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.

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 66	93.57 7	94.06 10	77.24 52	93.10 1	95.72 8	82.99 1	97.44 7	89.07 14	96.63 4	94.88 14

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 55	93.10 1	95.39 14	82.99 1	97.27 12

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 59	95.06 1	94.23 3	78.38 33	92.78 4	95.74 6	82.45 3	97.49 4	89.42 9	96.68 2	94.95 10

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 47	82.45 3	96.87 20	83.77 63	96.48 8	94.88 14

PC_three_1452									68.21 252	92.02 12	94.00 49	82.09 5	95.98 56	84.58 53	96.68 2	94.95 10

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 89	92.29 7	95.66 10	81.67 6	97.38 11	87.44 33	96.34 15	93.95 57
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 9	89.42 9	96.57 7	94.67 26

test_241102_TWO								94.06 10	77.24 52	92.78 4	95.72 8	81.26 8	97.44 7	89.07 14	96.58 6	94.26 46

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 59	93.49 9	92.73 64	77.33 49	92.12 9	95.78 4	80.98 9	97.40 9	89.08 12	96.41 12	93.33 89
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
test0726					95.27 5	71.25 59	93.60 6	94.11 6	77.33 49	92.81 3	95.79 3	80.98 9

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 35	92.05 11	95.74 6	80.83 11

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 53	92.24 71	69.03 103	89.57 90	93.39 30	77.53 45	89.79 18	94.12 42	78.98 12	96.58 35	85.66 40	95.72 24	94.58 31

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 40	78.35 13	96.77 24	89.59 8	94.22 60	94.67 26
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 93	91.06 16	96.03 1	76.84 14	97.03 17	89.09 11	95.65 27	94.47 36
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
dcpmvs_2		85.63 58	86.15 48	84.06 131	91.71 78	64.94 200	86.47 194	91.87 102	73.63 138	86.60 50	93.02 75	76.57 15	91.87 221	83.36 65	92.15 80	95.35 3

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 62	93.00 46	80.90 7	88.06 29	94.06 45	76.43 16	96.84 21	88.48 24	95.99 18	94.34 42

MCST-MVS		87.37 29	87.25 29	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 155	84.86 68	92.89 77	76.22 17	96.33 41	84.89 48	95.13 36	94.40 39

CSCG		86.41 45	86.19 46	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 130	83.16 98	91.07 123	75.94 18	95.19 80	79.94 101	94.38 56	93.55 81

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 59	80.26 11	87.78 33	94.27 35	75.89 19	96.81 23	87.45 32	96.44 9	93.05 104

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 81	74.62 118	88.90 22	93.85 55	75.75 20	96.00 54	87.80 28	94.63 48	95.04 8
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 64	71.95 49	92.40 24	94.74 2	75.71 91	89.16 19	95.10 16	75.65 21	96.19 46	87.07 34	96.01 17	94.79 21

9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 132	88.57 25	94.67 21	75.57 22	95.79 58	86.77 35	95.76 23

SD-MVS		88.06 14	88.50 14	86.71 54	92.60 69	72.71 29	91.81 41	93.19 35	77.87 36	90.32 17	94.00 49	74.83 23	93.78 139	87.63 30	94.27 59	93.65 74
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
DELS-MVS		85.41 63	85.30 65	85.77 70	88.49 169	67.93 136	85.52 224	93.44 27	78.70 29	83.63 94	89.03 170	74.57 24	95.71 61	80.26 99	94.04 61	93.66 70
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
patch_mono-2		83.65 83	84.54 73	80.99 224	90.06 112	65.83 180	84.21 253	88.74 205	71.60 176	85.01 62	92.44 87	74.51 25	83.50 343	82.15 81	92.15 80	93.64 76

train_agg		86.43 43	86.20 45	87.13 44	93.26 52	72.96 25	88.75 118	91.89 100	68.69 244	85.00 63	93.10 70	74.43 26	95.41 72	84.97 45	95.71 25	93.02 106

test_8					93.13 54	72.57 35	88.68 123	91.84 104	68.69 244	84.87 67	93.10 70	74.43 26	95.16 81

TEST9					93.26 52	72.96 25	88.75 118	91.89 100	68.44 249	85.00 63	93.10 70	74.36 28	95.41 72

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 114	92.29 7	95.97 2	74.28 29	97.24 13	88.58 21	96.91 1	94.87 16
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
test_prior2							88.85 115		75.41 97	84.91 65	93.54 59	74.28 29		83.31 66	95.86 20

TSAR-MVS + GP.		85.71 57	85.33 63	86.84 50	91.34 81	72.50 36	89.07 108	87.28 235	76.41 76	85.80 54	90.22 142	74.15 31	95.37 77	81.82 83	91.88 83	92.65 118

ZD-MVS					94.38 25	72.22 44		92.67 66	70.98 189	87.75 35	94.07 44	74.01 32	96.70 27	84.66 52	94.84 44

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 72	72.96 25	93.73 5	93.67 20	80.19 12	88.10 28	94.80 19	73.76 33	97.11 15	87.51 31	95.82 21	94.90 13
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 157	88.58 24	94.52 23	73.36 34	96.49 38	84.26 57	95.01 37	92.70 114
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
casdiffmvs_mvg		85.99 48	86.09 50	85.70 72	87.65 207	67.22 157	88.69 122	93.04 41	79.64 18	85.33 59	92.54 86	73.30 35	94.50 110	83.49 64	91.14 94	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
sasdasda		85.91 52	85.87 54	86.04 65	89.84 117	69.44 98	90.45 68	93.00 46	76.70 71	88.01 31	91.23 115	73.28 36	93.91 133	81.50 85	88.80 126	94.77 23

canonicalmvs		85.91 52	85.87 54	86.04 65	89.84 117	69.44 98	90.45 68	93.00 46	76.70 71	88.01 31	91.23 115	73.28 36	93.91 133	81.50 85	88.80 126	94.77 23

segment_acmp												73.08 38

DPM-MVS		84.93 70	84.29 77	86.84 50	90.20 105	73.04 23	87.12 173	93.04 41	69.80 216	82.85 102	91.22 117	73.06 39	96.02 52	76.72 131	94.63 48	91.46 157

NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 54	92.83 60	81.50 5	85.79 55	93.47 63	73.02 40	97.00 18	84.90 46	94.94 40	94.10 50

test_fmvsmconf_n		85.92 51	86.04 51	85.57 74	85.03 260	69.51 93	89.62 89	90.58 139	73.42 146	87.75 35	94.02 47	72.85 41	93.24 164	90.37 3	90.75 98	93.96 56

MGCFI-Net		85.06 69	85.51 59	83.70 145	89.42 130	63.01 241	89.43 93	92.62 72	76.43 75	87.53 38	91.34 113	72.82 42	93.42 159	81.28 88	88.74 129	94.66 29

nrg030		83.88 78	83.53 82	84.96 90	86.77 229	69.28 102	90.46 67	92.67 66	74.79 113	82.95 99	91.33 114	72.70 43	93.09 178	80.79 95	79.28 257	92.50 123

CDPH-MVS		85.76 56	85.29 66	87.17 43	93.49 47	71.08 64	88.58 126	92.42 79	68.32 251	84.61 74	93.48 61	72.32 44	96.15 48	79.00 104	95.43 30	94.28 45

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 69	77.57 41	83.84 89	94.40 32	72.24 45	96.28 43	85.65 41	95.30 35	93.62 77
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffmvs		85.11 67	85.14 67	85.01 88	87.20 221	65.77 183	87.75 155	92.83 60	77.84 37	84.36 80	92.38 88	72.15 46	93.93 132	81.27 89	90.48 101	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
DeepC-MVS	79.81 2	87.08 35	86.88 38	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 57	82.82 103	94.23 38	72.13 47	97.09 16	84.83 49	95.37 31	93.65 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_l_conf0.5_n		84.47 74	84.54 73	84.27 117	85.42 250	68.81 109	88.49 128	87.26 236	68.08 253	88.03 30	93.49 60	72.04 48	91.77 223	88.90 17	89.14 122	92.24 134

MVSMamba_PlusPlus		85.99 48	85.96 52	86.05 64	91.09 85	67.64 142	89.63 88	92.65 69	72.89 160	84.64 73	91.71 99	71.85 49	96.03 50	84.77 51	94.45 54	94.49 35

baseline		84.93 70	84.98 68	84.80 98	87.30 219	65.39 191	87.30 169	92.88 57	77.62 39	84.04 86	92.26 90	71.81 50	93.96 126	81.31 87	90.30 104	95.03 9

ZNCC-MVS		87.94 18	87.85 19	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 65	85.24 60	94.32 33	71.76 51	96.93 19	85.53 43	95.79 22	94.32 43

test_fmvsmconf0.1_n		85.61 59	85.65 57	85.50 75	82.99 307	69.39 100	89.65 86	90.29 152	73.31 149	87.77 34	94.15 41	71.72 52	93.23 165	90.31 4	90.67 100	93.89 61

MM		89.16 6	89.23 7	88.97 4	90.79 95	73.65 10	92.66 23	91.17 124	86.57 1	87.39 41	94.97 18	71.70 53	97.68 1	92.19 1	95.63 28	95.57 1

test12				86.80 52	92.63 67	70.70 75		91.79 106		82.71 105		71.67 54	96.16 47		94.50 51	93.54 82

UniMVSNet_NR-MVSNet		81.88 114	81.54 114	82.92 176	88.46 171	63.46 231	87.13 172	92.37 80	80.19 12	78.38 161	89.14 166	71.66 55	93.05 180	70.05 194	76.46 288	92.25 132

CS-MVS		86.69 39	86.95 35	85.90 69	90.76 96	67.57 145	92.83 17	93.30 32	79.67 17	84.57 76	92.27 89	71.47 56	95.02 91	84.24 59	93.46 67	95.13 7

DeepC-MVS_fast	79.65 3	86.91 36	86.62 40	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 73	84.22 81	93.36 66	71.44 57	96.76 25	80.82 93	95.33 33	94.16 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsm_n_1920		85.29 65	85.34 62	85.13 85	86.12 239	69.93 86	88.65 124	90.78 135	69.97 212	88.27 26	93.98 52	71.39 58	91.54 233	88.49 23	90.45 102	93.91 58

MVS_111021_HR		85.14 66	84.75 71	86.32 58	91.65 79	72.70 30	85.98 207	90.33 149	76.11 85	82.08 109	91.61 105	71.36 59	94.17 122	81.02 90	92.58 75	92.08 140

balanced_conf03		86.78 37	86.99 33	86.15 61	91.24 83	67.61 143	90.51 62	92.90 56	77.26 51	87.44 40	91.63 103	71.27 60	96.06 49	85.62 42	95.01 37	94.78 22

reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 107	88.96 20	95.54 12	71.20 61	96.54 36	86.28 37	93.49 65	93.06 102

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 107	88.96 20	95.54 12	71.20 61	96.54 36	86.28 37	93.49 65	93.06 102

ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 57	93.59 23	76.27 83	88.14 27	95.09 17	71.06 63	96.67 29	87.67 29	96.37 14	94.09 51

fmvsm_l_conf0.5_n_a		84.13 76	84.16 78	84.06 131	85.38 251	68.40 124	88.34 135	86.85 246	67.48 260	87.48 39	93.40 64	70.89 64	91.61 227	88.38 25	89.22 120	92.16 138

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 104	86.34 51	95.29 15	70.86 65	96.00 54	88.78 19	96.04 16	94.58 31
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 22	87.47 26	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 67	84.91 65	94.44 30	70.78 66	96.61 32	84.53 54	94.89 42	93.66 70

EI-MVSNet-Vis-set		84.19 75	83.81 80	85.31 79	88.18 180	67.85 137	87.66 157	89.73 167	80.05 14	82.95 99	89.59 155	70.74 67	94.82 99	80.66 96	84.72 179	93.28 91

GST-MVS		87.42 27	87.26 28	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 63	84.68 69	93.99 51	70.67 68	96.82 22	84.18 61	95.01 37	93.90 60

SPE-MVS-test		86.29 46	86.48 41	85.71 71	91.02 88	67.21 158	92.36 29	93.78 18	78.97 28	83.51 95	91.20 118	70.65 69	95.15 82	81.96 82	94.89 42	94.77 23

