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.

SED-MVS		78.97 1	84.56 1	72.45 1	81.70 3	86.20 3	77.82 4	59.97 6	88.89 1	65.96 1	86.00 6	84.02 1	70.03 1	76.19 4	76.17 5	79.22 19	94.46 1

DVP-MVS		77.54 2	84.41 2	69.54 6	79.93 4	86.08 4	77.20 8	60.31 4	88.62 2	62.54 2	86.67 3	83.77 2	58.04 32	75.84 6	75.69 7	79.21 20	94.17 2
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
SF-MVS		76.41 3	80.45 5	71.69 2	82.90 1	86.54 1	82.08 1	64.58 1	81.67 10	59.82 4	86.26 4	77.90 7	61.11 14	71.81 26	70.75 33	79.63 12	88.22 25

MSP-MVS		76.38 4	82.99 3	68.68 7	71.93 18	78.65 23	77.61 5	55.44 18	88.04 3	60.25 3	92.24 1	77.08 9	69.84 2	75.48 7	75.69 7	76.99 54	93.75 3
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
DPE-MVS		75.74 5	82.82 4	67.49 11	77.07 7	82.01 7	77.05 9	57.70 11	86.55 5	55.44 15	90.50 2	82.52 3	60.33 19	72.99 14	72.98 15	77.33 47	92.19 6
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		74.63 6	78.53 10	70.07 4	76.10 9	82.56 6	79.30 3	59.89 7	80.49 13	57.75 10	66.98 25	76.16 12	65.95 3	79.35 1	78.47 1	81.45 5	85.71 45

APDe-MVS		74.59 7	80.23 6	68.01 10	76.51 8	80.20 15	77.39 6	58.18 9	85.31 6	56.84 12	84.89 7	76.08 13	60.66 17	71.85 25	71.76 20	78.47 29	91.49 9

MCST-MVS		74.06 8	77.71 13	69.79 5	78.95 5	81.99 8	76.33 10	62.16 3	75.89 21	52.96 25	64.37 31	73.30 21	65.66 4	77.49 2	77.43 3	82.67 1	93.51 4

CNVR-MVS		73.87 9	78.60 9	68.35 9	73.32 13	81.97 9	76.19 11	59.29 8	80.12 14	56.70 13	67.09 24	76.48 10	64.26 6	75.88 5	75.75 6	80.32 7	92.93 5

SMA-MVS		73.31 10	79.53 7	66.05 13	71.25 20	80.13 16	74.99 12	56.09 14	84.14 7	54.48 18	73.74 16	80.23 4	61.43 11	74.96 8	74.09 11	78.08 36	89.42 14
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
xxxxxxxxxxxxxcwj		73.17 11	74.44 20	71.69 2	82.90 1	86.54 1	82.08 1	64.58 1	81.67 10	59.82 4	86.26 4	35.82 144	61.11 14	71.81 26	70.75 33	79.63 12	88.22 25

CSCG		72.98 12	76.86 15	68.46 8	78.23 6	81.74 10	77.26 7	60.00 5	75.61 24	59.06 6	62.72 33	77.42 8	56.63 45	74.24 10	77.18 4	79.56 14	89.13 18

HPM-MVS++		72.44 13	78.73 8	65.11 14	71.88 19	77.31 32	71.98 20	55.67 16	83.11 9	53.59 22	75.90 12	78.49 6	61.00 16	73.99 11	73.31 14	76.55 57	88.97 19

APD-MVS		71.86 14	77.91 12	64.80 16	70.39 25	75.69 42	74.02 14	56.14 13	83.59 8	52.92 26	84.67 8	73.46 20	59.30 26	69.47 41	69.66 41	76.02 63	88.84 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		71.50 15	77.27 14	64.77 17	69.64 27	79.26 17	73.53 15	54.73 24	79.32 16	54.23 19	74.81 13	74.61 17	59.40 25	73.00 13	72.17 18	77.10 53	87.72 30

NCCC		71.36 16	75.44 17	66.60 12	72.46 16	79.18 19	74.16 13	57.83 10	76.93 19	54.19 20	63.47 32	71.08 26	61.30 13	73.56 12	73.70 12	79.69 11	90.19 11

train_agg		70.74 17	76.53 16	63.98 19	70.33 26	75.16 46	72.33 19	55.78 15	75.74 22	50.41 34	80.08 11	73.15 22	57.75 37	71.96 24	70.94 30	77.25 51	88.69 22

TSAR-MVS + MP.		70.28 18	75.09 18	64.66 18	69.34 29	64.61 126	72.60 18	56.29 12	80.73 12	58.36 8	84.56 9	75.22 15	55.37 51	69.11 48	69.45 43	75.97 65	81.97 75
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DeepPCF-MVS	62.48 1	70.07 19	78.36 11	60.39 43	62.38 59	76.96 35	65.54 56	52.23 33	87.46 4	49.07 35	74.05 15	76.19 11	59.01 28	72.79 18	71.61 22	74.13 106	89.49 13

SteuartSystems-ACMMP		69.78 20	74.76 19	63.98 19	73.45 12	78.56 24	73.13 17	55.24 21	70.68 34	48.93 37	70.43 20	69.10 28	54.00 56	72.78 20	72.98 15	79.14 21	88.74 21
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		68.75 21	72.84 22	63.98 19	68.87 33	75.09 47	71.87 21	51.22 38	73.50 28	58.17 9	68.05 23	68.67 29	57.79 36	70.49 34	69.23 45	75.98 64	84.84 53

SD-MVS		68.30 22	72.58 24	63.31 25	69.24 30	67.85 99	70.81 26	53.65 29	79.64 15	58.52 7	74.31 14	75.37 14	53.52 62	65.63 72	63.56 103	74.13 106	81.73 79
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
zzz-MVS		67.78 23	72.46 25	62.33 30	66.09 39	74.21 52	70.05 28	51.54 36	77.27 17	54.61 17	60.30 41	71.51 25	56.73 43	69.19 46	68.63 54	74.96 86	86.11 42

DELS-MVS		67.36 24	70.34 38	63.89 22	69.12 31	81.55 11	70.82 25	55.02 22	53.38 74	48.83 38	56.45 47	59.35 55	60.05 23	74.93 9	74.78 9	79.51 15	91.95 7
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
MP-MVS		67.34 25	73.08 21	60.64 40	66.20 38	76.62 37	69.22 32	50.92 40	70.07 35	48.81 39	69.66 21	70.12 27	53.68 59	68.41 53	69.13 47	74.98 85	87.53 32
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS	60.65 2	67.33 26	71.52 32	62.44 28	59.79 80	74.84 49	68.89 33	55.56 17	73.91 27	53.50 23	55.00 52	65.63 34	60.08 22	71.99 23	71.33 26	76.85 55	87.94 29
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP-MVS		67.22 27	72.08 27	61.56 35	66.76 36	73.58 59	71.41 22	52.98 31	69.92 37	43.85 58	70.58 19	58.75 57	56.76 42	72.90 16	71.88 19	77.57 43	86.94 36

CANet		67.21 28	71.83 29	61.83 31	64.51 45	79.25 18	66.72 48	48.73 56	68.49 42	50.63 33	61.40 37	66.47 32	61.44 10	69.31 45	69.90 37	78.94 24	88.00 27

