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.

OPU-MVS				96.21 1	98.00 44	90.85 1	97.13 9				97.08 40	92.59 1	98.94 83	92.25 45	98.99 10	98.84 8

HPM-MVS++		95.14 9	94.91 11	95.83 2	98.25 29	89.65 2	95.92 59	96.96 52	91.75 7	94.02 35	96.83 51	88.12 21	99.55 12	93.41 24	98.94 12	98.28 45

DPM-MVS		92.58 69	91.74 77	95.08 12	96.19 98	89.31 3	92.66 227	96.56 93	83.44 186	91.68 92	95.04 116	86.60 42	98.99 76	85.60 137	97.92 69	96.93 116

3Dnovator+	87.14 4	92.42 72	91.37 80	95.55 4	95.63 120	88.73 4	97.07 13	96.77 72	90.84 16	84.02 232	96.62 63	75.95 156	99.34 33	87.77 111	97.68 75	98.59 18

CNVR-MVS		95.40 6	95.37 6	95.50 5	98.11 37	88.51 5	95.29 86	96.96 52	92.09 3	95.32 19	97.08 40	89.49 12	99.33 36	95.10 8	98.85 15	98.66 14

SMA-MVS		95.20 7	95.07 9	95.59 3	98.14 36	88.48 6	96.26 40	97.28 28	85.90 133	97.67 3	98.10 2	88.41 17	99.56 7	94.66 10	99.19 1	98.71 12
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
ETH3D cwj APD-0.16		93.91 41	93.53 49	95.06 13	96.76 81	87.78 7	94.92 111	97.21 34	84.33 168	93.89 38	97.09 39	87.20 33	99.29 41	91.90 61	98.44 51	98.12 59

SF-MVS		94.97 10	94.90 12	95.20 8	97.84 50	87.76 8	96.65 28	97.48 7	87.76 94	95.71 15	97.70 11	88.28 19	99.35 31	93.89 18	98.78 21	98.48 24

ETH3D-3000-0.1		94.61 16	94.44 18	95.12 11	97.70 55	87.71 9	95.98 56	97.44 12	86.67 120	95.25 21	97.31 25	87.73 25	99.24 44	93.11 31	98.76 26	98.40 35

ACMMP_NAP		94.74 14	94.56 16	95.28 6	98.02 43	87.70 10	95.68 68	97.34 19	88.28 79	95.30 20	97.67 13	85.90 49	99.54 16	93.91 17	98.95 11	98.60 17

canonicalmvs		93.27 58	92.75 64	94.85 27	95.70 118	87.66 11	96.33 35	96.41 99	90.00 34	94.09 33	94.60 133	82.33 88	98.62 105	92.40 41	92.86 157	98.27 47

alignmvs		93.08 62	92.50 69	94.81 32	95.62 121	87.61 12	95.99 54	96.07 122	89.77 38	94.12 32	94.87 121	80.56 106	98.66 101	92.42 40	93.10 152	98.15 56

MCST-MVS		94.45 20	94.20 28	95.19 9	98.46 18	87.50 13	95.00 106	97.12 40	87.13 107	92.51 73	96.30 74	89.24 14	99.34 33	93.46 21	98.62 44	98.73 11

NCCC		94.81 13	94.69 15	95.17 10	97.83 51	87.46 14	95.66 70	96.93 55	92.34 2	93.94 36	96.58 65	87.74 24	99.44 27	92.83 33	98.40 53	98.62 16

DPE-MVS		95.57 3	95.67 3	95.25 7	98.36 25	87.28 15	95.56 75	97.51 4	89.13 55	97.14 7	97.91 9	91.64 5	99.62 1	94.61 11	99.17 2	98.86 7

test_part2					98.55 11	87.22 16				96.40 11

ZNCC-MVS		94.47 18	94.28 22	95.03 14	98.52 14	86.96 17	96.85 23	97.32 24	88.24 80	93.15 55	97.04 42	86.17 44	99.62 1	92.40 41	98.81 18	98.52 20

zzz-MVS		94.47 18	94.30 21	95.00 16	98.42 20	86.95 18	95.06 104	96.97 49	91.07 13	93.14 56	97.56 14	84.30 67	99.56 7	93.43 22	98.75 27	98.47 28

MTAPA		94.42 24	94.22 25	95.00 16	98.42 20	86.95 18	94.36 154	96.97 49	91.07 13	93.14 56	97.56 14	84.30 67	99.56 7	93.43 22	98.75 27	98.47 28

nrg030		91.08 93	90.39 95	93.17 79	93.07 214	86.91 20	96.41 33	96.26 107	88.30 78	88.37 132	94.85 124	82.19 92	97.64 172	91.09 74	82.95 263	94.96 180

APD-MVS		94.24 30	94.07 34	94.75 36	98.06 41	86.90 21	95.88 60	96.94 54	85.68 139	95.05 23	97.18 35	87.31 31	99.07 58	91.90 61	98.61 45	98.28 45
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ETH3 D test6400		93.64 49	93.22 54	94.92 20	97.79 52	86.84 22	95.31 81	97.26 29	82.67 205	93.81 39	96.29 75	87.29 32	99.27 42	89.87 89	98.67 37	98.65 15

GST-MVS		94.21 33	93.97 38	94.90 24	98.41 22	86.82 23	96.54 30	97.19 35	88.24 80	93.26 51	96.83 51	85.48 53	99.59 4	91.43 71	98.40 53	98.30 41

HFP-MVS		94.52 17	94.40 19	94.86 25	98.61 9	86.81 24	96.94 15	97.34 19	88.63 68	93.65 43	97.21 32	86.10 45	99.49 23	92.35 43	98.77 24	98.30 41

#test#		94.32 28	94.14 31	94.86 25	98.61 9	86.81 24	96.43 31	97.34 19	87.51 100	93.65 43	97.21 32	86.10 45	99.49 23	91.68 65	98.77 24	98.30 41

TSAR-MVS + GP.		93.66 48	93.41 51	94.41 52	96.59 86	86.78 26	94.40 146	93.93 238	89.77 38	94.21 29	95.59 102	87.35 30	98.61 106	92.72 36	96.15 103	97.83 81

DeepC-MVS_fast	89.43 2	94.04 36	93.79 42	94.80 33	97.48 62	86.78 26	95.65 72	96.89 58	89.40 47	92.81 62	96.97 45	85.37 55	99.24 44	90.87 81	98.69 33	98.38 37
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		94.39 25	94.18 29	95.00 16	98.24 31	86.77 28	96.16 44	97.23 32	87.28 105	94.85 24	97.04 42	86.99 37	99.52 20	91.54 67	98.33 56	98.71 12

Regformer-2		94.33 27	94.22 25	94.68 38	95.54 123	86.75 29	94.57 134	96.70 81	91.84 6	94.41 25	96.56 67	87.19 34	99.13 54	93.50 20	97.65 77	98.16 55

SD-MVS		94.96 11	95.33 7	93.88 62	97.25 73	86.69 30	96.19 43	97.11 42	90.42 25	96.95 10	97.27 27	89.53 11	96.91 234	94.38 13	98.85 15	98.03 67
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
ACMMPR		94.43 22	94.28 22	94.91 22	98.63 8	86.69 30	96.94 15	97.32 24	88.63 68	93.53 50	97.26 29	85.04 59	99.54 16	92.35 43	98.78 21	98.50 22

region2R		94.43 22	94.27 24	94.92 20	98.65 7	86.67 32	96.92 19	97.23 32	88.60 70	93.58 47	97.27 27	85.22 56	99.54 16	92.21 46	98.74 29	98.56 19

MP-MVS-pluss		94.21 33	94.00 37	94.85 27	98.17 34	86.65 33	94.82 118	97.17 38	86.26 127	92.83 61	97.87 10	85.57 52	99.56 7	94.37 14	98.92 13	98.34 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		94.34 26	94.21 27	94.74 37	98.39 23	86.64 34	97.60 1	97.24 30	88.53 72	92.73 66	97.23 30	85.20 57	99.32 37	92.15 49	98.83 17	98.25 50

ZD-MVS					98.15 35	86.62 35		97.07 44	83.63 180	94.19 30	96.91 48	87.57 29	99.26 43	91.99 53	98.44 51

XVS		94.45 20	94.32 20	94.85 27	98.54 12	86.60 36	96.93 17	97.19 35	90.66 23	92.85 59	97.16 37	85.02 60	99.49 23	91.99 53	98.56 47	98.47 28

X-MVStestdata		88.31 159	86.13 201	94.85 27	98.54 12	86.60 36	96.93 17	97.19 35	90.66 23	92.85 59	23.41 353	85.02 60	99.49 23	91.99 53	98.56 47	98.47 28

MSP-MVS		95.42 5	95.56 5	94.98 19	98.49 16	86.52 38	96.91 20	97.47 8	91.73 8	96.10 13	96.69 58	89.90 9	99.30 39	94.70 9	98.04 64	99.13 1

TEST9					97.53 58	86.49 39	94.07 171	96.78 70	81.61 231	92.77 63	96.20 80	87.71 26	99.12 55

train_agg		93.44 53	93.08 57	94.52 45	97.53 58	86.49 39	94.07 171	96.78 70	81.86 224	92.77 63	96.20 80	87.63 27	99.12 55	92.14 50	98.69 33	97.94 72

test_0728_SECOND				95.01 15	98.79 1	86.43 41	97.09 11	97.49 5					99.61 3	95.62 5	99.08 7	98.99 5

PHI-MVS		93.89 43	93.65 47	94.62 42	96.84 79	86.43 41	96.69 27	97.49 5	85.15 154	93.56 49	96.28 76	85.60 51	99.31 38	92.45 38	98.79 19	98.12 59

3Dnovator	86.66 5	91.73 81	90.82 92	94.44 48	94.59 161	86.37 43	97.18 7	97.02 46	89.20 52	84.31 227	96.66 61	73.74 189	99.17 50	86.74 126	97.96 67	97.79 83

Regformer-1		94.22 32	94.13 32	94.51 46	95.54 123	86.36 44	94.57 134	96.44 96	91.69 9	94.32 28	96.56 67	87.05 36	99.03 64	93.35 25	97.65 77	98.15 56

TSAR-MVS + MP.		94.85 12	94.94 10	94.58 43	98.25 29	86.33 45	96.11 49	96.62 88	88.14 85	96.10 13	96.96 46	89.09 15	98.94 83	94.48 12	98.68 35	98.48 24
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		95.20 7	95.32 8	94.85 27	96.99 76	86.33 45	97.33 3	97.30 26	91.38 11	95.39 18	97.46 17	88.98 16	99.40 28	94.12 15	98.89 14	98.82 10
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS		94.25 29	94.07 34	94.77 35	98.47 17	86.31 47	96.71 26	96.98 48	89.04 57	91.98 82	97.19 34	85.43 54	99.56 7	92.06 52	98.79 19	98.44 33
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
test_8					97.49 61	86.30 48	94.02 176	96.76 73	81.86 224	92.70 67	96.20 80	87.63 27	99.02 68

