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.

SF-MVS		87.30 9	88.71 9	85.64 6	94.57 1	94.55 5	91.01 1	79.94 1	89.15 15	79.85 11	92.37 6	83.29 14	79.75 14	83.52 30	82.72 37	88.75 36	95.37 27

DPM-MVS		85.41 14	86.72 20	83.89 13	91.66 16	91.92 19	90.49 2	78.09 3	86.90 22	73.95 25	74.52 40	82.01 21	79.29 18	90.24 1	90.65 1	89.86 9	90.78 101

RA-MVS		89.09 1	91.24 1	86.58 1	90.46 21	94.66 3	90.27 3	76.84 9	95.13 1	90.11 1	94.39 4	86.45 4	83.52 5	87.21 6	83.04 33	88.26 53	97.83 4

MED-MVS		88.53 3	90.83 3	85.84 4	92.32 10	93.45 7	89.69 4	77.14 7	93.69 4	86.32 4	94.60 2	86.09 5	81.66 7	86.22 14	85.36 11	87.93 64	96.41 15

TPM-MVS					94.34 2	93.91 6	89.34 5			75.49 22	82.52 23	83.34 13	83.53 4			89.62 12	90.78 101
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
ACM-MVS					93.98 4	92.96 11	89.10 6		88.78 17	69.60 40	79.43 30	82.20 20	80.91 12			88.69 37	94.58 33

DPE-MVS		87.60 8	90.44 6	84.29 10	92.09 11	93.44 8	88.69 7	75.11 13	93.06 7	80.80 10	94.23 5	86.70 3	81.44 10	84.84 21	83.52 30	87.64 76	97.28 6
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SED-MVS		88.94 2	90.98 2	86.56 2	92.53 8	95.09 1	88.55 8	76.83 10	94.16 2	86.57 3	90.85 8	87.07 1	86.18 1	86.36 9	85.08 15	88.67 38	98.21 3

DVP-MVS++		87.98 5	89.76 8	85.89 3	92.57 7	94.57 4	88.34 9	76.61 11	92.40 9	83.40 7	89.26 13	85.57 8	86.04 2	86.24 13	84.89 17	88.39 49	95.42 24

APDe-MVS		86.37 10	88.41 11	84.00 12	91.43 18	91.83 20	88.34 9	74.67 14	91.19 10	81.76 9	91.13 7	81.94 23	80.07 13	83.38 31	82.58 39	87.69 74	96.78 12
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
TestfortrainingZip							88.32 11	77.84 4		88.26 2						90.10 7

aaEdge-Enhanced		87.94 6	89.84 7	85.72 5	91.74 14	92.20 17	88.32 11	77.84 4	92.47 8	85.03 6	94.60 2	85.70 7	81.31 11	83.94 28	83.57 29	90.10 7	96.41 15

MSP-MVS		87.87 7	90.57 5	84.73 8	89.38 31	91.60 21	88.24 13	74.15 16	93.55 5	82.28 8	94.99 1	83.21 15	85.96 3	87.67 5	84.67 20	88.32 50	98.29 1
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
MCST-MVS		85.75 12	86.99 16	84.31 9	94.07 3	92.80 12	88.15 14	79.10 2	85.66 26	70.72 34	76.50 38	80.45 27	82.17 6	88.35 2	87.49 3	91.63 2	97.65 5

DVP-MVS		88.07 4	90.73 4	84.97 7	91.98 12	95.01 2	87.86 15	76.88 8	93.90 3	85.15 5	90.11 10	86.90 2	79.46 17	86.26 12	84.67 20	88.50 46	98.25 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
CNVR-MVS		85.96 11	87.58 14	84.06 11	92.58 6	92.40 15	87.62 16	77.77 6	88.44 18	75.93 20	79.49 29	81.97 22	81.65 8	87.04 8	86.58 4	88.79 34	97.18 8

CSCG		82.90 24	84.52 27	81.02 21	91.85 13	93.43 9	87.14 17	74.01 18	81.96 37	76.14 18	70.84 42	82.49 17	69.71 91	82.32 45	85.18 14	87.26 92	95.40 26

SMA-MVS		85.24 15	88.27 12	81.72 18	91.74 14	90.71 25	86.71 18	73.16 23	90.56 13	74.33 24	83.07 21	85.88 6	77.16 26	86.28 11	85.58 8	87.23 93	95.77 17
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
NCCC		84.16 19	85.46 25	82.64 14	92.34 9	90.57 28	86.57 19	76.51 12	86.85 23	72.91 28	77.20 36	78.69 31	79.09 20	84.64 23	84.88 18	88.44 47	95.41 25

APD-MVS		84.83 16	87.00 15	82.30 16	89.61 29	89.21 41	86.51 20	73.64 20	90.98 11	77.99 16	89.89 11	80.04 29	79.18 19	82.00 52	81.37 58	86.88 102	95.49 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + ACMM		81.59 29	85.84 24	76.63 42	89.82 27	86.53 96	86.32 21	66.72 57	85.96 25	65.43 52	88.98 14	82.29 18	67.57 112	82.06 50	81.33 59	83.93 189	93.75 47

HPM-MVS++		85.64 13	88.43 10	82.39 15	92.65 5	90.24 31	85.83 22	74.21 15	90.68 12	75.63 21	86.77 16	84.15 11	78.68 21	86.33 10	85.26 12	87.32 88	95.60 21

SD-MVS		84.31 18	86.96 17	81.22 19	88.98 35	88.68 52	85.65 23	73.85 19	89.09 16	79.63 12	87.34 15	84.84 9	73.71 40	82.66 39	81.60 53	85.48 145	94.51 34
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.		84.39 17	86.58 21	81.83 17	88.09 43	86.47 97	85.63 24	73.62 21	90.13 14	79.24 13	89.67 12	82.99 16	77.72 24	81.22 57	80.92 71	86.68 108	94.66 32
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
train_agg		83.35 22	86.93 18	79.17 30	89.70 28	88.41 60	85.60 25	72.89 25	86.31 24	66.58 49	90.48 9	82.24 19	73.06 48	83.10 35	82.64 38	87.21 97	95.30 28

ACMMP_NAP		83.54 21	86.37 22	80.25 25	89.57 30	90.10 33	85.27 26	71.66 27	87.38 20	73.08 27	84.23 20	80.16 28	75.31 30	84.85 20	83.64 26	86.57 110	94.21 39

SteuartSystems-ACMMP		82.51 25	85.35 26	79.20 29	90.25 22	89.39 39	84.79 27	70.95 29	82.86 33	68.32 43	86.44 17	77.19 32	73.07 47	83.63 29	83.64 26	87.82 68	94.34 36
Skip Steuart: Steuart Systems R&D Blog.
MSLP-MVS++		78.57 41	77.33 58	80.02 26	88.39 39	84.79 115	84.62 28	66.17 61	75.96 58	78.40 14	61.59 68	71.47 49	73.54 43	78.43 99	78.88 101	88.97 31	90.18 113

HFP-MVS		82.48 26	84.12 28	80.56 22	90.15 23	87.55 75	84.28 29	69.67 36	85.22 27	77.95 17	84.69 19	75.94 35	75.04 32	81.85 53	81.17 65	86.30 118	92.40 74

CDPH-MVS		79.39 39	82.13 35	76.19 45	89.22 34	88.34 62	84.20 30	71.00 28	79.67 49	56.97 110	77.77 33	72.24 46	68.50 105	81.33 56	82.74 34	87.23 93	92.84 66

DELS-MVS		79.49 35	79.84 44	79.08 31	88.26 42	92.49 13	84.12 31	70.63 31	65.27 95	69.60 40	61.29 70	66.50 65	72.75 51	88.07 4	88.03 2	89.13 28	97.22 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
DeepC-MVS_fast	75.41 2	81.69 28	82.10 36	81.20 20	91.04 20	87.81 74	83.42 32	74.04 17	83.77 30	71.09 32	66.88 54	72.44 42	79.48 16	85.08 18	84.97 16	88.12 58	93.78 46
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS	74.46 3	80.30 34	81.05 39	79.42 27	87.42 45	88.50 57	83.23 33	73.27 22	82.78 34	71.01 33	62.86 65	69.93 55	74.80 34	84.30 24	84.20 23	86.79 105	94.77 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMPR		80.62 33	82.98 31	77.87 37	88.41 38	87.05 86	83.02 34	69.18 39	83.91 29	68.35 42	82.89 22	73.64 39	72.16 58	80.78 63	81.13 66	86.10 123	91.43 91

HQP-MVS		78.26 43	80.91 40	75.17 53	85.67 54	84.33 122	83.01 35	69.38 37	79.88 47	55.83 111	79.85 28	64.90 72	70.81 79	82.46 41	81.78 47	86.30 118	93.18 58

PGM-MVS		79.42 38	81.84 37	76.60 43	88.38 40	86.69 91	82.97 36	65.75 63	80.39 44	64.94 55	81.95 26	72.11 47	71.41 71	80.45 65	80.55 82	86.18 120	90.76 104

MGCNet		83.82 20	86.88 19	80.26 24	88.48 36	93.17 10	82.93 37	67.66 50	88.28 19	74.90 23	77.08 37	80.93 25	78.09 22	85.83 17	85.88 7	89.53 17	96.96 11

