.48 382	60.48 337	83.09 332	87.86 274	69.22 289	74.38 315	85.24 329	62.10 199	91.53 280	71.09 236	75.40 371	89.74 283

BH-w/o		78.21 252	77.33 257	80.84 279	88.81 167	65.13 228	84.87 282	87.85 275	69.75 276	74.52 312	84.74 342	61.34 215	93.11 207	58.24 366	85.84 216	84.27 413

FE-MVS		77.78 265	75.68 286	84.08 160	88.09 201	66.00 200	83.13 331	87.79 276	68.42 310	78.01 222	85.23 330	45.50 391	95.12 92	59.11 355	85.83 217	91.11 217

HY-MVS	69.67 12	77.95 261	77.15 259	80.36 289	87.57 240	60.21 342	83.37 326	87.78 277	66.11 337	75.37 287	87.06 282	63.27 175	90.48 317	61.38 335	82.43 274	90.40 249

guyue		81.13 171	80.64 165	82.60 236	86.52 276	63.92 265	86.69 224	87.73 278	73.97 166	80.83 171	89.69 198	56.70 267	91.33 290	78.26 153	85.40 224	92.54 163

1112_ss		77.40 276	76.43 277	80.32 291	89.11 160	60.41 339	83.65 317	87.72 279	62.13 394	73.05 330	86.72 287	62.58 190	89.97 325	62.11 328	80.80 293	90.59 241

mvs_anonymous		79.42 220	79.11 209	80.34 290	84.45 329	57.97 364	82.59 338	87.62 280	67.40 321	76.17 270	88.56 237	68.47 116	89.59 332	70.65 242	86.05 209	93.47 117

ACMH+	68.96 14	76.01 303	74.01 314	82.03 248	88.60 179	65.31 224	88.86 130	87.55 281	70.25 263	67.75 393	87.47 269	41.27 419	93.19 202	58.37 364	75.94 359	87.60 344

tfpnnormal		74.39 321	73.16 327	78.08 343	86.10 288	58.05 361	84.65 289	87.53 282	70.32 260	71.22 355	85.63 319	54.97 278	89.86 326	43.03 453	75.02 378	86.32 379

CHOSEN 1792x2688		77.63 272	75.69 285	83.44 191	89.98 122	68.58 129	78.70 398	87.50 283	56.38 440	75.80 275	86.84 283	58.67 247	91.40 287	61.58 333	85.75 218	90.34 251

ambc				75.24 381	73.16 462	50.51 447	63.05 477	87.47 284		64.28 429	77.81 438	17.80 477	89.73 330	57.88 369	60.64 451	85.49 395

Fast-Effi-MVS+-dtu		78.02 259	76.49 275	82.62 235	83.16 362	66.96 185	86.94 212	87.45 285	72.45 204	71.49 352	84.17 356	54.79 283	91.58 272	67.61 273	80.31 300	89.30 295

usedtu_blend_shiyan5		73.29 341	70.96 355	80.25 293	77.80 436	62.16 310	84.44 297	87.38 286	64.41 362	68.09 389	76.28 448	51.32 325	91.23 293	63.21 311	65.76 433	87.35 352

D2MVS		74.82 318	73.21 326	79.64 312	79.81 418	62.56 301	80.34 374	87.35 287	64.37 364	68.86 380	82.66 387	46.37 378	90.10 322	67.91 271	81.24 286	86.25 380

fmvsm_s_conf0.5_n_2		84.04 99	84.11 99	83.81 181	86.17 284	65.00 233	86.96 210	87.28 288	74.35 156	88.25 39	94.23 50	61.82 204	92.60 228	89.85 12	88.09 168	93.84 92

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 288	76.41 90	85.80 71	90.22 186	74.15 35	95.37 85	81.82 103	91.88 94	92.65 160

blended_shiyan6		73.38 336	71.17 352	80.01 300	78.36 432	61.48 321	82.43 340	87.27 290	65.40 350	68.56 384	77.55 440	51.94 317	91.01 304	63.27 310	65.76 433	87.55 347

blend_shiyan4		72.29 356	69.65 368	80.21 295	78.24 434	62.16 310	82.29 342	87.27 290	65.41 349	68.43 388	76.42 447	39.91 427	91.23 293	63.21 311	65.66 436	87.22 358

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 148	85.42 303	68.81 116	88.49 150	87.26 292	68.08 313	88.03 44	93.49 77	72.04 57	91.77 265	88.90 29	89.14 146	92.24 180

hse-mvs2		81.72 155	80.94 160	84.07 161	88.72 175	67.68 160	85.87 254	87.26 292	76.02 105	84.67 87	88.22 247	61.54 209	93.48 182	82.71 96	73.44 394	91.06 219

AUN-MVS		79.21 227	77.60 249	84.05 167	88.71 176	67.61 162	85.84 256	87.26 292	69.08 294	77.23 240	88.14 252	53.20 300	93.47 183	75.50 189	73.45 393	91.06 219

FE-blended-shiyan7		72.94 348	70.66 358	79.79 306	77.80 436	61.03 327	81.31 357	87.15 295	65.18 352	68.09 389	76.28 448	51.32 325	90.97 307	63.06 313	65.76 433	87.35 352

BH-RMVSNet		79.61 212	78.44 222	83.14 205	89.38 143	65.93 202	84.95 281	87.15 295	73.56 179	78.19 217	89.79 196	56.67 268	93.36 188	59.53 350	86.74 196	90.13 260

Test_1112_low_res		76.40 297	75.44 291	79.27 318	89.28 149	58.09 360	81.69 350	87.07 297	59.53 415	72.48 339	86.67 292	61.30 216	89.33 336	60.81 340	80.15 302	90.41 248

KD-MVS_self_test		68.81 389	67.59 394	72.46 412	74.29 453	45.45 462	77.93 410	87.00 298	63.12 378	63.99 433	78.99 430	42.32 411	84.77 398	56.55 384	64.09 441	87.16 362

mvsmamba		80.60 190	79.38 200	84.27 148	89.74 128	67.24 178	87.47 187	86.95 299	70.02 266	75.38 286	88.93 224	51.24 328	92.56 231	75.47 190	89.22 143	93.00 146

reproduce_monomvs		75.40 313	74.38 311	78.46 337	83.92 340	57.80 369	83.78 313	86.94 300	73.47 183	72.25 343	84.47 344	38.74 433	89.27 338	75.32 191	70.53 413	88.31 329

LS3D		76.95 284	74.82 303	83.37 195	90.45 107	67.36 172	89.15 120	86.94 300	61.87 397	69.52 374	90.61 174	51.71 322	94.53 122	46.38 441	86.71 197	88.21 333

miper_lstm_enhance		74.11 326	73.11 328	77.13 362	80.11 413	59.62 347	72.23 443	86.92 302	66.76 326	70.40 360	82.92 382	56.93 265	82.92 412	69.06 261	72.63 399	88.87 311

fmvsm_l_conf0.5_n_a		84.13 97	84.16 94	84.06 164	85.38 304	68.40 133	88.34 158	86.85 303	67.48 320	87.48 55	93.40 82	70.89 73	91.61 270	88.38 37	89.22 143	92.16 187

jason		81.39 167	80.29 174	84.70 121	86.63 274	69.90 94	85.95 251	86.77 304	63.24 377	81.07 164	89.47 207	61.08 222	92.15 250	78.33 149	90.07 128	92.05 190
jason: jason.
viewdifsd2359ckpt11		80.37 199	79.73 190	82.30 242	83.70 346	62.39 303	84.20 305	86.67 305	73.22 193	80.90 167	90.62 172	63.00 185	91.56 275	76.81 171	78.44 321	92.95 149

viewmsd2359difaftdt		80.37 199	79.73 190	82.30 242	83.70 346	62.39 303	84.20 305	86.67 305	73.22 193	80.90 167	90.62 172	63.00 185	91.56 275	76.81 171	78.44 321	92.95 149

