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.

UA-Net		78.65 38	83.96 45	72.46 43	84.87 1	76.15 44	89.06 30	55.70 47	77.25 68	53.14 117	79.73 82	82.09 128	59.69 73	92.21 5	90.93 3	92.32 3	89.36 7

mPP-MVS					82.97 2							92.12 36

DTE-MVSNet		77.28 50	84.87 36	68.42 64	82.94 3	72.70 66	81.60 66	61.78 13	85.03 14	51.40 124	92.11 6	96.00 6	49.42 127	89.73 23	82.52 47	83.39 80	75.98 87

zzz-MVS		82.61 10	85.04 33	79.79 6	82.59 4	73.90 59	92.42 9	62.39 12	84.54 18	80.21 9	79.86 80	90.74 56	70.63 16	90.01 21	89.71 8	90.48 21	86.49 37

MP-MVS		83.50 5	86.11 19	80.45 3	82.58 5	80.60 24	92.68 5	63.48 3	81.43 47	80.21 9	81.95 62	90.76 55	72.86 6	90.14 19	89.30 11	90.92 19	88.59 19
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.06 53	85.05 32	67.74 67	82.29 6	72.59 67	80.86 70	61.03 20	84.66 16	50.08 132	92.19 4	96.59 3	49.12 128	89.83 22	82.35 49	83.06 81	77.14 83

DVP-MVS++		82.03 19	88.03 3	75.05 28	82.08 7	78.96 31	88.98 32	56.44 40	89.29 4	72.39 38	93.25 1	93.86 18	63.42 50	85.46 47	81.36 57	86.96 47	94.00 1

HPM-MVS++		80.44 29	82.57 52	77.96 14	81.99 8	72.76 64	90.48 19	61.31 15	80.85 53	77.90 18	81.93 63	87.01 97	68.20 28	84.15 58	85.27 30	87.85 40	86.00 40

CP-MVS		84.06 3	86.79 10	80.86 2	81.81 9	79.66 29	92.67 6	64.48 1	83.13 33	82.32 3	80.89 73	92.97 27	72.51 9	91.74 6	90.02 6	91.40 17	89.14 8

SR-MVS					81.31 10			62.63 9				91.11 47

PS-CasMVS		77.46 49	85.80 23	67.73 68	81.24 11	72.88 63	80.63 72	61.28 16	84.14 22	50.53 129	92.13 5	96.76 1	50.12 123	91.02 9	84.46 32	82.60 88	79.19 70

APD-MVS		80.60 28	84.63 38	75.91 24	81.22 12	81.48 10	90.49 18	58.81 30	77.54 67	67.49 70	85.90 29	89.82 69	69.43 21	86.08 44	83.80 38	88.01 38	87.77 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVSNet		77.01 54	85.04 33	67.65 69	81.16 13	72.72 65	80.54 73	61.18 17	82.09 42	50.41 130	90.81 9	95.89 7	50.03 124	90.86 10	84.30 35	82.56 90	78.65 76

ACMMPR		83.94 4	87.20 4	80.14 4	81.04 14	81.92 8	92.57 8	63.14 5	84.35 21	79.45 12	83.37 46	92.04 37	72.82 8	90.66 12	88.96 12	91.80 6	89.13 9

train_agg		77.83 44	80.47 62	74.77 29	80.92 15	69.60 78	88.87 33	56.32 42	74.03 84	71.03 47	83.67 45	87.68 87	64.75 40	83.70 60	81.85 53	86.71 50	82.73 51

TSAR-MVS + MP.		81.23 24	86.13 17	75.52 26	80.74 16	83.22 3	90.55 17	55.12 50	80.87 52	67.62 68	88.01 17	92.38 32	70.61 17	86.64 40	83.10 43	88.51 33	88.67 16
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CPTT-MVS		82.32 13	85.00 35	79.19 7	80.73 17	80.86 21	91.68 13	62.59 10	82.55 38	75.53 29	73.88 119	92.28 33	73.74 5	90.07 20	87.65 19	90.87 20	87.74 25

PGM-MVS		83.03 7	85.67 26	79.95 5	80.69 18	81.09 15	92.40 10	63.06 6	79.38 61	80.21 9	80.31 76	91.44 42	71.75 12	90.46 15	88.53 15	91.57 9	88.50 20

WR-MVS_H		77.56 47	85.88 22	67.86 66	80.54 19	74.32 56	83.23 56	61.78 13	83.47 30	47.46 143	91.81 7	95.84 8	50.50 120	90.44 16	84.37 33	83.63 76	80.89 62

SteuartSystems-ACMMP		82.16 15	85.55 28	78.21 11	80.48 20	79.28 30	92.65 7	61.03 20	80.55 55	77.00 23	81.80 65	90.71 57	68.73 26	90.25 17	87.94 18	89.36 28	88.30 22
Skip Steuart: Steuart Systems R&D Blog.
XVS					80.47 21	81.29 12	93.33 3			77.45 20		90.19 65				91.52 11

X-MVStestdata					80.47 21	81.29 12	93.33 3			77.45 20		90.19 65				91.52 11

SD-MVS		82.13 17	86.80 9	76.67 20	80.36 23	80.66 22	89.48 26	56.93 35	82.50 39	67.55 69	87.05 22	91.40 44	72.84 7	88.66 28	88.32 16	92.85 2	89.04 11
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
X-MVS		81.61 22	84.73 37	77.97 12	80.31 24	81.29 12	93.53 2	62.50 11	81.41 48	77.45 20	72.04 129	90.19 65	62.50 57	90.57 13	88.87 13	91.54 10	88.73 15

SMA-MVS		82.15 16	85.93 21	77.74 16	80.13 25	80.25 26	91.01 16	60.61 24	85.54 12	78.61 15	83.21 49	86.96 100	65.95 34	88.10 32	87.59 20	90.11 22	89.83 5
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
ACMMP		83.17 6	86.75 11	79.01 8	80.11 26	82.01 7	92.29 11	60.35 26	82.20 41	78.32 16	80.59 74	93.14 24	70.67 15	91.30 8	89.36 10	92.30 4	88.62 17
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
ACMM	71.24 7	82.85 9	86.59 12	78.50 10	80.10 27	78.59 32	91.77 12	60.76 23	84.43 19	76.49 25	81.58 67	93.50 19	70.45 19	91.38 7	89.42 9	91.42 16	87.22 32
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HFP-MVS		82.37 11	86.28 15	77.81 15	79.94 28	80.96 18	91.13 15	63.30 4	84.04 23	71.81 40	82.39 57	89.59 72	69.16 24	89.08 26	88.83 14	91.49 13	89.10 10

ACMP	70.35 9	82.17 14	86.45 14	77.18 18	79.33 29	81.00 17	89.27 28	58.63 32	81.35 49	75.46 30	82.97 52	95.08 13	68.90 25	90.49 14	87.43 22	91.48 14	86.84 34
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OPM-MVS		81.44 23	85.68 25	76.49 21	79.27 30	78.21 35	89.84 23	58.67 31	85.25 13	76.26 26	85.28 34	92.88 28	66.03 33	87.20 38	85.40 28	88.86 32	85.58 44
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
APDe-MVS		81.08 26	86.12 18	75.20 27	79.25 31	80.91 19	90.38 21	57.05 34	85.83 11	66.07 79	87.34 21	91.27 45	69.45 20	85.99 45	82.55 45	88.98 31	88.95 13

