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.

DVP-MVS		89.60 1	90.35 1	87.33 35	95.27 2	71.25 52	93.49 5	92.73 49	77.33 45	92.12 4	95.78 4	80.98 3	97.40 2	89.08 4	96.41 4	93.33 66

MSP-MVS		89.51 2	89.91 3	88.30 6	94.28 21	73.46 15	92.90 11	94.11 2	80.27 13	91.35 7	94.16 28	78.35 6	96.77 16	89.59 1	94.22 50	94.67 9

DPE-MVS		89.48 3	89.98 2	88.01 11	94.80 6	72.69 28	91.59 32	94.10 4	75.90 77	92.29 2	95.66 6	81.67 1	97.38 4	87.44 14	96.34 7	93.95 36

APDe-MVS		89.15 4	89.63 4	87.73 24	94.49 14	71.69 48	93.83 2	93.96 7	75.70 80	91.06 8	96.03 1	76.84 7	97.03 10	89.09 3	95.65 18	94.47 16

SMA-MVS		89.08 5	89.23 5	88.61 2	94.25 22	73.73 7	92.40 17	93.63 12	74.77 96	92.29 2	95.97 2	74.28 21	97.24 6	88.58 7	96.91 1	94.87 7

HPM-MVS++		89.02 6	89.15 6	88.63 1	95.01 5	76.03 1	92.38 19	92.85 43	80.26 14	87.78 19	94.27 24	75.89 11	96.81 15	87.45 13	96.44 3	93.05 78

CNVR-MVS		88.93 7	89.13 7	88.33 4	94.77 7	73.82 6	90.51 48	93.00 34	80.90 10	88.06 17	94.06 32	76.43 8	96.84 13	88.48 8	95.99 9	94.34 20

SteuartSystems-ACMMP		88.72 8	88.86 8	88.32 5	92.14 61	72.96 22	93.73 3	93.67 11	80.19 15	88.10 16	94.80 9	73.76 25	97.11 8	87.51 12	95.82 14	94.90 6
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	80.84 1	88.10 9	88.56 9	86.73 46	92.24 59	69.03 90	89.57 74	93.39 22	77.53 42	89.79 10	94.12 30	78.98 5	96.58 27	85.66 18	95.72 15	94.58 12

SD-MVS		88.06 10	88.50 10	86.71 47	92.60 57	72.71 26	91.81 31	93.19 27	77.87 33	90.32 9	94.00 33	74.83 14	93.78 125	87.63 11	94.27 48	93.65 53
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
NCCC		88.06 10	88.01 13	88.24 7	94.41 18	73.62 8	91.22 39	92.83 44	81.50 7	85.79 30	93.47 42	73.02 31	97.00 12	84.90 22	94.94 30	94.10 27

ACMMP_NAP		88.05 12	88.08 12	87.94 14	93.70 33	73.05 19	90.86 42	93.59 13	76.27 73	88.14 15	95.09 8	71.06 45	96.67 20	87.67 10	96.37 6	94.09 28

TSAR-MVS + MP.		88.02 13	88.11 11	87.72 26	93.68 35	72.13 43	91.41 35	92.35 62	74.62 100	88.90 11	93.85 36	75.75 12	96.00 41	87.80 9	94.63 38	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
testtj		87.78 14	87.78 14	87.77 22	94.55 12	72.47 35	92.23 24	93.49 17	74.75 97	88.33 14	94.43 21	73.27 28	97.02 11	84.18 36	94.84 34	93.82 44

MP-MVS		87.71 15	87.64 16	87.93 17	94.36 20	73.88 4	92.71 16	92.65 53	77.57 38	83.84 58	94.40 23	72.24 37	96.28 32	85.65 19	95.30 26	93.62 55
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		87.67 16	87.72 15	87.54 31	93.64 36	72.04 45	89.80 67	93.50 16	75.17 91	86.34 25	95.29 7	70.86 46	96.00 41	88.78 6	96.04 8	94.58 12
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 17	87.47 18	87.94 14	94.58 10	73.54 12	93.04 8	93.24 24	76.78 58	84.91 38	94.44 19	70.78 47	96.61 23	84.53 29	94.89 32	93.66 48

zzz-MVS		87.53 18	87.41 19	87.90 18	94.18 26	74.25 2	90.23 57	92.02 73	79.45 19	85.88 27	94.80 9	68.07 70	96.21 34	86.69 15	95.34 22	93.23 70

ACMMPR		87.44 19	87.23 22	88.08 9	94.64 8	73.59 9	93.04 8	93.20 26	76.78 58	84.66 45	94.52 14	68.81 67	96.65 21	84.53 29	94.90 31	94.00 34

APD-MVS		87.44 19	87.52 17	87.19 37	94.24 23	72.39 37	91.86 30	92.83 44	73.01 128	88.58 12	94.52 14	73.36 26	96.49 28	84.26 33	95.01 28	92.70 86
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 21	87.26 20	87.89 21	94.12 28	72.97 21	92.39 18	93.43 20	76.89 55	84.68 44	93.99 34	70.67 50	96.82 14	84.18 36	95.01 28	93.90 39

region2R		87.42 21	87.20 23	88.09 8	94.63 9	73.55 10	93.03 10	93.12 29	76.73 61	84.45 48	94.52 14	69.09 64	96.70 19	84.37 32	94.83 35	94.03 31

MCST-MVS		87.37 23	87.25 21	87.73 24	94.53 13	72.46 36	89.82 65	93.82 9	73.07 126	84.86 43	92.89 54	76.22 9	96.33 30	84.89 24	95.13 27	94.40 17

#test#		87.33 24	87.13 24	87.94 14	94.58 10	73.54 12	92.34 21	93.24 24	75.23 88	84.91 38	94.44 19	70.78 47	96.61 23	83.75 39	94.89 32	93.66 48

MTAPA		87.23 25	87.00 25	87.90 18	94.18 26	74.25 2	86.58 163	92.02 73	79.45 19	85.88 27	94.80 9	68.07 70	96.21 34	86.69 15	95.34 22	93.23 70

XVS		87.18 26	86.91 28	88.00 12	94.42 16	73.33 17	92.78 12	92.99 36	79.14 21	83.67 61	94.17 27	67.45 77	96.60 25	83.06 44	94.50 41	94.07 29

HPM-MVS		87.11 27	86.98 26	87.50 33	93.88 31	72.16 42	92.19 25	93.33 23	76.07 76	83.81 59	93.95 35	69.77 59	96.01 40	85.15 20	94.66 37	94.32 22
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 27	86.92 27	87.68 30	94.20 25	73.86 5	93.98 1	92.82 47	76.62 63	83.68 60	94.46 18	67.93 72	95.95 43	84.20 35	94.39 44	93.23 70

DeepC-MVS	79.81 2	87.08 29	86.88 29	87.69 29	91.16 71	72.32 41	90.31 55	93.94 8	77.12 49	82.82 71	94.23 26	72.13 39	97.09 9	84.83 25	95.37 21	93.65 53
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	79.65 3	86.91 30	86.62 32	87.76 23	93.52 38	72.37 39	91.26 36	93.04 30	76.62 63	84.22 53	93.36 44	71.44 43	96.76 17	80.82 62	95.33 24	94.16 25
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		86.73 31	86.67 31	86.91 42	94.11 29	72.11 44	92.37 20	92.56 55	74.50 101	86.84 23	94.65 13	67.31 79	95.77 46	84.80 26	92.85 58	92.84 85

test_prior3		86.73 31	86.86 30	86.33 53	92.61 55	69.59 81	88.85 93	92.97 39	75.41 84	84.91 38	93.54 38	74.28 21	95.48 54	83.31 40	95.86 11	93.91 37

PGM-MVS		86.68 33	86.27 36	87.90 18	94.22 24	73.38 16	90.22 59	93.04 30	75.53 82	83.86 57	94.42 22	67.87 74	96.64 22	82.70 49	94.57 40	93.66 48

mPP-MVS		86.67 34	86.32 35	87.72 26	94.41 18	73.55 10	92.74 14	92.22 66	76.87 56	82.81 72	94.25 25	66.44 85	96.24 33	82.88 48	94.28 47	93.38 63

Regformer-2		86.63 35	86.53 33	86.95 41	89.33 110	71.24 54	88.43 105	92.05 72	82.50 1	86.88 22	90.09 108	74.45 16	95.61 50	84.38 31	90.63 79	94.01 33

CANet		86.45 36	86.10 40	87.51 32	90.09 90	70.94 59	89.70 71	92.59 54	81.78 4	81.32 89	91.43 81	70.34 52	97.23 7	84.26 33	93.36 54	94.37 18

train_agg		86.43 37	86.20 37	87.13 39	93.26 42	72.96 22	88.75 97	91.89 82	68.69 195	85.00 36	93.10 48	74.43 17	95.41 58	84.97 21	95.71 16	93.02 80

PHI-MVS		86.43 37	86.17 39	87.24 36	90.88 77	70.96 57	92.27 23	94.07 6	72.45 132	85.22 34	91.90 68	69.47 61	96.42 29	83.28 42	95.94 10	94.35 19

Regformer-1		86.41 39	86.33 34	86.64 48	89.33 110	70.93 60	88.43 105	91.39 101	82.14 3	86.65 24	90.09 108	74.39 19	95.01 75	83.97 38	90.63 79	93.97 35

CSCG		86.41 39	86.19 38	87.07 40	92.91 49	72.48 34	90.81 43	93.56 14	73.95 111	83.16 66	91.07 89	75.94 10	95.19 66	79.94 69	94.38 45	93.55 58