OPM-MVS		72.74 95	70.93 119	74.85 61	85.30 55	84.34 121	82.82 38	69.79 35	49.96 178	55.39 117	54.09 112	60.14 106	70.04 90	80.38 69	79.43 94	85.74 134	88.20 140
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MP-MVS		80.94 30	83.49 30	77.96 35	88.48 36	88.16 67	82.82 38	69.34 38	80.79 43	69.67 38	82.35 24	77.13 33	71.60 67	80.97 62	80.96 70	85.87 130	94.06 42
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		79.44 36	81.51 38	77.02 41	86.95 47	85.96 107	82.00 40	68.44 45	81.82 38	67.39 44	77.43 34	73.68 38	71.62 66	79.56 82	79.58 92	85.73 135	92.51 70

CLD-MVS		77.36 51	77.29 59	77.45 40	82.21 63	88.11 69	81.92 41	68.96 41	77.97 53	69.62 39	62.08 66	59.44 112	73.57 42	81.75 54	81.27 62	88.41 48	90.39 109
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
3Dnovator	70.49 5	78.42 42	76.77 63	80.35 23	91.43 18	90.27 30	81.84 42	70.79 30	72.10 65	71.95 29	50.02 138	67.86 62	77.46 25	82.89 36	84.24 22	88.61 41	89.99 116

PCF-MVS	70.85 4	75.73 60	76.55 66	74.78 62	83.67 57	88.04 72	81.47 43	70.62 33	69.24 79	57.52 108	60.59 74	69.18 57	70.65 82	77.11 115	77.65 119	84.75 172	94.01 43
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
ACMMP		77.61 48	79.59 45	75.30 52	85.87 53	85.58 108	81.42 44	67.38 53	79.38 50	62.61 73	78.53 31	65.79 67	68.80 103	78.56 96	78.50 107	85.75 132	90.80 100
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
QAPM		77.50 49	77.43 57	77.59 39	91.52 17	92.00 18	81.41 45	70.63 31	66.22 86	58.05 105	54.70 103	71.79 48	74.49 38	82.46 41	82.04 41	89.46 21	92.79 68

sasdasda		77.65 46	79.59 45	75.39 49	81.52 68	89.83 37	81.32 46	60.74 141	80.05 45	66.72 45	68.43 47	65.09 68	74.72 36	78.87 90	82.73 35	87.32 88	92.16 80

canonicalmvs		77.65 46	79.59 45	75.39 49	81.52 68	89.83 37	81.32 46	60.74 141	80.05 45	66.72 45	68.43 47	65.09 68	74.72 36	78.87 90	82.73 35	87.32 88	92.16 80

MVSMamba_PlusPlus		80.76 32	82.78 33	78.41 34	81.93 66	91.55 23	81.27 48	68.39 46	83.28 31	66.70 48	69.11 46	68.52 58	81.56 9	88.17 3	86.51 6	90.62 5	92.28 77

CANet		80.90 31	82.93 32	78.53 33	86.83 49	92.26 16	81.19 49	66.95 54	81.60 40	69.90 37	66.93 53	74.80 36	76.79 27	84.68 22	84.77 19	89.50 19	95.50 22

TSAR-MVS + GP.		82.27 27	85.98 23	77.94 36	80.72 76	88.25 66	81.12 50	67.71 49	87.10 21	73.31 26	85.23 18	83.68 12	76.64 28	80.43 66	81.47 56	88.15 57	95.66 20

X-MVS		78.16 44	80.55 41	75.38 51	87.99 44	86.27 102	81.05 51	68.98 40	78.33 51	61.07 93	75.25 39	72.27 43	67.52 114	80.03 73	80.52 83	85.66 142	91.20 95

CPTT-MVS		75.43 62	77.13 61	73.44 76	81.43 70	82.55 136	80.96 52	64.35 71	77.95 54	61.39 88	69.20 45	70.94 51	69.38 98	73.89 155	73.32 172	83.14 202	92.06 86

PHI-MVS		79.43 37	84.06 29	74.04 72	86.15 52	91.57 22	80.85 53	68.90 42	82.22 36	51.81 132	78.10 32	74.28 37	70.39 85	84.01 27	84.00 24	86.14 122	94.24 37

casdiffmvs_mvg		75.57 61	76.04 68	75.02 57	80.48 79	89.31 40	80.79 54	64.04 78	66.95 84	63.87 61	57.52 86	61.33 89	72.90 49	82.01 51	81.99 44	88.03 60	93.16 59
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DI_MVS_pp		73.94 80	74.85 78	72.88 83	76.57 129	86.80 89	80.41 55	61.47 130	62.35 110	59.44 102	47.91 146	68.12 59	72.24 57	82.84 38	81.50 55	87.15 99	94.42 35

3Dnovator+	70.16 6	77.87 45	77.29 59	78.55 32	89.25 33	88.32 63	80.09 56	67.95 48	74.89 63	71.83 30	52.05 128	70.68 52	76.27 29	82.27 46	82.04 41	85.92 127	90.77 103

MVS_Test		75.22 63	76.69 64	73.51 73	79.30 92	88.82 49	80.06 57	58.74 152	69.77 73	57.50 109	59.78 77	61.35 87	75.31 30	82.07 49	83.60 28	90.13 6	91.41 93

AdaColmap		76.23 57	73.55 93	79.35 28	89.38 31	85.00 112	79.99 58	73.04 24	76.60 57	71.17 31	55.18 101	57.99 123	77.87 23	76.82 120	76.82 126	84.67 174	86.45 151

LGP-MVS_train		72.02 103	73.18 96	70.67 105	82.13 64	80.26 159	79.58 59	63.04 96	70.09 70	51.98 130	65.06 58	55.62 149	62.49 142	75.97 131	76.32 133	84.80 171	88.93 128

EPNet		79.28 40	82.25 34	75.83 47	88.31 41	90.14 32	79.43 60	68.07 47	81.76 39	61.26 89	77.26 35	70.08 54	70.06 89	82.43 43	82.00 43	87.82 68	92.09 85
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MAR-MVS		77.19 52	78.37 54	75.81 48	89.87 26	90.58 27	79.33 61	65.56 65	77.62 55	58.33 104	59.24 80	67.98 60	74.83 33	82.37 44	83.12 32	86.95 100	87.67 144
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
hybridcas		74.86 69	74.70 80	75.04 56	79.57 84	89.12 42	78.97 62	64.02 79	65.29 94	65.36 53	54.81 102	60.39 101	73.16 45	80.41 67	80.49 84	89.18 27	92.39 75

viewmanbaseed2359cas		74.53 71	74.69 82	74.35 66	79.37 90	88.90 48	78.96 63	64.07 77	63.67 99	62.19 77	56.95 90	58.42 118	72.04 61	80.08 72	81.92 45	89.47 20	92.91 63

E2		75.18 66	75.21 74	75.15 54	79.77 82	89.10 43	78.62 64	64.19 74	65.19 96	65.90 50	58.15 83	58.36 119	72.56 53	80.74 64	81.78 47	89.84 10	93.19 57

Casviewmamba		75.20 64	75.26 73	75.13 55	80.13 81	88.67 53	78.61 65	64.02 79	67.43 82	66.72 45	56.60 92	60.53 94	73.45 44	80.41 67	81.03 67	87.84 66	92.13 84

viewmacassd2359aftdt		73.00 91	72.63 102	73.44 76	78.70 105	88.45 59	78.52 66	63.49 92	57.74 138	60.15 100	52.57 122	57.01 132	70.69 81	78.85 93	81.29 60	89.10 29	92.48 71

DeepPCF-MVS	76.94 1	83.08 23	87.77 13	77.60 38	90.11 24	90.96 24	78.48 67	72.63 26	93.10 6	65.84 51	80.67 27	81.55 24	74.80 34	85.94 16	85.39 10	83.75 191	96.77 13

viewcassd2359sk11		74.75 70	74.61 84	74.90 60	79.62 83	88.96 47	78.47 68	64.08 76	63.51 102	65.27 54	57.02 89	57.89 125	72.25 56	80.30 71	81.57 54	89.72 11	93.04 61

casdiffmvs		75.20 64	75.69 71	74.63 63	79.26 94	89.07 44	78.47 68	63.59 90	67.05 83	63.79 62	55.72 97	60.32 102	73.58 41	82.16 47	81.78 47	89.08 30	93.72 49
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewdifsd2359ckpt09		73.89 81	73.57 92	74.26 67	78.54 109	88.37 61	78.34 70	63.79 86	63.31 103	64.90 56	57.29 88	56.53 139	72.15 59	79.12 84	77.91 117	87.83 67	92.48 71

E5new		73.48 86	72.84 99	74.23 68	79.06 96	88.52 55	78.32 71	63.99 81	58.33 130	63.34 66	54.07 113	56.89 133	71.29 72	78.99 87	80.82 75	89.35 22	92.26 78

E5		73.48 86	72.84 99	74.23 68	79.06 96	88.52 55	78.32 71	63.99 81	58.33 130	63.34 66	54.07 113	56.89 133	71.29 72	78.99 87	80.82 75	89.35 22	92.26 78

viewdifsd2359ckpt13		74.11 77	74.06 87	74.18 70	79.34 91	89.07 44	78.31 73	64.25 73	62.52 108	62.06 78	55.80 95	56.70 137	72.29 55	80.35 70	81.47 56	88.80 33	92.47 73