NCCC		77.82 45	80.72 61	74.43 31	79.24 32	75.72 47	88.06 36	56.36 41	79.61 59	73.22 35	67.75 142	87.05 96	63.09 53	85.62 46	84.00 37	86.62 51	85.30 47

ACMMP_NAP		81.79 21	85.72 24	77.21 17	79.15 33	79.68 28	91.62 14	59.66 28	83.55 29	77.74 19	83.72 44	87.34 93	65.36 35	88.61 29	87.56 21	89.73 27	89.58 6

MSP-MVS		79.65 34	84.28 42	74.25 32	78.92 34	81.86 9	89.07 29	60.49 25	83.85 27	70.05 56	85.12 35	90.92 53	62.99 54	81.15 79	81.64 54	83.99 70	85.42 46
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
LGP-MVS_train		82.91 8	86.50 13	78.72 9	78.72 35	81.03 16	89.78 24	61.16 18	80.15 57	80.44 6	84.83 37	94.19 16	70.52 18	90.70 11	87.19 23	91.71 8	87.37 30

DeepC-MVS	73.80 3	82.34 12	86.87 8	77.06 19	78.62 36	84.34 1	90.30 22	63.54 2	83.10 34	71.30 45	86.91 24	90.54 62	67.12 30	87.81 35	87.05 24	91.46 15	88.37 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DPE-MVS		81.01 27	85.18 30	76.15 22	78.58 37	80.64 23	89.77 25	57.92 33	81.66 46	73.45 33	86.84 25	89.80 70	69.33 22	85.40 48	82.91 44	87.87 39	89.01 12
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CNVR-MVS		77.79 46	81.57 56	73.38 38	78.37 38	75.91 45	87.97 37	55.11 51	79.41 60	70.98 48	74.70 114	86.43 104	61.77 60	85.10 54	83.73 39	86.10 59	85.68 43

TSAR-MVS + ACMM		81.20 25	86.92 7	74.52 30	77.60 39	82.29 5	84.41 49	62.95 8	82.99 35	64.03 83	87.71 18	89.17 74	71.98 11	88.19 31	88.10 17	86.18 57	89.95 4

HQP-MVS		75.81 59	78.91 68	72.18 46	77.41 40	75.38 51	84.75 46	53.35 59	76.12 72	73.32 34	69.48 134	88.07 82	57.76 82	79.42 92	78.44 70	86.48 52	85.50 45

CDPH-MVS		77.22 52	81.05 60	72.75 42	77.29 41	77.46 39	86.36 43	54.02 57	73.00 90	69.75 59	77.78 95	88.90 77	61.31 64	84.09 59	82.54 46	87.79 41	83.57 49

TSAR-MVS + GP.		73.42 70	76.31 80	70.05 56	77.15 42	71.13 74	81.59 67	54.11 56	69.84 116	58.65 95	66.20 154	78.77 145	65.29 37	83.65 61	83.14 42	83.54 78	81.47 59

PMVS	70.37 8	81.82 20	87.08 6	75.68 25	77.06 43	77.23 40	87.77 39	56.25 43	83.33 32	67.18 74	89.48 15	87.94 84	77.70 1	93.02 2	92.57 2	88.13 37	86.00 40
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
AdaColmap		74.73 65	77.57 74	71.40 48	76.90 44	75.76 46	84.54 48	53.08 61	76.20 71	66.64 78	66.06 155	78.16 148	61.32 63	85.37 50	82.20 51	85.95 61	79.27 69

UniMVSNet_ETH3D		71.84 74	81.36 57	60.74 111	76.46 45	66.01 89	66.49 145	60.24 27	86.58 9	41.87 166	90.04 12	96.02 5	43.72 154	85.14 52	77.30 80	75.64 134	68.40 135

DeepC-MVS_fast	71.40 6	78.48 40	82.92 50	73.31 40	76.44 46	82.23 6	87.59 40	56.56 38	77.79 65	68.91 65	77.00 97	87.32 94	61.90 59	85.40 48	84.37 33	88.46 34	86.33 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DVP-MVS		80.31 30	85.60 27	74.15 34	76.23 47	78.39 33	86.62 41	55.79 46	86.47 10	71.32 44	90.96 8	89.02 76	69.28 23	84.62 57	81.64 54	85.66 62	88.09 23
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
WR-MVS		78.32 42	86.09 20	69.25 62	76.22 48	72.33 71	85.71 45	59.02 29	86.66 8	51.41 123	92.91 3	96.76 1	53.09 109	90.21 18	85.30 29	90.05 24	78.46 77

xxxxxxxxxxxxxcwj		78.58 39	83.87 46	72.41 44	76.04 49	75.72 47	83.86 51	51.81 66	84.00 25	70.65 52	81.27 69	95.06 14	64.64 41	83.28 66	80.28 60	87.44 43	87.49 28

SF-MVS		78.36 41	83.47 48	72.41 44	76.04 49	75.72 47	83.86 51	51.81 66	84.00 25	70.65 52	81.27 69	92.22 34	64.64 41	83.28 66	80.28 60	87.44 43	87.49 28

SED-MVS		79.70 33	85.16 31	73.34 39	75.83 51	78.11 36	88.77 34	56.45 39	84.85 15	69.45 61	90.70 10	88.38 79	63.16 52	85.12 53	81.28 58	86.40 54	87.63 26

COLMAP_ROB	75.87 2	84.34 2	89.80 2	77.97 12	75.52 52	82.76 4	90.39 20	54.21 55	89.37 3	83.18 2	89.90 13	95.58 11	72.34 10	92.31 4	90.04 5	92.17 5	88.61 18
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LS3D		79.33 36	84.03 44	73.84 35	75.37 53	78.09 37	83.30 55	52.94 62	84.42 20	76.01 27	84.16 40	87.44 92	65.34 36	86.30 42	82.08 52	90.09 23	85.70 42

TDRefinement		86.29 1	90.77 1	81.06 1	75.10 54	83.76 2	93.79 1	61.08 19	89.57 2	86.19 1	90.06 11	93.01 26	76.72 2	94.71 1	92.72 1	93.47 1	91.56 2

DPM-MVS		71.35 78	73.50 104	68.84 63	74.93 55	73.35 61	84.07 50	50.56 72	71.91 94	67.06 75	61.21 177	77.02 153	52.64 112	74.15 118	75.14 93	83.79 73	81.74 57

ambc			79.96 64		74.57 56	75.48 50	73.75 111		80.32 56	72.34 39	78.46 90	92.41 31	59.05 78	80.24 88	73.95 99	75.41 136	78.85 73

MVS_0304		73.74 69	77.16 77	69.74 59	74.24 57	73.47 60	84.70 47	49.62 76	62.26 145	67.27 72	75.87 102	87.57 89	57.49 85	81.20 78	79.50 67	85.10 64	80.27 64

DeepPCF-MVS	71.57 5	79.49 35	84.05 43	74.17 33	74.14 58	80.88 20	89.33 27	56.24 44	82.41 40	71.58 42	82.27 58	86.47 103	66.47 32	84.80 55	84.16 36	87.26 45	87.34 31