CDPH-MVS		67.03 29	71.64 30	61.65 34	69.10 32	76.84 36	71.35 24	55.42 19	67.02 45	42.83 63	65.27 29	64.60 38	53.16 65	69.70 40	71.40 24	78.02 38	86.67 37

MAR-MVS		66.85 30	69.81 39	63.39 24	73.56 11	80.51 14	69.87 29	51.51 37	67.78 44	46.44 44	51.09 65	61.60 50	60.38 18	72.67 21	73.61 13	78.59 26	81.44 83
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
DeepC-MVS_fast	60.18 3	66.84 31	70.69 36	62.36 29	62.76 53	73.21 62	67.96 36	52.31 32	72.26 31	51.03 28	56.50 46	64.26 39	63.37 7	71.64 28	70.85 31	76.70 56	86.10 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		66.77 32	72.21 26	60.44 42	61.23 67	70.00 82	64.26 61	47.79 68	72.98 29	56.32 14	71.35 18	72.33 23	55.68 50	65.49 73	66.66 68	77.35 45	86.62 38

MVS_0304		66.31 33	71.61 31	60.14 44	62.59 57	78.98 21	67.13 44	45.75 92	64.35 50	45.23 51	60.69 39	67.67 31	61.73 9	71.09 30	71.03 28	78.41 32	87.44 33

ACMMPR		66.20 34	71.51 33	60.00 46	65.34 43	74.04 54	69.39 31	50.92 40	71.97 32	46.04 46	66.79 26	65.68 33	53.07 66	68.93 50	69.12 48	75.21 79	84.05 59

3Dnovator	58.39 4	65.97 35	66.85 51	64.94 15	73.72 10	79.03 20	67.73 39	54.25 25	61.52 53	52.79 27	42.27 91	60.73 53	62.01 8	71.29 29	71.75 21	79.12 22	81.34 86

TSAR-MVS + ACMM		65.95 36	72.83 23	57.93 55	69.35 28	65.85 118	73.36 16	39.84 145	76.00 20	48.69 40	82.54 10	75.03 16	49.38 93	65.33 75	63.42 105	66.94 165	81.67 80

canonicalmvs		65.55 37	70.75 35	59.49 49	62.11 61	78.26 27	66.52 49	43.82 113	71.54 33	47.84 42	61.30 38	61.68 48	58.48 31	67.56 60	69.67 40	78.16 35	85.25 50

QAPM		65.47 38	67.82 44	62.72 27	72.56 14	81.17 13	67.43 42	55.38 20	56.07 66	43.29 61	43.60 86	65.38 36	59.10 27	72.20 22	70.76 32	78.56 27	85.59 48

PGM-MVS		65.35 39	70.43 37	59.43 50	65.78 41	73.75 56	69.41 30	48.18 64	68.80 41	45.37 50	65.88 28	64.04 40	52.68 72	68.94 49	68.68 53	75.18 80	82.93 66

PHI-MVS		65.17 40	72.07 28	57.11 65	63.02 52	77.35 31	67.04 45	48.14 66	68.03 43	37.56 90	66.00 27	65.39 35	53.19 64	70.68 31	70.57 36	73.72 113	86.46 41

CLD-MVS		64.69 41	67.25 46	61.69 33	68.22 35	78.33 25	63.09 64	47.59 71	69.64 38	53.98 21	54.87 53	53.94 71	57.87 33	72.79 18	71.34 25	79.40 17	69.87 153
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		64.66 42	67.11 49	61.80 32	71.04 21	77.91 28	62.75 67	54.78 23	51.43 77	47.54 43	53.77 56	54.85 68	56.84 40	70.59 32	71.50 23	77.86 39	89.70 12

EPNet		64.39 43	70.93 34	56.77 67	60.58 74	75.77 39	59.28 88	50.58 44	69.93 36	40.73 79	68.59 22	61.60 50	53.72 57	68.65 51	68.07 56	75.75 70	83.87 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		64.37 44	69.48 40	58.39 52	62.21 60	71.81 74	67.27 43	49.51 50	69.40 40	45.76 48	60.41 40	64.96 37	51.84 74	67.33 64	67.57 62	73.78 112	84.89 51

casdiffmvs		63.87 45	67.08 50	60.12 45	60.90 70	78.29 26	67.91 37	48.01 67	55.89 68	44.97 52	50.45 67	56.94 62	59.54 24	70.17 37	69.81 38	79.41 16	87.99 28

MVS_Test		63.75 46	67.24 47	59.68 48	60.01 76	76.99 34	68.13 35	45.17 95	57.45 61	43.74 59	53.07 59	56.16 66	61.33 12	70.27 35	71.11 27	79.72 10	85.63 47

X-MVS		63.53 47	68.62 41	57.60 59	64.77 44	73.06 63	65.82 54	50.53 45	65.77 47	42.02 71	58.20 43	63.42 43	47.83 105	68.25 57	68.50 55	74.61 96	83.16 65

ACMMP		63.27 48	67.85 43	57.93 55	62.64 56	72.30 71	68.23 34	48.77 55	66.50 46	43.05 62	62.07 34	57.84 61	49.98 85	66.58 68	66.46 73	74.93 87	83.17 63
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
ETV-MVS		62.88 49	68.18 42	56.70 68	58.47 88	74.89 48	60.26 81	43.96 110	58.27 60	42.37 69	61.47 36	56.56 63	57.80 34	68.00 58	68.74 51	77.34 46	89.33 17

CS-MVS		62.84 50	67.51 45	57.38 62	60.49 75	75.48 45	62.36 69	43.72 115	53.40 73	41.28 76	57.32 45	58.61 59	57.80 34	69.85 39	69.49 42	78.64 25	88.45 23

AdaColmap		62.79 51	62.63 66	62.98 26	70.82 22	72.90 66	67.84 38	54.09 27	65.14 48	50.71 31	41.78 93	47.64 100	60.17 21	67.41 63	66.83 66	74.28 101	76.69 110

3Dnovator+	55.76 7	62.70 52	65.10 58	59.90 47	65.89 40	72.15 72	62.94 66	49.82 49	62.77 52	49.06 36	43.62 85	61.47 52	58.60 30	68.51 52	66.75 67	73.08 127	80.40 95

OpenMVS	55.62 8	62.57 53	63.76 63	61.19 37	72.13 17	78.84 22	64.42 59	50.51 46	56.44 63	45.67 49	36.88 120	56.51 64	56.66 44	68.28 56	68.96 49	77.73 41	80.44 94

PVSNet_BlendedMVS		62.53 54	66.37 53	58.05 53	58.17 89	75.70 40	61.30 74	48.67 59	58.67 57	50.93 29	55.43 50	49.39 89	53.01 67	69.46 42	66.55 70	76.24 61	89.39 15

PVSNet_Blended		62.53 54	66.37 53	58.05 53	58.17 89	75.70 40	61.30 74	48.67 59	58.67 57	50.93 29	55.43 50	49.39 89	53.01 67	69.46 42	66.55 70	76.24 61	89.39 15