MVS_0304		86.37 41	85.81 44	88.02 10	90.13 88	72.39 37	89.66 72	92.75 48	81.64 6	82.66 75	92.04 64	64.44 101	97.35 5	84.76 27	94.25 49	94.33 21

agg_prior1		86.22 42	86.09 41	86.62 49	92.85 50	71.94 46	88.59 102	91.78 88	68.96 192	84.41 49	93.18 47	74.94 13	94.93 76	84.75 28	95.33 24	93.01 81

APD-MVS_3200maxsize		85.97 43	85.88 42	86.22 56	92.69 53	69.53 83	91.93 29	92.99 36	73.54 120	85.94 26	94.51 17	65.80 93	95.61 50	83.04 46	92.51 64	93.53 60

canonicalmvs		85.91 44	85.87 43	86.04 60	89.84 97	69.44 88	90.45 53	93.00 34	76.70 62	88.01 18	91.23 83	73.28 27	93.91 119	81.50 56	88.80 100	94.77 8

ACMMP		85.89 45	85.39 47	87.38 34	93.59 37	72.63 30	92.74 14	93.18 28	76.78 58	80.73 98	93.82 37	64.33 102	96.29 31	82.67 50	90.69 78	93.23 70
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
CDPH-MVS		85.76 46	85.29 51	87.17 38	93.49 39	71.08 55	88.58 103	92.42 60	68.32 200	84.61 46	93.48 40	72.32 36	96.15 37	79.00 72	95.43 20	94.28 23

TSAR-MVS + GP.		85.71 47	85.33 48	86.84 43	91.34 69	72.50 33	89.07 87	87.28 202	76.41 66	85.80 29	90.22 106	74.15 24	95.37 62	81.82 54	91.88 66	92.65 90

Regformer-4		85.68 48	85.45 46	86.35 52	88.95 127	69.67 80	88.29 115	91.29 103	81.73 5	85.36 32	90.01 110	72.62 33	95.35 63	83.28 42	87.57 114	94.03 31

alignmvs		85.48 49	85.32 49	85.96 61	89.51 105	69.47 85	89.74 69	92.47 56	76.17 74	87.73 20	91.46 80	70.32 53	93.78 125	81.51 55	88.95 97	94.63 11

3Dnovator+	77.84 4	85.48 49	84.47 60	88.51 3	91.08 72	73.49 14	93.18 7	93.78 10	80.79 11	76.66 159	93.37 43	60.40 159	96.75 18	77.20 90	93.73 53	95.29 2

MSLP-MVS++		85.43 51	85.76 45	84.45 94	91.93 64	70.24 68	90.71 45	92.86 42	77.46 44	84.22 53	92.81 58	67.16 81	92.94 163	80.36 65	94.35 46	90.16 162

DELS-MVS		85.41 52	85.30 50	85.77 62	88.49 143	67.93 118	85.52 194	93.44 19	78.70 28	83.63 63	89.03 135	74.57 15	95.71 48	80.26 67	94.04 51	93.66 48
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
HPM-MVS_fast		85.35 53	84.95 56	86.57 51	93.69 34	70.58 66	92.15 27	91.62 92	73.89 113	82.67 74	94.09 31	62.60 122	95.54 53	80.93 60	92.93 56	93.57 57

Regformer-3		85.23 54	85.07 53	85.70 63	88.95 127	69.01 92	88.29 115	89.91 141	80.95 9	85.01 35	90.01 110	72.45 35	94.19 105	82.50 51	87.57 114	93.90 39

abl_6		85.23 54	84.95 56	86.07 59	92.23 60	70.48 67	90.80 44	92.08 71	73.51 121	85.26 33	94.16 28	62.75 121	95.92 44	82.46 52	91.30 73	91.81 116

MVS_111021_HR		85.14 56	84.75 58	86.32 55	91.65 67	72.70 27	85.98 178	90.33 129	76.11 75	82.08 79	91.61 75	71.36 44	94.17 107	81.02 59	92.58 63	92.08 108

casdiffmvs		85.11 57	85.14 52	85.01 77	87.20 183	65.77 154	87.75 130	92.83 44	77.84 34	84.36 52	92.38 60	72.15 38	93.93 118	81.27 58	90.48 81	95.33 1

UA-Net		85.08 58	84.96 55	85.45 64	92.07 62	68.07 116	89.78 68	90.86 115	82.48 2	84.60 47	93.20 46	69.35 62	95.22 65	71.39 136	90.88 77	93.07 77

DPM-MVS		84.93 59	84.29 61	86.84 43	90.20 87	73.04 20	87.12 145	93.04 30	69.80 172	82.85 70	91.22 84	73.06 30	96.02 39	76.72 98	94.63 38	91.46 125

baseline		84.93 59	84.98 54	84.80 86	87.30 181	65.39 162	87.30 141	92.88 41	77.62 36	84.04 56	92.26 61	71.81 40	93.96 112	81.31 57	90.30 83	95.03 4

CS-MVS		84.76 61	84.61 59	85.22 72	89.66 99	66.43 141	90.23 57	93.56 14	76.52 65	82.59 76	85.93 215	70.41 51	95.80 45	79.93 70	92.68 62	93.42 62

EI-MVSNet-Vis-set		84.19 62	83.81 62	85.31 67	88.18 152	67.85 119	87.66 132	89.73 146	80.05 17	82.95 67	89.59 120	70.74 49	94.82 84	80.66 64	84.72 149	93.28 68

nrg030		83.88 63	83.53 63	84.96 79	86.77 191	69.28 89	90.46 52	92.67 51	74.79 95	82.95 67	91.33 82	72.70 32	93.09 157	80.79 63	79.28 214	92.50 94

EI-MVSNet-UG-set		83.81 64	83.38 65	85.09 75	87.87 161	67.53 123	87.44 138	89.66 147	79.74 18	82.23 78	89.41 129	70.24 54	94.74 87	79.95 68	83.92 157	92.99 82

CPTT-MVS		83.73 65	83.33 66	84.92 82	93.28 41	70.86 62	92.09 28	90.38 125	68.75 194	79.57 106	92.83 56	60.60 155	93.04 161	80.92 61	91.56 70	90.86 139

EPNet		83.72 66	82.92 72	86.14 58	84.22 224	69.48 84	91.05 41	85.27 223	81.30 8	76.83 154	91.65 72	66.09 88	95.56 52	76.00 103	93.85 52	93.38 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP_MVS		83.64 67	83.14 67	85.14 73	90.08 92	68.71 102	91.25 37	92.44 57	79.12 23	78.92 114	91.00 93	60.42 157	95.38 60	78.71 75	86.32 135	91.33 126

Effi-MVS+		83.62 68	83.08 68	85.24 70	88.38 148	67.45 124	88.89 91	89.15 163	75.50 83	82.27 77	88.28 154	69.61 60	94.45 95	77.81 84	87.84 112	93.84 43

ETV-MVS		83.53 69	82.92 72	85.36 66	90.09 90	67.18 131	89.10 86	92.24 64	72.97 129	80.18 102	85.32 231	68.66 68	95.63 49	74.95 110	92.74 59	93.26 69

OPM-MVS		83.50 70	82.95 71	85.14 73	88.79 135	70.95 58	89.13 85	91.52 95	77.55 41	80.96 96	91.75 70	60.71 151	94.50 94	79.67 71	86.51 133	89.97 176

Vis-MVSNet		83.46 71	82.80 75	85.43 65	90.25 86	68.74 100	90.30 56	90.13 135	76.33 72	80.87 97	92.89 54	61.00 148	94.20 104	72.45 131	90.97 75	93.35 65
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 72	83.45 64	83.28 132	92.74 52	62.28 220	88.17 120	89.50 151	75.22 89	81.49 88	92.74 59	66.75 82	95.11 69	72.85 127	91.58 69	92.45 95

EPP-MVSNet		83.40 73	83.02 70	84.57 90	90.13 88	64.47 180	92.32 22	90.73 116	74.45 103	79.35 109	91.10 87	69.05 66	95.12 68	72.78 128	87.22 122	94.13 26

3Dnovator	76.31 5	83.38 74	82.31 81	86.59 50	87.94 160	72.94 25	90.64 46	92.14 70	77.21 47	75.47 180	92.83 56	58.56 166	94.72 88	73.24 125	92.71 61	92.13 107

EIA-MVS		83.31 75	82.80 75	84.82 84	89.59 101	65.59 156	88.21 118	92.68 50	74.66 99	78.96 112	86.42 207	69.06 65	95.26 64	75.54 108	90.09 87	93.62 55

MVS_Test		83.15 76	83.06 69	83.41 129	86.86 187	63.21 206	86.11 176	92.00 76	74.31 104	82.87 69	89.44 128	70.03 55	93.21 148	77.39 89	88.50 108	93.81 45

IS-MVSNet		83.15 76	82.81 74	84.18 104	89.94 95	63.30 204	91.59 32	88.46 182	79.04 25	79.49 107	92.16 62	65.10 97	94.28 98	67.71 164	91.86 67	94.95 5

DP-MVS Recon		83.11 78	82.09 83	86.15 57	94.44 15	70.92 61	88.79 95	92.20 67	70.53 161	79.17 110	91.03 92	64.12 104	96.03 38	68.39 161	90.14 86	91.50 122

PAPM_NR		83.02 79	82.41 78	84.82 84	92.47 58	66.37 143	87.93 127	91.80 86	73.82 114	77.32 145	90.66 98	67.90 73	94.90 80	70.37 142	89.48 94	93.19 74

VDD-MVS		83.01 80	82.36 80	84.96 79	91.02 74	66.40 142	88.91 90	88.11 185	77.57 38	84.39 51	93.29 45	52.19 215	93.91 119	77.05 93	88.70 102	94.57 14

MVSFormer		82.85 81	82.05 84	85.24 70	87.35 176	70.21 69	90.50 49	90.38 125	68.55 197	81.32 89	89.47 123	61.68 134	93.46 141	78.98 73	90.26 84	92.05 109

