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 2	86.20 2	77.82 8	59.97 9	88.89 1	65.96 3	86.00 8	84.02 1	70.03 1	76.19 6	76.17 5	79.22 31	94.46 1

RA-MVS		78.02 2	82.95 5	72.27 2	74.25 13	84.05 6	80.18 3	60.14 7	88.18 3	74.18 1	87.86 5	79.64 8	65.61 9	76.31 5	71.47 29	79.27 30	93.16 5

MED-MVS		77.83 3	83.38 3	71.35 4	78.67 5	83.69 7	80.89 2	61.46 5	87.23 6	64.27 4	89.05 3	82.28 4	63.40 12	75.25 11	75.44 10	79.44 21	90.74 12

DVP-MVS		77.54 4	84.41 2	69.54 9	79.93 3	86.08 3	77.20 13	60.31 6	88.62 2	62.54 5	86.67 6	83.77 2	58.04 54	75.84 9	75.69 8	79.21 32	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
aaEdge-Enhanced		76.71 5	81.90 7	70.66 5	77.07 9	81.13 17	78.23 6	61.85 3	85.73 8	61.71 6	89.05 3	80.80 5	63.14 14	72.50 26	73.33 17	81.99 4	90.74 12

SF-MVS		76.41 6	80.45 9	71.69 3	82.90 1	86.54 1	82.08 1	64.58 1	81.67 15	59.82 9	86.26 7	77.90 11	61.11 21	71.81 31	70.75 38	79.63 16	88.22 28

MSP-MVS		76.38 7	82.99 4	68.68 10	71.93 22	78.65 31	77.61 10	55.44 22	88.04 4	60.25 8	92.24 1	77.08 14	69.84 2	75.48 10	75.69 8	76.99 85	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
DVP-MVS++		75.99 8	81.32 8	69.77 8	71.86 24	85.13 4	77.62 9	59.87 11	82.69 13	61.55 7	83.05 12	79.63 9	69.78 3	76.01 7	75.89 6	77.92 65	86.86 51

DPE-MVS		75.74 9	82.82 6	67.49 14	77.07 9	82.01 11	77.05 14	57.70 15	86.55 7	55.44 21	90.50 2	82.52 3	60.33 25	72.99 18	72.98 19	77.33 76	92.19 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		74.63 10	78.53 14	70.07 6	76.10 12	82.56 10	79.30 4	59.89 10	80.49 17	57.75 15	66.98 31	76.16 18	65.95 6	79.35 1	78.47 1	81.45 7	85.71 70

APDe-MVS		74.59 11	80.23 10	68.01 13	76.51 11	80.20 20	77.39 11	58.18 13	85.31 9	56.84 17	84.89 9	76.08 19	60.66 23	71.85 30	71.76 25	78.47 53	91.49 10
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MCST-MVS		74.06 12	77.71 17	69.79 7	78.95 4	81.99 12	76.33 16	62.16 2	75.89 24	52.96 30	64.37 36	73.30 26	65.66 8	77.49 2	77.43 3	82.67 1	93.51 4

CNVR-MVS		73.87 13	78.60 13	68.35 12	73.32 17	81.97 13	76.19 17	59.29 12	80.12 18	56.70 18	67.09 30	76.48 16	64.26 11	75.88 8	75.75 7	80.32 10	92.93 6

SMA-MVS		73.31 14	79.53 11	66.05 16	71.25 25	80.13 21	74.99 18	56.09 18	84.14 10	54.48 24	73.74 19	80.23 6	61.43 18	74.96 12	74.09 14	78.08 62	89.42 18
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
CSCG		72.98 15	76.86 19	68.46 11	78.23 7	81.74 14	77.26 12	60.00 8	75.61 27	59.06 10	62.72 38	77.42 13	56.63 68	74.24 14	77.18 4	79.56 18	89.13 22

HPM-MVS++		72.44 16	78.73 12	65.11 17	71.88 23	77.31 55	71.98 26	55.67 20	83.11 12	53.59 28	75.90 15	78.49 10	61.00 22	73.99 15	73.31 18	76.55 91	88.97 23

APD-MVS		71.86 17	77.91 16	64.80 19	70.39 29	75.69 67	74.02 20	56.14 17	83.59 11	52.92 31	84.67 10	73.46 25	59.30 34	69.47 47	69.66 51	76.02 98	88.84 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		71.50 18	77.27 18	64.77 20	69.64 31	79.26 23	73.53 21	54.73 28	79.32 20	54.23 25	74.81 16	74.61 23	59.40 32	73.00 17	72.17 22	77.10 84	87.72 33

NCCC		71.36 19	75.44 22	66.60 15	72.46 20	79.18 25	74.16 19	57.83 14	76.93 22	54.19 26	63.47 37	71.08 31	61.30 20	73.56 16	73.70 15	79.69 15	90.19 14

train_agg		70.74 20	76.53 20	63.98 24	70.33 30	75.16 76	72.33 25	55.78 19	75.74 25	50.41 40	80.08 14	73.15 27	57.75 58	71.96 29	70.94 35	77.25 80	88.69 26

MGCNet		70.65 21	76.30 21	64.05 23	67.54 40	80.89 18	68.89 39	49.94 53	77.93 21	55.92 20	68.22 28	73.10 28	62.14 15	71.10 35	71.81 24	79.87 11	91.03 11

TSAR-MVS + MP.		70.28 22	75.09 23	64.66 21	69.34 33	64.61 169	72.60 24	56.29 16	80.73 16	58.36 13	84.56 11	75.22 21	55.37 81	69.11 58	69.45 54	75.97 100	81.97 110
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 23	78.36 15	60.39 52	62.38 65	76.96 58	65.54 82	52.23 37	87.46 5	49.07 41	74.05 18	76.19 17	59.01 37	72.79 22	71.61 27	74.13 155	89.49 17

SteuartSystems-ACMMP		69.78 24	74.76 24	63.98 24	73.45 16	78.56 33	73.13 23	55.24 25	70.68 39	48.93 43	70.43 24	69.10 33	54.00 90	72.78 24	72.98 19	79.14 37	88.74 25
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		68.75 25	72.84 26	63.98 24	68.87 37	75.09 78	71.87 27	51.22 41	73.50 31	58.17 14	68.05 29	68.67 34	57.79 57	70.49 40	69.23 61	75.98 99	84.84 83

MVSMamba_PlusPlus		68.58 26	72.33 29	64.22 22	66.67 42	80.11 22	68.52 42	54.28 29	65.99 52	51.49 33	59.22 48	62.40 50	65.80 7	76.97 4	75.31 11	78.56 49	86.28 65

SD-MVS		68.30 27	72.58 28	63.31 29	69.24 34	67.85 140	70.81 32	53.65 34	79.64 19	58.52 12	74.31 17	75.37 20	53.52 96	65.63 101	63.56 139	74.13 155	81.73 115
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
DELS-MVS		67.36 28	70.34 42	63.89 27	69.12 35	81.55 15	70.82 31	55.02 26	53.38 88	48.83 44	56.45 54	59.35 63	60.05 29	74.93 13	74.78 12	79.51 19	91.95 8
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 29	73.08 25	60.64 45	66.20 44	76.62 60	69.22 38	50.92 43	70.07 40	48.81 45	69.66 26	70.12 32	53.68 93	68.41 69	69.13 63	74.98 130	87.53 37
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 30	71.52 35	62.44 32	59.79 102	74.84 80	68.89 39	55.56 21	73.91 30	53.50 29	55.00 60	65.63 38	60.08 27	71.99 28	71.33 32	76.85 86	87.94 31
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 31	72.08 31	61.56 38	66.76 41	73.58 89	71.41 28	52.98 35	69.92 42	43.85 83	70.58 23	58.75 65	56.76 66	72.90 20	71.88 23	77.57 71	86.94 50

CANet		67.21 32	71.83 33	61.83 34	64.51 50	79.25 24	66.72 73	48.73 61	68.49 47	50.63 39	61.40 42	66.47 36	61.44 17	69.31 52	69.90 44	78.94 45	88.00 29

CDPH-MVS		67.03 33	71.64 34	61.65 37	69.10 36	76.84 59	71.35 30	55.42 23	67.02 50	42.83 95	65.27 35	64.60 42	53.16 99	69.70 46	71.40 30	78.02 64	86.67 58

MAR-MVS		66.85 34	69.81 43	63.39 28	73.56 15	80.51 19	69.87 34	51.51 40	67.78 49	46.44 62	51.09 82	61.60 57	60.38 24	72.67 25	73.61 16	78.59 48	81.44 119
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 35	70.69 40	62.36 33	62.76 60	73.21 92	67.96 47	52.31 36	72.26 34	51.03 34	56.50 53	64.26 43	63.37 13	71.64 32	70.85 36	76.70 89	86.10 67
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 36	72.21 30	60.44 51	61.23 88	70.00 120	64.26 91	47.79 85	72.98 32	56.32 19	71.35 22	72.33 29	55.68 79	65.49 102	66.66 95	77.35 74	86.62 59

