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 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 96	91.06 16	96.03 1	76.84 14	97.03 17	89.09 16	95.65 27	94.47 38
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 120	92.29 7	95.97 2	74.28 29	97.24 13	88.58 27	96.91 1	94.87 17
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 59	93.60 6	94.11 6	77.33 52	92.81 3	95.79 3	80.98 9

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 52	92.12 9	95.78 4	80.98 9	97.40 9	89.08 17	96.41 12	93.33 96
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 34	92.12 9	95.78 4	81.46 7	97.40 9	89.42 14	96.57 7	94.67 28

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 34	92.78 4	95.74 6	82.45 3	97.49 4	89.42 14	96.68 2	94.95 11

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 37	92.05 11	95.74 6	80.83 11

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 55	93.10 1	95.72 8	82.99 1	97.44 7	89.07 19	96.63 4	94.88 15

test_241102_TWO								94.06 10	77.24 55	92.78 4	95.72 8	81.26 8	97.44 7	89.07 19	96.58 6	94.26 48

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 92	92.29 7	95.66 10	81.67 6	97.38 11	87.44 39	96.34 15	93.95 62
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
reproduce_model		87.28 30	87.39 28	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 121	88.80 24	95.61 11	70.29 74	96.44 39	86.20 45	93.08 69	93.16 104

reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 113	88.96 21	95.54 12	71.20 63	96.54 36	86.28 43	93.49 65	93.06 109

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 113	88.96 21	95.54 12	71.20 63	96.54 36	86.28 43	93.49 65	93.06 109

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 58	93.10 1	95.39 14	82.99 1	97.27 12

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 110	86.34 57	95.29 15	70.86 67	96.00 54	88.78 25	96.04 16	94.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
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 94	89.16 20	95.10 16	75.65 21	96.19 46	87.07 40	96.01 17	94.79 22

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 86	88.14 31	95.09 17	71.06 65	96.67 29	87.67 35	96.37 14	94.09 54

fmvsm_s_conf0.5_n_3		86.36 46	87.46 27	83.09 174	87.08 229	65.21 199	89.09 112	90.21 156	79.67 17	89.98 18	95.02 18	73.17 38	91.71 234	91.30 2	91.60 88	92.34 136

MM		89.16 6	89.23 7	88.97 4	90.79 95	73.65 10	92.66 23	91.17 125	86.57 1	87.39 47	94.97 19	71.70 55	97.68 1	92.19 1	95.63 28	95.57 1

MTAPA		87.23 31	87.00 33	87.90 22	94.18 35	74.25 5	86.58 199	92.02 93	79.45 20	85.88 59	94.80 20	68.07 100	96.21 45	86.69 42	95.34 32	93.23 99

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 32	94.80 20	73.76 33	97.11 15	87.51 37	95.82 21	94.90 14
Skip Steuart: Steuart Systems R&D Blog.
9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 141	88.57 26	94.67 22	75.57 22	95.79 58	86.77 41	95.76 23

SR-MVS		86.73 38	86.67 40	86.91 49	94.11 37	72.11 47	92.37 28	92.56 75	74.50 125	86.84 54	94.65 23	67.31 109	95.77 59	84.80 56	92.85 72	92.84 119

region2R		87.42 27	87.20 32	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 73	84.45 83	94.52 24	69.09 88	96.70 27	84.37 62	94.83 45	94.03 57

ACMMPR		87.44 25	87.23 31	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 70	84.66 78	94.52 24	68.81 94	96.65 30	84.53 60	94.90 41	94.00 59

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 166	88.58 25	94.52 24	73.36 34	96.49 38	84.26 63	95.01 37	92.70 121
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 52	85.88 55	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 151	85.94 58	94.51 27	65.80 127	95.61 62	83.04 77	92.51 76	93.53 89

CP-MVS		87.11 33	86.92 37	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 76	83.68 99	94.46 28	67.93 102	95.95 57	84.20 66	94.39 55	93.23 99

SR-MVS-dyc-post		85.77 57	85.61 61	86.23 59	93.06 58	70.63 76	91.88 38	92.27 84	73.53 152	85.69 62	94.45 29	65.00 135	95.56 63	82.75 81	91.87 84	92.50 130

RE-MVS-def			85.48 64		93.06 58	70.63 76	91.88 38	92.27 84	73.53 152	85.69 62	94.45 29	63.87 141		82.75 81	91.87 84	92.50 130

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 70	84.91 71	94.44 31	70.78 68	96.61 32	84.53 60	94.89 42	93.66 76

PGM-MVS		86.68 40	86.27 46	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 98	83.86 95	94.42 32	67.87 104	96.64 31	82.70 85	94.57 50	93.66 76

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 70	77.57 44	83.84 96	94.40 33	72.24 47	96.28 43	85.65 47	95.30 35	93.62 83
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 48	86.32 44	85.14 85	87.20 225	68.54 123	89.57 90	90.44 145	75.31 104	87.49 44	94.39 34	72.86 42	92.72 193	89.04 21	90.56 104	94.16 50

fmvsm_s_conf0.1_n_2		83.80 86	83.79 86	83.83 149	85.62 256	64.94 207	87.03 182	86.62 257	74.32 130	87.97 37	94.33 35	60.67 194	92.60 196	89.72 10	87.79 147	93.96 60

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 68	85.24 66	94.32 36	71.76 53	96.93 19	85.53 49	95.79 22	94.32 45

MVS_0304		87.69 20	87.55 24	88.12 13	89.45 130	71.76 51	91.47 49	89.54 176	82.14 3	86.65 55	94.28 37	68.28 99	97.46 6	90.81 4	95.31 34	95.15 7

test_fmvsmconf0.01_n		84.73 77	84.52 79	85.34 80	80.25 359	69.03 103	89.47 92	89.65 173	73.24 162	86.98 52	94.27 38	66.62 113	93.23 167	90.26 7	89.95 116	93.78 73

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 38	94.27 38	75.89 19	96.81 23	87.45 38	96.44 9	93.05 111

mPP-MVS		86.67 41	86.32 44	87.72 30	94.41 22	73.55 13	92.74 20	92.22 88	76.87 67	82.81 112	94.25 40	66.44 117	96.24 44	82.88 80	94.28 58	93.38 92

fmvsm_s_conf0.5_n_2		84.04 82	84.11 83	83.81 150	86.17 246	65.00 205	86.96 184	87.28 241	74.35 129	88.25 30	94.23 41	61.82 170	92.60 196	89.85 8	88.09 145	93.84 69

DeepC-MVS	79.81 2	87.08 35	86.88 39	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 60	82.82 111	94.23 41	72.13 49	97.09 16	84.83 55	95.37 31	93.65 80
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		87.18 32	86.91 38	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 22	83.67 100	94.17 43	67.45 107	96.60 33	83.06 75	94.50 51	94.07 55

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 44	78.35 13	96.77 24	89.59 13	94.22 60	94.67 28
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 61	85.65 60	85.50 77	82.99 318	69.39 100	89.65 86	90.29 154	73.31 158	87.77 39	94.15 45	71.72 54	93.23 167	90.31 6	90.67 103	93.89 66

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 48	89.79 19	94.12 46	78.98 12	96.58 35	85.66 46	95.72 24	94.58 33

HPM-MVS_fast		85.35 67	84.95 74	86.57 56	93.69 42	70.58 78	92.15 35	91.62 111	73.89 142	82.67 114	94.09 47	62.60 156	95.54 65	80.93 98	92.93 71	93.57 85

ZD-MVS					94.38 25	72.22 44		92.67 67	70.98 200	87.75 40	94.07 48	74.01 32	96.70 27	84.66 58	94.84 44

fmvsm_s_conf0.1_n_a		83.32 101	82.99 99	84.28 119	83.79 295	68.07 135	89.34 101	82.85 315	69.80 227	87.36 48	94.06 49	68.34 98	91.56 239	87.95 33	83.46 216	93.21 102

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 33	94.06 49	76.43 16	96.84 21	88.48 30	95.99 18	94.34 44

test_fmvsmconf_n		85.92 53	86.04 53	85.57 76	85.03 271	69.51 93	89.62 89	90.58 140	73.42 155	87.75 40	94.02 51	72.85 43	93.24 166	90.37 5	90.75 101	93.96 60

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 51	82.45 3	96.87 20	83.77 70	96.48 8	94.88 15

PC_three_1452									68.21 263	92.02 12	94.00 53	82.09 5	95.98 56	84.58 59	96.68 2	94.95 11

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 38	90.32 17	94.00 53	74.83 23	93.78 141	87.63 36	94.27 59	93.65 80
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 27	87.26 29	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 66	84.68 75	93.99 55	70.67 70	96.82 22	84.18 67	95.01 37	93.90 65

test_fmvsm_n_1920		85.29 68	85.34 66	85.13 88	86.12 248	69.93 86	88.65 130	90.78 136	69.97 223	88.27 29	93.98 56	71.39 60	91.54 241	88.49 29	90.45 106	93.91 63

fmvsm_s_conf0.1_n		83.56 94	83.38 92	84.10 128	84.86 273	67.28 157	89.40 98	83.01 310	70.67 205	87.08 50	93.96 57	68.38 97	91.45 247	88.56 28	84.50 191	93.56 86

HPM-MVS		87.11 33	86.98 35	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 89	83.81 97	93.95 58	69.77 80	96.01 53	85.15 50	94.66 47	94.32 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_5		85.22 69	85.55 62	84.25 124	86.26 243	67.40 153	89.18 104	89.31 184	72.50 171	88.31 28	93.86 59	69.66 81	91.96 222	89.81 9	91.05 97	93.38 92

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 82	74.62 124	88.90 23	93.85 60	75.75 20	96.00 54	87.80 34	94.63 48	95.04 9
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 56	85.39 65	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 70	80.73 136	93.82 61	64.33 137	96.29 42	82.67 86	90.69 102	93.23 99
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 92	83.41 91	84.28 119	86.14 247	68.12 133	89.43 94	82.87 314	70.27 216	87.27 49	93.80 62	69.09 88	91.58 237	88.21 32	83.65 210	93.14 106

fmvsm_s_conf0.5_n_4		85.39 66	85.75 59	84.30 117	86.70 237	65.83 184	88.77 122	89.78 167	75.46 100	88.35 27	93.73 63	69.19 87	93.06 182	91.30 2	88.44 140	94.02 58

fmvsm_s_conf0.5_n		83.80 86	83.71 87	84.07 134	86.69 238	67.31 156	89.46 93	83.07 309	71.09 197	86.96 53	93.70 64	69.02 93	91.47 246	88.79 24	84.62 190	93.44 91

test_prior2							88.85 120		75.41 101	84.91 71	93.54 65	74.28 29		83.31 73	95.86 20

fmvsm_l_conf0.5_n		84.47 78	84.54 77	84.27 121	85.42 260	68.81 109	88.49 134	87.26 243	68.08 264	88.03 34	93.49 66	72.04 50	91.77 230	88.90 23	89.14 127	92.24 143

VDDNet		81.52 131	80.67 134	84.05 139	90.44 101	64.13 225	89.73 84	85.91 268	71.11 196	83.18 105	93.48 67	50.54 290	93.49 155	73.40 171	88.25 142	94.54 36

CDPH-MVS		85.76 58	85.29 70	87.17 43	93.49 47	71.08 64	88.58 132	92.42 80	68.32 262	84.61 80	93.48 67	72.32 46	96.15 48	79.00 112	95.43 30	94.28 47

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 61	93.47 69	73.02 41	97.00 18	84.90 52	94.94 40	94.10 53

fmvsm_l_conf0.5_n_a		84.13 81	84.16 82	84.06 136	85.38 261	68.40 126	88.34 141	86.85 253	67.48 271	87.48 45	93.40 70	70.89 66	91.61 235	88.38 31	89.22 125	92.16 147

3Dnovator+	77.84 4	85.48 62	84.47 80	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 209	93.37 71	60.40 202	96.75 26	77.20 131	93.73 64	95.29 5

DeepC-MVS_fast	79.65 3	86.91 36	86.62 41	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 76	84.22 87	93.36 72	71.44 59	96.76 25	80.82 100	95.33 33	94.16 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 108	82.36 109	84.96 93	91.02 88	66.40 172	88.91 117	88.11 220	77.57 44	84.39 85	93.29 73	52.19 264	93.91 135	77.05 134	88.70 135	94.57 35

test_fmvsmvis_n_1920		84.02 83	83.87 84	84.49 109	84.12 287	69.37 101	88.15 149	87.96 225	70.01 221	83.95 94	93.23 74	68.80 95	91.51 244	88.61 26	89.96 115	92.57 126

