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.

APDe-MVS		88.00 2	90.50 2	85.08 3	90.95 6	91.58 5	92.03 1	75.53 9	91.15 1	80.10 11	92.27 4	88.34 8	80.80 3	88.00 12	86.99 16	91.09 4	95.16 3

ESAPD		88.63 1	91.29 1	85.53 1	90.87 7	92.20 1	91.98 2	76.00 4	90.55 4	82.09 4	93.85 1	90.75 1	81.25 1	88.62 6	87.59 11	90.96 7	95.48 1

v1.0		81.11 37	77.43 64	85.41 2	91.73 2	92.08 2	91.91 3	76.73 1	90.14 5	80.33 10	92.75 2	90.44 2	80.73 4	88.97 5	87.63 9	91.01 6	0.00 243

APD-MVS		86.84 8	88.91 10	84.41 6	90.66 10	90.10 9	90.78 4	75.64 6	87.38 14	78.72 15	90.68 7	86.82 12	80.15 5	87.13 22	86.45 25	90.51 17	93.83 10
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SMA-MVS		87.56 3	90.17 4	84.52 5	91.71 3	90.57 6	90.77 5	75.19 10	90.67 3	80.50 9	86.59 14	88.86 5	78.09 13	89.92 1	89.41 1	90.84 8	95.19 2

HSP-MVS		87.45 4	90.22 3	84.22 9	90.00 20	91.80 4	90.59 6	75.80 5	89.93 6	78.35 17	92.54 3	89.18 4	80.89 2	87.99 13	86.29 27	89.70 37	93.85 9

TSAR-MVS + MP.		86.88 7	89.23 6	84.14 10	89.78 23	88.67 28	90.59 6	73.46 23	88.99 8	80.52 8	91.26 5	88.65 6	79.91 6	86.96 27	86.22 28	90.59 15	93.83 10

CNVR-MVS		86.36 10	88.19 13	84.23 8	91.33 5	89.84 11	90.34 8	75.56 7	87.36 15	78.97 14	81.19 25	86.76 13	78.74 8	89.30 3	88.58 2	90.45 23	94.33 6

ACMMP_Plus		86.52 9	89.01 7	83.62 14	90.28 16	90.09 10	90.32 9	74.05 17	88.32 11	79.74 12	87.04 12	85.59 19	76.97 26	89.35 2	88.44 4	90.35 26	94.27 7

SteuartSystems-ACMMP		85.99 12	88.31 12	83.27 18	90.73 9	89.84 11	90.27 10	74.31 12	84.56 27	75.88 26	87.32 11	85.04 20	77.31 21	89.01 4	88.46 3	91.14 3	93.96 8
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		87.09 5	88.92 9	84.95 4	92.61 1	87.91 36	90.23 11	76.06 3	88.85 9	81.20 5	87.33 10	87.93 9	79.47 7	88.59 7	88.23 5	90.15 30	93.60 16

train_agg		84.86 21	87.21 18	82.11 24	90.59 12	85.47 53	89.81 12	73.55 22	83.95 29	73.30 34	89.84 9	87.23 11	75.61 29	86.47 31	85.46 35	89.78 33	92.06 28

NCCC		85.34 16	86.59 21	83.88 13	91.48 4	88.88 22	89.79 13	75.54 8	86.67 18	77.94 20	76.55 32	84.99 21	78.07 14	88.04 10	87.68 8	90.46 22	93.31 17

zzz-MVS		85.71 13	86.88 19	84.34 7	90.54 14	87.11 40	89.77 14	74.17 15	88.54 10	83.08 2	78.60 29	86.10 15	78.11 12	87.80 15	87.46 12	90.35 26	92.56 22

SD-MVS		86.96 6	89.45 5	84.05 12	90.13 17	89.23 19	89.77 14	74.59 11	89.17 7	80.70 6	89.93 8	89.67 3	78.47 9	87.57 17	86.79 19	90.67 14	93.76 12

HFP-MVS		86.15 11	87.95 14	84.06 11	90.80 8	89.20 20	89.62 16	74.26 13	87.52 12	80.63 7	86.82 13	84.19 25	78.22 11	87.58 16	87.19 14	90.81 9	93.13 20

ACMMPR		85.52 14	87.53 16	83.17 19	90.13 17	89.27 17	89.30 17	73.97 18	86.89 17	77.14 22	86.09 15	83.18 28	77.74 17	87.42 18	87.20 13	90.77 10	92.63 21

MCST-MVS		85.13 19	86.62 20	83.39 15	90.55 13	89.82 13	89.29 18	73.89 20	84.38 28	76.03 25	79.01 28	85.90 17	78.47 9	87.81 14	86.11 30	92.11 1	93.29 18

PGM-MVS		84.42 24	86.29 24	82.23 23	90.04 19	88.82 24	89.23 19	71.74 31	82.82 33	74.61 29	84.41 20	82.09 30	77.03 25	87.13 22	86.73 21	90.73 12	92.06 28

MP-MVS		85.50 15	87.40 17	83.28 17	90.65 11	89.51 16	89.16 20	74.11 16	83.70 30	78.06 19	85.54 17	84.89 23	77.31 21	87.40 19	87.14 15	90.41 24	93.65 15
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CSCG		85.28 18	87.68 15	82.49 22	89.95 21	91.99 3	88.82 21	71.20 33	86.41 19	79.63 13	79.26 26	88.36 7	73.94 36	86.64 29	86.67 22	91.40 2	94.41 4

CP-MVS		84.74 23	86.43 23	82.77 21	89.48 26	88.13 35	88.64 22	73.93 19	84.92 22	76.77 23	81.94 23	83.50 26	77.29 23	86.92 28	86.49 24	90.49 18	93.14 19

DeepC-MVS	78.47 2	84.81 22	86.03 25	83.37 16	89.29 28	90.38 8	88.61 23	76.50 2	86.25 20	77.22 21	75.12 36	80.28 40	77.59 19	88.39 8	88.17 6	91.02 5	93.66 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + ACMM		85.10 20	88.81 11	80.77 31	89.55 25	88.53 30	88.59 24	72.55 26	87.39 13	71.90 40	90.95 6	87.55 10	74.57 31	87.08 24	86.54 23	87.47 75	93.67 13

OPM-MVS		79.68 44	79.28 53	80.15 34	87.99 34	86.77 44	88.52 25	72.72 25	64.55 87	67.65 58	67.87 65	74.33 58	74.31 34	86.37 33	85.25 37	89.73 36	89.81 45

CDPH-MVS		82.64 29	85.03 30	79.86 35	89.41 27	88.31 32	88.32 26	71.84 30	80.11 41	67.47 59	82.09 22	81.44 36	71.85 51	85.89 36	86.15 29	90.24 28	91.25 34

ACMMP		83.42 27	85.27 28	81.26 27	88.47 32	88.49 31	88.31 27	72.09 28	83.42 31	72.77 37	82.65 21	78.22 44	75.18 30	86.24 34	85.76 32	90.74 11	92.13 27
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
X-MVS		83.23 28	85.20 29	80.92 30	89.71 24	88.68 25	88.21 28	73.60 21	82.57 34	71.81 43	77.07 30	81.92 32	71.72 53	86.98 26	86.86 17	90.47 19	92.36 25

DeepC-MVS_fast	78.24 3	84.27 25	85.50 27	82.85 20	90.46 15	89.24 18	87.83 29	74.24 14	84.88 23	76.23 24	75.26 35	81.05 38	77.62 18	88.02 11	87.62 10	90.69 13	92.41 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CPTT-MVS		81.77 33	83.10 34	80.21 33	85.93 46	86.45 47	87.72 30	70.98 34	82.54 35	71.53 46	74.23 41	81.49 35	76.31 28	82.85 58	81.87 54	88.79 53	92.26 26

HQP-MVS		81.19 36	83.27 33	78.76 42	87.40 36	85.45 54	86.95 31	70.47 36	81.31 38	66.91 62	79.24 27	76.63 49	71.67 54	84.43 44	83.78 45	89.19 46	92.05 30

