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 53	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 46	92.19 6

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	35.82 143	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 56	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 62	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 52	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 50	88.69 22

TSAR-MVS + MP.		70.28 18	75.09 18	64.66 18	69.34 29	64.61 125	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 64	81.97 74
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	74.13 105	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 63	84.84 52

SD-MVS		68.30 22	72.58 24	63.31 25	69.24 30	67.85 98	70.81 26	53.65 29	79.64 15	58.52 7	74.31 14	75.37 14	53.52 62	65.63 72	63.56 102	74.13 105	81.73 78
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 85	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 84	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 79	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 54	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 42	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 82
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 55	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 66	70.00 81	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 44	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 78	84.05 58

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 85

TSAR-MVS + ACMM		65.95 36	72.83 23	57.93 55	69.35 28	65.85 117	73.36 16	39.84 144	76.00 20	48.69 40	82.54 10	75.03 16	49.38 92	65.33 75	63.42 104	66.94 164	81.67 79

canonicalmvs		65.55 37	70.75 35	59.49 49	62.11 61	78.26 27	66.52 49	43.82 112	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 71	68.94 49	68.68 53	75.18 79	82.93 65

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 89	66.00 27	65.39 35	53.19 64	70.68 31	70.57 36	73.72 112	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	69.87 152
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 73	75.77 39	59.28 87	50.58 44	69.93 36	40.73 78	68.59 22	61.60 50	53.72 57	68.65 51	68.07 56	75.75 69	83.87 60
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 73	67.27 43	49.51 50	69.40 40	45.76 48	60.41 40	64.96 37	51.84 73	67.33 64	67.57 62	73.78 111	84.89 51

casdiffmvs		63.87 45	67.08 50	60.12 45	60.90 69	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 75	76.99 34	68.13 35	45.17 94	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	47.83 104	68.25 57	68.50 55	74.61 95	83.16 64

ACMMP		63.27 48	67.85 43	57.93 55	62.64 56	72.30 70	68.23 34	48.77 55	66.50 46	43.05 62	62.07 34	57.84 61	49.98 84	66.58 68	66.46 73	74.93 86	83.17 62
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 87	74.89 48	60.26 80	43.96 109	58.27 60	42.37 69	61.47 36	56.56 63	57.80 34	68.00 58	68.74 51	77.34 45	89.33 17

CS-MVS		62.84 50	67.51 45	57.38 62	60.49 74	75.48 45	62.36 69	43.72 114	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 99	60.17 21	67.41 63	66.83 66	74.28 100	76.69 109

3Dnovator+	55.76 7	62.70 52	65.10 58	59.90 47	65.89 40	72.15 71	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	73.08 126	80.40 94

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	36.88 119	56.51 64	56.66 44	68.28 56	68.96 49	77.73 41	80.44 93

PVSNet_BlendedMVS		62.53 54	66.37 53	58.05 53	58.17 88	75.70 40	61.30 73	48.67 59	58.67 57	50.93 29	55.43 50	49.39 88	53.01 67	69.46 42	66.55 70	76.24 60	89.39 15

PVSNet_Blended		62.53 54	66.37 53	58.05 53	58.17 88	75.70 40	61.30 73	48.67 59	58.67 57	50.93 29	55.43 50	49.39 88	53.01 67	69.46 42	66.55 70	76.24 60	89.39 15

MVSTER		62.51 56	67.22 48	57.02 66	55.05 109	69.23 90	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 58	82.04 71

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 89	39.85 82	25.15 181	44.76 112	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	38.42 153	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 86	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 85	72.22 131	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 93	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 67	87.70 31

DI_MVS_plusplus_trai		61.86 61	65.26 57	57.90 58	57.93 91	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 59	88.30 24

MSLP-MVS++		61.81 62	62.19 71	61.37 36	68.33 34	63.08 139	70.75 27	38.89 150	63.96 51	57.51 11	48.59 73	61.66 49	53.67 60	62.04 112	59.92 147	79.03 23	76.08 112

OPM-MVS		61.59 63	62.30 70	60.76 39	66.53 37	73.35 61	71.41 22	54.18 26	40.82 117	41.57 75	45.70 81	54.84 69	54.43 55	69.92 38	69.19 46	76.45 57	82.25 68

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	36.30 122	51.64 75	50.95 79	72.89 17	70.65 35	82.13 3	75.17 118

EIA-MVS		60.56 65	64.29 61	56.20 73	59.14 85	72.68 67	59.55 85	43.56 117	51.78 76	41.01 77	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 67	68.88 92	65.98 53	48.85 54	58.56 59	44.19 55	48.89 71	46.31 105	48.56 97	63.61 94	64.49 94	75.75 69	81.91 75
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 71	61.94 62	68.15 97	66.90 46	48.15 65	59.75 56	38.47 85	50.38 68	48.34 96	46.87 109	65.39 74	64.93 84	75.51 74	81.21 87

Effi-MVS+		59.63 68	61.78 77	57.12 64	61.56 64	71.63 74	63.61 62	47.59 71	47.18 90	37.79 86	45.29 82	49.93 85	56.27 46	67.45 61	67.06 64	75.91 65	83.93 59

CPTT-MVS		59.54 69	64.47 60	53.79 83	54.99 111	67.63 101	65.48 57	44.59 101	64.81 49	37.74 87	51.55 62	59.90 54	49.77 88	61.83 114	61.26 132	70.18 145	84.31 57

baseline2		59.20 70	61.72 78	56.27 72	59.61 81	74.12 53	58.65 90	49.42 51	48.10 87	40.12 81	49.10 70	44.15 114	51.24 76	66.65 67	67.88 60	78.56 27	82.06 70

baseline		58.65 71	61.99 72	54.75 78	54.70 113	71.85 72	60.20 81	43.91 110	55.99 67	40.13 80	53.50 57	50.91 81	55.76 48	61.29 122	61.73 124	73.83 109	78.68 102