UA-Net		85.08 72	84.96 73	85.45 78	92.07 73	68.07 135	89.78 82	90.86 135	82.48 2	84.60 81	93.20 75	69.35 84	95.22 81	71.39 189	90.88 100	93.07 108

TEST9					93.26 52	72.96 25	88.75 124	91.89 101	68.44 260	85.00 69	93.10 76	74.36 28	95.41 73

train_agg		86.43 43	86.20 47	87.13 44	93.26 52	72.96 25	88.75 124	91.89 101	68.69 255	85.00 69	93.10 76	74.43 26	95.41 73	84.97 51	95.71 25	93.02 113

test_8					93.13 54	72.57 35	88.68 129	91.84 105	68.69 255	84.87 73	93.10 76	74.43 26	95.16 83

LFMVS		81.82 124	81.23 125	83.57 156	91.89 76	63.43 241	89.84 78	81.85 326	77.04 63	83.21 104	93.10 76	52.26 263	93.43 160	71.98 184	89.95 116	93.85 67

旧先验1					91.96 74	65.79 187		86.37 261			93.08 80	69.31 86			92.74 73	88.74 271

dcpmvs_2		85.63 60	86.15 50	84.06 136	91.71 78	64.94 207	86.47 202	91.87 103	73.63 147	86.60 56	93.02 81	76.57 15	91.87 228	83.36 72	92.15 80	95.35 3

testdata				79.97 253	90.90 91	64.21 223		84.71 280	59.27 361	85.40 64	92.91 82	62.02 169	89.08 294	68.95 215	91.37 93	86.63 320

MCST-MVS		87.37 29	87.25 30	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 164	84.86 74	92.89 83	76.22 17	96.33 41	84.89 54	95.13 36	94.40 41

Vis-MVSNet		83.46 97	82.80 103	85.43 79	90.25 105	68.74 114	90.30 72	90.13 159	76.33 85	80.87 135	92.89 83	61.00 189	94.20 122	72.45 183	90.97 98	93.35 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		83.73 88	83.33 94	84.92 96	93.28 49	70.86 72	92.09 36	90.38 147	68.75 254	79.57 148	92.83 85	60.60 198	93.04 185	80.92 99	91.56 91	90.86 183

3Dnovator	76.31 5	83.38 100	82.31 110	86.59 55	87.94 196	72.94 28	90.64 60	92.14 92	77.21 57	75.47 235	92.83 85	58.56 209	94.72 105	73.24 174	92.71 74	92.13 148

MSLP-MVS++		85.43 64	85.76 58	84.45 110	91.93 75	70.24 79	90.71 59	92.86 58	77.46 50	84.22 87	92.81 87	67.16 111	92.94 187	80.36 104	94.35 57	90.16 211

test2506		77.30 233	76.49 230	79.74 258	90.08 109	52.02 374	87.86 160	63.10 416	74.88 116	80.16 142	92.79 88	38.29 382	92.35 209	68.74 218	92.50 77	94.86 18

ECVR-MVS		79.61 171	79.26 164	80.67 240	90.08 109	54.69 357	87.89 158	77.44 369	74.88 116	80.27 139	92.79 88	48.96 311	92.45 203	68.55 219	92.50 77	94.86 18

test1111		79.43 178	79.18 167	80.15 250	89.99 114	53.31 370	87.33 174	77.05 373	75.04 111	80.23 141	92.77 90	48.97 310	92.33 211	68.87 216	92.40 79	94.81 21

MG-MVS		83.41 98	83.45 90	83.28 164	92.74 65	62.28 261	88.17 147	89.50 178	75.22 105	81.49 126	92.74 91	66.75 112	95.11 87	72.85 177	91.58 90	92.45 133

casdiffmvs_mvg		85.99 50	86.09 52	85.70 74	87.65 211	67.22 161	88.69 128	93.04 41	79.64 19	85.33 65	92.54 92	73.30 35	94.50 112	83.49 71	91.14 96	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 90	84.54 77	80.99 232	90.06 113	65.83 184	84.21 261	88.74 211	71.60 187	85.01 68	92.44 93	74.51 25	83.50 353	82.15 88	92.15 80	93.64 82

casdiffmvs		85.11 71	85.14 71	85.01 91	87.20 225	65.77 188	87.75 161	92.83 60	77.84 39	84.36 86	92.38 94	72.15 48	93.93 134	81.27 96	90.48 105	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 39	86.95 36	85.90 71	90.76 96	67.57 148	92.83 17	93.30 32	79.67 17	84.57 82	92.27 95	71.47 58	95.02 93	84.24 65	93.46 67	95.13 8

baseline		84.93 74	84.98 72	84.80 101	87.30 223	65.39 196	87.30 175	92.88 57	77.62 42	84.04 92	92.26 96	71.81 52	93.96 128	81.31 94	90.30 108	95.03 10

QAPM		80.88 141	79.50 157	85.03 90	88.01 194	68.97 107	91.59 43	92.00 95	66.63 283	75.15 253	92.16 97	57.70 216	95.45 68	63.52 258	88.76 133	90.66 191

IS-MVSNet		83.15 103	82.81 102	84.18 126	89.94 116	63.30 243	91.59 43	88.46 217	79.04 26	79.49 149	92.16 97	65.10 132	94.28 117	67.71 225	91.86 86	94.95 11

BP-MVS1		84.32 79	83.71 87	86.17 61	87.84 201	67.85 139	89.38 99	89.64 174	77.73 40	83.98 93	92.12 99	56.89 226	95.43 70	84.03 68	91.75 87	95.24 6

新几何1				83.42 159	93.13 54	70.71 74		85.48 273	57.43 377	81.80 122	91.98 100	63.28 145	92.27 212	64.60 253	92.99 70	87.27 303

OpenMVS	72.83 10	79.77 169	78.33 184	84.09 132	85.17 265	69.91 87	90.57 61	90.97 130	66.70 277	72.17 297	91.91 101	54.70 241	93.96 128	61.81 279	90.95 99	88.41 280

PHI-MVS		86.43 43	86.17 49	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 172	85.22 67	91.90 102	69.47 83	96.42 40	83.28 74	95.94 19	94.35 43

VNet		82.21 116	82.41 107	81.62 213	90.82 93	60.93 276	84.47 252	89.78 167	76.36 84	84.07 91	91.88 103	64.71 136	90.26 271	70.68 196	88.89 129	93.66 76

EC-MVSNet		86.01 49	86.38 43	84.91 97	89.31 139	66.27 175	92.32 30	93.63 21	79.37 21	84.17 89	91.88 103	69.04 92	95.43 70	83.93 69	93.77 63	93.01 114

GDP-MVS		83.52 95	82.64 105	86.16 62	88.14 185	68.45 125	89.13 110	92.69 65	72.82 170	83.71 98	91.86 105	55.69 231	95.35 79	80.03 107	89.74 119	94.69 27

OPM-MVS		83.50 96	82.95 100	85.14 85	88.79 160	70.95 69	89.13 110	91.52 114	77.55 47	80.96 134	91.75 106	60.71 192	94.50 112	79.67 111	86.51 167	89.97 227
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVSMamba_PlusPlus		85.99 50	85.96 54	86.05 66	91.09 85	67.64 145	89.63 88	92.65 70	72.89 169	84.64 79	91.71 107	71.85 51	96.03 50	84.77 57	94.45 54	94.49 37

XVG-OURS-SEG-HR		80.81 144	79.76 151	83.96 146	85.60 257	68.78 111	83.54 275	90.50 143	70.66 208	76.71 208	91.66 108	60.69 193	91.26 252	76.94 135	81.58 238	91.83 153

EPNet		83.72 89	82.92 101	86.14 65	84.22 285	69.48 94	91.05 56	85.27 274	81.30 6	76.83 204	91.65 109	66.09 122	95.56 63	76.00 145	93.85 62	93.38 92
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 110	81.97 118	84.85 98	88.75 162	67.42 151	87.98 152	90.87 134	74.92 115	79.72 146	91.65 109	62.19 166	93.96 128	75.26 155	86.42 168	93.16 104

balanced_conf03		86.78 37	86.99 34	86.15 63	91.24 83	67.61 146	90.51 62	92.90 56	77.26 54	87.44 46	91.63 111	71.27 62	96.06 49	85.62 48	95.01 37	94.78 23

test222					91.50 80	68.26 130	84.16 262	83.20 307	54.63 388	79.74 145	91.63 111	58.97 207			91.42 92	86.77 316

MVS_111021_HR		85.14 70	84.75 75	86.32 58	91.65 79	72.70 30	85.98 215	90.33 151	76.11 88	82.08 117	91.61 113	71.36 61	94.17 124	81.02 97	92.58 75	92.08 149

原ACMM1				84.35 114	93.01 60	68.79 110		92.44 77	63.96 319	81.09 132	91.57 114	66.06 123	95.45 68	67.19 232	94.82 46	88.81 266

LPG-MVS_test		82.08 118	81.27 124	84.50 107	89.23 143	68.76 112	90.22 73	91.94 99	75.37 102	76.64 210	91.51 115	54.29 244	94.91 95	78.44 118	83.78 203	89.83 232

LGP-MVS_train				84.50 107	89.23 143	68.76 112		91.94 99	75.37 102	76.64 210	91.51 115	54.29 244	94.91 95	78.44 118	83.78 203	89.83 232

XVG-OURS		80.41 157	79.23 165	83.97 145	85.64 255	69.02 105	83.03 286	90.39 146	71.09 197	77.63 186	91.49 117	54.62 243	91.35 250	75.71 147	83.47 215	91.54 160

alignmvs		85.48 62	85.32 68	85.96 70	89.51 127	69.47 95	89.74 83	92.47 76	76.17 87	87.73 42	91.46 118	70.32 73	93.78 141	81.51 91	88.95 128	94.63 32

CANet		86.45 42	86.10 51	87.51 37	90.09 108	70.94 70	89.70 85	92.59 74	81.78 4	81.32 127	91.43 119	70.34 72	97.23 14	84.26 63	93.36 68	94.37 42

h-mvs33		83.15 103	82.19 111	86.02 69	90.56 98	70.85 73	88.15 149	89.16 192	76.02 90	84.67 76	91.39 120	61.54 175	95.50 66	82.71 83	75.48 317	91.72 156

MGCFI-Net		85.06 73	85.51 63	83.70 152	89.42 131	63.01 249	89.43 94	92.62 73	76.43 78	87.53 43	91.34 121	72.82 44	93.42 161	81.28 95	88.74 134	94.66 31

nrg030		83.88 84	83.53 89	84.96 93	86.77 235	69.28 102	90.46 67	92.67 67	74.79 119	82.95 107	91.33 122	72.70 45	93.09 180	80.79 102	79.28 268	92.50 130

sasdasda		85.91 54	85.87 56	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 74	88.01 35	91.23 123	73.28 36	93.91 135	81.50 92	88.80 131	94.77 24

canonicalmvs		85.91 54	85.87 56	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 74	88.01 35	91.23 123	73.28 36	93.91 135	81.50 92	88.80 131	94.77 24

DPM-MVS		84.93 74	84.29 81	86.84 50	90.20 106	73.04 23	87.12 179	93.04 41	69.80 227	82.85 110	91.22 125	73.06 40	96.02 52	76.72 139	94.63 48	91.46 166

Anonymous202405211		78.25 206	77.01 216	81.99 207	91.03 87	60.67 281	84.77 244	83.90 293	70.65 209	80.00 143	91.20 126	41.08 368	91.43 248	65.21 247	85.26 183	93.85 67

SPE-MVS-test		86.29 47	86.48 42	85.71 73	91.02 88	67.21 162	92.36 29	93.78 18	78.97 29	83.51 103	91.20 126	70.65 71	95.15 84	81.96 89	94.89 42	94.77 24

Anonymous20240529		80.19 164	78.89 172	84.10 128	90.60 97	64.75 212	88.95 116	90.90 132	65.97 291	80.59 137	91.17 128	49.97 295	93.73 147	69.16 213	82.70 227	93.81 71

EPP-MVSNet		83.40 99	83.02 98	84.57 105	90.13 107	64.47 218	92.32 30	90.73 137	74.45 128	79.35 151	91.10 129	69.05 91	95.12 85	72.78 178	87.22 156	94.13 52