E3new		74.17 75	73.83 90	74.57 64	79.40 88	88.76 50	78.30 74	63.89 84	61.21 115	64.38 60	55.65 98	57.34 130	71.87 62	79.73 79	81.28 61	89.55 15	92.86 64

E3		74.17 75	73.83 90	74.57 64	79.40 88	88.76 50	78.30 74	63.89 84	61.22 114	64.40 59	55.64 99	57.35 129	71.86 63	79.73 79	81.27 62	89.55 15	92.86 64

diffmvs		74.32 72	75.42 72	73.04 82	75.60 139	87.27 79	78.20 76	62.96 98	68.66 80	61.89 81	59.79 76	59.84 108	71.80 64	78.30 102	79.87 87	87.80 70	94.23 38
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVSTER		76.92 53	79.92 43	73.42 78	74.98 145	82.97 130	78.15 77	63.41 93	78.02 52	64.41 58	67.54 51	72.80 41	71.05 75	83.29 34	83.73 25	88.53 45	91.12 96

OpenMVS	67.62 8	74.92 68	73.91 88	76.09 46	90.10 25	90.38 29	78.01 78	66.35 59	66.09 89	62.80 70	46.33 163	64.55 74	71.77 65	79.92 75	80.88 72	87.52 80	89.20 125

E4		73.32 89	72.68 101	74.06 71	79.06 96	88.47 58	77.98 79	63.57 91	57.73 139	63.18 68	53.48 116	56.74 136	71.26 74	78.95 89	80.84 73	89.30 24	92.55 69

ACMP	68.86 7	72.15 101	72.25 103	72.03 95	80.96 72	80.87 152	77.93 80	64.13 75	69.29 77	60.79 96	64.04 61	53.54 161	63.91 131	73.74 158	75.27 145	84.45 181	88.98 127
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MS-PatchMatch		70.34 115	69.00 134	71.91 98	85.20 56	85.35 109	77.84 81	61.77 125	58.01 136	55.40 116	41.26 184	58.34 120	61.69 145	81.70 55	78.29 108	89.56 14	80.02 208

casdiffseed414692147		71.49 105	70.06 129	73.15 81	79.11 95	87.26 80	77.82 82	62.34 117	58.44 129	60.33 99	46.19 164	51.26 169	71.53 68	77.07 116	79.56 93	87.80 70	90.61 106

diffmvs_AUTHOR		73.73 83	74.73 79	72.56 89	75.05 143	87.15 84	77.82 82	62.29 118	66.22 86	61.10 92	57.92 84	59.72 110	71.43 69	78.25 104	79.68 90	87.71 73	94.17 40

ACMM	66.70 10	70.42 111	68.49 138	72.67 86	82.85 58	77.76 182	77.70 84	64.76 70	64.61 97	60.74 97	49.29 140	53.97 159	65.86 121	74.97 141	75.57 142	84.13 188	83.29 184
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EC-MVSNet		76.05 58	78.87 48	72.77 84	78.87 104	86.63 92	77.50 85	57.04 181	75.34 59	61.68 85	64.20 60	69.56 56	73.96 39	82.12 48	80.65 80	87.57 78	93.57 50

MVS_111021_HR		77.42 50	78.40 53	76.28 44	86.95 47	90.68 26	77.41 86	70.56 34	66.21 88	62.48 75	66.17 57	63.98 76	72.08 60	82.87 37	83.15 31	88.24 54	95.71 19

ET-MVSNet_ETH3D		71.38 108	74.70 80	67.51 134	51.61 250	88.06 71	77.29 87	60.95 140	63.61 100	48.36 148	66.60 55	60.67 92	79.55 15	73.56 161	80.58 81	87.30 91	89.80 118

viewmambaseed2359dif		72.54 99	72.88 98	72.13 94	74.78 147	86.45 98	77.24 88	61.65 129	62.61 107	61.83 82	55.85 93	57.51 127	70.64 83	75.71 133	77.90 118	86.65 109	94.16 41

dtuplus		72.12 102	72.21 105	72.01 96	74.74 148	86.54 95	77.22 89	61.74 128	60.26 121	61.52 87	54.43 109	57.46 128	70.32 87	75.64 135	77.35 122	86.51 114	93.75 47

GG-mvs-BLEND		54.54 228	77.58 56	27.67 261	0.03 282	90.09 34	77.20 90	0.02 276	66.83 85	0.05 282	59.90 75	73.33 40	0.04 278	78.40 100	79.30 96	88.65 39	95.20 29

E6new		72.71 96	72.05 106	73.49 74	79.01 100	88.31 64	77.06 91	62.71 110	56.63 144	62.00 79	52.31 123	55.75 146	70.93 77	78.51 97	80.72 78	89.20 25	92.14 82

E6		72.71 96	72.05 106	73.49 74	79.01 100	88.31 64	77.06 91	62.71 110	56.63 144	62.00 79	52.31 123	55.75 146	70.93 77	78.51 97	80.72 78	89.20 25	92.14 82

viewdifsd2359ckpt07		72.78 94	72.24 104	73.41 79	78.58 108	88.14 68	76.95 93	63.73 88	57.28 140	63.47 64	54.45 108	56.62 138	69.16 100	78.86 92	79.98 86	88.58 44	90.33 110

CostFormer		72.18 100	73.90 89	70.18 108	79.47 86	86.19 105	76.94 94	48.62 231	66.07 90	60.40 98	54.14 111	65.82 66	67.98 106	75.84 132	76.41 131	87.67 75	92.83 67

hybridnocas07		74.06 78	75.21 74	72.71 85	75.43 141	87.22 81	76.90 95	62.70 112	69.87 71	62.72 72	59.53 78	59.98 107	71.03 76	77.21 114	79.23 97	87.49 81	93.44 53

viewmamba		73.51 85	74.57 85	72.28 92	75.68 138	87.10 85	76.82 96	62.81 104	69.38 76	61.26 89	58.32 82	59.73 109	70.35 86	76.34 125	78.81 102	86.77 106	92.32 76

hybrid		73.86 82	75.13 76	72.38 90	75.05 143	87.04 87	76.72 97	62.53 114	69.51 75	62.37 76	59.27 79	60.40 100	70.21 88	77.07 116	79.17 98	87.39 84	93.46 52

baseline1		71.47 106	72.02 108	70.82 103	80.56 78	84.51 118	76.61 98	66.93 55	56.22 149	48.66 146	55.40 100	60.43 99	62.55 141	83.35 33	80.99 68	89.60 13	83.28 185

onestephybrid01		73.58 84	74.69 82	72.29 91	76.11 133	87.32 77	76.53 99	62.91 102	68.13 81	63.40 65	58.47 81	60.61 93	68.74 104	76.69 122	78.09 112	86.05 125	93.54 51

blend_shiyan4		66.60 147	67.24 151	65.85 143	68.02 186	76.25 194	75.94 100	58.03 158	64.52 98	53.78 123	52.14 125	60.47 95	53.51 191	67.10 215	66.76 222	85.79 131	83.46 181

XVS					82.43 59	86.27 102	75.70 101			61.07 93		72.27 43				85.67 139

X-MVStestdata					82.43 59	86.27 102	75.70 101			61.07 93		72.27 43				85.67 139

CHOSEN 1792x2688		72.55 98	71.98 109	73.22 80	86.57 50	92.41 14	75.63 103	66.77 56	62.08 112	52.32 129	30.27 240	50.74 173	66.14 120	86.22 14	85.41 9	91.90 1	96.75 14

FMVSNet3		70.41 113	71.89 111	68.68 122	70.89 172	79.42 166	75.63 103	60.97 137	65.32 91	51.06 134	47.37 151	62.05 81	64.90 125	82.49 40	82.27 40	88.64 40	84.34 174

Effi-MVS+		70.42 111	71.23 116	69.47 112	78.04 113	85.24 110	75.57 105	58.88 151	59.56 124	48.47 147	52.73 121	54.94 152	69.69 92	78.34 101	77.06 124	86.18 120	90.73 105

CANet_DTU		72.84 93	76.63 65	68.43 127	76.81 126	86.62 94	75.54 106	54.71 208	72.06 66	43.54 168	67.11 52	58.46 116	72.40 54	81.13 60	80.82 75	87.57 78	90.21 112

PVSNet_BlendedMVS		76.84 54	78.47 51	74.95 58	82.37 61	89.90 35	75.45 107	65.45 66	74.99 61	70.66 35	63.07 63	58.27 121	67.60 109	84.24 25	81.70 50	88.18 55	97.10 9

PVSNet_Blended		76.84 54	78.47 51	74.95 58	82.37 61	89.90 35	75.45 107	65.45 66	74.99 61	70.66 35	63.07 63	58.27 121	67.60 109	84.24 25	81.70 50	88.18 55	97.10 9

OMC-MVS		74.03 79	75.82 70	71.95 97	79.56 85	80.98 150	75.35 109	63.21 94	84.48 28	61.83 82	61.54 69	66.89 63	69.41 97	76.60 123	74.07 162	82.34 213	86.15 155