LGP-MVS_train		79.83 40	81.22 42	78.22 46	86.28 44	85.36 56	86.76 32	69.59 42	77.34 47	65.14 66	75.68 34	70.79 70	71.37 56	84.60 42	84.01 43	90.18 29	90.74 38

3Dnovator+	75.73 4	82.40 30	82.76 35	81.97 25	88.02 33	89.67 14	86.60 33	71.48 32	81.28 39	78.18 18	64.78 75	77.96 46	77.13 24	87.32 20	86.83 18	90.41 24	91.48 32

MVS_0304		81.73 34	83.86 31	79.26 38	86.22 45	89.18 21	86.41 34	67.15 58	75.28 53	70.75 50	74.59 38	83.49 27	74.42 33	87.05 25	86.34 26	90.58 16	91.08 36

PCF-MVS	73.28 6	79.42 46	80.41 48	78.26 44	84.88 56	88.17 33	86.08 35	69.85 39	75.23 55	68.43 54	68.03 64	78.38 43	71.76 52	81.26 73	80.65 74	88.56 58	91.18 35
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMM	72.26 8	78.86 52	78.13 57	79.71 36	86.89 40	83.40 67	86.02 36	70.50 35	75.28 53	71.49 47	63.01 80	69.26 80	73.57 38	84.11 46	83.98 44	89.76 35	87.84 57
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		79.35 47	81.23 41	77.16 51	85.01 53	86.92 43	85.87 37	60.89 127	80.07 43	75.35 28	72.96 44	73.21 61	68.43 69	85.41 39	84.63 41	87.41 76	85.44 80
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MSLP-MVS++		82.09 32	82.66 36	81.42 26	87.03 39	87.22 39	85.82 38	70.04 38	80.30 40	78.66 16	68.67 61	81.04 39	77.81 16	85.19 40	84.88 40	89.19 46	91.31 33

casdiffmvs1		79.56 45	81.02 44	77.86 48	84.19 57	87.00 41	85.73 39	63.24 84	79.22 45	72.05 39	73.55 43	76.93 48	73.25 40	80.92 79	80.20 80	88.69 55	89.31 48

ACMP	73.23 7	79.79 41	80.53 46	78.94 40	85.61 48	85.68 51	85.61 40	69.59 42	77.33 48	71.00 49	74.45 39	69.16 81	71.88 49	83.15 55	83.37 48	89.92 32	90.57 41
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PHI-MVS		82.36 31	85.89 26	78.24 45	86.40 43	89.52 15	85.52 41	69.52 44	82.38 36	65.67 64	81.35 24	82.36 29	73.07 42	87.31 21	86.76 20	89.24 44	91.56 31

CANet		81.62 35	83.41 32	79.53 37	87.06 38	88.59 29	85.47 42	67.96 54	76.59 50	74.05 30	74.69 37	81.98 31	72.98 43	86.14 35	85.47 34	89.68 38	90.42 42

DeepPCF-MVS	79.04 1	85.30 17	88.93 8	81.06 28	88.77 31	90.48 7	85.46 43	73.08 24	90.97 2	73.77 33	84.81 19	85.95 16	77.43 20	88.22 9	87.73 7	87.85 70	94.34 5

XVS					86.63 41	88.68 25	85.00 44			71.81 43		81.92 32				90.47 19

X-MVStestdata					86.63 41	88.68 25	85.00 44			71.81 43		81.92 32				90.47 19

AdaColmap		79.74 43	78.62 56	81.05 29	89.23 29	86.06 50	84.95 46	71.96 29	79.39 44	75.51 27	63.16 79	68.84 86	76.51 27	83.55 51	82.85 50	88.13 62	86.46 68

TSAR-MVS + GP.		83.69 26	86.58 22	80.32 32	85.14 50	86.96 42	84.91 47	70.25 37	84.71 26	73.91 32	85.16 18	85.63 18	77.92 15	85.44 37	85.71 33	89.77 34	92.45 23

casdiffmvs		77.90 56	78.63 55	77.06 52	82.85 61	86.44 48	84.45 48	64.35 77	71.84 62	69.93 52	70.80 52	72.99 62	72.00 48	80.84 81	79.80 84	88.76 54	87.71 59

DELS-MVS		79.15 50	81.07 43	76.91 53	83.54 58	87.31 38	84.45 48	64.92 73	69.98 64	69.34 53	71.62 49	76.26 50	69.84 61	86.57 30	85.90 31	89.39 42	89.88 44
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
3Dnovator	73.76 5	79.75 42	80.52 47	78.84 41	84.94 55	87.35 37	84.43 50	65.54 68	78.29 46	73.97 31	63.00 81	75.62 53	74.07 35	85.00 41	85.34 36	90.11 31	89.04 49

MAR-MVS		79.21 48	80.32 49	77.92 47	87.46 35	88.15 34	83.95 51	67.48 57	74.28 57	68.25 55	64.70 76	77.04 47	72.17 47	85.42 38	85.00 39	88.22 59	87.62 60
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
EPNet		79.08 51	80.62 45	77.28 50	88.90 30	83.17 70	83.65 52	72.41 27	74.41 56	67.15 61	76.78 31	74.37 57	64.43 105	83.70 50	83.69 46	87.15 81	88.19 53
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
abl_6				79.05 39	87.27 37	88.85 23	83.62 53	68.25 50	81.68 37	72.94 36	73.79 42	84.45 24	72.55 45			89.66 39	90.64 39

MVS_111021_HR		80.13 39	81.46 40	78.58 43	85.77 47	85.17 57	83.45 54	69.28 45	74.08 59	70.31 51	74.31 40	75.26 54	73.13 41	86.46 32	85.15 38	89.53 40	89.81 45

OMC-MVS		80.26 38	82.59 37	77.54 49	83.04 59	85.54 52	83.25 55	65.05 72	87.32 16	72.42 38	72.04 47	78.97 42	73.30 39	83.86 47	81.60 57	88.15 61	88.83 51

TSAR-MVS + COLMAP		78.34 54	81.64 39	74.48 65	80.13 79	85.01 58	81.73 56	65.93 67	84.75 25	61.68 76	85.79 16	66.27 93	71.39 55	82.91 57	80.78 65	86.01 134	85.98 70

TAPA-MVS	71.42 9	77.69 57	80.05 51	74.94 61	80.68 73	84.52 60	81.36 57	63.14 86	84.77 24	64.82 68	68.72 59	75.91 52	71.86 50	81.62 63	79.55 90	87.80 71	85.24 83
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Effi-MVS+		75.28 66	76.20 72	74.20 66	81.15 68	83.24 68	81.11 58	63.13 87	66.37 73	60.27 81	64.30 77	68.88 85	70.93 59	81.56 65	81.69 56	88.61 56	87.35 62

DI_MVS_plusplus_trai		75.13 67	76.12 73	73.96 67	78.18 89	81.55 79	80.97 59	62.54 107	68.59 69	65.13 67	61.43 82	74.81 56	69.32 64	81.01 78	79.59 88	87.64 73	85.89 71

canonicalmvs		79.16 49	82.37 38	75.41 58	82.33 65	86.38 49	80.80 60	63.18 85	82.90 32	67.34 60	72.79 45	76.07 51	69.62 62	83.46 54	84.41 42	89.20 45	90.60 40

diffmvs1		76.02 63	78.90 54	72.65 71	77.84 94	82.90 72	80.67 61	60.86 129	76.16 51	63.35 73	71.50 50	74.85 55	68.35 70	80.22 96	78.69 97	84.87 161	88.00 55

QAPM		78.47 53	80.22 50	76.43 55	85.03 52	86.75 45	80.62 62	66.00 65	73.77 60	65.35 65	65.54 72	78.02 45	72.69 44	83.71 49	83.36 49	88.87 52	90.41 43

MVS_111021_LR		78.13 55	79.85 52	76.13 56	81.12 69	81.50 81	80.28 63	65.25 70	76.09 52	71.32 48	76.49 33	72.87 63	72.21 46	82.79 59	81.29 59	86.59 117	87.91 56

