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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

HPM-MVS++		94.60 4	94.91 7	94.24 4	97.86 1	96.53 28	96.14 6	92.51 4	93.87 11	90.76 7	93.45 13	93.84 2	92.62 6	95.11 9	94.08 15	95.58 40	97.48 11

SMA-MVS		94.57 5	95.23 4	93.80 9	97.56 2	97.61 5	95.92 13	92.02 8	94.43 3	89.74 15	92.16 18	92.63 9	91.78 16	95.98 1	95.57 1	95.80 27	98.25 1

zzz-MVS		93.80 14	93.45 22	94.20 6	97.53 3	96.43 32	95.88 14	91.12 16	94.09 7	92.74 3	87.68 29	90.77 20	92.04 11	94.74 15	93.56 24	95.91 19	96.85 23

MP-MVS		93.35 17	93.59 20	93.08 19	97.39 4	96.82 18	95.38 20	90.71 18	90.82 31	88.07 23	92.83 16	90.29 23	91.32 23	94.03 23	93.19 31	95.61 38	97.16 16
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		93.69 16	93.66 19	93.72 12	97.37 5	96.66 25	95.93 12	92.50 5	93.40 15	88.35 21	87.36 31	92.33 10	92.18 10	94.89 11	94.09 14	96.00 16	96.91 22

ESAPD		95.35 1	95.97 1	94.63 2	97.35 6	97.95 1	97.09 2	93.48 1	93.91 9	90.13 11	96.41 2	95.14 1	92.88 4	95.64 3	94.53 8	96.86 2	98.21 2

CNVR-MVS		94.37 8	94.65 8	94.04 7	97.29 7	97.11 8	96.00 8	92.43 6	93.45 12	89.85 14	90.92 21	93.04 6	92.59 7	95.77 2	94.82 4	96.11 15	97.42 13

HFP-MVS		94.02 11	94.22 14	93.78 10	97.25 8	96.85 16	95.81 15	90.94 17	94.12 6	90.29 9	94.09 10	89.98 25	92.52 8	93.94 26	93.49 27	95.87 21	97.10 19

APD-MVS		94.37 8	94.47 12	94.26 3	97.18 9	96.99 12	96.53 5	92.68 3	92.45 20	89.96 12	94.53 7	91.63 15	92.89 3	94.58 18	93.82 19	96.31 11	97.26 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	88.76 1	93.10 19	93.02 26	93.19 18	97.13 10	96.51 29	95.35 21	91.19 15	93.14 17	88.14 22	85.26 37	89.49 29	91.45 19	95.17 7	95.07 2	95.85 24	96.48 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		95.23 2	95.69 2	94.70 1	97.12 11	97.81 3	97.19 1	92.83 2	95.06 2	90.98 5	96.47 1	92.77 8	93.38 1	95.34 6	94.21 12	96.68 4	98.17 3

ACMMPR		93.72 15	93.94 16	93.48 14	97.07 12	96.93 13	95.78 16	90.66 20	93.88 10	89.24 17	93.53 12	89.08 32	92.24 9	93.89 28	93.50 25	95.88 20	96.73 27

mPP-MVS					97.06 13							88.08 39

ACMMP_Plus		93.94 12	94.49 11	93.30 16	97.03 14	97.31 7	95.96 9	91.30 14	93.41 14	88.55 20	93.00 14	90.33 22	91.43 22	95.53 4	94.41 10	95.53 42	97.47 12

PGM-MVS		92.76 22	93.03 25	92.45 24	97.03 14	96.67 24	95.73 18	87.92 36	90.15 38	86.53 32	92.97 15	88.33 38	91.69 17	93.62 31	93.03 33	95.83 25	96.41 33

SteuartSystems-ACMMP		94.06 10	94.65 8	93.38 15	96.97 16	97.36 6	96.12 7	91.78 10	92.05 24	87.34 26	94.42 8	90.87 19	91.87 14	95.47 5	94.59 7	96.21 13	97.77 8
Skip Steuart: Steuart Systems R&D Blog.
X-MVS		92.36 26	92.75 27	91.90 28	96.89 17	96.70 21	95.25 22	90.48 23	91.50 29	83.95 43	88.20 27	88.82 34	89.11 32	93.75 29	93.43 28	95.75 32	96.83 25

train_agg		92.87 21	93.53 21	92.09 26	96.88 18	95.38 44	95.94 11	90.59 22	90.65 33	83.65 46	94.31 9	91.87 14	90.30 27	93.38 32	92.42 39	95.17 58	96.73 27

CP-MVS		93.25 18	93.26 23	93.24 17	96.84 19	96.51 29	95.52 19	90.61 21	92.37 21	88.88 18	90.91 22	89.52 28	91.91 13	93.64 30	92.78 38	95.69 33	97.09 20

HSP-MVS		94.83 3	95.37 3	94.21 5	96.82 20	97.94 2	96.69 3	92.37 7	93.97 8	90.29 9	96.16 3	93.71 3	92.70 5	94.80 13	93.13 32	96.37 8	97.90 6

MCST-MVS		93.81 13	94.06 15	93.53 13	96.79 21	96.85 16	95.95 10	91.69 12	92.20 22	87.17 28	90.83 23	93.41 4	91.96 12	94.49 20	93.50 25	97.61 1	97.12 18

EPNet		89.60 42	89.91 40	89.24 49	96.45 22	93.61 66	92.95 42	88.03 34	85.74 54	83.36 47	87.29 32	83.05 56	80.98 80	92.22 47	91.85 43	93.69 147	95.58 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CSCG		92.76 22	93.16 24	92.29 25	96.30 23	97.74 4	94.67 28	88.98 30	92.46 19	89.73 16	86.67 33	92.15 12	88.69 36	92.26 46	92.92 36	95.40 46	97.89 7

CDPH-MVS		91.14 34	92.01 29	90.11 38	96.18 24	96.18 35	94.89 26	88.80 32	88.76 43	77.88 73	89.18 26	87.71 41	87.29 52	93.13 35	93.31 30	95.62 37	95.84 41

DeepC-MVS	87.86 3	92.26 27	91.86 30	92.73 21	96.18 24	96.87 15	95.19 23	91.76 11	92.17 23	86.58 31	81.79 44	85.85 44	90.88 25	94.57 19	94.61 6	95.80 27	97.18 15
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		90.29 38	88.38 50	92.53 22	96.10 26	95.19 47	92.98 41	91.40 13	89.08 42	88.65 19	78.35 60	81.44 62	91.30 24	90.81 77	90.21 65	94.72 86	93.59 70

MSLP-MVS++		92.02 30	91.40 32	92.75 20	96.01 27	95.88 40	93.73 35	89.00 28	89.89 39	90.31 8	81.28 49	88.85 33	91.45 19	92.88 40	94.24 11	96.00 16	96.76 26

3Dnovator+	86.06 4	91.60 31	90.86 37	92.47 23	96.00 28	96.50 31	94.70 27	87.83 37	90.49 34	89.92 13	74.68 74	89.35 30	90.66 26	94.02 24	94.14 13	95.67 35	96.85 23

TSAR-MVS + ACMM		92.97 20	94.51 10	91.16 32	95.88 29	96.59 26	95.09 24	90.45 24	93.42 13	83.01 48	94.68 6	90.74 21	88.74 35	94.75 14	93.78 20	93.82 140	97.63 9

ACMMP		92.03 29	92.16 28	91.87 29	95.88 29	96.55 27	94.47 30	89.49 27	91.71 27	85.26 37	91.52 20	84.48 49	90.21 28	92.82 41	91.63 45	95.92 18	96.42 32
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
SD-MVS		94.53 6	95.22 5	93.73 11	95.69 31	97.03 10	95.77 17	91.95 9	94.41 4	91.35 4	94.97 4	93.34 5	91.80 15	94.72 16	93.99 16	95.82 26	98.07 4

