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 7	59.97 7	88.89 1	65.96 2	86.00 7	84.02 1	70.03 1	76.19 5	76.17 5	79.22 29	94.46 1

DVP-MVS		77.54 2	84.41 2	69.54 8	79.93 3	86.08 3	77.20 12	60.31 5	88.62 2	62.54 3	86.67 5	83.77 2	58.04 52	75.84 8	75.69 8	79.21 30	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
MED-MVS		76.90 3	82.85 4	69.95 5	77.07 7	83.40 7	79.41 2	58.66 11	87.23 5	61.71 4	89.05 3	82.28 4	63.40 10	73.34 15	73.93 14	79.44 21	90.74 11

aaEdge-Enhanced		76.71 4	81.90 6	70.66 3	77.07 7	81.13 15	78.23 5	61.85 3	85.73 7	61.71 4	89.05 3	80.80 5	63.14 12	72.50 25	73.33 17	81.99 4	90.74 11

SF-MVS		76.41 5	80.45 8	71.69 2	82.90 1	86.54 1	82.08 1	64.58 1	81.67 13	59.82 8	86.26 6	77.90 10	61.11 19	71.81 30	70.75 37	79.63 16	88.22 26

MSP-MVS		76.38 6	82.99 3	68.68 9	71.93 20	78.65 29	77.61 9	55.44 21	88.04 3	60.25 7	92.24 1	77.08 13	69.84 2	75.48 9	75.69 8	76.99 83	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 7	81.32 7	69.77 7	71.86 22	85.13 4	77.62 8	59.87 9	82.69 12	61.55 6	83.05 11	79.63 8	69.78 3	76.01 6	75.89 6	77.92 63	86.86 49

DPE-MVS		75.74 8	82.82 5	67.49 13	77.07 7	82.01 9	77.05 13	57.70 14	86.55 6	55.44 20	90.50 2	82.52 3	60.33 23	72.99 17	72.98 19	77.33 74	92.19 6
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		74.63 9	78.53 13	70.07 4	76.10 11	82.56 8	79.30 3	59.89 8	80.49 15	57.75 14	66.98 29	76.16 16	65.95 5	79.35 1	78.47 1	81.45 7	85.71 68

APDe-MVS		74.59 10	80.23 9	68.01 12	76.51 10	80.20 18	77.39 10	58.18 12	85.31 8	56.84 16	84.89 8	76.08 17	60.66 21	71.85 29	71.76 25	78.47 51	91.49 9
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MCST-MVS		74.06 11	77.71 16	69.79 6	78.95 4	81.99 10	76.33 14	62.16 2	75.89 22	52.96 28	64.37 34	73.30 24	65.66 7	77.49 2	77.43 3	82.67 1	93.51 4

CNVR-MVS		73.87 12	78.60 12	68.35 11	73.32 15	81.97 11	76.19 15	59.29 10	80.12 16	56.70 17	67.09 28	76.48 14	64.26 9	75.88 7	75.75 7	80.32 10	92.93 5

SMA-MVS		73.31 13	79.53 10	66.05 15	71.25 23	80.13 19	74.99 16	56.09 17	84.14 9	54.48 22	73.74 18	80.23 6	61.43 16	74.96 10	74.09 13	78.08 60	89.42 16
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 14	76.86 18	68.46 10	78.23 6	81.74 12	77.26 11	60.00 6	75.61 25	59.06 9	62.72 36	77.42 12	56.63 66	74.24 12	77.18 4	79.56 18	89.13 20

HPM-MVS++		72.44 15	78.73 11	65.11 16	71.88 21	77.31 53	71.98 24	55.67 19	83.11 11	53.59 26	75.90 14	78.49 9	61.00 20	73.99 13	73.31 18	76.55 89	88.97 21

APD-MVS		71.86 16	77.91 15	64.80 18	70.39 27	75.69 65	74.02 18	56.14 16	83.59 10	52.92 29	84.67 9	73.46 23	59.30 32	69.47 46	69.66 50	76.02 96	88.84 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		71.50 17	77.27 17	64.77 19	69.64 29	79.26 21	73.53 19	54.73 27	79.32 18	54.23 23	74.81 15	74.61 21	59.40 30	73.00 16	72.17 22	77.10 82	87.72 31

NCCC		71.36 18	75.44 21	66.60 14	72.46 18	79.18 23	74.16 17	57.83 13	76.93 20	54.19 24	63.47 35	71.08 29	61.30 18	73.56 14	73.70 15	79.69 15	90.19 13

train_agg		70.74 19	76.53 19	63.98 23	70.33 28	75.16 74	72.33 23	55.78 18	75.74 23	50.41 38	80.08 13	73.15 25	57.75 56	71.96 28	70.94 34	77.25 78	88.69 24

MGCNet		70.65 20	76.30 20	64.05 22	67.54 38	80.89 16	68.89 37	49.94 52	77.93 19	55.92 19	68.22 26	73.10 26	62.14 13	71.10 34	71.81 24	79.87 11	91.03 10

TSAR-MVS + MP.		70.28 21	75.09 22	64.66 20	69.34 31	64.61 167	72.60 22	56.29 15	80.73 14	58.36 12	84.56 10	75.22 19	55.37 79	69.11 57	69.45 53	75.97 98	81.97 108
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 22	78.36 14	60.39 51	62.38 63	76.96 56	65.54 80	52.23 36	87.46 4	49.07 39	74.05 17	76.19 15	59.01 35	72.79 21	71.61 27	74.13 153	89.49 15

SteuartSystems-ACMMP		69.78 23	74.76 23	63.98 23	73.45 14	78.56 31	73.13 21	55.24 24	70.68 37	48.93 41	70.43 23	69.10 31	54.00 88	72.78 23	72.98 19	79.14 35	88.74 23
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		68.75 24	72.84 25	63.98 23	68.87 35	75.09 76	71.87 25	51.22 40	73.50 29	58.17 13	68.05 27	68.67 32	57.79 55	70.49 39	69.23 60	75.98 97	84.84 81

MVSMamba_PlusPlus		68.58 25	72.33 28	64.22 21	66.67 40	80.11 20	68.52 40	54.28 28	65.99 50	51.49 31	59.22 46	62.40 48	65.80 6	76.97 4	75.31 10	78.56 47	86.28 63

SD-MVS		68.30 26	72.58 27	63.31 28	69.24 32	67.85 138	70.81 30	53.65 33	79.64 17	58.52 11	74.31 16	75.37 18	53.52 94	65.63 100	63.56 138	74.13 153	81.73 113
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 27	70.34 41	63.89 26	69.12 33	81.55 13	70.82 29	55.02 25	53.38 86	48.83 42	56.45 52	59.35 61	60.05 27	74.93 11	74.78 11	79.51 19	91.95 7
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
MP-MVS		67.34 28	73.08 24	60.64 44	66.20 42	76.62 58	69.22 36	50.92 42	70.07 38	48.81 43	69.66 24	70.12 30	53.68 91	68.41 68	69.13 62	74.98 128	87.53 35
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 29	71.52 34	62.44 31	59.79 100	74.84 78	68.89 37	55.56 20	73.91 28	53.50 27	55.00 58	65.63 36	60.08 25	71.99 27	71.33 31	76.85 84	87.94 29
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP-MVS		67.22 30	72.08 30	61.56 37	66.76 39	73.58 87	71.41 26	52.98 34	69.92 40	43.85 81	70.58 22	58.75 63	56.76 64	72.90 19	71.88 23	77.57 69	86.94 48

CANet		67.21 31	71.83 32	61.83 33	64.51 48	79.25 22	66.72 71	48.73 60	68.49 45	50.63 37	61.40 40	66.47 34	61.44 15	69.31 51	69.90 43	78.94 43	88.00 27

CDPH-MVS		67.03 32	71.64 33	61.65 36	69.10 34	76.84 57	71.35 28	55.42 22	67.02 48	42.83 93	65.27 33	64.60 40	53.16 97	69.70 45	71.40 29	78.02 62	86.67 56

MAR-MVS		66.85 33	69.81 42	63.39 27	73.56 13	80.51 17	69.87 32	51.51 39	67.78 47	46.44 60	51.09 80	61.60 55	60.38 22	72.67 24	73.61 16	78.59 46	81.44 117
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 34	70.69 39	62.36 32	62.76 58	73.21 90	67.96 45	52.31 35	72.26 32	51.03 32	56.50 51	64.26 41	63.37 11	71.64 31	70.85 35	76.70 87	86.10 65
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 35	72.21 29	60.44 50	61.23 86	70.00 118	64.26 89	47.79 84	72.98 30	56.32 18	71.35 21	72.33 27	55.68 77	65.49 101	66.66 94	77.35 72	86.62 57

ACMMPR		66.20 36	71.51 35	60.00 60	65.34 46	74.04 82	69.39 34	50.92 42	71.97 33	46.04 63	66.79 30	65.68 35	53.07 98	68.93 60	69.12 63	75.21 122	84.05 89