CANet		86.45 42	86.10 49	87.51 37	90.09 107	70.94 70	89.70 85	92.59 73	81.78 4	81.32 119	91.43 111	70.34 70	97.23 14	84.26 57	93.36 68	94.37 40

alignmvs		85.48 60	85.32 64	85.96 68	89.51 126	69.47 95	89.74 83	92.47 75	76.17 84	87.73 37	91.46 110	70.32 71	93.78 139	81.51 84	88.95 123	94.63 30

reproduce_model		87.28 30	87.39 27	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 115	88.80 23	95.61 11	70.29 72	96.44 39	86.20 39	93.08 69	93.16 97

EI-MVSNet-UG-set		83.81 79	83.38 85	85.09 86	87.87 196	67.53 146	87.44 165	89.66 168	79.74 16	82.23 108	89.41 164	70.24 73	94.74 102	79.95 100	83.92 193	92.99 109

MVS_Test		83.15 95	83.06 90	83.41 154	86.86 225	63.21 237	86.11 205	92.00 94	74.31 123	82.87 101	89.44 163	70.03 74	93.21 167	77.39 122	88.50 134	93.81 65

FC-MVSNet-test		81.52 123	82.02 108	80.03 244	88.42 174	55.97 334	87.95 148	93.42 29	77.10 58	77.38 182	90.98 130	69.96 75	91.79 222	68.46 213	84.50 182	92.33 128

FIs		82.07 111	82.42 98	81.04 223	88.80 158	58.34 295	88.26 138	93.49 26	76.93 62	78.47 160	91.04 124	69.92 76	92.34 204	69.87 198	84.97 176	92.44 127

UniMVSNet (Re)		81.60 122	81.11 119	83.09 167	88.38 175	64.41 212	87.60 158	93.02 45	78.42 32	78.56 157	88.16 195	69.78 77	93.26 163	69.58 201	76.49 287	91.60 148

HPM-MVS		87.11 33	86.98 34	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 86	83.81 90	93.95 54	69.77 78	96.01 53	85.15 44	94.66 47	94.32 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Effi-MVS+		83.62 86	83.08 89	85.24 81	88.38 175	67.45 147	88.89 113	89.15 187	75.50 96	82.27 107	88.28 191	69.61 79	94.45 112	77.81 117	87.84 139	93.84 64

PHI-MVS		86.43 43	86.17 47	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 161	85.22 61	91.90 95	69.47 80	96.42 40	83.28 67	95.94 19	94.35 41

UA-Net		85.08 68	84.96 69	85.45 76	92.07 73	68.07 133	89.78 82	90.86 134	82.48 2	84.60 75	93.20 69	69.35 81	95.22 79	71.39 181	90.88 97	93.07 101

ETV-MVS		84.90 72	84.67 72	85.59 73	89.39 133	68.66 120	88.74 120	92.64 71	79.97 15	84.10 84	85.71 259	69.32 82	95.38 74	80.82 93	91.37 91	92.72 113

旧先验1					91.96 74	65.79 182		86.37 253			93.08 74	69.31 83			92.74 73	88.74 261

fmvsm_s_conf0.5_n_a		83.63 85	83.41 84	84.28 115	86.14 238	68.12 131	89.43 93	82.87 306	70.27 205	87.27 43	93.80 57	69.09 84	91.58 229	88.21 26	83.65 201	93.14 99

region2R		87.42 27	87.20 31	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 70	84.45 77	94.52 23	69.09 84	96.70 27	84.37 56	94.83 45	94.03 54

EIA-MVS		83.31 94	82.80 96	84.82 96	89.59 122	65.59 186	88.21 139	92.68 65	74.66 117	78.96 147	86.42 246	69.06 86	95.26 78	75.54 143	90.09 108	93.62 77

EPP-MVSNet		83.40 91	83.02 91	84.57 102	90.13 106	64.47 210	92.32 30	90.73 136	74.45 122	79.35 143	91.10 121	69.05 87	95.12 83	72.78 170	87.22 147	94.13 49

EC-MVSNet		86.01 47	86.38 42	84.91 94	89.31 138	66.27 171	92.32 30	93.63 21	79.37 20	84.17 83	91.88 96	69.04 88	95.43 70	83.93 62	93.77 63	93.01 107

fmvsm_s_conf0.5_n		83.80 80	83.71 81	84.07 129	86.69 231	67.31 152	89.46 92	83.07 301	71.09 186	86.96 47	93.70 58	69.02 89	91.47 238	88.79 18	84.62 181	93.44 85

ACMMPR		87.44 25	87.23 30	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 67	84.66 72	94.52 23	68.81 90	96.65 30	84.53 54	94.90 41	94.00 55

test_fmvsmvis_n_1920		84.02 77	83.87 79	84.49 106	84.12 276	69.37 101	88.15 143	87.96 219	70.01 210	83.95 87	93.23 68	68.80 91	91.51 236	88.61 20	89.96 111	92.57 119

mvs_anonymous		79.42 171	79.11 160	80.34 238	84.45 271	57.97 301	82.59 280	87.62 228	67.40 261	76.17 216	88.56 184	68.47 92	89.59 276	70.65 189	86.05 166	93.47 84

fmvsm_s_conf0.1_n		83.56 87	83.38 85	84.10 123	84.86 262	67.28 153	89.40 97	83.01 302	70.67 194	87.08 44	93.96 53	68.38 93	91.45 239	88.56 22	84.50 182	93.56 80

fmvsm_s_conf0.1_n_a		83.32 93	82.99 92	84.28 115	83.79 284	68.07 133	89.34 99	82.85 307	69.80 216	87.36 42	94.06 45	68.34 94	91.56 231	87.95 27	83.46 206	93.21 95

MVS_0304		87.69 20	87.55 24	88.12 13	89.45 129	71.76 51	91.47 49	89.54 171	82.14 3	86.65 49	94.28 34	68.28 95	97.46 6	90.81 2	95.31 34	95.15 6

MTAPA		87.23 31	87.00 32	87.90 22	94.18 35	74.25 5	86.58 191	92.02 92	79.45 19	85.88 53	94.80 19	68.07 96	96.21 45	86.69 36	95.34 32	93.23 92

mamv4		76.81 232	78.23 180	72.54 342	86.12 239	65.75 184	78.76 333	82.07 315	64.12 302	72.97 274	91.02 127	67.97 97	68.08 406	83.04 70	78.02 269	83.80 352

CP-MVS		87.11 33	86.92 36	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 73	83.68 91	94.46 27	67.93 98	95.95 57	84.20 60	94.39 55	93.23 92

PAPM_NR		83.02 99	82.41 99	84.82 96	92.47 70	66.37 169	87.93 150	91.80 105	73.82 134	77.32 184	90.66 133	67.90 99	94.90 95	70.37 191	89.48 117	93.19 96

PGM-MVS		86.68 40	86.27 44	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 95	83.86 88	94.42 31	67.87 100	96.64 31	82.70 78	94.57 50	93.66 70

PAPR		81.66 121	80.89 124	83.99 139	90.27 103	64.00 218	86.76 187	91.77 108	68.84 242	77.13 194	89.50 156	67.63 101	94.88 97	67.55 219	88.52 133	93.09 100

Fast-Effi-MVS+		80.81 136	79.92 139	83.47 150	88.85 153	64.51 207	85.53 222	89.39 176	70.79 191	78.49 159	85.06 277	67.54 102	93.58 147	67.03 227	86.58 156	92.32 129

XVS		87.18 32	86.91 37	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 21	83.67 92	94.17 39	67.45 103	96.60 33	83.06 68	94.50 51	94.07 52

X-MVStestdata		80.37 152	77.83 188	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 21	83.67 92	12.47 418	67.45 103	96.60 33	83.06 68	94.50 51	94.07 52

SR-MVS		86.73 38	86.67 39	86.91 49	94.11 37	72.11 47	92.37 28	92.56 74	74.50 119	86.84 48	94.65 22	67.31 105	95.77 59	84.80 50	92.85 72	92.84 112

NR-MVSNet		80.23 154	79.38 151	82.78 185	87.80 199	63.34 234	86.31 199	91.09 128	79.01 26	72.17 286	89.07 168	67.20 106	92.81 189	66.08 233	75.65 301	92.20 135

MSLP-MVS++		85.43 62	85.76 56	84.45 107	91.93 75	70.24 79	90.71 59	92.86 58	77.46 47	84.22 81	92.81 81	67.16 107	92.94 184	80.36 97	94.35 57	90.16 201

MG-MVS		83.41 90	83.45 83	83.28 157	92.74 65	62.28 253	88.17 141	89.50 173	75.22 100	81.49 118	92.74 85	66.75 108	95.11 85	72.85 169	91.58 88	92.45 126

test_fmvsmconf0.01_n		84.73 73	84.52 75	85.34 78	80.25 348	69.03 103	89.47 91	89.65 169	73.24 153	86.98 46	94.27 35	66.62 109	93.23 165	90.26 5	89.95 112	93.78 67

EI-MVSNet		80.52 148	79.98 138	82.12 195	84.28 272	63.19 239	86.41 195	88.95 197	74.18 127	78.69 152	87.54 211	66.62 109	92.43 198	72.57 173	80.57 241	90.74 179

IterMVS-LS		80.06 157	79.38 151	82.11 196	85.89 242	63.20 238	86.79 184	89.34 177	74.19 126	75.45 229	86.72 231	66.62 109	92.39 200	72.58 172	76.86 282	90.75 178
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_ehance_all_eth		78.59 193	77.76 193	81.08 222	82.66 314	61.56 262	83.65 262	89.15 187	68.87 241	75.55 225	83.79 303	66.49 112	92.03 213	73.25 165	76.39 290	89.64 228

mPP-MVS		86.67 41	86.32 43	87.72 30	94.41 22	73.55 13	92.74 20	92.22 87	76.87 64	82.81 104	94.25 37	66.44 113	96.24 44	82.88 73	94.28 58	93.38 86

c3_l		78.75 187	77.91 185	81.26 216	82.89 309	61.56 262	84.09 256	89.13 189	69.97 212	75.56 224	84.29 292	66.36 114	92.09 212	73.47 162	75.48 305	90.12 204

GeoE		81.71 118	81.01 122	83.80 144	89.51 126	64.45 211	88.97 110	88.73 206	71.27 182	78.63 155	89.76 149	66.32 115	93.20 170	69.89 197	86.02 167	93.74 68

WR-MVS_H		78.51 194	78.49 170	78.56 272	88.02 189	56.38 328	88.43 129	92.67 66	77.14 56	73.89 263	87.55 210	66.25 116	89.24 283	58.92 295	73.55 331	90.06 211

PCF-MVS	73.52 7	80.38 150	78.84 165	85.01 88	87.71 204	68.99 106	83.65 262	91.46 118	63.00 315	77.77 176	90.28 138	66.10 117	95.09 89	61.40 274	88.22 137	90.94 172
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPNet		83.72 82	82.92 94	86.14 63	84.22 274	69.48 94	91.05 56	85.27 266	81.30 6	76.83 196	91.65 101	66.09 118	95.56 63	76.00 137	93.85 62	93.38 86
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM1				84.35 111	93.01 60	68.79 110		92.44 76	63.96 308	81.09 124	91.57 106	66.06 119	95.45 68	67.19 224	94.82 46	88.81 256

PVSNet_BlendedMVS		80.60 144	80.02 137	82.36 194	88.85 153	65.40 189	86.16 204	92.00 94	69.34 226	78.11 168	86.09 254	66.02 120	94.27 116	71.52 178	82.06 223	87.39 288

PVSNet_Blended		80.98 131	80.34 132	82.90 177	88.85 153	65.40 189	84.43 248	92.00 94	67.62 257	78.11 168	85.05 278	66.02 120	94.27 116	71.52 178	89.50 116	89.01 246

diffmvs		82.10 109	81.88 111	82.76 187	83.00 305	63.78 223	83.68 261	89.76 165	72.94 158	82.02 110	89.85 147	65.96 122	90.79 257	82.38 80	87.30 146	93.71 69
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS_3200maxsize		85.97 50	85.88 53	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 142	85.94 52	94.51 26	65.80 123	95.61 62	83.04 70	92.51 76	93.53 83

miper_enhance_ethall		77.87 212	76.86 212	80.92 227	81.65 328	61.38 264	82.68 279	88.98 194	65.52 285	75.47 226	82.30 330	65.76 124	92.00 215	72.95 168	76.39 290	89.39 234