PVSNet_Blended_VisFu		58.56 72	62.33 69	54.16 80	56.90 94	73.92 55	57.72 93	46.16 90	44.23 97	42.73 66	46.26 78	51.06 80	46.28 112	67.99 59	65.38 79	75.18 79	87.44 33

ACMM	53.73 9	57.91 73	58.27 92	57.49 60	63.10 50	66.45 111	65.65 55	49.02 53	53.69 72	42.67 67	36.41 121	46.07 108	50.38 82	64.74 81	64.63 90	74.14 104	75.91 113
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		57.87 74	63.63 64	51.15 97	52.18 119	70.20 80	58.14 92	37.32 160	56.49 62	31.06 120	57.38 44	50.05 83	53.67 60	64.98 79	65.04 83	74.57 96	81.29 86

ET-MVSNet_ETH3D		57.84 75	61.91 73	53.09 85	32.91 200	74.53 50	63.51 63	46.80 83	46.52 92	36.14 94	56.00 48	46.20 106	64.41 5	60.75 130	66.99 65	74.79 87	82.35 66

tpm cat1		57.41 76	58.26 93	56.42 70	60.80 71	72.56 68	64.35 60	38.43 152	49.18 85	46.36 45	36.69 120	43.50 117	54.47 53	61.39 120	62.64 112	74.11 107	81.81 76

IB-MVS	53.15 10	57.33 77	59.02 84	55.37 75	60.83 70	77.11 33	54.51 117	50.10 48	43.22 101	42.82 65	40.50 98	37.61 135	44.67 124	59.27 144	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 78	59.08 83	55.06 76	59.91 77	70.65 78	60.71 76	35.38 171	47.91 88	42.58 68	39.78 102	45.45 110	54.44 54	62.19 109	62.82 109	77.37 43	84.73 53

baseline1		57.21 79	60.53 80	53.33 84	62.50 58	69.86 83	57.33 97	50.59 43	43.39 100	30.00 126	48.60 72	51.09 79	42.36 136	69.38 44	68.03 57	77.20 51	73.39 125

HyFIR lowres test		57.12 80	59.11 82	54.80 77	61.55 65	77.55 30	59.02 88	45.00 96	41.84 114	33.93 106	22.44 188	49.16 91	51.02 78	68.39 54	68.71 52	78.26 34	85.70 46

MVS_111021_LR		57.06 81	60.60 79	52.93 86	56.25 98	65.14 123	55.16 115	41.21 136	52.32 75	44.89 53	53.92 55	49.27 90	52.16 72	61.46 118	60.54 140	67.92 157	81.53 81

DCV-MVSNet		56.80 82	58.96 85	54.28 79	59.96 76	66.74 109	60.37 79	44.87 98	41.01 116	36.81 92	47.57 76	47.87 98	48.23 100	64.41 84	65.17 81	75.45 75	79.95 96

test_part1		56.55 83	56.50 107	56.60 69	61.77 63	69.59 87	66.45 50	43.70 115	38.22 128	44.13 56	29.53 166	49.96 84	47.92 102	63.09 99	64.59 91	75.61 72	80.54 92

Anonymous20231211		56.40 84	57.00 102	55.70 74	59.78 80	72.49 69	61.29 75	46.83 82	40.50 118	40.46 79	22.12 190	49.73 86	51.07 77	64.39 85	65.30 80	74.74 89	84.44 56

PMMVS		55.74 85	62.68 65	47.64 126	44.34 169	65.58 121	47.22 152	37.96 156	56.43 64	34.11 104	61.51 35	47.41 100	54.55 52	65.88 70	62.49 116	67.67 159	79.48 97

Fast-Effi-MVS+		55.73 86	58.26 93	52.76 87	54.33 114	68.19 96	57.05 98	34.66 173	46.92 91	38.96 84	40.53 97	41.55 126	55.69 49	65.31 76	65.99 74	75.90 66	79.34 98

FC-MVSNet-train		55.68 87	57.00 102	54.13 81	63.37 47	66.16 113	46.77 155	52.14 34	42.36 108	37.67 88	48.50 74	41.42 128	51.28 75	61.58 117	63.22 106	73.56 114	75.76 115

FMVSNet3		55.66 88	59.68 81	50.96 99	50.59 133	66.49 110	57.57 94	46.61 84	49.30 82	28.77 131	39.61 103	51.42 76	43.85 129	68.29 55	68.80 50	78.35 33	73.86 120

OMC-MVS		55.48 89	61.85 75	48.04 125	41.55 176	60.32 156	56.80 102	31.78 193	75.67 23	42.30 70	51.52 63	54.15 70	49.91 86	60.28 135	57.59 154	65.91 167	73.42 123

tpm		54.94 90	57.86 98	51.54 95	59.48 83	67.04 105	58.34 91	34.60 175	41.93 113	34.41 101	42.40 90	47.14 101	49.07 95	61.46 118	61.67 128	73.31 121	83.39 61

