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.

DVP-MVS		78.77 2	84.89 1	71.62 5	78.04 4	82.05 1	81.64 11	57.96 7	87.53 1	66.64 2	88.77 1	86.31 1	63.16 10	79.99 7	78.56 7	82.31 23	91.03 1
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
SED-MVS		79.21 1	84.74 2	72.75 1	78.66 3	81.96 2	82.94 5	58.16 4	86.82 2	67.66 1	88.29 4	86.15 3	66.42 2	80.41 4	78.65 6	82.65 17	90.92 2

MSP-MVS		77.82 5	83.46 5	71.24 9	75.26 17	80.22 8	82.95 4	57.85 8	85.90 3	64.79 6	88.54 3	83.43 7	66.24 3	78.21 18	78.56 7	80.34 49	89.39 7
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
DVP-MVS++		78.76 3	84.44 3	72.14 2	76.63 8	81.93 3	82.92 6	58.10 5	85.86 4	66.53 3	87.86 5	86.16 2	66.45 1	80.46 3	78.53 9	82.19 28	90.29 4

APDe-MVS		77.58 6	82.93 6	71.35 7	77.86 5	80.55 7	83.38 1	57.61 10	85.57 5	61.11 22	86.10 7	82.98 8	64.76 4	78.29 15	76.78 23	83.40 6	90.20 5

DeepPCF-MVS	66.49 1	74.25 21	80.97 10	66.41 33	67.75 53	78.87 14	75.61 40	54.16 35	84.86 6	58.22 34	77.94 17	81.01 17	62.52 17	78.34 13	77.38 17	80.16 52	88.40 11

SMA-MVS		77.32 7	82.51 7	71.26 8	75.43 15	80.19 9	82.22 8	58.26 3	84.83 7	64.36 9	78.19 16	83.46 6	63.61 8	81.00 1	80.28 1	83.66 4	89.62 6
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
DPE-MVS		78.11 4	83.84 4	71.42 6	77.82 6	81.32 4	82.92 6	57.81 9	84.04 8	63.19 14	88.63 2	86.00 4	64.52 5	78.71 11	77.63 16	82.26 24	90.57 3
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
xxxxxxxxxxxxxcwj		74.63 17	77.07 28	71.79 3	79.32 1	80.76 5	82.96 2	57.49 11	82.82 9	64.79 6	83.69 10	52.03 122	62.83 14	77.13 27	75.21 32	83.35 7	87.85 16

SF-MVS		77.13 8	81.70 8	71.79 3	79.32 1	80.76 5	82.96 2	57.49 11	82.82 9	64.79 6	83.69 10	84.46 5	62.83 14	77.13 27	75.21 32	83.35 7	87.85 16

HPM-MVS++		76.01 10	80.47 12	70.81 10	76.60 9	74.96 37	80.18 18	58.36 2	81.96 11	63.50 13	78.80 15	82.53 11	64.40 6	78.74 10	78.84 5	81.81 34	87.46 19

ACMMP_NAP		76.15 9	81.17 9	70.30 12	74.09 21	79.47 11	81.59 13	57.09 15	81.38 12	63.89 12	79.02 14	80.48 19	62.24 19	80.05 6	79.12 4	82.94 12	88.64 9

SD-MVS		74.43 18	78.87 18	69.26 21	74.39 20	73.70 47	79.06 26	55.24 27	81.04 13	62.71 16	80.18 13	82.61 10	61.70 23	75.43 42	73.92 45	82.44 22	85.22 33
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
TSAR-MVS + MP.		75.22 14	80.06 13	69.56 18	74.61 19	72.74 51	80.59 15	55.70 25	80.80 14	62.65 17	86.25 6	82.92 9	62.07 21	76.89 30	75.66 31	81.77 36	85.19 34
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
zzz-MVS		74.25 21	77.97 24	69.91 16	73.43 25	74.06 45	79.69 20	56.44 19	80.74 15	64.98 5	68.72 31	79.98 21	62.92 13	78.24 17	77.77 15	81.99 32	86.30 24

APD-MVS		75.80 11	80.90 11	69.86 17	75.42 16	78.48 17	81.43 14	57.44 13	80.45 16	59.32 28	85.28 8	80.82 18	63.96 7	76.89 30	76.08 28	81.58 40	88.30 12
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OMC-MVS		65.16 58	71.35 46	57.94 75	52.95 150	68.82 65	69.00 60	38.28 168	79.89 17	55.20 43	62.76 53	68.31 60	56.14 61	71.30 65	68.70 79	76.06 100	79.67 61

CNVR-MVS		75.62 12	79.91 14	70.61 11	75.76 11	78.82 15	81.66 10	57.12 14	79.77 18	63.04 15	70.69 25	81.15 16	62.99 11	80.23 5	79.54 3	83.11 9	89.16 8

TSAR-MVS + ACMM		72.56 30	79.07 17	64.96 42	73.24 26	73.16 50	78.50 28	48.80 69	79.34 19	55.32 42	85.04 9	81.49 15	58.57 41	75.06 45	73.75 46	75.35 108	85.61 31

CSCG		74.68 16	79.22 16	69.40 19	75.69 13	80.01 10	79.12 25	52.83 43	79.34 19	63.99 11	70.49 26	82.02 12	60.35 34	77.48 25	77.22 20	84.38 1	87.97 15

HFP-MVS		74.87 15	78.86 20	70.21 13	73.99 22	77.91 19	80.36 17	56.63 17	78.41 21	64.27 10	74.54 21	77.75 29	62.96 12	78.70 12	77.82 13	83.02 10	86.91 22

NCCC		74.27 20	77.83 25	70.13 14	75.70 12	77.41 24	80.51 16	57.09 15	78.25 22	62.28 19	65.54 38	78.26 26	62.18 20	79.13 8	78.51 10	83.01 11	87.68 18

DeepC-MVS	66.32 2	73.85 24	78.10 23	68.90 24	67.92 51	79.31 12	78.16 30	59.28 1	78.24 23	61.13 21	67.36 37	76.10 34	63.40 9	79.11 9	78.41 11	83.52 5	88.16 13
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNLPA		62.78 68	66.31 67	58.65 68	58.47 104	68.41 68	65.98 82	41.22 145	78.02 24	56.04 38	46.65 127	59.50 92	57.50 47	69.67 82	65.27 129	72.70 140	76.67 81

ACMMPR		73.79 25	78.41 21	68.40 26	72.35 29	77.79 20	79.32 22	56.38 20	77.67 25	58.30 33	74.16 22	76.66 30	61.40 24	78.32 14	77.80 14	82.68 16	86.51 23

TSAR-MVS + COLMAP		62.65 69	69.90 55	54.19 99	46.31 187	66.73 87	65.49 88	41.36 143	76.57 26	46.31 79	76.80 18	56.68 102	53.27 82	69.50 83	66.65 107	72.40 145	76.36 88

DPM-MVS		72.80 28	75.90 31	69.19 22	75.51 14	77.68 21	81.62 12	54.83 28	75.96 27	62.06 20	63.96 45	76.58 31	58.55 42	76.66 34	76.77 24	82.60 19	83.68 43

CP-MVS		72.63 29	76.95 29	67.59 28	70.67 37	75.53 35	77.95 32	56.01 23	75.65 28	58.82 30	69.16 30	76.48 32	60.46 31	77.66 23	77.20 21	81.65 38	86.97 21