ACMMPR		66.20 37	71.51 36	60.00 61	65.34 48	74.04 84	69.39 36	50.92 43	71.97 35	46.04 65	66.79 32	65.68 37	53.07 100	68.93 61	69.12 64	75.21 124	84.05 91

3Dnovator	58.39 4	65.97 38	66.85 58	64.94 18	73.72 14	79.03 26	67.73 52	54.25 30	61.52 58	52.79 32	42.27 132	60.73 61	62.01 16	71.29 33	71.75 26	79.12 38	81.34 122

TSAR-MVS + ACMM		65.95 39	72.83 27	57.93 77	69.35 32	65.85 160	73.36 22	39.84 196	76.00 23	48.69 46	82.54 13	75.03 22	49.38 129	65.33 104	63.42 141	66.94 219	81.67 116

sasdasda		65.55 40	70.75 38	59.49 69	62.11 73	78.26 42	66.52 75	43.82 154	71.54 36	47.84 50	61.30 43	61.68 54	58.48 45	67.56 79	69.67 49	78.16 60	85.25 78

canonicalmvs		65.55 40	70.75 38	59.49 69	62.11 73	78.26 42	66.52 75	43.82 154	71.54 36	47.84 50	61.30 43	61.68 54	58.48 45	67.56 79	69.67 49	78.16 60	85.25 78

QAPM		65.47 42	67.82 50	62.72 31	72.56 18	81.17 16	67.43 59	55.38 24	56.07 75	43.29 92	43.60 124	65.38 40	59.10 35	72.20 27	70.76 37	78.56 49	85.59 74

PGM-MVS		65.35 43	70.43 41	59.43 71	65.78 46	73.75 86	69.41 35	48.18 74	68.80 46	45.37 74	65.88 34	64.04 44	52.68 107	68.94 60	68.68 73	75.18 125	82.93 100

PHI-MVS		65.17 44	72.07 32	57.11 92	63.02 58	77.35 54	67.04 69	48.14 79	68.03 48	37.56 124	66.00 33	65.39 39	53.19 98	70.68 37	70.57 40	73.72 163	86.46 62

CLD-MVS		64.69 45	67.25 52	61.69 36	68.22 39	78.33 38	63.09 96	47.59 88	69.64 43	53.98 27	54.87 61	53.94 89	57.87 55	72.79 22	71.34 31	79.40 26	69.87 205
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 46	67.11 55	61.80 35	71.04 26	77.91 49	62.75 99	54.78 27	51.43 93	47.54 52	53.77 64	54.85 84	56.84 64	70.59 38	71.50 28	77.86 66	89.70 16

EPNet		64.39 47	70.93 37	56.77 96	60.58 97	75.77 63	59.28 122	50.58 47	69.93 41	40.73 113	68.59 27	61.60 57	53.72 91	68.65 64	68.07 77	75.75 113	83.87 93
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		64.37 48	69.48 44	58.39 74	62.21 69	71.81 112	67.27 64	49.51 55	69.40 45	45.76 71	60.41 46	64.96 41	51.84 109	67.33 86	67.57 86	73.78 162	84.89 81

EC-MVSNet		64.30 49	68.19 46	59.76 65	62.97 59	75.31 74	67.26 65	44.19 148	60.73 61	47.52 54	55.84 56	62.12 52	57.67 59	70.71 36	67.47 88	78.97 43	85.13 80

casdiffmvs_mvg		64.26 50	67.60 51	60.36 53	62.26 68	78.54 34	69.39 36	48.33 72	56.54 70	45.36 75	52.86 70	57.36 70	58.42 47	70.28 41	70.24 42	78.43 55	87.39 41
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		63.87 51	67.08 56	60.12 60	60.90 93	78.29 41	67.91 49	48.01 83	55.89 79	44.97 78	50.45 86	56.94 71	59.54 30	70.17 44	69.81 46	79.41 24	87.99 30
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
E2		63.83 52	66.23 63	61.02 41	62.29 67	78.81 29	67.95 48	48.45 68	52.32 90	48.38 47	51.97 74	53.76 90	59.35 33	69.39 50	69.76 48	79.70 14	87.62 36

MVS_Test		63.75 53	67.24 53	59.68 66	60.01 98	76.99 57	68.13 45	45.17 132	57.45 69	43.74 85	53.07 68	56.16 78	61.33 19	70.27 42	71.11 33	79.72 13	85.63 73

Casviewmamba		63.73 54	66.47 60	60.53 50	63.39 52	77.99 48	67.69 53	48.45 68	55.29 82	45.83 69	50.75 85	56.46 75	60.08 27	69.12 56	69.33 55	77.74 68	86.33 64

hybridcas		63.61 55	66.22 64	60.56 47	62.13 72	78.59 32	68.59 41	48.14 79	54.14 85	45.68 72	49.27 92	56.60 72	59.44 31	69.17 54	69.03 67	79.41 24	86.75 57

X-MVS		63.53 56	68.62 45	57.60 81	64.77 49	73.06 94	65.82 80	50.53 48	65.77 53	42.02 106	58.20 51	63.42 47	47.83 140	68.25 74	68.50 74	74.61 142	83.16 97

viewcassd2359sk11		63.49 57	65.78 70	60.83 43	62.14 71	78.68 30	67.83 51	48.34 71	51.06 95	47.99 49	51.10 81	53.41 91	59.09 36	69.12 56	69.58 52	79.58 17	87.49 38

viewmanbaseed2359cas		63.30 58	65.85 69	60.31 54	61.55 83	78.41 37	68.44 43	47.39 94	50.91 96	46.42 63	50.98 84	53.99 88	58.60 43	69.11 58	70.10 43	79.48 20	87.46 39

ACMMP		63.27 59	67.85 49	57.93 77	62.64 63	72.30 107	68.23 44	48.77 60	66.50 51	43.05 93	62.07 39	57.84 68	49.98 120	66.58 92	66.46 102	74.93 131	83.17 95
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
CS-MVS		63.16 60	68.01 48	57.49 83	57.39 120	72.73 101	63.38 95	45.16 133	59.37 63	46.49 61	58.93 50	57.68 69	56.31 70	71.12 34	70.37 41	76.23 97	85.88 68

E3new		63.04 61	65.16 76	60.56 47	61.92 76	78.50 35	67.67 55	48.17 75	49.34 103	47.42 56	49.85 89	52.98 93	58.75 39	68.79 62	69.33 55	79.43 22	87.30 42

E3		63.03 62	65.15 77	60.56 47	61.92 76	78.49 36	67.68 54	48.17 75	49.33 104	47.43 55	49.85 89	52.99 92	58.75 39	68.79 62	69.32 57	79.43 22	87.30 42

viewdifsd2359ckpt13		62.95 63	65.29 73	60.21 55	62.21 69	78.86 27	67.26 65	48.16 77	50.15 99	45.82 70	50.17 88	51.84 104	58.68 42	69.24 53	69.88 45	79.15 36	86.86 51

ETV-MVS		62.88 64	68.18 47	56.70 97	58.47 110	74.89 79	60.26 114	43.96 151	58.27 68	42.37 102	61.47 41	56.56 73	57.80 56	68.00 77	68.74 71	77.34 75	89.33 21

AdaColmap		62.79 65	62.63 97	62.98 30	70.82 27	72.90 98	67.84 50	54.09 32	65.14 54	50.71 37	41.78 134	47.64 138	60.17 26	67.41 85	66.83 93	74.28 148	76.69 150

3Dnovator+	55.76 7	62.70 66	65.10 78	59.90 62	65.89 45	72.15 108	62.94 98	49.82 54	62.77 57	49.06 42	43.62 123	61.47 59	58.60 43	68.51 65	66.75 94	73.08 178	80.40 130

OpenMVS	55.62 8	62.57 67	63.76 91	61.19 40	72.13 21	78.84 28	64.42 89	50.51 49	56.44 72	45.67 73	36.88 164	56.51 74	56.66 67	68.28 73	68.96 68	77.73 69	80.44 129

PVSNet_BlendedMVS		62.53 68	66.37 61	58.05 75	58.17 111	75.70 65	61.30 107	48.67 64	58.67 64	50.93 35	55.43 58	49.39 127	53.01 102	69.46 48	66.55 98	76.24 95	89.39 19

PVSNet_Blended		62.53 68	66.37 61	58.05 75	58.17 111	75.70 65	61.30 107	48.67 64	58.67 64	50.93 35	55.43 58	49.39 127	53.01 102	69.46 48	66.55 98	76.24 95	89.39 19

MVSTER		62.51 70	67.22 54	57.02 94	55.05 148	69.23 128	63.02 97	46.88 104	61.11 60	43.95 82	59.20 49	58.86 64	56.80 65	69.13 55	70.98 34	76.41 93	82.04 107

viewdifsd2359ckpt09		62.50 71	64.48 81	60.19 58	61.23 88	77.58 51	67.62 56	48.43 70	51.16 94	47.53 53	51.23 80	51.93 101	58.78 38	67.17 88	65.88 108	77.54 72	86.38 63

E5new		62.48 72	64.43 83	60.20 56	61.57 80	78.31 39	67.43 59	48.06 81	47.28 118	46.73 58	48.48 100	52.64 96	58.20 50	68.45 66	69.07 65	79.20 33	86.77 55

