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 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

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

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

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.
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

v1.0		87.80 59	81.94 97	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	0.00 246

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

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

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_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
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

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

mPP-MVS					97.06 13							88.08 40

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

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

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.
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

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

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

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

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

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

EPNet		89.60 42	89.91 40	89.24 51	96.45 23	93.61 70	92.95 43	88.03 35	85.74 56	83.36 49	87.29 33	83.05 56	80.98 86	92.22 48	91.85 44	93.69 152	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 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

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 78	89.18 27	87.71 42	87.29 54	93.13 36	93.31 31	95.62 38	95.84 41

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
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 65	81.44 62	91.30 25	90.81 80	90.21 68	94.72 90	93.59 79

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

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 81	89.35 31	90.66 27	94.02 25	94.14 14	95.67 36	96.85 23

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	93.82 145	97.63 9

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
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

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 79	89.67 85	94.08 122	96.45 31

CANet		91.33 33	91.46 31	91.18 32	95.01 35	96.71 21	93.77 34	87.39 41	87.72 49	87.26 28	81.77 47	89.73 27	87.32 53	94.43 22	93.86 19	96.31 12	96.02 39

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

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 58	79.07 68	77.13 70	82.89 57	87.70 46	92.19 50	92.32 41	94.23 117	94.20 70
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 33	94.88 38	95.74 42	93.82 33	85.36 52	89.28 41	87.81 25	74.34 83	87.40 43	88.56 38	93.07 37	93.74 22	96.53 7	95.71 43

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	92.90 171	97.58 10

QAPM		89.49 43	89.58 43	89.38 48	94.73 40	95.94 39	92.35 46	85.00 55	85.69 57	80.03 64	76.97 72	87.81 41	87.87 44	92.18 51	92.10 42	96.33 10	96.40 34

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 50	81.29 58	87.96 29	85.43 47	87.69 47	93.90 28	92.93 36	96.33 10	95.69 44

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

OpenMVS	82.53 11	87.71 60	86.84 66	88.73 54	94.42 43	95.06 52	91.02 61	83.49 71	82.50 69	82.24 56	67.62 127	85.48 46	85.56 63	91.19 60	91.30 48	95.67 36	94.75 59

PLC	83.76 9	88.61 50	86.83 67	90.70 35	94.22 44	92.63 94	91.50 56	87.19 42	89.16 42	86.87 30	75.51 78	80.87 64	89.98 31	90.01 91	89.20 100	94.41 112	90.45 157
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		85.96 71	84.37 83	87.81 63	94.13 45	93.27 76	90.26 66	89.00 29	84.91 59	72.84 98	71.74 101	72.47 109	87.45 51	89.53 100	89.09 103	93.20 165	89.60 159

EPNet_dtu		81.98 106	83.82 85	79.83 164	94.10 46	85.97 190	87.29 110	84.08 63	80.61 87	59.96 194	81.62 50	77.19 87	62.91 213	87.21 127	86.38 146	90.66 194	87.77 175
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 38	93.91 47	96.79 20	94.49 30	86.54 45	86.57 53	82.85 52	81.68 49	89.70 28	87.57 50	94.64 18	93.93 18	96.67 6	96.15 37

OPM-MVS		87.56 62	85.80 76	89.62 46	93.90 48	94.09 63	94.12 32	88.18 34	75.40 122	77.30 82	76.41 73	77.93 84	88.79 35	92.20 49	90.82 55	95.40 47	93.72 78

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 68	85.05 41	78.96 61	84.24 51	84.25 69	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
CNLPA		88.40 51	87.00 65	90.03 41	93.73 50	94.28 60	89.56 73	85.81 49	91.87 26	87.55 26	69.53 115	81.49 61	89.23 32	89.45 101	88.59 111	94.31 116	93.82 75

HQP-MVS		89.13 46	89.58 43	88.60 57	93.53 51	93.67 68	93.29 39	87.58 40	88.53 46	75.50 83	87.60 31	80.32 67	87.07 55	90.66 85	89.95 75	94.62 98	96.35 35

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 95	93.95 72

ACMM	83.27 10	87.68 61	86.09 73	89.54 47	93.26 53	92.19 98	91.43 57	86.74 44	86.02 54	82.85 52	75.63 77	75.14 93	88.41 39	90.68 84	89.99 72	94.59 99	92.97 88
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 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 88	94.70 61

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

PCF-MVS	84.60 6	88.66 48	87.75 61	89.73 44	93.06 57	96.02 37	93.22 40	90.00 26	82.44 70	80.02 65	77.96 68	85.16 48	87.36 52	88.54 115	88.54 112	94.72 90	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 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 90	89.00 106	95.30 56	92.57 104
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		87.40 63	87.80 57	86.92 67	92.86 59	95.40 45	88.56 94	83.45 74	79.55 96	82.26 55	74.49 82	84.03 52	79.24 138	92.97 40	91.53 47	95.15 63	96.65 29

UA-Net		86.07 69	87.78 58	84.06 95	92.85 60	95.11 51	87.73 101	84.38 58	73.22 148	73.18 96	79.99 55	89.22 32	71.47 186	93.22 35	93.03 34	94.76 87	90.69 152

LGP-MVS_train		88.25 55	88.55 48	87.89 62	92.84 61	93.66 69	93.35 38	85.22 54	85.77 55	74.03 92	86.60 35	76.29 90	86.62 58	91.20 59	90.58 63	95.29 57	95.75 42

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 93	91.75 20	73.22 107	87.64 49	91.49 55	89.71 84	93.73 151	91.82 126

PVSNet_BlendedMVS		88.19 56	88.00 55	88.42 58	92.71 63	94.82 56	89.08 79	83.81 64	84.91 59	86.38 34	79.14 59	78.11 82	82.66 75	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 79	83.81 64	84.91 59	86.38 34	79.14 59	78.11 82	82.66 75	93.05 38	91.10 49	95.86 24	94.86 57

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 52	81.23 59	85.62 36	75.13 94	87.01 56	89.83 93	89.77 82	94.79 84	95.43 51
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSDG		83.87 85	81.02 111	87.19 66	92.17 66	89.80 138	89.15 77	85.72 50	80.61 87	79.24 67	66.66 131	68.75 121	82.69 74	87.95 123	87.44 126	94.19 118	85.92 193

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

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	89.06 106	90.78 56	95.34 53	95.46 49

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 77	88.30 41	91.17 61	93.59 24	95.36 51	97.04 21

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 81	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
CHOSEN 1792x2688		82.16 104	80.91 118	83.61 102	91.14 71	92.01 100	89.55 74	79.15 141	79.87 92	70.29 103	52.51 217	72.56 108	81.39 82	88.87 110	88.17 117	90.15 198	92.37 116