CLD-MVS		71.24 80	78.12 71	63.20 101	74.03 59	71.60 72	82.82 60	32.91 190	74.23 82	69.32 62	79.65 83	91.54 40	47.02 141	81.22 77	79.01 68	73.09 151	69.63 125
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPNet		66.87 110	68.89 136	64.53 92	73.97 60	61.13 126	78.46 78	61.03 20	56.78 169	53.41 114	66.91 149	70.91 170	43.49 155	76.08 112	76.68 85	76.81 124	73.73 98
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMH+	67.97 10	80.15 31	86.16 16	73.14 41	73.82 61	76.41 43	83.59 54	54.82 53	87.35 7	70.86 49	86.98 23	96.27 4	66.50 31	89.17 25	83.39 40	89.26 29	83.56 50

MCST-MVS		73.84 68	77.44 75	69.63 60	73.75 62	74.73 54	81.38 68	48.58 82	74.77 76	69.16 63	71.97 130	86.20 107	59.50 74	78.51 99	74.06 98	85.42 63	81.85 55

CSCG		76.95 55	82.08 54	70.97 49	73.32 63	78.35 34	81.08 69	47.19 91	83.47 30	69.82 58	80.44 75	87.19 95	64.59 43	81.01 82	77.26 81	89.83 26	86.84 34

OMC-MVS		79.95 32	85.28 29	73.74 37	72.95 64	80.10 27	87.87 38	48.13 86	84.62 17	79.42 13	80.27 77	92.49 30	64.14 45	87.25 37	85.11 31	89.92 25	87.10 33

CANet		71.07 81	75.09 89	66.39 81	72.57 65	71.53 73	82.38 62	47.10 93	59.81 151	59.81 91	74.97 109	84.37 120	54.25 103	79.89 90	77.64 79	82.25 91	77.40 81

TranMVSNet+NR-MVSNet		71.66 75	79.23 67	62.83 104	72.54 66	65.64 91	74.77 101	55.27 49	75.91 73	45.50 155	89.55 14	94.25 15	45.96 145	82.74 70	77.03 82	82.96 83	69.48 129

DROMVSNet		70.50 89	73.32 107	67.20 73	72.07 67	66.21 87	70.86 122	50.10 75	57.66 162	60.49 89	74.97 109	79.42 140	63.32 51	79.65 91	75.46 90	86.35 56	79.87 66

UniMVSNet_NR-MVSNet		70.82 85	77.44 75	63.11 102	71.75 68	66.02 88	73.93 108	55.00 52	70.90 100	46.77 149	86.68 26	91.54 40	46.91 142	81.07 81	76.32 87	84.28 68	69.54 126

PCF-MVS	65.25 14	73.99 67	76.74 79	70.79 51	71.61 69	75.33 52	83.76 53	50.40 74	74.88 75	74.50 32	67.60 143	85.36 113	58.30 80	78.61 98	74.25 96	86.15 58	81.13 61
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_part1		75.44 61	84.35 41	65.04 90	71.41 70	70.75 75	78.24 81	47.16 92	90.52 1	56.65 101	93.19 2	95.21 12	49.99 125	82.20 74	77.98 75	86.80 49	80.63 63

TSAR-MVS + COLMAP		75.85 58	81.06 58	69.77 58	71.15 71	76.90 41	82.93 58	52.43 64	79.25 62	70.13 54	82.78 53	87.00 98	60.02 69	80.30 86	79.61 66	81.95 94	81.61 58

PHI-MVS		75.17 63	78.37 69	71.43 47	71.13 72	72.46 69	82.28 63	50.55 73	73.39 87	79.05 14	73.65 121	87.50 91	61.98 58	81.10 80	78.48 69	83.60 77	81.99 54

DU-MVS		71.03 83	77.92 72	62.98 103	70.81 73	65.48 93	73.93 108	56.76 36	69.95 114	46.77 149	85.70 32	93.49 20	46.91 142	83.47 62	77.82 77	82.72 86	69.54 126

Baseline_NR-MVSNet		68.15 106	75.12 88	60.02 114	70.81 73	55.67 157	75.88 94	53.40 58	71.25 97	43.96 160	85.88 30	92.68 29	45.76 146	83.47 62	68.34 139	70.34 168	68.58 133

CNLPA		76.67 56	81.72 55	70.77 52	70.75 75	76.68 42	86.14 44	46.11 102	81.82 44	74.68 31	76.37 99	86.23 106	62.92 56	85.28 51	83.29 41	84.02 69	82.40 53

EG-PatchMatch MVS		71.50 77	76.82 78	65.30 87	70.74 76	66.50 85	74.23 105	43.25 125	72.02 93	59.11 93	79.85 81	86.88 101	63.95 46	80.29 87	75.25 92	80.51 101	76.98 84

3Dnovator+	72.94 4	78.78 37	83.05 49	73.80 36	70.70 77	81.34 11	88.33 35	56.01 45	81.33 50	72.87 37	78.06 93	81.15 130	63.83 47	87.39 36	85.82 26	91.06 18	86.28 39

MSLP-MVS++		76.66 57	82.32 53	70.06 55	70.51 78	80.27 25	79.77 75	55.58 48	77.79 65	63.09 84	67.25 146	89.50 73	71.01 14	88.10 32	85.74 27	80.39 102	87.56 27

PLC	64.88 15	75.76 60	80.22 63	70.57 54	70.46 79	77.75 38	82.01 64	48.84 81	80.74 54	70.85 50	71.32 131	84.82 116	63.69 48	84.73 56	82.35 49	87.54 42	79.80 67
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
UniMVSNet (Re)		71.29 79	78.14 70	63.30 99	70.29 80	66.57 84	75.98 93	54.74 54	70.20 109	46.20 153	85.08 36	93.21 22	48.19 133	82.50 72	78.33 72	84.40 67	71.08 117

Effi-MVS+-dtu		70.10 93	73.76 102	65.82 83	70.23 81	74.92 53	79.47 77	44.49 118	56.98 167	54.34 107	64.26 161	84.78 117	59.97 70	80.96 83	80.38 59	86.44 53	74.05 97

MAR-MVS		70.00 94	72.28 116	67.34 71	69.89 82	72.57 68	80.09 74	49.49 78	60.28 150	69.03 64	59.29 187	80.79 133	54.68 101	78.39 101	76.00 88	80.87 99	78.67 75
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
CS-MVS-test		69.71 96	71.95 122	67.11 74	69.81 83	61.69 121	70.86 122	45.70 106	51.07 186	66.95 76	74.48 116	75.90 157	64.34 44	86.44 41	79.64 65	85.02 65	74.40 94

ACMH	66.19 11	78.12 43	84.55 39	70.63 53	69.62 84	72.40 70	80.77 71	46.43 100	89.24 5	77.99 17	87.42 20	95.83 9	62.95 55	86.27 43	78.24 73	86.00 60	82.46 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
abl_6				65.41 86	69.37 85	74.02 58	82.50 61	47.39 90	66.39 129	56.63 102	60.61 180	82.76 126	53.68 106			82.92 84	78.39 78

EIA-MVS		63.24 132	64.16 155	62.16 105	69.30 86	63.20 112	72.40 113	40.82 150	48.31 203	51.50 121	59.63 185	62.23 189	57.33 86	78.00 103	71.94 112	81.59 97	65.82 147

FPMVS		70.46 90	74.89 90	65.28 88	69.09 87	61.42 122	77.07 89	46.92 96	76.73 70	53.53 113	67.33 144	75.07 160	67.23 29	83.41 64	81.54 56	77.86 120	78.73 74