TAPA-MVS	73.13 9	79.15 186	77.94 192	82.79 192	89.59 123	62.99 253	88.16 148	91.51 115	65.77 292	77.14 201	91.09 130	60.91 190	93.21 169	50.26 363	87.05 158	92.17 146
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 45	86.19 48	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 139	83.16 106	91.07 131	75.94 18	95.19 82	79.94 109	94.38 56	93.55 87

FIs		82.07 119	82.42 106	81.04 231	88.80 159	58.34 303	88.26 144	93.49 26	76.93 65	78.47 168	91.04 132	69.92 78	92.34 210	69.87 206	84.97 185	92.44 134

MVS_111021_LR		82.61 112	82.11 112	84.11 127	88.82 157	71.58 55	85.15 235	86.16 265	74.69 121	80.47 138	91.04 132	62.29 163	90.55 269	80.33 105	90.08 113	90.20 210

DP-MVS Recon		83.11 106	82.09 114	86.15 63	94.44 19	70.92 71	88.79 121	92.20 89	70.53 210	79.17 153	91.03 134	64.12 139	96.03 50	68.39 222	90.14 111	91.50 162

mamv4		76.81 240	78.23 188	72.54 353	86.12 248	65.75 189	78.76 342	82.07 323	64.12 313	72.97 285	91.02 135	67.97 101	68.08 418	83.04 77	78.02 280	83.80 364

HQP_MVS		83.64 91	83.14 95	85.14 85	90.08 109	68.71 116	91.25 52	92.44 77	79.12 24	78.92 157	91.00 136	60.42 200	95.38 75	78.71 116	86.32 169	91.33 167

plane_prior4											91.00 136

FC-MVSNet-test		81.52 131	82.02 116	80.03 252	88.42 175	55.97 342	87.95 154	93.42 29	77.10 61	77.38 190	90.98 138	69.96 77	91.79 229	68.46 221	84.50 191	92.33 137

Vis-MVSNet (Re-imp)		78.36 205	78.45 179	78.07 291	88.64 166	51.78 380	86.70 196	79.63 352	74.14 137	75.11 254	90.83 139	61.29 183	89.75 281	58.10 313	91.60 88	92.69 123

114514_t		80.68 150	79.51 156	84.20 125	94.09 38	67.27 158	89.64 87	91.11 128	58.75 367	74.08 271	90.72 140	58.10 212	95.04 92	69.70 207	89.42 123	90.30 207

PAPM_NR		83.02 107	82.41 107	84.82 99	92.47 70	66.37 173	87.93 156	91.80 106	73.82 143	77.32 192	90.66 141	67.90 103	94.90 97	70.37 199	89.48 122	93.19 103

LS3D		76.95 238	74.82 256	83.37 162	90.45 100	67.36 155	89.15 109	86.94 250	61.87 342	69.52 326	90.61 142	51.71 277	94.53 110	46.38 384	86.71 164	88.21 283

VPNet		78.69 198	78.66 175	78.76 275	88.31 178	55.72 346	84.45 255	86.63 256	76.79 69	78.26 172	90.55 143	59.30 205	89.70 283	66.63 236	77.05 291	90.88 182

UniMVSNet_ETH3D		79.10 188	78.24 186	81.70 212	86.85 232	60.24 288	87.28 176	88.79 206	74.25 134	76.84 203	90.53 144	49.48 301	91.56 239	67.98 223	82.15 231	93.29 97

ACMP	74.13 6	81.51 133	80.57 135	84.36 113	89.42 131	68.69 119	89.97 77	91.50 118	74.46 127	75.04 257	90.41 145	53.82 249	94.54 109	77.56 127	82.91 222	89.86 231
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
RRT-MVS		82.60 114	82.10 113	84.10 128	87.98 195	62.94 254	87.45 170	91.27 121	77.42 51	79.85 144	90.28 146	56.62 228	94.70 107	79.87 110	88.15 144	94.67 28

PCF-MVS	73.52 7	80.38 158	78.84 173	85.01 91	87.71 208	68.99 106	83.65 270	91.46 119	63.00 326	77.77 184	90.28 146	66.10 121	95.09 91	61.40 282	88.22 143	90.94 181
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 122	68.32 128					90.24 148

HQP-MVS		82.61 112	82.02 116	84.37 112	89.33 136	66.98 165	89.17 105	92.19 90	76.41 79	77.23 195	90.23 149	60.17 203	95.11 87	77.47 128	85.99 177	91.03 177

PS-MVSNAJss		82.07 119	81.31 123	84.34 115	86.51 241	67.27 158	89.27 102	91.51 115	71.75 182	79.37 150	90.22 150	63.15 150	94.27 118	77.69 126	82.36 230	91.49 163

TSAR-MVS + GP.		85.71 59	85.33 67	86.84 50	91.34 81	72.50 36	89.07 113	87.28 241	76.41 79	85.80 60	90.22 150	74.15 31	95.37 78	81.82 90	91.88 83	92.65 125

SDMVSNet		80.38 158	80.18 144	80.99 232	89.03 152	64.94 207	80.45 318	89.40 180	75.19 108	76.61 212	89.98 152	60.61 197	87.69 315	76.83 137	83.55 212	90.33 205

sd_testset		77.70 225	77.40 209	78.60 278	89.03 152	60.02 290	79.00 338	85.83 269	75.19 108	76.61 212	89.98 152	54.81 236	85.46 337	62.63 269	83.55 212	90.33 205

TranMVSNet+NR-MVSNet		80.84 142	80.31 141	82.42 200	87.85 200	62.33 259	87.74 162	91.33 120	80.55 9	77.99 180	89.86 154	65.23 131	92.62 194	67.05 234	75.24 327	92.30 139

diffmvs		82.10 117	81.88 119	82.76 195	83.00 316	63.78 231	83.68 269	89.76 169	72.94 167	82.02 118	89.85 155	65.96 126	90.79 265	82.38 87	87.30 155	93.71 75
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-RMVSNet		79.61 171	78.44 180	83.14 172	89.38 135	65.93 181	84.95 241	87.15 246	73.56 150	78.19 174	89.79 156	56.67 227	93.36 162	59.53 297	86.74 163	90.13 213

GeoE		81.71 126	81.01 130	83.80 151	89.51 127	64.45 219	88.97 115	88.73 212	71.27 193	78.63 163	89.76 157	66.32 119	93.20 172	69.89 205	86.02 176	93.74 74

AdaColmap		80.58 155	79.42 158	84.06 136	93.09 57	68.91 108	89.36 100	88.97 202	69.27 238	75.70 231	89.69 158	57.20 223	95.77 59	63.06 263	88.41 141	87.50 298

ACMM	73.20 8	80.78 149	79.84 150	83.58 155	89.31 139	68.37 127	89.99 76	91.60 112	70.28 215	77.25 193	89.66 159	53.37 254	93.53 154	74.24 163	82.85 223	88.85 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 212	76.79 223	81.97 208	90.40 102	71.07 65	87.59 165	84.55 283	66.03 290	72.38 294	89.64 160	57.56 218	86.04 329	59.61 296	83.35 217	88.79 267

test_yl		81.17 136	80.47 138	83.24 167	89.13 147	63.62 232	86.21 210	89.95 164	72.43 175	81.78 123	89.61 161	57.50 219	93.58 149	70.75 194	86.90 160	92.52 128

DCV-MVSNet		81.17 136	80.47 138	83.24 167	89.13 147	63.62 232	86.21 210	89.95 164	72.43 175	81.78 123	89.61 161	57.50 219	93.58 149	70.75 194	86.90 160	92.52 128

EI-MVSNet-Vis-set		84.19 80	83.81 85	85.31 81	88.18 182	67.85 139	87.66 163	89.73 171	80.05 14	82.95 107	89.59 163	70.74 69	94.82 101	80.66 103	84.72 188	93.28 98

PAPR		81.66 129	80.89 132	83.99 144	90.27 104	64.00 226	86.76 195	91.77 109	68.84 253	77.13 202	89.50 164	67.63 105	94.88 99	67.55 227	88.52 138	93.09 107

jajsoiax		79.29 183	77.96 191	83.27 165	84.68 276	66.57 171	89.25 103	90.16 158	69.20 243	75.46 237	89.49 165	45.75 336	93.13 178	76.84 136	80.80 248	90.11 215

MVSFormer		82.85 109	82.05 115	85.24 83	87.35 217	70.21 80	90.50 64	90.38 147	68.55 257	81.32 127	89.47 166	61.68 172	93.46 158	78.98 113	90.26 109	92.05 150

jason		81.39 134	80.29 142	84.70 103	86.63 240	69.90 88	85.95 216	86.77 254	63.24 322	81.07 133	89.47 166	61.08 188	92.15 216	78.33 121	90.07 114	92.05 150
jason: jason.
mvs_tets		79.13 187	77.77 200	83.22 169	84.70 275	66.37 173	89.17 105	90.19 157	69.38 236	75.40 240	89.46 168	44.17 348	93.15 176	76.78 138	80.70 250	90.14 212

UGNet		80.83 143	79.59 155	84.54 106	88.04 191	68.09 134	89.42 96	88.16 219	76.95 64	76.22 221	89.46 168	49.30 305	93.94 131	68.48 220	90.31 107	91.60 157
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		80.60 152	80.55 136	80.76 238	88.07 190	60.80 279	86.86 189	91.58 113	75.67 97	80.24 140	89.45 170	63.34 144	90.25 272	70.51 198	79.22 269	91.23 170

MVS_Test		83.15 103	83.06 97	83.41 161	86.86 231	63.21 245	86.11 213	92.00 95	74.31 131	82.87 109	89.44 171	70.03 76	93.21 169	77.39 130	88.50 139	93.81 71

EI-MVSNet-UG-set		83.81 85	83.38 92	85.09 89	87.87 199	67.53 149	87.44 171	89.66 172	79.74 16	82.23 116	89.41 172	70.24 75	94.74 104	79.95 108	83.92 202	92.99 116

RPSCF		73.23 293	71.46 297	78.54 281	82.50 328	59.85 291	82.18 292	82.84 316	58.96 364	71.15 308	89.41 172	45.48 340	84.77 344	58.82 305	71.83 357	91.02 179

UniMVSNet_NR-MVSNet		81.88 122	81.54 122	82.92 184	88.46 172	63.46 239	87.13 178	92.37 81	80.19 12	78.38 169	89.14 174	71.66 57	93.05 183	70.05 202	76.46 300	92.25 141

tttt0517		79.40 180	77.91 193	83.90 148	88.10 188	63.84 229	88.37 140	84.05 291	71.45 190	76.78 206	89.12 175	49.93 298	94.89 98	70.18 201	83.18 220	92.96 117

DU-MVS		81.12 138	80.52 137	82.90 185	87.80 203	63.46 239	87.02 183	91.87 103	79.01 27	78.38 169	89.07 176	65.02 133	93.05 183	70.05 202	76.46 300	92.20 144

NR-MVSNet		80.23 162	79.38 159	82.78 193	87.80 203	63.34 242	86.31 207	91.09 129	79.01 27	72.17 297	89.07 176	67.20 110	92.81 192	66.08 241	75.65 313	92.20 144

DELS-MVS		85.41 65	85.30 69	85.77 72	88.49 170	67.93 138	85.52 232	93.44 27	78.70 30	83.63 102	89.03 178	74.57 24	95.71 61	80.26 106	94.04 61	93.66 76
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		80.60 152	79.38 159	84.27 121	89.74 121	67.24 160	87.47 168	86.95 249	70.02 220	75.38 241	88.93 179	51.24 281	92.56 199	75.47 153	89.22 125	93.00 115

baseline1		76.98 237	76.75 226	77.66 296	88.13 186	55.66 347	85.12 236	81.89 324	73.04 165	76.79 205	88.90 180	62.43 161	87.78 314	63.30 262	71.18 361	89.55 241

DP-MVS		76.78 241	74.57 258	83.42 159	93.29 48	69.46 97	88.55 133	83.70 295	63.98 318	70.20 314	88.89 181	54.01 248	94.80 102	46.66 381	81.88 236	86.01 330

ab-mvs		79.51 174	78.97 171	81.14 228	88.46 172	60.91 277	83.84 266	89.24 189	70.36 212	79.03 154	88.87 182	63.23 148	90.21 273	65.12 248	82.57 228	92.28 140

PEN-MVS		77.73 222	77.69 204	77.84 293	87.07 230	53.91 364	87.91 157	91.18 124	77.56 46	73.14 283	88.82 183	61.23 184	89.17 292	59.95 292	72.37 351	90.43 201

tt0805		78.73 196	77.83 196	81.43 218	85.17 265	60.30 287	89.41 97	90.90 132	71.21 194	77.17 200	88.73 184	46.38 325	93.21 169	72.57 181	78.96 270	90.79 184

test_djsdf		80.30 161	79.32 162	83.27 165	83.98 291	65.37 197	90.50 64	90.38 147	68.55 257	76.19 222	88.70 185	56.44 229	93.46 158	78.98 113	80.14 258	90.97 180

