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.

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
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
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

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
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

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_one_0601					95.07 7	71.46 59		94.14 10	78.27 41	92.05 14	95.74 6	80.83 13

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

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
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

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

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

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

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
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

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

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

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

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

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

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

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.
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

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

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

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

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

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
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

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

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

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

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

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.
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

fmvsm_s_conf0.1_n_2		83.80 106	83.79 106	83.83 179	85.62 297	64.94 238	87.03 207	86.62 310	74.32 157	87.97 47	94.33 43	60.67 228	92.60 228	89.72 14	87.79 175	93.96 83

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

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

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

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

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

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_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

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
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

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

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
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

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_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

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

fmvsm_s_conf0.1_n_a		83.32 126	82.99 123	84.28 146	83.79 342	68.07 145	89.34 111	82.85 370	69.80 273	87.36 58	94.06 59	68.34 119	91.56 275	87.95 42	83.46 260	93.21 129

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

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

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

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

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
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

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

fmvsm_s_conf0.1_n		83.56 117	83.38 116	84.10 155	84.86 318	67.28 175	89.40 108	83.01 365	70.67 246	87.08 60	93.96 67	68.38 117	91.45 285	88.56 34	84.50 234	93.56 113

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
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	87.92 363	86.56 53	91.05 109	90.80 229

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

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
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
fmvsm_s_conf0.5_n_a		83.63 115	83.41 115	84.28 146	86.14 285	68.12 143	89.43 104	82.87 369	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_conf0.5_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

fmvsm_s_conf0.5_n		83.80 106	83.71 108	84.07 161	86.69 272	67.31 173	89.46 103	83.07 364	71.09 234	86.96 63	93.70 75	69.02 110	91.47 284	88.79 30	84.62 233	93.44 118

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

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 148	85.42 303	68.81 116	88.49 150	87.26 293	68.08 313	88.03 44	93.49 77	72.04 57	91.77 265	88.90 29	89.14 146	92.24 180

VDDNet		81.52 164	80.67 164	84.05 167	90.44 108	64.13 260	89.73 93	85.91 321	71.11 233	83.18 126	93.48 78	50.54 338	93.49 179	73.40 210	88.25 165	94.54 53

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

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

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

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

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

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
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

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

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

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

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

LFMVS		81.82 154	81.23 154	83.57 188	91.89 82	63.43 283	89.84 87	81.85 381	77.04 70	83.21 123	93.10 88	52.26 307	93.43 186	71.98 229	89.95 130	93.85 90

旧先验1					91.96 80	65.79 208		86.37 314			93.08 92	69.31 99			92.74 80	88.74 319

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

testdata				79.97 302	90.90 98	64.21 258		84.71 335	59.27 418	85.40 75	92.91 94	62.02 201	89.08 344	68.95 262	91.37 105	86.63 378

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

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
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

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

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

test2506		77.30 278	76.49 275	79.74 309	90.08 116	52.02 432	87.86 178	63.10 475	74.88 143	80.16 181	92.79 100	38.29 438	92.35 243	68.74 265	92.50 84	94.86 19

ECVR-MVS		79.61 212	79.26 205	80.67 283	90.08 116	54.69 414	87.89 176	77.44 428	74.88 143	80.27 178	92.79 100	48.96 361	92.45 237	68.55 266	92.50 84	94.86 19

test1111		79.43 219	79.18 208	80.15 297	89.99 121	53.31 427	87.33 199	77.05 432	75.04 136	80.23 180	92.77 102	48.97 360	92.33 245	68.87 263	92.40 86	94.81 22

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

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
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	83.50 409	82.15 101	92.15 90	93.64 108

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
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

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

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

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

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

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

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

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

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

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

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

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

新几何1				83.42 192	93.13 60	70.71 80		85.48 327	57.43 436	81.80 150	91.98 120	63.28 174	92.27 246	64.60 300	92.99 76	87.27 358

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	61.81 332	90.95 112	88.41 328

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

VNet		82.21 145	82.41 134	81.62 255	90.82 100	60.93 329	84.47 293	89.78 195	76.36 96	84.07 104	91.88 123	64.71 163	90.26 320	70.68 241	88.89 148	93.66 102

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

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

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

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

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).
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

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

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

EPNet		83.72 111	82.92 125	86.14 72	84.22 332	69.48 101	91.05 64	85.27 328	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
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

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

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

test222					91.50 86	68.26 137	84.16 307	83.20 362	54.63 447	79.74 184	91.63 135	58.97 244			91.42 103	86.77 373

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

原ACMM1				84.35 139	93.01 66	68.79 117		92.44 82	63.96 374	81.09 163	91.57 139	66.06 150	95.45 75	67.19 279	94.82 50	88.81 314

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

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

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

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

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

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

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

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

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

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

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

Anonymous202405211		78.25 250	77.01 261	81.99 249	91.03 94	60.67 335	84.77 284	83.90 348	70.65 250	80.00 182	91.20 153	41.08 422	91.43 286	65.21 294	85.26 225	93.85 90

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

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	49.97 345	93.73 164	69.16 260	82.70 272	93.81 94

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

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	50.26 420	87.05 190	92.17 186
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
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

FIs		82.07 148	82.42 133	81.04 274	88.80 171	58.34 359	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

MVS_111021_LR		82.61 140	82.11 141	84.11 154	88.82 166	71.58 57	85.15 274	86.16 318	74.69 148	80.47 177	91.04 159	62.29 195	90.55 317	80.33 120	90.08 127	90.20 257

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

mamv4		76.81 286	78.23 230	72.54 412	86.12 286	65.75 210	78.76 398	82.07 378	64.12 368	72.97 332	91.02 162	67.97 123	68.08 477	83.04 89	78.02 328	83.80 422

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_prior4											91.00 163

FC-MVSNet-test		81.52 164	82.02 145	80.03 299	88.42 187	55.97 399	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

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	90.79 311	82.77 93	87.93 173	93.59 111

Vis-MVSNet (Re-imp)		78.36 249	78.45 221	78.07 345	88.64 178	51.78 438	86.70 223	79.63 410	74.14 164	75.11 299	90.83 167	61.29 217	89.75 330	58.10 368	91.60 99	92.69 158

114514_t		80.68 186	79.51 197	84.20 152	94.09 42	67.27 176	89.64 96	91.11 152	58.75 425	74.08 317	90.72 168	58.10 251	95.04 99	69.70 254	89.42 140	90.30 254

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

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

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

viewdifsd2359ckpt11		80.37 199	79.73 190	82.30 242	83.70 346	62.39 303	84.20 305	86.67 306	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	86.67 306	73.22 193	80.90 167	90.62 172	63.00 185	91.56 275	76.81 171	78.44 321	92.95 149

LS3D		76.95 284	74.82 303	83.37 195	90.45 107	67.36 172	89.15 120	86.94 301	61.87 398	69.52 374	90.61 174	51.71 323	94.53 122	46.38 442	86.71 197	88.21 333

AstraMVS		80.81 178	80.14 179	82.80 225	86.05 289	63.96 262	86.46 233	85.90 322	73.71 174	80.85 170	90.56 175	54.06 291	91.57 274	79.72 131	83.97 245	92.86 152

VPNet		78.69 241	78.66 217	78.76 328	88.31 190	55.72 403	84.45 296	86.63 309	76.79 76	78.26 215	90.55 176	59.30 242	89.70 332	66.63 283	77.05 339	90.88 227

UniMVSNet_ETH3D		79.10 230	78.24 228	81.70 254	86.85 265	60.24 342	87.28 201	88.79 246	74.25 161	76.84 248	90.53 177	49.48 351	91.56 275	67.98 270	82.15 276	93.29 124

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
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

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

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

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	63.00 382	77.77 229	90.28 182	66.10 148	95.09 98	61.40 335	88.22 166	90.94 226
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 129	68.32 135					90.24 184

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

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

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

SDMVSNet		80.38 197	80.18 176	80.99 275	89.03 161	64.94 238	80.45 373	89.40 211	75.19 132	76.61 257	89.98 188	60.61 231	87.69 367	76.83 170	83.55 256	90.33 252

sd_testset		77.70 269	77.40 254	78.60 331	89.03 161	60.02 344	79.00 394	85.83 323	75.19 132	76.61 257	89.98 188	54.81 279	85.46 392	62.63 321	83.55 256	90.33 252