ETV-MVS		64.30 126	64.76 151	63.77 97	68.59 88	62.49 115	77.02 90	45.31 108	49.27 196	50.88 127	56.23 199	59.91 197	57.12 87	80.19 89	74.23 97	83.68 74	71.03 118

IS_MVSNet		68.20 105	74.41 98	60.96 109	68.55 89	64.36 107	71.47 117	48.33 84	70.11 111	43.30 162	80.90 72	74.54 162	47.19 140	81.25 76	77.97 76	86.94 48	71.76 115

MVS_111021_HR		72.37 72	76.12 83	68.00 65	68.55 89	64.30 109	82.93 58	48.98 80	74.25 81	65.39 80	73.59 122	84.11 121	59.48 75	82.61 71	78.38 71	82.66 87	75.59 89

EPP-MVSNet		69.51 97	76.17 81	61.74 108	68.38 91	66.60 83	71.77 115	46.98 94	73.60 86	41.79 167	82.06 61	69.65 175	52.51 113	83.41 64	79.94 64	89.02 30	77.94 79

TAPA-MVS	66.11 12	75.37 62	79.24 66	70.86 50	67.63 92	74.09 57	83.17 57	44.75 117	81.82 44	80.83 5	65.61 157	88.04 83	61.58 61	83.21 68	80.12 62	87.17 46	81.82 56
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_111021_LR		71.60 76	75.21 87	67.38 70	67.42 93	62.44 116	81.73 65	46.24 101	70.89 101	66.80 77	73.19 124	84.98 114	60.09 68	81.94 75	77.77 78	82.00 93	75.29 90

GeoE		68.11 107	72.10 120	63.47 98	67.32 94	62.42 117	78.32 79	43.22 126	64.06 138	55.72 105	73.97 118	84.58 118	55.35 95	76.09 111	70.41 124	80.89 98	73.14 102

test1111		64.34 124	71.57 125	55.90 139	67.25 95	60.24 134	66.66 143	51.63 70	73.36 88	34.69 183	75.63 105	80.67 134	39.43 166	78.17 102	71.69 114	75.71 131	61.23 163

Effi-MVS+		69.04 99	73.01 109	64.40 93	67.20 96	64.83 101	74.87 100	43.97 121	63.33 141	60.90 87	73.06 125	85.79 110	55.61 93	73.58 124	76.41 86	83.84 72	74.09 96

CS-MVS		63.93 127	63.82 158	64.07 94	67.17 97	64.71 102	72.30 114	41.91 138	49.21 197	60.24 90	56.66 196	52.75 202	59.16 77	78.72 97	72.69 108	83.64 75	71.99 112

MSDG		68.98 101	73.31 108	63.92 95	67.08 98	68.27 79	75.41 97	40.77 151	67.61 123	64.89 81	75.75 103	78.96 142	53.70 105	76.72 107	73.95 99	81.71 96	71.93 114

v7n		74.47 66	81.06 58	66.77 76	66.98 99	67.10 81	76.76 91	45.88 104	81.98 43	67.43 71	88.38 16	95.67 10	61.38 62	80.76 84	73.49 101	82.21 92	80.06 65

v1240		70.94 84	74.52 95	66.76 77	66.54 100	64.40 105	77.76 84	45.29 109	70.05 112	71.45 43	83.36 48	90.96 51	60.37 66	70.50 154	68.68 131	79.14 111	73.68 99

v1192		71.06 82	74.87 91	66.61 78	66.38 101	65.80 90	78.27 80	45.28 110	70.19 110	70.79 51	83.37 46	91.79 38	58.76 79	70.86 151	69.02 130	80.16 104	73.08 103

v1921920		70.82 85	74.46 97	66.58 79	66.33 102	64.35 108	77.72 85	45.07 112	70.39 106	71.18 46	83.15 50	90.62 60	59.97 70	70.90 149	68.43 138	79.19 110	73.39 100

test2506		59.86 154	64.01 156	55.02 143	66.19 103	61.34 123	63.84 159	51.79 68	70.75 103	34.39 187	62.65 169	39.92 224	38.92 168	79.39 93	73.43 102	75.00 138	60.56 166

ECVR-MVS		63.93 127	71.52 126	55.08 142	66.19 103	61.34 123	63.84 159	51.79 68	70.75 103	34.77 181	74.70 114	81.10 131	38.92 168	79.39 93	73.43 102	75.00 138	59.92 169

Anonymous202405211			72.22 117		66.19 103	61.09 128	62.23 166	45.87 105	71.25 97		79.33 88	86.16 109	37.36 176	73.54 125	69.84 128	75.45 135	64.32 153

TinyColmap		71.85 73	76.11 84	66.87 75	66.07 106	65.34 95	74.35 103	49.30 79	79.93 58	75.93 28	75.66 104	87.74 86	54.72 100	80.66 85	70.42 123	80.85 100	73.02 105

gm-plane-assit		56.76 164	57.64 183	55.73 140	66.01 107	55.45 160	74.96 99	30.54 200	73.71 85	56.04 104	81.81 64	30.91 227	43.83 152	58.77 191	54.71 189	63.02 183	48.13 197

v144192		70.68 88	74.40 99	66.34 82	65.94 108	64.38 106	77.63 86	45.18 111	69.97 113	70.11 55	82.70 55	90.77 54	59.84 72	71.43 147	68.46 134	79.31 109	73.08 103

canonicalmvs		66.37 111	74.37 100	57.04 132	65.89 109	65.06 96	62.58 164	42.55 128	76.82 69	46.87 148	67.33 144	86.38 105	45.49 148	76.77 106	71.85 113	78.87 114	76.35 85

v1144		70.45 91	74.50 96	65.73 84	65.74 110	64.88 99	77.33 88	44.16 119	70.59 105	69.63 60	83.15 50	91.42 43	57.79 81	71.29 148	68.53 133	79.72 107	71.63 116

NR-MVSNet		68.66 103	76.15 82	59.93 115	65.49 111	65.48 93	74.42 102	56.76 36	69.95 114	45.38 156	85.70 32	91.13 46	34.68 186	74.52 117	76.75 84	82.83 85	69.49 128

IterMVS-LS		65.76 115	70.85 130	59.81 117	65.33 112	57.78 142	64.63 155	48.02 87	65.65 132	51.05 126	81.31 68	77.47 151	54.94 99	69.46 162	69.36 129	74.90 140	74.95 91
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		70.25 92	74.59 94	65.19 89	65.32 113	66.46 86	76.60 92	44.84 115	67.38 125	67.21 73	82.75 54	90.56 61	57.70 83	71.69 144	68.63 132	79.44 108	74.67 93

FC-MVSNet-train		64.87 121	74.76 93	53.33 148	65.24 114	58.05 139	69.69 129	41.92 137	70.99 99	32.62 192	85.75 31	88.23 80	32.10 196	77.61 104	74.41 95	78.43 115	68.25 136

RPSCF		77.56 47	84.51 40	69.46 61	65.17 115	74.36 55	79.74 76	47.45 89	84.01 24	72.89 36	77.89 94	90.67 58	65.14 38	88.25 30	89.74 7	86.38 55	86.64 36