PVSNet_Blended_VisFu		82.62 103	81.83 112	84.96 90	90.80 94	69.76 90	88.74 120	91.70 109	69.39 224	78.96 147	88.46 186	65.47 125	94.87 98	74.42 152	88.57 131	90.24 199

API-MVS		81.99 113	81.23 117	84.26 119	90.94 90	70.18 85	91.10 55	89.32 178	71.51 178	78.66 154	88.28 191	65.26 126	95.10 88	64.74 244	91.23 93	87.51 286

TranMVSNet+NR-MVSNet		80.84 134	80.31 133	82.42 192	87.85 197	62.33 251	87.74 156	91.33 119	80.55 9	77.99 172	89.86 146	65.23 127	92.62 190	67.05 226	75.24 315	92.30 130

IS-MVSNet		83.15 95	82.81 95	84.18 121	89.94 115	63.30 235	91.59 43	88.46 211	79.04 25	79.49 141	92.16 91	65.10 128	94.28 115	67.71 217	91.86 86	94.95 10

DU-MVS		81.12 130	80.52 129	82.90 177	87.80 199	63.46 231	87.02 176	91.87 102	79.01 26	78.38 161	89.07 168	65.02 129	93.05 180	70.05 194	76.46 288	92.20 135

Baseline_NR-MVSNet		78.15 203	78.33 176	77.61 290	85.79 243	56.21 332	86.78 185	85.76 262	73.60 140	77.93 173	87.57 208	65.02 129	88.99 287	67.14 225	75.33 312	87.63 282

SR-MVS-dyc-post		85.77 55	85.61 58	86.23 59	93.06 58	70.63 76	91.88 38	92.27 83	73.53 143	85.69 56	94.45 28	65.00 131	95.56 63	82.75 74	91.87 84	92.50 123

VNet		82.21 108	82.41 99	81.62 205	90.82 93	60.93 268	84.47 244	89.78 164	76.36 81	84.07 85	91.88 96	64.71 132	90.26 263	70.68 188	88.89 124	93.66 70

Test By Simon												64.33 133

ACMMP		85.89 54	85.39 61	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 67	80.73 128	93.82 56	64.33 133	96.29 42	82.67 79	90.69 99	93.23 92
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
DP-MVS Recon		83.11 98	82.09 106	86.15 61	94.44 19	70.92 71	88.79 116	92.20 88	70.53 199	79.17 145	91.03 126	64.12 135	96.03 50	68.39 214	90.14 107	91.50 153

CLD-MVS		82.31 107	81.65 113	84.29 114	88.47 170	67.73 140	85.81 215	92.35 81	75.78 90	78.33 163	86.58 241	64.01 136	94.35 113	76.05 136	87.48 144	90.79 175
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RE-MVS-def			85.48 60		93.06 58	70.63 76	91.88 38	92.27 83	73.53 143	85.69 56	94.45 28	63.87 137		82.75 74	91.87 84	92.50 123

MVS		78.19 202	76.99 210	81.78 202	85.66 245	66.99 160	84.66 238	90.47 143	55.08 375	72.02 288	85.27 270	63.83 138	94.11 124	66.10 232	89.80 114	84.24 345

WR-MVS		79.49 167	79.22 158	80.27 240	88.79 159	58.35 294	85.06 230	88.61 209	78.56 30	77.65 177	88.34 189	63.81 139	90.66 260	64.98 242	77.22 277	91.80 146

VPA-MVSNet		80.60 144	80.55 128	80.76 230	88.07 187	60.80 271	86.86 181	91.58 112	75.67 94	80.24 132	89.45 162	63.34 140	90.25 264	70.51 190	79.22 258	91.23 161

新几何1				83.42 152	93.13 54	70.71 74		85.48 265	57.43 365	81.80 114	91.98 93	63.28 141	92.27 206	64.60 245	92.99 70	87.27 292

HY-MVS	69.67 12	77.95 209	77.15 206	80.36 237	87.57 212	60.21 281	83.37 269	87.78 226	66.11 276	75.37 233	87.06 226	63.27 142	90.48 262	61.38 275	82.43 219	90.40 193

XXY-MVS		75.41 257	75.56 235	74.96 319	83.59 289	57.82 305	80.59 307	83.87 286	66.54 273	74.93 250	88.31 190	63.24 143	80.09 361	62.16 266	76.85 283	86.97 301

ab-mvs		79.51 166	78.97 163	81.14 220	88.46 171	60.91 269	83.84 258	89.24 183	70.36 201	79.03 146	88.87 174	63.23 144	90.21 265	65.12 240	82.57 218	92.28 131

xiu_mvs_v2_base		81.69 119	81.05 120	83.60 147	89.15 145	68.03 135	84.46 246	90.02 158	70.67 194	81.30 122	86.53 244	63.17 145	94.19 121	75.60 142	88.54 132	88.57 265

pcd_1.5k_mvsjas		5.26 392	7.02 395	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	63.15 146	0.00 425	0.00 424	0.00 423	0.00 421

PS-MVSNAJss		82.07 111	81.31 115	84.34 112	86.51 234	67.27 154	89.27 100	91.51 114	71.75 171	79.37 142	90.22 142	63.15 146	94.27 116	77.69 118	82.36 220	91.49 154

PS-MVSNAJ		81.69 119	81.02 121	83.70 145	89.51 126	68.21 130	84.28 252	90.09 157	70.79 191	81.26 123	85.62 264	63.15 146	94.29 114	75.62 141	88.87 125	88.59 264

WTY-MVS		75.65 252	75.68 232	75.57 310	86.40 235	56.82 319	77.92 346	82.40 311	65.10 289	76.18 214	87.72 203	63.13 149	80.90 358	60.31 282	81.96 224	89.00 248

TransMVSNet (Re)		75.39 259	74.56 250	77.86 284	85.50 249	57.10 316	86.78 185	86.09 259	72.17 167	71.53 293	87.34 214	63.01 150	89.31 281	56.84 317	61.83 378	87.17 294

v8		79.97 160	79.02 162	82.80 182	84.09 277	64.50 209	87.96 147	90.29 152	74.13 129	75.24 241	86.81 228	62.88 151	93.89 136	74.39 153	75.40 310	90.00 213

HPM-MVS_fast		85.35 64	84.95 70	86.57 56	93.69 42	70.58 78	92.15 35	91.62 110	73.89 133	82.67 106	94.09 43	62.60 152	95.54 65	80.93 91	92.93 71	93.57 79

PAPM		77.68 218	76.40 225	81.51 208	87.29 220	61.85 258	83.78 259	89.59 170	64.74 294	71.23 295	88.70 177	62.59 153	93.66 146	52.66 337	87.03 150	89.01 246

1112_ss		77.40 223	76.43 224	80.32 239	89.11 150	60.41 278	83.65 262	87.72 227	62.13 328	73.05 273	86.72 231	62.58 154	89.97 269	62.11 268	80.80 237	90.59 185

LCM-MVSNet-Re		77.05 227	76.94 211	77.36 294	87.20 221	51.60 373	80.06 314	80.46 333	75.20 101	67.69 330	86.72 231	62.48 155	88.98 288	63.44 252	89.25 119	91.51 152

v148		78.72 189	77.80 190	81.47 209	82.73 312	61.96 257	86.30 200	88.08 216	73.26 151	76.18 214	85.47 267	62.46 156	92.36 202	71.92 177	73.82 329	90.09 207

baseline1		76.98 229	76.75 218	77.66 288	88.13 183	55.66 339	85.12 228	81.89 316	73.04 156	76.79 197	88.90 172	62.43 157	87.78 306	63.30 254	71.18 349	89.55 231

MAR-MVS		81.84 115	80.70 125	85.27 80	91.32 82	71.53 56	89.82 79	90.92 130	69.77 218	78.50 158	86.21 250	62.36 158	94.52 109	65.36 238	92.05 82	89.77 225
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
MVS_111021_LR		82.61 104	82.11 104	84.11 122	88.82 156	71.58 55	85.15 227	86.16 257	74.69 115	80.47 130	91.04 124	62.29 159	90.55 261	80.33 98	90.08 109	90.20 200

TAMVS		78.89 186	77.51 200	83.03 171	87.80 199	67.79 139	84.72 237	85.05 270	67.63 256	76.75 199	87.70 204	62.25 160	90.82 256	58.53 300	87.13 148	90.49 189

CP-MVSNet		78.22 199	78.34 175	77.84 285	87.83 198	54.54 351	87.94 149	91.17 124	77.65 38	73.48 268	88.49 185	62.24 161	88.43 298	62.19 265	74.07 324	90.55 186

OMC-MVS		82.69 102	81.97 110	84.85 95	88.75 161	67.42 148	87.98 146	90.87 133	74.92 109	79.72 138	91.65 101	62.19 162	93.96 126	75.26 147	86.42 159	93.16 97

cl____		77.72 215	76.76 216	80.58 233	82.49 318	60.48 276	83.09 274	87.87 222	69.22 230	74.38 260	85.22 273	62.10 163	91.53 234	71.09 183	75.41 309	89.73 227

DIV-MVS_self_test		77.72 215	76.76 216	80.58 233	82.48 319	60.48 276	83.09 274	87.86 223	69.22 230	74.38 260	85.24 271	62.10 163	91.53 234	71.09 183	75.40 310	89.74 226

testdata				79.97 245	90.90 91	64.21 215		84.71 272	59.27 349	85.40 58	92.91 76	62.02 165	89.08 286	68.95 207	91.37 91	86.63 309

eth_miper_zixun_eth		77.92 210	76.69 219	81.61 207	83.00 305	61.98 256	83.15 272	89.20 185	69.52 223	74.86 251	84.35 291	61.76 166	92.56 193	71.50 180	72.89 337	90.28 198

MVSFormer		82.85 101	82.05 107	85.24 81	87.35 213	70.21 80	90.50 64	90.38 145	68.55 246	81.32 119	89.47 158	61.68 167	93.46 156	78.98 105	90.26 105	92.05 141

lupinMVS		81.39 126	80.27 135	84.76 99	87.35 213	70.21 80	85.55 220	86.41 251	62.85 318	81.32 119	88.61 181	61.68 167	92.24 208	78.41 112	90.26 105	91.83 144

cdsmvs_eth3d_5k		19.96 386	26.61 388	0.00 406	0.00 429	0.00 431	0.00 417	89.26 182	0.00 424	0.00 425	88.61 181	61.62 169	0.00 425	0.00 424	0.00 423	0.00 421

h-mvs33		83.15 95	82.19 103	86.02 67	90.56 98	70.85 73	88.15 143	89.16 186	76.02 87	84.67 70	91.39 112	61.54 170	95.50 66	82.71 76	75.48 305	91.72 147

hse-mvs2		81.72 117	80.94 123	84.07 129	88.72 162	67.68 141	85.87 211	87.26 236	76.02 87	84.67 70	88.22 194	61.54 170	93.48 154	82.71 76	73.44 333	91.06 166

CDS-MVSNet		79.07 181	77.70 195	83.17 164	87.60 208	68.23 129	84.40 250	86.20 256	67.49 259	76.36 209	86.54 243	61.54 170	90.79 257	61.86 270	87.33 145	90.49 189
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v10		79.74 162	78.67 166	82.97 175	84.06 278	64.95 199	87.88 153	90.62 138	73.11 154	75.11 245	86.56 242	61.46 173	94.05 125	73.68 158	75.55 303	89.90 219

v1144		80.03 158	79.03 161	83.01 172	83.78 285	64.51 207	87.11 174	90.57 141	71.96 170	78.08 170	86.20 251	61.41 174	93.94 129	74.93 148	77.23 276	90.60 184

cl22		78.07 205	77.01 208	81.23 217	82.37 321	61.83 259	83.55 266	87.98 218	68.96 240	75.06 247	83.87 299	61.40 175	91.88 220	73.53 160	76.39 290	89.98 216

BH-w/o		78.21 200	77.33 204	80.84 228	88.81 157	65.13 196	84.87 234	87.85 224	69.75 219	74.52 257	84.74 284	61.34 176	93.11 177	58.24 304	85.84 170	84.27 344

Test_1112_low_res		76.40 242	75.44 237	79.27 259	89.28 140	58.09 297	81.69 289	87.07 240	59.53 347	72.48 281	86.67 236	61.30 177	89.33 280	60.81 280	80.15 246	90.41 192

