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.

FOURS1					98.86 1	85.54 67	98.29 1	97.49 6	89.79 47	96.29 18

test_0728_SECOND				95.01 17	98.79 2	86.43 39	97.09 16	97.49 6					99.61 4	95.62 21	99.08 7	98.99 9

DVP-MVS		95.67 3	96.02 3	94.64 39	98.78 3	85.93 55	97.09 16	96.73 79	90.27 31	97.04 11	98.05 13	91.47 8	99.55 16	95.62 21	99.08 7	98.45 36
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					98.78 3	85.93 55	97.19 11	97.47 11	90.27 31	97.64 4	98.13 3	91.47 8

SED-MVS		95.91 2	96.28 2	94.80 33	98.77 5	85.99 52	97.13 14	97.44 15	90.31 28	97.71 1	98.07 9	92.31 4	99.58 10	95.66 17	99.13 3	98.84 14

IU-MVS					98.77 5	86.00 50		96.84 65	81.26 273	97.26 7				95.50 23	99.13 3	99.03 8

test_241102_ONE					98.77 5	85.99 52		97.44 15	90.26 33	97.71 1	97.96 17	92.31 4	99.38 31

region2R		94.43 23	94.27 31	94.92 20	98.65 8	86.67 30	96.92 24	97.23 34	88.60 85	93.58 58	97.27 37	85.22 55	99.54 20	92.21 69	98.74 31	98.56 25

ACMMPR		94.43 23	94.28 29	94.91 21	98.63 9	86.69 28	96.94 20	97.32 27	88.63 83	93.53 61	97.26 39	85.04 59	99.54 20	92.35 65	98.78 26	98.50 27

HFP-MVS		94.52 19	94.40 23	94.86 24	98.61 10	86.81 25	96.94 20	97.34 23	88.63 83	93.65 56	97.21 41	86.10 45	99.49 26	92.35 65	98.77 28	98.30 48

test_one_0601					98.58 11	85.83 61		97.44 15	91.05 14	96.78 15	98.06 11	91.45 11

test_part2					98.55 12	87.22 19				96.40 17

XVS		94.45 21	94.32 25	94.85 25	98.54 13	86.60 34	96.93 22	97.19 35	90.66 24	92.85 73	97.16 47	85.02 60	99.49 26	91.99 79	98.56 50	98.47 33

X-MVStestdata		88.31 177	86.13 224	94.85 25	98.54 13	86.60 34	96.93 22	97.19 35	90.66 24	92.85 73	23.41 413	85.02 60	99.49 26	91.99 79	98.56 50	98.47 33

ZNCC-MVS		94.47 20	94.28 29	95.03 16	98.52 15	86.96 20	96.85 28	97.32 27	88.24 95	93.15 66	97.04 52	86.17 44	99.62 2	92.40 62	98.81 23	98.52 26

mPP-MVS		93.99 41	93.78 48	94.63 40	98.50 16	85.90 60	96.87 26	96.91 58	88.70 81	91.83 106	97.17 46	83.96 74	99.55 16	91.44 92	98.64 45	98.43 38

MSP-MVS		95.42 6	95.56 6	94.98 19	98.49 17	86.52 36	96.91 25	97.47 11	91.73 10	96.10 20	96.69 66	89.90 12	99.30 40	94.70 28	98.04 68	99.13 2
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
MP-MVS		94.25 28	94.07 39	94.77 35	98.47 18	86.31 44	96.71 31	96.98 49	89.04 69	91.98 97	97.19 44	85.43 53	99.56 12	92.06 78	98.79 24	98.44 37
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		94.45 21	94.20 35	95.19 13	98.46 19	87.50 16	95.00 125	97.12 41	87.13 125	92.51 87	96.30 83	89.24 17	99.34 34	93.46 42	98.62 46	98.73 18

PGM-MVS		93.96 43	93.72 51	94.68 38	98.43 20	86.22 47	95.30 104	97.78 1	87.45 121	93.26 63	97.33 35	84.62 67	99.51 24	90.75 104	98.57 49	98.32 47

MTAPA		94.42 25	94.22 32	95.00 18	98.42 21	86.95 21	94.36 171	96.97 50	91.07 13	93.14 67	97.56 26	84.30 70	99.56 12	93.43 43	98.75 30	98.47 33

GST-MVS		94.21 31	93.97 43	94.90 23	98.41 22	86.82 24	96.54 36	97.19 35	88.24 95	93.26 63	96.83 61	85.48 52	99.59 8	91.43 93	98.40 54	98.30 48

HPM-MVS		94.02 39	93.88 44	94.43 47	98.39 23	85.78 63	97.25 10	97.07 45	86.90 133	92.62 84	96.80 65	84.85 65	99.17 47	92.43 60	98.65 44	98.33 44
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		94.34 26	94.21 34	94.74 37	98.39 23	86.64 32	97.60 4	97.24 32	88.53 87	92.73 81	97.23 40	85.20 56	99.32 38	92.15 72	98.83 22	98.25 58

DPE-MVS		95.57 4	95.67 4	95.25 11	98.36 25	87.28 18	95.56 96	97.51 5	89.13 66	97.14 9	97.91 18	91.64 7	99.62 2	94.61 30	99.17 2	98.86 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
HPM-MVS_fast		93.40 61	93.22 62	93.94 58	98.36 25	84.83 76	97.15 13	96.80 71	85.77 159	92.47 88	97.13 48	82.38 92	99.07 53	90.51 107	98.40 54	97.92 80

DP-MVS Recon		91.95 84	91.28 91	93.96 57	98.33 27	85.92 57	94.66 148	96.66 85	82.69 235	90.03 134	95.82 105	82.30 96	99.03 58	84.57 175	96.48 106	96.91 133

APDe-MVS		95.46 5	95.64 5	94.91 21	98.26 28	86.29 46	97.46 6	97.40 20	89.03 71	96.20 19	98.10 7	89.39 16	99.34 34	95.88 16	99.03 11	99.10 4
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
TSAR-MVS + MP.		94.85 14	94.94 12	94.58 42	98.25 29	86.33 42	96.11 59	96.62 88	88.14 100	96.10 20	96.96 55	89.09 18	98.94 78	94.48 31	98.68 37	98.48 30
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
HPM-MVS++		95.14 10	94.91 13	95.83 4	98.25 29	89.65 4	95.92 75	96.96 52	91.75 9	94.02 50	96.83 61	88.12 24	99.55 16	93.41 45	98.94 16	98.28 51

CPTT-MVS		91.99 83	91.80 84	92.55 115	98.24 31	81.98 163	96.76 30	96.49 97	81.89 255	90.24 127	96.44 81	78.59 141	98.61 108	89.68 111	97.85 74	97.06 122

SR-MVS		94.23 30	94.17 37	94.43 47	98.21 32	85.78 63	96.40 38	96.90 59	88.20 98	94.33 41	97.40 32	84.75 66	99.03 58	93.35 46	97.99 69	98.48 30

MP-MVS-pluss		94.21 31	94.00 42	94.85 25	98.17 33	86.65 31	94.82 137	97.17 39	86.26 148	92.83 75	97.87 20	85.57 51	99.56 12	94.37 33	98.92 17	98.34 42
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ZD-MVS					98.15 34	86.62 33		97.07 45	83.63 209	94.19 44	96.91 57	87.57 31	99.26 42	91.99 79	98.44 53

SMA-MVS		95.20 8	95.07 11	95.59 6	98.14 35	88.48 8	96.26 46	97.28 31	85.90 156	97.67 3	98.10 7	88.41 20	99.56 12	94.66 29	99.19 1	98.71 20
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
CNVR-MVS		95.40 7	95.37 7	95.50 8	98.11 36	88.51 7	95.29 106	96.96 52	92.09 6	95.32 32	97.08 49	89.49 15	99.33 37	95.10 25	98.85 20	98.66 21

114514_t		89.51 140	88.50 153	92.54 116	98.11 36	81.99 162	95.16 117	96.36 105	70.19 383	85.81 211	95.25 127	76.70 160	98.63 105	82.07 216	96.86 97	97.00 127

ACMMP		93.24 65	92.88 69	94.30 51	98.09 38	85.33 70	96.86 27	97.45 14	88.33 91	90.15 132	97.03 53	81.44 109	99.51 24	90.85 103	95.74 115	98.04 72
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
APD-MVS		94.24 29	94.07 39	94.75 36	98.06 39	86.90 23	95.88 76	96.94 55	85.68 162	95.05 35	97.18 45	87.31 35	99.07 53	91.90 85	98.61 48	98.28 51
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CSCG		93.23 66	93.05 65	93.76 66	98.04 40	84.07 98	96.22 48	97.37 21	84.15 197	90.05 133	95.66 112	87.77 26	99.15 50	89.91 110	98.27 58	98.07 69

ACMMP_NAP		94.74 16	94.56 19	95.28 10	98.02 41	87.70 11	95.68 86	97.34 23	88.28 94	95.30 33	97.67 25	85.90 47	99.54 20	93.91 37	98.95 15	98.60 23

OPU-MVS				96.21 3	98.00 42	90.85 3	97.13 14				97.08 49	92.59 2	98.94 78	92.25 68	98.99 14	98.84 14

SR-MVS-dyc-post		93.82 45	93.82 45	93.82 62	97.92 43	84.57 82	96.28 43	96.76 75	87.46 119	93.75 54	97.43 30	84.24 71	99.01 63	92.73 54	97.80 76	97.88 81

RE-MVS-def			93.68 53		97.92 43	84.57 82	96.28 43	96.76 75	87.46 119	93.75 54	97.43 30	82.94 85		92.73 54	97.80 76	97.88 81

APD-MVS_3200maxsize		93.78 46	93.77 49	93.80 64	97.92 43	84.19 96	96.30 41	96.87 62	86.96 129	93.92 52	97.47 28	83.88 75	98.96 77	92.71 57	97.87 73	98.26 57

save fliter					97.85 46	85.63 66	95.21 113	96.82 68	89.44 54

SF-MVS		94.97 12	94.90 15	95.20 12	97.84 47	87.76 10	96.65 34	97.48 10	87.76 114	95.71 27	97.70 24	88.28 23	99.35 33	93.89 38	98.78 26	98.48 30

NCCC		94.81 15	94.69 18	95.17 14	97.83 48	87.46 17	95.66 89	96.93 56	92.34 4	93.94 51	96.58 76	87.74 27	99.44 29	92.83 53	98.40 54	98.62 22

9.14			94.47 20		97.79 49		96.08 60	97.44 15	86.13 154	95.10 34	97.40 32	88.34 22	99.22 44	93.25 47	98.70 34

CDPH-MVS		92.83 73	92.30 79	94.44 45	97.79 49	86.11 49	94.06 190	96.66 85	80.09 287	92.77 78	96.63 73	86.62 38	99.04 57	87.40 138	98.66 41	98.17 63

DVP-MVS++		95.98 1	96.36 1	94.82 31	97.78 51	86.00 50	98.29 1	97.49 6	90.75 19	97.62 5	98.06 11	92.59 2	99.61 4	95.64 19	99.02 12	98.86 11

MSC_two_6792asdad				96.52 1	97.78 51	90.86 1		96.85 63					99.61 4	96.03 14	99.06 9	99.07 5

No_MVS				96.52 1	97.78 51	90.86 1		96.85 63					99.61 4	96.03 14	99.06 9	99.07 5

dcpmvs_2		93.49 53	94.19 36	91.38 169	97.69 54	76.78 290	94.25 174	96.29 109	88.33 91	94.46 39	96.88 58	88.07 25	98.64 103	93.62 41	98.09 65	98.73 18

DP-MVS		87.25 217	85.36 252	92.90 96	97.65 55	83.24 122	94.81 138	92.00 305	74.99 347	81.92 305	95.00 138	72.66 216	99.05 55	66.92 360	92.33 188	96.40 152

PAPM_NR		91.22 98	90.78 102	92.52 117	97.60 56	81.46 176	94.37 170	96.24 117	86.39 145	87.41 175	94.80 148	82.06 104	98.48 116	82.80 201	95.37 126	97.61 97

patch_mono-2		93.74 48	94.32 25	92.01 135	97.54 57	78.37 257	93.40 220	97.19 35	88.02 103	94.99 36	97.21 41	88.35 21	98.44 126	94.07 35	98.09 65	99.23 1

TEST9					97.53 58	86.49 37	94.07 188	96.78 72	81.61 265	92.77 78	96.20 87	87.71 28	99.12 51

train_agg		93.44 56	93.08 64	94.52 44	97.53 58	86.49 37	94.07 188	96.78 72	81.86 256	92.77 78	96.20 87	87.63 29	99.12 51	92.14 73	98.69 35	97.94 77

test_8					97.49 60	86.30 45	94.02 193	96.76 75	81.86 256	92.70 82	96.20 87	87.63 29	99.02 61