IB-MVS	79.09 12	82.60 98	82.19 95	83.07 108	91.08 72	93.55 71	80.90 194	81.35 106	76.56 113	80.87 60	64.81 149	69.97 116	68.87 196	85.64 156	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
IS_MVSNet		86.18 68	88.18 53	83.85 99	91.02 73	94.72 58	87.48 105	82.46 91	81.05 82	70.28 104	76.98 71	82.20 60	76.65 153	93.97 26	93.38 30	95.18 60	94.97 54

casdiffmvs		87.83 58	87.45 63	88.28 61	91.01 74	95.16 50	91.42 58	82.08 98	84.68 62	83.26 50	80.75 54	77.48 86	86.53 60	89.82 94	89.84 79	95.38 50	94.43 64

HyFIR lowres test		81.62 116	79.45 139	84.14 94	91.00 75	93.38 74	88.27 95	78.19 149	76.28 115	70.18 105	48.78 221	73.69 102	83.52 70	87.05 131	87.83 124	93.68 153	89.15 162

COLMAP_ROB	76.78 15	80.50 129	78.49 145	82.85 109	90.96 76	89.65 143	86.20 144	83.40 75	77.15 110	66.54 134	62.27 157	65.62 132	77.89 147	85.23 174	84.70 184	92.11 178	84.83 199
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 75	87.72 62	82.76 111	90.95 77	93.01 85	89.99 67	75.46 182	82.67 67	64.91 156	83.14 41	80.09 68	80.68 91	92.03 53	91.03 51	94.57 101	92.08 118

FC-MVSNet-train		85.18 77	85.31 78	85.03 77	90.67 78	91.62 104	87.66 102	83.61 66	79.75 93	74.37 90	78.69 62	71.21 113	78.91 140	91.23 57	89.96 74	94.96 73	94.69 62

tfpn		81.79 110	80.06 128	83.82 100	90.61 79	92.91 89	87.62 103	82.34 93	73.66 145	67.46 127	64.99 146	55.50 211	79.77 122	91.12 73	89.62 86	95.14 64	92.59 101

thres600view7		82.53 101	81.02 111	84.28 89	90.61 79	93.05 83	88.57 92	82.67 85	74.12 138	68.56 121	65.09 144	62.13 163	80.40 98	91.15 66	89.02 105	94.88 79	92.59 101

view800		82.38 103	80.93 116	84.06 95	90.59 81	92.96 87	88.11 97	82.44 92	73.92 139	68.10 124	65.07 145	61.64 165	80.10 110	91.17 61	89.24 99	95.01 70	92.56 105

view600		82.51 102	81.00 115	84.27 90	90.56 82	92.95 88	88.57 92	82.57 90	74.16 137	68.70 120	65.13 143	62.15 162	80.36 104	91.15 66	88.98 107	94.87 81	92.48 113

thres400		82.68 96	81.15 108	84.47 83	90.52 83	92.89 90	88.95 89	82.71 83	74.33 134	69.22 116	65.31 140	62.61 152	80.63 93	90.96 77	89.50 93	94.79 84	92.45 115

EPP-MVSNet		86.55 66	87.76 59	85.15 76	90.52 83	94.41 59	87.24 113	82.32 94	81.79 75	73.60 94	78.57 63	82.41 58	82.07 80	91.23 57	90.39 65	95.14 64	95.48 48

ACMH	78.52 14	81.86 108	80.45 125	83.51 104	90.51 85	91.22 108	85.62 151	84.23 60	70.29 172	62.21 174	69.04 119	64.05 141	84.48 68	87.57 125	88.45 114	94.01 127	92.54 108
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		83.65 88	86.81 68	79.96 162	90.46 86	92.71 91	84.84 162	82.00 100	80.93 84	62.44 173	76.29 74	82.32 59	65.54 209	92.29 46	91.66 45	94.49 107	91.47 138

thres200		82.77 95	81.25 107	84.54 79	90.38 87	93.05 83	89.13 78	82.67 85	74.40 133	69.53 111	65.69 138	63.03 148	80.63 93	91.15 66	89.42 95	94.88 79	92.04 120

MS-PatchMatch		81.79 110	81.44 104	82.19 116	90.35 88	89.29 149	88.08 99	75.36 183	77.60 108	69.00 117	64.37 152	78.87 80	77.14 152	88.03 122	85.70 172	93.19 166	86.24 190

PatchMatch-RL		83.34 91	81.36 105	85.65 72	90.33 89	89.52 145	84.36 166	81.82 102	80.87 86	79.29 66	74.04 86	62.85 150	86.05 61	88.40 118	87.04 133	92.04 179	86.77 184

tfpn111		83.51 89	82.68 92	84.47 83	90.30 90	93.09 80	89.05 81	82.72 81	75.14 123	69.49 112	74.24 84	63.13 143	80.38 99	91.15 66	89.51 89	94.91 75	92.50 110

conf0.01		82.64 97	81.02 111	84.53 81	90.30 90	93.22 78	89.05 81	82.75 79	75.14 123	69.69 108	67.15 129	59.19 192	80.38 99	91.16 64	89.51 89	95.00 71	91.76 129

conf200view11		82.85 94	81.46 102	84.47 83	90.30 90	93.09 80	89.05 81	82.72 81	75.14 123	69.49 112	65.72 135	63.13 143	80.38 99	91.15 66	89.51 89	94.91 75	92.50 110

thres100view900		82.55 99	81.01 114	84.34 86	90.30 90	92.27 96	89.04 86	82.77 78	75.14 123	69.56 109	65.72 135	63.13 143	79.62 126	89.97 92	89.26 98	94.73 89	91.61 136

tfpn200view9		82.86 93	81.46 102	84.48 82	90.30 90	93.09 80	89.05 81	82.71 83	75.14 123	69.56 109	65.72 135	63.13 143	80.38 99	91.15 66	89.51 89	94.91 75	92.50 110

conf0.002		82.54 100	80.83 119	84.54 79	90.28 95	93.24 77	89.05 81	82.75 79	75.14 123	69.75 107	67.99 123	57.12 203	80.38 99	91.16 64	89.79 80	95.02 69	91.36 140

tfpn1000		81.03 120	81.70 99	80.25 160	90.18 96	91.35 105	83.96 169	81.15 109	78.00 107	62.11 176	73.37 94	65.75 130	69.17 195	88.68 113	87.44 126	94.93 74	87.29 179

conf0.05thres1000		81.00 121	79.12 140	83.20 106	90.14 97	92.15 99	87.05 127	82.09 97	68.11 191	66.19 138	59.67 189	61.10 176	79.05 139	90.47 88	89.11 102	94.68 93	93.22 82

tfpnview11		80.84 123	81.10 109	80.54 154	90.10 98	90.96 111	85.44 155	81.84 101	75.77 118	59.27 198	73.54 90	64.40 137	71.69 183	89.16 104	87.97 118	94.91 75	85.92 193