3Dnovator	58.39 4	65.97 37	66.85 57	64.94 17	73.72 12	79.03 24	67.73 50	54.25 29	61.52 56	52.79 30	42.27 130	60.73 59	62.01 14	71.29 32	71.75 26	79.12 36	81.34 120

TSAR-MVS + ACMM		65.95 38	72.83 26	57.93 76	69.35 30	65.85 158	73.36 20	39.84 195	76.00 21	48.69 44	82.54 12	75.03 20	49.38 127	65.33 103	63.42 140	66.94 217	81.67 114

sasdasda		65.55 39	70.75 37	59.49 68	62.11 71	78.26 40	66.52 73	43.82 153	71.54 34	47.84 48	61.30 41	61.68 52	58.48 43	67.56 78	69.67 48	78.16 58	85.25 76

canonicalmvs		65.55 39	70.75 37	59.49 68	62.11 71	78.26 40	66.52 73	43.82 153	71.54 34	47.84 48	61.30 41	61.68 52	58.48 43	67.56 78	69.67 48	78.16 58	85.25 76

QAPM		65.47 41	67.82 49	62.72 30	72.56 16	81.17 14	67.43 57	55.38 23	56.07 73	43.29 90	43.60 122	65.38 38	59.10 33	72.20 26	70.76 36	78.56 47	85.59 72

PGM-MVS		65.35 42	70.43 40	59.43 70	65.78 44	73.75 84	69.41 33	48.18 73	68.80 44	45.37 72	65.88 32	64.04 42	52.68 105	68.94 59	68.68 72	75.18 123	82.93 98

PHI-MVS		65.17 43	72.07 31	57.11 91	63.02 56	77.35 52	67.04 67	48.14 78	68.03 46	37.56 122	66.00 31	65.39 37	53.19 96	70.68 36	70.57 39	73.72 161	86.46 60

CLD-MVS		64.69 44	67.25 51	61.69 35	68.22 37	78.33 36	63.09 94	47.59 87	69.64 41	53.98 25	54.87 59	53.94 87	57.87 53	72.79 21	71.34 30	79.40 26	69.87 203
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 45	67.11 54	61.80 34	71.04 24	77.91 47	62.75 97	54.78 26	51.43 91	47.54 50	53.77 62	54.85 82	56.84 62	70.59 37	71.50 28	77.86 64	89.70 14

EPNet		64.39 46	70.93 36	56.77 95	60.58 95	75.77 61	59.28 120	50.58 46	69.93 39	40.73 111	68.59 25	61.60 55	53.72 89	68.65 63	68.07 76	75.75 111	83.87 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		64.37 47	69.48 43	58.39 73	62.21 67	71.81 110	67.27 62	49.51 54	69.40 43	45.76 69	60.41 44	64.96 39	51.84 107	67.33 85	67.57 85	73.78 160	84.89 79

EC-MVSNet		64.30 48	68.19 45	59.76 64	62.97 57	75.31 72	67.26 63	44.19 147	60.73 59	47.52 52	55.84 54	62.12 50	57.67 57	70.71 35	67.47 87	78.97 41	85.13 78

casdiffmvs_mvg		64.26 49	67.60 50	60.36 52	62.26 66	78.54 32	69.39 34	48.33 71	56.54 68	45.36 73	52.86 68	57.36 68	58.42 45	70.28 40	70.24 41	78.43 53	87.39 39
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 50	67.08 55	60.12 59	60.90 91	78.29 39	67.91 47	48.01 82	55.89 77	44.97 76	50.45 84	56.94 69	59.54 28	70.17 43	69.81 45	79.41 24	87.99 28
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 51	66.23 62	61.02 40	62.29 65	78.81 27	67.95 46	48.45 67	52.32 88	48.38 45	51.97 72	53.76 88	59.35 31	69.39 49	69.76 47	79.70 14	87.62 34

MVS_Test		63.75 52	67.24 52	59.68 65	60.01 96	76.99 55	68.13 43	45.17 131	57.45 67	43.74 83	53.07 66	56.16 76	61.33 17	70.27 41	71.11 32	79.72 13	85.63 71

Casviewmamba		63.73 53	66.47 59	60.53 49	63.39 50	77.99 46	67.69 51	48.45 67	55.29 80	45.83 67	50.75 83	56.46 73	60.08 25	69.12 55	69.33 54	77.74 66	86.33 62

hybridcas		63.61 54	66.22 63	60.56 46	62.13 70	78.59 30	68.59 39	48.14 78	54.14 83	45.68 70	49.27 90	56.60 70	59.44 29	69.17 53	69.03 66	79.41 24	86.75 55

X-MVS		63.53 55	68.62 44	57.60 80	64.77 47	73.06 92	65.82 78	50.53 47	65.77 51	42.02 104	58.20 49	63.42 45	47.83 138	68.25 73	68.50 73	74.61 140	83.16 95

viewcassd2359sk11		63.49 56	65.78 69	60.83 42	62.14 69	78.68 28	67.83 49	48.34 70	51.06 93	47.99 47	51.10 79	53.41 89	59.09 34	69.12 55	69.58 51	79.58 17	87.49 36

viewmanbaseed2359cas		63.30 57	65.85 68	60.31 53	61.55 81	78.41 35	68.44 41	47.39 93	50.91 94	46.42 61	50.98 82	53.99 86	58.60 41	69.11 57	70.10 42	79.48 20	87.46 37

ACMMP		63.27 58	67.85 48	57.93 76	62.64 61	72.30 105	68.23 42	48.77 59	66.50 49	43.05 91	62.07 37	57.84 66	49.98 118	66.58 91	66.46 101	74.93 129	83.17 93
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 59	68.01 47	57.49 82	57.39 118	72.73 99	63.38 93	45.16 132	59.37 61	46.49 59	58.93 48	57.68 67	56.31 68	71.12 33	70.37 40	76.23 95	85.88 66

E3new		63.04 60	65.16 75	60.56 46	61.92 74	78.50 33	67.67 53	48.17 74	49.34 101	47.42 54	49.85 87	52.98 91	58.75 37	68.79 61	69.33 54	79.43 22	87.30 40

E3		63.03 61	65.15 76	60.56 46	61.92 74	78.49 34	67.68 52	48.17 74	49.33 102	47.43 53	49.85 87	52.99 90	58.75 37	68.79 61	69.32 56	79.43 22	87.30 40

viewdifsd2359ckpt13		62.95 62	65.29 72	60.21 54	62.21 67	78.86 25	67.26 63	48.16 76	50.15 97	45.82 68	50.17 86	51.84 102	58.68 40	69.24 52	69.88 44	79.15 34	86.86 49

ETV-MVS		62.88 63	68.18 46	56.70 96	58.47 108	74.89 77	60.26 112	43.96 150	58.27 66	42.37 100	61.47 39	56.56 71	57.80 54	68.00 76	68.74 70	77.34 73	89.33 19

AdaColmap		62.79 64	62.63 96	62.98 29	70.82 25	72.90 96	67.84 48	54.09 31	65.14 52	50.71 35	41.78 132	47.64 136	60.17 24	67.41 84	66.83 92	74.28 146	76.69 148

3Dnovator+	55.76 7	62.70 65	65.10 77	59.90 61	65.89 43	72.15 106	62.94 96	49.82 53	62.77 55	49.06 40	43.62 121	61.47 57	58.60 41	68.51 64	66.75 93	73.08 176	80.40 128

OpenMVS	55.62 8	62.57 66	63.76 90	61.19 39	72.13 19	78.84 26	64.42 87	50.51 48	56.44 70	45.67 71	36.88 162	56.51 72	56.66 65	68.28 72	68.96 67	77.73 67	80.44 127

PVSNet_BlendedMVS		62.53 67	66.37 60	58.05 74	58.17 109	75.70 63	61.30 105	48.67 63	58.67 62	50.93 33	55.43 56	49.39 125	53.01 100	69.46 47	66.55 97	76.24 93	89.39 17

PVSNet_Blended		62.53 67	66.37 60	58.05 74	58.17 109	75.70 63	61.30 105	48.67 63	58.67 62	50.93 33	55.43 56	49.39 125	53.01 100	69.46 47	66.55 97	76.24 93	89.39 17

MVSTER		62.51 69	67.22 53	57.02 93	55.05 146	69.23 126	63.02 95	46.88 103	61.11 58	43.95 80	59.20 47	58.86 62	56.80 63	69.13 54	70.98 33	76.41 91	82.04 105

viewdifsd2359ckpt09		62.50 70	64.48 80	60.19 57	61.23 86	77.58 49	67.62 54	48.43 69	51.16 92	47.53 51	51.23 78	51.93 99	58.78 36	67.17 87	65.88 107	77.54 70	86.38 61

E5new		62.48 71	64.43 82	60.20 55	61.57 78	78.31 37	67.43 57	48.06 80	47.28 116	46.73 56	48.48 98	52.64 94	58.20 48	68.45 65	69.07 64	79.20 31	86.77 53