GBi-Net		54.66 91	58.42 90	50.26 107	49.36 142	65.81 118	56.80 102	46.61 84	49.30 82	28.77 131	39.61 103	51.42 76	42.71 132	64.25 87	65.54 76	77.32 47	73.03 128

test1		54.66 91	58.42 90	50.26 107	49.36 142	65.81 118	56.80 102	46.61 84	49.30 82	28.77 131	39.61 103	51.42 76	42.71 132	64.25 87	65.54 76	77.32 47	73.03 128

test-LLR		54.62 93	58.66 88	49.89 113	51.68 125	65.89 115	47.88 146	46.35 88	42.51 105	29.84 127	41.41 94	48.87 92	45.20 117	62.91 103	64.43 95	78.43 30	84.62 54

TSAR-MVS + COLMAP		54.37 94	62.43 68	44.98 140	34.33 196	58.94 163	54.11 120	34.15 184	74.06 26	34.57 100	71.63 17	42.03 125	47.88 103	61.26 123	57.33 157	64.83 170	71.74 138

EPMVS		54.07 95	56.06 109	51.75 94	56.74 96	70.80 76	55.32 113	34.20 181	46.46 93	36.59 93	40.38 100	42.55 120	49.77 88	61.25 124	60.90 136	77.86 39	70.08 149

v2v482		54.00 96	55.12 115	52.69 89	51.73 124	69.42 89	60.65 77	45.09 95	34.56 150	33.73 109	35.29 125	35.36 146	49.92 85	64.05 91	65.16 82	75.00 83	81.98 73

CNLPA		54.00 96	57.08 101	50.40 106	49.83 139	61.75 147	53.47 123	37.27 161	74.55 25	44.85 54	33.58 137	45.42 111	52.94 70	58.89 146	53.66 176	64.06 173	71.68 139

FMVSNet2		53.94 98	57.29 99	50.03 110	49.36 142	65.81 118	56.80 102	45.95 91	43.13 102	28.04 135	35.68 123	48.18 97	42.71 132	67.23 65	67.95 59	77.32 47	73.03 128

v8		53.77 99	54.82 119	52.54 90	52.12 120	66.95 108	60.56 78	43.23 123	37.17 139	35.35 96	34.96 128	37.50 137	49.51 91	63.67 93	64.59 91	74.48 97	78.91 101

GA-MVS		53.77 99	56.41 108	50.70 101	51.63 127	69.96 82	57.55 95	44.39 102	34.31 151	27.15 137	40.99 96	36.40 141	47.65 106	67.45 61	67.16 63	75.83 68	78.60 103

Effi-MVS+-dtu		53.63 101	54.85 118	52.20 92	59.32 84	61.33 150	56.42 108	40.24 142	43.84 99	34.22 103	39.49 107	46.18 107	53.00 69	58.72 150	57.49 156	69.99 148	76.91 108

thisisatest0530		53.61 102	57.22 100	49.40 118	51.30 129	68.22 95	52.72 131	43.34 121	42.72 104	35.31 97	43.57 87	44.14 115	44.37 127	63.00 101	64.86 86	69.34 151	74.00 119

v1144		53.47 103	54.65 120	52.10 93	51.93 122	69.81 84	59.32 86	44.77 100	33.21 157	32.52 112	33.55 138	34.34 154	49.29 93	64.58 82	64.81 88	74.74 89	82.27 67

v10		53.44 104	54.40 121	52.31 91	52.08 121	66.99 106	59.68 84	43.41 118	35.90 145	34.30 102	33.98 135	35.56 144	50.10 83	64.39 85	64.67 89	74.32 98	79.30 99

PatchmatchNet		53.37 105	55.62 113	50.75 100	55.93 105	70.54 79	51.39 136	36.41 164	44.85 95	37.26 90	39.40 109	42.54 121	47.83 104	60.29 134	60.88 138	75.69 71	70.87 143
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		53.36 106	55.65 112	50.68 103	55.34 107	59.04 161	55.00 116	39.98 143	38.72 126	33.22 110	44.52 84	47.05 102	49.63 90	61.82 115	61.77 123	70.92 140	76.61 111
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		53.30 107	58.66 88	47.04 129	44.94 163	65.89 115	47.88 146	35.95 167	42.51 105	29.84 127	41.41 94	48.87 92	45.20 117	62.91 103	64.43 95	78.43 30	84.62 54

tttt0517		53.05 108	56.73 106	48.76 121	50.35 135	67.51 102	51.96 135	43.34 121	42.00 112	33.88 107	43.19 88	43.49 118	44.37 127	62.58 108	64.86 86	68.67 153	73.46 122

MDTV_nov1_ep13		52.99 109	55.59 114	49.95 112	54.08 115	70.69 77	56.47 107	38.42 153	42.78 103	30.19 125	39.56 106	43.31 119	45.78 114	60.07 139	62.11 120	74.74 89	70.62 144

EPP-MVSNet		52.91 110	58.91 86	45.91 134	54.99 111	68.84 93	49.27 142	42.71 130	37.53 133	20.20 165	46.09 79	56.19 65	36.90 147	61.37 121	60.90 136	71.41 135	81.41 83

dps		52.84 111	52.92 132	52.74 88	59.89 78	69.49 88	54.47 118	37.38 159	42.49 107	39.53 83	35.33 124	32.71 159	51.83 74	60.45 131	61.12 133	73.33 120	68.86 158

v1192		52.69 112	53.86 124	51.31 96	51.22 130	69.76 85	57.37 96	44.39 102	32.21 160	31.39 119	32.41 145	32.44 162	49.19 94	64.25 87	64.17 97	74.31 99	81.81 76

V42		52.63 113	55.08 116	49.76 115	44.93 164	67.49 104	60.19 82	42.13 133	37.21 138	34.08 105	34.57 131	37.30 138	47.29 107	63.48 96	64.15 98	69.96 149	81.38 84