OMC-MVS		82.69 82	81.97 87	84.85 83	88.75 137	67.42 125	87.98 123	90.87 114	74.92 94	79.72 105	91.65 72	62.19 132	93.96 112	75.26 109	86.42 134	93.16 75

PVSNet_Blended_VisFu		82.62 83	81.83 89	84.96 79	90.80 79	69.76 78	88.74 99	91.70 91	69.39 179	78.96 112	88.46 149	65.47 94	94.87 83	74.42 113	88.57 104	90.24 160

MVS_111021_LR		82.61 84	82.11 82	84.11 105	88.82 132	71.58 49	85.15 197	86.16 216	74.69 98	80.47 100	91.04 90	62.29 129	90.55 221	80.33 66	90.08 88	90.20 161

HQP-MVS		82.61 84	82.02 85	84.37 96	89.33 110	66.98 134	89.17 80	92.19 68	76.41 66	77.23 148	90.23 105	60.17 160	95.11 69	77.47 87	85.99 140	91.03 133

CLD-MVS		82.31 86	81.65 90	84.29 101	88.47 144	67.73 122	85.81 185	92.35 62	75.78 78	78.33 125	86.58 202	64.01 105	94.35 96	76.05 102	87.48 119	90.79 140
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VNet		82.21 87	82.41 78	81.62 180	90.82 78	60.93 229	84.47 213	89.78 143	76.36 71	84.07 55	91.88 69	64.71 100	90.26 223	70.68 139	88.89 98	93.66 48

diffmvs		82.10 88	81.88 88	82.76 163	83.00 250	63.78 192	83.68 228	89.76 144	72.94 130	82.02 80	89.85 113	65.96 92	90.79 217	82.38 53	87.30 121	93.71 47

LPG-MVS_test		82.08 89	81.27 93	84.50 92	89.23 118	68.76 98	90.22 59	91.94 80	75.37 86	76.64 160	91.51 77	54.29 198	94.91 78	78.44 77	83.78 158	89.83 181

FIs		82.07 90	82.42 77	81.04 193	88.80 134	58.34 250	88.26 117	93.49 17	76.93 54	78.47 122	91.04 90	69.92 57	92.34 180	69.87 148	84.97 146	92.44 96

PS-MVSNAJss		82.07 90	81.31 92	84.34 99	86.51 194	67.27 129	89.27 78	91.51 96	71.75 141	79.37 108	90.22 106	63.15 115	94.27 99	77.69 85	82.36 180	91.49 123

API-MVS		81.99 92	81.23 94	84.26 102	90.94 75	70.18 74	91.10 40	89.32 156	71.51 147	78.66 118	88.28 154	65.26 95	95.10 72	64.74 193	91.23 74	87.51 235

UniMVSNet_NR-MVSNet		81.88 93	81.54 91	82.92 151	88.46 145	63.46 200	87.13 144	92.37 61	80.19 15	78.38 123	89.14 131	71.66 42	93.05 159	70.05 145	76.46 240	92.25 102

MAR-MVS		81.84 94	80.70 100	85.27 69	91.32 70	71.53 50	89.82 65	90.92 112	69.77 173	78.50 120	86.21 211	62.36 128	94.52 93	65.36 186	92.05 65	89.77 184
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
LFMVS		81.82 95	81.23 94	83.57 124	91.89 65	63.43 202	89.84 64	81.85 265	77.04 52	83.21 64	93.10 48	52.26 214	93.43 143	71.98 132	89.95 90	93.85 41

xiu_mvs_v2_base		81.69 96	81.05 97	83.60 122	89.15 121	68.03 117	84.46 215	90.02 137	70.67 158	81.30 92	86.53 205	63.17 114	94.19 105	75.60 107	88.54 106	88.57 216

PS-MVSNAJ		81.69 96	81.02 98	83.70 121	89.51 105	68.21 114	84.28 221	90.09 136	70.79 155	81.26 93	85.62 225	63.15 115	94.29 97	75.62 106	88.87 99	88.59 215

PAPR		81.66 98	80.89 99	83.99 114	90.27 85	64.00 187	86.76 159	91.77 90	68.84 193	77.13 152	89.50 121	67.63 75	94.88 82	67.55 166	88.52 107	93.09 76

UniMVSNet (Re)		81.60 99	81.11 96	83.09 142	88.38 148	64.41 181	87.60 133	93.02 33	78.42 31	78.56 119	88.16 157	69.78 58	93.26 147	69.58 151	76.49 239	91.60 118

FC-MVSNet-test		81.52 100	82.02 85	80.03 207	88.42 147	55.97 286	87.95 125	93.42 21	77.10 50	77.38 143	90.98 95	69.96 56	91.79 192	68.46 160	84.50 151	92.33 98

VDDNet		81.52 100	80.67 101	84.05 109	90.44 83	64.13 186	89.73 70	85.91 219	71.11 151	83.18 65	93.48 40	50.54 238	93.49 140	73.40 123	88.25 110	94.54 15

ACMP	74.13 6	81.51 102	80.57 102	84.36 97	89.42 107	68.69 105	89.97 63	91.50 99	74.46 102	75.04 196	90.41 102	53.82 203	94.54 91	77.56 86	82.91 172	89.86 180
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jason		81.39 103	80.29 109	84.70 88	86.63 193	69.90 76	85.95 179	86.77 207	63.24 247	81.07 95	89.47 123	61.08 147	92.15 185	78.33 80	90.07 89	92.05 109
jason: jason.
lupinMVS		81.39 103	80.27 110	84.76 87	87.35 176	70.21 69	85.55 190	86.41 211	62.85 253	81.32 89	88.61 144	61.68 134	92.24 183	78.41 79	90.26 84	91.83 114

test_yl		81.17 105	80.47 105	83.24 135	89.13 122	63.62 193	86.21 172	89.95 139	72.43 135	81.78 85	89.61 118	57.50 174	93.58 134	70.75 137	86.90 126	92.52 92

DCV-MVSNet		81.17 105	80.47 105	83.24 135	89.13 122	63.62 193	86.21 172	89.95 139	72.43 135	81.78 85	89.61 118	57.50 174	93.58 134	70.75 137	86.90 126	92.52 92

DU-MVS		81.12 107	80.52 104	82.90 152	87.80 164	63.46 200	87.02 148	91.87 84	79.01 26	78.38 123	89.07 133	65.02 98	93.05 159	70.05 145	76.46 240	92.20 104

PVSNet_Blended		80.98 108	80.34 107	82.90 152	88.85 129	65.40 159	84.43 217	92.00 76	67.62 203	78.11 130	85.05 237	66.02 90	94.27 99	71.52 134	89.50 93	89.01 199

mvs-test1		80.88 109	79.40 125	85.29 68	85.13 212	69.75 79	89.28 77	88.10 186	74.99 92	76.44 165	86.72 191	57.27 177	94.26 103	73.53 121	83.18 169	91.87 113

QAPM		80.88 109	79.50 123	85.03 76	88.01 159	68.97 94	91.59 32	92.00 76	66.63 214	75.15 193	92.16 62	57.70 171	95.45 56	63.52 197	88.76 101	90.66 145

1121		80.84 111	79.77 116	84.05 109	93.11 46	70.78 63	84.66 207	85.42 222	57.37 295	81.76 87	92.02 65	63.41 108	94.12 108	67.28 169	92.93 56	87.26 242

TranMVSNet+NR-MVSNet		80.84 111	80.31 108	82.42 167	87.85 162	62.33 218	87.74 131	91.33 102	80.55 12	77.99 133	89.86 112	65.23 96	92.62 170	67.05 174	75.24 258	92.30 100

UGNet		80.83 113	79.59 121	84.54 91	88.04 157	68.09 115	89.42 75	88.16 184	76.95 53	76.22 169	89.46 125	49.30 252	93.94 115	68.48 159	90.31 82	91.60 118
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
Fast-Effi-MVS+		80.81 114	79.92 113	83.47 125	88.85 129	64.51 177	85.53 192	89.39 154	70.79 155	78.49 121	85.06 236	67.54 76	93.58 134	67.03 175	86.58 131	92.32 99

XVG-OURS-SEG-HR		80.81 114	79.76 117	83.96 116	85.60 204	68.78 97	83.54 232	90.50 122	70.66 159	76.71 158	91.66 71	60.69 152	91.26 204	76.94 94	81.58 187	91.83 114

xiu_mvs_v1_base_debu		80.80 116	79.72 118	84.03 111	87.35 176	70.19 71	85.56 187	88.77 174	69.06 188	81.83 81	88.16 157	50.91 232	92.85 165	78.29 81	87.56 116	89.06 194

xiu_mvs_v1_base		80.80 116	79.72 118	84.03 111	87.35 176	70.19 71	85.56 187	88.77 174	69.06 188	81.83 81	88.16 157	50.91 232	92.85 165	78.29 81	87.56 116	89.06 194

xiu_mvs_v1_base_debi		80.80 116	79.72 118	84.03 111	87.35 176	70.19 71	85.56 187	88.77 174	69.06 188	81.83 81	88.16 157	50.91 232	92.85 165	78.29 81	87.56 116	89.06 194

ACMM	73.20 8	80.78 119	79.84 115	83.58 123	89.31 115	68.37 109	89.99 62	91.60 93	70.28 165	77.25 146	89.66 116	53.37 206	93.53 139	74.24 116	82.85 173	88.85 207
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
114514_t		80.68 120	79.51 122	84.20 103	94.09 30	67.27 129	89.64 73	91.11 109	58.75 287	74.08 203	90.72 97	58.10 168	95.04 74	69.70 149	89.42 95	90.30 159