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

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	90.79 311	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
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	46.89 372	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	46.89 372	94.82 108	76.85 167	89.57 136	93.80 96

mamba_0408		79.37 224	77.52 251	84.93 110	88.81 167	67.96 149	65.03 473	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	78.12 343	88.81 167	67.96 149	65.03 473	88.66 254	70.96 240	79.48 189	89.80 194	58.69 245	74.23 465	70.35 245	85.93 213	92.18 183

BH-RMVSNet		79.61 212	78.44 222	83.14 205	89.38 143	65.93 202	84.95 281	87.15 296	73.56 179	78.19 217	89.79 196	56.67 268	93.36 188	59.53 351	86.74 196	90.13 260

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

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

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	63.06 314	88.41 160	87.50 350

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
CNLPA		78.08 256	76.79 268	81.97 250	90.40 109	71.07 70	87.59 184	84.55 338	66.03 340	72.38 341	89.64 201	57.56 257	86.04 384	59.61 350	83.35 261	88.79 315

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

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

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

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	45.75 389	93.13 206	76.84 169	80.80 293	90.11 262

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

jason		81.39 167	80.29 174	84.70 121	86.63 274	69.90 94	85.95 251	86.77 305	63.24 378	81.07 164	89.47 207	61.08 222	92.15 250	78.33 149	90.07 128	92.05 190
jason: jason.
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	44.17 401	93.15 204	76.78 173	80.70 295	90.14 259

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	49.30 355	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
VPA-MVSNet		80.60 190	80.55 167	80.76 281	88.07 202	60.80 332	86.86 216	91.58 137	75.67 115	80.24 179	89.45 211	63.34 173	90.25 321	70.51 243	79.22 315	91.23 214

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

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

RPSCF		73.23 344	71.46 345	78.54 334	82.50 380	59.85 345	82.18 345	82.84 371	58.96 421	71.15 356	89.41 213	45.48 393	84.77 399	58.82 360	71.83 406	91.02 223

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

tttt0517		79.40 221	77.91 235	83.90 178	88.10 200	63.84 266	88.37 157	84.05 346	71.45 225	76.78 251	89.12 216	49.93 348	94.89 105	70.18 248	83.18 265	92.96 148

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

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

icg_test_0407_2		78.92 236	78.93 213	78.90 326	87.13 254	63.59 274	76.58 420	89.33 214	70.51 252	77.82 225	89.03 219	61.84 202	81.38 424	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	79.37 317	87.13 254	63.59 274	77.12 418	89.33 214	70.51 252	66.22 418	89.03 219	50.36 340	82.78 414	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

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
mvsmamba		80.60 190	79.38 200	84.27 148	89.74 128	67.24 178	87.47 187	86.95 300	70.02 266	75.38 286	88.93 224	51.24 329	92.56 231	75.47 190	89.22 143	93.00 146

baseline1		76.98 283	76.75 271	77.66 353	88.13 198	55.66 404	85.12 275	81.89 379	73.04 197	76.79 250	88.90 225	62.43 193	87.78 366	63.30 309	71.18 410	89.55 288

DP-MVS		76.78 287	74.57 306	83.42 192	93.29 52	69.46 104	88.55 149	83.70 350	63.98 373	70.20 362	88.89 226	54.01 292	94.80 111	46.66 439	81.88 281	86.01 388

ab-mvs		79.51 215	78.97 212	81.14 271	88.46 184	60.91 330	83.84 312	89.24 226	70.36 257	79.03 196	88.87 227	63.23 178	90.21 322	65.12 295	82.57 273	92.28 177

PEN-MVS		77.73 266	77.69 247	77.84 349	87.07 262	53.91 421	87.91 175	91.18 148	77.56 51	73.14 329	88.82 228	61.23 218	89.17 342	59.95 346	72.37 400	90.43 247

tt0805		78.73 239	77.83 239	81.43 260	85.17 309	60.30 341	89.41 107	90.90 157	71.21 231	77.17 245	88.73 229	46.38 378	93.21 197	72.57 220	78.96 316	90.79 230

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

PAPM		77.68 270	76.40 279	81.51 258	87.29 250	61.85 315	83.78 313	89.59 205	64.74 359	71.23 354	88.70 230	62.59 189	93.66 165	52.66 404	87.03 191	89.01 304

DTE-MVSNet		76.99 282	76.80 267	77.54 358	86.24 281	53.06 430	87.52 185	90.66 165	77.08 69	72.50 338	88.67 232	60.48 233	89.52 334	57.33 375	70.74 412	90.05 269

PS-CasMVS		78.01 260	78.09 231	77.77 351	87.71 224	54.39 418	88.02 169	91.22 146	77.50 54	73.26 327	88.64 233	60.73 225	88.41 358	61.88 330	73.88 389	90.53 243

cdsmvs_eth3d_5k		19.96 456	26.61 458	0.00 477	0.00 500	0.00 502	0.00 489	89.26 223	0.00 495	0.00 496	88.61 234	61.62 208	0.00 496	0.00 495	0.00 494	0.00 492

lupinMVS		81.39 167	80.27 175	84.76 119	87.35 241	70.21 86	85.55 264	86.41 312	62.85 385	81.32 158	88.61 234	61.68 206	92.24 248	78.41 148	90.26 123	91.83 193

F-COLMAP		76.38 298	74.33 312	82.50 238	89.28 149	66.95 186	88.41 153	89.03 235	64.05 371	66.83 407	88.61 234	46.78 374	92.89 217	57.48 372	78.55 318	87.67 342

mvs_anonymous		79.42 220	79.11 209	80.34 290	84.45 329	57.97 365	82.59 338	87.62 280	67.40 321	76.17 270	88.56 237	68.47 116	89.59 333	70.65 242	86.05 209	93.47 117

CP-MVSNet		78.22 251	78.34 225	77.84 349	87.83 214	54.54 416	87.94 173	91.17 149	77.65 46	73.48 325	88.49 238	62.24 197	88.43 357	62.19 326	74.07 385	90.55 242

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

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

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	64.33 366	69.87 371	88.38 241	53.66 294	93.58 166	58.86 359	82.73 270	87.86 339
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 216	79.22 207	80.27 292	88.79 172	58.35 358	85.06 278	88.61 258	78.56 35	77.65 230	88.34 242	63.81 172	90.66 316	64.98 297	77.22 337	91.80 195

XXY-MVS		75.41 312	75.56 289	74.96 384	83.59 349	57.82 369	80.59 370	83.87 349	66.54 334	74.93 305	88.31 243	63.24 177	80.09 430	62.16 327	76.85 343	86.97 369

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

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	87.51 349

thisisatest0530		79.40 221	77.76 244	84.31 142	87.69 228	65.10 231	87.36 197	84.26 344	70.04 265	77.42 234	88.26 246	49.94 346	94.79 112	70.20 247	84.70 232	93.03 143

hse-mvs2		81.72 155	80.94 160	84.07 161	88.72 175	67.68 160	85.87 254	87.26 293	76.02 105	84.67 87	88.22 247	61.54 209	93.48 182	82.71 96	73.44 394	91.06 219

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	50.91 332	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	50.91 332	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	50.91 332	92.85 219	78.29 150	87.56 179	89.06 299

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

AUN-MVS		79.21 227	77.60 249	84.05 167	88.71 176	67.61 162	85.84 256	87.26 293	69.08 294	77.23 240	88.14 252	53.20 300	93.47 183	75.50 189	73.45 393	91.06 219

Anonymous20231211		78.97 234	77.69 247	82.81 224	90.54 106	64.29 257	90.11 83	91.51 139	65.01 357	76.16 271	88.13 253	50.56 337	93.03 214	69.68 255	77.56 335	91.11 217

pm-mvs1		77.25 279	76.68 273	78.93 325	84.22 332	58.62 356	86.41 234	88.36 261	71.37 226	73.31 326	88.01 254	61.22 219	89.15 343	64.24 303	73.01 397	89.03 303

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	50.11 343	92.51 235	79.02 138	86.89 194	90.97 224

