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	75.70 110	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 53
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	74.77 143	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	93.33 119
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 34

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 65

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 103	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 81
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	74.69 144	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 129

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 134	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 136

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 134	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 136

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 97

fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 81	89.48 137	67.88 153	88.59 146	89.05 230	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	75.17 130	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 43
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	75.71 108	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 96	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 73

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 122	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 94	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	75.24 122	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 50

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 94	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	83.09 203	87.08 256	65.21 221	89.09 123	90.21 179	79.67 19	89.98 24	95.02 24	73.17 42	91.71 265	91.30 3	91.60 99	92.34 169

MM		89.16 8	89.23 10	88.97 4	90.79 102	73.65 10	92.66 28	91.17 145	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 98	79.94 17	89.74 27	94.86 26	68.63 110	94.20 136	90.83 5	91.39 104	94.38 57

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 224	92.02 107	79.45 22	85.88 70	94.80 27	68.07 118	96.21 50	86.69 52	95.34 36	93.23 122

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	87.33 242	67.30 174	89.50 101	90.98 150	76.25 97	90.56 22	94.75 29	68.38 113	94.24 135	90.80 7	92.32 89	94.19 67

9.14			88.26 19		92.84 69		91.52 56	94.75 1	73.93 165	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	74.50 148	86.84 64	94.65 31	67.31 127	95.77 64	84.80 68	92.85 78	92.84 150

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	69.09 101	96.70 31	84.37 74	94.83 49	94.03 76

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	68.81 107	96.65 34	84.53 72	94.90 45	94.00 78

APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 194	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 152
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	73.54 176	85.94 69	94.51 35	65.80 150	95.61 67	83.04 89	92.51 83	93.53 112

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	67.93 120	95.95 62	84.20 78	94.39 61	93.23 122

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 66	93.06 64	70.63 82	91.88 43	92.27 89	73.53 177	85.69 73	94.45 37	65.00 158	95.56 68	82.75 94	91.87 95	92.50 162

RE-MVS-def			85.48 75		93.06 64	70.63 82	91.88 43	92.27 89	73.53 177	85.69 73	94.45 37	63.87 166		82.75 94	91.87 95	92.50 162

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 98

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 113	83.86 108	94.42 40	67.87 122	96.64 35	82.70 98	94.57 56	93.66 98

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	93.62 105
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	87.20 247	68.54 130	89.57 99	90.44 168	75.31 121	87.49 54	94.39 42	72.86 47	92.72 221	89.04 27	90.56 118	94.16 68

fmvsm_s_conf0.1_n_2		83.80 102	83.79 102	83.83 175	85.62 293	64.94 234	87.03 203	86.62 301	74.32 153	87.97 47	94.33 43	60.67 224	92.60 224	89.72 14	87.79 171	93.96 79

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 186	87.12 255	66.01 199	88.56 148	89.43 206	75.59 112	89.32 28	94.32 44	72.89 46	91.21 290	90.11 11	92.33 87	93.16 129

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 62

MGCNet		87.69 24	87.55 29	88.12 13	89.45 138	71.76 53	91.47 57	89.54 202	82.14 3	86.65 66	94.28 46	68.28 116	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	80.25 407	69.03 110	89.47 102	89.65 198	73.24 188	86.98 62	94.27 47	66.62 134	93.23 191	90.26 10	89.95 130	93.78 94

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	93.05 138

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 96	76.87 74	82.81 132	94.25 49	66.44 138	96.24 49	82.88 92	94.28 64	93.38 115

fmvsm_s_conf0.5_n_2		84.04 95	84.11 95	83.81 177	86.17 280	65.00 229	86.96 206	87.28 283	74.35 152	88.25 39	94.23 50	61.82 200	92.60 224	89.85 12	88.09 164	93.84 88

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	82.82 131	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 102
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	84.00 168	87.78 218	66.09 196	89.96 86	90.80 158	77.37 57	86.72 65	94.20 52	72.51 51	92.78 220	89.08 22	92.33 87	93.13 133

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	67.45 125	96.60 37	83.06 87	94.50 57	94.07 74

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 34
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	82.99 365	69.39 107	89.65 95	90.29 177	73.31 184	87.77 49	94.15 55	71.72 61	93.23 191	90.31 9	90.67 117	93.89 85

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 43

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 130	73.89 166	82.67 134	94.09 57	62.60 184	95.54 70	80.93 111	92.93 77	93.57 108

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 235	87.75 50	94.07 58	74.01 36	96.70 31	84.66 70	94.84 48

fmvsm_s_conf0.1_n_a		83.32 122	82.99 119	84.28 142	83.79 338	68.07 145	89.34 111	82.85 361	69.80 269	87.36 58	94.06 59	68.34 115	91.56 271	87.95 42	83.46 256	93.21 125

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 60

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 312	69.51 100	89.62 98	90.58 163	73.42 180	87.75 50	94.02 61	72.85 48	93.24 190	90.37 8	90.75 115	93.96 79

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									68.21 308	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	93.65 102
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 84

test_fmvsm_n_1920		85.29 80	85.34 77	85.13 101	86.12 282	69.93 92	88.65 144	90.78 159	69.97 265	88.27 38	93.98 66	71.39 67	91.54 275	88.49 35	90.45 120	93.91 82

fmvsm_s_conf0.1_n		83.56 113	83.38 112	84.10 151	84.86 314	67.28 175	89.40 108	83.01 356	70.67 242	87.08 60	93.96 67	68.38 113	91.45 281	88.56 34	84.50 230	93.56 109

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 100	83.81 110	93.95 68	69.77 92	96.01 58	85.15 62	94.66 51	94.32 62
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		83.34 120	84.03 96	81.28 262	85.73 290	65.13 224	85.40 265	89.90 189	74.96 136	82.13 140	93.89 69	66.65 133	87.92 354	86.56 53	91.05 109	90.80 225

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 147	86.26 276	67.40 170	89.18 115	89.31 215	72.50 199	88.31 37	93.86 70	69.66 93	91.96 253	89.81 13	91.05 109	93.38 115

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 87	74.62 147	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	80.73 168	93.82 72	64.33 162	96.29 46	82.67 99	90.69 116	93.23 122
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		83.63 111	83.41 111	84.28 142	86.14 281	68.12 143	89.43 104	82.87 360	70.27 258	87.27 59	93.80 73	69.09 101	91.58 268	88.21 38	83.65 250	93.14 132

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 140	86.70 267	65.83 205	88.77 136	89.78 191	75.46 116	88.35 36	93.73 74	69.19 100	93.06 206	91.30 3	88.44 159	94.02 77

fmvsm_s_conf0.5_n		83.80 102	83.71 104	84.07 157	86.69 268	67.31 173	89.46 103	83.07 355	71.09 230	86.96 63	93.70 75	69.02 106	91.47 280	88.79 30	84.62 229	93.44 114

test_prior2							88.85 132		75.41 117	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	84.27 144	85.42 299	68.81 116	88.49 150	87.26 285	68.08 309	88.03 44	93.49 77	72.04 57	91.77 261	88.90 29	89.14 146	92.24 176

VDDNet		81.52 160	80.67 160	84.05 163	90.44 108	64.13 256	89.73 93	85.91 312	71.11 229	83.18 122	93.48 78	50.54 330	93.49 177	73.40 206	88.25 161	94.54 49

CDPH-MVS		85.76 68	85.29 81	87.17 48	93.49 51	71.08 69	88.58 147	92.42 85	68.32 307	84.61 91	93.48 78	72.32 52	96.15 53	79.00 136	95.43 34	94.28 64

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 72

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 181	87.32 244	65.13 224	88.86 130	91.63 129	75.41 117	88.23 40	93.45 81	68.56 111	92.47 232	89.52 18	92.78 79	93.20 127

fmvsm_l_conf0.5_n_a		84.13 93	84.16 94	84.06 160	85.38 300	68.40 133	88.34 158	86.85 295	67.48 316	87.48 55	93.40 82	70.89 73	91.61 266	88.38 37	89.22 143	92.16 183

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	76.66 250	93.37 83	60.40 232	96.75 30	77.20 158	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 68
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 130	82.36 132	84.96 107	91.02 95	66.40 191	88.91 128	88.11 259	77.57 49	84.39 96	93.29 85	52.19 304	93.91 152	77.05 161	88.70 154	94.57 45

test_fmvsmvis_n_1920		84.02 96	83.87 98	84.49 127	84.12 330	69.37 108	88.15 166	87.96 266	70.01 263	83.95 107	93.23 86	68.80 108	91.51 278	88.61 32	89.96 129	92.57 157

UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 156	82.48 2	84.60 92	93.20 87	69.35 97	95.22 88	71.39 230	90.88 114	93.07 135

TEST9					93.26 56	72.96 25	88.75 138	91.89 115	68.44 305	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	91.89 115	68.69 300	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 140

test_8					93.13 60	72.57 35	88.68 143	91.84 119	68.69 300	84.87 84	93.10 88	74.43 30	95.16 90

LFMVS		81.82 150	81.23 150	83.57 184	91.89 82	63.43 279	89.84 87	81.85 372	77.04 70	83.21 119	93.10 88	52.26 303	93.43 182	71.98 225	89.95 130	93.85 86

旧先验1					91.96 80	65.79 208		86.37 305			93.08 92	69.31 99			92.74 80	88.74 315

dcpmvs_2		85.63 70	86.15 60	84.06 160	91.71 84	64.94 234	86.47 228	91.87 117	73.63 172	86.60 67	93.02 93	76.57 18	91.87 259	83.36 84	92.15 90	95.35 3

testdata				79.97 294	90.90 98	64.21 254		84.71 326	59.27 409	85.40 75	92.91 94	62.02 197	89.08 335	68.95 258	91.37 105	86.63 369

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 192	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 56

Vis-MVSNet		83.46 116	82.80 123	85.43 90	90.25 112	68.74 121	90.30 80	90.13 182	76.33 93	80.87 165	92.89 95	61.00 219	94.20 136	72.45 222	90.97 111	93.35 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		83.73 106	83.33 114	84.92 111	93.28 53	70.86 78	92.09 41	90.38 170	68.75 299	79.57 183	92.83 97	60.60 228	93.04 209	80.92 112	91.56 102	90.86 224

3Dnovator	76.31 5	83.38 119	82.31 133	86.59 61	87.94 208	72.94 28	90.64 68	92.14 106	77.21 63	75.47 276	92.83 97	58.56 244	94.72 115	73.24 209	92.71 81	92.13 184

MSLP-MVS++		85.43 75	85.76 69	84.45 128	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 100	92.81 99	67.16 129	92.94 211	80.36 119	94.35 63	90.16 254

test2506		77.30 274	76.49 271	79.74 300	90.08 116	52.02 423	87.86 178	63.10 466	74.88 139	80.16 177	92.79 100	38.29 429	92.35 239	68.74 261	92.50 84	94.86 19

ECVR-MVS		79.61 208	79.26 201	80.67 279	90.08 116	54.69 405	87.89 176	77.44 419	74.88 139	80.27 174	92.79 100	48.96 353	92.45 233	68.55 262	92.50 84	94.86 19

test1111		79.43 215	79.18 204	80.15 291	89.99 121	53.31 418	87.33 195	77.05 423	75.04 132	80.23 176	92.77 102	48.97 352	92.33 241	68.87 259	92.40 86	94.81 22

MG-MVS		83.41 117	83.45 110	83.28 193	92.74 71	62.28 304	88.17 164	89.50 204	75.22 124	81.49 152	92.74 103	66.75 132	95.11 94	72.85 212	91.58 101	92.45 166

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		83.65 109	84.54 89	80.99 271	90.06 120	65.83 205	84.21 299	88.74 248	71.60 218	85.01 79	92.44 105	74.51 29	83.50 400	82.15 101	92.15 90	93.64 104

casdiffmvs		85.11 83	85.14 82	85.01 105	87.20 247	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

baseline		84.93 86	84.98 83	84.80 117	87.30 245	65.39 218	87.30 196	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	92.19 101	79.31 24	84.39 96	92.18 109	64.64 160	95.53 71	80.70 116	94.65 52	94.56 47

SymmetryMVS		85.38 78	84.81 86	87.07 50	91.47 87	72.47 38	91.65 47	88.06 263	79.31 24	84.39 96	92.18 109	64.64 160	95.53 71	80.70 116	90.91 113	93.21 125

QAPM		80.88 171	79.50 194	85.03 104	88.01 206	68.97 114	91.59 51	92.00 109	66.63 329	75.15 294	92.16 111	57.70 251	95.45 75	63.52 301	88.76 152	90.66 233

