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.

ESAPD		95.53 1	96.13 1	94.82 1	96.81 21	98.05 1	97.42 1	93.09 2	94.31 5	91.49 4	97.12 1	95.03 2	93.27 2	95.55 4	94.58 8	96.86 2	98.25 1

APDe-MVS		95.23 2	95.69 2	94.70 2	97.12 11	97.81 4	97.19 2	92.83 3	95.06 2	90.98 6	96.47 2	92.77 9	93.38 1	95.34 7	94.21 13	96.68 5	98.17 2

SMA-MVS		94.70 4	95.35 4	93.93 9	97.57 2	97.57 6	95.98 10	91.91 10	94.50 3	90.35 9	93.46 14	92.72 10	91.89 15	95.89 1	95.22 1	95.88 21	98.10 3

SD-MVS		94.53 6	95.22 5	93.73 12	95.69 32	97.03 11	95.77 18	91.95 9	94.41 4	91.35 5	94.97 5	93.34 6	91.80 17	94.72 17	93.99 17	95.82 28	98.07 4

TSAR-MVS + MP.		94.48 7	94.97 6	93.90 10	95.53 33	97.01 12	96.69 4	90.71 19	94.24 6	90.92 7	94.97 5	92.19 12	93.03 3	94.83 13	93.60 23	96.51 8	97.97 5

HSP-MVS		94.83 3	95.37 3	94.21 6	96.82 20	97.94 3	96.69 4	92.37 8	93.97 9	90.29 11	96.16 4	93.71 4	92.70 6	94.80 14	93.13 33	96.37 9	97.90 6

CSCG		92.76 22	93.16 24	92.29 26	96.30 24	97.74 5	94.67 29	88.98 31	92.46 20	89.73 17	86.67 34	92.15 13	88.69 37	92.26 47	92.92 37	95.40 47	97.89 7

SteuartSystems-ACMMP		94.06 10	94.65 8	93.38 16	96.97 16	97.36 7	96.12 8	91.78 11	92.05 25	87.34 27	94.42 9	90.87 20	91.87 16	95.47 6	94.59 7	96.21 14	97.77 8
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + ACMM		92.97 20	94.51 10	91.16 33	95.88 30	96.59 27	95.09 25	90.45 25	93.42 14	83.01 51	94.68 7	90.74 22	88.74 36	94.75 15	93.78 21	93.82 142	97.63 9

DeepPCF-MVS	88.51 2	92.64 25	94.42 13	90.56 37	94.84 39	96.92 15	91.31 59	89.61 27	95.16 1	84.55 43	89.91 26	91.45 17	90.15 30	95.12 9	94.81 5	92.90 167	97.58 10

HPM-MVS++		94.60 5	94.91 7	94.24 5	97.86 1	96.53 29	96.14 7	92.51 5	93.87 12	90.76 8	93.45 15	93.84 3	92.62 7	95.11 10	94.08 16	95.58 41	97.48 11

ACMMP_Plus		93.94 12	94.49 11	93.30 17	97.03 14	97.31 8	95.96 11	91.30 15	93.41 15	88.55 21	93.00 16	90.33 23	91.43 23	95.53 5	94.41 11	95.53 43	97.47 12

CNVR-MVS		94.37 8	94.65 8	94.04 8	97.29 7	97.11 9	96.00 9	92.43 7	93.45 13	89.85 16	90.92 22	93.04 7	92.59 8	95.77 2	94.82 4	96.11 16	97.42 13

APD-MVS		94.37 8	94.47 12	94.26 4	97.18 9	96.99 13	96.53 6	92.68 4	92.45 21	89.96 14	94.53 8	91.63 16	92.89 4	94.58 19	93.82 20	96.31 12	97.26 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS	87.86 3	92.26 27	91.86 30	92.73 22	96.18 25	96.87 16	95.19 24	91.76 12	92.17 24	86.58 32	81.79 46	85.85 45	90.88 26	94.57 20	94.61 6	95.80 29	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
MP-MVS		93.35 17	93.59 20	93.08 20	97.39 4	96.82 19	95.38 21	90.71 19	90.82 32	88.07 24	92.83 18	90.29 24	91.32 24	94.03 24	93.19 32	95.61 39	97.16 16
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TSAR-MVS + GP.		92.71 24	93.91 17	91.30 31	91.96 67	96.00 38	93.43 37	87.94 36	92.53 19	86.27 37	93.57 12	91.94 14	91.44 22	93.29 34	92.89 38	96.78 4	97.15 17

MCST-MVS		93.81 13	94.06 15	93.53 14	96.79 22	96.85 17	95.95 12	91.69 13	92.20 23	87.17 29	90.83 24	93.41 5	91.96 13	94.49 21	93.50 26	97.61 1	97.12 18

HFP-MVS		94.02 11	94.22 14	93.78 11	97.25 8	96.85 17	95.81 16	90.94 18	94.12 7	90.29 11	94.09 11	89.98 26	92.52 9	93.94 27	93.49 28	95.87 23	97.10 19

CP-MVS		93.25 18	93.26 23	93.24 18	96.84 19	96.51 30	95.52 20	90.61 22	92.37 22	88.88 19	90.91 23	89.52 29	91.91 14	93.64 31	92.78 39	95.69 34	97.09 20

canonicalmvs		89.36 44	89.92 39	88.70 55	91.38 69	95.92 40	91.81 54	82.61 89	90.37 36	82.73 54	82.09 44	79.28 76	88.30 41	91.17 61	93.59 24	95.36 51	97.04 21

NCCC		93.69 16	93.66 19	93.72 13	97.37 5	96.66 26	95.93 14	92.50 6	93.40 16	88.35 22	87.36 32	92.33 11	92.18 11	94.89 12	94.09 15	96.00 17	96.91 22

zzz-MVS		93.80 14	93.45 22	94.20 7	97.53 3	96.43 33	95.88 15	91.12 17	94.09 8	92.74 3	87.68 30	90.77 21	92.04 12	94.74 16	93.56 25	95.91 20	96.85 23

3Dnovator+	86.06 4	91.60 31	90.86 37	92.47 24	96.00 29	96.50 32	94.70 28	87.83 38	90.49 35	89.92 15	74.68 78	89.35 31	90.66 27	94.02 25	94.14 14	95.67 36	96.85 23

X-MVS		92.36 26	92.75 27	91.90 29	96.89 17	96.70 22	95.25 23	90.48 24	91.50 30	83.95 45	88.20 28	88.82 35	89.11 33	93.75 30	93.43 29	95.75 33	96.83 25

MSLP-MVS++		92.02 30	91.40 32	92.75 21	96.01 28	95.88 41	93.73 36	89.00 29	89.89 40	90.31 10	81.28 51	88.85 34	91.45 20	92.88 41	94.24 12	96.00 17	96.76 26

train_agg		92.87 21	93.53 21	92.09 27	96.88 18	95.38 46	95.94 13	90.59 23	90.65 34	83.65 48	94.31 10	91.87 15	90.30 28	93.38 33	92.42 40	95.17 61	96.73 27

ACMMPR		93.72 15	93.94 16	93.48 15	97.07 12	96.93 14	95.78 17	90.66 21	93.88 11	89.24 18	93.53 13	89.08 33	92.24 10	93.89 29	93.50 26	95.88 21	96.73 27

PVSNet_Blended_VisFu		87.40 63	87.80 57	86.92 67	92.86 59	95.40 45	88.56 93	83.45 74	79.55 93	82.26 55	74.49 79	84.03 52	79.24 135	92.97 40	91.53 47	95.15 63	96.65 29

DeepC-MVS_fast	88.76 1	93.10 19	93.02 26	93.19 19	97.13 10	96.51 30	95.35 22	91.19 16	93.14 18	88.14 23	85.26 38	89.49 30	91.45 20	95.17 8	95.07 2	95.85 26	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
CPTT-MVS		91.39 32	90.95 35	91.91 28	95.06 34	95.24 48	95.02 26	88.98 31	91.02 31	86.71 31	84.89 40	88.58 38	91.60 19	90.82 77	89.67 85	94.08 120	96.45 31