TSAR-MVS + MP.		94.48 7	94.97 6	93.90 8	95.53 32	97.01 11	96.69 3	90.71 18	94.24 5	90.92 6	94.97 4	92.19 11	93.03 2	94.83 12	93.60 22	96.51 7	97.97 5

CPTT-MVS		91.39 32	90.95 35	91.91 27	95.06 33	95.24 46	95.02 25	88.98 30	91.02 30	86.71 30	84.89 39	88.58 37	91.60 18	90.82 76	89.67 80	94.08 118	96.45 31

CANet		91.33 33	91.46 31	91.18 31	95.01 34	96.71 20	93.77 33	87.39 40	87.72 46	87.26 27	81.77 45	89.73 26	87.32 51	94.43 21	93.86 18	96.31 11	96.02 39

PHI-MVS		92.05 28	93.74 18	90.08 39	94.96 35	97.06 9	93.11 40	87.71 38	90.71 32	80.78 58	92.40 17	91.03 17	87.68 46	94.32 22	94.48 9	96.21 13	96.16 36

MAR-MVS		88.39 52	88.44 49	88.33 58	94.90 36	95.06 49	90.51 59	83.59 66	85.27 56	79.07 66	77.13 64	82.89 57	87.70 44	92.19 49	92.32 40	94.23 113	94.20 64
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
3Dnovator	85.17 5	90.48 37	89.90 41	91.16 32	94.88 37	95.74 41	93.82 32	85.36 51	89.28 40	87.81 24	74.34 76	87.40 42	88.56 37	93.07 36	93.74 21	96.53 6	95.71 43

DeepPCF-MVS	88.51 2	92.64 25	94.42 13	90.56 36	94.84 38	96.92 14	91.31 56	89.61 26	95.16 1	84.55 41	89.91 25	91.45 16	90.15 29	95.12 8	94.81 5	92.90 161	97.58 10

QAPM		89.49 43	89.58 43	89.38 47	94.73 39	95.94 38	92.35 44	85.00 54	85.69 55	80.03 62	76.97 66	87.81 40	87.87 43	92.18 50	92.10 41	96.33 9	96.40 34

MVS_111021_HR		90.56 36	91.29 34	89.70 44	94.71 40	95.63 42	91.81 52	86.38 45	87.53 47	81.29 55	87.96 28	85.43 46	87.69 45	93.90 27	92.93 35	96.33 9	95.69 44

abl_6				90.66 35	94.65 41	96.27 33	92.21 45	86.94 42	90.23 36	86.38 33	85.50 36	92.96 7	88.37 39			95.40 46	95.46 49

OpenMVS	82.53 11	87.71 57	86.84 63	88.73 52	94.42 42	95.06 49	91.02 58	83.49 69	82.50 64	82.24 53	67.62 119	85.48 45	85.56 60	91.19 59	91.30 47	95.67 35	94.75 56

PLC	83.76 9	88.61 49	86.83 64	90.70 34	94.22 43	92.63 86	91.50 54	87.19 41	89.16 41	86.87 29	75.51 71	80.87 63	89.98 30	90.01 88	89.20 92	94.41 108	90.45 149
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		85.96 67	84.37 77	87.81 60	94.13 44	93.27 71	90.26 62	89.00 28	84.91 57	72.84 91	71.74 94	72.47 100	87.45 49	89.53 94	89.09 95	93.20 156	89.60 151

EPNet_dtu		81.98 99	83.82 79	79.83 157	94.10 45	85.97 181	87.29 104	84.08 61	80.61 80	59.96 186	81.62 48	77.19 82	62.91 204	87.21 117	86.38 137	90.66 185	87.77 167
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_0304		90.88 35	91.35 33	90.34 37	93.91 46	96.79 19	94.49 29	86.54 44	86.57 50	82.85 49	81.68 47	89.70 27	87.57 48	94.64 17	93.93 17	96.67 5	96.15 37

OPM-MVS		87.56 59	85.80 70	89.62 45	93.90 47	94.09 60	94.12 31	88.18 33	75.40 113	77.30 76	76.41 67	77.93 80	88.79 34	92.20 48	90.82 54	95.40 46	93.72 69

DELS-MVS		89.71 41	89.68 42	89.74 42	93.75 48	96.22 34	93.76 34	85.84 47	82.53 63	85.05 39	78.96 57	84.24 50	84.25 65	94.91 10	94.91 3	95.78 31	96.02 39
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
CNLPA		88.40 50	87.00 62	90.03 40	93.73 49	94.28 57	89.56 69	85.81 48	91.87 25	87.55 25	69.53 108	81.49 61	89.23 31	89.45 95	88.59 102	94.31 112	93.82 68

HQP-MVS		89.13 45	89.58 43	88.60 55	93.53 50	93.67 64	93.29 38	87.58 39	88.53 44	75.50 78	87.60 30	80.32 66	87.07 53	90.66 82	89.95 72	94.62 94	96.35 35

OMC-MVS		90.23 39	90.40 38	90.03 40	93.45 51	95.29 45	91.89 51	86.34 46	93.25 16	84.94 40	81.72 46	86.65 43	88.90 33	91.69 53	90.27 64	94.65 91	93.95 66

ACMM	83.27 10	87.68 58	86.09 69	89.54 46	93.26 52	92.19 89	91.43 55	86.74 43	86.02 52	82.85 49	75.63 70	75.14 86	88.41 38	90.68 81	89.99 69	94.59 95	92.97 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_111021_LR		90.14 40	90.89 36	89.26 48	93.23 53	94.05 61	90.43 60	84.65 56	90.16 37	84.52 42	90.14 24	83.80 53	87.99 42	92.50 44	90.92 53	94.74 84	94.70 58

XVS					93.11 54	96.70 21	91.91 49			83.95 43		88.82 34				95.79 29

X-MVStestdata					93.11 54	96.70 21	91.91 49			83.95 43		88.82 34				95.79 29

PCF-MVS	84.60 6	88.66 47	87.75 59	89.73 43	93.06 56	96.02 36	93.22 39	90.00 25	82.44 65	80.02 63	77.96 61	85.16 47	87.36 50	88.54 107	88.54 103	94.72 86	95.61 46
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TAPA-MVS	84.37 7	88.91 46	88.93 46	88.89 50	93.00 57	94.85 52	92.00 48	84.84 55	91.68 28	80.05 61	79.77 53	84.56 48	88.17 41	90.11 87	89.00 98	95.30 53	92.57 96
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		87.40 60	87.80 56	86.92 64	92.86 58	95.40 43	88.56 87	83.45 72	79.55 88	82.26 52	74.49 75	84.03 51	79.24 130	92.97 39	91.53 46	95.15 60	96.65 29

UA-Net		86.07 65	87.78 57	84.06 88	92.85 59	95.11 48	87.73 94	84.38 57	73.22 140	73.18 89	79.99 52	89.22 31	71.47 178	93.22 34	93.03 33	94.76 83	90.69 144

LGP-MVS_train		88.25 54	88.55 47	87.89 59	92.84 60	93.66 65	93.35 37	85.22 53	85.77 53	74.03 85	86.60 34	76.29 84	86.62 56	91.20 58	90.58 61	95.29 54	95.75 42

TSAR-MVS + COLMAP		88.40 50	89.09 45	87.60 61	92.72 61	93.92 63	92.21 45	85.57 50	91.73 26	73.72 86	91.75 19	73.22 98	87.64 47	91.49 54	89.71 78	93.73 146	91.82 118

PVSNet_BlendedMVS		88.19 55	88.00 54	88.42 56	92.71 62	94.82 53	89.08 75	83.81 62	84.91 57	86.38 33	79.14 55	78.11 78	82.66 68	93.05 37	91.10 48	95.86 22	94.86 54

