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 8	88.89 1	65.96 2	86.00 7	84.02 1	70.03 1	76.19 5	76.17 5	79.22 30	94.46 1

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

DVP-MVS		77.54 3	84.41 2	69.54 8	79.93 3	86.08 3	77.20 12	60.31 6	88.62 2	62.54 4	86.67 5	83.77 2	58.04 53	75.84 8	75.69 8	79.21 31	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 4	81.90 6	70.66 4	77.07 9	81.13 16	78.23 5	61.85 3	85.73 7	61.71 5	89.05 3	80.80 5	63.14 13	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 14	59.82 8	86.26 6	77.90 10	61.11 20	71.81 30	70.75 37	79.63 16	88.22 27

MSP-MVS		76.38 6	82.99 4	68.68 9	71.93 21	78.65 30	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 84	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 23	85.13 4	77.62 8	59.87 10	82.69 12	61.55 6	83.05 11	79.63 8	69.78 3	76.01 6	75.89 6	77.92 64	86.86 50

DPE-MVS		75.74 8	82.82 5	67.49 13	77.07 9	82.01 10	77.05 13	57.70 14	86.55 6	55.44 20	90.50 2	82.52 3	60.33 24	72.99 17	72.98 19	77.33 75	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 5	76.10 12	82.56 9	79.30 3	59.89 9	80.49 16	57.75 14	66.98 30	76.16 17	65.95 6	79.35 1	78.47 1	81.45 7	85.71 69

APDe-MVS		74.59 10	80.23 9	68.01 12	76.51 11	80.20 19	77.39 10	58.18 12	85.31 8	56.84 16	84.89 8	76.08 18	60.66 22	71.85 29	71.76 25	78.47 52	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 11	76.33 15	62.16 2	75.89 23	52.96 29	64.37 35	73.30 25	65.66 8	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 16	81.97 12	76.19 16	59.29 11	80.12 17	56.70 17	67.09 29	76.48 15	64.26 10	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 24	80.13 20	74.99 17	56.09 17	84.14 9	54.48 23	73.74 18	80.23 6	61.43 17	74.96 11	74.09 14	78.08 61	89.42 17
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CSCG		72.98 14	76.86 18	68.46 10	78.23 7	81.74 13	77.26 11	60.00 7	75.61 26	59.06 9	62.72 37	77.42 12	56.63 67	74.24 13	77.18 4	79.56 18	89.13 21

HPM-MVS++		72.44 15	78.73 11	65.11 16	71.88 22	77.31 54	71.98 25	55.67 19	83.11 11	53.59 27	75.90 14	78.49 9	61.00 21	73.99 14	73.31 18	76.55 90	88.97 22

APD-MVS		71.86 16	77.91 15	64.80 18	70.39 28	75.69 66	74.02 19	56.14 16	83.59 10	52.92 30	84.67 9	73.46 24	59.30 33	69.47 46	69.66 50	76.02 97	88.84 23
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 30	79.26 22	73.53 20	54.73 27	79.32 19	54.23 24	74.81 15	74.61 22	59.40 31	73.00 16	72.17 22	77.10 83	87.72 32

NCCC		71.36 18	75.44 21	66.60 14	72.46 19	79.18 24	74.16 18	57.83 13	76.93 21	54.19 25	63.47 36	71.08 30	61.30 19	73.56 15	73.70 15	79.69 15	90.19 13

train_agg		70.74 19	76.53 19	63.98 23	70.33 29	75.16 75	72.33 24	55.78 18	75.74 24	50.41 39	80.08 13	73.15 26	57.75 57	71.96 28	70.94 34	77.25 79	88.69 25

MGCNet		70.65 20	76.30 20	64.05 22	67.54 39	80.89 17	68.89 38	49.94 52	77.93 20	55.92 19	68.22 27	73.10 27	62.14 14	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 32	64.61 168	72.60 23	56.29 15	80.73 15	58.36 12	84.56 10	75.22 20	55.37 80	69.11 57	69.45 53	75.97 99	81.97 109
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 64	76.96 57	65.54 81	52.23 36	87.46 4	49.07 40	74.05 17	76.19 16	59.01 36	72.79 21	71.61 27	74.13 154	89.49 16