OurMVSNet-221017-0		74.26 323	72.42 336	79.80 305	83.76 344	59.59 348	85.92 253	86.64 307	66.39 335	66.96 404	87.58 263	39.46 428	91.60 271	65.76 291	69.27 418	88.22 332

VPNet		78.69 241	78.66 217	78.76 327	88.31 190	55.72 402	84.45 296	86.63 308	76.79 76	78.26 215	90.55 176	59.30 242	89.70 331	66.63 283	77.05 339	90.88 227

fmvsm_s_conf0.1_n_2		83.80 106	83.79 106	83.83 179	85.62 297	64.94 238	87.03 207	86.62 309	74.32 157	87.97 47	94.33 43	60.67 228	92.60 228	89.72 14	87.79 175	93.96 83

USDC		70.33 376	68.37 377	76.21 368	80.60 407	56.23 395	79.19 390	86.49 310	60.89 402	61.29 443	85.47 324	31.78 455	89.47 335	53.37 400	76.21 357	82.94 432

lupinMVS		81.39 167	80.27 175	84.76 119	87.35 241	70.21 86	85.55 264	86.41 311	62.85 384	81.32 158	88.61 234	61.68 206	92.24 248	78.41 148	90.26 123	91.83 193

TR-MVS		77.44 274	76.18 281	81.20 269	88.24 192	63.24 286	84.61 290	86.40 312	67.55 318	77.81 227	86.48 301	54.10 289	93.15 204	57.75 370	82.72 271	87.20 359

旧先验1					91.96 80	65.79 208		86.37 313			93.08 92	69.31 99			92.74 80	88.74 319

GA-MVS		76.87 285	75.17 300	81.97 250	82.75 374	62.58 299	81.44 355	86.35 314	72.16 212	74.74 307	82.89 383	46.20 382	92.02 255	68.85 264	81.09 288	91.30 213

MonoMVSNet		76.49 294	75.80 283	78.58 331	81.55 394	58.45 356	86.36 239	86.22 315	74.87 145	74.73 308	83.73 365	51.79 321	88.73 350	70.78 238	72.15 403	88.55 325

CDS-MVSNet		79.07 231	77.70 246	83.17 204	87.60 231	68.23 141	84.40 301	86.20 316	67.49 319	76.36 263	86.54 299	61.54 209	90.79 310	61.86 330	87.33 184	90.49 245
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_111021_LR		82.61 140	82.11 141	84.11 154	88.82 166	71.58 57	85.15 274	86.16 317	74.69 148	80.47 177	91.04 159	62.29 195	90.55 316	80.33 120	90.08 127	90.20 257

MSDG		73.36 339	70.99 354	80.49 287	84.51 328	65.80 207	80.71 367	86.13 318	65.70 343	65.46 420	83.74 364	44.60 395	90.91 308	51.13 412	76.89 341	84.74 409

TransMVSNet (Re)		75.39 314	74.56 307	77.86 347	85.50 302	57.10 380	86.78 220	86.09 319	72.17 211	71.53 351	87.34 270	63.01 184	89.31 337	56.84 380	61.83 447	87.17 360

VDDNet		81.52 164	80.67 164	84.05 167	90.44 108	64.13 260	89.73 93	85.91 320	71.11 233	83.18 126	93.48 78	50.54 337	93.49 179	73.40 210	88.25 165	94.54 53

AstraMVS		80.81 178	80.14 179	82.80 225	86.05 289	63.96 262	86.46 233	85.90 321	73.71 174	80.85 170	90.56 175	54.06 291	91.57 274	79.72 131	83.97 245	92.86 152

sd_testset		77.70 269	77.40 254	78.60 330	89.03 161	60.02 343	79.00 393	85.83 322	75.19 132	76.61 257	89.98 188	54.81 279	85.46 391	62.63 320	83.55 256	90.33 252

Baseline_NR-MVSNet		78.15 255	78.33 226	77.61 354	85.79 292	56.21 396	86.78 220	85.76 323	73.60 178	77.93 224	87.57 264	65.02 160	88.99 344	67.14 280	75.33 373	87.63 343

Anonymous20240521		68.80 390	67.22 399	73.55 399	74.33 452	54.11 418	83.18 329	85.61 324	58.15 427	61.68 442	80.94 406	30.71 458	81.27 424	57.00 378	73.34 396	85.28 399

test_vis1_n_1920		75.52 309	75.78 284	74.75 388	79.84 417	57.44 376	83.26 328	85.52 325	62.83 385	79.34 194	86.17 308	45.10 393	79.71 430	78.75 143	81.21 287	87.10 366

新几何1				83.42 192	93.13 60	70.71 80		85.48 326	57.43 435	81.80 150	91.98 120	63.28 174	92.27 246	64.60 300	92.99 76	87.27 357

EPNet		83.72 111	82.92 125	86.14 72	84.22 332	69.48 101	91.05 64	85.27 327	81.30 6	76.83 249	91.65 133	66.09 149	95.56 68	76.00 181	93.85 68	93.38 119
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UnsupCasMVSNet_eth		67.33 401	65.99 405	71.37 418	73.48 459	51.47 440	75.16 430	85.19 328	65.20 351	60.78 445	80.93 408	42.35 410	77.20 441	57.12 375	53.69 464	85.44 397

SD_0403		74.65 320	74.77 304	74.29 392	86.20 283	47.42 456	83.71 315	85.12 329	69.30 285	68.50 386	87.95 256	59.40 241	86.05 382	49.38 423	83.35 261	89.40 291

mmtdpeth		74.16 325	73.01 329	77.60 356	83.72 345	61.13 323	85.10 276	85.10 330	72.06 213	77.21 244	80.33 413	43.84 402	85.75 385	77.14 164	52.61 466	85.91 390

IB-MVS	68.01 15	75.85 305	73.36 325	83.31 196	84.76 321	66.03 197	83.38 325	85.06 331	70.21 264	69.40 375	81.05 403	45.76 387	94.66 118	65.10 296	75.49 365	89.25 296
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
TAMVS		78.89 237	77.51 253	83.03 212	87.80 215	67.79 157	84.72 285	85.05 332	67.63 316	76.75 252	87.70 260	62.25 196	90.82 309	58.53 362	87.13 189	90.49 245

CL-MVSNet_self_test		72.37 354	71.46 345	75.09 382	79.49 424	53.53 422	80.76 365	85.01 333	69.12 293	70.51 358	82.05 396	57.92 253	84.13 402	52.27 405	66.00 432	87.60 344

testdata				79.97 301	90.90 98	64.21 258		84.71 334	59.27 417	85.40 75	92.91 94	62.02 201	89.08 343	68.95 262	91.37 105	86.63 377