Vis-MVSNet (Re-imp)		78.36 197	78.45 171	78.07 283	88.64 165	51.78 372	86.70 188	79.63 343	74.14 128	75.11 245	90.83 131	61.29 178	89.75 273	58.10 305	91.60 87	92.69 116

PEN-MVS		77.73 214	77.69 196	77.84 285	87.07 224	53.91 356	87.91 151	91.18 123	77.56 43	73.14 272	88.82 175	61.23 179	89.17 284	59.95 284	72.37 339	90.43 191

pm-mvs1		77.25 226	76.68 220	78.93 265	84.22 274	58.62 292	86.41 195	88.36 212	71.37 180	73.31 269	88.01 201	61.22 180	89.15 285	64.24 248	73.01 336	89.03 245

BH-untuned		79.47 168	78.60 168	82.05 197	89.19 144	65.91 178	86.07 206	88.52 210	72.18 166	75.42 230	87.69 205	61.15 181	93.54 151	60.38 281	86.83 153	86.70 307

v2v482		80.23 154	79.29 155	83.05 170	83.62 288	64.14 216	87.04 175	89.97 160	73.61 139	78.18 167	87.22 219	61.10 182	93.82 137	76.11 134	76.78 285	91.18 162

jason		81.39 126	80.29 134	84.70 100	86.63 233	69.90 88	85.95 208	86.77 247	63.24 311	81.07 125	89.47 158	61.08 183	92.15 210	78.33 113	90.07 110	92.05 141
jason: jason.
Vis-MVSNet		83.46 89	82.80 96	85.43 77	90.25 104	68.74 114	90.30 72	90.13 156	76.33 82	80.87 127	92.89 77	61.00 184	94.20 120	72.45 175	90.97 95	93.35 88
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAPA-MVS	73.13 9	79.15 178	77.94 184	82.79 184	89.59 122	62.99 245	88.16 142	91.51 114	65.77 281	77.14 193	91.09 122	60.91 185	93.21 167	50.26 352	87.05 149	92.17 137
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PS-CasMVS		78.01 208	78.09 181	77.77 287	87.71 204	54.39 353	88.02 145	91.22 121	77.50 46	73.26 270	88.64 180	60.73 186	88.41 299	61.88 269	73.88 328	90.53 187

OPM-MVS		83.50 88	82.95 93	85.14 83	88.79 159	70.95 69	89.13 107	91.52 113	77.55 44	80.96 126	91.75 98	60.71 187	94.50 110	79.67 103	86.51 158	89.97 217
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XVG-OURS-SEG-HR		80.81 136	79.76 143	83.96 141	85.60 247	68.78 111	83.54 267	90.50 142	70.66 197	76.71 200	91.66 100	60.69 188	91.26 244	76.94 127	81.58 228	91.83 144

v144192		79.47 168	78.37 174	82.78 185	83.35 293	63.96 219	86.96 177	90.36 148	69.99 211	77.50 179	85.67 262	60.66 189	93.77 141	74.27 154	76.58 286	90.62 182

V42		79.38 174	78.24 178	82.83 179	81.10 340	65.50 188	85.55 220	89.82 163	71.57 177	78.21 165	86.12 253	60.66 189	93.18 173	75.64 140	75.46 307	89.81 224

SDMVSNet		80.38 150	80.18 136	80.99 224	89.03 151	64.94 200	80.45 310	89.40 175	75.19 102	76.61 204	89.98 144	60.61 191	87.69 307	76.83 129	83.55 203	90.33 195

CPTT-MVS		83.73 81	83.33 87	84.92 93	93.28 49	70.86 72	92.09 36	90.38 145	68.75 243	79.57 140	92.83 79	60.60 192	93.04 182	80.92 92	91.56 89	90.86 174

DTE-MVSNet		76.99 228	76.80 214	77.54 293	86.24 236	53.06 365	87.52 160	90.66 137	77.08 59	72.50 280	88.67 179	60.48 193	89.52 277	57.33 312	70.74 351	90.05 212

HQP_MVS		83.64 84	83.14 88	85.14 83	90.08 108	68.71 116	91.25 52	92.44 76	79.12 23	78.92 149	91.00 128	60.42 194	95.38 74	78.71 108	86.32 160	91.33 158

plane_prior6					89.84 117	68.70 118						60.42 194

3Dnovator+	77.84 4	85.48 60	84.47 76	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 201	93.37 65	60.40 196	96.75 26	77.20 123	93.73 64	95.29 5

HQP2-MVS												60.17 197

HQP-MVS		82.61 104	82.02 108	84.37 109	89.33 135	66.98 161	89.17 102	92.19 89	76.41 76	77.23 187	90.23 141	60.17 197	95.11 85	77.47 120	85.99 168	91.03 168

VPNet		78.69 190	78.66 167	78.76 267	88.31 177	55.72 338	84.45 247	86.63 249	76.79 66	78.26 164	90.55 135	59.30 199	89.70 275	66.63 228	77.05 279	90.88 173

v1192		79.59 165	78.43 173	83.07 169	83.55 290	64.52 206	86.93 179	90.58 139	70.83 190	77.78 175	85.90 255	59.15 200	93.94 129	73.96 157	77.19 278	90.76 177

test222					91.50 80	68.26 128	84.16 254	83.20 299	54.63 376	79.74 137	91.63 103	58.97 201			91.42 90	86.77 305

CHOSEN 1792x2688		77.63 219	75.69 231	83.44 151	89.98 114	68.58 122	78.70 334	87.50 231	56.38 370	75.80 221	86.84 227	58.67 202	91.40 241	61.58 273	85.75 172	90.34 194

3Dnovator	76.31 5	83.38 92	82.31 102	86.59 55	87.94 193	72.94 28	90.64 60	92.14 91	77.21 54	75.47 226	92.83 79	58.56 203	94.72 103	73.24 166	92.71 74	92.13 139

v1921920		79.22 176	78.03 182	82.80 182	83.30 295	63.94 220	86.80 183	90.33 149	69.91 214	77.48 180	85.53 265	58.44 204	93.75 143	73.60 159	76.85 283	90.71 180

FA-MVS(test-final)		80.96 132	79.91 140	84.10 123	88.30 178	65.01 198	84.55 243	90.01 159	73.25 152	79.61 139	87.57 208	58.35 205	94.72 103	71.29 182	86.25 162	92.56 120

114514_t		80.68 142	79.51 148	84.20 120	94.09 38	67.27 154	89.64 87	91.11 127	58.75 355	74.08 262	90.72 132	58.10 206	95.04 90	69.70 199	89.42 118	90.30 197

v7n		78.97 184	77.58 199	83.14 165	83.45 292	65.51 187	88.32 136	91.21 122	73.69 137	72.41 282	86.32 249	57.93 207	93.81 138	69.18 204	75.65 301	90.11 205

CL-MVSNet_self_test		72.37 291	71.46 286	75.09 318	79.49 361	53.53 358	80.76 303	85.01 271	69.12 234	70.51 299	82.05 334	57.92 208	84.13 338	52.27 339	66.00 370	87.60 283

baseline2		75.70 251	73.83 262	81.30 215	83.26 296	61.79 260	82.57 281	80.65 329	66.81 263	66.88 339	83.42 311	57.86 209	92.19 209	63.47 251	79.57 251	89.91 218

QAPM		80.88 133	79.50 149	85.03 87	88.01 191	68.97 107	91.59 43	92.00 94	66.63 272	75.15 244	92.16 91	57.70 210	95.45 68	63.52 250	88.76 128	90.66 181

HyFIR lowres test		77.53 220	75.40 239	83.94 142	89.59 122	66.62 165	80.36 311	88.64 208	56.29 371	76.45 206	85.17 274	57.64 211	93.28 162	61.34 276	83.10 211	91.91 143

CNLPA		78.08 204	76.79 215	81.97 200	90.40 102	71.07 65	87.59 159	84.55 275	66.03 279	72.38 283	89.64 152	57.56 212	86.04 320	59.61 288	83.35 207	88.79 257

test_yl		81.17 128	80.47 130	83.24 160	89.13 146	63.62 224	86.21 202	89.95 161	72.43 164	81.78 115	89.61 153	57.50 213	93.58 147	70.75 186	86.90 151	92.52 121

DCV-MVSNet		81.17 128	80.47 130	83.24 160	89.13 146	63.62 224	86.21 202	89.95 161	72.43 164	81.78 115	89.61 153	57.50 213	93.58 147	70.75 186	86.90 151	92.52 121

sss		73.60 275	73.64 264	73.51 333	82.80 310	55.01 347	76.12 353	81.69 319	62.47 324	74.68 254	85.85 258	57.32 215	78.11 369	60.86 279	80.93 234	87.39 288

Effi-MVS+-dtu		80.03 158	78.57 169	84.42 108	85.13 258	68.74 114	88.77 117	88.10 215	74.99 106	74.97 249	83.49 310	57.27 216	93.36 160	73.53 160	80.88 235	91.18 162

AdaColmap		80.58 147	79.42 150	84.06 131	93.09 57	68.91 108	89.36 98	88.97 196	69.27 227	75.70 222	89.69 150	57.20 217	95.77 59	63.06 255	88.41 135	87.50 287

v1240		78.99 183	77.78 191	82.64 188	83.21 297	63.54 228	86.62 190	90.30 151	69.74 221	77.33 183	85.68 261	57.04 218	93.76 142	73.13 167	76.92 280	90.62 182

miper_lstm_enhance		74.11 269	73.11 269	77.13 298	80.11 350	59.62 286	72.23 374	86.92 245	66.76 265	70.40 301	82.92 320	56.93 219	82.92 347	69.06 206	72.63 338	88.87 253

BH-RMVSNet		79.61 163	78.44 172	83.14 165	89.38 134	65.93 177	84.95 233	87.15 239	73.56 141	78.19 166	89.79 148	56.67 220	93.36 160	59.53 289	86.74 154	90.13 203

RRT-MVS		82.60 106	82.10 105	84.10 123	87.98 192	62.94 246	87.45 164	91.27 120	77.42 48	79.85 136	90.28 138	56.62 221	94.70 105	79.87 102	88.15 138	94.67 26

test_djsdf		80.30 153	79.32 154	83.27 158	83.98 280	65.37 192	90.50 64	90.38 145	68.55 246	76.19 213	88.70 177	56.44 222	93.46 156	78.98 105	80.14 247	90.97 171

EPNet_dtu		75.46 255	74.86 246	77.23 297	82.57 316	54.60 350	86.89 180	83.09 300	71.64 172	66.25 350	85.86 257	55.99 223	88.04 303	54.92 326	86.55 157	89.05 244
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CostFormer		75.24 260	73.90 260	79.27 259	82.65 315	58.27 296	80.80 300	82.73 309	61.57 331	75.33 238	83.13 316	55.52 224	91.07 253	64.98 242	78.34 267	88.45 267

tpmrst		72.39 289	72.13 280	73.18 337	80.54 345	49.91 384	79.91 318	79.08 348	63.11 313	71.69 291	79.95 353	55.32 225	82.77 348	65.66 237	73.89 327	86.87 302

1314		76.53 236	75.30 243	80.21 241	83.93 281	62.32 252	84.66 238	88.81 199	60.23 340	70.16 306	84.07 298	55.30 226	90.73 259	67.37 221	83.21 209	87.59 285

tfpnnormal		74.39 264	73.16 268	78.08 282	86.10 241	58.05 298	84.65 240	87.53 230	70.32 203	71.22 296	85.63 263	54.97 227	89.86 270	43.03 384	75.02 317	86.32 311

sd_testset		77.70 217	77.40 201	78.60 270	89.03 151	60.02 282	79.00 329	85.83 261	75.19 102	76.61 204	89.98 144	54.81 228	85.46 328	62.63 261	83.55 203	90.33 195

GBi-Net		78.40 195	77.40 201	81.40 212	87.60 208	63.01 241	88.39 131	89.28 179	71.63 173	75.34 234	87.28 215	54.80 229	91.11 247	62.72 257	79.57 251	90.09 207

test1		78.40 195	77.40 201	81.40 212	87.60 208	63.01 241	88.39 131	89.28 179	71.63 173	75.34 234	87.28 215	54.80 229	91.11 247	62.72 257	79.57 251	90.09 207

