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 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
SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 112	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
test0726					95.27 5	71.25 57	93.60 6	94.11 6	77.33 49	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 57	93.49 9	92.73 61	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
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

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 57	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

test_one_0601					95.07 7	71.46 55		94.14 5	78.27 35	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 63	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_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

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 42	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_241102_ONE					95.30 2	70.98 63		94.06 10	77.17 55	93.10 1	95.39 11	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 78	93.50 25	75.17 104	86.34 48	95.29 12	70.86 63	96.00 51	88.78 19	96.04 16	94.58 31
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 61	71.95 49	92.40 24	94.74 2	75.71 91	89.16 19	95.10 13	75.65 21	96.19 43	87.07 34	96.01 17	94.79 21

ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 54	93.59 23	76.27 83	88.14 24	95.09 14	71.06 61	96.67 29	87.67 29	96.37 14	94.09 51

MM		89.16 6	89.23 7	88.97 4	90.79 92	73.65 10	92.66 23	91.17 121	86.57 1	87.39 38	94.97 15	71.70 53	97.68 1	92.19 1	95.63 28	95.57 1

MTAPA		87.23 28	87.00 29	87.90 22	94.18 35	74.25 5	86.58 188	92.02 89	79.45 19	85.88 50	94.80 16	68.07 93	96.21 42	86.69 36	95.34 32	93.23 92

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 69	72.96 25	93.73 5	93.67 20	80.19 12	88.10 25	94.80 16	73.76 33	97.11 15	87.51 31	95.82 21	94.90 13
Skip Steuart: Steuart Systems R&D Blog.
9.14			88.26 15		92.84 60		91.52 45	94.75 1	73.93 129	88.57 22	94.67 18	75.57 22	95.79 55	86.77 35	95.76 23

SR-MVS		86.73 35	86.67 36	86.91 46	94.11 37	72.11 47	92.37 28	92.56 71	74.50 116	86.84 45	94.65 19	67.31 102	95.77 56	84.80 47	92.85 69	92.84 109

region2R		87.42 25	87.20 28	88.09 14	94.63 14	73.55 13	93.03 14	93.12 37	76.73 70	84.45 74	94.52 20	69.09 81	96.70 27	84.37 53	94.83 45	94.03 54

ACMMPR		87.44 23	87.23 27	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 67	84.66 69	94.52 20	68.81 87	96.65 30	84.53 51	94.90 41	94.00 55

APD-MVS		87.44 23	87.52 23	87.19 42	94.24 32	72.39 39	91.86 40	92.83 57	73.01 153	88.58 21	94.52 20	73.36 34	96.49 36	84.26 54	95.01 37	92.70 111
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 47	85.88 50	86.22 57	92.69 63	69.53 89	91.93 37	92.99 46	73.54 139	85.94 49	94.51 23	65.80 120	95.61 59	83.04 67	92.51 73	93.53 83

CP-MVS		87.11 30	86.92 33	87.68 34	94.20 34	73.86 7	93.98 3	92.82 60	76.62 73	83.68 88	94.46 24	67.93 95	95.95 54	84.20 57	94.39 55	93.23 92

SR-MVS-dyc-post		85.77 52	85.61 55	86.23 56	93.06 55	70.63 73	91.88 38	92.27 80	73.53 140	85.69 53	94.45 25	65.00 128	95.56 60	82.75 71	91.87 81	92.50 120

RE-MVS-def			85.48 57		93.06 55	70.63 73	91.88 38	92.27 80	73.53 140	85.69 53	94.45 25	63.87 134		82.75 71	91.87 81	92.50 120

HFP-MVS		87.58 22	87.47 24	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 67	84.91 62	94.44 27	70.78 64	96.61 32	84.53 51	94.89 42	93.66 70

PGM-MVS		86.68 37	86.27 41	87.90 22	94.22 33	73.38 18	90.22 70	93.04 38	75.53 95	83.86 85	94.42 28	67.87 97	96.64 31	82.70 75	94.57 50	93.66 70

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 66	77.57 41	83.84 86	94.40 29	72.24 45	96.28 40	85.65 38	95.30 35	93.62 77
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
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 57	94.32 30	71.76 51	96.93 19	85.53 40	95.79 22	94.32 43

MVS_0304		87.69 20	87.55 22	88.12 13	89.45 126	71.76 51	91.47 46	89.54 168	82.14 3	86.65 46	94.28 31	68.28 92	97.46 6	90.81 2	95.31 34	95.15 6

test_fmvsmconf0.01_n		84.73 70	84.52 72	85.34 75	80.25 343	69.03 100	89.47 88	89.65 166	73.24 149	86.98 43	94.27 32	66.62 106	93.23 162	90.26 5	89.95 109	93.78 67

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 56	80.26 11	87.78 30	94.27 32	75.89 19	96.81 23	87.45 32	96.44 9	93.05 101

mPP-MVS		86.67 38	86.32 40	87.72 30	94.41 22	73.55 13	92.74 20	92.22 84	76.87 64	82.81 101	94.25 34	66.44 110	96.24 41	82.88 70	94.28 58	93.38 86

DeepC-MVS	79.81 2	87.08 32	86.88 35	87.69 33	91.16 81	72.32 43	90.31 68	93.94 14	77.12 57	82.82 100	94.23 35	72.13 47	97.09 16	84.83 46	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
XVS		87.18 29	86.91 34	88.00 17	94.42 20	73.33 19	92.78 18	92.99 46	79.14 21	83.67 89	94.17 36	67.45 100	96.60 33	83.06 65	94.50 51	94.07 52

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 37	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
test_fmvsmconf0.1_n		85.61 56	85.65 54	85.50 72	82.99 302	69.39 97	89.65 83	90.29 149	73.31 145	87.77 31	94.15 38	71.72 52	93.23 162	90.31 4	90.67 97	93.89 61

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 50	92.24 68	69.03 100	89.57 87	93.39 30	77.53 45	89.79 18	94.12 39	78.98 12	96.58 35	85.66 37	95.72 24	94.58 31

HPM-MVS_fast		85.35 61	84.95 67	86.57 53	93.69 42	70.58 75	92.15 35	91.62 107	73.89 130	82.67 103	94.09 40	62.60 149	95.54 62	80.93 88	92.93 68	93.57 79

ZD-MVS					94.38 25	72.22 44		92.67 63	70.98 184	87.75 32	94.07 41	74.01 32	96.70 27	84.66 49	94.84 44

fmvsm_s_conf0.1_n_a		83.32 90	82.99 89	84.28 112	83.79 280	68.07 130	89.34 96	82.85 302	69.80 211	87.36 39	94.06 42	68.34 91	91.56 228	87.95 27	83.46 203	93.21 95

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 59	93.00 43	80.90 7	88.06 26	94.06 42	76.43 16	96.84 21	88.48 24	95.99 18	94.34 42

test_fmvsmconf_n		85.92 48	86.04 48	85.57 71	85.03 257	69.51 90	89.62 86	90.58 136	73.42 142	87.75 32	94.02 44	72.85 41	93.24 161	90.37 3	90.75 95	93.96 56

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 44	82.45 3	96.87 20	83.77 60	96.48 8	94.88 14

PC_three_1452									68.21 247	92.02 12	94.00 46	82.09 5	95.98 53	84.58 50	96.68 2	94.95 10

SD-MVS		88.06 14	88.50 14	86.71 51	92.60 66	72.71 29	91.81 41	93.19 35	77.87 36	90.32 17	94.00 46	74.83 23	93.78 136	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
GST-MVS		87.42 25	87.26 25	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 63	84.68 66	93.99 48	70.67 66	96.82 22	84.18 58	95.01 37	93.90 60

test_fmvsm_n_1920		85.29 62	85.34 59	85.13 82	86.12 236	69.93 83	88.65 121	90.78 132	69.97 207	88.27 23	93.98 49	71.39 58	91.54 230	88.49 23	90.45 99	93.91 58

fmvsm_s_conf0.1_n		83.56 84	83.38 82	84.10 120	84.86 259	67.28 150	89.40 94	83.01 297	70.67 189	87.08 41	93.96 50	68.38 90	91.45 236	88.56 22	84.50 179	93.56 80

HPM-MVS		87.11 30	86.98 31	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 86	83.81 87	93.95 51	69.77 75	96.01 50	85.15 41	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
TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 47	92.35 78	74.62 115	88.90 20	93.85 52	75.75 20	96.00 51	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
ACMMP		85.89 51	85.39 58	87.38 39	93.59 45	72.63 33	92.74 20	93.18 36	76.78 67	80.73 125	93.82 53	64.33 130	96.29 39	82.67 76	90.69 96	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
fmvsm_s_conf0.5_n_a		83.63 82	83.41 81	84.28 112	86.14 235	68.12 128	89.43 90	82.87 301	70.27 200	87.27 40	93.80 54	69.09 81	91.58 226	88.21 26	83.65 198	93.14 98

fmvsm_s_conf0.5_n		83.80 77	83.71 78	84.07 126	86.69 228	67.31 149	89.46 89	83.07 296	71.09 181	86.96 44	93.70 55	69.02 86	91.47 235	88.79 18	84.62 178	93.44 85

test_prior2							88.85 112		75.41 97	84.91 62	93.54 56	74.28 29		83.31 63	95.86 20

fmvsm_l_conf0.5_n		84.47 71	84.54 70	84.27 114	85.42 247	68.81 106	88.49 125	87.26 233	68.08 248	88.03 27	93.49 57	72.04 48	91.77 220	88.90 17	89.14 119	92.24 131

VDDNet		81.52 120	80.67 123	84.05 131	90.44 98	64.13 214	89.73 81	85.91 256	71.11 180	83.18 94	93.48 58	50.54 278	93.49 150	73.40 158	88.25 133	94.54 34

CDPH-MVS		85.76 53	85.29 63	87.17 43	93.49 47	71.08 61	88.58 123	92.42 76	68.32 246	84.61 71	93.48 58	72.32 44	96.15 45	79.00 101	95.43 30	94.28 45

NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 51	92.83 57	81.50 5	85.79 52	93.47 60	73.02 40	97.00 18	84.90 43	94.94 40	94.10 50

fmvsm_l_conf0.5_n_a		84.13 73	84.16 75	84.06 128	85.38 248	68.40 121	88.34 132	86.85 242	67.48 255	87.48 36	93.40 61	70.89 62	91.61 224	88.38 25	89.22 117	92.16 135

3Dnovator+	77.84 4	85.48 57	84.47 73	88.51 7	91.08 83	73.49 16	93.18 11	93.78 18	80.79 8	76.66 197	93.37 62	60.40 193	96.75 26	77.20 120	93.73 64	95.29 5

DeepC-MVS_fast	79.65 3	86.91 33	86.62 37	87.76 27	93.52 46	72.37 41	91.26 48	93.04 38	76.62 73	84.22 78	93.36 63	71.44 57	96.76 25	80.82 90	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
VDD-MVS		83.01 97	82.36 98	84.96 87	91.02 85	66.40 165	88.91 109	88.11 211	77.57 41	84.39 76	93.29 64	52.19 252	93.91 130	77.05 122	88.70 127	94.57 33

test_fmvsmvis_n_1920		84.02 74	83.87 76	84.49 103	84.12 273	69.37 98	88.15 140	87.96 216	70.01 205	83.95 84	93.23 65	68.80 88	91.51 233	88.61 20	89.96 108	92.57 116

UA-Net		85.08 65	84.96 66	85.45 73	92.07 70	68.07 130	89.78 79	90.86 131	82.48 2	84.60 72	93.20 66	69.35 78	95.22 76	71.39 176	90.88 94	93.07 100

TEST9					93.26 50	72.96 25	88.75 115	91.89 97	68.44 244	85.00 60	93.10 67	74.36 28	95.41 69

train_agg		86.43 40	86.20 42	87.13 44	93.26 50	72.96 25	88.75 115	91.89 97	68.69 239	85.00 60	93.10 67	74.43 26	95.41 69	84.97 42	95.71 25	93.02 103

test_8					93.13 52	72.57 35	88.68 120	91.84 101	68.69 239	84.87 64	93.10 67	74.43 26	95.16 78