E5		62.48 72	64.43 83	60.20 56	61.57 80	78.31 39	67.43 59	48.06 81	47.28 118	46.73 58	48.48 100	52.64 96	58.20 50	68.45 66	69.07 65	79.20 33	86.77 55

CHOSEN 1792x2688		62.48 72	64.06 88	60.64 45	72.50 19	84.18 5	62.43 100	53.77 33	47.90 117	39.85 117	25.15 234	44.76 155	53.72 91	77.29 3	77.61 2	81.60 6	91.53 9

CostFormer		62.45 75	65.68 71	58.67 73	63.29 55	77.65 50	67.62 56	38.42 207	54.04 86	46.00 66	48.27 103	57.89 67	56.97 62	67.03 89	67.79 84	79.74 12	87.09 47

E4		62.36 76	64.27 86	60.14 59	61.58 79	78.25 44	67.38 62	47.91 84	46.78 123	46.58 60	48.07 104	52.52 98	58.23 49	68.32 71	68.96 68	79.19 35	86.98 49

PCF-MVS	55.99 6	62.31 77	66.60 59	57.32 86	59.12 109	73.68 88	67.53 58	48.71 62	61.35 59	42.83 95	51.33 79	63.48 46	53.48 97	65.64 100	64.87 123	72.22 183	85.83 69
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		62.30 78	66.05 65	57.92 79	57.08 122	75.60 71	66.90 70	47.06 102	55.45 81	43.37 90	53.45 66	55.60 80	57.21 61	66.57 93	68.00 80	75.89 103	87.70 35
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewmacassd2359aftdt		62.09 79	64.24 87	59.58 68	60.94 91	78.01 45	68.04 46	46.83 106	46.59 125	45.11 77	47.34 106	52.79 94	57.50 60	68.43 68	69.54 53	79.08 39	87.01 48

E6new		61.98 80	63.77 89	59.90 62	61.52 85	78.01 45	67.09 67	47.57 91	45.71 129	45.97 67	46.87 108	51.47 107	58.17 52	68.20 75	69.31 59	79.07 40	86.81 53

E6		61.98 80	63.77 89	59.90 62	61.52 85	78.01 45	67.09 67	47.57 91	45.71 129	45.97 67	46.87 108	51.47 107	58.17 52	68.20 75	69.31 59	79.07 40	86.81 53

hybridnocas07		61.95 82	65.92 67	57.32 86	56.68 128	75.77 63	65.53 83	46.69 111	55.99 76	43.65 86	53.11 67	55.36 82	56.11 72	66.44 95	67.60 85	75.28 122	87.16 46

DI_MVS_pp		61.86 83	65.26 74	57.90 80	57.93 116	74.51 82	66.30 77	46.49 116	49.96 101	41.62 109	42.69 129	61.77 53	58.74 41	70.25 43	69.32 57	76.31 94	88.30 27

diffmvs_AUTHOR		61.85 84	65.54 72	57.54 82	56.64 129	75.64 70	66.65 74	46.55 115	53.31 89	42.72 99	51.70 76	55.51 81	56.91 63	66.66 90	68.09 76	75.77 112	87.89 32

MSLP-MVS++		61.81 85	62.19 102	61.37 39	68.33 38	63.08 190	70.75 33	38.89 203	63.96 56	57.51 16	48.59 97	61.66 56	53.67 94	62.04 153	59.92 192	79.03 42	76.08 153

hybrid		61.79 86	65.87 68	57.03 93	56.18 134	75.51 73	65.47 85	46.32 119	55.94 78	43.47 88	52.97 69	55.80 79	55.45 80	66.17 96	67.53 87	75.28 122	87.17 45

SPE-MVS-test		61.68 87	65.97 66	56.67 98	57.77 117	72.59 104	57.63 130	45.54 127	58.53 67	47.11 57	59.45 47	56.34 76	55.15 82	64.52 117	65.03 121	76.80 87	85.34 77

OPM-MVS		61.59 88	62.30 101	60.76 44	66.53 43	73.35 91	71.41 28	54.18 31	40.82 160	41.57 110	45.70 115	54.84 85	54.43 87	69.92 45	69.19 62	76.45 92	82.25 104
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
viewmamba		61.51 89	65.19 75	57.21 89	57.74 118	75.65 69	64.78 88	47.08 101	55.24 83	42.97 94	52.09 73	54.80 86	56.05 74	65.74 98	67.28 89	74.63 141	85.53 76

viewdifsd2359ckpt07		61.43 90	63.33 94	59.20 72	61.66 78	77.47 53	66.75 72	46.85 105	45.54 131	45.32 76	48.59 97	51.61 106	56.09 73	67.46 81	68.01 79	78.54 52	84.67 85

MS-PatchMatch		61.41 91	61.88 106	60.85 42	70.57 28	75.98 62	66.29 78	46.91 103	50.56 98	48.28 48	36.30 167	51.64 105	50.95 115	72.89 21	70.65 39	82.13 3	75.17 164

onestephybrid01		61.26 92	65.06 79	56.82 95	57.98 114	75.52 72	64.18 92	46.76 109	54.24 84	43.46 89	52.35 71	55.20 83	55.00 83	65.65 99	66.25 103	73.56 165	86.21 66

casdiffseed414692147		60.92 93	62.03 103	59.63 67	62.33 66	76.41 61	67.31 63	47.59 88	48.83 111	43.83 84	41.47 135	47.12 143	58.26 48	67.43 84	68.40 75	78.47 53	84.57 88

viewmambaseed2359dif		60.68 94	63.59 93	57.29 88	56.93 124	75.24 75	65.36 86	45.82 125	49.89 102	43.57 87	49.83 91	51.89 103	56.33 69	64.86 112	65.71 110	75.75 113	87.72 33

EIA-MVS		60.56 95	64.29 85	56.20 103	59.14 108	72.68 103	59.55 120	43.56 158	51.78 92	41.01 112	55.47 57	51.93 101	55.87 76	65.01 108	66.57 97	78.06 63	86.60 61

dtuplus		60.37 96	63.09 95	57.19 90	57.03 123	75.16 76	65.19 87	45.85 124	48.37 114	43.35 91	48.48 100	52.00 100	55.90 75	64.94 111	65.48 114	75.85 108	87.18 44

ACMP	56.21 5	59.78 97	61.81 108	57.41 85	61.15 90	68.88 130	65.98 79	48.85 59	58.56 66	44.19 81	48.89 95	46.31 147	48.56 134	63.61 133	64.49 131	75.75 113	81.91 111
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		59.69 98	62.59 98	56.31 101	61.94 75	68.15 137	66.90 70	48.15 78	59.75 62	38.47 120	50.38 87	48.34 135	46.87 147	65.39 103	64.93 122	75.51 118	81.21 124

Effi-MVS+		59.63 99	61.78 109	57.12 91	61.56 82	71.63 113	63.61 93	47.59 88	47.18 120	37.79 121	45.29 116	49.93 123	56.27 71	67.45 82	67.06 91	75.91 101	83.93 92

CPTT-MVS		59.54 100	64.47 82	53.79 115	54.99 150	67.63 144	65.48 84	44.59 142	64.81 55	37.74 122	51.55 77	59.90 62	49.77 125	61.83 157	61.26 174	70.18 199	84.31 90

baseline2		59.20 101	61.72 110	56.27 102	59.61 104	74.12 83	58.65 125	49.42 56	48.10 115	40.12 116	49.10 94	44.15 158	51.24 112	66.65 91	67.88 83	78.56 49	82.06 106

MGCFI-Net		59.19 102	66.89 57	50.20 146	57.15 121	68.62 133	54.79 159	39.20 201	70.99 38	32.93 153	60.83 45	61.00 60	45.54 154	63.77 131	60.71 183	71.59 187	82.29 102

GeoE		58.97 103	60.94 111	56.67 98	61.27 87	72.71 102	61.35 106	45.69 126	49.19 108	41.22 111	39.55 151	49.58 126	52.79 106	64.79 113	65.89 107	77.73 69	84.87 82

baseline		58.65 104	61.99 104	54.75 110	54.70 152	71.85 111	60.20 115	43.91 152	55.99 76	40.13 115	53.50 65	50.91 120	55.76 77	61.29 165	61.73 166	73.83 159	78.68 141

PVSNet_Blended_VisFu		58.56 105	62.33 100	54.16 112	56.90 125	73.92 85	57.72 129	46.16 122	44.23 136	42.73 98	46.26 110	51.06 118	46.28 150	67.99 78	65.38 116	75.18 125	87.44 40

ACMM	53.73 9	57.91 106	58.27 131	57.49 83	63.10 56	66.45 154	65.65 81	49.02 58	53.69 87	42.67 100	36.41 166	46.07 150	50.38 118	64.74 115	64.63 128	74.14 154	75.91 154
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		57.87 107	63.63 92	51.15 133	52.18 159	70.20 119	58.14 128	37.32 214	56.49 71	31.06 163	57.38 52	50.05 122	53.67 94	64.98 110	65.04 120	74.57 143	81.29 123