MS-PatchMatch		73.83 330	72.67 332	77.30 360	83.87 341	66.02 198	81.82 346	84.66 335	61.37 401	68.61 383	82.82 385	47.29 366	88.21 358	59.27 352	84.32 241	77.68 456

ET-MVSNet_ETH3D		78.63 242	76.63 274	84.64 122	86.73 270	69.47 102	85.01 279	84.61 336	69.54 280	66.51 414	86.59 295	50.16 341	91.75 266	76.26 176	84.24 242	92.69 158

CNLPA		78.08 256	76.79 268	81.97 250	90.40 109	71.07 70	87.59 184	84.55 337	66.03 340	72.38 341	89.64 201	57.56 257	86.04 383	59.61 349	83.35 261	88.79 315

MIMVSNet1		68.58 392	66.78 402	73.98 396	80.07 414	51.82 436	80.77 364	84.37 338	64.40 363	59.75 451	82.16 395	36.47 444	83.63 406	42.73 454	70.33 414	86.48 378

KD-MVS_2432*1600		66.22 411	63.89 414	73.21 402	75.47 450	53.42 424	70.76 450	84.35 339	64.10 368	66.52 412	78.52 432	34.55 449	84.98 395	50.40 415	50.33 469	81.23 444

miper_refine_blended		66.22 411	63.89 414	73.21 402	75.47 450	53.42 424	70.76 450	84.35 339	64.10 368	66.52 412	78.52 432	34.55 449	84.98 395	50.40 415	50.33 469	81.23 444

test_0402		72.79 351	70.44 362	79.84 304	88.13 198	65.99 201	85.93 252	84.29 341	65.57 345	67.40 400	85.49 323	46.92 370	92.61 227	35.88 467	74.38 384	80.94 446

EU-MVSNet		68.53 394	67.61 393	71.31 421	78.51 431	47.01 459	84.47 293	84.27 342	42.27 468	66.44 415	84.79 341	40.44 424	83.76 404	58.76 360	68.54 423	83.17 426

thisisatest0530		79.40 221	77.76 244	84.31 142	87.69 228	65.10 231	87.36 197	84.26 343	70.04 265	77.42 234	88.26 246	49.94 345	94.79 112	70.20 247	84.70 232	93.03 143

COLMAP_ROB	66.92 17	73.01 346	70.41 363	80.81 280	87.13 254	65.63 211	88.30 160	84.19 344	62.96 382	63.80 435	87.69 261	38.04 438	92.56 231	46.66 438	74.91 379	84.24 414
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tttt0517		79.40 221	77.91 235	83.90 178	88.10 200	63.84 266	88.37 157	84.05 345	71.45 225	76.78 251	89.12 216	49.93 347	94.89 105	70.18 248	83.18 265	92.96 148

CMPMVS	51.72 21	70.19 378	68.16 380	76.28 367	73.15 463	57.55 374	79.47 385	83.92 346	48.02 461	56.48 461	84.81 340	43.13 406	86.42 379	62.67 319	81.81 282	84.89 407
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous202405211		78.25 250	77.01 261	81.99 249	91.03 94	60.67 334	84.77 284	83.90 347	70.65 250	80.00 182	91.20 153	41.08 421	91.43 286	65.21 294	85.26 225	93.85 90

XXY-MVS		75.41 312	75.56 289	74.96 383	83.59 349	57.82 368	80.59 369	83.87 348	66.54 334	74.93 305	88.31 243	63.24 177	80.09 429	62.16 326	76.85 343	86.97 368

DP-MVS		76.78 287	74.57 306	83.42 192	93.29 52	69.46 104	88.55 149	83.70 349	63.98 372	70.20 362	88.89 226	54.01 292	94.80 111	46.66 438	81.88 281	86.01 387

tfpn200view9		76.42 296	75.37 295	79.55 315	89.13 156	57.65 372	85.17 272	83.60 350	73.41 185	76.45 260	86.39 303	52.12 309	91.95 258	48.33 429	83.75 250	89.07 297

thres400		76.50 291	75.37 295	79.86 303	89.13 156	57.65 372	85.17 272	83.60 350	73.41 185	76.45 260	86.39 303	52.12 309	91.95 258	48.33 429	83.75 250	90.00 270

SixPastTwentyTwo		73.37 337	71.26 351	79.70 309	85.08 314	57.89 366	85.57 260	83.56 352	71.03 238	65.66 419	85.88 312	42.10 414	92.57 230	59.11 355	63.34 442	88.65 321

thres200		75.55 308	74.47 309	78.82 326	87.78 218	57.85 367	83.07 334	83.51 353	72.44 206	75.84 274	84.42 345	52.08 312	91.75 266	47.41 436	83.64 255	86.86 370

IterMVS-SCA-FT		75.43 311	73.87 318	80.11 298	82.69 376	64.85 243	81.57 352	83.47 354	69.16 292	70.49 359	84.15 357	51.95 315	88.15 359	69.23 258	72.14 404	87.34 354

CVMVSNet		72.99 347	72.58 334	74.25 393	84.28 330	50.85 445	86.41 234	83.45 355	44.56 465	73.23 328	87.54 267	49.38 352	85.70 386	65.90 289	78.44 321	86.19 382

ITE_SJBPF				78.22 339	81.77 390	60.57 335		83.30 356	69.25 288	67.54 395	87.20 276	36.33 445	87.28 371	54.34 394	74.62 382	86.80 371

thisisatest0515		77.33 277	75.38 294	83.18 203	85.27 308	63.80 267	82.11 345	83.27 357	65.06 354	75.91 272	83.84 361	49.54 349	94.27 131	67.24 278	86.19 206	91.48 208

mvs5depth		69.45 385	67.45 396	75.46 378	73.93 454	55.83 400	79.19 390	83.23 358	66.89 323	71.63 350	83.32 374	33.69 451	85.09 394	59.81 347	55.34 462	85.46 396

thres100view900		76.50 291	75.55 290	79.33 317	89.52 133	56.99 381	85.83 257	83.23 358	73.94 168	76.32 264	87.12 279	51.89 318	91.95 258	48.33 429	83.75 250	89.07 297

thres600view7		76.50 291	75.44 291	79.68 310	89.40 141	57.16 378	85.53 266	83.23 358	73.79 172	76.26 265	87.09 280	51.89 318	91.89 261	48.05 434	83.72 253	90.00 270

test222					91.50 86	68.26 137	84.16 307	83.20 361	54.63 446	79.74 184	91.63 135	58.97 244			91.42 103	86.77 372

EPNet_dtu		75.46 310	74.86 302	77.23 361	82.57 379	54.60 414	86.89 214	83.09 362	71.64 218	66.25 416	85.86 313	55.99 272	88.04 361	54.92 391	86.55 199	89.05 302
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
fmvsm_s_conf0.5_n		83.80 106	83.71 108	84.07 161	86.69 272	67.31 173	89.46 103	83.07 363	71.09 234	86.96 63	93.70 75	69.02 110	91.47 284	88.79 30	84.62 233	93.44 118

fmvsm_s_conf0.1_n		83.56 117	83.38 116	84.10 155	84.86 318	67.28 175	89.40 108	83.01 364	70.67 246	87.08 60	93.96 67	68.38 117	91.45 285	88.56 34	84.50 234	93.56 113

testing91		76.54 289	75.66 288	79.18 321	88.43 186	55.89 399	81.08 359	83.00 365	73.76 173	75.34 288	84.29 350	46.20 382	90.07 323	64.33 301	84.50 234	91.58 203