SixPastTwentyTwo		77.24 51	83.65 47	69.78 57	65.14 116	64.85 100	77.44 87	47.74 88	82.76 37	68.52 66	87.65 19	93.31 21	71.68 13	89.49 24	82.41 48	88.14 36	85.05 48

test20.03		53.49 177	60.95 174	44.78 189	64.73 117	47.25 187	61.58 167	43.30 124	65.86 131	22.85 214	66.87 151	79.85 137	22.99 208	62.38 183	56.95 184	53.25 199	47.46 199

Vis-MVSNet (Re-imp)		60.99 148	67.78 139	53.06 150	64.66 118	53.49 170	67.40 139	49.52 77	68.55 121	28.00 205	79.53 87	71.41 169	33.08 194	75.30 114	71.28 116	75.69 133	54.91 181

TransMVSNet (Re)		63.49 130	73.86 101	51.39 160	64.26 119	56.07 154	61.17 168	42.23 131	78.81 63	34.80 180	85.94 28	90.63 59	34.35 190	72.73 134	67.98 145	71.50 158	64.84 151

v2v482		69.01 100	73.39 106	63.89 96	63.86 120	62.99 113	75.26 98	42.05 134	70.22 108	68.46 67	82.64 56	91.61 39	55.38 94	70.89 150	66.93 152	78.30 116	68.48 134

Fast-Effi-MVS+		67.71 108	72.54 113	62.07 106	63.83 121	63.68 110	75.74 96	39.94 157	60.89 149	54.29 108	73.00 126	86.19 108	56.85 88	78.46 100	73.23 104	81.74 95	72.36 109

DCV-MVSNet		64.34 124	72.84 111	54.42 146	63.79 122	62.09 119	62.50 165	42.72 127	74.32 80	41.34 169	66.96 147	88.57 78	39.18 167	75.20 115	70.35 125	77.01 123	72.37 108

v8		68.77 102	73.50 104	63.26 100	63.74 123	64.47 104	74.22 106	42.07 133	67.30 126	64.89 81	82.08 60	90.23 64	56.50 91	71.85 143	66.57 153	78.14 117	72.02 111

Anonymous20231211		63.69 129	72.86 110	53.00 151	63.72 124	60.25 133	60.33 170	40.96 147	72.49 91	38.91 172	81.77 66	88.17 81	37.60 175	73.30 126	68.01 144	76.47 129	66.06 146

Gipuma		73.40 71	79.27 65	66.55 80	63.64 125	59.35 135	70.28 126	45.92 103	83.79 28	71.78 41	84.04 42	93.07 25	68.69 27	87.90 34	76.76 83	78.98 113	69.96 122
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LTVRE_ROB	75.99 1	82.04 18	87.16 5	76.07 23	63.57 126	70.27 77	86.48 42	62.99 7	89.00 6	80.32 7	86.25 27	91.04 49	74.66 4	92.58 3	90.29 4	88.42 35	90.72 3
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
PVSNet_Blended_VisFu		70.70 87	73.62 103	67.28 72	63.53 127	72.96 62	77.97 82	52.10 65	63.65 139	62.66 85	71.14 132	73.46 164	63.55 49	79.35 96	75.34 91	83.90 71	79.43 68

USDC		68.53 104	71.82 123	64.68 91	63.53 127	61.87 120	70.12 127	46.98 94	77.89 64	76.58 24	68.55 139	86.88 101	50.50 120	73.73 121	65.62 155	80.39 102	68.21 137

gg-mvs-nofinetune		56.45 169	61.04 173	51.10 163	63.42 129	49.40 184	53.71 195	52.52 63	74.77 76	46.93 146	77.31 96	53.88 201	26.42 206	62.51 182	57.81 182	63.60 182	51.57 189

pmmvs-eth3d		65.36 119	70.09 133	59.85 116	63.05 130	53.61 169	74.29 104	46.45 99	68.14 122	51.45 122	78.83 89	85.78 111	49.87 126	70.44 155	70.45 122	74.00 144	63.38 157

casdiffmvs		66.19 113	72.34 114	59.02 119	62.75 131	60.61 132	69.06 132	41.38 143	69.49 117	54.11 109	84.00 43	89.74 71	49.12 128	70.74 153	62.70 170	77.70 122	69.14 131

pmmvs6		64.78 122	75.82 85	51.89 154	62.41 132	57.13 147	60.24 171	45.59 107	82.90 36	34.69 183	84.83 37	93.18 23	36.22 180	76.43 108	71.13 118	72.21 155	65.12 150

DI_MVS_plusplus_trai		63.43 131	67.54 140	58.63 121	62.34 133	58.06 138	65.75 151	42.15 132	63.05 142	53.28 115	75.88 101	75.92 156	50.18 122	68.04 165	64.20 161	78.07 118	67.65 138

CDS-MVSNet		57.90 161	63.57 161	51.28 161	62.30 134	53.17 171	64.70 154	51.61 71	57.41 163	32.75 191	63.73 163	70.53 171	27.12 202	72.49 135	73.02 106	69.22 172	54.68 182
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tfpnnormal		61.41 143	71.33 128	49.83 169	61.73 135	54.90 165	58.52 179	41.24 144	75.20 74	32.00 197	82.13 59	87.87 85	35.63 184	72.75 133	66.30 154	69.87 169	60.14 167

OpenMVS	60.79 16	64.42 123	69.72 134	58.23 128	61.63 136	62.17 118	64.11 157	37.54 175	67.17 127	55.71 106	65.89 156	74.89 161	52.67 111	72.20 139	68.29 141	77.73 121	77.39 82

thres600view7		58.87 158	65.91 144	50.66 166	61.27 137	56.32 151	59.88 174	40.63 153	64.88 134	32.10 196	64.82 158	69.83 173	36.72 178	72.99 130	72.55 109	73.34 147	59.97 168

Fast-Effi-MVS+-dtu		63.22 133	65.55 145	60.49 113	61.24 138	64.70 103	74.15 107	53.24 60	51.46 180	49.67 135	58.03 193	78.42 146	48.05 135	72.03 140	71.14 117	76.60 128	63.09 158

pmmvs4		61.12 146	64.61 152	57.04 132	60.88 139	52.15 174	70.59 125	44.82 116	61.35 148	46.91 147	72.08 128	73.27 165	46.79 144	65.06 171	67.76 148	72.28 153	60.58 165

3Dnovator	65.69 13	69.43 98	75.74 86	62.06 107	60.78 140	70.50 76	75.85 95	39.57 160	74.44 78	57.41 98	75.91 100	77.73 150	55.34 96	76.86 105	75.61 89	83.44 79	79.14 71

Vis-MVSNet		69.95 95	77.69 73	60.91 110	60.67 141	66.71 82	77.94 83	48.58 82	69.10 119	45.78 154	80.21 78	83.58 124	53.41 108	82.92 69	80.11 63	79.08 112	81.21 60
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
pm-mvs1		61.97 138	72.01 121	50.25 168	60.64 142	55.23 161	58.67 178	42.44 129	74.40 79	33.63 189	81.03 71	89.86 68	34.87 185	72.93 132	67.95 146	71.28 159	62.65 159

MS-PatchMatch		56.31 171	60.22 178	51.73 157	60.53 143	55.53 159	63.41 161	37.18 176	51.34 183	37.44 174	60.53 181	62.19 190	45.52 147	64.25 177	63.17 168	66.33 177	64.56 152

thres400		57.25 163	63.73 159	49.70 171	60.19 144	54.95 164	58.16 180	39.60 159	62.42 143	31.98 199	62.33 171	69.20 176	35.96 182	70.07 159	68.03 143	72.28 153	59.12 172