SD_0403		74.65 320	74.77 304	74.29 393	86.20 283	47.42 457	83.71 315	85.12 330	69.30 285	68.50 387	87.95 256	59.40 241	86.05 383	49.38 424	83.35 261	89.40 291

LTVRE_ROB	69.57 13	76.25 299	74.54 308	81.41 261	88.60 179	64.38 256	79.24 389	89.12 233	70.76 245	69.79 373	87.86 257	49.09 358	93.20 200	56.21 387	80.16 301	86.65 377
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
testing3-2		75.12 317	75.19 299	74.91 385	90.40 109	45.09 468	80.29 376	78.42 420	78.37 40	76.54 259	87.75 258	44.36 399	87.28 372	57.04 378	83.49 258	92.37 172

WTY-MVS		75.65 307	75.68 286	75.57 375	86.40 279	56.82 384	77.92 412	82.40 374	65.10 354	76.18 268	87.72 259	63.13 183	80.90 427	60.31 344	81.96 279	89.00 306

TAMVS		78.89 237	77.51 253	83.03 212	87.80 215	67.79 157	84.72 285	85.05 333	67.63 316	76.75 252	87.70 260	62.25 196	90.82 310	58.53 363	87.13 189	90.49 245

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	60.38 343	86.83 195	86.70 375

COLMAP_ROB	66.92 17	73.01 347	70.41 364	80.81 280	87.13 254	65.63 211	88.30 160	84.19 345	62.96 383	63.80 436	87.69 261	38.04 439	92.56 231	46.66 439	74.91 379	84.24 415
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 323	72.42 336	79.80 306	83.76 344	59.59 349	85.92 253	86.64 308	66.39 335	66.96 405	87.58 263	39.46 429	91.60 271	65.76 291	69.27 418	88.22 332

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

Baseline_NR-MVSNet		78.15 255	78.33 226	77.61 355	85.79 292	56.21 397	86.78 220	85.76 324	73.60 178	77.93 224	87.57 264	65.02 160	88.99 345	67.14 280	75.33 373	87.63 343

WR-MVS_H		78.51 246	78.49 220	78.56 333	88.02 204	56.38 393	88.43 151	92.67 72	77.14 65	73.89 319	87.55 266	66.25 145	89.24 340	58.92 358	73.55 392	90.06 268

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

CVMVSNet		72.99 348	72.58 334	74.25 394	84.28 330	50.85 446	86.41 234	83.45 356	44.56 466	73.23 328	87.54 267	49.38 353	85.70 387	65.90 289	78.44 321	86.19 383

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	67.75 394	87.47 269	41.27 420	93.19 202	58.37 365	75.94 359	87.60 344

TransMVSNet (Re)		75.39 314	74.56 307	77.86 348	85.50 302	57.10 381	86.78 220	86.09 320	72.17 211	71.53 351	87.34 270	63.01 184	89.31 338	56.84 381	61.83 448	87.17 361

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	62.72 317	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	62.72 317	79.57 307	90.09 264

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	62.38 323	79.38 312	89.61 286

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	49.06 359	91.11 297	60.91 339	78.52 319	90.09 264

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

ITE_SJBPF				78.22 340	81.77 390	60.57 336		83.30 357	69.25 288	67.54 396	87.20 276	36.33 446	87.28 372	54.34 395	74.62 382	86.80 372

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

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

thres100view900		76.50 291	75.55 290	79.33 318	89.52 133	56.99 382	85.83 257	83.23 359	73.94 168	76.32 264	87.12 279	51.89 319	91.95 258	48.33 430	83.75 250	89.07 297

thres600view7		76.50 291	75.44 291	79.68 311	89.40 141	57.16 379	85.53 266	83.23 359	73.79 172	76.26 265	87.09 280	51.89 319	91.89 261	48.05 435	83.72 253	90.00 270

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	32.78 453	92.11 251	69.99 251	80.43 299	88.09 335

HY-MVS	69.67 12	77.95 261	77.15 259	80.36 289	87.57 240	60.21 343	83.37 326	87.78 277	66.11 337	75.37 287	87.06 282	63.27 175	90.48 318	61.38 336	82.43 274	90.40 249

CHOSEN 1792x2688		77.63 272	75.69 285	83.44 191	89.98 122	68.58 129	78.70 399	87.50 283	56.38 441	75.80 275	86.84 283	58.67 247	91.40 287	61.58 334	85.75 218	90.34 251

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

AllTest		70.96 368	68.09 383	79.58 314	85.15 311	63.62 270	84.58 291	79.83 407	62.31 392	60.32 449	86.73 285	32.02 454	88.96 348	50.28 418	71.57 408	86.15 384

TestCases				79.58 314	85.15 311	63.62 270		79.83 407	62.31 392	60.32 449	86.73 285	32.02 454	88.96 348	50.28 418	71.57 408	86.15 384

LCM-MVSNet-Re		77.05 281	76.94 264	77.36 359	87.20 251	51.60 439	80.06 379	80.46 398	75.20 131	67.69 395	86.72 287	62.48 191	88.98 346	63.44 307	89.25 141	91.51 205

1112_ss		77.40 276	76.43 277	80.32 291	89.11 160	60.41 340	83.65 317	87.72 279	62.13 395	73.05 330	86.72 287	62.58 190	89.97 326	62.11 329	80.80 293	90.59 241

ab-mvs-re		7.23 459	9.64 462	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	86.72 287	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

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.
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	66.78 408	86.70 291	41.95 417	91.51 282	55.64 388	78.14 327	87.17 361
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 297	75.44 291	79.27 319	89.28 149	58.09 361	81.69 351	87.07 298	59.53 416	72.48 339	86.67 292	61.30 216	89.33 337	60.81 341	80.15 302	90.41 248

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	62.72 317	79.57 307	89.45 290

pmmvs6		74.69 319	73.39 323	78.61 330	81.38 398	57.48 376	86.64 226	87.95 271	64.99 358	70.18 363	86.61 294	50.43 339	89.52 334	62.12 328	70.18 415	88.83 313

ET-MVSNet_ETH3D		78.63 242	76.63 274	84.64 122	86.73 270	69.47 102	85.01 279	84.61 337	69.54 280	66.51 415	86.59 295	50.16 342	91.75 266	76.26 176	84.24 242	92.69 158

testgi		66.67 408	66.53 404	67.08 443	75.62 449	41.69 478	75.93 423	76.50 435	66.11 337	65.20 426	86.59 295	35.72 448	74.71 462	43.71 451	73.38 395	84.84 409

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
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

CDS-MVSNet		79.07 231	77.70 246	83.17 204	87.60 231	68.23 141	84.40 301	86.20 317	67.49 319	76.36 263	86.54 299	61.54 209	90.79 311	61.86 331	87.33 184	90.49 245
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
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

TR-MVS		77.44 274	76.18 281	81.20 269	88.24 192	63.24 286	84.61 290	86.40 313	67.55 318	77.81 227	86.48 301	54.10 289	93.15 204	57.75 371	82.72 271	87.20 360

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

tfpn200view9		76.42 296	75.37 295	79.55 316	89.13 156	57.65 373	85.17 272	83.60 351	73.41 185	76.45 260	86.39 303	52.12 309	91.95 258	48.33 430	83.75 250	89.07 297

thres400		76.50 291	75.37 295	79.86 304	89.13 156	57.65 373	85.17 272	83.60 351	73.41 185	76.45 260	86.39 303	52.12 309	91.95 258	48.33 430	83.75 250	90.00 270

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

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
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

test_vis1_n_1920		75.52 309	75.78 284	74.75 389	79.84 417	57.44 377	83.26 328	85.52 326	62.83 386	79.34 194	86.17 308	45.10 394	79.71 431	78.75 143	81.21 287	87.10 367

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

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	87.39 351

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

SixPastTwentyTwo		73.37 338	71.26 351	79.70 310	85.08 314	57.89 367	85.57 260	83.56 353	71.03 238	65.66 420	85.88 312	42.10 415	92.57 230	59.11 356	63.34 443	88.65 321