ET-MVSNet_ETH3D		57.84 108	61.91 105	53.09 118	32.91 255	74.53 81	63.51 94	46.80 108	46.52 126	36.14 130	56.00 55	46.20 148	64.41 10	60.75 173	66.99 92	74.79 132	82.35 101

viewdifsd2359ckpt11		57.53 109	59.36 119	55.39 105	55.17 146	72.10 109	61.49 103	45.16 133	42.72 145	42.15 104	46.03 112	47.43 139	54.14 89	61.84 155	62.46 157	74.23 149	82.96 98

viewmsd2359difaftdt		57.53 109	59.36 119	55.39 105	55.17 146	72.10 109	61.49 103	45.16 133	42.72 145	42.15 104	46.03 112	47.42 140	54.15 88	61.84 155	62.46 157	74.23 149	82.96 98

tpm cat1		57.41 111	58.26 132	56.42 100	60.80 95	72.56 105	64.35 90	38.43 206	49.18 109	46.36 64	36.69 165	43.50 162	54.47 85	61.39 163	62.64 152	74.11 157	81.81 112

IB-MVS	53.15 10	57.33 112	59.02 123	55.37 107	60.83 94	77.11 56	54.51 160	50.10 52	43.22 142	42.82 97	40.50 141	37.61 183	44.67 164	59.27 188	69.81 46	79.29 29	85.59 74
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		57.23 113	59.08 122	55.06 108	59.91 100	70.65 117	60.71 110	35.38 225	47.91 116	42.58 101	39.78 146	45.45 152	54.44 86	62.19 150	62.82 149	77.37 73	84.73 84

baseline1		57.21 114	60.53 113	53.33 117	62.50 64	69.86 122	57.33 134	50.59 46	43.39 141	30.00 169	48.60 96	51.09 117	42.36 177	69.38 51	68.03 78	77.20 81	73.39 177

FA-MVS(training)		57.15 115	60.42 114	53.34 116	58.15 113	72.77 99	59.79 118	38.68 204	49.01 110	36.56 129	40.79 139	45.44 153	53.04 101	65.23 107	67.93 82	73.82 160	81.80 114

HyFIR lowres test		57.12 116	59.11 121	54.80 109	61.55 83	77.55 52	59.02 123	45.00 137	41.84 157	33.93 147	22.44 241	49.16 130	51.02 114	68.39 70	68.71 72	78.26 59	85.70 72

MVS_111021_LR		57.06 117	60.60 112	52.93 119	56.25 132	65.14 167	55.16 157	41.21 186	52.32 90	44.89 79	53.92 63	49.27 129	52.16 108	61.46 161	60.54 184	67.92 211	81.53 118

DCV-MVSNet		56.80 118	58.96 124	54.28 111	59.96 99	66.74 152	60.37 113	44.87 139	41.01 159	36.81 127	47.57 105	47.87 137	48.23 137	64.41 119	65.17 118	75.45 119	79.95 134

Anonymous20231211		56.40 119	57.00 144	55.70 104	59.78 103	72.49 106	61.29 109	46.83 106	40.50 163	40.46 114	22.12 243	49.73 124	51.07 113	64.39 120	65.30 117	74.74 135	84.44 89

0.4-1-1-0.2		56.13 120	60.14 115	51.44 130	45.97 200	73.09 93	56.79 144	45.39 128	47.03 121	34.23 139	43.14 128	51.20 116	47.33 143	63.12 137	63.30 142	78.95 44	80.11 132

0.3-1-1-0.015		56.04 121	60.09 116	51.32 131	46.02 198	73.04 97	56.64 145	45.36 129	46.70 124	34.01 143	43.24 126	51.25 112	46.98 146	63.12 137	63.20 145	78.90 46	80.11 132

PMMVS		55.74 122	62.68 96	47.64 167	44.34 213	65.58 164	47.22 205	37.96 210	56.43 73	34.11 141	61.51 40	47.41 141	54.55 84	65.88 97	62.49 156	67.67 213	79.48 136

Fast-Effi-MVS+		55.73 123	58.26 132	52.76 120	54.33 153	68.19 136	57.05 135	34.66 227	46.92 122	38.96 119	40.53 140	41.55 171	55.69 78	65.31 105	65.99 104	75.90 102	79.34 137

FC-MVSNet-train		55.68 124	57.00 144	54.13 113	63.37 53	66.16 156	46.77 209	52.14 38	42.36 151	37.67 123	48.50 99	41.42 173	51.28 111	61.58 160	63.22 144	73.56 165	75.76 157

FMVSNet3		55.66 125	59.68 118	50.96 135	50.59 173	66.49 153	57.57 131	46.61 112	49.30 105	28.77 174	39.61 147	51.42 109	43.85 169	68.29 72	68.80 70	78.35 58	73.86 167

0.4-1-1-0.1		55.63 126	59.73 117	50.85 136	45.99 199	72.77 99	56.11 151	45.23 131	45.84 128	33.32 151	42.60 130	51.06 118	45.68 153	62.99 142	62.97 148	78.76 47	79.90 135

OMC-MVS		55.48 127	61.85 107	48.04 166	41.55 222	60.32 208	56.80 139	31.78 248	75.67 26	42.30 103	51.52 78	54.15 87	49.91 122	60.28 179	57.59 204	65.91 222	73.42 175

tpm		54.94 128	57.86 137	51.54 129	59.48 106	67.04 148	58.34 127	34.60 230	41.93 156	34.41 137	42.40 131	47.14 142	49.07 132	61.46 161	61.67 170	73.31 173	83.39 94

GBi-Net		54.66 129	58.42 129	50.26 144	49.36 182	65.81 161	56.80 139	46.61 112	49.30 105	28.77 174	39.61 147	51.42 109	42.71 173	64.25 123	65.54 111	77.32 77	73.03 180

test1		54.66 129	58.42 129	50.26 144	49.36 182	65.81 161	56.80 139	46.61 112	49.30 105	28.77 174	39.61 147	51.42 109	42.71 173	64.25 123	65.54 111	77.32 77	73.03 180

test-LLR		54.62 131	58.66 127	49.89 151	51.68 165	65.89 158	47.88 199	46.35 117	42.51 148	29.84 170	41.41 136	48.87 131	45.20 157	62.91 144	64.43 132	78.43 55	84.62 86

dmvs_re		54.51 132	57.04 143	51.56 128	56.51 130	62.63 194	55.56 153	50.45 50	45.31 132	24.75 192	43.94 122	39.99 178	42.74 172	66.53 94	65.44 115	79.33 28	75.46 159

TSAR-MVS + COLMAP		54.37 133	62.43 99	44.98 185	34.33 245	58.94 216	54.11 165	34.15 239	74.06 29	34.57 136	71.63 21	42.03 170	47.88 139	61.26 166	57.33 209	64.83 225	71.74 191

EPMVS		54.07 134	56.06 150	51.75 127	56.74 127	70.80 115	55.32 155	34.20 236	46.46 127	36.59 128	40.38 143	42.55 165	49.77 125	61.25 167	60.90 179	77.86 66	70.08 202

v2v482		54.00 135	55.12 157	52.69 122	51.73 164	69.42 127	60.65 111	45.09 136	34.56 195	33.73 150	35.29 171	35.36 194	49.92 121	64.05 129	65.16 119	75.00 129	81.98 109

CNLPA		54.00 135	57.08 142	50.40 143	49.83 179	61.75 199	53.47 169	37.27 215	74.55 28	44.85 80	33.58 183	45.42 154	52.94 105	58.89 190	53.66 230	64.06 229	71.68 192

FMVSNet2		53.94 137	57.29 139	50.03 148	49.36 182	65.81 161	56.80 139	45.95 123	43.13 143	28.04 178	35.68 168	48.18 136	42.71 173	67.23 87	67.95 81	77.32 77	73.03 180

v8		53.77 138	54.82 162	52.54 123	52.12 160	66.95 151	60.56 112	43.23 164	37.17 184	35.35 132	34.96 174	37.50 185	49.51 128	63.67 132	64.59 129	74.48 145	78.91 140

GA-MVS		53.77 138	56.41 149	50.70 138	51.63 167	69.96 121	57.55 132	44.39 143	34.31 196	27.15 180	40.99 138	36.40 189	47.65 142	67.45 82	67.16 90	75.83 109	78.60 142

Effi-MVS+-dtu		53.63 140	54.85 161	52.20 125	59.32 107	61.33 202	56.42 148	40.24 194	43.84 138	34.22 140	39.49 152	46.18 149	53.00 104	58.72 194	57.49 208	69.99 202	76.91 148

thisisatest0530		53.61 141	57.22 140	49.40 156	51.30 169	68.22 135	52.72 177	43.34 162	42.72 145	35.31 133	43.57 125	44.14 159	44.37 167	63.00 141	64.86 124	69.34 205	74.00 166

v1144		53.47 142	54.65 163	52.10 126	51.93 162	69.81 123	59.32 121	44.77 141	33.21 202	32.52 155	33.55 184	34.34 203	49.29 130	64.58 116	64.81 126	74.74 135	82.27 103

blend_shiyan4		53.44 143	57.17 141	49.10 159	46.19 196	65.49 165	58.38 126	42.54 174	48.56 113	34.01 143	44.21 119	51.25 112	36.84 190	57.58 198	57.87 199	76.63 90	75.23 161

v10		53.44 143	54.40 164	52.31 124	52.08 161	66.99 149	59.68 119	43.41 159	35.90 190	34.30 138	33.98 181	35.56 191	50.10 119	64.39 120	64.67 127	74.32 146	79.30 138