CANet_DTU		61.22 145	67.07 142	54.40 147	59.89 145	63.62 111	70.98 120	36.77 178	50.49 188	47.15 144	62.45 170	80.81 132	37.90 174	71.87 142	70.09 127	73.69 145	70.19 120

v148		64.92 120	70.58 132	58.32 125	59.89 145	57.09 148	66.04 147	35.27 181	69.11 118	60.66 88	79.57 84	90.93 52	53.91 104	69.81 161	62.22 171	74.14 142	65.31 149

DELS-MVS		65.54 118	71.79 124	58.24 127	59.68 147	65.55 92	70.99 119	38.69 166	62.29 144	49.27 137	75.03 108	81.42 129	50.93 116	73.71 123	71.35 115	79.90 106	73.20 101
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
QAPM		66.36 112	72.76 112	58.90 120	59.57 148	65.01 97	64.05 158	41.17 146	73.09 89	56.82 100	69.42 135	77.78 149	55.07 98	73.00 129	72.07 111	76.71 125	78.96 72

MVS_Test		62.58 136	67.46 141	56.89 134	59.52 149	55.90 156	64.94 153	38.83 163	57.08 166	56.55 103	76.53 98	84.49 119	47.45 137	66.95 168	62.01 172	74.04 143	69.27 130

test-LLR		46.01 197	45.06 214	47.11 184	59.39 150	36.72 207	51.28 200	40.95 148	36.41 219	34.45 185	46.14 213	47.02 215	38.00 172	51.78 207	48.53 201	58.60 187	48.84 194

test0.0.03 1		45.40 200	49.55 203	40.57 198	59.39 150	44.36 195	53.37 196	40.95 148	47.14 204	19.23 217	45.49 215	60.24 195	19.24 213	54.82 202	51.98 195	51.21 203	42.82 207

GA-MVS		60.73 150	64.24 154	56.64 136	59.38 152	57.45 146	65.07 152	36.65 180	57.39 164	58.17 96	73.43 123	69.10 178	47.38 138	64.47 176	63.63 165	73.19 149	64.22 154

PatchMatch-RL		60.96 149	63.00 166	58.57 123	59.16 153	52.18 173	67.38 140	41.99 135	57.85 160	48.16 139	53.55 207	69.77 174	59.47 76	73.73 121	72.49 110	75.27 137	61.44 162

PM-MVS		65.66 116	71.25 129	59.14 118	58.92 154	54.88 166	73.66 112	38.55 167	66.12 130	49.91 134	69.87 133	86.97 99	60.61 65	76.30 109	74.75 94	73.19 149	69.83 123

thres200		56.35 170	62.36 167	49.34 173	58.87 155	56.32 151	55.91 188	40.63 153	58.51 154	31.34 200	58.81 191	67.31 180	35.96 182	72.99 130	65.51 156	73.34 147	57.07 175

baseline1		52.90 181	58.38 180	46.51 188	58.87 155	50.01 182	54.17 193	40.45 156	56.81 168	29.25 204	62.72 168	58.99 198	30.25 198	65.05 172	60.57 175	66.07 178	54.54 183

ET-MVSNet_ETH3D		60.33 152	62.10 169	58.27 126	58.61 157	58.05 139	68.06 136	41.20 145	51.40 181	51.10 125	64.06 162	49.42 208	50.61 119	74.72 116	70.29 126	80.05 105	66.74 142

thres100view900		53.88 174	59.19 179	47.68 182	58.57 158	52.74 172	54.45 192	38.07 170	53.23 174	31.01 201	56.41 197	66.40 185	32.80 195	65.03 173	64.43 160	71.18 162	56.10 178

tfpn200view9		56.07 172	61.85 170	49.34 173	58.57 158	56.48 150	58.01 182	40.72 152	53.23 174	31.01 201	56.41 197	66.40 185	34.18 191	73.02 128	68.06 142	73.53 146	59.35 171

testgi		51.94 184	61.37 172	40.94 196	58.38 160	47.03 189	65.88 148	30.49 202	70.87 102	22.64 215	57.53 195	87.59 88	18.30 215	63.01 179	54.32 191	49.93 205	49.27 192

FMVSNet1		61.92 139	71.36 127	50.90 164	57.67 161	59.29 136	59.48 176	44.14 120	70.24 107	34.72 182	75.45 107	84.94 115	36.75 177	72.33 136	68.45 135	72.66 152	68.83 132

IterMVS-SCA-FT		62.67 134	68.00 138	56.45 138	56.92 162	64.92 98	57.51 183	38.12 168	59.44 152	53.62 112	74.74 113	71.60 167	64.84 39	70.24 157	65.27 157	67.70 176	69.83 123

IB-MVS	57.02 17	61.37 144	65.39 146	56.69 135	56.65 163	60.85 131	70.70 124	37.90 173	49.37 195	45.37 157	48.75 211	79.14 141	53.55 107	76.26 110	70.85 121	75.97 130	72.50 107
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
thisisatest0515		66.95 109	72.29 115	60.72 112	56.37 164	56.05 155	71.08 118	38.81 164	67.59 124	53.26 116	78.21 91	79.79 139	60.11 67	75.69 113	73.02 106	84.69 66	75.66 88

PVSNet_BlendedMVS		61.75 140	65.07 149	57.87 130	56.27 165	60.99 129	65.81 149	43.75 122	51.27 184	54.08 110	62.12 172	78.84 143	50.67 117	71.49 145	63.91 163	76.64 126	66.86 140

PVSNet_Blended		61.75 140	65.07 149	57.87 130	56.27 165	60.99 129	65.81 149	43.75 122	51.27 184	54.08 110	62.12 172	78.84 143	50.67 117	71.49 145	63.91 163	76.64 126	66.86 140

diffmvs		62.64 135	69.66 135	54.46 145	56.19 167	55.06 163	67.36 141	36.74 179	64.18 136	50.58 128	79.54 85	87.55 90	45.13 150	68.04 165	63.20 166	70.78 163	70.02 121

GBi-Net		56.54 167	63.26 164	48.70 177	55.88 168	57.61 143	57.26 184	41.75 139	49.06 198	32.37 193	61.81 174	67.02 181	34.58 187	72.33 136	68.45 135	70.38 165	66.56 143

test1		56.54 167	63.26 164	48.70 177	55.88 168	57.61 143	57.26 184	41.75 139	49.06 198	32.37 193	61.81 174	67.02 181	34.58 187	72.33 136	68.45 135	70.38 165	66.56 143

FMVSNet2		57.80 162	65.39 146	48.94 176	55.88 168	57.61 143	57.26 184	42.37 130	58.21 155	33.19 190	68.36 140	75.55 159	34.58 187	66.91 169	64.55 159	70.38 165	66.56 143

tpm cat1		50.98 185	51.28 197	50.62 167	55.74 171	49.92 183	63.13 162	38.12 168	52.38 178	47.61 142	60.11 182	44.51 221	44.86 151	51.31 209	47.49 204	54.25 198	53.24 185

new-patchmatchnet		47.33 193	60.49 177	31.99 212	55.69 172	33.86 212	36.84 220	33.31 189	72.36 92	14.33 220	80.09 79	92.14 35	13.27 217	63.54 178	40.09 212	38.51 214	41.32 211