EPNet_dtu		75.46 310	74.86 302	77.23 362	82.57 379	54.60 415	86.89 214	83.09 363	71.64 218	66.25 417	85.86 313	55.99 272	88.04 362	54.92 392	86.55 199	89.05 302
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 333	73.64 321	73.51 401	82.80 373	55.01 412	76.12 422	81.69 382	62.47 391	74.68 309	85.85 314	57.32 260	78.11 438	60.86 340	80.93 289	87.39 351

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

test_cas_vis1_n_1920		73.76 331	73.74 320	73.81 399	75.90 445	59.77 346	80.51 371	82.40 374	58.30 427	81.62 155	85.69 316	44.35 400	76.41 449	76.29 175	78.61 317	85.23 401

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

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

tfpnnormal		74.39 321	73.16 327	78.08 344	86.10 288	58.05 362	84.65 289	87.53 282	70.32 260	71.22 355	85.63 319	54.97 278	89.86 327	43.03 454	75.02 378	86.32 380

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

SSC-MVS3.2		73.35 341	73.39 323	73.23 402	85.30 307	49.01 453	74.58 437	81.57 383	75.21 130	73.68 322	85.58 321	52.53 301	82.05 419	54.33 396	77.69 333	88.63 322

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

test_0402		72.79 352	70.44 363	79.84 305	88.13 198	65.99 201	85.93 252	84.29 342	65.57 345	67.40 401	85.49 323	46.92 371	92.61 227	35.88 468	74.38 384	80.94 447

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

USDC		70.33 377	68.37 378	76.21 369	80.60 407	56.23 396	79.19 391	86.49 311	60.89 403	61.29 444	85.47 324	31.78 456	89.47 336	53.37 401	76.21 357	82.94 433

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	90.77 314	74.99 193	76.58 346	88.23 331

MVP-Stereo		76.12 300	74.46 310	81.13 272	85.37 305	69.79 95	84.42 300	87.95 271	65.03 356	67.46 398	85.33 327	53.28 299	91.73 268	58.01 369	83.27 263	81.85 442
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 254	76.99 263	81.78 252	85.66 295	66.99 182	84.66 287	90.47 171	55.08 446	72.02 346	85.27 328	63.83 171	94.11 141	66.10 287	89.80 133	84.24 415

DIV-MVS_self_test		77.72 267	76.76 269	80.58 285	82.48 382	60.48 338	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

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	45.50 392	95.12 92	59.11 356	85.83 217	91.11 217

cl____		77.72 267	76.76 269	80.58 285	82.49 381	60.48 338	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

HyFIR lowres test		77.53 273	75.40 293	83.94 177	89.59 130	66.62 188	80.36 374	88.64 257	56.29 442	76.45 260	85.17 332	57.64 256	93.28 190	61.34 337	83.10 266	91.91 192

pmmvs4		74.03 329	71.91 340	80.39 288	81.96 387	68.32 135	81.45 355	82.14 376	59.32 417	69.87 371	85.13 333	52.40 305	88.13 361	60.21 345	74.74 381	84.73 411

TDRefinement		67.49 400	64.34 412	76.92 364	73.47 461	61.07 327	84.86 283	82.98 367	59.77 413	58.30 456	85.13 333	26.06 464	87.89 364	47.92 436	60.59 453	81.81 443

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

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

ttmdpeth		59.91 428	57.10 432	68.34 438	67.13 475	46.65 462	74.64 436	67.41 465	48.30 461	62.52 442	85.04 337	20.40 474	75.93 454	42.55 456	45.90 476	82.44 436

test_fmvs1_n		70.86 370	70.24 366	72.73 410	72.51 468	55.28 409	81.27 359	79.71 409	51.49 457	78.73 201	84.87 338	27.54 463	77.02 443	76.06 179	79.97 305	85.88 392

WBMVS		73.43 335	72.81 331	75.28 381	87.91 209	50.99 445	78.59 402	81.31 388	65.51 348	74.47 313	84.83 339	46.39 377	86.68 376	58.41 364	77.86 329	88.17 334

CMPMVS	51.72 21	70.19 379	68.16 381	76.28 368	73.15 464	57.55 375	79.47 386	83.92 347	48.02 462	56.48 462	84.81 340	43.13 407	86.42 380	62.67 320	81.81 282	84.89 408
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 395	67.61 394	71.31 422	78.51 431	47.01 460	84.47 293	84.27 343	42.27 469	66.44 416	84.79 341	40.44 425	83.76 405	58.76 361	68.54 423	83.17 427

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	58.24 367	85.84 216	84.27 414

pmmvs5		71.55 363	70.20 367	75.61 374	77.83 436	56.39 392	81.74 349	80.89 389	57.76 432	67.46 398	84.49 343	49.26 356	85.32 394	57.08 377	75.29 374	85.11 405

reproduce_monomvs		75.40 313	74.38 311	78.46 338	83.92 340	57.80 370	83.78 313	86.94 301	73.47 183	72.25 343	84.47 344	38.74 434	89.27 339	75.32 191	70.53 413	88.31 329

thres200		75.55 308	74.47 309	78.82 327	87.78 218	57.85 368	83.07 334	83.51 354	72.44 206	75.84 274	84.42 345	52.08 312	91.75 266	47.41 437	83.64 255	86.86 371

test_fmvs1		70.93 369	70.52 361	72.16 414	73.71 457	55.05 411	80.82 362	78.77 418	51.21 458	78.58 206	84.41 346	31.20 458	76.94 444	75.88 183	80.12 304	84.47 413

testing3		68.56 394	67.67 393	71.22 423	87.33 246	42.87 473	83.06 335	71.54 453	70.36 257	69.08 379	84.38 347	30.33 460	85.69 388	37.50 466	75.45 369	85.09 406

test_fmvs2		68.35 397	67.48 396	70.98 425	69.50 471	51.95 434	80.05 380	76.38 436	49.33 460	74.65 310	84.38 347	23.30 472	75.40 460	74.51 198	75.17 377	85.60 395

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

myMVS_eth3d28		73.62 332	73.53 322	73.90 398	88.20 193	47.41 458	78.06 409	79.37 412	74.29 160	73.98 318	84.29 350	44.67 395	83.54 408	51.47 410	87.39 183	90.74 234

testing91		76.54 289	75.66 288	79.18 322	88.43 186	55.89 400	81.08 360	83.00 366	73.76 173	75.34 288	84.29 350	46.20 383	90.07 324	64.33 301	84.50 234	91.58 203

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

testing99		76.09 302	75.12 301	79.00 323	88.16 195	55.50 406	80.79 364	81.40 386	73.30 189	75.17 296	84.27 353	44.48 398	90.02 325	64.28 302	84.22 243	91.48 208

UWE-MVS		72.13 360	71.49 344	74.03 396	86.66 273	47.70 455	81.40 357	76.89 434	63.60 377	75.59 277	84.22 354	39.94 427	85.62 389	48.98 427	86.13 208	88.77 316

FE-MVSNET3		76.43 295	75.32 297	79.76 308	83.00 366	60.72 333	81.74 349	88.76 251	68.99 299	72.98 331	84.19 355	56.41 271	90.27 319	62.39 322	79.40 311	88.31 329

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

IterMVS-SCA-FT		75.43 311	73.87 318	80.11 298	82.69 376	64.85 243	81.57 353	83.47 355	69.16 292	70.49 359	84.15 357	51.95 315	88.15 360	69.23 258	72.14 404	87.34 355

1314		76.53 290	75.30 298	80.21 295	83.93 339	62.32 307	84.66 287	88.81 245	60.23 409	70.16 365	84.07 358	55.30 277	90.73 315	67.37 276	83.21 264	87.59 346

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

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	64.22 431	83.85 360	35.10 449	92.56 231	57.44 373	80.83 292	82.16 440

thisisatest0515		77.33 277	75.38 294	83.18 203	85.27 308	63.80 267	82.11 346	83.27 358	65.06 355	75.91 272	83.84 361	49.54 350	94.27 131	67.24 278	86.19 206	91.48 208

test20.03		67.45 401	66.95 402	68.94 432	75.48 450	44.84 469	77.50 414	77.67 424	66.66 328	63.01 438	83.80 362	47.02 370	78.40 436	42.53 457	68.86 422	83.58 424