TDRefinement		67.49 399	64.34 411	76.92 363	73.47 460	61.07 326	84.86 283	82.98 366	59.77 412	58.30 455	85.13 333	26.06 463	87.89 363	47.92 435	60.59 452	81.81 442

OpenMVS_ROB	64.09 19	70.56 373	68.19 379	77.65 353	80.26 410	59.41 351	85.01 279	82.96 367	58.76 423	65.43 421	82.33 391	37.63 440	91.23 293	45.34 448	76.03 358	82.32 436

fmvsm_s_conf0.5_n_a		83.63 115	83.41 115	84.28 146	86.14 285	68.12 143	89.43 104	82.87 368	70.27 262	87.27 59	93.80 73	69.09 105	91.58 272	88.21 38	83.65 254	93.14 136

fmvsm_s_conf0.1_n_a		83.32 126	82.99 123	84.28 146	83.79 342	68.07 145	89.34 111	82.85 369	69.80 273	87.36 58	94.06 59	68.34 119	91.56 275	87.95 42	83.46 260	93.21 129

RPSCF		73.23 343	71.46 345	78.54 333	82.50 380	59.85 344	82.18 344	82.84 370	58.96 420	71.15 356	89.41 213	45.48 392	84.77 398	58.82 359	71.83 406	91.02 223

CostFormer		75.24 315	73.90 317	79.27 318	82.65 378	58.27 359	80.80 362	82.73 371	61.57 398	75.33 292	83.13 378	55.52 275	91.07 303	64.98 297	78.34 326	88.45 326

IterMVS		74.29 322	72.94 330	78.35 338	81.53 395	63.49 280	81.58 351	82.49 372	68.06 314	69.99 368	83.69 367	51.66 323	85.54 389	65.85 290	71.64 407	86.01 387
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_cas_vis1_n_1920		73.76 331	73.74 320	73.81 398	75.90 444	59.77 345	80.51 370	82.40 373	58.30 426	81.62 155	85.69 316	44.35 399	76.41 448	76.29 175	78.61 317	85.23 400

WTY-MVS		75.65 307	75.68 286	75.57 374	86.40 279	56.82 383	77.92 411	82.40 373	65.10 353	76.18 268	87.72 259	63.13 183	80.90 426	60.31 343	81.96 279	89.00 306

pmmvs4		74.03 329	71.91 340	80.39 288	81.96 387	68.32 135	81.45 354	82.14 375	59.32 416	69.87 371	85.13 333	52.40 305	88.13 360	60.21 344	74.74 381	84.73 410

FMVSNet5		69.50 384	67.96 384	74.15 394	82.97 370	55.35 407	80.01 380	82.12 376	62.56 389	63.02 436	81.53 400	36.92 441	81.92 419	48.42 428	74.06 386	85.17 403

mamv4		76.81 286	78.23 230	72.54 411	86.12 286	65.75 210	78.76 397	82.07 377	64.12 367	72.97 332	91.02 162	67.97 123	68.08 476	83.04 89	78.02 328	83.80 421

baseline1		76.98 283	76.75 271	77.66 352	88.13 198	55.66 403	85.12 275	81.89 378	73.04 197	76.79 250	88.90 225	62.43 193	87.78 365	63.30 309	71.18 410	89.55 288

UnsupCasMVSNet_bld		63.70 420	61.53 426	70.21 427	73.69 457	51.39 441	72.82 441	81.89 378	55.63 443	57.81 457	71.80 462	38.67 434	78.61 434	49.26 425	52.21 467	80.63 448

LFMVS		81.82 154	81.23 154	83.57 188	91.89 82	63.43 283	89.84 87	81.85 380	77.04 70	83.21 123	93.10 88	52.26 307	93.43 186	71.98 229	89.95 130	93.85 90

sss		73.60 333	73.64 321	73.51 400	82.80 373	55.01 411	76.12 421	81.69 381	62.47 390	74.68 309	85.85 314	57.32 260	78.11 437	60.86 339	80.93 289	87.39 350

SSC-MVS3.2		73.35 340	73.39 323	73.23 401	85.30 307	49.01 452	74.58 436	81.57 382	75.21 130	73.68 322	85.58 321	52.53 301	82.05 418	54.33 395	77.69 333	88.63 322

pmmvs-eth3d		70.50 374	67.83 388	78.52 335	77.37 440	66.18 195	81.82 346	81.51 383	58.90 421	63.90 434	80.42 411	42.69 409	86.28 380	58.56 361	65.30 438	83.11 428

TinyColmap		67.30 402	64.81 409	74.76 387	81.92 389	56.68 387	80.29 375	81.49 384	60.33 406	56.27 462	83.22 375	24.77 467	87.66 367	45.52 446	69.47 417	79.95 451

testing99		76.09 302	75.12 301	79.00 322	88.16 195	55.50 405	80.79 363	81.40 385	73.30 189	75.17 296	84.27 353	44.48 397	90.02 324	64.28 302	84.22 243	91.48 208

tpmvs		71.09 366	69.29 371	76.49 366	82.04 386	56.04 397	78.92 395	81.37 386	64.05 370	67.18 402	78.28 434	49.74 348	89.77 328	49.67 422	72.37 400	83.67 422

WBMVS		73.43 335	72.81 331	75.28 380	87.91 209	50.99 444	78.59 401	81.31 387	65.51 348	74.47 313	84.83 339	46.39 376	86.68 375	58.41 363	77.86 329	88.17 334

pmmvs5		71.55 362	70.20 366	75.61 373	77.83 435	56.39 391	81.74 348	80.89 388	57.76 431	67.46 397	84.49 343	49.26 355	85.32 393	57.08 376	75.29 374	85.11 404

ANet_high		50.57 442	46.10 446	63.99 446	48.67 491	39.13 479	70.99 449	80.85 389	61.39 400	31.18 480	57.70 476	17.02 478	73.65 467	31.22 473	15.89 488	79.18 453

LCM-MVSNet		54.25 433	49.68 443	67.97 440	53.73 488	45.28 465	66.85 465	80.78 390	35.96 477	39.45 478	62.23 471	8.70 487	78.06 438	48.24 432	51.20 468	80.57 449

PVSNet	64.34 18	72.08 360	70.87 357	75.69 372	86.21 282	56.44 390	74.37 437	80.73 391	62.06 395	70.17 364	82.23 394	42.86 408	83.31 410	54.77 392	84.45 238	87.32 355

baseline2		75.70 306	73.83 319	81.30 265	83.26 356	61.79 317	82.57 339	80.65 392	66.81 324	66.88 405	83.42 373	57.86 254	92.19 249	63.47 306	79.57 307	89.91 275

ppachtmachnet_test		70.04 380	67.34 398	78.14 341	79.80 419	61.13 323	79.19 390	80.59 393	59.16 418	65.27 422	79.29 425	46.75 374	87.29 370	49.33 424	66.72 427	86.00 389