MVSTER		62.51 56	67.22 48	57.02 66	55.05 110	69.23 91	63.02 65	46.88 81	61.11 55	43.95 57	59.20 42	58.86 56	56.80 41	69.13 47	70.98 29	76.41 59	82.04 72

CHOSEN 1792x2688		62.48 57	64.06 62	60.64 40	72.50 15	84.18 5	62.43 68	53.77 28	47.90 90	39.85 83	25.15 182	44.76 113	53.72 57	77.29 3	77.61 2	81.60 4	91.53 8

CostFormer		62.45 58	65.68 56	58.67 51	63.29 49	77.65 29	67.62 40	38.42 154	54.04 71	46.00 47	48.27 75	57.89 60	56.97 39	67.03 66	67.79 61	79.74 9	87.09 35

PCF-MVS	55.99 6	62.31 59	66.60 52	57.32 63	59.12 87	73.68 58	67.53 41	48.71 57	61.35 54	42.83 63	51.33 64	63.48 42	53.48 63	65.64 71	64.87 86	72.22 132	85.83 44
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		62.30 60	66.05 55	57.92 57	57.08 94	75.60 44	66.90 46	47.06 79	55.45 70	43.37 60	53.45 58	55.60 67	57.21 38	66.57 69	68.00 58	75.89 68	87.70 31

DI_MVS_plusplus_trai		61.86 61	65.26 57	57.90 58	57.93 92	74.51 51	66.30 51	46.49 87	49.96 81	41.62 74	42.69 89	61.77 47	58.74 29	70.25 36	69.32 44	76.31 60	88.30 24

MSLP-MVS++		61.81 62	62.19 71	61.37 36	68.33 34	63.08 140	70.75 27	38.89 151	63.96 51	57.51 11	48.59 73	61.66 49	53.67 60	62.04 113	59.92 148	79.03 23	76.08 113

OPM-MVS		61.59 63	62.30 70	60.76 39	66.53 37	73.35 61	71.41 22	54.18 26	40.82 118	41.57 75	45.70 81	54.84 69	54.43 55	69.92 38	69.19 46	76.45 58	82.25 69

MS-PatchMatch		61.41 64	61.88 74	60.85 38	70.57 24	75.98 38	66.29 52	46.91 80	50.56 79	48.28 41	36.30 123	51.64 75	50.95 80	72.89 17	70.65 35	82.13 3	75.17 119

EIA-MVS		60.56 65	64.29 61	56.20 74	59.14 86	72.68 68	59.55 86	43.56 118	51.78 76	41.01 78	55.47 49	51.93 74	55.87 47	65.01 78	66.57 69	78.06 37	86.60 40

ACMP	56.21 5	59.78 66	61.81 76	57.41 61	61.15 68	68.88 93	65.98 53	48.85 54	58.56 59	44.19 55	48.89 71	46.31 106	48.56 98	63.61 95	64.49 95	75.75 70	81.91 76
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		59.69 67	62.59 67	56.31 72	61.94 62	68.15 98	66.90 46	48.15 65	59.75 56	38.47 86	50.38 68	48.34 97	46.87 110	65.39 74	64.93 85	75.51 75	81.21 88

Effi-MVS+		59.63 68	61.78 77	57.12 64	61.56 64	71.63 75	63.61 62	47.59 71	47.18 91	37.79 87	45.29 82	49.93 85	56.27 46	67.45 61	67.06 64	75.91 66	83.93 60

CPTT-MVS		59.54 69	64.47 60	53.79 84	54.99 112	67.63 102	65.48 57	44.59 102	64.81 49	37.74 88	51.55 62	59.90 54	49.77 89	61.83 115	61.26 133	70.18 146	84.31 58

baseline2		59.20 70	61.72 78	56.27 73	59.61 82	74.12 53	58.65 91	49.42 51	48.10 88	40.12 82	49.10 70	44.15 115	51.24 77	66.65 67	67.88 60	78.56 27	82.06 71

GeoE		58.97 71	60.94 79	56.67 69	61.27 66	72.71 67	61.35 73	45.69 93	49.19 85	41.22 77	39.55 107	49.58 88	52.79 71	64.79 80	65.89 75	77.73 41	84.87 52

baseline		58.65 72	61.99 72	54.75 79	54.70 114	71.85 73	60.20 82	43.91 111	55.99 67	40.13 81	53.50 57	50.91 81	55.76 48	61.29 123	61.73 125	73.83 110	78.68 103

PVSNet_Blended_VisFu		58.56 73	62.33 69	54.16 81	56.90 95	73.92 55	57.72 94	46.16 90	44.23 98	42.73 66	46.26 78	51.06 80	46.28 113	67.99 59	65.38 80	75.18 80	87.44 33

ACMM	53.73 9	57.91 74	58.27 93	57.49 60	63.10 50	66.45 112	65.65 55	49.02 53	53.69 72	42.67 67	36.41 122	46.07 109	50.38 83	64.74 82	64.63 91	74.14 105	75.91 114
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		57.87 75	63.63 64	51.15 98	52.18 120	70.20 81	58.14 93	37.32 161	56.49 62	31.06 121	57.38 44	50.05 83	53.67 60	64.98 79	65.04 84	74.57 97	81.29 87

ET-MVSNet_ETH3D		57.84 76	61.91 73	53.09 86	32.91 201	74.53 50	63.51 63	46.80 83	46.52 93	36.14 95	56.00 48	46.20 107	64.41 5	60.75 131	66.99 65	74.79 88	82.35 67

tpm cat1		57.41 77	58.26 94	56.42 71	60.80 72	72.56 69	64.35 60	38.43 153	49.18 86	46.36 45	36.69 121	43.50 118	54.47 53	61.39 121	62.64 113	74.11 108	81.81 77

IB-MVS	53.15 10	57.33 78	59.02 85	55.37 76	60.83 71	77.11 33	54.51 118	50.10 48	43.22 102	42.82 65	40.50 98	37.61 136	44.67 125	59.27 145	69.81 38	79.29 18	85.59 48
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
tpmrst		57.23 79	59.08 84	55.06 77	59.91 78	70.65 79	60.71 77	35.38 172	47.91 89	42.58 68	39.78 102	45.45 111	54.44 54	62.19 110	62.82 110	77.37 44	84.73 54

baseline1		57.21 80	60.53 81	53.33 85	62.50 58	69.86 84	57.33 98	50.59 43	43.39 101	30.00 127	48.60 72	51.09 79	42.36 137	69.38 44	68.03 57	77.20 52	73.39 126

HyFIR lowres test		57.12 81	59.11 83	54.80 78	61.55 65	77.55 30	59.02 89	45.00 97	41.84 115	33.93 107	22.44 189	49.16 92	51.02 79	68.39 54	68.71 52	78.26 34	85.70 46

MVS_111021_LR		57.06 82	60.60 80	52.93 87	56.25 99	65.14 124	55.16 116	41.21 137	52.32 75	44.89 53	53.92 55	49.27 91	52.16 73	61.46 119	60.54 141	67.92 158	81.53 82