FMVSNet2		78.20 201	77.21 205	81.20 218	87.60 208	62.89 247	87.47 162	89.02 192	71.63 173	75.29 240	87.28 215	54.80 229	91.10 250	62.38 262	79.38 255	89.61 229

Fast-Effi-MVS+-dtu		78.02 207	76.49 222	82.62 189	83.16 301	66.96 163	86.94 178	87.45 233	72.45 161	71.49 294	84.17 296	54.79 232	91.58 229	67.61 218	80.31 244	89.30 237

MVSTER		79.01 182	77.88 187	82.38 193	83.07 302	64.80 203	84.08 257	88.95 197	69.01 239	78.69 152	87.17 222	54.70 233	92.43 198	74.69 149	80.57 241	89.89 220

OpenMVS	72.83 10	79.77 161	78.33 176	84.09 127	85.17 254	69.91 87	90.57 61	90.97 129	66.70 266	72.17 286	91.91 94	54.70 233	93.96 126	61.81 271	90.95 96	88.41 269

XVG-OURS		80.41 149	79.23 157	83.97 140	85.64 246	69.02 105	83.03 278	90.39 144	71.09 186	77.63 178	91.49 109	54.62 235	91.35 242	75.71 139	83.47 205	91.54 151

LPG-MVS_test		82.08 110	81.27 116	84.50 104	89.23 142	68.76 112	90.22 73	91.94 98	75.37 98	76.64 202	91.51 107	54.29 236	94.91 93	78.44 110	83.78 194	89.83 222

LGP-MVS_train				84.50 104	89.23 142	68.76 112		91.94 98	75.37 98	76.64 202	91.51 107	54.29 236	94.91 93	78.44 110	83.78 194	89.83 222

TR-MVS		77.44 221	76.18 227	81.20 218	88.24 179	63.24 236	84.61 241	86.40 252	67.55 258	77.81 174	86.48 245	54.10 238	93.15 174	57.75 308	82.72 216	87.20 293

FMVSNet3		77.88 211	76.85 213	80.97 226	86.84 227	62.36 250	86.52 193	88.77 201	71.13 184	75.34 234	86.66 237	54.07 239	91.10 250	62.72 257	79.57 251	89.45 233

DP-MVS		76.78 233	74.57 249	83.42 152	93.29 48	69.46 97	88.55 127	83.70 287	63.98 307	70.20 303	88.89 173	54.01 240	94.80 100	46.66 370	81.88 226	86.01 319

ACMP	74.13 6	81.51 125	80.57 127	84.36 110	89.42 130	68.69 119	89.97 77	91.50 117	74.46 121	75.04 248	90.41 137	53.82 241	94.54 107	77.56 119	82.91 212	89.86 221
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	70.83 11	78.05 206	76.37 226	83.08 168	91.88 77	67.80 138	88.19 140	89.46 174	64.33 300	69.87 312	88.38 188	53.66 242	93.58 147	58.86 296	82.73 215	87.86 278
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
dmvs_testset		62.63 353	64.11 344	58.19 383	78.55 366	24.76 421	75.28 360	65.94 399	67.91 255	60.34 377	76.01 380	53.56 243	73.94 396	31.79 402	67.65 363	75.88 390

CANet_DTU		80.61 143	79.87 141	82.83 179	85.60 247	63.17 240	87.36 166	88.65 207	76.37 80	75.88 219	88.44 187	53.51 244	93.07 179	73.30 164	89.74 115	92.25 132

WB-MVSnew		71.96 296	71.65 284	72.89 338	84.67 268	51.88 370	82.29 283	77.57 355	62.31 325	73.67 266	83.00 318	53.49 245	81.10 357	45.75 377	82.13 222	85.70 325

ACMM	73.20 8	80.78 141	79.84 142	83.58 148	89.31 138	68.37 125	89.99 76	91.60 111	70.28 204	77.25 185	89.66 151	53.37 246	93.53 152	74.24 155	82.85 213	88.85 254
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		76.12 245	74.46 253	81.13 221	85.37 252	69.79 89	84.42 249	87.95 220	65.03 291	67.46 333	85.33 269	53.28 247	91.73 226	58.01 306	83.27 208	81.85 371
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
AUN-MVS		79.21 177	77.60 198	84.05 134	88.71 163	67.61 143	85.84 213	87.26 236	69.08 235	77.23 187	88.14 199	53.20 248	93.47 155	75.50 144	73.45 332	91.06 166

anonymousdsp		78.60 192	77.15 206	82.98 174	80.51 346	67.08 159	87.24 171	89.53 172	65.66 283	75.16 243	87.19 221	52.52 249	92.25 207	77.17 124	79.34 256	89.61 229

CR-MVSNet		73.37 278	71.27 290	79.67 253	81.32 338	65.19 194	75.92 355	80.30 336	59.92 343	72.73 277	81.19 338	52.50 250	86.69 312	59.84 285	77.71 272	87.11 298

Patchmtry		70.74 305	69.16 308	75.49 313	80.72 342	54.07 355	74.94 366	80.30 336	58.34 356	70.01 307	81.19 338	52.50 250	86.54 314	53.37 334	71.09 350	85.87 324

pmmvs4		74.03 272	71.91 281	80.39 236	81.96 324	68.32 126	81.45 293	82.14 313	59.32 348	69.87 312	85.13 275	52.40 252	88.13 302	60.21 283	74.74 320	84.73 341

RPMNet		73.51 276	70.49 298	82.58 190	81.32 338	65.19 194	75.92 355	92.27 83	57.60 363	72.73 277	76.45 378	52.30 253	95.43 70	48.14 365	77.71 272	87.11 298

LFMVS		81.82 116	81.23 117	83.57 149	91.89 76	63.43 233	89.84 78	81.85 318	77.04 60	83.21 96	93.10 70	52.26 254	93.43 158	71.98 176	89.95 112	93.85 62

VDD-MVS		83.01 100	82.36 101	84.96 90	91.02 88	66.40 168	88.91 112	88.11 214	77.57 41	84.39 79	93.29 67	52.19 255	93.91 133	77.05 126	88.70 130	94.57 33

tfpn200view9		76.42 241	75.37 241	79.55 257	89.13 146	57.65 308	85.17 225	83.60 288	73.41 147	76.45 206	86.39 247	52.12 256	91.95 216	48.33 361	83.75 197	89.07 239

thres400		76.50 237	75.37 241	79.86 247	89.13 146	57.65 308	85.17 225	83.60 288	73.41 147	76.45 206	86.39 247	52.12 256	91.95 216	48.33 361	83.75 197	90.00 213

Syy-MVS		68.05 330	67.85 320	68.67 366	84.68 265	40.97 409	78.62 335	73.08 380	66.65 270	66.74 342	79.46 357	52.11 258	82.30 350	32.89 401	76.38 293	82.75 364

thres200		75.55 253	74.47 252	78.82 266	87.78 202	57.85 304	83.07 276	83.51 291	72.44 163	75.84 220	84.42 287	52.08 259	91.75 224	47.41 368	83.64 202	86.86 303

PMMVS		69.34 319	68.67 310	71.35 351	75.67 377	62.03 255	75.17 361	73.46 378	50.00 388	68.68 322	79.05 360	52.07 260	78.13 368	61.16 277	82.77 214	73.90 392

tpm cat1		70.57 307	68.31 313	77.35 295	82.41 320	57.95 302	78.08 343	80.22 338	52.04 382	68.54 325	77.66 373	52.00 261	87.84 305	51.77 340	72.07 344	86.25 312

IterMVS-SCA-FT		75.43 256	73.87 261	80.11 243	82.69 313	64.85 202	81.57 291	83.47 292	69.16 233	70.49 300	84.15 297	51.95 262	88.15 301	69.23 203	72.14 343	87.34 290

SCA		74.22 267	72.33 278	79.91 246	84.05 279	62.17 254	79.96 317	79.29 346	66.30 275	72.38 283	80.13 351	51.95 262	88.60 296	59.25 291	77.67 274	88.96 250

thres100view900		76.50 237	75.55 236	79.33 258	89.52 125	56.99 317	85.83 214	83.23 296	73.94 131	76.32 210	87.12 223	51.89 264	91.95 216	48.33 361	83.75 197	89.07 239

thres600view7		76.50 237	75.44 237	79.68 252	89.40 132	57.16 314	85.53 222	83.23 296	73.79 135	76.26 211	87.09 224	51.89 264	91.89 219	48.05 366	83.72 200	90.00 213

tpm2		73.26 281	71.46 286	78.63 268	83.34 294	56.71 322	80.65 306	80.40 335	56.63 369	73.55 267	82.02 335	51.80 266	91.24 245	56.35 321	78.42 265	87.95 275

MonoMVSNet		76.49 240	75.80 229	78.58 271	81.55 331	58.45 293	86.36 198	86.22 255	74.87 112	74.73 253	83.73 305	51.79 267	88.73 293	70.78 185	72.15 342	88.55 266

LS3D		76.95 230	74.82 247	83.37 155	90.45 100	67.36 151	89.15 106	86.94 243	61.87 330	69.52 315	90.61 134	51.71 268	94.53 108	46.38 373	86.71 155	88.21 272

IterMVS		74.29 265	72.94 271	78.35 278	81.53 332	63.49 230	81.58 290	82.49 310	68.06 254	69.99 309	83.69 307	51.66 269	85.54 326	65.85 235	71.64 346	86.01 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 291	71.71 283	74.35 326	82.19 322	52.00 367	79.22 325	77.29 360	64.56 296	72.95 275	83.68 308	51.35 270	83.26 346	58.33 303	75.80 299	87.81 279

sam_mvs1												51.32 271				88.96 250

mvsmamba		80.60 144	79.38 151	84.27 117	89.74 120	67.24 156	87.47 162	86.95 242	70.02 209	75.38 232	88.93 171	51.24 272	92.56 193	75.47 145	89.22 120	93.00 108

PatchmatchNet		73.12 283	71.33 289	78.49 276	83.18 299	60.85 270	79.63 319	78.57 350	64.13 301	71.73 290	79.81 356	51.20 273	85.97 321	57.40 311	76.36 295	88.66 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
patchmatchnet-post											74.00 387	51.12 274	88.60 296

xiu_mvs_v1_base_debu		80.80 138	79.72 144	84.03 136	87.35 213	70.19 82	85.56 217	88.77 201	69.06 236	81.83 111	88.16 195	50.91 275	92.85 186	78.29 114	87.56 141	89.06 241

xiu_mvs_v1_base		80.80 138	79.72 144	84.03 136	87.35 213	70.19 82	85.56 217	88.77 201	69.06 236	81.83 111	88.16 195	50.91 275	92.85 186	78.29 114	87.56 141	89.06 241

xiu_mvs_v1_base_debi		80.80 138	79.72 144	84.03 136	87.35 213	70.19 82	85.56 217	88.77 201	69.06 236	81.83 111	88.16 195	50.91 275	92.85 186	78.29 114	87.56 141	89.06 241

Patchmatch-test		64.82 348	63.24 349	69.57 359	79.42 362	49.82 385	63.49 405	69.05 391	51.98 384	59.95 380	80.13 351	50.91 275	70.98 399	40.66 390	73.57 330	87.90 277

Patchmatch-RL test		70.24 311	67.78 324	77.61 290	77.43 370	59.57 288	71.16 378	70.33 385	62.94 317	68.65 323	72.77 390	50.62 279	85.49 327	69.58 201	66.58 367	87.77 280

Anonymous20231211		78.97 184	77.69 196	82.81 181	90.54 99	64.29 214	90.11 75	91.51 114	65.01 292	76.16 217	88.13 200	50.56 280	93.03 183	69.68 200	77.56 275	91.11 164

VDDNet		81.52 123	80.67 126	84.05 134	90.44 101	64.13 217	89.73 84	85.91 260	71.11 185	83.18 97	93.48 61	50.54 281	93.49 153	73.40 163	88.25 136	94.54 34

pmmvs6		74.69 263	73.39 265	78.61 269	81.38 335	57.48 311	86.64 189	87.95 220	64.99 293	70.18 304	86.61 238	50.43 282	89.52 277	62.12 267	70.18 354	88.83 255

test_post											5.46 419	50.36 283	84.24 337

ET-MVSNet_ETH3D		78.63 191	76.63 221	84.64 101	86.73 230	69.47 95	85.01 231	84.61 274	69.54 222	66.51 348	86.59 239	50.16 284	91.75 224	76.26 133	84.24 190	92.69 116

sam_mvs												50.01 285

Anonymous20240529		80.19 156	78.89 164	84.10 123	90.60 97	64.75 204	88.95 111	90.90 131	65.97 280	80.59 129	91.17 120	49.97 286	93.73 145	69.16 205	82.70 217	93.81 65

thisisatest0530		79.40 172	77.76 193	84.31 113	87.69 206	65.10 197	87.36 166	84.26 281	70.04 208	77.42 181	88.26 193	49.94 287	94.79 101	70.20 192	84.70 180	93.03 105