IS-MVSNet		83.15 125	82.81 122	84.18 149	89.94 123	63.30 281	91.59 51	88.46 256	79.04 30	79.49 184	92.16 111	65.10 155	94.28 130	67.71 268	91.86 97	94.95 12

viewmacassd2359aftdt		83.76 105	83.66 106	84.07 157	86.59 271	64.56 243	86.88 211	91.82 120	75.72 107	83.34 118	92.15 113	68.24 117	92.88 214	79.05 132	89.15 145	94.77 25

BP-MVS1		84.32 91	83.71 104	86.17 68	87.84 213	67.85 154	89.38 109	89.64 199	77.73 45	83.98 106	92.12 114	56.89 262	95.43 77	84.03 80	91.75 98	95.24 7

E4		84.10 94	83.99 97	84.45 128	87.58 235	64.99 230	86.54 226	92.25 92	76.38 90	83.37 117	92.09 115	69.88 90	93.58 166	79.78 126	88.03 167	94.77 25

新几何1				83.42 188	93.13 60	70.71 80		85.48 318	57.43 427	81.80 146	91.98 116	63.28 170	92.27 242	64.60 296	92.99 76	87.27 350

OpenMVS	72.83 10	79.77 206	78.33 222	84.09 155	85.17 305	69.91 93	90.57 69	90.97 151	66.70 323	72.17 340	91.91 117	54.70 280	93.96 144	61.81 323	90.95 112	88.41 324

PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 200	85.22 78	91.90 118	69.47 95	96.42 44	83.28 86	95.94 23	94.35 59

VNet		82.21 141	82.41 130	81.62 251	90.82 100	60.93 320	84.47 289	89.78 191	76.36 92	84.07 104	91.88 119	64.71 159	90.26 311	70.68 237	88.89 148	93.66 98

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	91.88 119	69.04 105	95.43 77	83.93 81	93.77 69	93.01 141

GDP-MVS		83.52 114	82.64 126	86.16 69	88.14 197	68.45 132	89.13 121	92.69 70	72.82 198	83.71 111	91.86 121	55.69 270	95.35 86	80.03 122	89.74 134	94.69 33

KinetiMVS		83.31 123	82.61 127	85.39 91	87.08 256	67.56 165	88.06 168	91.65 128	77.80 44	82.21 139	91.79 122	57.27 257	94.07 142	77.77 151	89.89 132	94.56 47

E2		84.00 97	83.87 98	84.39 131	87.70 226	64.95 231	86.40 233	92.23 93	75.85 104	83.21 119	91.78 123	70.09 85	93.55 171	79.52 129	88.05 165	94.66 37

E3		84.00 97	83.87 98	84.39 131	87.70 226	64.95 231	86.40 233	92.23 93	75.85 104	83.21 119	91.78 123	70.09 85	93.55 171	79.52 129	88.05 165	94.66 37

OPM-MVS		83.50 115	82.95 120	85.14 98	88.79 172	70.95 74	89.13 121	91.52 134	77.55 52	80.96 162	91.75 125	60.71 222	94.50 124	79.67 128	86.51 196	89.97 270
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	72.89 197	84.64 90	91.71 126	71.85 58	96.03 55	84.77 69	94.45 60	94.49 52

viewmanbaseed2359cas		83.66 108	83.55 108	84.00 168	86.81 263	64.53 244	86.65 221	91.75 125	74.89 138	83.15 124	91.68 127	68.74 109	92.83 218	79.02 134	89.24 142	94.63 40

XVG-OURS-SEG-HR		80.81 174	79.76 185	83.96 172	85.60 294	68.78 118	83.54 318	90.50 166	70.66 245	76.71 249	91.66 128	60.69 223	91.26 287	76.94 162	81.58 279	91.83 189

EPNet		83.72 107	82.92 121	86.14 72	84.22 328	69.48 101	91.05 64	85.27 319	81.30 6	76.83 245	91.65 129	66.09 145	95.56 68	76.00 177	93.85 68	93.38 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 134	81.97 143	84.85 114	88.75 174	67.42 168	87.98 170	90.87 155	74.92 137	79.72 181	91.65 129	62.19 194	93.96 144	75.26 188	86.42 197	93.16 129

viewdifsd2359ckpt07		82.83 133	82.78 125	82.99 210	86.51 273	62.58 295	85.09 273	90.83 157	75.22 124	82.28 136	91.63 131	69.43 96	92.03 249	77.71 152	86.32 198	94.34 60

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	91.63 131	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

test222					91.50 86	68.26 137	84.16 302	83.20 353	54.63 438	79.74 180	91.63 131	58.97 240			91.42 103	86.77 364

MVS_111021_HR		85.14 82	84.75 87	86.32 65	91.65 85	72.70 30	85.98 246	90.33 174	76.11 99	82.08 141	91.61 134	71.36 68	94.17 139	81.02 110	92.58 82	92.08 185

原ACMM1				84.35 135	93.01 66	68.79 117		92.44 82	63.96 365	81.09 159	91.57 135	66.06 146	95.45 75	67.19 275	94.82 50	88.81 310

viewcassd2359sk11		83.89 99	83.74 103	84.34 136	87.76 221	64.91 237	86.30 237	92.22 96	75.47 115	83.04 125	91.52 136	70.15 83	93.53 174	79.26 131	87.96 168	94.57 45

LPG-MVS_test		82.08 143	81.27 149	84.50 125	89.23 152	68.76 119	90.22 81	91.94 113	75.37 119	76.64 251	91.51 137	54.29 283	94.91 102	78.44 142	83.78 243	89.83 275

LGP-MVS_train				84.50 125	89.23 152	68.76 119		91.94 113	75.37 119	76.64 251	91.51 137	54.29 283	94.91 102	78.44 142	83.78 243	89.83 275

XVG-OURS		80.41 191	79.23 202	83.97 171	85.64 292	69.02 112	83.03 331	90.39 169	71.09 230	77.63 227	91.49 139	54.62 282	91.35 284	75.71 180	83.47 255	91.54 200

alignmvs		85.48 73	85.32 79	85.96 77	89.51 134	69.47 102	89.74 92	92.47 81	76.17 98	87.73 52	91.46 140	70.32 80	93.78 158	81.51 104	88.95 147	94.63 40

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	81.32 154	91.43 141	70.34 79	97.23 17	84.26 75	93.36 74	94.37 58

h-mvs33		83.15 125	82.19 136	86.02 76	90.56 105	70.85 79	88.15 166	89.16 225	76.02 101	84.67 87	91.39 142	61.54 205	95.50 73	82.71 96	75.48 362	91.72 196

MGCFI-Net		85.06 85	85.51 74	83.70 179	89.42 139	63.01 287	89.43 104	92.62 78	76.43 85	87.53 53	91.34 143	72.82 49	93.42 183	81.28 108	88.74 153	94.66 37

nrg030		83.88 100	83.53 109	84.96 107	86.77 265	69.28 109	90.46 75	92.67 72	74.79 142	82.95 126	91.33 144	72.70 50	93.09 204	80.79 115	79.28 310	92.50 162

E3new		83.78 104	83.60 107	84.31 138	87.76 221	64.89 238	86.24 240	92.20 99	75.15 131	82.87 128	91.23 145	70.11 84	93.52 176	79.05 132	87.79 171	94.51 51

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	91.23 145	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	91.23 145	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	87.12 200	93.04 46	69.80 269	82.85 130	91.22 148	73.06 44	96.02 57	76.72 170	94.63 54	91.46 206

Anonymous202405211		78.25 246	77.01 257	81.99 245	91.03 94	60.67 326	84.77 280	83.90 339	70.65 246	80.00 178	91.20 149	41.08 414	91.43 282	65.21 290	85.26 221	93.85 86

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	83.51 116	91.20 149	70.65 78	95.15 91	81.96 102	94.89 46	94.77 25

Anonymous20240529		80.19 201	78.89 210	84.10 151	90.60 104	64.75 241	88.95 127	90.90 153	65.97 337	80.59 170	91.17 151	49.97 337	93.73 164	69.16 256	82.70 268	93.81 90

EPP-MVSNet		83.40 118	83.02 118	84.57 123	90.13 114	64.47 249	92.32 35	90.73 160	74.45 151	79.35 189	91.10 152	69.05 104	95.12 92	72.78 213	87.22 182	94.13 70

TAPA-MVS	73.13 9	79.15 224	77.94 230	82.79 224	89.59 130	62.99 291	88.16 165	91.51 135	65.77 338	77.14 242	91.09 153	60.91 220	93.21 193	50.26 411	87.05 186	92.17 182
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	73.95 163	83.16 123	91.07 154	75.94 21	95.19 89	79.94 124	94.38 62	93.55 110

FIs		82.07 144	82.42 129	81.04 270	88.80 171	58.34 350	88.26 161	93.49 31	76.93 72	78.47 207	91.04 155	69.92 89	92.34 240	69.87 249	84.97 223	92.44 167

MVS_111021_LR		82.61 136	82.11 137	84.11 150	88.82 166	71.58 57	85.15 270	86.16 309	74.69 144	80.47 173	91.04 155	62.29 191	90.55 308	80.33 120	90.08 127	90.20 253

DP-MVS Recon		83.11 128	82.09 139	86.15 70	94.44 23	70.92 76	88.79 135	92.20 99	70.53 247	79.17 191	91.03 157	64.12 164	96.03 55	68.39 265	90.14 125	91.50 202

mamv4		76.81 282	78.23 226	72.54 403	86.12 282	65.75 210	78.76 389	82.07 369	64.12 359	72.97 328	91.02 158	67.97 119	68.08 468	83.04 89	78.02 324	83.80 413

HQP_MVS		83.64 110	83.14 115	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 195	91.00 159	60.42 230	95.38 82	78.71 140	86.32 198	91.33 207

plane_prior4											91.00 159

FC-MVSNet-test		81.52 160	82.02 141	80.03 293	88.42 187	55.97 390	87.95 172	93.42 34	77.10 68	77.38 231	90.98 161	69.96 88	91.79 260	68.46 264	84.50 230	92.33 170

diffmvs_AUTHOR		82.38 139	82.27 135	82.73 229	83.26 352	63.80 263	83.89 306	89.76 193	73.35 183	82.37 135	90.84 162	66.25 141	90.79 302	82.77 93	87.93 169	93.59 107

Vis-MVSNet (Re-imp)		78.36 245	78.45 217	78.07 336	88.64 178	51.78 429	86.70 219	79.63 401	74.14 160	75.11 295	90.83 163	61.29 213	89.75 321	58.10 359	91.60 99	92.69 154

114514_t		80.68 182	79.51 193	84.20 148	94.09 42	67.27 176	89.64 96	91.11 148	58.75 416	74.08 313	90.72 164	58.10 247	95.04 99	69.70 250	89.42 140	90.30 250

viewdifsd2359ckpt13		82.91 131	82.29 134	84.77 118	86.96 259	66.90 187	87.47 187	91.62 130	72.19 205	81.68 149	90.71 165	66.92 131	93.28 186	75.90 178	87.15 184	94.12 71

viewdifsd2359ckpt09		83.34 120	82.55 128	85.70 81	87.64 230	67.72 159	88.43 151	91.68 127	71.91 212	81.65 150	90.68 166	67.10 130	94.75 113	76.17 173	87.70 174	94.62 42

PAPM_NR		83.02 129	82.41 130	84.82 115	92.47 76	66.37 192	87.93 174	91.80 121	73.82 167	77.32 233	90.66 167	67.90 121	94.90 104	70.37 240	89.48 139	93.19 128

viewdifsd2359ckpt11		80.37 195	79.73 186	82.30 238	83.70 342	62.39 299	84.20 300	86.67 297	73.22 189	80.90 163	90.62 168	63.00 181	91.56 271	76.81 167	78.44 317	92.95 145

viewmsd2359difaftdt		80.37 195	79.73 186	82.30 238	83.70 342	62.39 299	84.20 300	86.67 297	73.22 189	80.90 163	90.62 168	63.00 181	91.56 271	76.81 167	78.44 317	92.95 145

LS3D		76.95 280	74.82 299	83.37 191	90.45 107	67.36 172	89.15 120	86.94 292	61.87 389	69.52 370	90.61 170	51.71 317	94.53 122	46.38 433	86.71 193	88.21 329

AstraMVS		80.81 174	80.14 175	82.80 221	86.05 285	63.96 258	86.46 229	85.90 313	73.71 170	80.85 166	90.56 171	54.06 287	91.57 270	79.72 127	83.97 241	92.86 148

VPNet		78.69 237	78.66 213	78.76 319	88.31 190	55.72 394	84.45 292	86.63 300	76.79 76	78.26 211	90.55 172	59.30 238	89.70 323	66.63 279	77.05 335	90.88 223

UniMVSNet_ETH3D		79.10 226	78.24 224	81.70 250	86.85 261	60.24 333	87.28 197	88.79 242	74.25 157	76.84 244	90.53 173	49.48 343	91.56 271	67.98 266	82.15 272	93.29 120