DCV-MVSNet		56.80 83	58.96 86	54.28 80	59.96 77	66.74 110	60.37 80	44.87 99	41.01 117	36.81 93	47.57 76	47.87 99	48.23 101	64.41 85	65.17 82	75.45 76	79.95 97

test_part1		56.55 84	56.50 108	56.60 70	61.77 63	69.59 88	66.45 50	43.70 116	38.22 129	44.13 56	29.53 167	49.96 84	47.92 103	63.09 100	64.59 92	75.61 73	80.54 93

Anonymous20231211		56.40 85	57.00 103	55.70 75	59.78 81	72.49 70	61.29 76	46.83 82	40.50 119	40.46 80	22.12 191	49.73 86	51.07 78	64.39 86	65.30 81	74.74 90	84.44 57

PMMVS		55.74 86	62.68 65	47.64 127	44.34 170	65.58 122	47.22 153	37.96 157	56.43 64	34.11 105	61.51 35	47.41 101	54.55 52	65.88 70	62.49 117	67.67 160	79.48 98

Fast-Effi-MVS+		55.73 87	58.26 94	52.76 88	54.33 115	68.19 97	57.05 99	34.66 174	46.92 92	38.96 85	40.53 97	41.55 127	55.69 49	65.31 76	65.99 74	75.90 67	79.34 99

FC-MVSNet-train		55.68 88	57.00 103	54.13 82	63.37 47	66.16 114	46.77 156	52.14 34	42.36 109	37.67 89	48.50 74	41.42 129	51.28 76	61.58 118	63.22 107	73.56 115	75.76 116

FMVSNet3		55.66 89	59.68 82	50.96 100	50.59 134	66.49 111	57.57 95	46.61 84	49.30 82	28.77 132	39.61 103	51.42 76	43.85 130	68.29 55	68.80 50	78.35 33	73.86 121

OMC-MVS		55.48 90	61.85 75	48.04 126	41.55 177	60.32 157	56.80 103	31.78 194	75.67 23	42.30 70	51.52 63	54.15 70	49.91 87	60.28 136	57.59 155	65.91 168	73.42 124

tpm		54.94 91	57.86 99	51.54 96	59.48 84	67.04 106	58.34 92	34.60 176	41.93 114	34.41 102	42.40 90	47.14 102	49.07 96	61.46 119	61.67 129	73.31 122	83.39 62

GBi-Net		54.66 92	58.42 91	50.26 108	49.36 143	65.81 119	56.80 103	46.61 84	49.30 82	28.77 132	39.61 103	51.42 76	42.71 133	64.25 88	65.54 77	77.32 48	73.03 129

test1		54.66 92	58.42 91	50.26 108	49.36 143	65.81 119	56.80 103	46.61 84	49.30 82	28.77 132	39.61 103	51.42 76	42.71 133	64.25 88	65.54 77	77.32 48	73.03 129

test-LLR		54.62 94	58.66 89	49.89 114	51.68 126	65.89 116	47.88 147	46.35 88	42.51 106	29.84 128	41.41 94	48.87 93	45.20 118	62.91 104	64.43 96	78.43 30	84.62 55

TSAR-MVS + COLMAP		54.37 95	62.43 68	44.98 141	34.33 197	58.94 164	54.11 121	34.15 185	74.06 26	34.57 101	71.63 17	42.03 126	47.88 104	61.26 124	57.33 158	64.83 171	71.74 139

EPMVS		54.07 96	56.06 110	51.75 95	56.74 97	70.80 77	55.32 114	34.20 182	46.46 94	36.59 94	40.38 100	42.55 121	49.77 89	61.25 125	60.90 137	77.86 39	70.08 150

v2v482		54.00 97	55.12 116	52.69 90	51.73 125	69.42 90	60.65 78	45.09 96	34.56 151	33.73 110	35.29 126	35.36 147	49.92 86	64.05 92	65.16 83	75.00 84	81.98 74

CNLPA		54.00 97	57.08 102	50.40 107	49.83 140	61.75 148	53.47 124	37.27 162	74.55 25	44.85 54	33.58 138	45.42 112	52.94 70	58.89 147	53.66 177	64.06 174	71.68 140

FMVSNet2		53.94 99	57.29 100	50.03 111	49.36 143	65.81 119	56.80 103	45.95 91	43.13 103	28.04 136	35.68 124	48.18 98	42.71 133	67.23 65	67.95 59	77.32 48	73.03 129

v8		53.77 100	54.82 120	52.54 91	52.12 121	66.95 109	60.56 79	43.23 124	37.17 140	35.35 97	34.96 129	37.50 138	49.51 92	63.67 94	64.59 92	74.48 98	78.91 102

GA-MVS		53.77 100	56.41 109	50.70 102	51.63 128	69.96 83	57.55 96	44.39 103	34.31 152	27.15 138	40.99 96	36.40 142	47.65 107	67.45 61	67.16 63	75.83 69	78.60 104

Effi-MVS+-dtu		53.63 102	54.85 119	52.20 93	59.32 85	61.33 151	56.42 109	40.24 143	43.84 100	34.22 104	39.49 108	46.18 108	53.00 69	58.72 151	57.49 157	69.99 149	76.91 109

thisisatest0530		53.61 103	57.22 101	49.40 119	51.30 130	68.22 96	52.72 132	43.34 122	42.72 105	35.31 98	43.57 87	44.14 116	44.37 128	63.00 102	64.86 87	69.34 152	74.00 120

v1144		53.47 104	54.65 121	52.10 94	51.93 123	69.81 85	59.32 87	44.77 101	33.21 158	32.52 113	33.55 139	34.34 155	49.29 94	64.58 83	64.81 89	74.74 90	82.27 68

v10		53.44 105	54.40 122	52.31 92	52.08 122	66.99 107	59.68 85	43.41 119	35.90 146	34.30 103	33.98 136	35.56 145	50.10 84	64.39 86	64.67 90	74.32 99	79.30 100

PatchmatchNet		53.37 106	55.62 114	50.75 101	55.93 106	70.54 80	51.39 137	36.41 165	44.85 96	37.26 91	39.40 110	42.54 122	47.83 105	60.29 135	60.88 139	75.69 72	70.87 144
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		53.36 107	55.65 113	50.68 104	55.34 108	59.04 162	55.00 117	39.98 144	38.72 127	33.22 111	44.52 84	47.05 103	49.63 91	61.82 116	61.77 124	70.92 141	76.61 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		53.30 108	58.66 89	47.04 130	44.94 164	65.89 116	47.88 147	35.95 168	42.51 106	29.84 128	41.41 94	48.87 93	45.20 118	62.91 104	64.43 96	78.43 30	84.62 55

tttt0517		53.05 109	56.73 107	48.76 122	50.35 136	67.51 103	51.96 136	43.34 122	42.00 113	33.88 108	43.19 88	43.49 119	44.37 128	62.58 109	64.86 87	68.67 154	73.46 123

MDTV_nov1_ep13		52.99 110	55.59 115	49.95 113	54.08 116	70.69 78	56.47 108	38.42 154	42.78 104	30.19 126	39.56 106	43.31 120	45.78 115	60.07 140	62.11 121	74.74 90	70.62 145