MP-MVS		74.31 19	78.87 18	68.99 23	73.49 24	78.56 16	79.25 24	56.51 18	75.33 29	60.69 24	75.30 20	79.12 24	61.81 22	77.78 22	77.93 12	82.18 30	88.06 14
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		73.67 26	77.39 26	69.33 20	76.26 10	78.19 18	78.77 27	54.54 32	75.33 29	59.99 26	67.96 33	79.23 23	62.43 18	78.00 19	75.71 30	84.02 2	87.30 20

SteuartSystems-ACMMP		75.23 13	79.60 15	70.13 14	76.81 7	78.92 13	81.74 9	57.99 6	75.30 31	59.83 27	75.69 19	78.45 25	60.48 30	80.58 2	79.77 2	83.94 3	88.52 10
Skip Steuart: Steuart Systems R&D Blog.
TAPA-MVS	54.74 10	60.85 77	66.61 64	54.12 101	47.38 183	65.33 99	65.35 90	36.51 177	75.16 32	48.82 72	54.70 86	63.51 73	53.31 81	68.36 95	64.97 135	73.37 128	74.27 101
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DeepC-MVS_fast	65.08 3	72.00 31	76.11 30	67.21 30	68.93 47	77.46 22	76.54 36	54.35 33	74.92 33	58.64 32	65.18 39	74.04 44	62.62 16	77.92 20	77.02 22	82.16 31	86.21 25
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
train_agg		73.89 23	78.25 22	68.80 25	75.25 18	72.27 53	79.75 19	56.05 22	74.87 34	58.97 29	81.83 12	79.76 22	61.05 27	77.39 26	76.01 29	81.71 37	85.61 31

canonicalmvs		65.62 55	72.06 42	58.11 71	63.94 75	71.05 56	64.49 96	43.18 128	74.08 35	47.35 74	64.17 43	71.97 48	51.17 95	71.87 60	70.74 58	78.51 67	80.56 58

PGM-MVS		72.89 27	77.13 27	67.94 27	72.47 28	77.25 25	79.27 23	54.63 31	73.71 36	57.95 35	72.38 23	75.33 36	60.75 28	78.25 16	77.36 19	82.57 20	85.62 30

ACMMP		71.57 32	75.84 32	66.59 32	70.30 41	76.85 30	78.46 29	53.95 36	73.52 37	55.56 40	70.13 27	71.36 49	58.55 42	77.00 29	76.23 27	82.71 15	85.81 29
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
HQP-MVS		70.88 35	75.02 35	66.05 36	71.69 32	74.47 42	77.51 33	53.17 40	72.89 38	54.88 46	70.03 28	70.48 51	57.26 50	76.02 37	75.01 37	81.78 35	86.21 25

X-MVS		71.18 34	75.66 34	65.96 37	71.71 31	76.96 27	77.26 34	55.88 24	72.75 39	54.48 50	64.39 42	74.47 39	54.19 68	77.84 21	77.37 18	82.21 27	85.85 28

CPTT-MVS		68.76 43	73.01 39	63.81 49	65.42 64	73.66 48	76.39 38	52.08 45	72.61 40	50.33 64	60.73 61	72.65 47	59.43 39	73.32 55	72.12 51	79.19 62	85.99 27

NP-MVS									72.00 41

CLD-MVS		67.02 51	71.57 44	61.71 54	71.01 36	74.81 39	71.62 54	38.91 161	71.86 42	60.70 23	64.97 40	67.88 64	51.88 92	76.77 33	74.98 38	76.11 96	69.75 124
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TSAR-MVS + GP.		69.71 36	73.92 38	64.80 44	68.27 49	70.56 58	71.90 52	50.75 53	71.38 43	57.46 37	68.68 32	75.42 35	60.10 35	73.47 54	73.99 44	80.32 50	83.97 39

abl_6				64.36 46	70.08 42	77.45 23	72.88 51	50.15 58	71.31 44	54.77 49	62.79 52	77.99 28	56.80 55			81.50 41	83.91 40

3Dnovator+	62.63 4	69.51 37	72.62 41	65.88 38	68.21 50	76.47 31	73.50 50	52.74 44	70.85 45	58.65 31	55.97 78	69.95 52	61.11 26	76.80 32	75.09 34	81.09 44	83.23 46

PHI-MVS		69.27 40	74.84 36	62.76 53	66.83 56	74.83 38	73.88 48	49.32 63	70.61 46	50.93 62	69.62 29	74.84 37	57.25 51	75.53 41	74.32 42	78.35 69	84.17 38

AdaColmap		67.89 47	68.85 58	66.77 31	73.73 23	74.30 44	75.28 41	53.58 38	70.24 47	57.59 36	51.19 103	59.19 93	60.74 29	75.33 44	73.72 47	79.69 57	77.96 72

MSLP-MVS++		68.17 45	70.72 50	65.19 40	69.41 44	70.64 57	74.99 42	45.76 81	70.20 48	60.17 25	56.42 76	73.01 45	61.14 25	72.80 57	70.54 62	79.70 55	81.42 54

CDPH-MVS		71.47 33	75.82 33	66.41 33	72.97 27	77.15 26	78.14 31	54.71 29	69.88 49	53.07 58	70.98 24	74.83 38	56.95 54	76.22 35	76.57 25	82.62 18	85.09 35

LGP-MVS_train		68.87 41	72.03 43	65.18 41	69.33 45	74.03 46	76.67 35	53.88 37	68.46 50	52.05 61	63.21 49	63.89 71	56.31 58	75.99 38	74.43 41	82.83 14	84.18 37

ACMP	61.42 5	68.72 44	71.37 45	65.64 39	69.06 46	74.45 43	75.88 39	53.30 39	68.10 51	55.74 39	61.53 60	62.29 78	56.97 53	74.70 48	74.23 43	82.88 13	84.31 36
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	59.98 8	67.32 50	71.04 48	62.97 52	64.77 66	74.49 41	74.78 44	49.54 60	67.44 52	54.39 53	58.35 70	72.81 46	55.79 64	71.54 62	69.24 72	78.57 64	83.41 44
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
3Dnovator	60.86 6	66.99 53	70.32 52	63.11 51	66.63 57	74.52 40	71.56 55	45.76 81	67.37 53	55.00 45	54.31 89	68.19 61	58.49 44	73.97 51	73.63 48	81.22 43	80.23 59

PLC	52.09 14	59.21 84	62.47 87	55.41 94	53.24 148	64.84 107	64.47 97	40.41 154	65.92 54	44.53 90	46.19 135	55.69 109	55.33 65	68.24 99	65.30 128	74.50 114	71.09 115
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMM	60.30 7	67.58 49	68.82 59	66.13 35	70.59 38	72.01 55	76.54 36	54.26 34	65.64 55	54.78 48	50.35 106	61.72 82	58.74 40	75.79 40	75.03 35	81.88 33	81.17 56
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet		68.77 42	73.01 39	63.83 48	68.30 48	75.19 36	73.73 49	47.90 70	63.86 56	54.84 47	67.51 35	74.36 42	57.62 46	74.22 50	73.57 49	80.56 47	82.36 48

MAR-MVS		68.04 46	70.74 49	64.90 43	71.68 33	76.33 32	74.63 45	50.48 57	63.81 57	55.52 41	54.88 84	69.90 53	57.39 49	75.42 43	74.79 39	79.71 54	80.03 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
QAPM		65.27 56	69.49 57	60.35 58	65.43 63	72.20 54	65.69 86	47.23 72	63.46 58	49.14 69	53.56 90	71.04 50	57.01 52	72.60 58	71.41 54	77.62 73	82.14 50