ACMP	74.13 6	81.51 162	80.57 162	84.36 134	89.42 139	68.69 126	89.97 85	91.50 138	74.46 150	75.04 298	90.41 174	53.82 289	94.54 121	77.56 154	82.91 263	89.86 274
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SSM_0407		81.58 157	80.48 165	84.87 113	88.81 167	67.96 149	87.37 192	89.25 220	71.06 232	79.48 185	90.39 175	59.57 235	94.48 126	72.45 222	85.93 209	92.18 179

SSM_0404		81.91 147	80.84 158	85.13 101	89.24 151	68.26 137	87.84 179	89.25 220	71.06 232	80.62 169	90.39 175	59.57 235	94.65 119	72.45 222	87.19 183	92.47 165

viewmambaseed2359dif		80.41 191	79.84 183	82.12 240	82.95 367	62.50 298	83.39 319	88.06 263	67.11 318	80.98 161	90.31 177	66.20 143	91.01 298	74.62 192	84.90 224	92.86 148

RRT-MVS		82.60 138	82.10 138	84.10 151	87.98 207	62.94 292	87.45 190	91.27 141	77.42 56	79.85 179	90.28 178	56.62 265	94.70 117	79.87 125	88.15 163	94.67 34

PCF-MVS	73.52 7	80.38 193	78.84 211	85.01 105	87.71 224	68.99 113	83.65 312	91.46 139	63.00 373	77.77 225	90.28 178	66.10 144	95.09 98	61.40 326	88.22 162	90.94 222
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					90.24 180

HQP-MVS		82.61 136	82.02 141	84.37 133	89.33 144	66.98 183	89.17 116	92.19 101	76.41 86	77.23 236	90.23 181	60.17 233	95.11 94	77.47 155	85.99 207	91.03 217

PS-MVSNAJss		82.07 144	81.31 148	84.34 136	86.51 273	67.27 176	89.27 112	91.51 135	71.75 213	79.37 188	90.22 182	63.15 176	94.27 131	77.69 153	82.36 271	91.49 203

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 283	76.41 86	85.80 71	90.22 182	74.15 35	95.37 85	81.82 103	91.88 94	92.65 156

SDMVSNet		80.38 193	80.18 172	80.99 271	89.03 161	64.94 234	80.45 364	89.40 207	75.19 128	76.61 253	89.98 184	60.61 227	87.69 358	76.83 166	83.55 252	90.33 248

sd_testset		77.70 265	77.40 250	78.60 322	89.03 161	60.02 335	79.00 385	85.83 314	75.19 128	76.61 253	89.98 184	54.81 275	85.46 383	62.63 312	83.55 252	90.33 248

TranMVSNet+NR-MVSNet		80.84 172	80.31 169	82.42 235	87.85 212	62.33 302	87.74 181	91.33 140	80.55 9	77.99 219	89.86 186	65.23 154	92.62 222	67.05 277	75.24 372	92.30 172

diffmvs		82.10 142	81.88 144	82.76 227	83.00 362	63.78 265	83.68 311	89.76 193	72.94 195	82.02 142	89.85 187	65.96 149	90.79 302	82.38 100	87.30 181	93.71 96
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		81.53 158	80.16 173	85.62 84	85.51 296	68.25 139	88.84 133	92.19 101	71.31 223	80.50 171	89.83 188	46.89 364	94.82 108	76.85 163	89.57 136	93.80 92

StellarMVS		81.53 158	80.16 173	85.62 84	85.51 296	68.25 139	88.84 133	92.19 101	71.31 223	80.50 171	89.83 188	46.89 364	94.82 108	76.85 163	89.57 136	93.80 92

mamba_0408		79.37 220	77.52 247	84.93 110	88.81 167	67.96 149	65.03 464	88.66 250	70.96 236	79.48 185	89.80 190	58.69 241	94.65 119	70.35 241	85.93 209	92.18 179

SSM_04072		77.67 267	77.52 247	78.12 334	88.81 167	67.96 149	65.03 464	88.66 250	70.96 236	79.48 185	89.80 190	58.69 241	74.23 456	70.35 241	85.93 209	92.18 179

BH-RMVSNet		79.61 208	78.44 218	83.14 201	89.38 143	65.93 202	84.95 277	87.15 288	73.56 175	78.19 213	89.79 192	56.67 264	93.36 184	59.53 342	86.74 192	90.13 256

GeoE		81.71 152	81.01 155	83.80 178	89.51 134	64.45 250	88.97 126	88.73 249	71.27 226	78.63 201	89.76 193	66.32 140	93.20 196	69.89 248	86.02 206	93.74 95

guyue		81.13 167	80.64 161	82.60 232	86.52 272	63.92 261	86.69 220	87.73 274	73.97 162	80.83 167	89.69 194	56.70 263	91.33 286	78.26 149	85.40 220	92.54 159

AdaColmap		80.58 189	79.42 195	84.06 160	93.09 63	68.91 115	89.36 110	88.97 236	69.27 282	75.70 272	89.69 194	57.20 259	95.77 64	63.06 306	88.41 160	87.50 344

ACMM	73.20 8	80.78 181	79.84 183	83.58 183	89.31 147	68.37 134	89.99 84	91.60 132	70.28 257	77.25 234	89.66 196	53.37 294	93.53 174	74.24 198	82.85 264	88.85 308
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 252	76.79 264	81.97 246	90.40 109	71.07 70	87.59 184	84.55 329	66.03 336	72.38 337	89.64 197	57.56 253	86.04 375	59.61 341	83.35 257	88.79 311

test_yl		81.17 165	80.47 166	83.24 196	89.13 156	63.62 266	86.21 241	89.95 187	72.43 203	81.78 147	89.61 198	57.50 254	93.58 166	70.75 235	86.90 188	92.52 160

DCV-MVSNet		81.17 165	80.47 166	83.24 196	89.13 156	63.62 266	86.21 241	89.95 187	72.43 203	81.78 147	89.61 198	57.50 254	93.58 166	70.75 235	86.90 188	92.52 160

EI-MVSNet-Vis-set		84.19 92	83.81 101	85.31 93	88.18 194	67.85 154	87.66 182	89.73 196	80.05 15	82.95 126	89.59 200	70.74 76	94.82 108	80.66 118	84.72 227	93.28 121

PAPR		81.66 155	80.89 157	83.99 170	90.27 111	64.00 257	86.76 218	91.77 124	68.84 298	77.13 243	89.50 201	67.63 123	94.88 106	67.55 270	88.52 157	93.09 134

jajsoiax		79.29 221	77.96 229	83.27 194	84.68 319	66.57 190	89.25 113	90.16 181	69.20 287	75.46 278	89.49 202	45.75 381	93.13 202	76.84 165	80.80 289	90.11 258

MVSFormer		82.85 132	82.05 140	85.24 95	87.35 237	70.21 86	90.50 72	90.38 170	68.55 302	81.32 154	89.47 203	61.68 202	93.46 180	78.98 137	90.26 123	92.05 186

jason		81.39 163	80.29 170	84.70 121	86.63 270	69.90 94	85.95 247	86.77 296	63.24 369	81.07 160	89.47 203	61.08 218	92.15 246	78.33 145	90.07 128	92.05 186
jason: jason.
mvs_tets		79.13 225	77.77 239	83.22 198	84.70 318	66.37 192	89.17 116	90.19 180	69.38 279	75.40 281	89.46 205	44.17 393	93.15 200	76.78 169	80.70 291	90.14 255

UGNet		80.83 173	79.59 192	84.54 124	88.04 203	68.09 144	89.42 106	88.16 258	76.95 71	76.22 262	89.46 205	49.30 347	93.94 147	68.48 263	90.31 121	91.60 197
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		80.60 186	80.55 163	80.76 277	88.07 202	60.80 323	86.86 212	91.58 133	75.67 111	80.24 175	89.45 207	63.34 169	90.25 312	70.51 239	79.22 311	91.23 210

MVS_Test		83.15 125	83.06 117	83.41 190	86.86 260	63.21 283	86.11 244	92.00 109	74.31 154	82.87 128	89.44 208	70.03 87	93.21 193	77.39 157	88.50 158	93.81 90

EI-MVSNet-UG-set		83.81 101	83.38 112	85.09 103	87.87 211	67.53 166	87.44 191	89.66 197	79.74 18	82.23 138	89.41 209	70.24 82	94.74 114	79.95 123	83.92 242	92.99 143

RPSCF		73.23 337	71.46 341	78.54 325	82.50 376	59.85 336	82.18 337	82.84 362	58.96 412	71.15 352	89.41 209	45.48 385	84.77 390	58.82 351	71.83 402	91.02 219

UniMVSNet_NR-MVSNet		81.88 148	81.54 147	82.92 214	88.46 184	63.46 277	87.13 199	92.37 86	80.19 12	78.38 208	89.14 211	71.66 64	93.05 207	70.05 245	76.46 345	92.25 174

tttt0517		79.40 217	77.91 231	83.90 174	88.10 200	63.84 262	88.37 157	84.05 337	71.45 221	76.78 247	89.12 212	49.93 340	94.89 105	70.18 244	83.18 261	92.96 144

DU-MVS		81.12 168	80.52 164	82.90 215	87.80 215	63.46 277	87.02 204	91.87 117	79.01 31	78.38 208	89.07 213	65.02 156	93.05 207	70.05 245	76.46 345	92.20 177

NR-MVSNet		80.23 199	79.38 196	82.78 225	87.80 215	63.34 280	86.31 236	91.09 149	79.01 31	72.17 340	89.07 213	67.20 128	92.81 219	66.08 284	75.65 358	92.20 177

icg_test_0407_2		78.92 232	78.93 209	78.90 317	87.13 250	63.59 270	76.58 411	89.33 210	70.51 248	77.82 221	89.03 215	61.84 198	81.38 415	72.56 218	85.56 216	91.74 192

IMVS_0407		80.61 184	79.90 181	82.75 228	87.13 250	63.59 270	85.33 266	89.33 210	70.51 248	77.82 221	89.03 215	61.84 198	92.91 212	72.56 218	85.56 216	91.74 192

IMVS_0404		77.16 276	76.42 274	79.37 308	87.13 250	63.59 270	77.12 409	89.33 210	70.51 248	66.22 409	89.03 215	50.36 332	82.78 405	72.56 218	85.56 216	91.74 192

IMVS_0403		80.80 177	80.12 176	82.87 217	87.13 250	63.59 270	85.19 267	89.33 210	70.51 248	78.49 205	89.03 215	63.26 172	93.27 188	72.56 218	85.56 216	91.74 192

DELS-MVS		85.41 76	85.30 80	85.77 79	88.49 182	67.93 152	85.52 264	93.44 32	78.70 34	83.63 115	89.03 215	74.57 27	95.71 66	80.26 121	94.04 67	93.66 98
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		80.60 186	79.38 196	84.27 144	89.74 128	67.24 178	87.47 187	86.95 291	70.02 262	75.38 282	88.93 220	51.24 321	92.56 227	75.47 186	89.22 143	93.00 142

baseline1		76.98 279	76.75 267	77.66 344	88.13 198	55.66 395	85.12 271	81.89 370	73.04 193	76.79 246	88.90 221	62.43 189	87.78 357	63.30 305	71.18 406	89.55 284

DP-MVS		76.78 283	74.57 302	83.42 188	93.29 52	69.46 104	88.55 149	83.70 341	63.98 364	70.20 358	88.89 222	54.01 288	94.80 111	46.66 430	81.88 277	86.01 379

ab-mvs		79.51 211	78.97 208	81.14 267	88.46 184	60.91 321	83.84 307	89.24 222	70.36 253	79.03 192	88.87 223	63.23 174	90.21 313	65.12 291	82.57 269	92.28 173

PEN-MVS		77.73 262	77.69 243	77.84 340	87.07 258	53.91 412	87.91 175	91.18 144	77.56 51	73.14 325	88.82 224	61.23 214	89.17 333	59.95 337	72.37 396	90.43 243

tt0805		78.73 235	77.83 235	81.43 256	85.17 305	60.30 332	89.41 107	90.90 153	71.21 227	77.17 241	88.73 225	46.38 370	93.21 193	72.57 216	78.96 312	90.79 226

test_djsdf		80.30 198	79.32 199	83.27 194	83.98 334	65.37 219	90.50 72	90.38 170	68.55 302	76.19 263	88.70 226	56.44 266	93.46 180	78.98 137	80.14 299	90.97 220

PAPM		77.68 266	76.40 275	81.51 254	87.29 246	61.85 309	83.78 308	89.59 201	64.74 351	71.23 350	88.70 226	62.59 185	93.66 165	52.66 395	87.03 187	89.01 300