MVS_Test		75.37 65	77.13 68	73.31 69	79.07 84	81.32 83	79.98 64	60.12 148	69.72 67	64.11 70	70.53 53	73.22 60	68.90 65	80.14 98	79.48 92	87.67 72	85.50 78

PLC	68.99 11	75.68 64	75.31 75	76.12 57	82.94 60	81.26 84	79.94 65	66.10 63	77.15 49	66.86 63	59.13 94	68.53 87	73.73 37	80.38 89	79.04 94	87.13 85	81.68 130
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		77.20 58	77.54 61	76.80 54	82.63 62	84.31 61	79.77 66	64.64 74	85.17 21	73.18 35	56.37 113	69.81 77	74.53 32	81.12 76	78.69 97	86.04 133	87.29 64

LS3D		74.08 70	73.39 80	74.88 62	85.05 51	82.62 74	79.71 67	68.66 48	72.82 61	58.80 87	57.61 106	61.31 106	71.07 58	80.32 93	78.87 96	86.00 136	80.18 144

MSDG		71.52 83	69.87 104	73.44 68	82.21 66	79.35 108	79.52 68	64.59 75	66.15 75	61.87 75	53.21 161	56.09 138	65.85 102	78.94 114	78.50 99	86.60 116	76.85 172

CANet_DTU		73.29 74	76.96 69	69.00 113	77.04 112	82.06 76	79.49 69	56.30 180	67.85 70	53.29 127	71.12 51	70.37 75	61.81 124	81.59 64	80.96 63	86.09 128	84.73 92

diffmvs		74.38 69	76.65 71	71.74 74	77.05 111	81.86 77	79.30 70	60.54 133	69.54 68	62.16 74	69.70 56	70.74 71	66.73 80	79.18 112	78.14 110	84.63 165	87.42 61

PVSNet_Blended_VisFu		76.57 59	77.90 58	75.02 60	80.56 74	86.58 46	79.24 71	66.18 62	64.81 84	68.18 56	65.61 70	71.45 66	67.05 72	84.16 45	81.80 55	88.90 50	90.92 37

v7		70.33 95	69.87 104	70.88 78	74.79 141	81.04 86	79.22 72	60.57 132	57.70 137	56.65 109	54.23 145	55.29 143	66.95 75	78.28 121	77.47 122	87.12 88	85.05 87

v10		70.22 97	69.76 108	70.74 84	74.79 141	80.30 102	79.22 72	59.81 151	57.71 136	56.58 110	54.22 147	55.31 141	66.95 75	78.28 121	77.47 122	87.12 88	85.07 86

v1144		69.93 106	69.36 119	70.61 89	74.89 134	80.93 87	79.11 74	60.64 130	55.97 165	55.31 116	53.85 152	54.14 151	66.54 83	78.10 123	77.44 124	87.14 84	85.09 85

v2v482		70.05 101	69.46 113	70.74 84	74.62 153	80.32 101	79.00 75	60.62 131	57.41 138	56.89 102	55.43 120	55.14 144	66.39 85	77.25 146	77.14 129	86.90 95	83.57 107

OpenMVS	70.44 10	76.15 62	76.82 70	75.37 59	85.01 53	84.79 59	78.99 76	62.07 113	71.27 63	67.88 57	57.91 105	72.36 64	70.15 60	82.23 61	81.41 58	88.12 63	87.78 58

v1141		69.96 105	69.44 116	70.58 92	74.78 143	80.50 97	78.85 77	60.30 138	56.95 144	56.74 106	54.68 136	56.26 135	65.93 99	77.38 140	76.72 146	86.88 98	83.57 107

divwei89l23v2f112		69.97 103	69.44 116	70.58 92	74.78 143	80.50 97	78.85 77	60.30 138	56.97 143	56.75 105	54.67 137	56.27 134	65.92 100	77.37 141	76.72 146	86.88 98	83.58 106

v18		70.10 99	69.52 111	70.77 83	74.66 152	77.06 142	78.84 79	58.84 165	60.01 118	59.23 83	55.06 125	57.47 118	66.34 88	77.50 138	76.75 139	86.71 110	82.77 116

v1		69.97 103	69.45 115	70.59 90	74.78 143	80.51 96	78.84 79	60.30 138	56.98 141	56.81 104	54.69 135	56.29 133	65.91 101	77.37 141	76.71 149	86.89 97	83.59 104

v6		70.35 92	69.94 101	70.83 79	74.68 149	80.62 92	78.81 81	60.16 146	58.81 125	58.17 92	55.01 126	57.31 122	66.32 91	77.53 134	76.73 145	86.82 102	83.62 101

v1neww		70.34 93	69.93 102	70.82 80	74.68 149	80.61 93	78.80 82	60.17 143	58.74 127	58.10 93	55.00 127	57.28 123	66.33 89	77.53 134	76.74 141	86.82 102	83.61 102

v7new		70.34 93	69.93 102	70.82 80	74.68 149	80.61 93	78.80 82	60.17 143	58.74 127	58.10 93	55.00 127	57.28 123	66.33 89	77.53 134	76.74 141	86.82 102	83.61 102

v16		70.07 100	69.46 113	70.79 82	74.74 147	77.08 141	78.79 84	58.86 160	59.75 119	59.15 84	54.87 132	57.33 120	66.38 86	77.61 132	76.77 134	86.81 107	82.79 114

v8		70.23 96	69.86 106	70.67 88	74.69 148	79.82 104	78.79 84	59.18 156	58.80 126	58.20 91	55.00 127	57.33 120	66.31 92	77.51 137	76.71 149	86.82 102	83.88 100

v17		70.03 102	69.43 118	70.72 86	74.75 146	77.09 140	78.78 86	58.85 162	59.53 122	58.72 88	54.87 132	57.39 119	66.38 86	77.60 133	76.75 139	86.83 101	82.80 112

v15		69.61 108	68.88 125	70.46 94	74.81 140	77.03 145	78.75 87	58.83 166	57.06 140	57.18 98	54.55 138	56.37 129	66.13 96	77.70 129	76.76 136	87.03 92	82.69 119

V14		69.59 109	68.86 126	70.45 96	74.83 139	77.04 143	78.70 88	58.83 166	56.95 144	57.08 100	54.41 139	56.34 130	66.15 93	77.77 128	76.76 136	87.08 90	82.74 117

V9		69.58 110	68.83 127	70.46 94	74.85 138	77.04 143	78.65 89	58.85 162	56.83 147	57.12 99	54.26 143	56.31 131	66.14 95	77.83 127	76.76 136	87.13 85	82.79 114

ACMH+	66.54 13	71.36 84	70.09 99	72.85 70	82.59 63	81.13 85	78.56 90	68.04 52	61.55 107	52.52 133	51.50 180	54.14 151	68.56 68	78.85 115	79.50 91	86.82 102	83.94 99

Effi-MVS+-dtu		71.82 81	71.86 90	71.78 73	78.77 85	80.47 99	78.55 91	61.67 120	60.68 112	55.49 114	58.48 98	65.48 95	68.85 66	76.92 151	75.55 166	87.35 77	85.46 79

Fast-Effi-MVS+		73.11 75	73.66 78	72.48 72	77.72 104	80.88 90	78.55 91	58.83 166	65.19 81	60.36 80	59.98 89	62.42 104	71.22 57	81.66 62	80.61 76	88.20 60	84.88 91

v1192		69.50 114	68.83 127	70.29 100	74.49 154	80.92 89	78.55 91	60.54 133	55.04 174	54.21 119	52.79 169	52.33 176	66.92 77	77.88 126	77.35 127	87.04 91	85.51 77

v12		69.54 111	68.79 129	70.41 97	74.88 135	77.03 145	78.54 94	58.85 162	56.71 148	56.87 103	54.13 148	56.23 136	66.15 93	77.89 125	76.74 141	87.17 80	82.80 112

v13		69.52 113	68.76 131	70.41 97	74.88 135	77.02 147	78.52 95	58.86 160	56.61 156	56.91 101	54.00 150	56.17 137	66.11 97	77.93 124	76.74 141	87.21 79	82.83 111

tpmp4_e23		68.32 128	67.08 156	69.76 107	77.86 93	75.22 171	78.37 96	56.17 182	66.06 77	64.27 69	57.15 110	54.89 146	63.40 109	70.97 194	68.29 203	78.46 193	77.00 171