MVS_0304		69.49 38	73.96 37	64.28 47	67.92 51	76.13 33	74.90 43	47.60 71	63.29 59	54.09 54	67.44 36	76.35 33	59.53 38	75.81 39	75.03 35	81.62 39	83.70 42

RPSCF		46.41 182	54.42 154	37.06 197	25.70 220	45.14 204	45.39 193	20.81 214	62.79 60	35.10 134	44.92 149	55.60 110	43.56 131	56.12 191	52.45 199	51.80 208	63.91 169

EPNet		65.14 59	69.54 56	60.00 60	66.61 58	67.67 76	67.53 66	55.32 26	62.67 61	46.22 81	67.74 34	65.93 68	48.07 111	72.17 59	72.12 51	76.28 92	78.47 69
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
casdiffmvs		64.09 60	68.13 62	59.37 65	61.81 81	68.32 69	68.48 63	44.45 98	61.95 62	49.12 70	63.04 50	69.67 55	53.83 72	70.46 74	66.06 117	78.55 65	77.43 74

LS3D		60.20 80	61.70 89	58.45 70	64.18 71	67.77 73	67.19 68	48.84 68	61.67 63	41.27 107	45.89 139	51.81 124	54.18 69	68.78 88	66.50 112	75.03 111	69.48 130

OpenMVS	57.13 9	62.81 67	65.75 71	59.39 64	66.47 59	69.52 63	64.26 98	43.07 130	61.34 64	50.19 65	47.29 124	64.41 70	54.60 67	70.18 79	68.62 81	77.73 71	78.89 65

MVS_111021_HR		67.62 48	70.39 51	64.39 45	69.77 43	70.45 60	71.44 56	51.72 49	60.77 65	55.06 44	62.14 57	66.40 67	58.13 45	76.13 36	74.79 39	80.19 51	82.04 51

MVS_111021_LR		63.05 66	66.43 66	59.10 66	61.33 85	63.77 116	65.87 83	43.58 117	60.20 66	53.70 56	62.09 58	62.38 77	55.84 63	70.24 78	68.08 84	74.30 116	78.28 71

diffmvs		61.64 73	66.55 65	55.90 90	56.63 126	63.71 117	67.13 71	41.27 144	59.49 67	46.70 77	63.93 46	68.01 63	50.46 96	67.30 118	65.51 125	73.24 133	77.87 73

DROMVSNet		67.01 52	70.27 54	63.21 50	67.21 54	70.47 59	69.01 58	46.96 74	59.16 68	53.23 57	64.01 44	69.71 54	60.37 32	74.92 46	71.24 56	82.50 21	82.41 47

DELS-MVS		65.87 54	70.30 53	60.71 56	64.05 74	72.68 52	70.90 57	45.43 85	57.49 69	49.05 71	64.43 41	68.66 57	55.11 66	74.31 49	73.02 50	79.70 55	81.51 53
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
CS-MVS		64.01 61	68.64 60	58.60 69	60.26 93	65.23 104	65.49 88	38.99 160	57.42 70	46.13 82	63.70 47	68.62 58	59.83 36	73.63 52	70.66 59	80.93 45	81.24 55

MVS_Test		62.40 71	66.23 68	57.94 75	59.77 99	64.77 108	66.50 76	41.76 139	57.26 71	49.33 68	62.68 54	67.47 66	53.50 77	68.57 93	66.25 114	76.77 85	76.58 83

DI_MVS_plusplus_trai		61.88 72	65.17 76	58.06 72	60.05 94	65.26 101	66.03 80	44.22 100	55.75 72	46.73 76	54.64 87	68.12 62	54.13 70	69.13 86	66.66 106	77.18 80	76.61 82

GeoE		62.43 70	64.79 79	59.68 63	64.15 73	67.17 83	68.80 61	44.42 99	55.65 73	47.38 73	51.54 100	62.51 76	54.04 71	69.99 80	68.07 85	79.28 60	78.57 67

OPM-MVS		69.33 39	71.05 47	67.32 29	72.34 30	75.70 34	79.57 21	56.34 21	55.21 74	53.81 55	59.51 65	68.96 56	59.67 37	77.61 24	76.44 26	82.19 28	83.88 41
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PVSNet_BlendedMVS		61.63 74	64.82 77	57.91 77	57.21 122	67.55 78	63.47 102	46.08 79	54.72 75	52.46 59	58.59 68	60.73 85	51.82 93	70.46 74	65.20 131	76.44 89	76.50 86

PVSNet_Blended		61.63 74	64.82 77	57.91 77	57.21 122	67.55 78	63.47 102	46.08 79	54.72 75	52.46 59	58.59 68	60.73 85	51.82 93	70.46 74	65.20 131	76.44 89	76.50 86

Effi-MVS+		63.28 63	65.96 70	60.17 59	64.26 70	68.06 71	68.78 62	45.71 83	54.08 77	46.64 78	55.92 79	63.13 75	55.94 62	70.38 77	71.43 53	79.68 58	78.70 66

test2506		55.82 119	59.57 117	51.46 119	60.39 91	64.55 110	58.69 124	48.87 66	53.91 78	26.99 170	48.97 112	41.72 187	37.71 159	70.96 69	69.49 69	76.08 97	67.37 144

ECVR-MVS		56.44 114	60.74 96	51.42 120	60.39 91	64.55 110	58.69 124	48.87 66	53.91 78	26.76 172	45.55 144	53.43 116	37.71 159	70.96 69	69.49 69	76.08 97	67.32 146

test_part1		63.06 65	65.27 74	60.47 57	66.24 62	70.17 61	71.86 53	45.36 87	53.75 80	49.61 66	44.85 150	65.53 69	48.93 102	71.39 63	70.65 60	80.82 46	80.59 57

CS-MVS-test		65.21 57	68.35 61	61.55 55	62.29 79	66.33 93	69.01 58	46.96 74	53.61 81	49.35 67	63.37 48	67.61 65	60.37 32	74.92 46	71.04 57	81.34 42	81.58 52

CostFormer		56.57 112	59.13 122	53.60 103	57.52 112	61.12 132	66.94 73	35.95 179	53.44 82	44.68 89	55.87 80	54.44 112	48.21 108	60.37 167	58.33 174	68.27 166	70.33 122

MSDG		58.46 93	58.97 124	57.85 79	66.27 61	66.23 94	67.72 64	42.33 134	53.43 83	43.68 93	43.39 162	45.35 165	49.75 99	68.66 91	67.77 90	77.38 77	67.96 139

ETV-MVS		63.23 64	66.08 69	59.91 61	63.13 78	68.13 70	67.62 65	44.62 95	53.39 84	46.23 80	58.74 67	58.19 96	57.45 48	73.60 53	71.38 55	80.39 48	79.13 63

CANet_DTU		58.88 86	64.68 80	52.12 117	55.77 130	66.75 86	63.92 99	37.04 175	53.32 85	37.45 129	59.81 63	61.81 81	44.43 128	68.25 97	67.47 96	74.12 118	75.33 96

COLMAP_ROB	46.52 15	51.99 148	54.86 152	48.63 144	49.13 177	61.73 126	60.53 114	36.57 176	53.14 86	32.95 141	37.10 184	38.68 199	40.49 146	65.72 141	63.08 150	72.11 149	64.60 166
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FC-MVSNet-train		58.40 94	63.15 86	52.85 112	64.29 69	61.84 125	55.98 145	46.47 77	53.06 87	34.96 136	61.95 59	56.37 106	39.49 149	68.67 90	68.36 83	75.92 102	71.81 112