APDe-MVS		95.46 4	95.64 4	94.91 22	98.26 28	86.29 49	97.46 2	97.40 17	89.03 58	96.20 12	98.10 2	89.39 13	99.34 33	95.88 1	99.03 9	99.10 3

PGM-MVS		93.96 40	93.72 45	94.68 38	98.43 19	86.22 50	95.30 84	97.78 1	87.45 103	93.26 51	97.33 24	84.62 65	99.51 21	90.75 83	98.57 46	98.32 40

test12				94.34 53	97.13 74	86.15 51		96.29 105		91.04 103		85.08 58	99.01 70		98.13 61	97.86 79

CDPH-MVS		92.83 64	92.30 71	94.44 48	97.79 52	86.11 52	94.06 173	96.66 85	80.09 250	92.77 63	96.63 62	86.62 39	99.04 63	87.40 116	98.66 40	98.17 54

RRT_MVS		88.86 145	87.68 155	92.39 116	92.02 242	86.09 53	94.38 152	94.94 199	85.45 146	87.14 154	93.84 164	65.88 278	97.11 220	88.73 100	86.77 235	93.98 227

IU-MVS					98.77 4	86.00 54		96.84 63	81.26 238	97.26 6				95.50 7	99.13 3	99.03 4

SED-MVS		95.91 1	96.28 1	94.80 33	98.77 4	85.99 55	97.13 9	97.44 12	90.31 26	97.71 1	98.07 4	92.31 2	99.58 5	95.66 2	99.13 3	98.84 8

test_241102_ONE					98.77 4	85.99 55		97.44 12	90.26 30	97.71 1	97.96 8	92.31 2	99.38 29

test_prior4						85.96 57	94.11 166

DVP-MVS		95.67 2	96.02 2	94.64 40	98.78 2	85.93 58	97.09 11	96.73 77	90.27 28	97.04 8	98.05 6	91.47 6	99.55 12	95.62 5	99.08 7	98.45 32

test0726					98.78 2	85.93 58	97.19 6	97.47 8	90.27 28	97.64 4	98.13 1	91.47 6

agg_prior1		93.29 57	92.97 61	94.26 55	97.38 64	85.92 60	93.92 181	96.72 79	81.96 218	92.16 78	96.23 78	87.85 22	98.97 79	91.95 57	98.55 49	97.90 76

agg_prior					97.38 64	85.92 60		96.72 79		92.16 78			98.97 79

DP-MVS Recon		91.95 76	91.28 82	93.96 60	98.33 27	85.92 60	94.66 129	96.66 85	82.69 204	90.03 113	95.82 95	82.30 89	99.03 64	84.57 149	96.48 100	96.91 117

mPP-MVS		93.99 38	93.78 43	94.63 41	98.50 15	85.90 63	96.87 21	96.91 56	88.70 66	91.83 88	97.17 36	83.96 74	99.55 12	91.44 70	98.64 43	98.43 34

DeepC-MVS	88.79 3	93.31 56	92.99 60	94.26 55	96.07 105	85.83 64	94.89 113	96.99 47	89.02 59	89.56 115	97.37 23	82.51 85	99.38 29	92.20 47	98.30 57	97.57 90
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		94.23 31	94.17 30	94.43 50	98.21 33	85.78 65	96.40 34	96.90 57	88.20 83	94.33 27	97.40 21	84.75 64	99.03 64	93.35 25	97.99 65	98.48 24

HPM-MVS		94.02 37	93.88 39	94.43 50	98.39 23	85.78 65	97.25 5	97.07 44	86.90 115	92.62 70	96.80 55	84.85 63	99.17 50	92.43 39	98.65 42	98.33 39
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CANet		93.54 51	93.20 56	94.55 44	95.65 119	85.73 67	94.94 109	96.69 83	91.89 5	90.69 105	95.88 93	81.99 97	99.54 16	93.14 30	97.95 68	98.39 36

xxxxxxxxxxxxxcwj		94.65 15	94.70 14	94.48 47	97.85 48	85.63 68	95.21 92	95.47 169	89.44 44	95.71 15	97.70 11	88.28 19	99.35 31	93.89 18	98.78 21	98.48 24

save fliter					97.85 48	85.63 68	95.21 92	96.82 67	89.44 44

Regformer-4		93.91 41	93.81 41	94.19 57	95.36 127	85.47 70	94.68 126	96.41 99	91.60 10	93.75 40	96.71 56	85.95 48	99.10 57	93.21 29	96.65 94	98.01 69

OpenMVS	83.78 11	88.74 149	87.29 164	93.08 82	92.70 225	85.39 71	96.57 29	96.43 98	78.74 267	80.85 278	96.07 87	69.64 237	99.01 70	78.01 243	96.65 94	94.83 187

ACMMP		93.24 59	92.88 63	94.30 54	98.09 40	85.33 72	96.86 22	97.45 11	88.33 76	90.15 111	97.03 44	81.44 100	99.51 21	90.85 82	95.74 106	98.04 66
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
EPNet		91.79 78	91.02 88	94.10 58	90.10 306	85.25 73	96.03 53	92.05 273	92.83 1	87.39 151	95.78 96	79.39 122	99.01 70	88.13 108	97.48 79	98.05 65
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		93.43 54	93.25 53	93.97 59	95.42 126	85.04 74	93.06 218	97.13 39	90.74 20	91.84 86	95.09 115	86.32 43	99.21 47	91.22 73	98.45 50	97.65 85
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
MVS_111021_HR		93.45 52	93.31 52	93.84 63	96.99 76	84.84 75	93.24 211	97.24 30	88.76 64	91.60 93	95.85 94	86.07 47	98.66 101	91.91 58	98.16 60	98.03 67

HPM-MVS_fast		93.40 55	93.22 54	93.94 61	98.36 25	84.83 76	97.15 8	96.80 69	85.77 136	92.47 74	97.13 38	82.38 86	99.07 58	90.51 85	98.40 53	97.92 75

CNLPA		89.07 139	87.98 149	92.34 118	96.87 78	84.78 77	94.08 170	93.24 250	81.41 234	84.46 217	95.13 114	75.57 162	96.62 244	77.21 250	93.84 137	95.61 161

UA-Net		92.83 64	92.54 68	93.68 70	96.10 103	84.71 78	95.66 70	96.39 101	91.92 4	93.22 53	96.49 69	83.16 78	98.87 87	84.47 151	95.47 111	97.45 95

Regformer-3		93.68 47	93.64 48	93.81 67	95.36 127	84.61 79	94.68 126	95.83 142	91.27 12	93.60 46	96.71 56	85.75 50	98.86 90	92.87 32	96.65 94	97.96 71

QAPM		89.51 125	88.15 146	93.59 72	94.92 146	84.58 80	96.82 24	96.70 81	78.43 270	83.41 248	96.19 83	73.18 197	99.30 39	77.11 252	96.54 97	96.89 118

SR-MVS-dyc-post		93.82 44	93.82 40	93.82 64	97.92 45	84.57 81	96.28 38	96.76 73	87.46 101	93.75 40	97.43 18	84.24 69	99.01 70	92.73 34	97.80 72	97.88 77

RE-MVS-def			93.68 46		97.92 45	84.57 81	96.28 38	96.76 73	87.46 101	93.75 40	97.43 18	82.94 80		92.73 34	97.80 72	97.88 77

API-MVS		90.66 100	90.07 102	92.45 112	96.36 94	84.57 81	96.06 52	95.22 188	82.39 207	89.13 121	94.27 145	80.32 108	98.46 115	80.16 220	96.71 92	94.33 210

UniMVSNet (Re)		89.80 119	89.07 123	92.01 127	93.60 200	84.52 84	94.78 121	97.47 8	89.26 50	86.44 169	92.32 209	82.10 93	97.39 199	84.81 146	80.84 295	94.12 218

test_prior3		93.60 50	93.53 49	93.82 64	97.29 69	84.49 85	94.12 164	96.88 59	87.67 97	92.63 68	96.39 72	86.62 39	98.87 87	91.50 68	98.67 37	98.11 61

test_prior				93.82 64	97.29 69	84.49 85		96.88 59					98.87 87			98.11 61

MAR-MVS		90.30 107	89.37 116	93.07 84	96.61 85	84.48 87	95.68 68	95.67 153	82.36 209	87.85 140	92.85 192	76.63 150	98.80 97	80.01 221	96.68 93	95.91 149
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
xiu_mvs_v1_base_debu		90.64 101	90.05 103	92.40 113	93.97 187	84.46 88	93.32 201	95.46 170	85.17 151	92.25 75	94.03 148	70.59 223	98.57 108	90.97 76	94.67 122	94.18 213

xiu_mvs_v1_base		90.64 101	90.05 103	92.40 113	93.97 187	84.46 88	93.32 201	95.46 170	85.17 151	92.25 75	94.03 148	70.59 223	98.57 108	90.97 76	94.67 122	94.18 213

xiu_mvs_v1_base_debi		90.64 101	90.05 103	92.40 113	93.97 187	84.46 88	93.32 201	95.46 170	85.17 151	92.25 75	94.03 148	70.59 223	98.57 108	90.97 76	94.67 122	94.18 213

1121		90.42 106	89.49 112	93.20 77	97.27 71	84.46 88	92.63 228	95.51 167	71.01 330	91.20 101	96.21 79	82.92 81	99.05 60	80.56 213	98.07 63	96.10 142

MVS_111021_LR		92.47 71	92.29 72	92.98 87	95.99 108	84.43 92	93.08 216	96.09 120	88.20 83	91.12 102	95.72 99	81.33 102	97.76 163	91.74 63	97.37 82	96.75 121

PCF-MVS	84.11 10	87.74 174	86.08 205	92.70 101	94.02 181	84.43 92	89.27 294	95.87 139	73.62 312	84.43 219	94.33 139	78.48 134	98.86 90	70.27 293	94.45 130	94.81 188
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
新几何1				93.10 81	97.30 68	84.35 94		95.56 162	71.09 329	91.26 100	96.24 77	82.87 82	98.86 90	79.19 232	98.10 62	96.07 144

abl_6		93.18 61	93.05 58	93.57 73	97.52 60	84.27 95	95.53 76	96.67 84	87.85 91	93.20 54	97.22 31	80.35 107	99.18 49	91.91 58	97.21 83	97.26 100

APD-MVS_3200maxsize		93.78 45	93.77 44	93.80 68	97.92 45	84.19 96	96.30 36	96.87 61	86.96 111	93.92 37	97.47 16	83.88 75	98.96 82	92.71 37	97.87 70	98.26 49

NR-MVSNet		88.58 154	87.47 160	91.93 134	93.04 216	84.16 97	94.77 122	96.25 109	89.05 56	80.04 292	93.29 178	79.02 125	97.05 226	81.71 196	80.05 305	94.59 196