EPP-MVSNet		52.91 111	58.91 87	45.91 135	54.99 112	68.84 94	49.27 143	42.71 131	37.53 134	20.20 166	46.09 79	56.19 65	36.90 148	61.37 122	60.90 137	71.41 136	81.41 84

dps		52.84 112	52.92 133	52.74 89	59.89 79	69.49 89	54.47 119	37.38 160	42.49 108	39.53 84	35.33 125	32.71 160	51.83 75	60.45 132	61.12 134	73.33 121	68.86 159

v1192		52.69 113	53.86 125	51.31 97	51.22 131	69.76 86	57.37 97	44.39 103	32.21 161	31.39 120	32.41 146	32.44 163	49.19 95	64.25 88	64.17 98	74.31 100	81.81 77

V42		52.63 114	55.08 117	49.76 116	44.93 165	67.49 105	60.19 83	42.13 134	37.21 139	34.08 106	34.57 132	37.30 139	47.29 108	63.48 97	64.15 99	69.96 150	81.38 85

MSDG		52.58 115	51.40 146	53.95 83	65.48 42	64.31 134	61.44 72	44.02 108	44.17 99	32.92 112	30.40 158	31.81 167	46.35 112	62.13 111	62.55 115	73.49 117	64.41 167

Fast-Effi-MVS+-dtu		52.47 116	55.89 111	48.48 124	56.25 99	65.07 125	58.75 90	23.79 205	41.27 116	27.07 140	37.95 115	41.34 130	50.85 81	62.90 106	62.34 119	74.17 104	80.37 96

v144192		52.43 117	53.63 127	51.03 99	51.06 132	69.60 87	56.94 101	44.84 100	32.15 162	30.88 122	32.45 145	32.71 160	48.36 99	62.98 103	63.52 104	74.10 109	82.02 73

thres100view900		52.33 118	53.91 124	50.48 106	56.10 101	67.79 100	56.18 111	49.18 52	35.86 148	25.22 146	34.74 130	34.10 156	42.41 136	64.45 84	62.62 114	73.81 111	77.85 105

v1921920		51.95 119	53.19 129	50.51 105	50.82 133	69.14 92	55.45 113	44.34 107	31.53 166	30.53 124	31.96 148	31.67 168	48.31 100	63.12 99	63.28 106	73.59 114	81.60 81

v148		51.72 120	53.15 130	50.05 110	50.15 138	67.51 103	56.98 100	42.85 129	32.60 160	32.41 115	33.88 137	34.71 152	44.45 126	61.06 126	63.00 109	73.45 118	79.24 101

TAPA-MVS	47.92 11	51.66 121	57.88 98	44.40 144	36.46 192	58.42 167	53.82 123	30.83 195	69.51 39	34.97 100	46.90 77	49.67 87	46.99 109	58.00 154	54.64 173	63.33 180	68.00 161
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		51.53 122	57.98 97	44.01 148	55.96 105	66.16 114	47.65 149	42.84 130	39.82 122	19.09 174	44.97 83	50.28 82	27.20 180	63.43 98	63.84 100	71.33 138	77.33 107

v1240		51.42 123	52.66 135	49.97 112	50.31 137	68.70 95	54.05 122	43.85 112	30.78 170	30.22 125	31.43 151	31.03 175	47.98 102	62.62 108	63.16 108	73.40 119	80.93 90

pmmvs4		51.28 124	52.50 137	49.85 115	49.54 142	63.02 141	52.83 131	43.41 119	44.65 97	35.71 96	34.38 133	32.25 164	45.14 121	60.21 139	60.03 145	72.44 131	72.98 132

Vis-MVSNet		51.13 125	58.04 96	43.06 154	47.68 150	67.71 101	49.10 144	39.09 150	37.75 132	22.57 157	51.03 66	48.78 95	32.42 165	62.12 112	61.80 123	67.49 162	77.12 108
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		51.04 126	58.79 88	42.00 160	40.59 179	65.32 123	46.65 158	39.26 148	39.90 121	27.30 137	54.12 54	52.03 73	30.93 169	59.85 142	59.62 150	67.23 164	80.70 91
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
tfpn200view9		50.91 127	52.45 138	49.11 121	56.10 101	64.53 129	53.06 128	47.31 76	35.86 148	25.22 146	34.74 130	34.10 156	41.08 139	60.84 128	61.37 131	71.90 135	75.70 117

SCA		50.88 128	53.70 126	47.59 128	55.99 103	55.81 176	43.14 170	33.45 188	45.16 95	37.14 92	41.83 92	43.82 117	44.43 127	60.37 133	60.02 146	71.38 137	68.90 158

gg-mvs-nofinetune		50.82 129	55.83 112	44.97 142	60.63 73	75.69 42	53.40 125	34.48 178	20.05 205	6.93 200	18.27 197	52.70 72	33.57 156	70.50 33	72.93 17	80.84 6	80.68 92

thres200		50.76 130	52.52 136	48.70 123	55.98 104	64.60 127	55.29 115	47.34 74	33.91 155	24.36 149	34.33 134	33.90 158	37.27 146	60.84 128	62.41 118	71.99 133	77.63 106

thres400		50.39 131	52.22 139	48.26 125	55.02 111	66.32 113	52.97 129	48.33 63	32.68 159	22.94 155	33.21 141	33.38 159	37.27 146	62.74 107	61.38 130	73.04 128	75.81 115

EG-PatchMatch MVS		50.23 132	50.89 149	49.47 117	59.54 83	70.88 76	52.46 133	44.01 109	26.22 191	31.91 116	24.97 183	31.45 171	33.48 158	64.79 80	66.51 72	75.40 77	71.39 142

IterMVS		50.23 132	53.27 128	46.68 131	47.59 152	60.58 155	53.10 127	36.62 164	36.07 144	25.89 143	39.42 109	40.05 133	43.65 131	60.22 138	61.35 132	73.23 123	75.23 118
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		50.14 134	52.78 134	47.06 129	45.56 161	63.56 137	54.22 120	43.74 114	34.10 154	25.37 145	29.79 164	42.06 125	38.70 142	64.25 88	65.54 77	74.75 89	70.18 149

ACMH	47.82 13	50.10 135	49.60 155	50.69 103	63.36 48	66.99 107	56.83 102	52.13 35	31.06 169	17.74 179	28.22 171	26.24 191	45.17 120	60.88 127	63.80 101	68.91 153	70.00 152
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		49.85 136	56.99 105	41.52 163	52.79 118	57.06 170	41.44 175	43.13 125	56.13 65	19.24 173	52.11 60	48.38 96	22.14 187	58.19 153	58.38 153	70.35 144	68.71 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		49.59 137	49.75 154	49.40 119	55.88 107	59.86 159	56.31 110	45.33 94	48.57 87	28.32 135	31.54 150	36.81 141	46.27 114	57.17 159	55.88 168	64.29 173	58.42 185

UniMVSNet_NR-MVSNet		49.56 138	53.04 131	45.49 138	51.59 129	64.42 133	46.97 154	51.01 39	37.87 130	16.42 180	39.87 101	34.91 151	33.43 160	59.59 143	62.70 111	73.52 116	71.94 135