CANet_DTU		80.61 121	79.87 114	82.83 155	85.60 204	63.17 209	87.36 139	88.65 178	76.37 70	75.88 176	88.44 150	53.51 205	93.07 158	73.30 124	89.74 92	92.25 102

VPA-MVSNet		80.60 122	80.55 103	80.76 197	88.07 156	60.80 232	86.86 153	91.58 94	75.67 81	80.24 101	89.45 127	63.34 109	90.25 224	70.51 141	79.22 215	91.23 129

PVSNet_BlendedMVS		80.60 122	80.02 111	82.36 169	88.85 129	65.40 159	86.16 174	92.00 76	69.34 182	78.11 130	86.09 214	66.02 90	94.27 99	71.52 134	82.06 182	87.39 237

AdaColmap		80.58 124	79.42 124	84.06 108	93.09 47	68.91 95	89.36 76	88.97 169	69.27 183	75.70 179	89.69 115	57.20 180	95.77 46	63.06 203	88.41 109	87.50 236

EI-MVSNet		80.52 125	79.98 112	82.12 170	84.28 222	63.19 208	86.41 166	88.95 170	74.18 108	78.69 116	87.54 171	66.62 83	92.43 175	72.57 130	80.57 199	90.74 143

XVG-OURS		80.41 126	79.23 130	83.97 115	85.64 203	69.02 91	83.03 236	90.39 124	71.09 152	77.63 139	91.49 79	54.62 197	91.35 202	75.71 104	83.47 165	91.54 120

PCF-MVS	73.52 7	80.38 127	78.84 137	85.01 77	87.71 168	68.99 93	83.65 229	91.46 100	63.00 250	77.77 137	90.28 103	66.10 87	95.09 73	61.40 219	88.22 111	90.94 137
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		80.37 128	77.83 160	88.00 12	94.42 16	73.33 17	92.78 12	92.99 36	79.14 21	83.67 61	12.47 331	67.45 77	96.60 25	83.06 44	94.50 41	94.07 29

test_djsdf		80.30 129	79.32 128	83.27 133	83.98 229	65.37 163	90.50 49	90.38 125	68.55 197	76.19 170	88.70 140	56.44 185	93.46 141	78.98 73	80.14 206	90.97 136

v2v482		80.23 130	79.29 129	83.05 145	83.62 234	64.14 185	87.04 147	89.97 138	73.61 117	78.18 129	87.22 179	61.10 146	93.82 122	76.11 101	76.78 237	91.18 130

NR-MVSNet		80.23 130	79.38 126	82.78 161	87.80 164	63.34 203	86.31 169	91.09 110	79.01 26	72.17 221	89.07 133	67.20 80	92.81 169	66.08 181	75.65 249	92.20 104

Anonymous20240529		80.19 132	78.89 136	84.10 106	90.60 80	64.75 174	88.95 89	90.90 113	65.97 222	80.59 99	91.17 86	49.97 243	93.73 131	69.16 155	82.70 177	93.81 45

IterMVS-LS		80.06 133	79.38 126	82.11 171	85.89 199	63.20 207	86.79 156	89.34 155	74.19 107	75.45 182	86.72 191	66.62 83	92.39 177	72.58 129	76.86 234	90.75 142
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		80.03 134	78.57 142	84.42 95	85.13 212	68.74 100	88.77 96	88.10 186	74.99 92	74.97 197	83.49 252	57.27 177	93.36 144	73.53 121	80.88 193	91.18 130

v1144		80.03 134	79.03 133	83.01 147	83.78 232	64.51 177	87.11 146	90.57 120	71.96 140	78.08 132	86.20 212	61.41 139	93.94 115	74.93 111	77.23 228	90.60 148

v8		79.97 136	79.02 134	82.80 158	84.09 226	64.50 179	87.96 124	90.29 132	74.13 110	75.24 191	86.81 188	62.88 120	93.89 121	74.39 114	75.40 254	90.00 173

DI_MVS_plusplus_test		79.89 137	78.58 141	83.85 120	82.89 254	65.32 164	86.12 175	89.55 149	69.64 177	70.55 234	85.82 220	57.24 179	93.81 123	76.85 95	88.55 105	92.41 97

test_normal		79.81 138	78.45 144	83.89 119	82.70 258	65.40 159	85.82 184	89.48 152	69.39 179	70.12 243	85.66 223	57.15 181	93.71 132	77.08 92	88.62 103	92.56 91

OpenMVS	72.83 10	79.77 139	78.33 150	84.09 107	85.17 209	69.91 75	90.57 47	90.97 111	66.70 210	72.17 221	91.91 67	54.70 195	93.96 112	61.81 216	90.95 76	88.41 220

v10		79.74 140	78.67 138	82.97 150	84.06 227	64.95 171	87.88 129	90.62 118	73.11 125	75.11 194	86.56 203	61.46 138	94.05 111	73.68 119	75.55 251	89.90 178

BH-RMVSNet		79.61 141	78.44 146	83.14 140	89.38 109	65.93 149	84.95 202	87.15 203	73.56 119	78.19 128	89.79 114	56.67 184	93.36 144	59.53 233	86.74 129	90.13 164

v1192		79.59 142	78.43 147	83.07 144	83.55 236	64.52 176	86.93 151	90.58 119	70.83 154	77.78 136	85.90 216	59.15 163	93.94 115	73.96 118	77.19 230	90.76 141

ab-mvs		79.51 143	78.97 135	81.14 191	88.46 145	60.91 230	83.84 226	89.24 161	70.36 163	79.03 111	88.87 138	63.23 113	90.21 225	65.12 188	82.57 178	92.28 101

WR-MVS		79.49 144	79.22 131	80.27 204	88.79 135	58.35 249	85.06 199	88.61 180	78.56 29	77.65 138	88.34 152	63.81 107	90.66 220	64.98 191	77.22 229	91.80 117

v144192		79.47 145	78.37 148	82.78 161	83.35 238	63.96 188	86.96 149	90.36 128	69.99 169	77.50 140	85.67 222	60.66 153	93.77 127	74.27 115	76.58 238	90.62 146

BH-untuned		79.47 145	78.60 140	82.05 172	89.19 120	65.91 150	86.07 177	88.52 181	72.18 137	75.42 183	87.69 166	61.15 145	93.54 138	60.38 226	86.83 128	86.70 255

mvs_anonymous		79.42 147	79.11 132	80.34 202	84.45 221	57.97 256	82.59 237	87.62 195	67.40 207	76.17 173	88.56 147	68.47 69	89.59 234	70.65 140	86.05 139	93.47 61

thisisatest0530		79.40 148	77.76 164	84.31 100	87.69 170	65.10 170	87.36 139	84.26 234	70.04 168	77.42 142	88.26 156	49.94 244	94.79 86	70.20 143	84.70 150	93.03 79

tttt0517		79.40 148	77.91 158	83.90 118	88.10 155	63.84 190	88.37 112	84.05 236	71.45 148	76.78 156	89.12 132	49.93 246	94.89 81	70.18 144	83.18 169	92.96 83

V42		79.38 150	78.24 152	82.83 155	81.10 279	65.50 158	85.55 190	89.82 142	71.57 146	78.21 127	86.12 213	60.66 153	93.18 152	75.64 105	75.46 253	89.81 183

jajsoiax		79.29 151	77.96 156	83.27 133	84.68 218	66.57 140	89.25 79	90.16 134	69.20 185	75.46 181	89.49 122	45.75 275	93.13 155	76.84 96	80.80 195	90.11 165

v1921920		79.22 152	78.03 155	82.80 158	83.30 240	63.94 189	86.80 155	90.33 129	69.91 170	77.48 141	85.53 226	58.44 167	93.75 129	73.60 120	76.85 235	90.71 144

TAPA-MVS	73.13 9	79.15 153	77.94 157	82.79 160	89.59 101	62.99 213	88.16 121	91.51 96	65.77 223	77.14 151	91.09 88	60.91 149	93.21 148	50.26 282	87.05 124	92.17 106
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 154	77.77 163	83.22 137	84.70 217	66.37 143	89.17 80	90.19 133	69.38 181	75.40 184	89.46 125	44.17 281	93.15 153	76.78 97	80.70 197	90.14 163

UniMVSNet_ETH3D		79.10 155	78.24 152	81.70 179	86.85 188	60.24 237	87.28 142	88.79 173	74.25 106	76.84 153	90.53 101	49.48 249	91.56 198	67.98 162	82.15 181	93.29 67

CDS-MVSNet		79.07 156	77.70 165	83.17 139	87.60 171	68.23 113	84.40 219	86.20 215	67.49 205	76.36 166	86.54 204	61.54 137	90.79 217	61.86 215	87.33 120	90.49 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 157	77.88 159	82.38 168	83.07 247	64.80 173	84.08 225	88.95 170	69.01 191	78.69 116	87.17 182	54.70 195	92.43 175	74.69 112	80.57 199	89.89 179

v1240		78.99 158	77.78 162	82.64 164	83.21 242	63.54 197	86.62 162	90.30 131	69.74 176	77.33 144	85.68 221	57.04 182	93.76 128	73.13 126	76.92 232	90.62 146

Anonymous20231211		78.97 159	77.69 166	82.81 157	90.54 81	64.29 183	90.11 61	91.51 96	65.01 232	76.16 174	88.13 161	50.56 237	93.03 162	69.68 150	77.56 226	91.11 132

v7n		78.97 159	77.58 168	83.14 140	83.45 237	65.51 157	88.32 113	91.21 105	73.69 116	72.41 218	86.32 210	57.93 169	93.81 123	69.18 154	75.65 249	90.11 165