LFMVS		81.82 113	81.23 114	83.57 146	91.89 73	63.43 230	89.84 75	81.85 313	77.04 60	83.21 93	93.10 67	52.26 251	93.43 155	71.98 171	89.95 109	93.85 62

旧先验1					91.96 71	65.79 179		86.37 249			93.08 71	69.31 80			92.74 70	88.74 258

dcpmvs_2		85.63 55	86.15 45	84.06 128	91.71 75	64.94 197	86.47 191	91.87 99	73.63 135	86.60 47	93.02 72	76.57 15	91.87 218	83.36 62	92.15 77	95.35 3

testdata				79.97 242	90.90 88	64.21 212		84.71 267	59.27 344	85.40 55	92.91 73	62.02 162	89.08 282	68.95 202	91.37 88	86.63 305

MCST-MVS		87.37 27	87.25 26	87.73 28	94.53 17	72.46 38	89.82 76	93.82 16	73.07 151	84.86 65	92.89 74	76.22 17	96.33 38	84.89 45	95.13 36	94.40 39

Vis-MVSNet		83.46 86	82.80 93	85.43 74	90.25 101	68.74 111	90.30 69	90.13 153	76.33 82	80.87 124	92.89 74	61.00 181	94.20 117	72.45 170	90.97 92	93.35 88
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		83.73 78	83.33 84	84.92 90	93.28 49	70.86 69	92.09 36	90.38 142	68.75 238	79.57 137	92.83 76	60.60 189	93.04 179	80.92 89	91.56 86	90.86 171

3Dnovator	76.31 5	83.38 89	82.31 99	86.59 52	87.94 190	72.94 28	90.64 57	92.14 88	77.21 54	75.47 222	92.83 76	58.56 200	94.72 100	73.24 161	92.71 71	92.13 136

MSLP-MVS++		85.43 59	85.76 53	84.45 104	91.93 72	70.24 76	90.71 56	92.86 55	77.46 47	84.22 78	92.81 78	67.16 104	92.94 181	80.36 94	94.35 57	90.16 198

test2506		77.30 222	76.49 219	79.74 247	90.08 105	52.02 361	87.86 151	63.10 399	74.88 108	80.16 131	92.79 79	38.29 365	92.35 200	68.74 205	92.50 74	94.86 17

ECVR-MVS		79.61 160	79.26 153	80.67 229	90.08 105	54.69 344	87.89 149	77.44 353	74.88 108	80.27 128	92.79 79	48.96 299	92.45 194	68.55 206	92.50 74	94.86 17

test1111		79.43 167	79.18 156	80.15 239	89.99 110	53.31 357	87.33 165	77.05 357	75.04 105	80.23 130	92.77 81	48.97 298	92.33 202	68.87 203	92.40 76	94.81 20

MG-MVS		83.41 87	83.45 80	83.28 154	92.74 62	62.28 250	88.17 138	89.50 170	75.22 100	81.49 115	92.74 82	66.75 105	95.11 82	72.85 164	91.58 85	92.45 123

casdiffmvs_mvg		85.99 45	86.09 47	85.70 69	87.65 204	67.22 154	88.69 119	93.04 38	79.64 18	85.33 56	92.54 83	73.30 35	94.50 107	83.49 61	91.14 91	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 80	84.54 70	80.99 221	90.06 109	65.83 177	84.21 249	88.74 202	71.60 171	85.01 59	92.44 84	74.51 25	83.50 338	82.15 78	92.15 77	93.64 76

casdiffmvs		85.11 64	85.14 64	85.01 85	87.20 218	65.77 180	87.75 152	92.83 57	77.84 37	84.36 77	92.38 85	72.15 46	93.93 129	81.27 86	90.48 98	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 36	86.95 32	85.90 66	90.76 93	67.57 142	92.83 17	93.30 32	79.67 17	84.57 73	92.27 86	71.47 56	95.02 88	84.24 56	93.46 65	95.13 7

baseline		84.93 67	84.98 65	84.80 95	87.30 216	65.39 188	87.30 166	92.88 54	77.62 39	84.04 83	92.26 87	71.81 50	93.96 123	81.31 84	90.30 101	95.03 9

QAPM		80.88 130	79.50 146	85.03 84	88.01 188	68.97 104	91.59 42	92.00 91	66.63 267	75.15 240	92.16 88	57.70 207	95.45 65	63.52 245	88.76 125	90.66 178

IS-MVSNet		83.15 92	82.81 92	84.18 118	89.94 112	63.30 232	91.59 42	88.46 208	79.04 25	79.49 138	92.16 88	65.10 125	94.28 112	67.71 212	91.86 83	94.95 10

新几何1				83.42 149	93.13 52	70.71 71		85.48 261	57.43 360	81.80 111	91.98 90	63.28 138	92.27 203	64.60 240	92.99 67	87.27 288

OpenMVS	72.83 10	79.77 158	78.33 173	84.09 124	85.17 251	69.91 84	90.57 58	90.97 126	66.70 261	72.17 281	91.91 91	54.70 230	93.96 123	61.81 266	90.95 93	88.41 266

PHI-MVS		86.43 40	86.17 44	87.24 41	90.88 89	70.96 65	92.27 32	94.07 9	72.45 157	85.22 58	91.90 92	69.47 77	96.42 37	83.28 64	95.94 19	94.35 41

VNet		82.21 105	82.41 96	81.62 202	90.82 90	60.93 264	84.47 240	89.78 161	76.36 81	84.07 82	91.88 93	64.71 129	90.26 260	70.68 183	88.89 121	93.66 70

EC-MVSNet		86.01 44	86.38 39	84.91 91	89.31 135	66.27 168	92.32 30	93.63 21	79.37 20	84.17 80	91.88 93	69.04 85	95.43 67	83.93 59	93.77 63	93.01 104

OPM-MVS		83.50 85	82.95 90	85.14 80	88.79 156	70.95 66	89.13 104	91.52 110	77.55 44	80.96 123	91.75 95	60.71 184	94.50 107	79.67 100	86.51 155	89.97 214
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVSMamba_PlusPlus		85.99 45	85.96 49	86.05 61	91.09 82	67.64 139	89.63 85	92.65 66	72.89 156	84.64 70	91.71 96	71.85 49	96.03 47	84.77 48	94.45 54	94.49 35

XVG-OURS-SEG-HR		80.81 133	79.76 140	83.96 138	85.60 244	68.78 108	83.54 262	90.50 139	70.66 192	76.71 196	91.66 97	60.69 185	91.26 241	76.94 123	81.58 225	91.83 141

EPNet		83.72 79	82.92 91	86.14 60	84.22 271	69.48 91	91.05 53	85.27 262	81.30 6	76.83 192	91.65 98	66.09 115	95.56 60	76.00 133	93.85 62	93.38 86
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 99	81.97 107	84.85 92	88.75 158	67.42 145	87.98 143	90.87 130	74.92 107	79.72 135	91.65 98	62.19 159	93.96 123	75.26 142	86.42 156	93.16 97

balanced_conf03		86.78 34	86.99 30	86.15 58	91.24 80	67.61 140	90.51 59	92.90 53	77.26 51	87.44 37	91.63 100	71.27 60	96.06 46	85.62 39	95.01 37	94.78 22

test222					91.50 77	68.26 125	84.16 250	83.20 294	54.63 371	79.74 134	91.63 100	58.97 198			91.42 87	86.77 301

MVS_111021_HR		85.14 63	84.75 68	86.32 55	91.65 76	72.70 30	85.98 204	90.33 146	76.11 85	82.08 106	91.61 102	71.36 59	94.17 119	81.02 87	92.58 72	92.08 137

原ACMM1				84.35 108	93.01 57	68.79 107		92.44 73	63.96 303	81.09 121	91.57 103	66.06 116	95.45 65	67.19 219	94.82 46	88.81 253

LPG-MVS_test		82.08 107	81.27 113	84.50 101	89.23 139	68.76 109	90.22 70	91.94 95	75.37 98	76.64 198	91.51 104	54.29 233	94.91 90	78.44 107	83.78 191	89.83 219

LGP-MVS_train				84.50 101	89.23 139	68.76 109		91.94 95	75.37 98	76.64 198	91.51 104	54.29 233	94.91 90	78.44 107	83.78 191	89.83 219

XVG-OURS		80.41 146	79.23 154	83.97 137	85.64 243	69.02 102	83.03 273	90.39 141	71.09 181	77.63 175	91.49 106	54.62 232	91.35 239	75.71 135	83.47 202	91.54 148

alignmvs		85.48 57	85.32 61	85.96 65	89.51 123	69.47 92	89.74 80	92.47 72	76.17 84	87.73 34	91.46 107	70.32 69	93.78 136	81.51 81	88.95 120	94.63 30

CANet		86.45 39	86.10 46	87.51 37	90.09 104	70.94 67	89.70 82	92.59 70	81.78 4	81.32 116	91.43 108	70.34 68	97.23 14	84.26 54	93.36 66	94.37 40

h-mvs33		83.15 92	82.19 100	86.02 64	90.56 95	70.85 70	88.15 140	89.16 183	76.02 87	84.67 67	91.39 109	61.54 167	95.50 63	82.71 73	75.48 302	91.72 144

MGCFI-Net		85.06 66	85.51 56	83.70 142	89.42 127	63.01 238	89.43 90	92.62 69	76.43 75	87.53 35	91.34 110	72.82 42	93.42 156	81.28 85	88.74 126	94.66 29

nrg030		83.88 75	83.53 79	84.96 87	86.77 226	69.28 99	90.46 64	92.67 63	74.79 111	82.95 96	91.33 111	72.70 43	93.09 175	80.79 92	79.28 254	92.50 120

sasdasda		85.91 49	85.87 51	86.04 62	89.84 114	69.44 95	90.45 65	93.00 43	76.70 71	88.01 28	91.23 112	73.28 36	93.91 130	81.50 82	88.80 123	94.77 23

canonicalmvs		85.91 49	85.87 51	86.04 62	89.84 114	69.44 95	90.45 65	93.00 43	76.70 71	88.01 28	91.23 112	73.28 36	93.91 130	81.50 82	88.80 123	94.77 23

DPM-MVS		84.93 67	84.29 74	86.84 47	90.20 102	73.04 23	87.12 170	93.04 38	69.80 211	82.85 99	91.22 114	73.06 39	96.02 49	76.72 127	94.63 48	91.46 154

Anonymous202405211		78.25 195	77.01 205	81.99 196	91.03 84	60.67 269	84.77 232	83.90 280	70.65 193	80.00 132	91.20 115	41.08 352	91.43 237	65.21 234	85.26 171	93.85 62

CS-MVS-test		86.29 43	86.48 38	85.71 68	91.02 85	67.21 155	92.36 29	93.78 18	78.97 28	83.51 92	91.20 115	70.65 67	95.15 79	81.96 79	94.89 42	94.77 23

Anonymous20240529		80.19 153	78.89 161	84.10 120	90.60 94	64.75 201	88.95 108	90.90 128	65.97 275	80.59 126	91.17 117	49.97 283	93.73 142	69.16 200	82.70 214	93.81 65

EPP-MVSNet		83.40 88	83.02 88	84.57 99	90.13 103	64.47 207	92.32 30	90.73 133	74.45 119	79.35 140	91.10 118	69.05 84	95.12 80	72.78 165	87.22 144	94.13 49

TAPA-MVS	73.13 9	79.15 175	77.94 181	82.79 181	89.59 119	62.99 242	88.16 139	91.51 111	65.77 276	77.14 189	91.09 119	60.91 182	93.21 164	50.26 347	87.05 146	92.17 134
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 42	86.19 43	87.07 45	92.91 58	72.48 37	90.81 55	93.56 24	73.95 127	83.16 95	91.07 120	75.94 18	95.19 77	79.94 98	94.38 56	93.55 81

FIs		82.07 108	82.42 95	81.04 220	88.80 155	58.34 291	88.26 135	93.49 26	76.93 62	78.47 157	91.04 121	69.92 73	92.34 201	69.87 193	84.97 173	92.44 124

MVS_111021_LR		82.61 101	82.11 101	84.11 119	88.82 153	71.58 53	85.15 224	86.16 253	74.69 113	80.47 127	91.04 121	62.29 156	90.55 258	80.33 95	90.08 106	90.20 197