PatchmatchNet		53.37 145	55.62 155	50.75 137	55.93 140	70.54 118	51.39 182	36.41 218	44.85 134	37.26 125	39.40 154	42.54 166	47.83 140	60.29 178	60.88 181	75.69 116	70.87 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test2506		53.36 146	57.36 138	48.68 162	55.53 142	68.11 138	54.31 162	46.25 120	43.54 139	22.21 204	40.19 144	43.69 161	36.56 194	64.15 127	65.94 105	77.20 81	75.91 154

IterMVS-LS		53.36 146	55.65 154	50.68 140	55.34 144	59.04 214	55.00 158	39.98 195	38.71 172	33.22 152	44.52 118	47.05 144	49.63 127	61.82 158	61.77 165	70.92 194	76.61 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		53.30 148	58.66 127	47.04 170	44.94 207	65.89 158	47.88 199	35.95 221	42.51 148	29.84 170	41.41 136	48.87 131	45.20 157	62.91 144	64.43 132	78.43 55	84.62 86

tttt0517		53.05 149	56.73 148	48.76 160	50.35 175	67.51 145	51.96 181	43.34 162	42.00 155	33.88 148	43.19 127	43.49 163	44.37 167	62.58 149	64.86 124	68.67 207	73.46 174

MDTV_nov1_ep13		52.99 150	55.59 156	49.95 150	54.08 154	70.69 116	56.47 147	38.42 207	42.78 144	30.19 168	39.56 150	43.31 164	45.78 152	60.07 183	62.11 162	74.74 135	70.62 197

EPP-MVSNet		52.91 151	58.91 125	45.91 176	54.99 150	68.84 131	49.27 189	42.71 172	37.53 178	20.20 216	46.09 111	56.19 77	36.90 189	61.37 164	60.90 179	71.41 188	81.41 120

dps		52.84 152	52.92 179	52.74 121	59.89 101	69.49 126	54.47 161	37.38 213	42.49 150	39.53 118	35.33 170	32.71 213	51.83 110	60.45 175	61.12 176	73.33 172	68.86 211

v1192		52.69 153	53.86 169	51.31 132	51.22 170	69.76 124	57.37 133	44.39 143	32.21 205	31.39 162	32.41 192	32.44 216	49.19 131	64.25 123	64.17 134	74.31 147	81.81 112

V42		52.63 154	55.08 158	49.76 153	44.93 208	67.49 147	60.19 116	42.13 182	37.21 183	34.08 142	34.57 177	37.30 186	47.29 144	63.48 135	64.15 135	69.96 203	81.38 121

MSDG		52.58 155	51.40 192	53.95 114	65.48 47	64.31 177	61.44 105	44.02 149	44.17 137	32.92 154	30.40 205	31.81 220	46.35 149	62.13 151	62.55 154	73.49 168	64.41 219

ECVR-MVS		52.52 156	55.88 152	48.60 163	55.53 142	68.11 138	54.31 162	46.25 120	43.54 139	21.75 208	32.76 189	39.83 181	36.56 194	64.15 127	65.94 105	77.20 81	76.81 149

Fast-Effi-MVS+-dtu		52.47 157	55.89 151	48.48 164	56.25 132	65.07 168	58.75 124	23.79 261	41.27 158	27.07 182	37.95 159	41.34 174	50.85 116	62.90 146	62.34 160	74.17 153	80.37 131

v144192		52.43 158	53.63 173	51.03 134	51.06 171	69.60 125	56.94 137	44.84 140	32.15 206	30.88 164	32.45 191	32.71 213	48.36 135	62.98 143	63.52 140	74.10 158	82.02 108

thres100view900		52.33 159	53.91 168	50.48 142	56.10 135	67.79 141	56.18 150	49.18 57	35.86 192	25.22 189	34.74 175	34.10 205	42.41 176	64.45 118	62.62 153	73.81 161	77.85 143

v1921920		51.95 160	53.19 175	50.51 141	50.82 172	69.14 129	55.45 154	44.34 147	31.53 210	30.53 166	31.96 194	31.67 221	48.31 136	63.12 137	63.28 143	73.59 164	81.60 117

v148		51.72 161	53.15 176	50.05 147	50.15 177	67.51 145	56.98 136	42.85 169	32.60 204	32.41 157	33.88 182	34.71 199	44.45 165	61.06 168	63.00 147	73.45 169	79.24 139

TAPA-MVS	47.92 11	51.66 162	57.88 136	44.40 189	36.46 238	58.42 219	53.82 167	30.83 251	69.51 44	34.97 135	46.90 107	49.67 125	46.99 145	58.00 197	54.64 225	63.33 236	68.00 213
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		51.53 163	57.98 135	44.01 193	55.96 139	66.16 156	47.65 201	42.84 171	39.82 167	19.09 225	44.97 117	50.28 121	27.20 233	63.43 136	63.84 136	71.33 190	77.33 145

v1240		51.42 164	52.66 181	49.97 149	50.31 176	68.70 132	54.05 166	43.85 153	30.78 214	30.22 167	31.43 198	31.03 228	47.98 138	62.62 148	63.16 146	73.40 170	80.93 126

pmmvs4		51.28 165	52.50 183	49.85 152	49.54 181	63.02 191	52.83 176	43.41 159	44.65 135	35.71 131	34.38 178	32.25 217	45.14 160	60.21 182	60.03 189	72.44 182	72.98 183

Vis-MVSNet		51.13 166	58.04 134	43.06 199	47.68 189	67.71 142	49.10 190	39.09 202	37.75 176	22.57 201	51.03 83	48.78 133	32.42 218	62.12 152	61.80 164	67.49 216	77.12 146
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		51.04 167	58.79 126	42.00 209	40.59 224	65.32 166	46.65 211	39.26 199	39.90 166	27.30 179	54.12 62	52.03 99	30.93 222	59.85 185	59.62 194	67.23 218	80.70 127
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		50.91 168	52.45 184	49.11 158	56.10 135	64.53 172	53.06 173	47.31 97	35.86 192	25.22 189	34.74 175	34.10 205	41.08 179	60.84 170	61.37 172	71.90 186	75.70 158

SCA		50.88 169	53.70 171	47.59 168	55.99 137	55.81 230	43.14 224	33.45 242	45.16 133	37.14 126	41.83 133	43.82 160	44.43 166	60.37 176	60.02 190	71.38 189	68.90 210

gg-mvs-nofinetune		50.82 170	55.83 153	44.97 186	60.63 96	75.69 67	53.40 170	34.48 232	20.05 258	6.93 257	18.27 252	52.70 95	33.57 207	70.50 39	72.93 21	80.84 8	80.68 128

thres200		50.76 171	52.52 182	48.70 161	55.98 138	64.60 170	55.29 156	47.34 95	33.91 199	24.36 193	34.33 179	33.90 207	37.27 187	60.84 170	62.41 159	71.99 184	77.63 144

test1111		50.62 172	54.98 160	45.55 180	53.84 156	68.48 134	48.99 191	47.25 98	40.60 162	15.64 235	31.51 197	38.32 182	33.01 214	64.34 122	66.62 96	74.55 144	74.95 165

usedtu_blend_shiyan5		50.54 173	53.98 166	46.52 172	33.34 248	64.26 179	56.80 139	42.26 176	28.39 224	34.01 143	44.21 119	51.25 112	36.84 190	56.84 208	57.68 200	75.86 104	75.23 161

thres400		50.39 174	52.22 185	48.26 165	55.02 149	66.32 155	52.97 174	48.33 72	32.68 203	22.94 199	33.21 186	33.38 212	37.27 187	62.74 147	61.38 171	73.04 179	75.81 156

EG-PatchMatch MVS		50.23 175	50.89 195	49.47 154	59.54 105	70.88 114	52.46 178	44.01 150	26.22 242	31.91 158	24.97 235	31.45 224	33.48 209	64.79 113	66.51 101	75.40 120	71.39 194

IterMVS		50.23 175	53.27 174	46.68 171	47.59 191	60.58 206	53.10 172	36.62 217	36.07 188	25.89 185	39.42 153	40.05 177	43.65 170	60.22 181	61.35 173	73.23 174	75.23 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dtuonly		50.17 177	54.11 165	45.57 179	42.23 220	60.12 209	53.78 168	34.65 228	40.73 161	25.46 187	35.49 169	44.55 156	49.82 123	60.74 174	60.21 188	67.50 215	76.92 147

FMVSNet1		50.14 178	52.78 180	47.06 169	45.56 204	63.56 187	54.22 164	43.74 156	34.10 198	25.37 188	29.79 212	42.06 169	38.70 183	64.25 123	65.54 111	74.75 133	70.18 201