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

MSDG		73.36 340	70.99 355	80.49 287	84.51 328	65.80 207	80.71 368	86.13 319	65.70 343	65.46 421	83.74 364	44.60 396	90.91 309	51.13 413	76.89 341	84.74 410

MonoMVSNet		76.49 294	75.80 283	78.58 332	81.55 394	58.45 357	86.36 239	86.22 316	74.87 145	74.73 308	83.73 365	51.79 322	88.73 351	70.78 238	72.15 403	88.55 325

testing11		75.14 316	74.01 314	78.53 335	88.16 195	56.38 393	80.74 367	80.42 400	70.67 246	72.69 337	83.72 366	43.61 405	89.86 327	62.29 325	83.76 249	89.36 293

IterMVS		74.29 322	72.94 330	78.35 339	81.53 395	63.49 280	81.58 352	82.49 373	68.06 314	69.99 368	83.69 367	51.66 324	85.54 390	65.85 290	71.64 407	86.01 388
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 355	71.71 342	74.35 392	82.19 385	52.00 433	79.22 390	77.29 430	64.56 361	72.95 333	83.68 368	51.35 325	83.26 412	58.33 366	75.80 360	87.81 340

UWE-MVS-28		65.32 415	64.93 409	66.49 444	78.70 429	38.55 481	77.86 413	64.39 473	62.00 397	64.13 432	83.60 369	41.44 418	76.00 453	31.39 473	80.89 290	84.92 407

sc_t1		72.19 359	69.51 370	80.23 294	84.81 319	61.09 326	84.68 286	80.22 404	60.70 405	71.27 353	83.58 370	36.59 444	89.24 340	60.41 342	63.31 444	90.37 250

testing222		74.04 327	72.66 333	78.19 341	87.89 210	55.36 407	81.06 361	79.20 415	71.30 229	74.65 310	83.57 371	39.11 433	88.67 353	51.43 412	85.75 218	90.53 243

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

baseline2		75.70 306	73.83 319	81.30 265	83.26 356	61.79 317	82.57 339	80.65 393	66.81 324	66.88 406	83.42 373	57.86 254	92.19 249	63.47 306	79.57 307	89.91 275

mvs5depth		69.45 386	67.45 397	75.46 379	73.93 455	55.83 401	79.19 391	83.23 359	66.89 323	71.63 350	83.32 374	33.69 452	85.09 395	59.81 348	55.34 463	85.46 397

TinyColmap		67.30 403	64.81 410	74.76 388	81.92 389	56.68 388	80.29 376	81.49 385	60.33 407	56.27 463	83.22 375	24.77 468	87.66 368	45.52 447	69.47 417	79.95 452

mvsany_test1		62.30 424	61.26 428	65.41 446	69.52 470	54.86 413	66.86 465	49.78 486	46.65 463	68.50 387	83.21 376	49.15 357	66.28 478	56.93 380	60.77 451	75.11 462

test_vis1_n		69.85 384	69.21 373	71.77 416	72.66 467	55.27 410	81.48 354	76.21 437	52.03 454	75.30 293	83.20 377	28.97 461	76.22 451	74.60 197	78.41 325	83.81 421

CostFormer		75.24 315	73.90 317	79.27 319	82.65 378	58.27 360	80.80 363	82.73 372	61.57 399	75.33 292	83.13 378	55.52 275	91.07 303	64.98 297	78.34 326	88.45 326

MVStest1		56.63 432	52.76 438	68.25 439	61.67 481	53.25 429	71.67 446	68.90 463	38.59 474	50.59 470	83.05 379	25.08 466	70.66 471	36.76 467	38.56 477	80.83 448

WB-MVSnew		71.96 362	71.65 343	72.89 408	84.67 326	51.88 436	82.29 343	77.57 425	62.31 392	73.67 323	83.00 380	53.49 297	81.10 426	45.75 446	82.13 277	85.70 394

ETVMVS		72.25 358	71.05 354	75.84 371	87.77 220	51.91 435	79.39 387	74.98 441	69.26 287	73.71 321	82.95 381	40.82 424	86.14 382	46.17 443	84.43 239	89.47 289

miper_lstm_enhance		74.11 326	73.11 328	77.13 363	80.11 413	59.62 348	72.23 444	86.92 303	66.76 326	70.40 360	82.92 382	56.93 265	82.92 413	69.06 261	72.63 399	88.87 311

GA-MVS		76.87 285	75.17 300	81.97 250	82.75 374	62.58 299	81.44 356	86.35 315	72.16 212	74.74 307	82.89 383	46.20 383	92.02 255	68.85 264	81.09 288	91.30 213

K. test v3		71.19 365	68.51 377	79.21 321	83.04 365	57.78 371	84.35 302	76.91 433	72.90 200	62.99 439	82.86 384	39.27 430	91.09 302	61.65 333	52.66 466	88.75 317

MS-PatchMatch		73.83 330	72.67 332	77.30 361	83.87 341	66.02 198	81.82 347	84.66 336	61.37 402	68.61 383	82.82 385	47.29 367	88.21 359	59.27 353	84.32 241	77.68 457

lessismore_v0				78.97 324	81.01 404	57.15 380		65.99 468		61.16 445	82.82 385	39.12 432	91.34 289	59.67 349	46.92 473	88.43 327

D2MVS		74.82 318	73.21 326	79.64 313	79.81 418	62.56 301	80.34 375	87.35 287	64.37 365	68.86 380	82.66 387	46.37 379	90.10 323	67.91 271	81.24 286	86.25 381

Anonymous20231206		68.60 392	67.80 390	71.02 424	80.23 412	50.75 447	78.30 407	80.47 397	56.79 439	66.11 419	82.63 388	46.35 380	78.95 434	43.62 452	75.70 361	83.36 426

MIMVSNet		70.69 372	69.30 371	74.88 386	84.52 327	56.35 395	75.87 426	79.42 411	64.59 360	67.76 393	82.41 389	41.10 421	81.54 422	46.64 441	81.34 284	86.75 374

UBG		73.08 346	72.27 338	75.51 377	88.02 204	51.29 443	78.35 406	77.38 429	65.52 346	73.87 320	82.36 390	45.55 390	86.48 379	55.02 391	84.39 240	88.75 317

OpenMVS_ROB	64.09 19	70.56 374	68.19 380	77.65 354	80.26 410	59.41 352	85.01 279	82.96 368	58.76 424	65.43 422	82.33 391	37.63 441	91.23 293	45.34 449	76.03 358	82.32 437

miper_enhance_ethall		77.87 264	76.86 265	80.92 278	81.65 391	61.38 323	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

test0.0.03 1		68.00 399	67.69 392	68.90 433	77.55 439	47.43 456	75.70 427	72.95 452	66.66 328	66.56 411	82.29 393	48.06 364	75.87 455	44.97 450	74.51 383	83.41 425

PVSNet	64.34 18	72.08 361	70.87 358	75.69 373	86.21 282	56.44 391	74.37 438	80.73 392	62.06 396	70.17 364	82.23 394	42.86 409	83.31 411	54.77 393	84.45 238	87.32 356

MIMVSNet1		68.58 393	66.78 403	73.98 397	80.07 414	51.82 437	80.77 365	84.37 339	64.40 364	59.75 452	82.16 395	36.47 445	83.63 407	42.73 455	70.33 414	86.48 379

CL-MVSNet_self_test		72.37 355	71.46 345	75.09 383	79.49 424	53.53 423	80.76 366	85.01 334	69.12 293	70.51 358	82.05 396	57.92 253	84.13 403	52.27 406	66.00 432	87.60 344

tpm2		73.26 343	71.46 345	78.63 329	83.34 354	56.71 387	80.65 369	80.40 401	56.63 440	73.55 324	82.02 397	51.80 321	91.24 292	56.35 386	78.42 324	87.95 336

PatchMatch-RL		72.38 354	70.90 357	76.80 366	88.60 179	67.38 171	79.53 385	76.17 438	62.75 388	69.36 376	82.00 398	45.51 391	84.89 398	53.62 399	80.58 296	78.12 456