TAMVS		78.89 161	77.51 169	83.03 146	87.80 164	67.79 121	84.72 206	85.05 226	67.63 202	76.75 157	87.70 165	62.25 130	90.82 216	58.53 243	87.13 123	90.49 153

v148		78.72 162	77.80 161	81.47 183	82.73 257	61.96 223	86.30 170	88.08 188	73.26 124	76.18 171	85.47 228	62.46 126	92.36 179	71.92 133	73.82 271	90.09 167

VPNet		78.69 163	78.66 139	78.76 230	88.31 150	55.72 288	84.45 216	86.63 209	76.79 57	78.26 126	90.55 100	59.30 162	89.70 233	66.63 176	77.05 231	90.88 138

ET-MVSNet_ETH3D		78.63 164	76.63 184	84.64 89	86.73 192	69.47 85	85.01 200	84.61 229	69.54 178	66.51 277	86.59 200	50.16 241	91.75 193	76.26 100	84.24 155	92.69 88

anonymousdsp		78.60 165	77.15 174	82.98 149	80.51 285	67.08 132	87.24 143	89.53 150	65.66 225	75.16 192	87.19 181	52.52 208	92.25 182	77.17 91	79.34 213	89.61 185

WR-MVS_H		78.51 166	78.49 143	78.56 233	88.02 158	56.38 281	88.43 105	92.67 51	77.14 48	73.89 204	87.55 170	66.25 86	89.24 240	58.92 238	73.55 273	90.06 171

GBi-Net		78.40 167	77.40 170	81.40 185	87.60 171	63.01 210	88.39 109	89.28 157	71.63 143	75.34 186	87.28 175	54.80 191	91.11 207	62.72 204	79.57 208	90.09 167

test1		78.40 167	77.40 170	81.40 185	87.60 171	63.01 210	88.39 109	89.28 157	71.63 143	75.34 186	87.28 175	54.80 191	91.11 207	62.72 204	79.57 208	90.09 167

Vis-MVSNet (Re-imp)		78.36 169	78.45 144	78.07 241	88.64 139	51.78 304	86.70 160	79.63 286	74.14 109	75.11 194	90.83 96	61.29 142	89.75 231	58.10 247	91.60 68	92.69 88

Anonymous202405211		78.25 170	77.01 176	81.99 174	91.03 73	60.67 233	84.77 205	83.90 238	70.65 160	80.00 103	91.20 85	41.08 297	91.43 200	65.21 187	85.26 144	93.85 41

CP-MVSNet		78.22 171	78.34 149	77.84 243	87.83 163	54.54 293	87.94 126	91.17 107	77.65 35	73.48 206	88.49 148	62.24 131	88.43 254	62.19 210	74.07 266	90.55 151

BH-w/o		78.21 172	77.33 172	80.84 195	88.81 133	65.13 169	84.87 203	87.85 192	69.75 174	74.52 201	84.74 240	61.34 140	93.11 156	58.24 246	85.84 142	84.27 283

FMVSNet2		78.20 173	77.21 173	81.20 189	87.60 171	62.89 214	87.47 137	89.02 166	71.63 143	75.29 190	87.28 175	54.80 191	91.10 210	62.38 208	79.38 212	89.61 185

MVS		78.19 174	76.99 177	81.78 177	85.66 202	66.99 133	84.66 207	90.47 123	55.08 305	72.02 223	85.27 232	63.83 106	94.11 110	66.10 180	89.80 91	84.24 284

Baseline_NR-MVSNet		78.15 175	78.33 150	77.61 248	85.79 200	56.21 284	86.78 157	85.76 220	73.60 118	77.93 134	87.57 169	65.02 98	88.99 244	67.14 173	75.33 255	87.63 232

CNLPA		78.08 176	76.79 181	81.97 175	90.40 84	71.07 56	87.59 134	84.55 230	66.03 221	72.38 219	89.64 117	57.56 173	86.04 273	59.61 232	83.35 166	88.79 210

PLC	70.83 11	78.05 177	76.37 188	83.08 143	91.88 66	67.80 120	88.19 119	89.46 153	64.33 240	69.87 249	88.38 151	53.66 204	93.58 134	58.86 239	82.73 175	87.86 228
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 178	76.49 185	82.62 165	83.16 246	66.96 136	86.94 150	87.45 200	72.45 132	71.49 229	84.17 243	54.79 194	91.58 197	67.61 165	80.31 203	89.30 190

PS-CasMVS		78.01 179	78.09 154	77.77 245	87.71 168	54.39 295	88.02 122	91.22 104	77.50 43	73.26 208	88.64 143	60.73 150	88.41 255	61.88 214	73.88 270	90.53 152

HY-MVS	69.67 12	77.95 180	77.15 174	80.36 201	87.57 175	60.21 238	83.37 234	87.78 193	66.11 218	75.37 185	87.06 186	63.27 111	90.48 222	61.38 220	82.43 179	90.40 157

FMVSNet3		77.88 181	76.85 179	80.97 194	86.84 189	62.36 217	86.52 165	88.77 174	71.13 150	75.34 186	86.66 198	54.07 201	91.10 210	62.72 204	79.57 208	89.45 188

PEN-MVS		77.73 182	77.69 166	77.84 243	87.07 186	53.91 297	87.91 128	91.18 106	77.56 40	73.14 210	88.82 139	61.23 143	89.17 241	59.95 229	72.37 277	90.43 155

PAPM		77.68 183	76.40 187	81.51 182	87.29 182	61.85 224	83.78 227	89.59 148	64.74 234	71.23 230	88.70 140	62.59 123	93.66 133	52.66 272	87.03 125	89.01 199

CHOSEN 1792x2688		77.63 184	75.69 191	83.44 126	89.98 94	68.58 107	78.70 275	87.50 198	56.38 300	75.80 178	86.84 187	58.67 165	91.40 201	61.58 218	85.75 143	90.34 158

HyFIR lowres test		77.53 185	75.40 197	83.94 117	89.59 101	66.62 138	80.36 257	88.64 179	56.29 301	76.45 162	85.17 233	57.64 172	93.28 146	61.34 221	83.10 171	91.91 112

FMVSNet1		77.44 186	76.12 190	81.40 185	86.81 190	63.01 210	88.39 109	89.28 157	70.49 162	74.39 202	87.28 175	49.06 255	91.11 207	60.91 223	78.52 217	90.09 167

TR-MVS		77.44 186	76.18 189	81.20 189	88.24 151	63.24 205	84.61 211	86.40 212	67.55 204	77.81 135	86.48 206	54.10 200	93.15 153	57.75 250	82.72 176	87.20 243

1112_ss		77.40 188	76.43 186	80.32 203	89.11 126	60.41 236	83.65 229	87.72 194	62.13 261	73.05 211	86.72 191	62.58 124	89.97 228	62.11 213	80.80 195	90.59 150

thisisatest0515		77.33 189	75.38 198	83.18 138	85.27 208	63.80 191	82.11 242	83.27 248	65.06 230	75.91 175	83.84 247	49.54 248	94.27 99	67.24 171	86.19 137	91.48 124

pm-mvs1		77.25 190	76.68 183	78.93 227	84.22 224	58.62 248	86.41 166	88.36 183	71.37 149	73.31 207	88.01 162	61.22 144	89.15 242	64.24 195	73.01 275	89.03 198

LCM-MVSNet-Re		77.05 191	76.94 178	77.36 251	87.20 183	51.60 305	80.06 260	80.46 278	75.20 90	67.69 264	86.72 191	62.48 125	88.98 245	63.44 199	89.25 96	91.51 121

DTE-MVSNet		76.99 192	76.80 180	77.54 250	86.24 196	53.06 301	87.52 135	90.66 117	77.08 51	72.50 216	88.67 142	60.48 156	89.52 235	57.33 254	70.74 288	90.05 172

baseline1		76.98 193	76.75 182	77.66 246	88.13 153	55.66 289	85.12 198	81.89 263	73.04 127	76.79 155	88.90 136	62.43 127	87.78 262	63.30 201	71.18 286	89.55 187

LS3D		76.95 194	74.82 204	83.37 130	90.45 82	67.36 128	89.15 84	86.94 205	61.87 263	69.52 252	90.61 99	51.71 226	94.53 92	46.38 302	86.71 130	88.21 222

GA-MVS		76.87 195	75.17 202	81.97 175	82.75 256	62.58 215	81.44 251	86.35 214	72.16 139	74.74 199	82.89 257	46.20 270	92.02 187	68.85 158	81.09 191	91.30 128

DP-MVS		76.78 196	74.57 206	83.42 127	93.29 40	69.46 87	88.55 104	83.70 240	63.98 244	70.20 239	88.89 137	54.01 202	94.80 85	46.66 299	81.88 185	86.01 267

cascas		76.72 197	74.64 205	82.99 148	85.78 201	65.88 151	82.33 240	89.21 162	60.85 269	72.74 213	81.02 275	47.28 262	93.75 129	67.48 167	85.02 145	89.34 189

1314		76.53 198	75.30 201	80.21 205	83.93 230	62.32 219	84.66 207	88.81 172	60.23 273	70.16 242	84.07 245	55.30 189	90.73 219	67.37 168	83.21 168	87.59 234

thres100view900		76.50 199	75.55 194	79.33 221	89.52 104	56.99 270	85.83 183	83.23 249	73.94 112	76.32 167	87.12 183	51.89 223	91.95 188	48.33 290	83.75 160	89.07 192

thres600view7		76.50 199	75.44 195	79.68 214	89.40 108	57.16 267	85.53 192	83.23 249	73.79 115	76.26 168	87.09 184	51.89 223	91.89 191	48.05 295	83.72 163	90.00 173

thres400		76.50 199	75.37 199	79.86 210	89.13 122	57.65 262	85.17 195	83.60 241	73.41 122	76.45 162	86.39 208	52.12 216	91.95 188	48.33 290	83.75 160	90.00 173

tfpn200view9		76.42 202	75.37 199	79.55 220	89.13 122	57.65 262	85.17 195	83.60 241	73.41 122	76.45 162	86.39 208	52.12 216	91.95 188	48.33 290	83.75 160	89.07 192