DP-MVS Recon		83.11 95	82.09 103	86.15 58	94.44 19	70.92 68	88.79 113	92.20 85	70.53 194	79.17 142	91.03 123	64.12 132	96.03 47	68.39 209	90.14 104	91.50 150

mamv4		76.81 229	78.23 177	72.54 337	86.12 236	65.75 181	78.76 328	82.07 310	64.12 297	72.97 270	91.02 124	67.97 94	68.08 401	83.04 67	78.02 266	83.80 347

HQP_MVS		83.64 81	83.14 85	85.14 80	90.08 105	68.71 113	91.25 49	92.44 73	79.12 23	78.92 146	91.00 125	60.42 191	95.38 71	78.71 105	86.32 157	91.33 155

plane_prior4											91.00 125

FC-MVSNet-test		81.52 120	82.02 105	80.03 241	88.42 171	55.97 329	87.95 145	93.42 29	77.10 58	77.38 179	90.98 127	69.96 72	91.79 219	68.46 208	84.50 179	92.33 125

Vis-MVSNet (Re-imp)		78.36 194	78.45 168	78.07 279	88.64 162	51.78 367	86.70 185	79.63 338	74.14 125	75.11 241	90.83 128	61.29 175	89.75 270	58.10 300	91.60 84	92.69 113

114514_t		80.68 139	79.51 145	84.20 117	94.09 38	67.27 151	89.64 84	91.11 124	58.75 350	74.08 258	90.72 129	58.10 203	95.04 87	69.70 194	89.42 115	90.30 194

PAPM_NR		83.02 96	82.41 96	84.82 93	92.47 67	66.37 166	87.93 147	91.80 102	73.82 131	77.32 181	90.66 130	67.90 96	94.90 92	70.37 186	89.48 114	93.19 96

LS3D		76.95 227	74.82 244	83.37 152	90.45 97	67.36 148	89.15 103	86.94 240	61.87 325	69.52 310	90.61 131	51.71 265	94.53 105	46.38 368	86.71 152	88.21 268

VPNet		78.69 187	78.66 164	78.76 264	88.31 174	55.72 333	84.45 243	86.63 245	76.79 66	78.26 161	90.55 132	59.30 196	89.70 272	66.63 223	77.05 276	90.88 170

UniMVSNet_ETH3D		79.10 177	78.24 175	81.70 201	86.85 223	60.24 276	87.28 167	88.79 197	74.25 122	76.84 191	90.53 133	49.48 289	91.56 228	67.98 210	82.15 218	93.29 90

ACMP	74.13 6	81.51 122	80.57 124	84.36 107	89.42 127	68.69 116	89.97 74	91.50 114	74.46 118	75.04 244	90.41 134	53.82 238	94.54 104	77.56 116	82.91 209	89.86 218
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
RRT-MVS		82.60 103	82.10 102	84.10 120	87.98 189	62.94 243	87.45 161	91.27 117	77.42 48	79.85 133	90.28 135	56.62 218	94.70 102	79.87 99	88.15 135	94.67 26

PCF-MVS	73.52 7	80.38 147	78.84 162	85.01 85	87.71 201	68.99 103	83.65 257	91.46 115	63.00 310	77.77 173	90.28 135	66.10 114	95.09 86	61.40 269	88.22 134	90.94 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 118	68.32 123					90.24 137

HQP-MVS		82.61 101	82.02 105	84.37 106	89.33 132	66.98 158	89.17 99	92.19 86	76.41 76	77.23 184	90.23 138	60.17 194	95.11 82	77.47 117	85.99 165	91.03 165

PS-MVSNAJss		82.07 108	81.31 112	84.34 109	86.51 231	67.27 151	89.27 97	91.51 111	71.75 166	79.37 139	90.22 139	63.15 143	94.27 113	77.69 115	82.36 217	91.49 151

TSAR-MVS + GP.		85.71 54	85.33 60	86.84 47	91.34 78	72.50 36	89.07 105	87.28 232	76.41 76	85.80 51	90.22 139	74.15 31	95.37 74	81.82 80	91.88 80	92.65 115

SDMVSNet		80.38 147	80.18 133	80.99 221	89.03 148	64.94 197	80.45 305	89.40 172	75.19 102	76.61 200	89.98 141	60.61 188	87.69 303	76.83 125	83.55 200	90.33 192

sd_testset		77.70 214	77.40 198	78.60 267	89.03 148	60.02 278	79.00 324	85.83 257	75.19 102	76.61 200	89.98 141	54.81 225	85.46 323	62.63 256	83.55 200	90.33 192

TranMVSNet+NR-MVSNet		80.84 131	80.31 130	82.42 189	87.85 194	62.33 248	87.74 153	91.33 116	80.55 9	77.99 169	89.86 143	65.23 124	92.62 187	67.05 221	75.24 312	92.30 127

diffmvs		82.10 106	81.88 108	82.76 184	83.00 300	63.78 220	83.68 256	89.76 162	72.94 154	82.02 107	89.85 144	65.96 119	90.79 254	82.38 77	87.30 143	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
BH-RMVSNet		79.61 160	78.44 169	83.14 162	89.38 131	65.93 174	84.95 229	87.15 236	73.56 138	78.19 163	89.79 145	56.67 217	93.36 157	59.53 284	86.74 151	90.13 200

GeoE		81.71 115	81.01 119	83.80 141	89.51 123	64.45 208	88.97 107	88.73 203	71.27 177	78.63 152	89.76 146	66.32 112	93.20 167	69.89 192	86.02 164	93.74 68

AdaColmap		80.58 144	79.42 147	84.06 128	93.09 54	68.91 105	89.36 95	88.97 193	69.27 222	75.70 218	89.69 147	57.20 214	95.77 56	63.06 250	88.41 132	87.50 283

ACMM	73.20 8	80.78 138	79.84 139	83.58 145	89.31 135	68.37 122	89.99 73	91.60 108	70.28 199	77.25 182	89.66 148	53.37 243	93.53 149	74.24 150	82.85 210	88.85 251
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 201	76.79 212	81.97 197	90.40 99	71.07 62	87.59 156	84.55 270	66.03 274	72.38 279	89.64 149	57.56 209	86.04 316	59.61 283	83.35 204	88.79 254

test_yl		81.17 125	80.47 127	83.24 157	89.13 143	63.62 221	86.21 199	89.95 158	72.43 160	81.78 112	89.61 150	57.50 210	93.58 144	70.75 181	86.90 148	92.52 118

DCV-MVSNet		81.17 125	80.47 127	83.24 157	89.13 143	63.62 221	86.21 199	89.95 158	72.43 160	81.78 112	89.61 150	57.50 210	93.58 144	70.75 181	86.90 148	92.52 118

EI-MVSNet-Vis-set		84.19 72	83.81 77	85.31 76	88.18 177	67.85 134	87.66 154	89.73 164	80.05 14	82.95 96	89.59 152	70.74 65	94.82 96	80.66 93	84.72 176	93.28 91

PAPR		81.66 118	80.89 121	83.99 136	90.27 100	64.00 215	86.76 184	91.77 105	68.84 237	77.13 190	89.50 153	67.63 98	94.88 94	67.55 214	88.52 130	93.09 99

jajsoiax		79.29 172	77.96 180	83.27 155	84.68 262	66.57 164	89.25 98	90.16 152	69.20 227	75.46 224	89.49 154	45.75 324	93.13 173	76.84 124	80.80 234	90.11 202

MVSFormer		82.85 98	82.05 104	85.24 78	87.35 210	70.21 77	90.50 61	90.38 142	68.55 241	81.32 116	89.47 155	61.68 164	93.46 153	78.98 102	90.26 102	92.05 138

jason		81.39 123	80.29 131	84.70 97	86.63 230	69.90 85	85.95 205	86.77 243	63.24 306	81.07 122	89.47 155	61.08 180	92.15 207	78.33 110	90.07 107	92.05 138
jason: jason.
mvs_tets		79.13 176	77.77 189	83.22 159	84.70 261	66.37 166	89.17 99	90.19 151	69.38 220	75.40 227	89.46 157	44.17 334	93.15 171	76.78 126	80.70 236	90.14 199

UGNet		80.83 132	79.59 144	84.54 100	88.04 185	68.09 129	89.42 92	88.16 210	76.95 61	76.22 208	89.46 157	49.30 293	93.94 126	68.48 207	90.31 100	91.60 145
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		80.60 141	80.55 125	80.76 227	88.07 184	60.80 267	86.86 178	91.58 109	75.67 94	80.24 129	89.45 159	63.34 137	90.25 261	70.51 185	79.22 255	91.23 158

MVS_Test		83.15 92	83.06 87	83.41 151	86.86 222	63.21 234	86.11 202	92.00 91	74.31 120	82.87 98	89.44 160	70.03 71	93.21 164	77.39 119	88.50 131	93.81 65

EI-MVSNet-UG-set		83.81 76	83.38 82	85.09 83	87.87 193	67.53 143	87.44 162	89.66 165	79.74 16	82.23 105	89.41 161	70.24 70	94.74 99	79.95 97	83.92 190	92.99 106

RPSCF		73.23 277	71.46 281	78.54 270	82.50 312	59.85 279	82.18 279	82.84 303	58.96 347	71.15 292	89.41 161	45.48 328	84.77 330	58.82 292	71.83 342	91.02 167

UniMVSNet_NR-MVSNet		81.88 111	81.54 111	82.92 173	88.46 168	63.46 228	87.13 169	92.37 77	80.19 12	78.38 158	89.14 163	71.66 55	93.05 177	70.05 189	76.46 285	92.25 129

tttt0517		79.40 169	77.91 182	83.90 140	88.10 182	63.84 218	88.37 131	84.05 278	71.45 174	76.78 194	89.12 164	49.93 286	94.89 93	70.18 188	83.18 207	92.96 107

DU-MVS		81.12 127	80.52 126	82.90 174	87.80 196	63.46 228	87.02 173	91.87 99	79.01 26	78.38 158	89.07 165	65.02 126	93.05 177	70.05 189	76.46 285	92.20 132

NR-MVSNet		80.23 151	79.38 148	82.78 182	87.80 196	63.34 231	86.31 196	91.09 125	79.01 26	72.17 281	89.07 165	67.20 103	92.81 186	66.08 228	75.65 298	92.20 132

DELS-MVS		85.41 60	85.30 62	85.77 67	88.49 166	67.93 133	85.52 221	93.44 27	78.70 29	83.63 91	89.03 167	74.57 24	95.71 58	80.26 96	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
mvsmamba		80.60 141	79.38 148	84.27 114	89.74 117	67.24 153	87.47 159	86.95 239	70.02 204	75.38 228	88.93 168	51.24 269	92.56 190	75.47 141	89.22 117	93.00 105

baseline1		76.98 226	76.75 215	77.66 284	88.13 180	55.66 334	85.12 225	81.89 311	73.04 152	76.79 193	88.90 169	62.43 154	87.78 302	63.30 249	71.18 346	89.55 228

DP-MVS		76.78 230	74.57 246	83.42 149	93.29 48	69.46 94	88.55 124	83.70 282	63.98 302	70.20 298	88.89 170	54.01 237	94.80 97	46.66 365	81.88 223	86.01 315

ab-mvs		79.51 163	78.97 160	81.14 217	88.46 168	60.91 265	83.84 254	89.24 180	70.36 196	79.03 143	88.87 171	63.23 141	90.21 262	65.12 235	82.57 215	92.28 128

PEN-MVS		77.73 211	77.69 193	77.84 281	87.07 221	53.91 351	87.91 148	91.18 120	77.56 43	73.14 268	88.82 172	61.23 176	89.17 280	59.95 279	72.37 336	90.43 188

tt0805		78.73 185	77.83 185	81.43 207	85.17 251	60.30 275	89.41 93	90.90 128	71.21 178	77.17 188	88.73 173	46.38 313	93.21 164	72.57 168	78.96 256	90.79 172

test_djsdf		80.30 150	79.32 151	83.27 155	83.98 277	65.37 189	90.50 61	90.38 142	68.55 241	76.19 209	88.70 174	56.44 219	93.46 153	78.98 102	80.14 244	90.97 168