PatchT		68.46 328	67.85 320	70.29 357	80.70 343	43.93 401	72.47 373	74.88 372	60.15 341	70.55 298	76.57 377	49.94 287	81.59 353	50.58 346	74.83 319	85.34 330

tttt0517		79.40 172	77.91 185	83.90 143	88.10 185	63.84 221	88.37 134	84.05 283	71.45 179	76.78 198	89.12 167	49.93 289	94.89 96	70.18 193	83.18 210	92.96 110

tpmvs		71.09 301	69.29 306	76.49 302	82.04 323	56.04 333	78.92 331	81.37 323	64.05 305	67.18 337	78.28 368	49.74 290	89.77 272	49.67 355	72.37 339	83.67 353

thisisatest0515		77.33 224	75.38 240	83.18 163	85.27 253	63.80 222	82.11 285	83.27 295	65.06 290	75.91 218	83.84 301	49.54 291	94.27 116	67.24 223	86.19 163	91.48 155

UniMVSNet_ETH3D		79.10 180	78.24 178	81.70 204	86.85 226	60.24 280	87.28 170	88.79 200	74.25 125	76.84 195	90.53 136	49.48 292	91.56 231	67.98 215	82.15 221	93.29 90

dmvs_re		71.14 300	70.58 296	72.80 339	81.96 324	59.68 285	75.60 359	79.34 345	68.55 246	69.27 319	80.72 346	49.42 293	76.54 377	52.56 338	77.79 271	82.19 369

CVMVSNet		72.99 286	72.58 275	74.25 327	84.28 272	50.85 380	86.41 195	83.45 293	44.56 395	73.23 271	87.54 211	49.38 294	85.70 323	65.90 234	78.44 264	86.19 314

MDTV_nov1_ep13_2view						37.79 411	75.16 362		55.10 374	66.53 345		49.34 295		53.98 330		87.94 276

UGNet		80.83 135	79.59 147	84.54 103	88.04 188	68.09 132	89.42 95	88.16 213	76.95 61	76.22 212	89.46 160	49.30 296	93.94 129	68.48 212	90.31 103	91.60 148
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
pmmvs5		71.55 297	70.20 303	75.61 309	77.83 368	56.39 327	81.74 288	80.89 325	57.76 361	67.46 333	84.49 285	49.26 297	85.32 330	57.08 314	75.29 313	85.11 336

mvsany_test1		62.30 354	61.26 358	65.41 375	69.52 399	54.86 348	66.86 395	49.78 415	46.65 392	68.50 326	83.21 314	49.15 298	66.28 407	56.93 316	60.77 381	75.11 391

LTVRE_ROB	69.57 13	76.25 244	74.54 251	81.41 211	88.60 166	64.38 213	79.24 324	89.12 190	70.76 193	69.79 314	87.86 202	49.09 299	93.20 170	56.21 322	80.16 245	86.65 308
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
FMVSNet1		77.44 221	76.12 228	81.40 212	86.81 228	63.01 241	88.39 131	89.28 179	70.49 200	74.39 259	87.28 215	49.06 300	91.11 247	60.91 278	78.52 262	90.09 207

test1111		79.43 170	79.18 159	80.15 242	89.99 113	53.31 362	87.33 168	77.05 362	75.04 105	80.23 133	92.77 84	48.97 301	92.33 205	68.87 208	92.40 79	94.81 20

ECVR-MVS		79.61 163	79.26 156	80.67 232	90.08 108	54.69 349	87.89 152	77.44 358	74.88 110	80.27 131	92.79 82	48.96 302	92.45 197	68.55 211	92.50 77	94.86 17

MDTV_nov1_ep13			69.97 304		83.18 299	53.48 359	77.10 351	80.18 339	60.45 337	69.33 318	80.44 347	48.89 303	86.90 311	51.60 342	78.51 263

test_post1							78.90 332				5.43 420	48.81 304	85.44 329	59.25 291

test-LLR		72.94 287	72.43 276	74.48 324	81.35 336	58.04 299	78.38 338	77.46 356	66.66 267	69.95 310	79.00 362	48.06 305	79.24 363	66.13 230	84.83 177	86.15 315

test0.0.03 1		68.00 331	67.69 325	68.90 363	77.55 369	47.43 389	75.70 358	72.95 382	66.66 267	66.56 344	82.29 331	48.06 305	75.87 385	44.97 381	74.51 322	83.41 355

our_test_3		69.14 320	67.00 333	75.57 310	79.80 356	58.80 290	77.96 344	77.81 353	59.55 346	62.90 370	78.25 369	47.43 307	83.97 339	51.71 341	67.58 364	83.93 350

MS-PatchMatch		73.83 273	72.67 273	77.30 296	83.87 283	66.02 174	81.82 286	84.66 273	61.37 334	68.61 324	82.82 323	47.29 308	88.21 300	59.27 290	84.32 189	77.68 386

cascas		76.72 234	74.64 248	82.99 173	85.78 244	65.88 179	82.33 282	89.21 184	60.85 336	72.74 276	81.02 341	47.28 309	93.75 143	67.48 220	85.02 175	89.34 236

WB-MVS		54.94 363	54.72 364	55.60 389	73.50 388	20.90 423	74.27 369	61.19 406	59.16 350	50.61 398	74.15 386	47.19 310	75.78 386	17.31 414	35.07 408	70.12 396

test20.03		67.45 333	66.95 334	68.94 362	75.48 379	44.84 399	77.50 347	77.67 354	66.66 267	63.01 368	83.80 302	47.02 311	78.40 367	42.53 387	68.86 361	83.58 354

test_0402		72.79 288	70.44 299	79.84 248	88.13 183	65.99 176	85.93 209	84.29 279	65.57 284	67.40 335	85.49 266	46.92 312	92.61 191	35.88 398	74.38 323	80.94 376

F-COLMAP		76.38 243	74.33 255	82.50 191	89.28 140	66.95 164	88.41 130	89.03 191	64.05 305	66.83 340	88.61 181	46.78 313	92.89 185	57.48 309	78.55 261	87.67 281

ppachtmachnet_test		70.04 313	67.34 331	78.14 281	79.80 356	61.13 265	79.19 326	80.59 330	59.16 350	65.27 355	79.29 359	46.75 314	87.29 309	49.33 356	66.72 365	86.00 321

WBMVS		73.43 277	72.81 272	75.28 316	87.91 194	50.99 379	78.59 337	81.31 324	65.51 287	74.47 258	84.83 281	46.39 315	86.68 313	58.41 301	77.86 270	88.17 273

tt0805		78.73 188	77.83 188	81.43 210	85.17 254	60.30 279	89.41 96	90.90 131	71.21 183	77.17 192	88.73 176	46.38 316	93.21 167	72.57 173	78.96 259	90.79 175

D2MVS		74.82 262	73.21 267	79.64 254	79.81 355	62.56 249	80.34 312	87.35 234	64.37 299	68.86 321	82.66 325	46.37 317	90.10 266	67.91 216	81.24 231	86.25 312

Anonymous20231206		68.60 324	67.80 323	71.02 354	80.23 349	50.75 381	78.30 342	80.47 332	56.79 368	66.11 351	82.63 326	46.35 318	78.95 365	43.62 383	75.70 300	83.36 356

SSC-MVS		53.88 366	53.59 366	54.75 391	72.87 394	19.59 424	73.84 371	60.53 408	57.58 364	49.18 402	73.45 389	46.34 319	75.47 389	16.20 417	32.28 410	69.20 397

CHOSEN 280x420		66.51 340	64.71 341	71.90 345	81.45 333	63.52 229	57.98 408	68.95 392	53.57 378	62.59 371	76.70 376	46.22 320	75.29 391	55.25 324	79.68 250	76.88 388

testing91		76.54 235	75.66 234	79.18 262	88.43 173	55.89 335	81.08 297	83.00 303	73.76 136	75.34 234	84.29 292	46.20 321	90.07 267	64.33 246	84.50 182	91.58 150

GA-MVS		76.87 231	75.17 244	81.97 200	82.75 311	62.58 248	81.44 294	86.35 254	72.16 168	74.74 252	82.89 321	46.20 321	92.02 214	68.85 209	81.09 233	91.30 160

MDA-MVSNet_test_wron		65.03 346	62.92 350	71.37 349	75.93 374	56.73 320	69.09 390	74.73 374	57.28 366	54.03 395	77.89 370	45.88 323	74.39 394	49.89 354	61.55 379	82.99 362

YYNet1		65.03 346	62.91 351	71.38 348	75.85 376	56.60 324	69.12 389	74.66 376	57.28 366	54.12 394	77.87 371	45.85 324	74.48 393	49.95 353	61.52 380	83.05 360

EPMVS		69.02 321	68.16 315	71.59 347	79.61 359	49.80 386	77.40 348	66.93 396	62.82 320	70.01 307	79.05 360	45.79 325	77.86 371	56.58 319	75.26 314	87.13 297

IB-MVS	68.01 15	75.85 250	73.36 266	83.31 156	84.76 263	66.03 173	83.38 268	85.06 269	70.21 207	69.40 316	81.05 340	45.76 326	94.66 106	65.10 241	75.49 304	89.25 238
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
jajsoiax		79.29 175	77.96 183	83.27 158	84.68 265	66.57 167	89.25 101	90.16 155	69.20 232	75.46 228	89.49 157	45.75 327	93.13 176	76.84 128	80.80 237	90.11 205

UBG		73.08 284	72.27 279	75.51 312	88.02 189	51.29 377	78.35 341	77.38 359	65.52 285	73.87 264	82.36 328	45.55 328	86.48 316	55.02 325	84.39 188	88.75 259

PatchMatch-RL		72.38 290	70.90 294	76.80 301	88.60 166	67.38 150	79.53 320	76.17 368	62.75 321	69.36 317	82.00 336	45.51 329	84.89 334	53.62 332	80.58 240	78.12 385

FE-MVS		77.78 213	75.68 232	84.08 128	88.09 186	66.00 175	83.13 273	87.79 225	68.42 250	78.01 171	85.23 272	45.50 330	95.12 83	59.11 293	85.83 171	91.11 164

RPSCF		73.23 282	71.46 286	78.54 273	82.50 317	59.85 283	82.18 284	82.84 308	58.96 352	71.15 297	89.41 164	45.48 331	84.77 335	58.82 297	71.83 345	91.02 170

test_vis1_n_1920		75.52 254	75.78 230	74.75 323	79.84 354	57.44 312	83.26 270	85.52 264	62.83 319	79.34 144	86.17 252	45.10 332	79.71 362	78.75 107	81.21 232	87.10 300

MSDG		73.36 280	70.99 293	80.49 235	84.51 270	65.80 181	80.71 305	86.13 258	65.70 282	65.46 353	83.74 304	44.60 333	90.91 255	51.13 345	76.89 281	84.74 340

PVSNet_0	57.27 20	61.67 356	59.27 359	68.85 364	79.61 359	57.44 312	68.01 391	73.44 379	55.93 372	58.54 384	70.41 395	44.58 334	77.55 372	47.01 369	35.91 407	71.55 395

testing99		76.09 247	75.12 245	79.00 263	88.16 181	55.50 341	80.79 301	81.40 322	73.30 150	75.17 242	84.27 294	44.48 335	90.02 268	64.28 247	84.22 191	91.48 155

test_cas_vis1_n_1920		73.76 274	73.74 263	73.81 331	75.90 375	59.77 284	80.51 308	82.40 311	58.30 357	81.62 117	85.69 260	44.35 336	76.41 380	76.29 132	78.61 260	85.23 332

mvs_tets		79.13 179	77.77 192	83.22 162	84.70 264	66.37 169	89.17 102	90.19 154	69.38 225	75.40 231	89.46 160	44.17 337	93.15 174	76.78 130	80.70 239	90.14 202