Anonymous20231211		68.44 130	66.37 158	70.86 102	77.58 118	83.49 127	75.15 110	61.89 122	52.54 171	58.50 103	28.89 242	56.78 135	69.29 99	74.96 143	76.61 127	82.73 205	91.36 94

Anonymous202405211			66.35 159		78.00 114	84.41 120	74.85 111	63.18 95	51.00 174		31.37 237	53.73 160	69.67 93	76.28 126	76.84 125	83.21 201	90.85 99

DCV-MVSNet		69.13 125	69.07 133	69.21 114	77.65 117	77.52 184	74.68 112	57.85 163	54.92 159	55.34 118	55.74 96	55.56 150	66.35 119	75.05 140	76.56 129	83.35 196	88.13 141

baseline		72.89 92	74.46 86	71.07 101	75.99 134	87.50 76	74.57 113	60.49 144	70.72 69	57.60 107	60.63 73	60.97 90	70.79 80	75.27 139	76.33 132	86.94 101	89.79 119

GBi-Net		69.21 121	70.40 125	67.81 131	69.49 177	78.65 172	74.54 114	60.97 137	65.32 91	51.06 134	47.37 151	62.05 81	63.43 133	77.49 109	78.22 109	87.37 85	83.73 177

test1		69.21 121	70.40 125	67.81 131	69.49 177	78.65 172	74.54 114	60.97 137	65.32 91	51.06 134	47.37 151	62.05 81	63.43 133	77.49 109	78.22 109	87.37 85	83.73 177

FMVSNet2		68.06 134	68.57 137	67.45 136	69.49 177	78.65 172	74.54 114	60.23 149	56.29 148	49.64 144	42.13 180	57.08 131	63.43 133	81.15 59	80.99 68	87.37 85	83.73 177

thres100view900		67.14 145	66.09 161	68.38 128	77.70 115	83.84 126	74.52 117	66.33 60	49.16 182	43.40 170	43.24 168	41.34 196	62.59 140	79.31 83	75.92 137	85.73 135	89.81 117

TSAR-MVS + COLMAP		73.09 90	76.86 62	68.71 121	74.97 146	82.49 137	74.51 118	61.83 123	83.16 32	49.31 145	82.22 25	51.62 168	68.94 102	78.76 95	75.52 144	82.67 207	84.23 175

tpm cat1		67.47 141	67.05 153	67.98 130	76.63 127	81.51 144	74.49 119	47.65 236	61.18 116	61.12 91	42.51 175	53.02 164	64.74 127	70.11 202	71.50 194	83.22 199	89.49 121

MSDG		65.57 153	61.57 194	70.24 107	82.02 65	76.47 191	74.46 120	68.73 44	56.52 146	50.33 140	38.47 199	41.10 200	62.42 143	72.12 178	72.94 179	83.47 195	73.37 230

usedtu_blend_shiyan5		62.84 176	63.39 176	62.21 176	48.58 255	75.44 203	74.43 121	57.47 169	39.26 226	53.78 123	52.14 125	60.47 95	53.51 191	66.38 217	66.54 224	85.46 146	83.46 181

CS-MVS		75.84 59	78.61 50	72.61 88	79.03 99	86.74 90	74.43 121	60.27 147	74.15 64	62.78 71	66.26 56	64.25 75	72.81 50	83.36 32	81.69 52	86.32 116	93.85 45

0.4-1-1-0.2		70.06 117	70.92 121	69.06 119	67.65 191	84.98 113	74.41 123	62.76 107	63.03 104	53.95 121	51.07 132	60.32 102	67.52 114	73.73 159	74.85 149	88.04 59	88.45 137

0.3-1-1-0.015		70.01 118	70.93 119	68.93 120	67.63 193	84.94 114	74.17 124	62.69 113	62.88 105	53.78 123	51.37 131	60.47 95	67.27 116	73.70 160	74.70 151	88.00 61	88.47 136

TAPA-MVS	67.10 9	71.45 107	73.47 95	69.10 116	77.04 124	80.78 153	73.81 125	62.10 119	80.80 42	51.28 133	60.91 71	63.80 78	67.98 106	74.59 145	72.42 186	82.37 212	80.97 205
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
0.4-1-1-0.1		69.62 119	70.57 124	68.51 125	67.55 195	84.77 116	73.54 126	62.45 116	62.23 111	53.25 127	50.57 136	60.25 105	66.36 118	73.49 163	74.34 159	87.90 65	88.30 139

thres200		65.58 152	64.74 167	66.56 140	77.52 120	81.61 140	73.44 127	62.95 99	46.23 194	42.45 177	42.76 170	41.18 198	58.12 169	76.24 127	75.59 141	84.89 164	89.58 120

FA-MVS(training)		70.24 116	71.77 112	68.45 126	77.52 120	86.03 106	73.33 128	49.12 230	63.55 101	55.77 112	48.91 143	56.26 141	67.78 108	77.60 108	79.62 91	87.19 98	90.40 108

dmvs_re		67.60 137	67.21 152	68.06 129	74.07 150	79.01 168	73.31 129	68.74 43	58.27 132	42.07 179	49.72 139	43.96 189	60.66 151	76.79 121	78.04 115	89.51 18	84.69 170

MGCFI-Net		74.26 73	78.69 49	69.10 116	80.64 77	87.32 77	73.21 130	59.20 150	79.76 48	50.18 142	68.10 49	64.86 73	64.65 128	78.28 103	80.83 74	86.69 107	91.69 90

ETV-MVS		76.25 56	80.22 42	71.63 100	78.23 111	87.95 73	72.75 131	60.27 147	77.50 56	57.73 106	71.53 41	66.60 64	73.16 45	80.99 61	81.23 64	87.63 77	95.73 18

MVS_111021_LR		74.26 73	75.95 69	72.27 93	79.43 87	85.04 111	72.71 132	65.27 68	70.92 68	63.58 63	69.32 44	60.31 104	69.43 96	77.01 118	77.15 123	83.22 199	91.93 88

PLC	64.00 12	68.54 129	66.66 155	70.74 104	80.28 80	74.88 210	72.64 133	63.70 89	69.26 78	55.71 113	47.24 154	55.31 151	70.42 84	72.05 180	70.67 205	81.66 220	77.19 216
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
viewdifsd2359ckpt11		69.15 123	68.30 140	70.14 109	73.44 157	82.79 132	72.24 134	61.20 133	54.59 164	61.70 84	53.16 117	52.89 165	67.57 112	71.81 183	72.73 183	84.66 175	90.10 114

viewmsd2359difaftdt		69.14 124	68.29 141	70.13 110	73.44 157	82.79 132	72.24 134	61.20 133	54.60 163	61.68 85	53.16 117	52.87 166	67.58 111	71.82 181	72.73 183	84.66 175	90.10 114

test2506		69.26 120	70.79 122	67.48 135	78.64 106	86.40 99	72.22 136	62.75 108	58.05 134	45.24 158	50.76 133	54.93 153	58.05 171	79.82 76	79.70 88	87.96 62	85.90 160

ECVR-MVS		67.93 136	68.49 138	67.28 138	78.64 106	86.40 99	72.22 136	62.75 108	58.05 134	44.06 166	40.92 188	48.20 178	58.05 171	79.82 76	79.70 88	87.96 62	86.32 154

PVSNet_Blended_VisFu		71.76 104	73.54 94	69.69 111	79.01 100	87.16 83	72.05 138	61.80 124	56.46 147	59.66 101	53.88 115	62.48 79	59.08 165	81.17 58	78.90 100	86.53 112	94.74 31

v2v482		63.68 168	62.85 184	64.65 152	68.01 187	80.46 157	71.90 139	57.60 166	44.26 201	42.82 175	39.80 195	38.62 217	61.56 146	73.06 167	74.86 148	86.03 126	88.90 130

v8		63.44 170	62.58 186	64.43 154	68.28 185	78.07 177	71.82 140	54.85 205	46.70 192	45.20 159	39.40 196	40.91 201	60.54 153	72.85 171	74.39 158	85.92 127	85.76 162

GeoE		68.96 127	69.32 131	68.54 123	76.61 128	83.12 129	71.78 141	56.87 183	60.21 122	54.86 119	45.95 165	54.79 155	64.27 129	74.59 145	75.54 143	86.84 104	91.01 98

EIA-MVS		73.48 86	76.05 67	70.47 106	78.12 112	87.21 82	71.78 141	60.63 143	69.66 74	55.56 115	64.86 59	60.69 91	69.53 94	77.35 113	78.59 103	87.22 95	94.01 43

FMVSNet1		63.48 169	63.07 180	63.97 159	65.31 209	76.37 193	71.77 143	57.90 162	43.32 205	45.66 155	35.06 223	49.43 175	58.57 167	77.49 109	78.22 109	84.59 178	81.60 203

tfpn200view9		65.90 151	64.96 165	67.00 139	77.70 115	81.58 142	71.71 144	62.94 101	49.16 182	43.40 170	43.24 168	41.34 196	61.42 147	76.24 127	74.63 153	84.84 166	88.52 134

v10		63.00 173	62.22 189	63.90 161	67.88 189	77.78 181	71.59 145	54.34 209	45.37 198	42.76 176	38.53 198	38.93 215	61.05 150	74.39 149	74.52 156	85.75 132	86.04 157

thres400		65.18 157	64.44 169	66.04 141	76.40 130	82.63 134	71.52 146	64.27 72	44.93 200	40.69 186	41.86 181	40.79 202	58.12 169	77.67 107	74.64 152	85.26 154	88.56 133