DTE-MVSNet		76.99 278	76.80 263	77.54 349	86.24 277	53.06 421	87.52 185	90.66 161	77.08 69	72.50 334	88.67 228	60.48 229	89.52 325	57.33 366	70.74 408	90.05 265

PS-CasMVS		78.01 256	78.09 227	77.77 342	87.71 224	54.39 409	88.02 169	91.22 142	77.50 54	73.26 323	88.64 229	60.73 221	88.41 349	61.88 321	73.88 385	90.53 239

cdsmvs_eth3d_5k		19.96 447	26.61 449	0.00 468	0.00 491	0.00 493	0.00 480	89.26 219	0.00 486	0.00 487	88.61 230	61.62 204	0.00 487	0.00 486	0.00 485	0.00 483

lupinMVS		81.39 163	80.27 171	84.76 119	87.35 237	70.21 86	85.55 260	86.41 303	62.85 376	81.32 154	88.61 230	61.68 202	92.24 244	78.41 144	90.26 123	91.83 189

F-COLMAP		76.38 294	74.33 308	82.50 234	89.28 149	66.95 186	88.41 153	89.03 231	64.05 362	66.83 398	88.61 230	46.78 366	92.89 213	57.48 363	78.55 314	87.67 338

mvs_anonymous		79.42 216	79.11 205	80.34 286	84.45 325	57.97 356	82.59 333	87.62 276	67.40 317	76.17 266	88.56 233	68.47 112	89.59 324	70.65 238	86.05 205	93.47 113

CP-MVSNet		78.22 247	78.34 221	77.84 340	87.83 214	54.54 407	87.94 173	91.17 145	77.65 46	73.48 321	88.49 234	62.24 193	88.43 348	62.19 317	74.07 381	90.55 238

PVSNet_Blended_VisFu		82.62 135	81.83 145	84.96 107	90.80 101	69.76 97	88.74 140	91.70 126	69.39 278	78.96 193	88.46 235	65.47 152	94.87 107	74.42 195	88.57 155	90.24 252

CANet_DTU		80.61 184	79.87 182	82.83 218	85.60 294	63.17 286	87.36 193	88.65 252	76.37 91	75.88 269	88.44 236	53.51 292	93.07 205	73.30 207	89.74 134	92.25 174

PLC	70.83 11	78.05 254	76.37 276	83.08 205	91.88 83	67.80 156	88.19 163	89.46 205	64.33 357	69.87 367	88.38 237	53.66 290	93.58 166	58.86 350	82.73 266	87.86 335
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 212	79.22 203	80.27 288	88.79 172	58.35 349	85.06 274	88.61 254	78.56 35	77.65 226	88.34 238	63.81 168	90.66 307	64.98 293	77.22 333	91.80 191

XXY-MVS		75.41 308	75.56 285	74.96 375	83.59 345	57.82 360	80.59 361	83.87 340	66.54 330	74.93 301	88.31 239	63.24 173	80.09 421	62.16 318	76.85 339	86.97 360

Effi-MVS+		83.62 112	83.08 116	85.24 95	88.38 188	67.45 167	88.89 129	89.15 226	75.50 114	82.27 137	88.28 240	69.61 94	94.45 127	77.81 150	87.84 170	93.84 88

API-MVS		81.99 146	81.23 150	84.26 146	90.94 97	70.18 91	91.10 63	89.32 214	71.51 220	78.66 200	88.28 240	65.26 153	95.10 97	64.74 295	91.23 107	87.51 343

thisisatest0530		79.40 217	77.76 240	84.31 138	87.69 228	65.10 227	87.36 193	84.26 335	70.04 261	77.42 230	88.26 242	49.94 338	94.79 112	70.20 243	84.70 228	93.03 139

hse-mvs2		81.72 151	80.94 156	84.07 157	88.72 175	67.68 160	85.87 250	87.26 285	76.02 101	84.67 87	88.22 243	61.54 205	93.48 178	82.71 96	73.44 390	91.06 215

xiu_mvs_v1_base_debu		80.80 177	79.72 188	84.03 165	87.35 237	70.19 88	85.56 257	88.77 243	69.06 291	81.83 143	88.16 244	50.91 324	92.85 215	78.29 146	87.56 175	89.06 295

xiu_mvs_v1_base		80.80 177	79.72 188	84.03 165	87.35 237	70.19 88	85.56 257	88.77 243	69.06 291	81.83 143	88.16 244	50.91 324	92.85 215	78.29 146	87.56 175	89.06 295

xiu_mvs_v1_base_debi		80.80 177	79.72 188	84.03 165	87.35 237	70.19 88	85.56 257	88.77 243	69.06 291	81.83 143	88.16 244	50.91 324	92.85 215	78.29 146	87.56 175	89.06 295

UniMVSNet (Re)		81.60 156	81.11 152	83.09 203	88.38 188	64.41 251	87.60 183	93.02 50	78.42 37	78.56 203	88.16 244	69.78 91	93.26 189	69.58 252	76.49 344	91.60 197

AUN-MVS		79.21 223	77.60 245	84.05 163	88.71 176	67.61 162	85.84 252	87.26 285	69.08 290	77.23 236	88.14 248	53.20 296	93.47 179	75.50 185	73.45 389	91.06 215

Anonymous20231211		78.97 230	77.69 243	82.81 220	90.54 106	64.29 253	90.11 83	91.51 135	65.01 349	76.16 267	88.13 249	50.56 329	93.03 210	69.68 251	77.56 331	91.11 213

pm-mvs1		77.25 275	76.68 269	78.93 316	84.22 328	58.62 347	86.41 230	88.36 257	71.37 222	73.31 322	88.01 250	61.22 215	89.15 334	64.24 299	73.01 393	89.03 299

LuminaMVS		80.68 182	79.62 191	83.83 175	85.07 311	68.01 148	86.99 205	88.83 240	70.36 253	81.38 153	87.99 251	50.11 335	92.51 231	79.02 134	86.89 190	90.97 220

SD_0403		74.65 316	74.77 300	74.29 384	86.20 279	47.42 448	83.71 310	85.12 321	69.30 281	68.50 381	87.95 252	59.40 237	86.05 374	49.38 415	83.35 257	89.40 287

LTVRE_ROB	69.57 13	76.25 295	74.54 304	81.41 257	88.60 179	64.38 252	79.24 380	89.12 229	70.76 241	69.79 369	87.86 253	49.09 350	93.20 196	56.21 378	80.16 297	86.65 368
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		75.12 313	75.19 295	74.91 376	90.40 109	45.09 459	80.29 367	78.42 411	78.37 40	76.54 255	87.75 254	44.36 391	87.28 363	57.04 369	83.49 254	92.37 168

WTY-MVS		75.65 303	75.68 282	75.57 366	86.40 275	56.82 375	77.92 403	82.40 365	65.10 346	76.18 264	87.72 255	63.13 179	80.90 418	60.31 335	81.96 275	89.00 302

TAMVS		78.89 233	77.51 249	83.03 208	87.80 215	67.79 157	84.72 281	85.05 324	67.63 312	76.75 248	87.70 256	62.25 192	90.82 301	58.53 354	87.13 185	90.49 241

BH-untuned		79.47 213	78.60 214	82.05 243	89.19 154	65.91 203	86.07 245	88.52 255	72.18 206	75.42 280	87.69 257	61.15 216	93.54 173	60.38 334	86.83 191	86.70 366

COLMAP_ROB	66.92 17	73.01 340	70.41 356	80.81 276	87.13 250	65.63 211	88.30 160	84.19 336	62.96 374	63.80 427	87.69 257	38.04 430	92.56 227	46.66 430	74.91 375	84.24 406
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 319	72.42 332	79.80 298	83.76 340	59.59 340	85.92 249	86.64 299	66.39 331	66.96 396	87.58 259	39.46 420	91.60 267	65.76 287	69.27 414	88.22 328

FA-MVS(test-final)		80.96 170	79.91 180	84.10 151	88.30 191	65.01 228	84.55 288	90.01 185	73.25 187	79.61 182	87.57 260	58.35 246	94.72 115	71.29 231	86.25 201	92.56 158

Baseline_NR-MVSNet		78.15 251	78.33 222	77.61 346	85.79 288	56.21 388	86.78 216	85.76 315	73.60 174	77.93 220	87.57 260	65.02 156	88.99 336	67.14 276	75.33 369	87.63 339

WR-MVS_H		78.51 242	78.49 216	78.56 324	88.02 204	56.38 384	88.43 151	92.67 72	77.14 65	73.89 315	87.55 262	66.25 141	89.24 331	58.92 349	73.55 388	90.06 264

EI-MVSNet		80.52 190	79.98 178	82.12 240	84.28 326	63.19 285	86.41 230	88.95 237	74.18 159	78.69 198	87.54 263	66.62 134	92.43 234	72.57 216	80.57 293	90.74 230

CVMVSNet		72.99 341	72.58 330	74.25 385	84.28 326	50.85 437	86.41 230	83.45 347	44.56 457	73.23 324	87.54 263	49.38 345	85.70 378	65.90 285	78.44 317	86.19 374

ACMH+	68.96 14	76.01 299	74.01 310	82.03 244	88.60 179	65.31 220	88.86 130	87.55 277	70.25 259	67.75 385	87.47 265	41.27 412	93.19 198	58.37 356	75.94 355	87.60 340

TransMVSNet (Re)		75.39 310	74.56 303	77.86 339	85.50 298	57.10 372	86.78 216	86.09 311	72.17 207	71.53 347	87.34 266	63.01 180	89.31 329	56.84 372	61.83 439	87.17 352

GBi-Net		78.40 243	77.40 250	81.40 258	87.60 231	63.01 287	88.39 154	89.28 216	71.63 215	75.34 284	87.28 267	54.80 276	91.11 291	62.72 308	79.57 303	90.09 260

test1		78.40 243	77.40 250	81.40 258	87.60 231	63.01 287	88.39 154	89.28 216	71.63 215	75.34 284	87.28 267	54.80 276	91.11 291	62.72 308	79.57 303	90.09 260

FMVSNet2		78.20 249	77.21 254	81.20 265	87.60 231	62.89 293	87.47 187	89.02 232	71.63 215	75.29 290	87.28 267	54.80 276	91.10 294	62.38 314	79.38 308	89.61 282

FMVSNet1		77.44 270	76.12 278	81.40 258	86.81 263	63.01 287	88.39 154	89.28 216	70.49 252	74.39 310	87.28 267	49.06 351	91.11 291	60.91 330	78.52 315	90.09 260

v2v482		80.23 199	79.29 200	83.05 207	83.62 344	64.14 255	87.04 202	89.97 186	73.61 173	78.18 214	87.22 271	61.10 217	93.82 156	76.11 174	76.78 341	91.18 211

ITE_SJBPF				78.22 331	81.77 386	60.57 327		83.30 348	69.25 284	67.54 387	87.20 272	36.33 437	87.28 363	54.34 386	74.62 378	86.80 363

anonymousdsp		78.60 239	77.15 255	82.98 212	80.51 405	67.08 181	87.24 198	89.53 203	65.66 340	75.16 293	87.19 273	52.52 298	92.25 243	77.17 159	79.34 309	89.61 282

MVSTER		79.01 228	77.88 234	82.38 236	83.07 359	64.80 240	84.08 305	88.95 237	69.01 294	78.69 198	87.17 274	54.70 280	92.43 234	74.69 191	80.57 293	89.89 273

thres100view900		76.50 287	75.55 286	79.33 309	89.52 133	56.99 373	85.83 253	83.23 350	73.94 164	76.32 260	87.12 275	51.89 313	91.95 254	48.33 421	83.75 246	89.07 293

thres600view7		76.50 287	75.44 287	79.68 302	89.40 141	57.16 370	85.53 262	83.23 350	73.79 168	76.26 261	87.09 276	51.89 313	91.89 257	48.05 426	83.72 249	90.00 266

XVG-ACMP-BASELINE		76.11 297	74.27 309	81.62 251	83.20 355	64.67 242	83.60 315	89.75 195	69.75 272	71.85 343	87.09 276	32.78 444	92.11 247	69.99 247	80.43 295	88.09 331

HY-MVS	69.67 12	77.95 257	77.15 255	80.36 285	87.57 236	60.21 334	83.37 321	87.78 273	66.11 333	75.37 283	87.06 278	63.27 171	90.48 309	61.38 327	82.43 270	90.40 245

CHOSEN 1792x2688		77.63 268	75.69 281	83.44 187	89.98 122	68.58 129	78.70 390	87.50 279	56.38 432	75.80 271	86.84 279	58.67 243	91.40 283	61.58 325	85.75 214	90.34 247

v8		79.97 205	79.02 207	82.80 221	84.09 331	64.50 248	87.96 171	90.29 177	74.13 161	75.24 291	86.81 280	62.88 183	93.89 155	74.39 196	75.40 367	90.00 266