EPNet_dtu		56.63 166	60.77 175	51.80 155	55.47 173	44.63 193	69.83 128	38.74 165	50.27 189	47.64 141	58.01 194	72.27 166	33.71 192	68.60 164	67.72 149	65.39 179	63.86 155
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20231206		50.28 189	57.94 182	41.35 195	55.45 174	43.65 197	58.06 181	34.12 184	62.02 147	24.25 212	59.33 186	79.80 138	24.49 207	59.55 185	54.28 192	51.74 201	46.94 201

baseline2		53.55 176	55.19 188	51.62 158	55.27 175	51.95 175	60.89 169	34.23 183	46.69 207	42.47 163	53.56 206	50.01 205	45.33 149	64.63 175	61.22 174	71.56 156	58.28 174

tttt0517		61.44 142	63.85 157	58.62 122	55.20 176	55.61 158	68.80 133	38.02 171	55.70 171	50.01 133	66.93 148	48.90 209	56.69 89	73.84 120	71.10 119	82.99 82	74.89 92

CVMVSNet		60.45 151	63.72 160	56.63 137	54.82 177	53.75 167	68.41 135	41.95 136	55.07 173	48.03 140	58.08 192	68.67 179	55.09 97	69.14 163	68.34 139	71.51 157	72.97 106

thisisatest0530		61.02 147	63.44 163	58.19 129	54.75 178	55.09 162	68.03 138	38.02 171	55.45 172	49.06 138	66.58 152	48.69 210	56.69 89	73.07 127	71.10 119	82.60 88	74.14 95

FC-MVSNet-test		60.28 153	70.83 131	47.96 180	54.69 179	47.12 188	68.06 136	41.68 142	71.42 95	23.73 213	84.70 39	77.41 152	28.92 200	82.33 73	73.08 105	70.68 164	59.77 170

UGNet		65.61 117	74.79 92	54.91 144	54.54 180	68.20 80	70.97 121	48.21 85	67.14 128	41.67 168	74.15 117	80.65 135	36.10 181	79.39 93	77.99 74	77.95 119	76.01 86
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
FMVSNet3		54.77 173	60.73 176	47.81 181	54.29 181	56.88 149	55.89 189	41.75 139	49.06 198	32.37 193	61.81 174	67.02 181	33.67 193	62.88 180	61.96 173	68.88 173	65.53 148

our_test_3					52.72 182	53.66 168	69.11 131

CostFormer		52.59 182	55.14 189	49.61 172	52.72 182	50.40 178	66.28 146	33.78 186	52.85 176	43.43 161	66.30 153	51.37 204	41.78 162	54.92 201	51.18 197	59.68 186	58.98 173

HyFIR lowres test		59.15 157	62.28 168	55.49 141	52.42 184	62.59 114	71.76 116	39.74 158	50.25 190	41.92 165	62.88 167	69.16 177	55.85 92	62.77 181	67.18 151	71.27 160	61.11 164

dps		49.71 190	51.97 195	47.07 185	52.37 185	47.00 190	53.02 198	40.52 155	44.91 211	41.23 170	64.55 159	44.27 222	40.12 165	57.71 194	51.97 196	55.14 196	53.41 184

V42		65.79 114	72.11 119	58.42 124	51.89 186	58.69 137	73.80 110	34.50 182	65.40 133	57.10 99	79.54 85	89.09 75	57.51 84	71.98 141	67.83 147	75.70 132	72.26 110

CR-MVSNet		53.82 175	55.40 187	51.98 153	51.57 187	50.23 179	45.00 210	44.97 113	46.90 205	52.60 118	67.91 141	46.99 217	48.37 131	59.15 189	59.53 179	69.38 171	57.07 175

IterMVS		59.24 156	64.45 153	53.16 149	50.98 188	61.29 125	66.51 144	32.85 191	58.17 156	46.31 152	72.58 127	70.23 172	54.26 102	64.81 174	60.24 176	68.04 175	63.81 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPMNet		50.92 186	50.32 199	51.62 158	50.25 189	50.23 179	59.16 177	46.70 98	46.90 205	42.39 164	48.97 210	37.23 225	41.78 162	57.30 196	56.18 186	69.44 170	55.43 179

MDTV_nov1_ep13_2view		58.09 160	63.54 162	51.74 156	50.13 190	46.56 192	66.95 142	33.41 188	63.52 140	58.77 94	74.84 111	84.10 122	43.12 156	65.95 170	54.69 190	58.04 190	55.13 180

tpm		46.67 194	49.20 205	43.72 191	49.60 191	36.60 209	53.93 194	26.84 206	52.70 177	58.05 97	69.04 136	47.96 213	30.06 199	48.33 212	42.76 207	43.88 208	47.01 200

TAMVS		46.64 195	53.62 192	38.49 201	49.56 192	36.87 206	53.16 197	25.76 208	56.33 170	22.55 216	60.72 179	61.80 192	27.12 202	59.50 186	58.33 181	52.79 200	41.82 210

PatchmatchNet		48.67 192	50.10 200	46.99 187	48.29 193	41.00 200	55.54 190	38.94 161	51.38 182	45.15 158	63.22 165	48.45 212	42.83 157	53.80 206	48.50 203	51.19 204	44.37 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CMPMVS	45.32 18	58.10 159	65.24 148	49.76 170	47.88 194	46.86 191	48.16 208	32.82 192	58.06 157	61.35 86	59.64 184	80.00 136	47.27 139	70.15 158	64.10 162	61.08 184	77.85 80
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		59.77 155	66.07 143	52.42 152	47.81 195	51.86 176	62.98 163	32.28 195	62.08 146	47.10 145	59.94 183	83.42 125	53.08 110	70.06 160	63.19 167	71.26 161	71.96 113

CHOSEN 1792x2688		52.99 180	56.91 184	48.42 179	47.32 196	50.10 181	64.18 156	33.85 185	45.46 210	36.95 176	55.20 203	66.49 184	51.20 115	59.28 187	59.81 177	57.01 192	61.99 161

MVSTER		53.08 179	56.39 185	49.21 175	47.19 197	51.08 177	60.14 173	31.74 197	40.63 215	38.97 171	55.78 200	46.74 218	42.47 159	67.29 167	62.99 169	74.73 141	70.23 119

tpmrst		43.31 206	46.14 210	40.02 200	47.05 198	36.48 210	48.01 209	32.17 196	49.50 194	37.26 175	63.66 164	47.04 214	31.98 197	42.00 218	40.55 211	43.64 209	43.75 205

pmmvs5		50.64 187	58.01 181	42.05 194	47.01 199	43.67 196	49.27 206	29.43 203	50.77 187	33.83 188	68.69 138	76.16 155	27.82 201	57.53 195	57.07 183	64.95 180	52.18 187

SCA		52.47 183	53.97 191	50.71 165	46.95 200	57.79 141	60.18 172	46.89 97	51.92 179	46.71 151	60.73 178	49.97 206	47.69 136	56.39 198	52.98 194	55.82 194	48.03 198

MIMVSNet1		56.72 165	68.69 137	42.76 193	46.70 201	42.81 198	69.13 130	30.52 201	81.01 51	32.00 197	74.82 112	91.10 48	26.83 204	73.98 119	64.72 158	51.40 202	52.38 186