MDA-MVSNet-bldmvs		66.68 338	63.66 347	75.75 307	79.28 363	60.56 275	73.92 370	78.35 351	64.43 297	50.13 400	79.87 355	44.02 338	83.67 341	46.10 375	56.86 386	83.03 361

mmtdpeth		74.16 268	73.01 270	77.60 292	83.72 287	61.13 265	85.10 229	85.10 268	72.06 169	77.21 191	80.33 349	43.84 339	85.75 322	77.14 125	52.61 396	85.91 322

gg-mvs-nofinetune		69.95 314	67.96 318	75.94 305	83.07 302	54.51 352	77.23 350	70.29 386	63.11 313	70.32 302	62.33 399	43.62 340	88.69 294	53.88 331	87.76 140	84.62 342

testing11		75.14 261	74.01 257	78.53 274	88.16 181	56.38 328	80.74 304	80.42 334	70.67 194	72.69 279	83.72 306	43.61 341	89.86 270	62.29 264	83.76 196	89.36 235

GG-mvs-BLEND				75.38 315	81.59 330	55.80 337	79.32 323	69.63 388		67.19 336	73.67 388	43.24 342	88.90 292	50.41 347	84.50 182	81.45 373

CMPMVS	51.72 21	70.19 312	68.16 315	76.28 303	73.15 393	57.55 310	79.47 321	83.92 284	48.02 391	56.48 391	84.81 282	43.13 343	86.42 317	62.67 260	81.81 227	84.89 338
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dp		66.80 337	65.43 339	70.90 356	79.74 358	48.82 387	75.12 364	74.77 373	59.61 345	64.08 363	77.23 374	42.89 344	80.72 359	48.86 359	66.58 367	83.16 358

PVSNet	64.34 18	72.08 295	70.87 295	75.69 308	86.21 237	56.44 326	74.37 368	80.73 328	62.06 329	70.17 305	82.23 332	42.86 345	83.31 345	54.77 327	84.45 186	87.32 291

pmmvs-eth3d		70.50 309	67.83 322	78.52 275	77.37 371	66.18 172	81.82 286	81.51 320	58.90 353	63.90 365	80.42 348	42.69 346	86.28 318	58.56 299	65.30 372	83.11 359

UnsupCasMVSNet_eth		67.33 334	65.99 338	71.37 349	73.48 389	51.47 375	75.16 362	85.19 267	65.20 288	60.78 376	80.93 345	42.35 347	77.20 373	57.12 313	53.69 394	85.44 329

KD-MVS_self_test		68.81 322	67.59 328	72.46 343	74.29 383	45.45 394	77.93 345	87.00 241	63.12 312	63.99 364	78.99 364	42.32 348	84.77 335	56.55 320	64.09 375	87.16 296

ADS-MVSNet2		66.20 345	63.33 348	74.82 321	79.92 352	58.75 291	67.55 393	75.19 370	53.37 379	65.25 356	75.86 381	42.32 348	80.53 360	41.57 388	68.91 359	85.18 333

ADS-MVSNet		64.36 349	62.88 352	68.78 365	79.92 352	47.17 390	67.55 393	71.18 384	53.37 379	65.25 356	75.86 381	42.32 348	73.99 395	41.57 388	68.91 359	85.18 333

SixPastTwentyTwo		73.37 278	71.26 291	79.70 251	85.08 259	57.89 303	85.57 216	83.56 290	71.03 188	65.66 352	85.88 256	42.10 351	92.57 192	59.11 293	63.34 376	88.65 263

JIA-IIPM		66.32 342	62.82 353	76.82 300	77.09 372	61.72 261	65.34 401	75.38 369	58.04 360	64.51 360	62.32 400	42.05 352	86.51 315	51.45 343	69.22 358	82.21 368

ACMH	67.68 16	75.89 249	73.93 259	81.77 203	88.71 163	66.61 166	88.62 125	89.01 193	69.81 215	66.78 341	86.70 235	41.95 353	91.51 236	55.64 323	78.14 268	87.17 294
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	68.96 14	76.01 248	74.01 257	82.03 198	88.60 166	65.31 193	88.86 114	87.55 229	70.25 206	67.75 329	87.47 213	41.27 354	93.19 172	58.37 302	75.94 298	87.60 283

MIMVSNet		70.69 306	69.30 305	74.88 320	84.52 269	56.35 330	75.87 357	79.42 344	64.59 295	67.76 328	82.41 327	41.10 355	81.54 354	46.64 372	81.34 229	86.75 306

Anonymous202405211		78.25 198	77.01 208	81.99 199	91.03 87	60.67 273	84.77 236	83.90 285	70.65 198	80.00 135	91.20 118	41.08 356	91.43 240	65.21 239	85.26 174	93.85 62

N_pmnet		52.79 369	53.26 367	51.40 393	78.99 365	7.68 427	69.52 385	3.89 426	51.63 385	57.01 389	74.98 385	40.83 357	65.96 408	37.78 395	64.67 373	80.56 380

ETVMVS		72.25 293	71.05 292	75.84 306	87.77 203	51.91 369	79.39 322	74.98 371	69.26 228	73.71 265	82.95 319	40.82 358	86.14 319	46.17 374	84.43 187	89.47 232

EU-MVSNet		68.53 327	67.61 327	71.31 352	78.51 367	47.01 391	84.47 244	84.27 280	42.27 398	66.44 349	84.79 283	40.44 359	83.76 340	58.76 298	68.54 362	83.17 357

DSMNet-mixed		57.77 361	56.90 363	60.38 381	67.70 402	35.61 412	69.18 387	53.97 413	32.30 411	57.49 388	79.88 354	40.39 360	68.57 405	38.78 394	72.37 339	76.97 387

UWE-MVS		72.13 294	71.49 285	74.03 329	86.66 232	47.70 388	81.40 295	76.89 364	63.60 310	75.59 223	84.22 295	39.94 361	85.62 325	48.98 358	86.13 165	88.77 258

OurMVSNet-221017-0		74.26 266	72.42 277	79.80 249	83.76 286	59.59 287	85.92 210	86.64 248	66.39 274	66.96 338	87.58 207	39.46 362	91.60 228	65.76 236	69.27 357	88.22 271

K. test v3		71.19 299	68.51 311	79.21 261	83.04 304	57.78 307	84.35 251	76.91 363	72.90 159	62.99 369	82.86 322	39.27 363	91.09 252	61.65 272	52.66 395	88.75 259

lessismore_v0				78.97 264	81.01 341	57.15 315		65.99 398		61.16 375	82.82 323	39.12 364	91.34 243	59.67 287	46.92 402	88.43 268

testing222		74.04 270	72.66 274	78.19 280	87.89 195	55.36 342	81.06 298	79.20 347	71.30 181	74.65 255	83.57 309	39.11 365	88.67 295	51.43 344	85.75 172	90.53 187

reproduce_monomvs		75.40 258	74.38 254	78.46 277	83.92 282	57.80 306	83.78 259	86.94 243	73.47 145	72.25 285	84.47 286	38.74 366	89.27 282	75.32 146	70.53 352	88.31 270

UnsupCasMVSNet_bld		63.70 351	61.53 357	70.21 358	73.69 387	51.39 376	72.82 372	81.89 316	55.63 373	57.81 387	71.80 392	38.67 367	78.61 366	49.26 357	52.21 397	80.63 378

new-patchmatchnet		61.73 355	61.73 356	61.70 379	72.74 395	24.50 422	69.16 388	78.03 352	61.40 332	56.72 390	75.53 384	38.42 368	76.48 379	45.95 376	57.67 385	84.13 347

MVS-HIRNet		59.14 359	57.67 361	63.57 377	81.65 328	43.50 402	71.73 375	65.06 401	39.59 402	51.43 397	57.73 405	38.34 369	82.58 349	39.53 391	73.95 326	64.62 401

test2506		77.30 225	76.49 222	79.74 250	90.08 108	52.02 366	87.86 154	63.10 404	74.88 110	80.16 134	92.79 82	38.29 370	92.35 203	68.74 210	92.50 77	94.86 17

COLMAP_ROB	66.92 17	73.01 285	70.41 300	80.81 229	87.13 223	65.63 185	88.30 137	84.19 282	62.96 316	63.80 366	87.69 205	38.04 371	92.56 193	46.66 370	74.91 318	84.24 345
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TESTMET0.1,1		69.89 315	69.00 309	72.55 341	79.27 364	56.85 318	78.38 338	74.71 375	57.64 362	68.09 327	77.19 375	37.75 372	76.70 376	63.92 249	84.09 192	84.10 348

OpenMVS_ROB	64.09 19	70.56 308	68.19 314	77.65 289	80.26 347	59.41 289	85.01 231	82.96 305	58.76 354	65.43 354	82.33 329	37.63 373	91.23 246	45.34 380	76.03 297	82.32 367

FMVSNet5		69.50 317	67.96 318	74.15 328	82.97 308	55.35 343	80.01 316	82.12 314	62.56 323	63.02 367	81.53 337	36.92 374	81.92 352	48.42 360	74.06 325	85.17 335

MIMVSNet1		68.58 325	66.78 335	73.98 330	80.07 351	51.82 371	80.77 302	84.37 276	64.40 298	59.75 381	82.16 333	36.47 375	83.63 342	42.73 385	70.33 353	86.48 310

ITE_SJBPF				78.22 279	81.77 327	60.57 274		83.30 294	69.25 229	67.54 331	87.20 220	36.33 376	87.28 310	54.34 329	74.62 321	86.80 304

test-mter		71.41 298	70.39 301	74.48 324	81.35 336	58.04 299	78.38 338	77.46 356	60.32 339	69.95 310	79.00 362	36.08 377	79.24 363	66.13 230	84.83 177	86.15 315

testgi		66.67 339	66.53 336	67.08 373	75.62 378	41.69 408	75.93 354	76.50 365	66.11 276	65.20 358	86.59 239	35.72 378	74.71 392	43.71 382	73.38 334	84.84 339

EG-PatchMatch MVS		74.04 270	71.82 282	80.71 231	84.92 261	67.42 148	85.86 212	88.08 216	66.04 278	64.22 362	83.85 300	35.10 379	92.56 193	57.44 310	80.83 236	82.16 370

KD-MVS_2432*1600		66.22 343	63.89 345	73.21 334	75.47 380	53.42 360	70.76 381	84.35 277	64.10 303	66.52 346	78.52 366	34.55 380	84.98 332	50.40 348	50.33 399	81.23 374

miper_refine_blended		66.22 343	63.89 345	73.21 334	75.47 380	53.42 360	70.76 381	84.35 277	64.10 303	66.52 346	78.52 366	34.55 380	84.98 332	50.40 348	50.33 399	81.23 374

mvs5depth		69.45 318	67.45 330	75.46 314	73.93 384	55.83 336	79.19 326	83.23 296	66.89 262	71.63 292	83.32 312	33.69 382	85.09 331	59.81 286	55.34 392	85.46 328

XVG-ACMP-BASELINE		76.11 246	74.27 256	81.62 205	83.20 298	64.67 205	83.60 265	89.75 166	69.75 219	71.85 289	87.09 224	32.78 383	92.11 211	69.99 196	80.43 243	88.09 274

AllTest		70.96 302	68.09 317	79.58 255	85.15 256	63.62 224	84.58 242	79.83 340	62.31 325	60.32 378	86.73 229	32.02 384	88.96 290	50.28 350	71.57 347	86.15 315

TestCases				79.58 255	85.15 256	63.62 224		79.83 340	62.31 325	60.32 378	86.73 229	32.02 384	88.96 290	50.28 350	71.57 347	86.15 315

USDC		70.33 310	68.37 312	76.21 304	80.60 344	56.23 331	79.19 326	86.49 250	60.89 335	61.29 374	85.47 267	31.78 386	89.47 279	53.37 334	76.21 296	82.94 363

myMVS_eth3d		67.02 336	66.29 337	69.21 361	84.68 265	42.58 404	78.62 335	73.08 380	66.65 270	66.74 342	79.46 357	31.53 387	82.30 350	39.43 393	76.38 293	82.75 364

test_fmvs1		70.93 303	70.52 297	72.16 344	73.71 386	55.05 346	80.82 299	78.77 349	51.21 387	78.58 156	84.41 288	31.20 388	76.94 375	75.88 138	80.12 248	84.47 343