CSCG		93.23 60	93.05 58	93.76 69	98.04 42	84.07 98	96.22 42	97.37 18	84.15 170	90.05 112	95.66 100	87.77 23	99.15 53	89.91 88	98.27 58	98.07 63

OMC-MVS		91.23 89	90.62 94	93.08 82	96.27 96	84.07 98	93.52 196	95.93 132	86.95 112	89.51 116	96.13 86	78.50 133	98.35 124	85.84 135	92.90 156	96.83 119

ETV-MVS		92.74 66	92.66 65	92.97 88	95.20 135	84.04 100	95.07 101	96.51 94	90.73 21	92.96 58	91.19 249	84.06 72	98.34 125	91.72 64	96.54 97	96.54 128

ET-MVSNet_ETH3D		87.51 187	85.91 212	92.32 119	93.70 198	83.93 101	92.33 238	90.94 304	84.16 169	72.09 331	92.52 203	69.90 232	95.85 286	89.20 95	88.36 214	97.17 105

OPM-MVS		90.12 110	89.56 111	91.82 140	93.14 211	83.90 102	94.16 162	95.74 149	88.96 60	87.86 139	95.43 105	72.48 205	97.91 158	88.10 109	90.18 183	93.65 248

MVSFormer		91.68 83	91.30 81	92.80 94	93.86 190	83.88 103	95.96 57	95.90 136	84.66 164	91.76 89	94.91 119	77.92 138	97.30 202	89.64 91	97.11 84	97.24 101

lupinMVS		90.92 94	90.21 98	93.03 85	93.86 190	83.88 103	92.81 224	93.86 240	79.84 253	91.76 89	94.29 142	77.92 138	98.04 148	90.48 86	97.11 84	97.17 105

Vis-MVSNet		91.75 80	91.23 83	93.29 74	95.32 130	83.78 105	96.14 46	95.98 128	89.89 35	90.45 107	96.58 65	75.09 166	98.31 129	84.75 147	96.90 88	97.78 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet_NR-MVSNet		89.92 117	89.29 118	91.81 142	93.39 205	83.72 106	94.43 144	97.12 40	89.80 37	86.46 166	93.32 175	83.16 78	97.23 212	84.92 143	81.02 291	94.49 205

DU-MVS		89.34 135	88.50 135	91.85 139	93.04 216	83.72 106	94.47 141	96.59 90	89.50 43	86.46 166	93.29 178	77.25 142	97.23 212	84.92 143	81.02 291	94.59 196

FMVSNet2		87.19 202	85.82 214	91.30 158	94.01 182	83.67 108	94.79 120	94.94 199	83.57 181	83.88 235	92.05 224	66.59 270	96.51 255	77.56 247	85.01 245	93.73 245

FMVSNet3		87.40 192	86.11 203	91.30 158	93.79 195	83.64 109	94.20 161	94.81 212	83.89 175	84.37 220	91.87 229	68.45 255	96.56 252	78.23 240	85.36 242	93.70 247

MVS		87.44 190	86.10 204	91.44 154	92.61 228	83.62 110	92.63 228	95.66 155	67.26 336	81.47 270	92.15 215	77.95 137	98.22 133	79.71 224	95.48 110	92.47 288

CDS-MVSNet		89.45 128	88.51 134	92.29 122	93.62 199	83.61 111	93.01 219	94.68 217	81.95 219	87.82 142	93.24 180	78.69 129	96.99 229	80.34 217	93.23 150	96.28 133
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
jason		90.80 95	90.10 101	92.90 91	93.04 216	83.53 112	93.08 216	94.15 232	80.22 247	91.41 97	94.91 119	76.87 144	97.93 157	90.28 87	96.90 88	97.24 101
jason: jason.
EI-MVSNet-Vis-set		93.01 63	92.92 62	93.29 74	95.01 139	83.51 113	94.48 138	95.77 146	90.87 15	92.52 72	96.67 60	84.50 66	99.00 75	91.99 53	94.44 131	97.36 96

test1172		93.97 39	94.07 34	93.66 71	98.11 37	83.45 114	96.26 40	96.84 63	88.33 76	94.19 30	97.43 18	84.24 69	99.01 70	93.26 27	97.98 66	98.52 20

MSLP-MVS++		93.72 46	94.08 33	92.65 103	97.31 67	83.43 115	95.79 63	97.33 22	90.03 33	93.58 47	96.96 46	84.87 62	97.76 163	92.19 48	98.66 40	96.76 120

VNet		92.24 74	91.91 75	93.24 76	96.59 86	83.43 115	94.84 117	96.44 96	89.19 53	94.08 34	95.90 92	77.85 141	98.17 135	88.90 98	93.38 146	98.13 58

Effi-MVS+		91.59 84	91.11 85	93.01 86	94.35 174	83.39 117	94.60 131	95.10 193	87.10 108	90.57 106	93.10 186	81.43 101	98.07 146	89.29 94	94.48 129	97.59 89

UGNet		89.95 115	88.95 126	92.95 89	94.51 164	83.31 118	95.70 67	95.23 186	89.37 48	87.58 146	93.94 156	64.00 286	98.78 98	83.92 157	96.31 102	96.74 122
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
DP-MVS		87.25 197	85.36 226	92.90 91	97.65 56	83.24 119	94.81 119	92.00 275	74.99 300	81.92 268	95.00 117	72.66 202	99.05 60	66.92 315	92.33 164	96.40 129

EI-MVSNet-UG-set		92.74 66	92.62 66	93.12 80	94.86 150	83.20 120	94.40 146	95.74 149	90.71 22	92.05 81	96.60 64	84.00 73	98.99 76	91.55 66	93.63 139	97.17 105

PVSNet_Blended_VisFu		91.38 86	90.91 90	92.80 94	96.39 93	83.17 121	94.87 115	96.66 85	83.29 190	89.27 120	94.46 137	80.29 109	99.17 50	87.57 114	95.37 114	96.05 146

GBi-Net		87.26 195	85.98 208	91.08 167	94.01 182	83.10 122	95.14 98	94.94 199	83.57 181	84.37 220	91.64 233	66.59 270	96.34 267	78.23 240	85.36 242	93.79 238

test1		87.26 195	85.98 208	91.08 167	94.01 182	83.10 122	95.14 98	94.94 199	83.57 181	84.37 220	91.64 233	66.59 270	96.34 267	78.23 240	85.36 242	93.79 238

FMVSNet1		85.85 234	84.11 247	91.08 167	92.81 223	83.10 122	95.14 98	94.94 199	81.64 229	82.68 257	91.64 233	59.01 317	96.34 267	75.37 267	83.78 253	93.79 238

AdaColmap		89.89 118	89.07 123	92.37 117	97.41 63	83.03 125	94.42 145	95.92 133	82.81 202	86.34 171	94.65 131	73.89 185	99.02 68	80.69 210	95.51 109	95.05 175

VDD-MVS		90.74 96	89.92 108	93.20 77	96.27 96	83.02 126	95.73 65	93.86 240	88.42 75	92.53 71	96.84 50	62.09 295	98.64 103	90.95 79	92.62 160	97.93 74

CANet_DTU		90.26 109	89.41 115	92.81 93	93.46 204	83.01 127	93.48 197	94.47 220	89.43 46	87.76 144	94.23 146	70.54 227	99.03 64	84.97 142	96.39 101	96.38 130

TranMVSNet+NR-MVSNet		88.84 146	87.95 150	91.49 150	92.68 226	83.01 127	94.92 111	96.31 104	89.88 36	85.53 186	93.85 163	76.63 150	96.96 230	81.91 189	79.87 308	94.50 203

pmmvs4		85.43 240	83.86 251	90.16 202	90.02 309	82.97 129	90.27 277	92.67 261	75.93 292	80.73 279	91.74 232	71.05 216	95.73 292	78.85 234	83.46 260	91.78 301

LS3D		87.89 169	86.32 196	92.59 105	96.07 105	82.92 130	95.23 90	94.92 204	75.66 293	82.89 255	95.98 89	72.48 205	99.21 47	68.43 307	95.23 119	95.64 160

VPA-MVSNet		89.62 121	88.96 125	91.60 148	93.86 190	82.89 131	95.46 77	97.33 22	87.91 88	88.43 131	93.31 176	74.17 180	97.40 196	87.32 119	82.86 268	94.52 201

test_part1		86.16 228	84.40 244	91.46 153	92.63 227	82.80 132	96.42 32	96.05 125	73.47 313	82.06 264	91.43 242	63.89 288	97.43 187	84.51 150	79.11 314	94.14 216

HY-MVS	83.01 12	89.03 141	87.94 151	92.29 122	94.86 150	82.77 133	92.08 248	94.49 219	81.52 233	86.93 157	92.79 198	78.32 136	98.23 131	79.93 222	90.55 178	95.88 151

plane_prior6					94.52 163	82.75 134						74.23 177

plane_prior3						82.75 134			90.26 30	86.91 159

plane_prior7					94.70 157	82.74 136

HQP_MVS		90.60 104	90.19 99	91.82 140	94.70 157	82.73 137	95.85 61	96.22 112	90.81 17	86.91 159	94.86 122	74.23 177	98.12 136	88.15 106	89.99 184	94.63 192

plane_prior						82.73 137	95.21 92		89.66 41						89.88 189

PatchMatch-RL		86.77 215	85.54 220	90.47 191	95.88 111	82.71 139	90.54 274	92.31 266	79.82 254	84.32 225	91.57 240	68.77 251	96.39 263	73.16 283	93.48 144	92.32 294

PLC	84.53 7	89.06 140	88.03 148	92.15 125	97.27 71	82.69 140	94.29 156	95.44 175	79.71 255	84.01 233	94.18 147	76.68 149	98.75 99	77.28 249	93.41 145	95.02 176
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ab-mvs		89.41 131	88.35 139	92.60 104	95.15 137	82.65 141	92.20 243	95.60 160	83.97 174	88.55 128	93.70 170	74.16 181	98.21 134	82.46 179	89.37 196	96.94 115

TAMVS		89.21 136	88.29 143	91.96 132	93.71 196	82.62 142	93.30 205	94.19 230	82.22 211	87.78 143	93.94 156	78.83 126	96.95 231	77.70 245	92.98 155	96.32 131

PS-MVSNAJ		91.18 91	90.92 89	91.96 132	95.26 133	82.60 143	92.09 247	95.70 151	86.27 126	91.84 86	92.46 204	79.70 117	98.99 76	89.08 96	95.86 105	94.29 211

xiu_mvs_v2_base		91.13 92	90.89 91	91.86 137	94.97 142	82.42 144	92.24 241	95.64 158	86.11 132	91.74 91	93.14 184	79.67 120	98.89 86	89.06 97	95.46 112	94.28 212

NP-MVS					94.37 171	82.42 144					93.98 154

test_yl		90.69 98	90.02 106	92.71 99	95.72 116	82.41 146	94.11 166	95.12 191	85.63 140	91.49 94	94.70 127	74.75 170	98.42 120	86.13 133	92.53 161	97.31 98