FE-MVSNET2		72.88 351	71.28 349	77.67 352	78.30 434	57.78 371	84.43 298	88.92 243	69.56 279	64.61 428	81.67 399	46.73 376	88.54 356	59.33 352	67.99 424	86.69 376

FMVSNet5		69.50 385	67.96 385	74.15 395	82.97 370	55.35 408	80.01 381	82.12 377	62.56 390	63.02 437	81.53 400	36.92 442	81.92 420	48.42 429	74.06 386	85.17 404

CR-MVSNet		73.37 338	71.27 350	79.67 312	81.32 401	65.19 226	75.92 424	80.30 402	59.92 412	72.73 335	81.19 401	52.50 303	86.69 375	59.84 347	77.71 331	87.11 365

Patchmtry		70.74 371	69.16 374	75.49 378	80.72 405	54.07 420	74.94 435	80.30 402	58.34 426	70.01 366	81.19 401	52.50 303	86.54 377	53.37 401	71.09 411	85.87 393

IB-MVS	68.01 15	75.85 305	73.36 325	83.31 196	84.76 321	66.03 197	83.38 325	85.06 332	70.21 264	69.40 375	81.05 403	45.76 388	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
cascas		76.72 288	74.64 305	82.99 214	85.78 293	65.88 204	82.33 342	89.21 227	60.85 404	72.74 334	81.02 404	47.28 368	93.75 162	67.48 275	85.02 226	89.34 294

LF4IMVS		64.02 420	62.19 424	69.50 430	70.90 469	53.29 428	76.13 421	77.18 431	52.65 452	58.59 454	80.98 405	23.55 471	76.52 447	53.06 403	66.66 428	78.68 455

Anonymous20240521		68.80 391	67.22 400	73.55 400	74.33 453	54.11 419	83.18 329	85.61 325	58.15 428	61.68 443	80.94 406	30.71 459	81.27 425	57.00 379	73.34 396	85.28 400

gm-plane-assit					81.40 397	53.83 422			62.72 389		80.94 406		92.39 240	63.40 308

UnsupCasMVSNet_eth		67.33 402	65.99 406	71.37 419	73.48 460	51.47 441	75.16 431	85.19 329	65.20 352	60.78 446	80.93 408	42.35 411	77.20 442	57.12 376	53.69 465	85.44 398

dmvs_re		71.14 366	70.58 360	72.80 409	81.96 387	59.68 347	75.60 428	79.34 413	68.55 306	69.27 378	80.72 409	49.42 352	76.54 446	52.56 405	77.79 330	82.19 439

MDTV_nov1_ep13			69.97 368		83.18 360	53.48 424	77.10 419	80.18 406	60.45 406	69.33 377	80.44 410	48.89 362	86.90 374	51.60 409	78.51 320

pmmvs-eth3d		70.50 375	67.83 389	78.52 336	77.37 441	66.18 195	81.82 347	81.51 384	58.90 422	63.90 435	80.42 411	42.69 410	86.28 381	58.56 362	65.30 439	83.11 429

tt0320		70.49 376	68.03 384	77.89 347	84.78 320	59.12 353	83.55 321	80.44 399	58.13 429	67.43 400	80.41 412	39.26 431	87.54 369	55.12 390	63.18 445	86.99 368

mmtdpeth		74.16 325	73.01 329	77.60 357	83.72 345	61.13 324	85.10 276	85.10 331	72.06 213	77.21 244	80.33 413	43.84 403	85.75 386	77.14 164	52.61 467	85.91 391

tt0320-xc		70.11 380	67.45 397	78.07 345	85.33 306	59.51 351	83.28 327	78.96 417	58.77 423	67.10 404	80.28 414	36.73 443	87.42 370	56.83 382	59.77 455	87.29 357

PM-MVS		66.41 410	64.14 413	73.20 405	73.92 456	56.45 390	78.97 395	64.96 472	63.88 375	64.72 427	80.24 415	19.84 476	83.44 410	66.24 284	64.52 441	79.71 453

SCA		74.22 324	72.33 337	79.91 303	84.05 337	62.17 309	79.96 382	79.29 414	66.30 336	72.38 341	80.13 416	51.95 315	88.60 354	59.25 354	77.67 334	88.96 308

Patchmatch-test		64.82 418	63.24 419	69.57 429	79.42 425	49.82 451	63.49 477	69.05 461	51.98 455	59.95 451	80.13 416	50.91 332	70.98 470	40.66 460	73.57 391	87.90 338

tpmrst		72.39 353	72.13 339	73.18 406	80.54 408	49.91 450	79.91 383	79.08 416	63.11 380	71.69 349	79.95 418	55.32 276	82.77 415	65.66 292	73.89 388	86.87 370

DSMNet-mixed		57.77 431	56.90 433	60.38 452	67.70 473	35.61 483	69.18 457	53.97 484	32.30 482	57.49 459	79.88 419	40.39 426	68.57 476	38.78 464	72.37 400	76.97 458

MDA-MVSNet-bldmvs		66.68 407	63.66 417	75.75 372	79.28 426	60.56 337	73.92 440	78.35 421	64.43 362	50.13 471	79.87 420	44.02 402	83.67 406	46.10 444	56.86 457	83.03 431

PatchmatchNet		73.12 345	71.33 348	78.49 337	83.18 360	60.85 331	79.63 384	78.57 419	64.13 367	71.73 348	79.81 421	51.20 330	85.97 385	57.40 374	76.36 356	88.66 320
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FE-MVSNET		67.25 404	65.33 408	73.02 407	75.86 446	52.54 431	80.26 378	80.56 395	63.80 376	60.39 447	79.70 422	41.41 419	84.66 401	43.34 453	62.62 446	81.86 441

Syy-MVS		68.05 398	67.85 387	68.67 436	84.68 323	40.97 479	78.62 400	73.08 450	66.65 331	66.74 409	79.46 423	52.11 311	82.30 417	32.89 471	76.38 354	82.75 434

myMVS_eth3d		67.02 405	66.29 405	69.21 431	84.68 323	42.58 474	78.62 400	73.08 450	66.65 331	66.74 409	79.46 423	31.53 457	82.30 417	39.43 463	76.38 354	82.75 434

ppachtmachnet_test		70.04 381	67.34 399	78.14 342	79.80 419	61.13 324	79.19 391	80.59 394	59.16 419	65.27 423	79.29 425	46.75 375	87.29 371	49.33 425	66.72 427	86.00 390

EPMVS		69.02 389	68.16 381	71.59 417	79.61 422	49.80 452	77.40 415	66.93 466	62.82 387	70.01 366	79.05 426	45.79 387	77.86 440	56.58 384	75.26 375	87.13 364

PMMVS		69.34 387	68.67 376	71.35 421	75.67 448	62.03 312	75.17 430	73.46 448	50.00 459	68.68 381	79.05 426	52.07 313	78.13 437	61.16 338	82.77 269	73.90 463

test-LLR		72.94 349	72.43 335	74.48 390	81.35 399	58.04 363	78.38 403	77.46 426	66.66 328	69.95 369	79.00 428	48.06 364	79.24 432	66.13 285	84.83 229	86.15 384

test-mter		71.41 364	70.39 365	74.48 390	81.35 399	58.04 363	78.38 403	77.46 426	60.32 408	69.95 369	79.00 428	36.08 447	79.24 432	66.13 285	84.83 229	86.15 384

KD-MVS_self_test		68.81 390	67.59 395	72.46 413	74.29 454	45.45 463	77.93 411	87.00 299	63.12 379	63.99 434	78.99 430	42.32 412	84.77 399	56.55 385	64.09 442	87.16 363

test_fmvs3		63.36 422	61.82 425	67.98 440	62.51 480	46.96 461	77.37 416	74.03 447	45.24 465	67.50 397	78.79 431	12.16 484	72.98 469	72.77 218	66.02 431	83.99 419