MSDG		52.58 114	51.40 145	53.95 82	65.48 42	64.31 133	61.44 72	44.02 107	44.17 98	32.92 111	30.40 157	31.81 166	46.35 111	62.13 110	62.55 114	73.49 116	64.41 166

Fast-Effi-MVS+-dtu		52.47 115	55.89 110	48.48 123	56.25 98	65.07 124	58.75 89	23.79 204	41.27 115	27.07 139	37.95 114	41.34 129	50.85 80	62.90 105	62.34 118	74.17 103	80.37 95

v144192		52.43 116	53.63 126	51.03 98	51.06 131	69.60 86	56.94 100	44.84 99	32.15 161	30.88 121	32.45 144	32.71 159	48.36 98	62.98 102	63.52 103	74.10 108	82.02 72

thres100view900		52.33 117	53.91 123	50.48 105	56.10 100	67.79 99	56.18 110	49.18 52	35.86 147	25.22 145	34.74 129	34.10 155	42.41 135	64.45 83	62.62 113	73.81 110	77.85 104

v1921920		51.95 118	53.19 128	50.51 104	50.82 132	69.14 91	55.45 112	44.34 106	31.53 165	30.53 123	31.96 147	31.67 167	48.31 99	63.12 98	63.28 105	73.59 113	81.60 80

v148		51.72 119	53.15 129	50.05 109	50.15 137	67.51 102	56.98 99	42.85 128	32.60 159	32.41 114	33.88 136	34.71 151	44.45 125	61.06 125	63.00 108	73.45 117	79.24 100

TAPA-MVS	47.92 11	51.66 120	57.88 97	44.40 143	36.46 191	58.42 166	53.82 122	30.83 194	69.51 39	34.97 99	46.90 77	49.67 87	46.99 108	58.00 153	54.64 172	63.33 179	68.00 160
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		51.53 121	57.98 96	44.01 147	55.96 104	66.16 113	47.65 148	42.84 129	39.82 121	19.09 173	44.97 83	50.28 82	27.20 179	63.43 97	63.84 99	71.33 137	77.33 106

v1240		51.42 122	52.66 134	49.97 111	50.31 136	68.70 94	54.05 121	43.85 111	30.78 169	30.22 124	31.43 150	31.03 174	47.98 101	62.62 107	63.16 107	73.40 118	80.93 89

pmmvs4		51.28 123	52.50 136	49.85 114	49.54 141	63.02 140	52.83 130	43.41 118	44.65 96	35.71 95	34.38 132	32.25 163	45.14 120	60.21 138	60.03 144	72.44 130	72.98 131

Vis-MVSNet		51.13 124	58.04 95	43.06 153	47.68 149	67.71 100	49.10 143	39.09 149	37.75 131	22.57 156	51.03 66	48.78 94	32.42 164	62.12 111	61.80 122	67.49 161	77.12 107
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		51.04 125	58.79 87	42.00 159	40.59 178	65.32 122	46.65 157	39.26 147	39.90 120	27.30 136	54.12 54	52.03 73	30.93 168	59.85 141	59.62 149	67.23 163	80.70 90
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		50.91 126	52.45 137	49.11 120	56.10 100	64.53 128	53.06 127	47.31 76	35.86 147	25.22 145	34.74 129	34.10 155	41.08 138	60.84 127	61.37 130	71.90 134	75.70 116

SCA		50.88 127	53.70 125	47.59 127	55.99 102	55.81 175	43.14 169	33.45 187	45.16 94	37.14 91	41.83 92	43.82 116	44.43 126	60.37 132	60.02 145	71.38 136	68.90 157

gg-mvs-nofinetune		50.82 128	55.83 111	44.97 141	60.63 72	75.69 42	53.40 124	34.48 177	20.05 203	6.93 198	18.27 196	52.70 72	33.57 155	70.50 33	72.93 17	80.84 6	80.68 91

thres200		50.76 129	52.52 135	48.70 122	55.98 103	64.60 126	55.29 114	47.34 74	33.91 154	24.36 148	34.33 133	33.90 157	37.27 145	60.84 127	62.41 117	71.99 132	77.63 105

thres400		50.39 130	52.22 138	48.26 124	55.02 110	66.32 112	52.97 128	48.33 63	32.68 158	22.94 154	33.21 140	33.38 158	37.27 145	62.74 106	61.38 129	73.04 127	75.81 114

EG-PatchMatch MVS		50.23 131	50.89 148	49.47 116	59.54 82	70.88 75	52.46 132	44.01 108	26.22 190	31.91 115	24.97 182	31.45 170	33.48 157	64.79 80	66.51 72	75.40 76	71.39 141

IterMVS		50.23 131	53.27 127	46.68 130	47.59 151	60.58 154	53.10 126	36.62 163	36.07 143	25.89 142	39.42 108	40.05 132	43.65 130	60.22 137	61.35 131	73.23 122	75.23 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		50.14 133	52.78 133	47.06 128	45.56 160	63.56 136	54.22 119	43.74 113	34.10 153	25.37 144	29.79 163	42.06 124	38.70 141	64.25 87	65.54 76	74.75 88	70.18 148