PAPM		77.68 226	76.40 233	81.51 216	87.29 224	61.85 266	83.78 267	89.59 175	64.74 305	71.23 306	88.70 185	62.59 157	93.66 148	52.66 347	87.03 159	89.01 256

DTE-MVSNet		76.99 236	76.80 222	77.54 301	86.24 244	53.06 373	87.52 166	90.66 138	77.08 62	72.50 291	88.67 187	60.48 199	89.52 285	57.33 320	70.74 363	90.05 222

PS-CasMVS		78.01 216	78.09 189	77.77 295	87.71 208	54.39 361	88.02 151	91.22 122	77.50 49	73.26 281	88.64 188	60.73 191	88.41 307	61.88 277	73.88 340	90.53 197

cdsmvs_eth3d_5k		19.96 398	26.61 400	0.00 418	0.00 441	0.00 443	0.00 429	89.26 188	0.00 436	0.00 437	88.61 189	61.62 174	0.00 437	0.00 436	0.00 435	0.00 433

lupinMVS		81.39 134	80.27 143	84.76 102	87.35 217	70.21 80	85.55 228	86.41 259	62.85 329	81.32 127	88.61 189	61.68 172	92.24 214	78.41 120	90.26 109	91.83 153

F-COLMAP		76.38 251	74.33 264	82.50 199	89.28 141	66.95 168	88.41 136	89.03 197	64.05 316	66.83 351	88.61 189	46.78 322	92.89 188	57.48 317	78.55 272	87.67 292

mvs_anonymous		79.42 179	79.11 168	80.34 246	84.45 282	57.97 309	82.59 288	87.62 234	67.40 272	76.17 225	88.56 192	68.47 96	89.59 284	70.65 197	86.05 175	93.47 90

CP-MVSNet		78.22 207	78.34 183	77.84 293	87.83 202	54.54 359	87.94 155	91.17 125	77.65 41	73.48 279	88.49 193	62.24 165	88.43 306	62.19 273	74.07 336	90.55 196

PVSNet_Blended_VisFu		82.62 111	81.83 120	84.96 93	90.80 94	69.76 90	88.74 126	91.70 110	69.39 235	78.96 155	88.46 194	65.47 129	94.87 100	74.42 160	88.57 136	90.24 209

CANet_DTU		80.61 151	79.87 149	82.83 187	85.60 257	63.17 248	87.36 172	88.65 213	76.37 83	75.88 228	88.44 195	53.51 252	93.07 181	73.30 172	89.74 119	92.25 141

PLC	70.83 11	78.05 214	76.37 234	83.08 176	91.88 77	67.80 141	88.19 146	89.46 179	64.33 311	69.87 323	88.38 196	53.66 250	93.58 149	58.86 304	82.73 225	87.86 289
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 175	79.22 166	80.27 248	88.79 160	58.35 302	85.06 238	88.61 215	78.56 31	77.65 185	88.34 197	63.81 143	90.66 268	64.98 250	77.22 289	91.80 155

XXY-MVS		75.41 265	75.56 243	74.96 327	83.59 300	57.82 313	80.59 315	83.87 294	66.54 284	74.93 259	88.31 198	63.24 147	80.09 372	62.16 274	76.85 295	86.97 312

Effi-MVS+		83.62 93	83.08 96	85.24 83	88.38 176	67.45 150	88.89 118	89.15 193	75.50 99	82.27 115	88.28 199	69.61 82	94.45 114	77.81 125	87.84 146	93.84 69

API-MVS		81.99 121	81.23 125	84.26 123	90.94 90	70.18 85	91.10 55	89.32 183	71.51 189	78.66 162	88.28 199	65.26 130	95.10 90	64.74 252	91.23 95	87.51 297

thisisatest0530		79.40 180	77.76 201	84.31 116	87.69 210	65.10 203	87.36 172	84.26 289	70.04 219	77.42 189	88.26 201	49.94 296	94.79 103	70.20 200	84.70 189	93.03 112

hse-mvs2		81.72 125	80.94 131	84.07 134	88.72 163	67.68 144	85.87 219	87.26 243	76.02 90	84.67 76	88.22 202	61.54 175	93.48 156	82.71 83	73.44 345	91.06 175

xiu_mvs_v1_base_debu		80.80 146	79.72 152	84.03 141	87.35 217	70.19 82	85.56 225	88.77 207	69.06 247	81.83 119	88.16 203	50.91 284	92.85 189	78.29 122	87.56 149	89.06 251

xiu_mvs_v1_base		80.80 146	79.72 152	84.03 141	87.35 217	70.19 82	85.56 225	88.77 207	69.06 247	81.83 119	88.16 203	50.91 284	92.85 189	78.29 122	87.56 149	89.06 251

xiu_mvs_v1_base_debi		80.80 146	79.72 152	84.03 141	87.35 217	70.19 82	85.56 225	88.77 207	69.06 247	81.83 119	88.16 203	50.91 284	92.85 189	78.29 122	87.56 149	89.06 251

UniMVSNet (Re)		81.60 130	81.11 127	83.09 174	88.38 176	64.41 220	87.60 164	93.02 45	78.42 33	78.56 165	88.16 203	69.78 79	93.26 165	69.58 209	76.49 299	91.60 157

AUN-MVS		79.21 185	77.60 206	84.05 139	88.71 164	67.61 146	85.84 221	87.26 243	69.08 246	77.23 195	88.14 207	53.20 256	93.47 157	75.50 152	73.45 344	91.06 175

Anonymous20231211		78.97 192	77.69 204	82.81 189	90.54 99	64.29 222	90.11 75	91.51 115	65.01 303	76.16 226	88.13 208	50.56 289	93.03 186	69.68 208	77.56 287	91.11 173

pm-mvs1		77.25 234	76.68 228	78.93 273	84.22 285	58.62 300	86.41 203	88.36 218	71.37 191	73.31 280	88.01 209	61.22 185	89.15 293	64.24 256	73.01 348	89.03 255

LTVRE_ROB	69.57 13	76.25 252	74.54 260	81.41 219	88.60 167	64.38 221	79.24 333	89.12 196	70.76 204	69.79 325	87.86 210	49.09 308	93.20 172	56.21 331	80.16 256	86.65 319
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		75.12 270	75.19 252	74.91 328	90.40 102	45.09 409	80.29 321	78.42 361	78.37 36	76.54 214	87.75 211	44.36 346	87.28 318	57.04 323	83.49 214	92.37 135

WTY-MVS		75.65 260	75.68 240	75.57 318	86.40 242	56.82 327	77.92 356	82.40 319	65.10 300	76.18 223	87.72 212	63.13 153	80.90 369	60.31 290	81.96 234	89.00 258

TAMVS		78.89 194	77.51 208	83.03 179	87.80 203	67.79 142	84.72 245	85.05 278	67.63 267	76.75 207	87.70 213	62.25 164	90.82 264	58.53 308	87.13 157	90.49 199

BH-untuned		79.47 176	78.60 176	82.05 205	89.19 145	65.91 182	86.07 214	88.52 216	72.18 177	75.42 239	87.69 214	61.15 186	93.54 153	60.38 289	86.83 162	86.70 318

COLMAP_ROB	66.92 17	73.01 296	70.41 311	80.81 237	87.13 228	65.63 190	88.30 143	84.19 290	62.96 327	63.80 378	87.69 214	38.04 383	92.56 199	46.66 381	74.91 330	84.24 357
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 275	72.42 288	79.80 257	83.76 297	59.59 295	85.92 218	86.64 255	66.39 285	66.96 349	87.58 216	39.46 374	91.60 236	65.76 244	69.27 369	88.22 282

FA-MVS(test-final)		80.96 140	79.91 148	84.10 128	88.30 179	65.01 204	84.55 251	90.01 162	73.25 161	79.61 147	87.57 217	58.35 211	94.72 105	71.29 190	86.25 171	92.56 127

Baseline_NR-MVSNet		78.15 211	78.33 184	77.61 298	85.79 252	56.21 340	86.78 193	85.76 270	73.60 149	77.93 181	87.57 217	65.02 133	88.99 295	67.14 233	75.33 324	87.63 293

WR-MVS_H		78.51 202	78.49 178	78.56 280	88.02 192	56.38 336	88.43 135	92.67 67	77.14 59	73.89 273	87.55 219	66.25 120	89.24 291	58.92 303	73.55 343	90.06 221

EI-MVSNet		80.52 156	79.98 146	82.12 203	84.28 283	63.19 247	86.41 203	88.95 203	74.18 136	78.69 160	87.54 220	66.62 113	92.43 204	72.57 181	80.57 252	90.74 188

CVMVSNet		72.99 297	72.58 286	74.25 336	84.28 283	50.85 388	86.41 203	83.45 301	44.56 407	73.23 282	87.54 220	49.38 303	85.70 332	65.90 242	78.44 275	86.19 325

ACMH+	68.96 14	76.01 256	74.01 266	82.03 206	88.60 167	65.31 198	88.86 119	87.55 235	70.25 217	67.75 340	87.47 222	41.27 366	93.19 174	58.37 310	75.94 310	87.60 294

TransMVSNet (Re)		75.39 267	74.56 259	77.86 292	85.50 259	57.10 324	86.78 193	86.09 267	72.17 178	71.53 304	87.34 223	63.01 154	89.31 289	56.84 326	61.83 390	87.17 305

GBi-Net		78.40 203	77.40 209	81.40 220	87.60 212	63.01 249	88.39 137	89.28 185	71.63 184	75.34 243	87.28 224	54.80 237	91.11 255	62.72 265	79.57 262	90.09 217

test1		78.40 203	77.40 209	81.40 220	87.60 212	63.01 249	88.39 137	89.28 185	71.63 184	75.34 243	87.28 224	54.80 237	91.11 255	62.72 265	79.57 262	90.09 217

FMVSNet2		78.20 209	77.21 213	81.20 226	87.60 212	62.89 255	87.47 168	89.02 198	71.63 184	75.29 249	87.28 224	54.80 237	91.10 258	62.38 270	79.38 266	89.61 239

FMVSNet1		77.44 229	76.12 236	81.40 220	86.81 234	63.01 249	88.39 137	89.28 185	70.49 211	74.39 268	87.28 224	49.06 309	91.11 255	60.91 286	78.52 273	90.09 217

v2v482		80.23 162	79.29 163	83.05 178	83.62 299	64.14 224	87.04 181	89.97 163	73.61 148	78.18 175	87.22 228	61.10 187	93.82 139	76.11 142	76.78 297	91.18 171

ITE_SJBPF				78.22 287	81.77 338	60.57 282		83.30 302	69.25 240	67.54 342	87.20 229	36.33 388	87.28 318	54.34 338	74.62 333	86.80 315

anonymousdsp		78.60 200	77.15 214	82.98 182	80.51 357	67.08 163	87.24 177	89.53 177	65.66 294	75.16 252	87.19 230	52.52 258	92.25 213	77.17 132	79.34 267	89.61 239

MVSTER		79.01 190	77.88 195	82.38 201	83.07 313	64.80 211	84.08 265	88.95 203	69.01 250	78.69 160	87.17 231	54.70 241	92.43 204	74.69 157	80.57 252	89.89 230

thres100view900		76.50 245	75.55 244	79.33 266	89.52 126	56.99 325	85.83 222	83.23 304	73.94 140	76.32 219	87.12 232	51.89 273	91.95 223	48.33 372	83.75 206	89.07 249

thres600view7		76.50 245	75.44 245	79.68 260	89.40 133	57.16 322	85.53 230	83.23 304	73.79 144	76.26 220	87.09 233	51.89 273	91.89 226	48.05 377	83.72 209	90.00 223

XVG-ACMP-BASELINE		76.11 254	74.27 265	81.62 213	83.20 309	64.67 213	83.60 273	89.75 170	69.75 230	71.85 300	87.09 233	32.78 395	92.11 217	69.99 204	80.43 254	88.09 285

HY-MVS	69.67 12	77.95 217	77.15 214	80.36 245	87.57 216	60.21 289	83.37 277	87.78 232	66.11 287	75.37 242	87.06 235	63.27 146	90.48 270	61.38 283	82.43 229	90.40 203