CDS-MVSNet		49.25 139	53.97 123	43.75 150	47.53 153	64.53 129	48.59 145	42.27 133	33.77 156	26.64 141	40.46 99	42.26 124	30.01 172	61.77 117	61.71 126	67.48 163	73.28 128
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	44.22 14	49.14 140	51.75 142	46.10 134	42.78 175	55.60 179	53.11 126	34.46 179	55.69 69	32.47 114	34.16 135	41.45 128	48.91 97	57.13 160	54.09 174	64.84 170	64.10 168
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH+	47.85 12	49.13 141	48.86 161	49.44 118	56.75 96	62.01 147	56.62 107	47.55 73	37.49 135	23.98 150	26.68 176	29.46 182	43.12 132	57.45 158	58.85 152	68.62 155	70.05 151

NR-MVSNet		48.84 142	51.76 141	45.44 139	57.66 93	60.64 153	47.39 150	47.63 69	37.26 136	13.31 184	37.31 117	29.64 181	33.53 157	63.52 96	62.09 122	73.10 126	71.89 138

CR-MVSNet		48.82 143	51.85 140	45.29 140	46.74 155	55.95 174	52.06 134	34.21 180	42.17 110	31.74 117	32.92 143	42.53 123	45.00 122	58.80 148	61.11 135	61.99 185	69.47 154

thres600view7		48.44 144	50.23 152	46.35 133	54.05 117	64.60 127	50.18 140	47.34 74	31.73 165	20.74 164	32.28 147	32.62 162	33.79 155	60.84 128	56.11 166	71.99 133	73.40 125

test-mter		48.31 145	55.04 118	40.45 167	34.12 198	59.02 163	41.77 174	28.05 199	38.43 128	22.67 156	39.35 111	44.40 114	41.88 138	60.30 134	61.68 128	74.20 102	82.12 70

PatchT		48.11 146	51.27 148	44.43 143	50.13 139	61.58 149	33.59 188	32.92 190	40.38 120	31.74 117	30.60 157	36.93 140	45.00 122	58.80 148	61.11 135	73.19 124	69.47 154

TranMVSNet+NR-MVSNet		48.06 147	51.36 147	44.21 146	50.38 135	62.09 146	47.28 151	50.88 42	36.11 143	13.25 185	37.51 116	31.60 170	30.70 170	59.34 144	62.53 116	72.81 129	70.31 147

TransMVSNet (Re)		47.46 148	48.94 160	45.74 137	57.96 91	64.29 135	48.26 146	48.47 62	26.33 190	19.33 171	29.45 168	31.28 174	25.31 184	63.05 101	62.70 111	75.10 83	65.47 165

DU-MVS		47.33 149	50.86 150	43.20 153	44.43 168	60.64 153	46.97 154	47.63 69	37.26 136	16.42 180	37.31 117	31.39 172	33.43 160	57.53 156	59.98 147	70.35 144	71.94 135

v7n		47.22 150	48.38 162	45.87 136	48.20 149	63.58 136	50.69 138	40.93 141	26.60 189	26.44 142	26.52 177	29.65 180	38.19 144	58.22 152	60.23 144	70.79 142	73.83 122

UA-Net		47.19 151	53.02 132	40.38 168	55.31 109	60.02 158	38.41 181	38.68 152	36.42 142	22.47 159	51.95 61	58.72 58	25.62 183	54.11 172	53.40 178	61.79 186	56.51 188

Baseline_NR-MVSNet		47.14 152	50.83 151	42.84 156	44.43 168	63.31 139	44.50 166	50.36 47	37.71 133	11.25 190	30.84 154	32.09 165	30.96 168	57.53 156	63.73 102	75.53 74	70.60 146

pmmvs5		47.02 153	50.02 153	43.51 152	43.48 173	62.65 143	47.24 152	37.78 159	30.59 171	24.80 148	35.26 127	30.43 176	34.36 153	59.05 146	60.28 143	73.40 119	71.92 137

UniMVSNet (Re)		46.89 154	51.65 144	41.34 165	45.60 160	62.71 142	44.05 167	47.10 78	37.24 138	13.55 183	36.90 119	34.54 154	26.76 181	57.56 155	59.90 149	70.98 140	72.69 133

thisisatest0515		46.88 155	49.57 156	43.74 151	45.33 163	60.46 156	46.19 160	41.06 140	30.34 172	29.73 130	32.50 144	31.63 169	35.43 151	58.75 150	61.71 126	64.70 172	71.59 141

tfpnnormal		46.61 156	46.82 169	46.37 132	52.70 119	62.31 144	50.39 139	47.17 77	25.74 193	21.80 160	23.13 187	24.15 199	33.45 159	60.28 136	60.77 140	72.70 130	71.39 142

pm-mvs1		46.14 157	49.34 159	42.41 157	48.93 146	62.22 145	44.98 164	42.68 132	27.66 183	20.76 163	29.88 163	34.96 150	26.41 182	60.03 141	60.42 142	70.70 143	70.20 148

IterMVS-SCA-FT		45.87 158	51.55 145	39.24 171	46.22 156	59.43 160	52.89 130	31.93 191	36.01 145	23.68 151	38.86 112	39.88 135	39.05 141	56.25 165	58.17 154	41.70 206	72.25 134

MIMVSNet		45.62 159	49.56 157	41.02 166	38.17 183	64.43 132	49.48 142	35.43 171	36.53 141	20.06 168	22.58 188	35.16 149	28.75 177	61.97 114	62.20 120	74.20 102	64.07 169

gm-plane-assit		45.41 160	48.03 164	42.34 158	56.49 98	40.48 204	24.54 208	34.15 185	14.44 211	6.59 201	17.82 198	35.32 148	49.82 88	72.93 15	74.11 10	82.47 2	81.12 89

ADS-MVSNet		45.39 161	46.42 170	44.19 147	48.74 148	57.52 168	43.91 168	31.93 191	35.89 147	27.11 139	30.12 159	32.06 166	45.30 116	53.13 178	55.19 170	68.15 157	61.07 177

GG-mvs-BLEND		44.87 162	64.59 59	21.86 204	0.01 220	73.70 57	55.99 112	0.01 217	50.70 78	0.01 221	49.18 69	63.61 41	0.01 216	63.83 93	64.50 94	75.13 82	86.62 38

pmmvs-eth3d		44.67 163	45.27 175	43.98 149	42.56 176	55.72 178	44.97 165	40.81 142	31.96 164	29.13 131	26.09 179	25.27 196	36.69 149	55.13 169	56.62 163	69.68 151	66.12 164

MDTV_nov1_ep13_2view		44.44 164	45.75 173	42.91 155	46.13 157	63.43 138	46.53 159	34.20 182	29.08 178	19.95 169	26.23 178	27.89 186	35.88 150	53.36 177	56.43 164	74.74 90	63.86 170

CMPMVS	33.64 16	44.39 165	46.41 171	42.03 159	44.21 171	56.50 172	46.73 157	26.48 204	34.20 153	35.14 99	24.22 184	34.64 153	40.52 140	56.50 164	56.07 167	59.12 190	62.74 173
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		44.31 166	51.67 143	35.72 181	51.82 124	55.24 180	34.57 187	41.63 135	39.10 125	8.84 197	45.93 80	46.63 105	14.45 197	54.09 173	57.03 160	63.00 181	63.65 171