E5		62.48 71	64.43 82	60.20 55	61.57 78	78.31 37	67.43 57	48.06 80	47.28 116	46.73 56	48.48 98	52.64 94	58.20 48	68.45 65	69.07 64	79.20 31	86.77 53

CHOSEN 1792x2688		62.48 71	64.06 87	60.64 44	72.50 17	84.18 5	62.43 98	53.77 32	47.90 115	39.85 115	25.15 232	44.76 153	53.72 89	77.29 3	77.61 2	81.60 6	91.53 8

CostFormer		62.45 74	65.68 70	58.67 72	63.29 53	77.65 48	67.62 54	38.42 206	54.04 84	46.00 64	48.27 101	57.89 65	56.97 60	67.03 88	67.79 83	79.74 12	87.09 45

E4		62.36 75	64.27 85	60.14 58	61.58 77	78.25 42	67.38 60	47.91 83	46.78 121	46.58 58	48.07 102	52.52 96	58.23 47	68.32 70	68.96 67	79.19 33	86.98 47

PCF-MVS	55.99 6	62.31 76	66.60 58	57.32 85	59.12 107	73.68 86	67.53 56	48.71 61	61.35 57	42.83 93	51.33 77	63.48 44	53.48 95	65.64 99	64.87 122	72.22 181	85.83 67
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		62.30 77	66.05 64	57.92 78	57.08 120	75.60 69	66.90 68	47.06 101	55.45 79	43.37 88	53.45 64	55.60 78	57.21 59	66.57 92	68.00 79	75.89 101	87.70 33
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 78	64.24 86	59.58 67	60.94 89	78.01 43	68.04 44	46.83 105	46.59 123	45.11 75	47.34 104	52.79 92	57.50 58	68.43 67	69.54 52	79.08 37	87.01 46

E6new		61.98 79	63.77 88	59.90 61	61.52 83	78.01 43	67.09 65	47.57 90	45.71 127	45.97 65	46.87 106	51.47 105	58.17 50	68.20 74	69.31 58	79.07 38	86.81 51

E6		61.98 79	63.77 88	59.90 61	61.52 83	78.01 43	67.09 65	47.57 90	45.71 127	45.97 65	46.87 106	51.47 105	58.17 50	68.20 74	69.31 58	79.07 38	86.81 51

hybridnocas07		61.95 81	65.92 66	57.32 85	56.68 126	75.77 61	65.53 81	46.69 110	55.99 74	43.65 84	53.11 65	55.36 80	56.11 70	66.44 94	67.60 84	75.28 120	87.16 44

DI_MVS_pp		61.86 82	65.26 73	57.90 79	57.93 114	74.51 80	66.30 75	46.49 115	49.96 99	41.62 107	42.69 127	61.77 51	58.74 39	70.25 42	69.32 56	76.31 92	88.30 25

diffmvs_AUTHOR		61.85 83	65.54 71	57.54 81	56.64 127	75.64 68	66.65 72	46.55 114	53.31 87	42.72 97	51.70 74	55.51 79	56.91 61	66.66 89	68.09 75	75.77 110	87.89 30

MSLP-MVS++		61.81 84	62.19 101	61.37 38	68.33 36	63.08 188	70.75 31	38.89 202	63.96 54	57.51 15	48.59 95	61.66 54	53.67 92	62.04 152	59.92 191	79.03 40	76.08 151

hybrid		61.79 85	65.87 67	57.03 92	56.18 132	75.51 71	65.47 83	46.32 118	55.94 76	43.47 86	52.97 67	55.80 77	55.45 78	66.17 95	67.53 86	75.28 120	87.17 43

SPE-MVS-test		61.68 86	65.97 65	56.67 97	57.77 115	72.59 102	57.63 128	45.54 126	58.53 65	47.11 55	59.45 45	56.34 74	55.15 80	64.52 116	65.03 120	76.80 85	85.34 75

OPM-MVS		61.59 87	62.30 100	60.76 43	66.53 41	73.35 89	71.41 26	54.18 30	40.82 158	41.57 108	45.70 113	54.84 83	54.43 85	69.92 44	69.19 61	76.45 90	82.25 102
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
viewmamba		61.51 88	65.19 74	57.21 88	57.74 116	75.65 67	64.78 86	47.08 100	55.24 81	42.97 92	52.09 71	54.80 84	56.05 72	65.74 97	67.28 88	74.63 139	85.53 74

viewdifsd2359ckpt07		61.43 89	63.33 93	59.20 71	61.66 76	77.47 51	66.75 70	46.85 104	45.54 129	45.32 74	48.59 95	51.61 104	56.09 71	67.46 80	68.01 78	78.54 50	84.67 83

MS-PatchMatch		61.41 90	61.88 105	60.85 41	70.57 26	75.98 60	66.29 76	46.91 102	50.56 96	48.28 46	36.30 165	51.64 103	50.95 113	72.89 20	70.65 38	82.13 3	75.17 162

onestephybrid01		61.26 91	65.06 78	56.82 94	57.98 112	75.52 70	64.18 90	46.76 108	54.24 82	43.46 87	52.35 69	55.20 81	55.00 81	65.65 98	66.25 102	73.56 163	86.21 64

casdiffseed414692147		60.92 92	62.03 102	59.63 66	62.33 64	76.41 59	67.31 61	47.59 87	48.83 109	43.83 82	41.47 133	47.12 141	58.26 46	67.43 83	68.40 74	78.47 51	84.57 86

viewmambaseed2359dif		60.68 93	63.59 92	57.29 87	56.93 122	75.24 73	65.36 84	45.82 124	49.89 100	43.57 85	49.83 89	51.89 101	56.33 67	64.86 111	65.71 109	75.75 111	87.72 31

EIA-MVS		60.56 94	64.29 84	56.20 102	59.14 106	72.68 101	59.55 118	43.56 157	51.78 90	41.01 110	55.47 55	51.93 99	55.87 74	65.01 107	66.57 96	78.06 61	86.60 59

dtuplus		60.37 95	63.09 94	57.19 89	57.03 121	75.16 74	65.19 85	45.85 123	48.37 112	43.35 89	48.48 98	52.00 98	55.90 73	64.94 110	65.48 113	75.85 106	87.18 42

ACMP	56.21 5	59.78 96	61.81 107	57.41 84	61.15 88	68.88 128	65.98 77	48.85 58	58.56 64	44.19 79	48.89 93	46.31 145	48.56 132	63.61 132	64.49 130	75.75 111	81.91 109
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		59.69 97	62.59 97	56.31 100	61.94 73	68.15 135	66.90 68	48.15 77	59.75 60	38.47 118	50.38 85	48.34 133	46.87 145	65.39 102	64.93 121	75.51 116	81.21 122

Effi-MVS+		59.63 98	61.78 108	57.12 90	61.56 80	71.63 111	63.61 91	47.59 87	47.18 118	37.79 119	45.29 114	49.93 121	56.27 69	67.45 81	67.06 90	75.91 99	83.93 90

CPTT-MVS		59.54 99	64.47 81	53.79 114	54.99 148	67.63 142	65.48 82	44.59 141	64.81 53	37.74 120	51.55 75	59.90 60	49.77 123	61.83 156	61.26 173	70.18 197	84.31 88

baseline2		59.20 100	61.72 109	56.27 101	59.61 102	74.12 81	58.65 123	49.42 55	48.10 113	40.12 114	49.10 92	44.15 156	51.24 110	66.65 90	67.88 82	78.56 47	82.06 104

MGCFI-Net		59.19 101	66.89 56	50.20 145	57.15 119	68.62 131	54.79 157	39.20 200	70.99 36	32.93 151	60.83 43	61.00 58	45.54 152	63.77 130	60.71 182	71.59 185	82.29 100

GeoE		58.97 102	60.94 110	56.67 97	61.27 85	72.71 100	61.35 104	45.69 125	49.19 106	41.22 109	39.55 149	49.58 124	52.79 104	64.79 112	65.89 106	77.73 67	84.87 80

baseline		58.65 103	61.99 103	54.75 109	54.70 150	71.85 109	60.20 113	43.91 151	55.99 74	40.13 113	53.50 63	50.91 118	55.76 75	61.29 164	61.73 165	73.83 157	78.68 139

PVSNet_Blended_VisFu		58.56 104	62.33 99	54.16 111	56.90 123	73.92 83	57.72 127	46.16 121	44.23 134	42.73 96	46.26 108	51.06 116	46.28 148	67.99 77	65.38 115	75.18 123	87.44 38

ACMM	53.73 9	57.91 105	58.27 130	57.49 82	63.10 54	66.45 152	65.65 79	49.02 57	53.69 85	42.67 98	36.41 164	46.07 148	50.38 116	64.74 114	64.63 127	74.14 152	75.91 152
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		57.87 106	63.63 91	51.15 132	52.18 157	70.20 117	58.14 126	37.32 213	56.49 69	31.06 161	57.38 50	50.05 120	53.67 92	64.98 109	65.04 119	74.57 141	81.29 121