CHOSEN 1792x2688		77.63 227	75.69 239	83.44 158	89.98 115	68.58 122	78.70 343	87.50 237	56.38 382	75.80 230	86.84 236	58.67 208	91.40 249	61.58 281	85.75 181	90.34 204

v8		79.97 168	79.02 170	82.80 190	84.09 288	64.50 217	87.96 153	90.29 154	74.13 138	75.24 250	86.81 237	62.88 155	93.89 138	74.39 161	75.40 322	90.00 223

AllTest		70.96 313	68.09 328	79.58 263	85.15 267	63.62 232	84.58 250	79.83 349	62.31 336	60.32 390	86.73 238	32.02 396	88.96 298	50.28 361	71.57 359	86.15 326

TestCases				79.58 263	85.15 267	63.62 232		79.83 349	62.31 336	60.32 390	86.73 238	32.02 396	88.96 298	50.28 361	71.57 359	86.15 326

LCM-MVSNet-Re		77.05 235	76.94 219	77.36 302	87.20 225	51.60 381	80.06 323	80.46 342	75.20 107	67.69 341	86.72 240	62.48 159	88.98 296	63.44 260	89.25 124	91.51 161

1112_ss		77.40 231	76.43 232	80.32 247	89.11 151	60.41 286	83.65 270	87.72 233	62.13 339	73.05 284	86.72 240	62.58 158	89.97 277	62.11 276	80.80 248	90.59 195

ab-mvs-re		7.23 401	9.64 404	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	86.72 240	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

IterMVS-LS		80.06 165	79.38 159	82.11 204	85.89 251	63.20 246	86.79 192	89.34 182	74.19 135	75.45 238	86.72 240	66.62 113	92.39 206	72.58 180	76.86 294	90.75 187
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 257	73.93 268	81.77 211	88.71 164	66.61 170	88.62 131	89.01 199	69.81 226	66.78 352	86.70 244	41.95 364	91.51 244	55.64 332	78.14 279	87.17 305
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 250	75.44 245	79.27 267	89.28 141	58.09 305	81.69 297	87.07 247	59.53 359	72.48 292	86.67 245	61.30 182	89.33 288	60.81 288	80.15 257	90.41 202

FMVSNet3		77.88 219	76.85 221	80.97 234	86.84 233	62.36 258	86.52 201	88.77 207	71.13 195	75.34 243	86.66 246	54.07 247	91.10 258	62.72 265	79.57 262	89.45 243

pmmvs6		74.69 272	73.39 275	78.61 277	81.38 346	57.48 319	86.64 197	87.95 226	64.99 304	70.18 315	86.61 247	50.43 291	89.52 285	62.12 275	70.18 366	88.83 265

ET-MVSNet_ETH3D		78.63 199	76.63 229	84.64 104	86.73 236	69.47 95	85.01 239	84.61 282	69.54 233	66.51 359	86.59 248	50.16 293	91.75 231	76.26 141	84.24 199	92.69 123

testgi		66.67 350	66.53 347	67.08 384	75.62 390	41.69 419	75.93 365	76.50 376	66.11 287	65.20 369	86.59 248	35.72 390	74.71 404	43.71 393	73.38 346	84.84 351

CLD-MVS		82.31 115	81.65 121	84.29 118	88.47 171	67.73 143	85.81 223	92.35 82	75.78 93	78.33 171	86.58 250	64.01 140	94.35 115	76.05 144	87.48 152	90.79 184
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 170	78.67 174	82.97 183	84.06 289	64.95 206	87.88 159	90.62 139	73.11 163	75.11 254	86.56 251	61.46 178	94.05 127	73.68 166	75.55 315	89.90 229

CDS-MVSNet		79.07 189	77.70 203	83.17 171	87.60 212	68.23 131	84.40 258	86.20 264	67.49 270	76.36 218	86.54 252	61.54 175	90.79 265	61.86 278	87.33 154	90.49 199
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 127	81.05 128	83.60 154	89.15 146	68.03 137	84.46 254	90.02 161	70.67 205	81.30 130	86.53 253	63.17 149	94.19 123	75.60 150	88.54 137	88.57 276

TR-MVS		77.44 229	76.18 235	81.20 226	88.24 180	63.24 244	84.61 249	86.40 260	67.55 269	77.81 182	86.48 254	54.10 246	93.15 176	57.75 316	82.72 226	87.20 304

EIA-MVS		83.31 102	82.80 103	84.82 99	89.59 123	65.59 191	88.21 145	92.68 66	74.66 123	78.96 155	86.42 255	69.06 90	95.26 80	75.54 151	90.09 112	93.62 83

tfpn200view9		76.42 249	75.37 249	79.55 265	89.13 147	57.65 316	85.17 233	83.60 296	73.41 156	76.45 215	86.39 256	52.12 265	91.95 223	48.33 372	83.75 206	89.07 249

thres400		76.50 245	75.37 249	79.86 255	89.13 147	57.65 316	85.17 233	83.60 296	73.41 156	76.45 215	86.39 256	52.12 265	91.95 223	48.33 372	83.75 206	90.00 223

v7n		78.97 192	77.58 207	83.14 172	83.45 303	65.51 192	88.32 142	91.21 123	73.69 146	72.41 293	86.32 258	57.93 213	93.81 140	69.18 212	75.65 313	90.11 215

MAR-MVS		81.84 123	80.70 133	85.27 82	91.32 82	71.53 56	89.82 79	90.92 131	69.77 229	78.50 166	86.21 259	62.36 162	94.52 111	65.36 246	92.05 82	89.77 235
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		80.03 166	79.03 169	83.01 180	83.78 296	64.51 215	87.11 180	90.57 142	71.96 181	78.08 178	86.20 260	61.41 179	93.94 131	74.93 156	77.23 288	90.60 194

test_vis1_n_1920		75.52 262	75.78 238	74.75 332	79.84 365	57.44 320	83.26 278	85.52 272	62.83 330	79.34 152	86.17 261	45.10 341	79.71 373	78.75 115	81.21 242	87.10 311

V42		79.38 182	78.24 186	82.83 187	81.10 351	65.50 193	85.55 228	89.82 166	71.57 188	78.21 173	86.12 262	60.66 195	93.18 175	75.64 148	75.46 319	89.81 234

PVSNet_BlendedMVS		80.60 152	80.02 145	82.36 202	88.85 154	65.40 194	86.16 212	92.00 95	69.34 237	78.11 176	86.09 263	66.02 124	94.27 118	71.52 186	82.06 233	87.39 299

v1192		79.59 173	78.43 181	83.07 177	83.55 301	64.52 214	86.93 187	90.58 140	70.83 201	77.78 183	85.90 264	59.15 206	93.94 131	73.96 165	77.19 290	90.76 186

SixPastTwentyTwo		73.37 288	71.26 302	79.70 259	85.08 270	57.89 311	85.57 224	83.56 298	71.03 199	65.66 363	85.88 265	42.10 362	92.57 198	59.11 301	63.34 388	88.65 273

EPNet_dtu		75.46 263	74.86 255	77.23 305	82.57 327	54.60 358	86.89 188	83.09 308	71.64 183	66.25 361	85.86 266	55.99 230	88.04 311	54.92 335	86.55 166	89.05 254
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 285	73.64 273	73.51 343	82.80 321	55.01 355	76.12 364	81.69 327	62.47 335	74.68 263	85.85 267	57.32 221	78.11 380	60.86 287	80.93 244	87.39 299

ETV-MVS		84.90 76	84.67 76	85.59 75	89.39 134	68.66 120	88.74 126	92.64 72	79.97 15	84.10 90	85.71 268	69.32 85	95.38 75	80.82 100	91.37 93	92.72 120

test_cas_vis1_n_1920		73.76 283	73.74 272	73.81 341	75.90 387	59.77 292	80.51 316	82.40 319	58.30 369	81.62 125	85.69 269	44.35 347	76.41 391	76.29 140	78.61 271	85.23 343

v1240		78.99 191	77.78 199	82.64 196	83.21 308	63.54 236	86.62 198	90.30 153	69.74 232	77.33 191	85.68 270	57.04 224	93.76 144	73.13 175	76.92 292	90.62 192

v144192		79.47 176	78.37 182	82.78 193	83.35 304	63.96 227	86.96 184	90.36 150	69.99 222	77.50 187	85.67 271	60.66 195	93.77 143	74.27 162	76.58 298	90.62 192

tfpnnormal		74.39 273	73.16 279	78.08 290	86.10 250	58.05 306	84.65 248	87.53 236	70.32 214	71.22 307	85.63 272	54.97 235	89.86 278	43.03 395	75.02 329	86.32 322

PS-MVSNAJ		81.69 127	81.02 129	83.70 152	89.51 127	68.21 132	84.28 260	90.09 160	70.79 202	81.26 131	85.62 273	63.15 150	94.29 116	75.62 149	88.87 130	88.59 275

SSC-MVS3.2		73.35 291	73.39 275	73.23 344	85.30 263	49.01 395	74.58 379	81.57 328	75.21 106	73.68 276	85.58 274	52.53 257	82.05 362	54.33 339	77.69 285	88.63 274

v1921920		79.22 184	78.03 190	82.80 190	83.30 306	63.94 228	86.80 191	90.33 151	69.91 225	77.48 188	85.53 275	58.44 210	93.75 145	73.60 167	76.85 295	90.71 190

test_0402		72.79 299	70.44 310	79.84 256	88.13 186	65.99 180	85.93 217	84.29 287	65.57 295	67.40 346	85.49 276	46.92 321	92.61 195	35.88 409	74.38 335	80.94 388

v148		78.72 197	77.80 198	81.47 217	82.73 323	61.96 265	86.30 208	88.08 222	73.26 160	76.18 223	85.47 277	62.46 160	92.36 208	71.92 185	73.82 341	90.09 217

USDC		70.33 321	68.37 323	76.21 312	80.60 355	56.23 339	79.19 335	86.49 258	60.89 347	61.29 386	85.47 277	31.78 398	89.47 287	53.37 344	76.21 308	82.94 375

MVP-Stereo		76.12 253	74.46 262	81.13 229	85.37 262	69.79 89	84.42 257	87.95 226	65.03 302	67.46 344	85.33 279	53.28 255	91.73 233	58.01 314	83.27 218	81.85 383
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 210	76.99 218	81.78 210	85.66 254	66.99 164	84.66 246	90.47 144	55.08 387	72.02 299	85.27 280	63.83 142	94.11 126	66.10 240	89.80 118	84.24 357

DIV-MVS_self_test		77.72 223	76.76 224	80.58 241	82.48 330	60.48 284	83.09 282	87.86 229	69.22 241	74.38 269	85.24 281	62.10 167	91.53 242	71.09 191	75.40 322	89.74 236

FE-MVS		77.78 221	75.68 240	84.08 133	88.09 189	66.00 179	83.13 281	87.79 231	68.42 261	78.01 179	85.23 282	45.50 339	95.12 85	59.11 301	85.83 180	91.11 173

cl____		77.72 223	76.76 224	80.58 241	82.49 329	60.48 284	83.09 282	87.87 228	69.22 241	74.38 269	85.22 283	62.10 167	91.53 242	71.09 191	75.41 321	89.73 237

HyFIR lowres test		77.53 228	75.40 247	83.94 147	89.59 123	66.62 169	80.36 319	88.64 214	56.29 383	76.45 215	85.17 284	57.64 217	93.28 164	61.34 284	83.10 221	91.91 152

pmmvs4		74.03 281	71.91 292	80.39 244	81.96 335	68.32 128	81.45 301	82.14 321	59.32 360	69.87 323	85.13 285	52.40 261	88.13 310	60.21 291	74.74 332	84.73 353

TDRefinement		67.49 343	64.34 354	76.92 307	73.47 402	61.07 275	84.86 243	82.98 312	59.77 356	58.30 397	85.13 285	26.06 406	87.89 312	47.92 378	60.59 395	81.81 384

Fast-Effi-MVS+		80.81 144	79.92 147	83.47 157	88.85 154	64.51 215	85.53 230	89.39 181	70.79 202	78.49 167	85.06 287	67.54 106	93.58 149	67.03 235	86.58 165	92.32 138

PVSNet_Blended		80.98 139	80.34 140	82.90 185	88.85 154	65.40 194	84.43 256	92.00 95	67.62 268	78.11 176	85.05 288	66.02 124	94.27 118	71.52 186	89.50 121	89.01 256

ttmdpeth		59.91 370	57.10 374	68.34 379	67.13 416	46.65 403	74.64 378	67.41 406	48.30 402	62.52 384	85.04 289	20.40 416	75.93 396	42.55 397	45.90 417	82.44 378

test_fmvs1_n		70.86 315	70.24 313	72.73 351	72.51 409	55.28 352	81.27 304	79.71 351	51.49 398	78.73 159	84.87 290	27.54 405	77.02 385	76.06 143	79.97 260	85.88 334