FE-MVSNET		67.25 403	65.33 407	73.02 406	75.86 445	52.54 430	80.26 377	80.56 394	63.80 375	60.39 446	79.70 422	41.41 418	84.66 400	43.34 452	62.62 445	81.86 440

Gipuma		45.18 447	41.86 450	55.16 460	77.03 442	51.52 439	32.50 485	80.52 395	32.46 480	27.12 483	35.02 484	9.52 486	75.50 456	22.31 481	60.21 453	38.45 483
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231206		68.60 391	67.80 389	71.02 423	80.23 412	50.75 446	78.30 406	80.47 396	56.79 438	66.11 418	82.63 388	46.35 379	78.95 433	43.62 451	75.70 361	83.36 425

LCM-MVSNet-Re		77.05 281	76.94 264	77.36 358	87.20 251	51.60 438	80.06 378	80.46 397	75.20 131	67.69 394	86.72 287	62.48 191	88.98 345	63.44 307	89.25 141	91.51 205

tt0320		70.49 375	68.03 383	77.89 346	84.78 320	59.12 352	83.55 321	80.44 398	58.13 428	67.43 399	80.41 412	39.26 430	87.54 368	55.12 389	63.18 444	86.99 367

testing11		75.14 316	74.01 314	78.53 334	88.16 195	56.38 392	80.74 366	80.42 399	70.67 246	72.69 337	83.72 366	43.61 404	89.86 326	62.29 324	83.76 249	89.36 293

tpm2		73.26 342	71.46 345	78.63 328	83.34 354	56.71 386	80.65 368	80.40 400	56.63 439	73.55 324	82.02 397	51.80 320	91.24 292	56.35 385	78.42 324	87.95 336

CR-MVSNet		73.37 337	71.27 350	79.67 311	81.32 401	65.19 226	75.92 423	80.30 401	59.92 411	72.73 335	81.19 401	52.50 303	86.69 374	59.84 346	77.71 331	87.11 364

Patchmtry		70.74 370	69.16 373	75.49 377	80.72 405	54.07 419	74.94 434	80.30 401	58.34 425	70.01 366	81.19 401	52.50 303	86.54 376	53.37 400	71.09 411	85.87 392

sc_t1		72.19 358	69.51 369	80.23 294	84.81 319	61.09 325	84.68 286	80.22 403	60.70 404	71.27 353	83.58 370	36.59 443	89.24 339	60.41 341	63.31 443	90.37 250

tpm cat1		70.57 372	68.31 378	77.35 359	82.41 383	57.95 365	78.08 407	80.22 403	52.04 452	68.54 385	77.66 439	52.00 314	87.84 364	51.77 406	72.07 405	86.25 380

MDTV_nov1_ep13			69.97 367		83.18 360	53.48 423	77.10 418	80.18 405	60.45 405	69.33 377	80.44 410	48.89 361	86.90 373	51.60 408	78.51 320

AllTest		70.96 367	68.09 382	79.58 313	85.15 311	63.62 270	84.58 291	79.83 406	62.31 391	60.32 448	86.73 285	32.02 453	88.96 347	50.28 417	71.57 408	86.15 383

TestCases				79.58 313	85.15 311	63.62 270		79.83 406	62.31 391	60.32 448	86.73 285	32.02 453	88.96 347	50.28 417	71.57 408	86.15 383

test_fmvs1_n		70.86 369	70.24 365	72.73 409	72.51 467	55.28 408	81.27 358	79.71 408	51.49 456	78.73 201	84.87 338	27.54 462	77.02 442	76.06 179	79.97 305	85.88 391

Vis-MVSNet (Re-imp)		78.36 249	78.45 221	78.07 344	88.64 178	51.78 437	86.70 223	79.63 409	74.14 164	75.11 299	90.83 167	61.29 217	89.75 329	58.10 367	91.60 99	92.69 158

MIMVSNet		70.69 371	69.30 370	74.88 385	84.52 327	56.35 394	75.87 425	79.42 410	64.59 359	67.76 392	82.41 389	41.10 420	81.54 421	46.64 440	81.34 284	86.75 373

myMVS_eth3d28		73.62 332	73.53 322	73.90 397	88.20 193	47.41 457	78.06 408	79.37 411	74.29 160	73.98 318	84.29 350	44.67 394	83.54 407	51.47 409	87.39 183	90.74 234

dmvs_re		71.14 365	70.58 359	72.80 408	81.96 387	59.68 346	75.60 427	79.34 412	68.55 306	69.27 378	80.72 409	49.42 351	76.54 445	52.56 404	77.79 330	82.19 438

SCA		74.22 324	72.33 337	79.91 302	84.05 337	62.17 309	79.96 381	79.29 413	66.30 336	72.38 341	80.13 416	51.95 315	88.60 353	59.25 353	77.67 334	88.96 308

testing222		74.04 327	72.66 333	78.19 340	87.89 210	55.36 406	81.06 360	79.20 414	71.30 229	74.65 310	83.57 371	39.11 432	88.67 352	51.43 411	85.75 218	90.53 243

tpmrst		72.39 352	72.13 339	73.18 405	80.54 408	49.91 449	79.91 382	79.08 415	63.11 379	71.69 349	79.95 418	55.32 276	82.77 414	65.66 292	73.89 388	86.87 369

tt0320-xc		70.11 379	67.45 396	78.07 344	85.33 306	59.51 350	83.28 327	78.96 416	58.77 422	67.10 403	80.28 414	36.73 442	87.42 369	56.83 381	59.77 454	87.29 356

test_fmvs1		70.93 368	70.52 360	72.16 413	73.71 456	55.05 410	80.82 361	78.77 417	51.21 457	78.58 206	84.41 346	31.20 457	76.94 443	75.88 183	80.12 304	84.47 412

PatchmatchNet		73.12 344	71.33 348	78.49 336	83.18 360	60.85 330	79.63 383	78.57 418	64.13 366	71.73 348	79.81 421	51.20 329	85.97 384	57.40 373	76.36 356	88.66 320
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
testing3-2		75.12 317	75.19 299	74.91 384	90.40 109	45.09 467	80.29 375	78.42 419	78.37 40	76.54 259	87.75 258	44.36 398	87.28 371	57.04 377	83.49 258	92.37 172

MDA-MVSNet-bldmvs		66.68 406	63.66 416	75.75 371	79.28 426	60.56 336	73.92 439	78.35 420	64.43 361	50.13 470	79.87 420	44.02 401	83.67 405	46.10 443	56.86 456	83.03 430

new-patchmatchnet		61.73 424	61.73 425	61.70 449	72.74 465	24.50 492	69.16 457	78.03 421	61.40 399	56.72 460	75.53 454	38.42 435	76.48 447	45.95 444	57.67 455	84.13 416

our_test_3		69.14 387	67.00 400	75.57 374	79.80 419	58.80 353	77.96 409	77.81 422	59.55 414	62.90 439	78.25 435	47.43 365	83.97 403	51.71 407	67.58 426	83.93 419

test20.03		67.45 400	66.95 401	68.94 431	75.48 449	44.84 468	77.50 413	77.67 423	66.66 328	63.01 437	83.80 362	47.02 369	78.40 435	42.53 456	68.86 422	83.58 423