MVS_Test		86.93 65	87.24 64	86.56 68	90.10 98	93.47 72	90.31 65	80.12 121	83.55 65	78.12 73	79.58 57	79.80 71	85.45 64	90.17 89	90.59 62	95.29 57	93.53 80

tfpn_n400		80.63 125	80.79 120	80.43 157	90.02 100	91.08 109	85.34 157	81.79 103	72.93 151	59.27 198	73.54 90	64.40 137	71.61 184	89.05 108	88.21 115	94.56 102	86.32 188

tfpnconf		80.63 125	80.79 120	80.43 157	90.02 100	91.08 109	85.34 157	81.79 103	72.93 151	59.27 198	73.54 90	64.40 137	71.61 184	89.05 108	88.21 115	94.56 102	86.32 188

thresconf0.02		81.14 119	80.93 116	81.39 134	90.01 102	91.31 106	86.79 135	82.28 95	76.97 112	61.46 185	74.24 84	62.08 164	72.98 178	88.70 112	87.90 119	94.81 83	85.28 196

ACMH+	79.08 13	81.84 109	80.06 128	83.91 98	89.92 103	90.62 114	86.21 143	83.48 73	73.88 141	65.75 147	66.38 132	65.30 133	84.63 67	85.90 151	87.25 130	93.45 158	91.13 142

tfpn_ndepth		81.77 112	82.29 94	81.15 142	89.79 104	91.71 103	85.49 154	81.63 105	79.17 99	64.76 157	73.04 95	68.14 126	70.62 190	88.72 111	87.88 121	94.63 97	87.38 177

Effi-MVS+		85.33 76	85.08 79	85.63 73	89.69 105	93.42 73	89.90 68	80.31 118	79.32 97	72.48 100	73.52 93	74.03 98	86.55 59	90.99 75	89.98 73	94.83 82	94.27 69

Anonymous202405211			82.75 91		89.58 106	92.97 86	89.04 86	84.13 62	78.72 102		57.18 201	76.64 89	83.13 73	89.55 99	89.92 76	93.38 160	94.28 67

tttt0517		85.11 79	85.81 75	84.30 88	89.24 107	92.68 93	87.12 126	80.11 122	81.98 73	74.31 91	78.08 67	73.57 103	79.90 114	91.01 74	89.58 87	95.11 68	93.77 76

DI_MVS_plusplus_trai		86.41 67	85.54 77	87.42 65	89.24 107	93.13 79	92.16 49	82.65 87	82.30 71	80.75 62	68.30 122	80.41 66	85.01 66	90.56 86	90.07 70	94.70 92	94.01 71

thisisatest0530		85.15 78	85.86 74	84.33 87	89.19 109	92.57 95	87.22 114	80.11 122	82.15 72	74.41 89	78.15 66	73.80 101	79.90 114	90.99 75	89.58 87	95.13 66	93.75 77

Anonymous20240521		85.88 73	86.17 70	85.54 74	89.10 110	89.85 136	89.34 75	80.70 111	83.04 66	78.08 75	76.19 75	79.00 78	82.42 78	89.67 97	90.30 66	93.63 155	95.12 52

UGNet		85.90 72	88.23 52	83.18 107	88.96 111	94.10 62	87.52 104	83.60 67	81.66 76	77.90 77	80.76 53	83.19 55	66.70 206	91.13 72	90.71 60	94.39 113	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
diffmvs1		87.00 64	87.76 59	86.12 69	88.93 112	93.78 67	90.40 64	80.42 113	88.25 48	78.77 70	79.52 58	79.73 73	85.07 65	89.30 103	89.49 94	93.11 167	94.28 67

Anonymous20231211		84.42 83	83.02 88	86.05 70	88.85 113	92.70 92	88.92 90	83.40 75	79.99 90	78.31 72	55.83 208	78.92 79	83.33 71	89.06 106	89.76 83	93.50 157	94.90 55

DWT-MVSNet_training		80.51 128	78.05 159	83.39 105	88.64 114	88.33 166	86.11 145	76.33 167	79.65 94	78.64 71	69.62 113	58.89 197	80.82 87	80.50 207	82.03 202	89.77 201	87.36 178

MVSTER		86.03 70	86.12 71	85.93 71	88.62 115	89.93 134	89.33 76	79.91 126	81.87 74	81.35 57	81.07 52	74.91 95	80.66 92	92.13 52	90.10 69	95.68 35	92.80 93

TDRefinement		79.05 158	77.05 181	81.39 134	88.45 116	89.00 156	86.92 130	82.65 87	74.21 136	64.41 158	59.17 192	59.16 193	74.52 166	85.23 174	85.09 179	91.37 186	87.51 176

diffmvs		85.72 74	86.10 72	85.27 75	88.43 117	93.34 75	88.98 88	80.17 120	84.21 63	77.41 81	78.53 64	76.01 91	83.28 72	88.09 120	88.61 110	93.34 161	93.92 73

IterMVS-LS		83.28 92	82.95 90	83.65 101	88.39 118	88.63 160	86.80 134	78.64 145	76.56 113	73.43 95	72.52 100	75.35 92	80.81 89	86.43 146	88.51 113	93.84 144	92.66 98
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+		83.77 87	82.98 89	84.69 78	87.98 119	91.87 101	88.10 98	77.70 155	78.10 106	73.04 97	69.13 117	68.51 122	86.66 57	90.49 87	89.85 78	94.67 94	92.88 90

gg-mvs-nofinetune		75.64 201	77.26 177	73.76 206	87.92 120	92.20 97	87.32 109	64.67 225	51.92 232	35.35 237	46.44 225	77.05 88	71.97 180	92.64 44	91.02 52	95.34 53	89.53 160

RPSCF		83.46 90	83.36 87	83.59 103	87.75 121	87.35 172	84.82 163	79.46 137	83.84 64	78.12 73	82.69 42	79.87 69	82.60 77	82.47 200	81.13 204	88.78 206	86.13 191

Effi-MVS+-dtu		82.05 105	81.76 98	82.38 113	87.72 122	90.56 115	86.90 132	78.05 151	73.85 142	66.85 133	71.29 103	71.90 111	82.00 81	86.64 141	85.48 176	92.76 173	92.58 103

CostFormer		80.94 122	80.21 127	81.79 122	87.69 123	88.58 161	87.47 106	70.66 199	80.02 89	77.88 78	73.03 96	71.40 112	78.24 144	79.96 210	79.63 206	88.82 205	88.84 163