TAMVS		44.27 167	49.35 158	38.35 175	44.74 166	61.04 152	39.07 179	31.82 193	29.95 174	18.34 177	33.55 139	39.94 134	30.01 172	56.85 162	57.58 156	66.13 167	66.54 162

MVS-HIRNet		43.98 168	43.63 179	44.39 145	47.66 151	59.31 161	32.66 194	33.88 187	30.15 173	33.75 109	16.82 203	28.39 185	45.25 117	53.92 176	55.00 172	73.16 125	61.80 174

UniMVSNet_ETH3D		43.97 169	46.01 172	41.59 161	38.31 182	56.20 173	49.69 141	38.18 156	28.18 179	19.88 170	27.82 173	30.20 177	33.41 162	54.18 171	56.30 165	70.05 148	69.17 156

RPMNet		43.70 170	48.17 163	38.48 174	45.52 162	55.95 174	37.66 182	26.63 203	42.17 110	25.47 144	29.59 166	37.61 136	33.87 154	50.85 183	52.02 182	61.75 187	69.00 157

PatchMatch-RL		43.37 171	44.93 176	41.56 162	37.94 184	51.70 182	40.02 177	35.75 169	39.04 126	30.71 123	35.14 128	27.43 188	46.58 111	51.99 179	50.55 186	58.38 192	58.64 183

FMVSNet5		43.29 172	47.07 167	38.87 172	30.46 203	50.99 184	45.87 161	37.19 163	42.17 110	19.32 172	26.77 175	40.51 131	30.26 171	56.82 163	55.81 169	70.10 147	56.46 189

test0.0.03 1		43.07 173	46.95 168	38.54 173	51.68 126	58.77 165	35.28 183	46.35 88	32.05 163	12.44 186	28.53 170	35.52 146	14.40 198	57.12 161	56.93 161	71.11 139	59.69 179

anonymousdsp		43.03 174	47.19 166	38.18 176	36.00 194	56.92 171	38.44 180	34.56 177	24.22 195	22.53 158	29.69 165	29.92 178	35.21 152	53.96 175	58.98 151	62.32 184	76.66 111

USDC		42.80 175	45.57 174	39.58 169	34.55 196	51.13 183	42.61 171	36.21 166	39.59 123	23.65 152	33.13 142	20.87 205	37.86 145	55.35 168	57.16 159	62.61 182	61.75 175

pmnet_mix02		42.41 176	43.24 181	41.44 164	45.80 159	57.46 169	42.19 172	41.57 136	29.38 176	23.39 153	26.08 180	23.96 200	27.31 179	51.50 180	53.76 176	68.36 156	60.58 178

CHOSEN 280x420		42.39 177	47.40 165	36.54 179	33.56 199	39.66 207	40.67 176	26.88 202	34.66 150	18.03 178	30.09 160	45.59 110	44.82 124	54.46 170	54.00 175	55.28 199	73.32 127

pmmvs6		41.90 178	44.01 178	39.43 170	44.45 167	58.77 165	41.92 173	39.22 149	21.74 198	19.08 175	17.40 201	31.33 173	24.28 186	55.94 166	56.67 162	67.60 161	66.24 163

Anonymous20231206		40.63 179	43.29 180	37.53 177	48.88 147	55.81 176	34.99 184	44.98 98	28.16 180	10.16 194	17.26 202	27.50 187	18.28 191	54.00 174	55.07 171	67.85 159	65.23 166

CVMVSNet		38.91 180	44.49 177	32.40 190	34.57 195	47.20 195	34.81 185	34.20 182	31.45 167	8.95 196	38.86 112	36.38 143	24.30 185	47.77 187	46.94 198	57.59 194	62.85 172

COLMAP_ROB	34.79 15	38.65 181	40.72 184	36.23 180	36.41 193	49.22 191	45.51 163	27.60 201	37.81 131	20.54 165	23.37 186	24.25 198	28.11 178	51.02 182	48.55 189	59.22 189	50.82 199
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		38.23 182	41.72 183	34.15 183	40.56 180	50.07 187	33.17 191	44.35 106	27.64 185	5.54 207	30.84 154	26.67 189	14.99 195	45.64 190	52.38 181	66.29 166	58.83 182

WR-MVS		37.61 183	42.15 182	32.31 192	43.64 172	51.85 181	29.39 200	43.35 121	27.65 184	4.40 209	29.90 162	29.80 179	10.46 202	46.73 189	51.98 183	62.60 183	57.16 186

TinyColmap		37.18 184	37.37 197	36.95 178	31.17 202	45.21 198	39.71 178	34.65 175	29.83 175	20.20 166	18.54 196	13.72 213	38.27 143	50.33 184	51.57 184	57.71 193	52.42 196

CP-MVSNet		37.09 185	40.62 185	32.99 185	37.56 186	48.25 192	32.75 192	43.05 126	27.88 182	5.93 203	31.27 152	25.82 194	15.09 193	43.37 197	48.82 187	63.54 178	58.90 180

DTE-MVSNet		36.91 186	40.44 186	32.79 188	40.74 178	47.55 194	30.71 198	44.39 103	27.03 187	4.32 210	30.88 153	25.99 192	12.73 200	45.58 191	50.80 185	63.86 175	55.23 192

PS-CasMVS		36.84 187	40.23 189	32.89 186	37.44 187	48.09 193	32.68 193	42.97 128	27.36 186	5.89 204	30.08 161	25.48 195	14.96 196	43.28 198	48.71 188	63.39 179	58.63 184

WR-MVS_H		36.29 188	40.35 188	31.55 194	37.80 185	49.94 189	30.57 199	41.11 139	26.90 188	4.14 211	30.72 156	28.85 183	10.45 203	42.47 199	47.99 193	65.24 169	55.54 190

SixPastTwentyTwo		36.11 189	37.80 193	34.13 184	37.13 190	46.72 196	34.58 186	34.96 173	21.20 201	11.66 187	29.15 169	19.88 206	29.77 174	44.93 192	48.34 190	56.67 196	54.41 194

test20.03		36.00 190	38.92 190	32.60 189	45.92 158	50.99 184	28.05 204	43.69 117	21.62 199	6.03 202	17.61 200	25.91 193	8.34 209	51.26 181	52.60 180	63.58 176	52.46 195

TDRefinement		35.76 191	38.23 191	32.88 187	19.09 212	46.04 197	43.29 169	29.49 196	33.49 157	19.04 176	22.29 190	17.82 208	29.69 176	48.60 186	47.24 196	56.65 197	52.12 197

LTVRE_ROB	32.83 17	35.10 192	37.46 194	32.35 191	43.12 174	49.99 188	28.52 202	33.23 189	12.73 212	8.18 198	27.71 174	21.34 203	32.64 164	46.92 188	48.11 191	48.41 203	55.45 191
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		34.96 193	38.17 192	31.22 195	22.78 207	40.82 203	33.56 189	23.61 206	29.16 177	21.43 162	28.00 172	21.43 202	31.90 166	44.33 195	42.12 201	54.07 201	61.34 176

testgi		34.51 194	37.42 195	31.12 196	47.37 154	50.34 186	24.38 209	41.21 137	20.32 203	5.64 206	20.56 192	26.55 190	8.06 210	49.28 185	52.65 179	60.05 188	42.23 205