WB-MVSnew		71.96 361	71.65 343	72.89 407	84.67 326	51.88 435	82.29 342	77.57 424	62.31 391	73.67 323	83.00 380	53.49 297	81.10 425	45.75 445	82.13 277	85.70 393

test-LLR		72.94 348	72.43 335	74.48 389	81.35 399	58.04 362	78.38 402	77.46 425	66.66 328	69.95 369	79.00 428	48.06 363	79.24 431	66.13 285	84.83 229	86.15 383

test-mter		71.41 363	70.39 364	74.48 389	81.35 399	58.04 362	78.38 402	77.46 425	60.32 407	69.95 369	79.00 428	36.08 446	79.24 431	66.13 285	84.83 229	86.15 383

ECVR-MVS		79.61 212	79.26 205	80.67 283	90.08 116	54.69 413	87.89 176	77.44 427	74.88 143	80.27 178	92.79 100	48.96 360	92.45 237	68.55 266	92.50 84	94.86 19

UBG		73.08 345	72.27 338	75.51 376	88.02 204	51.29 442	78.35 405	77.38 428	65.52 346	73.87 320	82.36 390	45.55 389	86.48 378	55.02 390	84.39 240	88.75 317

tpm		72.37 354	71.71 342	74.35 391	82.19 385	52.00 432	79.22 389	77.29 429	64.56 360	72.95 333	83.68 368	51.35 324	83.26 411	58.33 365	75.80 360	87.81 340

LF4IMVS		64.02 419	62.19 423	69.50 429	70.90 468	53.29 427	76.13 420	77.18 430	52.65 451	58.59 453	80.98 405	23.55 470	76.52 446	53.06 402	66.66 428	78.68 454

test1111		79.43 219	79.18 208	80.15 297	89.99 121	53.31 426	87.33 199	77.05 431	75.04 136	80.23 180	92.77 102	48.97 359	92.33 245	68.87 263	92.40 86	94.81 22

K. test v3		71.19 364	68.51 376	79.21 320	83.04 365	57.78 370	84.35 302	76.91 432	72.90 200	62.99 438	82.86 384	39.27 429	91.09 302	61.65 332	52.66 465	88.75 317

UWE-MVS		72.13 359	71.49 344	74.03 395	86.66 273	47.70 454	81.40 356	76.89 433	63.60 376	75.59 277	84.22 354	39.94 426	85.62 388	48.98 426	86.13 208	88.77 316

testgi		66.67 407	66.53 403	67.08 442	75.62 448	41.69 477	75.93 422	76.50 434	66.11 337	65.20 425	86.59 295	35.72 447	74.71 461	43.71 450	73.38 395	84.84 408

test_fmvs2		68.35 396	67.48 395	70.98 424	69.50 470	51.95 433	80.05 379	76.38 435	49.33 459	74.65 310	84.38 347	23.30 471	75.40 459	74.51 198	75.17 377	85.60 394

test_vis1_n		69.85 383	69.21 372	71.77 415	72.66 466	55.27 409	81.48 353	76.21 436	52.03 453	75.30 293	83.20 377	28.97 460	76.22 450	74.60 197	78.41 325	83.81 420

PatchMatch-RL		72.38 353	70.90 356	76.80 365	88.60 179	67.38 171	79.53 384	76.17 437	62.75 387	69.36 376	82.00 398	45.51 390	84.89 397	53.62 398	80.58 296	78.12 455

JIA-IIPM		66.32 410	62.82 422	76.82 364	77.09 441	61.72 318	65.34 470	75.38 438	58.04 430	64.51 428	62.32 470	42.05 415	86.51 377	51.45 410	69.22 419	82.21 437

ADS-MVSNet2		66.20 413	63.33 417	74.82 386	79.92 415	58.75 354	67.55 462	75.19 439	53.37 449	65.25 423	75.86 451	42.32 411	80.53 428	41.57 457	68.91 420	85.18 401

ETVMVS		72.25 357	71.05 353	75.84 370	87.77 220	51.91 434	79.39 386	74.98 440	69.26 287	73.71 321	82.95 381	40.82 423	86.14 381	46.17 442	84.43 239	89.47 289

PatchT		68.46 395	67.85 386	70.29 426	80.70 406	43.93 470	72.47 442	74.88 441	60.15 409	70.55 357	76.57 444	49.94 345	81.59 420	50.58 413	74.83 380	85.34 398

dp		66.80 405	65.43 406	70.90 425	79.74 421	48.82 453	75.12 432	74.77 442	59.61 413	64.08 432	77.23 441	42.89 407	80.72 427	48.86 427	66.58 429	83.16 427

MDA-MVSNet_test_wron		65.03 415	62.92 419	71.37 418	75.93 443	56.73 384	69.09 459	74.73 443	57.28 436	54.03 465	77.89 436	45.88 384	74.39 463	49.89 421	61.55 448	82.99 431

TESTMET0.1,1		69.89 382	69.00 374	72.55 410	79.27 427	56.85 382	78.38 402	74.71 444	57.64 432	68.09 389	77.19 442	37.75 439	76.70 444	63.92 304	84.09 244	84.10 417

YYNet1		65.03 415	62.91 420	71.38 417	75.85 446	56.60 388	69.12 458	74.66 445	57.28 436	54.12 464	77.87 437	45.85 385	74.48 462	49.95 420	61.52 449	83.05 429

test_fmvs3		63.36 421	61.82 424	67.98 439	62.51 479	46.96 460	77.37 415	74.03 446	45.24 464	67.50 396	78.79 431	12.16 483	72.98 468	72.77 218	66.02 431	83.99 418

PMMVS		69.34 386	68.67 375	71.35 420	75.67 447	62.03 312	75.17 429	73.46 447	50.00 458	68.68 381	79.05 426	52.07 313	78.13 436	61.16 337	82.77 269	73.90 462

PVSNet_0	57.27 20	61.67 425	59.27 428	68.85 433	79.61 422	57.44 376	68.01 460	73.44 448	55.93 442	58.54 454	70.41 465	44.58 396	77.55 440	47.01 437	35.91 477	71.55 465

Syy-MVS		68.05 397	67.85 386	68.67 435	84.68 323	40.97 478	78.62 399	73.08 449	66.65 331	66.74 408	79.46 423	52.11 311	82.30 416	32.89 470	76.38 354	82.75 433

myMVS_eth3d		67.02 404	66.29 404	69.21 430	84.68 323	42.58 473	78.62 399	73.08 449	66.65 331	66.74 408	79.46 423	31.53 456	82.30 416	39.43 462	76.38 354	82.75 433

test0.0.03 1		68.00 398	67.69 391	68.90 432	77.55 438	47.43 455	75.70 426	72.95 451	66.66 328	66.56 410	82.29 393	48.06 363	75.87 454	44.97 449	74.51 383	83.41 424

testing3		68.56 393	67.67 392	71.22 422	87.33 246	42.87 472	83.06 335	71.54 452	70.36 257	69.08 379	84.38 347	30.33 459	85.69 387	37.50 465	75.45 369	85.09 405

ADS-MVSNet		64.36 418	62.88 421	68.78 434	79.92 415	47.17 458	67.55 462	71.18 453	53.37 449	65.25 423	75.86 451	42.32 411	73.99 465	41.57 457	68.91 420	85.18 401