Vis-MVSNet		84.38 84	86.68 69	81.70 127	87.65 124	94.89 54	88.14 96	80.90 110	74.48 132	68.23 123	77.53 69	80.72 65	69.98 192	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
tpmp4_e23		79.82 140	77.96 164	82.00 118	87.59 125	86.93 176	87.81 100	72.21 194	79.99 90	78.02 76	67.83 125	64.77 134	78.74 141	79.99 209	78.90 209	87.65 211	87.29 179

test-LLR		79.47 152	79.84 133	79.03 168	87.47 126	82.40 215	81.24 189	78.05 151	73.72 143	62.69 170	73.76 87	74.42 96	73.49 173	84.61 182	82.99 195	91.25 188	87.01 182

test0.0.03 1		76.03 195	78.51 144	73.12 210	87.47 126	85.13 201	76.32 213	78.05 151	73.19 150	50.98 218	70.64 105	69.28 119	55.53 217	85.33 172	84.38 187	90.39 196	81.63 210

tpmrst		76.55 185	75.99 192	77.20 183	87.32 128	83.05 208	82.86 175	65.62 220	78.61 104	67.22 132	69.19 116	65.71 131	75.87 157	76.75 221	75.33 222	84.31 226	83.28 205

CDS-MVSNet		81.63 115	82.09 96	81.09 144	87.21 129	90.28 125	87.46 107	80.33 117	69.06 185	70.66 101	71.30 102	73.87 99	67.99 199	89.58 98	89.87 77	92.87 172	90.69 152
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm cat1		77.78 174	75.28 202	80.70 150	87.14 130	85.84 192	85.81 147	70.40 200	77.44 109	78.80 69	63.72 153	64.01 142	76.55 154	75.60 224	75.21 223	85.51 222	85.12 197

tpm		76.30 193	76.05 191	76.59 189	86.97 131	83.01 209	83.83 170	67.06 214	71.83 158	63.87 164	69.56 114	62.88 149	73.41 175	79.79 211	78.59 210	84.41 225	86.68 185

EPMVS		77.53 176	78.07 156	76.90 187	86.89 132	84.91 202	82.18 184	66.64 216	81.00 83	64.11 162	72.75 99	69.68 117	74.42 168	79.36 213	78.13 212	87.14 214	80.68 214

PatchmatchNet		78.67 166	78.85 143	78.46 174	86.85 133	86.03 189	83.77 171	68.11 210	80.88 85	66.19 138	72.90 98	73.40 105	78.06 145	79.25 214	77.71 215	87.75 210	81.75 209
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
USDC		80.69 124	79.89 132	81.62 130	86.48 134	89.11 154	86.53 139	78.86 142	81.15 81	63.48 166	72.98 97	59.12 195	81.16 84	87.10 129	85.01 180	93.23 164	84.77 200

Fast-Effi-MVS+-dtu		79.95 136	80.69 122	79.08 167	86.36 135	89.14 153	85.85 146	72.28 193	72.85 153	59.32 197	70.43 108	68.42 123	77.57 148	86.14 148	86.44 145	93.11 167	91.39 139

tfpnnormal		77.46 177	74.86 205	80.49 155	86.34 136	88.92 157	84.33 167	81.26 107	61.39 217	61.70 182	51.99 218	53.66 218	74.84 163	88.63 114	87.38 129	94.50 106	92.08 118

dps		78.02 171	75.94 193	80.44 156	86.06 137	86.62 182	82.58 176	69.98 203	75.14 123	77.76 80	69.08 118	59.93 185	78.47 142	79.47 212	77.96 213	87.78 209	83.40 204

LTVRE_ROB	74.41 16	75.78 200	74.72 206	77.02 186	85.88 138	89.22 150	82.44 179	77.17 158	50.57 233	45.45 225	65.44 139	52.29 221	81.25 83	85.50 163	87.42 128	89.94 200	92.62 99
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		76.40 191	75.47 200	77.48 179	85.86 139	90.22 127	82.45 178	73.96 188	59.64 222	59.60 196	52.75 216	62.20 161	68.44 198	88.23 119	87.50 125	94.55 104	87.78 174

CR-MVSNet		78.71 165	78.86 142	78.55 173	85.85 140	85.15 199	82.30 181	68.23 208	74.71 130	65.37 151	64.39 151	69.59 118	77.18 150	85.10 178	84.87 181	92.34 177	88.21 168

GA-MVS		79.52 151	79.71 136	79.30 166	85.68 141	90.36 123	84.55 164	78.44 146	70.47 171	57.87 205	68.52 121	61.38 174	76.21 155	89.40 102	87.89 120	93.04 170	89.96 158

TransMVSNet (Re)		76.57 184	75.16 203	78.22 176	85.60 142	87.24 173	82.46 177	81.23 108	59.80 221	59.05 203	57.07 202	59.14 194	66.60 207	88.09 120	86.82 134	94.37 114	87.95 173

RPMNet		77.07 179	77.63 170	76.42 190	85.56 143	85.15 199	81.37 186	65.27 222	74.71 130	60.29 193	63.71 154	66.59 128	73.64 172	82.71 197	82.12 200	92.38 176	88.39 166

MDTV_nov1_ep13		79.14 154	79.49 138	78.74 171	85.40 144	86.89 177	84.32 168	70.29 201	78.85 101	69.42 114	75.37 79	73.29 106	75.64 158	80.61 206	79.48 208	87.36 212	81.91 208

UniMVSNet (Re)		81.22 117	81.08 110	81.39 134	85.35 145	91.76 102	84.93 161	82.88 77	76.13 116	65.02 155	64.94 147	63.09 147	75.17 160	87.71 124	89.04 104	94.97 72	94.88 56

UniMVSNet_NR-MVSNet		81.87 107	81.33 106	82.50 112	85.31 146	91.30 107	85.70 148	84.25 59	75.89 117	64.21 159	66.95 130	64.65 136	80.22 106	87.07 130	89.18 101	95.27 59	94.29 65

IterMVS		78.79 164	79.71 136	77.71 177	85.26 147	85.91 191	84.54 165	69.84 205	73.38 147	61.25 188	70.53 107	70.35 114	74.43 167	85.21 176	83.80 190	90.95 192	88.77 164
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		80.25 131	79.98 131	80.56 153	85.20 148	90.94 112	85.65 150	83.58 69	75.74 119	61.36 187	65.30 141	56.75 205	72.38 179	88.46 117	88.80 108	95.16 62	93.87 74