DeepC-MVS_fast	89.43 2	94.04 38	93.79 47	94.80 33	97.48 61	86.78 26	95.65 91	96.89 60	89.40 56	92.81 76	96.97 54	85.37 54	99.24 43	90.87 102	98.69 35	98.38 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		89.89 131	89.07 137	92.37 124	97.41 62	83.03 134	94.42 163	95.92 145	82.81 232	86.34 201	94.65 155	73.89 200	99.02 61	80.69 242	95.51 119	95.05 207

agg_prior					97.38 63	85.92 57		96.72 81		92.16 93			98.97 75

原ACMM1				92.01 135	97.34 64	81.05 187		96.81 70	78.89 303	90.45 124	95.92 100	82.65 89	98.84 88	80.68 243	98.26 59	96.14 163

MSLP-MVS++		93.72 49	94.08 38	92.65 110	97.31 65	83.43 116	95.79 81	97.33 25	90.03 36	93.58 58	96.96 55	84.87 64	97.76 179	92.19 71	98.66 41	96.76 139

新几何1				93.10 83	97.30 66	84.35 94		95.56 174	71.09 379	91.26 117	96.24 85	82.87 87	98.86 84	79.19 264	98.10 64	96.07 169

test_prior				93.82 62	97.29 67	84.49 86		96.88 61					98.87 82			98.11 68

PLC	84.53 7	89.06 157	88.03 166	92.15 133	97.27 68	82.69 148	94.29 172	95.44 186	79.71 292	84.01 271	94.18 173	76.68 161	98.75 94	77.28 282	93.41 167	95.02 208
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
SD-MVS		94.96 13	95.33 8	93.88 59	97.25 69	86.69 28	96.19 49	97.11 43	90.42 27	96.95 13	97.27 37	89.53 14	96.91 255	94.38 32	98.85 20	98.03 73
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
test12				94.34 50	97.13 70	86.15 48		96.29 109		91.04 119		85.08 58	99.01 63		98.13 63	97.86 83

MG-MVS		91.77 87	91.70 87	92.00 138	97.08 71	80.03 218	93.60 214	95.18 200	87.85 111	90.89 120	96.47 80	82.06 104	98.36 131	85.07 167	97.04 90	97.62 96

SteuartSystems-ACMMP		95.20 8	95.32 9	94.85 25	96.99 72	86.33 42	97.33 7	97.30 29	91.38 12	95.39 31	97.46 29	88.98 19	99.40 30	94.12 34	98.89 18	98.82 16
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		93.45 55	93.31 59	93.84 61	96.99 72	84.84 75	93.24 232	97.24 32	88.76 78	91.60 111	95.85 103	86.07 46	98.66 101	91.91 83	98.16 61	98.03 73

CNLPA		89.07 156	87.98 167	92.34 125	96.87 74	84.78 78	94.08 187	93.24 271	81.41 269	84.46 255	95.13 135	75.57 176	96.62 265	77.21 283	93.84 157	95.61 191

PHI-MVS		93.89 44	93.65 55	94.62 41	96.84 75	86.43 39	96.69 32	97.49 6	85.15 175	93.56 60	96.28 84	85.60 50	99.31 39	92.45 59	98.79 24	98.12 67

旧先验1					96.79 76	81.81 166		95.67 166			96.81 63	86.69 37			97.66 81	96.97 129

LFMVS		90.08 123	89.13 136	92.95 94	96.71 77	82.32 158	96.08 60	89.91 356	86.79 134	92.15 94	96.81 63	62.60 319	98.34 134	87.18 142	93.90 155	98.19 61

CS-MVS-test		94.02 39	94.29 28	93.24 76	96.69 78	83.24 122	97.49 5	96.92 57	92.14 5	92.90 71	95.77 108	85.02 60	98.33 136	93.03 50	98.62 46	98.13 65

Anonymous202405211		87.68 193	86.13 224	92.31 127	96.66 79	80.74 197	94.87 133	91.49 322	80.47 283	89.46 141	95.44 119	54.72 368	98.23 142	82.19 212	89.89 219	97.97 75

TAPA-MVS	84.62 6	88.16 181	87.01 191	91.62 159	96.64 80	80.65 198	94.39 166	96.21 122	76.38 332	86.19 205	95.44 119	79.75 124	98.08 161	62.75 377	95.29 128	96.13 164
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MAR-MVS		90.30 118	89.37 130	93.07 87	96.61 81	84.48 87	95.68 86	95.67 166	82.36 240	87.85 166	92.85 218	76.63 162	98.80 90	80.01 252	96.68 101	95.91 175
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
VNet		92.24 82	91.91 83	93.24 76	96.59 82	83.43 116	94.84 136	96.44 98	89.19 64	94.08 49	95.90 101	77.85 152	98.17 146	88.90 120	93.38 168	98.13 65

TSAR-MVS + GP.		93.66 50	93.41 58	94.41 49	96.59 82	86.78 26	94.40 164	93.93 255	89.77 48	94.21 43	95.59 115	87.35 34	98.61 108	92.72 56	96.15 111	97.83 86

MVSMamba_PlusPlus		93.44 56	93.54 57	93.14 81	96.58 84	83.05 133	96.06 64	96.50 96	84.42 194	94.09 46	95.56 116	85.01 63	98.69 100	94.96 26	98.66 41	97.67 94

CS-MVS		94.12 37	94.44 22	93.17 79	96.55 85	83.08 132	97.63 3	96.95 54	91.71 11	93.50 62	96.21 86	85.61 49	98.24 141	93.64 40	98.17 60	98.19 61

test222					96.55 85	81.70 168	92.22 267	95.01 207	68.36 386	90.20 129	96.14 92	80.26 119			97.80 76	96.05 172

Anonymous20240529		88.09 183	86.59 206	92.58 114	96.53 87	81.92 165	95.99 70	95.84 153	74.11 356	89.06 147	95.21 130	61.44 328	98.81 89	83.67 189	87.47 260	97.01 126

Anonymous20231211		86.59 244	85.13 257	90.98 191	96.52 88	81.50 172	96.14 56	96.16 123	73.78 359	83.65 279	92.15 242	63.26 316	97.37 221	82.82 200	81.74 319	94.06 255

DeepPCF-MVS	89.96 1	94.20 33	94.77 17	92.49 118	96.52 88	80.00 220	94.00 196	97.08 44	90.05 35	95.65 29	97.29 36	89.66 13	98.97 75	93.95 36	98.71 32	98.50 27

testdata				90.49 205	96.40 90	77.89 269		95.37 192	72.51 371	93.63 57	96.69 66	82.08 103	97.65 187	83.08 193	97.39 84	95.94 174

PVSNet_Blended_VisFu		91.38 94	90.91 99	92.80 101	96.39 91	83.17 125	94.87 133	96.66 85	83.29 220	89.27 143	94.46 162	80.29 118	99.17 47	87.57 136	95.37 126	96.05 172

API-MVS		90.66 111	90.07 113	92.45 120	96.36 92	84.57 82	96.06 64	95.22 199	82.39 238	89.13 144	94.27 170	80.32 117	98.46 120	80.16 251	96.71 100	94.33 243

F-COLMAP		87.95 186	86.80 196	91.40 168	96.35 93	80.88 193	94.73 143	95.45 184	79.65 293	82.04 303	94.61 156	71.13 230	98.50 114	76.24 295	91.05 203	94.80 221

VDD-MVS		90.74 107	89.92 119	93.20 78	96.27 94	83.02 135	95.73 83	93.86 259	88.42 90	92.53 85	96.84 60	62.09 321	98.64 103	90.95 100	92.62 183	97.93 79

OMC-MVS		91.23 97	90.62 104	93.08 85	96.27 94	84.07 98	93.52 216	95.93 144	86.95 130	89.51 138	96.13 93	78.50 143	98.35 133	85.84 161	92.90 177	96.83 138

DPM-MVS		92.58 77	91.74 86	95.08 15	96.19 96	89.31 5	92.66 251	96.56 93	83.44 215	91.68 110	95.04 137	86.60 40	98.99 70	85.60 163	97.92 72	96.93 131

CHOSEN 1792x2688		88.84 162	87.69 173	92.30 128	96.14 97	81.42 178	90.01 322	95.86 152	74.52 352	87.41 175	93.94 182	75.46 177	98.36 131	80.36 247	95.53 118	97.12 120

balanced_conf03		93.98 42	94.22 32	93.26 75	96.13 98	83.29 121	96.27 45	96.52 94	89.82 43	95.56 30	95.51 117	84.50 68	98.79 91	94.83 27	98.86 19	97.72 91

thres100view900		87.63 198	86.71 199	90.38 213	96.12 99	78.55 250	95.03 124	91.58 318	87.15 124	88.06 162	92.29 238	68.91 269	98.10 151	70.13 338	91.10 198	94.48 238

PVSNet_BlendedMVS		89.98 126	89.70 121	90.82 194	96.12 99	81.25 181	93.92 201	96.83 66	83.49 214	89.10 145	92.26 239	81.04 113	98.85 86	86.72 150	87.86 255	92.35 326

PVSNet_Blended		90.73 108	90.32 107	91.98 139	96.12 99	81.25 181	92.55 255	96.83 66	82.04 248	89.10 145	92.56 229	81.04 113	98.85 86	86.72 150	95.91 113	95.84 179

UA-Net		92.83 73	92.54 76	93.68 68	96.10 102	84.71 79	95.66 89	96.39 103	91.92 7	93.22 65	96.49 79	83.16 81	98.87 82	84.47 177	95.47 122	97.45 105

MM		95.10 11	94.91 13	95.68 5	96.09 103	88.34 9	96.68 33	94.37 239	95.08 1	94.68 37	97.72 23	82.94 85	99.64 1	97.85 1	98.76 29	99.06 7

thres600view7		87.65 195	86.67 201	90.59 198	96.08 104	78.72 246	94.88 132	91.58 318	87.06 127	88.08 161	92.30 237	68.91 269	98.10 151	70.05 341	91.10 198	94.96 212

DeepC-MVS	88.79 3	93.31 62	92.99 67	94.26 52	96.07 105	85.83 61	94.89 131	96.99 48	89.02 72	89.56 137	97.37 34	82.51 91	99.38 31	92.20 70	98.30 57	97.57 100
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		87.89 187	86.32 217	92.59 113	96.07 105	82.92 139	95.23 111	94.92 215	75.66 339	82.89 292	95.98 98	72.48 219	99.21 45	68.43 348	95.23 131	95.64 188

h-mvs33		90.80 105	90.15 111	92.75 104	96.01 107	82.66 149	95.43 98	95.53 178	89.80 44	93.08 68	95.64 113	75.77 169	99.00 68	92.07 75	78.05 356	96.60 145

SDMVSNet		90.19 121	89.61 124	91.93 143	96.00 108	83.09 131	92.89 245	95.98 140	88.73 79	86.85 188	95.20 131	72.09 223	97.08 241	88.90 120	89.85 221	95.63 189

sd_testset		88.59 171	87.85 171	90.83 193	96.00 108	80.42 205	92.35 261	94.71 229	88.73 79	86.85 188	95.20 131	67.31 279	96.43 284	79.64 257	89.85 221	95.63 189

HyFIR lowres test		88.09 183	86.81 195	91.93 143	96.00 108	80.63 199	90.01 322	95.79 156	73.42 363	87.68 171	92.10 247	73.86 201	97.96 170	80.75 241	91.70 192	97.19 114

tfpn200view9		87.58 202	86.64 202	90.41 210	95.99 111	78.64 248	94.58 151	91.98 307	86.94 131	88.09 159	91.77 257	69.18 265	98.10 151	70.13 338	91.10 198	94.48 238

thres400		87.62 200	86.64 202	90.57 199	95.99 111	78.64 248	94.58 151	91.98 307	86.94 131	88.09 159	91.77 257	69.18 265	98.10 151	70.13 338	91.10 198	94.96 212

MVS_111021_LR		92.47 79	92.29 80	92.98 91	95.99 111	84.43 91	93.08 237	96.09 131	88.20 98	91.12 118	95.72 111	81.33 111	97.76 179	91.74 87	97.37 85	96.75 140

PatchMatch-RL		86.77 239	85.54 246	90.47 209	95.88 114	82.71 147	90.54 306	92.31 295	79.82 291	84.32 263	91.57 269	68.77 271	96.39 286	73.16 319	93.48 166	92.32 327

EPP-MVSNet		91.70 90	91.56 88	92.13 134	95.88 114	80.50 203	97.33 7	95.25 196	86.15 151	89.76 136	95.60 114	83.42 79	98.32 138	87.37 140	93.25 171	97.56 101