V42		62.86 175	62.97 181	62.74 171	60.84 228	78.99 170	71.46 147	57.13 180	46.85 190	44.28 165	38.87 197	40.73 204	57.63 178	72.60 175	74.14 160	85.09 159	88.63 132

tpmrst		67.15 144	68.12 145	66.03 142	76.21 131	80.98 150	71.27 148	45.05 242	60.69 119	50.63 138	46.95 159	54.15 158	65.30 122	71.80 184	71.77 190	87.72 72	90.48 107

v1144		63.00 173	62.39 188	63.70 162	67.72 190	80.27 158	71.23 149	56.40 184	42.51 206	40.81 185	38.12 203	37.73 218	60.42 155	74.46 147	74.55 155	85.64 143	89.12 126

baseline2		71.22 110	73.01 97	69.13 115	75.76 136	86.34 101	71.23 149	62.78 106	62.62 106	52.85 128	57.32 87	54.31 156	63.27 136	79.74 78	79.31 95	88.89 32	91.43 91

gg-mvs-nofinetune		62.34 179	66.19 160	57.86 204	76.15 132	88.61 54	71.18 151	41.24 260	25.74 261	13.16 266	22.91 258	63.97 77	54.52 188	85.06 19	85.25 13	90.92 3	91.78 89

Fast-Effi-MVS+		67.59 138	67.56 148	67.62 133	73.67 153	81.14 149	71.12 152	54.79 207	58.88 126	50.61 139	46.70 161	47.05 182	69.12 101	76.06 130	76.44 130	86.43 115	86.65 149

HyFIR lowres test		68.39 131	68.28 143	68.52 124	80.85 73	88.11 69	71.08 153	58.09 157	54.87 161	47.80 151	27.55 248	55.80 145	64.97 124	79.11 85	79.14 99	88.31 51	93.35 54

CNLPA		71.37 109	70.27 127	72.66 87	80.79 75	81.33 146	71.07 154	65.75 63	82.36 35	64.80 57	42.46 176	56.49 140	72.70 52	73.00 169	70.52 207	80.84 226	85.76 162

tpm		64.85 158	66.02 162	63.48 163	74.52 149	78.38 175	70.98 155	44.99 244	51.61 173	43.28 172	47.66 149	53.18 162	60.57 152	70.58 196	71.30 201	86.54 111	89.45 123

IterMVS-LS		66.08 150	66.56 157	65.51 144	73.67 153	74.88 210	70.89 156	53.55 215	50.42 176	48.32 149	50.59 135	55.66 148	61.83 144	73.93 154	74.42 157	84.82 170	86.01 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+-dtu		63.05 172	64.72 168	61.11 182	71.21 170	76.81 190	70.72 157	43.13 252	52.51 172	35.34 220	46.55 162	46.36 183	61.40 148	71.57 188	71.44 196	84.84 166	87.79 143

dtuonly		62.74 177	63.91 172	61.36 181	61.12 227	71.54 228	70.69 158	50.99 225	52.81 170	40.13 188	42.43 177	51.07 171	62.78 138	71.77 185	71.63 192	82.47 210	86.15 155

Effi-MVS+-dtu		64.58 160	64.08 170	65.16 147	73.04 159	75.17 209	70.68 159	56.23 187	54.12 166	44.71 163	47.42 150	51.10 170	63.82 132	68.08 212	66.32 232	82.47 210	86.38 152

SPE-MVS-test		75.09 67	77.84 55	71.87 99	79.27 93	86.92 88	70.53 160	60.36 145	75.13 60	63.13 69	67.92 50	65.08 70	71.43 69	78.15 105	78.51 106	86.53 112	93.16 59

LS3D		64.54 162	62.14 190	67.34 137	80.85 73	75.79 198	69.99 161	65.87 62	60.77 118	44.35 164	42.43 177	45.95 185	65.01 123	69.88 203	68.69 214	77.97 241	71.43 238

v1192		62.25 182	61.64 193	62.96 166	66.88 198	79.72 162	69.96 162	55.77 191	41.58 211	39.42 191	37.05 208	35.96 231	60.50 154	74.30 152	74.09 161	85.24 155	88.76 131

CDS-MVSNet		64.22 163	65.89 163	62.28 175	70.05 174	80.59 154	69.91 163	57.98 159	43.53 204	46.58 153	48.22 145	50.76 172	46.45 221	75.68 134	76.08 135	82.70 206	86.34 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test1111		66.72 146	67.80 146	65.45 145	77.42 122	86.63 92	69.69 164	62.98 97	55.29 155	39.47 190	40.12 193	47.11 181	55.70 183	79.96 74	80.00 85	87.47 82	85.49 165

GA-MVS		64.55 161	65.76 164	63.12 165	69.68 176	81.56 143	69.59 165	58.16 156	45.23 199	35.58 219	47.01 158	41.82 193	59.41 161	79.62 81	78.54 104	86.32 116	86.56 150

thres600view7		63.77 167	63.14 179	64.51 153	75.49 140	81.61 140	69.59 165	62.95 99	43.96 203	38.90 195	41.09 185	40.24 211	55.25 186	76.24 127	71.54 193	84.89 164	87.30 145

v144192		62.05 186	61.46 195	62.73 172	66.59 202	79.87 161	69.30 167	55.88 189	41.50 213	39.41 192	37.23 206	36.45 226	59.62 159	72.69 174	73.51 167	85.61 144	88.93 128

MDTV_nov1_ep13		65.21 156	67.28 150	62.79 168	70.91 171	81.72 139	69.28 168	49.50 228	58.08 133	43.94 167	50.50 137	56.02 143	58.86 166	70.72 193	73.37 170	84.24 184	80.52 207

EPMVS		66.21 148	67.49 149	64.73 151	75.81 135	84.20 124	68.94 169	44.37 246	61.55 113	48.07 150	49.21 142	54.87 154	62.88 137	71.82 181	71.40 198	88.28 52	79.37 211

EPNet_dtu		66.17 149	70.13 128	61.54 180	81.04 71	77.39 186	68.87 170	62.50 115	69.78 72	33.51 227	63.77 62	56.22 142	37.65 240	72.20 177	72.18 189	85.69 138	79.38 210
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMH+	60.36 13	61.16 192	58.38 212	64.42 155	77.37 123	74.35 216	68.45 171	62.81 104	45.86 196	38.48 199	35.71 218	37.35 221	59.81 158	67.24 214	69.80 211	79.58 234	78.32 214

v1921920		61.66 190	61.10 198	62.31 174	66.32 203	79.57 164	68.41 172	55.49 197	41.03 214	38.69 196	36.64 214	35.27 234	59.60 160	73.23 165	73.41 169	85.37 150	88.51 135

thisisatest0530		68.38 132	70.98 118	65.35 146	72.61 160	84.42 119	68.21 173	57.98 159	59.77 123	50.80 137	54.63 104	58.48 115	57.92 173	76.99 119	77.47 120	84.60 177	85.07 167

v148		62.00 187	61.19 197	62.96 166	67.46 196	79.49 165	67.87 174	57.66 165	42.30 207	45.02 161	38.20 202	38.89 216	54.77 187	69.83 204	72.60 185	84.96 160	87.01 147

UniMVSNet_ETH3D		57.83 211	56.46 226	59.43 195	63.24 218	73.22 220	67.70 175	55.58 194	36.17 238	36.84 209	32.64 232	35.14 235	51.50 199	65.81 225	69.81 210	81.73 219	82.44 198

ACMH	59.42 14	61.59 191	59.22 210	64.36 156	78.92 103	78.26 176	67.65 176	67.48 52	39.81 219	30.98 235	38.25 201	34.59 237	61.37 149	70.55 197	73.47 168	79.74 233	79.59 209
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		67.99 135	70.61 123	64.94 149	71.94 165	83.96 125	67.62 177	57.98 159	59.30 125	49.90 143	54.50 107	57.98 124	57.92 173	76.48 124	77.47 120	84.24 184	84.58 171

IterMVS-SCA-FT		60.21 199	62.97 181	57.00 215	66.64 201	71.84 224	67.53 178	46.93 239	47.56 187	36.77 211	46.85 160	48.21 177	52.51 195	70.36 199	72.40 187	71.63 260	83.53 180

CR-MVSNet		62.31 180	64.75 166	59.47 194	68.63 183	71.29 230	67.53 178	43.18 250	55.83 151	41.40 180	41.04 186	55.85 144	57.29 179	72.76 172	73.27 174	78.77 238	83.23 186

Patchmtry						78.06 178	67.53 178	43.18 250		41.40 180

IterMVS		61.87 189	63.55 174	59.90 190	67.29 197	72.20 223	67.34 181	48.56 232	47.48 188	37.86 206	47.07 156	48.27 176	54.08 189	72.12 178	73.71 165	84.30 183	83.99 176
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1240		61.09 193	60.55 202	61.72 179	65.92 207	79.28 167	67.16 182	54.91 204	39.79 220	38.10 203	36.08 217	34.64 236	59.15 164	72.86 170	73.36 171	85.10 157	87.84 142