DCV-MVSNet		90.69 98	90.02 106	92.71 99	95.72 116	82.41 146	94.11 166	95.12 191	85.63 140	91.49 94	94.70 127	74.75 170	98.42 120	86.13 133	92.53 161	97.31 98

CS-MVS		92.60 68	92.56 67	92.73 98	95.55 122	82.35 148	96.14 46	96.85 62	88.71 65	91.44 96	91.51 241	84.13 71	98.48 112	91.27 72	97.47 80	97.34 97

LFMVS		90.08 111	89.13 122	92.95 89	96.71 82	82.32 149	96.08 50	89.91 322	86.79 116	92.15 80	96.81 53	62.60 292	98.34 125	87.18 120	93.90 135	98.19 53

MVP-Stereo		85.97 231	84.86 236	89.32 233	90.92 284	82.19 150	92.11 246	94.19 230	78.76 266	78.77 301	91.63 236	68.38 256	96.56 252	75.01 272	93.95 134	89.20 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
VDDNet		89.56 124	88.49 137	92.76 96	95.07 138	82.09 151	96.30 36	93.19 251	81.05 242	91.88 84	96.86 49	61.16 305	98.33 127	88.43 104	92.49 163	97.84 80

CLD-MVS		89.47 127	88.90 128	91.18 162	94.22 175	82.07 152	92.13 245	96.09 120	87.90 89	85.37 201	92.45 205	74.38 175	97.56 176	87.15 121	90.43 179	93.93 228
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
114514_t		89.51 125	88.50 135	92.54 108	98.11 37	81.99 153	95.16 97	96.36 103	70.19 332	85.81 178	95.25 109	76.70 148	98.63 104	82.07 185	96.86 90	97.00 113

casdiffmvs		92.51 70	92.43 70	92.74 97	94.41 170	81.98 154	94.54 136	96.23 111	89.57 42	91.96 83	96.17 84	82.58 84	98.01 150	90.95 79	95.45 113	98.23 51

CPTT-MVS		91.99 75	91.80 76	92.55 107	98.24 31	81.98 154	96.76 25	96.49 95	81.89 223	90.24 109	96.44 71	78.59 131	98.61 106	89.68 90	97.85 71	97.06 109

Anonymous20240529		88.09 165	86.59 186	92.58 106	96.53 89	81.92 156	95.99 54	95.84 141	74.11 308	89.06 124	95.21 111	61.44 300	98.81 96	83.67 162	87.47 225	97.01 112

旧先验1					96.79 80	81.81 157		95.67 153			96.81 53	86.69 38			97.66 76	96.97 114

baseline		92.39 73	92.29 72	92.69 102	94.46 167	81.77 158	94.14 163	96.27 106	89.22 51	91.88 84	96.00 88	82.35 87	97.99 152	91.05 75	95.27 118	98.30 41

test222					96.55 88	81.70 159	92.22 242	95.01 196	68.36 335	90.20 110	96.14 85	80.26 110			97.80 72	96.05 146

HQP5-MVS						81.56 160

HQP-MVS		89.80 119	89.28 119	91.34 157	94.17 176	81.56 160	94.39 148	96.04 126	88.81 61	85.43 195	93.97 155	73.83 187	97.96 154	87.11 123	89.77 191	94.50 203

Anonymous20231211		86.59 219	85.13 229	90.98 176	96.52 90	81.50 162	96.14 46	96.16 116	73.78 310	83.65 242	92.15 215	63.26 290	97.37 200	82.82 173	81.74 281	94.06 223

LTVRE_ROB	82.13 13	86.26 227	84.90 235	90.34 198	94.44 169	81.50 162	92.31 240	94.89 205	83.03 195	79.63 296	92.67 199	69.69 236	97.79 161	71.20 289	86.26 236	91.72 302
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
LPG-MVS_test		89.45 128	88.90 128	91.12 163	94.47 165	81.49 164	95.30 84	96.14 117	86.73 118	85.45 192	95.16 112	69.89 233	98.10 138	87.70 112	89.23 200	93.77 242

LGP-MVS_train				91.12 163	94.47 165	81.49 164		96.14 117	86.73 118	85.45 192	95.16 112	69.89 233	98.10 138	87.70 112	89.23 200	93.77 242

XVG-OURS		89.40 133	88.70 131	91.52 149	94.06 179	81.46 166	91.27 264	96.07 122	86.14 130	88.89 126	95.77 97	68.73 252	97.26 208	87.39 117	89.96 186	95.83 154

PAPM_NR		91.22 90	90.78 93	92.52 109	97.60 57	81.46 166	94.37 153	96.24 110	86.39 125	87.41 148	94.80 126	82.06 95	98.48 112	82.80 174	95.37 114	97.61 87

CHOSEN 1792x2688		88.84 146	87.69 154	92.30 121	96.14 99	81.42 168	90.01 284	95.86 140	74.52 305	87.41 148	93.94 156	75.46 163	98.36 122	80.36 216	95.53 108	97.12 108

IS-MVSNet		91.43 85	91.09 87	92.46 111	95.87 113	81.38 169	96.95 14	93.69 245	89.72 40	89.50 117	95.98 89	78.57 132	97.77 162	83.02 168	96.50 99	98.22 52

ACMP	84.23 8	89.01 143	88.35 139	90.99 174	94.73 154	81.27 170	95.07 101	95.89 138	86.48 122	83.67 241	94.30 141	69.33 241	97.99 152	87.10 125	88.55 207	93.72 246
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PVSNet_BlendedMVS		89.98 113	89.70 109	90.82 178	96.12 100	81.25 171	93.92 181	96.83 65	83.49 185	89.10 122	92.26 212	81.04 104	98.85 93	86.72 129	87.86 223	92.35 293

PVSNet_Blended		90.73 97	90.32 97	91.98 130	96.12 100	81.25 171	92.55 232	96.83 65	82.04 216	89.10 122	92.56 202	81.04 104	98.85 93	86.72 129	95.91 104	95.84 153

ACMM	84.12 9	89.14 137	88.48 138	91.12 163	94.65 160	81.22 173	95.31 81	96.12 119	85.31 150	85.92 177	94.34 138	70.19 231	98.06 147	85.65 136	88.86 205	94.08 222
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-OURS-SEG-HR		89.95 115	89.45 113	91.47 152	94.00 185	81.21 174	91.87 250	96.06 124	85.78 135	88.55 128	95.73 98	74.67 173	97.27 206	88.71 101	89.64 193	95.91 149

WTY-MVS		89.60 122	88.92 127	91.67 146	95.47 125	81.15 175	92.38 236	94.78 214	83.11 193	89.06 124	94.32 140	78.67 130	96.61 247	81.57 197	90.89 177	97.24 101

AUN-MVS		87.78 173	86.54 188	91.48 151	94.82 152	81.05 176	93.91 184	93.93 238	83.00 196	86.93 157	93.53 171	69.50 239	97.67 169	86.14 132	77.12 323	95.73 158

原ACMM1				92.01 127	97.34 66	81.05 176		96.81 68	78.89 262	90.45 107	95.92 91	82.65 83	98.84 95	80.68 211	98.26 59	96.14 137

FIs		90.51 105	90.35 96	90.99 174	93.99 186	80.98 178	95.73 65	97.54 3	89.15 54	86.72 163	94.68 129	81.83 99	97.24 210	85.18 140	88.31 215	94.76 190

1112_ss		88.42 155	87.33 163	91.72 144	94.92 146	80.98 178	92.97 221	94.54 218	78.16 275	83.82 237	93.88 161	78.78 128	97.91 158	79.45 227	89.41 195	96.26 134

PAPR		90.02 112	89.27 120	92.29 122	95.78 114	80.95 180	92.68 226	96.22 112	81.91 221	86.66 164	93.75 169	82.23 90	98.44 119	79.40 231	94.79 121	97.48 93

cascas		86.43 225	84.98 232	90.80 179	92.10 239	80.92 181	90.24 278	95.91 135	73.10 317	83.57 245	88.39 302	65.15 281	97.46 183	84.90 145	91.43 169	94.03 225

F-COLMAP		87.95 168	86.80 176	91.40 155	96.35 95	80.88 182	94.73 124	95.45 173	79.65 256	82.04 266	94.61 132	71.13 215	98.50 111	76.24 260	91.05 175	94.80 189

PS-MVSNAJss		89.97 114	89.62 110	91.02 171	91.90 245	80.85 183	95.26 89	95.98 128	86.26 127	86.21 173	94.29 142	79.70 117	97.65 170	88.87 99	88.10 217	94.57 198

Fast-Effi-MVS+		89.41 131	88.64 132	91.71 145	94.74 153	80.81 184	93.54 195	95.10 193	83.11 193	86.82 162	90.67 266	79.74 116	97.75 166	80.51 215	93.55 140	96.57 126

sss		88.93 144	88.26 145	90.94 177	94.05 180	80.78 185	91.71 255	95.38 179	81.55 232	88.63 127	93.91 160	75.04 167	95.47 302	82.47 178	91.61 168	96.57 126

Anonymous202405211		87.68 175	86.13 201	92.31 120	96.66 83	80.74 186	94.87 115	91.49 290	80.47 246	89.46 118	95.44 103	54.72 329	98.23 131	82.19 183	89.89 188	97.97 70

TAPA-MVS	84.62 6	88.16 163	87.01 171	91.62 147	96.64 84	80.65 187	94.39 148	96.21 115	76.38 286	86.19 174	95.44 103	79.75 115	98.08 145	62.75 330	95.29 116	96.13 138
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
HyFIR lowres test		88.09 165	86.81 175	91.93 134	96.00 107	80.63 188	90.01 284	95.79 145	73.42 314	87.68 145	92.10 220	73.86 186	97.96 154	80.75 209	91.70 167	97.19 104

ACMH	80.38 17	85.36 241	83.68 253	90.39 193	94.45 168	80.63 188	94.73 124	94.85 208	82.09 213	77.24 309	92.65 200	60.01 312	97.58 174	72.25 286	84.87 246	92.96 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XXY-MVS		87.65 177	86.85 174	90.03 209	92.14 236	80.60 190	93.76 187	95.23 186	82.94 198	84.60 212	94.02 151	74.27 176	95.49 301	81.04 202	83.68 256	94.01 226

anonymousdsp		87.84 170	87.09 168	90.12 205	89.13 316	80.54 191	94.67 128	95.55 163	82.05 214	83.82 237	92.12 217	71.47 213	97.15 216	87.15 121	87.80 224	92.67 282

testing_2		83.40 269	81.02 276	90.56 184	85.06 338	80.51 192	91.37 262	95.57 161	82.92 199	67.06 339	85.54 326	49.47 339	97.24 210	86.74 126	85.44 241	93.93 228