EPNet_dtu		52.05 146	58.26 130	44.81 169	54.10 143	50.09 188	52.01 166	40.82 148	53.03 88	27.41 167	54.90 83	57.96 100	26.72 192	62.97 153	62.70 156	67.78 168	66.19 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Fast-Effi-MVS+		60.36 78	63.35 85	56.87 85	58.70 101	65.86 96	65.08 92	37.11 174	53.00 89	45.36 86	52.12 97	56.07 108	56.27 59	71.28 66	69.42 71	78.71 63	75.69 93

DCV-MVSNet		59.49 81	64.00 83	54.23 98	61.81 81	64.33 112	61.42 108	43.77 110	52.85 90	38.94 121	55.62 81	62.15 80	43.24 136	69.39 84	67.66 93	76.22 94	75.97 90

baseline		55.19 128	60.88 94	48.55 145	49.87 174	58.10 161	58.70 123	34.75 183	52.82 91	39.48 120	60.18 62	60.86 84	45.41 123	61.05 163	60.74 166	63.10 183	72.41 110

USDC		51.11 152	53.71 157	48.08 152	44.76 192	55.99 170	53.01 165	40.90 146	52.49 92	36.14 132	44.67 151	33.66 209	43.27 135	63.23 152	61.10 163	70.39 160	64.82 164

ACMH+	53.71 12	59.26 83	60.28 102	58.06 72	64.17 72	68.46 67	67.51 67	50.93 52	52.46 93	35.83 133	40.83 176	45.12 169	52.32 88	69.88 81	69.00 77	77.59 75	76.21 89

test1111		55.24 125	59.98 110	49.71 129	59.80 98	64.10 114	56.48 139	49.34 62	52.27 94	21.56 186	44.49 152	51.96 123	35.93 170	70.59 73	69.07 75	75.13 110	67.40 142

PVSNet_Blended_VisFu		63.65 62	66.92 63	59.83 62	60.03 95	73.44 49	66.33 77	48.95 65	52.20 95	50.81 63	56.07 77	60.25 89	53.56 74	73.23 56	70.01 67	79.30 59	83.24 45

Anonymous20231211		57.71 103	60.79 95	54.13 100	61.68 83	65.81 97	60.81 113	43.70 114	51.97 96	39.67 116	34.82 191	63.59 72	43.31 134	68.55 94	66.63 108	75.59 103	74.13 103

pmmvs4		54.66 132	56.07 143	53.00 110	54.63 137	57.08 167	60.43 115	44.10 102	51.69 97	40.55 111	46.55 131	44.79 174	45.95 121	62.54 156	63.66 145	72.36 146	66.20 153

MS-PatchMatch		58.19 100	60.20 105	55.85 91	65.17 65	64.16 113	64.82 93	41.48 142	50.95 98	42.17 102	45.38 145	56.42 104	48.08 110	68.30 96	66.70 105	73.39 127	69.46 132

IS_MVSNet		57.95 101	64.26 82	50.60 123	61.62 84	65.25 103	57.18 132	45.42 86	50.79 99	26.49 174	57.81 72	60.05 90	34.51 175	71.24 67	70.20 66	78.36 68	74.44 100

tpm cat1		53.30 138	53.41 160	53.17 109	58.16 105	59.15 150	63.73 101	38.27 169	50.73 100	46.98 75	45.57 143	44.00 181	49.20 101	55.90 194	54.02 193	62.65 185	64.50 167

Anonymous202405211			60.60 98		63.44 77	66.71 90	61.00 112	47.23 72	50.62 101		36.85 186	60.63 88	43.03 137	69.17 85	67.72 92	75.41 105	72.54 109

UGNet		57.03 106	65.25 75	47.44 157	46.54 186	66.73 87	56.30 140	43.28 126	50.06 102	32.99 140	62.57 55	63.26 74	33.31 180	68.25 97	67.58 94	72.20 148	78.29 70
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
EIA-MVS		61.53 76	63.79 84	58.89 67	63.82 76	67.61 77	65.35 90	42.15 138	49.98 103	45.66 84	57.47 74	56.62 103	56.59 57	70.91 71	69.15 73	79.78 53	74.80 98

Vis-MVSNet (Re-imp)		50.37 157	57.73 137	41.80 183	57.53 111	54.35 173	45.70 191	45.24 88	49.80 104	13.43 202	58.23 71	56.42 104	20.11 203	62.96 154	63.36 148	68.76 164	58.96 188

EPP-MVSNet		59.39 82	65.45 73	52.32 116	60.96 87	67.70 75	58.42 126	44.75 93	49.71 105	27.23 169	59.03 66	62.20 79	43.34 133	70.71 72	69.13 74	79.25 61	79.63 62

Effi-MVS+-dtu		60.34 79	62.32 88	58.03 74	64.31 68	67.44 80	65.99 81	42.26 135	49.55 106	42.00 103	48.92 114	59.79 91	56.27 59	68.07 104	67.03 98	77.35 78	75.45 95

FMVSNet2		55.04 130	59.95 111	49.31 132	52.42 153	61.44 127	57.03 133	44.08 103	49.55 106	30.40 153	46.89 125	58.84 94	38.22 154	67.07 124	66.21 115	73.69 123	69.65 125

UA-Net		58.50 91	64.68 80	51.30 121	66.97 55	67.13 84	53.68 161	45.65 84	49.51 108	31.58 148	62.91 51	68.47 59	35.85 171	68.20 100	67.28 97	74.03 119	69.24 134

UniMVSNet_NR-MVSNet		56.94 109	61.14 92	52.05 118	60.02 96	65.21 105	57.44 130	52.93 42	49.37 109	24.31 181	54.62 88	50.54 130	39.04 151	68.69 89	68.84 78	78.53 66	70.72 117

Baseline_NR-MVSNet		53.50 136	57.89 133	48.37 148	54.60 138	59.25 149	56.10 141	51.84 46	49.32 110	17.92 196	45.38 145	47.68 142	36.93 166	68.11 102	65.95 119	72.84 135	69.57 128

baseline1		54.48 133	58.69 125	49.57 130	60.63 90	58.29 159	55.70 147	44.95 91	49.20 111	29.62 157	54.77 85	54.75 111	35.29 172	67.15 122	64.08 141	71.21 155	62.58 176

GBi-Net		55.20 126	60.25 103	49.31 132	52.42 153	61.44 127	57.03 133	44.04 104	49.18 112	30.47 150	48.28 116	58.19 96	38.22 154	68.05 105	66.96 99	73.69 123	69.65 125

test1		55.20 126	60.25 103	49.31 132	52.42 153	61.44 127	57.03 133	44.04 104	49.18 112	30.47 150	48.28 116	58.19 96	38.22 154	68.05 105	66.96 99	73.69 123	69.65 125

FMVSNet3		54.78 131	59.58 116	49.17 135	52.37 156	61.31 131	56.72 138	44.04 104	49.18 112	30.47 150	48.28 116	58.19 96	38.09 157	65.48 144	65.20 131	73.31 130	69.45 133

TranMVSNet+NR-MVSNet		55.87 117	60.14 107	50.88 122	59.46 100	63.82 115	57.93 128	52.98 41	48.94 115	20.52 189	52.87 92	47.33 147	36.81 167	69.12 87	69.03 76	77.56 76	69.89 123