CMPMVS	56.49 17	73.84 209	71.73 214	76.31 193	85.20 148	85.67 194	75.80 214	73.23 191	62.26 214	65.40 150	53.40 215	59.70 187	71.77 182	80.25 208	79.56 207	86.45 217	81.28 211
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TinyColmap		76.73 181	73.95 208	79.96 162	85.16 150	85.64 195	82.34 180	78.19 149	70.63 168	62.06 177	60.69 177	49.61 225	80.81 89	85.12 177	83.69 191	91.22 190	82.27 207

gm-plane-assit		70.29 213	70.65 215	69.88 214	85.03 151	78.50 225	58.41 235	65.47 221	50.39 234	40.88 230	49.60 220	50.11 224	75.14 161	91.43 56	89.78 81	94.32 115	84.73 201

FC-MVSNet-test		76.53 188	81.62 101	70.58 213	84.99 152	85.73 193	74.81 216	78.85 143	77.00 111	39.13 236	75.90 76	73.50 104	54.08 221	86.54 143	85.99 168	91.65 182	86.68 185

DU-MVS		81.20 118	80.30 126	82.25 114	84.98 153	90.94 112	85.70 148	83.58 69	75.74 119	64.21 159	65.30 141	59.60 189	80.22 106	86.89 134	89.31 96	94.77 86	94.29 65

Baseline_NR-MVSNet		79.84 138	78.37 152	81.55 132	84.98 153	86.66 179	85.06 159	83.49 71	75.57 121	63.31 167	58.22 199	60.97 177	78.00 146	86.89 134	87.13 131	94.47 108	93.15 84

TranMVSNet+NR-MVSNet		80.52 127	79.84 133	81.33 137	84.92 155	90.39 119	85.53 153	84.22 61	74.27 135	60.68 192	64.93 148	59.96 184	77.48 149	86.75 139	89.28 97	95.12 67	93.29 81

pm-mvs1		78.51 169	77.75 169	79.40 165	84.83 156	89.30 148	83.55 173	79.38 138	62.64 213	63.68 165	58.73 197	64.68 135	70.78 189	89.79 95	87.84 122	94.17 119	91.28 141

testgi		71.92 211	74.20 207	69.27 216	84.58 157	83.06 207	73.40 218	74.39 185	64.04 211	46.17 224	68.90 120	57.15 202	48.89 227	84.07 187	83.08 194	88.18 208	79.09 219

thisisatest0515		79.76 142	80.59 124	78.80 170	84.40 158	88.91 158	79.48 200	76.94 161	72.29 156	67.33 131	67.82 126	65.99 129	70.80 188	88.50 116	87.84 122	93.86 142	92.75 96

FMVSNet3		84.44 82	84.64 82	84.21 91	84.32 159	90.13 129	89.85 69	80.37 114	81.17 78	75.50 83	69.63 110	79.69 74	79.62 126	89.72 96	90.52 64	95.59 40	91.58 137

GBi-Net		84.51 80	84.80 80	84.17 92	84.20 160	89.95 131	89.70 70	80.37 114	81.17 78	75.50 83	69.63 110	79.69 74	79.75 123	90.73 81	90.72 57	95.52 44	91.71 131

test1		84.51 80	84.80 80	84.17 92	84.20 160	89.95 131	89.70 70	80.37 114	81.17 78	75.50 83	69.63 110	79.69 74	79.75 123	90.73 81	90.72 57	95.52 44	91.71 131

FMVSNet2		83.87 85	83.73 86	84.05 97	84.20 160	89.95 131	89.70 70	80.21 119	79.17 99	74.89 87	65.91 133	77.49 85	79.75 123	90.87 78	91.00 53	95.52 44	91.71 131

WR-MVS		76.63 183	78.02 162	75.02 200	84.14 163	89.76 140	78.34 208	80.64 112	69.56 182	52.32 213	61.26 161	61.24 175	60.66 214	84.45 184	87.07 132	93.99 128	92.77 94

v16		79.65 147	77.91 165	81.69 128	84.04 164	86.65 181	87.20 116	76.32 168	72.41 155	68.71 119	61.13 168	62.52 154	79.93 113	85.55 161	86.22 149	93.92 133	90.91 146

v17		79.59 149	77.88 166	81.60 131	84.03 165	86.66 179	87.13 125	76.31 169	72.09 157	68.29 122	61.15 167	62.57 153	79.90 114	85.55 161	86.20 154	93.93 131	90.93 144

v18		79.71 146	77.98 163	81.73 126	84.02 166	86.67 178	87.37 108	76.35 166	72.61 154	68.86 118	61.35 160	62.65 151	79.94 112	85.49 164	86.21 151	93.85 143	90.92 145

v1neww		80.09 133	78.45 148	82.00 118	83.97 167	90.49 116	87.18 120	79.67 131	71.49 160	67.44 128	61.24 163	62.41 158	79.83 118	85.49 164	86.19 156	93.88 139	91.86 123

v7new		80.09 133	78.45 148	82.00 118	83.97 167	90.49 116	87.18 120	79.67 131	71.49 160	67.44 128	61.24 163	62.41 158	79.83 118	85.49 164	86.19 156	93.88 139	91.86 123

v8		79.90 137	78.39 151	81.66 129	83.97 167	89.81 137	87.16 123	77.40 157	71.49 160	67.71 125	61.24 163	62.49 155	79.83 118	85.48 168	86.17 160	93.89 137	92.02 122

v6		80.11 132	78.47 146	82.01 117	83.97 167	90.49 116	87.19 119	79.67 131	71.59 159	67.51 126	61.26 161	62.46 157	79.81 121	85.49 164	86.18 159	93.89 137	91.86 123

divwei89l23v2f112		79.75 144	78.04 160	81.75 123	83.90 171	90.37 121	87.21 115	79.90 127	70.20 175	66.18 140	60.92 171	61.48 170	79.52 133	85.36 170	86.17 160	93.81 146	91.77 127

v15		79.13 155	77.37 171	81.19 139	83.90 171	86.56 183	87.01 128	76.15 173	70.20 175	66.48 135	60.71 176	61.55 167	79.60 128	85.59 159	86.19 156	93.98 129	90.80 151

V14		79.11 156	77.35 173	81.16 141	83.90 171	86.54 184	86.94 129	76.10 175	70.14 177	66.41 137	60.59 178	61.54 168	79.59 129	85.64 156	86.20 154	94.04 125	90.82 149

v1141		79.75 144	78.04 160	81.75 123	83.89 174	90.37 121	87.20 116	79.89 128	70.23 173	66.18 140	60.92 171	61.48 170	79.54 130	85.36 170	86.17 160	93.81 146	91.76 129

v11		79.02 160	77.36 172	80.95 147	83.89 174	86.48 187	86.53 139	75.77 181	69.69 181	65.21 154	60.36 183	60.24 182	80.32 105	87.20 128	86.54 142	93.96 130	91.02 143

v1		79.76 142	78.06 158	81.74 125	83.89 174	90.38 120	87.20 116	79.88 129	70.23 173	66.17 143	60.92 171	61.56 166	79.50 134	85.37 169	86.17 160	93.81 146	91.77 127