V42		68.76 124	69.63 109	67.74 124	64.93 207	78.01 126	78.30 97	56.48 179	58.65 129	56.30 111	54.26 143	57.03 126	64.85 104	77.47 139	77.01 131	85.60 148	84.96 89

v11		69.37 116	68.65 135	70.20 101	74.87 137	76.97 148	78.29 98	58.55 170	56.38 159	56.04 112	54.02 149	54.98 145	66.47 84	78.30 120	76.91 132	86.97 93	83.02 110

CostFormer		68.92 121	69.58 110	68.15 120	75.98 124	76.17 160	78.22 99	51.86 196	65.80 78	61.56 77	63.57 78	62.83 102	61.85 122	70.40 201	68.67 198	79.42 189	79.62 151

v144192		69.34 117	68.68 134	70.12 102	74.06 157	80.54 95	78.08 100	60.54 133	54.99 176	54.13 120	52.92 166	52.80 172	66.73 80	77.13 148	76.72 146	87.15 81	85.63 73

ACMH	65.37 14	70.71 88	70.00 100	71.54 75	82.51 64	82.47 75	77.78 101	68.13 51	56.19 162	46.06 169	54.30 140	51.20 188	68.68 67	80.66 83	80.72 67	86.07 129	84.45 95
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1921920		69.03 120	68.32 140	69.86 105	74.03 158	80.37 100	77.55 102	60.25 142	54.62 177	53.59 125	52.36 176	51.50 187	66.75 79	77.17 147	76.69 151	86.96 94	85.56 74

MS-PatchMatch		70.17 98	70.49 97	69.79 106	80.98 71	77.97 132	77.51 103	58.95 158	62.33 100	55.22 117	53.14 162	65.90 94	62.03 119	79.08 113	77.11 130	84.08 170	77.91 162

PVSNet_BlendedMVS		76.21 60	77.52 62	74.69 63	79.46 81	83.79 64	77.50 104	64.34 78	69.88 65	71.88 41	68.54 62	70.42 73	67.05 72	83.48 52	79.63 86	87.89 68	86.87 66

PVSNet_Blended		76.21 60	77.52 62	74.69 63	79.46 81	83.79 64	77.50 104	64.34 78	69.88 65	71.88 41	68.54 62	70.42 73	67.05 72	83.48 52	79.63 86	87.89 68	86.87 66

Anonymous20240521		73.65 72	75.78 74	71.16 77	80.19 77	79.27 109	77.45 106	61.68 119	66.73 72	58.72 88	65.31 73	69.96 76	62.19 116	81.29 72	80.97 62	86.74 109	86.91 65

CHOSEN 1792x2688		69.20 119	69.26 120	69.13 111	76.86 113	78.93 113	77.27 107	60.12 148	61.86 104	54.42 118	42.54 207	61.61 105	66.91 78	78.55 118	78.14 110	79.23 191	83.23 109

Fast-Effi-MVS+-dtu		68.34 127	69.47 112	67.01 142	75.15 130	77.97 132	77.12 108	55.40 183	57.87 131	46.68 166	56.17 116	60.39 107	62.36 114	76.32 159	76.25 155	85.35 152	81.34 131

Anonymous20231211		71.90 80	72.48 86	71.21 76	80.14 78	81.53 80	76.92 109	62.89 90	64.46 88	58.94 85	43.80 203	70.98 69	62.22 115	80.70 82	80.19 82	86.18 122	85.73 72

MVSTER		72.06 79	74.24 77	69.51 109	70.39 186	75.97 161	76.91 110	57.36 175	64.64 86	61.39 78	68.86 58	63.76 99	63.46 108	81.44 66	79.70 85	87.56 74	85.31 82

Anonymous202405211			72.16 88		80.85 72	81.85 78	76.88 111	65.40 69	62.89 98		46.35 199	67.99 89	62.05 118	81.15 75	80.38 78	85.97 138	84.50 93

v1240		68.64 125	67.89 147	69.51 109	73.89 160	80.26 103	76.73 112	59.97 150	53.43 186	53.08 128	51.82 179	50.84 190	66.62 82	76.79 153	76.77 134	86.78 108	85.34 81

HyFIR lowres test		69.47 115	68.94 124	70.09 103	76.77 114	82.93 71	76.63 113	60.17 143	59.00 124	54.03 121	40.54 213	65.23 96	67.89 71	76.54 158	78.30 106	85.03 156	80.07 145

Vis-MVSNet		72.77 77	77.20 67	67.59 128	74.19 156	84.01 62	76.61 114	61.69 118	60.62 114	50.61 143	70.25 55	71.31 68	55.57 167	83.85 48	82.28 51	86.90 95	88.08 54
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TDRefinement		66.09 156	65.03 181	67.31 135	69.73 190	76.75 150	75.33 115	64.55 76	60.28 116	49.72 150	45.63 201	42.83 216	60.46 132	75.75 161	75.95 161	84.08 170	78.04 161

tpm cat1		65.41 158	63.81 189	67.28 137	75.61 128	72.88 181	75.32 116	52.85 190	62.97 96	63.66 71	53.24 160	53.29 168	61.83 123	65.54 212	64.14 215	74.43 209	74.60 184

COLMAP_ROB	62.73 15	67.66 141	66.76 160	68.70 116	80.49 76	77.98 130	75.29 117	62.95 89	63.62 92	49.96 147	47.32 198	50.72 191	58.57 138	76.87 152	75.50 167	84.94 159	75.33 182
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
UGNet		72.78 76	77.67 60	67.07 141	71.65 178	83.24 68	75.20 118	63.62 81	64.93 83	56.72 107	71.82 48	73.30 59	49.02 188	81.02 77	80.70 72	86.22 121	88.67 52
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
GBi-Net		70.78 86	73.37 81	67.76 122	72.95 166	78.00 127	75.15 119	62.72 98	64.13 89	51.44 135	58.37 99	69.02 82	57.59 144	81.33 69	80.72 67	86.70 111	82.02 122

test1		70.78 86	73.37 81	67.76 122	72.95 166	78.00 127	75.15 119	62.72 98	64.13 89	51.44 135	58.37 99	69.02 82	57.59 144	81.33 69	80.72 67	86.70 111	82.02 122

FMVSNet2		70.39 91	72.67 85	67.72 125	72.95 166	78.00 127	75.15 119	62.69 102	63.29 94	51.25 139	55.64 117	68.49 88	57.59 144	80.91 80	80.35 79	86.70 111	82.02 122

IterMVS-LS		71.69 82	72.82 84	70.37 99	77.54 106	76.34 157	75.13 122	60.46 136	61.53 108	57.57 96	64.89 74	67.33 90	66.04 98	77.09 150	77.37 126	85.48 150	85.18 84
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPNet_dtu		68.08 132	71.00 93	64.67 160	79.64 80	68.62 197	75.05 123	63.30 83	66.36 74	45.27 173	67.40 67	66.84 92	43.64 202	75.37 165	74.98 172	81.15 182	77.44 165
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPP-MVSNet		74.00 71	77.41 65	70.02 104	80.53 75	83.91 63	74.99 124	62.68 103	65.06 82	49.77 149	68.68 60	72.09 65	63.06 111	82.49 60	80.73 66	89.12 48	88.91 50

FMVSNet3		70.49 90	72.90 83	67.67 126	72.88 169	77.98 130	74.96 125	62.72 98	64.13 89	51.44 135	58.37 99	69.02 82	57.43 147	79.43 108	79.57 89	86.59 117	81.81 129