Patchmatch-RL test		70.24 377	67.78 390	77.61 354	77.43 439	59.57 349	71.16 447	70.33 454	62.94 383	68.65 382	72.77 460	50.62 335	85.49 390	69.58 256	66.58 429	87.77 341

gg-mvs-nofinetune		69.95 381	67.96 384	75.94 369	83.07 363	54.51 416	77.23 416	70.29 455	63.11 379	70.32 361	62.33 469	43.62 403	88.69 351	53.88 397	87.76 177	84.62 411

door-mid								69.98 456

GG-mvs-BLEND				75.38 379	81.59 393	55.80 401	79.32 387	69.63 457		67.19 401	73.67 458	43.24 405	88.90 349	50.41 414	84.50 234	81.45 443

FPMVS		53.68 436	51.64 438	59.81 452	65.08 476	51.03 443	69.48 455	69.58 458	41.46 469	40.67 476	72.32 461	16.46 479	70.00 473	24.24 480	65.42 437	58.40 476

door								69.44 459

Patchmatch-test		64.82 417	63.24 418	69.57 428	79.42 425	49.82 450	63.49 476	69.05 460	51.98 454	59.95 450	80.13 416	50.91 331	70.98 469	40.66 459	73.57 391	87.90 338

CHOSEN 280x420		66.51 408	64.71 410	71.90 414	81.45 396	63.52 279	57.98 479	68.95 461	53.57 448	62.59 440	76.70 443	46.22 381	75.29 460	55.25 388	79.68 306	76.88 458

MVStest1		56.63 431	52.76 437	68.25 438	61.67 480	53.25 428	71.67 445	68.90 462	38.59 473	50.59 469	83.05 379	25.08 465	70.66 470	36.76 466	38.56 476	80.83 447

EGC-MVSNET		52.07 440	47.05 444	67.14 441	83.51 351	60.71 333	80.50 371	67.75 463	0.07 491	0.43 492	75.85 453	24.26 468	81.54 421	28.82 474	62.25 446	59.16 474

ttmdpeth		59.91 427	57.10 431	68.34 437	67.13 474	46.65 461	74.64 435	67.41 464	48.30 460	62.52 441	85.04 337	20.40 473	75.93 453	42.55 455	45.90 475	82.44 435

EPMVS		69.02 388	68.16 380	71.59 416	79.61 422	49.80 451	77.40 414	66.93 465	62.82 386	70.01 366	79.05 426	45.79 386	77.86 439	56.58 383	75.26 375	87.13 363

APD_test1		53.31 437	49.93 442	63.42 448	65.68 475	50.13 448	71.59 446	66.90 466	34.43 478	40.58 477	71.56 463	8.65 488	76.27 449	34.64 469	55.36 461	63.86 472

lessismore_v0				78.97 323	81.01 404	57.15 379		65.99 467		61.16 444	82.82 385	39.12 431	91.34 289	59.67 348	46.92 472	88.43 327

dmvs_testset		62.63 422	64.11 413	58.19 453	78.55 430	24.76 491	75.28 428	65.94 468	67.91 315	60.34 447	76.01 450	53.56 295	73.94 466	31.79 471	67.65 425	75.88 460

pmmvs3		57.79 429	54.26 434	68.37 436	64.02 478	56.72 385	75.12 432	65.17 469	40.20 470	52.93 466	69.86 466	20.36 474	75.48 457	45.45 447	55.25 463	72.90 464

MVS-HIRNet		59.14 428	57.67 430	63.57 447	81.65 391	43.50 471	71.73 444	65.06 470	39.59 472	51.43 467	57.73 475	38.34 436	82.58 415	39.53 460	73.95 387	64.62 471

PM-MVS		66.41 409	64.14 412	73.20 404	73.92 455	56.45 389	78.97 394	64.96 471	63.88 374	64.72 426	80.24 415	19.84 475	83.44 409	66.24 284	64.52 440	79.71 452

UWE-MVS-28		65.32 414	64.93 408	66.49 443	78.70 429	38.55 480	77.86 412	64.39 472	62.00 396	64.13 431	83.60 369	41.44 417	76.00 452	31.39 472	80.89 290	84.92 406

PMVS	37.38 22	44.16 448	40.28 452	55.82 458	40.82 493	42.54 475	65.12 471	63.99 473	34.43 478	24.48 484	57.12 477	3.92 493	76.17 451	17.10 485	55.52 460	48.75 479
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test2506		77.30 278	76.49 275	79.74 308	90.08 116	52.02 431	87.86 178	63.10 474	74.88 143	80.16 181	92.79 100	38.29 437	92.35 243	68.74 265	92.50 84	94.86 19

test_method		31.52 452	29.28 456	38.23 467	27.03 495	6.50 498	20.94 487	62.21 475	4.05 489	22.35 487	52.50 480	13.33 480	47.58 487	27.04 477	34.04 479	60.62 473

WB-MVS		54.94 432	54.72 433	55.60 459	73.50 458	20.90 493	74.27 438	61.19 476	59.16 418	50.61 468	74.15 456	47.19 368	75.78 455	17.31 484	35.07 478	70.12 466

test_vis1_rt		60.28 426	58.42 429	65.84 444	67.25 473	55.60 404	70.44 452	60.94 477	44.33 466	59.00 452	66.64 467	24.91 466	68.67 474	62.80 315	69.48 416	73.25 463

SSC-MVS		53.88 435	53.59 435	54.75 461	72.87 464	19.59 494	73.84 440	60.53 478	57.58 434	49.18 472	73.45 459	46.34 380	75.47 458	16.20 487	32.28 480	69.20 467

testf1		45.72 444	41.96 448	57.00 454	56.90 482	45.32 463	66.14 467	59.26 479	26.19 482	30.89 481	60.96 473	4.14 491	70.64 471	26.39 478	46.73 473	55.04 477

APD_test2		45.72 444	41.96 448	57.00 454	56.90 482	45.32 463	66.14 467	59.26 479	26.19 482	30.89 481	60.96 473	4.14 491	70.64 471	26.39 478	46.73 473	55.04 477

test_f		52.09 439	50.82 440	55.90 457	53.82 487	42.31 476	59.42 478	58.31 481	36.45 476	56.12 463	70.96 464	12.18 482	57.79 483	53.51 399	56.57 458	67.60 468

new_pmnet		50.91 441	50.29 441	52.78 462	68.58 471	34.94 484	63.71 474	56.63 482	39.73 471	44.95 473	65.47 468	21.93 472	58.48 482	34.98 468	56.62 457	64.92 470

DSMNet-mixed		57.77 430	56.90 432	60.38 451	67.70 472	35.61 482	69.18 456	53.97 483	32.30 481	57.49 458	79.88 419	40.39 425	68.57 475	38.78 463	72.37 400	76.97 457

PMMVS2		40.82 449	38.86 453	46.69 464	53.84 486	16.45 495	48.61 482	49.92 484	37.49 474	31.67 479	60.97 472	8.14 489	56.42 484	28.42 475	30.72 481	67.19 469

mvsany_test1		62.30 423	61.26 427	65.41 445	69.52 469	54.86 412	66.86 464	49.78 485	46.65 462	68.50 386	83.21 376	49.15 356	66.28 477	56.93 379	60.77 450	75.11 461

test_vis3_rt		49.26 443	47.02 445	56.00 456	54.30 485	45.27 466	66.76 466	48.08 486	36.83 475	44.38 474	53.20 479	7.17 490	64.07 479	56.77 382	55.66 459	58.65 475