V9		79.08 157	77.32 175	81.14 143	83.89 174	86.52 185	86.85 133	76.06 176	70.02 178	66.42 136	60.44 181	61.52 169	79.54 130	85.68 155	86.21 151	94.08 122	90.83 148

v12		79.03 159	77.28 176	81.06 145	83.88 178	86.49 186	86.62 137	76.02 177	69.99 179	66.18 140	60.34 184	61.44 172	79.54 130	85.70 154	86.21 151	94.11 121	90.82 149

v13		78.99 161	77.25 178	81.02 146	83.87 179	86.47 188	86.60 138	75.96 179	69.87 180	66.07 144	60.25 185	61.41 173	79.49 135	85.72 153	86.22 149	94.14 120	90.84 147

v2v482		79.84 138	78.07 156	81.90 121	83.75 180	90.21 128	87.17 122	79.85 130	70.65 167	65.93 146	61.93 158	60.07 183	80.82 87	85.25 173	86.71 136	93.88 139	91.70 134

v7		79.79 141	78.28 153	81.54 133	83.73 181	90.34 124	87.27 111	78.27 148	70.50 169	65.59 148	60.59 178	60.47 179	80.46 96	86.90 133	86.63 139	93.92 133	92.56 105

v10		79.62 148	78.19 154	81.28 138	83.73 181	89.69 142	87.27 111	76.86 162	70.50 169	65.46 149	60.58 180	60.47 179	80.44 97	86.91 132	86.63 139	93.93 131	92.55 107

v1144		79.38 153	77.83 167	81.18 140	83.62 183	90.23 126	87.15 124	78.35 147	69.13 184	64.02 163	60.20 186	59.41 190	80.14 109	86.78 137	86.57 141	93.81 146	92.53 109

v148		78.59 167	76.84 184	80.62 152	83.61 184	89.16 152	83.65 172	79.24 140	69.38 183	69.34 115	59.88 188	60.41 181	75.19 159	83.81 188	84.63 185	92.70 174	90.63 154

SixPastTwentyTwo		76.02 196	75.72 197	76.36 191	83.38 185	87.54 170	75.50 215	76.22 171	65.50 204	57.05 206	70.64 105	53.97 217	74.54 165	80.96 205	82.12 200	91.44 184	89.35 161

CVMVSNet		76.70 182	78.46 147	74.64 204	83.34 186	84.48 203	81.83 185	74.58 184	68.88 186	51.23 217	69.77 109	70.05 115	67.49 202	84.27 185	83.81 189	89.38 203	87.96 172

v1192		78.94 162	77.33 174	80.82 149	83.25 187	89.90 135	86.91 131	77.72 154	68.63 188	62.61 172	59.17 192	57.53 201	80.62 95	86.89 134	86.47 144	93.79 150	92.75 96

DTE-MVSNet		75.14 203	75.44 201	74.80 202	83.18 188	87.19 174	78.25 210	80.11 122	66.05 199	48.31 221	60.88 174	54.67 214	64.54 211	82.57 198	86.17 160	94.43 111	90.53 156

PEN-MVS		76.02 196	76.07 189	75.95 195	83.17 189	87.97 168	79.65 198	80.07 125	66.57 197	51.45 215	60.94 170	55.47 212	66.81 205	82.72 196	86.80 135	94.59 99	92.03 121

TAMVS		76.42 189	77.16 180	75.56 196	83.05 190	85.55 196	80.58 196	71.43 196	65.40 208	61.04 191	67.27 128	69.22 120	67.99 199	84.88 180	84.78 183	89.28 204	83.01 206

pmmvs4		79.99 135	78.08 155	82.22 115	83.04 191	87.16 175	84.95 160	78.80 144	78.64 103	74.53 88	64.61 150	59.41 190	79.45 136	84.13 186	84.54 186	92.53 175	88.08 170

v144192		78.81 163	77.22 179	80.67 151	82.95 192	89.79 139	86.40 141	77.42 156	68.26 190	63.13 168	59.50 190	58.13 199	80.08 111	85.93 150	86.08 165	94.06 124	92.83 92

v1921920		78.57 168	76.99 182	80.41 159	82.93 193	89.63 144	86.38 142	77.14 159	68.31 189	61.80 180	58.89 196	56.79 204	80.19 108	86.50 145	86.05 167	94.02 126	92.76 95

CHOSEN 280x420		80.28 130	81.66 100	78.67 172	82.92 194	79.24 224	85.36 156	66.79 215	78.11 105	70.32 102	75.03 80	79.87 69	81.09 85	89.07 105	83.16 193	85.54 221	87.17 181

WR-MVS_H		75.84 199	76.93 183	74.57 205	82.86 195	89.50 146	78.34 208	79.36 139	66.90 195	52.51 212	60.20 186	59.71 186	59.73 215	83.61 189	85.77 170	94.65 95	92.84 91

v1240		78.15 170	76.53 185	80.04 161	82.85 196	89.48 147	85.61 152	76.77 163	67.05 193	61.18 190	58.37 198	56.16 209	79.89 117	86.11 149	86.08 165	93.92 133	92.47 114

V42		79.59 149	78.43 150	80.94 148	82.79 197	89.71 141	86.66 136	76.73 164	71.38 163	67.42 130	61.01 169	62.30 160	78.39 143	85.56 160	86.48 143	93.65 154	92.60 100

CP-MVSNet		76.36 192	76.41 186	76.32 192	82.73 198	88.64 159	79.39 201	79.62 134	67.21 192	53.70 209	60.72 175	55.22 213	67.91 201	83.52 190	86.34 147	94.55 104	93.19 83

PS-CasMVS		75.90 198	75.86 196	75.96 194	82.59 199	88.46 164	79.23 204	79.56 136	66.00 200	52.77 211	59.48 191	54.35 216	67.14 204	83.37 193	86.23 148	94.47 108	93.10 85

test20.03		68.31 217	70.05 217	66.28 221	82.41 200	80.84 219	67.35 226	76.11 174	58.44 224	40.80 231	53.77 213	54.54 215	42.28 233	83.07 194	81.96 203	88.73 207	77.76 221