ET-MVSNet_ETH3D		57.84 107	61.91 104	53.09 117	32.91 252	74.53 79	63.51 92	46.80 107	46.52 124	36.14 128	56.00 53	46.20 146	64.41 8	60.75 172	66.99 91	74.79 130	82.35 99

viewdifsd2359ckpt11		57.53 108	59.36 118	55.39 104	55.17 144	72.10 107	61.49 101	45.16 132	42.72 143	42.15 102	46.03 110	47.43 137	54.14 87	61.84 154	62.46 156	74.23 147	82.96 96

viewmsd2359difaftdt		57.53 108	59.36 118	55.39 104	55.17 144	72.10 107	61.49 101	45.16 132	42.72 143	42.15 102	46.03 110	47.42 138	54.15 86	61.84 154	62.46 156	74.23 147	82.96 96

tpm cat1		57.41 110	58.26 131	56.42 99	60.80 93	72.56 103	64.35 88	38.43 205	49.18 107	46.36 62	36.69 163	43.50 160	54.47 83	61.39 162	62.64 151	74.11 155	81.81 110

IB-MVS	53.15 10	57.33 111	59.02 122	55.37 106	60.83 92	77.11 54	54.51 158	50.10 51	43.22 140	42.82 95	40.50 139	37.61 181	44.67 162	59.27 187	69.81 45	79.29 28	85.59 72
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		57.23 112	59.08 121	55.06 107	59.91 98	70.65 115	60.71 108	35.38 224	47.91 114	42.58 99	39.78 144	45.45 150	54.44 84	62.19 149	62.82 148	77.37 71	84.73 82

baseline1		57.21 113	60.53 112	53.33 116	62.50 62	69.86 120	57.33 132	50.59 45	43.39 139	30.00 167	48.60 94	51.09 115	42.36 175	69.38 50	68.03 77	77.20 79	73.39 175

FA-MVS(training)		57.15 114	60.42 113	53.34 115	58.15 111	72.77 97	59.79 116	38.68 203	49.01 108	36.56 127	40.79 137	45.44 151	53.04 99	65.23 106	67.93 81	73.82 158	81.80 112

HyFIR lowres test		57.12 115	59.11 120	54.80 108	61.55 81	77.55 50	59.02 121	45.00 136	41.84 155	33.93 145	22.44 239	49.16 128	51.02 112	68.39 69	68.71 71	78.26 57	85.70 70

MVS_111021_LR		57.06 116	60.60 111	52.93 118	56.25 130	65.14 165	55.16 155	41.21 185	52.32 88	44.89 77	53.92 61	49.27 127	52.16 106	61.46 160	60.54 183	67.92 209	81.53 116

DCV-MVSNet		56.80 117	58.96 123	54.28 110	59.96 97	66.74 150	60.37 111	44.87 138	41.01 157	36.81 125	47.57 103	47.87 135	48.23 135	64.41 118	65.17 117	75.45 117	79.95 132

Anonymous20231211		56.40 118	57.00 143	55.70 103	59.78 101	72.49 104	61.29 107	46.83 105	40.50 161	40.46 112	22.12 241	49.73 122	51.07 111	64.39 119	65.30 116	74.74 133	84.44 87

0.4-1-1-0.2		56.13 119	60.14 114	51.44 129	45.97 198	73.09 91	56.79 142	45.39 127	47.03 119	34.23 137	43.14 126	51.20 114	47.33 141	63.12 136	63.30 141	78.95 42	80.11 130

0.3-1-1-0.015		56.04 120	60.09 115	51.32 130	46.02 196	73.04 95	56.64 143	45.36 128	46.70 122	34.01 141	43.24 124	51.25 110	46.98 144	63.12 136	63.20 144	78.90 44	80.11 130

PMMVS		55.74 121	62.68 95	47.64 166	44.34 211	65.58 162	47.22 203	37.96 209	56.43 71	34.11 139	61.51 38	47.41 139	54.55 82	65.88 96	62.49 155	67.67 211	79.48 134

Fast-Effi-MVS+		55.73 122	58.26 131	52.76 119	54.33 151	68.19 134	57.05 133	34.66 226	46.92 120	38.96 117	40.53 138	41.55 169	55.69 76	65.31 104	65.99 103	75.90 100	79.34 135

FC-MVSNet-train		55.68 123	57.00 143	54.13 112	63.37 51	66.16 154	46.77 207	52.14 37	42.36 149	37.67 121	48.50 97	41.42 171	51.28 109	61.58 159	63.22 143	73.56 163	75.76 155

FMVSNet3		55.66 124	59.68 117	50.96 134	50.59 171	66.49 151	57.57 129	46.61 111	49.30 103	28.77 172	39.61 145	51.42 107	43.85 167	68.29 71	68.80 69	78.35 56	73.86 165

0.4-1-1-0.1		55.63 125	59.73 116	50.85 135	45.99 197	72.77 97	56.11 149	45.23 130	45.84 126	33.32 149	42.60 128	51.06 116	45.68 151	62.99 141	62.97 147	78.76 45	79.90 133

OMC-MVS		55.48 126	61.85 106	48.04 165	41.55 220	60.32 206	56.80 137	31.78 247	75.67 24	42.30 101	51.52 76	54.15 85	49.91 120	60.28 178	57.59 203	65.91 220	73.42 173

tpm		54.94 127	57.86 136	51.54 128	59.48 104	67.04 146	58.34 125	34.60 229	41.93 154	34.41 135	42.40 129	47.14 140	49.07 130	61.46 160	61.67 169	73.31 171	83.39 92

GBi-Net		54.66 128	58.42 128	50.26 143	49.36 180	65.81 159	56.80 137	46.61 111	49.30 103	28.77 172	39.61 145	51.42 107	42.71 171	64.25 122	65.54 110	77.32 75	73.03 178

test1		54.66 128	58.42 128	50.26 143	49.36 180	65.81 159	56.80 137	46.61 111	49.30 103	28.77 172	39.61 145	51.42 107	42.71 171	64.25 122	65.54 110	77.32 75	73.03 178

test-LLR		54.62 130	58.66 126	49.89 150	51.68 163	65.89 156	47.88 197	46.35 116	42.51 146	29.84 168	41.41 134	48.87 129	45.20 155	62.91 143	64.43 131	78.43 53	84.62 84

dmvs_re		54.51 131	57.04 142	51.56 127	56.51 128	62.63 192	55.56 151	50.45 49	45.31 130	24.75 190	43.94 120	39.99 176	42.74 170	66.53 93	65.44 114	79.33 27	75.46 157

TSAR-MVS + COLMAP		54.37 132	62.43 98	44.98 184	34.33 242	58.94 214	54.11 163	34.15 238	74.06 27	34.57 134	71.63 20	42.03 168	47.88 137	61.26 165	57.33 208	64.83 223	71.74 189

EPMVS		54.07 133	56.06 149	51.75 126	56.74 125	70.80 113	55.32 153	34.20 235	46.46 125	36.59 126	40.38 141	42.55 163	49.77 123	61.25 166	60.90 178	77.86 64	70.08 200

v2v482		54.00 134	55.12 156	52.69 121	51.73 162	69.42 125	60.65 109	45.09 135	34.56 193	33.73 148	35.29 169	35.36 192	49.92 119	64.05 128	65.16 118	75.00 127	81.98 107

CNLPA		54.00 134	57.08 141	50.40 142	49.83 177	61.75 197	53.47 167	37.27 214	74.55 26	44.85 78	33.58 181	45.42 152	52.94 103	58.89 189	53.66 229	64.06 227	71.68 190

FMVSNet2		53.94 136	57.29 138	50.03 147	49.36 180	65.81 159	56.80 137	45.95 122	43.13 141	28.04 176	35.68 166	48.18 134	42.71 171	67.23 86	67.95 80	77.32 75	73.03 178

v8		53.77 137	54.82 161	52.54 122	52.12 158	66.95 149	60.56 110	43.23 163	37.17 182	35.35 130	34.96 172	37.50 183	49.51 126	63.67 131	64.59 128	74.48 143	78.91 138

GA-MVS		53.77 137	56.41 148	50.70 137	51.63 165	69.96 119	57.55 130	44.39 142	34.31 194	27.15 178	40.99 136	36.40 187	47.65 140	67.45 81	67.16 89	75.83 107	78.60 140