ACMMP		92.03 29	92.16 28	91.87 30	95.88 30	96.55 28	94.47 31	89.49 28	91.71 28	85.26 39	91.52 21	84.48 50	90.21 29	92.82 42	91.63 46	95.92 19	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
PGM-MVS		92.76 22	93.03 25	92.45 25	97.03 14	96.67 25	95.73 19	87.92 37	90.15 39	86.53 33	92.97 17	88.33 39	91.69 18	93.62 32	93.03 34	95.83 27	96.41 33

QAPM		89.49 43	89.58 43	89.38 48	94.73 40	95.94 39	92.35 46	85.00 55	85.69 56	80.03 64	76.97 69	87.81 41	87.87 44	92.18 51	92.10 42	96.33 10	96.40 34

HQP-MVS		89.13 46	89.58 43	88.60 57	93.53 51	93.67 67	93.29 39	87.58 40	88.53 46	75.50 82	87.60 31	80.32 67	87.07 55	90.66 83	89.95 75	94.62 96	96.35 35

PHI-MVS		92.05 28	93.74 18	90.08 40	94.96 36	97.06 10	93.11 41	87.71 39	90.71 33	80.78 61	92.40 19	91.03 18	87.68 48	94.32 23	94.48 10	96.21 14	96.16 36

MVS_0304		90.88 35	91.35 33	90.34 38	93.91 47	96.79 20	94.49 30	86.54 45	86.57 52	82.85 52	81.68 49	89.70 28	87.57 50	94.64 18	93.93 18	96.67 6	96.15 37

UGNet		85.90 71	88.23 52	83.18 104	88.96 109	94.10 62	87.52 103	83.60 67	81.66 73	77.90 76	80.76 53	83.19 55	66.70 202	91.13 72	90.71 60	94.39 111	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
CANet		91.33 33	91.46 31	91.18 32	95.01 35	96.71 21	93.77 34	87.39 41	87.72 48	87.26 28	81.77 47	89.73 27	87.32 53	94.43 22	93.86 19	96.31 12	96.02 39

DELS-MVS		89.71 41	89.68 42	89.74 43	93.75 49	96.22 35	93.76 35	85.84 48	82.53 67	85.05 41	78.96 60	84.24 51	84.25 68	94.91 11	94.91 3	95.78 32	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
CDPH-MVS		91.14 34	92.01 29	90.11 39	96.18 25	96.18 36	94.89 27	88.80 33	88.76 45	77.88 77	89.18 27	87.71 42	87.29 54	93.13 36	93.31 31	95.62 38	95.84 41

LGP-MVS_train		88.25 55	88.55 48	87.89 62	92.84 61	93.66 68	93.35 38	85.22 54	85.77 54	74.03 89	86.60 35	76.29 89	86.62 58	91.20 59	90.58 63	95.29 57	95.75 42

3Dnovator	85.17 5	90.48 37	89.90 41	91.16 33	94.88 38	95.74 42	93.82 33	85.36 52	89.28 41	87.81 25	74.34 80	87.40 43	88.56 38	93.07 37	93.74 22	96.53 7	95.71 43

MVS_111021_HR		90.56 36	91.29 34	89.70 45	94.71 41	95.63 43	91.81 54	86.38 46	87.53 49	81.29 58	87.96 29	85.43 47	87.69 47	93.90 28	92.93 36	96.33 10	95.69 44

anonymousdsp		77.94 168	79.00 137	76.71 184	79.03 212	87.83 165	79.58 196	72.87 188	65.80 198	58.86 200	65.82 130	62.48 152	75.99 153	86.77 134	88.66 106	93.92 131	95.68 45

PCF-MVS	84.60 6	88.66 48	87.75 60	89.73 44	93.06 57	96.02 37	93.22 40	90.00 26	82.44 69	80.02 65	77.96 65	85.16 48	87.36 52	88.54 112	88.54 109	94.72 88	95.61 46
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPNet		89.60 42	89.91 40	89.24 51	96.45 23	93.61 69	92.95 43	88.03 35	85.74 55	83.36 49	87.29 33	83.05 56	80.98 85	92.22 48	91.85 44	93.69 149	95.58 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPP-MVSNet		86.55 65	87.76 59	85.15 75	90.52 83	94.41 59	87.24 112	82.32 94	81.79 72	73.60 91	78.57 62	82.41 58	82.07 79	91.23 57	90.39 65	95.14 64	95.48 48

casdiffmvs1		89.19 45	89.09 45	89.31 49	91.86 68	95.44 44	92.81 45	82.23 96	88.97 44	85.78 38	82.59 43	81.31 63	87.87 44	89.06 103	90.78 56	95.34 53	95.46 49

abl_6				90.66 36	94.65 42	96.27 34	92.21 47	86.94 43	90.23 37	86.38 34	85.50 37	92.96 8	88.37 40			95.40 47	95.46 49

ACMP	83.90 8	88.32 54	88.06 54	88.62 56	92.18 65	93.98 65	91.28 60	85.24 53	86.69 51	81.23 59	85.62 36	75.13 93	87.01 56	89.83 91	89.77 82	94.79 82	95.43 51
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20240521		85.88 72	86.17 69	85.54 73	89.10 108	89.85 133	89.34 74	80.70 111	83.04 65	78.08 74	76.19 72	79.00 77	82.42 77	89.67 95	90.30 66	93.63 152	95.12 52

CLD-MVS		88.66 48	88.52 49	88.82 53	91.37 70	94.22 61	92.82 44	82.08 98	88.27 47	85.14 40	81.86 45	78.53 80	85.93 62	91.17 61	90.61 61	95.55 42	95.00 53
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS_MVSNet		86.18 67	88.18 53	83.85 96	91.02 73	94.72 58	87.48 104	82.46 91	81.05 79	70.28 101	76.98 68	82.20 60	76.65 150	93.97 26	93.38 30	95.18 60	94.97 54

Anonymous20231211		84.42 80	83.02 85	86.05 69	88.85 110	92.70 91	88.92 89	83.40 75	79.99 87	78.31 71	55.83 204	78.92 78	83.33 70	89.06 103	89.76 83	93.50 154	94.90 55

UniMVSNet (Re)		81.22 114	81.08 107	81.39 131	85.35 142	91.76 99	84.93 158	82.88 77	76.13 113	65.02 151	64.94 143	63.09 143	75.17 157	87.71 120	89.04 101	94.97 70	94.88 56

PVSNet_BlendedMVS		88.19 56	88.00 55	88.42 58	92.71 63	94.82 56	89.08 78	83.81 64	84.91 58	86.38 34	79.14 58	78.11 81	82.66 74	93.05 38	91.10 49	95.86 24	94.86 57

PVSNet_Blended		88.19 56	88.00 55	88.42 58	92.71 63	94.82 56	89.08 78	83.81 64	84.91 58	86.38 34	79.14 58	78.11 81	82.66 74	93.05 38	91.10 49	95.86 24	94.86 57

OpenMVS	82.53 11	87.71 60	86.84 65	88.73 54	94.42 43	95.06 52	91.02 61	83.49 71	82.50 68	82.24 56	67.62 123	85.48 46	85.56 63	91.19 60	91.30 48	95.67 36	94.75 59

IB-MVS	79.09 12	82.60 95	82.19 92	83.07 105	91.08 72	93.55 70	80.90 191	81.35 106	76.56 110	80.87 60	64.81 145	69.97 113	68.87 192	85.64 152	90.06 71	95.36 51	94.74 60
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
MVS_111021_LR		90.14 40	90.89 36	89.26 50	93.23 54	94.05 64	90.43 63	84.65 57	90.16 38	84.52 44	90.14 25	83.80 53	87.99 43	92.50 45	90.92 54	94.74 86	94.70 61