FMVSNet1		81.64 114	80.61 123	82.84 110	82.36 201	89.20 151	88.67 91	79.58 135	70.79 166	72.63 99	58.95 195	72.26 110	79.34 137	90.73 81	90.72 57	94.47 108	91.62 135

pmmvs6		74.83 204	72.89 211	77.09 184	82.11 202	87.50 171	80.88 195	76.97 160	52.79 231	61.91 179	46.66 224	60.49 178	69.28 194	86.74 140	85.46 177	91.39 185	90.56 155

pmmvs5		76.93 180	76.33 187	77.62 178	81.97 203	88.40 165	81.32 188	74.35 186	65.42 207	61.42 186	63.07 155	57.95 200	73.23 176	85.60 158	85.35 178	93.41 159	88.55 165

v7n		77.22 178	76.23 188	78.38 175	81.89 204	89.10 155	82.24 183	76.36 165	65.96 201	61.21 189	56.56 203	55.79 210	75.07 162	86.55 142	86.68 137	93.52 156	92.95 89

our_test_3					81.81 205	83.96 206	76.61 212

Anonymous20231206		70.80 212	70.59 216	71.04 212	81.60 206	82.49 214	74.64 217	75.87 180	64.17 210	49.27 219	44.85 228	53.59 219	54.68 220	83.07 194	82.34 199	90.17 197	83.65 203

v748		76.17 194	75.10 204	77.43 180	81.60 206	88.01 167	79.02 205	76.28 170	64.47 209	64.14 161	56.55 204	56.26 208	70.40 191	82.50 199	85.77 170	93.11 167	92.15 117

ADS-MVSNet		74.53 206	75.69 198	73.17 209	81.57 208	80.71 220	79.27 203	63.03 228	79.27 98	59.94 195	67.86 124	68.32 125	71.08 187	77.33 218	76.83 218	84.12 228	79.53 215

testpf		63.91 222	65.23 222	62.38 225	81.32 209	69.95 234	62.71 233	54.16 238	61.29 218	48.73 220	57.31 200	52.50 220	50.97 223	67.50 233	68.86 234	76.36 236	79.21 217

test-mter		77.79 173	80.02 130	75.18 199	81.18 210	82.85 210	80.52 197	62.03 230	73.62 146	62.16 175	73.55 89	73.83 100	73.81 171	84.67 181	83.34 192	91.37 186	88.31 167

TESTMET0.1,1		77.78 174	79.84 133	75.38 198	80.86 211	82.40 215	81.24 189	62.72 229	73.72 143	62.69 170	73.76 87	74.42 96	73.49 173	84.61 182	82.99 195	91.25 188	87.01 182

MDTV_nov1_ep13_2view		73.21 210	72.91 210	73.56 208	80.01 212	84.28 205	78.62 206	66.43 217	68.64 187	59.12 201	60.39 182	59.69 188	69.81 193	78.82 216	77.43 217	87.36 212	81.11 213

FPMVS		63.63 224	60.08 230	67.78 218	80.01 212	71.50 232	72.88 220	69.41 207	61.82 216	53.11 210	45.12 227	42.11 232	50.86 224	66.69 234	63.84 236	80.41 232	69.46 232

V4		76.55 185	75.89 194	77.32 181	79.95 214	88.50 162	81.07 192	73.62 189	65.47 206	61.71 181	56.31 205	58.87 198	74.28 170	83.48 191	85.62 174	93.28 162	92.98 87

v52		76.55 185	75.89 194	77.31 182	79.94 215	88.49 163	81.07 192	73.62 189	65.49 205	61.66 183	56.29 206	58.90 196	74.30 169	83.47 192	85.62 174	93.28 162	92.99 86

anonymousdsp		77.94 172	79.00 141	76.71 188	79.03 216	87.83 169	79.58 199	72.87 192	65.80 202	58.86 204	65.82 134	62.48 156	75.99 156	86.77 138	88.66 109	93.92 133	95.68 45

N_pmnet		66.85 218	66.63 219	67.11 220	78.73 217	74.66 228	70.53 223	71.07 197	66.46 198	46.54 223	51.68 219	51.91 222	55.48 218	74.68 225	72.38 229	80.29 233	74.65 225

PMMVS		81.65 113	84.05 84	78.86 169	78.56 218	82.63 212	83.10 174	67.22 213	81.39 77	70.11 106	84.91 39	79.74 72	82.12 79	87.31 126	85.70 172	92.03 180	86.67 187

PatchT		76.42 189	77.81 168	74.80 202	78.46 219	84.30 204	71.82 222	65.03 224	73.89 140	65.37 151	61.58 159	66.70 127	77.18 150	85.10 178	84.87 181	90.94 193	88.21 168

MVS-HIRNet		68.83 215	66.39 220	71.68 211	77.58 220	75.52 227	66.45 227	65.05 223	62.16 215	62.84 169	44.76 229	56.60 207	71.96 181	78.04 217	75.06 224	86.18 219	72.56 227

LP		68.35 216	67.23 218	69.67 215	77.49 221	79.38 223	72.84 221	61.37 231	66.94 194	55.08 207	47.00 223	50.35 223	65.16 210	75.61 223	76.03 219	86.08 220	75.28 224

pmmvs-eth3d		74.32 207	71.96 213	77.08 185	77.33 222	82.71 211	78.41 207	76.02 177	66.65 196	65.98 145	54.23 212	49.02 227	73.14 177	82.37 201	82.69 197	91.61 183	86.05 192

new-patchmatchnet		63.80 223	63.31 226	64.37 222	76.49 223	75.99 226	63.73 230	70.99 198	57.27 225	43.08 227	45.86 226	43.80 229	45.13 232	73.20 228	70.68 233	86.80 216	76.34 223

FMVSNet5		75.50 202	76.07 189	74.83 201	76.16 224	81.19 218	81.34 187	70.21 202	73.20 149	61.59 184	58.97 194	68.33 124	68.50 197	85.87 152	85.85 169	91.18 191	79.11 218

PM-MVS		74.17 208	73.10 209	75.41 197	76.07 225	82.53 213	77.56 211	71.69 195	71.04 164	61.92 178	61.23 166	47.30 228	74.82 164	81.78 203	79.80 205	90.42 195	88.05 171

MIMVSNet		74.69 205	75.60 199	73.62 207	76.02 226	85.31 198	81.21 191	67.43 211	71.02 165	59.07 202	54.48 209	64.07 140	66.14 208	86.52 144	86.64 138	91.83 181	81.17 212