IS-MVSNet		91.43 93	91.09 96	92.46 119	95.87 116	81.38 179	96.95 19	93.69 265	89.72 50	89.50 140	95.98 98	78.57 142	97.77 178	83.02 195	96.50 105	98.22 60

test_fmvsm_n_1920		94.71 17	95.11 10	93.50 71	95.79 117	84.62 80	96.15 54	97.64 2	89.85 42	97.19 8	97.89 19	86.28 43	98.71 99	97.11 6	98.08 67	97.17 115

PAPR		90.02 125	89.27 135	92.29 129	95.78 118	80.95 191	92.68 250	96.22 119	81.91 252	86.66 192	93.75 194	82.23 98	98.44 126	79.40 263	94.79 137	97.48 103

Vis-MVSNet (Re-imp)		89.59 138	89.44 127	90.03 226	95.74 119	75.85 304	95.61 93	90.80 340	87.66 118	87.83 167	95.40 122	76.79 158	96.46 282	78.37 269	96.73 99	97.80 87

test_yl		90.69 109	90.02 117	92.71 106	95.72 120	82.41 156	94.11 183	95.12 202	85.63 163	91.49 112	94.70 150	74.75 184	98.42 129	86.13 156	92.53 185	97.31 107

DCV-MVSNet		90.69 109	90.02 117	92.71 106	95.72 120	82.41 156	94.11 183	95.12 202	85.63 163	91.49 112	94.70 150	74.75 184	98.42 129	86.13 156	92.53 185	97.31 107

sasdasda		93.27 63	92.75 71	94.85 25	95.70 122	87.66 12	96.33 39	96.41 101	90.00 37	94.09 46	94.60 157	82.33 94	98.62 106	92.40 62	92.86 178	98.27 53

canonicalmvs		93.27 63	92.75 71	94.85 25	95.70 122	87.66 12	96.33 39	96.41 101	90.00 37	94.09 46	94.60 157	82.33 94	98.62 106	92.40 62	92.86 178	98.27 53

mamv4		90.92 102	91.78 85	88.33 283	95.67 124	70.75 364	92.92 244	96.02 139	81.90 253	88.11 158	95.34 123	85.88 48	96.97 250	95.22 24	95.01 133	97.26 110

CANet		93.54 52	93.20 63	94.55 43	95.65 125	85.73 65	94.94 128	96.69 84	91.89 8	90.69 122	95.88 102	81.99 106	99.54 20	93.14 49	97.95 71	98.39 39

3Dnovator+	87.14 4	92.42 80	91.37 89	95.55 7	95.63 126	88.73 6	97.07 18	96.77 74	90.84 16	84.02 270	96.62 74	75.95 168	99.34 34	87.77 133	97.68 80	98.59 24

MGCFI-Net		93.03 70	92.63 74	94.23 53	95.62 127	85.92 57	96.08 60	96.33 107	89.86 41	93.89 53	94.66 154	82.11 101	98.50 114	92.33 67	92.82 181	98.27 53

fmvsm_s_conf0.5_n		93.76 47	94.06 41	92.86 98	95.62 127	83.17 125	96.14 56	96.12 128	88.13 101	95.82 26	98.04 16	83.43 77	98.48 116	96.97 9	96.23 109	96.92 132

test2506		87.21 221	86.28 219	90.02 228	95.62 127	73.64 327	96.25 47	71.38 411	87.89 109	90.45 124	96.65 70	55.29 365	98.09 159	86.03 158	96.94 92	98.33 44

ECVR-MVS		89.09 155	88.53 151	90.77 196	95.62 127	75.89 303	96.16 52	84.22 389	87.89 109	90.20 129	96.65 70	63.19 317	98.10 151	85.90 159	96.94 92	98.33 44

alignmvs		93.08 69	92.50 77	94.81 32	95.62 127	87.61 15	95.99 70	96.07 133	89.77 48	94.12 45	94.87 143	80.56 115	98.66 101	92.42 61	93.10 174	98.15 64

test1111		89.10 153	88.64 148	90.48 206	95.53 132	74.97 312	96.08 60	84.89 387	88.13 101	90.16 131	96.65 70	63.29 315	98.10 151	86.14 154	96.90 94	98.39 39

WTY-MVS		89.60 137	88.92 141	91.67 158	95.47 133	81.15 185	92.38 259	94.78 226	83.11 224	89.06 147	94.32 165	78.67 140	96.61 268	81.57 228	90.89 205	97.24 111

DELS-MVS		93.43 60	93.25 61	93.97 56	95.42 134	85.04 72	93.06 239	97.13 40	90.74 21	91.84 104	95.09 136	86.32 42	99.21 45	91.22 94	98.45 52	97.65 95
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
thres200		87.21 221	86.24 221	90.12 222	95.36 135	78.53 251	93.26 230	92.10 301	86.42 144	88.00 164	91.11 282	69.24 264	98.00 167	69.58 342	91.04 204	93.83 268

Vis-MVSNet		91.75 88	91.23 92	93.29 73	95.32 136	83.78 105	96.14 56	95.98 140	89.89 39	90.45 124	96.58 76	75.09 180	98.31 139	84.75 173	96.90 94	97.78 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
fmvsm_l_conf0.5_n_a		94.20 33	94.40 23	93.60 69	95.29 137	84.98 73	95.61 93	96.28 112	86.31 146	96.75 16	97.86 21	87.40 33	98.74 96	97.07 7	97.02 91	97.07 121

fmvsm_l_conf0.5_n		94.29 27	94.46 21	93.79 65	95.28 138	85.43 68	95.68 86	96.43 99	86.56 140	96.84 14	97.81 22	87.56 32	98.77 93	97.14 5	96.82 98	97.16 119

BH-RMVSNet		88.37 175	87.48 178	91.02 186	95.28 138	79.45 232	92.89 245	93.07 276	85.45 168	86.91 184	94.84 147	70.35 244	97.76 179	73.97 313	94.59 143	95.85 178

COLMAP_ROB	80.39 16	83.96 293	82.04 302	89.74 240	95.28 138	79.75 226	94.25 174	92.28 296	75.17 345	78.02 347	93.77 192	58.60 351	97.84 176	65.06 369	85.92 273	91.63 339
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PS-MVSNAJ		91.18 99	90.92 98	91.96 141	95.26 141	82.60 152	92.09 272	95.70 164	86.27 147	91.84 104	92.46 231	79.70 126	98.99 70	89.08 118	95.86 114	94.29 244

BH-untuned		88.60 170	88.13 165	90.01 229	95.24 142	78.50 253	93.29 228	94.15 248	84.75 187	84.46 255	93.40 199	75.76 171	97.40 217	77.59 279	94.52 146	94.12 250

EC-MVSNet		93.44 56	93.71 52	92.63 111	95.21 143	82.43 153	97.27 9	96.71 82	90.57 26	92.88 72	95.80 106	83.16 81	98.16 147	93.68 39	98.14 62	97.31 107

ETV-MVS		92.74 75	92.66 73	92.97 92	95.20 144	84.04 100	95.07 121	96.51 95	90.73 22	92.96 70	91.19 276	84.06 72	98.34 134	91.72 88	96.54 103	96.54 150

mvsmamba		90.33 117	89.69 122	92.25 132	95.17 145	81.64 169	95.27 109	93.36 270	84.88 181	89.51 138	94.27 170	69.29 263	97.42 209	89.34 115	96.12 112	97.68 93

GeoE		90.05 124	89.43 128	91.90 148	95.16 146	80.37 206	95.80 80	94.65 232	83.90 202	87.55 174	94.75 149	78.18 147	97.62 191	81.28 231	93.63 159	97.71 92

EIA-MVS		91.95 84	91.94 82	91.98 139	95.16 146	80.01 219	95.36 99	96.73 79	88.44 88	89.34 142	92.16 241	83.82 76	98.45 124	89.35 114	97.06 89	97.48 103

ab-mvs		89.41 145	88.35 157	92.60 112	95.15 148	82.65 150	92.20 268	95.60 173	83.97 201	88.55 153	93.70 195	74.16 196	98.21 145	82.46 206	89.37 229	96.94 130

VDDNet		89.56 139	88.49 155	92.76 103	95.07 149	82.09 160	96.30 41	93.19 273	81.05 278	91.88 102	96.86 59	61.16 336	98.33 136	88.43 126	92.49 187	97.84 85

fmvsm_s_conf0.5_n_a		93.57 51	93.76 50	93.00 90	95.02 150	83.67 108	96.19 49	96.10 130	87.27 123	95.98 24	98.05 13	83.07 84	98.45 124	96.68 11	95.51 119	96.88 135

AllTest		83.42 300	81.39 306	89.52 250	95.01 151	77.79 274	93.12 234	90.89 338	77.41 323	76.12 359	93.34 200	54.08 371	97.51 198	68.31 349	84.27 286	93.26 292

TestCases				89.52 250	95.01 151	77.79 274		90.89 338	77.41 323	76.12 359	93.34 200	54.08 371	97.51 198	68.31 349	84.27 286	93.26 292

EI-MVSNet-Vis-set		93.01 71	92.92 68	93.29 73	95.01 151	83.51 115	94.48 156	95.77 157	90.87 15	92.52 86	96.67 68	84.50 68	99.00 68	91.99 79	94.44 149	97.36 106

xiu_mvs_v2_base		91.13 100	90.89 100	91.86 149	94.97 154	82.42 154	92.24 266	95.64 171	86.11 155	91.74 109	93.14 211	79.67 129	98.89 81	89.06 119	95.46 123	94.28 245

tttt0517		88.61 169	87.78 172	91.11 181	94.96 155	77.81 272	95.35 100	89.69 360	85.09 177	88.05 163	94.59 159	66.93 285	98.48 116	83.27 192	92.13 190	97.03 125

baseline1		88.10 182	87.28 184	90.57 199	94.96 155	80.07 214	94.27 173	91.29 327	86.74 136	87.41 175	94.00 179	76.77 159	96.20 295	80.77 240	79.31 352	95.44 193

Test_1112_low_res		87.65 195	86.51 210	91.08 182	94.94 157	79.28 240	91.77 278	94.30 242	76.04 337	83.51 283	92.37 234	77.86 151	97.73 183	78.69 268	89.13 235	96.22 159

1112_ss		88.42 173	87.33 182	91.72 156	94.92 158	80.98 189	92.97 242	94.54 233	78.16 319	83.82 274	93.88 187	78.78 138	97.91 174	79.45 259	89.41 228	96.26 158

QAPM		89.51 140	88.15 164	93.59 70	94.92 158	84.58 81	96.82 29	96.70 83	78.43 313	83.41 285	96.19 90	73.18 211	99.30 40	77.11 285	96.54 103	96.89 134

MVS_0304		94.18 36	93.80 46	95.34 9	94.91 160	87.62 14	95.97 72	93.01 278	92.58 3	94.22 42	97.20 43	80.56 115	99.59 8	97.04 8	98.68 37	98.81 17

BH-w/o		87.57 203	87.05 189	89.12 260	94.90 161	77.90 268	92.41 257	93.51 267	82.89 231	83.70 277	91.34 270	75.75 172	97.07 243	75.49 299	93.49 164	92.39 324

thisisatest0530		88.67 167	87.61 175	91.86 149	94.87 162	80.07 214	94.63 149	89.90 357	84.00 200	88.46 155	93.78 191	66.88 287	98.46 120	83.30 191	92.65 182	97.06 122

EI-MVSNet-UG-set		92.74 75	92.62 75	93.12 82	94.86 163	83.20 124	94.40 164	95.74 160	90.71 23	92.05 95	96.60 75	84.00 73	98.99 70	91.55 90	93.63 159	97.17 115

HY-MVS	83.01 12	89.03 158	87.94 169	92.29 129	94.86 163	82.77 141	92.08 273	94.49 234	81.52 268	86.93 182	92.79 224	78.32 146	98.23 142	79.93 253	90.55 208	95.88 177

hse-mvs2		89.88 132	89.34 131	91.51 163	94.83 165	81.12 186	93.94 199	93.91 258	89.80 44	93.08 68	93.60 196	75.77 169	97.66 186	92.07 75	77.07 363	95.74 184

AUN-MVS		87.78 191	86.54 209	91.48 165	94.82 166	81.05 187	93.91 203	93.93 255	83.00 227	86.93 182	93.53 197	69.50 257	97.67 184	86.14 154	77.12 362	95.73 186

Fast-Effi-MVS+		89.41 145	88.64 148	91.71 157	94.74 167	80.81 195	93.54 215	95.10 204	83.11 224	86.82 190	90.67 297	79.74 125	97.75 182	80.51 246	93.55 161	96.57 148