PAPM		77.68 215	76.40 222	81.51 205	87.29 217	61.85 255	83.78 255	89.59 167	64.74 289	71.23 290	88.70 174	62.59 150	93.66 143	52.66 332	87.03 147	89.01 243

DTE-MVSNet		76.99 225	76.80 211	77.54 288	86.24 233	53.06 360	87.52 157	90.66 134	77.08 59	72.50 276	88.67 176	60.48 190	89.52 274	57.33 307	70.74 348	90.05 209

PS-CasMVS		78.01 205	78.09 178	77.77 283	87.71 201	54.39 348	88.02 142	91.22 118	77.50 46	73.26 266	88.64 177	60.73 183	88.41 295	61.88 264	73.88 325	90.53 184

cdsmvs_eth3d_5k		19.96 381	26.61 383	0.00 401	0.00 424	0.00 426	0.00 412	89.26 179	0.00 419	0.00 420	88.61 178	61.62 166	0.00 420	0.00 419	0.00 418	0.00 416

lupinMVS		81.39 123	80.27 132	84.76 96	87.35 210	70.21 77	85.55 217	86.41 247	62.85 313	81.32 116	88.61 178	61.68 164	92.24 205	78.41 109	90.26 102	91.83 141

F-COLMAP		76.38 240	74.33 251	82.50 188	89.28 137	66.95 161	88.41 127	89.03 188	64.05 300	66.83 335	88.61 178	46.78 310	92.89 182	57.48 304	78.55 258	87.67 277

mvs_anonymous		79.42 168	79.11 157	80.34 235	84.45 268	57.97 297	82.59 275	87.62 225	67.40 256	76.17 212	88.56 181	68.47 89	89.59 273	70.65 184	86.05 163	93.47 84

CP-MVSNet		78.22 196	78.34 172	77.84 281	87.83 195	54.54 346	87.94 146	91.17 121	77.65 38	73.48 264	88.49 182	62.24 158	88.43 294	62.19 260	74.07 321	90.55 183

PVSNet_Blended_VisFu		82.62 100	81.83 109	84.96 87	90.80 91	69.76 87	88.74 117	91.70 106	69.39 219	78.96 144	88.46 183	65.47 122	94.87 95	74.42 147	88.57 128	90.24 196

CANet_DTU		80.61 140	79.87 138	82.83 176	85.60 244	63.17 237	87.36 163	88.65 204	76.37 80	75.88 215	88.44 184	53.51 241	93.07 176	73.30 159	89.74 112	92.25 129

PLC	70.83 11	78.05 203	76.37 223	83.08 165	91.88 74	67.80 135	88.19 137	89.46 171	64.33 295	69.87 307	88.38 185	53.66 239	93.58 144	58.86 291	82.73 212	87.86 274
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 164	79.22 155	80.27 237	88.79 156	58.35 290	85.06 226	88.61 206	78.56 30	77.65 174	88.34 186	63.81 136	90.66 257	64.98 237	77.22 274	91.80 143

XXY-MVS		75.41 254	75.56 232	74.96 314	83.59 284	57.82 301	80.59 302	83.87 281	66.54 268	74.93 246	88.31 187	63.24 140	80.09 356	62.16 261	76.85 280	86.97 297

Effi-MVS+		83.62 83	83.08 86	85.24 78	88.38 172	67.45 144	88.89 110	89.15 184	75.50 96	82.27 104	88.28 188	69.61 76	94.45 109	77.81 114	87.84 136	93.84 64

API-MVS		81.99 110	81.23 114	84.26 116	90.94 87	70.18 82	91.10 52	89.32 175	71.51 173	78.66 151	88.28 188	65.26 123	95.10 85	64.74 239	91.23 90	87.51 282

thisisatest0530		79.40 169	77.76 190	84.31 110	87.69 203	65.10 194	87.36 163	84.26 276	70.04 203	77.42 178	88.26 190	49.94 284	94.79 98	70.20 187	84.70 177	93.03 102

hse-mvs2		81.72 114	80.94 120	84.07 126	88.72 159	67.68 138	85.87 208	87.26 233	76.02 87	84.67 67	88.22 191	61.54 167	93.48 151	82.71 73	73.44 330	91.06 163

xiu_mvs_v1_base_debu		80.80 135	79.72 141	84.03 133	87.35 210	70.19 79	85.56 214	88.77 198	69.06 231	81.83 108	88.16 192	50.91 272	92.85 183	78.29 111	87.56 138	89.06 238

xiu_mvs_v1_base		80.80 135	79.72 141	84.03 133	87.35 210	70.19 79	85.56 214	88.77 198	69.06 231	81.83 108	88.16 192	50.91 272	92.85 183	78.29 111	87.56 138	89.06 238

xiu_mvs_v1_base_debi		80.80 135	79.72 141	84.03 133	87.35 210	70.19 79	85.56 214	88.77 198	69.06 231	81.83 108	88.16 192	50.91 272	92.85 183	78.29 111	87.56 138	89.06 238

UniMVSNet (Re)		81.60 119	81.11 116	83.09 164	88.38 172	64.41 209	87.60 155	93.02 42	78.42 32	78.56 154	88.16 192	69.78 74	93.26 160	69.58 196	76.49 284	91.60 145

AUN-MVS		79.21 174	77.60 195	84.05 131	88.71 160	67.61 140	85.84 210	87.26 233	69.08 230	77.23 184	88.14 196	53.20 245	93.47 152	75.50 140	73.45 329	91.06 163

Anonymous20231211		78.97 181	77.69 193	82.81 178	90.54 96	64.29 211	90.11 72	91.51 111	65.01 287	76.16 213	88.13 197	50.56 277	93.03 180	69.68 195	77.56 272	91.11 161

pm-mvs1		77.25 223	76.68 217	78.93 262	84.22 271	58.62 288	86.41 192	88.36 209	71.37 175	73.31 265	88.01 198	61.22 177	89.15 281	64.24 243	73.01 333	89.03 242

LTVRE_ROB	69.57 13	76.25 241	74.54 248	81.41 208	88.60 163	64.38 210	79.24 319	89.12 187	70.76 188	69.79 309	87.86 199	49.09 296	93.20 167	56.21 317	80.16 242	86.65 304
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
WTY-MVS		75.65 249	75.68 229	75.57 305	86.40 232	56.82 314	77.92 341	82.40 306	65.10 284	76.18 210	87.72 200	63.13 146	80.90 353	60.31 277	81.96 221	89.00 245

TAMVS		78.89 183	77.51 197	83.03 168	87.80 196	67.79 136	84.72 233	85.05 265	67.63 251	76.75 195	87.70 201	62.25 157	90.82 253	58.53 295	87.13 145	90.49 186

BH-untuned		79.47 165	78.60 165	82.05 194	89.19 141	65.91 175	86.07 203	88.52 207	72.18 162	75.42 226	87.69 202	61.15 178	93.54 148	60.38 276	86.83 150	86.70 303

COLMAP_ROB	66.92 17	73.01 280	70.41 295	80.81 226	87.13 220	65.63 182	88.30 134	84.19 277	62.96 311	63.80 361	87.69 202	38.04 366	92.56 190	46.66 365	74.91 315	84.24 340
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 262	72.42 272	79.80 246	83.76 282	59.59 283	85.92 207	86.64 244	66.39 269	66.96 333	87.58 204	39.46 358	91.60 225	65.76 231	69.27 353	88.22 267

FA-MVS(test-final)		80.96 129	79.91 137	84.10 120	88.30 175	65.01 195	84.55 239	90.01 156	73.25 148	79.61 136	87.57 205	58.35 202	94.72 100	71.29 177	86.25 159	92.56 117

Baseline_NR-MVSNet		78.15 200	78.33 173	77.61 286	85.79 240	56.21 327	86.78 182	85.76 258	73.60 137	77.93 170	87.57 205	65.02 126	88.99 283	67.14 220	75.33 309	87.63 278

WR-MVS_H		78.51 191	78.49 167	78.56 269	88.02 186	56.38 323	88.43 126	92.67 63	77.14 56	73.89 259	87.55 207	66.25 113	89.24 279	58.92 290	73.55 328	90.06 208

EI-MVSNet		80.52 145	79.98 135	82.12 192	84.28 269	63.19 236	86.41 192	88.95 194	74.18 124	78.69 149	87.54 208	66.62 106	92.43 195	72.57 168	80.57 238	90.74 176

CVMVSNet		72.99 281	72.58 270	74.25 322	84.28 269	50.85 375	86.41 192	83.45 288	44.56 390	73.23 267	87.54 208	49.38 291	85.70 318	65.90 229	78.44 261	86.19 310

ACMH+	68.96 14	76.01 245	74.01 253	82.03 195	88.60 163	65.31 190	88.86 111	87.55 226	70.25 201	67.75 324	87.47 210	41.27 350	93.19 169	58.37 297	75.94 295	87.60 279

TransMVSNet (Re)		75.39 255	74.56 247	77.86 280	85.50 246	57.10 311	86.78 182	86.09 255	72.17 163	71.53 288	87.34 211	63.01 147	89.31 278	56.84 312	61.83 374	87.17 290

GBi-Net		78.40 192	77.40 198	81.40 209	87.60 205	63.01 238	88.39 128	89.28 176	71.63 168	75.34 230	87.28 212	54.80 226	91.11 244	62.72 252	79.57 248	90.09 204

test1		78.40 192	77.40 198	81.40 209	87.60 205	63.01 238	88.39 128	89.28 176	71.63 168	75.34 230	87.28 212	54.80 226	91.11 244	62.72 252	79.57 248	90.09 204

FMVSNet2		78.20 198	77.21 202	81.20 215	87.60 205	62.89 244	87.47 159	89.02 189	71.63 168	75.29 236	87.28 212	54.80 226	91.10 247	62.38 257	79.38 252	89.61 226

FMVSNet1		77.44 218	76.12 225	81.40 209	86.81 225	63.01 238	88.39 128	89.28 176	70.49 195	74.39 255	87.28 212	49.06 297	91.11 244	60.91 273	78.52 259	90.09 204

v2v482		80.23 151	79.29 152	83.05 167	83.62 283	64.14 213	87.04 172	89.97 157	73.61 136	78.18 164	87.22 216	61.10 179	93.82 134	76.11 130	76.78 282	91.18 159

ITE_SJBPF				78.22 275	81.77 322	60.57 270		83.30 289	69.25 224	67.54 326	87.20 217	36.33 371	87.28 306	54.34 324	74.62 318	86.80 300

anonymousdsp		78.60 189	77.15 203	82.98 171	80.51 341	67.08 156	87.24 168	89.53 169	65.66 278	75.16 239	87.19 218	52.52 246	92.25 204	77.17 121	79.34 253	89.61 226

MVSTER		79.01 179	77.88 184	82.38 190	83.07 297	64.80 200	84.08 253	88.95 194	69.01 234	78.69 149	87.17 219	54.70 230	92.43 195	74.69 144	80.57 238	89.89 217

thres100view900		76.50 234	75.55 233	79.33 255	89.52 122	56.99 312	85.83 211	83.23 291	73.94 128	76.32 206	87.12 220	51.89 261	91.95 213	48.33 356	83.75 194	89.07 236

thres600view7		76.50 234	75.44 234	79.68 249	89.40 129	57.16 309	85.53 219	83.23 291	73.79 132	76.26 207	87.09 221	51.89 261	91.89 216	48.05 361	83.72 197	90.00 210

XVG-ACMP-BASELINE		76.11 243	74.27 252	81.62 202	83.20 293	64.67 202	83.60 260	89.75 163	69.75 214	71.85 284	87.09 221	32.78 378	92.11 208	69.99 191	80.43 240	88.09 270

HY-MVS	69.67 12	77.95 206	77.15 203	80.36 234	87.57 209	60.21 277	83.37 264	87.78 223	66.11 271	75.37 229	87.06 223	63.27 139	90.48 259	61.38 270	82.43 216	90.40 190