SteuartSystems-ACMMP		69.78 23	74.76 23	63.98 23	73.45 15	78.56 32	73.13 22	55.24 24	70.68 38	48.93 42	70.43 23	69.10 32	54.00 89	72.78 23	72.98 19	79.14 36	88.74 24
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		68.75 24	72.84 25	63.98 23	68.87 36	75.09 77	71.87 26	51.22 40	73.50 30	58.17 13	68.05 28	68.67 33	57.79 56	70.49 39	69.23 60	75.98 98	84.84 82

MVSMamba_PlusPlus		68.58 25	72.33 28	64.22 21	66.67 41	80.11 21	68.52 41	54.28 28	65.99 51	51.49 32	59.22 47	62.40 49	65.80 7	76.97 4	75.31 11	78.56 48	86.28 64

SD-MVS		68.30 26	72.58 27	63.31 28	69.24 33	67.85 139	70.81 31	53.65 33	79.64 18	58.52 11	74.31 16	75.37 19	53.52 95	65.63 100	63.56 138	74.13 154	81.73 114
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 34	81.55 14	70.82 30	55.02 25	53.38 87	48.83 43	56.45 53	59.35 62	60.05 28	74.93 12	74.78 12	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 43	76.62 59	69.22 37	50.92 42	70.07 39	48.81 44	69.66 25	70.12 31	53.68 92	68.41 68	69.13 62	74.98 129	87.53 36
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	59.79 101	74.84 79	68.89 38	55.56 20	73.91 29	53.50 28	55.00 59	65.63 37	60.08 26	71.99 27	71.33 31	76.85 85	87.94 30
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 40	73.58 88	71.41 27	52.98 34	69.92 41	43.85 82	70.58 22	58.75 64	56.76 65	72.90 19	71.88 23	77.57 70	86.94 49

CANet		67.21 31	71.83 32	61.83 33	64.51 49	79.25 23	66.72 72	48.73 60	68.49 46	50.63 38	61.40 41	66.47 35	61.44 16	69.31 51	69.90 43	78.94 44	88.00 28

CDPH-MVS		67.03 32	71.64 33	61.65 36	69.10 35	76.84 58	71.35 29	55.42 22	67.02 49	42.83 94	65.27 34	64.60 41	53.16 98	69.70 45	71.40 29	78.02 63	86.67 57

MAR-MVS		66.85 33	69.81 42	63.39 27	73.56 14	80.51 18	69.87 33	51.51 39	67.78 48	46.44 61	51.09 81	61.60 56	60.38 23	72.67 24	73.61 16	78.59 47	81.44 118
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 59	73.21 91	67.96 46	52.31 35	72.26 33	51.03 33	56.50 52	64.26 42	63.37 12	71.64 31	70.85 35	76.70 88	86.10 66
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 87	70.00 119	64.26 90	47.79 84	72.98 31	56.32 18	71.35 21	72.33 28	55.68 78	65.49 101	66.66 94	77.35 73	86.62 58

ACMMPR		66.20 36	71.51 35	60.00 60	65.34 47	74.04 83	69.39 35	50.92 42	71.97 34	46.04 64	66.79 31	65.68 36	53.07 99	68.93 60	69.12 63	75.21 123	84.05 90

3Dnovator	58.39 4	65.97 37	66.85 57	64.94 17	73.72 13	79.03 25	67.73 51	54.25 29	61.52 57	52.79 31	42.27 131	60.73 60	62.01 15	71.29 32	71.75 26	79.12 37	81.34 121

TSAR-MVS + ACMM		65.95 38	72.83 26	57.93 76	69.35 31	65.85 159	73.36 21	39.84 195	76.00 22	48.69 45	82.54 12	75.03 21	49.38 128	65.33 103	63.42 140	66.94 218	81.67 115

sasdasda		65.55 39	70.75 37	59.49 68	62.11 72	78.26 41	66.52 74	43.82 153	71.54 35	47.84 49	61.30 42	61.68 53	58.48 44	67.56 78	69.67 48	78.16 59	85.25 77

canonicalmvs		65.55 39	70.75 37	59.49 68	62.11 72	78.26 41	66.52 74	43.82 153	71.54 35	47.84 49	61.30 42	61.68 53	58.48 44	67.56 78	69.67 48	78.16 59	85.25 77