EPP-MVSNet		67.58 139	71.10 117	63.48 163	75.71 137	83.35 128	66.85 183	57.83 164	53.02 169	41.15 183	55.82 94	67.89 61	56.01 182	74.40 148	72.92 180	83.33 197	90.30 111

pmmvs4		63.14 171	62.46 187	63.94 160	66.03 205	76.40 192	66.82 184	57.60 166	56.74 142	50.26 141	40.81 189	37.51 220	59.26 163	71.75 186	71.48 195	83.68 194	82.53 195

dps		64.08 164	63.22 178	65.08 148	75.27 142	79.65 163	66.68 185	46.63 240	56.94 141	55.67 114	43.96 167	43.63 191	64.00 130	69.50 207	69.82 209	82.25 214	79.02 212

tfpnnormal		58.97 205	56.48 225	61.89 177	71.27 169	76.21 195	66.65 186	61.76 126	32.90 247	36.41 212	27.83 246	29.14 254	50.64 208	73.06 167	73.05 178	84.58 179	83.15 188

UGNet		67.57 140	71.69 113	62.76 170	69.88 175	82.58 135	66.43 187	58.64 153	54.71 162	51.87 131	61.74 67	62.01 84	45.46 226	74.78 144	74.99 146	84.24 184	91.02 97
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
PatchmatchNet		65.43 155	67.71 147	62.78 169	73.49 155	82.83 131	66.42 188	45.40 241	60.40 120	45.27 157	49.22 141	57.60 126	60.01 157	70.61 194	71.38 199	86.08 124	81.91 201
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Vis-MVSNet		65.53 154	69.83 130	60.52 185	70.80 173	84.59 117	66.37 189	55.47 198	48.40 185	40.62 187	57.67 85	58.43 117	45.37 227	77.49 109	76.24 134	84.47 180	85.99 159
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
usedtu_dtu_shiyan1		62.43 178	64.08 170	60.50 186	59.68 233	80.58 155	66.18 190	61.75 127	53.08 168	36.05 215	36.33 215	41.74 194	51.86 197	77.70 106	77.95 116	87.47 82	81.17 204

IS_MVSNet		67.29 143	71.98 109	61.82 178	76.92 125	84.32 123	65.90 191	58.22 155	55.75 153	39.22 193	54.51 106	62.47 80	45.99 224	78.83 94	78.52 105	84.70 173	89.47 122

FC-MVSNet-train		68.83 128	68.29 141	69.47 112	78.35 110	79.94 160	64.72 192	66.38 58	54.96 158	54.51 120	56.75 91	47.91 180	66.91 117	75.57 138	75.75 138	85.92 127	87.12 146

IB-MVS	64.48 11	69.02 126	68.97 135	69.09 118	81.75 67	89.01 46	64.50 193	64.91 69	56.65 143	62.59 74	47.89 147	45.23 186	51.99 196	69.18 208	81.88 46	88.77 35	92.93 62
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
pm-mvs1		59.21 204	59.58 209	58.77 200	67.97 188	77.07 189	64.12 194	57.20 177	34.73 244	36.86 208	35.34 220	40.54 206	43.34 231	74.32 151	73.30 173	83.13 203	81.77 202

PMMVS		70.37 114	75.06 77	64.90 150	71.46 166	81.88 138	64.10 195	55.64 193	71.31 67	46.69 152	70.69 43	58.56 113	69.53 94	79.03 86	75.63 140	81.96 217	88.32 138

UniMVSNet_NR-MVSNet		62.30 181	63.51 175	60.89 183	69.48 180	77.83 180	64.07 196	63.94 83	50.03 177	31.17 233	44.82 166	41.12 199	51.37 202	71.02 190	74.81 150	85.30 153	84.95 168

DU-MVS		60.87 195	61.82 192	59.76 192	66.69 199	75.87 196	64.07 196	61.96 120	49.31 180	31.17 233	42.76 170	36.95 223	51.37 202	69.67 205	73.20 177	83.30 198	84.95 168

test-LLR		68.23 133	71.61 114	64.28 157	71.37 167	81.32 147	63.98 198	61.03 135	58.62 127	42.96 173	52.74 119	61.65 85	57.74 176	75.64 135	78.09 112	88.61 41	93.21 55

TESTMET0.1,1		67.38 142	71.61 114	62.45 173	66.05 204	81.32 147	63.98 198	55.36 199	58.62 127	42.96 173	52.74 119	61.65 85	57.74 176	75.64 135	78.09 112	88.61 41	93.21 55

MIMVSNet		57.78 213	59.71 208	55.53 221	54.79 244	77.10 188	63.89 200	45.02 243	46.59 193	36.79 210	28.36 245	40.77 203	45.84 225	74.97 141	76.58 128	86.87 103	73.60 228

FMVSNet5		58.86 206	60.24 204	57.25 211	52.66 248	66.25 244	63.77 201	52.86 220	57.85 137	37.92 205	36.12 216	52.22 167	51.37 202	70.88 192	71.43 197	84.92 161	66.91 249

NR-MVSNet		61.08 194	62.09 191	59.90 190	71.96 164	75.87 196	63.60 202	61.96 120	49.31 180	27.95 238	42.76 170	33.85 241	48.82 211	74.35 150	74.05 163	85.13 156	84.45 172

TransMVSNet (Re)		57.83 211	56.90 223	58.91 199	72.26 162	74.69 213	63.57 203	61.42 131	32.30 250	32.65 229	33.97 229	35.96 231	39.17 238	73.84 157	72.84 181	84.37 182	74.69 223

EG-PatchMatch MVS		58.73 208	58.03 215	59.55 193	72.32 161	80.49 156	63.44 204	55.55 195	32.49 249	38.31 202	28.87 243	37.22 222	42.84 232	74.30 152	75.70 139	84.84 166	77.14 217

TranMVSNet+NR-MVSNet		60.38 198	61.30 196	59.30 196	68.34 184	75.57 202	63.38 205	63.78 87	46.74 191	27.73 239	42.56 174	36.84 224	47.66 216	70.36 199	74.59 154	84.91 163	82.46 196

pmmvs5		59.72 200	60.24 204	59.11 198	62.77 221	77.33 187	63.17 206	54.00 212	40.21 218	37.23 207	40.41 190	35.99 230	51.75 198	72.55 176	72.74 182	85.72 137	82.45 197

wanda-best-256-512		57.69 214	57.90 217	57.46 209	48.58 255	75.44 203	63.15 207	57.47 169	39.27 223	38.64 197	34.66 225	40.34 207	51.44 200	66.38 217	66.54 224	85.46 146	82.64 190

FE-blended-shiyan7		57.69 214	57.90 217	57.46 209	48.58 255	75.44 203	63.15 207	57.47 169	39.27 223	38.64 197	34.66 225	40.34 207	51.44 200	66.38 217	66.54 224	85.46 146	82.64 190

FE-MVSNET3		61.91 188	63.26 177	60.33 188	48.58 255	75.44 203	63.15 207	57.47 169	39.27 223	53.78 123	52.14 125	60.47 95	53.51 191	66.38 217	66.54 224	85.46 146	82.59 192

USDC		59.69 201	60.03 206	59.28 197	64.04 214	71.84 224	63.15 207	55.36 199	54.90 160	35.02 221	48.34 144	29.79 253	58.16 168	70.60 195	71.33 200	79.99 231	73.42 229

blended_shiyan8		57.49 218	57.71 220	57.24 212	48.52 259	75.34 207	62.85 211	57.32 176	38.77 228	38.43 200	34.41 228	40.31 209	50.92 205	66.25 222	66.37 229	85.37 150	82.55 194

blended_shiyan6		57.50 217	57.73 219	57.23 213	48.51 260	75.34 207	62.85 211	57.33 174	38.78 227	38.38 201	34.46 227	40.29 210	50.91 206	66.27 221	66.37 229	85.37 150	82.59 192

CMPMVS	43.63 17	57.67 216	55.43 227	60.28 189	72.01 163	79.00 169	62.77 213	53.23 217	41.77 210	45.42 156	30.74 239	39.03 214	53.01 194	64.81 231	64.65 238	75.26 252	68.03 247
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
COLMAP_ROB	51.17 15	55.13 223	52.90 236	57.73 206	73.47 156	67.21 242	62.13 214	55.82 190	47.83 186	34.39 223	31.60 236	34.24 238	44.90 228	63.88 236	62.52 245	75.67 250	63.02 257
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
gbinet_0.2-2-1-0.02		56.72 221	57.64 221	55.64 220	45.57 263	74.69 213	62.04 215	57.17 179	35.71 242	35.71 217	33.73 230	41.66 195	48.54 212	66.06 224	66.43 228	84.83 169	85.22 166

v7n		57.04 220	56.64 224	57.52 207	62.85 220	74.75 212	61.76 216	51.80 223	35.58 243	36.02 216	32.33 234	33.61 242	50.16 209	67.73 213	70.34 208	82.51 208	82.12 199

UniMVSNet (Re)		60.62 196	62.93 183	57.92 203	67.64 192	77.90 179	61.75 217	61.24 132	49.83 179	29.80 237	42.57 173	40.62 205	43.36 230	70.49 198	73.27 174	83.76 190	85.81 161