PVSNet_Blended		88.19 55	88.00 54	88.42 56	92.71 62	94.82 53	89.08 75	83.81 62	84.91 57	86.38 33	79.14 55	78.11 78	82.66 68	93.05 37	91.10 48	95.86 22	94.86 54

ACMP	83.90 8	88.32 53	88.06 53	88.62 54	92.18 64	93.98 62	91.28 57	85.24 52	86.69 49	81.23 56	85.62 35	75.13 87	87.01 54	89.83 90	89.77 77	94.79 80	95.43 50
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSDG		83.87 77	81.02 102	87.19 63	92.17 65	89.80 130	89.15 72	85.72 49	80.61 80	79.24 65	66.66 123	68.75 112	82.69 67	87.95 113	87.44 116	94.19 114	85.92 185

TSAR-MVS + GP.		92.71 24	93.91 17	91.30 30	91.96 66	96.00 37	93.43 36	87.94 35	92.53 18	86.27 36	93.57 11	91.94 13	91.44 21	93.29 33	92.89 37	96.78 3	97.15 17

canonicalmvs		89.36 44	89.92 39	88.70 53	91.38 67	95.92 39	91.81 52	82.61 87	90.37 35	82.73 51	82.09 42	79.28 75	88.30 40	91.17 60	93.59 23	95.36 49	97.04 21

CLD-MVS		88.66 47	88.52 48	88.82 51	91.37 68	94.22 58	92.82 43	82.08 95	88.27 45	85.14 38	81.86 43	78.53 77	85.93 59	91.17 60	90.61 59	95.55 41	95.00 51
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CHOSEN 1792x2688		82.16 96	80.91 109	83.61 95	91.14 69	92.01 91	89.55 70	79.15 132	79.87 84	70.29 96	52.51 208	72.56 99	81.39 75	88.87 102	88.17 108	90.15 189	92.37 108

IB-MVS	79.09 12	82.60 90	82.19 87	83.07 101	91.08 70	93.55 67	80.90 186	81.35 102	76.56 104	80.87 57	64.81 141	69.97 107	68.87 187	85.64 146	90.06 68	95.36 49	94.74 57
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
IS_MVSNet		86.18 64	88.18 52	83.85 92	91.02 71	94.72 55	87.48 99	82.46 89	81.05 75	70.28 97	76.98 65	82.20 60	76.65 145	93.97 25	93.38 29	95.18 57	94.97 52

HyFIR lowres test		81.62 109	79.45 130	84.14 87	91.00 72	93.38 70	88.27 88	78.19 141	76.28 106	70.18 98	48.78 212	73.69 94	83.52 66	87.05 121	87.83 114	93.68 148	89.15 154

COLMAP_ROB	76.78 15	80.50 122	78.49 136	82.85 102	90.96 73	89.65 135	86.20 136	83.40 73	77.15 101	66.54 126	62.27 150	65.62 122	77.89 139	85.23 165	84.70 175	92.11 169	84.83 192
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CANet_DTU		85.43 70	87.72 60	82.76 104	90.95 74	93.01 80	89.99 63	75.46 173	82.67 62	64.91 148	83.14 40	80.09 67	80.68 85	92.03 52	91.03 50	94.57 97	92.08 110

FC-MVSNet-train		85.18 72	85.31 72	85.03 70	90.67 75	91.62 95	87.66 95	83.61 64	79.75 85	74.37 84	78.69 58	71.21 104	78.91 132	91.23 56	89.96 71	94.96 68	94.69 59

tfpn		81.79 103	80.06 118	83.82 93	90.61 76	92.91 83	87.62 96	82.34 91	73.66 137	67.46 120	64.99 138	55.50 202	79.77 114	91.12 72	89.62 81	95.14 61	92.59 93

thres600view7		82.53 93	81.02 102	84.28 82	90.61 76	93.05 78	88.57 85	82.67 83	74.12 129	68.56 114	65.09 136	62.13 153	80.40 92	91.15 65	89.02 97	94.88 74	92.59 93

view800		82.38 95	80.93 107	84.06 88	90.59 78	92.96 81	88.11 90	82.44 90	73.92 131	68.10 117	65.07 137	61.64 155	80.10 104	91.17 60	89.24 91	95.01 65	92.56 97

view600		82.51 94	81.00 106	84.27 83	90.56 79	92.95 82	88.57 85	82.57 88	74.16 128	68.70 113	65.13 135	62.15 152	80.36 98	91.15 65	88.98 99	94.87 76	92.48 105

thres400		82.68 88	81.15 99	84.47 78	90.52 80	92.89 84	88.95 83	82.71 81	74.33 125	69.22 109	65.31 132	62.61 142	80.63 87	90.96 74	89.50 86	94.79 80	92.45 107

EPP-MVSNet		86.55 62	87.76 58	85.15 69	90.52 80	94.41 56	87.24 107	82.32 92	81.79 68	73.60 87	78.57 59	82.41 58	82.07 72	91.23 56	90.39 63	95.14 61	95.48 48

ACMH	78.52 14	81.86 101	80.45 115	83.51 97	90.51 82	91.22 99	85.62 143	84.23 59	70.29 163	62.21 166	69.04 112	64.05 131	84.48 64	87.57 115	88.45 105	94.01 123	92.54 100
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		83.65 80	86.81 65	79.96 155	90.46 83	92.71 85	84.84 154	82.00 96	80.93 77	62.44 165	76.29 68	82.32 59	65.54 200	92.29 45	91.66 44	94.49 103	91.47 130

thres200		82.77 87	81.25 98	84.54 74	90.38 84	93.05 78	89.13 73	82.67 83	74.40 124	69.53 104	65.69 130	63.03 138	80.63 87	91.15 65	89.42 87	94.88 74	92.04 112

MS-PatchMatch		81.79 103	81.44 95	82.19 109	90.35 85	89.29 141	88.08 92	75.36 174	77.60 99	69.00 110	64.37 145	78.87 76	77.14 144	88.03 112	85.70 163	93.19 157	86.24 182

PatchMatch-RL		83.34 83	81.36 96	85.65 67	90.33 86	89.52 137	84.36 158	81.82 98	80.87 79	79.29 64	74.04 79	62.85 140	86.05 58	88.40 109	87.04 123	92.04 170	86.77 176

tfpn111		83.51 81	82.68 84	84.47 78	90.30 87	93.09 75	89.05 77	82.72 79	75.14 114	69.49 105	74.24 77	63.13 133	80.38 93	91.15 65	89.51 82	94.91 70	92.50 102

conf0.01		82.64 89	81.02 102	84.53 76	90.30 87	93.22 73	89.05 77	82.75 77	75.14 114	69.69 101	67.15 121	59.19 182	80.38 93	91.16 63	89.51 82	95.00 66	91.76 121

conf200view11		82.85 86	81.46 93	84.47 78	90.30 87	93.09 75	89.05 77	82.72 79	75.14 114	69.49 105	65.72 127	63.13 133	80.38 93	91.15 65	89.51 82	94.91 70	92.50 102

thres100view900		82.55 91	81.01 105	84.34 81	90.30 87	92.27 87	89.04 82	82.77 76	75.14 114	69.56 102	65.72 127	63.13 133	79.62 118	89.97 89	89.26 90	94.73 85	91.61 128

tfpn200view9		82.86 85	81.46 93	84.48 77	90.30 87	93.09 75	89.05 77	82.71 81	75.14 114	69.56 102	65.72 127	63.13 133	80.38 93	91.15 65	89.51 82	94.91 70	92.50 102

conf0.002		82.54 92	80.83 110	84.54 74	90.28 92	93.24 72	89.05 77	82.75 77	75.14 114	69.75 100	67.99 116	57.12 193	80.38 93	91.16 63	89.79 75	95.02 64	91.36 132

tfpn1000		81.03 113	81.70 90	80.25 153	90.18 93	91.35 96	83.96 161	81.15 105	78.00 98	62.11 168	73.37 87	65.75 120	69.17 186	88.68 105	87.44 116	94.93 69	87.29 171

conf0.05thres1000		81.00 114	79.12 131	83.20 99	90.14 94	92.15 90	87.05 119	82.09 94	68.11 182	66.19 130	59.67 182	61.10 166	79.05 131	90.47 85	89.11 94	94.68 89	93.22 74

tfpnview11		80.84 116	81.10 100	80.54 147	90.10 95	90.96 103	85.44 147	81.84 97	75.77 109	59.27 190	73.54 83	64.40 127	71.69 175	89.16 98	87.97 109	94.91 70	85.92 185