WBMVS		73.43 287	72.81 283	75.28 324	87.91 197	50.99 387	78.59 346	81.31 333	65.51 298	74.47 267	84.83 291	46.39 324	86.68 322	58.41 309	77.86 281	88.17 284

CMPMVS	51.72 21	70.19 323	68.16 326	76.28 311	73.15 405	57.55 318	79.47 330	83.92 292	48.02 403	56.48 403	84.81 292	43.13 354	86.42 326	62.67 268	81.81 237	84.89 350
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 338	67.61 338	71.31 363	78.51 379	47.01 401	84.47 252	84.27 288	42.27 410	66.44 360	84.79 293	40.44 371	83.76 349	58.76 306	68.54 374	83.17 369

BH-w/o		78.21 208	77.33 212	80.84 236	88.81 158	65.13 202	84.87 242	87.85 230	69.75 230	74.52 266	84.74 294	61.34 181	93.11 179	58.24 312	85.84 179	84.27 356

pmmvs5		71.55 308	70.20 314	75.61 317	77.83 380	56.39 335	81.74 296	80.89 334	57.76 373	67.46 344	84.49 295	49.26 306	85.32 339	57.08 322	75.29 325	85.11 347

reproduce_monomvs		75.40 266	74.38 263	78.46 285	83.92 293	57.80 314	83.78 267	86.94 250	73.47 154	72.25 296	84.47 296	38.74 378	89.27 290	75.32 154	70.53 364	88.31 281

thres200		75.55 261	74.47 261	78.82 274	87.78 206	57.85 312	83.07 284	83.51 299	72.44 174	75.84 229	84.42 297	52.08 268	91.75 231	47.41 379	83.64 211	86.86 314

test_fmvs1		70.93 314	70.52 308	72.16 355	73.71 398	55.05 354	80.82 307	78.77 359	51.21 399	78.58 164	84.41 298	31.20 400	76.94 386	75.88 146	80.12 259	84.47 355

testing3		68.56 337	67.67 337	71.22 364	87.33 222	42.87 414	83.06 285	71.54 394	70.36 212	69.08 331	84.38 299	30.33 402	85.69 333	37.50 407	75.45 320	85.09 348

test_fmvs2		68.35 340	67.48 340	70.98 366	69.50 412	51.95 376	80.05 324	76.38 377	49.33 401	74.65 264	84.38 299	23.30 414	75.40 402	74.51 159	75.17 328	85.60 337

eth_miper_zixun_eth		77.92 218	76.69 227	81.61 215	83.00 316	61.98 264	83.15 280	89.20 191	69.52 234	74.86 260	84.35 301	61.76 171	92.56 199	71.50 188	72.89 349	90.28 208

myMVS_eth3d28		73.62 284	73.53 274	73.90 340	88.20 181	47.41 399	78.06 353	79.37 354	74.29 133	73.98 272	84.29 302	44.67 342	83.54 352	51.47 353	87.39 153	90.74 188

testing91		76.54 243	75.66 242	79.18 270	88.43 174	55.89 343	81.08 305	83.00 311	73.76 145	75.34 243	84.29 302	46.20 330	90.07 275	64.33 254	84.50 191	91.58 159

c3_l		78.75 195	77.91 193	81.26 224	82.89 320	61.56 270	84.09 264	89.13 195	69.97 223	75.56 233	84.29 302	66.36 118	92.09 218	73.47 170	75.48 317	90.12 214

testing99		76.09 255	75.12 254	79.00 271	88.16 183	55.50 349	80.79 309	81.40 331	73.30 159	75.17 251	84.27 305	44.48 345	90.02 276	64.28 255	84.22 200	91.48 164

UWE-MVS		72.13 305	71.49 296	74.03 338	86.66 239	47.70 397	81.40 303	76.89 375	63.60 321	75.59 232	84.22 306	39.94 373	85.62 334	48.98 369	86.13 174	88.77 268

Fast-Effi-MVS+-dtu		78.02 215	76.49 230	82.62 197	83.16 312	66.96 167	86.94 186	87.45 239	72.45 172	71.49 305	84.17 307	54.79 240	91.58 237	67.61 226	80.31 255	89.30 247

IterMVS-SCA-FT		75.43 264	73.87 270	80.11 251	82.69 324	64.85 210	81.57 299	83.47 300	69.16 244	70.49 311	84.15 308	51.95 271	88.15 309	69.23 211	72.14 355	87.34 301

1314		76.53 244	75.30 251	80.21 249	83.93 292	62.32 260	84.66 246	88.81 205	60.23 352	70.16 317	84.07 309	55.30 234	90.73 267	67.37 229	83.21 219	87.59 296

cl22		78.07 213	77.01 216	81.23 225	82.37 332	61.83 267	83.55 274	87.98 224	68.96 251	75.06 256	83.87 310	61.40 180	91.88 227	73.53 168	76.39 302	89.98 226

EG-PatchMatch MVS		74.04 279	71.82 293	80.71 239	84.92 272	67.42 151	85.86 220	88.08 222	66.04 289	64.22 373	83.85 311	35.10 391	92.56 199	57.44 318	80.83 247	82.16 382

thisisatest0515		77.33 232	75.38 248	83.18 170	85.27 264	63.80 230	82.11 293	83.27 303	65.06 301	75.91 227	83.84 312	49.54 300	94.27 118	67.24 231	86.19 172	91.48 164

test20.03		67.45 344	66.95 345	68.94 373	75.48 391	44.84 410	77.50 358	77.67 365	66.66 278	63.01 380	83.80 313	47.02 320	78.40 378	42.53 398	68.86 373	83.58 366

miper_ehance_all_eth		78.59 201	77.76 201	81.08 230	82.66 325	61.56 270	83.65 270	89.15 193	68.87 252	75.55 234	83.79 314	66.49 116	92.03 219	73.25 173	76.39 302	89.64 238

MSDG		73.36 290	70.99 304	80.49 243	84.51 281	65.80 186	80.71 313	86.13 266	65.70 293	65.46 364	83.74 315	44.60 343	90.91 263	51.13 356	76.89 293	84.74 352

MonoMVSNet		76.49 248	75.80 237	78.58 279	81.55 342	58.45 301	86.36 206	86.22 263	74.87 118	74.73 262	83.73 316	51.79 276	88.73 301	70.78 193	72.15 354	88.55 277

testing11		75.14 269	74.01 266	78.53 282	88.16 183	56.38 336	80.74 312	80.42 343	70.67 205	72.69 290	83.72 317	43.61 352	89.86 278	62.29 272	83.76 205	89.36 245

IterMVS		74.29 274	72.94 282	78.35 286	81.53 343	63.49 238	81.58 298	82.49 318	68.06 265	69.99 320	83.69 318	51.66 278	85.54 335	65.85 243	71.64 358	86.01 330
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 302	71.71 294	74.35 335	82.19 333	52.00 375	79.22 334	77.29 371	64.56 307	72.95 286	83.68 319	51.35 279	83.26 356	58.33 311	75.80 311	87.81 290

UWE-MVS-28		65.32 357	64.93 351	66.49 385	78.70 377	38.55 422	77.86 357	64.39 414	62.00 341	64.13 374	83.60 320	41.44 365	76.00 395	31.39 414	80.89 245	84.92 349

testing222		74.04 279	72.66 285	78.19 288	87.89 198	55.36 350	81.06 306	79.20 357	71.30 192	74.65 264	83.57 321	39.11 377	88.67 303	51.43 355	85.75 181	90.53 197

Effi-MVS+-dtu		80.03 166	78.57 177	84.42 111	85.13 269	68.74 114	88.77 122	88.10 221	74.99 112	74.97 258	83.49 322	57.27 222	93.36 162	73.53 168	80.88 246	91.18 171

baseline2		75.70 259	73.83 271	81.30 223	83.26 307	61.79 268	82.57 289	80.65 338	66.81 274	66.88 350	83.42 323	57.86 215	92.19 215	63.47 259	79.57 262	89.91 228

mvs5depth		69.45 329	67.45 341	75.46 322	73.93 396	55.83 344	79.19 335	83.23 304	66.89 273	71.63 303	83.32 324	33.69 394	85.09 340	59.81 294	55.34 404	85.46 339

TinyColmap		67.30 346	64.81 352	74.76 331	81.92 337	56.68 331	80.29 321	81.49 330	60.33 350	56.27 404	83.22 325	24.77 410	87.66 316	45.52 389	69.47 368	79.95 393

mvsany_test1		62.30 366	61.26 370	65.41 387	69.52 411	54.86 356	66.86 407	49.78 427	46.65 404	68.50 337	83.21 326	49.15 307	66.28 419	56.93 325	60.77 393	75.11 403

test_vis1_n		69.85 327	69.21 318	71.77 357	72.66 408	55.27 353	81.48 300	76.21 378	52.03 395	75.30 248	83.20 327	28.97 403	76.22 393	74.60 158	78.41 277	83.81 363

CostFormer		75.24 268	73.90 269	79.27 267	82.65 326	58.27 304	80.80 308	82.73 317	61.57 343	75.33 247	83.13 328	55.52 232	91.07 261	64.98 250	78.34 278	88.45 278

MVStest1		56.63 374	52.76 380	68.25 380	61.67 422	53.25 372	71.67 388	68.90 404	38.59 415	50.59 411	83.05 329	25.08 408	70.66 412	36.76 408	38.56 418	80.83 389

WB-MVSnew		71.96 307	71.65 295	72.89 349	84.67 279	51.88 378	82.29 291	77.57 366	62.31 336	73.67 277	83.00 330	53.49 253	81.10 368	45.75 388	82.13 232	85.70 336

ETVMVS		72.25 304	71.05 303	75.84 314	87.77 207	51.91 377	79.39 331	74.98 382	69.26 239	73.71 275	82.95 331	40.82 370	86.14 328	46.17 385	84.43 196	89.47 242

miper_lstm_enhance		74.11 278	73.11 280	77.13 306	80.11 361	59.62 294	72.23 386	86.92 252	66.76 276	70.40 312	82.92 332	56.93 225	82.92 357	69.06 214	72.63 350	88.87 263

GA-MVS		76.87 239	75.17 253	81.97 208	82.75 322	62.58 256	81.44 302	86.35 262	72.16 179	74.74 261	82.89 333	46.20 330	92.02 220	68.85 217	81.09 243	91.30 169

K. test v3		71.19 310	68.51 322	79.21 269	83.04 315	57.78 315	84.35 259	76.91 374	72.90 168	62.99 381	82.86 334	39.27 375	91.09 260	61.65 280	52.66 407	88.75 269

MS-PatchMatch		73.83 282	72.67 284	77.30 304	83.87 294	66.02 178	81.82 294	84.66 281	61.37 346	68.61 335	82.82 335	47.29 317	88.21 308	59.27 298	84.32 198	77.68 398

lessismore_v0				78.97 272	81.01 352	57.15 323		65.99 409		61.16 387	82.82 335	39.12 376	91.34 251	59.67 295	46.92 414	88.43 279

D2MVS		74.82 271	73.21 278	79.64 262	79.81 366	62.56 257	80.34 320	87.35 240	64.37 310	68.86 332	82.66 337	46.37 326	90.10 274	67.91 224	81.24 241	86.25 323

Anonymous20231206		68.60 335	67.80 334	71.02 365	80.23 360	50.75 389	78.30 351	80.47 341	56.79 380	66.11 362	82.63 338	46.35 327	78.95 376	43.62 394	75.70 312	83.36 368

MIMVSNet		70.69 317	69.30 316	74.88 329	84.52 280	56.35 338	75.87 368	79.42 353	64.59 306	67.76 339	82.41 339	41.10 367	81.54 365	46.64 383	81.34 239	86.75 317

UBG		73.08 295	72.27 290	75.51 320	88.02 192	51.29 385	78.35 350	77.38 370	65.52 296	73.87 274	82.36 340	45.55 337	86.48 325	55.02 334	84.39 197	88.75 269