ET-MVSNet_ETH3D		58.38 95	61.57 90	54.67 97	42.15 200	65.26 101	65.70 84	43.82 109	48.84 116	42.34 100	59.76 64	47.76 141	56.68 56	67.02 125	68.60 82	77.33 79	73.73 107

Vis-MVSNet		58.48 92	65.70 72	50.06 128	53.40 147	67.20 82	60.24 116	43.32 125	48.83 117	30.23 154	62.38 56	61.61 83	40.35 147	71.03 68	69.77 68	72.82 136	79.11 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVSTER		57.19 105	61.11 93	52.62 114	50.82 170	58.79 152	61.55 106	37.86 171	48.81 118	41.31 106	57.43 75	52.10 121	48.60 106	68.19 101	66.75 104	75.56 104	75.68 94

TDRefinement		49.31 162	52.44 168	45.67 165	30.44 213	59.42 146	59.24 120	39.78 158	48.76 119	31.20 149	35.73 188	29.90 213	42.81 138	64.24 151	62.59 158	70.55 158	66.43 149

IterMVS-LS		58.30 97	61.39 91	54.71 96	59.92 97	58.40 156	59.42 118	43.64 115	48.71 120	40.25 114	57.53 73	58.55 95	52.15 90	65.42 146	65.34 127	72.85 134	75.77 91
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpmrst		48.08 173	49.88 185	45.98 161	52.71 151	48.11 193	53.62 162	33.70 192	48.70 121	39.74 115	48.96 113	46.23 159	40.29 148	50.14 208	49.28 204	55.80 199	57.71 191

UniMVSNet (Re)		55.15 129	60.39 101	49.03 138	55.31 132	64.59 109	55.77 146	50.63 54	48.66 122	20.95 187	51.47 101	50.40 131	34.41 177	67.81 109	67.89 87	77.11 83	71.88 111

DU-MVS		55.41 123	59.59 114	50.54 125	54.60 138	62.97 119	57.44 130	51.80 47	48.62 123	24.31 181	51.99 98	47.00 150	39.04 151	68.11 102	67.75 91	76.03 101	70.72 117

NR-MVSNet		55.35 124	59.46 119	50.56 124	61.33 85	62.97 119	57.91 129	51.80 47	48.62 123	20.59 188	51.99 98	44.73 175	34.10 178	68.58 92	68.64 80	77.66 72	70.67 121

SCA		50.99 154	53.22 164	48.40 147	51.07 166	56.78 168	50.25 170	39.05 159	48.31 125	41.38 105	49.54 108	46.70 155	46.00 120	58.31 177	56.28 177	62.65 185	56.60 193

thisisatest0530		56.68 111	59.68 112	53.19 108	52.97 149	60.96 135	59.41 119	40.51 150	48.26 126	41.06 109	52.67 93	46.30 157	49.78 97	67.66 113	67.83 88	75.39 106	74.07 105

EPMVS		44.66 188	47.86 192	40.92 186	47.97 181	44.70 205	47.58 182	33.27 194	48.11 127	29.58 158	49.65 107	44.38 179	34.65 174	51.71 203	47.90 206	52.49 207	48.57 208

PatchmatchNet		49.92 161	51.29 175	48.32 149	51.83 160	51.86 182	53.38 164	37.63 173	47.90 128	40.83 110	48.54 115	45.30 166	45.19 125	56.86 184	53.99 195	61.08 190	54.57 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tttt0517		56.53 113	59.59 114	52.95 111	52.66 152	60.99 134	59.21 121	40.51 150	47.89 129	40.40 112	52.50 96	46.04 161	49.78 97	67.75 111	67.83 88	75.15 109	74.17 102

v8		58.88 86	60.57 100	56.92 84	57.35 117	65.69 98	66.69 75	42.64 132	47.89 129	45.77 83	49.04 111	52.98 118	52.77 84	67.51 115	65.57 124	76.26 93	75.30 97

V42		56.97 108	60.14 107	53.28 106	48.16 179	62.78 122	66.30 78	37.93 170	47.44 131	42.68 98	48.19 119	52.59 120	51.90 91	67.46 116	65.94 120	72.72 138	76.55 85

baseline2		55.89 116	57.82 134	53.64 102	57.36 116	61.09 133	59.75 117	40.45 152	47.38 132	41.26 108	51.23 102	46.90 152	48.11 109	65.63 143	64.38 140	74.90 112	68.16 138

GG-mvs-BLEND		36.62 205	53.39 161	17.06 214	0.01 226	58.61 153	48.63 176	0.01 223	47.13 133	0.02 227	43.98 155	60.64 87	0.03 222	54.92 198	51.47 201	53.64 205	56.99 192

v10		59.17 85	60.60 98	57.50 80	57.95 107	66.73 87	67.09 72	44.11 101	46.85 134	45.42 85	48.18 120	51.07 126	53.63 73	67.84 108	66.59 110	76.79 84	76.92 79

tpm		48.82 168	51.27 176	45.96 162	54.10 143	47.35 195	56.05 142	30.23 201	46.70 135	43.21 95	52.54 95	47.55 145	37.28 164	54.11 199	50.50 202	54.90 202	60.12 185

CHOSEN 1792x2688		55.85 118	58.01 132	53.33 105	57.26 121	62.82 121	63.29 104	41.55 141	46.65 136	38.34 122	34.55 192	53.50 114	52.43 87	67.10 123	67.56 95	67.13 170	73.92 106

PatchMatch-RL		50.11 160	51.56 174	48.43 146	46.23 188	51.94 181	50.21 171	38.62 167	46.62 137	37.51 127	42.43 173	39.38 196	52.24 89	60.98 164	59.56 170	65.76 174	60.01 186

v2v482		58.69 89	60.12 109	57.03 83	57.16 124	66.05 95	67.17 69	43.52 119	46.33 138	45.19 87	49.46 110	51.02 127	52.51 86	67.30 118	66.03 118	76.61 86	74.62 99

PMMVS		49.20 166	54.28 156	43.28 177	34.13 208	45.70 203	48.98 175	26.09 210	46.31 139	34.92 137	55.22 82	53.47 115	47.48 114	59.43 169	59.04 172	68.05 167	60.77 181

IB-MVS	54.11 11	58.36 96	60.70 97	55.62 92	58.67 102	68.02 72	61.56 105	43.15 129	46.09 140	44.06 92	44.24 154	50.99 129	48.71 105	66.70 128	70.33 63	77.60 74	78.50 68
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
ACMH	52.42 13	58.24 98	59.56 118	56.70 87	66.34 60	69.59 62	66.71 74	49.12 64	46.08 141	28.90 161	42.67 171	41.20 188	52.60 85	71.39 63	70.28 64	76.51 88	75.72 92
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		54.08 134	58.68 126	48.71 142	50.90 169	61.35 130	56.73 137	43.94 108	45.91 142	29.32 160	42.72 170	56.26 107	37.70 161	68.05 105	66.96 99	73.69 123	69.50 129

Fast-Effi-MVS+-dtu		56.30 115	59.29 121	52.82 113	58.64 103	64.89 106	65.56 87	32.89 197	45.80 143	35.04 135	45.89 139	54.14 113	49.41 100	67.16 121	66.45 113	75.37 107	70.69 119