CHOSEN 1792x2688		77.63 216	75.69 228	83.44 148	89.98 111	68.58 119	78.70 329	87.50 228	56.38 365	75.80 217	86.84 224	58.67 199	91.40 238	61.58 268	85.75 169	90.34 191

v8		79.97 157	79.02 159	82.80 179	84.09 274	64.50 206	87.96 144	90.29 149	74.13 126	75.24 237	86.81 225	62.88 148	93.89 133	74.39 148	75.40 307	90.00 210

AllTest		70.96 297	68.09 312	79.58 252	85.15 253	63.62 221	84.58 238	79.83 335	62.31 320	60.32 373	86.73 226	32.02 379	88.96 286	50.28 345	71.57 344	86.15 311

TestCases				79.58 252	85.15 253	63.62 221		79.83 335	62.31 320	60.32 373	86.73 226	32.02 379	88.96 286	50.28 345	71.57 344	86.15 311

LCM-MVSNet-Re		77.05 224	76.94 208	77.36 289	87.20 218	51.60 368	80.06 309	80.46 328	75.20 101	67.69 325	86.72 228	62.48 152	88.98 284	63.44 247	89.25 116	91.51 149

1112_ss		77.40 220	76.43 221	80.32 236	89.11 147	60.41 274	83.65 257	87.72 224	62.13 323	73.05 269	86.72 228	62.58 151	89.97 266	62.11 263	80.80 234	90.59 182

ab-mvs-re		7.23 384	9.64 387	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	86.72 228	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

IterMVS-LS		80.06 154	79.38 148	82.11 193	85.89 239	63.20 235	86.79 181	89.34 174	74.19 123	75.45 225	86.72 228	66.62 106	92.39 197	72.58 167	76.86 279	90.75 175
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 246	73.93 255	81.77 200	88.71 160	66.61 163	88.62 122	89.01 190	69.81 210	66.78 336	86.70 232	41.95 349	91.51 233	55.64 318	78.14 265	87.17 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 239	75.44 234	79.27 256	89.28 137	58.09 293	81.69 284	87.07 237	59.53 342	72.48 277	86.67 233	61.30 174	89.33 277	60.81 275	80.15 243	90.41 189

FMVSNet3		77.88 208	76.85 210	80.97 223	86.84 224	62.36 247	86.52 190	88.77 198	71.13 179	75.34 230	86.66 234	54.07 236	91.10 247	62.72 252	79.57 248	89.45 230

pmmvs6		74.69 259	73.39 261	78.61 266	81.38 330	57.48 306	86.64 186	87.95 217	64.99 288	70.18 299	86.61 235	50.43 279	89.52 274	62.12 262	70.18 350	88.83 252

ET-MVSNet_ETH3D		78.63 188	76.63 218	84.64 98	86.73 227	69.47 92	85.01 227	84.61 269	69.54 217	66.51 343	86.59 236	50.16 281	91.75 221	76.26 129	84.24 187	92.69 113

testgi		66.67 334	66.53 331	67.08 368	75.62 373	41.69 403	75.93 349	76.50 360	66.11 271	65.20 353	86.59 236	35.72 373	74.71 387	43.71 377	73.38 331	84.84 334

CLD-MVS		82.31 104	81.65 110	84.29 111	88.47 167	67.73 137	85.81 212	92.35 78	75.78 90	78.33 160	86.58 238	64.01 133	94.35 110	76.05 132	87.48 141	90.79 172
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 159	78.67 163	82.97 172	84.06 275	64.95 196	87.88 150	90.62 135	73.11 150	75.11 241	86.56 239	61.46 170	94.05 122	73.68 153	75.55 300	89.90 216

CDS-MVSNet		79.07 178	77.70 192	83.17 161	87.60 205	68.23 126	84.40 246	86.20 252	67.49 254	76.36 205	86.54 240	61.54 167	90.79 254	61.86 265	87.33 142	90.49 186
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 116	81.05 117	83.60 144	89.15 142	68.03 132	84.46 242	90.02 155	70.67 189	81.30 119	86.53 241	63.17 142	94.19 118	75.60 138	88.54 129	88.57 262

TR-MVS		77.44 218	76.18 224	81.20 215	88.24 176	63.24 233	84.61 237	86.40 248	67.55 253	77.81 171	86.48 242	54.10 235	93.15 171	57.75 303	82.72 213	87.20 289

EIA-MVS		83.31 91	82.80 93	84.82 93	89.59 119	65.59 183	88.21 136	92.68 62	74.66 114	78.96 144	86.42 243	69.06 83	95.26 75	75.54 139	90.09 105	93.62 77

tfpn200view9		76.42 238	75.37 238	79.55 254	89.13 143	57.65 303	85.17 222	83.60 283	73.41 143	76.45 202	86.39 244	52.12 253	91.95 213	48.33 356	83.75 194	89.07 236

thres400		76.50 234	75.37 238	79.86 244	89.13 143	57.65 303	85.17 222	83.60 283	73.41 143	76.45 202	86.39 244	52.12 253	91.95 213	48.33 356	83.75 194	90.00 210

v7n		78.97 181	77.58 196	83.14 162	83.45 287	65.51 184	88.32 133	91.21 119	73.69 134	72.41 278	86.32 246	57.93 204	93.81 135	69.18 199	75.65 298	90.11 202

MAR-MVS		81.84 112	80.70 122	85.27 77	91.32 79	71.53 54	89.82 76	90.92 127	69.77 213	78.50 155	86.21 247	62.36 155	94.52 106	65.36 233	92.05 79	89.77 222
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		80.03 155	79.03 158	83.01 169	83.78 281	64.51 204	87.11 171	90.57 138	71.96 165	78.08 167	86.20 248	61.41 171	93.94 126	74.93 143	77.23 273	90.60 181

test_vis1_n_1920		75.52 251	75.78 227	74.75 318	79.84 349	57.44 307	83.26 265	85.52 260	62.83 314	79.34 141	86.17 249	45.10 329	79.71 357	78.75 104	81.21 229	87.10 296

V42		79.38 171	78.24 175	82.83 176	81.10 335	65.50 185	85.55 217	89.82 160	71.57 172	78.21 162	86.12 250	60.66 186	93.18 170	75.64 136	75.46 304	89.81 221

PVSNet_BlendedMVS		80.60 141	80.02 134	82.36 191	88.85 150	65.40 186	86.16 201	92.00 91	69.34 221	78.11 165	86.09 251	66.02 117	94.27 113	71.52 173	82.06 220	87.39 284

v1192		79.59 162	78.43 170	83.07 166	83.55 285	64.52 203	86.93 176	90.58 136	70.83 185	77.78 172	85.90 252	59.15 197	93.94 126	73.96 152	77.19 275	90.76 174

SixPastTwentyTwo		73.37 273	71.26 286	79.70 248	85.08 256	57.89 299	85.57 213	83.56 285	71.03 183	65.66 347	85.88 253	42.10 347	92.57 189	59.11 288	63.34 372	88.65 260

EPNet_dtu		75.46 252	74.86 243	77.23 292	82.57 311	54.60 345	86.89 177	83.09 295	71.64 167	66.25 345	85.86 254	55.99 220	88.04 299	54.92 321	86.55 154	89.05 241
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 270	73.64 260	73.51 328	82.80 305	55.01 342	76.12 348	81.69 314	62.47 319	74.68 250	85.85 255	57.32 212	78.11 364	60.86 274	80.93 231	87.39 284

ETV-MVS		84.90 69	84.67 69	85.59 70	89.39 130	68.66 117	88.74 117	92.64 68	79.97 15	84.10 81	85.71 256	69.32 79	95.38 71	80.82 90	91.37 88	92.72 110

test_cas_vis1_n_1920		73.76 269	73.74 259	73.81 326	75.90 370	59.77 280	80.51 303	82.40 306	58.30 352	81.62 114	85.69 257	44.35 333	76.41 375	76.29 128	78.61 257	85.23 327

v1240		78.99 180	77.78 188	82.64 185	83.21 292	63.54 225	86.62 187	90.30 148	69.74 216	77.33 180	85.68 258	57.04 215	93.76 139	73.13 162	76.92 277	90.62 179

v144192		79.47 165	78.37 171	82.78 182	83.35 288	63.96 216	86.96 174	90.36 145	69.99 206	77.50 176	85.67 259	60.66 186	93.77 138	74.27 149	76.58 283	90.62 179

tfpnnormal		74.39 260	73.16 264	78.08 278	86.10 238	58.05 294	84.65 236	87.53 227	70.32 198	71.22 291	85.63 260	54.97 224	89.86 267	43.03 379	75.02 314	86.32 307

PS-MVSNAJ		81.69 116	81.02 118	83.70 142	89.51 123	68.21 127	84.28 248	90.09 154	70.79 186	81.26 120	85.62 261	63.15 143	94.29 111	75.62 137	88.87 122	88.59 261

v1921920		79.22 173	78.03 179	82.80 179	83.30 290	63.94 217	86.80 180	90.33 146	69.91 209	77.48 177	85.53 262	58.44 201	93.75 140	73.60 154	76.85 280	90.71 177

test_0402		72.79 283	70.44 294	79.84 245	88.13 180	65.99 173	85.93 206	84.29 274	65.57 279	67.40 330	85.49 263	46.92 309	92.61 188	35.88 393	74.38 320	80.94 371

v148		78.72 186	77.80 187	81.47 206	82.73 307	61.96 254	86.30 197	88.08 213	73.26 147	76.18 210	85.47 264	62.46 153	92.36 199	71.92 172	73.82 326	90.09 204

USDC		70.33 305	68.37 307	76.21 299	80.60 339	56.23 326	79.19 321	86.49 246	60.89 330	61.29 369	85.47 264	31.78 381	89.47 276	53.37 329	76.21 293	82.94 358

MVP-Stereo		76.12 242	74.46 250	81.13 218	85.37 249	69.79 86	84.42 245	87.95 217	65.03 286	67.46 328	85.33 266	53.28 244	91.73 223	58.01 301	83.27 205	81.85 366
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 199	76.99 207	81.78 199	85.66 242	66.99 157	84.66 234	90.47 140	55.08 370	72.02 283	85.27 267	63.83 135	94.11 121	66.10 227	89.80 111	84.24 340

DIV-MVS_self_test		77.72 212	76.76 213	80.58 230	82.48 314	60.48 272	83.09 269	87.86 220	69.22 225	74.38 256	85.24 268	62.10 160	91.53 231	71.09 178	75.40 307	89.74 223

FE-MVS		77.78 210	75.68 229	84.08 125	88.09 183	66.00 172	83.13 268	87.79 222	68.42 245	78.01 168	85.23 269	45.50 327	95.12 80	59.11 288	85.83 168	91.11 161

cl____		77.72 212	76.76 213	80.58 230	82.49 313	60.48 272	83.09 269	87.87 219	69.22 225	74.38 256	85.22 270	62.10 160	91.53 231	71.09 178	75.41 306	89.73 224

HyFIR lowres test		77.53 217	75.40 236	83.94 139	89.59 119	66.62 162	80.36 306	88.64 205	56.29 366	76.45 202	85.17 271	57.64 208	93.28 159	61.34 271	83.10 208	91.91 140

pmmvs4		74.03 267	71.91 276	80.39 233	81.96 319	68.32 123	81.45 288	82.14 308	59.32 343	69.87 307	85.13 272	52.40 249	88.13 298	60.21 278	74.74 317	84.73 336

TDRefinement		67.49 327	64.34 337	76.92 294	73.47 385	61.07 263	84.86 231	82.98 299	59.77 339	58.30 380	85.13 272	26.06 389	87.89 300	47.92 362	60.59 379	81.81 367

Fast-Effi-MVS+		80.81 133	79.92 136	83.47 147	88.85 150	64.51 204	85.53 219	89.39 173	70.79 186	78.49 156	85.06 274	67.54 99	93.58 144	67.03 222	86.58 153	92.32 126

PVSNet_Blended		80.98 128	80.34 129	82.90 174	88.85 150	65.40 186	84.43 244	92.00 91	67.62 252	78.11 165	85.05 275	66.02 117	94.27 113	71.52 173	89.50 113	89.01 243

m2depth		59.91 353	57.10 357	68.34 363	67.13 399	46.65 388	74.64 362	67.41 390	48.30 385	62.52 367	85.04 276	20.40 399	75.93 379	42.55 381	45.90 400	82.44 361

test_fmvs1_n		70.86 299	70.24 297	72.73 335	72.51 392	55.28 339	81.27 291	79.71 337	51.49 381	78.73 148	84.87 277	27.54 388	77.02 369	76.06 131	79.97 246	85.88 318