Effi-MVS+-dtu		53.63 139	54.85 160	52.20 124	59.32 105	61.33 200	56.42 146	40.24 193	43.84 136	34.22 138	39.49 150	46.18 147	53.00 102	58.72 193	57.49 207	69.99 200	76.91 146

thisisatest0530		53.61 140	57.22 139	49.40 155	51.30 167	68.22 133	52.72 175	43.34 161	42.72 143	35.31 131	43.57 123	44.14 157	44.37 165	63.00 140	64.86 123	69.34 203	74.00 164

v1144		53.47 141	54.65 162	52.10 125	51.93 160	69.81 121	59.32 119	44.77 140	33.21 200	32.52 153	33.55 182	34.34 201	49.29 128	64.58 115	64.81 125	74.74 133	82.27 101

blend_shiyan4		53.44 142	57.17 140	49.10 158	46.19 194	65.49 163	58.38 124	42.54 173	48.56 111	34.01 141	44.21 117	51.25 110	36.84 188	57.58 197	57.87 198	76.63 88	75.23 159

v10		53.44 142	54.40 163	52.31 123	52.08 159	66.99 147	59.68 117	43.41 158	35.90 188	34.30 136	33.98 179	35.56 189	50.10 117	64.39 119	64.67 126	74.32 144	79.30 136

PatchmatchNet		53.37 144	55.62 154	50.75 136	55.93 138	70.54 116	51.39 180	36.41 217	44.85 132	37.26 123	39.40 152	42.54 164	47.83 138	60.29 177	60.88 180	75.69 114	70.87 194
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test2506		53.36 145	57.36 137	48.68 161	55.53 140	68.11 136	54.31 160	46.25 119	43.54 137	22.21 202	40.19 142	43.69 159	36.56 192	64.15 126	65.94 104	77.20 79	75.91 152

IterMVS-LS		53.36 145	55.65 153	50.68 139	55.34 142	59.04 212	55.00 156	39.98 194	38.72 170	33.22 150	44.52 116	47.05 142	49.63 125	61.82 157	61.77 164	70.92 192	76.61 150
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		53.30 147	58.66 126	47.04 169	44.94 205	65.89 156	47.88 197	35.95 220	42.51 146	29.84 168	41.41 134	48.87 129	45.20 155	62.91 143	64.43 131	78.43 53	84.62 84

tttt0517		53.05 148	56.73 147	48.76 159	50.35 173	67.51 143	51.96 179	43.34 161	42.00 153	33.88 146	43.19 125	43.49 161	44.37 165	62.58 148	64.86 123	68.67 205	73.46 172

MDTV_nov1_ep13		52.99 149	55.59 155	49.95 149	54.08 152	70.69 114	56.47 145	38.42 206	42.78 142	30.19 166	39.56 148	43.31 162	45.78 150	60.07 182	62.11 161	74.74 133	70.62 195

EPP-MVSNet		52.91 150	58.91 124	45.91 175	54.99 148	68.84 129	49.27 187	42.71 171	37.53 176	20.20 214	46.09 109	56.19 75	36.90 187	61.37 163	60.90 178	71.41 186	81.41 118

dps		52.84 151	52.92 178	52.74 120	59.89 99	69.49 124	54.47 159	37.38 212	42.49 148	39.53 116	35.33 168	32.71 211	51.83 108	60.45 174	61.12 175	73.33 170	68.86 209

v1192		52.69 152	53.86 168	51.31 131	51.22 168	69.76 122	57.37 131	44.39 142	32.21 203	31.39 160	32.41 190	32.44 214	49.19 129	64.25 122	64.17 133	74.31 145	81.81 110

V42		52.63 153	55.08 157	49.76 152	44.93 206	67.49 145	60.19 114	42.13 181	37.21 181	34.08 140	34.57 175	37.30 184	47.29 142	63.48 134	64.15 134	69.96 201	81.38 119

MSDG		52.58 154	51.40 191	53.95 113	65.48 45	64.31 175	61.44 103	44.02 148	44.17 135	32.92 152	30.40 203	31.81 218	46.35 147	62.13 150	62.55 153	73.49 166	64.41 217

ECVR-MVS		52.52 155	55.88 151	48.60 162	55.53 140	68.11 136	54.31 160	46.25 119	43.54 137	21.75 206	32.76 187	39.83 179	36.56 192	64.15 126	65.94 104	77.20 79	76.81 147

Fast-Effi-MVS+-dtu		52.47 156	55.89 150	48.48 163	56.25 130	65.07 166	58.75 122	23.79 260	41.27 156	27.07 180	37.95 157	41.34 172	50.85 114	62.90 145	62.34 159	74.17 151	80.37 129

v144192		52.43 157	53.63 172	51.03 133	51.06 169	69.60 123	56.94 135	44.84 139	32.15 204	30.88 162	32.45 189	32.71 211	48.36 133	62.98 142	63.52 139	74.10 156	82.02 106

thres100view900		52.33 158	53.91 167	50.48 141	56.10 133	67.79 139	56.18 148	49.18 56	35.86 190	25.22 187	34.74 173	34.10 203	42.41 174	64.45 117	62.62 152	73.81 159	77.85 141

v1921920		51.95 159	53.19 174	50.51 140	50.82 170	69.14 127	55.45 152	44.34 146	31.53 208	30.53 164	31.96 192	31.67 219	48.31 134	63.12 136	63.28 142	73.59 162	81.60 115

v148		51.72 160	53.15 175	50.05 146	50.15 175	67.51 143	56.98 134	42.85 168	32.60 202	32.41 155	33.88 180	34.71 197	44.45 163	61.06 167	63.00 146	73.45 167	79.24 137

TAPA-MVS	47.92 11	51.66 161	57.88 135	44.40 188	36.46 235	58.42 217	53.82 165	30.83 250	69.51 42	34.97 133	46.90 105	49.67 123	46.99 143	58.00 196	54.64 224	63.33 234	68.00 211
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		51.53 162	57.98 134	44.01 192	55.96 137	66.16 154	47.65 199	42.84 170	39.82 165	19.09 223	44.97 115	50.28 119	27.20 231	63.43 135	63.84 135	71.33 188	77.33 143

v1240		51.42 163	52.66 180	49.97 148	50.31 174	68.70 130	54.05 164	43.85 152	30.78 212	30.22 165	31.43 196	31.03 226	47.98 136	62.62 147	63.16 145	73.40 168	80.93 124

pmmvs4		51.28 164	52.50 182	49.85 151	49.54 179	63.02 189	52.83 174	43.41 158	44.65 133	35.71 129	34.38 176	32.25 215	45.14 158	60.21 181	60.03 188	72.44 180	72.98 181

Vis-MVSNet		51.13 165	58.04 133	43.06 198	47.68 187	67.71 140	49.10 188	39.09 201	37.75 174	22.57 199	51.03 81	48.78 131	32.42 216	62.12 151	61.80 163	67.49 214	77.12 144
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		51.04 166	58.79 125	42.00 208	40.59 222	65.32 164	46.65 209	39.26 198	39.90 164	27.30 177	54.12 60	52.03 97	30.93 220	59.85 184	59.62 193	67.23 216	80.70 125
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		50.91 167	52.45 183	49.11 157	56.10 133	64.53 170	53.06 171	47.31 96	35.86 190	25.22 187	34.74 173	34.10 203	41.08 177	60.84 169	61.37 171	71.90 184	75.70 156

SCA		50.88 168	53.70 170	47.59 167	55.99 135	55.81 228	43.14 222	33.45 241	45.16 131	37.14 124	41.83 131	43.82 158	44.43 164	60.37 175	60.02 189	71.38 187	68.90 208

gg-mvs-nofinetune		50.82 169	55.83 152	44.97 185	60.63 94	75.69 65	53.40 168	34.48 231	20.05 256	6.93 254	18.27 249	52.70 93	33.57 205	70.50 38	72.93 21	80.84 8	80.68 126

thres200		50.76 170	52.52 181	48.70 160	55.98 136	64.60 168	55.29 154	47.34 94	33.91 197	24.36 191	34.33 177	33.90 205	37.27 185	60.84 169	62.41 158	71.99 182	77.63 142

test1111		50.62 171	54.98 159	45.55 179	53.84 154	68.48 132	48.99 189	47.25 97	40.60 160	15.64 233	31.51 195	38.32 180	33.01 212	64.34 121	66.62 95	74.55 142	74.95 163

usedtu_blend_shiyan5		50.54 172	53.98 165	46.52 171	33.34 245	64.26 177	56.80 137	42.26 175	28.39 222	34.01 141	44.21 117	51.25 110	36.84 188	56.84 207	57.68 199	75.86 102	75.23 159

thres400		50.39 173	52.22 184	48.26 164	55.02 147	66.32 153	52.97 172	48.33 71	32.68 201	22.94 197	33.21 184	33.38 210	37.27 185	62.74 146	61.38 170	73.04 177	75.81 154

EG-PatchMatch MVS		50.23 174	50.89 194	49.47 153	59.54 103	70.88 112	52.46 176	44.01 149	26.22 240	31.91 156	24.97 233	31.45 222	33.48 207	64.79 112	66.51 100	75.40 118	71.39 192