FMVSNet1		68.84 122	70.47 98	66.94 143	71.35 183	77.68 135	74.71 126	62.35 112	56.93 146	49.94 148	50.01 186	64.59 97	57.07 150	81.33 69	80.72 67	86.25 120	82.00 125

GA-MVS		68.14 129	69.17 122	66.93 144	73.77 161	78.50 123	74.45 127	58.28 171	55.11 173	48.44 153	60.08 87	53.99 154	61.50 125	78.43 119	77.57 120	85.13 154	80.54 138

pmmvs4		67.89 135	67.39 153	68.48 118	71.60 180	73.57 180	74.45 127	60.98 126	64.65 85	57.97 95	54.95 130	51.73 185	61.88 121	73.78 173	75.11 170	83.99 172	77.91 162

IS_MVSNet		73.33 73	77.34 66	68.65 117	81.29 67	83.47 66	74.45 127	63.58 82	65.75 79	48.49 152	67.11 69	70.61 72	54.63 172	84.51 43	83.58 47	89.48 41	86.34 69

CDS-MVSNet		67.65 142	69.83 107	65.09 155	75.39 129	76.55 152	74.42 130	63.75 80	53.55 185	49.37 151	59.41 92	62.45 103	44.44 200	79.71 102	79.82 83	83.17 176	77.36 166
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
DWT-MVSNet_training		67.24 150	65.96 168	68.74 114	76.15 120	74.36 178	74.37 131	56.66 178	61.82 105	60.51 79	58.23 104	49.76 196	65.07 103	70.04 202	70.39 189	79.70 188	77.11 169

tfpn111		68.38 126	69.23 121	67.39 131	77.83 96	78.93 113	74.28 132	62.81 91	56.64 150	46.70 162	56.24 114	53.47 161	56.59 153	80.41 84	78.43 100	86.11 125	80.53 139

conf0.01		67.72 139	67.99 144	67.39 131	77.82 101	78.94 111	74.28 132	62.81 91	56.64 150	46.70 162	53.33 157	48.59 201	56.59 153	80.34 91	78.43 100	86.16 124	79.67 150

conf0.002		67.52 147	67.64 148	67.39 131	77.80 103	78.94 111	74.28 132	62.81 91	56.64 150	46.70 162	53.65 153	46.28 209	56.59 153	80.33 92	78.37 105	86.17 123	79.23 154

conf200view11		68.11 130	68.72 132	67.39 131	77.83 96	78.93 113	74.28 132	62.81 91	56.64 150	46.70 162	52.65 171	53.47 161	56.59 153	80.41 84	78.43 100	86.11 125	80.53 139

tfpn200view9		68.11 130	68.72 132	67.40 130	77.83 96	78.93 113	74.28 132	62.81 91	56.64 150	46.82 160	52.65 171	53.47 161	56.59 153	80.41 84	78.43 100	86.11 125	80.52 141

thres400		67.95 134	68.62 136	67.17 138	77.90 90	78.59 122	74.27 137	62.72 98	56.34 161	45.77 171	53.00 164	53.35 166	56.46 158	80.21 97	78.43 100	85.91 141	80.43 142

v148		67.85 136	67.53 149	68.23 119	73.25 164	77.57 138	74.26 138	57.36 175	55.70 167	57.45 97	53.53 154	55.42 140	61.96 120	75.23 166	73.92 175	85.08 155	81.32 132

thres200		67.98 133	68.55 137	67.30 136	77.89 92	78.86 117	74.18 139	62.75 96	56.35 160	46.48 167	52.98 165	53.54 157	56.46 158	80.41 84	77.97 112	86.05 131	79.78 149

thres100view900		67.60 145	68.02 143	67.12 140	77.83 96	77.75 134	73.90 140	62.52 108	56.64 150	46.82 160	52.65 171	53.47 161	55.92 162	78.77 116	77.62 119	85.72 146	79.23 154

thres600view7		67.68 140	68.43 138	66.80 145	77.90 90	78.86 117	73.84 141	62.75 96	56.07 163	44.70 177	52.85 168	52.81 171	55.58 166	80.41 84	77.77 115	86.05 131	80.28 143

UniMVSNet_NR-MVSNet		70.59 89	72.19 87	68.72 115	77.72 104	80.72 91	73.81 142	69.65 41	61.99 102	43.23 180	60.54 85	57.50 117	58.57 138	79.56 106	81.07 61	89.34 43	83.97 97

DU-MVS		69.63 107	70.91 94	68.13 121	75.99 122	79.54 105	73.81 142	69.20 46	61.20 110	43.23 180	58.52 96	53.50 158	58.57 138	79.22 110	80.45 77	87.97 65	83.97 97

view600		67.63 144	68.36 139	66.77 146	77.84 94	78.66 120	73.74 144	62.62 105	56.04 164	44.98 174	52.86 167	52.83 170	55.48 169	80.36 90	77.75 116	85.95 140	80.02 146

FC-MVSNet-train		72.60 78	75.07 76	69.71 108	81.10 70	78.79 119	73.74 144	65.23 71	66.10 76	53.34 126	70.36 54	63.40 101	56.92 152	81.44 66	80.96 63	87.93 66	84.46 94

UA-Net		74.47 68	77.80 59	70.59 90	85.33 49	85.40 55	73.54 146	65.98 66	60.65 113	56.00 113	72.11 46	79.15 41	54.63 172	83.13 56	82.25 52	88.04 64	81.92 128

NR-MVSNet		68.79 123	70.56 96	66.71 149	77.48 107	79.54 105	73.52 147	69.20 46	61.20 110	39.76 191	58.52 96	50.11 194	51.37 183	80.26 95	80.71 71	88.97 49	83.59 104

TranMVSNet+NR-MVSNet		69.25 118	70.81 95	67.43 129	77.23 110	79.46 107	73.48 148	69.66 40	60.43 115	39.56 192	58.82 95	53.48 160	55.74 165	79.59 104	81.21 60	88.89 51	82.70 118

IterMVS		66.36 154	68.30 141	64.10 162	69.48 193	74.61 176	73.41 149	50.79 202	57.30 139	48.28 154	60.64 84	59.92 110	60.85 131	74.14 171	72.66 181	81.80 179	78.82 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EG-PatchMatch MVS		67.24 150	66.94 157	67.60 127	78.73 86	81.35 82	73.28 150	59.49 153	46.89 212	51.42 138	43.65 204	53.49 159	55.50 168	81.38 68	80.66 73	87.15 81	81.17 133

v7n		67.05 152	66.94 157	67.17 138	72.35 171	78.97 110	73.26 151	58.88 159	51.16 197	50.90 140	48.21 191	50.11 194	60.96 127	77.70 129	77.38 125	86.68 114	85.05 87

view800		67.35 149	68.22 142	66.35 150	77.83 96	78.62 121	72.97 152	62.58 106	55.71 166	44.13 178	52.69 170	52.24 180	54.58 174	80.27 94	78.19 108	86.01 134	79.79 148

IB-MVS	66.94 12	71.21 85	71.66 91	70.68 87	79.18 83	82.83 73	72.61 153	61.77 117	59.66 120	63.44 72	53.26 159	59.65 111	59.16 137	76.78 154	82.11 53	87.90 67	87.33 63
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
UniMVSNet (Re)		69.53 112	71.90 89	66.76 147	76.42 115	80.93 87	72.59 154	68.03 53	61.75 106	41.68 188	58.34 102	57.23 125	53.27 179	79.53 107	80.62 75	88.57 57	84.90 90

Baseline_NR-MVSNet		67.53 146	68.77 130	66.09 151	75.99 122	74.75 175	72.43 155	68.41 49	61.33 109	38.33 196	51.31 181	54.13 153	56.03 161	79.22 110	78.19 108	85.37 151	82.45 120