AllTest		70.96 359	68.09 374	79.58 305	85.15 307	63.62 266	84.58 287	79.83 398	62.31 383	60.32 440	86.73 281	32.02 445	88.96 339	50.28 409	71.57 404	86.15 375

TestCases				79.58 305	85.15 307	63.62 266		79.83 398	62.31 383	60.32 440	86.73 281	32.02 445	88.96 339	50.28 409	71.57 404	86.15 375

LCM-MVSNet-Re		77.05 277	76.94 260	77.36 350	87.20 247	51.60 430	80.06 370	80.46 389	75.20 127	67.69 386	86.72 283	62.48 187	88.98 337	63.44 303	89.25 141	91.51 201

1112_ss		77.40 272	76.43 273	80.32 287	89.11 160	60.41 331	83.65 312	87.72 275	62.13 386	73.05 326	86.72 283	62.58 186	89.97 317	62.11 320	80.80 289	90.59 237

ab-mvs-re		7.23 450	9.64 453	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	86.72 283	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

IterMVS-LS		80.06 202	79.38 196	82.11 242	85.89 286	63.20 284	86.79 215	89.34 209	74.19 158	75.45 279	86.72 283	66.62 134	92.39 236	72.58 215	76.86 338	90.75 229
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 300	73.93 312	81.77 249	88.71 176	66.61 189	88.62 145	89.01 233	69.81 268	66.78 399	86.70 287	41.95 409	91.51 278	55.64 379	78.14 323	87.17 352
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 293	75.44 287	79.27 310	89.28 149	58.09 352	81.69 343	87.07 289	59.53 407	72.48 335	86.67 288	61.30 212	89.33 328	60.81 332	80.15 298	90.41 244

FMVSNet3		77.88 259	76.85 262	80.97 273	86.84 262	62.36 301	86.52 227	88.77 243	71.13 228	75.34 284	86.66 289	54.07 286	91.10 294	62.72 308	79.57 303	89.45 286

pmmvs6		74.69 315	73.39 319	78.61 321	81.38 394	57.48 367	86.64 222	87.95 267	64.99 350	70.18 359	86.61 290	50.43 331	89.52 325	62.12 319	70.18 411	88.83 309

ET-MVSNet_ETH3D		78.63 238	76.63 270	84.64 122	86.73 266	69.47 102	85.01 275	84.61 328	69.54 276	66.51 406	86.59 291	50.16 334	91.75 262	76.26 172	84.24 238	92.69 154

testgi		66.67 399	66.53 395	67.08 434	75.62 440	41.69 469	75.93 414	76.50 426	66.11 333	65.20 417	86.59 291	35.72 439	74.71 453	43.71 442	73.38 391	84.84 400

CLD-MVS		82.31 140	81.65 146	84.29 141	88.47 183	67.73 158	85.81 254	92.35 87	75.78 106	78.33 210	86.58 293	64.01 165	94.35 128	76.05 176	87.48 178	90.79 226
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 207	78.67 212	82.97 213	84.06 332	64.95 231	87.88 177	90.62 162	73.11 191	75.11 295	86.56 294	61.46 208	94.05 143	73.68 201	75.55 360	89.90 272

CDS-MVSNet		79.07 227	77.70 242	83.17 200	87.60 231	68.23 141	84.40 296	86.20 308	67.49 315	76.36 259	86.54 295	61.54 205	90.79 302	61.86 322	87.33 180	90.49 241
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 153	81.05 153	83.60 181	89.15 155	68.03 147	84.46 291	90.02 184	70.67 242	81.30 157	86.53 296	63.17 175	94.19 138	75.60 183	88.54 156	88.57 320

TR-MVS		77.44 270	76.18 277	81.20 265	88.24 192	63.24 282	84.61 286	86.40 304	67.55 314	77.81 223	86.48 297	54.10 285	93.15 200	57.75 362	82.72 267	87.20 351

EIA-MVS		83.31 123	82.80 123	84.82 115	89.59 130	65.59 213	88.21 162	92.68 71	74.66 146	78.96 193	86.42 298	69.06 103	95.26 87	75.54 184	90.09 126	93.62 105

tfpn200view9		76.42 292	75.37 291	79.55 307	89.13 156	57.65 364	85.17 268	83.60 342	73.41 181	76.45 256	86.39 299	52.12 305	91.95 254	48.33 421	83.75 246	89.07 293

thres400		76.50 287	75.37 291	79.86 296	89.13 156	57.65 364	85.17 268	83.60 342	73.41 181	76.45 256	86.39 299	52.12 305	91.95 254	48.33 421	83.75 246	90.00 266

v7n		78.97 230	77.58 246	83.14 201	83.45 348	65.51 214	88.32 159	91.21 143	73.69 171	72.41 336	86.32 301	57.93 248	93.81 157	69.18 255	75.65 358	90.11 258

MAR-MVS		81.84 149	80.70 159	85.27 94	91.32 89	71.53 58	89.82 88	90.92 152	69.77 271	78.50 204	86.21 302	62.36 190	94.52 123	65.36 289	92.05 93	89.77 278
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		80.03 203	79.03 206	83.01 209	83.78 339	64.51 246	87.11 201	90.57 165	71.96 211	78.08 217	86.20 303	61.41 209	93.94 147	74.93 190	77.23 332	90.60 236

test_vis1_n_1920		75.52 305	75.78 280	74.75 380	79.84 413	57.44 368	83.26 323	85.52 317	62.83 377	79.34 190	86.17 304	45.10 386	79.71 422	78.75 139	81.21 283	87.10 358

V42		79.38 219	78.24 224	82.83 218	81.10 399	65.50 215	85.55 260	89.82 190	71.57 219	78.21 212	86.12 305	60.66 225	93.18 199	75.64 181	75.46 364	89.81 277

PVSNet_BlendedMVS		80.60 186	80.02 177	82.36 237	88.85 163	65.40 216	86.16 243	92.00 109	69.34 280	78.11 215	86.09 306	66.02 147	94.27 131	71.52 227	82.06 274	87.39 345

v1192		79.59 210	78.43 219	83.07 206	83.55 346	64.52 245	86.93 209	90.58 163	70.83 238	77.78 224	85.90 307	59.15 239	93.94 147	73.96 200	77.19 334	90.76 228

SixPastTwentyTwo		73.37 332	71.26 347	79.70 301	85.08 310	57.89 358	85.57 256	83.56 344	71.03 234	65.66 411	85.88 308	42.10 407	92.57 226	59.11 347	63.34 434	88.65 317

EPNet_dtu		75.46 306	74.86 298	77.23 353	82.57 375	54.60 406	86.89 210	83.09 354	71.64 214	66.25 408	85.86 309	55.99 268	88.04 353	54.92 383	86.55 195	89.05 298
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 329	73.64 317	73.51 392	82.80 369	55.01 403	76.12 413	81.69 373	62.47 382	74.68 305	85.85 310	57.32 256	78.11 429	60.86 331	80.93 285	87.39 345

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	85.71 311	69.32 98	95.38 82	80.82 113	91.37 105	92.72 151

test_cas_vis1_n_1920		73.76 327	73.74 316	73.81 390	75.90 436	59.77 337	80.51 362	82.40 365	58.30 418	81.62 151	85.69 312	44.35 392	76.41 440	76.29 171	78.61 313	85.23 392

v1240		78.99 229	77.78 238	82.64 230	83.21 354	63.54 274	86.62 223	90.30 176	69.74 274	77.33 232	85.68 313	57.04 260	93.76 161	73.13 210	76.92 336	90.62 234

v144192		79.47 213	78.37 220	82.78 225	83.35 349	63.96 258	86.96 206	90.36 173	69.99 264	77.50 228	85.67 314	60.66 225	93.77 160	74.27 197	76.58 342	90.62 234

tfpnnormal		74.39 317	73.16 323	78.08 335	86.10 284	58.05 353	84.65 285	87.53 278	70.32 256	71.22 351	85.63 315	54.97 274	89.86 318	43.03 445	75.02 374	86.32 371

PS-MVSNAJ		81.69 153	81.02 154	83.70 179	89.51 134	68.21 142	84.28 298	90.09 183	70.79 239	81.26 158	85.62 316	63.15 176	94.29 129	75.62 182	88.87 149	88.59 319

SSC-MVS3.2		73.35 335	73.39 319	73.23 393	85.30 303	49.01 444	74.58 428	81.57 374	75.21 126	73.68 318	85.58 317	52.53 297	82.05 410	54.33 387	77.69 329	88.63 318

v1921920		79.22 222	78.03 228	82.80 221	83.30 351	63.94 260	86.80 214	90.33 174	69.91 267	77.48 229	85.53 318	58.44 245	93.75 162	73.60 202	76.85 339	90.71 232

test_0402		72.79 344	70.44 355	79.84 297	88.13 198	65.99 201	85.93 248	84.29 333	65.57 341	67.40 392	85.49 319	46.92 363	92.61 223	35.88 459	74.38 380	80.94 438

v148		78.72 236	77.80 237	81.47 255	82.73 371	61.96 308	86.30 237	88.08 261	73.26 186	76.18 264	85.47 320	62.46 188	92.36 238	71.92 226	73.82 386	90.09 260

USDC		70.33 368	68.37 369	76.21 360	80.60 403	56.23 387	79.19 382	86.49 302	60.89 394	61.29 435	85.47 320	31.78 447	89.47 327	53.37 392	76.21 353	82.94 424

VortexMVS		78.57 241	77.89 233	80.59 280	85.89 286	62.76 294	85.61 255	89.62 200	72.06 209	74.99 299	85.38 322	55.94 269	90.77 305	74.99 189	76.58 342	88.23 327

MVP-Stereo		76.12 296	74.46 306	81.13 268	85.37 301	69.79 95	84.42 295	87.95 267	65.03 348	67.46 389	85.33 323	53.28 295	91.73 264	58.01 360	83.27 259	81.85 433
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 250	76.99 259	81.78 248	85.66 291	66.99 182	84.66 283	90.47 167	55.08 437	72.02 342	85.27 324	63.83 167	94.11 141	66.10 283	89.80 133	84.24 406

DIV-MVS_self_test		77.72 263	76.76 265	80.58 281	82.48 378	60.48 329	83.09 327	87.86 270	69.22 285	74.38 311	85.24 325	62.10 195	91.53 276	71.09 232	75.40 367	89.74 279

FE-MVS		77.78 261	75.68 282	84.08 156	88.09 201	66.00 200	83.13 326	87.79 272	68.42 306	78.01 218	85.23 326	45.50 384	95.12 92	59.11 347	85.83 213	91.11 213

cl____		77.72 263	76.76 265	80.58 281	82.49 377	60.48 329	83.09 327	87.87 269	69.22 285	74.38 311	85.22 327	62.10 195	91.53 276	71.09 232	75.41 366	89.73 280

HyFIR lowres test		77.53 269	75.40 289	83.94 173	89.59 130	66.62 188	80.36 365	88.64 253	56.29 433	76.45 256	85.17 328	57.64 252	93.28 186	61.34 328	83.10 262	91.91 188

pmmvs4		74.03 325	71.91 336	80.39 284	81.96 383	68.32 135	81.45 347	82.14 367	59.32 408	69.87 367	85.13 329	52.40 301	88.13 352	60.21 336	74.74 377	84.73 402

TDRefinement		67.49 391	64.34 403	76.92 355	73.47 452	61.07 319	84.86 279	82.98 358	59.77 404	58.30 447	85.13 329	26.06 455	87.89 355	47.92 427	60.59 444	81.81 434

Fast-Effi-MVS+		80.81 174	79.92 179	83.47 185	88.85 163	64.51 246	85.53 262	89.39 208	70.79 239	78.49 205	85.06 331	67.54 124	93.58 166	67.03 278	86.58 194	92.32 171

PVSNet_Blended		80.98 169	80.34 168	82.90 215	88.85 163	65.40 216	84.43 293	92.00 109	67.62 313	78.11 215	85.05 332	66.02 147	94.27 131	71.52 227	89.50 138	89.01 300

ttmdpeth		59.91 419	57.10 423	68.34 429	67.13 466	46.65 453	74.64 427	67.41 456	48.30 452	62.52 433	85.04 333	20.40 465	75.93 445	42.55 447	45.90 467	82.44 427

test_fmvs1_n		70.86 361	70.24 358	72.73 401	72.51 459	55.28 400	81.27 350	79.71 400	51.49 448	78.73 197	84.87 334	27.54 454	77.02 434	76.06 175	79.97 301	85.88 383