Baseline_NR-MVSNet		59.47 202	60.28 203	58.54 201	66.69 199	73.90 217	61.63 218	62.90 103	49.15 184	26.87 240	35.18 222	37.62 219	48.20 214	69.67 205	73.61 166	84.92 161	82.82 189

TDRefinement		52.70 233	51.02 243	54.66 225	57.41 240	65.06 248	61.47 219	54.94 202	44.03 202	33.93 225	30.13 241	27.57 257	46.17 223	61.86 238	62.48 246	74.01 256	66.06 250

CHOSEN 280x420		62.23 184	66.57 156	57.17 214	59.88 231	68.92 237	61.20 220	42.28 254	54.17 165	39.57 189	47.78 148	64.97 71	62.68 139	73.85 156	69.52 212	77.43 242	86.75 148

ADS-MVSNet		58.40 210	59.16 211	57.52 207	65.80 208	74.57 215	60.26 221	40.17 261	50.51 175	38.01 204	40.11 194	44.72 187	59.36 162	64.91 229	66.55 223	81.53 221	72.72 233

pmmvs6		54.20 230	53.54 232	54.97 222	63.22 219	72.98 221	60.17 222	52.32 222	26.77 260	34.30 224	23.29 257	36.23 228	40.33 237	68.77 209	68.76 213	79.47 236	78.00 215

UA-Net		64.62 159	68.23 144	60.42 187	77.53 119	81.38 145	60.08 223	57.47 169	47.01 189	44.75 162	60.68 72	71.32 50	41.84 234	73.27 164	72.25 188	80.83 227	71.68 236

SCA		63.90 166	66.67 154	60.66 184	73.75 151	71.78 226	59.87 224	43.66 248	61.13 117	45.03 160	51.64 129	59.45 111	57.92 173	70.96 191	70.80 203	83.71 192	80.92 206

PatchMatch-RL		62.22 185	60.69 200	64.01 158	68.74 182	75.75 199	59.27 225	60.35 146	56.09 150	53.80 122	47.06 157	36.45 226	64.80 126	68.22 211	67.22 218	77.10 244	74.02 225

test-mter		64.06 165	69.24 132	58.01 202	59.07 235	77.40 185	59.13 226	48.11 234	55.64 154	39.18 194	51.56 130	58.54 114	55.38 185	73.52 162	76.00 136	87.22 95	92.05 87

TAMVS		58.86 206	60.91 199	56.47 218	62.38 223	77.57 183	58.97 227	52.98 218	38.76 229	36.17 213	42.26 179	47.94 179	46.45 221	70.23 201	70.79 204	81.86 218	78.82 213

thisisatest0515		59.37 203	60.68 201	57.84 205	64.39 213	75.65 201	58.56 228	53.86 213	41.55 212	42.12 178	40.40 191	39.59 212	47.09 219	71.69 187	73.79 164	81.02 225	82.08 200

MDTV_nov1_ep13_2view		54.47 229	54.61 228	54.30 228	60.50 229	73.82 218	57.92 229	43.38 249	39.43 222	32.51 230	33.23 231	34.05 239	47.26 218	62.36 237	66.21 233	84.24 184	73.19 231

pmmvs-eth3d		55.20 222	53.95 231	56.65 216	57.34 241	67.77 240	57.54 230	53.74 214	40.93 215	41.09 184	31.19 238	29.10 255	49.07 210	65.54 226	67.28 217	81.14 223	75.81 218

TinyColmap		52.66 234	50.09 246	55.65 219	59.72 232	64.02 253	57.15 231	52.96 219	40.28 217	32.51 230	32.42 233	20.97 268	56.65 181	63.95 235	65.15 237	74.91 253	63.87 255

Vis-MVSNet (Re-imp)		62.25 182	68.74 136	54.68 224	73.70 152	78.74 171	56.51 232	57.49 168	55.22 156	26.86 241	54.56 105	61.35 87	31.06 244	73.10 166	74.90 147	82.49 209	83.31 183

our_test_3					63.32 216	71.07 232	55.90 233

dtuonlycased		50.09 242	48.12 250	52.39 230	52.04 249	68.20 239	55.54 234	49.33 229	36.78 233	32.91 228	24.24 253	39.38 213	48.29 213	46.71 259	50.09 260	76.23 246	71.43 238

CVMVSNet		54.92 227	58.16 213	51.13 235	62.61 222	68.44 238	55.45 235	52.38 221	42.28 208	21.45 250	47.10 155	46.10 184	37.96 239	64.42 234	63.81 239	76.92 245	75.01 222

RPMNet		58.63 209	62.80 185	53.76 229	67.59 194	71.29 230	54.60 236	38.13 262	55.83 151	35.70 218	41.58 183	53.04 163	47.89 215	66.10 223	67.38 216	78.65 240	84.40 173

pmnet_mix02		53.92 231	53.30 233	54.65 226	61.89 224	71.33 229	54.54 237	54.17 211	40.38 216	34.65 222	34.76 224	30.68 252	40.44 236	60.97 239	63.71 240	82.19 215	71.24 240

RPSCF		55.07 224	58.06 214	51.57 232	48.87 254	58.95 260	53.68 238	41.26 259	62.42 109	45.88 154	54.38 110	54.26 157	53.75 190	57.15 247	53.53 259	66.01 262	65.75 251

test0.0.03 1		57.35 219	59.89 207	54.38 227	71.37 167	73.45 219	52.71 239	61.03 135	46.11 195	26.33 242	41.73 182	44.08 188	29.72 246	71.43 189	70.90 202	85.10 157	71.56 237

FE-MVSNET2		50.42 239	51.98 241	48.61 240	44.79 264	68.96 236	52.01 240	55.50 196	32.55 248	19.88 254	21.60 262	28.20 256	35.80 241	68.31 210	71.76 191	83.69 193	72.45 234

anonymousdsp		54.99 225	57.24 222	52.36 231	53.82 246	71.75 227	51.49 241	48.14 233	33.74 245	33.66 226	38.34 200	36.13 229	47.54 217	64.53 233	70.60 206	79.53 235	85.59 164

Anonymous20231206		52.23 235	52.80 237	51.56 233	64.70 212	69.41 234	51.01 242	58.60 154	36.63 235	22.44 249	21.80 260	31.42 248	30.52 245	66.79 216	67.83 215	82.10 216	75.73 219

PM-MVS		50.11 241	50.38 245	49.80 236	47.23 262	62.08 256	50.91 243	44.84 245	41.90 209	36.10 214	35.22 221	26.05 261	46.83 220	57.64 245	55.42 257	72.90 257	74.32 224

LTVRE_ROB	47.26 16	49.41 245	49.91 247	48.82 238	64.76 211	69.79 233	49.05 244	47.12 238	20.36 267	16.52 258	36.65 213	26.96 258	50.76 207	60.47 240	63.16 243	64.73 263	72.00 235
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
PEN-MVS		51.04 236	52.94 235	48.82 238	61.45 226	66.00 245	48.68 245	57.20 177	36.87 232	15.36 260	36.98 209	32.72 243	28.77 250	57.63 246	66.37 229	81.44 222	74.00 226

CP-MVSNet		50.57 238	52.60 239	48.21 242	58.77 237	65.82 246	48.17 246	56.29 186	37.41 231	16.59 257	37.14 207	31.95 245	29.21 247	56.60 249	63.71 240	80.22 229	75.56 220

PatchmatchNet2					56.14 242	64.21 251	48.11 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PS-CasMVS		50.17 240	52.02 240	48.02 243	58.60 238	65.54 247	48.04 248	56.19 188	36.42 237	16.42 259	35.68 219	31.33 249	28.85 249	56.42 251	63.54 242	80.01 230	75.18 221

PatchT		60.46 197	63.85 173	56.51 217	65.95 206	75.68 200	47.34 249	41.39 257	53.89 167	41.40 180	37.84 204	50.30 174	57.29 179	72.76 172	73.27 174	85.67 139	83.23 186

SixPastTwentyTwo		49.11 246	49.22 248	48.99 237	58.54 239	64.14 252	47.18 250	47.75 235	31.15 252	24.42 245	41.01 187	26.55 259	44.04 229	54.76 255	58.70 252	71.99 259	68.21 245

N_pmnet		47.67 248	47.00 252	48.45 241	54.72 245	62.78 254	46.95 251	51.25 224	36.01 240	26.09 244	26.59 250	25.93 263	35.50 243	55.67 253	59.01 250	76.22 248	63.04 256

usedtu_dtu_shiyan2		40.99 256	42.22 259	39.56 255	22.63 273	59.44 259	46.80 252	43.69 247	19.05 269	21.04 251	16.27 271	23.77 265	27.46 253	53.16 257	55.09 258	75.73 249	68.78 243

MDA-MVSNet-bldmvs		44.15 253	42.27 258	46.34 246	38.34 266	62.31 255	46.28 253	55.74 192	29.83 253	20.98 252	27.11 249	16.45 274	41.98 233	41.11 265	57.47 253	74.72 254	61.65 261