MVS_Test		86.93 61	87.24 61	86.56 65	90.10 95	93.47 68	90.31 61	80.12 114	83.55 61	78.12 69	79.58 54	79.80 70	85.45 61	90.17 86	90.59 60	95.29 54	93.53 71

tfpn_n400		80.63 118	80.79 111	80.43 150	90.02 97	91.08 101	85.34 149	81.79 99	72.93 143	59.27 190	73.54 83	64.40 127	71.61 176	89.05 100	88.21 106	94.56 98	86.32 180

tfpnconf		80.63 118	80.79 111	80.43 150	90.02 97	91.08 101	85.34 149	81.79 99	72.93 143	59.27 190	73.54 83	64.40 127	71.61 176	89.05 100	88.21 106	94.56 98	86.32 180

thresconf0.02		81.14 112	80.93 107	81.39 127	90.01 99	91.31 97	86.79 127	82.28 93	76.97 103	61.46 177	74.24 77	62.08 154	72.98 170	88.70 104	87.90 110	94.81 79	85.28 188

ACMH+	79.08 13	81.84 102	80.06 118	83.91 91	89.92 100	90.62 107	86.21 135	83.48 71	73.88 133	65.75 139	66.38 124	65.30 123	84.63 63	85.90 141	87.25 120	93.45 151	91.13 134

tfpn_ndepth		81.77 105	82.29 86	81.15 135	89.79 101	91.71 94	85.49 146	81.63 101	79.17 91	64.76 149	73.04 88	68.14 117	70.62 181	88.72 103	87.88 112	94.63 93	87.38 169

Effi-MVS+		85.33 71	85.08 73	85.63 68	89.69 102	93.42 69	89.90 64	80.31 112	79.32 89	72.48 93	73.52 86	74.03 91	86.55 57	90.99 73	89.98 70	94.83 77	94.27 63

DI_MVS_plusplus_trai		86.41 63	85.54 71	87.42 62	89.24 103	93.13 74	92.16 47	82.65 85	82.30 66	80.75 59	68.30 115	80.41 65	85.01 62	90.56 83	90.07 67	94.70 88	94.01 65

UGNet		85.90 68	88.23 51	83.18 100	88.96 104	94.10 59	87.52 98	83.60 65	81.66 69	77.90 72	80.76 51	83.19 55	66.70 197	91.13 71	90.71 58	94.39 109	96.06 38
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
DWT-MVSNet_training		80.51 121	78.05 150	83.39 98	88.64 105	88.33 157	86.11 137	76.33 158	79.65 86	78.64 68	69.62 106	58.89 187	80.82 81	80.50 198	82.03 193	89.77 192	87.36 170

MVSTER		86.03 66	86.12 68	85.93 66	88.62 106	89.93 127	89.33 71	79.91 117	81.87 67	81.35 54	81.07 50	74.91 88	80.66 86	92.13 51	90.10 66	95.68 34	92.80 86

TDRefinement		79.05 150	77.05 172	81.39 127	88.45 107	89.00 148	86.92 122	82.65 85	74.21 127	64.41 150	59.17 185	59.16 183	74.52 158	85.23 165	85.09 170	91.37 177	87.51 168

IterMVS-LS		83.28 84	82.95 83	83.65 94	88.39 108	88.63 151	86.80 126	78.64 136	76.56 104	73.43 88	72.52 93	75.35 85	80.81 83	86.43 136	88.51 104	93.84 139	92.66 90
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+		83.77 79	82.98 82	84.69 73	87.98 109	91.87 92	88.10 91	77.70 147	78.10 97	73.04 90	69.13 110	68.51 113	86.66 55	90.49 84	89.85 74	94.67 90	92.88 82

gg-mvs-nofinetune		75.64 193	77.26 168	73.76 198	87.92 110	92.20 88	87.32 103	64.67 217	51.92 223	35.35 230	46.44 216	77.05 83	71.97 172	92.64 43	91.02 51	95.34 51	89.53 152

diffmvs		85.70 69	86.35 67	84.95 71	87.75 111	90.96 103	89.09 74	78.56 137	86.50 51	80.44 60	77.86 62	83.93 52	81.64 74	85.52 153	86.79 126	92.21 168	92.87 83

RPSCF		83.46 82	83.36 81	83.59 96	87.75 111	87.35 163	84.82 155	79.46 128	83.84 60	78.12 69	82.69 41	79.87 68	82.60 70	82.47 191	81.13 195	88.78 197	86.13 183

Effi-MVS+-dtu		82.05 98	81.76 89	82.38 106	87.72 113	90.56 108	86.90 124	78.05 143	73.85 134	66.85 125	71.29 96	71.90 102	82.00 73	86.64 131	85.48 167	92.76 163	92.58 95

CostFormer		80.94 115	80.21 117	81.79 115	87.69 114	88.58 152	87.47 100	70.66 191	80.02 82	77.88 73	73.03 89	71.40 103	78.24 136	79.96 201	79.63 197	88.82 196	88.84 155

Vis-MVSNet		84.38 76	86.68 66	81.70 120	87.65 115	94.89 51	88.14 89	80.90 106	74.48 123	68.23 116	77.53 63	80.72 64	69.98 183	92.68 42	91.90 42	95.33 52	94.58 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
tpmp4_e23		79.82 133	77.96 155	82.00 111	87.59 116	86.93 167	87.81 93	72.21 186	79.99 83	78.02 71	67.83 118	64.77 124	78.74 133	79.99 200	78.90 200	87.65 202	87.29 171

test-LLR		79.47 144	79.84 123	79.03 161	87.47 117	82.40 206	81.24 181	78.05 143	73.72 135	62.69 162	73.76 80	74.42 89	73.49 165	84.61 173	82.99 186	91.25 179	87.01 174

test0.0.03 1		76.03 187	78.51 135	73.12 202	87.47 117	85.13 192	76.32 204	78.05 143	73.19 142	50.98 210	70.64 98	69.28 110	55.53 208	85.33 163	84.38 178	90.39 187	81.63 203

tpmrst		76.55 177	75.99 183	77.20 175	87.32 119	83.05 199	82.86 167	65.62 212	78.61 95	67.22 124	69.19 109	65.71 121	75.87 149	76.75 212	75.33 214	84.31 218	83.28 198

CDS-MVSNet		81.63 108	82.09 88	81.09 137	87.21 120	90.28 118	87.46 101	80.33 111	69.06 176	70.66 94	71.30 95	73.87 92	67.99 190	89.58 93	89.87 73	92.87 162	90.69 144
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm cat1		77.78 166	75.28 193	80.70 143	87.14 121	85.84 183	85.81 139	70.40 192	77.44 100	78.80 67	63.72 146	64.01 132	76.55 146	75.60 215	75.21 215	85.51 214	85.12 190

tpm		76.30 185	76.05 182	76.59 181	86.97 122	83.01 200	83.83 162	67.06 206	71.83 149	63.87 156	69.56 107	62.88 139	73.41 167	79.79 202	78.59 201	84.41 217	86.68 177