WBMVS		73.43 331	72.81 327	75.28 372	87.91 209	50.99 436	78.59 393	81.31 379	65.51 344	74.47 309	84.83 335	46.39 369	86.68 367	58.41 355	77.86 325	88.17 330

CMPMVS	51.72 21	70.19 370	68.16 372	76.28 359	73.15 455	57.55 366	79.47 377	83.92 338	48.02 453	56.48 453	84.81 336	43.13 399	86.42 371	62.67 311	81.81 278	84.89 399
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 386	67.61 385	71.31 413	78.51 427	47.01 451	84.47 289	84.27 334	42.27 460	66.44 407	84.79 337	40.44 417	83.76 396	58.76 352	68.54 419	83.17 418

BH-w/o		78.21 248	77.33 253	80.84 275	88.81 167	65.13 224	84.87 278	87.85 271	69.75 272	74.52 308	84.74 338	61.34 211	93.11 203	58.24 358	85.84 212	84.27 405

pmmvs5		71.55 354	70.20 359	75.61 365	77.83 429	56.39 383	81.74 341	80.89 380	57.76 423	67.46 389	84.49 339	49.26 348	85.32 385	57.08 368	75.29 370	85.11 396

reproduce_monomvs		75.40 309	74.38 307	78.46 329	83.92 336	57.80 361	83.78 308	86.94 292	73.47 179	72.25 339	84.47 340	38.74 425	89.27 330	75.32 187	70.53 409	88.31 325

thres200		75.55 304	74.47 305	78.82 318	87.78 218	57.85 359	83.07 329	83.51 345	72.44 202	75.84 270	84.42 341	52.08 308	91.75 262	47.41 428	83.64 251	86.86 362

test_fmvs1		70.93 360	70.52 353	72.16 405	73.71 448	55.05 402	80.82 353	78.77 409	51.21 449	78.58 202	84.41 342	31.20 449	76.94 435	75.88 179	80.12 300	84.47 404

testing3		68.56 385	67.67 384	71.22 414	87.33 242	42.87 464	83.06 330	71.54 444	70.36 253	69.08 375	84.38 343	30.33 451	85.69 379	37.50 457	75.45 365	85.09 397

test_fmvs2		68.35 388	67.48 387	70.98 416	69.50 462	51.95 425	80.05 371	76.38 427	49.33 451	74.65 306	84.38 343	23.30 463	75.40 451	74.51 194	75.17 373	85.60 386

eth_miper_zixun_eth		77.92 258	76.69 268	81.61 253	83.00 362	61.98 307	83.15 325	89.20 224	69.52 277	74.86 302	84.35 345	61.76 201	92.56 227	71.50 229	72.89 394	90.28 251

myMVS_eth3d28		73.62 328	73.53 318	73.90 389	88.20 193	47.41 449	78.06 400	79.37 403	74.29 156	73.98 314	84.29 346	44.67 387	83.54 399	51.47 401	87.39 179	90.74 230

testing91		76.54 285	75.66 284	79.18 313	88.43 186	55.89 391	81.08 351	83.00 357	73.76 169	75.34 284	84.29 346	46.20 375	90.07 315	64.33 297	84.50 230	91.58 199

c3_l		78.75 234	77.91 231	81.26 263	82.89 368	61.56 313	84.09 304	89.13 228	69.97 265	75.56 274	84.29 346	66.36 139	92.09 248	73.47 205	75.48 362	90.12 257

testing99		76.09 298	75.12 297	79.00 314	88.16 195	55.50 397	80.79 355	81.40 377	73.30 185	75.17 292	84.27 349	44.48 390	90.02 316	64.28 298	84.22 239	91.48 204

UWE-MVS		72.13 351	71.49 340	74.03 387	86.66 269	47.70 446	81.40 349	76.89 425	63.60 368	75.59 273	84.22 350	39.94 419	85.62 380	48.98 418	86.13 204	88.77 312

FE-MVSNET3		76.43 291	75.32 293	79.76 299	83.00 362	60.72 324	81.74 341	88.76 247	68.99 295	72.98 327	84.19 351	56.41 267	90.27 310	62.39 313	79.40 307	88.31 325

Fast-Effi-MVS+-dtu		78.02 255	76.49 271	82.62 231	83.16 358	66.96 185	86.94 208	87.45 281	72.45 200	71.49 348	84.17 352	54.79 279	91.58 268	67.61 269	80.31 296	89.30 291

IterMVS-SCA-FT		75.43 307	73.87 314	80.11 292	82.69 372	64.85 239	81.57 345	83.47 346	69.16 288	70.49 355	84.15 353	51.95 311	88.15 351	69.23 254	72.14 400	87.34 347

1314		76.53 286	75.30 294	80.21 290	83.93 335	62.32 303	84.66 283	88.81 241	60.23 400	70.16 361	84.07 354	55.30 273	90.73 306	67.37 272	83.21 260	87.59 342

cl22		78.07 253	77.01 257	81.23 264	82.37 380	61.83 310	83.55 316	87.98 265	68.96 296	75.06 297	83.87 355	61.40 210	91.88 258	73.53 203	76.39 347	89.98 269

EG-PatchMatch MVS		74.04 323	71.82 337	80.71 278	84.92 313	67.42 168	85.86 251	88.08 261	66.04 335	64.22 422	83.85 356	35.10 440	92.56 227	57.44 364	80.83 288	82.16 431

thisisatest0515		77.33 273	75.38 290	83.18 199	85.27 304	63.80 263	82.11 338	83.27 349	65.06 347	75.91 268	83.84 357	49.54 342	94.27 131	67.24 274	86.19 202	91.48 204

test20.03		67.45 392	66.95 393	68.94 423	75.48 441	44.84 460	77.50 405	77.67 415	66.66 324	63.01 429	83.80 358	47.02 362	78.40 427	42.53 448	68.86 418	83.58 415

miper_ehance_all_eth		78.59 240	77.76 240	81.08 269	82.66 373	61.56 313	83.65 312	89.15 226	68.87 297	75.55 275	83.79 359	66.49 137	92.03 249	73.25 208	76.39 347	89.64 281

MSDG		73.36 334	70.99 349	80.49 283	84.51 324	65.80 207	80.71 359	86.13 310	65.70 339	65.46 412	83.74 360	44.60 388	90.91 300	51.13 404	76.89 337	84.74 401

MonoMVSNet		76.49 290	75.80 279	78.58 323	81.55 390	58.45 348	86.36 235	86.22 307	74.87 141	74.73 304	83.73 361	51.79 316	88.73 342	70.78 234	72.15 399	88.55 321

testing11		75.14 312	74.01 310	78.53 326	88.16 195	56.38 384	80.74 358	80.42 391	70.67 242	72.69 333	83.72 362	43.61 397	89.86 318	62.29 316	83.76 245	89.36 289

IterMVS		74.29 318	72.94 326	78.35 330	81.53 391	63.49 276	81.58 344	82.49 364	68.06 310	69.99 364	83.69 363	51.66 318	85.54 381	65.85 286	71.64 403	86.01 379
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 347	71.71 338	74.35 383	82.19 381	52.00 424	79.22 381	77.29 421	64.56 353	72.95 329	83.68 364	51.35 319	83.26 403	58.33 357	75.80 356	87.81 336

UWE-MVS-28		65.32 406	64.93 400	66.49 435	78.70 425	38.55 472	77.86 404	64.39 464	62.00 388	64.13 423	83.60 365	41.44 410	76.00 444	31.39 464	80.89 286	84.92 398

sc_t1		72.19 350	69.51 361	80.23 289	84.81 315	61.09 318	84.68 282	80.22 395	60.70 396	71.27 349	83.58 366	36.59 435	89.24 331	60.41 333	63.31 435	90.37 246

testing222		74.04 323	72.66 329	78.19 332	87.89 210	55.36 398	81.06 352	79.20 406	71.30 225	74.65 306	83.57 367	39.11 424	88.67 344	51.43 403	85.75 214	90.53 239

Effi-MVS+-dtu		80.03 203	78.57 215	84.42 130	85.13 309	68.74 121	88.77 136	88.10 260	74.99 133	74.97 300	83.49 368	57.27 257	93.36 184	73.53 203	80.88 287	91.18 211

baseline2		75.70 302	73.83 315	81.30 261	83.26 352	61.79 311	82.57 334	80.65 384	66.81 320	66.88 397	83.42 369	57.86 250	92.19 245	63.47 302	79.57 303	89.91 271

mvs5depth		69.45 377	67.45 388	75.46 370	73.93 446	55.83 392	79.19 382	83.23 350	66.89 319	71.63 346	83.32 370	33.69 443	85.09 386	59.81 339	55.34 454	85.46 388

TinyColmap		67.30 394	64.81 401	74.76 379	81.92 385	56.68 379	80.29 367	81.49 376	60.33 398	56.27 454	83.22 371	24.77 459	87.66 359	45.52 438	69.47 413	79.95 443

mvsany_test1		62.30 415	61.26 419	65.41 437	69.52 461	54.86 404	66.86 456	49.78 477	46.65 454	68.50 381	83.21 372	49.15 349	66.28 469	56.93 371	60.77 442	75.11 453

test_vis1_n		69.85 375	69.21 364	71.77 407	72.66 458	55.27 401	81.48 346	76.21 428	52.03 445	75.30 289	83.20 373	28.97 452	76.22 442	74.60 193	78.41 321	83.81 412

CostFormer		75.24 311	73.90 313	79.27 310	82.65 374	58.27 351	80.80 354	82.73 363	61.57 390	75.33 288	83.13 374	55.52 271	91.07 297	64.98 293	78.34 322	88.45 322

MVStest1		56.63 423	52.76 429	68.25 430	61.67 472	53.25 420	71.67 437	68.90 454	38.59 465	50.59 461	83.05 375	25.08 457	70.66 462	36.76 458	38.56 468	80.83 439

WB-MVSnew		71.96 353	71.65 339	72.89 399	84.67 322	51.88 427	82.29 336	77.57 416	62.31 383	73.67 319	83.00 376	53.49 293	81.10 417	45.75 437	82.13 273	85.70 385

ETVMVS		72.25 349	71.05 348	75.84 362	87.77 220	51.91 426	79.39 378	74.98 432	69.26 283	73.71 317	82.95 377	40.82 416	86.14 373	46.17 434	84.43 235	89.47 285

miper_lstm_enhance		74.11 322	73.11 324	77.13 354	80.11 409	59.62 339	72.23 435	86.92 294	66.76 322	70.40 356	82.92 378	56.93 261	82.92 404	69.06 257	72.63 395	88.87 307

GA-MVS		76.87 281	75.17 296	81.97 246	82.75 370	62.58 295	81.44 348	86.35 306	72.16 208	74.74 303	82.89 379	46.20 375	92.02 251	68.85 260	81.09 284	91.30 209

K. test v3		71.19 356	68.51 368	79.21 312	83.04 361	57.78 362	84.35 297	76.91 424	72.90 196	62.99 430	82.86 380	39.27 421	91.09 296	61.65 324	52.66 457	88.75 313

MS-PatchMatch		73.83 326	72.67 328	77.30 352	83.87 337	66.02 198	81.82 339	84.66 327	61.37 393	68.61 379	82.82 381	47.29 359	88.21 350	59.27 344	84.32 237	77.68 448

lessismore_v0				78.97 315	81.01 400	57.15 371		65.99 459		61.16 436	82.82 381	39.12 423	91.34 285	59.67 340	46.92 464	88.43 323

D2MVS		74.82 314	73.21 322	79.64 304	79.81 414	62.56 297	80.34 366	87.35 282	64.37 356	68.86 376	82.66 383	46.37 371	90.10 314	67.91 267	81.24 282	86.25 372

Anonymous20231206		68.60 383	67.80 381	71.02 415	80.23 408	50.75 438	78.30 398	80.47 388	56.79 430	66.11 410	82.63 384	46.35 372	78.95 425	43.62 443	75.70 357	83.36 417

MIMVSNet		70.69 363	69.30 362	74.88 377	84.52 323	56.35 386	75.87 417	79.42 402	64.59 352	67.76 384	82.41 385	41.10 413	81.54 413	46.64 432	81.34 280	86.75 365

UBG		73.08 339	72.27 334	75.51 368	88.02 204	51.29 434	78.35 397	77.38 420	65.52 342	73.87 316	82.36 386	45.55 382	86.48 370	55.02 382	84.39 236	88.75 313