FC-MVSNet-train		85.18 76	85.31 75	85.03 76	90.67 78	91.62 101	87.66 101	83.61 66	79.75 90	74.37 88	78.69 61	71.21 110	78.91 137	91.23 57	89.96 74	94.96 71	94.69 62

Vis-MVSNet		84.38 81	86.68 68	81.70 124	87.65 121	94.89 54	88.14 95	80.90 110	74.48 129	68.23 120	77.53 66	80.72 65	69.98 188	92.68 43	91.90 43	95.33 55	94.58 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
casdiffmvs		87.83 58	87.45 62	88.28 61	91.01 74	95.16 50	91.42 58	82.08 98	84.68 61	83.26 50	80.75 54	77.48 85	86.53 60	89.82 92	89.84 79	95.38 50	94.43 64

UniMVSNet_NR-MVSNet		81.87 104	81.33 103	82.50 109	85.31 143	91.30 104	85.70 145	84.25 59	75.89 114	64.21 155	66.95 126	64.65 132	80.22 105	87.07 126	89.18 98	95.27 59	94.29 65

DU-MVS		81.20 115	80.30 122	82.25 111	84.98 150	90.94 109	85.70 145	83.58 69	75.74 116	64.21 155	65.30 137	59.60 185	80.22 105	86.89 130	89.31 93	94.77 84	94.29 65

Anonymous202405211			82.75 88		89.58 106	92.97 85	89.04 85	84.13 62	78.72 99		57.18 197	76.64 88	83.13 72	89.55 97	89.92 76	93.38 157	94.28 67

Effi-MVS+		85.33 75	85.08 76	85.63 72	89.69 105	93.42 72	89.90 67	80.31 117	79.32 94	72.48 97	73.52 90	74.03 97	86.55 59	90.99 74	89.98 73	94.83 80	94.27 68

MAR-MVS		88.39 53	88.44 50	88.33 60	94.90 37	95.06 52	90.51 62	83.59 68	85.27 57	79.07 68	77.13 67	82.89 57	87.70 46	92.19 50	92.32 41	94.23 115	94.20 69
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
DI_MVS_plusplus_trai		86.41 66	85.54 74	87.42 65	89.24 107	93.13 78	92.16 49	82.65 87	82.30 70	80.75 62	68.30 119	80.41 66	85.01 65	90.56 84	90.07 70	94.70 90	94.01 70

OMC-MVS		90.23 39	90.40 38	90.03 41	93.45 52	95.29 47	91.89 53	86.34 47	93.25 17	84.94 42	81.72 48	86.65 44	88.90 34	91.69 54	90.27 67	94.65 93	93.95 71

diffmvs		85.72 73	86.10 71	85.27 74	88.43 114	93.34 74	88.98 87	80.17 119	84.21 62	77.41 80	78.53 63	76.01 90	83.28 71	88.09 116	88.61 107	93.34 158	93.92 72

NR-MVSNet		80.25 128	79.98 127	80.56 150	85.20 145	90.94 109	85.65 147	83.58 69	75.74 116	61.36 183	65.30 137	56.75 201	72.38 176	88.46 113	88.80 105	95.16 62	93.87 73

CNLPA		88.40 51	87.00 64	90.03 41	93.73 50	94.28 60	89.56 72	85.81 49	91.87 26	87.55 26	69.53 112	81.49 61	89.23 32	89.45 99	88.59 108	94.31 114	93.82 74

OPM-MVS		87.56 62	85.80 73	89.62 46	93.90 48	94.09 63	94.12 32	88.18 34	75.40 119	77.30 81	76.41 70	77.93 83	88.79 35	92.20 49	90.82 55	95.40 47	93.72 75

AdaColmap		90.29 38	88.38 51	92.53 23	96.10 27	95.19 49	92.98 42	91.40 14	89.08 43	88.65 20	78.35 64	81.44 62	91.30 25	90.81 78	90.21 68	94.72 88	93.59 76

MVS_Test		86.93 64	87.24 63	86.56 68	90.10 98	93.47 71	90.31 64	80.12 120	83.55 64	78.12 72	79.58 57	79.80 71	85.45 64	90.17 87	90.59 62	95.29 57	93.53 77

TranMVSNet+NR-MVSNet		80.52 124	79.84 129	81.33 134	84.92 152	90.39 116	85.53 150	84.22 61	74.27 132	60.68 188	64.93 144	59.96 180	77.48 146	86.75 135	89.28 94	95.12 66	93.29 78

conf0.05thres1000		81.00 118	79.12 136	83.20 103	90.14 97	92.15 96	87.05 124	82.09 97	68.11 187	66.19 134	59.67 185	61.10 172	79.05 136	90.47 86	89.11 99	94.68 91	93.22 79

CP-MVSNet		76.36 188	76.41 182	76.32 188	82.73 194	88.64 155	79.39 197	79.62 131	67.21 188	53.70 205	60.72 171	55.22 209	67.91 197	83.52 186	86.34 143	94.55 102	93.19 80

Baseline_NR-MVSNet		79.84 135	78.37 148	81.55 129	84.98 150	86.66 175	85.06 156	83.49 71	75.57 118	63.31 163	58.22 195	60.97 173	78.00 143	86.89 130	87.13 127	94.47 106	93.15 81

PS-CasMVS		75.90 194	75.86 192	75.96 190	82.59 195	88.46 160	79.23 200	79.56 133	66.00 196	52.77 207	59.48 187	54.35 212	67.14 200	83.37 189	86.23 144	94.47 106	93.10 82

v52		76.55 181	75.89 190	77.31 178	79.94 211	88.49 159	81.07 189	73.62 185	65.49 201	61.66 179	56.29 202	58.90 192	74.30 166	83.47 188	85.62 170	93.28 159	92.99 83

V4		76.55 181	75.89 190	77.32 177	79.95 210	88.50 158	81.07 189	73.62 185	65.47 202	61.71 177	56.31 201	58.87 194	74.28 167	83.48 187	85.62 170	93.28 159	92.98 84

ACMM	83.27 10	87.68 61	86.09 72	89.54 47	93.26 53	92.19 95	91.43 57	86.74 44	86.02 53	82.85 52	75.63 74	75.14 92	88.41 39	90.68 82	89.99 72	94.59 97	92.97 85
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v7n		77.22 174	76.23 184	78.38 171	81.89 200	89.10 152	82.24 180	76.36 161	65.96 197	61.21 185	56.56 199	55.79 206	75.07 159	86.55 138	86.68 133	93.52 153	92.95 86

Fast-Effi-MVS+		83.77 84	82.98 86	84.69 77	87.98 116	91.87 98	88.10 97	77.70 152	78.10 103	73.04 94	69.13 114	68.51 119	86.66 57	90.49 85	89.85 78	94.67 92	92.88 87

WR-MVS_H		75.84 195	76.93 179	74.57 201	82.86 191	89.50 143	78.34 204	79.36 136	66.90 191	52.51 208	60.20 182	59.71 182	59.73 211	83.61 185	85.77 166	94.65 93	92.84 88

v144192		78.81 159	77.22 175	80.67 148	82.95 188	89.79 136	86.40 138	77.42 153	68.26 186	63.13 164	59.50 186	58.13 195	80.08 110	85.93 146	86.08 161	94.06 122	92.83 89

MVSTER		86.03 69	86.12 70	85.93 70	88.62 112	89.93 131	89.33 75	79.91 123	81.87 71	81.35 57	81.07 52	74.91 94	80.66 91	92.13 52	90.10 69	95.68 35	92.80 90

WR-MVS		76.63 179	78.02 158	75.02 196	84.14 159	89.76 137	78.34 204	80.64 112	69.56 178	52.32 209	61.26 157	61.24 171	60.66 210	84.45 180	87.07 128	93.99 126	92.77 91

v1921920		78.57 164	76.99 178	80.41 156	82.93 189	89.63 141	86.38 139	77.14 156	68.31 185	61.80 176	58.89 192	56.79 200	80.19 107	86.50 141	86.05 163	94.02 124	92.76 92

v1192		78.94 158	77.33 170	80.82 146	83.25 183	89.90 132	86.91 128	77.72 151	68.63 184	62.61 168	59.17 188	57.53 197	80.62 94	86.89 130	86.47 140	93.79 147	92.75 93