KD-MVS_2432*1600		66.22 412	63.89 415	73.21 403	75.47 451	53.42 425	70.76 451	84.35 340	64.10 369	66.52 413	78.52 432	34.55 450	84.98 396	50.40 416	50.33 470	81.23 445

miper_refine_blended		66.22 412	63.89 415	73.21 403	75.47 451	53.42 425	70.76 451	84.35 340	64.10 369	66.52 413	78.52 432	34.55 450	84.98 396	50.40 416	50.33 470	81.23 445

tpmvs		71.09 367	69.29 372	76.49 367	82.04 386	56.04 398	78.92 396	81.37 387	64.05 371	67.18 403	78.28 434	49.74 349	89.77 329	49.67 423	72.37 400	83.67 423

our_test_3		69.14 388	67.00 401	75.57 375	79.80 419	58.80 354	77.96 410	77.81 423	59.55 415	62.90 440	78.25 435	47.43 366	83.97 404	51.71 408	67.58 426	83.93 420

MDA-MVSNet_test_wron		65.03 416	62.92 420	71.37 419	75.93 444	56.73 385	69.09 460	74.73 444	57.28 437	54.03 466	77.89 436	45.88 385	74.39 464	49.89 422	61.55 449	82.99 432

YYNet1		65.03 416	62.91 421	71.38 418	75.85 447	56.60 389	69.12 459	74.66 446	57.28 437	54.12 465	77.87 437	45.85 386	74.48 463	49.95 421	61.52 450	83.05 430

ambc				75.24 382	73.16 463	50.51 448	63.05 478	87.47 284		64.28 430	77.81 438	17.80 478	89.73 331	57.88 370	60.64 452	85.49 396

tpm cat1		70.57 373	68.31 379	77.35 360	82.41 383	57.95 366	78.08 408	80.22 404	52.04 453	68.54 386	77.66 439	52.00 314	87.84 365	51.77 407	72.07 405	86.25 381

blended_shiyan6		73.38 336	71.17 352	80.01 301	78.36 432	61.48 322	82.43 340	87.27 291	65.40 350	68.56 385	77.55 440	51.94 317	91.01 304	63.27 311	65.76 433	87.55 347

blended_shiyan8		73.38 336	71.17 352	80.02 300	78.36 432	61.51 321	82.43 340	87.28 288	65.40 350	68.61 383	77.53 441	51.91 318	91.00 307	63.28 310	65.76 433	87.53 348

dp		66.80 406	65.43 407	70.90 426	79.74 421	48.82 454	75.12 433	74.77 443	59.61 414	64.08 433	77.23 442	42.89 408	80.72 428	48.86 428	66.58 429	83.16 428

TESTMET0.1,1		69.89 383	69.00 375	72.55 411	79.27 427	56.85 383	78.38 403	74.71 445	57.64 433	68.09 390	77.19 443	37.75 440	76.70 445	63.92 304	84.09 244	84.10 418

CHOSEN 280x420		66.51 409	64.71 411	71.90 415	81.45 396	63.52 279	57.98 480	68.95 462	53.57 449	62.59 441	76.70 444	46.22 382	75.29 461	55.25 389	79.68 306	76.88 459

PatchT		68.46 396	67.85 387	70.29 427	80.70 406	43.93 471	72.47 443	74.88 442	60.15 410	70.55 357	76.57 445	49.94 346	81.59 421	50.58 414	74.83 380	85.34 399

mvsany_test3		53.99 435	51.45 440	61.61 451	55.51 485	44.74 470	63.52 476	45.41 490	43.69 468	58.11 457	76.45 446	17.99 477	63.76 481	54.77 393	47.59 472	76.34 460

RPMNet		73.51 334	70.49 362	82.58 237	81.32 401	65.19 226	75.92 424	92.27 93	57.60 434	72.73 335	76.45 446	52.30 306	95.43 77	48.14 434	77.71 331	87.11 365

blend_shiyan4		72.29 357	69.65 369	80.21 295	78.24 435	62.16 310	82.29 343	87.27 291	65.41 349	68.43 389	76.42 448	39.91 428	91.23 293	63.21 312	65.66 437	87.22 359

FE-blended-shiyan7		72.94 349	70.66 359	79.79 307	77.80 437	61.03 328	81.31 358	87.15 296	65.18 353	68.09 390	76.28 449	51.32 326	90.97 308	63.06 314	65.76 433	87.35 353

usedtu_blend_shiyan5		73.29 342	70.96 356	80.25 293	77.80 437	62.16 310	84.44 297	87.38 286	64.41 363	68.09 390	76.28 449	51.32 326	91.23 293	63.21 312	65.76 433	87.35 353

dmvs_testset		62.63 423	64.11 414	58.19 454	78.55 430	24.76 492	75.28 429	65.94 469	67.91 315	60.34 448	76.01 451	53.56 295	73.94 467	31.79 472	67.65 425	75.88 461

ADS-MVSNet2		66.20 414	63.33 418	74.82 387	79.92 415	58.75 355	67.55 463	75.19 440	53.37 450	65.25 424	75.86 452	42.32 412	80.53 429	41.57 458	68.91 420	85.18 402

ADS-MVSNet		64.36 419	62.88 422	68.78 435	79.92 415	47.17 459	67.55 463	71.18 454	53.37 450	65.25 424	75.86 452	42.32 412	73.99 466	41.57 458	68.91 420	85.18 402

EGC-MVSNET		52.07 441	47.05 445	67.14 442	83.51 351	60.71 334	80.50 372	67.75 464	0.07 492	0.43 493	75.85 454	24.26 469	81.54 422	28.82 475	62.25 447	59.16 475

new-patchmatchnet		61.73 425	61.73 426	61.70 450	72.74 466	24.50 493	69.16 458	78.03 422	61.40 400	56.72 461	75.53 455	38.42 436	76.48 448	45.95 445	57.67 456	84.13 417

N_pmnet		52.79 439	53.26 437	51.40 464	78.99 428	7.68 498	69.52 455	3.89 497	51.63 456	57.01 460	74.98 456	40.83 423	65.96 479	37.78 465	64.67 440	80.56 451

WB-MVS		54.94 433	54.72 434	55.60 460	73.50 459	20.90 494	74.27 439	61.19 477	59.16 419	50.61 469	74.15 457	47.19 369	75.78 456	17.31 485	35.07 479	70.12 467

patchmatchnet-post											74.00 458	51.12 331	88.60 354

GG-mvs-BLEND				75.38 380	81.59 393	55.80 402	79.32 388	69.63 458		67.19 402	73.67 459	43.24 406	88.90 350	50.41 415	84.50 234	81.45 444

SSC-MVS		53.88 436	53.59 436	54.75 462	72.87 465	19.59 495	73.84 441	60.53 479	57.58 435	49.18 473	73.45 460	46.34 381	75.47 459	16.20 488	32.28 481	69.20 468

Patchmatch-RL test		70.24 378	67.78 391	77.61 355	77.43 440	59.57 350	71.16 448	70.33 455	62.94 384	68.65 382	72.77 461	50.62 336	85.49 391	69.58 256	66.58 429	87.77 341

FPMVS		53.68 437	51.64 439	59.81 453	65.08 477	51.03 444	69.48 456	69.58 459	41.46 470	40.67 477	72.32 462	16.46 480	70.00 474	24.24 481	65.42 438	58.40 477

UnsupCasMVSNet_bld		63.70 421	61.53 427	70.21 428	73.69 458	51.39 442	72.82 442	81.89 379	55.63 444	57.81 458	71.80 463	38.67 435	78.61 435	49.26 426	52.21 468	80.63 449

APD_test1		53.31 438	49.93 443	63.42 449	65.68 476	50.13 449	71.59 447	66.90 467	34.43 479	40.58 478	71.56 464	8.65 489	76.27 450	34.64 470	55.36 462	63.86 473

test_f		52.09 440	50.82 441	55.90 458	53.82 488	42.31 477	59.42 479	58.31 482	36.45 477	56.12 464	70.96 465	12.18 483	57.79 484	53.51 400	56.57 459	67.60 469