Test_1112_low_res		76.40 203	75.44 195	79.27 222	89.28 116	58.09 252	81.69 246	87.07 204	59.53 280	72.48 217	86.67 197	61.30 141	89.33 238	60.81 225	80.15 205	90.41 156

F-COLMAP		76.38 204	74.33 211	82.50 166	89.28 116	66.95 137	88.41 108	89.03 165	64.05 242	66.83 273	88.61 144	46.78 265	92.89 164	57.48 251	78.55 216	87.67 231

LTVRE_ROB	69.57 13	76.25 205	74.54 208	81.41 184	88.60 140	64.38 182	79.24 268	89.12 164	70.76 157	69.79 251	87.86 163	49.09 254	93.20 150	56.21 260	80.16 204	86.65 256
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
MVP-Stereo		76.12 206	74.46 210	81.13 192	85.37 207	69.79 77	84.42 218	87.95 190	65.03 231	67.46 266	85.33 230	53.28 207	91.73 195	58.01 248	83.27 167	81.85 303
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 207	74.27 212	81.62 180	83.20 243	64.67 175	83.60 231	89.75 145	69.75 174	71.85 224	87.09 184	32.78 317	92.11 186	69.99 147	80.43 202	88.09 224

ACMH+	68.96 14	76.01 208	74.01 213	82.03 173	88.60 140	65.31 165	88.86 92	87.55 196	70.25 166	67.75 263	87.47 173	41.27 295	93.19 151	58.37 244	75.94 245	87.60 233

ACMH	67.68 16	75.89 209	73.93 214	81.77 178	88.71 138	66.61 139	88.62 101	89.01 167	69.81 171	66.78 274	86.70 196	41.95 294	91.51 199	55.64 261	78.14 222	87.17 244
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 210	73.36 220	83.31 131	84.76 216	66.03 146	83.38 233	85.06 225	70.21 167	69.40 253	81.05 274	45.76 274	94.66 89	65.10 189	75.49 252	89.25 191
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
testing_2		75.73 211	73.34 221	82.89 154	77.37 306	65.22 166	84.10 224	90.54 121	69.09 187	60.46 303	81.15 273	40.48 299	92.84 168	76.36 99	80.54 201	90.60 148

baseline2		75.70 212	73.83 217	81.30 188	83.26 241	61.79 225	82.57 238	80.65 274	66.81 208	66.88 272	83.42 253	57.86 170	92.19 184	63.47 198	79.57 208	89.91 177

WTY-MVS		75.65 213	75.68 192	75.57 266	86.40 195	56.82 272	77.92 282	82.40 258	65.10 229	76.18 171	87.72 164	63.13 118	80.90 296	60.31 227	81.96 183	89.00 201

thres200		75.55 214	74.47 209	78.82 229	87.78 167	57.85 259	83.07 235	83.51 244	72.44 134	75.84 177	84.42 242	52.08 218	91.75 193	47.41 297	83.64 164	86.86 251

EPNet_dtu		75.46 215	74.86 203	77.23 254	82.57 261	54.60 292	86.89 152	83.09 252	71.64 142	66.25 279	85.86 218	55.99 186	88.04 259	54.92 263	86.55 132	89.05 197
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 216	73.87 216	80.11 206	82.69 259	64.85 172	81.57 249	83.47 245	69.16 186	70.49 236	84.15 244	51.95 221	88.15 257	69.23 153	72.14 280	87.34 239

XXY-MVS		75.41 217	75.56 193	74.96 271	83.59 235	57.82 260	80.59 256	83.87 239	66.54 215	74.93 198	88.31 153	63.24 112	80.09 299	62.16 211	76.85 235	86.97 249

TransMVSNet (Re)		75.39 218	74.56 207	77.86 242	85.50 206	57.10 269	86.78 157	86.09 218	72.17 138	71.53 228	87.34 174	63.01 119	89.31 239	56.84 257	61.83 308	87.17 244

CostFormer		75.24 219	73.90 215	79.27 222	82.65 260	58.27 251	80.80 252	82.73 256	61.57 264	75.33 189	83.13 255	55.52 187	91.07 213	64.98 191	78.34 221	88.45 218

D2MVS		74.82 220	73.21 222	79.64 217	79.81 292	62.56 216	80.34 258	87.35 201	64.37 239	68.86 256	82.66 261	46.37 267	90.10 227	67.91 163	81.24 190	86.25 260

pmmvs6		74.69 221	73.39 219	78.61 232	81.38 274	57.48 265	86.64 161	87.95 190	64.99 233	70.18 240	86.61 199	50.43 239	89.52 235	62.12 212	70.18 290	88.83 208

PatchFormer-LS_test		74.50 222	73.05 225	78.86 228	82.95 252	59.55 243	81.65 247	82.30 259	67.44 206	71.62 227	78.15 296	52.34 212	88.92 249	65.05 190	75.90 246	88.12 223

tfpnnormal		74.39 223	73.16 223	78.08 240	86.10 198	58.05 253	84.65 210	87.53 197	70.32 164	71.22 231	85.63 224	54.97 190	89.86 229	43.03 311	75.02 259	86.32 259

IterMVS		74.29 224	72.94 226	78.35 237	81.53 271	63.49 199	81.58 248	82.49 257	68.06 201	69.99 246	83.69 250	51.66 227	85.54 276	65.85 183	71.64 283	86.01 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 225	72.42 230	79.80 212	83.76 233	59.59 241	85.92 181	86.64 208	66.39 216	66.96 271	87.58 168	39.46 302	91.60 196	65.76 184	69.27 292	88.22 221

SCA		74.22 226	72.33 231	79.91 209	84.05 228	62.17 221	79.96 262	79.29 288	66.30 217	72.38 219	80.13 282	51.95 221	88.60 252	59.25 235	77.67 225	88.96 203

miper_lstm_enhance		74.11 227	73.11 224	77.13 255	80.11 288	59.62 240	72.23 304	86.92 206	66.76 209	70.40 237	82.92 256	56.93 183	82.92 290	69.06 156	72.63 276	88.87 206

EG-PatchMatch MVS		74.04 228	71.82 235	80.71 198	84.92 215	67.42 125	85.86 182	88.08 188	66.04 220	64.22 291	83.85 246	35.10 316	92.56 173	57.44 252	80.83 194	82.16 302

pmmvs4		74.03 229	71.91 233	80.39 200	81.96 266	68.32 110	81.45 250	82.14 261	59.32 281	69.87 249	85.13 234	52.40 211	88.13 258	60.21 228	74.74 262	84.73 281

MS-PatchMatch		73.83 230	72.67 227	77.30 253	83.87 231	66.02 147	81.82 243	84.66 228	61.37 267	68.61 259	82.82 259	47.29 261	88.21 256	59.27 234	84.32 154	77.68 315

DWT-MVSNet_test		73.70 231	71.86 234	79.21 224	82.91 253	58.94 245	82.34 239	82.17 260	65.21 227	71.05 233	78.31 293	44.21 280	90.17 226	63.29 202	77.28 227	88.53 217

sss		73.60 232	73.64 218	73.51 281	82.80 255	55.01 291	76.12 288	81.69 266	62.47 258	74.68 200	85.85 219	57.32 176	78.11 306	60.86 224	80.93 192	87.39 237

SixPastTwentyTwo		73.37 233	71.26 241	79.70 213	85.08 214	57.89 258	85.57 186	83.56 243	71.03 153	65.66 281	85.88 217	42.10 292	92.57 172	59.11 237	63.34 307	88.65 214

CR-MVSNet		73.37 233	71.27 240	79.67 215	81.32 277	65.19 167	75.92 290	80.30 280	59.92 276	72.73 214	81.19 271	52.50 209	86.69 267	59.84 230	77.71 223	87.11 247

MSDG		73.36 235	70.99 242	80.49 199	84.51 220	65.80 152	80.71 254	86.13 217	65.70 224	65.46 282	83.74 249	44.60 278	90.91 215	51.13 277	76.89 233	84.74 280

tpm2		73.26 236	71.46 237	78.63 231	83.34 239	56.71 275	80.65 255	80.40 279	56.63 299	73.55 205	82.02 268	51.80 225	91.24 205	56.35 259	78.42 220	87.95 225

RPSCF		73.23 237	71.46 237	78.54 234	82.50 262	59.85 239	82.18 241	82.84 255	58.96 284	71.15 232	89.41 129	45.48 277	84.77 281	58.82 240	71.83 282	91.02 135

PatchmatchNet		73.12 238	71.33 239	78.49 236	83.18 244	60.85 231	79.63 264	78.57 290	64.13 241	71.73 225	79.81 287	51.20 230	85.97 274	57.40 253	76.36 242	88.66 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
COLMAP_ROB	66.92 17	73.01 239	70.41 246	80.81 196	87.13 185	65.63 155	88.30 114	84.19 235	62.96 251	63.80 294	87.69 166	38.04 308	92.56 173	46.66 299	74.91 260	84.24 284
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 240	72.58 228	74.25 278	84.28 222	50.85 310	86.41 166	83.45 246	44.56 316	73.23 209	87.54 171	49.38 250	85.70 275	65.90 182	78.44 219	86.19 262

test-LLR		72.94 241	72.43 229	74.48 275	81.35 275	58.04 254	78.38 276	77.46 296	66.66 211	69.95 247	79.00 291	48.06 258	79.24 300	66.13 178	84.83 147	86.15 263

test_0402		72.79 242	70.44 245	79.84 211	88.13 153	65.99 148	85.93 180	84.29 232	65.57 226	67.40 268	85.49 227	46.92 264	92.61 171	35.88 320	74.38 265	80.94 306

tpmrst		72.39 243	72.13 232	73.18 283	80.54 284	49.91 313	79.91 263	79.08 289	63.11 248	71.69 226	79.95 284	55.32 188	82.77 291	65.66 185	73.89 269	86.87 250