IterMVS-LS		83.28 89	82.95 87	83.65 98	88.39 115	88.63 156	86.80 131	78.64 142	76.56 110	73.43 92	72.52 97	75.35 91	80.81 88	86.43 142	88.51 110	93.84 141	92.66 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LTVRE_ROB	74.41 16	75.78 196	74.72 202	77.02 182	85.88 135	89.22 147	82.44 176	77.17 155	50.57 229	45.45 221	65.44 135	52.29 217	81.25 82	85.50 159	87.42 124	89.94 196	92.62 95
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
V42		79.59 145	78.43 146	80.94 145	82.79 193	89.71 138	86.66 133	76.73 160	71.38 159	67.42 127	61.01 165	62.30 156	78.39 140	85.56 156	86.48 139	93.65 151	92.60 96

tfpn		81.79 107	80.06 124	83.82 97	90.61 79	92.91 88	87.62 102	82.34 93	73.66 142	67.46 124	64.99 142	55.50 207	79.77 119	91.12 73	89.62 86	95.14 64	92.59 97

thres600view7		82.53 98	81.02 108	84.28 86	90.61 79	93.05 82	88.57 91	82.67 85	74.12 135	68.56 118	65.09 140	62.13 159	80.40 97	91.15 66	89.02 102	94.88 77	92.59 97

Effi-MVS+-dtu		82.05 102	81.76 95	82.38 110	87.72 119	90.56 112	86.90 129	78.05 148	73.85 139	66.85 129	71.29 100	71.90 108	82.00 80	86.64 137	85.48 172	92.76 169	92.58 99

TAPA-MVS	84.37 7	88.91 47	88.93 47	88.89 52	93.00 58	94.85 55	92.00 50	84.84 56	91.68 29	80.05 63	79.77 56	84.56 49	88.17 42	90.11 88	89.00 103	95.30 56	92.57 100
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
view800		82.38 100	80.93 113	84.06 92	90.59 81	92.96 86	88.11 96	82.44 92	73.92 136	68.10 121	65.07 141	61.64 161	80.10 109	91.17 61	89.24 96	95.01 68	92.56 101

v7		79.79 138	78.28 149	81.54 130	83.73 177	90.34 121	87.27 110	78.27 145	70.50 165	65.59 144	60.59 174	60.47 175	80.46 95	86.90 129	86.63 135	93.92 131	92.56 101

v10		79.62 144	78.19 150	81.28 135	83.73 177	89.69 139	87.27 110	76.86 158	70.50 165	65.46 145	60.58 176	60.47 175	80.44 96	86.91 128	86.63 135	93.93 129	92.55 103

ACMH	78.52 14	81.86 105	80.45 121	83.51 101	90.51 85	91.22 105	85.62 148	84.23 60	70.29 168	62.21 170	69.04 116	64.05 137	84.48 67	87.57 121	88.45 111	94.01 125	92.54 104
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1144		79.38 149	77.83 163	81.18 137	83.62 179	90.23 123	87.15 122	78.35 144	69.13 180	64.02 159	60.20 182	59.41 186	80.14 108	86.78 133	86.57 137	93.81 143	92.53 105

tfpn111		83.51 86	82.68 89	84.47 82	90.30 90	93.09 79	89.05 80	82.72 81	75.14 120	69.49 109	74.24 81	63.13 139	80.38 98	91.15 66	89.51 87	94.91 73	92.50 106

conf200view11		82.85 91	81.46 99	84.47 82	90.30 90	93.09 79	89.05 80	82.72 81	75.14 120	69.49 109	65.72 131	63.13 139	80.38 98	91.15 66	89.51 87	94.91 73	92.50 106

tfpn200view9		82.86 90	81.46 99	84.48 81	90.30 90	93.09 79	89.05 80	82.71 83	75.14 120	69.56 106	65.72 131	63.13 139	80.38 98	91.15 66	89.51 87	94.91 73	92.50 106

view600		82.51 99	81.00 112	84.27 87	90.56 82	92.95 87	88.57 91	82.57 90	74.16 134	68.70 117	65.13 139	62.15 158	80.36 103	91.15 66	88.98 104	94.87 79	92.48 109

v1240		78.15 166	76.53 181	80.04 158	82.85 192	89.48 144	85.61 149	76.77 159	67.05 189	61.18 186	58.37 194	56.16 205	79.89 114	86.11 145	86.08 161	93.92 131	92.47 110

thres400		82.68 93	81.15 105	84.47 82	90.52 83	92.89 89	88.95 88	82.71 83	74.33 131	69.22 113	65.31 136	62.61 148	80.63 92	90.96 75	89.50 91	94.79 82	92.45 111

CHOSEN 1792x2688		82.16 101	80.91 115	83.61 99	91.14 71	92.01 97	89.55 73	79.15 138	79.87 89	70.29 100	52.51 213	72.56 105	81.39 81	88.87 107	88.17 114	90.15 194	92.37 112

v748		76.17 190	75.10 200	77.43 176	81.60 202	88.01 163	79.02 201	76.28 166	64.47 205	64.14 157	56.55 200	56.26 204	70.40 187	82.50 195	85.77 166	93.11 164	92.15 113

tfpnnormal		77.46 173	74.86 201	80.49 152	86.34 133	88.92 154	84.33 164	81.26 107	61.39 213	61.70 178	51.99 214	53.66 214	74.84 160	88.63 111	87.38 125	94.50 104	92.08 114

CANet_DTU		85.43 74	87.72 61	82.76 108	90.95 77	93.01 84	89.99 66	75.46 178	82.67 66	64.91 152	83.14 41	80.09 68	80.68 90	92.03 53	91.03 51	94.57 99	92.08 114

thres200		82.77 92	81.25 104	84.54 78	90.38 87	93.05 82	89.13 77	82.67 85	74.40 130	69.53 108	65.69 134	63.03 144	80.63 92	91.15 66	89.42 92	94.88 77	92.04 116

PEN-MVS		76.02 192	76.07 185	75.95 191	83.17 185	87.97 164	79.65 195	80.07 122	66.57 193	51.45 211	60.94 166	55.47 208	66.81 201	82.72 192	86.80 131	94.59 97	92.03 117

v8		79.90 134	78.39 147	81.66 126	83.97 163	89.81 134	87.16 121	77.40 154	71.49 156	67.71 122	61.24 159	62.49 151	79.83 115	85.48 164	86.17 156	93.89 135	92.02 118

v1neww		80.09 130	78.45 144	82.00 115	83.97 163	90.49 113	87.18 118	79.67 128	71.49 156	67.44 125	61.24 159	62.41 154	79.83 115	85.49 160	86.19 152	93.88 137	91.86 119

v7new		80.09 130	78.45 144	82.00 115	83.97 163	90.49 113	87.18 118	79.67 128	71.49 156	67.44 125	61.24 159	62.41 154	79.83 115	85.49 160	86.19 152	93.88 137	91.86 119

v6		80.11 129	78.47 142	82.01 114	83.97 163	90.49 113	87.19 117	79.67 128	71.59 155	67.51 123	61.26 157	62.46 153	79.81 118	85.49 160	86.18 155	93.89 135	91.86 119