PVSNet_0	57.27 20	61.67 426	59.27 429	68.85 434	79.61 422	57.44 377	68.01 461	73.44 449	55.93 443	58.54 455	70.41 466	44.58 397	77.55 441	47.01 438	35.91 478	71.55 466

pmmvs3		57.79 430	54.26 435	68.37 437	64.02 479	56.72 386	75.12 433	65.17 470	40.20 471	52.93 467	69.86 467	20.36 475	75.48 458	45.45 448	55.25 464	72.90 465

test_vis1_rt		60.28 427	58.42 430	65.84 445	67.25 474	55.60 405	70.44 453	60.94 478	44.33 467	59.00 453	66.64 468	24.91 467	68.67 475	62.80 316	69.48 416	73.25 464

new_pmnet		50.91 442	50.29 442	52.78 463	68.58 472	34.94 485	63.71 475	56.63 483	39.73 472	44.95 474	65.47 469	21.93 473	58.48 483	34.98 469	56.62 458	64.92 471

gg-mvs-nofinetune		69.95 382	67.96 385	75.94 370	83.07 363	54.51 417	77.23 417	70.29 456	63.11 380	70.32 361	62.33 470	43.62 404	88.69 352	53.88 398	87.76 177	84.62 412

JIA-IIPM		66.32 411	62.82 423	76.82 365	77.09 442	61.72 318	65.34 471	75.38 439	58.04 431	64.51 429	62.32 471	42.05 416	86.51 378	51.45 411	69.22 419	82.21 438

LCM-MVSNet		54.25 434	49.68 444	67.97 441	53.73 489	45.28 466	66.85 466	80.78 391	35.96 478	39.45 479	62.23 472	8.70 488	78.06 439	48.24 433	51.20 469	80.57 450

PMMVS2		40.82 450	38.86 454	46.69 465	53.84 487	16.45 496	48.61 483	49.92 485	37.49 475	31.67 480	60.97 473	8.14 490	56.42 485	28.42 476	30.72 482	67.19 470

testf1		45.72 445	41.96 449	57.00 455	56.90 483	45.32 464	66.14 468	59.26 480	26.19 483	30.89 482	60.96 474	4.14 492	70.64 472	26.39 479	46.73 474	55.04 478

APD_test2		45.72 445	41.96 449	57.00 455	56.90 483	45.32 464	66.14 468	59.26 480	26.19 483	30.89 482	60.96 474	4.14 492	70.64 472	26.39 479	46.73 474	55.04 478

MVS-HIRNet		59.14 429	57.67 431	63.57 448	81.65 391	43.50 472	71.73 445	65.06 471	39.59 473	51.43 468	57.73 476	38.34 437	82.58 416	39.53 461	73.95 387	64.62 472

ANet_high		50.57 443	46.10 447	63.99 447	48.67 492	39.13 480	70.99 450	80.85 390	61.39 401	31.18 481	57.70 477	17.02 479	73.65 468	31.22 474	15.89 489	79.18 454

PMVS	37.38 22	44.16 449	40.28 453	55.82 459	40.82 494	42.54 476	65.12 472	63.99 474	34.43 479	24.48 485	57.12 478	3.92 494	76.17 452	17.10 486	55.52 461	48.75 480
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		45.42 447	45.38 448	45.55 466	73.36 462	26.85 490	67.72 462	34.19 492	54.15 448	49.65 472	56.41 479	25.43 465	62.94 482	19.45 483	28.09 483	46.86 482

test_vis3_rt		49.26 444	47.02 446	56.00 457	54.30 486	45.27 467	66.76 467	48.08 487	36.83 476	44.38 475	53.20 480	7.17 491	64.07 480	56.77 383	55.66 460	58.65 476

test_method		31.52 453	29.28 457	38.23 468	27.03 496	6.50 499	20.94 488	62.21 476	4.05 490	22.35 488	52.50 481	13.33 481	47.58 488	27.04 478	34.04 480	60.62 474

kuosan		39.70 451	40.40 452	37.58 469	64.52 478	26.98 488	65.62 470	33.02 493	46.12 464	42.79 476	48.99 482	24.10 470	46.56 490	12.16 491	26.30 484	39.20 483

DeepMVS_CX				27.40 472	40.17 495	26.90 489		24.59 496	17.44 488	23.95 486	48.61 483	9.77 486	26.48 491	18.06 484	24.47 485	28.83 485

MVE	26.22 23	30.37 455	25.89 459	43.81 467	44.55 493	35.46 484	28.87 487	39.07 491	18.20 487	18.58 489	40.18 484	2.68 495	47.37 489	17.07 487	23.78 486	48.60 481
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 448	41.86 451	55.16 461	77.03 443	51.52 440	32.50 486	80.52 396	32.46 481	27.12 484	35.02 485	9.52 487	75.50 457	22.31 482	60.21 454	38.45 484
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 452	30.64 455	35.15 470	52.87 490	27.67 487	57.09 481	47.86 488	24.64 485	16.40 490	33.05 486	11.23 485	54.90 486	14.46 489	18.15 487	22.87 486

EMVS		30.81 454	29.65 456	34.27 471	50.96 491	25.95 491	56.58 482	46.80 489	24.01 486	15.53 491	30.68 487	12.47 482	54.43 487	12.81 490	17.05 488	22.43 487

tmp_tt		18.61 457	21.40 460	10.23 474	4.82 497	10.11 497	34.70 485	30.74 495	1.48 491	23.91 487	26.07 488	28.42 462	13.41 493	27.12 477	15.35 490	7.17 488

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	12.47 489	67.45 129	96.60 37	83.06 87	94.50 57	94.07 78

test_post											5.46 490	50.36 340	84.24 402

test_post1							78.90 397				5.43 491	48.81 363	85.44 393	59.25 354

wuyk23d		16.82 458	15.94 461	19.46 473	58.74 482	31.45 486	39.22 484	3.74 498	6.84 489	6.04 492	2.70 492	1.27 496	24.29 492	10.54 492	14.40 491	2.63 489

testmvs		6.04 461	8.02 464	0.10 476	0.08 498	0.03 501	69.74 454	0.04 499	0.05 493	0.31 494	1.68 493	0.02 498	0.04 494	0.24 493	0.02 492	0.25 491

test123		6.12 460	8.11 463	0.14 475	0.06 499	0.09 500	71.05 449	0.03 500	0.04 494	0.25 495	1.30 494	0.05 497	0.03 495	0.21 494	0.01 493	0.29 490

mmdepth		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

monomultidepth		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

test_blank		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet_test		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

DCPMVS		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

pcd_1.5k_mvsjas		5.26 462	7.02 465	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	63.15 180	0.00 496	0.00 495	0.00 494	0.00 492

sosnet-low-res		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

sosnet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uncertanet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

Regformer		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

uanet		0.00 463	0.00 466	0.00 477	0.00 500	0.00 502	0.00 489	0.00 501	0.00 495	0.00 496	0.00 495	0.00 499	0.00 496	0.00 495	0.00 494	0.00 492

TestfortrainingZip							93.28 12

WAC-MVS						42.58 474								39.46 462

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 153	91.30 18

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

eth-test2					0.00 500

eth-test					0.00 500

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

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

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

GSMVS																88.96 308

test_part2					95.06 8	72.65 32				91.80 16

sam_mvs1												51.32 326				88.96 308

sam_mvs												50.01 344

MTGPA								92.02 111

MTMP							92.18 39	32.83 494

test9_res														84.90 64	95.70 30	92.87 151

agg_prior2														82.91 91	95.45 33	92.70 156

agg_prior					92.85 68	71.94 52		91.78 127		84.41 95			94.93 101

test_prior4						72.60 34	89.01 125

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

旧先验2							86.56 229		58.10 430	87.04 61			88.98 346	74.07 203

新几何2							86.29 243

无先验							87.48 186	88.98 238	60.00 411				94.12 140	67.28 277		88.97 307

原ACMM2							86.86 216

testdata2													91.01 304	62.37 324

segment_acmp												73.08 43

testdata1							84.14 308		75.71 112

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

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 234

plane_prior5								92.44 82					95.38 82	78.71 144	86.32 202	91.33 211

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

n2								0.00 501

nn								0.00 501

door-mid								69.98 457

test11								92.23 97

door								69.44 460

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

HQP3-MVS								92.19 105							85.99 211

HQP2-MVS												60.17 237

MDTV_nov1_ep13_2view						37.79 482	75.16 431		55.10 445	66.53 412		49.34 354		53.98 397		87.94 337

ACMMP++_ref															81.95 280

ACMMP++															81.25 285

Test By Simon												64.33 166

High-resolution multi-view benchmark