E-PMN		31.77 451	30.64 454	35.15 469	52.87 489	27.67 486	57.09 480	47.86 487	24.64 484	16.40 489	33.05 485	11.23 484	54.90 485	14.46 488	18.15 486	22.87 485

EMVS		30.81 453	29.65 455	34.27 470	50.96 490	25.95 490	56.58 481	46.80 488	24.01 485	15.53 490	30.68 486	12.47 481	54.43 486	12.81 489	17.05 487	22.43 486

mvsany_test3		53.99 434	51.45 439	61.61 450	55.51 484	44.74 469	63.52 475	45.41 489	43.69 467	58.11 456	76.45 445	17.99 476	63.76 480	54.77 392	47.59 471	76.34 459

MVE	26.22 23	30.37 454	25.89 458	43.81 466	44.55 492	35.46 483	28.87 486	39.07 490	18.20 486	18.58 488	40.18 483	2.68 494	47.37 488	17.07 486	23.78 485	48.60 480
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dongtai		45.42 446	45.38 447	45.55 465	73.36 461	26.85 489	67.72 461	34.19 491	54.15 447	49.65 471	56.41 478	25.43 464	62.94 481	19.45 482	28.09 482	46.86 481

kuosan		39.70 450	40.40 451	37.58 468	64.52 477	26.98 487	65.62 469	33.02 492	46.12 463	42.79 475	48.99 481	24.10 469	46.56 489	12.16 490	26.30 483	39.20 482

MTMP							92.18 39	32.83 493

tmp_tt		18.61 456	21.40 459	10.23 473	4.82 496	10.11 496	34.70 484	30.74 494	1.48 490	23.91 486	26.07 487	28.42 461	13.41 492	27.12 476	15.35 489	7.17 487

DeepMVS_CX				27.40 471	40.17 494	26.90 488		24.59 495	17.44 487	23.95 485	48.61 482	9.77 485	26.48 490	18.06 483	24.47 484	28.83 484

N_pmnet		52.79 438	53.26 436	51.40 463	78.99 428	7.68 497	69.52 454	3.89 496	51.63 455	57.01 459	74.98 455	40.83 422	65.96 478	37.78 464	64.67 439	80.56 450

wuyk23d		16.82 457	15.94 460	19.46 472	58.74 481	31.45 485	39.22 483	3.74 497	6.84 488	6.04 491	2.70 491	1.27 495	24.29 491	10.54 491	14.40 490	2.63 488

testmvs		6.04 460	8.02 463	0.10 475	0.08 497	0.03 500	69.74 453	0.04 498	0.05 492	0.31 493	1.68 492	0.02 497	0.04 493	0.24 492	0.02 491	0.25 490

test123		6.12 459	8.11 462	0.14 474	0.06 498	0.09 499	71.05 448	0.03 499	0.04 493	0.25 494	1.30 493	0.05 496	0.03 494	0.21 493	0.01 492	0.29 489

mmdepth		0.00 462	0.00 465	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	0.00 494	0.00 498	0.00 495	0.00 494	0.00 493	0.00 491

monomultidepth		0.00 462	0.00 465	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	0.00 494	0.00 498	0.00 495	0.00 494	0.00 493	0.00 491

test_blank		0.00 462	0.00 465	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	0.00 494	0.00 498	0.00 495	0.00 494	0.00 493	0.00 491

uanet_test		0.00 462	0.00 465	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	0.00 494	0.00 498	0.00 495	0.00 494	0.00 493	0.00 491

DCPMVS		0.00 462	0.00 465	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	0.00 494	0.00 498	0.00 495	0.00 494	0.00 493	0.00 491

pcd_1.5k_mvsjas		5.26 461	7.02 464	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	0.00 494	63.15 180	0.00 495	0.00 494	0.00 493	0.00 491

sosnet-low-res		0.00 462	0.00 465	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	0.00 494	0.00 498	0.00 495	0.00 494	0.00 493	0.00 491

sosnet		0.00 462	0.00 465	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	0.00 494	0.00 498	0.00 495	0.00 494	0.00 493	0.00 491

uncertanet		0.00 462	0.00 465	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	0.00 494	0.00 498	0.00 495	0.00 494	0.00 493	0.00 491

Regformer		0.00 462	0.00 465	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	0.00 494	0.00 498	0.00 495	0.00 494	0.00 493	0.00 491

n2								0.00 500

nn								0.00 500

ab-mvs-re		7.23 458	9.64 461	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	86.72 287	0.00 498	0.00 495	0.00 494	0.00 493	0.00 491

uanet		0.00 462	0.00 465	0.00 476	0.00 499	0.00 501	0.00 488	0.00 500	0.00 494	0.00 495	0.00 494	0.00 498	0.00 495	0.00 494	0.00 493	0.00 491

TestfortrainingZip							93.28 12

WAC-MVS						42.58 473								39.46 461

PC_three_1452									68.21 312	92.02 15	94.00 63	82.09 5	95.98 61	84.58 71	96.68 2	94.95 12

eth-test2					0.00 499

eth-test					0.00 499

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 61	82.45 3	96.87 24	83.77 82	96.48 8	94.88 16

test_0728_THIRD									78.38 38	92.12 12	95.78 4	81.46 9	97.40 9	89.42 19	96.57 7	94.67 38

GSMVS																88.96 308

test_part2					95.06 8	72.65 32				91.80 16

sam_mvs1												51.32 325				88.96 308

sam_mvs												50.01 343

test_post1							78.90 396				5.43 490	48.81 362	85.44 392	59.25 353

test_post											5.46 489	50.36 339	84.24 401

patchmatchnet-post											74.00 457	51.12 330	88.60 353

gm-plane-assit					81.40 397	53.83 421			62.72 388		80.94 406		92.39 240	63.40 308

test9_res														84.90 64	95.70 30	92.87 151

agg_prior2														82.91 91	95.45 33	92.70 156

test_prior4						72.60 34	89.01 125

test_prior2							88.85 132		75.41 121	84.91 82	93.54 76	74.28 33		83.31 85	95.86 24

旧先验2							86.56 229		58.10 429	87.04 61			88.98 345	74.07 203

新几何2							86.29 243

原ACMM2							86.86 216

testdata2													91.01 304	62.37 323

segment_acmp												73.08 43

testdata1							84.14 308		75.71 112

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 234

plane_prior4											91.00 163

plane_prior3						68.60 128			78.44 36	78.92 199

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 124

plane_prior						68.71 123	90.38 78		77.62 47						86.16 207

HQP5-MVS						66.98 183

HQP-NCC					89.33 144		89.17 116		76.41 90	77.23 240

ACMP_Plane					89.33 144		89.17 116		76.41 90	77.23 240

BP-MVS														77.47 159

HQP4-MVS										77.24 239			95.11 94			91.03 221

HQP2-MVS												60.17 237

NP-MVS					89.62 129	68.32 135					90.24 184

MDTV_nov1_ep13_2view						37.79 481	75.16 430		55.10 444	66.53 411		49.34 353		53.98 396		87.94 337

ACMMP++_ref															81.95 280

ACMMP++															81.25 285

Test By Simon												64.33 166

High-resolution multi-view benchmark