OpenMVS_ROB	64.09 19	70.56 365	68.19 371	77.65 345	80.26 406	59.41 343	85.01 275	82.96 359	58.76 415	65.43 413	82.33 387	37.63 432	91.23 289	45.34 440	76.03 354	82.32 428

miper_enhance_ethall		77.87 260	76.86 261	80.92 274	81.65 387	61.38 315	82.68 332	88.98 234	65.52 342	75.47 276	82.30 388	65.76 151	92.00 252	72.95 211	76.39 347	89.39 288

test0.0.03 1		68.00 390	67.69 383	68.90 424	77.55 430	47.43 447	75.70 418	72.95 443	66.66 324	66.56 402	82.29 389	48.06 356	75.87 446	44.97 441	74.51 379	83.41 416

PVSNet	64.34 18	72.08 352	70.87 351	75.69 364	86.21 278	56.44 382	74.37 429	80.73 383	62.06 387	70.17 360	82.23 390	42.86 401	83.31 402	54.77 384	84.45 234	87.32 348

MIMVSNet1		68.58 384	66.78 394	73.98 388	80.07 410	51.82 428	80.77 356	84.37 330	64.40 355	59.75 443	82.16 391	36.47 436	83.63 398	42.73 446	70.33 410	86.48 370

CL-MVSNet_self_test		72.37 347	71.46 341	75.09 374	79.49 420	53.53 414	80.76 357	85.01 325	69.12 289	70.51 354	82.05 392	57.92 249	84.13 394	52.27 397	66.00 428	87.60 340

tpm2		73.26 336	71.46 341	78.63 320	83.34 350	56.71 378	80.65 360	80.40 392	56.63 431	73.55 320	82.02 393	51.80 315	91.24 288	56.35 377	78.42 320	87.95 332

PatchMatch-RL		72.38 346	70.90 350	76.80 357	88.60 179	67.38 171	79.53 376	76.17 429	62.75 379	69.36 372	82.00 394	45.51 383	84.89 389	53.62 390	80.58 292	78.12 447

FE-MVSNET2		72.88 343	71.28 345	77.67 343	78.30 428	57.78 362	84.43 293	88.92 239	69.56 275	64.61 419	81.67 395	46.73 368	88.54 347	59.33 343	67.99 420	86.69 367

FMVSNet5		69.50 376	67.96 376	74.15 386	82.97 366	55.35 399	80.01 372	82.12 368	62.56 381	63.02 428	81.53 396	36.92 433	81.92 411	48.42 420	74.06 382	85.17 395

CR-MVSNet		73.37 332	71.27 346	79.67 303	81.32 397	65.19 222	75.92 415	80.30 393	59.92 403	72.73 331	81.19 397	52.50 299	86.69 366	59.84 338	77.71 327	87.11 356

Patchmtry		70.74 362	69.16 365	75.49 369	80.72 401	54.07 411	74.94 426	80.30 393	58.34 417	70.01 362	81.19 397	52.50 299	86.54 368	53.37 392	71.09 407	85.87 384

IB-MVS	68.01 15	75.85 301	73.36 321	83.31 192	84.76 317	66.03 197	83.38 320	85.06 323	70.21 260	69.40 371	81.05 399	45.76 380	94.66 118	65.10 292	75.49 361	89.25 292
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		76.72 284	74.64 301	82.99 210	85.78 289	65.88 204	82.33 335	89.21 223	60.85 395	72.74 330	81.02 400	47.28 360	93.75 162	67.48 271	85.02 222	89.34 290

LF4IMVS		64.02 411	62.19 415	69.50 421	70.90 460	53.29 419	76.13 412	77.18 422	52.65 443	58.59 445	80.98 401	23.55 462	76.52 438	53.06 394	66.66 424	78.68 446

Anonymous20240521		68.80 382	67.22 391	73.55 391	74.33 444	54.11 410	83.18 324	85.61 316	58.15 419	61.68 434	80.94 402	30.71 450	81.27 416	57.00 370	73.34 392	85.28 391

gm-plane-assit					81.40 393	53.83 413			62.72 380		80.94 402		92.39 236	63.40 304

UnsupCasMVSNet_eth		67.33 393	65.99 397	71.37 410	73.48 451	51.47 432	75.16 422	85.19 320	65.20 345	60.78 437	80.93 404	42.35 403	77.20 433	57.12 367	53.69 456	85.44 389

dmvs_re		71.14 357	70.58 352	72.80 400	81.96 383	59.68 338	75.60 419	79.34 404	68.55 302	69.27 374	80.72 405	49.42 344	76.54 437	52.56 396	77.79 326	82.19 430

MDTV_nov1_ep13			69.97 360		83.18 356	53.48 415	77.10 410	80.18 397	60.45 397	69.33 373	80.44 406	48.89 354	86.90 365	51.60 400	78.51 316

pmmvs-eth3d		70.50 366	67.83 380	78.52 327	77.37 432	66.18 195	81.82 339	81.51 375	58.90 413	63.90 426	80.42 407	42.69 402	86.28 372	58.56 353	65.30 430	83.11 420

tt0320		70.49 367	68.03 375	77.89 338	84.78 316	59.12 344	83.55 316	80.44 390	58.13 420	67.43 391	80.41 408	39.26 422	87.54 360	55.12 381	63.18 436	86.99 359

mmtdpeth		74.16 321	73.01 325	77.60 348	83.72 341	61.13 316	85.10 272	85.10 322	72.06 209	77.21 240	80.33 409	43.84 395	85.75 377	77.14 160	52.61 458	85.91 382

tt0320-xc		70.11 371	67.45 388	78.07 336	85.33 302	59.51 342	83.28 322	78.96 408	58.77 414	67.10 395	80.28 410	36.73 434	87.42 361	56.83 373	59.77 446	87.29 349

PM-MVS		66.41 401	64.14 404	73.20 396	73.92 447	56.45 381	78.97 386	64.96 463	63.88 366	64.72 418	80.24 411	19.84 467	83.44 401	66.24 280	64.52 432	79.71 444

SCA		74.22 320	72.33 333	79.91 295	84.05 333	62.17 305	79.96 373	79.29 405	66.30 332	72.38 337	80.13 412	51.95 311	88.60 345	59.25 345	77.67 330	88.96 304

Patchmatch-test		64.82 409	63.24 410	69.57 420	79.42 421	49.82 442	63.49 468	69.05 452	51.98 446	59.95 442	80.13 412	50.91 324	70.98 461	40.66 451	73.57 387	87.90 334

tpmrst		72.39 345	72.13 335	73.18 397	80.54 404	49.91 441	79.91 374	79.08 407	63.11 371	71.69 345	79.95 414	55.32 272	82.77 406	65.66 288	73.89 384	86.87 361

DSMNet-mixed		57.77 422	56.90 424	60.38 443	67.70 464	35.61 474	69.18 448	53.97 475	32.30 473	57.49 450	79.88 415	40.39 418	68.57 467	38.78 455	72.37 396	76.97 449

MDA-MVSNet-bldmvs		66.68 398	63.66 408	75.75 363	79.28 422	60.56 328	73.92 431	78.35 412	64.43 354	50.13 462	79.87 416	44.02 394	83.67 397	46.10 435	56.86 448	83.03 422

PatchmatchNet		73.12 338	71.33 344	78.49 328	83.18 356	60.85 322	79.63 375	78.57 410	64.13 358	71.73 344	79.81 417	51.20 322	85.97 376	57.40 365	76.36 352	88.66 316
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FE-MVSNET		67.25 395	65.33 399	73.02 398	75.86 437	52.54 422	80.26 369	80.56 386	63.80 367	60.39 438	79.70 418	41.41 411	84.66 392	43.34 444	62.62 437	81.86 432

Syy-MVS		68.05 389	67.85 378	68.67 427	84.68 319	40.97 470	78.62 391	73.08 441	66.65 327	66.74 400	79.46 419	52.11 307	82.30 408	32.89 462	76.38 350	82.75 425

myMVS_eth3d		67.02 396	66.29 396	69.21 422	84.68 319	42.58 465	78.62 391	73.08 441	66.65 327	66.74 400	79.46 419	31.53 448	82.30 408	39.43 454	76.38 350	82.75 425

ppachtmachnet_test		70.04 372	67.34 390	78.14 333	79.80 415	61.13 316	79.19 382	80.59 385	59.16 410	65.27 414	79.29 421	46.75 367	87.29 362	49.33 416	66.72 423	86.00 381

EPMVS		69.02 380	68.16 372	71.59 408	79.61 418	49.80 443	77.40 406	66.93 457	62.82 378	70.01 362	79.05 422	45.79 379	77.86 431	56.58 375	75.26 371	87.13 355

PMMVS		69.34 378	68.67 367	71.35 412	75.67 439	62.03 306	75.17 421	73.46 439	50.00 450	68.68 377	79.05 422	52.07 309	78.13 428	61.16 329	82.77 265	73.90 454

test-LLR		72.94 342	72.43 331	74.48 381	81.35 395	58.04 354	78.38 394	77.46 417	66.66 324	69.95 365	79.00 424	48.06 356	79.24 423	66.13 281	84.83 225	86.15 375

test-mter		71.41 355	70.39 357	74.48 381	81.35 395	58.04 354	78.38 394	77.46 417	60.32 399	69.95 365	79.00 424	36.08 438	79.24 423	66.13 281	84.83 225	86.15 375

KD-MVS_self_test		68.81 381	67.59 386	72.46 404	74.29 445	45.45 454	77.93 402	87.00 290	63.12 370	63.99 425	78.99 426	42.32 404	84.77 390	56.55 376	64.09 433	87.16 354

test_fmvs3		63.36 413	61.82 416	67.98 431	62.51 471	46.96 452	77.37 407	74.03 438	45.24 456	67.50 388	78.79 427	12.16 475	72.98 460	72.77 214	66.02 427	83.99 410

KD-MVS_2432*1600		66.22 403	63.89 406	73.21 394	75.47 442	53.42 416	70.76 442	84.35 331	64.10 360	66.52 404	78.52 428	34.55 441	84.98 387	50.40 407	50.33 461	81.23 436

miper_refine_blended		66.22 403	63.89 406	73.21 394	75.47 442	53.42 416	70.76 442	84.35 331	64.10 360	66.52 404	78.52 428	34.55 441	84.98 387	50.40 407	50.33 461	81.23 436

tpmvs		71.09 358	69.29 363	76.49 358	82.04 382	56.04 389	78.92 387	81.37 378	64.05 362	67.18 394	78.28 430	49.74 341	89.77 320	49.67 414	72.37 396	83.67 414

our_test_3		69.14 379	67.00 392	75.57 366	79.80 415	58.80 345	77.96 401	77.81 414	59.55 406	62.90 431	78.25 431	47.43 358	83.97 395	51.71 399	67.58 422	83.93 411

MDA-MVSNet_test_wron		65.03 407	62.92 411	71.37 410	75.93 435	56.73 376	69.09 451	74.73 435	57.28 428	54.03 457	77.89 432	45.88 377	74.39 455	49.89 413	61.55 440	82.99 423

YYNet1		65.03 407	62.91 412	71.38 409	75.85 438	56.60 380	69.12 450	74.66 437	57.28 428	54.12 456	77.87 433	45.85 378	74.48 454	49.95 412	61.52 441	83.05 421

ambc				75.24 373	73.16 454	50.51 439	63.05 469	87.47 280		64.28 421	77.81 434	17.80 469	89.73 322	57.88 361	60.64 443	85.49 387

tpm cat1		70.57 364	68.31 370	77.35 351	82.41 379	57.95 357	78.08 399	80.22 395	52.04 444	68.54 380	77.66 435	52.00 310	87.84 356	51.77 398	72.07 401	86.25 372

dp		66.80 397	65.43 398	70.90 417	79.74 417	48.82 445	75.12 424	74.77 434	59.61 405	64.08 424	77.23 436	42.89 400	80.72 419	48.86 419	66.58 425	83.16 419

TESTMET0.1,1		69.89 374	69.00 366	72.55 402	79.27 423	56.85 374	78.38 394	74.71 436	57.64 424	68.09 383	77.19 437	37.75 431	76.70 436	63.92 300	84.09 240	84.10 409

CHOSEN 280x420		66.51 400	64.71 402	71.90 406	81.45 392	63.52 275	57.98 471	68.95 453	53.57 440	62.59 432	76.70 438	46.22 374	75.29 452	55.25 380	79.68 302	76.88 450

PatchT		68.46 387	67.85 378	70.29 418	80.70 402	43.93 462	72.47 434	74.88 433	60.15 401	70.55 353	76.57 439	49.94 338	81.59 412	50.58 405	74.83 376	85.34 390

mvsany_test3		53.99 426	51.45 431	61.61 442	55.51 476	44.74 461	63.52 467	45.41 481	43.69 459	58.11 448	76.45 440	17.99 468	63.76 472	54.77 384	47.59 463	76.34 451