EPMVS		77.53 168	78.07 147	76.90 179	86.89 123	84.91 193	82.18 176	66.64 208	81.00 76	64.11 154	72.75 92	69.68 108	74.42 160	79.36 204	78.13 203	87.14 206	80.68 207

PatchmatchNet		78.67 158	78.85 134	78.46 166	86.85 124	86.03 180	83.77 163	68.11 202	80.88 78	66.19 130	72.90 91	73.40 96	78.06 137	79.25 205	77.71 207	87.75 201	81.75 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous20240521		82.08 97	79.66 128	84.91 72	86.79 125	91.13 100	87.60 97	82.95 74	74.05 130	76.29 77	64.40 143	55.57 201	81.02 79	89.34 97	89.69 79	94.82 78	93.41 72

USDC		80.69 117	79.89 122	81.62 123	86.48 126	89.11 146	86.53 131	78.86 133	81.15 74	63.48 158	72.98 90	59.12 185	81.16 77	87.10 119	85.01 171	93.23 155	84.77 193

Fast-Effi-MVS+-dtu		79.95 129	80.69 113	79.08 160	86.36 127	89.14 145	85.85 138	72.28 185	72.85 145	59.32 189	70.43 101	68.42 114	77.57 140	86.14 138	86.44 136	93.11 158	91.39 131

tfpnnormal		77.46 169	74.86 196	80.49 148	86.34 128	88.92 149	84.33 159	81.26 103	61.39 208	61.70 174	51.99 209	53.66 209	74.84 155	88.63 106	87.38 119	94.50 102	92.08 110

dps		78.02 163	75.94 184	80.44 149	86.06 129	86.62 173	82.58 168	69.98 195	75.14 114	77.76 75	69.08 111	59.93 175	78.47 134	79.47 203	77.96 204	87.78 200	83.40 197

LTVRE_ROB	74.41 16	75.78 192	74.72 197	77.02 178	85.88 130	89.22 142	82.44 171	77.17 150	50.57 224	45.45 217	65.44 131	52.29 212	81.25 76	85.50 154	87.42 118	89.94 191	92.62 91
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
EG-PatchMatch MVS		76.40 183	75.47 191	77.48 171	85.86 131	90.22 120	82.45 170	73.96 180	59.64 213	59.60 188	52.75 207	62.20 151	68.44 189	88.23 110	87.50 115	94.55 100	87.78 166

CR-MVSNet		78.71 157	78.86 133	78.55 165	85.85 132	85.15 190	82.30 173	68.23 200	74.71 121	65.37 143	64.39 144	69.59 109	77.18 142	85.10 169	84.87 172	92.34 167	88.21 160

GA-MVS		79.52 143	79.71 126	79.30 159	85.68 133	90.36 116	84.55 156	78.44 138	70.47 162	57.87 197	68.52 114	61.38 164	76.21 147	89.40 96	87.89 111	93.04 160	89.96 150

TransMVSNet (Re)		76.57 176	75.16 194	78.22 168	85.60 134	87.24 164	82.46 169	81.23 104	59.80 212	59.05 195	57.07 194	59.14 184	66.60 198	88.09 111	86.82 124	94.37 110	87.95 165

RPMNet		77.07 171	77.63 161	76.42 182	85.56 135	85.15 190	81.37 178	65.27 214	74.71 121	60.29 185	63.71 147	66.59 119	73.64 164	82.71 188	82.12 191	92.38 166	88.39 158

MDTV_nov1_ep13		79.14 146	79.49 129	78.74 163	85.40 136	86.89 168	84.32 160	70.29 193	78.85 93	69.42 107	75.37 72	73.29 97	75.64 150	80.61 197	79.48 199	87.36 203	81.91 201

UniMVSNet (Re)		81.22 110	81.08 101	81.39 127	85.35 137	91.76 93	84.93 153	82.88 75	76.13 107	65.02 147	64.94 139	63.09 137	75.17 152	87.71 114	89.04 96	94.97 67	94.88 53

UniMVSNet_NR-MVSNet		81.87 100	81.33 97	82.50 105	85.31 138	91.30 98	85.70 140	84.25 58	75.89 108	64.21 151	66.95 122	64.65 126	80.22 100	87.07 120	89.18 93	95.27 56	94.29 61

IterMVS		78.79 156	79.71 126	77.71 169	85.26 139	85.91 182	84.54 157	69.84 197	73.38 139	61.25 180	70.53 100	70.35 105	74.43 159	85.21 167	83.80 181	90.95 183	88.77 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		80.25 124	79.98 121	80.56 146	85.20 140	90.94 105	85.65 142	83.58 67	75.74 110	61.36 179	65.30 133	56.75 195	72.38 171	88.46 108	88.80 100	95.16 59	93.87 67

CMPMVS	56.49 17	73.84 201	71.73 205	76.31 185	85.20 140	85.67 185	75.80 205	73.23 183	62.26 205	65.40 142	53.40 206	59.70 177	71.77 174	80.25 199	79.56 198	86.45 209	81.28 204
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TinyColmap		76.73 173	73.95 199	79.96 155	85.16 142	85.64 186	82.34 172	78.19 141	70.63 159	62.06 169	60.69 170	49.61 216	80.81 83	85.12 168	83.69 182	91.22 181	82.27 200

gm-plane-assit		70.29 205	70.65 206	69.88 206	85.03 143	78.50 216	58.41 227	65.47 213	50.39 225	40.88 222	49.60 211	50.11 215	75.14 153	91.43 55	89.78 76	94.32 111	84.73 194

FC-MVSNet-test		76.53 180	81.62 92	70.58 205	84.99 144	85.73 184	74.81 207	78.85 134	77.00 102	39.13 229	75.90 69	73.50 95	54.08 212	86.54 133	85.99 159	91.65 173	86.68 177

DU-MVS		81.20 111	80.30 116	82.25 107	84.98 145	90.94 105	85.70 140	83.58 67	75.74 110	64.21 151	65.30 133	59.60 179	80.22 100	86.89 124	89.31 88	94.77 82	94.29 61

Baseline_NR-MVSNet		79.84 131	78.37 143	81.55 125	84.98 145	86.66 170	85.06 151	83.49 69	75.57 112	63.31 159	58.22 192	60.97 167	78.00 138	86.89 124	87.13 121	94.47 104	93.15 76

TranMVSNet+NR-MVSNet		80.52 120	79.84 123	81.33 130	84.92 147	90.39 112	85.53 145	84.22 60	74.27 126	60.68 184	64.93 140	59.96 174	77.48 141	86.75 129	89.28 89	95.12 63	93.29 73

pm-mvs1		78.51 161	77.75 160	79.40 158	84.83 148	89.30 140	83.55 165	79.38 129	62.64 204	63.68 157	58.73 190	64.68 125	70.78 180	89.79 91	87.84 113	94.17 115	91.28 133

testgi		71.92 203	74.20 198	69.27 208	84.58 149	83.06 198	73.40 209	74.39 177	64.04 202	46.17 216	68.90 113	57.15 192	48.89 219	84.07 178	83.08 185	88.18 199	79.09 212

FMVSNet3		84.44 75	84.64 76	84.21 84	84.32 150	90.13 122	89.85 65	80.37 108	81.17 71	75.50 78	69.63 103	79.69 72	79.62 118	89.72 92	90.52 62	95.59 39	91.58 129

GBi-Net		84.51 73	84.80 74	84.17 85	84.20 151	89.95 124	89.70 66	80.37 108	81.17 71	75.50 78	69.63 103	79.69 72	79.75 115	90.73 78	90.72 55	95.52 43	91.71 123

test1		84.51 73	84.80 74	84.17 85	84.20 151	89.95 124	89.70 66	80.37 108	81.17 71	75.50 78	69.63 103	79.69 72	79.75 115	90.73 78	90.72 55	95.52 43	91.71 123