IterMVS		50.23 174	53.27 173	46.68 170	47.59 189	60.58 204	53.10 170	36.62 216	36.07 186	25.89 183	39.42 151	40.05 175	43.65 168	60.22 180	61.35 172	73.23 172	75.23 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dtuonly		50.17 176	54.11 164	45.57 178	42.23 218	60.12 207	53.78 166	34.65 227	40.73 159	25.46 185	35.49 167	44.55 154	49.82 121	60.74 173	60.21 187	67.50 213	76.92 145

FMVSNet1		50.14 177	52.78 179	47.06 168	45.56 202	63.56 185	54.22 162	43.74 155	34.10 196	25.37 186	29.79 210	42.06 167	38.70 181	64.25 122	65.54 110	74.75 131	70.18 199

ACMH	47.82 13	50.10 178	49.60 200	50.69 138	63.36 52	66.99 147	56.83 136	52.13 38	31.06 211	17.74 230	28.22 220	26.24 242	45.17 157	60.88 168	63.80 136	68.91 204	70.00 202
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		49.85 179	56.99 145	41.52 211	52.79 155	57.06 221	41.44 227	43.13 164	56.13 72	19.24 222	52.11 70	48.38 132	22.14 239	58.19 195	58.38 196	70.35 195	68.71 210
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FE-MVSNET3		49.74 180	53.74 169	45.07 183	33.34 245	64.26 177	48.12 192	42.26 175	28.39 222	34.01 141	44.21 117	51.25 110	36.84 188	56.84 207	57.68 199	75.86 102	73.60 169

LS3D		49.59 181	49.75 199	49.40 155	55.88 139	59.86 209	56.31 147	45.33 129	48.57 110	28.32 175	31.54 194	36.81 186	46.27 149	57.17 202	55.88 219	64.29 226	58.42 237

usedtu_dtu_shiyan1		49.57 182	53.64 171	44.82 186	42.15 219	67.70 141	49.68 185	46.75 109	40.11 163	18.63 227	29.92 207	34.46 200	35.01 197	65.00 108	66.55 97	76.72 86	71.76 188

UniMVSNet_NR-MVSNet		49.56 183	53.04 176	45.49 180	51.59 166	64.42 174	46.97 204	51.01 41	37.87 172	16.42 231	39.87 143	34.91 196	33.43 209	59.59 185	62.70 149	73.52 165	71.94 184

CDS-MVSNet		49.25 184	53.97 166	43.75 194	47.53 190	64.53 170	48.59 190	42.27 174	33.77 198	26.64 181	40.46 140	42.26 166	30.01 223	61.77 158	61.71 166	67.48 215	73.28 177
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	44.22 14	49.14 185	51.75 187	46.10 174	42.78 216	55.60 231	53.11 169	34.46 232	55.69 78	32.47 154	34.16 178	41.45 170	48.91 131	57.13 203	54.09 226	64.84 222	64.10 218
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH+	47.85 12	49.13 186	48.86 210	49.44 154	56.75 124	62.01 196	56.62 144	47.55 92	37.49 177	23.98 192	26.68 226	29.46 233	43.12 169	57.45 201	58.85 195	68.62 206	70.05 201

NR-MVSNet		48.84 187	51.76 186	45.44 181	57.66 117	60.64 202	47.39 200	47.63 85	37.26 178	13.31 236	37.31 159	29.64 232	33.53 206	63.52 133	62.09 162	73.10 175	71.89 187

CR-MVSNet		48.82 188	51.85 185	45.29 182	46.74 192	55.95 226	52.06 177	34.21 233	42.17 150	31.74 157	32.92 186	42.53 165	45.00 159	58.80 190	61.11 176	61.99 240	69.47 204

thres600view7		48.44 189	50.23 197	46.35 173	54.05 153	64.60 168	50.18 183	47.34 94	31.73 207	20.74 212	32.28 191	32.62 213	33.79 204	60.84 169	56.11 217	71.99 182	73.40 174

test-mter		48.31 190	55.04 158	40.45 216	34.12 243	59.02 213	41.77 226	28.05 254	38.43 171	22.67 198	39.35 153	44.40 155	41.88 176	60.30 176	61.68 168	74.20 149	82.12 103

PatchT		48.11 191	51.27 193	44.43 187	50.13 176	61.58 198	33.59 241	32.92 243	40.38 162	31.74 157	30.60 202	36.93 185	45.00 159	58.80 190	61.11 176	73.19 173	69.47 204

TranMVSNet+NR-MVSNet		48.06 192	51.36 192	44.21 190	50.38 172	62.09 195	47.28 201	50.88 44	36.11 185	13.25 237	37.51 158	31.60 221	30.70 221	59.34 186	62.53 154	72.81 178	70.31 197

TransMVSNet (Re)		47.46 193	48.94 207	45.74 177	57.96 113	64.29 176	48.26 191	48.47 66	26.33 239	19.33 220	29.45 213	31.28 225	25.31 235	63.05 139	62.70 149	75.10 126	65.47 215

DU-MVS		47.33 194	50.86 195	43.20 197	44.43 209	60.64 202	46.97 204	47.63 85	37.26 178	16.42 231	37.31 159	31.39 223	33.43 209	57.53 199	59.98 190	70.35 195	71.94 184

v7n		47.22 195	48.38 212	45.87 176	48.20 186	63.58 184	50.69 181	40.93 189	26.60 238	26.44 182	26.52 227	29.65 231	38.19 183	58.22 194	60.23 186	70.79 193	73.83 166

UA-Net		47.19 196	53.02 177	40.38 217	55.31 143	60.02 208	38.41 233	38.68 203	36.42 184	22.47 201	51.95 73	58.72 64	25.62 234	54.11 223	53.40 230	61.79 241	56.51 241

Baseline_NR-MVSNet		47.14 197	50.83 196	42.84 200	44.43 209	63.31 187	44.50 218	50.36 50	37.71 175	11.25 242	30.84 199	32.09 216	30.96 219	57.53 199	63.73 137	75.53 115	70.60 196

pmmvs5		47.02 198	50.02 198	43.51 196	43.48 214	62.65 191	47.24 202	37.78 211	30.59 213	24.80 189	35.26 170	30.43 227	34.36 200	59.05 188	60.28 185	73.40 168	71.92 186

UniMVSNet (Re)		46.89 199	51.65 189	41.34 213	45.60 201	62.71 190	44.05 219	47.10 99	37.24 180	13.55 235	36.90 161	34.54 199	26.76 232	57.56 198	59.90 192	70.98 191	72.69 182

thisisatest0515		46.88 200	49.57 201	43.74 195	45.33 204	60.46 205	46.19 211	41.06 188	30.34 214	29.73 170	32.50 188	31.63 220	35.43 195	58.75 192	61.71 166	64.70 225	71.59 191

tfpnnormal		46.61 201	46.82 219	46.37 172	52.70 156	62.31 193	50.39 182	47.17 98	25.74 242	21.80 203	23.13 237	24.15 250	33.45 208	60.28 178	60.77 181	72.70 179	71.39 192

pm-mvs1		46.14 202	49.34 204	42.41 205	48.93 183	62.22 194	44.98 216	42.68 172	27.66 232	20.76 211	29.88 209	34.96 195	26.41 233	60.03 183	60.42 184	70.70 194	70.20 198

wanda-best-256-512		46.10 203	49.06 205	42.66 201	33.34 245	64.26 177	48.12 192	42.26 175	28.39 222	21.80 203	28.97 215	33.62 206	34.55 198	56.84 207	57.68 199	75.86 102	73.66 168

FE-blended-shiyan7		46.10 203	49.06 205	42.66 201	33.34 245	64.26 177	48.12 192	42.26 175	28.39 222	21.80 203	28.96 216	33.62 206	34.55 198	56.84 207	57.68 199	75.86 102	73.67 167

blended_shiyan6		45.97 205	48.94 207	42.50 204	33.34 245	64.17 181	47.94 196	42.22 179	28.07 229	21.66 208	28.80 217	33.57 208	34.06 201	56.79 212	57.55 205	75.79 108	73.60 169

blended_shiyan8		45.96 206	48.92 209	42.51 203	33.32 250	64.17 181	47.95 195	42.22 179	28.06 230	21.70 207	28.77 218	33.56 209	34.06 201	56.76 213	57.55 205	75.79 108	73.59 171

IterMVS-SCA-FT		45.87 207	51.55 190	39.24 221	46.22 193	59.43 210	52.89 173	31.93 244	36.01 187	23.68 193	38.86 154	39.88 178	39.05 179	56.25 216	58.17 197	41.70 261	72.25 183

MIMVSNet		45.62 208	49.56 202	41.02 214	38.17 226	64.43 173	49.48 186	35.43 223	36.53 183	20.06 216	22.58 238	35.16 194	28.75 228	61.97 153	62.20 160	74.20 149	64.07 219

gm-plane-assit		45.41 209	48.03 214	42.34 206	56.49 129	40.48 258	24.54 263	34.15 238	14.44 264	6.59 255	17.82 251	35.32 193	49.82 121	72.93 18	74.11 12	82.47 2	81.12 123