Anonymous20240521		68.80 323	67.22 332	73.55 332	74.33 382	54.11 354	83.18 271	85.61 263	58.15 358	61.68 373	80.94 343	30.71 389	81.27 356	57.00 315	73.34 335	85.28 331

testing3		68.56 326	67.67 326	71.22 353	87.33 218	42.87 403	83.06 277	71.54 383	70.36 201	69.08 320	84.38 289	30.33 390	85.69 324	37.50 396	75.45 308	85.09 337

test_vis1_n		69.85 316	69.21 307	71.77 346	72.66 396	55.27 345	81.48 292	76.21 367	52.03 383	75.30 239	83.20 315	28.97 391	76.22 382	74.60 150	78.41 266	83.81 351

tmp_tt		18.61 387	21.40 390	10.23 403	4.82 426	10.11 426	34.70 413	30.74 424	1.48 420	23.91 416	26.07 417	28.42 392	13.41 422	27.12 406	15.35 419	7.17 417

test_fmvs1_n		70.86 304	70.24 302	72.73 340	72.51 397	55.28 344	81.27 296	79.71 342	51.49 386	78.73 151	84.87 280	27.54 393	77.02 374	76.06 135	79.97 249	85.88 323

TDRefinement		67.49 332	64.34 342	76.92 299	73.47 390	61.07 267	84.86 235	82.98 304	59.77 344	58.30 385	85.13 275	26.06 394	87.89 304	47.92 367	60.59 383	81.81 372

dongtai		45.42 377	45.38 378	45.55 395	73.36 391	26.85 419	67.72 392	34.19 421	54.15 377	49.65 401	56.41 408	25.43 395	62.94 411	19.45 412	28.09 412	46.86 411

MVStest1		56.63 362	52.76 368	68.25 369	61.67 410	53.25 364	71.67 376	68.90 393	38.59 403	50.59 399	83.05 317	25.08 396	70.66 400	36.76 397	38.56 406	80.83 377

test_vis1_rt		60.28 357	58.42 360	65.84 374	67.25 403	55.60 340	70.44 383	60.94 407	44.33 396	59.00 382	66.64 397	24.91 397	68.67 404	62.80 256	69.48 355	73.25 393

TinyColmap		67.30 335	64.81 340	74.76 322	81.92 326	56.68 323	80.29 313	81.49 321	60.33 338	56.27 392	83.22 313	24.77 398	87.66 308	45.52 378	69.47 356	79.95 381

EGC-MVSNET		52.07 371	47.05 375	67.14 372	83.51 291	60.71 272	80.50 309	67.75 394	0.07 421	0.43 422	75.85 383	24.26 399	81.54 354	28.82 404	62.25 377	59.16 404

kuosan		39.70 381	40.40 382	37.58 398	64.52 407	26.98 417	65.62 400	33.02 422	46.12 393	42.79 405	48.99 411	24.10 400	46.56 419	12.16 420	26.30 413	39.20 412

LF4IMVS		64.02 350	62.19 354	69.50 360	70.90 398	53.29 363	76.13 352	77.18 361	52.65 381	58.59 383	80.98 342	23.55 401	76.52 378	53.06 336	66.66 366	78.68 384

test_fmvs2		68.35 329	67.48 329	70.98 355	69.50 400	51.95 368	80.05 315	76.38 366	49.33 389	74.65 255	84.38 289	23.30 402	75.40 390	74.51 151	75.17 316	85.60 326

new_pmnet		50.91 372	50.29 372	52.78 392	68.58 401	34.94 414	63.71 403	56.63 412	39.73 401	44.95 403	65.47 398	21.93 403	58.48 412	34.98 399	56.62 387	64.92 400

ttmdpeth		59.91 358	57.10 362	68.34 368	67.13 404	46.65 393	74.64 367	67.41 395	48.30 390	62.52 372	85.04 279	20.40 404	75.93 384	42.55 386	45.90 405	82.44 366

pmmvs3		57.79 360	54.26 365	68.37 367	64.02 408	56.72 321	75.12 364	65.17 400	40.20 400	52.93 396	69.86 396	20.36 405	75.48 388	45.45 379	55.25 393	72.90 394

PM-MVS		66.41 341	64.14 343	73.20 336	73.92 385	56.45 325	78.97 330	64.96 402	63.88 309	64.72 359	80.24 350	19.84 406	83.44 344	66.24 229	64.52 374	79.71 382

mvsany_test3		53.99 365	51.45 370	61.61 380	55.51 414	44.74 400	63.52 404	45.41 419	43.69 397	58.11 386	76.45 378	17.99 407	63.76 410	54.77 327	47.59 401	76.34 389

ambc				75.24 317	73.16 392	50.51 382	63.05 406	87.47 232		64.28 361	77.81 372	17.80 408	89.73 274	57.88 307	60.64 382	85.49 327

ANet_high		50.57 373	46.10 377	63.99 376	48.67 421	39.13 410	70.99 380	80.85 326	61.39 333	31.18 410	57.70 406	17.02 409	73.65 397	31.22 403	15.89 418	79.18 383

FPMVS		53.68 367	51.64 369	59.81 382	65.08 406	51.03 378	69.48 386	69.58 389	41.46 399	40.67 406	72.32 391	16.46 410	70.00 403	24.24 410	65.42 371	58.40 406

test_method		31.52 383	29.28 387	38.23 397	27.03 425	6.50 428	20.94 416	62.21 405	4.05 419	22.35 417	52.50 410	13.33 411	47.58 417	27.04 407	34.04 409	60.62 403

EMVS		30.81 384	29.65 386	34.27 400	50.96 420	25.95 420	56.58 410	46.80 418	24.01 415	15.53 420	30.68 416	12.47 412	54.43 416	12.81 419	17.05 417	22.43 416

test_f		52.09 370	50.82 371	55.90 387	53.82 417	42.31 407	59.42 407	58.31 411	36.45 406	56.12 393	70.96 394	12.18 413	57.79 413	53.51 333	56.57 388	67.60 398

test_fmvs3		63.36 352	61.82 355	67.98 370	62.51 409	46.96 392	77.37 349	74.03 377	45.24 394	67.50 332	78.79 365	12.16 414	72.98 398	72.77 171	66.02 369	83.99 349

E-PMN		31.77 382	30.64 385	35.15 399	52.87 419	27.67 416	57.09 409	47.86 417	24.64 414	16.40 419	33.05 415	11.23 415	54.90 415	14.46 418	18.15 416	22.87 415

DeepMVS_CX				27.40 401	40.17 424	26.90 418		24.59 425	17.44 417	23.95 415	48.61 412	9.77 416	26.48 420	18.06 413	24.47 414	28.83 414

Gipuma		45.18 378	41.86 381	55.16 390	77.03 373	51.52 374	32.50 414	80.52 331	32.46 410	27.12 413	35.02 414	9.52 417	75.50 387	22.31 411	60.21 384	38.45 413
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		54.25 364	49.68 374	67.97 371	53.73 418	45.28 397	66.85 396	80.78 327	35.96 407	39.45 408	62.23 401	8.70 418	78.06 370	48.24 364	51.20 398	80.57 379

APD_test1		53.31 368	49.93 373	63.42 378	65.68 405	50.13 383	71.59 377	66.90 397	34.43 408	40.58 407	71.56 393	8.65 419	76.27 381	34.64 400	55.36 391	63.86 402

PMMVS2		40.82 380	38.86 384	46.69 394	53.84 416	16.45 425	48.61 411	49.92 414	37.49 404	31.67 409	60.97 402	8.14 420	56.42 414	28.42 405	30.72 411	67.19 399

test_vis3_rt		49.26 374	47.02 376	56.00 386	54.30 415	45.27 398	66.76 397	48.08 416	36.83 405	44.38 404	53.20 409	7.17 421	64.07 409	56.77 318	55.66 389	58.65 405

testf1		45.72 375	41.96 379	57.00 384	56.90 412	45.32 395	66.14 398	59.26 409	26.19 412	30.89 411	60.96 403	4.14 422	70.64 401	26.39 408	46.73 403	55.04 407

APD_test2		45.72 375	41.96 379	57.00 384	56.90 412	45.32 395	66.14 398	59.26 409	26.19 412	30.89 411	60.96 403	4.14 422	70.64 401	26.39 408	46.73 403	55.04 407

PMVS	37.38 22	44.16 379	40.28 383	55.82 388	40.82 423	42.54 406	65.12 402	63.99 403	34.43 408	24.48 414	57.12 407	3.92 424	76.17 383	17.10 415	55.52 390	48.75 409
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 385	25.89 389	43.81 396	44.55 422	35.46 413	28.87 415	39.07 420	18.20 416	18.58 418	40.18 413	2.68 425	47.37 418	17.07 416	23.78 415	48.60 410
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		16.82 388	15.94 391	19.46 402	58.74 411	31.45 415	39.22 412	3.74 427	6.84 418	6.04 421	2.70 421	1.27 426	24.29 421	10.54 421	14.40 420	2.63 418

test123		6.12 390	8.11 393	0.14 404	0.06 428	0.09 429	71.05 379	0.03 429	0.04 423	0.25 424	1.30 423	0.05 427	0.03 424	0.21 423	0.01 422	0.29 419

testmvs		6.04 391	8.02 394	0.10 405	0.08 427	0.03 430	69.74 384	0.04 428	0.05 422	0.31 423	1.68 422	0.02 428	0.04 423	0.24 422	0.02 421	0.25 420

mmdepth		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

monomultidepth		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

test_blank		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

uanet_test		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

DCPMVS		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

sosnet-low-res		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

sosnet		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

uncertanet		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

Regformer		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

ab-mvs-re		7.23 389	9.64 392	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	86.72 231	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

uanet		0.00 393	0.00 396	0.00 406	0.00 429	0.00 431	0.00 417	0.00 430	0.00 424	0.00 425	0.00 424	0.00 429	0.00 425	0.00 424	0.00 423	0.00 421

WAC-MVS						42.58 404								39.46 392

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 120	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 6	96.44 9	94.41 37

No_MVS				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 6	96.44 9	94.41 37

eth-test2					0.00 429

eth-test					0.00 429

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 263	92.39 6				88.94 16	96.63 4	94.85 19

save fliter					93.80 40	72.35 42	90.47 66	91.17 124	74.31 123

test_0728_SECOND				87.71 32	95.34 1	71.43 58	93.49 9	94.23 3					97.49 4	89.08 12	96.41 12	94.21 47

GSMVS																88.96 250

test_part2					95.06 8	72.65 32				91.80 13

MTGPA								92.02 92

MTMP							92.18 34	32.83 423

gm-plane-assit					81.40 334	53.83 357			62.72 322		80.94 343		92.39 200	63.40 253

test9_res														84.90 46	95.70 26	92.87 111

agg_prior2														82.91 72	95.45 29	92.70 114

agg_prior					92.85 62	71.94 50		91.78 107		84.41 78			94.93 92

test_prior4						72.60 34	89.01 109

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 58

旧先验2							86.56 192		58.10 359	87.04 45			88.98 288	74.07 156

新几何2							86.29 201

无先验							87.48 161	88.98 194	60.00 342				94.12 123	67.28 222		88.97 249

原ACMM2							86.86 181

testdata2													91.01 254	62.37 263

testdata1							84.14 255		75.71 91

plane_prior7					90.08 108	68.51 123

plane_prior5								92.44 76					95.38 74	78.71 108	86.32 160	91.33 158

plane_prior4											91.00 128

plane_prior3						68.60 121			78.44 31	78.92 149

plane_prior2							91.25 52		79.12 23

plane_prior1					89.90 116

plane_prior						68.71 116	90.38 70		77.62 39						86.16 164

n2								0.00 430

nn								0.00 430

door-mid								69.98 387

test11								92.23 86

door								69.44 390

HQP5-MVS						66.98 161

HQP-NCC					89.33 135		89.17 102		76.41 76	77.23 187

ACMP_Plane					89.33 135		89.17 102		76.41 76	77.23 187

BP-MVS														77.47 120

HQP4-MVS										77.24 186			95.11 85			91.03 168

HQP3-MVS								92.19 89							85.99 168

NP-MVS					89.62 121	68.32 126					90.24 140

ACMMP++_ref															81.95 225

ACMMP++															81.25 230

High-resolution multi-view benchmark