RPMNet		73.51 330	70.49 354	82.58 233	81.32 397	65.19 222	75.92 415	92.27 89	57.60 425	72.73 331	76.45 440	52.30 302	95.43 77	48.14 425	77.71 327	87.11 356

dmvs_testset		62.63 414	64.11 405	58.19 445	78.55 426	24.76 483	75.28 420	65.94 460	67.91 311	60.34 439	76.01 442	53.56 291	73.94 458	31.79 463	67.65 421	75.88 452

ADS-MVSNet2		66.20 405	63.33 409	74.82 378	79.92 411	58.75 346	67.55 454	75.19 431	53.37 441	65.25 415	75.86 443	42.32 404	80.53 420	41.57 449	68.91 416	85.18 393

ADS-MVSNet		64.36 410	62.88 413	68.78 426	79.92 411	47.17 450	67.55 454	71.18 445	53.37 441	65.25 415	75.86 443	42.32 404	73.99 457	41.57 449	68.91 416	85.18 393

EGC-MVSNET		52.07 432	47.05 436	67.14 433	83.51 347	60.71 325	80.50 363	67.75 455	0.07 483	0.43 484	75.85 445	24.26 460	81.54 413	28.82 466	62.25 438	59.16 466

new-patchmatchnet		61.73 416	61.73 417	61.70 441	72.74 457	24.50 484	69.16 449	78.03 413	61.40 391	56.72 452	75.53 446	38.42 427	76.48 439	45.95 436	57.67 447	84.13 408

N_pmnet		52.79 430	53.26 428	51.40 455	78.99 424	7.68 489	69.52 446	3.89 488	51.63 447	57.01 451	74.98 447	40.83 415	65.96 470	37.78 456	64.67 431	80.56 442

WB-MVS		54.94 424	54.72 425	55.60 451	73.50 450	20.90 485	74.27 430	61.19 468	59.16 410	50.61 460	74.15 448	47.19 361	75.78 447	17.31 476	35.07 470	70.12 458

patchmatchnet-post											74.00 449	51.12 323	88.60 345

GG-mvs-BLEND				75.38 371	81.59 389	55.80 393	79.32 379	69.63 449		67.19 393	73.67 450	43.24 398	88.90 341	50.41 406	84.50 230	81.45 435

SSC-MVS		53.88 427	53.59 427	54.75 453	72.87 456	19.59 486	73.84 432	60.53 470	57.58 426	49.18 464	73.45 451	46.34 373	75.47 450	16.20 479	32.28 472	69.20 459

Patchmatch-RL test		70.24 369	67.78 382	77.61 346	77.43 431	59.57 341	71.16 439	70.33 446	62.94 375	68.65 378	72.77 452	50.62 328	85.49 382	69.58 252	66.58 425	87.77 337

FPMVS		53.68 428	51.64 430	59.81 444	65.08 468	51.03 435	69.48 447	69.58 450	41.46 461	40.67 468	72.32 453	16.46 471	70.00 465	24.24 472	65.42 429	58.40 468

UnsupCasMVSNet_bld		63.70 412	61.53 418	70.21 419	73.69 449	51.39 433	72.82 433	81.89 370	55.63 435	57.81 449	71.80 454	38.67 426	78.61 426	49.26 417	52.21 459	80.63 440

APD_test1		53.31 429	49.93 434	63.42 440	65.68 467	50.13 440	71.59 438	66.90 458	34.43 470	40.58 469	71.56 455	8.65 480	76.27 441	34.64 461	55.36 453	63.86 464

test_f		52.09 431	50.82 432	55.90 449	53.82 479	42.31 468	59.42 470	58.31 473	36.45 468	56.12 455	70.96 456	12.18 474	57.79 475	53.51 391	56.57 450	67.60 460

PVSNet_0	57.27 20	61.67 417	59.27 420	68.85 425	79.61 418	57.44 368	68.01 452	73.44 440	55.93 434	58.54 446	70.41 457	44.58 389	77.55 432	47.01 429	35.91 469	71.55 457

pmmvs3		57.79 421	54.26 426	68.37 428	64.02 470	56.72 377	75.12 424	65.17 461	40.20 462	52.93 458	69.86 458	20.36 466	75.48 449	45.45 439	55.25 455	72.90 456

test_vis1_rt		60.28 418	58.42 421	65.84 436	67.25 465	55.60 396	70.44 444	60.94 469	44.33 458	59.00 444	66.64 459	24.91 458	68.67 466	62.80 307	69.48 412	73.25 455

new_pmnet		50.91 433	50.29 433	52.78 454	68.58 463	34.94 476	63.71 466	56.63 474	39.73 463	44.95 465	65.47 460	21.93 464	58.48 474	34.98 460	56.62 449	64.92 462

gg-mvs-nofinetune		69.95 373	67.96 376	75.94 361	83.07 359	54.51 408	77.23 408	70.29 447	63.11 371	70.32 357	62.33 461	43.62 396	88.69 343	53.88 389	87.76 173	84.62 403

JIA-IIPM		66.32 402	62.82 414	76.82 356	77.09 433	61.72 312	65.34 462	75.38 430	58.04 422	64.51 420	62.32 462	42.05 408	86.51 369	51.45 402	69.22 415	82.21 429

LCM-MVSNet		54.25 425	49.68 435	67.97 432	53.73 480	45.28 457	66.85 457	80.78 382	35.96 469	39.45 470	62.23 463	8.70 479	78.06 430	48.24 424	51.20 460	80.57 441

PMMVS2		40.82 441	38.86 445	46.69 456	53.84 478	16.45 487	48.61 474	49.92 476	37.49 466	31.67 471	60.97 464	8.14 481	56.42 476	28.42 467	30.72 473	67.19 461

testf1		45.72 436	41.96 440	57.00 446	56.90 474	45.32 455	66.14 459	59.26 471	26.19 474	30.89 473	60.96 465	4.14 483	70.64 463	26.39 470	46.73 465	55.04 469

APD_test2		45.72 436	41.96 440	57.00 446	56.90 474	45.32 455	66.14 459	59.26 471	26.19 474	30.89 473	60.96 465	4.14 483	70.64 463	26.39 470	46.73 465	55.04 469

MVS-HIRNet		59.14 420	57.67 422	63.57 439	81.65 387	43.50 463	71.73 436	65.06 462	39.59 464	51.43 459	57.73 467	38.34 428	82.58 407	39.53 452	73.95 383	64.62 463

ANet_high		50.57 434	46.10 438	63.99 438	48.67 483	39.13 471	70.99 441	80.85 381	61.39 392	31.18 472	57.70 468	17.02 470	73.65 459	31.22 465	15.89 480	79.18 445

PMVS	37.38 22	44.16 440	40.28 444	55.82 450	40.82 485	42.54 467	65.12 463	63.99 465	34.43 470	24.48 476	57.12 469	3.92 485	76.17 443	17.10 477	55.52 452	48.75 471
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		45.42 438	45.38 439	45.55 457	73.36 453	26.85 481	67.72 453	34.19 483	54.15 439	49.65 463	56.41 470	25.43 456	62.94 473	19.45 474	28.09 474	46.86 473

test_vis3_rt		49.26 435	47.02 437	56.00 448	54.30 477	45.27 458	66.76 458	48.08 478	36.83 467	44.38 466	53.20 471	7.17 482	64.07 471	56.77 374	55.66 451	58.65 467

test_method		31.52 444	29.28 448	38.23 459	27.03 487	6.50 490	20.94 479	62.21 467	4.05 481	22.35 479	52.50 472	13.33 472	47.58 479	27.04 469	34.04 471	60.62 465

kuosan		39.70 442	40.40 443	37.58 460	64.52 469	26.98 479	65.62 461	33.02 484	46.12 455	42.79 467	48.99 473	24.10 461	46.56 481	12.16 482	26.30 475	39.20 474

DeepMVS_CX				27.40 463	40.17 486	26.90 480		24.59 487	17.44 479	23.95 477	48.61 474	9.77 477	26.48 482	18.06 475	24.47 476	28.83 476

MVE	26.22 23	30.37 446	25.89 450	43.81 458	44.55 484	35.46 475	28.87 478	39.07 482	18.20 478	18.58 480	40.18 475	2.68 486	47.37 480	17.07 478	23.78 477	48.60 472
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 439	41.86 442	55.16 452	77.03 434	51.52 431	32.50 477	80.52 387	32.46 472	27.12 475	35.02 476	9.52 478	75.50 448	22.31 473	60.21 445	38.45 475
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 443	30.64 446	35.15 461	52.87 481	27.67 478	57.09 472	47.86 479	24.64 476	16.40 481	33.05 477	11.23 476	54.90 477	14.46 480	18.15 478	22.87 477

EMVS		30.81 445	29.65 447	34.27 462	50.96 482	25.95 482	56.58 473	46.80 480	24.01 477	15.53 482	30.68 478	12.47 473	54.43 478	12.81 481	17.05 479	22.43 478

tmp_tt		18.61 448	21.40 451	10.23 465	4.82 488	10.11 488	34.70 476	30.74 486	1.48 482	23.91 478	26.07 479	28.42 453	13.41 484	27.12 468	15.35 481	7.17 479

X-MVStestdata		80.37 195	77.83 235	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 480	67.45 125	96.60 37	83.06 87	94.50 57	94.07 74

test_post											5.46 481	50.36 332	84.24 393

test_post1							78.90 388				5.43 482	48.81 355	85.44 384	59.25 345

wuyk23d		16.82 449	15.94 452	19.46 464	58.74 473	31.45 477	39.22 475	3.74 489	6.84 480	6.04 483	2.70 483	1.27 487	24.29 483	10.54 483	14.40 482	2.63 480

testmvs		6.04 452	8.02 455	0.10 467	0.08 489	0.03 492	69.74 445	0.04 490	0.05 484	0.31 485	1.68 484	0.02 489	0.04 485	0.24 484	0.02 483	0.25 482

test123		6.12 451	8.11 454	0.14 466	0.06 490	0.09 491	71.05 440	0.03 491	0.04 485	0.25 486	1.30 485	0.05 488	0.03 486	0.21 485	0.01 484	0.29 481

mmdepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

monomultidepth		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

test_blank		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet_test		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

DCPMVS		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

pcd_1.5k_mvsjas		5.26 453	7.02 456	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	63.15 176	0.00 487	0.00 486	0.00 485	0.00 483

sosnet-low-res		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

sosnet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uncertanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

Regformer		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

uanet		0.00 454	0.00 457	0.00 468	0.00 491	0.00 493	0.00 480	0.00 492	0.00 486	0.00 487	0.00 486	0.00 490	0.00 487	0.00 486	0.00 485	0.00 483

TestfortrainingZip							93.28 12

WAC-MVS						42.58 465								39.46 453

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 149	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 54

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 54

eth-test2					0.00 491

eth-test					0.00 491

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 320	92.39 6				88.94 28	96.63 4	94.85 21

save fliter					93.80 44	72.35 44	90.47 74	91.17 145	74.31 154

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 66

GSMVS																88.96 304

test_part2					95.06 8	72.65 32				91.80 16

sam_mvs1												51.32 320				88.96 304

sam_mvs												50.01 336

MTGPA								92.02 107

MTMP							92.18 39	32.83 485

test9_res														84.90 64	95.70 30	92.87 147

agg_prior2														82.91 91	95.45 33	92.70 152

agg_prior					92.85 68	71.94 52		91.78 123		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 82

旧先验2							86.56 225		58.10 421	87.04 61			88.98 337	74.07 199

新几何2							86.29 239

无先验							87.48 186	88.98 234	60.00 402				94.12 140	67.28 273		88.97 303

原ACMM2							86.86 212

testdata2													91.01 298	62.37 315

segment_acmp												73.08 43

testdata1							84.14 303		75.71 108

test12				86.80 58	92.63 73	70.70 81		91.79 122		82.71 133		71.67 63	96.16 52		94.50 57	93.54 111

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 230

plane_prior5								92.44 82					95.38 82	78.71 140	86.32 198	91.33 207

plane_prior3						68.60 128			78.44 36	78.92 195

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 124

plane_prior						68.71 123	90.38 78		77.62 47						86.16 203

n2								0.00 492

nn								0.00 492

door-mid								69.98 448

test11								92.23 93

door								69.44 451

HQP5-MVS						66.98 183

HQP-NCC					89.33 144		89.17 116		76.41 86	77.23 236

ACMP_Plane					89.33 144		89.17 116		76.41 86	77.23 236

BP-MVS														77.47 155

HQP4-MVS										77.24 235			95.11 94			91.03 217

HQP3-MVS								92.19 101							85.99 207

HQP2-MVS												60.17 233

MDTV_nov1_ep13_2view						37.79 473	75.16 422		55.10 436	66.53 403		49.34 346		53.98 388		87.94 333

ACMMP++_ref															81.95 276

ACMMP++															81.25 281

Test By Simon												64.33 162

High-resolution multi-view benchmark