FMVSNet2		83.87 77	83.73 80	84.05 90	84.20 151	89.95 124	89.70 66	80.21 113	79.17 91	74.89 82	65.91 125	77.49 81	79.75 115	90.87 75	91.00 52	95.52 43	91.71 123

WR-MVS		76.63 175	78.02 153	75.02 192	84.14 154	89.76 132	78.34 199	80.64 107	69.56 173	52.32 205	61.26 154	61.24 165	60.66 205	84.45 175	87.07 122	93.99 124	92.77 87

v16		79.65 139	77.91 156	81.69 121	84.04 155	86.65 172	87.20 109	76.32 159	72.41 147	68.71 112	61.13 161	62.52 144	79.93 107	85.55 151	86.22 140	93.92 129	90.91 138

v17		79.59 141	77.88 157	81.60 124	84.03 156	86.66 170	87.13 118	76.31 160	72.09 148	68.29 115	61.15 160	62.57 143	79.90 108	85.55 151	86.20 145	93.93 127	90.93 136

v18		79.71 138	77.98 154	81.73 119	84.02 157	86.67 169	87.37 102	76.35 157	72.61 146	68.86 111	61.35 153	62.65 141	79.94 106	85.49 155	86.21 142	93.85 138	90.92 137

v1neww		80.09 126	78.45 139	82.00 111	83.97 158	90.49 109	87.18 113	79.67 122	71.49 151	67.44 121	61.24 156	62.41 148	79.83 110	85.49 155	86.19 147	93.88 135	91.86 115

v7new		80.09 126	78.45 139	82.00 111	83.97 158	90.49 109	87.18 113	79.67 122	71.49 151	67.44 121	61.24 156	62.41 148	79.83 110	85.49 155	86.19 147	93.88 135	91.86 115

v8		79.90 130	78.39 142	81.66 122	83.97 158	89.81 129	87.16 116	77.40 149	71.49 151	67.71 118	61.24 156	62.49 145	79.83 110	85.48 159	86.17 151	93.89 133	92.02 114

v6		80.11 125	78.47 137	82.01 110	83.97 158	90.49 109	87.19 112	79.67 122	71.59 150	67.51 119	61.26 154	62.46 147	79.81 113	85.49 155	86.18 150	93.89 133	91.86 115

divwei89l23v2f112		79.75 136	78.04 151	81.75 116	83.90 162	90.37 114	87.21 108	79.90 118	70.20 166	66.18 132	60.92 164	61.48 160	79.52 125	85.36 161	86.17 151	93.81 141	91.77 119

v15		79.13 147	77.37 162	81.19 132	83.90 162	86.56 174	87.01 120	76.15 164	70.20 166	66.48 127	60.71 169	61.55 157	79.60 120	85.59 149	86.19 147	93.98 125	90.80 143

V14		79.11 148	77.35 164	81.16 134	83.90 162	86.54 175	86.94 121	76.10 166	70.14 168	66.41 129	60.59 171	61.54 158	79.59 121	85.64 146	86.20 145	94.04 121	90.82 141

v1141		79.75 136	78.04 151	81.75 116	83.89 165	90.37 114	87.20 109	79.89 119	70.23 164	66.18 132	60.92 164	61.48 160	79.54 122	85.36 161	86.17 151	93.81 141	91.76 121

v11		79.02 152	77.36 163	80.95 140	83.89 165	86.48 178	86.53 131	75.77 172	69.69 172	65.21 146	60.36 176	60.24 172	80.32 99	87.20 118	86.54 133	93.96 126	91.02 135

v1		79.76 135	78.06 149	81.74 118	83.89 165	90.38 113	87.20 109	79.88 120	70.23 164	66.17 135	60.92 164	61.56 156	79.50 126	85.37 160	86.17 151	93.81 141	91.77 119

V9		79.08 149	77.32 166	81.14 136	83.89 165	86.52 176	86.85 125	76.06 167	70.02 169	66.42 128	60.44 174	61.52 159	79.54 122	85.68 145	86.21 142	94.08 118	90.83 140

v12		79.03 151	77.28 167	81.06 138	83.88 169	86.49 177	86.62 129	76.02 168	69.99 170	66.18 132	60.34 177	61.44 162	79.54 122	85.70 144	86.21 142	94.11 117	90.82 141

v13		78.99 153	77.25 169	81.02 139	83.87 170	86.47 179	86.60 130	75.96 170	69.87 171	66.07 136	60.25 178	61.41 163	79.49 127	85.72 143	86.22 140	94.14 116	90.84 139

v2v482		79.84 131	78.07 147	81.90 114	83.75 171	90.21 121	87.17 115	79.85 121	70.65 158	65.93 138	61.93 151	60.07 173	80.82 81	85.25 164	86.71 127	93.88 135	91.70 126

v7		79.79 134	78.28 144	81.54 126	83.73 172	90.34 117	87.27 105	78.27 140	70.50 160	65.59 140	60.59 171	60.47 169	80.46 90	86.90 123	86.63 130	93.92 129	92.56 97

v10		79.62 140	78.19 145	81.28 131	83.73 172	89.69 134	87.27 105	76.86 153	70.50 160	65.46 141	60.58 173	60.47 169	80.44 91	86.91 122	86.63 130	93.93 127	92.55 99

v1144		79.38 145	77.83 158	81.18 133	83.62 174	90.23 119	87.15 117	78.35 139	69.13 175	64.02 155	60.20 179	59.41 180	80.14 103	86.78 127	86.57 132	93.81 141	92.53 101

v148		78.59 159	76.84 175	80.62 145	83.61 175	89.16 144	83.65 164	79.24 131	69.38 174	69.34 108	59.88 181	60.41 171	75.19 151	83.81 179	84.63 176	92.70 164	90.63 146

SixPastTwentyTwo		76.02 188	75.72 188	76.36 183	83.38 176	87.54 161	75.50 206	76.22 162	65.50 195	57.05 198	70.64 98	53.97 208	74.54 157	80.96 196	82.12 191	91.44 175	89.35 153

CVMVSNet		76.70 174	78.46 138	74.64 196	83.34 177	84.48 194	81.83 177	74.58 175	68.88 177	51.23 209	69.77 102	70.05 106	67.49 193	84.27 176	83.81 180	89.38 194	87.96 164

v1192		78.94 154	77.33 165	80.82 142	83.25 178	89.90 128	86.91 123	77.72 146	68.63 179	62.61 164	59.17 185	57.53 191	80.62 89	86.89 124	86.47 135	93.79 145	92.75 89

DTE-MVSNet		75.14 195	75.44 192	74.80 194	83.18 179	87.19 165	78.25 201	80.11 115	66.05 190	48.31 213	60.88 167	54.67 205	64.54 202	82.57 189	86.17 151	94.43 107	90.53 148

PEN-MVS		76.02 188	76.07 180	75.95 187	83.17 180	87.97 159	79.65 190	80.07 116	66.57 188	51.45 207	60.94 163	55.47 203	66.81 196	82.72 187	86.80 125	94.59 95	92.03 113

TAMVS		76.42 181	77.16 171	75.56 188	83.05 181	85.55 187	80.58 188	71.43 188	65.40 199	61.04 183	67.27 120	69.22 111	67.99 190	84.88 171	84.78 174	89.28 195	83.01 199

pmmvs4		79.99 128	78.08 146	82.22 108	83.04 182	87.16 166	84.95 152	78.80 135	78.64 94	74.53 83	64.61 142	59.41 180	79.45 128	84.13 177	84.54 177	92.53 165	88.08 162

v144192		78.81 155	77.22 170	80.67 144	82.95 183	89.79 131	86.40 133	77.42 148	68.26 181	63.13 160	59.50 183	58.13 189	80.08 105	85.93 140	86.08 156	94.06 120	92.83 85

v1921920		78.57 160	76.99 173	80.41 152	82.93 184	89.63 136	86.38 134	77.14 151	68.31 180	61.80 172	58.89 189	56.79 194	80.19 102	86.50 135	86.05 158	94.02 122	92.76 88