ACMH	47.82 13	50.10 134	49.60 154	50.69 102	63.36 48	66.99 106	56.83 101	52.13 35	31.06 168	17.74 178	28.22 170	26.24 190	45.17 119	60.88 126	63.80 100	68.91 152	70.00 151
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		49.85 135	56.99 104	41.52 162	52.79 117	57.06 169	41.44 174	43.13 124	56.13 65	19.24 172	52.11 60	48.38 95	22.14 186	58.19 152	58.38 152	70.35 143	68.71 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		49.59 136	49.75 153	49.40 118	55.88 106	59.86 158	56.31 109	45.33 93	48.57 86	28.32 134	31.54 149	36.81 140	46.27 113	57.17 158	55.88 167	64.29 172	58.42 184

UniMVSNet_NR-MVSNet		49.56 137	53.04 130	45.49 137	51.59 128	64.42 132	46.97 153	51.01 39	37.87 129	16.42 179	39.87 101	34.91 150	33.43 159	59.59 142	62.70 110	73.52 115	71.94 134

CDS-MVSNet		49.25 138	53.97 122	43.75 149	47.53 152	64.53 128	48.59 144	42.27 132	33.77 155	26.64 140	40.46 99	42.26 123	30.01 171	61.77 116	61.71 125	67.48 162	73.28 127
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	44.22 14	49.14 139	51.75 141	46.10 133	42.78 174	55.60 178	53.11 125	34.46 178	55.69 69	32.47 113	34.16 134	41.45 127	48.91 96	57.13 159	54.09 173	64.84 169	64.10 167
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH+	47.85 12	49.13 140	48.86 160	49.44 117	56.75 95	62.01 146	56.62 106	47.55 73	37.49 134	23.98 149	26.68 175	29.46 181	43.12 131	57.45 157	58.85 151	68.62 154	70.05 150

NR-MVSNet		48.84 141	51.76 140	45.44 138	57.66 92	60.64 152	47.39 149	47.63 69	37.26 135	13.31 183	37.31 116	29.64 180	33.53 156	63.52 95	62.09 121	73.10 125	71.89 137

CR-MVSNet		48.82 142	51.85 139	45.29 139	46.74 154	55.95 173	52.06 133	34.21 179	42.17 109	31.74 116	32.92 142	42.53 122	45.00 121	58.80 147	61.11 134	61.99 184	69.47 153

thres600view7		48.44 143	50.23 151	46.35 132	54.05 116	64.60 126	50.18 139	47.34 74	31.73 164	20.74 163	32.28 146	32.62 161	33.79 154	60.84 127	56.11 165	71.99 132	73.40 124

test-mter		48.31 144	55.04 117	40.45 166	34.12 197	59.02 162	41.77 173	28.05 198	38.43 127	22.67 155	39.35 110	44.40 113	41.88 137	60.30 133	61.68 127	74.20 101	82.12 69

PatchT		48.11 145	51.27 147	44.43 142	50.13 138	61.58 148	33.59 187	32.92 189	40.38 119	31.74 116	30.60 156	36.93 139	45.00 121	58.80 147	61.11 134	73.19 123	69.47 153

TranMVSNet+NR-MVSNet		48.06 146	51.36 146	44.21 145	50.38 134	62.09 145	47.28 150	50.88 42	36.11 142	13.25 184	37.51 115	31.60 169	30.70 169	59.34 143	62.53 115	72.81 128	70.31 146

TransMVSNet (Re)		47.46 147	48.94 159	45.74 136	57.96 90	64.29 134	48.26 145	48.47 62	26.33 189	19.33 170	29.45 167	31.28 173	25.31 183	63.05 100	62.70 110	75.10 82	65.47 164

DU-MVS		47.33 148	50.86 149	43.20 152	44.43 167	60.64 152	46.97 153	47.63 69	37.26 135	16.42 179	37.31 116	31.39 171	33.43 159	57.53 155	59.98 146	70.35 143	71.94 134

v7n		47.22 149	48.38 161	45.87 135	48.20 148	63.58 135	50.69 137	40.93 140	26.60 188	26.44 141	26.52 176	29.65 179	38.19 143	58.22 151	60.23 143	70.79 141	73.83 121

UA-Net		47.19 150	53.02 131	40.38 167	55.31 108	60.02 157	38.41 180	38.68 151	36.42 141	22.47 158	51.95 61	58.72 58	25.62 182	54.11 171	53.40 177	61.79 185	56.51 187

Baseline_NR-MVSNet		47.14 151	50.83 150	42.84 155	44.43 167	63.31 138	44.50 165	50.36 47	37.71 132	11.25 189	30.84 153	32.09 164	30.96 167	57.53 155	63.73 101	75.53 73	70.60 145

pmmvs5		47.02 152	50.02 152	43.51 151	43.48 172	62.65 142	47.24 151	37.78 158	30.59 170	24.80 147	35.26 126	30.43 175	34.36 152	59.05 145	60.28 142	73.40 118	71.92 136

UniMVSNet (Re)		46.89 153	51.65 143	41.34 164	45.60 159	62.71 141	44.05 166	47.10 78	37.24 137	13.55 182	36.90 118	34.54 153	26.76 180	57.56 154	59.90 148	70.98 139	72.69 132

thisisatest0515		46.88 154	49.57 155	43.74 150	45.33 162	60.46 155	46.19 159	41.06 139	30.34 171	29.73 129	32.50 143	31.63 168	35.43 150	58.75 149	61.71 125	64.70 171	71.59 140

tfpnnormal		46.61 155	46.82 168	46.37 131	52.70 118	62.31 143	50.39 138	47.17 77	25.74 192	21.80 159	23.13 186	24.15 198	33.45 158	60.28 135	60.77 139	72.70 129	71.39 141

pm-mvs1		46.14 156	49.34 158	42.41 156	48.93 145	62.22 144	44.98 163	42.68 131	27.66 182	20.76 162	29.88 162	34.96 149	26.41 181	60.03 140	60.42 141	70.70 142	70.20 147