MDA-MVSNet-bldmvs		34.31 195	34.11 201	34.54 182	24.73 205	49.66 190	33.42 190	43.03 127	21.59 200	11.10 191	19.81 194	12.68 214	31.41 167	35.59 204	48.05 192	63.56 177	51.39 198

N_pmnet		34.09 196	35.74 199	32.17 193	37.25 189	43.17 201	32.26 196	35.57 170	26.22 191	10.60 193	20.44 193	19.38 207	20.20 189	44.59 194	47.00 197	57.13 195	49.35 202

RPSCF		33.61 197	40.43 187	25.65 200	16.00 214	32.41 209	31.73 197	13.33 213	50.13 80	23.12 154	31.56 149	40.09 132	32.73 163	41.14 203	37.05 204	36.99 209	50.63 200

EU-MVSNet		33.00 198	36.49 198	28.92 197	33.10 200	42.86 202	29.32 201	35.99 167	22.94 196	5.83 205	25.29 181	24.43 197	15.21 192	41.22 202	41.65 203	54.08 200	57.01 187

pmmvs3		31.22 199	33.62 202	28.43 198	22.82 206	40.26 206	26.40 205	22.05 208	16.89 209	10.99 192	14.72 205	16.26 209	29.70 175	44.82 193	47.39 195	58.61 191	54.98 193

FC-MVSNet-test		30.97 200	37.38 196	23.49 203	37.42 188	33.68 208	19.43 211	39.27 147	31.37 168	1.67 217	38.56 114	28.85 183	6.06 213	41.40 200	43.80 200	37.10 208	44.03 204

new-patchmatchnet		30.47 201	32.80 204	27.75 199	36.81 191	43.98 199	24.85 207	39.29 146	20.52 202	4.06 212	15.94 204	16.05 210	9.57 204	41.32 201	42.05 202	51.94 202	49.74 201

MIMVSNet1		29.60 202	33.37 203	25.20 202	19.52 210	43.94 200	26.29 206	37.92 158	19.95 206	3.79 213	12.64 209	21.99 201	7.70 211	43.83 196	46.32 199	55.97 198	44.92 203

FPMVS		26.87 203	28.19 205	25.32 201	27.09 204	29.49 210	32.28 195	17.79 210	28.09 181	11.33 188	19.38 195	14.69 211	20.88 188	35.11 205	32.82 206	42.56 205	37.75 206

PMVS	18.18 18	21.95 204	22.85 206	20.90 205	21.92 208	14.78 212	19.95 210	17.31 211	15.69 210	11.32 189	13.70 206	13.91 212	15.02 194	34.92 206	31.72 207	39.85 207	35.20 207
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		19.10 205	22.71 207	14.89 207	10.93 216	24.08 211	14.22 212	13.94 212	18.68 207	2.93 214	12.84 208	11.27 215	11.94 201	30.57 208	30.58 208	35.38 210	30.93 208

Gipuma		17.16 206	17.83 208	16.36 206	18.76 213	12.15 215	11.97 213	27.78 200	17.94 208	4.86 208	2.53 216	2.73 220	8.90 207	34.32 207	36.09 205	25.92 211	19.06 211
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		13.92 207	17.14 209	10.16 210	1.69 219	6.92 218	11.25 214	5.74 214	22.41 197	8.11 199	10.40 210	20.91 204	13.73 199	22.17 209	13.98 211	20.44 212	23.18 209

PMMVS2		12.25 208	14.17 210	10.00 211	11.39 215	14.35 213	8.21 215	19.29 209	9.31 213	0.19 220	7.38 212	6.19 218	1.10 215	19.26 210	21.13 210	19.85 213	21.56 210

E-PMN		10.66 209	8.30 212	13.42 208	19.91 209	7.87 216	4.30 218	29.47 197	8.37 216	1.70 216	3.67 213	1.29 223	9.12 206	8.98 214	13.59 212	16.03 214	14.30 214

EMVS		10.15 210	7.67 213	13.05 209	19.22 211	7.77 217	4.48 216	29.34 198	8.65 215	1.67 217	3.55 214	1.36 222	9.15 205	8.15 215	11.79 214	14.44 215	12.43 215

MVE	10.35 19	9.76 211	11.08 211	8.22 212	4.43 217	13.04 214	3.36 219	23.57 207	5.74 217	1.76 215	3.09 215	1.75 221	6.78 212	12.78 212	23.04 209	9.44 216	18.09 212
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 212	0.01 214	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.01 218	0.00 222	0.02 217	0.00 224	0.00 218	0.01 216	0.01 215	0.00 219	0.03 216

test123		0.01 212	0.01 214	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.01 218	0.00 222	0.02 217	0.00 224	0.01 216	0.00 217	0.01 215	0.00 219	0.03 216

uanet_test		0.00 214	0.00 216	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 220	0.00 222	0.00 219	0.00 224	0.00 218	0.00 217	0.00 217	0.00 219	0.00 218

sosnet-low-res		0.00 214	0.00 216	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 220	0.00 222	0.00 219	0.00 224	0.00 218	0.00 217	0.00 217	0.00 219	0.00 218

sosnet		0.00 214	0.00 216	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 220	0.00 222	0.00 219	0.00 224	0.00 218	0.00 217	0.00 217	0.00 219	0.00 218

RE-MVS-def										21.59 161

9.14												80.07 5

SR-MVS					63.74 46			48.51 61				73.80 18

Anonymous202405211			56.81 106		60.91 69	73.48 60	59.82 84	48.68 58	39.26 124		24.00 185	46.77 104	50.73 82	65.28 77	65.72 76	75.37 78	83.17 63

our_test_3					49.68 141	61.50 150	45.84 162

ambc			35.52 200		38.36 181	40.40 205	28.38 203		25.20 194	14.87 182	13.22 207	7.54 217	19.34 190	55.63 167	47.79 194	47.91 204	58.89 181

MTAPA										54.82 16		71.98 24

MTMP										50.64 32		68.31 30

Patchmatch-RL test							0.69 221

tmp_tt				4.41 213	2.56 218	1.81 220	2.61 220	0.27 216	20.12 204	9.81 195	17.69 199	9.04 216	1.96 214	12.88 211	12.11 213	9.23 217

XVS					62.70 54	73.06 63	61.80 70			42.02 71		63.42 43				74.68 94

X-MVStestdata					62.70 54	73.06 63	61.80 70			42.02 71		63.42 43				74.68 94

abl_6				63.79 23	70.80 23	81.22 12	65.26 58	53.25 30	77.02 18	53.02 24	65.14 30	73.74 19	60.30 20			80.13 8	90.27 10

mPP-MVS					63.08 51							62.34 46

NP-MVS									72.62 30

Patchmtry						64.49 131	52.06 134	34.21 180		31.74 117

DeepMVS_CX						5.87 219	4.32 217	1.74 215	9.04 214	1.30 219	7.97 211	3.16 219	8.56 208	9.74 213		6.30 218	14.51 213

High-resolution multi-view benchmark