MDTV_nov1_ep13		64.37 169	65.24 176	63.37 171	68.94 195	70.81 188	72.40 156	50.29 205	60.10 117	53.91 123	60.07 88	59.15 113	57.21 148	69.43 205	67.30 205	77.47 196	69.78 203

tfpn		66.58 153	67.18 154	65.88 152	77.82 101	78.45 124	72.07 157	62.52 108	55.35 170	43.21 182	52.54 175	46.12 210	53.68 175	80.02 99	78.23 107	85.99 137	79.55 152

v52		65.23 160	66.24 163	64.06 163	61.94 211	76.42 154	72.06 158	54.30 185	49.94 201	50.04 146	47.41 196	52.42 174	60.23 134	75.71 162	76.22 156	85.78 143	85.56 74

V4		65.23 160	66.23 164	64.06 163	61.94 211	76.42 154	72.05 159	54.31 184	49.91 203	50.06 145	47.42 195	52.40 175	60.24 133	75.71 162	76.22 156	85.78 143	85.56 74

conf0.05thres1000		66.26 155	66.77 159	65.66 153	77.45 108	78.10 125	71.85 160	62.44 111	51.47 196	43.00 183	47.92 193	51.66 186	53.40 177	79.71 102	77.97 112	85.82 142	80.56 137

USDC		67.36 148	67.90 146	66.74 148	71.72 176	75.23 169	71.58 161	60.28 141	67.45 71	50.54 144	60.93 83	45.20 213	62.08 117	76.56 157	74.50 173	84.25 169	75.38 181

tpm		62.41 185	63.15 191	61.55 180	72.24 172	63.79 212	71.31 162	46.12 218	57.82 132	55.33 115	59.90 90	54.74 147	53.63 176	67.24 211	64.29 213	70.65 220	74.25 188

tfpnnormal		64.27 171	63.64 190	65.02 156	75.84 125	75.61 163	71.24 163	62.52 108	47.79 209	42.97 184	42.65 206	44.49 214	52.66 181	78.77 116	76.86 133	84.88 160	79.29 153

gg-mvs-nofinetune		62.55 182	65.05 180	59.62 191	78.72 87	77.61 136	70.83 164	53.63 186	39.71 224	22.04 228	36.36 217	64.32 98	47.53 190	81.16 74	79.03 95	85.00 157	77.17 167

TransMVSNet (Re)		64.74 167	65.66 173	63.66 167	77.40 109	75.33 166	69.86 165	62.67 104	47.63 210	41.21 189	50.01 186	52.33 176	45.31 199	79.57 105	77.69 118	85.49 149	77.07 170

pm-mvs1		65.62 157	67.42 151	63.53 168	73.66 162	76.39 156	69.66 166	60.87 128	49.73 204	43.97 179	51.24 182	57.00 127	48.16 189	79.89 100	77.84 114	84.85 163	79.82 147

PatchmatchNet		64.21 174	64.65 183	63.69 166	71.29 184	68.66 196	69.63 167	51.70 198	63.04 95	53.77 124	59.83 91	58.34 115	60.23 134	68.54 208	66.06 210	75.56 204	68.08 207
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thresconf0.02		64.77 166	65.90 169	63.44 169	76.37 116	75.17 174	69.51 168	61.28 121	56.98 141	39.01 194	56.24 114	48.68 200	49.78 186	77.13 148	75.61 164	84.71 164	71.53 198

v748		65.12 162	65.24 176	64.98 157	69.77 189	76.45 153	69.47 169	57.06 177	49.93 202	50.70 141	47.87 194	49.50 198	57.14 149	73.64 175	75.18 169	85.75 145	84.14 96

pmmvs-eth3d		63.52 177	62.44 199	64.77 159	66.82 201	70.12 191	69.41 170	59.48 154	54.34 181	52.71 129	46.24 200	44.35 215	56.93 151	72.37 178	73.77 176	83.30 174	75.91 175

tpmrst		62.00 189	62.35 200	61.58 179	71.62 179	64.14 209	69.07 171	48.22 214	62.21 101	53.93 122	58.26 103	55.30 142	55.81 164	63.22 217	62.62 218	70.85 219	70.70 201

dps		64.00 175	62.99 192	65.18 154	73.29 163	72.07 184	68.98 172	53.07 189	57.74 135	58.41 90	55.55 119	47.74 205	60.89 130	69.53 204	67.14 207	76.44 201	71.19 200

PatchMatch-RL		67.78 138	66.65 161	69.10 112	73.01 165	72.69 182	68.49 173	61.85 116	62.93 97	60.20 82	56.83 112	50.42 192	69.52 63	75.62 164	74.46 174	81.51 180	73.62 191

TinyColmap		62.84 180	61.03 205	64.96 158	69.61 191	71.69 185	68.48 174	59.76 152	55.41 169	47.69 158	47.33 197	34.20 226	62.76 113	74.52 168	72.59 182	81.44 181	71.47 199

tfpn_ndepth		65.09 163	67.12 155	62.73 172	75.75 127	76.23 158	68.00 175	60.36 137	58.16 130	40.27 190	54.89 131	54.22 150	46.80 195	76.69 156	75.66 163	85.19 153	73.98 190

MDTV_nov1_ep13_2view		60.16 199	60.51 207	59.75 189	65.39 204	69.05 195	68.00 175	48.29 212	51.99 191	45.95 170	48.01 192	49.64 197	53.39 178	68.83 207	66.52 209	77.47 196	69.55 204

pmmvs6		62.41 185	62.88 193	61.87 178	71.38 182	75.18 173	67.76 177	59.45 155	41.64 220	42.52 187	37.33 215	52.91 169	46.87 194	77.67 131	76.26 154	83.23 175	79.18 156

tfpnview11		64.33 170	66.17 165	62.18 174	76.25 117	75.23 169	67.45 178	61.16 122	55.50 168	36.38 201	55.35 121	51.89 182	46.96 191	77.28 145	76.10 160	84.86 162	71.85 197

tfpn_n400		64.23 172	66.05 166	62.12 176	76.20 118	75.24 167	67.43 179	61.15 123	54.04 183	36.38 201	55.35 121	51.89 182	46.94 192	77.31 143	76.15 158	84.59 166	72.36 194

tfpnconf		64.23 172	66.05 166	62.12 176	76.20 118	75.24 167	67.43 179	61.15 123	54.04 183	36.38 201	55.35 121	51.89 182	46.94 192	77.31 143	76.15 158	84.59 166	72.36 194

RPSCF		67.64 143	71.25 92	63.43 170	61.86 213	70.73 189	67.26 181	50.86 201	74.20 58	58.91 86	67.49 66	69.33 79	64.10 106	71.41 187	68.45 202	77.61 195	77.17 167

pmmvs5		62.37 188	64.04 187	60.42 185	65.03 205	71.67 186	67.17 182	52.70 193	50.30 198	44.80 175	54.23 145	51.19 189	49.37 187	72.88 177	73.48 178	83.45 173	74.55 185

anonymousdsp		65.28 159	67.98 145	62.13 175	58.73 221	73.98 179	67.10 183	50.69 203	48.41 207	47.66 159	54.27 141	52.75 173	61.45 126	76.71 155	80.20 80	87.13 85	89.53 47

our_test_3					67.93 197	70.99 187	66.89 184

MIMVSNet		58.52 204	61.34 204	55.22 206	60.76 214	67.01 202	66.81 185	49.02 208	56.43 158	38.90 195	40.59 212	54.54 149	40.57 210	73.16 176	71.65 184	75.30 207	66.00 210

Vis-MVSNet (Re-imp)		67.83 137	73.52 79	61.19 181	78.37 88	76.72 151	66.80 186	62.96 88	65.50 80	34.17 207	67.19 68	69.68 78	39.20 211	79.39 109	79.44 93	85.68 147	76.73 173

PMMVS		65.06 164	69.17 122	60.26 187	55.25 230	63.43 213	66.71 187	43.01 227	62.41 99	50.64 142	69.44 57	67.04 91	63.29 110	74.36 170	73.54 177	82.68 177	73.99 189

test-LLR		64.42 168	64.36 185	64.49 161	75.02 132	63.93 210	66.61 188	61.96 114	54.41 178	47.77 156	57.46 107	60.25 108	55.20 170	70.80 195	69.33 193	80.40 186	74.38 186