IterMVS-SCA-FT		45.87 157	51.55 144	39.24 170	46.22 155	59.43 159	52.89 129	31.93 190	36.01 144	23.68 150	38.86 111	39.88 134	39.05 140	56.25 164	58.17 153	41.70 205	72.25 133

MIMVSNet		45.62 158	49.56 156	41.02 165	38.17 182	64.43 131	49.48 141	35.43 170	36.53 140	20.06 167	22.58 187	35.16 148	28.75 176	61.97 113	62.20 119	74.20 101	64.07 168

gm-plane-assit		45.41 159	48.03 163	42.34 157	56.49 97	40.48 203	24.54 207	34.15 184	14.44 209	6.59 199	17.82 197	35.32 147	49.82 87	72.93 15	74.11 10	82.47 2	81.12 88

ADS-MVSNet		45.39 160	46.42 169	44.19 146	48.74 147	57.52 167	43.91 167	31.93 190	35.89 146	27.11 138	30.12 158	32.06 165	45.30 115	53.13 177	55.19 169	68.15 156	61.07 176

GG-mvs-BLEND		44.87 161	64.59 59	21.86 203	0.01 218	73.70 57	55.99 111	0.01 215	50.70 78	0.01 219	49.18 69	63.61 41	0.01 214	63.83 92	64.50 93	75.13 81	86.62 38

pmmvs-eth3d		44.67 162	45.27 174	43.98 148	42.56 175	55.72 177	44.97 164	40.81 141	31.96 163	29.13 130	26.09 178	25.27 195	36.69 148	55.13 168	56.62 162	69.68 150	66.12 163

MDTV_nov1_ep13_2view		44.44 163	45.75 172	42.91 154	46.13 156	63.43 137	46.53 158	34.20 181	29.08 177	19.95 168	26.23 177	27.89 185	35.88 149	53.36 176	56.43 163	74.74 89	63.86 169

CMPMVS	33.64 16	44.39 164	46.41 170	42.03 158	44.21 170	56.50 171	46.73 156	26.48 203	34.20 152	35.14 98	24.22 183	34.64 152	40.52 139	56.50 163	56.07 166	59.12 189	62.74 172
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		44.31 165	51.67 142	35.72 180	51.82 123	55.24 179	34.57 186	41.63 134	39.10 124	8.84 196	45.93 80	46.63 104	14.45 196	54.09 172	57.03 159	63.00 180	63.65 170

TAMVS		44.27 166	49.35 157	38.35 174	44.74 165	61.04 151	39.07 178	31.82 192	29.95 173	18.34 176	33.55 138	39.94 133	30.01 171	56.85 161	57.58 155	66.13 166	66.54 161

MVS-HIRNet		43.98 167	43.63 178	44.39 144	47.66 150	59.31 160	32.66 193	33.88 186	30.15 172	33.75 108	16.82 202	28.39 184	45.25 116	53.92 175	55.00 171	73.16 124	61.80 173

UniMVSNet_ETH3D		43.97 168	46.01 171	41.59 160	38.31 181	56.20 172	49.69 140	38.18 155	28.18 178	19.88 169	27.82 172	30.20 176	33.41 161	54.18 170	56.30 164	70.05 147	69.17 155

RPMNet		43.70 169	48.17 162	38.48 173	45.52 161	55.95 173	37.66 181	26.63 202	42.17 109	25.47 143	29.59 165	37.61 135	33.87 153	50.85 182	52.02 181	61.75 186	69.00 156

PatchMatch-RL		43.37 170	44.93 175	41.56 161	37.94 183	51.70 181	40.02 176	35.75 168	39.04 125	30.71 122	35.14 127	27.43 187	46.58 110	51.99 178	50.55 185	58.38 191	58.64 182

FMVSNet5		43.29 171	47.07 166	38.87 171	30.46 202	50.99 183	45.87 160	37.19 162	42.17 109	19.32 171	26.77 174	40.51 130	30.26 170	56.82 162	55.81 168	70.10 146	56.46 188

test0.0.03 1		43.07 172	46.95 167	38.54 172	51.68 125	58.77 164	35.28 182	46.35 88	32.05 162	12.44 185	28.53 169	35.52 145	14.40 197	57.12 160	56.93 160	71.11 138	59.69 178

anonymousdsp		43.03 173	47.19 165	38.18 175	36.00 193	56.92 170	38.44 179	34.56 176	24.22 194	22.53 157	29.69 164	29.92 177	35.21 151	53.96 174	58.98 150	62.32 183	76.66 110

USDC		42.80 174	45.57 173	39.58 168	34.55 195	51.13 182	42.61 170	36.21 165	39.59 122	23.65 151	33.13 141	20.87 203	37.86 144	55.35 167	57.16 158	62.61 181	61.75 174

pmnet_mix02		42.41 175	43.24 180	41.44 163	45.80 158	57.46 168	42.19 171	41.57 135	29.38 175	23.39 152	26.08 179	23.96 199	27.31 178	51.50 179	53.76 175	68.36 155	60.58 177

CHOSEN 280x420		42.39 176	47.40 164	36.54 178	33.56 198	39.66 206	40.67 175	26.88 201	34.66 149	18.03 177	30.09 159	45.59 109	44.82 123	54.46 169	54.00 174	55.28 198	73.32 126

pmmvs6		41.90 177	44.01 177	39.43 169	44.45 166	58.77 164	41.92 172	39.22 148	21.74 196	19.08 174	17.40 200	31.33 172	24.28 185	55.94 165	56.67 161	67.60 160	66.24 162