FPMVS		39.11 258	36.39 260	42.28 250	55.97 243	45.94 267	46.23 254	41.57 256	35.73 241	22.61 247	23.46 256	19.82 270	28.32 252	43.57 262	40.67 264	58.96 266	45.54 265

WR-MVS_H		49.62 244	52.63 238	46.11 248	58.80 236	67.58 241	46.14 255	54.94 202	36.51 236	13.63 265	36.75 212	35.67 233	22.10 259	56.43 250	62.76 244	81.06 224	72.73 232

DTE-MVSNet		49.82 243	51.92 242	47.37 244	61.75 225	64.38 250	45.89 256	57.33 174	36.11 239	12.79 267	36.87 210	31.92 246	25.73 255	58.01 244	65.22 236	80.75 228	70.93 242

WR-MVS		51.02 237	54.56 229	46.90 245	63.84 215	69.23 235	44.78 257	56.38 185	38.19 230	14.19 262	37.38 205	36.82 225	22.39 258	60.14 241	66.20 234	79.81 232	73.95 227

MVS-HIRNet		53.86 232	53.02 234	54.85 223	60.30 230	72.36 222	44.63 258	42.20 255	39.45 221	43.47 169	21.66 261	34.00 240	55.47 184	65.42 227	67.16 219	83.02 204	71.08 241

EU-MVSNet		44.84 251	47.85 251	41.32 254	49.26 253	56.59 263	43.07 259	47.64 237	33.03 246	13.82 263	36.78 211	30.99 250	24.37 256	53.80 256	55.57 256	69.78 261	68.21 245

FE-MVSNET		44.36 252	46.68 253	41.65 251	37.55 267	61.05 257	42.06 260	54.34 209	27.09 258	9.86 272	20.55 263	25.56 264	28.72 251	60.12 242	66.83 221	77.36 243	65.56 252

testgi		48.51 247	50.53 244	46.16 247	64.78 210	67.15 243	41.54 261	54.81 206	29.12 255	17.03 256	32.07 235	31.98 244	20.15 262	65.26 228	67.00 220	78.67 239	61.10 262

test20.03		47.23 250	48.69 249	45.53 249	63.28 217	64.39 249	41.01 262	56.93 182	29.16 254	15.21 261	23.90 254	30.76 251	17.51 265	64.63 232	65.26 235	79.21 237	62.71 259

new-patchmatchnet		42.21 254	42.97 255	41.33 253	53.05 247	59.89 258	39.38 263	49.61 227	28.26 257	12.10 268	22.17 259	21.54 267	19.22 263	50.96 258	56.04 255	74.61 255	61.92 260

MIMVSNet1		40.84 257	43.46 254	37.79 257	32.14 268	58.92 261	39.24 264	50.83 226	27.00 259	11.29 269	16.76 269	26.53 260	17.75 264	57.14 248	61.12 248	75.46 251	56.78 263

pmmvs3		41.86 255	42.29 257	41.36 252	39.80 265	52.66 265	38.93 265	35.85 266	23.40 264	20.22 253	19.30 264	20.84 269	40.56 235	55.98 252	58.79 251	72.80 258	65.03 253

ambc			42.30 256		50.36 252	49.51 266	35.47 266		32.04 251	23.53 246	17.36 266	8.95 278	29.06 248	64.88 230	56.26 254	61.29 265	67.12 248

FC-MVSNet-test		47.24 249	54.37 230	38.93 256	59.49 234	58.25 262	34.48 267	53.36 216	45.66 197	6.66 273	50.62 134	42.02 192	16.62 266	58.39 243	61.21 247	62.99 264	64.40 254

gm-plane-assit		54.99 225	57.99 216	51.49 234	69.27 181	54.42 264	32.32 268	42.59 253	21.18 265	13.71 264	23.61 255	43.84 190	60.21 156	87.09 7	86.55 5	90.81 4	89.28 124

new_pmnet		33.19 259	35.52 261	30.47 259	27.55 272	45.31 268	29.29 269	30.92 267	29.00 256	9.88 271	18.77 265	17.64 272	26.77 254	44.07 261	45.98 262	58.41 267	47.87 264

PMVS	27.44 18	32.08 260	29.07 264	35.60 258	48.33 261	24.79 271	26.97 270	41.34 258	20.45 266	22.50 248	17.11 268	18.64 271	20.44 261	41.99 264	38.06 265	54.02 268	42.44 266
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		28.15 262	34.48 262	20.76 263	6.76 277	21.18 273	21.03 271	18.41 270	36.77 234	17.52 255	15.67 272	31.63 247	24.05 257	41.03 266	26.69 268	36.82 271	68.38 244

Gipuma		24.91 263	24.61 265	25.26 262	31.47 269	21.59 272	18.06 272	37.53 263	25.43 262	10.03 270	4.18 277	4.25 281	14.85 267	43.20 263	47.03 261	39.62 270	26.55 273
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WB-MVS		30.42 261	32.63 263	27.84 260	51.51 251	41.64 269	17.75 273	55.06 201	20.11 268	2.46 278	26.13 252	16.63 273	3.90 274	44.91 260	44.54 263	36.34 272	34.48 269

DeepMVS_CX						19.81 275	17.01 274	10.02 271	23.61 263	5.85 274	17.21 267	8.03 279	21.13 260	22.60 269		21.42 276	30.01 270

PMMVS2		20.45 264	22.31 266	18.27 266	20.52 274	26.73 270	14.85 275	28.43 269	13.69 272	0.79 279	10.35 273	9.10 277	3.83 276	27.64 268	32.87 266	41.17 269	35.81 267

tmp_tt				16.09 267	13.07 275	8.12 278	13.61 276	2.08 272	55.09 157	30.10 236	40.26 192	22.83 266	5.35 272	29.91 267	25.25 269	32.33 273

EMVS		14.40 266	10.71 272	18.70 265	28.15 271	12.09 277	7.06 277	36.89 264	11.00 274	3.56 277	4.95 275	2.27 284	13.91 268	10.13 274	16.06 271	22.63 275	18.51 275

E-PMN		15.08 265	11.65 270	19.08 264	28.73 270	12.31 276	6.95 278	36.87 265	10.71 275	3.63 276	5.13 274	2.22 285	13.81 269	11.34 272	18.50 270	24.49 274	21.32 274

MVE	15.98 19	14.37 267	16.36 268	12.04 268	7.72 276	20.24 274	5.90 279	29.05 268	8.28 276	3.92 275	4.72 276	2.42 282	9.57 270	18.89 270	31.46 267	16.07 277	28.53 271
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Patchmatch-RL test							2.17 280

VLMVS_CLIP		11.46 268	18.27 267	3.50 269	3.73 278	5.54 279	2.13 281	0.48 273	18.85 270	0.26 281	28.51 244	9.68 276	7.31 271	17.28 271	13.56 272	7.11 278	34.49 268

VLMVS		9.08 269	15.28 269	1.84 270	1.39 279	3.31 280	1.20 282	0.09 275	18.54 271	0.39 280	27.68 247	12.43 275	3.90 274	9.16 275	8.34 274	4.04 279	27.51 272

MVS_clip		6.46 270	10.77 271	1.43 271	0.96 280	2.36 281	0.77 283	0.18 274	11.97 273	0.04 283	16.38 270	7.57 280	5.17 273	10.69 273	8.74 273	1.48 280	17.71 276

testmvs		0.05 272	0.08 274	0.01 273	0.00 283	0.01 283	0.03 284	0.01 277	0.05 278	0.00 284	0.14 280	0.01 286	0.03 280	0.05 277	0.05 276	0.01 281	0.24 279

MVS_baseline		1.61 271	2.81 273	0.21 272	0.06 281	0.07 282	0.02 285	0.00 278	2.84 277	0.00 284	4.11 278	2.29 283	1.18 277	1.23 276	1.30 275	0.00 282	7.85 277

test123		0.05 272	0.08 274	0.01 273	0.00 283	0.01 283	0.01 286	0.00 278	0.05 278	0.00 284	0.16 279	0.00 287	0.04 278	0.02 278	0.05 276	0.00 282	0.26 278

uanet_test		0.00 274	0.00 276	0.00 275	0.00 283	0.00 285	0.00 287	0.00 278	0.00 280	0.00 284	0.00 281	0.00 287	0.00 281	0.00 279	0.00 278	0.00 282	0.00 280

sosnet-low-res		0.00 274	0.00 276	0.00 275	0.00 283	0.00 285	0.00 287	0.00 278	0.00 280	0.00 284	0.00 281	0.00 287	0.00 281	0.00 279	0.00 278	0.00 282	0.00 280

sosnet		0.00 274	0.00 276	0.00 275	0.00 283	0.00 285	0.00 287	0.00 278	0.00 280	0.00 284	0.00 281	0.00 287	0.00 281	0.00 279	0.00 278	0.00 282	0.00 280

PatchmatchNet1												25.98 262	35.57 242	55.54 254	59.02 249	76.23 246	62.78 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3										26.10 243	26.55 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
RE-MVS-def										31.47 232

9.14												84.47 10

SR-MVS					86.33 51			67.54 51				80.78 26

MTAPA										78.32 15		79.42 30

MTMP										76.04 19		76.65 34

mPP-MVS					86.96 46							70.61 53

NP-MVS									81.60 40

High-resolution multi-view benchmark