ADS-MVSNet		45.39 210	46.42 220	44.19 191	48.74 185	57.52 219	43.91 220	31.93 244	35.89 189	27.11 179	30.12 204	32.06 217	45.30 153	53.13 229	55.19 221	68.15 208	61.07 229

gbinet_0.2-2-1-0.02		45.07 211	48.55 211	41.00 215	30.59 255	63.61 183	46.97 204	41.88 182	25.18 244	18.93 226	27.74 223	34.25 202	32.89 213	56.40 215	57.32 209	74.75 131	75.37 158

GG-mvs-BLEND		44.87 212	64.59 79	21.86 257	0.01 275	73.70 85	55.99 150	0.01 272	50.70 95	0.01 277	49.18 91	63.61 43	0.01 271	63.83 129	64.50 129	75.13 125	86.62 57

pmmvs-eth3d		44.67 213	45.27 225	43.98 193	42.56 217	55.72 230	44.97 217	40.81 191	31.96 206	29.13 171	26.09 229	25.27 247	36.69 191	55.13 220	56.62 214	69.68 202	66.12 214

MDTV_nov1_ep13_2view		44.44 214	45.75 223	42.91 199	46.13 195	63.43 186	46.53 210	34.20 235	29.08 220	19.95 217	26.23 228	27.89 237	35.88 194	53.36 228	56.43 215	74.74 133	63.86 220

CMPMVS	33.64 16	44.39 215	46.41 221	42.03 207	44.21 212	56.50 224	46.73 208	26.48 259	34.20 195	35.14 132	24.22 234	34.64 198	40.52 178	56.50 214	56.07 218	59.12 245	62.74 225
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		44.31 216	51.67 188	35.72 231	51.82 161	55.24 232	34.57 240	41.63 183	39.10 168	8.84 249	45.93 112	46.63 144	14.45 251	54.09 224	57.03 211	63.00 235	63.65 222

TAMVS		44.27 217	49.35 203	38.35 225	44.74 207	61.04 201	39.07 231	31.82 246	29.95 216	18.34 228	33.55 182	39.94 177	30.01 223	56.85 206	57.58 204	66.13 219	66.54 212

MVS-HIRNet		43.98 218	43.63 229	44.39 189	47.66 188	59.31 211	32.66 247	33.88 240	30.15 215	33.75 147	16.82 256	28.39 236	45.25 154	53.92 227	55.00 223	73.16 174	61.80 226

UniMVSNet_ETH3D		43.97 219	46.01 222	41.59 209	38.31 225	56.20 225	49.69 184	38.18 208	28.18 226	19.88 219	27.82 222	30.20 228	33.41 211	54.18 222	56.30 216	70.05 199	69.17 206

RPMNet		43.70 220	48.17 213	38.48 224	45.52 203	55.95 226	37.66 235	26.63 258	42.17 150	25.47 184	29.59 212	37.61 181	33.87 203	50.85 234	52.02 234	61.75 242	69.00 207

PatchMatch-RL		43.37 221	44.93 226	41.56 210	37.94 227	51.70 234	40.02 229	35.75 221	39.04 169	30.71 163	35.14 171	27.43 239	46.58 146	51.99 230	50.55 238	58.38 247	58.64 235

FMVSNet5		43.29 222	47.07 217	38.87 222	30.46 256	50.99 236	45.87 213	37.19 215	42.17 150	19.32 221	26.77 225	40.51 173	30.26 222	56.82 211	55.81 220	70.10 198	56.46 242

test0.0.03 1		43.07 223	46.95 218	38.54 223	51.68 163	58.77 215	35.28 236	46.35 116	32.05 205	12.44 238	28.53 219	35.52 190	14.40 252	57.12 204	56.93 212	71.11 190	59.69 231

anonymousdsp		43.03 224	47.19 216	38.18 226	36.00 237	56.92 222	38.44 232	34.56 230	24.22 246	22.53 200	29.69 211	29.92 229	35.21 196	53.96 226	58.98 194	62.32 239	76.66 149

USDC		42.80 225	45.57 224	39.58 218	34.55 241	51.13 235	42.61 223	36.21 218	39.59 166	23.65 194	33.13 185	20.87 257	37.86 184	55.35 219	57.16 210	62.61 237	61.75 227

pmnet_mix02		42.41 226	43.24 232	41.44 212	45.80 200	57.46 220	42.19 224	41.57 184	29.38 218	23.39 195	26.08 230	23.96 251	27.31 230	51.50 231	53.76 228	68.36 207	60.58 230

CHOSEN 280x420		42.39 227	47.40 215	36.54 229	33.56 244	39.66 261	40.67 228	26.88 257	34.66 192	18.03 229	30.09 205	45.59 149	44.82 161	54.46 221	54.00 227	55.28 254	73.32 176

pmmvs6		41.90 228	44.01 228	39.43 219	44.45 208	58.77 215	41.92 225	39.22 199	21.74 249	19.08 224	17.40 254	31.33 224	24.28 237	55.94 217	56.67 213	67.60 212	66.24 213

Anonymous20231206		40.63 229	43.29 231	37.53 227	48.88 184	55.81 228	34.99 237	44.98 137	28.16 227	10.16 246	17.26 255	27.50 238	18.28 243	54.00 225	55.07 222	67.85 210	65.23 216

dtuonlycased		40.33 230	41.14 235	39.39 220	35.91 238	57.86 218	46.10 212	31.75 248	28.52 221	19.94 218	18.33 248	35.38 191	38.79 180	41.36 254	42.69 254	63.84 229	63.79 221

FE-MVSNET2		39.87 231	43.46 230	35.69 232	30.82 254	56.74 223	37.91 234	42.85 168	24.70 245	8.15 251	18.01 250	23.67 252	23.12 238	56.86 205	61.26 173	71.25 189	62.95 223

CVMVSNet		38.91 232	44.49 227	32.40 241	34.57 240	47.20 247	34.81 238	34.20 235	31.45 209	8.95 248	38.86 154	36.38 188	24.30 236	47.77 239	46.94 250	57.59 249	62.85 224

COLMAP_ROB	34.79 15	38.65 233	40.72 236	36.23 230	36.41 236	49.22 243	45.51 215	27.60 256	37.81 173	20.54 213	23.37 236	24.25 249	28.11 229	51.02 233	48.55 241	59.22 244	50.82 253
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		38.23 234	41.72 234	34.15 234	40.56 223	50.07 239	33.17 244	44.35 145	27.64 234	5.54 261	30.84 199	26.67 240	14.99 249	45.64 242	52.38 233	66.29 218	58.83 234

WR-MVS		37.61 235	42.15 233	32.31 243	43.64 213	51.85 233	29.39 254	43.35 160	27.65 233	4.40 263	29.90 208	29.80 230	10.46 256	46.73 241	51.98 235	62.60 238	57.16 239

TinyColmap		37.18 236	37.37 249	36.95 228	31.17 253	45.21 252	39.71 230	34.65 227	29.83 217	20.20 214	18.54 247	13.72 267	38.27 182	50.33 235	51.57 236	57.71 248	52.42 250

CP-MVSNet		37.09 237	40.62 237	32.99 236	37.56 229	48.25 244	32.75 245	43.05 165	27.88 231	5.93 257	31.27 197	25.82 245	15.09 247	43.37 249	48.82 239	63.54 232	58.90 232

DTE-MVSNet		36.91 238	40.44 238	32.79 239	40.74 221	47.55 246	30.71 252	44.39 142	27.03 236	4.32 264	30.88 198	25.99 243	12.73 254	45.58 243	50.80 237	63.86 228	55.23 245

PS-CasMVS		36.84 239	40.23 241	32.89 237	37.44 230	48.09 245	32.68 246	42.97 167	27.36 235	5.89 258	30.08 206	25.48 246	14.96 250	43.28 250	48.71 240	63.39 233	58.63 236

WR-MVS_H		36.29 240	40.35 240	31.55 245	37.80 228	49.94 241	30.57 253	41.11 187	26.90 237	4.14 265	30.72 201	28.85 234	10.45 257	42.47 252	47.99 245	65.24 221	55.54 243

SixPastTwentyTwo		36.11 241	37.80 245	34.13 235	37.13 233	46.72 250	34.58 239	34.96 225	21.20 252	11.66 239	29.15 214	19.88 258	29.77 225	44.93 244	48.34 242	56.67 251	54.41 247

test20.03		36.00 242	38.92 242	32.60 240	45.92 199	50.99 236	28.05 259	43.69 156	21.62 250	6.03 256	17.61 253	25.91 244	8.34 263	51.26 232	52.60 232	63.58 230	52.46 249

TDRefinement		35.76 243	38.23 243	32.88 238	19.09 266	46.04 251	43.29 221	29.49 251	33.49 199	19.04 225	22.29 240	17.82 262	29.69 227	48.60 237	47.24 248	56.65 252	52.12 251