MDTV_nov1_ep13		49.60 191	51.57 196	47.31 183	46.28 202	44.61 194	59.82 175	30.96 198	48.80 202	50.20 131	59.26 188	52.38 203	38.56 170	56.20 199	49.70 200	58.04 190	50.01 190

E-PMN		43.83 205	49.81 202	36.84 205	46.09 203	31.86 215	42.77 214	25.85 207	57.76 161	25.53 207	55.50 201	62.47 188	43.77 153	70.78 152	39.51 213	37.04 215	30.79 216

baseline		53.46 178	61.55 171	44.01 190	45.83 204	48.77 185	57.26 184	28.75 204	49.99 191	38.85 173	68.78 137	75.65 158	38.30 171	60.80 184	59.78 178	55.10 197	67.07 139

pmnet_mix02		45.67 199	55.99 186	33.63 211	45.77 205	31.22 216	42.04 215	27.60 205	64.14 137	24.89 208	75.50 106	82.30 127	21.88 210	54.53 204	41.22 209	39.62 212	43.05 206

anonymousdsp		74.76 64	82.59 51	65.63 85	45.61 206	61.13 126	89.06 30	32.58 193	74.11 83	59.55 92	84.06 41	94.12 17	75.24 3	88.94 27	86.95 25	91.74 7	88.81 14

EMVS		43.85 204	49.91 201	36.77 206	45.46 207	32.70 213	44.09 212	25.33 209	57.88 159	26.62 206	58.99 190	61.14 194	42.77 158	70.26 156	38.52 216	36.38 216	29.87 217

MIMVSNet		45.83 198	53.46 194	36.94 204	45.38 208	39.50 203	52.20 199	30.68 199	57.09 165	24.53 211	55.22 202	71.54 168	21.74 211	55.81 200	51.08 198	47.11 206	43.96 204

N_pmnet		39.50 212	51.01 198	26.09 214	44.48 209	25.59 220	40.20 218	21.49 212	64.20 135	7.98 224	73.86 120	76.67 154	13.66 216	50.17 210	36.69 218	28.71 219	29.86 218

EPMVS		40.11 210	44.96 216	34.44 208	41.55 210	32.65 214	41.74 216	32.39 194	49.89 193	24.83 209	64.44 160	46.38 219	26.57 205	44.75 216	39.47 214	39.59 213	37.16 213

MVS-HIRNet		44.56 202	45.52 212	43.44 192	40.98 211	31.03 217	39.52 219	36.96 177	42.80 213	44.37 159	53.80 205	60.04 196	41.85 161	47.97 214	41.08 210	56.99 193	41.95 209

ADS-MVSNet		40.61 208	46.31 208	33.96 209	40.70 212	30.42 218	40.42 217	33.44 187	58.01 158	30.87 203	63.05 166	54.48 200	22.67 209	44.35 217	39.23 215	35.64 217	34.64 214

FMVSNet5		39.83 211	45.08 213	33.71 210	39.24 213	39.56 202	48.77 207	23.55 211	39.45 216	24.55 210	33.73 218	44.57 220	20.97 212	58.27 193	54.23 193	45.16 207	45.77 202

new_pmnet		35.76 214	45.64 211	24.22 215	38.59 214	25.83 219	31.87 222	19.24 215	49.06 198	9.01 223	54.34 204	64.73 187	12.46 218	49.21 211	44.91 205	34.17 218	31.41 215

PatchT		50.55 188	53.55 193	47.05 186	37.59 215	42.26 199	50.55 203	37.56 174	46.37 208	52.60 118	66.91 149	43.54 223	48.37 131	59.15 189	59.53 179	55.62 195	57.07 175

MDA-MVSNet-bldmvs		62.46 137	72.13 118	51.19 162	34.32 216	56.10 153	68.65 134	38.85 162	69.05 120	49.50 136	78.17 92	85.43 112	51.32 114	86.67 39	67.40 150	64.46 181	62.08 160

test-mter		44.18 203	47.60 207	40.18 199	33.20 217	39.03 204	55.28 191	13.91 219	39.07 218	36.63 177	48.09 212	49.52 207	41.12 164	54.55 203	50.91 199	60.97 185	52.03 188

CHOSEN 280x420		40.24 209	44.14 217	35.69 207	32.36 218	23.58 221	50.30 204	21.21 213	40.94 214	18.84 218	32.75 219	48.65 211	48.13 134	59.16 188	55.31 188	43.28 210	48.62 196

TESTMET0.1,1		41.79 207	45.06 214	37.97 202	31.32 219	36.72 207	51.28 200	14.17 218	36.41 219	34.45 185	46.14 213	47.02 215	38.00 172	51.78 207	48.53 201	58.60 187	48.84 194

pmmvs3		46.64 195	54.13 190	37.90 203	31.23 220	40.68 201	49.83 205	15.34 217	46.31 209	36.34 178	53.15 208	74.40 163	36.36 179	58.43 192	56.64 185	58.32 189	49.29 191

PMMVS		45.37 201	49.29 204	40.79 197	27.75 221	35.07 211	50.88 202	19.88 214	39.27 217	35.78 179	50.11 209	61.29 193	42.04 160	54.13 205	55.95 187	68.43 174	49.19 193

PMMVS2		34.11 215	48.55 206	17.26 216	25.45 222	20.72 222	35.08 221	16.26 216	58.71 153	4.16 226	59.22 189	78.40 147	3.65 220	57.24 197	38.31 217	18.94 222	27.28 219

MVE	28.01 19	35.86 213	43.56 218	26.88 213	22.33 223	19.75 223	30.85 223	23.88 210	49.90 192	10.48 222	43.64 216	61.87 191	48.99 130	47.26 215	42.15 208	24.76 220	40.37 212
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				7.47 219	8.89 224	3.32 225	4.35 226	1.14 222	15.58 223	15.76 219	8.50 222	5.90 229	2.00 221	20.02 219	21.51 219	12.70 223

test_method		13.28 217	15.83 219	10.30 218	1.05 225	2.18 226	15.40 224	2.23 221	22.43 221	13.84 221	22.00 221	33.14 226	9.78 219	17.80 220	9.93 220	19.50 221	3.31 221

test123		0.53 218	0.60 221	0.46 220	0.22 226	0.25 227	0.33 229	0.13 224	0.66 224	1.37 227	1.10 223	0.00 231	0.43 222	0.68 222	0.61 221	0.26 225	0.88 222

testmvs		0.47 219	0.69 220	0.21 221	0.17 227	0.17 228	0.35 228	0.16 223	0.66 224	0.18 229	1.05 224	0.99 230	0.27 223	0.62 223	0.54 222	0.15 226	0.77 223

GG-mvs-BLEND		31.54 216	46.27 209	14.37 217	0.07 228	48.65 186	42.97 213	0.08 225	44.04 212	1.21 228	39.77 217	57.94 199	0.15 224	48.19 213	42.82 206	41.70 211	42.46 208

uanet_test		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 231	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 231	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 231	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

RE-MVS-def										70.04 57

9.14												83.64 123

MTAPA										80.26 8		90.53 63

MTMP										82.07 4		91.00 50

Patchmatch-RL test							2.05 227

NP-MVS									71.39 96

Patchmtry						37.73 205	45.00 210	44.97 113		52.60 118

DeepMVS_CX						8.52 224	9.75 225	3.19 220	16.70 222	5.02 225	23.06 220	19.33 228	18.69 214	13.75 221		11.34 224	25.07 220

High-resolution multi-view benchmark