ACMP	84.23 8	89.01 160	88.35 157	90.99 189	94.73 168	81.27 180	95.07 121	95.89 150	86.48 141	83.67 278	94.30 166	69.33 259	97.99 168	87.10 147	88.55 240	93.72 278
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet	78.82 18	85.55 264	84.65 268	88.23 287	94.72 169	71.93 347	87.12 366	92.75 285	78.80 306	84.95 244	90.53 299	64.43 308	96.71 262	74.74 308	93.86 156	96.06 171

LCM-MVSNet-Re		88.30 178	88.32 160	88.27 284	94.71 170	72.41 346	93.15 233	90.98 334	87.77 113	79.25 338	91.96 253	78.35 145	95.75 317	83.04 194	95.62 117	96.65 144

HQP_MVS		90.60 115	90.19 109	91.82 152	94.70 171	82.73 145	95.85 77	96.22 119	90.81 17	86.91 184	94.86 144	74.23 192	98.12 149	88.15 127	89.99 215	94.63 224

plane_prior7					94.70 171	82.74 144

ACMH+	81.04 14	85.05 277	83.46 286	89.82 236	94.66 173	79.37 234	94.44 161	94.12 251	82.19 244	78.04 346	92.82 221	58.23 352	97.54 195	73.77 316	82.90 304	92.54 317

ACMM	84.12 9	89.14 152	88.48 156	91.12 178	94.65 174	81.22 183	95.31 102	96.12 128	85.31 171	85.92 209	94.34 163	70.19 247	98.06 163	85.65 162	88.86 238	94.08 254
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test_fmvsmconf_n		94.60 18	94.81 16	93.98 55	94.62 175	84.96 74	96.15 54	97.35 22	89.37 57	96.03 23	98.11 5	86.36 41	99.01 63	97.45 2	97.83 75	97.96 76

plane_prior1					94.59 176

casdiffmvs_mvg		92.96 72	92.83 70	93.35 72	94.59 176	83.40 118	95.00 125	96.34 106	90.30 30	92.05 95	96.05 95	83.43 77	98.15 148	92.07 75	95.67 116	98.49 29
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
3Dnovator	86.66 5	91.73 89	90.82 101	94.44 45	94.59 176	86.37 41	97.18 12	97.02 47	89.20 63	84.31 265	96.66 69	73.74 204	99.17 47	86.74 148	97.96 70	97.79 88

FA-MVS(test-final)		89.66 135	88.91 142	91.93 143	94.57 179	80.27 207	91.36 288	94.74 228	84.87 182	89.82 135	92.61 228	74.72 187	98.47 119	83.97 183	93.53 162	97.04 124

FE-MVS		87.40 210	86.02 230	91.57 161	94.56 180	79.69 228	90.27 309	93.72 264	80.57 281	88.80 150	91.62 265	65.32 302	98.59 110	74.97 307	94.33 151	96.44 151

plane_prior6					94.52 181	82.75 142						74.23 192

UGNet		89.95 128	88.95 140	92.95 94	94.51 182	83.31 120	95.70 85	95.23 197	89.37 57	87.58 172	93.94 182	64.00 310	98.78 92	83.92 184	96.31 108	96.74 141
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
LPG-MVS_test		89.45 143	88.90 143	91.12 178	94.47 183	81.49 174	95.30 104	96.14 124	86.73 137	85.45 226	95.16 133	69.89 250	98.10 151	87.70 134	89.23 233	93.77 274

LGP-MVS_train				91.12 178	94.47 183	81.49 174		96.14 124	86.73 137	85.45 226	95.16 133	69.89 250	98.10 151	87.70 134	89.23 233	93.77 274

baseline		92.39 81	92.29 80	92.69 109	94.46 185	81.77 167	94.14 180	96.27 113	89.22 62	91.88 102	96.00 96	82.35 93	97.99 168	91.05 96	95.27 130	98.30 48

ACMH	80.38 17	85.36 269	83.68 283	90.39 211	94.45 186	80.63 199	94.73 143	94.85 220	82.09 245	77.24 351	92.65 226	60.01 342	97.58 192	72.25 323	84.87 281	92.96 306
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LTVRE_ROB	82.13 13	86.26 254	84.90 263	90.34 215	94.44 187	81.50 172	92.31 265	94.89 216	83.03 226	79.63 335	92.67 225	69.69 253	97.79 177	71.20 327	86.26 272	91.72 337
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
testing91		87.11 226	86.18 222	89.92 232	94.43 188	75.38 311	91.53 285	92.27 297	86.48 141	86.50 193	90.24 305	61.19 334	97.53 196	82.10 214	90.88 206	96.84 137

casdiffmvs		92.51 78	92.43 78	92.74 105	94.41 189	81.98 163	94.54 154	96.23 118	89.57 52	91.96 99	96.17 91	82.58 90	98.01 166	90.95 100	95.45 124	98.23 59
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ETVMVS		84.43 287	82.92 296	88.97 266	94.37 190	74.67 315	91.23 294	88.35 369	83.37 218	86.06 208	89.04 331	55.38 363	95.67 320	67.12 356	91.34 196	96.58 147

MVS_Test		91.31 96	91.11 94	91.93 143	94.37 190	80.14 211	93.46 219	95.80 155	86.46 143	91.35 116	93.77 192	82.21 99	98.09 159	87.57 136	94.95 134	97.55 102

NP-MVS					94.37 190	82.42 154					93.98 180

TR-MVS		86.78 236	85.76 242	89.82 236	94.37 190	78.41 255	92.47 256	92.83 282	81.11 277	86.36 199	92.40 233	68.73 272	97.48 200	73.75 317	89.85 221	93.57 282

Effi-MVS+		91.59 92	91.11 94	93.01 89	94.35 194	83.39 119	94.60 150	95.10 204	87.10 126	90.57 123	93.10 213	81.43 110	98.07 162	89.29 116	94.48 147	97.59 99

testing11		86.44 251	85.35 253	89.69 244	94.29 195	75.40 310	91.30 290	90.53 343	84.76 186	85.06 241	90.13 311	58.95 350	97.45 204	82.08 215	91.09 202	96.21 161

RRT-MVS		90.85 104	90.70 103	91.30 172	94.25 196	76.83 289	94.85 135	96.13 127	89.04 69	90.23 128	94.88 142	70.15 248	98.72 97	91.86 86	94.88 135	98.34 42

testing99		86.72 240	85.73 245	89.69 244	94.23 197	74.91 314	91.35 289	90.97 335	86.14 152	86.36 199	90.22 306	59.41 346	97.48 200	82.24 211	90.66 207	96.69 143

CLD-MVS		89.47 142	88.90 143	91.18 177	94.22 198	82.07 161	92.13 270	96.09 131	87.90 107	85.37 235	92.45 232	74.38 190	97.56 194	87.15 143	90.43 210	93.93 259
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UBG		85.51 265	84.57 271	88.35 280	94.21 199	71.78 351	90.07 320	89.66 362	82.28 242	85.91 210	89.01 332	61.30 329	97.06 244	76.58 291	92.06 191	96.22 159

HQP-NCC					94.17 200		94.39 166		88.81 75	85.43 229

ACMP_Plane					94.17 200		94.39 166		88.81 75	85.43 229

HQP-MVS		89.80 133	89.28 134	91.34 171	94.17 200	81.56 170	94.39 166	96.04 136	88.81 75	85.43 229	93.97 181	73.83 202	97.96 170	87.11 145	89.77 224	94.50 235

testing222		84.84 282	83.32 287	89.43 254	94.15 203	75.94 302	91.09 297	89.41 365	84.90 180	85.78 212	89.44 326	52.70 376	96.28 293	70.80 333	91.57 194	96.07 169

WBMVS		84.97 279	84.18 274	87.34 306	94.14 204	71.62 355	90.20 316	92.35 292	81.61 265	84.06 268	90.76 293	61.82 324	96.52 276	78.93 266	83.81 289	93.89 260

XVG-OURS		89.40 147	88.70 147	91.52 162	94.06 205	81.46 176	91.27 292	96.07 133	86.14 152	88.89 149	95.77 108	68.73 272	97.26 229	87.39 139	89.96 217	95.83 180

sss		88.93 161	88.26 163	90.94 192	94.05 206	80.78 196	91.71 280	95.38 190	81.55 267	88.63 152	93.91 186	75.04 181	95.47 329	82.47 205	91.61 193	96.57 148

PCF-MVS	84.11 10	87.74 192	86.08 228	92.70 108	94.02 207	84.43 91	89.27 335	95.87 151	73.62 361	84.43 257	94.33 164	78.48 144	98.86 84	70.27 334	94.45 148	94.81 220
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GBi-Net		87.26 215	85.98 232	91.08 182	94.01 208	83.10 128	95.14 118	94.94 210	83.57 210	84.37 258	91.64 261	66.59 292	96.34 290	78.23 273	85.36 277	93.79 269

test1		87.26 215	85.98 232	91.08 182	94.01 208	83.10 128	95.14 118	94.94 210	83.57 210	84.37 258	91.64 261	66.59 292	96.34 290	78.23 273	85.36 277	93.79 269

FMVSNet2		87.19 223	85.82 238	91.30 172	94.01 208	83.67 108	94.79 139	94.94 210	83.57 210	83.88 273	92.05 251	66.59 292	96.51 277	77.56 280	85.01 280	93.73 277

XVG-OURS-SEG-HR		89.95 128	89.45 126	91.47 166	94.00 211	81.21 184	91.87 276	96.06 135	85.78 158	88.55 153	95.73 110	74.67 188	97.27 227	88.71 123	89.64 226	95.91 175

FIs		90.51 116	90.35 106	90.99 189	93.99 212	80.98 189	95.73 83	97.54 4	89.15 65	86.72 191	94.68 152	81.83 108	97.24 231	85.18 166	88.31 248	94.76 222

xiu_mvs_v1_base_debu		90.64 112	90.05 114	92.40 121	93.97 213	84.46 88	93.32 223	95.46 181	85.17 172	92.25 90	94.03 174	70.59 239	98.57 111	90.97 97	94.67 139	94.18 246

xiu_mvs_v1_base		90.64 112	90.05 114	92.40 121	93.97 213	84.46 88	93.32 223	95.46 181	85.17 172	92.25 90	94.03 174	70.59 239	98.57 111	90.97 97	94.67 139	94.18 246

xiu_mvs_v1_base_debi		90.64 112	90.05 114	92.40 121	93.97 213	84.46 88	93.32 223	95.46 181	85.17 172	92.25 90	94.03 174	70.59 239	98.57 111	90.97 97	94.67 139	94.18 246

VPA-MVSNet		89.62 136	88.96 139	91.60 160	93.86 216	82.89 140	95.46 97	97.33 25	87.91 106	88.43 156	93.31 203	74.17 195	97.40 217	87.32 141	82.86 305	94.52 232

MVSFormer		91.68 91	91.30 90	92.80 101	93.86 216	83.88 103	95.96 73	95.90 148	84.66 190	91.76 107	94.91 140	77.92 149	97.30 223	89.64 112	97.11 87	97.24 111

lupinMVS		90.92 102	90.21 108	93.03 88	93.86 216	83.88 103	92.81 248	93.86 259	79.84 290	91.76 107	94.29 167	77.92 149	98.04 164	90.48 108	97.11 87	97.17 115

IterMVS-LS		88.36 176	87.91 170	89.70 243	93.80 219	78.29 260	93.73 208	95.08 206	85.73 160	84.75 247	91.90 255	79.88 122	96.92 254	83.83 185	82.51 306	93.89 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MSDG		84.86 281	83.09 292	90.14 221	93.80 219	80.05 216	89.18 338	93.09 275	78.89 303	78.19 344	91.91 254	65.86 301	97.27 227	68.47 347	88.45 244	93.11 301

FMVSNet3		87.40 210	86.11 226	91.30 172	93.79 221	83.64 110	94.20 178	94.81 224	83.89 203	84.37 258	91.87 256	68.45 275	96.56 273	78.23 273	85.36 277	93.70 279

fmvsm_s_conf0.1_n		93.46 54	93.66 54	92.85 99	93.75 222	83.13 127	96.02 68	95.74 160	87.68 116	95.89 25	98.17 2	82.78 88	98.46 120	96.71 10	96.17 110	96.98 128

FC-MVSNet-test		90.27 119	90.18 110	90.53 201	93.71 223	79.85 225	95.77 82	97.59 3	89.31 59	86.27 202	94.67 153	81.93 107	97.01 248	84.26 179	88.09 251	94.71 223

TAMVS		89.21 151	88.29 161	91.96 141	93.71 223	82.62 151	93.30 227	94.19 246	82.22 243	87.78 169	93.94 182	78.83 136	96.95 252	77.70 278	92.98 176	96.32 154