WBMVS		73.43 272	72.81 267	75.28 311	87.91 191	50.99 374	78.59 332	81.31 319	65.51 282	74.47 254	84.83 278	46.39 312	86.68 309	58.41 296	77.86 267	88.17 269

CMPMVS	51.72 21	70.19 307	68.16 310	76.28 298	73.15 388	57.55 305	79.47 316	83.92 279	48.02 386	56.48 386	84.81 279	43.13 339	86.42 313	62.67 255	81.81 224	84.89 333
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 322	67.61 322	71.31 347	78.51 362	47.01 386	84.47 240	84.27 275	42.27 393	66.44 344	84.79 280	40.44 355	83.76 335	58.76 293	68.54 358	83.17 352

BH-w/o		78.21 197	77.33 201	80.84 225	88.81 154	65.13 193	84.87 230	87.85 221	69.75 214	74.52 253	84.74 281	61.34 173	93.11 174	58.24 299	85.84 167	84.27 339

pmmvs5		71.55 292	70.20 298	75.61 304	77.83 363	56.39 322	81.74 283	80.89 320	57.76 356	67.46 328	84.49 282	49.26 294	85.32 325	57.08 309	75.29 310	85.11 331

thres200		75.55 250	74.47 249	78.82 263	87.78 199	57.85 300	83.07 271	83.51 286	72.44 159	75.84 216	84.42 283	52.08 256	91.75 221	47.41 363	83.64 199	86.86 299

test_fmvs1		70.93 298	70.52 292	72.16 339	73.71 381	55.05 341	80.82 294	78.77 344	51.21 382	78.58 153	84.41 284	31.20 383	76.94 370	75.88 134	80.12 245	84.47 338

testing3		68.56 321	67.67 321	71.22 348	87.33 215	42.87 398	83.06 272	71.54 378	70.36 196	69.08 315	84.38 285	30.33 385	85.69 319	37.50 391	75.45 305	85.09 332

test_fmvs2		68.35 324	67.48 324	70.98 350	69.50 395	51.95 363	80.05 310	76.38 361	49.33 384	74.65 251	84.38 285	23.30 397	75.40 385	74.51 146	75.17 313	85.60 321

eth_miper_zixun_eth		77.92 207	76.69 216	81.61 204	83.00 300	61.98 253	83.15 267	89.20 182	69.52 218	74.86 247	84.35 287	61.76 163	92.56 190	71.50 175	72.89 334	90.28 195

testing91		76.54 232	75.66 231	79.18 259	88.43 170	55.89 330	81.08 292	83.00 298	73.76 133	75.34 230	84.29 288	46.20 318	90.07 264	64.33 241	84.50 179	91.58 147

c3_l		78.75 184	77.91 182	81.26 213	82.89 304	61.56 259	84.09 252	89.13 186	69.97 207	75.56 220	84.29 288	66.36 111	92.09 209	73.47 157	75.48 302	90.12 201

testing99		76.09 244	75.12 242	79.00 260	88.16 178	55.50 336	80.79 296	81.40 317	73.30 146	75.17 238	84.27 290	44.48 332	90.02 265	64.28 242	84.22 188	91.48 152

UWE-MVS		72.13 289	71.49 280	74.03 324	86.66 229	47.70 383	81.40 290	76.89 359	63.60 305	75.59 219	84.22 291	39.94 357	85.62 320	48.98 353	86.13 162	88.77 255

Fast-Effi-MVS+-dtu		78.02 204	76.49 219	82.62 186	83.16 296	66.96 160	86.94 175	87.45 230	72.45 157	71.49 289	84.17 292	54.79 229	91.58 226	67.61 213	80.31 241	89.30 234

IterMVS-SCA-FT		75.43 253	73.87 257	80.11 240	82.69 308	64.85 199	81.57 286	83.47 287	69.16 228	70.49 295	84.15 293	51.95 259	88.15 297	69.23 198	72.14 340	87.34 286

1314		76.53 233	75.30 240	80.21 238	83.93 278	62.32 249	84.66 234	88.81 196	60.23 335	70.16 301	84.07 294	55.30 223	90.73 256	67.37 216	83.21 206	87.59 281

cl22		78.07 202	77.01 205	81.23 214	82.37 316	61.83 256	83.55 261	87.98 215	68.96 235	75.06 243	83.87 295	61.40 172	91.88 217	73.53 155	76.39 287	89.98 213

EG-PatchMatch MVS		74.04 265	71.82 277	80.71 228	84.92 258	67.42 145	85.86 209	88.08 213	66.04 273	64.22 357	83.85 296	35.10 374	92.56 190	57.44 305	80.83 233	82.16 365

thisisatest0515		77.33 221	75.38 237	83.18 160	85.27 250	63.80 219	82.11 280	83.27 290	65.06 285	75.91 214	83.84 297	49.54 288	94.27 113	67.24 218	86.19 160	91.48 152

test20.03		67.45 328	66.95 329	68.94 357	75.48 374	44.84 394	77.50 342	77.67 349	66.66 262	63.01 363	83.80 298	47.02 308	78.40 362	42.53 382	68.86 357	83.58 349

miper_ehance_all_eth		78.59 190	77.76 190	81.08 219	82.66 309	61.56 259	83.65 257	89.15 184	68.87 236	75.55 221	83.79 299	66.49 109	92.03 210	73.25 160	76.39 287	89.64 225

MSDG		73.36 275	70.99 288	80.49 232	84.51 267	65.80 178	80.71 300	86.13 254	65.70 277	65.46 348	83.74 300	44.60 330	90.91 252	51.13 340	76.89 278	84.74 335

MonoMVSNet		76.49 237	75.80 226	78.58 268	81.55 326	58.45 289	86.36 195	86.22 251	74.87 110	74.73 249	83.73 301	51.79 264	88.73 289	70.78 180	72.15 339	88.55 263

testing11		75.14 257	74.01 253	78.53 271	88.16 178	56.38 323	80.74 299	80.42 329	70.67 189	72.69 275	83.72 302	43.61 337	89.86 267	62.29 259	83.76 193	89.36 232

IterMVS		74.29 261	72.94 266	78.35 274	81.53 327	63.49 227	81.58 285	82.49 305	68.06 249	69.99 304	83.69 303	51.66 266	85.54 321	65.85 230	71.64 343	86.01 315
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 286	71.71 278	74.35 321	82.19 317	52.00 362	79.22 320	77.29 355	64.56 291	72.95 271	83.68 304	51.35 267	83.26 341	58.33 298	75.80 296	87.81 275

testing222		74.04 265	72.66 269	78.19 276	87.89 192	55.36 337	81.06 293	79.20 342	71.30 176	74.65 251	83.57 305	39.11 361	88.67 291	51.43 339	85.75 169	90.53 184

Effi-MVS+-dtu		80.03 155	78.57 166	84.42 105	85.13 255	68.74 111	88.77 114	88.10 212	74.99 106	74.97 245	83.49 306	57.27 213	93.36 157	73.53 155	80.88 232	91.18 159

baseline2		75.70 248	73.83 258	81.30 212	83.26 291	61.79 257	82.57 276	80.65 324	66.81 258	66.88 334	83.42 307	57.86 206	92.19 206	63.47 246	79.57 248	89.91 215

mvs5depth		69.45 313	67.45 325	75.46 309	73.93 379	55.83 331	79.19 321	83.23 291	66.89 257	71.63 287	83.32 308	33.69 377	85.09 326	59.81 281	55.34 388	85.46 323

TinyColmap		67.30 330	64.81 335	74.76 317	81.92 321	56.68 318	80.29 308	81.49 316	60.33 333	56.27 387	83.22 309	24.77 393	87.66 304	45.52 373	69.47 352	79.95 376

mvsany_test1		62.30 349	61.26 353	65.41 370	69.52 394	54.86 343	66.86 390	49.78 410	46.65 387	68.50 321	83.21 310	49.15 295	66.28 402	56.93 311	60.77 377	75.11 386

test_vis1_n		69.85 311	69.21 302	71.77 341	72.66 391	55.27 340	81.48 287	76.21 362	52.03 378	75.30 235	83.20 311	28.97 386	76.22 377	74.60 145	78.41 263	83.81 346

CostFormer		75.24 256	73.90 256	79.27 256	82.65 310	58.27 292	80.80 295	82.73 304	61.57 326	75.33 234	83.13 312	55.52 221	91.07 250	64.98 237	78.34 264	88.45 264

MVStest1		56.63 357	52.76 363	68.25 364	61.67 405	53.25 359	71.67 371	68.90 388	38.59 398	50.59 394	83.05 313	25.08 391	70.66 395	36.76 392	38.56 401	80.83 372

WB-MVSnew		71.96 291	71.65 279	72.89 333	84.67 265	51.88 365	82.29 278	77.57 350	62.31 320	73.67 262	83.00 314	53.49 242	81.10 352	45.75 372	82.13 219	85.70 320

ETVMVS		72.25 288	71.05 287	75.84 301	87.77 200	51.91 364	79.39 317	74.98 366	69.26 223	73.71 261	82.95 315	40.82 354	86.14 315	46.17 369	84.43 184	89.47 229

miper_lstm_enhance		74.11 264	73.11 265	77.13 293	80.11 345	59.62 282	72.23 369	86.92 241	66.76 260	70.40 296	82.92 316	56.93 216	82.92 342	69.06 201	72.63 335	88.87 250

GA-MVS		76.87 228	75.17 241	81.97 197	82.75 306	62.58 245	81.44 289	86.35 250	72.16 164	74.74 248	82.89 317	46.20 318	92.02 211	68.85 204	81.09 230	91.30 157

K. test v3		71.19 294	68.51 306	79.21 258	83.04 299	57.78 302	84.35 247	76.91 358	72.90 155	62.99 364	82.86 318	39.27 359	91.09 249	61.65 267	52.66 391	88.75 256

MS-PatchMatch		73.83 268	72.67 268	77.30 291	83.87 279	66.02 171	81.82 281	84.66 268	61.37 329	68.61 319	82.82 319	47.29 305	88.21 296	59.27 285	84.32 186	77.68 381

lessismore_v0				78.97 261	81.01 336	57.15 310		65.99 393		61.16 370	82.82 319	39.12 360	91.34 240	59.67 282	46.92 397	88.43 265

D2MVS		74.82 258	73.21 263	79.64 251	79.81 350	62.56 246	80.34 307	87.35 231	64.37 294	68.86 316	82.66 321	46.37 314	90.10 263	67.91 211	81.24 228	86.25 308

Anonymous20231206		68.60 319	67.80 318	71.02 349	80.23 344	50.75 376	78.30 337	80.47 327	56.79 363	66.11 346	82.63 322	46.35 315	78.95 360	43.62 378	75.70 297	83.36 351

MIMVSNet		70.69 301	69.30 300	74.88 315	84.52 266	56.35 325	75.87 352	79.42 339	64.59 290	67.76 323	82.41 323	41.10 351	81.54 349	46.64 367	81.34 226	86.75 302

UBG		73.08 279	72.27 274	75.51 307	88.02 186	51.29 372	78.35 336	77.38 354	65.52 280	73.87 260	82.36 324	45.55 325	86.48 312	55.02 320	84.39 185	88.75 256

OpenMVS_ROB	64.09 19	70.56 303	68.19 309	77.65 285	80.26 342	59.41 285	85.01 227	82.96 300	58.76 349	65.43 349	82.33 325	37.63 368	91.23 243	45.34 375	76.03 294	82.32 362

miper_enhance_ethall		77.87 209	76.86 209	80.92 224	81.65 323	61.38 261	82.68 274	88.98 191	65.52 280	75.47 222	82.30 326	65.76 121	92.00 212	72.95 163	76.39 287	89.39 231

test0.0.03 1		68.00 326	67.69 320	68.90 358	77.55 364	47.43 384	75.70 353	72.95 377	66.66 262	66.56 339	82.29 327	48.06 302	75.87 380	44.97 376	74.51 319	83.41 350