EPP-MVSNet		91.70 82	91.56 79	92.13 126	95.88 111	80.50 193	97.33 3	95.25 185	86.15 129	89.76 114	95.60 101	83.42 77	98.32 128	87.37 118	93.25 149	97.56 91

MVSTER		88.84 146	88.29 143	90.51 188	92.95 221	80.44 194	93.73 188	95.01 196	84.66 164	87.15 152	93.12 185	72.79 201	97.21 214	87.86 110	87.36 228	93.87 233

diffmvs		91.37 87	91.23 83	91.77 143	93.09 213	80.27 195	92.36 237	95.52 166	87.03 110	91.40 98	94.93 118	80.08 111	97.44 186	92.13 51	94.56 127	97.61 87

pm-mvs1		86.61 217	85.54 220	89.82 217	91.44 258	80.18 196	95.28 88	94.85 208	83.84 176	81.66 269	92.62 201	72.45 207	96.48 257	79.67 225	78.06 317	92.82 280

WR-MVS		88.38 156	87.67 156	90.52 187	93.30 208	80.18 196	93.26 208	95.96 130	88.57 71	85.47 191	92.81 196	76.12 152	96.91 234	81.24 200	82.29 271	94.47 208

jajsoiax		88.24 161	87.50 158	90.48 190	90.89 286	80.14 198	95.31 81	95.65 157	84.97 158	84.24 229	94.02 151	65.31 280	97.42 189	88.56 102	88.52 209	93.89 230

V42		87.68 175	86.86 173	90.15 203	90.58 297	80.14 198	94.24 159	95.28 184	83.66 179	85.67 181	91.33 244	74.73 172	97.41 194	84.43 152	81.83 278	92.89 277

MVS_Test		91.31 88	91.11 85	91.93 134	94.37 171	80.14 198	93.46 199	95.80 144	86.46 123	91.35 99	93.77 167	82.21 91	98.09 144	87.57 114	94.95 120	97.55 92

thisisatest0530		88.67 150	87.61 157	91.86 137	94.87 149	80.07 201	94.63 130	89.90 323	84.00 173	88.46 130	93.78 166	66.88 265	98.46 115	83.30 164	92.65 159	97.06 109

baseline1		88.10 164	87.28 165	90.57 182	94.96 143	80.07 201	94.27 157	91.29 295	86.74 117	87.41 148	94.00 153	76.77 147	96.20 271	80.77 208	79.31 313	95.44 165

tfpnnormal		84.72 255	83.23 259	89.20 236	92.79 224	80.05 203	94.48 138	95.81 143	82.38 208	81.08 276	91.21 248	69.01 248	96.95 231	61.69 332	80.59 298	90.58 322

MSDG		84.86 253	83.09 260	90.14 204	93.80 193	80.05 203	89.18 297	93.09 252	78.89 262	78.19 302	91.91 227	65.86 279	97.27 206	68.47 306	88.45 211	93.11 269

MG-MVS		91.77 79	91.70 78	92.00 129	97.08 75	80.03 205	93.60 194	95.18 189	87.85 91	90.89 104	96.47 70	82.06 95	98.36 122	85.07 141	97.04 87	97.62 86

EIA-MVS		91.95 76	91.94 74	91.98 130	95.16 136	80.01 206	95.36 78	96.73 77	88.44 73	89.34 119	92.16 214	83.82 76	98.45 118	89.35 93	97.06 86	97.48 93

DeepPCF-MVS	89.96 1	94.20 35	94.77 13	92.49 110	96.52 90	80.00 207	94.00 178	97.08 43	90.05 32	95.65 17	97.29 26	89.66 10	98.97 79	93.95 16	98.71 30	98.50 22

pmmvs-eth3d		80.97 292	78.72 299	87.74 271	84.99 339	79.97 208	90.11 283	91.65 285	75.36 295	73.51 326	86.03 323	59.45 315	93.96 319	75.17 269	72.21 331	89.29 327

mvs_tets		88.06 167	87.28 165	90.38 195	90.94 282	79.88 209	95.22 91	95.66 155	85.10 155	84.21 230	93.94 156	63.53 289	97.40 196	88.50 103	88.40 213	93.87 233

IB-MVS	80.51 15	85.24 246	83.26 258	91.19 161	92.13 237	79.86 210	91.75 253	91.29 295	83.28 191	80.66 281	88.49 301	61.28 301	98.46 115	80.99 205	79.46 311	95.25 171
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
FC-MVSNet-test		90.27 108	90.18 100	90.53 185	93.71 196	79.85 211	95.77 64	97.59 2	89.31 49	86.27 172	94.67 130	81.93 98	97.01 228	84.26 153	88.09 219	94.71 191

COLMAP_ROB	80.39 16	83.96 261	82.04 268	89.74 221	95.28 131	79.75 212	94.25 158	92.28 267	75.17 298	78.02 305	93.77 167	58.60 318	97.84 160	65.06 323	85.92 237	91.63 304
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
1314		87.51 187	86.57 187	90.34 198	92.42 231	79.74 213	92.63 228	95.35 183	78.35 271	80.14 289	91.62 237	74.05 182	97.15 216	81.05 201	93.53 141	94.12 218

thisisatest0515		87.33 193	85.99 207	91.37 156	93.49 202	79.55 214	90.63 273	89.56 329	80.17 248	87.56 147	90.86 260	67.07 262	98.28 130	81.50 198	93.02 154	96.29 132

v10		87.25 197	86.38 191	89.85 215	91.19 270	79.50 215	94.48 138	95.45 173	83.79 177	83.62 243	91.19 249	75.13 165	97.42 189	81.94 188	80.60 297	92.63 284

VPNet		88.20 162	87.47 160	90.39 193	93.56 201	79.46 216	94.04 174	95.54 165	88.67 67	86.96 156	94.58 135	69.33 241	97.15 216	84.05 156	80.53 300	94.56 199

BH-RMVSNet		88.37 157	87.48 159	91.02 171	95.28 131	79.45 217	92.89 223	93.07 253	85.45 146	86.91 159	94.84 125	70.35 228	97.76 163	73.97 278	94.59 126	95.85 152

v8		87.50 189	86.71 179	89.89 214	91.37 264	79.40 218	94.50 137	95.38 179	84.81 161	83.60 244	91.33 244	76.05 153	97.42 189	82.84 172	80.51 302	92.84 279

ACMH+	81.04 14	85.05 249	83.46 257	89.82 217	94.66 159	79.37 219	94.44 143	94.12 235	82.19 212	78.04 304	92.82 195	58.23 319	97.54 177	73.77 280	82.90 267	92.54 285

EG-PatchMatch MVS		82.37 276	80.34 281	88.46 255	90.27 303	79.35 220	92.80 225	94.33 225	77.14 282	73.26 328	90.18 274	47.47 344	96.72 239	70.25 294	87.32 230	89.30 326

v1144		87.61 183	86.79 177	90.06 208	91.01 277	79.34 221	93.95 180	95.42 178	83.36 189	85.66 182	91.31 247	74.98 168	97.42 189	83.37 163	82.06 274	93.42 257

CR-MVSNet		85.35 242	83.76 252	90.12 205	90.58 297	79.34 221	85.24 328	91.96 279	78.27 272	85.55 184	87.87 312	71.03 217	95.61 293	73.96 279	89.36 197	95.40 167

RPMNet		83.95 262	81.53 272	91.21 160	90.58 297	79.34 221	85.24 328	96.76 73	71.44 327	85.55 184	82.97 334	70.87 219	98.91 85	61.01 334	89.36 197	95.40 167

PAPM		86.68 216	85.39 224	90.53 185	93.05 215	79.33 224	89.79 287	94.77 215	78.82 264	81.95 267	93.24 180	76.81 145	97.30 202	66.94 313	93.16 151	94.95 183

test_djsdf		89.03 141	88.64 132	90.21 200	90.74 292	79.28 225	95.96 57	95.90 136	84.66 164	85.33 203	92.94 190	74.02 183	97.30 202	89.64 91	88.53 208	94.05 224

Test_1112_low_res		87.65 177	86.51 189	91.08 167	94.94 145	79.28 225	91.77 252	94.30 226	76.04 291	83.51 246	92.37 207	77.86 140	97.73 167	78.69 236	89.13 202	96.22 135

v7n		86.81 210	85.76 218	89.95 213	90.72 293	79.25 227	95.07 101	95.92 133	84.45 167	82.29 260	90.86 260	72.60 204	97.53 178	79.42 230	80.52 301	93.08 271

v2v482		87.84 170	87.06 169	90.17 201	90.99 278	79.23 228	94.00 178	95.13 190	84.87 159	85.53 186	92.07 223	74.45 174	97.45 184	84.71 148	81.75 280	93.85 236

v1192		87.25 197	86.33 195	90.00 212	90.76 291	79.04 229	93.80 185	95.48 168	82.57 206	85.48 190	91.18 251	73.38 196	97.42 189	82.30 181	82.06 274	93.53 251

UniMVSNet_ETH3D		87.53 186	86.37 192	91.00 173	92.44 230	78.96 230	94.74 123	95.61 159	84.07 172	85.36 202	94.52 136	59.78 314	97.34 201	82.93 169	87.88 222	96.71 123

thres600view7		87.65 177	86.67 181	90.59 181	96.08 104	78.72 231	94.88 114	91.58 286	87.06 109	88.08 135	92.30 210	68.91 249	98.10 138	70.05 300	91.10 171	94.96 180

GA-MVS		86.61 217	85.27 227	90.66 180	91.33 267	78.71 232	90.40 276	93.81 243	85.34 149	85.12 205	89.57 287	61.25 302	97.11 220	80.99 205	89.59 194	96.15 136

tfpn200view9		87.58 184	86.64 182	90.41 192	95.99 108	78.64 233	94.58 132	91.98 277	86.94 113	88.09 133	91.77 230	69.18 246	98.10 138	70.13 297	91.10 171	94.48 206

thres400		87.62 182	86.64 182	90.57 182	95.99 108	78.64 233	94.58 132	91.98 277	86.94 113	88.09 133	91.77 230	69.18 246	98.10 138	70.13 297	91.10 171	94.96 180

thres100view900		87.63 180	86.71 179	90.38 195	96.12 100	78.55 235	95.03 105	91.58 286	87.15 106	88.06 136	92.29 211	68.91 249	98.10 138	70.13 297	91.10 171	94.48 206

thres200		87.21 201	86.24 199	90.12 205	95.36 127	78.53 236	93.26 208	92.10 271	86.42 124	88.00 138	91.11 255	69.24 245	98.00 151	69.58 301	91.04 176	93.83 237

MS-PatchMatch		85.05 249	84.16 246	87.73 272	91.42 262	78.51 237	91.25 265	93.53 246	77.50 277	80.15 288	91.58 238	61.99 296	95.51 298	75.69 264	94.35 132	89.16 329

BH-untuned		88.60 153	88.13 147	90.01 211	95.24 134	78.50 238	93.29 206	94.15 232	84.75 162	84.46 217	93.40 172	75.76 157	97.40 196	77.59 246	94.52 128	94.12 218