TESTMET0.1,1		61.10 196	64.36 185	57.29 199	57.53 223	63.93 210	66.61 188	36.22 233	54.41 178	47.77 156	57.46 107	60.25 108	55.20 170	70.80 195	69.33 193	80.40 186	74.38 186

CVMVSNet		62.55 182	65.89 170	58.64 195	66.95 199	69.15 194	66.49 190	56.29 181	52.46 190	32.70 208	59.27 93	58.21 116	50.09 185	71.77 186	71.39 186	79.31 190	78.99 157

CR-MVSNet		64.83 165	65.54 174	64.01 165	70.64 185	69.41 192	65.97 191	52.74 191	57.81 133	52.65 130	54.27 141	56.31 131	60.92 128	72.20 183	73.09 179	81.12 183	75.69 178

Patchmtry						65.80 206	65.97 191	52.74 191		52.65 130

test-mter		60.84 197	64.62 184	56.42 202	55.99 228	64.18 208	65.39 193	34.23 235	54.39 180	46.21 168	57.40 109	59.49 112	55.86 163	71.02 193	69.65 192	80.87 185	76.20 174

FMVSNet5		57.24 205	60.02 208	53.99 210	56.45 225	62.74 217	65.27 194	47.03 215	55.14 172	39.55 193	40.88 210	53.42 165	41.83 204	72.35 179	71.10 188	73.79 211	64.50 213

tfpn1000		63.81 176	66.31 162	60.90 183	75.76 126	75.74 162	65.14 195	60.14 147	56.47 157	35.99 204	55.11 124	52.30 178	43.42 203	76.21 160	75.34 168	84.97 158	73.01 193

CHOSEN 280x420		58.70 203	61.88 202	54.98 207	55.45 229	50.55 233	64.92 196	40.36 229	55.21 171	38.13 197	48.31 190	63.76 99	63.03 112	73.73 174	68.58 200	68.00 225	73.04 192

GG-mvs-BLEND		46.86 223	67.51 150	22.75 236	0.05 245	76.21 159	64.69 197	0.04 242	61.90 103	0.09 246	55.57 118	71.32 67	0.08 242	70.54 197	67.19 206	71.58 217	69.86 202

EPMVS		60.00 200	61.97 201	57.71 198	68.46 196	63.17 216	64.54 198	48.23 213	63.30 93	44.72 176	60.19 86	56.05 139	50.85 184	65.27 214	62.02 220	69.44 222	63.81 214

LTVRE_ROB	59.44 16	61.82 194	62.64 196	60.87 184	72.83 170	77.19 139	64.37 199	58.97 157	33.56 233	28.00 215	52.59 174	42.21 217	63.93 107	74.52 168	76.28 153	77.15 198	82.13 121
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
PM-MVS		60.48 198	60.94 206	59.94 188	58.85 220	66.83 203	64.27 200	51.39 199	55.03 175	48.03 155	50.00 188	40.79 220	58.26 141	69.20 206	67.13 208	78.84 192	77.60 164

TAMVS		59.58 201	62.81 195	55.81 204	66.03 203	65.64 207	63.86 201	48.74 209	49.95 200	37.07 200	54.77 134	58.54 114	44.44 200	72.29 180	71.79 183	74.70 208	66.66 209

RPMNet		61.71 195	62.88 193	60.34 186	69.51 192	69.41 192	63.48 202	49.23 206	57.81 133	45.64 172	50.51 184	50.12 193	53.13 180	68.17 210	68.49 201	81.07 184	75.62 180

PEN-MVS		62.96 179	65.77 172	59.70 190	73.98 159	75.45 164	63.39 203	67.61 56	52.49 189	25.49 218	53.39 155	49.12 199	40.85 209	71.94 185	77.26 128	86.86 100	80.72 136

CMPMVS	47.78 17	62.49 184	62.52 197	62.46 173	70.01 188	70.66 190	62.97 204	51.84 197	51.98 192	56.71 108	42.87 205	53.62 155	57.80 143	72.23 181	70.37 190	75.45 206	75.91 175
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CP-MVSNet		62.68 181	65.49 175	59.40 193	71.84 174	75.34 165	62.87 205	67.04 59	52.64 188	27.19 216	53.38 156	48.15 203	41.40 207	71.26 188	75.68 162	86.07 129	82.00 125

PS-CasMVS		62.38 187	65.06 179	59.25 194	71.73 175	75.21 172	62.77 206	66.99 60	51.94 194	26.96 217	52.00 178	47.52 206	41.06 208	71.16 191	75.60 165	85.97 138	81.97 127

SixPastTwentyTwo		61.84 192	62.45 198	61.12 182	69.20 194	72.20 183	62.03 207	57.40 174	46.54 213	38.03 198	57.14 111	41.72 218	58.12 142	69.67 203	71.58 185	81.94 178	78.30 160

WR-MVS_H		61.83 193	65.87 171	57.12 200	71.72 176	76.87 149	61.45 208	66.19 61	51.97 193	22.92 226	53.13 163	52.30 178	33.80 216	71.03 192	75.00 171	86.65 115	80.78 135

DTE-MVSNet		61.85 191	64.96 182	58.22 196	74.32 155	74.39 177	61.01 209	67.85 55	51.76 195	21.91 229	53.28 158	48.17 202	37.74 212	72.22 182	76.44 152	86.52 119	78.49 159

WR-MVS		63.03 178	67.40 152	57.92 197	75.14 131	77.60 137	60.56 210	66.10 63	54.11 182	23.88 219	53.94 151	53.58 156	34.50 215	73.93 172	77.71 117	87.35 77	80.94 134

Anonymous20231206		56.36 208	57.80 212	54.67 208	70.08 187	66.39 204	60.46 211	57.54 173	49.50 206	29.30 212	33.86 223	46.64 207	35.18 214	70.44 199	68.88 197	75.47 205	68.88 206

FPMVS		51.87 216	50.00 221	54.07 209	66.83 200	57.25 223	60.25 212	50.91 200	50.25 199	34.36 206	36.04 220	32.02 228	41.49 206	58.98 229	56.07 229	70.56 221	59.36 223

MDA-MVSNet-bldmvs		53.37 215	53.01 218	53.79 212	43.67 238	67.95 199	59.69 213	57.92 172	43.69 216	32.41 209	41.47 208	27.89 235	52.38 182	56.97 231	65.99 211	76.68 199	67.13 208

test0.0.03 1		58.80 202	61.58 203	55.56 205	75.02 132	68.45 198	59.58 214	61.96 114	52.74 187	29.57 211	49.75 189	54.56 148	31.46 218	71.19 189	69.77 191	75.75 202	64.57 212

ADS-MVSNet		55.94 209	58.01 210	53.54 213	62.48 210	58.48 222	59.12 215	46.20 217	59.65 121	42.88 185	52.34 177	53.31 167	46.31 197	62.00 221	60.02 225	64.23 231	60.24 222

LP		53.62 214	53.43 215	53.83 211	58.51 222	62.59 219	57.31 216	46.04 219	47.86 208	42.69 186	36.08 219	36.86 224	46.53 196	64.38 215	64.25 214	71.92 216	62.00 219

PatchT		61.97 190	64.04 187	59.55 192	60.49 215	67.40 200	56.54 217	48.65 210	56.69 149	52.65 130	51.10 183	52.14 181	60.92 128	72.20 183	73.09 179	78.03 194	75.69 178

EU-MVSNet		54.63 210	58.69 209	49.90 216	56.99 224	62.70 218	56.41 218	50.64 204	45.95 215	23.14 223	50.42 185	46.51 208	36.63 213	65.51 213	64.85 212	75.57 203	74.91 183

testgi		54.39 212	57.86 211	50.35 215	71.59 181	67.24 201	54.95 219	53.25 188	43.36 217	23.78 220	44.64 202	47.87 204	24.96 227	70.45 198	68.66 199	73.60 212	62.78 217