PatchMatch-RL		72.38 244	70.90 243	76.80 258	88.60 140	67.38 127	79.53 265	76.17 302	62.75 255	69.36 254	82.00 269	45.51 276	84.89 280	53.62 268	80.58 198	78.12 314

tpm		72.37 245	71.71 236	74.35 277	82.19 264	52.00 302	79.22 269	77.29 298	64.56 236	72.95 212	83.68 251	51.35 228	83.26 289	58.33 245	75.80 247	87.81 229

PVSNet	64.34 18	72.08 246	70.87 244	75.69 264	86.21 197	56.44 279	74.37 300	80.73 273	62.06 262	70.17 241	82.23 266	42.86 287	83.31 288	54.77 264	84.45 153	87.32 240

RPMNet		71.62 247	68.94 254	79.67 215	81.32 277	65.19 167	75.92 290	78.30 292	57.60 293	72.73 214	76.45 305	52.30 213	86.69 267	48.14 294	77.71 223	87.11 247

pmmvs5		71.55 248	70.20 248	75.61 265	77.83 303	56.39 280	81.74 245	80.89 270	57.76 291	67.46 266	84.49 241	49.26 253	85.32 279	57.08 256	75.29 256	85.11 277

test-mter		71.41 249	70.39 247	74.48 275	81.35 275	58.04 254	78.38 276	77.46 296	60.32 272	69.95 247	79.00 291	36.08 314	79.24 300	66.13 178	84.83 147	86.15 263

K. test v3		71.19 250	68.51 256	79.21 224	83.04 249	57.78 261	84.35 220	76.91 300	72.90 131	62.99 297	82.86 258	39.27 303	91.09 212	61.65 217	52.66 320	88.75 211

tpmvs		71.09 251	69.29 251	76.49 259	82.04 265	56.04 285	78.92 273	81.37 269	64.05 242	67.18 270	78.28 294	49.74 247	89.77 230	49.67 285	72.37 277	83.67 289

AllTest		70.96 252	68.09 262	79.58 218	85.15 210	63.62 193	84.58 212	79.83 284	62.31 259	60.32 304	86.73 189	32.02 318	88.96 247	50.28 280	71.57 284	86.15 263

Patchmtry		70.74 253	69.16 252	75.49 268	80.72 281	54.07 296	74.94 299	80.30 280	58.34 288	70.01 244	81.19 271	52.50 209	86.54 269	53.37 269	71.09 287	85.87 270

MIMVSNet		70.69 254	69.30 250	74.88 272	84.52 219	56.35 282	75.87 292	79.42 287	64.59 235	67.76 262	82.41 263	41.10 296	81.54 295	46.64 301	81.34 188	86.75 254

tpm cat1		70.57 255	68.31 258	77.35 252	82.41 263	57.95 257	78.08 280	80.22 282	52.04 311	68.54 260	77.66 300	52.00 220	87.84 261	51.77 273	72.07 281	86.25 260

OpenMVS_ROB	64.09 19	70.56 256	68.19 259	77.65 247	80.26 286	59.41 244	85.01 200	82.96 254	58.76 286	65.43 283	82.33 264	37.63 310	91.23 206	45.34 307	76.03 244	82.32 300

pmmvs-eth3d		70.50 257	67.83 266	78.52 235	77.37 306	66.18 145	81.82 243	81.51 267	58.90 285	63.90 293	80.42 280	42.69 288	86.28 272	58.56 242	65.30 304	83.11 295

USDC		70.33 258	68.37 257	76.21 261	80.60 283	56.23 283	79.19 270	86.49 210	60.89 268	61.29 300	85.47 228	31.78 320	89.47 237	53.37 269	76.21 243	82.94 299

Patchmatch-RL test		70.24 259	67.78 268	77.61 248	77.43 305	59.57 242	71.16 306	70.33 314	62.94 252	68.65 258	72.77 312	50.62 236	85.49 277	69.58 151	66.58 301	87.77 230

CMPMVS	51.72 21	70.19 260	68.16 260	76.28 260	73.15 320	57.55 264	79.47 266	83.92 237	48.02 315	56.48 315	84.81 238	43.13 285	86.42 271	62.67 207	81.81 186	84.89 278
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ppachtmachnet_test		70.04 261	67.34 271	78.14 239	79.80 293	61.13 227	79.19 270	80.59 275	59.16 283	65.27 284	79.29 288	46.75 266	87.29 264	49.33 286	66.72 299	86.00 269

gg-mvs-nofinetune		69.95 262	67.96 263	75.94 262	83.07 247	54.51 294	77.23 285	70.29 315	63.11 248	70.32 238	62.33 318	43.62 283	88.69 251	53.88 267	87.76 113	84.62 282

TESTMET0.1,1		69.89 263	69.00 253	72.55 284	79.27 300	56.85 271	78.38 276	74.71 308	57.64 292	68.09 261	77.19 302	37.75 309	76.70 311	63.92 196	84.09 156	84.10 287

FMVSNet5		69.50 264	67.96 263	74.15 279	82.97 251	55.35 290	80.01 261	82.12 262	62.56 257	63.02 295	81.53 270	36.92 311	81.92 293	48.42 289	74.06 267	85.17 276

PMMVS		69.34 265	68.67 255	71.35 290	75.67 312	62.03 222	75.17 294	73.46 310	50.00 314	68.68 257	79.05 289	52.07 219	78.13 305	61.16 222	82.77 174	73.90 318

our_test_3		69.14 266	67.00 272	75.57 266	79.80 293	58.80 246	77.96 281	77.81 294	59.55 279	62.90 298	78.25 295	47.43 260	83.97 283	51.71 274	67.58 298	83.93 288

EPMVS		69.02 267	68.16 260	71.59 286	79.61 296	49.80 315	77.40 284	66.93 322	62.82 254	70.01 244	79.05 289	45.79 273	77.86 308	56.58 258	75.26 257	87.13 246

Anonymous20231206		68.60 268	67.80 267	71.02 292	80.23 287	50.75 311	78.30 279	80.47 277	56.79 298	66.11 280	82.63 262	46.35 268	78.95 302	43.62 310	75.70 248	83.36 292

MIMVSNet1		68.58 269	66.78 274	73.98 280	80.07 289	51.82 303	80.77 253	84.37 231	64.40 238	59.75 307	82.16 267	36.47 312	83.63 286	42.73 312	70.33 289	86.48 258

EU-MVSNet		68.53 270	67.61 270	71.31 291	78.51 302	47.01 319	84.47 213	84.27 233	42.27 317	66.44 278	84.79 239	40.44 300	83.76 284	58.76 241	68.54 297	83.17 293

PatchT		68.46 271	67.85 265	70.29 294	80.70 282	43.93 322	72.47 303	74.88 305	60.15 274	70.55 234	76.57 304	49.94 244	81.59 294	50.58 278	74.83 261	85.34 273

test0.0.03 1		68.00 272	67.69 269	68.90 299	77.55 304	47.43 317	75.70 293	72.95 312	66.66 211	66.56 275	82.29 265	48.06 258	75.87 315	44.97 308	74.51 264	83.41 291

TDRefinement		67.49 273	64.34 280	76.92 256	73.47 318	61.07 228	84.86 204	82.98 253	59.77 277	58.30 310	85.13 234	26.06 322	87.89 260	47.92 296	60.59 312	81.81 304

test20.03		67.45 274	66.95 273	68.94 298	75.48 314	44.84 321	77.50 283	77.67 295	66.66 211	63.01 296	83.80 248	47.02 263	78.40 304	42.53 313	68.86 296	83.58 290

UnsupCasMVSNet_eth		67.33 275	65.99 276	71.37 288	73.48 317	51.47 307	75.16 295	85.19 224	65.20 228	60.78 302	80.93 278	42.35 289	77.20 310	57.12 255	53.69 319	85.44 272

TinyColmap		67.30 276	64.81 278	74.76 274	81.92 267	56.68 276	80.29 259	81.49 268	60.33 271	56.27 316	83.22 254	24.77 323	87.66 263	45.52 305	69.47 291	79.95 310

dp		66.80 277	65.43 277	70.90 293	79.74 295	48.82 316	75.12 297	74.77 306	59.61 278	64.08 292	77.23 301	42.89 286	80.72 297	48.86 288	66.58 301	83.16 294

MDA-MVSNet-bldmvs		66.68 278	63.66 282	75.75 263	79.28 299	60.56 235	73.92 301	78.35 291	64.43 237	50.13 321	79.87 286	44.02 282	83.67 285	46.10 303	56.86 315	83.03 297

testgi		66.67 279	66.53 275	67.08 304	75.62 313	41.69 325	75.93 289	76.50 301	66.11 218	65.20 287	86.59 200	35.72 315	74.71 319	43.71 309	73.38 274	84.84 279

CHOSEN 280x420		66.51 280	64.71 279	71.90 285	81.45 272	63.52 198	57.98 325	68.95 321	53.57 307	62.59 299	76.70 303	46.22 269	75.29 318	55.25 262	79.68 207	76.88 317

PM-MVS		66.41 281	64.14 281	73.20 282	73.92 315	56.45 278	78.97 272	64.96 326	63.88 246	64.72 288	80.24 281	19.84 327	83.44 287	66.24 177	64.52 306	79.71 311