HyFIR lowres test		56.87 110	58.60 128	54.84 95	56.62 127	69.27 64	64.77 94	42.21 136	45.66 144	37.50 128	33.08 194	57.47 101	53.33 80	65.46 145	67.94 86	74.60 113	71.35 114

MDTV_nov1_ep13		50.32 158	52.43 169	47.86 155	49.87 174	54.70 171	58.10 127	34.29 187	45.59 145	37.71 126	47.44 123	47.42 146	41.86 141	58.07 180	55.21 186	65.34 177	58.56 189

v1144		58.88 86	60.16 106	57.39 81	58.03 106	67.26 81	67.14 70	44.46 97	45.17 146	44.33 91	47.81 121	49.92 134	53.20 83	67.77 110	66.62 109	77.15 81	76.58 83

IterMVS-SCA-FT		52.18 145	57.75 136	45.68 164	51.01 168	62.06 123	55.10 154	34.75 183	44.85 147	32.86 142	51.13 104	51.22 125	48.74 103	62.47 157	61.51 161	51.61 209	71.02 116

MIMVSNet		43.79 191	48.53 189	38.27 193	41.46 201	48.97 191	50.81 169	32.88 198	44.55 148	22.07 184	32.05 195	47.15 148	24.76 195	58.73 174	56.09 180	57.63 198	52.14 197

IterMVS		53.45 137	57.12 140	49.17 135	49.23 176	60.93 136	59.05 122	34.63 185	44.53 149	33.22 138	51.09 105	51.01 128	48.38 107	62.43 158	60.79 165	70.54 159	69.05 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		50.47 155	52.61 166	47.98 153	49.03 178	52.94 177	48.27 177	38.86 163	44.41 150	39.59 117	44.34 153	44.65 177	46.63 117	58.97 172	60.31 167	65.48 175	62.66 173

RPMNet		46.41 182	48.72 188	43.72 173	47.77 182	52.94 177	46.02 190	33.92 189	44.41 150	31.82 147	36.89 185	37.42 204	37.41 162	53.88 200	54.02 193	65.37 176	61.47 179

ADS-MVSNet		40.67 198	43.38 204	37.50 196	44.36 194	39.79 212	42.09 204	32.67 199	44.34 152	28.87 162	40.76 178	40.37 193	30.22 184	48.34 212	45.87 211	46.81 213	44.21 212

v148		55.58 122	57.61 138	53.20 107	54.59 140	61.86 124	61.18 109	38.70 166	44.30 153	42.25 101	47.53 122	50.24 133	48.73 104	65.15 147	62.61 157	73.79 121	71.61 113

v1192		58.51 90	59.66 113	57.17 82	57.82 108	67.72 74	66.21 79	44.83 92	44.15 154	43.49 94	46.68 126	47.94 138	53.55 75	67.39 117	66.51 111	77.13 82	77.20 77

v144192		58.23 99	59.40 120	56.87 85	57.56 109	66.89 85	65.70 84	45.01 90	44.06 155	42.88 96	46.61 128	48.09 137	53.49 78	66.94 126	65.90 121	76.61 86	77.29 75

dps		50.42 156	51.20 177	49.51 131	55.88 129	56.07 169	53.73 159	38.89 162	43.66 156	40.36 113	45.66 141	37.63 203	45.23 124	59.05 170	56.18 178	62.94 184	60.16 184

FC-MVSNet-test		39.65 202	48.35 190	29.49 207	44.43 193	39.28 213	30.23 216	40.44 153	43.59 157	3.12 223	53.00 91	42.03 184	10.02 219	55.09 196	54.77 188	48.66 211	50.71 201

v1921920		57.89 102	59.02 123	56.58 88	57.55 110	66.66 91	64.72 95	44.70 94	43.55 158	42.73 97	46.17 136	46.93 151	53.51 76	66.78 127	65.75 123	76.29 91	77.28 76

pmmvs-eth3d		51.33 151	52.25 170	50.26 127	50.82 170	54.65 172	56.03 143	43.45 124	43.51 159	37.20 130	39.20 180	39.04 198	42.28 139	61.85 161	62.78 154	71.78 151	64.72 165

TinyColmap		47.08 179	47.56 193	46.52 160	42.35 199	53.44 176	51.77 167	40.70 149	43.44 160	31.92 146	29.78 202	23.72 219	45.04 126	61.99 160	59.54 171	67.35 169	61.03 180

PatchT		48.08 173	51.03 178	44.64 170	42.96 197	50.12 187	40.36 206	35.09 181	43.17 161	39.59 117	42.00 174	39.96 195	46.63 117	58.97 172	60.31 167	63.21 182	62.66 173

CMPMVS	37.70 17	49.24 164	52.71 165	45.19 166	45.97 189	51.23 184	47.44 183	29.31 202	43.04 162	44.69 88	34.45 193	48.35 136	43.64 130	62.59 155	59.82 169	60.08 191	69.48 130
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
GA-MVS		55.67 120	58.33 129	52.58 115	55.23 135	63.09 118	61.08 110	40.15 156	42.95 163	37.02 131	52.61 94	47.68 142	47.51 113	65.92 139	65.35 126	74.49 115	70.68 120

thres200		52.39 143	55.37 149	48.90 139	57.39 115	60.18 140	55.60 148	43.73 112	42.93 164	27.41 167	43.35 163	45.09 170	36.61 168	66.36 131	63.92 144	72.66 141	65.78 158

thres400		52.38 144	55.51 146	48.74 141	57.49 113	60.10 142	55.45 150	43.54 118	42.90 165	26.72 173	43.34 164	45.03 173	36.61 168	66.20 136	64.53 138	72.66 141	66.43 149

thres100view900		52.04 147	54.81 153	48.80 140	57.31 118	59.33 147	55.30 152	42.92 131	42.85 166	27.81 165	43.00 168	45.06 171	36.99 165	64.74 149	63.51 146	72.47 144	65.21 162

tfpn200view9		52.53 141	55.51 146	49.06 137	57.31 118	60.24 139	55.42 151	43.77 110	42.85 166	27.81 165	43.00 168	45.06 171	37.32 163	66.38 130	64.54 137	72.71 139	66.54 148

v1240		57.55 104	58.63 127	56.29 89	57.30 120	66.48 92	63.77 100	44.56 96	42.77 168	42.48 99	45.64 142	46.28 158	53.46 79	66.32 133	65.80 122	76.16 95	77.13 78

thres600view7		51.91 150	55.14 150	48.14 150	57.43 114	60.18 140	54.60 156	43.73 112	42.61 169	25.20 177	43.10 167	44.47 178	35.19 173	66.36 131	63.28 149	72.66 141	66.01 156

UniMVSNet_ETH3D		52.62 140	55.98 144	48.70 143	51.04 167	60.71 137	56.87 136	46.74 76	42.52 170	26.96 171	42.50 172	45.95 162	37.87 158	66.22 135	65.15 134	72.74 137	68.78 137

CDS-MVSNet		52.42 142	57.06 141	47.02 159	53.92 145	58.30 158	55.50 149	46.47 77	42.52 170	29.38 159	49.50 109	52.85 119	28.49 190	66.70 128	66.89 102	68.34 165	62.63 175
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
WR-MVS		48.78 169	55.06 151	41.45 184	55.50 131	60.40 138	43.77 199	49.99 59	41.92 172	8.10 215	45.24 148	45.56 163	17.47 204	61.57 162	64.60 136	73.85 120	66.14 155

thisisatest0515		53.85 135	56.84 142	50.37 126	50.25 173	58.17 160	55.99 144	39.90 157	41.88 173	38.16 124	45.91 138	45.30 166	44.58 127	66.15 137	66.89 102	73.36 129	73.57 108