Anonymous20231206		40.63 178	43.29 179	37.53 176	48.88 146	55.81 175	34.99 183	44.98 97	28.16 179	10.16 193	17.26 201	27.50 186	18.28 190	54.00 173	55.07 170	67.85 158	65.23 165

CVMVSNet		38.91 179	44.49 176	32.40 189	34.57 194	47.20 194	34.81 184	34.20 181	31.45 166	8.95 195	38.86 111	36.38 142	24.30 184	47.77 186	46.94 197	57.59 193	62.85 171

COLMAP_ROB	34.79 15	38.65 180	40.72 183	36.23 179	36.41 192	49.22 190	45.51 162	27.60 200	37.81 130	20.54 164	23.37 185	24.25 197	28.11 177	51.02 181	48.55 188	59.22 188	50.82 198
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		38.23 181	41.72 182	34.15 182	40.56 179	50.07 186	33.17 190	44.35 105	27.64 184	5.54 205	30.84 153	26.67 188	14.99 194	45.64 189	52.38 180	66.29 165	58.83 181

WR-MVS		37.61 182	42.15 181	32.31 191	43.64 171	51.85 180	29.39 199	43.35 120	27.65 183	4.40 207	29.90 161	29.80 178	10.46 200	46.73 188	51.98 182	62.60 182	57.16 185

TinyColmap		37.18 183	37.37 196	36.95 177	31.17 201	45.21 197	39.71 177	34.65 174	29.83 174	20.20 165	18.54 195	13.72 211	38.27 142	50.33 183	51.57 183	57.71 192	52.42 195

CP-MVSNet		37.09 184	40.62 184	32.99 184	37.56 185	48.25 191	32.75 191	43.05 125	27.88 181	5.93 201	31.27 151	25.82 193	15.09 192	43.37 196	48.82 186	63.54 177	58.90 179

DTE-MVSNet		36.91 185	40.44 185	32.79 187	40.74 177	47.55 193	30.71 197	44.39 102	27.03 186	4.32 208	30.88 152	25.99 191	12.73 198	45.58 190	50.80 184	63.86 174	55.23 191

PS-CasMVS		36.84 186	40.23 188	32.89 185	37.44 186	48.09 192	32.68 192	42.97 127	27.36 185	5.89 202	30.08 160	25.48 194	14.96 195	43.28 197	48.71 187	63.39 178	58.63 183

WR-MVS_H		36.29 187	40.35 187	31.55 193	37.80 184	49.94 188	30.57 198	41.11 138	26.90 187	4.14 209	30.72 155	28.85 182	10.45 201	42.47 198	47.99 192	65.24 168	55.54 189

SixPastTwentyTwo		36.11 188	37.80 192	34.13 183	37.13 189	46.72 195	34.58 185	34.96 172	21.20 199	11.66 186	29.15 168	19.88 204	29.77 173	44.93 191	48.34 189	56.67 195	54.41 193

test20.03		36.00 189	38.92 189	32.60 188	45.92 157	50.99 183	28.05 203	43.69 116	21.62 197	6.03 200	17.61 199	25.91 192	8.34 207	51.26 180	52.60 179	63.58 175	52.46 194

TDRefinement		35.76 190	38.23 190	32.88 186	19.09 211	46.04 196	43.29 168	29.49 195	33.49 156	19.04 175	22.29 189	17.82 206	29.69 175	48.60 185	47.24 195	56.65 196	52.12 196

LTVRE_ROB	32.83 17	35.10 191	37.46 193	32.35 190	43.12 173	49.99 187	28.52 201	33.23 188	12.73 210	8.18 197	27.71 173	21.34 202	32.64 163	46.92 187	48.11 190	48.41 202	55.45 190
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		34.96 192	38.17 191	31.22 194	22.78 206	40.82 202	33.56 188	23.61 205	29.16 176	21.43 161	28.00 171	21.43 201	31.90 165	44.33 194	42.12 200	54.07 200	61.34 175

testgi		34.51 193	37.42 194	31.12 195	47.37 153	50.34 185	24.38 208	41.21 136	20.32 201	5.64 204	20.56 191	26.55 189	8.06 208	49.28 184	52.65 178	60.05 187	42.23 204

MDA-MVSNet-bldmvs		34.31 194	34.11 200	34.54 181	24.73 204	49.66 189	33.42 189	43.03 126	21.59 198	11.10 190	19.81 193	12.68 212	31.41 166	35.59 203	48.05 191	63.56 176	51.39 197

N_pmnet		34.09 195	35.74 198	32.17 192	37.25 188	43.17 200	32.26 195	35.57 169	26.22 190	10.60 192	20.44 192	19.38 205	20.20 188	44.59 193	47.00 196	57.13 194	49.35 201

RPSCF		33.61 196	40.43 186	25.65 199	16.00 213	32.41 208	31.73 196	13.33 212	50.13 80	23.12 153	31.56 148	40.09 131	32.73 162	41.14 202	37.05 203	36.99 208	50.63 199

EU-MVSNet		33.00 197	36.49 197	28.92 196	33.10 199	42.86 201	29.32 200	35.99 166	22.94 195	5.83 203	25.29 180	24.43 196	15.21 191	41.22 201	41.65 202	54.08 199	57.01 186

pmmvs3		31.22 198	33.62 201	28.43 197	22.82 205	40.26 205	26.40 204	22.05 207	16.89 207	10.99 191	14.72 204	16.26 207	29.70 174	44.82 192	47.39 194	58.61 190	54.98 192