TransMVSNet (Re)		84.43 258	83.06 261	88.54 253	91.72 251	78.44 239	95.18 95	92.82 257	82.73 203	79.67 295	92.12 217	73.49 191	95.96 281	71.10 292	68.73 338	91.21 313

TR-MVS		86.78 212	85.76 218	89.82 217	94.37 171	78.41 240	92.47 233	92.83 256	81.11 241	86.36 170	92.40 206	68.73 252	97.48 181	73.75 281	89.85 190	93.57 250

CHOSEN 280x420		85.15 247	83.99 249	88.65 251	92.47 229	78.40 241	79.68 343	92.76 258	74.90 302	81.41 272	89.59 286	69.85 235	95.51 298	79.92 223	95.29 116	92.03 298

MIMVSNet		82.59 274	80.53 279	88.76 246	91.51 257	78.32 242	86.57 321	90.13 316	79.32 257	80.70 280	88.69 300	52.98 334	93.07 329	66.03 318	88.86 205	94.90 184

EI-MVSNet		89.10 138	88.86 130	89.80 220	91.84 247	78.30 243	93.70 191	95.01 196	85.73 137	87.15 152	95.28 107	79.87 114	97.21 214	83.81 159	87.36 228	93.88 232

IterMVS-LS		88.36 158	87.91 152	89.70 224	93.80 193	78.29 244	93.73 188	95.08 195	85.73 137	84.75 210	91.90 228	79.88 113	96.92 233	83.83 158	82.51 269	93.89 230
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v144192		87.19 202	86.35 194	89.74 221	90.64 295	78.24 245	93.92 181	95.43 176	81.93 220	85.51 188	91.05 257	74.21 179	97.45 184	82.86 171	81.56 282	93.53 251

test_0402		81.30 289	79.17 295	87.67 273	93.19 210	78.17 246	92.98 220	91.71 282	75.25 297	76.02 316	90.31 272	59.23 316	96.37 264	50.22 344	83.63 257	88.47 335

WR-MVS_H		87.80 172	87.37 162	89.10 239	93.23 209	78.12 247	95.61 73	97.30 26	87.90 89	83.72 239	92.01 225	79.65 121	96.01 279	76.36 257	80.54 299	93.16 267

v1921920		86.97 207	86.06 206	89.69 225	90.53 300	78.11 248	93.80 185	95.43 176	81.90 222	85.33 203	91.05 257	72.66 202	97.41 194	82.05 186	81.80 279	93.53 251

XVG-ACMP-BASELINE		86.00 230	84.84 237	89.45 232	91.20 269	78.00 249	91.70 256	95.55 163	85.05 157	82.97 254	92.25 213	54.49 330	97.48 181	82.93 169	87.45 227	92.89 277

FMVSNet5		81.52 285	79.60 291	87.27 282	91.17 271	77.95 250	91.49 260	92.26 268	76.87 283	76.16 313	87.91 311	51.67 335	92.34 331	67.74 312	81.16 285	91.52 305

GG-mvs-BLEND				87.94 270	89.73 314	77.91 251	87.80 311	78.23 351		80.58 282	83.86 329	59.88 313	95.33 304	71.20 289	92.22 165	90.60 321

BH-w/o		87.57 185	87.05 170	89.12 238	94.90 148	77.90 252	92.41 234	93.51 247	82.89 201	83.70 240	91.34 243	75.75 158	97.07 224	75.49 265	93.49 142	92.39 291

testdata				90.49 189	96.40 92	77.89 253		95.37 181	72.51 322	93.63 45	96.69 58	82.08 94	97.65 170	83.08 166	97.39 81	95.94 148

pmmvs6		83.42 267	81.60 271	88.87 244	88.01 329	77.87 254	94.96 107	94.24 229	74.67 304	78.80 300	91.09 256	60.17 311	96.49 256	77.06 254	75.40 326	92.23 296

Baseline_NR-MVSNet		87.07 205	86.63 184	88.40 256	91.44 258	77.87 254	94.23 160	92.57 263	84.12 171	85.74 180	92.08 221	77.25 142	96.04 276	82.29 182	79.94 306	91.30 310

tttt0517		88.61 152	87.78 153	91.11 166	94.96 143	77.81 256	95.35 79	89.69 326	85.09 156	88.05 137	94.59 134	66.93 263	98.48 112	83.27 165	92.13 166	97.03 111

AllTest		83.42 267	81.39 273	89.52 229	95.01 139	77.79 257	93.12 213	90.89 306	77.41 278	76.12 314	93.34 173	54.08 332	97.51 179	68.31 308	84.27 250	93.26 260

TestCases				89.52 229	95.01 139	77.79 257		90.89 306	77.41 278	76.12 314	93.34 173	54.08 332	97.51 179	68.31 308	84.27 250	93.26 260

v1240		86.78 212	85.85 213	89.56 227	90.45 301	77.79 257	93.61 193	95.37 181	81.65 228	85.43 195	91.15 253	71.50 212	97.43 187	81.47 199	82.05 276	93.47 255

gg-mvs-nofinetune		81.77 280	79.37 292	88.99 243	90.85 288	77.73 260	86.29 322	79.63 349	74.88 303	83.19 253	69.05 344	60.34 309	96.11 275	75.46 266	94.64 125	93.11 269

Fast-Effi-MVS+-dtu		87.44 190	86.72 178	89.63 226	92.04 240	77.68 261	94.03 175	93.94 237	85.81 134	82.42 259	91.32 246	70.33 229	97.06 225	80.33 218	90.23 182	94.14 216

mvs-test1		89.45 128	89.14 121	90.38 195	93.33 206	77.63 262	94.95 108	94.36 223	87.70 95	87.10 155	92.81 196	73.45 192	98.03 149	85.57 138	93.04 153	95.48 163

cl-mvsnet2		86.78 212	85.98 208	89.18 237	92.34 232	77.62 263	90.84 270	94.13 234	81.33 236	83.97 234	90.15 275	73.96 184	96.60 249	84.19 154	82.94 264	93.33 258

miper_enhance_ethall		86.90 208	86.18 200	89.06 240	91.66 255	77.58 264	90.22 280	94.82 211	79.16 259	84.48 216	89.10 291	79.19 124	96.66 242	84.06 155	82.94 264	92.94 275

MVS_0304		83.46 266	81.92 269	88.10 266	90.63 296	77.49 265	93.26 208	93.75 244	80.04 251	80.44 285	87.24 318	47.94 342	95.55 295	75.79 263	88.16 216	91.26 311

D2MVS		85.90 232	85.09 230	88.35 258	90.79 289	77.42 266	91.83 251	95.70 151	80.77 244	80.08 291	90.02 278	66.74 268	96.37 264	81.88 190	87.97 221	91.26 311

miper_ehance_all_eth		87.22 200	86.62 185	89.02 242	92.13 237	77.40 267	90.91 269	94.81 212	81.28 237	84.32 225	90.08 277	79.26 123	96.62 244	83.81 159	82.94 264	93.04 272

cl_fuxian		87.14 204	86.50 190	89.04 241	92.20 234	77.26 268	91.22 266	94.70 216	82.01 217	84.34 224	90.43 270	78.81 127	96.61 247	83.70 161	81.09 288	93.25 262

v148		87.04 206	86.32 196	89.21 235	90.94 282	77.26 268	93.71 190	94.43 221	84.84 160	84.36 223	90.80 263	76.04 154	97.05 226	82.12 184	79.60 310	93.31 259

PMMVS		85.71 237	84.96 233	87.95 269	88.90 319	77.09 270	88.68 303	90.06 318	72.32 323	86.47 165	90.76 265	72.15 208	94.40 313	81.78 193	93.49 142	92.36 292

ITE_SJBPF				88.24 262	91.88 246	77.05 271		92.92 254	85.54 143	80.13 290	93.30 177	57.29 321	96.20 271	72.46 285	84.71 247	91.49 306

pmmvs5		84.21 259	82.84 265	88.34 259	88.95 318	76.94 272	92.41 234	91.91 281	75.63 294	80.28 286	91.18 251	64.59 284	95.57 294	77.09 253	83.47 259	92.53 286

IterMVS-SCA-FT		85.45 239	84.53 243	88.18 264	91.71 252	76.87 273	90.19 281	92.65 262	85.40 148	81.44 271	90.54 267	66.79 266	95.00 310	81.04 202	81.05 289	92.66 283

baseline2		86.50 222	85.39 224	89.84 216	91.12 274	76.70 274	91.88 249	88.58 331	82.35 210	79.95 293	90.95 259	73.42 194	97.63 173	80.27 219	89.95 187	95.19 172

SCA		86.32 226	85.18 228	89.73 223	92.15 235	76.60 275	91.12 267	91.69 284	83.53 184	85.50 189	88.81 295	66.79 266	96.48 257	76.65 255	90.35 181	96.12 139

CP-MVSNet		87.63 180	87.26 167	88.74 249	93.12 212	76.59 276	95.29 86	96.58 91	88.43 74	83.49 247	92.98 189	75.28 164	95.83 287	78.97 233	81.15 287	93.79 238

cl-mvsnet_		86.52 221	85.78 215	88.75 247	92.03 241	76.46 277	90.74 271	94.30 226	81.83 226	83.34 250	90.78 264	75.74 160	96.57 250	81.74 194	81.54 283	93.22 264

cl-mvsnet1		86.53 220	85.78 215	88.75 247	92.02 242	76.45 278	90.74 271	94.30 226	81.83 226	83.34 250	90.82 262	75.75 158	96.57 250	81.73 195	81.52 284	93.24 263

Effi-MVS+-dtu		88.65 151	88.35 139	89.54 228	93.33 206	76.39 279	94.47 141	94.36 223	87.70 95	85.43 195	89.56 288	73.45 192	97.26 208	85.57 138	91.28 170	94.97 177

Patchmtry		82.71 272	80.93 278	88.06 267	90.05 308	76.37 280	84.74 330	91.96 279	72.28 324	81.32 274	87.87 312	71.03 217	95.50 300	68.97 303	80.15 304	92.32 294

PS-CasMVS		87.32 194	86.88 172	88.63 252	92.99 220	76.33 281	95.33 80	96.61 89	88.22 82	83.30 252	93.07 187	73.03 199	95.79 290	78.36 238	81.00 293	93.75 244

OpenMVS_ROB	74.94 19	79.51 300	77.03 306	86.93 291	87.00 332	76.23 282	92.33 238	90.74 309	68.93 334	74.52 322	88.23 306	49.58 338	96.62 244	57.64 340	84.29 249	87.94 337