JIA-IIPM		66.32 282	62.82 288	76.82 257	77.09 308	61.72 226	65.34 320	75.38 303	58.04 290	64.51 289	62.32 319	42.05 293	86.51 270	51.45 276	69.22 293	82.21 301

ADS-MVSNet2		66.20 283	63.33 283	74.82 273	79.92 290	58.75 247	67.55 317	75.19 304	53.37 308	65.25 285	75.86 306	42.32 290	80.53 298	41.57 314	68.91 294	85.18 274

YYNet1		65.03 284	62.91 286	71.38 287	75.85 311	56.60 277	69.12 314	74.66 309	57.28 296	54.12 317	77.87 298	45.85 272	74.48 320	49.95 283	61.52 310	83.05 296

MDA-MVSNet_test_wron		65.03 284	62.92 285	71.37 288	75.93 310	56.73 273	69.09 315	74.73 307	57.28 296	54.03 318	77.89 297	45.88 271	74.39 321	49.89 284	61.55 309	82.99 298

Patchmatch-test		64.82 286	63.24 284	69.57 296	79.42 298	49.82 314	63.49 323	69.05 320	51.98 312	59.95 306	80.13 282	50.91 232	70.98 324	40.66 316	73.57 272	87.90 227

ADS-MVSNet		64.36 287	62.88 287	68.78 301	79.92 290	47.17 318	67.55 317	71.18 313	53.37 308	65.25 285	75.86 306	42.32 290	73.99 322	41.57 314	68.91 294	85.18 274

LF4IMVS		64.02 288	62.19 289	69.50 297	70.90 322	53.29 300	76.13 287	77.18 299	52.65 310	58.59 308	80.98 276	23.55 324	76.52 312	53.06 271	66.66 300	78.68 313

UnsupCasMVSNet_bld		63.70 289	61.53 291	70.21 295	73.69 316	51.39 308	72.82 302	81.89 263	55.63 303	57.81 311	71.80 314	38.67 305	78.61 303	49.26 287	52.21 321	80.63 307

new-patchmatchnet		61.73 290	61.73 290	61.70 307	72.74 321	24.50 335	69.16 313	78.03 293	61.40 265	56.72 314	75.53 308	38.42 306	76.48 313	45.95 304	57.67 314	84.13 286

PVSNet_0	57.27 20	61.67 291	59.27 292	68.85 300	79.61 296	57.44 266	68.01 316	73.44 311	55.93 302	58.54 309	70.41 315	44.58 279	77.55 309	47.01 298	35.91 324	71.55 320

MVS-HIRNet		59.14 292	57.67 293	63.57 306	81.65 269	43.50 323	71.73 305	65.06 325	39.59 321	51.43 320	57.73 322	38.34 307	82.58 292	39.53 317	73.95 268	64.62 323

pmmvs3		57.79 293	54.26 295	68.37 302	64.02 326	56.72 274	75.12 297	65.17 324	40.20 319	52.93 319	69.86 316	20.36 326	75.48 317	45.45 306	55.25 318	72.90 319

DSMNet-mixed		57.77 294	56.90 294	60.38 308	67.70 324	35.61 328	69.18 312	53.97 329	32.30 326	57.49 312	79.88 285	40.39 301	68.57 326	38.78 318	72.37 277	76.97 316

LCM-MVSNet		54.25 295	49.68 299	67.97 303	53.73 329	45.28 320	66.85 319	80.78 272	35.96 323	39.45 324	62.23 320	8.70 335	78.06 307	48.24 293	51.20 322	80.57 308

FPMVS		53.68 296	51.64 297	59.81 309	65.08 325	51.03 309	69.48 311	69.58 318	41.46 318	40.67 323	72.32 313	16.46 330	70.00 325	24.24 325	65.42 303	58.40 324

N_pmnet		52.79 297	53.26 296	51.40 313	78.99 301	7.68 338	69.52 310	3.89 337	51.63 313	57.01 313	74.98 309	40.83 298	65.96 327	37.78 319	64.67 305	80.56 309

new_pmnet		50.91 298	50.29 298	52.78 312	68.58 323	34.94 330	63.71 322	56.63 328	39.73 320	44.95 322	65.47 317	21.93 325	58.48 328	34.98 321	56.62 316	64.92 322

ANet_high		50.57 299	46.10 300	63.99 305	48.67 332	39.13 326	70.99 308	80.85 271	61.39 266	31.18 326	57.70 323	17.02 329	73.65 323	31.22 322	15.89 330	79.18 312

Gipuma		45.18 300	41.86 301	55.16 311	77.03 309	51.52 306	32.50 331	80.52 276	32.46 325	27.12 327	35.02 327	9.52 334	75.50 316	22.31 326	60.21 313	38.45 327
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 301	40.28 302	55.82 310	40.82 334	42.54 324	65.12 321	63.99 327	34.43 324	24.48 328	57.12 324	3.92 337	76.17 314	17.10 328	55.52 317	48.75 325
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 302	38.86 303	46.69 314	53.84 328	16.45 336	48.61 328	49.92 330	37.49 322	31.67 325	60.97 321	8.14 336	56.42 329	28.42 323	30.72 325	67.19 321

E-PMN		31.77 303	30.64 304	35.15 316	52.87 330	27.67 332	57.09 326	47.86 331	24.64 327	16.40 332	33.05 328	11.23 332	54.90 330	14.46 330	18.15 328	22.87 329

EMVS		30.81 304	29.65 305	34.27 317	50.96 331	25.95 334	56.58 327	46.80 332	24.01 328	15.53 333	30.68 329	12.47 331	54.43 331	12.81 331	17.05 329	22.43 330

MVE	26.22 23	30.37 305	25.89 307	43.81 315	44.55 333	35.46 329	28.87 332	39.07 333	18.20 329	18.58 331	40.18 326	2.68 338	47.37 332	17.07 329	23.78 327	48.60 326
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 306	26.61 306	0.00 323	0.00 339	0.00 341	0.00 333	89.26 160	0.00 335	0.00 337	88.61 144	61.62 136	0.00 338	0.00 335	0.00 335	0.00 335

tmp_tt		18.61 307	21.40 308	10.23 320	4.82 336	10.11 337	34.70 330	30.74 335	1.48 332	23.91 330	26.07 330	28.42 321	13.41 335	27.12 324	15.35 331	7.17 331

wuyk23d		16.82 308	15.94 309	19.46 319	58.74 327	31.45 331	39.22 329	3.74 338	6.84 331	6.04 334	2.70 334	1.27 339	24.29 334	10.54 332	14.40 332	2.63 332

ab-mvs-re		7.23 309	9.64 310	0.00 323	0.00 339	0.00 341	0.00 333	0.00 341	0.00 335	0.00 337	86.72 191	0.00 342	0.00 338	0.00 335	0.00 335	0.00 335

test123		6.12 310	8.11 311	0.14 321	0.06 338	0.09 339	71.05 307	0.03 340	0.04 334	0.25 336	1.30 336	0.05 340	0.03 337	0.21 334	0.01 334	0.29 333

testmvs		6.04 311	8.02 312	0.10 322	0.08 337	0.03 340	69.74 309	0.04 339	0.05 333	0.31 335	1.68 335	0.02 341	0.04 336	0.24 333	0.02 333	0.25 334

pcd_1.5k_mvsjas		5.26 312	7.02 313	0.00 323	0.00 339	0.00 341	0.00 333	0.00 341	0.00 335	0.00 337	0.00 337	63.15 115	0.00 338	0.00 335	0.00 335	0.00 335

sosnet-low-res		0.00 313	0.00 314	0.00 323	0.00 339	0.00 341	0.00 333	0.00 341	0.00 335	0.00 337	0.00 337	0.00 342	0.00 338	0.00 335	0.00 335	0.00 335

sosnet		0.00 313	0.00 314	0.00 323	0.00 339	0.00 341	0.00 333	0.00 341	0.00 335	0.00 337	0.00 337	0.00 342	0.00 338	0.00 335	0.00 335	0.00 335

uncertanet		0.00 313	0.00 314	0.00 323	0.00 339	0.00 341	0.00 333	0.00 341	0.00 335	0.00 337	0.00 337	0.00 342	0.00 338	0.00 335	0.00 335	0.00 335

Regformer		0.00 313	0.00 314	0.00 323	0.00 339	0.00 341	0.00 333	0.00 341	0.00 335	0.00 337	0.00 337	0.00 342	0.00 338	0.00 335	0.00 335	0.00 335

uanet		0.00 313	0.00 314	0.00 323	0.00 339	0.00 341	0.00 333	0.00 341	0.00 335	0.00 337	0.00 337	0.00 342	0.00 338	0.00 335	0.00 335	0.00 335

save filter2										88.41 13	94.67 12	72.46 34	94.59 90	86.67 17	95.86 11	91.94 111

save fliter					93.80 32	72.35 40	90.47 51	91.17 107	74.31 104

test_0728_THIRD									78.38 32	92.12 4	95.78 4	81.46 2	97.40 2	89.42 2	96.57 2	94.67 9

test_0728_SECOND				87.71 28	95.34 1	71.43 51	93.49 5	94.23 1					97.49 1	89.08 4	96.41 4	94.21 24

test0726					95.27 2	71.25 52	93.60 4	94.11 2	77.33 45	92.81 1	95.79 3	80.98 3

GSMVS																88.96 203

test_part2					95.06 4	72.65 29				91.80 6

test_part1				0.00 323		0.00 341	0.00 333	94.09 5				0.00 342	0.00 338	0.00 335	0.00 335	0.00 335

sam_mvs1												51.32 229				88.96 203

sam_mvs												50.01 242

ambc				75.24 270	73.16 319	50.51 312<

High-resolution multi-view benchmark