LTVRE_ROB	32.83 17	35.10 244	37.46 246	32.35 242	43.12 215	49.99 240	28.52 256	33.23 242	12.73 266	8.18 250	27.71 224	21.34 255	32.64 215	46.92 240	48.11 243	48.41 258	55.45 244
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		34.96 245	38.17 244	31.22 246	22.78 261	40.82 257	33.56 242	23.61 261	29.16 219	21.43 210	28.00 221	21.43 254	31.90 217	44.33 247	42.12 255	54.07 256	61.34 228

testgi		34.51 246	37.42 247	31.12 247	47.37 191	50.34 238	24.38 264	41.21 185	20.32 254	5.64 260	20.56 242	26.55 241	8.06 264	49.28 236	52.65 231	60.05 243	42.23 259

MDA-MVSNet-bldmvs		34.31 247	34.11 255	34.54 233	24.73 258	49.66 242	33.42 243	43.03 166	21.59 251	11.10 243	19.81 245	12.68 268	31.41 218	35.59 259	48.05 244	63.56 231	51.39 252

N_pmnet		34.09 248	35.74 252	32.17 244	37.25 232	43.17 255	32.26 249	35.57 222	26.22 240	10.60 245	20.44 244	19.38 261	20.20 241	44.59 246	47.00 249	57.13 250	49.35 256

RPSCF		33.61 249	40.43 239	25.65 253	16.00 268	32.41 263	31.73 251	13.33 268	50.13 98	23.12 196	31.56 193	40.09 174	32.73 214	41.14 257	37.05 258	36.99 264	50.63 254

FE-MVSNET		33.52 250	37.02 250	29.45 248	23.65 259	47.19 249	28.15 258	40.92 190	20.01 257	3.42 268	16.28 257	19.67 260	17.80 244	47.90 238	54.52 225	62.73 236	53.53 248

EU-MVSNet		33.00 251	36.49 251	28.92 249	33.10 251	42.86 256	29.32 255	35.99 219	22.94 247	5.83 259	25.29 231	24.43 248	15.21 246	41.22 256	41.65 257	54.08 255	57.01 240

pmmvs3		31.22 252	33.62 256	28.43 250	22.82 260	40.26 260	26.40 260	22.05 263	16.89 261	10.99 244	14.72 259	16.26 263	29.70 226	44.82 245	47.39 247	58.61 246	54.98 246

usedtu_dtu_shiyan2		31.12 253	34.28 254	27.44 252	11.70 269	47.20 247	32.04 250	31.41 249	14.11 265	8.15 251	13.22 261	19.80 259	16.49 245	42.54 251	45.42 252	64.82 224	57.66 238

FC-MVSNet-test		30.97 254	37.38 248	23.49 256	37.42 231	33.68 262	19.43 266	39.27 197	31.37 210	1.67 272	38.56 156	28.85 234	6.06 267	41.40 253	43.80 253	37.10 263	44.03 258

new-patchmatchnet		30.47 255	32.80 258	27.75 251	36.81 234	43.98 253	24.85 262	39.29 196	20.52 253	4.06 266	15.94 258	16.05 264	9.57 258	41.32 255	42.05 256	51.94 257	49.74 255

MIMVSNet1		29.60 256	33.37 257	25.20 255	19.52 264	43.94 254	26.29 261	37.92 210	19.95 258	3.79 267	12.64 264	21.99 253	7.70 265	43.83 248	46.32 251	55.97 253	44.92 257

FPMVS		26.87 257	28.19 259	25.32 254	27.09 257	29.49 265	32.28 248	17.79 265	28.09 228	11.33 240	19.38 246	14.69 265	20.88 240	35.11 260	32.82 261	42.56 260	37.75 260

WB-MVS		22.51 258	25.28 260	19.27 259	35.74 239	31.57 264	11.45 269	40.75 192	15.01 263	0.98 275	20.48 243	12.53 269	1.77 269	36.11 258	35.01 260	24.91 267	26.27 263

PMVS	18.18 18	21.95 259	22.85 261	20.90 258	21.92 262	14.78 267	19.95 265	17.31 266	15.69 262	11.32 241	13.70 260	13.91 266	15.02 248	34.92 261	31.72 262	39.85 262	35.20 261
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		19.10 260	22.71 262	14.89 261	10.93 271	24.08 266	14.22 267	13.94 267	18.68 259	2.93 269	12.84 263	11.27 270	11.94 255	30.57 263	30.58 263	35.38 265	30.93 262

Gipuma		17.16 261	17.83 263	16.36 260	18.76 267	12.15 270	11.97 268	27.78 255	17.94 260	4.86 262	2.53 271	2.73 275	8.90 261	34.32 262	36.09 259	25.92 266	19.06 266
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		13.92 262	17.14 264	10.16 264	1.69 274	6.92 273	11.25 270	5.74 269	22.41 248	8.11 253	10.40 265	20.91 256	13.73 253	22.17 264	13.98 266	20.44 268	23.18 264

PMMVS2		12.25 263	14.17 265	10.00 265	11.39 270	14.35 268	8.21 271	19.29 264	9.31 267	0.19 276	7.38 267	6.19 273	1.10 270	19.26 265	21.13 265	19.85 269	21.56 265

E-PMN		10.66 264	8.30 267	13.42 262	19.91 263	7.87 271	4.30 274	29.47 252	8.37 270	1.70 271	3.67 268	1.29 278	9.12 260	8.98 269	13.59 267	16.03 270	14.30 269

EMVS		10.15 265	7.67 268	13.05 263	19.22 265	7.77 272	4.48 272	29.34 253	8.65 269	1.67 272	3.55 269	1.36 277	9.15 259	8.15 270	11.79 269	14.44 271	12.43 270

MVE	10.35 19	9.76 266	11.08 266	8.22 266	4.43 272	13.04 269	3.36 275	23.57 262	5.74 271	1.76 270	3.09 270	1.75 276	6.78 266	12.78 267	23.04 264	9.44 272	18.09 267
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 267	0.01 269	0.00 268	0.00 276	0.00 276	0.00 278	0.00 273	0.01 272	0.00 278	0.02 272	0.00 279	0.00 273	0.01 271	0.01 270	0.00 275	0.03 271

test123		0.01 267	0.01 269	0.00 268	0.00 276	0.00 276	0.00 278	0.00 273	0.01 272	0.00 278	0.02 272	0.00 279	0.01 271	0.00 272	0.01 270	0.00 275	0.03 271

uanet_test		0.00 269	0.00 271	0.00 268	0.00 276	0.00 276	0.00 278	0.00 273	0.00 274	0.00 278	0.00 274	0.00 279	0.00 273	0.00 272	0.00 272	0.00 275	0.00 273

sosnet-low-res		0.00 269	0.00 271	0.00 268	0.00 276	0.00 276	0.00 278	0.00 273	0.00 274	0.00 278	0.00 274	0.00 279	0.00 273	0.00 272	0.00 272	0.00 275	0.00 273

sosnet		0.00 269	0.00 271	0.00 268	0.00 276	0.00 276	0.00 278	0.00 273	0.00 274	0.00 278	0.00 274	0.00 279	0.00 273	0.00 272	0.00 272	0.00 275	0.00 273

TestfortrainingZip							78.23 5	61.85 3		68.16 1						81.99 4

TPM-MVS					78.45 5	83.50 6	78.26 4			58.88 10	72.62 19	77.54 11	69.42 4			80.40 9	85.71 68
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										21.59 209

9.14												80.07 7

SR-MVS					63.74 49			48.51 65				73.80 22

Anonymous202405211			56.81 146		60.91 90	73.48 88	59.82 115	48.68 62	39.26 167		24.00 235	46.77 143	50.73 115	65.28 105	65.72 108	75.37 119	83.17 93

our_test_3					49.68 178	61.50 199	45.84 214

ambc			35.52 253		38.36 224	40.40 259	28.38 257		25.20 243	14.87 234	13.22 261	7.54 272	19.34 242	55.63 218	47.79 246	47.91 259	58.89 233

MTAPA										54.82 21		71.98 28

MTMP										50.64 36		68.31 33

Patchmatch-RL test							0.69 277

tmp_tt				4.41 267	2.56 273	1.81 275	2.61 276	0.27 271	20.12 255	9.81 247	17.69 252	9.04 271	1.96 268	12.88 266	12.11 268	9.23 273

XVS					62.70 59	73.06 92	61.80 99			42.02 104		63.42 45				74.68 137

X-MVStestdata					62.70 59	73.06 92	61.80 99			42.02 104		63.42 45				74.68 137

mPP-MVS					63.08 55							62.34 49

NP-MVS									72.62 31

Patchmtry						64.49 172	52.06 177	34.21 233		31.74 157

DeepMVS_CX						5.87 274	4.32 273	1.74 270	9.04 268	1.30 274	7.97 266	3.16 274	8.56 262	9.74 268		6.30 274	14.51 268

High-resolution multi-view benchmark