IterMVS		84.88 252	83.98 250	87.60 274	91.44 258	76.03 283	90.18 282	92.41 265	83.24 192	81.06 277	90.42 271	66.60 269	94.28 316	79.46 226	80.98 294	92.48 287
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet (Re-imp)		89.59 123	89.44 114	90.03 209	95.74 115	75.85 284	95.61 73	90.80 308	87.66 99	87.83 141	95.40 106	76.79 146	96.46 260	78.37 237	96.73 91	97.80 82

eth_miper_zixun_eth		86.50 222	85.77 217	88.68 250	91.94 244	75.81 285	90.47 275	94.89 205	82.05 214	84.05 231	90.46 269	75.96 155	96.77 238	82.76 175	79.36 312	93.46 256

PEN-MVS		86.80 211	86.27 198	88.40 256	92.32 233	75.71 286	95.18 95	96.38 102	87.97 86	82.82 256	93.15 183	73.39 195	95.92 282	76.15 261	79.03 316	93.59 249

PatchmatchNet		85.85 234	84.70 239	89.29 234	91.76 250	75.54 287	88.49 305	91.30 294	81.63 230	85.05 206	88.70 299	71.71 209	96.24 270	74.61 275	89.05 203	96.08 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TDRefinement		79.81 298	77.34 302	87.22 287	79.24 347	75.48 288	93.12 213	92.03 274	76.45 285	75.01 319	91.58 238	49.19 340	96.44 261	70.22 296	69.18 335	89.75 325

DTE-MVSNet		86.11 229	85.48 222	87.98 268	91.65 256	74.92 289	94.93 110	95.75 148	87.36 104	82.26 261	93.04 188	72.85 200	95.82 288	74.04 277	77.46 321	93.20 265

miper_lstm_enhance		85.27 245	84.59 242	87.31 281	91.28 268	74.63 290	87.69 314	94.09 236	81.20 240	81.36 273	89.85 283	74.97 169	94.30 315	81.03 204	79.84 309	93.01 273

USDC		82.76 271	81.26 275	87.26 283	91.17 271	74.55 291	89.27 294	93.39 249	78.26 273	75.30 318	92.08 221	54.43 331	96.63 243	71.64 287	85.79 240	90.61 319

ppachtmachnet_test		81.84 279	80.07 286	87.15 289	88.46 323	74.43 292	89.04 299	92.16 270	75.33 296	77.75 306	88.99 292	66.20 274	95.37 303	65.12 322	77.60 319	91.65 303

mvs_anonymous		89.37 134	89.32 117	89.51 231	93.47 203	74.22 293	91.65 258	94.83 210	82.91 200	85.45 192	93.79 165	81.23 103	96.36 266	86.47 131	94.09 133	97.94 72

ADS-MVSNet2		81.66 282	79.71 290	87.50 277	91.35 265	74.19 294	83.33 335	88.48 332	72.90 319	82.24 262	85.77 324	64.98 282	93.20 327	64.57 324	83.74 254	95.12 173

Patchmatch-test		81.37 287	79.30 293	87.58 275	90.92 284	74.16 295	80.99 341	87.68 336	70.52 331	76.63 312	88.81 295	71.21 214	92.76 330	60.01 338	86.93 234	95.83 154

MDA-MVSNet-bldmvs		78.85 304	76.31 307	86.46 297	89.76 313	73.88 296	88.79 301	90.42 311	79.16 259	59.18 344	88.33 304	60.20 310	94.04 318	62.00 331	68.96 336	91.48 307

DWT-MVSNet_test		84.95 251	83.68 253	88.77 245	91.43 261	73.75 297	91.74 254	90.98 302	80.66 245	83.84 236	87.36 316	62.44 293	97.11 220	78.84 235	85.81 238	95.46 164

MIMVSNet1		79.38 301	77.28 303	85.69 304	86.35 334	73.67 298	91.61 259	92.75 259	78.11 276	72.64 330	88.12 307	48.16 341	91.97 335	60.32 335	77.49 320	91.43 308

our_test_3		81.93 278	80.46 280	86.33 300	88.46 323	73.48 299	88.46 306	91.11 297	76.46 284	76.69 311	88.25 305	66.89 264	94.36 314	68.75 304	79.08 315	91.14 315

JIA-IIPM		81.04 290	78.98 298	87.25 284	88.64 320	73.48 299	81.75 340	89.61 328	73.19 316	82.05 265	73.71 341	66.07 277	95.87 285	71.18 291	84.60 248	92.41 290

RRT_test8_iter05		86.90 208	86.36 193	88.52 254	93.00 219	73.27 301	94.32 155	95.96 130	85.50 145	84.26 228	92.86 191	60.76 307	97.70 168	88.32 105	82.29 271	94.60 195

TinyColmap		79.76 299	77.69 301	85.97 302	91.71 252	73.12 302	89.55 288	90.36 313	75.03 299	72.03 332	90.19 273	46.22 345	96.19 273	63.11 328	81.03 290	88.59 334

UnsupCasMVSNet_bld		76.23 309	73.27 312	85.09 309	83.79 341	72.92 303	85.65 327	93.47 248	71.52 326	68.84 337	79.08 339	49.77 337	93.21 326	66.81 317	60.52 345	89.13 331

test0.0.03 1		82.41 275	81.69 270	84.59 311	88.23 326	72.89 304	90.24 278	87.83 334	83.41 187	79.86 294	89.78 284	67.25 259	88.99 342	65.18 321	83.42 261	91.90 300

EPNet_dtu		86.49 224	85.94 211	88.14 265	90.24 304	72.82 305	94.11 166	92.20 269	86.66 121	79.42 298	92.36 208	73.52 190	95.81 289	71.26 288	93.66 138	95.80 156
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MDA-MVSNet_test_wron		79.21 303	77.19 305	85.29 306	88.22 327	72.77 306	85.87 324	90.06 318	74.34 306	62.62 343	87.56 315	66.14 275	91.99 334	66.90 316	73.01 328	91.10 317

EPMVS		83.90 264	82.70 266	87.51 276	90.23 305	72.67 307	88.62 304	81.96 346	81.37 235	85.01 207	88.34 303	66.31 273	94.45 312	75.30 268	87.12 231	95.43 166

YYNet1		79.22 302	77.20 304	85.28 307	88.20 328	72.66 308	85.87 324	90.05 320	74.33 307	62.70 342	87.61 314	66.09 276	92.03 333	66.94 313	72.97 329	91.15 314

UnsupCasMVSNet_eth		80.07 296	78.27 300	85.46 305	85.24 337	72.63 309	88.45 307	94.87 207	82.99 197	71.64 334	88.07 308	56.34 323	91.75 336	73.48 282	63.36 343	92.01 299

OurMVSNet-221017-0		85.35 242	84.64 241	87.49 278	90.77 290	72.59 310	94.01 177	94.40 222	84.72 163	79.62 297	93.17 182	61.91 297	96.72 239	81.99 187	81.16 285	93.16 267

CostFormer		85.77 236	84.94 234	88.26 261	91.16 273	72.58 311	89.47 292	91.04 301	76.26 289	86.45 168	89.97 280	70.74 221	96.86 237	82.35 180	87.07 233	95.34 170

LCM-MVSNet-Re		88.30 160	88.32 142	88.27 260	94.71 156	72.41 312	93.15 212	90.98 302	87.77 93	79.25 299	91.96 226	78.35 135	95.75 291	83.04 167	95.62 107	96.65 124

PVSNet	78.82 18	85.55 238	84.65 240	88.23 263	94.72 155	71.93 313	87.12 318	92.75 259	78.80 265	84.95 208	90.53 268	64.43 285	96.71 241	74.74 273	93.86 136	96.06 145

ADS-MVSNet		81.56 284	79.78 288	86.90 293	91.35 265	71.82 314	83.33 335	89.16 330	72.90 319	82.24 262	85.77 324	64.98 282	93.76 320	64.57 324	83.74 254	95.12 173

test-LLR		85.87 233	85.41 223	87.25 284	90.95 280	71.67 315	89.55 288	89.88 324	83.41 187	84.54 214	87.95 309	67.25 259	95.11 307	81.82 191	93.37 147	94.97 177

test-mter		84.54 257	83.64 255	87.25 284	90.95 280	71.67 315	89.55 288	89.88 324	79.17 258	84.54 214	87.95 309	55.56 325	95.11 307	81.82 191	93.37 147	94.97 177

tpm2		84.08 260	82.94 262	87.48 279	91.39 263	71.27 317	89.23 296	90.37 312	71.95 325	84.64 211	89.33 289	67.30 258	96.55 254	75.17 269	87.09 232	94.63 192

Patchmatch-RL test		81.67 281	79.96 287	86.81 296	85.42 336	71.23 318	82.17 339	87.50 337	78.47 269	77.19 310	82.50 335	70.81 220	93.48 323	82.66 176	72.89 330	95.71 159

TESTMET0.1,1		83.74 265	82.85 264	86.42 299	89.96 310	71.21 319	89.55 288	87.88 333	77.41 278	83.37 249	87.31 317	56.71 322	93.65 322	80.62 212	92.85 158	94.40 209

PVSNet_0	73.20 20	77.22 306	74.83 311	84.37 313	90.70 294	71.10 320	83.09 337	89.67 327	72.81 321	73.93 325	83.13 333	60.79 306	93.70 321	68.54 305	50.84 347	88.30 336

tpm cat1		81.96 277	80.27 282	87.01 290	91.09 275	71.02 321	87.38 317	91.53 289	66.25 337	80.17 287	86.35 322	68.22 257	96.15 274	69.16 302	82.29 271	93.86 235

tpmvs		83.35 270	82.07 267	87.20 288	91.07 276	71.00 322	88.31 308	91.70 283	78.91 261	80.49 284	87.18 319	69.30 244	97.08 223	68.12 311	83.56 258	93.51 254

PatchT		82.68 273	81.27 274	86.89 294	90.09 307	70.94 323	84.06 332	90.15 315	74.91 301	85.63 183	83.57 331	69.37 240	94.87 311	65.19 320	88.50 210	94.84 186

SixPastTwentyTwo		83.91 263	82.90 263	86.92 292	90.99 278	70.67 324	93.48 197	91.99 276	85.54 143	77.62 308	92.11 219	60.59 308	96.87 236	76.05 262	77.75 318	93.20 265

RPSCF		85.07 248	84.27 245	87.48 279	92.91 222	70.62 325	91.69 257	92.46 264	76.20 290	82.67 258	95.22 110	63.94 287	97.29 205	77.51 248	85.80 239	94.53 200

pmmvs3		71.81 312	68.71 315	81.11 321	75.86 348	70.42 326	86.74 319	83.66 343	58.95 343	68.64 338	80.89 337	36.93 348	89.52 341	63.10 329	63.59 342	83.39 339

Anonymous20231206		81.03 291	79.77 289	84.82 310	87.85 331	70.26 327	91.42 261	92.08 272	73.67 311	77.75 306	89.25 290	62.43 294	93.08 328	61.50 333	82.00 277	91.12 316

PM-MVS		78.11 305	76.12 309	84.09 316	83.54 342	70.08 328	88.97 300	85.27 341	79.93 252	74.73 321	86.43 321	34.70 349	93.48 323	79.43 229	72.06 332	88.72 332