ET-MVSNet_ETH3D		87.51 205	85.91 236	92.32 126	93.70 225	83.93 101	92.33 263	90.94 336	84.16 196	72.09 379	92.52 230	69.90 249	95.85 311	89.20 117	88.36 247	97.17 115

test_fmvsmvis_n_1920		93.44 56	93.55 56	93.10 83	93.67 226	84.26 95	95.83 79	96.14 124	89.00 73	92.43 89	97.50 27	83.37 80	98.72 97	96.61 12	97.44 83	96.32 154

CDS-MVSNet		89.45 143	88.51 152	92.29 129	93.62 227	83.61 113	93.01 240	94.68 231	81.95 250	87.82 168	93.24 207	78.69 139	96.99 249	80.34 248	93.23 172	96.28 157
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UniMVSNet (Re)		89.80 133	89.07 137	92.01 135	93.60 228	84.52 85	94.78 140	97.47 11	89.26 61	86.44 198	92.32 236	82.10 102	97.39 220	84.81 172	80.84 334	94.12 250

VPNet		88.20 180	87.47 179	90.39 211	93.56 229	79.46 231	94.04 191	95.54 177	88.67 82	86.96 181	94.58 160	69.33 259	97.15 236	84.05 182	80.53 339	94.56 230

thisisatest0515		87.33 213	85.99 231	91.37 170	93.49 230	79.55 229	90.63 305	89.56 364	80.17 285	87.56 173	90.86 287	67.07 284	98.28 140	81.50 229	93.02 175	96.29 156

mvs_anonymous		89.37 149	89.32 132	89.51 252	93.47 231	74.22 321	91.65 283	94.83 222	82.91 230	85.45 226	93.79 190	81.23 112	96.36 289	86.47 152	94.09 152	97.94 77

CANet_DTU		90.26 120	89.41 129	92.81 100	93.46 232	83.01 136	93.48 217	94.47 235	89.43 55	87.76 170	94.23 172	70.54 243	99.03 58	84.97 168	96.39 107	96.38 153

testing3		80.46 329	79.59 326	83.06 358	93.44 233	64.64 389	93.33 222	85.47 384	84.34 195	79.93 331	90.84 289	44.35 394	92.39 369	57.06 392	87.56 259	92.16 331

UniMVSNet_NR-MVSNet		89.92 130	89.29 133	91.81 154	93.39 234	83.72 106	94.43 162	97.12 41	89.80 44	86.46 195	93.32 202	83.16 81	97.23 232	84.92 169	81.02 330	94.49 237

Effi-MVS+-dtu		88.65 168	88.35 157	89.54 249	93.33 235	76.39 297	94.47 159	94.36 240	87.70 115	85.43 229	89.56 325	73.45 207	97.26 229	85.57 164	91.28 197	94.97 209

WR-MVS		88.38 174	87.67 174	90.52 203	93.30 236	80.18 209	93.26 230	95.96 143	88.57 86	85.47 225	92.81 222	76.12 164	96.91 255	81.24 232	82.29 310	94.47 240

WR-MVS_H		87.80 190	87.37 181	89.10 261	93.23 237	78.12 263	95.61 93	97.30 29	87.90 107	83.72 276	92.01 252	79.65 130	96.01 303	76.36 292	80.54 338	93.16 299

test_0402		81.30 322	79.17 332	87.67 298	93.19 238	78.17 262	92.98 241	91.71 312	75.25 344	76.02 361	90.31 304	59.23 347	96.37 287	50.22 397	83.63 294	88.47 382

OPM-MVS		90.12 122	89.56 125	91.82 152	93.14 239	83.90 102	94.16 179	95.74 160	88.96 74	87.86 165	95.43 121	72.48 219	97.91 174	88.10 131	90.18 214	93.65 280
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CP-MVSNet		87.63 198	87.26 186	88.74 272	93.12 240	76.59 294	95.29 106	96.58 91	88.43 89	83.49 284	92.98 216	75.28 178	95.83 312	78.97 265	81.15 326	93.79 269

diffmvs		91.37 95	91.23 92	91.77 155	93.09 241	80.27 207	92.36 260	95.52 179	87.03 128	91.40 115	94.93 139	80.08 120	97.44 207	92.13 74	94.56 144	97.61 97
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
nrg030		91.08 101	90.39 105	93.17 79	93.07 242	86.91 22	96.41 37	96.26 114	88.30 93	88.37 157	94.85 146	82.19 100	97.64 189	91.09 95	82.95 300	94.96 212

UWE-MVS		83.69 299	83.09 292	85.48 339	93.06 243	65.27 387	90.92 300	86.14 379	79.90 289	86.26 203	90.72 296	57.17 356	95.81 314	71.03 332	92.62 183	95.35 198

PAPM		86.68 241	85.39 250	90.53 201	93.05 244	79.33 239	89.79 325	94.77 227	78.82 305	81.95 304	93.24 207	76.81 157	97.30 223	66.94 358	93.16 173	94.95 215

DU-MVS		89.34 150	88.50 153	91.85 151	93.04 245	83.72 106	94.47 159	96.59 90	89.50 53	86.46 195	93.29 205	77.25 154	97.23 232	84.92 169	81.02 330	94.59 227

NR-MVSNet		88.58 172	87.47 179	91.93 143	93.04 245	84.16 97	94.77 141	96.25 116	89.05 68	80.04 329	93.29 205	79.02 135	97.05 246	81.71 227	80.05 344	94.59 227

jason		90.80 105	90.10 112	92.90 96	93.04 245	83.53 114	93.08 237	94.15 248	80.22 284	91.41 114	94.91 140	76.87 156	97.93 173	90.28 109	96.90 94	97.24 111
jason: jason.
PS-CasMVS		87.32 214	86.88 192	88.63 275	92.99 248	76.33 299	95.33 101	96.61 89	88.22 97	83.30 289	93.07 214	73.03 213	95.79 316	78.36 270	81.00 332	93.75 276

test_vis1_n_1920		89.39 148	89.84 120	88.04 291	92.97 249	72.64 341	94.71 145	96.03 138	86.18 150	91.94 101	96.56 78	61.63 325	95.74 318	93.42 44	95.11 132	95.74 184

MVSTER		88.84 162	88.29 161	90.51 204	92.95 250	80.44 204	93.73 208	95.01 207	84.66 190	87.15 179	93.12 212	72.79 215	97.21 234	87.86 132	87.36 263	93.87 264

RPSCF		85.07 276	84.27 273	87.48 304	92.91 251	70.62 366	91.69 282	92.46 290	76.20 336	82.67 295	95.22 128	63.94 311	97.29 226	77.51 281	85.80 274	94.53 231

FMVSNet1		85.85 260	84.11 276	91.08 182	92.81 252	83.10 128	95.14 118	94.94 210	81.64 263	82.68 294	91.64 261	59.01 349	96.34 290	75.37 301	83.78 290	93.79 269

tfpnnormal		84.72 284	83.23 290	89.20 258	92.79 253	80.05 216	94.48 156	95.81 154	82.38 239	81.08 314	91.21 275	69.01 268	96.95 252	61.69 379	80.59 337	90.58 362

OpenMVS	83.78 11	88.74 166	87.29 183	93.08 85	92.70 254	85.39 69	96.57 35	96.43 99	78.74 308	80.85 316	96.07 94	69.64 254	99.01 63	78.01 276	96.65 102	94.83 219

TranMVSNet+NR-MVSNet		88.84 162	87.95 168	91.49 164	92.68 255	83.01 136	94.92 130	96.31 108	89.88 40	85.53 220	93.85 189	76.63 162	96.96 251	81.91 220	79.87 347	94.50 235

MVS		87.44 208	86.10 227	91.44 167	92.61 256	83.62 111	92.63 252	95.66 168	67.26 388	81.47 308	92.15 242	77.95 148	98.22 144	79.71 255	95.48 121	92.47 320

fmvsm_s_conf0.1_n_a		93.19 67	93.26 60	92.97 92	92.49 257	83.62 111	96.02 68	95.72 163	86.78 135	96.04 22	98.19 1	82.30 96	98.43 128	96.38 13	95.42 125	96.86 136

CHOSEN 280x420		85.15 275	83.99 279	88.65 274	92.47 258	78.40 256	79.68 401	92.76 284	74.90 349	81.41 310	89.59 323	69.85 252	95.51 325	79.92 254	95.29 128	92.03 332

test_fmvsmconf0.1_n		94.20 33	94.31 27	93.88 59	92.46 259	84.80 77	96.18 51	96.82 68	89.29 60	95.68 28	98.11 5	85.10 57	98.99 70	97.38 3	97.75 79	97.86 83

UniMVSNet_ETH3D		87.53 204	86.37 214	91.00 188	92.44 260	78.96 245	94.74 142	95.61 172	84.07 199	85.36 236	94.52 161	59.78 344	97.34 222	82.93 196	87.88 254	96.71 142

1314		87.51 205	86.57 207	90.34 215	92.42 261	79.74 227	92.63 252	95.35 194	78.35 314	80.14 326	91.62 265	74.05 197	97.15 236	81.05 233	93.53 162	94.12 250

cl22		86.78 236	85.98 232	89.18 259	92.34 262	77.62 279	90.84 302	94.13 250	81.33 271	83.97 272	90.15 310	73.96 199	96.60 270	84.19 180	82.94 301	93.33 290

PEN-MVS		86.80 235	86.27 220	88.40 278	92.32 263	75.71 306	95.18 115	96.38 104	87.97 104	82.82 293	93.15 210	73.39 209	95.92 307	76.15 296	79.03 354	93.59 281

tt0805		86.92 231	85.74 244	90.48 206	92.22 264	79.98 221	95.63 92	94.88 218	83.83 205	84.74 248	92.80 223	57.61 354	97.67 184	85.48 165	84.42 284	93.79 269

c3_l		87.14 225	86.50 211	89.04 263	92.20 265	77.26 283	91.22 295	94.70 230	82.01 249	84.34 262	90.43 302	78.81 137	96.61 268	83.70 188	81.09 327	93.25 294

SCA		86.32 253	85.18 256	89.73 242	92.15 266	76.60 293	91.12 296	91.69 314	83.53 213	85.50 223	88.81 336	66.79 288	96.48 279	76.65 288	90.35 212	96.12 165

XXY-MVS		87.65 195	86.85 194	90.03 226	92.14 267	80.60 201	93.76 207	95.23 197	82.94 229	84.60 250	94.02 177	74.27 191	95.49 328	81.04 234	83.68 293	94.01 258

miper_ehance_all_eth		87.22 220	86.62 205	89.02 264	92.13 268	77.40 282	90.91 301	94.81 224	81.28 272	84.32 263	90.08 313	79.26 132	96.62 265	83.81 186	82.94 301	93.04 304

IB-MVS	80.51 15	85.24 274	83.26 289	91.19 176	92.13 268	79.86 224	91.75 279	91.29 327	83.28 221	80.66 319	88.49 342	61.28 330	98.46 120	80.99 237	79.46 350	95.25 201
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		86.43 252	84.98 260	90.80 195	92.10 270	80.92 192	90.24 313	95.91 147	73.10 366	83.57 282	88.39 343	65.15 304	97.46 203	84.90 171	91.43 195	94.03 257

Fast-Effi-MVS+-dtu		87.44 208	86.72 198	89.63 247	92.04 271	77.68 278	94.03 192	93.94 254	85.81 157	82.42 296	91.32 273	70.33 245	97.06 244	80.33 249	90.23 213	94.14 249

cl____		86.52 247	85.78 239	88.75 270	92.03 272	76.46 295	90.74 303	94.30 242	81.83 258	83.34 287	90.78 292	75.74 174	96.57 271	81.74 225	81.54 321	93.22 296

DIV-MVS_self_test		86.53 246	85.78 239	88.75 270	92.02 273	76.45 296	90.74 303	94.30 242	81.83 258	83.34 287	90.82 290	75.75 172	96.57 271	81.73 226	81.52 322	93.24 295

eth_miper_zixun_eth		86.50 248	85.77 241	88.68 273	91.94 274	75.81 305	90.47 307	94.89 216	82.05 246	84.05 269	90.46 301	75.96 167	96.77 259	82.76 202	79.36 351	93.46 288

Syy-MVS		80.07 333	79.78 321	80.94 367	91.92 275	59.93 398	89.75 327	87.40 376	81.72 260	78.82 340	87.20 360	66.29 296	91.29 379	47.06 399	87.84 256	91.60 340

myMVS_eth3d		79.67 338	78.79 337	82.32 364	91.92 275	64.08 390	89.75 327	87.40 376	81.72 260	78.82 340	87.20 360	45.33 392	91.29 379	59.09 387	87.84 256	91.60 340