PEN-MVS		49.21 165	54.32 155	43.24 178	54.33 141	59.26 148	47.04 185	51.37 51	41.67 174	9.97 210	46.22 134	41.80 186	22.97 200	60.52 165	64.03 142	73.73 122	66.75 147

CP-MVSNet		48.37 170	53.53 159	42.34 180	51.35 163	58.01 162	46.56 186	50.54 55	41.62 175	10.61 206	46.53 132	40.68 192	23.18 198	58.71 175	61.83 159	71.81 150	67.36 145

PS-CasMVS		48.18 172	53.25 163	42.27 181	51.26 164	57.94 163	46.51 187	50.52 56	41.30 176	10.56 207	45.35 147	40.34 194	23.04 199	58.66 176	61.79 160	71.74 152	67.38 143

FPMVS		38.36 204	40.41 208	35.97 199	38.92 205	39.85 211	45.50 192	25.79 211	41.13 177	18.70 193	30.10 200	24.56 217	31.86 182	49.42 210	46.80 209	55.04 200	51.03 200

DTE-MVSNet		48.03 175	53.28 162	41.91 182	54.64 136	57.50 165	44.63 198	51.66 50	41.02 178	7.97 216	46.26 133	40.90 189	20.24 202	60.45 166	62.89 153	72.33 147	63.97 168

FMVSNet5		40.96 196	45.81 197	35.29 202	34.30 207	44.55 206	47.28 184	28.84 204	40.76 179	21.62 185	29.85 201	42.44 183	24.77 194	57.53 182	55.00 187	54.93 201	50.56 202

WR-MVS_H		47.65 176	53.67 158	40.63 187	51.45 161	59.74 145	44.71 197	49.37 61	40.69 180	7.61 217	46.04 137	44.34 180	17.32 205	57.79 181	61.18 162	73.30 131	65.86 157

PM-MVS		44.55 189	48.13 191	40.37 188	32.85 212	46.82 200	46.11 189	29.28 203	40.48 181	29.99 155	39.98 179	34.39 208	41.80 142	56.08 192	53.88 197	62.19 188	65.31 160

pm-mvs1		51.02 153	55.55 145	45.73 163	54.16 142	58.52 154	50.92 168	42.56 133	40.32 182	25.67 176	43.66 159	50.34 132	30.06 185	65.85 140	63.97 143	70.99 157	66.21 152

pmnet_mix02		40.48 200	43.80 202	36.61 198	45.79 190	40.45 210	42.12 203	33.18 195	40.30 183	24.11 183	38.76 182	37.11 205	24.30 196	52.97 201	46.66 210	50.17 210	50.33 203

v7n		55.67 120	57.46 139	53.59 104	56.06 128	65.29 100	61.06 111	43.26 127	40.17 184	37.99 125	40.79 177	45.27 168	47.09 115	67.67 112	66.21 115	76.08 97	76.82 80

CVMVSNet		46.38 184	52.01 172	39.81 189	42.40 198	50.26 186	46.15 188	37.68 172	40.03 185	15.09 199	46.56 130	47.56 144	33.72 179	56.50 189	55.65 182	63.80 181	67.53 140

MDTV_nov1_ep13_2view		47.62 177	49.72 186	45.18 167	48.05 180	53.70 175	54.90 155	33.80 191	39.90 186	29.79 156	38.85 181	41.89 185	39.17 150	58.99 171	55.55 183	65.34 177	59.17 187

TransMVSNet (Re)		51.92 149	55.38 148	47.88 154	60.95 88	59.90 143	53.95 158	45.14 89	39.47 187	24.85 178	43.87 157	46.51 156	29.15 187	67.55 114	65.23 130	73.26 132	65.16 163

test-LLR		49.28 163	50.29 181	48.10 151	55.26 133	47.16 196	49.52 172	43.48 122	39.22 188	31.98 144	43.65 160	47.93 139	41.29 144	56.80 185	55.36 184	67.08 171	61.94 177

TESTMET0.1,1		46.09 185	50.29 181	41.18 185	36.91 206	47.16 196	49.52 172	20.32 215	39.22 188	31.98 144	43.65 160	47.93 139	41.29 144	56.80 185	55.36 184	67.08 171	61.94 177

TAMVS		44.02 190	49.18 187	37.99 195	47.03 185	45.97 202	45.04 194	28.47 205	39.11 190	20.23 190	43.22 166	48.52 135	28.49 190	58.15 179	57.95 176	58.71 193	51.36 199

test-mter		45.30 186	50.37 180	39.38 190	33.65 210	46.99 198	47.59 181	18.59 216	38.75 191	28.00 164	43.28 165	46.82 154	41.50 143	57.28 183	55.78 181	66.93 173	63.70 170

Anonymous20231206		42.28 193	45.89 196	38.07 194	51.96 158	48.98 190	43.66 200	38.81 165	38.74 192	14.32 201	26.74 207	40.90 189	20.94 201	56.64 188	54.67 190	58.71 193	54.59 195

EG-PatchMatch MVS		56.98 107	58.24 131	55.50 93	64.66 67	68.62 66	61.48 107	43.63 116	38.44 193	41.44 104	38.05 183	46.18 160	43.95 129	71.71 61	70.61 61	77.87 70	74.08 104

ambc			45.54 199		50.66 172	52.63 180	40.99 205		38.36 194	24.67 179	22.62 212	13.94 222	29.14 188	65.71 142	58.06 175	58.60 195	67.43 141

tfpnnormal		50.16 159	52.19 171	47.78 156	56.86 125	58.37 157	54.15 157	44.01 107	38.35 195	25.94 175	36.10 187	37.89 201	34.50 176	65.93 138	63.42 147	71.26 154	65.28 161

pmmvs5		47.07 180	51.02 179	42.46 179	45.18 191	51.47 183	48.23 179	33.09 196	38.17 196	28.62 163	46.60 129	43.48 182	30.74 183	58.28 178	58.63 173	68.92 163	60.48 182

test0.0.03 1		43.15 192	46.95 194	38.72 192	55.26 133	50.56 185	42.48 202	43.48 122	38.16 197	15.11 198	35.07 190	44.69 176	16.47 206	55.95 193	54.34 192	59.54 192	49.87 206

CHOSEN 280x420		40.80 197	45.05 200	35.84 201	32.95 211	29.57 216	44.98 195	23.71 213	37.54 198	18.42 194	31.36 198	47.07 149	46.41 119	56.71 187	54.65 191	48.55 212	58.47 190

N_pmnet		32.67 210	36.85 211	27.79 210	40.55 202	32.13 215	35.80 211	26.79 208	37.24 199	9.10 212	32.02 196	30.94 212	16.30 207	47.22 213	41.21 212	38.21 216	37.21 213

anonymousdsp		52.84 139	57.78 135	47.06 158	40.24 203	58.95 151	53.70 160	33.54 193	36.51 200	32.69 143	43.88 156	45.40 164	47.97 112	67.17 120	70.28 64	74.22 117	82.29 49

MVS-HIRNet		42.24 194	41.15 207	43.51 174	44.06 196	40.74 208	35.77 212	35.35 180	35.38 201	38.34 122	25.63 209	38.55 200	43.48 132	50.77 205	47.03 208	64.07 179	49.98 204

pmmvs6		48.35 171	51.64 173	44.51 171	51.92 159	57.94 163	49.44 174	42.17 137	34.45 202	24.62 180	28.87 205	46.90 152	29.07 189	64.60 150	63.08 150	69.83 161	65.68 159