MDTV_nov1_ep13			83.56 256		91.69 254	69.93 329	87.75 313	91.54 288	78.60 268	84.86 209	88.90 294	69.54 238	96.03 277	70.25 294	88.93 204

LF4IMVS		80.37 295	79.07 297	84.27 315	86.64 333	69.87 330	89.39 293	91.05 300	76.38 286	74.97 320	90.00 279	47.85 343	94.25 317	74.55 276	80.82 296	88.69 333

K. test v3		81.59 283	80.15 285	85.91 303	89.89 312	69.42 331	92.57 231	87.71 335	85.56 142	73.44 327	89.71 285	55.58 324	95.52 297	77.17 251	69.76 334	92.78 281

tpm		84.73 254	84.02 248	86.87 295	90.33 302	68.90 332	89.06 298	89.94 321	80.85 243	85.75 179	89.86 282	68.54 254	95.97 280	77.76 244	84.05 252	95.75 157

lessismore_v0				86.04 301	88.46 323	68.78 333		80.59 347		73.01 329	90.11 276	55.39 326	96.43 262	75.06 271	65.06 340	92.90 276

gm-plane-assit					89.60 315	68.00 334			77.28 281		88.99 292		97.57 175	79.44 228

tpmrst		85.35 242	84.99 231	86.43 298	90.88 287	67.88 335	88.71 302	91.43 292	80.13 249	86.08 176	88.80 297	73.05 198	96.02 278	82.48 177	83.40 262	95.40 167

test20.03		79.95 297	79.08 296	82.55 319	85.79 335	67.74 336	91.09 268	91.08 298	81.23 239	74.48 323	89.96 281	61.63 298	90.15 340	60.08 336	76.38 324	89.76 324

CMPMVS	59.16 21	80.52 294	79.20 294	84.48 312	83.98 340	67.63 337	89.95 286	93.84 242	64.79 339	66.81 340	91.14 254	57.93 320	95.17 305	76.25 259	88.10 217	90.65 318
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
testgi		80.94 293	80.20 284	83.18 317	87.96 330	66.29 338	91.28 263	90.70 310	83.70 178	78.12 303	92.84 193	51.37 336	90.82 339	63.34 327	82.46 270	92.43 289

new_pmnet		72.15 311	70.13 314	78.20 324	82.95 344	65.68 339	83.91 333	82.40 345	62.94 341	64.47 341	79.82 338	42.85 347	86.26 345	57.41 341	74.44 327	82.65 341

Gipuma		57.99 318	54.91 320	67.24 331	88.51 321	65.59 340	52.21 351	90.33 314	43.58 349	42.84 349	51.18 350	20.29 355	85.07 346	34.77 349	70.45 333	51.05 348
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dp		81.47 286	80.23 283	85.17 308	89.92 311	65.49 341	86.74 319	90.10 317	76.30 288	81.10 275	87.12 320	62.81 291	95.92 282	68.13 310	79.88 307	94.09 221

CVMVSNet		84.69 256	84.79 238	84.37 313	91.84 247	64.92 342	93.70 191	91.47 291	66.19 338	86.16 175	95.28 107	67.18 261	93.33 325	80.89 207	90.42 180	94.88 185

EU-MVSNet		81.32 288	80.95 277	82.42 320	88.50 322	63.67 343	93.32 201	91.33 293	64.02 340	80.57 283	92.83 194	61.21 304	92.27 332	76.34 258	80.38 303	91.32 309

ambc				83.06 318	79.99 346	63.51 344	77.47 344	92.86 255		74.34 324	84.45 328	28.74 350	95.06 309	73.06 284	68.89 337	90.61 319

new-patchmatchnet		76.41 308	75.17 310	80.13 322	82.65 345	59.61 345	87.66 315	91.08 298	78.23 274	69.85 335	83.22 332	54.76 328	91.63 338	64.14 326	64.89 341	89.16 329

LCM-MVSNet		66.00 314	62.16 318	77.51 326	64.51 354	58.29 346	83.87 334	90.90 305	48.17 347	54.69 345	73.31 342	16.83 358	86.75 344	65.47 319	61.67 344	87.48 338

FPMVS		64.63 315	62.55 317	70.88 328	70.80 350	56.71 347	84.42 331	84.42 342	51.78 346	49.57 346	81.61 336	23.49 352	81.48 348	40.61 348	76.25 325	74.46 344

ANet_high		58.88 317	54.22 321	72.86 327	56.50 357	56.67 348	80.75 342	86.00 338	73.09 318	37.39 350	64.63 347	22.17 353	79.49 350	43.51 346	23.96 351	82.43 342

MVS-HIRNet		73.70 310	72.20 313	78.18 325	91.81 249	56.42 349	82.94 338	82.58 344	55.24 344	68.88 336	66.48 345	55.32 327	95.13 306	58.12 339	88.42 212	83.01 340

DSMNet-mixed		76.94 307	76.29 308	78.89 323	83.10 343	56.11 350	87.78 312	79.77 348	60.65 342	75.64 317	88.71 298	61.56 299	88.34 343	60.07 337	89.29 199	92.21 297

MDTV_nov1_ep13_2view						55.91 351	87.62 316		73.32 315	84.59 213		70.33 229		74.65 274		95.50 162

DeepMVS_CX				56.31 334	74.23 349	51.81 352		56.67 357	44.85 348	48.54 348	75.16 340	27.87 351	58.74 354	40.92 347	52.22 346	58.39 347

MVE	39.65 23	43.39 320	38.59 326	57.77 333	56.52 356	48.77 353	55.38 350	58.64 356	29.33 353	28.96 353	52.65 349	4.68 360	64.62 353	28.11 351	33.07 349	59.93 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		59.60 316	56.40 319	69.21 330	68.83 351	46.58 354	73.02 348	77.48 352	55.07 345	49.21 347	72.95 343	17.43 357	80.04 349	49.32 345	44.33 348	80.99 343

PMVS	47.18 22	52.22 319	48.46 322	63.48 332	45.72 358	46.20 355	73.41 347	78.31 350	41.03 350	30.06 352	65.68 346	6.05 359	83.43 347	30.04 350	65.86 339	60.80 345
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		43.23 321	42.29 323	46.03 335	65.58 353	37.41 356	73.51 346	64.62 353	33.99 351	28.47 354	47.87 351	19.90 356	67.91 351	22.23 352	24.45 350	32.77 349

wuyk23d		21.27 325	20.48 328	23.63 338	68.59 352	36.41 357	49.57 352	6.85 360	9.37 354	7.89 356	4.46 358	4.03 361	31.37 355	17.47 354	16.07 354	3.12 352

EMVS		42.07 322	41.12 324	44.92 336	63.45 355	35.56 358	73.65 345	63.48 354	33.05 352	26.88 355	45.45 352	21.27 354	67.14 352	19.80 353	23.02 352	32.06 350

N_pmnet		68.89 313	68.44 316	70.23 329	89.07 317	28.79 359	88.06 309	19.50 359	69.47 333	71.86 333	84.93 327	61.24 303	91.75 336	54.70 342	77.15 322	90.15 323

tmp_tt		35.64 323	39.24 325	24.84 337	14.87 359	23.90 360	62.71 349	51.51 358	6.58 355	36.66 351	62.08 348	44.37 346	30.34 356	52.40 343	22.00 353	20.27 351

test123		8.76 327	11.22 330	1.39 339	0.85 361	0.97 361	85.76 326	0.35 362	0.54 357	2.45 358	8.14 357	0.60 362	0.48 357	2.16 356	0.17 356	2.71 353

testmvs		8.92 326	11.52 329	1.12 340	1.06 360	0.46 362	86.02 323	0.65 361	0.62 356	2.74 357	9.52 356	0.31 363	0.45 358	2.38 355	0.39 355	2.46 354

uanet_test		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

cdsmvs_eth3d_5k		22.14 324	29.52 327	0.00 341	0.00 362	0.00 363	0.00 353	95.76 147	0.00 358	0.00 359	94.29 142	75.66 161	0.00 359	0.00 357	0.00 357	0.00 355

pcd_1.5k_mvsjas		6.64 329	8.86 332	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	79.70 117	0.00 359	0.00 357	0.00 357	0.00 355

sosnet-low-res		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

sosnet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uncertanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

Regformer		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

ab-mvs-re		7.82 328	10.43 331	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	93.88 161	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

uanet		0.00 330	0.00 333	0.00 341	0.00 362	0.00 363	0.00 353	0.00 363	0.00 358	0.00 359	0.00 359	0.00 364	0.00 359	0.00 357	0.00 357	0.00 355

test_241102_TWO								97.44 12	90.31 26	97.62 5	98.07 4	91.46 8	99.58 5	95.66 2	99.12 6	98.98 6

9.14			94.47 17		97.79 52		96.08 50	97.44 12	86.13 131	95.10 22	97.40 21	88.34 18	99.22 46	93.25 28	98.70 32

test_0728_THIRD									90.75 19	97.04 8	98.05 6	92.09 4	99.55 12	95.64 4	99.13 3	99.13 1

GSMVS																96.12 139

sam_mvs1												71.70 210				96.12 139

sam_mvs												70.60 222

MTGPA								96.97 49

test_post1							88.00 310				9.81 355	69.31 243	95.53 296	76.65 255

test_post											10.29 354	70.57 226	95.91 284

patchmatchnet-post											83.76 330	71.53 211	96.48 257

MTMP							96.16 44	60.64 355

test9_res														91.91 58	98.71 30	98.07 63

agg_prior2														90.54 84	98.68 35	98.27 47

test_prior2							94.12 164		87.67 97	92.63 68	96.39 72	86.62 39		91.50 68	98.67 37

旧先验2							93.36 200		71.25 328	94.37 26			97.13 219	86.74 126

新几何2							93.11 215

无先验							93.28 207	96.26 107	73.95 309				99.05 60	80.56 213		96.59 125

原ACMM2							92.94 222

testdata2													98.75 99	78.30 239

segment_acmp												87.16 35

testdata1							92.15 244		87.94 87

plane_prior5								96.22 112					98.12 136	88.15 106	89.99 184	94.63 192

plane_prior4											94.86 122

plane_prior2							95.85 61		90.81 17

plane_prior1					94.59 161

n2								0.00 363

nn								0.00 363

door-mid								85.49 339

test11								96.57 92

door								85.33 340

HQP-NCC					94.17 176		94.39 148		88.81 61	85.43 195

ACMP_Plane					94.17 176		94.39 148		88.81 61	85.43 195

BP-MVS														87.11 123

HQP4-MVS										85.43 195			97.96 154			94.51 202

HQP3-MVS								96.04 126							89.77 191

HQP2-MVS												73.83 187

ACMMP++_ref															87.47 225

ACMMP++															88.01 220

Test By Simon												80.02 112

High-resolution multi-view benchmark