PS-MVSNAJss		89.97 127	89.62 123	91.02 186	91.90 277	80.85 194	95.26 110	95.98 140	86.26 148	86.21 204	94.29 167	79.70 126	97.65 187	88.87 122	88.10 249	94.57 229

ITE_SJBPF				88.24 286	91.88 278	77.05 286		92.92 279	85.54 166	80.13 327	93.30 204	57.29 355	96.20 295	72.46 322	84.71 282	91.49 343

EI-MVSNet		89.10 153	88.86 145	89.80 239	91.84 279	78.30 259	93.70 211	95.01 207	85.73 160	87.15 179	95.28 125	79.87 123	97.21 234	83.81 186	87.36 263	93.88 263

CVMVSNet		84.69 285	84.79 266	84.37 350	91.84 279	64.92 388	93.70 211	91.47 323	66.19 390	86.16 206	95.28 125	67.18 283	93.33 360	80.89 239	90.42 211	94.88 217

dmvs_re		84.20 290	83.22 291	87.14 316	91.83 281	77.81 272	90.04 321	90.19 348	84.70 189	81.49 307	89.17 329	64.37 309	91.13 381	71.58 325	85.65 276	92.46 321

MVS-HIRNet		73.70 356	72.20 359	78.18 374	91.81 282	56.42 406	82.94 392	82.58 393	55.24 400	68.88 387	66.48 403	55.32 364	95.13 333	58.12 389	88.42 245	83.01 391

PatchmatchNet		85.85 260	84.70 267	89.29 256	91.76 283	75.54 307	88.49 347	91.30 326	81.63 264	85.05 242	88.70 340	71.71 224	96.24 294	74.61 310	89.05 236	96.08 168
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TransMVSNet (Re)		84.43 287	83.06 294	88.54 276	91.72 284	78.44 254	95.18 115	92.82 283	82.73 234	79.67 334	92.12 244	73.49 206	95.96 305	71.10 331	68.73 385	91.21 349

IterMVS-SCA-FT		85.45 266	84.53 272	88.18 288	91.71 285	76.87 288	90.19 317	92.65 288	85.40 169	81.44 309	90.54 298	66.79 288	95.00 337	81.04 234	81.05 328	92.66 315

TinyColmap		79.76 337	77.69 340	85.97 333	91.71 285	73.12 332	89.55 329	90.36 346	75.03 346	72.03 380	90.19 308	46.22 391	96.19 297	63.11 375	81.03 329	88.59 381

MDTV_nov1_ep13			83.56 285		91.69 287	69.93 370	87.75 359	91.54 320	78.60 310	84.86 245	88.90 335	69.54 256	96.03 301	70.25 335	88.93 237

miper_enhance_ethall		86.90 232	86.18 222	89.06 262	91.66 288	77.58 280	90.22 315	94.82 223	79.16 299	84.48 254	89.10 330	79.19 134	96.66 263	84.06 181	82.94 301	92.94 307

DTE-MVSNet		86.11 255	85.48 248	87.98 292	91.65 289	74.92 313	94.93 129	95.75 159	87.36 122	82.26 298	93.04 215	72.85 214	95.82 313	74.04 312	77.46 360	93.20 297

MIMVSNet		82.59 306	80.53 311	88.76 269	91.51 290	78.32 258	86.57 370	90.13 350	79.32 295	80.70 318	88.69 341	52.98 375	93.07 365	66.03 364	88.86 238	94.90 216

WB-MVSnew		83.77 297	83.28 288	85.26 344	91.48 291	71.03 360	91.89 274	87.98 370	78.91 301	84.78 246	90.22 306	69.11 267	94.02 349	64.70 370	90.44 209	90.71 357

pm-mvs1		86.61 242	85.54 246	89.82 236	91.44 292	80.18 209	95.28 108	94.85 220	83.84 204	81.66 306	92.62 227	72.45 221	96.48 279	79.67 256	78.06 355	92.82 312

Baseline_NR-MVSNet		87.07 227	86.63 204	88.40 278	91.44 292	77.87 270	94.23 177	92.57 289	84.12 198	85.74 214	92.08 248	77.25 154	96.04 300	82.29 210	79.94 345	91.30 347

IterMVS		84.88 280	83.98 280	87.60 299	91.44 292	76.03 301	90.18 318	92.41 291	83.24 222	81.06 315	90.42 303	66.60 291	94.28 346	79.46 258	80.98 333	92.48 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MS-PatchMatch		85.05 277	84.16 275	87.73 297	91.42 295	78.51 252	91.25 293	93.53 266	77.50 322	80.15 325	91.58 267	61.99 322	95.51 325	75.69 298	94.35 150	89.16 375

tpm2		84.08 291	82.94 295	87.48 304	91.39 296	71.27 356	89.23 337	90.37 345	71.95 375	84.64 249	89.33 327	67.30 280	96.55 275	75.17 303	87.09 267	94.63 224

v8		87.50 207	86.71 199	89.89 233	91.37 297	79.40 233	94.50 155	95.38 190	84.81 185	83.60 281	91.33 271	76.05 165	97.42 209	82.84 199	80.51 341	92.84 311

ADS-MVSNet2		81.66 315	79.71 324	87.50 302	91.35 298	74.19 322	83.33 389	88.48 368	72.90 368	82.24 299	85.77 372	64.98 305	93.20 363	64.57 371	83.74 291	95.12 204

ADS-MVSNet		81.56 317	79.78 321	86.90 321	91.35 298	71.82 349	83.33 389	89.16 366	72.90 368	82.24 299	85.77 372	64.98 305	93.76 354	64.57 371	83.74 291	95.12 204

GA-MVS		86.61 242	85.27 255	90.66 197	91.33 300	78.71 247	90.40 308	93.81 262	85.34 170	85.12 239	89.57 324	61.25 331	97.11 240	80.99 237	89.59 227	96.15 162

miper_lstm_enhance		85.27 273	84.59 270	87.31 307	91.28 301	74.63 316	87.69 360	94.09 252	81.20 276	81.36 311	89.85 319	74.97 183	94.30 345	81.03 236	79.84 348	93.01 305

XVG-ACMP-BASELINE		86.00 256	84.84 265	89.45 253	91.20 302	78.00 265	91.70 281	95.55 175	85.05 178	82.97 291	92.25 240	54.49 369	97.48 200	82.93 196	87.45 262	92.89 309

v10		87.25 217	86.38 213	89.85 234	91.19 303	79.50 230	94.48 156	95.45 184	83.79 206	83.62 280	91.19 276	75.13 179	97.42 209	81.94 219	80.60 336	92.63 316

FMVSNet5		81.52 318	79.60 325	87.27 308	91.17 304	77.95 266	91.49 286	92.26 298	76.87 328	76.16 358	87.91 352	51.67 377	92.34 370	67.74 353	81.16 324	91.52 342

USDC		82.76 303	81.26 308	87.26 309	91.17 304	74.55 317	89.27 335	93.39 269	78.26 317	75.30 365	92.08 248	54.43 370	96.63 264	71.64 324	85.79 275	90.61 359

CostFormer		85.77 262	84.94 262	88.26 285	91.16 306	72.58 344	89.47 333	91.04 333	76.26 335	86.45 197	89.97 316	70.74 237	96.86 258	82.35 208	87.07 268	95.34 199

test_cas_vis1_n_1920		88.83 165	88.85 146	88.78 268	91.15 307	76.72 291	93.85 204	94.93 214	83.23 223	92.81 76	96.00 96	61.17 335	94.45 340	91.67 89	94.84 136	95.17 203

baseline2		86.50 248	85.39 250	89.84 235	91.12 308	76.70 292	91.88 275	88.58 367	82.35 241	79.95 330	90.95 286	73.42 208	97.63 190	80.27 250	89.95 218	95.19 202

tpm cat1		81.96 309	80.27 315	87.01 317	91.09 309	71.02 361	87.38 364	91.53 321	66.25 389	80.17 324	86.35 368	68.22 277	96.15 298	69.16 343	82.29 310	93.86 266

tpmvs		83.35 302	82.07 301	87.20 314	91.07 310	71.00 362	88.31 350	91.70 313	78.91 301	80.49 322	87.18 362	69.30 262	97.08 241	68.12 352	83.56 295	93.51 286

v1144		87.61 201	86.79 197	90.06 225	91.01 311	79.34 236	93.95 198	95.42 189	83.36 219	85.66 216	91.31 274	74.98 182	97.42 209	83.37 190	82.06 312	93.42 289

v2v482		87.84 188	87.06 188	90.17 218	90.99 312	79.23 243	94.00 196	95.13 201	84.87 182	85.53 220	92.07 250	74.45 189	97.45 204	84.71 174	81.75 318	93.85 267

SixPastTwentyTwo		83.91 295	82.90 297	86.92 320	90.99 312	70.67 365	93.48 217	91.99 306	85.54 166	77.62 350	92.11 246	60.59 338	96.87 257	76.05 297	77.75 357	93.20 297

test-LLR		85.87 259	85.41 249	87.25 310	90.95 314	71.67 353	89.55 329	89.88 358	83.41 216	84.54 252	87.95 350	67.25 281	95.11 334	81.82 222	93.37 169	94.97 209

test-mter		84.54 286	83.64 284	87.25 310	90.95 314	71.67 353	89.55 329	89.88 358	79.17 298	84.54 252	87.95 350	55.56 361	95.11 334	81.82 222	93.37 169	94.97 209

v148		87.04 228	86.32 217	89.21 257	90.94 316	77.26 283	93.71 210	94.43 236	84.84 184	84.36 261	90.80 291	76.04 166	97.05 246	82.12 213	79.60 349	93.31 291

mvs_tets		88.06 185	87.28 184	90.38 213	90.94 316	79.88 223	95.22 112	95.66 168	85.10 176	84.21 267	93.94 182	63.53 313	97.40 217	88.50 125	88.40 246	93.87 264

MVP-Stereo		85.97 257	84.86 264	89.32 255	90.92 318	82.19 159	92.11 271	94.19 246	78.76 307	78.77 343	91.63 264	68.38 276	96.56 273	75.01 306	93.95 154	89.20 374
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Patchmatch-test		81.37 320	79.30 328	87.58 300	90.92 318	74.16 323	80.99 396	87.68 374	70.52 381	76.63 356	88.81 336	71.21 229	92.76 367	60.01 385	86.93 269	95.83 180

jajsoiax		88.24 179	87.50 177	90.48 206	90.89 320	80.14 211	95.31 102	95.65 170	84.97 179	84.24 266	94.02 177	65.31 303	97.42 209	88.56 124	88.52 242	93.89 260

tpmrst		85.35 270	84.99 259	86.43 329	90.88 321	67.88 377	88.71 344	91.43 324	80.13 286	86.08 207	88.80 338	73.05 212	96.02 302	82.48 204	83.40 299	95.40 195

gg-mvs-nofinetune		81.77 312	79.37 327	88.99 265	90.85 322	77.73 277	86.29 371	79.63 400	74.88 350	83.19 290	69.05 402	60.34 339	96.11 299	75.46 300	94.64 142	93.11 301

D2MVS		85.90 258	85.09 258	88.35 280	90.79 323	77.42 281	91.83 277	95.70 164	80.77 280	80.08 328	90.02 314	66.74 290	96.37 287	81.88 221	87.97 253	91.26 348

OurMVSNet-221017-0		85.35 270	84.64 269	87.49 303	90.77 324	72.59 343	94.01 194	94.40 238	84.72 188	79.62 336	93.17 209	61.91 323	96.72 260	81.99 218	81.16 324	93.16 299

v1192		87.25 217	86.33 216	90.00 230	90.76 325	79.04 244	93.80 205	95.48 180	82.57 236	85.48 224	91.18 278	73.38 210	97.42 209	82.30 209	82.06 312	93.53 283

test_djsdf		89.03 158	88.64 148	90.21 217	90.74 326	79.28 240	95.96 73	95.90 148	84.66 190	85.33 237	92.94 217	74.02 198	97.30 223	89.64 112	88.53 241	94.05 256

v7n		86.81 234	85.76 242	89.95 231	90.72 327	79.25 242	95.07 121	95.92 145	84.45 193	82.29 297	90.86 287	72.60 218	97.53 196	79.42 262	80.52 340	93.08 303