PVSNet	64.34 18	72.08 290	70.87 290	75.69 303	86.21 234	56.44 321	74.37 363	80.73 323	62.06 324	70.17 300	82.23 328	42.86 341	83.31 340	54.77 322	84.45 183	87.32 287

MIMVSNet1		68.58 320	66.78 330	73.98 325	80.07 346	51.82 366	80.77 297	84.37 271	64.40 293	59.75 376	82.16 329	36.47 370	83.63 337	42.73 380	70.33 349	86.48 306

CL-MVSNet_self_test		72.37 286	71.46 281	75.09 313	79.49 356	53.53 353	80.76 298	85.01 266	69.12 229	70.51 294	82.05 330	57.92 205	84.13 333	52.27 334	66.00 366	87.60 279

tpm2		73.26 276	71.46 281	78.63 265	83.34 289	56.71 317	80.65 301	80.40 330	56.63 364	73.55 263	82.02 331	51.80 263	91.24 242	56.35 316	78.42 262	87.95 271

PatchMatch-RL		72.38 285	70.90 289	76.80 296	88.60 163	67.38 147	79.53 315	76.17 363	62.75 316	69.36 312	82.00 332	45.51 326	84.89 329	53.62 327	80.58 237	78.12 380

FMVSNet5		69.50 312	67.96 313	74.15 323	82.97 303	55.35 338	80.01 311	82.12 309	62.56 318	63.02 362	81.53 333	36.92 369	81.92 347	48.42 355	74.06 322	85.17 330

CR-MVSNet		73.37 273	71.27 285	79.67 250	81.32 333	65.19 191	75.92 350	80.30 331	59.92 338	72.73 273	81.19 334	52.50 247	86.69 308	59.84 280	77.71 269	87.11 294

Patchmtry		70.74 300	69.16 303	75.49 308	80.72 337	54.07 350	74.94 361	80.30 331	58.34 351	70.01 302	81.19 334	52.50 247	86.54 310	53.37 329	71.09 347	85.87 319

IB-MVS	68.01 15	75.85 247	73.36 262	83.31 153	84.76 260	66.03 170	83.38 263	85.06 264	70.21 202	69.40 311	81.05 336	45.76 323	94.66 103	65.10 236	75.49 301	89.25 235
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		76.72 231	74.64 245	82.99 170	85.78 241	65.88 176	82.33 277	89.21 181	60.85 331	72.74 272	81.02 337	47.28 306	93.75 140	67.48 215	85.02 172	89.34 233

LF4IMVS		64.02 345	62.19 349	69.50 355	70.90 393	53.29 358	76.13 347	77.18 356	52.65 376	58.59 378	80.98 338	23.55 396	76.52 373	53.06 331	66.66 362	78.68 379

Anonymous20240521		68.80 318	67.22 327	73.55 327	74.33 377	54.11 349	83.18 266	85.61 259	58.15 353	61.68 368	80.94 339	30.71 384	81.27 351	57.00 310	73.34 332	85.28 326

gm-plane-assit					81.40 329	53.83 352			62.72 317		80.94 339		92.39 197	63.40 248

UnsupCasMVSNet_eth		67.33 329	65.99 333	71.37 344	73.48 384	51.47 370	75.16 357	85.19 263	65.20 283	60.78 371	80.93 341	42.35 343	77.20 368	57.12 308	53.69 390	85.44 324

dmvs_re		71.14 295	70.58 291	72.80 334	81.96 319	59.68 281	75.60 354	79.34 340	68.55 241	69.27 314	80.72 342	49.42 290	76.54 372	52.56 333	77.79 268	82.19 364

MDTV_nov1_ep13			69.97 299		83.18 294	53.48 354	77.10 346	80.18 334	60.45 332	69.33 313	80.44 343	48.89 300	86.90 307	51.60 337	78.51 260

pmmvs-eth3d		70.50 304	67.83 317	78.52 272	77.37 366	66.18 169	81.82 281	81.51 315	58.90 348	63.90 360	80.42 344	42.69 342	86.28 314	58.56 294	65.30 368	83.11 354

PM-MVS		66.41 336	64.14 338	73.20 331	73.92 380	56.45 320	78.97 325	64.96 397	63.88 304	64.72 354	80.24 345	19.84 401	83.44 339	66.24 224	64.52 370	79.71 377

SCA		74.22 263	72.33 273	79.91 243	84.05 276	62.17 251	79.96 312	79.29 341	66.30 270	72.38 279	80.13 346	51.95 259	88.60 292	59.25 286	77.67 271	88.96 247

Patchmatch-test		64.82 343	63.24 344	69.57 354	79.42 357	49.82 380	63.49 400	69.05 386	51.98 379	59.95 375	80.13 346	50.91 272	70.98 394	40.66 385	73.57 327	87.90 273

tpmrst		72.39 284	72.13 275	73.18 332	80.54 340	49.91 379	79.91 313	79.08 343	63.11 308	71.69 286	79.95 348	55.32 222	82.77 343	65.66 232	73.89 324	86.87 298

DSMNet-mixed		57.77 356	56.90 358	60.38 376	67.70 397	35.61 407	69.18 382	53.97 408	32.30 406	57.49 383	79.88 349	40.39 356	68.57 400	38.78 389	72.37 336	76.97 382

MDA-MVSNet-bldmvs		66.68 333	63.66 342	75.75 302	79.28 358	60.56 271	73.92 365	78.35 346	64.43 292	50.13 395	79.87 350	44.02 335	83.67 336	46.10 370	56.86 382	83.03 356

PatchmatchNet		73.12 278	71.33 284	78.49 273	83.18 294	60.85 266	79.63 314	78.57 345	64.13 296	71.73 285	79.81 351	51.20 270	85.97 317	57.40 306	76.36 292	88.66 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Syy-MVS		68.05 325	67.85 315	68.67 361	84.68 262	40.97 404	78.62 330	73.08 375	66.65 265	66.74 337	79.46 352	52.11 255	82.30 345	32.89 396	76.38 290	82.75 359

myMVS_eth3d		67.02 331	66.29 332	69.21 356	84.68 262	42.58 399	78.62 330	73.08 375	66.65 265	66.74 337	79.46 352	31.53 382	82.30 345	39.43 388	76.38 290	82.75 359

ppachtmachnet_test		70.04 308	67.34 326	78.14 277	79.80 351	61.13 262	79.19 321	80.59 325	59.16 345	65.27 350	79.29 354	46.75 311	87.29 305	49.33 351	66.72 361	86.00 317

EPMVS		69.02 316	68.16 310	71.59 342	79.61 354	49.80 381	77.40 343	66.93 391	62.82 315	70.01 302	79.05 355	45.79 322	77.86 366	56.58 314	75.26 311	87.13 293

PMMVS		69.34 314	68.67 305	71.35 346	75.67 372	62.03 252	75.17 356	73.46 373	50.00 383	68.68 317	79.05 355	52.07 257	78.13 363	61.16 272	82.77 211	73.90 387

test-LLR		72.94 282	72.43 271	74.48 319	81.35 331	58.04 295	78.38 333	77.46 351	66.66 262	69.95 305	79.00 357	48.06 302	79.24 358	66.13 225	84.83 174	86.15 311

test-mter		71.41 293	70.39 296	74.48 319	81.35 331	58.04 295	78.38 333	77.46 351	60.32 334	69.95 305	79.00 357	36.08 372	79.24 358	66.13 225	84.83 174	86.15 311

KD-MVS_self_test		68.81 317	67.59 323	72.46 338	74.29 378	45.45 389	77.93 340	87.00 238	63.12 307	63.99 359	78.99 359	42.32 344	84.77 330	56.55 315	64.09 371	87.16 292

test_fmvs3		63.36 347	61.82 350	67.98 365	62.51 404	46.96 387	77.37 344	74.03 372	45.24 389	67.50 327	78.79 360	12.16 409	72.98 393	72.77 166	66.02 365	83.99 344

KD-MVS_2432*1600		66.22 338	63.89 340	73.21 329	75.47 375	53.42 355	70.76 376	84.35 272	64.10 298	66.52 341	78.52 361	34.55 375	84.98 327	50.40 343	50.33 394	81.23 369

miper_refine_blended		66.22 338	63.89 340	73.21 329	75.47 375	53.42 355	70.76 376	84.35 272	64.10 298	66.52 341	78.52 361	34.55 375	84.98 327	50.40 343	50.33 394	81.23 369

tpmvs		71.09 296	69.29 301	76.49 297	82.04 318	56.04 328	78.92 326	81.37 318	64.05 300	67.18 332	78.28 363	49.74 287	89.77 269	49.67 350	72.37 336	83.67 348

our_test_3		69.14 315	67.00 328	75.57 305	79.80 351	58.80 286	77.96 339	77.81 348	59.55 341	62.90 365	78.25 364	47.43 304	83.97 334	51.71 336	67.58 360	83.93 345

MDA-MVSNet_test_wron		65.03 341	62.92 345	71.37 344	75.93 369	56.73 315	69.09 385	74.73 369	57.28 361	54.03 390	77.89 365	45.88 320	74.39 389	49.89 349	61.55 375	82.99 357

YYNet1		65.03 341	62.91 346	71.38 343	75.85 371	56.60 319	69.12 384	74.66 371	57.28 361	54.12 389	77.87 366	45.85 321	74.48 388	49.95 348	61.52 376	83.05 355

ambc				75.24 312	73.16 387	50.51 377	63.05 401	87.47 229		64.28 356	77.81 367	17.80 403	89.73 271	57.88 302	60.64 378	85.49 322

tpm cat1		70.57 302	68.31 308	77.35 290	82.41 315	57.95 298	78.08 338	80.22 333	52.04 377	68.54 320	77.66 368	52.00 258	87.84 301	51.77 335	72.07 341	86.25 308

dp		66.80 332	65.43 334	70.90 351	79.74 353	48.82 382	75.12 359	74.77 368	59.61 340	64.08 358	77.23 369	42.89 340	80.72 354	48.86 354	66.58 363	83.16 353

TESTMET0.1,1		69.89 310	69.00 304	72.55 336	79.27 359	56.85 313	78.38 333	74.71 370	57.64 357	68.09 322	77.19 370	37.75 367	76.70 371	63.92 244	84.09 189	84.10 343

CHOSEN 280x420		66.51 335	64.71 336	71.90 340	81.45 328	63.52 226	57.98 403	68.95 387	53.57 373	62.59 366	76.70 371	46.22 317	75.29 386	55.25 319	79.68 247	76.88 383

PatchT		68.46 323	67.85 315	70.29 352	80.70 338	43.93 396	72.47 368	74.88 367	60.15 336	70.55 293	76.57 372	49.94 284	81.59 348	50.58 341	74.83 316	85.34 325

mvsany_test3		53.99 360	51.45 365	61.61 375	55.51 409	44.74 395	63.52 399	45.41 414	43.69 392	58.11 381	76.45 373	17.99 402	63.76 405	54.77 322	47.59 396	76.34 384

RPMNet		73.51 271	70.49 293	82.58 187	81.32 333	65.19 191	75.92 350	92.27 80	57.60 358	72.73 273	76.45 373	52.30 250	95.43 67	48.14 360	77.71 269	87.11 294

dmvs_testset		62.63 348	64.11 339	58.19 378	78.55 361	24.76 416	75.28 355	65.94 394	67.91 250	60.34 372	76.01 375	53.56 240	73.94 391	31.79 397	67.65 359	75.88 385

ADS-MVSNet2		66.20 340	63.33 343	74.82 316	79.92 347	58.75 287	67.55 388	75.19 365	53.37 374	65.25 351	75.86 376	42.32 344	80.53 355	41.57 383	68.91 355	85.18 328

ADS-MVSNet		64.36 344	62.88 347	68.78 360	79.92 347	47.17 385	67.55 388	71.18 379	53.37 374	65.25 351	75.86 376	42.32 344	73.99 390	41.57 383	68.91 355	85.18 328