OpenMVS_ROB	64.09 19	70.56 319	68.19 325	77.65 297	80.26 358	59.41 297	85.01 239	82.96 313	58.76 366	65.43 365	82.33 341	37.63 385	91.23 254	45.34 391	76.03 309	82.32 379

miper_enhance_ethall		77.87 220	76.86 220	80.92 235	81.65 339	61.38 272	82.68 287	88.98 200	65.52 296	75.47 235	82.30 342	65.76 128	92.00 221	72.95 176	76.39 302	89.39 244

test0.0.03 1		68.00 342	67.69 336	68.90 374	77.55 381	47.43 398	75.70 369	72.95 393	66.66 278	66.56 355	82.29 343	48.06 314	75.87 397	44.97 392	74.51 334	83.41 367

PVSNet	64.34 18	72.08 306	70.87 306	75.69 316	86.21 245	56.44 334	74.37 380	80.73 337	62.06 340	70.17 316	82.23 344	42.86 356	83.31 355	54.77 336	84.45 195	87.32 302

MIMVSNet1		68.58 336	66.78 346	73.98 339	80.07 362	51.82 379	80.77 310	84.37 284	64.40 309	59.75 393	82.16 345	36.47 387	83.63 351	42.73 396	70.33 365	86.48 321

CL-MVSNet_self_test		72.37 302	71.46 297	75.09 326	79.49 372	53.53 366	80.76 311	85.01 279	69.12 245	70.51 310	82.05 346	57.92 214	84.13 347	52.27 349	66.00 382	87.60 294

tpm2		73.26 292	71.46 297	78.63 276	83.34 305	56.71 330	80.65 314	80.40 344	56.63 381	73.55 278	82.02 347	51.80 275	91.24 253	56.35 330	78.42 276	87.95 286

PatchMatch-RL		72.38 301	70.90 305	76.80 309	88.60 167	67.38 154	79.53 329	76.17 379	62.75 332	69.36 328	82.00 348	45.51 338	84.89 343	53.62 342	80.58 251	78.12 397

FMVSNet5		69.50 328	67.96 329	74.15 337	82.97 319	55.35 351	80.01 325	82.12 322	62.56 334	63.02 379	81.53 349	36.92 386	81.92 363	48.42 371	74.06 337	85.17 346

CR-MVSNet		73.37 288	71.27 301	79.67 261	81.32 349	65.19 200	75.92 366	80.30 345	59.92 355	72.73 288	81.19 350	52.50 259	86.69 321	59.84 293	77.71 283	87.11 309

Patchmtry		70.74 316	69.16 319	75.49 321	80.72 353	54.07 363	74.94 377	80.30 345	58.34 368	70.01 318	81.19 350	52.50 259	86.54 323	53.37 344	71.09 362	85.87 335

IB-MVS	68.01 15	75.85 258	73.36 277	83.31 163	84.76 274	66.03 177	83.38 276	85.06 277	70.21 218	69.40 327	81.05 352	45.76 335	94.66 108	65.10 249	75.49 316	89.25 248
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		76.72 242	74.64 257	82.99 181	85.78 253	65.88 183	82.33 290	89.21 190	60.85 348	72.74 287	81.02 353	47.28 318	93.75 145	67.48 228	85.02 184	89.34 246

LF4IMVS		64.02 362	62.19 366	69.50 371	70.90 410	53.29 371	76.13 363	77.18 372	52.65 393	58.59 395	80.98 354	23.55 413	76.52 389	53.06 346	66.66 378	78.68 396

Anonymous20240521		68.80 334	67.22 343	73.55 342	74.33 394	54.11 362	83.18 279	85.61 271	58.15 370	61.68 385	80.94 355	30.71 401	81.27 367	57.00 324	73.34 347	85.28 342

gm-plane-assit					81.40 345	53.83 365			62.72 333		80.94 355		92.39 206	63.40 261

UnsupCasMVSNet_eth		67.33 345	65.99 349	71.37 360	73.48 401	51.47 383	75.16 373	85.19 275	65.20 299	60.78 388	80.93 357	42.35 358	77.20 384	57.12 321	53.69 406	85.44 340

dmvs_re		71.14 311	70.58 307	72.80 350	81.96 335	59.68 293	75.60 370	79.34 355	68.55 257	69.27 330	80.72 358	49.42 302	76.54 388	52.56 348	77.79 282	82.19 381

MDTV_nov1_ep13			69.97 315		83.18 310	53.48 367	77.10 362	80.18 348	60.45 349	69.33 329	80.44 359	48.89 312	86.90 320	51.60 352	78.51 274

pmmvs-eth3d		70.50 320	67.83 333	78.52 283	77.37 383	66.18 176	81.82 294	81.51 329	58.90 365	63.90 377	80.42 360	42.69 357	86.28 327	58.56 307	65.30 384	83.11 371

mmtdpeth		74.16 277	73.01 281	77.60 300	83.72 298	61.13 273	85.10 237	85.10 276	72.06 180	77.21 199	80.33 361	43.84 350	85.75 331	77.14 133	52.61 408	85.91 333

PM-MVS		66.41 352	64.14 355	73.20 347	73.92 397	56.45 333	78.97 339	64.96 413	63.88 320	64.72 370	80.24 362	19.84 418	83.44 354	66.24 237	64.52 386	79.71 394

SCA		74.22 276	72.33 289	79.91 254	84.05 290	62.17 262	79.96 326	79.29 356	66.30 286	72.38 294	80.13 363	51.95 271	88.60 304	59.25 299	77.67 286	88.96 260

Patchmatch-test		64.82 360	63.24 361	69.57 370	79.42 373	49.82 393	63.49 417	69.05 402	51.98 396	59.95 392	80.13 363	50.91 284	70.98 411	40.66 401	73.57 342	87.90 288

tpmrst		72.39 300	72.13 291	73.18 348	80.54 356	49.91 392	79.91 327	79.08 358	63.11 324	71.69 302	79.95 365	55.32 233	82.77 358	65.66 245	73.89 339	86.87 313

DSMNet-mixed		57.77 373	56.90 375	60.38 393	67.70 414	35.61 424	69.18 399	53.97 425	32.30 423	57.49 400	79.88 366	40.39 372	68.57 417	38.78 405	72.37 351	76.97 399

MDA-MVSNet-bldmvs		66.68 349	63.66 359	75.75 315	79.28 374	60.56 283	73.92 382	78.35 362	64.43 308	50.13 412	79.87 367	44.02 349	83.67 350	46.10 386	56.86 398	83.03 373

PatchmatchNet		73.12 294	71.33 300	78.49 284	83.18 310	60.85 278	79.63 328	78.57 360	64.13 312	71.73 301	79.81 368	51.20 282	85.97 330	57.40 319	76.36 307	88.66 272
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Syy-MVS		68.05 341	67.85 331	68.67 377	84.68 276	40.97 420	78.62 344	73.08 391	66.65 281	66.74 353	79.46 369	52.11 267	82.30 360	32.89 412	76.38 305	82.75 376

myMVS_eth3d		67.02 347	66.29 348	69.21 372	84.68 276	42.58 415	78.62 344	73.08 391	66.65 281	66.74 353	79.46 369	31.53 399	82.30 360	39.43 404	76.38 305	82.75 376

ppachtmachnet_test		70.04 324	67.34 342	78.14 289	79.80 367	61.13 273	79.19 335	80.59 339	59.16 362	65.27 366	79.29 371	46.75 323	87.29 317	49.33 367	66.72 377	86.00 332

EPMVS		69.02 332	68.16 326	71.59 358	79.61 370	49.80 394	77.40 359	66.93 407	62.82 331	70.01 318	79.05 372	45.79 334	77.86 382	56.58 328	75.26 326	87.13 308

PMMVS		69.34 330	68.67 321	71.35 362	75.67 389	62.03 263	75.17 372	73.46 389	50.00 400	68.68 333	79.05 372	52.07 269	78.13 379	61.16 285	82.77 224	73.90 404

test-LLR		72.94 298	72.43 287	74.48 333	81.35 347	58.04 307	78.38 347	77.46 367	66.66 278	69.95 321	79.00 374	48.06 314	79.24 374	66.13 238	84.83 186	86.15 326

test-mter		71.41 309	70.39 312	74.48 333	81.35 347	58.04 307	78.38 347	77.46 367	60.32 351	69.95 321	79.00 374	36.08 389	79.24 374	66.13 238	84.83 186	86.15 326

KD-MVS_self_test		68.81 333	67.59 339	72.46 354	74.29 395	45.45 404	77.93 355	87.00 248	63.12 323	63.99 376	78.99 376	42.32 359	84.77 344	56.55 329	64.09 387	87.16 307

test_fmvs3		63.36 364	61.82 367	67.98 381	62.51 421	46.96 402	77.37 360	74.03 388	45.24 406	67.50 343	78.79 377	12.16 426	72.98 410	72.77 179	66.02 381	83.99 361

KD-MVS_2432*1600		66.22 354	63.89 357	73.21 345	75.47 392	53.42 368	70.76 393	84.35 285	64.10 314	66.52 357	78.52 378	34.55 392	84.98 341	50.40 359	50.33 411	81.23 386

miper_refine_blended		66.22 354	63.89 357	73.21 345	75.47 392	53.42 368	70.76 393	84.35 285	64.10 314	66.52 357	78.52 378	34.55 392	84.98 341	50.40 359	50.33 411	81.23 386

tpmvs		71.09 312	69.29 317	76.49 310	82.04 334	56.04 341	78.92 340	81.37 332	64.05 316	67.18 348	78.28 380	49.74 299	89.77 280	49.67 366	72.37 351	83.67 365

our_test_3		69.14 331	67.00 344	75.57 318	79.80 367	58.80 298	77.96 354	77.81 364	59.55 358	62.90 382	78.25 381	47.43 316	83.97 348	51.71 351	67.58 376	83.93 362

MDA-MVSNet_test_wron		65.03 358	62.92 362	71.37 360	75.93 386	56.73 328	69.09 402	74.73 385	57.28 378	54.03 407	77.89 382	45.88 332	74.39 406	49.89 365	61.55 391	82.99 374

YYNet1		65.03 358	62.91 363	71.38 359	75.85 388	56.60 332	69.12 401	74.66 387	57.28 378	54.12 406	77.87 383	45.85 333	74.48 405	49.95 364	61.52 392	83.05 372

ambc				75.24 325	73.16 404	50.51 390	63.05 418	87.47 238		64.28 372	77.81 384	17.80 420	89.73 282	57.88 315	60.64 394	85.49 338

tpm cat1		70.57 318	68.31 324	77.35 303	82.41 331	57.95 310	78.08 352	80.22 347	52.04 394	68.54 336	77.66 385	52.00 270	87.84 313	51.77 350	72.07 356	86.25 323

dp		66.80 348	65.43 350	70.90 367	79.74 369	48.82 396	75.12 375	74.77 384	59.61 357	64.08 375	77.23 386	42.89 355	80.72 370	48.86 370	66.58 379	83.16 370

TESTMET0.1,1		69.89 326	69.00 320	72.55 352	79.27 375	56.85 326	78.38 347	74.71 386	57.64 374	68.09 338	77.19 387	37.75 384	76.70 387	63.92 257	84.09 201	84.10 360

CHOSEN 280x420		66.51 351	64.71 353	71.90 356	81.45 344	63.52 237	57.98 420	68.95 403	53.57 390	62.59 383	76.70 388	46.22 329	75.29 403	55.25 333	79.68 261	76.88 400

PatchT		68.46 339	67.85 331	70.29 368	80.70 354	43.93 412	72.47 385	74.88 383	60.15 353	70.55 309	76.57 389	49.94 296	81.59 364	50.58 357	74.83 331	85.34 341

mvsany_test3		53.99 377	51.45 382	61.61 392	55.51 426	44.74 411	63.52 416	45.41 431	43.69 409	58.11 398	76.45 390	17.99 419	63.76 422	54.77 336	47.59 413	76.34 401

RPMNet		73.51 286	70.49 309	82.58 198	81.32 349	65.19 200	75.92 366	92.27 84	57.60 375	72.73 288	76.45 390	52.30 262	95.43 70	48.14 376	77.71 283	87.11 309

dmvs_testset		62.63 365	64.11 356	58.19 395	78.55 378	24.76 433	75.28 371	65.94 410	67.91 266	60.34 389	76.01 392	53.56 251	73.94 408	31.79 413	67.65 375	75.88 402

ADS-MVSNet2		66.20 356	63.33 360	74.82 330	79.92 363	58.75 299	67.55 405	75.19 381	53.37 391	65.25 367	75.86 393	42.32 359	80.53 371	41.57 399	68.91 371	85.18 344