SixPastTwentyTwo		47.55 178	50.25 183	44.41 172	47.30 184	54.31 174	47.81 180	40.36 155	33.76 203	19.93 191	43.75 158	32.77 211	42.07 140	59.82 168	60.94 164	68.98 162	66.37 151

EU-MVSNet		40.63 199	45.65 198	34.78 203	39.11 204	46.94 199	40.02 207	34.03 188	33.50 204	10.37 208	35.57 189	37.80 202	23.65 197	51.90 202	50.21 203	61.49 189	63.62 171

MDA-MVSNet-bldmvs		41.36 195	43.15 205	39.27 191	28.74 215	52.68 179	44.95 196	40.84 147	32.89 205	18.13 195	31.61 197	22.09 220	38.97 153	50.45 207	56.11 179	64.01 180	56.23 194

test20.03		40.38 201	44.20 201	35.92 200	53.73 146	49.05 189	38.54 208	43.49 121	32.55 206	9.54 211	27.88 206	39.12 197	12.24 211	56.28 190	54.69 189	57.96 197	49.83 207

MIMVSNet1		35.51 206	41.41 206	28.63 208	27.53 217	43.36 207	38.09 209	33.82 190	32.01 207	6.77 218	21.63 213	35.43 206	11.97 213	55.05 197	53.99 195	53.59 206	48.36 209

new-patchmatchnet		33.24 209	37.20 210	28.62 209	44.32 195	38.26 214	29.68 217	36.05 178	31.97 208	6.33 219	26.59 208	27.33 214	11.12 218	50.08 209	41.05 213	44.23 214	45.15 211

gg-mvs-nofinetune		49.07 167	52.56 167	45.00 168	61.99 80	59.78 144	53.55 163	41.63 140	31.62 209	12.08 204	29.56 203	53.28 117	29.57 186	66.27 134	64.49 139	71.19 156	62.92 172

testgi		38.71 203	43.64 203	32.95 204	52.30 157	48.63 192	35.59 213	35.05 182	31.58 210	9.03 214	30.29 199	40.75 191	11.19 217	55.30 195	53.47 198	54.53 204	45.48 210

Gipuma		25.87 211	26.91 214	24.66 211	28.98 214	20.17 219	20.46 218	34.62 186	29.55 211	9.10 212	4.91 222	5.31 226	15.76 208	49.37 211	49.10 205	39.03 215	29.95 215
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new_pmnet		23.19 212	28.17 213	17.37 212	17.03 221	24.92 217	19.66 219	16.16 219	27.05 212	4.42 220	20.77 214	19.20 221	12.19 212	37.71 214	36.38 214	34.77 217	31.17 214

PMVS	27.84 18	33.81 208	35.28 212	32.09 205	34.13 208	24.81 218	32.51 215	26.48 209	26.41 213	19.37 192	23.76 210	24.02 218	25.18 193	50.78 204	47.24 207	54.89 203	49.95 205
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		35.10 207	38.47 209	31.17 206	26.37 219	40.47 209	34.51 214	18.09 217	24.75 214	16.88 197	23.05 211	26.69 215	32.69 181	50.73 206	51.60 200	58.46 196	51.98 198

gm-plane-assit		44.74 187	45.95 195	43.33 176	60.88 89	46.79 201	36.97 210	32.24 200	24.15 215	11.79 205	29.26 204	32.97 210	46.64 116	65.09 148	62.95 152	71.45 153	60.42 183

test_method		12.44 217	14.66 217	9.85 218	1.30 225	3.32 225	13.00 221	3.21 220	22.42 216	10.22 209	14.13 215	25.64 216	11.43 216	19.75 217	11.61 220	19.96 220	5.79 221

LTVRE_ROB	44.17 16	47.06 181	50.15 184	43.44 175	51.39 162	58.42 155	42.90 201	43.51 120	22.27 217	14.85 200	41.94 175	34.57 207	45.43 122	62.28 159	62.77 155	62.56 187	68.83 136
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
tmp_tt				5.40 219	3.97 224	2.35 226	3.26 226	0.44 222	17.56 218	12.09 203	11.48 218	7.14 224	1.98 220	15.68 219	15.49 219	10.69 223

PMMVS2		15.84 213	19.68 215	11.35 216	15.74 222	16.95 220	13.31 220	17.64 218	16.08 219	0.36 226	13.12 216	11.47 223	1.69 221	28.82 215	27.24 216	19.38 221	24.09 217

EMVS		14.49 215	12.45 219	16.87 215	27.02 218	12.56 223	8.13 222	27.19 207	15.05 220	3.14 222	6.69 220	2.67 228	15.08 210	14.60 220	18.05 218	20.67 219	17.56 220

E-PMN		15.09 214	13.19 218	17.30 213	27.80 216	12.62 222	7.81 223	27.54 206	14.62 221	3.19 221	6.89 219	2.52 229	15.09 209	15.93 218	20.22 217	22.38 218	19.53 218

DeepMVS_CX						6.95 224	5.98 225	2.25 221	11.73 222	2.07 225	11.85 217	5.43 225	11.75 215	11.40 221		8.10 224	18.38 219

MVE	12.28 19	13.53 216	15.72 216	10.96 217	7.39 223	15.71 221	6.05 224	23.73 212	10.29 223	3.01 224	5.77 221	3.41 227	11.91 214	20.11 216	29.79 215	13.67 222	24.98 216
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 218	0.02 220	0.00 220	0.00 227	0.00 227	0.01 228	0.00 224	0.01 224	0.00 228	0.03 224	0.00 230	0.01 223	0.01 222	0.01 221	0.00 225	0.06 223

test123		0.01 218	0.02 220	0.00 220	0.00 227	0.00 227	0.00 229	0.00 224	0.01 224	0.00 228	0.04 223	0.00 230	0.01 223	0.00 223	0.01 221	0.00 225	0.07 222

uanet_test		0.00 220	0.00 222	0.00 220	0.00 227	0.00 227	0.00 229	0.00 224	0.00 226	0.00 228	0.00 225	0.00 230	0.00 225	0.00 223	0.00 223	0.00 225	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 220	0.00 227	0.00 227	0.00 229	0.00 224	0.00 226	0.00 228	0.00 225	0.00 230	0.00 225	0.00 223	0.00 223	0.00 225	0.00 224

sosnet		0.00 220	0.00 222	0.00 220	0.00 227	0.00 227	0.00 229	0.00 224	0.00 226	0.00 228	0.00 225	0.00 230	0.00 225	0.00 223	0.00 223	0.00 225	0.00 224

RE-MVS-def										33.01 139

9.14												81.81 13

SR-MVS					71.46 35			54.67 30				81.54 14

our_test_3					51.15 165	57.31 166	55.12 153

MTAPA										65.14 4		80.20 20

MTMP										62.63 18		78.04 27

Patchmatch-RL test							1.04 227

XVS					70.49 39	76.96 27	74.36 46			54.48 50		74.47 39				82.24 25

X-MVStestdata					70.49 39	76.96 27	74.36 46			54.48 50		74.47 39				82.24 25

mPP-MVS					71.67 34							74.36 42

Patchmtry						47.61 194	48.27 177	38.86 163		39.59 117

High-resolution multi-view benchmark