TSAR-MVS + COLMAP		88.40 51	89.09 45	87.60 64	92.72 62	93.92 66	92.21 47	85.57 51	91.73 27	73.72 90	91.75 20	73.22 104	87.64 49	91.49 55	89.71 84	93.73 148	91.82 122

divwei89l23v2f112		79.75 140	78.04 156	81.75 120	83.90 167	90.37 118	87.21 113	79.90 124	70.20 171	66.18 136	60.92 167	61.48 166	79.52 130	85.36 166	86.17 156	93.81 143	91.77 123

v1		79.76 139	78.06 154	81.74 122	83.89 170	90.38 117	87.20 114	79.88 126	70.23 169	66.17 139	60.92 167	61.56 162	79.50 131	85.37 165	86.17 156	93.81 143	91.77 123

conf0.01		82.64 94	81.02 108	84.53 80	90.30 90	93.22 77	89.05 80	82.75 79	75.14 120	69.69 105	67.15 125	59.19 188	80.38 98	91.16 64	89.51 87	95.00 69	91.76 125

v1141		79.75 140	78.04 156	81.75 120	83.89 170	90.37 118	87.20 114	79.89 125	70.23 169	66.18 136	60.92 167	61.48 166	79.54 127	85.36 166	86.17 156	93.81 143	91.76 125

GBi-Net		84.51 77	84.80 77	84.17 89	84.20 156	89.95 128	89.70 69	80.37 113	81.17 75	75.50 82	69.63 107	79.69 73	79.75 120	90.73 79	90.72 57	95.52 44	91.71 127

test1		84.51 77	84.80 77	84.17 89	84.20 156	89.95 128	89.70 69	80.37 113	81.17 75	75.50 82	69.63 107	79.69 73	79.75 120	90.73 79	90.72 57	95.52 44	91.71 127

FMVSNet2		83.87 82	83.73 83	84.05 94	84.20 156	89.95 128	89.70 69	80.21 118	79.17 96	74.89 86	65.91 129	77.49 84	79.75 120	90.87 76	91.00 53	95.52 44	91.71 127

v2v482		79.84 135	78.07 152	81.90 118	83.75 176	90.21 125	87.17 120	79.85 127	70.65 163	65.93 142	61.93 154	60.07 179	80.82 86	85.25 169	86.71 132	93.88 137	91.70 130

FMVSNet1		81.64 111	80.61 120	82.84 107	82.36 197	89.20 148	88.67 90	79.58 132	70.79 162	72.63 96	58.95 191	72.26 107	79.34 134	90.73 79	90.72 57	94.47 106	91.62 131

thres100view900		82.55 96	81.01 111	84.34 85	90.30 90	92.27 93	89.04 85	82.77 78	75.14 120	69.56 106	65.72 131	63.13 139	79.62 123	89.97 90	89.26 95	94.73 87	91.61 132

FMVSNet3		84.44 79	84.64 79	84.21 88	84.32 155	90.13 126	89.85 68	80.37 113	81.17 75	75.50 82	69.63 107	79.69 73	79.62 123	89.72 94	90.52 64	95.59 40	91.58 133

Vis-MVSNet (Re-imp)		83.65 85	86.81 67	79.96 159	90.46 86	92.71 90	84.84 159	82.00 100	80.93 81	62.44 169	76.29 71	82.32 59	65.54 205	92.29 46	91.66 45	94.49 105	91.47 134

Fast-Effi-MVS+-dtu		79.95 133	80.69 119	79.08 164	86.36 132	89.14 150	85.85 143	72.28 189	72.85 150	59.32 193	70.43 105	68.42 120	77.57 145	86.14 144	86.44 141	93.11 164	91.39 135

conf0.002		82.54 97	80.83 116	84.54 78	90.28 95	93.24 76	89.05 80	82.75 79	75.14 120	69.75 104	67.99 120	57.12 199	80.38 98	91.16 64	89.79 80	95.02 67	91.36 136

pm-mvs1		78.51 165	77.75 165	79.40 162	84.83 153	89.30 145	83.55 170	79.38 135	62.64 209	63.68 161	58.73 193	64.68 131	70.78 185	89.79 93	87.84 119	94.17 117	91.28 137

ACMH+	79.08 13	81.84 106	80.06 124	83.91 95	89.92 103	90.62 111	86.21 140	83.48 73	73.88 138	65.75 143	66.38 128	65.30 129	84.63 66	85.90 147	87.25 126	93.45 155	91.13 138

v11		79.02 156	77.36 168	80.95 144	83.89 170	86.48 183	86.53 136	75.77 177	69.69 177	65.21 150	60.36 179	60.24 178	80.32 104	87.20 124	86.54 138	93.96 128	91.02 139

v17		79.59 145	77.88 162	81.60 128	84.03 161	86.66 175	87.13 123	76.31 165	72.09 153	68.29 119	61.15 163	62.57 149	79.90 113	85.55 157	86.20 150	93.93 129	90.93 140

v18		79.71 142	77.98 159	81.73 123	84.02 162	86.67 174	87.37 107	76.35 162	72.61 151	68.86 115	61.35 156	62.65 147	79.94 111	85.49 160	86.21 147	93.85 140	90.92 141

v16		79.65 143	77.91 161	81.69 125	84.04 160	86.65 177	87.20 114	76.32 164	72.41 152	68.71 116	61.13 164	62.52 150	79.93 112	85.55 157	86.22 145	93.92 131	90.91 142

v13		78.99 157	77.25 174	81.02 143	83.87 175	86.47 184	86.60 135	75.96 175	69.87 176	66.07 140	60.25 181	61.41 169	79.49 132	85.72 149	86.22 145	94.14 118	90.84 143

V9		79.08 153	77.32 171	81.14 140	83.89 170	86.52 181	86.85 130	76.06 172	70.02 174	66.42 132	60.44 177	61.52 165	79.54 127	85.68 151	86.21 147	94.08 120	90.83 144

v12		79.03 155	77.28 172	81.06 142	83.88 174	86.49 182	86.62 134	76.02 173	69.99 175	66.18 136	60.34 180	61.44 168	79.54 127	85.70 150	86.21 147	94.11 119	90.82 145

V14		79.11 152	77.35 169	81.16 138	83.90 167	86.54 180	86.94 126	76.10 171	70.14 173	66.41 133	60.59 174	61.54 164	79.59 126	85.64 152	86.20 150	94.04 123	90.82 145

v15		79.13 151	77.37 167	81.19 136	83.90 167	86.56 179	87.01 125	76.15 169	70.20 171	66.48 131	60.71 172	61.55 163	79.60 125	85.59 155	86.19 152	93.98 127	90.80 147

UA-Net		86.07 68	87.78 58	84.06 92	92.85 60	95.11 51	87.73 100	84.38 58	73.22 145	73.18 93	79.99 55	89.22 32	71.47 183	93.22 35	93.03 34	94.76 85	90.69 148

CDS-MVSNet		81.63 112	82.09 93	81.09 141	87.21 126	90.28 122	87.46 106	80.33 116	69.06 181	70.66 98	71.30 99	73.87 98	67.99 195	89.58 96	89.87 77	92.87 168	90.69 148
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v148		78.59 163	76.84 180	80.62 149	83.61 180	89.16 149	83.65 169	79.24 137	69.38 179	69.34 112	59.88 184	60.41 177	75.19 156	83.81 184	84.63 181	92.70 170	90.63 150

pmmvs6		74.83 200	72.89 207	77.09 180	82.11 198	87.50 167	80.88 192	76.97 157	52.79 227	61.91 175	46.66 220	60.49 174	69.28 190	86.74 136	85.46 173	91.39 181	90.56 151

DTE-MVSNet		75.14 199	75.44 197	74.80 198	83.18 184	87.19 170	78.25 206	80.11 121	66.05 195	48.31 217	60.88 170	54.67 210	64.54 207	82.57 194	86.17 156	94.43 109	90.53 152

PLC	83.76 9	88.61 50	86.83 66	90.70 35	94.22 44	92.63 92	91.50 56	87.19 42	89.16 42	86.87 30	75.51 75	80.87 64	89.98 31	90.01 89	89.20 97	94.41 110	90.45 153
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GA-MVS		79.52 147	79.71 132	79.30 163	85.68 138	90.36 120	84.55 161	78.44 143	70.47 167	57.87 201	68.52 118	61.38 170	76.21 152	89.40 100	87.89 117	93.04 166	89.96 154