EGC-MVSNET		52.07 366	47.05 370	67.14 367	83.51 286	60.71 268	80.50 304	67.75 389	0.07 416	0.43 417	75.85 378	24.26 394	81.54 349	28.82 399	62.25 373	59.16 399

new-patchmatchnet		61.73 350	61.73 351	61.70 374	72.74 390	24.50 417	69.16 383	78.03 347	61.40 327	56.72 385	75.53 379	38.42 363	76.48 374	45.95 371	57.67 381	84.13 342

N_pmnet		52.79 364	53.26 362	51.40 388	78.99 360	7.68 422	69.52 380	3.89 421	51.63 380	57.01 384	74.98 380	40.83 353	65.96 403	37.78 390	64.67 369	80.56 375

WB-MVS		54.94 358	54.72 359	55.60 384	73.50 383	20.90 418	74.27 364	61.19 401	59.16 345	50.61 393	74.15 381	47.19 307	75.78 381	17.31 409	35.07 403	70.12 391

patchmatchnet-post											74.00 382	51.12 271	88.60 292

GG-mvs-BLEND				75.38 310	81.59 325	55.80 332	79.32 318	69.63 383		67.19 331	73.67 383	43.24 338	88.90 288	50.41 342	84.50 179	81.45 368

SSC-MVS		53.88 361	53.59 361	54.75 386	72.87 389	19.59 419	73.84 366	60.53 403	57.58 359	49.18 397	73.45 384	46.34 316	75.47 384	16.20 412	32.28 405	69.20 392

Patchmatch-RL test		70.24 306	67.78 319	77.61 286	77.43 365	59.57 284	71.16 373	70.33 380	62.94 312	68.65 318	72.77 385	50.62 276	85.49 322	69.58 196	66.58 363	87.77 276

FPMVS		53.68 362	51.64 364	59.81 377	65.08 401	51.03 373	69.48 381	69.58 384	41.46 394	40.67 401	72.32 386	16.46 405	70.00 398	24.24 405	65.42 367	58.40 401

UnsupCasMVSNet_bld		63.70 346	61.53 352	70.21 353	73.69 382	51.39 371	72.82 367	81.89 311	55.63 368	57.81 382	71.80 387	38.67 362	78.61 361	49.26 352	52.21 392	80.63 373

APD_test1		53.31 363	49.93 368	63.42 373	65.68 400	50.13 378	71.59 372	66.90 392	34.43 403	40.58 402	71.56 388	8.65 414	76.27 376	34.64 395	55.36 387	63.86 397

test_f		52.09 365	50.82 366	55.90 382	53.82 412	42.31 402	59.42 402	58.31 406	36.45 401	56.12 388	70.96 389	12.18 408	57.79 408	53.51 328	56.57 384	67.60 393

PVSNet_0	57.27 20	61.67 351	59.27 354	68.85 359	79.61 354	57.44 307	68.01 386	73.44 374	55.93 367	58.54 379	70.41 390	44.58 331	77.55 367	47.01 364	35.91 402	71.55 390

pmmvs3		57.79 355	54.26 360	68.37 362	64.02 403	56.72 316	75.12 359	65.17 395	40.20 395	52.93 391	69.86 391	20.36 400	75.48 383	45.45 374	55.25 389	72.90 389

test_vis1_rt		60.28 352	58.42 355	65.84 369	67.25 398	55.60 335	70.44 378	60.94 402	44.33 391	59.00 377	66.64 392	24.91 392	68.67 399	62.80 251	69.48 351	73.25 388

new_pmnet		50.91 367	50.29 367	52.78 387	68.58 396	34.94 409	63.71 398	56.63 407	39.73 396	44.95 398	65.47 393	21.93 398	58.48 407	34.98 394	56.62 383	64.92 395

gg-mvs-nofinetune		69.95 309	67.96 313	75.94 300	83.07 297	54.51 347	77.23 345	70.29 381	63.11 308	70.32 297	62.33 394	43.62 336	88.69 290	53.88 326	87.76 137	84.62 337

JIA-IIPM		66.32 337	62.82 348	76.82 295	77.09 367	61.72 258	65.34 396	75.38 364	58.04 355	64.51 355	62.32 395	42.05 348	86.51 311	51.45 338	69.22 354	82.21 363

LCM-MVSNet		54.25 359	49.68 369	67.97 366	53.73 413	45.28 392	66.85 391	80.78 322	35.96 402	39.45 403	62.23 396	8.70 413	78.06 365	48.24 359	51.20 393	80.57 374

PMMVS2		40.82 375	38.86 379	46.69 389	53.84 411	16.45 420	48.61 406	49.92 409	37.49 399	31.67 404	60.97 397	8.14 415	56.42 409	28.42 400	30.72 406	67.19 394

testf1		45.72 370	41.96 374	57.00 379	56.90 407	45.32 390	66.14 393	59.26 404	26.19 407	30.89 406	60.96 398	4.14 417	70.64 396	26.39 403	46.73 398	55.04 402

APD_test2		45.72 370	41.96 374	57.00 379	56.90 407	45.32 390	66.14 393	59.26 404	26.19 407	30.89 406	60.96 398	4.14 417	70.64 396	26.39 403	46.73 398	55.04 402

MVS-HIRNet		59.14 354	57.67 356	63.57 372	81.65 323	43.50 397	71.73 370	65.06 396	39.59 397	51.43 392	57.73 400	38.34 364	82.58 344	39.53 386	73.95 323	64.62 396

ANet_high		50.57 368	46.10 372	63.99 371	48.67 416	39.13 405	70.99 375	80.85 321	61.39 328	31.18 405	57.70 401	17.02 404	73.65 392	31.22 398	15.89 413	79.18 378

PMVS	37.38 22	44.16 374	40.28 378	55.82 383	40.82 418	42.54 401	65.12 397	63.99 398	34.43 403	24.48 409	57.12 402	3.92 419	76.17 378	17.10 410	55.52 386	48.75 404
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		45.42 372	45.38 373	45.55 390	73.36 386	26.85 414	67.72 387	34.19 416	54.15 372	49.65 396	56.41 403	25.43 390	62.94 406	19.45 407	28.09 407	46.86 406

test_vis3_rt		49.26 369	47.02 371	56.00 381	54.30 410	45.27 393	66.76 392	48.08 411	36.83 400	44.38 399	53.20 404	7.17 416	64.07 404	56.77 313	55.66 385	58.65 400

test_method		31.52 378	29.28 382	38.23 392	27.03 420	6.50 423	20.94 411	62.21 400	4.05 414	22.35 412	52.50 405	13.33 406	47.58 412	27.04 402	34.04 404	60.62 398

kuosan		39.70 376	40.40 377	37.58 393	64.52 402	26.98 412	65.62 395	33.02 417	46.12 388	42.79 400	48.99 406	24.10 395	46.56 414	12.16 415	26.30 408	39.20 407

DeepMVS_CX				27.40 396	40.17 419	26.90 413		24.59 420	17.44 412	23.95 410	48.61 407	9.77 411	26.48 415	18.06 408	24.47 409	28.83 409

MVE	26.22 23	30.37 380	25.89 384	43.81 391	44.55 417	35.46 408	28.87 410	39.07 415	18.20 411	18.58 413	40.18 408	2.68 420	47.37 413	17.07 411	23.78 410	48.60 405
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 373	41.86 376	55.16 385	77.03 368	51.52 369	32.50 409	80.52 326	32.46 405	27.12 408	35.02 409	9.52 412	75.50 382	22.31 406	60.21 380	38.45 408
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 377	30.64 380	35.15 394	52.87 414	27.67 411	57.09 404	47.86 412	24.64 409	16.40 414	33.05 410	11.23 410	54.90 410	14.46 413	18.15 411	22.87 410

EMVS		30.81 379	29.65 381	34.27 395	50.96 415	25.95 415	56.58 405	46.80 413	24.01 410	15.53 415	30.68 411	12.47 407	54.43 411	12.81 414	17.05 412	22.43 411

tmp_tt		18.61 382	21.40 385	10.23 398	4.82 421	10.11 421	34.70 408	30.74 419	1.48 415	23.91 411	26.07 412	28.42 387	13.41 417	27.12 401	15.35 414	7.17 412

X-MVStestdata		80.37 149	77.83 185	88.00 17	94.42 20	73.33 19	92.78 18	92.99 46	79.14 21	83.67 89	12.47 413	67.45 100	96.60 33	83.06 65	94.50 51	94.07 52

test_post											5.46 414	50.36 280	84.24 332

test_post1							78.90 327				5.43 415	48.81 301	85.44 324	59.25 286

wuyk23d		16.82 383	15.94 386	19.46 397	58.74 406	31.45 410	39.22 407	3.74 422	6.84 413	6.04 416	2.70 416	1.27 421	24.29 416	10.54 416	14.40 415	2.63 413

testmvs		6.04 386	8.02 389	0.10 400	0.08 422	0.03 425	69.74 379	0.04 423	0.05 417	0.31 418	1.68 417	0.02 423	0.04 418	0.24 417	0.02 416	0.25 415

test123		6.12 385	8.11 388	0.14 399	0.06 423	0.09 424	71.05 374	0.03 424	0.04 418	0.25 419	1.30 418	0.05 422	0.03 419	0.21 418	0.01 417	0.29 414

test_blank		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

uanet_test		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

DCPMVS		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

pcd_1.5k_mvsjas		5.26 387	7.02 390	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	63.15 143	0.00 420	0.00 419	0.00 418	0.00 416

sosnet-low-res		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

sosnet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

uncertanet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

Regformer		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

uanet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 418	0.00 416

WAC-MVS						42.58 399								39.46 387

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 117	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 46					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 46					97.53 2	89.67 6	96.44 9	94.41 37

eth-test2					0.00 424

eth-test					0.00 424

IU-MVS					95.30 2	71.25 57		92.95 52	66.81 258	92.39 6				88.94 16	96.63 4	94.85 19

save fliter					93.80 40	72.35 42	90.47 63	91.17 121	74.31 120

test_0728_SECOND				87.71 32	95.34 1	71.43 56	93.49 9	94.23 3					97.49 4	89.08 12	96.41 12	94.21 47

GSMVS																88.96 247

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 268				88.96 247

sam_mvs												50.01 282

MTGPA								92.02 89

MTMP							92.18 34	32.83 418

test9_res														84.90 43	95.70 26	92.87 108

agg_prior2														82.91 69	95.45 29	92.70 111

agg_prior					92.85 59	71.94 50		91.78 104		84.41 75			94.93 89

test_prior4						72.60 34	89.01 106

test_prior				86.33 54	92.61 65	69.59 88		92.97 51					95.48 64			93.91 58

旧先验2							86.56 189		58.10 354	87.04 42			88.98 284	74.07 151

新几何2							86.29 198

无先验							87.48 158	88.98 191	60.00 337				94.12 120	67.28 217		88.97 246

原ACMM2							86.86 178

testdata2													91.01 251	62.37 258

segment_acmp												73.08 38

testdata1							84.14 251		75.71 91

test12				86.80 49	92.63 64	70.70 72		91.79 103		82.71 102		71.67 54	96.16 44		94.50 51	93.54 82

plane_prior7					90.08 105	68.51 120

plane_prior6					89.84 114	68.70 115						60.42 191

plane_prior5								92.44 73					95.38 71	78.71 105	86.32 157	91.33 155

plane_prior3						68.60 118			78.44 31	78.92 146

plane_prior2							91.25 49		79.12 23

plane_prior1					89.90 113

plane_prior						68.71 113	90.38 67		77.62 39						86.16 161

n2								0.00 425

nn								0.00 425

door-mid								69.98 382

test11								92.23 83

door								69.44 385

HQP5-MVS						66.98 158

HQP-NCC					89.33 132		89.17 99		76.41 76	77.23 184

ACMP_Plane					89.33 132		89.17 99		76.41 76	77.23 184

BP-MVS														77.47 117

HQP4-MVS										77.24 183			95.11 82			91.03 165

HQP3-MVS								92.19 86							85.99 165

HQP2-MVS												60.17 194

MDTV_nov1_ep13_2view						37.79 406	75.16 357		55.10 369	66.53 340		49.34 292		53.98 325		87.94 272

ACMMP++_ref															81.95 222

ACMMP++															81.25 227

Test By Simon												64.33 130

High-resolution multi-view benchmark