EU-MVSNet		69.98 214	72.30 212	67.28 219	75.67 227	79.39 222	73.12 219	69.94 204	63.59 212	42.80 228	62.93 156	56.71 206	55.07 219	79.13 215	78.55 211	87.06 215	85.82 195

test2356		63.96 221	64.10 225	63.78 223	74.71 228	71.55 231	65.83 228	67.38 212	57.11 226	40.41 232	53.58 214	41.13 234	49.35 226	77.00 220	77.57 216	85.01 224	70.79 228

testus		63.31 225	64.48 224	61.94 227	73.99 229	71.99 230	63.56 232	63.25 227	57.01 227	39.41 235	54.38 210	38.73 238	46.24 231	77.01 219	77.93 214	85.20 223	74.29 226

PMVS	50.48 18	55.81 232	51.93 234	60.33 228	72.90 230	49.34 243	48.78 240	69.51 206	43.49 238	54.25 208	36.26 238	41.04 235	39.71 237	65.07 236	60.70 237	76.85 235	67.58 233
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmv		56.62 230	56.41 232	56.86 230	71.92 231	67.58 236	52.17 238	65.69 218	40.60 239	28.53 240	37.90 233	31.52 240	40.10 235	72.64 229	74.73 226	82.78 230	69.91 229

test1235678		56.61 231	56.40 233	56.86 230	71.92 231	67.58 236	52.17 238	65.69 218	40.58 240	28.52 241	37.89 234	31.49 241	40.10 235	72.64 229	74.72 227	82.78 230	69.90 230

1111		57.32 229	57.20 231	57.46 229	71.89 233	67.50 238	52.34 236	58.78 233	46.57 236	39.69 233	37.38 235	38.78 236	46.37 229	74.15 226	74.36 228	75.70 237	61.66 235

.test1245		41.43 237	38.48 239	44.88 235	71.89 233	67.50 238	52.34 236	58.78 233	46.57 236	39.69 233	37.38 235	38.78 236	46.37 229	74.15 226	1.18 243	0.20 247	3.76 244

ambc			61.92 227		70.98 235	73.54 229	63.64 231		60.06 220	52.23 214	38.44 232	19.17 246	57.12 216	82.33 202	75.03 225	83.21 229	84.89 198

pmmvs3		61.89 226	61.74 228	62.06 226	64.30 236	70.83 233	64.22 229	52.14 240	48.78 235	44.47 226	41.67 231	41.70 233	63.03 212	76.06 222	76.02 220	84.18 227	77.14 222

test12356		50.02 233	51.22 235	48.61 234	63.00 237	60.15 241	47.60 242	56.49 235	38.02 241	24.74 243	36.14 239	25.93 243	24.79 240	66.19 235	71.68 231	75.07 238	60.44 237

MDA-MVSNet-bldmvs		66.22 219	64.49 223	68.24 217	61.67 238	82.11 217	70.07 224	76.16 172	59.14 223	47.94 222	54.35 211	35.82 239	67.33 203	64.94 237	75.68 221	86.30 218	79.36 216

new_pmnet		59.28 227	61.47 229	56.73 232	61.66 239	68.29 235	59.57 234	54.91 236	60.83 219	34.38 238	44.66 230	43.65 230	49.90 225	71.66 231	71.56 232	79.94 234	69.67 231

no-one		44.14 235	43.91 238	44.40 236	59.91 240	61.10 240	34.07 245	60.09 232	27.71 243	14.44 245	19.11 241	19.28 245	23.90 242	47.36 241	66.69 235	73.98 239	66.11 234

Gipuma		49.17 234	47.05 236	51.65 233	59.67 241	48.39 244	41.98 243	63.47 226	55.64 230	33.33 239	14.90 242	13.78 247	41.34 234	69.31 232	72.30 230	70.11 240	55.00 239
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		65.00 220	66.24 221	63.55 224	58.41 242	80.01 221	69.00 225	74.03 187	55.81 229	41.88 229	36.81 237	49.48 226	47.89 228	81.32 204	82.40 198	90.08 199	77.88 220

EMVS		30.49 240	25.44 242	36.39 239	51.47 243	29.89 248	20.17 248	54.00 239	26.49 244	12.02 247	13.94 245	8.84 248	34.37 238	25.04 244	34.37 242	46.29 245	39.53 242

E-PMN		31.40 238	26.80 241	36.78 238	51.39 244	29.96 247	20.20 247	54.17 237	25.93 245	12.75 246	14.73 243	8.58 249	34.10 239	27.36 243	37.83 241	48.07 244	43.18 241

PMMVS2		41.68 236	44.74 237	38.10 237	46.97 245	52.32 242	40.63 244	48.08 241	35.51 242	7.36 248	26.86 240	24.64 244	16.72 243	55.24 239	59.03 238	68.85 241	59.59 238

tmp_tt				32.73 240	43.96 246	21.15 249	26.71 246	8.99 244	65.67 203	51.39 216	56.01 207	42.64 231	11.76 244	56.60 238	50.81 240	53.55 243

MVE	30.17 19	30.88 239	33.52 240	27.80 242	23.78 247	39.16 246	18.69 249	46.90 242	21.88 246	15.39 244	14.37 244	7.31 250	24.41 241	41.63 242	56.22 239	37.64 246	54.07 240
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
GG-mvs-BLEND		57.56 228	82.61 93	28.34 241	0.22 248	90.10 130	79.37 202	0.14 246	79.56 95	0.40 249	71.25 104	83.40 54	0.30 247	86.27 147	83.87 188	89.59 202	83.83 202

testmvs		1.03 241	1.63 243	0.34 243	0.09 249	0.35 250	0.61 251	0.16 245	1.49 247	0.10 250	3.15 246	0.15 251	0.86 246	1.32 245	1.18 243	0.20 247	3.76 244

test123		0.87 242	1.40 244	0.25 244	0.03 250	0.25 251	0.35 252	0.08 247	1.21 248	0.05 251	2.84 247	0.03 252	0.89 245	0.43 246	1.16 245	0.13 249	3.87 243

sosnet-low-res		0.00 243	0.00 245	0.00 245	0.00 251	0.00 252	0.00 253	0.00 248	0.00 249	0.00 252	0.00 248	0.00 253	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

sosnet		0.00 243	0.00 245	0.00 245	0.00 251	0.00 252	0.00 253	0.00 248	0.00 249	0.00 252	0.00 248	0.00 253	0.00 248	0.00 247	0.00 246	0.00 250	0.00 246

MTAPA										92.97 2		91.03 18

MTMP										93.14 1		90.21 25

Patchmatch-RL test							8.55 250

NP-MVS									87.47 51

Patchmtry						85.54 197	82.30 181	68.23 208		65.37 151

DeepMVS_CX						48.31 245	48.03 241	26.08 243	56.42 228	25.77 242	47.51 222	31.31 242	51.30 222	48.49 240		53.61 242	61.52 236

High-resolution multi-view benchmark