FC-MVSNet-test		30.97 199	37.38 195	23.49 202	37.42 187	33.68 207	19.43 210	39.27 146	31.37 167	1.67 215	38.56 113	28.85 182	6.06 211	41.40 199	43.80 199	37.10 207	44.03 203

new-patchmatchnet		30.47 200	32.80 203	27.75 198	36.81 190	43.98 198	24.85 206	39.29 145	20.52 200	4.06 210	15.94 203	16.05 208	9.57 202	41.32 200	42.05 201	51.94 201	49.74 200

MIMVSNet1		29.60 201	33.37 202	25.20 201	19.52 209	43.94 199	26.29 205	37.92 157	19.95 204	3.79 211	12.64 208	21.99 200	7.70 209	43.83 195	46.32 198	55.97 197	44.92 202

FPMVS		26.87 202	28.19 204	25.32 200	27.09 203	29.49 209	32.28 194	17.79 209	28.09 180	11.33 187	19.38 194	14.69 209	20.88 187	35.11 204	32.82 205	42.56 204	37.75 205

PMVS	18.18 18	21.95 203	22.85 205	20.90 204	21.92 207	14.78 211	19.95 209	17.31 210	15.69 208	11.32 188	13.70 205	13.91 210	15.02 193	34.92 205	31.72 206	39.85 206	35.20 206
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		19.10 204	22.71 206	14.89 206	10.93 215	24.08 210	14.22 211	13.94 211	18.68 205	2.93 212	12.84 207	11.27 213	11.94 199	30.57 207	30.58 207	35.38 209	30.93 207

Gipuma		17.16 205	17.83 207	16.36 205	18.76 212	12.15 214	11.97 212	27.78 199	17.94 206	4.86 206	2.53 214	2.73 218	8.90 205	34.32 206	36.09 204	25.92 210	19.06 209
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		12.25 206	14.17 208	10.00 209	11.39 214	14.35 212	8.21 213	19.29 208	9.31 211	0.19 218	7.38 210	6.19 216	1.10 213	19.26 208	21.13 209	19.85 211	21.56 208

E-PMN		10.66 207	8.30 210	13.42 207	19.91 208	7.87 215	4.30 216	29.47 196	8.37 214	1.70 214	3.67 211	1.29 221	9.12 204	8.98 212	13.59 210	16.03 212	14.30 212

EMVS		10.15 208	7.67 211	13.05 208	19.22 210	7.77 216	4.48 214	29.34 197	8.65 213	1.67 215	3.55 212	1.36 220	9.15 203	8.15 213	11.79 212	14.44 213	12.43 213

MVE	10.35 19	9.76 209	11.08 209	8.22 210	4.43 216	13.04 213	3.36 217	23.57 206	5.74 215	1.76 213	3.09 213	1.75 219	6.78 210	12.78 210	23.04 208	9.44 214	18.09 210
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 210	0.01 212	0.00 212	0.00 219	0.00 219	0.00 220	0.00 216	0.01 216	0.00 220	0.02 215	0.00 222	0.00 216	0.01 214	0.01 213	0.00 217	0.03 214

test123		0.01 210	0.01 212	0.00 212	0.00 219	0.00 219	0.00 220	0.00 216	0.01 216	0.00 220	0.02 215	0.00 222	0.01 214	0.00 215	0.01 213	0.00 217	0.03 214

uanet_test		0.00 212	0.00 214	0.00 212	0.00 219	0.00 219	0.00 220	0.00 216	0.00 218	0.00 220	0.00 217	0.00 222	0.00 216	0.00 215	0.00 215	0.00 217	0.00 216

sosnet-low-res		0.00 212	0.00 214	0.00 212	0.00 219	0.00 219	0.00 220	0.00 216	0.00 218	0.00 220	0.00 217	0.00 222	0.00 216	0.00 215	0.00 215	0.00 217	0.00 216

sosnet		0.00 212	0.00 214	0.00 212	0.00 219	0.00 219	0.00 220	0.00 216	0.00 218	0.00 220	0.00 217	0.00 222	0.00 216	0.00 215	0.00 215	0.00 217	0.00 216

RE-MVS-def										21.59 160

9.14												80.07 5

SR-MVS					63.74 46			48.51 61				73.80 18

Anonymous202405211			56.81 105		60.91 68	73.48 60	59.82 83	48.68 58	39.26 123		24.00 184	46.77 103	50.73 81	65.28 77	65.72 75	75.37 77	83.17 62

our_test_3					49.68 140	61.50 149	45.84 161

ambc			35.52 199		38.36 180	40.40 204	28.38 202		25.20 193	14.87 181	13.22 206	7.54 215	19.34 189	55.63 166	47.79 193	47.91 203	58.89 180

MTAPA										54.82 16		71.98 24

MTMP										50.64 32		68.31 30

Patchmatch-RL test							0.69 219

tmp_tt				4.41 211	2.56 217	1.81 218	2.61 218	0.27 214	20.12 202	9.81 194	17.69 198	9.04 214	1.96 212	12.88 209	12.11 211	9.23 215

XVS					62.70 54	73.06 63	61.80 70			42.02 71		63.42 43				74.68 93

X-MVStestdata					62.70 54	73.06 63	61.80 70			42.02 71		63.42 43				74.68 93

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						64.49 130	52.06 133	34.21 179		31.74 116

DeepMVS_CX						5.87 217	4.32 215	1.74 213	9.04 212	1.30 217	7.97 209	3.16 217	8.56 206	9.74 211		6.30 216	14.51 211

High-resolution multi-view benchmark