ADS-MVSNet		64.36 361	62.88 364	68.78 376	79.92 363	47.17 400	67.55 405	71.18 395	53.37 391	65.25 367	75.86 393	42.32 359	73.99 407	41.57 399	68.91 371	85.18 344

EGC-MVSNET		52.07 383	47.05 387	67.14 383	83.51 302	60.71 280	80.50 317	67.75 405	0.07 433	0.43 434	75.85 395	24.26 411	81.54 365	28.82 416	62.25 389	59.16 416

new-patchmatchnet		61.73 367	61.73 368	61.70 391	72.74 407	24.50 434	69.16 400	78.03 363	61.40 344	56.72 402	75.53 396	38.42 380	76.48 390	45.95 387	57.67 397	84.13 359

N_pmnet		52.79 381	53.26 379	51.40 405	78.99 376	7.68 439	69.52 397	3.89 438	51.63 397	57.01 401	74.98 397	40.83 369	65.96 420	37.78 406	64.67 385	80.56 392

WB-MVS		54.94 375	54.72 376	55.60 401	73.50 400	20.90 435	74.27 381	61.19 418	59.16 362	50.61 410	74.15 398	47.19 319	75.78 398	17.31 426	35.07 420	70.12 408

patchmatchnet-post											74.00 399	51.12 283	88.60 304

GG-mvs-BLEND				75.38 323	81.59 341	55.80 345	79.32 332	69.63 399		67.19 347	73.67 400	43.24 353	88.90 300	50.41 358	84.50 191	81.45 385

SSC-MVS		53.88 378	53.59 378	54.75 403	72.87 406	19.59 436	73.84 383	60.53 420	57.58 376	49.18 414	73.45 401	46.34 328	75.47 401	16.20 429	32.28 422	69.20 409

Patchmatch-RL test		70.24 322	67.78 335	77.61 298	77.43 382	59.57 296	71.16 390	70.33 396	62.94 328	68.65 334	72.77 402	50.62 288	85.49 336	69.58 209	66.58 379	87.77 291

FPMVS		53.68 379	51.64 381	59.81 394	65.08 418	51.03 386	69.48 398	69.58 400	41.46 411	40.67 418	72.32 403	16.46 422	70.00 415	24.24 422	65.42 383	58.40 418

UnsupCasMVSNet_bld		63.70 363	61.53 369	70.21 369	73.69 399	51.39 384	72.82 384	81.89 324	55.63 385	57.81 399	71.80 404	38.67 379	78.61 377	49.26 368	52.21 409	80.63 390

APD_test1		53.31 380	49.93 385	63.42 390	65.68 417	50.13 391	71.59 389	66.90 408	34.43 420	40.58 419	71.56 405	8.65 431	76.27 392	34.64 411	55.36 403	63.86 414

test_f		52.09 382	50.82 383	55.90 399	53.82 429	42.31 418	59.42 419	58.31 423	36.45 418	56.12 405	70.96 406	12.18 425	57.79 425	53.51 343	56.57 400	67.60 410

PVSNet_0	57.27 20	61.67 368	59.27 371	68.85 375	79.61 370	57.44 320	68.01 403	73.44 390	55.93 384	58.54 396	70.41 407	44.58 344	77.55 383	47.01 380	35.91 419	71.55 407

pmmvs3		57.79 372	54.26 377	68.37 378	64.02 420	56.72 329	75.12 375	65.17 411	40.20 412	52.93 408	69.86 408	20.36 417	75.48 400	45.45 390	55.25 405	72.90 406

test_vis1_rt		60.28 369	58.42 372	65.84 386	67.25 415	55.60 348	70.44 395	60.94 419	44.33 408	59.00 394	66.64 409	24.91 409	68.67 416	62.80 264	69.48 367	73.25 405

new_pmnet		50.91 384	50.29 384	52.78 404	68.58 413	34.94 426	63.71 415	56.63 424	39.73 413	44.95 415	65.47 410	21.93 415	58.48 424	34.98 410	56.62 399	64.92 412

gg-mvs-nofinetune		69.95 325	67.96 329	75.94 313	83.07 313	54.51 360	77.23 361	70.29 397	63.11 324	70.32 313	62.33 411	43.62 351	88.69 302	53.88 341	87.76 148	84.62 354

JIA-IIPM		66.32 353	62.82 365	76.82 308	77.09 384	61.72 269	65.34 413	75.38 380	58.04 372	64.51 371	62.32 412	42.05 363	86.51 324	51.45 354	69.22 370	82.21 380

LCM-MVSNet		54.25 376	49.68 386	67.97 382	53.73 430	45.28 407	66.85 408	80.78 336	35.96 419	39.45 420	62.23 413	8.70 430	78.06 381	48.24 375	51.20 410	80.57 391

PMMVS2		40.82 392	38.86 396	46.69 406	53.84 428	16.45 437	48.61 423	49.92 426	37.49 416	31.67 421	60.97 414	8.14 432	56.42 426	28.42 417	30.72 423	67.19 411

testf1		45.72 387	41.96 391	57.00 396	56.90 424	45.32 405	66.14 410	59.26 421	26.19 424	30.89 423	60.96 415	4.14 434	70.64 413	26.39 420	46.73 415	55.04 419

APD_test2		45.72 387	41.96 391	57.00 396	56.90 424	45.32 405	66.14 410	59.26 421	26.19 424	30.89 423	60.96 415	4.14 434	70.64 413	26.39 420	46.73 415	55.04 419

MVS-HIRNet		59.14 371	57.67 373	63.57 389	81.65 339	43.50 413	71.73 387	65.06 412	39.59 414	51.43 409	57.73 417	38.34 381	82.58 359	39.53 402	73.95 338	64.62 413

ANet_high		50.57 385	46.10 389	63.99 388	48.67 433	39.13 421	70.99 392	80.85 335	61.39 345	31.18 422	57.70 418	17.02 421	73.65 409	31.22 415	15.89 430	79.18 395

PMVS	37.38 22	44.16 391	40.28 395	55.82 400	40.82 435	42.54 417	65.12 414	63.99 415	34.43 420	24.48 426	57.12 419	3.92 436	76.17 394	17.10 427	55.52 402	48.75 421
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		45.42 389	45.38 390	45.55 407	73.36 403	26.85 431	67.72 404	34.19 433	54.15 389	49.65 413	56.41 420	25.43 407	62.94 423	19.45 424	28.09 424	46.86 423

test_vis3_rt		49.26 386	47.02 388	56.00 398	54.30 427	45.27 408	66.76 409	48.08 428	36.83 417	44.38 416	53.20 421	7.17 433	64.07 421	56.77 327	55.66 401	58.65 417

test_method		31.52 395	29.28 399	38.23 409	27.03 437	6.50 440	20.94 428	62.21 417	4.05 431	22.35 429	52.50 422	13.33 423	47.58 429	27.04 419	34.04 421	60.62 415

kuosan		39.70 393	40.40 394	37.58 410	64.52 419	26.98 429	65.62 412	33.02 434	46.12 405	42.79 417	48.99 423	24.10 412	46.56 431	12.16 432	26.30 425	39.20 424

DeepMVS_CX				27.40 413	40.17 436	26.90 430		24.59 437	17.44 429	23.95 427	48.61 424	9.77 428	26.48 432	18.06 425	24.47 426	28.83 426

MVE	26.22 23	30.37 397	25.89 401	43.81 408	44.55 434	35.46 425	28.87 427	39.07 432	18.20 428	18.58 430	40.18 425	2.68 437	47.37 430	17.07 428	23.78 427	48.60 422
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 390	41.86 393	55.16 402	77.03 385	51.52 382	32.50 426	80.52 340	32.46 422	27.12 425	35.02 426	9.52 429	75.50 399	22.31 423	60.21 396	38.45 425
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 394	30.64 397	35.15 411	52.87 431	27.67 428	57.09 421	47.86 429	24.64 426	16.40 431	33.05 427	11.23 427	54.90 427	14.46 430	18.15 428	22.87 427

EMVS		30.81 396	29.65 398	34.27 412	50.96 432	25.95 432	56.58 422	46.80 430	24.01 427	15.53 432	30.68 428	12.47 424	54.43 428	12.81 431	17.05 429	22.43 428

tmp_tt		18.61 399	21.40 402	10.23 415	4.82 438	10.11 438	34.70 425	30.74 436	1.48 432	23.91 428	26.07 429	28.42 404	13.41 434	27.12 418	15.35 431	7.17 429

X-MVStestdata		80.37 160	77.83 196	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 22	83.67 100	12.47 430	67.45 107	96.60 33	83.06 75	94.50 51	94.07 55

test_post											5.46 431	50.36 292	84.24 346

test_post1							78.90 341				5.43 432	48.81 313	85.44 338	59.25 299

wuyk23d		16.82 400	15.94 403	19.46 414	58.74 423	31.45 427	39.22 424	3.74 439	6.84 430	6.04 433	2.70 433	1.27 438	24.29 433	10.54 433	14.40 432	2.63 430

testmvs		6.04 403	8.02 406	0.10 417	0.08 439	0.03 442	69.74 396	0.04 440	0.05 434	0.31 435	1.68 434	0.02 440	0.04 435	0.24 434	0.02 433	0.25 432

test123		6.12 402	8.11 405	0.14 416	0.06 440	0.09 441	71.05 391	0.03 441	0.04 435	0.25 436	1.30 435	0.05 439	0.03 436	0.21 435	0.01 434	0.29 431

mmdepth		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

monomultidepth		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

test_blank		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uanet_test		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

DCPMVS		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

pcd_1.5k_mvsjas		5.26 404	7.02 407	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	63.15 150	0.00 437	0.00 436	0.00 435	0.00 433

sosnet-low-res		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

sosnet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uncertanet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

Regformer		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

uanet		0.00 405	0.00 408	0.00 418	0.00 441	0.00 443	0.00 429	0.00 442	0.00 436	0.00 437	0.00 436	0.00 441	0.00 437	0.00 436	0.00 435	0.00 433

WAC-MVS						42.58 415								39.46 403

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 126	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 11	96.44 9	94.41 39

No_MVS				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 11	96.44 9	94.41 39

eth-test2					0.00 441

eth-test					0.00 441

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 274	92.39 6				88.94 22	96.63 4	94.85 20

save fliter					93.80 40	72.35 42	90.47 66	91.17 125	74.31 131

test_0728_SECOND				87.71 32	95.34 1	71.43 58	93.49 9	94.23 3					97.49 4	89.08 17	96.41 12	94.21 49

GSMVS																88.96 260

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 280				88.96 260

sam_mvs												50.01 294

MTGPA								92.02 93

MTMP							92.18 34	32.83 435

test9_res														84.90 52	95.70 26	92.87 118

agg_prior2														82.91 79	95.45 29	92.70 121

agg_prior					92.85 62	71.94 50		91.78 108		84.41 84			94.93 94

test_prior4						72.60 34	89.01 114

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 63

旧先验2							86.56 200		58.10 371	87.04 51			88.98 296	74.07 164

新几何2							86.29 209

无先验							87.48 167	88.98 200	60.00 354				94.12 125	67.28 230		88.97 259

原ACMM2							86.86 189

testdata2													91.01 262	62.37 271

segment_acmp												73.08 39

testdata1							84.14 263		75.71 94

test12				86.80 52	92.63 67	70.70 75		91.79 107		82.71 113		71.67 56	96.16 47		94.50 51	93.54 88

plane_prior7					90.08 109	68.51 124

plane_prior6					89.84 118	68.70 118						60.42 200

plane_prior5								92.44 77					95.38 75	78.71 116	86.32 169	91.33 167

plane_prior3						68.60 121			78.44 32	78.92 157

plane_prior2							91.25 52		79.12 24

plane_prior1					89.90 117

plane_prior						68.71 116	90.38 70		77.62 42						86.16 173

n2								0.00 442

nn								0.00 442

door-mid								69.98 398

test11								92.23 87

door								69.44 401

HQP5-MVS						66.98 165

HQP-NCC					89.33 136		89.17 105		76.41 79	77.23 195

ACMP_Plane					89.33 136		89.17 105		76.41 79	77.23 195

BP-MVS														77.47 128

HQP4-MVS										77.24 194			95.11 87			91.03 177

HQP3-MVS								92.19 90							85.99 177

HQP2-MVS												60.17 203

MDTV_nov1_ep13_2view						37.79 423	75.16 373		55.10 386	66.53 356		49.34 304		53.98 340		87.94 287

ACMMP++_ref															81.95 235

ACMMP++															81.25 240

Test By Simon												64.33 137

High-resolution multi-view benchmark