CHOSEN 280x420		80.28 123	81.66 91	78.67 164	82.92 185	79.24 215	85.36 148	66.79 207	78.11 96	70.32 95	75.03 73	79.87 68	81.09 78	89.07 99	83.16 184	85.54 213	87.17 173

WR-MVS_H		75.84 191	76.93 174	74.57 197	82.86 186	89.50 138	78.34 199	79.36 130	66.90 186	52.51 204	60.20 179	59.71 176	59.73 206	83.61 180	85.77 161	94.65 91	92.84 84

v1240		78.15 162	76.53 176	80.04 154	82.85 187	89.48 139	85.61 144	76.77 154	67.05 184	61.18 182	58.37 191	56.16 199	79.89 109	86.11 139	86.08 156	93.92 129	92.47 106

V42		79.59 141	78.43 141	80.94 141	82.79 188	89.71 133	86.66 128	76.73 155	71.38 154	67.42 123	61.01 162	62.30 150	78.39 135	85.56 150	86.48 134	93.65 149	92.60 92

CP-MVSNet		76.36 184	76.41 177	76.32 184	82.73 189	88.64 150	79.39 192	79.62 125	67.21 183	53.70 201	60.72 168	55.22 204	67.91 192	83.52 181	86.34 138	94.55 100	93.19 75

PS-CasMVS		75.90 190	75.86 187	75.96 186	82.59 190	88.46 155	79.23 195	79.56 127	66.00 191	52.77 203	59.48 184	54.35 207	67.14 195	83.37 184	86.23 139	94.47 104	93.10 77

test20.03		68.31 209	70.05 208	66.28 213	82.41 191	80.84 210	67.35 218	76.11 165	58.44 215	40.80 223	53.77 204	54.54 206	42.28 225	83.07 185	81.96 194	88.73 198	77.76 214

FMVSNet1		81.64 107	80.61 114	82.84 103	82.36 192	89.20 143	88.67 84	79.58 126	70.79 157	72.63 92	58.95 188	72.26 101	79.34 129	90.73 78	90.72 55	94.47 104	91.62 127

pmmvs6		74.83 196	72.89 202	77.09 176	82.11 193	87.50 162	80.88 187	76.97 152	52.79 222	61.91 171	46.66 215	60.49 168	69.28 185	86.74 130	85.46 168	91.39 176	90.56 147

pmmvs5		76.93 172	76.33 178	77.62 170	81.97 194	88.40 156	81.32 180	74.35 178	65.42 198	61.42 178	63.07 148	57.95 190	73.23 168	85.60 148	85.35 169	93.41 152	88.55 157

v7n		77.22 170	76.23 179	78.38 167	81.89 195	89.10 147	82.24 175	76.36 156	65.96 192	61.21 181	56.56 195	55.79 200	75.07 154	86.55 132	86.68 128	93.52 150	92.95 81

our_test_3					81.81 196	83.96 197	76.61 203

Anonymous20231206		70.80 204	70.59 207	71.04 204	81.60 197	82.49 205	74.64 208	75.87 171	64.17 201	49.27 211	44.85 219	53.59 210	54.68 211	83.07 185	82.34 190	90.17 188	83.65 196

v748		76.17 186	75.10 195	77.43 172	81.60 197	88.01 158	79.02 196	76.28 161	64.47 200	64.14 153	56.55 196	56.26 198	70.40 182	82.50 190	85.77 161	93.11 158	92.15 109

ADS-MVSNet		74.53 198	75.69 189	73.17 201	81.57 199	80.71 211	79.27 194	63.03 220	79.27 90	59.94 187	67.86 117	68.32 116	71.08 179	77.33 209	76.83 210	84.12 220	79.53 208

testpf		63.91 214	65.23 213	62.38 218	81.32 200	69.95 226	62.71 225	54.16 230	61.29 209	48.73 212	57.31 193	52.50 211	50.97 215	67.50 225	68.86 226	76.36 228	79.21 210

test-mter		77.79 165	80.02 120	75.18 191	81.18 201	82.85 201	80.52 189	62.03 222	73.62 138	62.16 167	73.55 82	73.83 93	73.81 163	84.67 172	83.34 183	91.37 177	88.31 159

TESTMET0.1,1		77.78 166	79.84 123	75.38 190	80.86 202	82.40 206	81.24 181	62.72 221	73.72 135	62.69 162	73.76 80	74.42 89	73.49 165	84.61 173	82.99 186	91.25 179	87.01 174

MDTV_nov1_ep13_2view		73.21 202	72.91 201	73.56 200	80.01 203	84.28 196	78.62 197	66.43 209	68.64 178	59.12 193	60.39 175	59.69 178	69.81 184	78.82 207	77.43 209	87.36 203	81.11 206

FPMVS		63.63 216	60.08 222	67.78 210	80.01 203	71.50 224	72.88 211	69.41 199	61.82 207	53.11 202	45.12 218	42.11 223	50.86 216	66.69 226	63.84 228	80.41 224	69.46 225

V4		76.55 177	75.89 185	77.32 173	79.95 205	88.50 153	81.07 184	73.62 181	65.47 197	61.71 173	56.31 197	58.87 188	74.28 162	83.48 182	85.62 165	93.28 153	92.98 79

v52		76.55 177	75.89 185	77.31 174	79.94 206	88.49 154	81.07 184	73.62 181	65.49 196	61.66 175	56.29 198	58.90 186	74.30 161	83.47 183	85.62 165	93.28 153	92.99 78

anonymousdsp		77.94 164	79.00 132	76.71 180	79.03 207	87.83 160	79.58 191	72.87 184	65.80 193	58.86 196	65.82 126	62.48 146	75.99 148	86.77 128	88.66 101	93.92 129	95.68 45

N_pmnet		66.85 210	66.63 210	67.11 212	78.73 208	74.66 220	70.53 214	71.07 189	66.46 189	46.54 215	51.68 210	51.91 213	55.48 209	74.68 217	72.38 221	80.29 225	74.65 218

PMMVS		81.65 106	84.05 78	78.86 162	78.56 209	82.63 203	83.10 166	67.22 205	81.39 70	70.11 99	84.91 38	79.74 71	82.12 71	87.31 116	85.70 163	92.03 171	86.67 179

PatchT		76.42 181	77.81 159	74.80 194	78.46 210	84.30 195	71.82 213	65.03 216	73.89 132	65.37 143	61.58 152	66.70 118	77.18 142	85.10 169	84.87 172	90.94 184	88.21 160

MVS-HIRNet		68.83 207	66.39 211	71.68 203	77.58 211	75.52 219	66.45 219	65.05 215	62.16 206	62.84 161	44.76 220	56.60 197	71.96 173	78.04 208	75.06 216	86.18 211	72.56 220

LP		68.35 208	67.23 209	69.67 207	77.49 212	79.38 214	72.84 212	61.37 223	66.94 185	55.08 199	47.00 214	50.35 214	65.16 201	75.61 214	76.03 211	86.08 212	75.28 217

pmmvs-eth3d		74.32 199	71.96 204	77.08 177	77.33 213	82.71 202	78.41 198	76.02 168	66.65 187	65.98 137	54.23 203	49.02 218	73.14 169	82.37 192	82.69 188	91.61 174	86.05 184

new-patchmatchnet		63.80 215	63.31 217	64.37 215	76.49 214	75.99 218	63.73 222	70.99 190	57.27 216	43.08 219	45.86 217	43.80 220	45.13 224	73.20 220	70.68 225	86.80 208	76.34 216