LS3D		85.96 70	84.37 80	87.81 63	94.13 45	93.27 75	90.26 65	89.00 29	84.91 58	72.84 95	71.74 98	72.47 106	87.45 51	89.53 98	89.09 100	93.20 162	89.60 155

gg-mvs-nofinetune		75.64 197	77.26 173	73.76 202	87.92 117	92.20 94	87.32 108	64.67 221	51.92 228	35.35 233	46.44 221	77.05 87	71.97 177	92.64 44	91.02 52	95.34 53	89.53 156

SixPastTwentyTwo		76.02 192	75.72 193	76.36 187	83.38 181	87.54 166	75.50 211	76.22 167	65.50 200	57.05 202	70.64 102	53.97 213	74.54 162	80.96 201	82.12 196	91.44 180	89.35 157

HyFIR lowres test		81.62 113	79.45 135	84.14 91	91.00 75	93.38 73	88.27 94	78.19 146	76.28 112	70.18 102	48.78 217	73.69 100	83.52 69	87.05 127	87.83 120	93.68 150	89.15 158

CostFormer		80.94 119	80.21 123	81.79 119	87.69 120	88.58 157	87.47 105	70.66 195	80.02 86	77.88 77	73.03 93	71.40 109	78.24 141	79.96 206	79.63 202	88.82 201	88.84 159

IterMVS		78.79 160	79.71 132	77.71 173	85.26 144	85.91 187	84.54 162	69.84 201	73.38 144	61.25 184	70.53 104	70.35 111	74.43 164	85.21 172	83.80 186	90.95 188	88.77 160
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs5		76.93 176	76.33 183	77.62 174	81.97 199	88.40 161	81.32 185	74.35 182	65.42 203	61.42 182	63.07 151	57.95 196	73.23 173	85.60 154	85.35 174	93.41 156	88.55 161

RPMNet		77.07 175	77.63 166	76.42 186	85.56 140	85.15 195	81.37 183	65.27 218	74.71 127	60.29 189	63.71 150	66.59 125	73.64 169	82.71 193	82.12 196	92.38 172	88.39 162

test-mter		77.79 169	80.02 126	75.18 195	81.18 206	82.85 206	80.52 194	62.03 226	73.62 143	62.16 171	73.55 86	73.83 99	73.81 168	84.67 177	83.34 188	91.37 182	88.31 163

CR-MVSNet		78.71 161	78.86 138	78.55 169	85.85 137	85.15 195	82.30 178	68.23 204	74.71 127	65.37 147	64.39 147	69.59 115	77.18 147	85.10 174	84.87 177	92.34 173	88.21 164

PatchT		76.42 185	77.81 164	74.80 198	78.46 215	84.30 200	71.82 218	65.03 220	73.89 137	65.37 147	61.58 155	66.70 124	77.18 147	85.10 174	84.87 177	90.94 189	88.21 164

pmmvs4		79.99 132	78.08 151	82.22 112	83.04 187	87.16 171	84.95 157	78.80 141	78.64 100	74.53 87	64.61 146	59.41 186	79.45 133	84.13 182	84.54 182	92.53 171	88.08 166

PM-MVS		74.17 204	73.10 205	75.41 193	76.07 221	82.53 209	77.56 207	71.69 191	71.04 160	61.92 174	61.23 162	47.30 224	74.82 161	81.78 199	79.80 201	90.42 191	88.05 167

CVMVSNet		76.70 178	78.46 143	74.64 200	83.34 182	84.48 199	81.83 182	74.58 180	68.88 182	51.23 213	69.77 106	70.05 112	67.49 198	84.27 181	83.81 185	89.38 199	87.96 168

TransMVSNet (Re)		76.57 180	75.16 199	78.22 172	85.60 139	87.24 169	82.46 174	81.23 108	59.80 217	59.05 199	57.07 198	59.14 190	66.60 203	88.09 116	86.82 130	94.37 112	87.95 169

EG-PatchMatch MVS		76.40 187	75.47 196	77.48 175	85.86 136	90.22 124	82.45 175	73.96 184	59.64 218	59.60 192	52.75 212	62.20 157	68.44 194	88.23 115	87.50 121	94.55 102	87.78 170

EPNet_dtu		81.98 103	83.82 82	79.83 161	94.10 46	85.97 186	87.29 109	84.08 63	80.61 84	59.96 190	81.62 50	77.19 86	62.91 209	87.21 123	86.38 142	90.66 190	87.77 171
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TDRefinement		79.05 154	77.05 177	81.39 131	88.45 113	89.00 153	86.92 127	82.65 87	74.21 133	64.41 154	59.17 188	59.16 189	74.52 163	85.23 170	85.09 175	91.37 182	87.51 172

tfpn_ndepth		81.77 109	82.29 91	81.15 139	89.79 104	91.71 100	85.49 151	81.63 105	79.17 96	64.76 153	73.04 92	68.14 123	70.62 186	88.72 108	87.88 118	94.63 95	87.38 173

DWT-MVSNet_training		80.51 125	78.05 155	83.39 102	88.64 111	88.33 162	86.11 142	76.33 163	79.65 91	78.64 70	69.62 110	58.89 193	80.82 86	80.50 203	82.03 198	89.77 197	87.36 174

tfpn1000		81.03 117	81.70 96	80.25 157	90.18 96	91.35 102	83.96 166	81.15 109	78.00 104	62.11 172	73.37 91	65.75 126	69.17 191	88.68 110	87.44 122	94.93 72	87.29 175

tpmp4_e23		79.82 137	77.96 160	82.00 115	87.59 122	86.93 172	87.81 99	72.21 190	79.99 87	78.02 75	67.83 122	64.77 130	78.74 138	79.99 205	78.90 205	87.65 207	87.29 175

CHOSEN 280x420		80.28 127	81.66 97	78.67 168	82.92 190	79.24 220	85.36 153	66.79 211	78.11 102	70.32 99	75.03 77	79.87 69	81.09 84	89.07 102	83.16 189	85.54 217	87.17 177

test-LLR		79.47 148	79.84 129	79.03 165	87.47 123	82.40 211	81.24 186	78.05 148	73.72 140	62.69 166	73.76 84	74.42 95	73.49 170	84.61 178	82.99 191	91.25 184	87.01 178

TESTMET0.1,1		77.78 170	79.84 129	75.38 194	80.86 207	82.40 211	81.24 186	62.72 225	73.72 140	62.69 166	73.76 84	74.42 95	73.49 170	84.61 178	82.99 191	91.25 184	87.01 178

PatchMatch-RL		83.34 88	81.36 102	85.65 71	90.33 89	89.52 142	84.36 163	81.82 102	80.87 83	79.29 66	74.04 83	62.85 146	86.05 61	88.40 114	87.04 129	92.04 175	86.77 180

FC-MVSNet-test		76.53 184	81.62 98	70.58 209	84.99 149	85.73 189	74.81 212	78.85 140	77.00 108	39.13 232	75.90 73	73.50 101	54.08 217	86.54 139	85.99 164	91.65 178	86.68 181

tpm		76.30 189	76.05 187	76.59 185	86.97 128	83.01 205	83.83 167	67.06 210	71.83 154	63.87 160	69.56 111	62.88 145	73.41 172	79.79 207	78.59 206	84.41 221	86.68 181

PMMVS		81.65 110	84.05 81	78.86 166	78.56 214	82.63 208	83.10 171	67.22 209	81.39 74	70.11 103	84.91 39	79.74 72	82.12 78	87.31 122	85.70 168	92.03 176	86.67 183

tfpn_n400		80.63 122	80.79 117	80.43 154	90.02 100	91.08 106	85.34 154	81.79 103	72.93 148	59.27 194	73.54 87	64.40 133	71.61 181	89.05 105	88.21 112	94.56 100	86.32 184

tfpnconf		80.63 122	80.79 117	80.43 154	90.02 100	91.08 106	85.34 154	81.79 103	72.93 148	59.27 194	73.54 87	64.40 133	71.61 181	89.05 105	88.21 112	94.56 100	86.32 184