PVSNet_0	73.20 20	77.22 349	74.83 355	84.37 350	90.70 328	71.10 359	83.09 391	89.67 361	72.81 370	73.93 373	83.13 383	60.79 337	93.70 356	68.54 346	50.84 404	88.30 383

v144192		87.19 223	86.35 215	89.74 240	90.64 329	78.24 261	93.92 201	95.43 187	81.93 251	85.51 222	91.05 284	74.21 194	97.45 204	82.86 198	81.56 320	93.53 283

test_fmvs1		87.34 212	87.56 176	86.68 326	90.59 330	71.80 350	94.01 194	94.04 253	78.30 315	91.97 98	95.22 128	56.28 359	93.71 355	92.89 52	94.71 138	94.52 232

V42		87.68 193	86.86 193	90.15 220	90.58 331	80.14 211	94.24 176	95.28 195	83.66 208	85.67 215	91.33 271	74.73 186	97.41 215	84.43 178	81.83 316	92.89 309

CR-MVSNet		85.35 270	83.76 282	90.12 222	90.58 331	79.34 236	85.24 379	91.96 309	78.27 316	85.55 218	87.87 353	71.03 232	95.61 321	73.96 314	89.36 230	95.40 195

RPMNet		83.95 294	81.53 305	91.21 175	90.58 331	79.34 236	85.24 379	96.76 75	71.44 377	85.55 218	82.97 386	70.87 235	98.91 80	61.01 381	89.36 230	95.40 195

v1921920		86.97 230	86.06 229	89.69 244	90.53 334	78.11 264	93.80 205	95.43 187	81.90 253	85.33 237	91.05 284	72.66 216	97.41 215	82.05 217	81.80 317	93.53 283

v1240		86.78 236	85.85 237	89.56 248	90.45 335	77.79 274	93.61 213	95.37 192	81.65 262	85.43 229	91.15 280	71.50 227	97.43 208	81.47 230	82.05 314	93.47 287

tpm		84.73 283	84.02 278	86.87 323	90.33 336	68.90 373	89.06 340	89.94 355	80.85 279	85.75 213	89.86 318	68.54 274	95.97 304	77.76 277	84.05 288	95.75 183

EG-PatchMatch MVS		82.37 308	80.34 314	88.46 277	90.27 337	79.35 235	92.80 249	94.33 241	77.14 327	73.26 376	90.18 309	47.47 387	96.72 260	70.25 335	87.32 265	89.30 371

EPNet_dtu		86.49 250	85.94 235	88.14 289	90.24 338	72.82 336	94.11 183	92.20 299	86.66 139	79.42 337	92.36 235	73.52 205	95.81 314	71.26 326	93.66 158	95.80 182
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		83.90 296	82.70 300	87.51 301	90.23 339	72.67 339	88.62 346	81.96 395	81.37 270	85.01 243	88.34 344	66.31 295	94.45 340	75.30 302	87.12 266	95.43 194

EPNet		91.79 86	91.02 97	94.10 54	90.10 340	85.25 71	96.03 67	92.05 303	92.83 2	87.39 178	95.78 107	79.39 131	99.01 63	88.13 129	97.48 82	98.05 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PatchT		82.68 305	81.27 307	86.89 322	90.09 341	70.94 363	84.06 386	90.15 349	74.91 348	85.63 217	83.57 381	69.37 258	94.87 339	65.19 366	88.50 243	94.84 218

Patchmtry		82.71 304	80.93 310	88.06 290	90.05 342	76.37 298	84.74 384	91.96 309	72.28 374	81.32 312	87.87 353	71.03 232	95.50 327	68.97 344	80.15 343	92.32 327

pmmvs4		85.43 267	83.86 281	90.16 219	90.02 343	82.97 138	90.27 309	92.67 287	75.93 338	80.73 317	91.74 259	71.05 231	95.73 319	78.85 267	83.46 297	91.78 336

TESTMET0.1,1		83.74 298	82.85 298	86.42 330	89.96 344	71.21 358	89.55 329	87.88 371	77.41 323	83.37 286	87.31 358	56.71 357	93.65 357	80.62 244	92.85 180	94.40 241

dp		81.47 319	80.23 316	85.17 345	89.92 345	65.49 385	86.74 368	90.10 351	76.30 334	81.10 313	87.12 363	62.81 318	95.92 307	68.13 351	79.88 346	94.09 253

K. test v3		81.59 316	80.15 318	85.91 336	89.89 346	69.42 372	92.57 254	87.71 373	85.56 165	73.44 375	89.71 322	55.58 360	95.52 324	77.17 284	69.76 379	92.78 313

MDA-MVSNet-bldmvs		78.85 343	76.31 348	86.46 327	89.76 347	73.88 324	88.79 343	90.42 344	79.16 299	59.18 398	88.33 345	60.20 340	94.04 348	62.00 378	68.96 383	91.48 344

test_fmvs1_n		87.03 229	87.04 190	86.97 318	89.74 348	71.86 348	94.55 153	94.43 236	78.47 311	91.95 100	95.50 118	51.16 379	93.81 353	93.02 51	94.56 144	95.26 200

GG-mvs-BLEND				87.94 294	89.73 349	77.91 267	87.80 355	78.23 404		80.58 320	83.86 379	59.88 343	95.33 331	71.20 327	92.22 189	90.60 361

EGC-MVSNET		61.97 368	56.37 373	78.77 372	89.63 350	73.50 328	89.12 339	82.79 392	0.21 418	1.24 419	84.80 376	39.48 397	90.04 386	44.13 401	75.94 368	72.79 400

gm-plane-assit					89.60 351	68.00 375			77.28 326		88.99 333		97.57 193	79.44 260

MonoMVSNet		86.89 233	86.55 208	87.92 295	89.46 352	73.75 325	94.12 181	93.10 274	87.82 112	85.10 240	90.76 293	69.59 255	94.94 338	86.47 152	82.50 307	95.07 206

test_fmvsmconf0.01_n		93.19 67	93.02 66	93.71 67	89.25 353	84.42 93	96.06 64	96.29 109	89.06 67	94.68 37	98.13 3	79.22 133	98.98 74	97.22 4	97.24 86	97.74 90

anonymousdsp		87.84 188	87.09 187	90.12 222	89.13 354	80.54 202	94.67 147	95.55 175	82.05 246	83.82 274	92.12 244	71.47 228	97.15 236	87.15 143	87.80 258	92.67 314

N_pmnet		68.89 362	68.44 364	70.23 382	89.07 355	28.79 421	88.06 352	19.50 421	69.47 384	71.86 381	84.93 375	61.24 332	91.75 376	54.70 394	77.15 361	90.15 363

pmmvs5		84.21 289	82.84 299	88.34 282	88.95 356	76.94 287	92.41 257	91.91 311	75.63 340	80.28 323	91.18 278	64.59 307	95.57 322	77.09 286	83.47 296	92.53 318

PMMVS		85.71 263	84.96 261	87.95 293	88.90 357	77.09 285	88.68 345	90.06 352	72.32 373	86.47 194	90.76 293	72.15 222	94.40 342	81.78 224	93.49 164	92.36 325

JIA-IIPM		81.04 323	78.98 336	87.25 310	88.64 358	73.48 329	81.75 395	89.61 363	73.19 365	82.05 302	73.71 398	66.07 300	95.87 310	71.18 329	84.60 283	92.41 323

test_vis1_n		86.56 245	86.49 212	86.78 325	88.51 359	72.69 338	94.68 146	93.78 263	79.55 294	90.70 121	95.31 124	48.75 384	93.28 361	93.15 48	93.99 153	94.38 242

Gipuma		57.99 374	54.91 376	67.24 388	88.51 359	65.59 384	52.21 409	90.33 347	43.58 406	42.84 409	51.18 410	20.29 412	85.07 400	34.77 407	70.45 377	51.05 409
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EU-MVSNet		81.32 321	80.95 309	82.42 363	88.50 361	63.67 392	93.32 223	91.33 325	64.02 393	80.57 321	92.83 220	61.21 333	92.27 371	76.34 293	80.38 342	91.32 346

our_test_3		81.93 310	80.46 313	86.33 331	88.46 362	73.48 329	88.46 348	91.11 329	76.46 330	76.69 355	88.25 346	66.89 286	94.36 343	68.75 345	79.08 353	91.14 351

ppachtmachnet_test		81.84 311	80.07 319	87.15 315	88.46 362	74.43 320	89.04 341	92.16 300	75.33 343	77.75 348	88.99 333	66.20 297	95.37 330	65.12 368	77.60 358	91.65 338

lessismore_v0				86.04 332	88.46 362	68.78 374		80.59 398		73.01 377	90.11 312	55.39 362	96.43 284	75.06 305	65.06 390	92.90 308

test0.0.03 1		82.41 307	81.69 303	84.59 348	88.23 365	72.89 335	90.24 313	87.83 372	83.41 216	79.86 332	89.78 320	67.25 281	88.99 392	65.18 367	83.42 298	91.90 335

MDA-MVSNet_test_wron		79.21 342	77.19 344	85.29 342	88.22 366	72.77 337	85.87 373	90.06 352	74.34 353	62.62 395	87.56 356	66.14 298	91.99 374	66.90 361	73.01 371	91.10 354

YYNet1		79.22 341	77.20 343	85.28 343	88.20 367	72.66 340	85.87 373	90.05 354	74.33 354	62.70 393	87.61 355	66.09 299	92.03 372	66.94 358	72.97 372	91.15 350

pmmvs6		83.42 300	81.60 304	88.87 267	88.01 368	77.87 270	94.96 127	94.24 245	74.67 351	78.80 342	91.09 283	60.17 341	96.49 278	77.06 287	75.40 369	92.23 329

testgi		80.94 327	80.20 317	83.18 356	87.96 369	66.29 382	91.28 291	90.70 342	83.70 207	78.12 345	92.84 219	51.37 378	90.82 383	63.34 374	82.46 308	92.43 322

mvsany_test1		85.42 268	85.30 254	85.77 337	87.95 370	75.41 309	87.61 363	80.97 397	76.82 329	88.68 151	95.83 104	77.44 153	90.82 383	85.90 159	86.51 270	91.08 355

Anonymous20231206		81.03 324	79.77 323	84.82 347	87.85 371	70.26 368	91.42 287	92.08 302	73.67 360	77.75 348	89.25 328	62.43 320	93.08 364	61.50 380	82.00 315	91.12 352

dmvs_testset		74.57 355	75.81 353	70.86 381	87.72 372	40.47 416	87.05 367	77.90 406	82.75 233	71.15 384	85.47 374	67.98 278	84.12 403	45.26 400	76.98 364	88.00 384

test_fmvs2		83.98 292	84.03 277	83.83 355	87.16 373	67.53 381	93.93 200	92.89 280	77.62 321	86.89 187	93.53 197	47.18 388	92.02 373	90.54 105	86.51 270	91.93 334

OpenMVS_ROB	74.94 19	79.51 339	77.03 346	86.93 319	87.00 374	76.23 300	92.33 263	90.74 341	68.93 385	74.52 370	88.23 347	49.58 382	96.62 265	57.64 390	84.29 285	87.94 385

LF4IMVS		80.37 331	79.07 335	84.27 352	86.64 375	69.87 371	89.39 334	91.05 332	76.38 332	74.97 367	90.00 315	47.85 386	94.25 347	74.55 311	80.82 335	88.69 380

MIMVSNet1		79.38 340	77.28 342	85.69 338	86.35 376	73.67 326	91.61 284	92.75 285	78.11 320	72.64 378	88.12 348	48.16 385	91.97 375	60.32 382	77.49 359	91.43 345

KD-MVS_2432*1600		78.50 344	76.02 351	85.93 334	86.22 377	74.47 318	84.80 382	92.33 293	79.29 296	76.98 353	85.92 370	53.81 373	93.97 350	67.39 354	57.42 399	89.36 369

miper_refine_blended		78.50 344	76.02 351	85.93 334	86.22 377	74.47 318	84.80 382	92.33 293	79.29 296	76.98 353	85.92 370	53.81 373	93.97 350	67.39 354	57.42 399	89.36 369

CL-MVSNet_self_test		81.74 313	80.53 311	85.36 341	85.96 379	72.45 345	90.25 311	93.07 276	81.24 274	79.85 333	87.29 359	70.93 234	92.52 368	66.95 357	69.23 381	91.11 353

test_vis1_rt		77.96 347	76.46 347	82.48 362	85.89 380	71.74 352	90.25 311	78.89 401	71.03 380	71.30 383	81.35 390	42.49 396	91.05 382	84.55 176	82.37 309	84.65 388

test20.03		79.95 335	79.08 334	82.55 360	85.79 381	67.74 379	91.09 297	91.08 330	81.23 275	74.48 371	89.96 317	61.63 325	90.15 385	60.08 383	76.38 365	89.76 366

Anonymous20240521		80.44 330	79.21 330	84.11 353	85.75 382	67.89 376	92.86 247	93.23 272	75.61 341	75.59 364	87.47 357	50.03 380	94.33 344	71.14 330	81.21 323	90.12 364