FMVSNet5		75.50 194	76.07 180	74.83 193	76.16 215	81.19 209	81.34 179	70.21 194	73.20 141	61.59 176	58.97 187	68.33 115	68.50 188	85.87 142	85.85 160	91.18 182	79.11 211

PM-MVS		74.17 200	73.10 200	75.41 189	76.07 216	82.53 204	77.56 202	71.69 187	71.04 155	61.92 170	61.23 159	47.30 219	74.82 156	81.78 194	79.80 196	90.42 186	88.05 163

MIMVSNet		74.69 197	75.60 190	73.62 199	76.02 217	85.31 189	81.21 183	67.43 203	71.02 156	59.07 194	54.48 200	64.07 130	66.14 199	86.52 134	86.64 129	91.83 172	81.17 205

EU-MVSNet		69.98 206	72.30 203	67.28 211	75.67 218	79.39 213	73.12 210	69.94 196	63.59 203	42.80 220	62.93 149	56.71 196	55.07 210	79.13 206	78.55 202	87.06 207	85.82 187

test2356		63.96 213	64.10 216	63.78 216	74.71 219	71.55 223	65.83 220	67.38 204	57.11 217	40.41 224	53.58 205	41.13 225	49.35 218	77.00 211	77.57 208	85.01 216	70.79 221

Anonymous20231211		62.95 218	60.42 221	65.89 214	74.22 220	78.37 217	67.66 217	74.47 176	40.37 232	39.59 227	27.51 231	38.26 230	52.13 213	75.39 216	77.89 206	87.28 205	85.16 189

testus		63.31 217	64.48 215	61.94 220	73.99 221	71.99 222	63.56 224	63.25 219	57.01 218	39.41 228	54.38 201	38.73 229	46.24 223	77.01 210	77.93 205	85.20 215	74.29 219

PMVS	50.48 18	55.81 225	51.93 226	60.33 221	72.90 222	49.34 235	48.78 232	69.51 198	43.49 229	54.25 200	36.26 229	41.04 226	39.71 229	65.07 228	60.70 229	76.85 227	67.58 226
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmv		56.62 223	56.41 224	56.86 223	71.92 223	67.58 228	52.17 230	65.69 210	40.60 230	28.53 233	37.90 224	31.52 232	40.10 227	72.64 221	74.73 218	82.78 222	69.91 222

test1235678		56.61 224	56.40 225	56.86 223	71.92 223	67.58 228	52.17 230	65.69 210	40.58 231	28.52 234	37.89 225	31.49 233	40.10 227	72.64 221	74.72 219	82.78 222	69.90 223

1111		57.32 222	57.20 223	57.46 222	71.89 225	67.50 230	52.34 228	58.78 225	46.57 227	39.69 225	37.38 226	38.78 227	46.37 221	74.15 218	74.36 220	75.70 229	61.66 228

.test1245		41.43 230	38.48 231	44.88 228	71.89 225	67.50 230	52.34 228	58.78 225	46.57 227	39.69 225	37.38 226	38.78 227	46.37 221	74.15 218	1.18 235	0.20 239	3.76 237

ambc			61.92 218		70.98 227	73.54 221	63.64 223		60.06 211	52.23 206	38.44 223	19.17 238	57.12 207	82.33 193	75.03 217	83.21 221	84.89 191

pmmvs3		61.89 219	61.74 219	62.06 219	64.30 228	70.83 225	64.22 221	52.14 232	48.78 226	44.47 218	41.67 222	41.70 224	63.03 203	76.06 213	76.02 212	84.18 219	77.14 215

test12356		50.02 226	51.22 227	48.61 227	63.00 229	60.15 233	47.60 234	56.49 227	38.02 233	24.74 236	36.14 230	25.93 235	24.79 232	66.19 227	71.68 223	75.07 230	60.44 230

MDA-MVSNet-bldmvs		66.22 211	64.49 214	68.24 209	61.67 230	82.11 208	70.07 215	76.16 163	59.14 214	47.94 214	54.35 202	35.82 231	67.33 194	64.94 229	75.68 213	86.30 210	79.36 209

new_pmnet		59.28 220	61.47 220	56.73 225	61.66 231	68.29 227	59.57 226	54.91 228	60.83 210	34.38 231	44.66 221	43.65 221	49.90 217	71.66 223	71.56 224	79.94 226	69.67 224

no-one		44.14 228	43.91 230	44.40 229	59.91 232	61.10 232	34.07 237	60.09 224	27.71 235	14.44 238	19.11 233	19.28 237	23.90 234	47.36 233	66.69 227	73.98 231	66.11 227

Gipuma		49.17 227	47.05 228	51.65 226	59.67 233	48.39 236	41.98 235	63.47 218	55.64 221	33.33 232	14.90 234	13.78 239	41.34 226	69.31 224	72.30 222	70.11 232	55.00 232
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		65.00 212	66.24 212	63.55 217	58.41 234	80.01 212	69.00 216	74.03 179	55.81 220	41.88 221	36.81 228	49.48 217	47.89 220	81.32 195	82.40 189	90.08 190	77.88 213

EMVS		30.49 233	25.44 234	36.39 232	51.47 235	29.89 240	20.17 240	54.00 231	26.49 236	12.02 240	13.94 237	8.84 240	34.37 230	25.04 236	34.37 234	46.29 237	39.53 235

E-PMN		31.40 231	26.80 233	36.78 231	51.39 236	29.96 239	20.20 239	54.17 229	25.93 237	12.75 239	14.73 235	8.58 241	34.10 231	27.36 235	37.83 233	48.07 236	43.18 234

PMMVS2		41.68 229	44.74 229	38.10 230	46.97 237	52.32 234	40.63 236	48.08 233	35.51 234	7.36 241	26.86 232	24.64 236	16.72 235	55.24 231	59.03 230	68.85 233	59.59 231

tmp_tt				32.73 233	43.96 238	21.15 241	26.71 238	8.99 236	65.67 194	51.39 208	56.01 199	42.64 222	11.76 236	56.60 230	50.81 232	53.55 235

MVE	30.17 19	30.88 232	33.52 232	27.80 235	23.78 239	39.16 238	18.69 241	46.90 234	21.88 238	15.39 237	14.37 236	7.31 242	24.41 233	41.63 234	56.22 231	37.64 238	54.07 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		57.56 221	82.61 85	28.34 234	0.22 240	90.10 123	79.37 193	0.14 238	79.56 87	0.40 242	71.25 97	83.40 54	0.30 239	86.27 137	83.87 179	89.59 193	83.83 195

testmvs		1.03 234	1.63 235	0.34 236	0.09 241	0.35 242	0.61 243	0.16 237	1.49 239	0.10 243	3.15 238	0.15 243	0.86 238	1.32 237	1.18 235	0.20 239	3.76 237

test123		0.87 235	1.40 236	0.25 237	0.03 242	0.25 243	0.35 244	0.08 239	1.21 240	0.05 244	2.84 239	0.03 244	0.89 237	0.43 238	1.16 237	0.13 241	3.87 236

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 243	0.00 244	0.00 245	0.00 240	0.00 241	0.00 245	0.00 240	0.00 245	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

MTAPA										92.97 2		91.03 17

MTMP										93.14 1		90.21 24

Patchmatch-RL test							8.55 242

NP-MVS									87.47 48

Patchmtry						85.54 188	82.30 173	68.23 200		65.37 143

DeepMVS_CX						48.31 237	48.03 233	26.08 235	56.42 219	25.77 235	47.51 213	31.31 234	51.30 214	48.49 232		53.61 234	61.52 229

High-resolution multi-view benchmark