MIMVSNet1		49.27 217	53.25 217	44.62 222	44.61 235	61.52 221	53.61 220	52.18 194	41.62 221	18.68 231	28.14 231	41.58 219	25.50 225	68.46 209	69.04 195	73.15 213	62.37 218

test20.03		53.93 213	56.28 214	51.19 214	72.19 173	65.83 205	53.20 221	61.08 125	42.74 218	22.08 227	37.07 216	45.76 212	24.29 231	70.44 199	69.04 195	74.31 210	63.05 216

N_pmnet		47.35 220	50.13 220	44.11 223	59.98 216	51.64 231	51.86 222	44.80 223	49.58 205	20.76 230	40.65 211	40.05 222	29.64 220	59.84 227	55.15 230	57.63 233	54.00 230

test2356		47.20 221	48.62 225	45.54 221	56.38 226	54.89 227	50.62 223	45.08 222	38.65 225	23.40 221	36.23 218	31.10 230	29.31 221	62.76 219	62.49 219	68.48 224	54.23 229

pmmvs3		47.65 218	49.08 223	45.99 220	44.61 235	54.79 228	50.04 224	31.95 238	33.91 231	29.90 210	30.37 225	33.53 227	46.31 197	63.50 216	63.67 216	73.14 214	63.77 215

ambc			53.42 216		64.99 206	63.36 214	49.96 225		47.07 211	37.12 199	28.97 227	16.36 241	41.82 205	75.10 167	67.34 204	71.55 218	75.72 177

MVS-HIRNet		54.41 211	52.10 219	57.11 201	58.99 219	56.10 225	49.68 226	49.10 207	46.18 214	52.15 134	33.18 224	46.11 211	56.10 160	63.19 218	59.70 226	76.64 200	60.25 221

FC-MVSNet-test		56.90 207	65.20 178	47.21 218	66.98 198	63.20 215	49.11 227	58.60 169	59.38 123	11.50 238	65.60 71	56.68 128	24.66 230	71.17 190	71.36 187	72.38 215	69.02 205

testpf		47.41 219	48.47 226	46.18 219	66.30 202	50.67 232	48.15 228	42.60 228	37.10 228	28.75 213	40.97 209	39.01 223	30.82 219	52.95 234	53.74 233	60.46 232	64.87 211

gm-plane-assit		57.00 206	57.62 213	56.28 203	76.10 121	62.43 220	47.62 229	46.57 216	33.84 232	23.24 222	37.52 214	40.19 221	59.61 136	79.81 101	77.55 121	84.55 168	72.03 196

new-patchmatchnet		46.97 222	49.47 222	44.05 224	62.82 209	56.55 224	45.35 230	52.01 195	42.47 219	17.04 234	35.73 221	35.21 225	21.84 236	61.27 222	54.83 231	65.26 230	60.26 220

testus		45.61 225	49.06 224	41.59 226	56.13 227	55.28 226	43.51 231	39.64 231	37.74 226	18.23 232	35.52 222	31.28 229	24.69 229	62.46 220	62.90 217	67.33 226	58.26 225

PMVS	39.38 18	46.06 224	43.30 230	49.28 217	62.93 208	38.75 239	41.88 232	53.50 187	33.33 234	35.46 205	28.90 228	31.01 231	33.04 217	58.61 230	54.63 232	68.86 223	57.88 226
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1111		43.08 226	44.02 229	41.98 225	59.22 217	49.27 235	41.48 233	45.63 220	35.01 229	23.06 224	28.60 229	30.15 232	27.22 222	60.42 225	57.97 227	55.27 236	46.74 232

.test1245		30.81 233	29.14 236	32.77 232	59.22 217	49.27 235	41.48 233	45.63 220	35.01 229	23.06 224	28.60 229	30.15 232	27.22 222	60.42 225	0.10 240	0.01 244	0.43 241

testmv		42.58 227	44.36 227	40.49 227	54.63 231	52.76 229	41.21 235	44.37 224	28.83 235	12.87 235	27.16 232	25.03 236	23.01 232	60.83 223	61.13 221	66.88 227	54.81 227

test1235678		42.57 228	44.36 227	40.49 227	54.63 231	52.75 230	41.21 235	44.37 224	28.82 236	12.87 235	27.15 233	25.01 237	23.01 232	60.83 223	61.13 221	66.88 227	54.81 227

new_pmnet		38.40 229	42.64 231	33.44 231	37.54 241	45.00 237	36.60 237	32.72 237	40.27 222	12.72 237	29.89 226	28.90 234	24.78 228	53.17 233	52.90 234	56.31 234	48.34 231

test12356		35.10 232	38.50 232	31.13 233	44.14 237	43.70 238	32.27 238	34.42 234	26.51 238	9.47 239	25.22 235	20.34 238	10.86 239	53.47 232	56.15 228	55.59 235	44.11 233

Gipuma		36.38 230	35.80 234	37.07 229	45.76 234	33.90 240	29.81 239	48.47 211	39.91 223	18.02 233	8.00 242	8.14 244	25.14 226	59.29 228	61.02 223	55.19 237	40.31 234
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
no-one		36.35 231	37.59 233	34.91 230	46.13 233	49.89 234	27.99 240	43.56 226	20.91 240	7.03 241	14.64 238	15.50 242	18.92 237	42.95 235	60.20 224	65.84 229	59.03 224

PMMVS2		25.60 234	29.75 235	20.76 237	28.00 242	30.93 241	23.10 241	29.18 239	23.14 239	1.46 245	18.23 237	16.54 240	5.08 240	40.22 236	41.40 236	37.76 238	37.79 236

DeepMVS_CX						18.74 245	18.55 242	8.02 240	26.96 237	7.33 240	23.81 236	13.05 243	25.99 224	25.17 239		22.45 243	36.25 237

EMVS		20.98 236	17.15 239	25.44 235	39.51 240	19.37 244	12.66 243	39.59 232	19.10 241	6.62 243	9.27 240	4.40 246	22.43 234	17.99 241	24.40 239	31.81 240	25.53 239

E-PMN		21.77 235	18.24 238	25.89 234	40.22 239	19.58 243	12.46 244	39.87 230	18.68 242	6.71 242	9.57 239	4.31 247	22.36 235	19.89 240	27.28 238	33.73 239	28.34 238

MVE	19.12 19	20.47 237	23.27 237	17.20 238	12.66 244	25.41 242	10.52 245	34.14 236	14.79 243	6.53 244	8.79 241	4.68 245	16.64 238	29.49 238	41.63 235	22.73 242	38.11 235
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				14.50 239	14.68 243	7.17 246	10.46 246	2.21 241	37.73 227	28.71 214	25.26 234	16.98 239	4.37 241	31.49 237	29.77 237	26.56 241

Patchmatch-RL test							2.85 247

testmvs		0.09 238	0.15 240	0.02 240	0.01 246	0.02 247	0.05 248	0.01 243	0.11 244	0.01 247	0.26 244	0.01 248	0.06 244	0.10 242	0.10 240	0.01 244	0.43 241

test123		0.09 238	0.14 241	0.02 240	0.00 247	0.02 247	0.02 249	0.01 243	0.09 245	0.00 248	0.30 243	0.00 249	0.08 242	0.03 243	0.09 242	0.01 244	0.45 240

sosnet-low-res		0.00 240	0.00 242	0.00 242	0.00 247	0.00 249	0.00 250	0.00 245	0.00 246	0.00 248	0.00 245	0.00 249	0.00 245	0.00 244	0.00 243	0.00 247	0.00 243

sosnet		0.00 240	0.00 242	0.00 242	0.00 247	0.00 249	0.00 250	0.00 245	0.00 246	0.00 248	0.00 245	0.00 249	0.00 245	0.00 244	0.00 243	0.00 247	0.00 243

MTAPA										83.48 1		86.45 14

MTMP										82.66 3		84.91 22

mPP-MVS					89.90 22							81.29 37

NP-MVS									80.10 42

High-resolution multi-view benchmark