KD-MVS_self_test		80.20 332	79.24 329	83.07 357	85.64 383	65.29 386	91.01 299	93.93 255	78.71 309	76.32 357	86.40 367	59.20 348	92.93 366	72.59 321	69.35 380	91.00 356

Patchmatch-RL test		81.67 314	79.96 320	86.81 324	85.42 384	71.23 357	82.17 394	87.50 375	78.47 311	77.19 352	82.50 388	70.81 236	93.48 358	82.66 203	72.89 373	95.71 187

UnsupCasMVSNet_eth		80.07 333	78.27 339	85.46 340	85.24 385	72.63 342	88.45 349	94.87 219	82.99 228	71.64 382	88.07 349	56.34 358	91.75 376	73.48 318	63.36 393	92.01 333

pmmvs-eth3d		80.97 326	78.72 338	87.74 296	84.99 386	79.97 222	90.11 319	91.65 316	75.36 342	73.51 374	86.03 369	59.45 345	93.96 352	75.17 303	72.21 374	89.29 373

mvs5depth		80.98 325	79.15 333	86.45 328	84.57 387	73.29 331	87.79 356	91.67 315	80.52 282	82.20 301	89.72 321	55.14 366	95.93 306	73.93 315	66.83 387	90.12 364

CMPMVS	59.16 21	80.52 328	79.20 331	84.48 349	83.98 388	67.63 380	89.95 324	93.84 261	64.79 392	66.81 390	91.14 281	57.93 353	95.17 332	76.25 294	88.10 249	90.65 358
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		76.23 353	73.27 357	85.09 346	83.79 389	72.92 334	85.65 376	93.47 268	71.52 376	68.84 388	79.08 393	49.77 381	93.21 362	66.81 362	60.52 396	89.13 377

PM-MVS		78.11 346	76.12 350	84.09 354	83.54 390	70.08 369	88.97 342	85.27 386	79.93 288	74.73 369	86.43 366	34.70 402	93.48 358	79.43 261	72.06 375	88.72 379

dongtai		58.82 373	58.24 371	60.56 390	83.13 391	45.09 414	82.32 393	48.22 420	67.61 387	61.70 397	69.15 401	38.75 398	76.05 409	32.01 408	41.31 408	60.55 405

DSMNet-mixed		76.94 350	76.29 349	78.89 371	83.10 392	56.11 407	87.78 357	79.77 399	60.65 397	75.64 363	88.71 339	61.56 327	88.34 393	60.07 384	89.29 232	92.21 330

new_pmnet		72.15 358	70.13 361	78.20 373	82.95 393	65.68 383	83.91 387	82.40 394	62.94 395	64.47 392	79.82 392	42.85 395	86.26 398	57.41 391	74.44 370	82.65 393

new-patchmatchnet		76.41 352	75.17 354	80.13 368	82.65 394	59.61 399	87.66 361	91.08 330	78.23 318	69.85 386	83.22 382	54.76 367	91.63 378	64.14 373	64.89 391	89.16 375

m2depth		76.55 351	74.64 356	82.29 365	82.25 395	67.81 378	89.76 326	85.69 382	70.35 382	75.76 362	91.69 260	46.88 389	89.77 387	66.16 363	63.23 394	89.30 371

WB-MVS		67.92 363	67.49 365	69.21 385	81.09 396	41.17 415	88.03 353	78.00 405	73.50 362	62.63 394	83.11 385	63.94 311	86.52 396	25.66 411	51.45 403	79.94 396

SSC-MVS		67.06 364	66.56 366	68.56 387	80.54 397	40.06 417	87.77 358	77.37 408	72.38 372	61.75 396	82.66 387	63.37 314	86.45 397	24.48 412	48.69 406	79.16 398

APD_test1		69.04 361	66.26 367	77.36 376	80.51 398	62.79 395	85.46 378	83.51 391	54.11 402	59.14 399	84.79 377	23.40 409	89.61 388	55.22 393	70.24 378	79.68 397

ambc				83.06 358	79.99 399	63.51 393	77.47 402	92.86 281		74.34 372	84.45 378	28.74 403	95.06 336	73.06 320	68.89 384	90.61 359

test_fmvs3		77.67 348	77.16 345	79.22 370	79.52 400	61.14 396	92.34 262	91.64 317	73.98 357	78.86 339	86.59 364	27.38 406	87.03 394	88.12 130	75.97 367	89.50 368

TDRefinement		79.81 336	77.34 341	87.22 313	79.24 401	75.48 308	93.12 234	92.03 304	76.45 331	75.01 366	91.58 267	49.19 383	96.44 283	70.22 337	69.18 382	89.75 367

MVStest1		72.91 357	69.70 362	82.54 361	78.14 402	73.05 333	88.21 351	86.21 378	60.69 396	64.70 391	90.53 299	46.44 390	85.70 399	58.78 388	53.62 401	88.87 378

kuosan		53.51 375	53.30 378	54.13 394	76.06 403	45.36 413	80.11 400	48.36 419	59.63 398	54.84 400	63.43 407	37.41 399	62.07 414	20.73 414	39.10 409	54.96 408

pmmvs3		71.81 360	68.71 363	81.11 366	75.86 404	70.42 367	86.74 368	83.66 390	58.95 399	68.64 389	80.89 391	36.93 400	89.52 389	63.10 376	63.59 392	83.39 389

mvsany_test3		74.95 354	73.26 358	80.02 369	74.61 405	63.16 394	85.53 377	78.42 402	74.16 355	74.89 368	86.46 365	36.02 401	89.09 391	82.39 207	66.91 386	87.82 386

DeepMVS_CX				56.31 393	74.23 406	51.81 409		56.67 417	44.85 405	48.54 405	75.16 396	27.87 405	58.74 415	40.92 405	52.22 402	58.39 407

test_f		71.95 359	70.87 360	75.21 377	74.21 407	59.37 400	85.07 381	85.82 381	65.25 391	70.42 385	83.13 383	23.62 407	82.93 405	78.32 271	71.94 376	83.33 390

test_vis3_rt		65.12 366	62.60 368	72.69 379	71.44 408	60.71 397	87.17 365	65.55 412	63.80 394	53.22 402	65.65 405	14.54 416	89.44 390	76.65 288	65.38 389	67.91 403

FPMVS		64.63 367	62.55 369	70.88 380	70.80 409	56.71 402	84.42 385	84.42 388	51.78 403	49.57 403	81.61 389	23.49 408	81.48 406	40.61 406	76.25 366	74.46 399

testf1		59.54 370	56.11 374	69.85 383	69.28 410	56.61 404	80.37 398	76.55 409	42.58 407	45.68 406	75.61 394	11.26 417	84.18 401	43.20 403	60.44 397	68.75 401

APD_test2		59.54 370	56.11 374	69.85 383	69.28 410	56.61 404	80.37 398	76.55 409	42.58 407	45.68 406	75.61 394	11.26 417	84.18 401	43.20 403	60.44 397	68.75 401

PMMVS2		59.60 369	56.40 372	69.21 385	68.83 412	46.58 411	73.02 406	77.48 407	55.07 401	49.21 404	72.95 400	17.43 414	80.04 407	49.32 398	44.33 407	80.99 395

wuyk23d		21.27 383	20.48 386	23.63 398	68.59 413	36.41 419	49.57 410	6.85 422	9.37 414	7.89 416	4.46 418	4.03 421	31.37 416	17.47 416	16.07 415	3.12 413

E-PMN		43.23 379	42.29 381	46.03 395	65.58 414	37.41 418	73.51 404	64.62 413	33.99 410	28.47 414	47.87 411	19.90 413	67.91 411	22.23 413	24.45 411	32.77 410

LCM-MVSNet		66.00 365	62.16 370	77.51 375	64.51 415	58.29 401	83.87 388	90.90 337	48.17 404	54.69 401	73.31 399	16.83 415	86.75 395	65.47 365	61.67 395	87.48 387

EMVS		42.07 380	41.12 382	44.92 396	63.45 416	35.56 420	73.65 403	63.48 414	33.05 411	26.88 415	45.45 412	21.27 411	67.14 412	19.80 415	23.02 413	32.06 411

MVE	39.65 23	43.39 378	38.59 384	57.77 391	56.52 417	48.77 410	55.38 408	58.64 416	29.33 412	28.96 413	52.65 409	4.68 420	64.62 413	28.11 410	33.07 410	59.93 406
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		58.88 372	54.22 377	72.86 378	56.50 418	56.67 403	80.75 397	86.00 380	73.09 367	37.39 410	64.63 406	22.17 410	79.49 408	43.51 402	23.96 412	82.43 394

test_method		50.52 377	48.47 379	56.66 392	52.26 419	18.98 423	41.51 411	81.40 396	10.10 413	44.59 408	75.01 397	28.51 404	68.16 410	53.54 395	49.31 405	82.83 392

PMVS	47.18 22	52.22 376	48.46 380	63.48 389	45.72 420	46.20 412	73.41 405	78.31 403	41.03 409	30.06 412	65.68 404	6.05 419	83.43 404	30.04 409	65.86 388	60.80 404
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		35.64 381	39.24 383	24.84 397	14.87 421	23.90 422	62.71 407	51.51 418	6.58 415	36.66 411	62.08 408	44.37 393	30.34 417	52.40 396	22.00 414	20.27 412

testmvs		8.92 384	11.52 387	1.12 400	1.06 422	0.46 425	86.02 372	0.65 423	0.62 416	2.74 417	9.52 416	0.31 423	0.45 419	2.38 417	0.39 416	2.46 415

test123		8.76 385	11.22 388	1.39 399	0.85 423	0.97 424	85.76 375	0.35 424	0.54 417	2.45 418	8.14 417	0.60 422	0.48 418	2.16 418	0.17 417	2.71 414

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

eth-test2					0.00 424

eth-test					0.00 424

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

cdsmvs_eth3d_5k		22.14 382	29.52 385	0.00 401	0.00 424	0.00 426	0.00 412	95.76 158	0.00 419	0.00 420	94.29 167	75.66 175	0.00 420	0.00 419	0.00 418	0.00 416

pcd_1.5k_mvsjas		6.64 387	8.86 390	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	79.70 126	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

ab-mvs-re		7.82 386	10.43 389	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	93.88 187	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						64.08 390								59.14 386

PC_three_1452									82.47 237	97.09 10	97.07 51	92.72 1	98.04 164	92.70 58	99.02 12	98.86 11

test_241102_TWO								97.44 15	90.31 28	97.62 5	98.07 9	91.46 10	99.58 10	95.66 17	99.12 6	98.98 10

test_0728_THIRD									90.75 19	97.04 11	98.05 13	92.09 6	99.55 16	95.64 19	99.13 3	99.13 2

GSMVS																96.12 165

sam_mvs1												71.70 225				96.12 165

sam_mvs												70.60 238

MTGPA								96.97 50

test_post1							88.00 354				9.81 415	69.31 261	95.53 323	76.65 288

test_post											10.29 414	70.57 242	95.91 309

patchmatchnet-post											83.76 380	71.53 226	96.48 279

MTMP							96.16 52	60.64 415

test9_res														91.91 83	98.71 32	98.07 69

agg_prior2														90.54 105	98.68 37	98.27 53

test_prior4						85.96 54	94.11 183

test_prior2							94.12 181		87.67 117	92.63 83	96.39 82	86.62 38		91.50 91	98.67 40

旧先验2							93.36 221		71.25 378	94.37 40			97.13 239	86.74 148

新几何2							93.11 236

无先验							93.28 229	96.26 114	73.95 358				99.05 55	80.56 245		96.59 146

原ACMM2							92.94 243

testdata2													98.75 94	78.30 272

segment_acmp												87.16 36

testdata1							92.15 269		87.94 105

plane_prior5								96.22 119					98.12 149	88.15 127	89.99 215	94.63 224

plane_prior4											94.86 144

plane_prior3						82.75 142			90.26 33	86.91 184

plane_prior2							95.85 77		90.81 17

plane_prior						82.73 145	95.21 113		89.66 51						89.88 220

n2								0.00 425

nn								0.00 425

door-mid								85.49 383

test11								96.57 92

door								85.33 385

HQP5-MVS						81.56 170

BP-MVS														87.11 145

HQP4-MVS										85.43 229			97.96 170			94.51 234

HQP3-MVS								96.04 136							89.77 224

HQP2-MVS												73.83 202

MDTV_nov1_ep13_2view						55.91 408	87.62 362		73.32 364	84.59 251		70.33 245		74.65 309		95.50 192

ACMMP++_ref															87.47 260

ACMMP++															88.01 252

Test By Simon												80.02 121

High-resolution multi-view benchmark