MS-PatchMatch		81.79 107	81.44 101	82.19 113	90.35 88	89.29 146	88.08 98	75.36 179	77.60 105	69.00 114	64.37 148	78.87 79	77.14 149	88.03 118	85.70 168	93.19 163	86.24 186

RPSCF		83.46 87	83.36 84	83.59 100	87.75 118	87.35 168	84.82 160	79.46 134	83.84 63	78.12 72	82.69 42	79.87 69	82.60 76	82.47 196	81.13 200	88.78 202	86.13 187

pmmvs-eth3d		74.32 203	71.96 209	77.08 181	77.33 218	82.71 207	78.41 203	76.02 173	66.65 192	65.98 141	54.23 208	49.02 223	73.14 174	82.37 197	82.69 193	91.61 179	86.05 188

tfpnview11		80.84 120	81.10 106	80.54 151	90.10 98	90.96 108	85.44 152	81.84 101	75.77 115	59.27 194	73.54 87	64.40 133	71.69 180	89.16 101	87.97 115	94.91 73	85.92 189

MSDG		83.87 82	81.02 108	87.19 66	92.17 66	89.80 135	89.15 76	85.72 50	80.61 84	79.24 67	66.66 127	68.75 118	82.69 73	87.95 119	87.44 122	94.19 116	85.92 189

EU-MVSNet		69.98 210	72.30 208	67.28 215	75.67 223	79.39 218	73.12 215	69.94 200	63.59 208	42.80 224	62.93 152	56.71 202	55.07 215	79.13 211	78.55 207	87.06 211	85.82 191

thresconf0.02		81.14 116	80.93 113	81.39 131	90.01 102	91.31 103	86.79 132	82.28 95	76.97 109	61.46 181	74.24 81	62.08 160	72.98 175	88.70 109	87.90 116	94.81 81	85.28 192

tpm cat1		77.78 170	75.28 198	80.70 147	87.14 127	85.84 188	85.81 144	70.40 196	77.44 106	78.80 69	63.72 149	64.01 138	76.55 151	75.60 220	75.21 219	85.51 218	85.12 193

ambc			61.92 223		70.98 231	73.54 225	63.64 227		60.06 216	52.23 210	38.44 228	19.17 242	57.12 212	82.33 198	75.03 221	83.21 225	84.89 194

COLMAP_ROB	76.78 15	80.50 126	78.49 141	82.85 106	90.96 76	89.65 140	86.20 141	83.40 75	77.15 107	66.54 130	62.27 153	65.62 128	77.89 144	85.23 170	84.70 180	92.11 174	84.83 195
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
USDC		80.69 121	79.89 128	81.62 127	86.48 131	89.11 151	86.53 136	78.86 139	81.15 78	63.48 162	72.98 94	59.12 191	81.16 83	87.10 125	85.01 176	93.23 161	84.77 196

gm-plane-assit		70.29 209	70.65 211	69.88 210	85.03 148	78.50 221	58.41 231	65.47 217	50.39 230	40.88 226	49.60 216	50.11 220	75.14 158	91.43 56	89.78 81	94.32 113	84.73 197

GG-mvs-BLEND		57.56 224	82.61 90	28.34 237	0.22 244	90.10 127	79.37 198	0.14 242	79.56 92	0.40 245	71.25 101	83.40 54	0.30 243	86.27 143	83.87 184	89.59 198	83.83 198

Anonymous20231206		70.80 208	70.59 212	71.04 208	81.60 202	82.49 210	74.64 213	75.87 176	64.17 206	49.27 215	44.85 224	53.59 215	54.68 216	83.07 190	82.34 195	90.17 193	83.65 199

dps		78.02 167	75.94 189	80.44 153	86.06 134	86.62 178	82.58 173	69.98 199	75.14 120	77.76 79	69.08 115	59.93 181	78.47 139	79.47 208	77.96 209	87.78 205	83.40 200

tpmrst		76.55 181	75.99 188	77.20 179	87.32 125	83.05 204	82.86 172	65.62 216	78.61 101	67.22 128	69.19 113	65.71 127	75.87 154	76.75 217	75.33 218	84.31 222	83.28 201

TAMVS		76.42 185	77.16 176	75.56 192	83.05 186	85.55 192	80.58 193	71.43 192	65.40 204	61.04 187	67.27 124	69.22 117	67.99 195	84.88 176	84.78 179	89.28 200	83.01 202

TinyColmap		76.73 177	73.95 204	79.96 159	85.16 147	85.64 191	82.34 177	78.19 146	70.63 164	62.06 173	60.69 173	49.61 221	80.81 88	85.12 173	83.69 187	91.22 186	82.27 203

MDTV_nov1_ep13		79.14 150	79.49 134	78.74 167	85.40 141	86.89 173	84.32 165	70.29 197	78.85 98	69.42 111	75.37 76	73.29 103	75.64 155	80.61 202	79.48 204	87.36 208	81.91 204

PatchmatchNet		78.67 162	78.85 139	78.46 170	86.85 130	86.03 185	83.77 168	68.11 206	80.88 82	66.19 134	72.90 95	73.40 102	78.06 142	79.25 210	77.71 211	87.75 206	81.75 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test0.0.03 1		76.03 191	78.51 140	73.12 206	87.47 123	85.13 197	76.32 209	78.05 148	73.19 147	50.98 214	70.64 102	69.28 116	55.53 213	85.33 168	84.38 183	90.39 192	81.63 206

CMPMVS	56.49 17	73.84 205	71.73 210	76.31 189	85.20 145	85.67 190	75.80 210	73.23 187	62.26 210	65.40 146	53.40 211	59.70 183	71.77 179	80.25 204	79.56 203	86.45 213	81.28 207
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		74.69 201	75.60 195	73.62 203	76.02 222	85.31 194	81.21 188	67.43 207	71.02 161	59.07 198	54.48 205	64.07 136	66.14 204	86.52 140	86.64 134	91.83 177	81.17 208

MDTV_nov1_ep13_2view		73.21 206	72.91 206	73.56 204	80.01 208	84.28 201	78.62 202	66.43 213	68.64 183	59.12 197	60.39 178	59.69 184	69.81 189	78.82 212	77.43 213	87.36 208	81.11 209

EPMVS		77.53 172	78.07 152	76.90 183	86.89 129	84.91 198	82.18 181	66.64 212	81.00 80	64.11 158	72.75 96	69.68 114	74.42 165	79.36 209	78.13 208	87.14 210	80.68 210

ADS-MVSNet		74.53 202	75.69 194	73.17 205	81.57 204	80.71 216	79.27 199	63.03 224	79.27 95	59.94 191	67.86 121	68.32 122	71.08 184	77.33 214	76.83 214	84.12 224	79.53 211

MDA-MVSNet-bldmvs		66.22 215	64.49 219	68.24 213	61.67 234	82.11 213	70.07 220	76.16 168	59.14 219	47.94 218	54.35 207	35.82 235	67.33 199	64.94 233	75.68 217	86.30 214	79.36 212

testpf		63.91 218	65.23 218	62.38 221	81.32 205	69.95 230	62.71 229	54.16 234	61.29 214	48.73 216	57.31 196	52.50 216	50.97 219	67.50 229	68.86 230	76.36 232	79.21 213

FMVSNet5		75.50 198	76.07 185	74.83 197	76.16 220	81.19 214	81.34 184	70.21 198	73.20 146	61.59 180	58.97 190	68.33 121	68.50 193	85.87 148	85.85 165	91.18 187	79.11 214

testgi		71.92 207	74.20 203	69.27 212	84.58 154	83.06 203	73.40 214	74.39 181	64.04 207	46.17 220	68.90 117	57.15 198	48.89 223	84.07 183	83.08 190	88.18 204	79.09 215