ACMH	47.82 13	50.10 179	49.60 201	50.69 139	63.36 54	66.99 149	56.83 138	52.13 39	31.06 213	17.74 232	28.22 222	26.24 244	45.17 159	60.88 169	63.80 137	68.91 206	70.00 204
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		49.85 180	56.99 146	41.52 212	52.79 157	57.06 223	41.44 229	43.13 165	56.13 74	19.24 224	52.11 72	48.38 134	22.14 241	58.19 196	58.38 197	70.35 197	68.71 212
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FE-MVSNET3		49.74 181	53.74 170	45.07 184	33.34 248	64.26 179	48.12 194	42.26 176	28.39 224	34.01 143	44.21 119	51.25 112	36.84 190	56.84 208	57.68 200	75.86 104	73.60 171

LS3D		49.59 182	49.75 200	49.40 156	55.88 141	59.86 211	56.31 149	45.33 130	48.57 112	28.32 177	31.54 196	36.81 188	46.27 151	57.17 203	55.88 220	64.29 228	58.42 239

usedtu_dtu_shiyan1		49.57 183	53.64 172	44.82 187	42.15 221	67.70 143	49.68 187	46.75 110	40.11 165	18.63 229	29.92 209	34.46 202	35.01 199	65.00 109	66.55 98	76.72 88	71.76 190

UniMVSNet_NR-MVSNet		49.56 184	53.04 177	45.49 181	51.59 168	64.42 176	46.97 206	51.01 42	37.87 174	16.42 233	39.87 145	34.91 198	33.43 211	59.59 186	62.70 150	73.52 167	71.94 186

CDS-MVSNet		49.25 185	53.97 167	43.75 195	47.53 192	64.53 172	48.59 192	42.27 175	33.77 200	26.64 183	40.46 142	42.26 168	30.01 225	61.77 159	61.71 167	67.48 217	73.28 179
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	44.22 14	49.14 186	51.75 188	46.10 175	42.78 218	55.60 233	53.11 171	34.46 233	55.69 80	32.47 156	34.16 180	41.45 172	48.91 133	57.13 204	54.09 227	64.84 224	64.10 220
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH+	47.85 12	49.13 187	48.86 211	49.44 155	56.75 126	62.01 198	56.62 146	47.55 93	37.49 179	23.98 194	26.68 228	29.46 235	43.12 171	57.45 202	58.85 196	68.62 208	70.05 203

NR-MVSNet		48.84 188	51.76 187	45.44 182	57.66 119	60.64 204	47.39 202	47.63 86	37.26 180	13.31 238	37.31 161	29.64 234	33.53 208	63.52 134	62.09 163	73.10 177	71.89 189

CR-MVSNet		48.82 189	51.85 186	45.29 183	46.74 194	55.95 228	52.06 179	34.21 234	42.17 152	31.74 159	32.92 188	42.53 167	45.00 161	58.80 191	61.11 177	61.99 242	69.47 206

thres600view7		48.44 190	50.23 198	46.35 174	54.05 155	64.60 170	50.18 185	47.34 95	31.73 209	20.74 214	32.28 193	32.62 215	33.79 206	60.84 170	56.11 218	71.99 184	73.40 176

test-mter		48.31 191	55.04 159	40.45 217	34.12 246	59.02 215	41.77 228	28.05 255	38.43 173	22.67 200	39.35 155	44.40 157	41.88 178	60.30 177	61.68 169	74.20 151	82.12 105

PatchT		48.11 192	51.27 194	44.43 188	50.13 178	61.58 200	33.59 244	32.92 244	40.38 164	31.74 159	30.60 204	36.93 187	45.00 161	58.80 191	61.11 177	73.19 175	69.47 206

TranMVSNet+NR-MVSNet		48.06 193	51.36 193	44.21 191	50.38 174	62.09 197	47.28 203	50.88 45	36.11 187	13.25 239	37.51 160	31.60 223	30.70 223	59.34 187	62.53 155	72.81 180	70.31 199

TransMVSNet (Re)		47.46 194	48.94 208	45.74 178	57.96 115	64.29 178	48.26 193	48.47 67	26.33 241	19.33 222	29.45 215	31.28 227	25.31 237	63.05 140	62.70 150	75.10 128	65.47 217

DU-MVS		47.33 195	50.86 196	43.20 198	44.43 211	60.64 204	46.97 206	47.63 86	37.26 180	16.42 233	37.31 161	31.39 225	33.43 211	57.53 200	59.98 191	70.35 197	71.94 186

v7n		47.22 196	48.38 213	45.87 177	48.20 188	63.58 186	50.69 183	40.93 190	26.60 240	26.44 184	26.52 229	29.65 233	38.19 185	58.22 195	60.23 187	70.79 195	73.83 168

UA-Net		47.19 197	53.02 178	40.38 218	55.31 145	60.02 210	38.41 235	38.68 204	36.42 186	22.47 203	51.95 75	58.72 66	25.62 236	54.11 224	53.40 231	61.79 243	56.51 243

Baseline_NR-MVSNet		47.14 198	50.83 197	42.84 201	44.43 211	63.31 189	44.50 220	50.36 51	37.71 177	11.25 244	30.84 201	32.09 218	30.96 221	57.53 200	63.73 138	75.53 117	70.60 198

pmmvs5		47.02 199	50.02 199	43.51 197	43.48 216	62.65 193	47.24 204	37.78 212	30.59 215	24.80 191	35.26 172	30.43 229	34.36 202	59.05 189	60.28 186	73.40 170	71.92 188

UniMVSNet (Re)		46.89 200	51.65 190	41.34 214	45.60 203	62.71 192	44.05 221	47.10 100	37.24 182	13.55 237	36.90 163	34.54 201	26.76 234	57.56 199	59.90 193	70.98 193	72.69 184

thisisatest0515		46.88 201	49.57 202	43.74 196	45.33 206	60.46 207	46.19 213	41.06 189	30.34 216	29.73 172	32.50 190	31.63 222	35.43 197	58.75 193	61.71 167	64.70 227	71.59 193

tfpnnormal		46.61 202	46.82 220	46.37 173	52.70 158	62.31 195	50.39 184	47.17 99	25.74 244	21.80 205	23.13 239	24.15 252	33.45 210	60.28 179	60.77 182	72.70 181	71.39 194

pm-mvs1		46.14 203	49.34 205	42.41 206	48.93 185	62.22 196	44.98 218	42.68 173	27.66 234	20.76 213	29.88 211	34.96 197	26.41 235	60.03 184	60.42 185	70.70 196	70.20 200

wanda-best-256-512		46.10 204	49.06 206	42.66 202	33.34 248	64.26 179	48.12 194	42.26 176	28.39 224	21.80 205	28.97 217	33.62 208	34.55 200	56.84 208	57.68 200	75.86 104	73.66 170

FE-blended-shiyan7		46.10 204	49.06 206	42.66 202	33.34 248	64.26 179	48.12 194	42.26 176	28.39 224	21.80 205	28.96 218	33.62 208	34.55 200	56.84 208	57.68 200	75.86 104	73.67 169

blended_shiyan6		45.97 206	48.94 208	42.50 205	33.34 248	64.17 183	47.94 198	42.22 180	28.07 231	21.66 210	28.80 219	33.57 210	34.06 203	56.79 213	57.55 206	75.79 110	73.60 171

blended_shiyan8		45.96 207	48.92 210	42.51 204	33.32 253	64.17 183	47.95 197	42.22 180	28.06 232	21.70 209	28.77 220	33.56 211	34.06 203	56.76 214	57.55 206	75.79 110	73.59 173

IterMVS-SCA-FT		45.87 208	51.55 191	39.24 222	46.22 195	59.43 212	52.89 175	31.93 245	36.01 189	23.68 195	38.86 156	39.88 180	39.05 181	56.25 217	58.17 198	41.70 264	72.25 185

MIMVSNet		45.62 209	49.56 203	41.02 215	38.17 229	64.43 175	49.48 188	35.43 224	36.53 185	20.06 218	22.58 240	35.16 196	28.75 230	61.97 154	62.20 161	74.20 151	64.07 221

gm-plane-assit		45.41 210	48.03 215	42.34 207	56.49 131	40.48 261	24.54 266	34.15 239	14.44 266	6.59 258	17.82 255	35.32 195	49.82 123	72.93 19	74.11 13	82.47 2	81.12 125

ADS-MVSNet		45.39 211	46.42 221	44.19 192	48.74 187	57.52 221	43.91 222	31.93 245	35.89 191	27.11 181	30.12 206	32.06 219	45.30 155	53.13 230	55.19 222	68.15 210	61.07 231

gbinet_0.2-2-1-0.02		45.07 212	48.55 212	41.00 216	30.59 258	63.61 185	46.97 206	41.88 183	25.18 246	18.93 228	27.74 225	34.25 204	32.89 215	56.40 216	57.32 210	74.75 133	75.37 160

GG-mvs-BLEND		44.87 213	64.59 80	21.86 258	0.01 281	73.70 87	55.99 152	0.01 276	50.70 97	0.01 282	49.18 93	63.61 45	0.01 278	63.83 130	64.50 130	75.13 127	86.62 59

pmmvs-eth3d		44.67 214	45.27 226	43.98 194	42.56 219	55.72 232	44.97 219	40.81 192	31.96 208	29.13 173	26.09 231	25.27 249	36.69 193	55.13 221	56.62 215	69.68 204	66.12 216

MDTV_nov1_ep13_2view		44.44 215	45.75 224	42.91 200	46.13 197	63.43 188	46.53 212	34.20 236	29.08 222	19.95 219	26.23 230	27.89 239	35.88 196	53.36 229	56.43 216	74.74 135	63.86 222

CMPMVS	33.64 16	44.39 216	46.41 222	42.03 208	44.21 214	56.50 226	46.73 210	26.48 260	34.20 197	35.14 134	24.22 236	34.64 200	40.52 180	56.50 215	56.07 219	59.12 247	62.74 227
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		44.31 217	51.67 189	35.72 232	51.82 163	55.24 234	34.57 243	41.63 184	39.10 170	8.84 252	45.93 114	46.63 146	14.45 254	54.09 225	57.03 212	63.00 237	63.65 224

TAMVS		44.27 218	49.35 204	38.35 226	44.74 209	61.04 203	39.07 233	31.82 247	29.95 218	18.34 230	33.55 184	39.94 179	30.01 225	56.85 207	57.58 205	66.13 221	66.54 214

MVS-HIRNet		43.98 219	43.63 230	44.39 190	47.66 190	59.31 213	32.66 250	33.88 241	30.15 217	33.75 149	16.82 260	28.39 238	45.25 156	53.92 228	55.00 224	73.16 176	61.80 228

UniMVSNet_ETH3D		43.97 220	46.01 223	41.59 210	38.31 228	56.20 227	49.69 186	38.18 209	28.18 228	19.88 221	27.82 224	30.20 230	33.41 213	54.18 223	56.30 217	70.05 201	69.17 208

RPMNet		43.70 221	48.17 214	38.48 225	45.52 205	55.95 228	37.66 237	26.63 259	42.17 152	25.47 186	29.59 214	37.61 183	33.87 205	50.85 235	52.02 235	61.75 244	69.00 209

PatchMatch-RL		43.37 222	44.93 227	41.56 211	37.94 230	51.70 236	40.02 231	35.75 222	39.04 171	30.71 165	35.14 173	27.43 241	46.58 148	51.99 231	50.55 239	58.38 249	58.64 237

FMVSNet5		43.29 223	47.07 218	38.87 223	30.46 259	50.99 238	45.87 215	37.19 216	42.17 152	19.32 223	26.77 227	40.51 175	30.26 224	56.82 212	55.81 221	70.10 200	56.46 244

test0.0.03 1		43.07 224	46.95 219	38.54 224	51.68 165	58.77 217	35.28 238	46.35 117	32.05 207	12.44 240	28.53 221	35.52 192	14.40 255	57.12 205	56.93 213	71.11 192	59.69 233

anonymousdsp		43.03 225	47.19 217	38.18 227	36.00 240	56.92 224	38.44 234	34.56 231	24.22 248	22.53 202	29.69 213	29.92 231	35.21 198	53.96 227	58.98 195	62.32 241	76.66 151

USDC		42.80 226	45.57 225	39.58 219	34.55 244	51.13 237	42.61 225	36.21 219	39.59 168	23.65 196	33.13 187	20.87 259	37.86 186	55.35 220	57.16 211	62.61 239	61.75 229

pmnet_mix02		42.41 227	43.24 233	41.44 213	45.80 202	57.46 222	42.19 226	41.57 185	29.38 220	23.39 197	26.08 232	23.96 253	27.31 232	51.50 232	53.76 229	68.36 209	60.58 232

CHOSEN 280x420		42.39 228	47.40 216	36.54 230	33.56 247	39.66 264	40.67 230	26.88 258	34.66 194	18.03 231	30.09 207	45.59 151	44.82 163	54.46 222	54.00 228	55.28 257	73.32 178

pmmvs6		41.90 229	44.01 229	39.43 220	44.45 210	58.77 217	41.92 227	39.22 200	21.74 251	19.08 226	17.40 258	31.33 226	24.28 239	55.94 218	56.67 214	67.60 214	66.24 215

Anonymous20231206		40.63 230	43.29 232	37.53 228	48.88 186	55.81 230	34.99 239	44.98 138	28.16 229	10.16 249	17.26 259	27.50 240	18.28 246	54.00 226	55.07 223	67.85 212	65.23 218

dtuonlycased		40.33 231	41.14 236	39.39 221	35.91 241	57.86 220	46.10 214	31.75 249	28.52 223	19.94 220	18.33 251	35.38 193	38.79 182	41.36 256	42.69 256	63.84 231	63.79 223

FE-MVSNET2		39.87 232	43.46 231	35.69 233	30.82 257	56.74 225	37.91 236	42.85 169	24.70 247	8.15 254	18.01 253	23.67 254	23.12 240	56.86 206	61.26 174	71.25 191	62.95 225

CVMVSNet		38.91 233	44.49 228	32.40 242	34.57 243	47.20 249	34.81 240	34.20 236	31.45 211	8.95 251	38.86 156	36.38 190	24.30 238	47.77 240	46.94 252	57.59 251	62.85 226

COLMAP_ROB	34.79 15	38.65 234	40.72 237	36.23 231	36.41 239	49.22 245	45.51 217	27.60 257	37.81 175	20.54 215	23.37 238	24.25 251	28.11 231	51.02 234	48.55 242	59.22 246	50.82 255
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		38.23 235	41.72 235	34.15 235	40.56 225	50.07 241	33.17 247	44.35 146	27.64 236	5.54 264	30.84 201	26.67 242	14.99 252	45.64 243	52.38 234	66.29 220	58.83 236

WR-MVS		37.61 236	42.15 234	32.31 244	43.64 215	51.85 235	29.39 257	43.35 161	27.65 235	4.40 266	29.90 210	29.80 232	10.46 259	46.73 242	51.98 236	62.60 240	57.16 241

TinyColmap		37.18 237	37.37 250	36.95 229	31.17 256	45.21 255	39.71 232	34.65 228	29.83 219	20.20 216	18.54 250	13.72 270	38.27 184	50.33 236	51.57 237	57.71 250	52.42 252

CP-MVSNet		37.09 238	40.62 238	32.99 237	37.56 232	48.25 246	32.75 248	43.05 166	27.88 233	5.93 260	31.27 199	25.82 247	15.09 250	43.37 251	48.82 240	63.54 234	58.90 234

DTE-MVSNet		36.91 239	40.44 239	32.79 240	40.74 223	47.55 248	30.71 255	44.39 143	27.03 238	4.32 267	30.88 200	25.99 245	12.73 257	45.58 244	50.80 238	63.86 230	55.23 247

PS-CasMVS		36.84 240	40.23 242	32.89 238	37.44 233	48.09 247	32.68 249	42.97 168	27.36 237	5.89 261	30.08 208	25.48 248	14.96 253	43.28 252	48.71 241	63.39 235	58.63 238

WR-MVS_H		36.29 241	40.35 241	31.55 246	37.80 231	49.94 243	30.57 256	41.11 188	26.90 239	4.14 268	30.72 203	28.85 236	10.45 260	42.47 254	47.99 246	65.24 223	55.54 245

SixPastTwentyTwo		36.11 242	37.80 246	34.13 236	37.13 236	46.72 252	34.58 242	34.96 226	21.20 254	11.66 241	29.15 216	19.88 260	29.77 227	44.93 245	48.34 243	56.67 254	54.41 249

test20.03		36.00 243	38.92 243	32.60 241	45.92 201	50.99 238	28.05 262	43.69 157	21.62 252	6.03 259	17.61 257	25.91 246	8.34 266	51.26 233	52.60 233	63.58 232	52.46 251

TDRefinement		35.76 244	38.23 244	32.88 239	19.09 269	46.04 254	43.29 223	29.49 252	33.49 201	19.04 227	22.29 242	17.82 265	29.69 229	48.60 238	47.24 249	56.65 255	52.12 253

LTVRE_ROB	32.83 17	35.10 245	37.46 247	32.35 243	43.12 217	49.99 242	28.52 259	33.23 243	12.73 268	8.18 253	27.71 226	21.34 257	32.64 217	46.92 241	48.11 244	48.41 261	55.45 246
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		34.96 246	38.17 245	31.22 247	22.78 264	40.82 260	33.56 245	23.61 262	29.16 221	21.43 212	28.00 223	21.43 256	31.90 219	44.33 249	42.12 257	54.07 259	61.34 230

testgi		34.51 247	37.42 248	31.12 248	47.37 193	50.34 240	24.38 267	41.21 186	20.32 256	5.64 263	20.56 244	26.55 243	8.06 267	49.28 237	52.65 232	60.05 245	42.23 262

MDA-MVSNet-bldmvs		34.31 248	34.11 256	34.54 234	24.73 261	49.66 244	33.42 246	43.03 167	21.59 253	11.10 245	19.81 248	12.68 271	31.41 220	35.59 261	48.05 245	63.56 233	51.39 254

N_pmnet		34.09 249	35.74 253	32.17 245	37.25 235	43.17 258	32.26 252	35.57 223	26.22 242	10.60 248	20.44 246	19.38 264	20.20 244	44.59 247	47.00 250	57.13 253	49.35 258