MIMVSNet1		65.00 216	66.24 217	63.55 220	58.41 238	80.01 217	69.00 221	74.03 183	55.81 225	41.88 225	36.81 233	49.48 222	47.89 224	81.32 200	82.40 194	90.08 195	77.88 216

test20.03		68.31 213	70.05 213	66.28 217	82.41 196	80.84 215	67.35 222	76.11 170	58.44 220	40.80 227	53.77 209	54.54 211	42.28 229	83.07 190	81.96 199	88.73 203	77.76 217

pmmvs3		61.89 222	61.74 224	62.06 222	64.30 232	70.83 229	64.22 225	52.14 236	48.78 231	44.47 222	41.67 227	41.70 229	63.03 208	76.06 218	76.02 216	84.18 223	77.14 218

new-patchmatchnet		63.80 219	63.31 222	64.37 218	76.49 219	75.99 222	63.73 226	70.99 194	57.27 221	43.08 223	45.86 222	43.80 225	45.13 228	73.20 224	70.68 229	86.80 212	76.34 219

LP		68.35 212	67.23 214	69.67 211	77.49 217	79.38 219	72.84 217	61.37 227	66.94 190	55.08 203	47.00 219	50.35 219	65.16 206	75.61 219	76.03 215	86.08 216	75.28 220

N_pmnet		66.85 214	66.63 215	67.11 216	78.73 213	74.66 224	70.53 219	71.07 193	66.46 194	46.54 219	51.68 215	51.91 218	55.48 214	74.68 221	72.38 225	80.29 229	74.65 221

testus		63.31 221	64.48 220	61.94 223	73.99 225	71.99 226	63.56 228	63.25 223	57.01 223	39.41 231	54.38 206	38.73 234	46.24 227	77.01 215	77.93 210	85.20 219	74.29 222

MVS-HIRNet		68.83 211	66.39 216	71.68 207	77.58 216	75.52 223	66.45 223	65.05 219	62.16 211	62.84 165	44.76 225	56.60 203	71.96 178	78.04 213	75.06 220	86.18 215	72.56 223

test2356		63.96 217	64.10 221	63.78 219	74.71 224	71.55 227	65.83 224	67.38 208	57.11 222	40.41 228	53.58 210	41.13 230	49.35 222	77.00 216	77.57 212	85.01 220	70.79 224

testmv		56.62 226	56.41 228	56.86 226	71.92 227	67.58 232	52.17 234	65.69 214	40.60 235	28.53 236	37.90 229	31.52 236	40.10 231	72.64 225	74.73 222	82.78 226	69.91 225

test1235678		56.61 227	56.40 229	56.86 226	71.92 227	67.58 232	52.17 234	65.69 214	40.58 236	28.52 237	37.89 230	31.49 237	40.10 231	72.64 225	74.72 223	82.78 226	69.90 226

new_pmnet		59.28 223	61.47 225	56.73 228	61.66 235	68.29 231	59.57 230	54.91 232	60.83 215	34.38 234	44.66 226	43.65 226	49.90 221	71.66 227	71.56 228	79.94 230	69.67 227

FPMVS		63.63 220	60.08 226	67.78 214	80.01 208	71.50 228	72.88 216	69.41 203	61.82 212	53.11 206	45.12 223	42.11 228	50.86 220	66.69 230	63.84 232	80.41 228	69.46 228

PMVS	50.48 18	55.81 228	51.93 230	60.33 224	72.90 226	49.34 239	48.78 236	69.51 202	43.49 234	54.25 204	36.26 234	41.04 231	39.71 233	65.07 232	60.70 233	76.85 231	67.58 229
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
no-one		44.14 231	43.91 234	44.40 232	59.91 236	61.10 236	34.07 241	60.09 228	27.71 239	14.44 241	19.11 237	19.28 241	23.90 238	47.36 237	66.69 231	73.98 235	66.11 230

1111		57.32 225	57.20 227	57.46 225	71.89 229	67.50 234	52.34 232	58.78 229	46.57 232	39.69 229	37.38 231	38.78 232	46.37 225	74.15 222	74.36 224	75.70 233	61.66 231

DeepMVS_CX						48.31 241	48.03 237	26.08 239	56.42 224	25.77 238	47.51 218	31.31 238	51.30 218	48.49 236		53.61 238	61.52 232

test12356		50.02 229	51.22 231	48.61 230	63.00 233	60.15 237	47.60 238	56.49 231	38.02 237	24.74 239	36.14 235	25.93 239	24.79 236	66.19 231	71.68 227	75.07 234	60.44 233

PMMVS2		41.68 232	44.74 233	38.10 233	46.97 241	52.32 238	40.63 240	48.08 237	35.51 238	7.36 244	26.86 236	24.64 240	16.72 239	55.24 235	59.03 234	68.85 237	59.59 234

Gipuma		49.17 230	47.05 232	51.65 229	59.67 237	48.39 240	41.98 239	63.47 222	55.64 226	33.33 235	14.90 238	13.78 243	41.34 230	69.31 228	72.30 226	70.11 236	55.00 235
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	30.17 19	30.88 235	33.52 236	27.80 238	23.78 243	39.16 242	18.69 245	46.90 238	21.88 242	15.39 240	14.37 240	7.31 246	24.41 237	41.63 238	56.22 235	37.64 242	54.07 236
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.40 234	26.80 237	36.78 234	51.39 240	29.96 243	20.20 243	54.17 233	25.93 241	12.75 242	14.73 239	8.58 245	34.10 235	27.36 239	37.83 237	48.07 240	43.18 237

EMVS		30.49 236	25.44 238	36.39 235	51.47 239	29.89 244	20.17 244	54.00 235	26.49 240	12.02 243	13.94 241	8.84 244	34.37 234	25.04 240	34.37 238	46.29 241	39.53 238

test123		0.87 238	1.40 240	0.25 240	0.03 246	0.25 247	0.35 248	0.08 243	1.21 244	0.05 247	2.84 243	0.03 248	0.89 241	0.43 242	1.16 241	0.13 245	3.87 239

.test1245		41.43 233	38.48 235	44.88 231	71.89 229	67.50 234	52.34 232	58.78 229	46.57 232	39.69 229	37.38 231	38.78 232	46.37 225	74.15 222	1.18 239	0.20 243	3.76 240

testmvs		1.03 237	1.63 239	0.34 239	0.09 245	0.35 246	0.61 247	0.16 241	1.49 243	0.10 246	3.15 242	0.15 247	0.86 242	1.32 241	1.18 239	0.20 243	3.76 240

v1.0		87.80 59	81.94 94	94.63 3	97.35 6	97.95 2	97.09 3	93.48 1	93.91 10	90.13 13	96.41 3	95.14 1	92.88 5	95.64 3	94.53 9	96.86 2	0.00 242

sosnet-low-res		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

sosnet		0.00 239	0.00 241	0.00 241	0.00 247	0.00 248	0.00 249	0.00 244	0.00 245	0.00 248	0.00 244	0.00 249	0.00 244	0.00 243	0.00 242	0.00 246	0.00 242

our_test_3					81.81 201	83.96 202	76.61 208

MTAPA										92.97 2		91.03 18

MTMP										93.14 1		90.21 25

Patchmatch-RL test							8.55 246

tmp_tt				32.73 236	43.96 242	21.15 245	26.71 242	8.99 240	65.67 199	51.39 212	56.01 203	42.64 227	11.76 240	56.60 234	50.81 236	53.55 239

XVS					93.11 55	96.70 22	91.91 51			83.95 45		88.82 35				95.79 30

X-MVStestdata					93.11 55	96.70 22	91.91 51			83.95 45		88.82 35				95.79 30

mPP-MVS					97.06 13							88.08 40

NP-MVS									87.47 50

Patchmtry						85.54 193	82.30 178	68.23 204		65.37 147

High-resolution multi-view benchmark