RPSCF		33.61 250	40.43 240	25.65 254	16.00 271	32.41 266	31.73 254	13.33 269	50.13 100	23.12 198	31.56 195	40.09 176	32.73 216	41.14 259	37.05 260	36.99 267	50.63 256

FE-MVSNET		33.52 251	37.02 251	29.45 249	23.65 262	47.19 251	28.15 261	40.92 191	20.01 259	3.42 271	16.28 261	19.67 262	17.80 247	47.90 239	54.52 226	62.73 238	53.53 250

EU-MVSNet		33.00 252	36.49 252	28.92 250	33.10 254	42.86 259	29.32 258	35.99 220	22.94 249	5.83 262	25.29 233	24.43 250	15.21 249	41.22 258	41.65 259	54.08 258	57.01 242

pmmvs3		31.22 253	33.62 257	28.43 251	22.82 263	40.26 263	26.40 263	22.05 264	16.89 263	10.99 246	14.72 263	16.26 266	29.70 228	44.82 246	47.39 248	58.61 248	54.98 248

usedtu_dtu_shiyan2		31.12 254	34.28 255	27.44 253	11.70 272	47.20 249	32.04 253	31.41 250	14.11 267	8.15 254	13.22 265	19.80 261	16.49 248	42.54 253	45.42 254	64.82 226	57.66 240

FC-MVSNet-test		30.97 255	37.38 249	23.49 257	37.42 234	33.68 265	19.43 269	39.27 198	31.37 212	1.67 275	38.56 158	28.85 236	6.06 270	41.40 255	43.80 255	37.10 266	44.03 261

new-patchmatchnet		30.47 256	32.80 259	27.75 252	36.81 237	43.98 256	24.85 265	39.29 197	20.52 255	4.06 269	15.94 262	16.05 267	9.57 261	41.32 257	42.05 258	51.94 260	49.74 257

MIMVSNet1		29.60 257	33.37 258	25.20 256	19.52 267	43.94 257	26.29 264	37.92 211	19.95 260	3.79 270	12.64 268	21.99 255	7.70 268	43.83 250	46.32 253	55.97 256	44.92 260

FPMVS		26.87 258	28.19 260	25.32 255	27.09 260	29.49 268	32.28 251	17.79 266	28.09 230	11.33 242	19.38 249	14.69 268	20.88 242	35.11 262	32.82 263	42.56 263	37.75 263

WB-MVS		22.51 259	25.28 261	19.27 260	35.74 242	31.57 267	11.45 272	40.75 193	15.01 265	0.98 278	20.48 245	12.53 272	1.77 273	36.11 260	35.01 262	24.91 270	26.27 266

PMVS	18.18 18	21.95 260	22.85 262	20.90 259	21.92 265	14.78 270	19.95 268	17.31 267	15.69 264	11.32 243	13.70 264	13.91 269	15.02 251	34.92 263	31.72 264	39.85 265	35.20 264
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		19.10 261	22.71 263	14.89 262	10.93 274	24.08 269	14.22 270	13.94 268	18.68 261	2.93 272	12.84 267	11.27 273	11.94 258	30.57 265	30.58 265	35.38 268	30.93 265

Gipuma		17.16 262	17.83 264	16.36 261	18.76 270	12.15 273	11.97 271	27.78 256	17.94 262	4.86 265	2.53 277	2.73 280	8.90 264	34.32 264	36.09 261	25.92 269	19.06 270
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		13.92 263	17.14 265	10.16 265	1.69 278	6.92 276	11.25 273	5.74 270	22.41 250	8.11 256	10.40 269	20.91 258	13.73 256	22.17 266	13.98 268	20.44 271	23.18 267

PMMVS2		12.25 264	14.17 266	10.00 266	11.39 273	14.35 271	8.21 274	19.29 265	9.31 270	0.19 279	7.38 272	6.19 276	1.10 276	19.26 267	21.13 267	19.85 272	21.56 269

E-PMN		10.66 265	8.30 269	13.42 263	19.91 266	7.87 274	4.30 277	29.47 253	8.37 273	1.70 274	3.67 274	1.29 284	9.12 263	8.98 272	13.59 269	16.03 273	14.30 274

EMVS		10.15 266	7.67 270	13.05 264	19.22 268	7.77 275	4.48 275	29.34 254	8.65 272	1.67 275	3.55 275	1.36 283	9.15 262	8.15 273	11.79 271	14.44 274	12.43 275

MVE	10.35 19	9.76 267	11.08 268	8.22 267	4.43 275	13.04 272	3.36 278	23.57 263	5.74 275	1.76 273	3.09 276	1.75 282	6.78 269	12.78 269	23.04 266	9.44 275	18.09 271
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
VLMVS_CLIP		7.02 268	11.44 267	1.87 269	2.24 277	3.07 278	0.81 280	0.27 272	10.75 269	0.07 280	17.95 254	5.68 277	4.56 271	11.47 270	9.13 272	3.26 278	22.06 268

VLMVS		3.53 269	6.13 271	0.48 270	0.40 279	0.82 280	0.27 282	0.02 275	6.70 274	0.05 281	9.77 270	5.39 278	1.43 275	3.00 274	2.55 273	0.74 279	14.71 272

MVS_clip		1.91 270	3.26 272	0.34 271	0.20 280	0.48 281	0.12 283	0.03 274	3.08 276	0.00 283	5.21 273	2.59 281	1.44 274	2.82 275	2.43 274	0.23 280	6.18 276

MVS_baseline		0.62 271	1.11 273	0.06 272	0.01 281	0.01 282	0.00 284	0.00 277	0.81 277	0.00 283	1.69 278	0.82 285	0.33 277	0.26 276	0.27 275	0.00 281	3.90 277

testmvs		0.01 272	0.01 274	0.00 273	0.00 283	0.00 283	0.00 284	0.00 277	0.01 278	0.00 283	0.02 279	0.00 286	0.00 280	0.01 277	0.01 276	0.00 281	0.03 278

test123		0.01 272	0.01 274	0.00 273	0.00 283	0.00 283	0.00 284	0.00 277	0.01 278	0.00 283	0.02 279	0.00 286	0.01 278	0.00 278	0.01 276	0.00 281	0.03 278

uanet_test		0.00 274	0.00 276	0.00 273	0.00 283	0.00 283	0.00 284	0.00 277	0.00 280	0.00 283	0.00 281	0.00 286	0.00 280	0.00 278	0.00 278	0.00 281	0.00 280

sosnet-low-res		0.00 274	0.00 276	0.00 273	0.00 283	0.00 283	0.00 284	0.00 277	0.00 280	0.00 283	0.00 281	0.00 286	0.00 280	0.00 278	0.00 278	0.00 281	0.00 280

sosnet		0.00 274	0.00 276	0.00 273	0.00 283	0.00 283	0.00 284	0.00 277	0.00 280	0.00 283	0.00 281	0.00 286	0.00 280	0.00 278	0.00 278	0.00 281	0.00 280

ACM-MVS					78.03 8	82.69 9	77.05 14		82.01 14	54.59 23	69.80 25	77.02 15	67.12 5			79.40 26	89.79 15

PatchmatchNet2					40.39 226	46.58 253	34.70 241
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1												19.42 263	20.22 243	44.40 248	46.98 251	57.14 252	49.28 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3										10.66 247	20.37 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
TestfortrainingZip							78.23 6	61.85 3		68.16 2						81.99 4

TPM-MVS					78.45 6	83.50 8	78.26 5			58.88 11	72.62 20	77.54 12	69.42 4			80.40 9	85.71 70
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										21.59 211

9.14												80.07 7

SR-MVS					63.74 51			48.51 66				73.80 24

Anonymous202405211			56.81 147		60.91 92	73.48 90	59.82 117	48.68 63	39.26 169		24.00 237	46.77 145	50.73 117	65.28 106	65.72 109	75.37 121	83.17 95

our_test_3					49.68 180	61.50 201	45.84 216

ambc			35.52 254		38.36 227	40.40 262	28.38 260		25.20 245	14.87 236	13.22 265	7.54 275	19.34 245	55.63 219	47.79 247	47.91 262	58.89 235

MTAPA										54.82 22		71.98 30

MTMP										50.64 38		68.31 35

Patchmatch-RL test							0.69 281

tmp_tt				4.41 268	2.56 276	1.81 279	2.61 279	0.27 272	20.12 257	9.81 250	17.69 256	9.04 274	1.96 272	12.88 268	12.11 270	9.23 276

XVS					62.70 61	73.06 94	61.80 101			42.02 106		63.42 47				74.68 139

X-MVStestdata					62.70 61	73.06 94	61.80 101			42.02 106		63.42 47				74.68 139

mPP-MVS					63.08 57							62.34 51

NP-MVS									72.62 33

Patchmtry						64.49 174	52.06 179	34.21 234		31.74 159

DeepMVS_CX						5.87 277	4.32 276	1.74 271	9.04 271	1.30 277	7.97 271	3.16 279	8.56 265	9.74 271		6.30 277	14.51 273

High-resolution multi-view benchmark