QAPM		65.47 41	67.82 49	62.72 30	72.56 17	81.17 15	67.43 58	55.38 23	56.07 74	43.29 91	43.60 123	65.38 39	59.10 34	72.20 26	70.76 36	78.56 48	85.59 73

PGM-MVS		65.35 42	70.43 40	59.43 70	65.78 45	73.75 85	69.41 34	48.18 73	68.80 45	45.37 73	65.88 33	64.04 43	52.68 106	68.94 59	68.68 72	75.18 124	82.93 99

PHI-MVS		65.17 43	72.07 31	57.11 91	63.02 57	77.35 53	67.04 68	48.14 78	68.03 47	37.56 123	66.00 32	65.39 38	53.19 97	70.68 36	70.57 39	73.72 162	86.46 61

CLD-MVS		64.69 44	67.25 51	61.69 35	68.22 38	78.33 37	63.09 95	47.59 87	69.64 42	53.98 26	54.87 60	53.94 88	57.87 54	72.79 21	71.34 30	79.40 26	69.87 204
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 25	77.91 48	62.75 98	54.78 26	51.43 92	47.54 51	53.77 63	54.85 83	56.84 63	70.59 37	71.50 28	77.86 65	89.70 15

EPNet		64.39 46	70.93 36	56.77 95	60.58 96	75.77 62	59.28 121	50.58 46	69.93 40	40.73 112	68.59 26	61.60 56	53.72 90	68.65 63	68.07 76	75.75 112	83.87 92
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 68	71.81 111	67.27 63	49.51 54	69.40 44	45.76 70	60.41 45	64.96 40	51.84 108	67.33 85	67.57 85	73.78 161	84.89 80

EC-MVSNet		64.30 48	68.19 45	59.76 64	62.97 58	75.31 73	67.26 64	44.19 147	60.73 60	47.52 53	55.84 55	62.12 51	57.67 58	70.71 35	67.47 87	78.97 42	85.13 79

casdiffmvs_mvg		64.26 49	67.60 50	60.36 52	62.26 67	78.54 33	69.39 35	48.33 71	56.54 69	45.36 74	52.86 69	57.36 69	58.42 46	70.28 40	70.24 41	78.43 54	87.39 40
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 92	78.29 40	67.91 48	48.01 82	55.89 78	44.97 77	50.45 85	56.94 70	59.54 29	70.17 43	69.81 45	79.41 24	87.99 29
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 66	78.81 28	67.95 47	48.45 67	52.32 89	48.38 46	51.97 73	53.76 89	59.35 32	69.39 49	69.76 47	79.70 14	87.62 35

MVS_Test		63.75 52	67.24 52	59.68 65	60.01 97	76.99 56	68.13 44	45.17 131	57.45 68	43.74 84	53.07 67	56.16 77	61.33 18	70.27 41	71.11 32	79.72 13	85.63 72

Casviewmamba		63.73 53	66.47 59	60.53 49	63.39 51	77.99 47	67.69 52	48.45 67	55.29 81	45.83 68	50.75 84	56.46 74	60.08 26	69.12 55	69.33 54	77.74 67	86.33 63

hybridcas		63.61 54	66.22 63	60.56 46	62.13 71	78.59 31	68.59 40	48.14 78	54.14 84	45.68 71	49.27 91	56.60 71	59.44 30	69.17 53	69.03 66	79.41 24	86.75 56

X-MVS		63.53 55	68.62 44	57.60 80	64.77 48	73.06 93	65.82 79	50.53 47	65.77 52	42.02 105	58.20 50	63.42 46	47.83 139	68.25 73	68.50 73	74.61 141	83.16 96

viewcassd2359sk11		63.49 56	65.78 69	60.83 42	62.14 70	78.68 29	67.83 50	48.34 70	51.06 94	47.99 48	51.10 80	53.41 90	59.09 35	69.12 55	69.58 51	79.58 17	87.49 37

viewmanbaseed2359cas		63.30 57	65.85 68	60.31 53	61.55 82	78.41 36	68.44 42	47.39 93	50.91 95	46.42 62	50.98 83	53.99 87	58.60 42	69.11 57	70.10 42	79.48 20	87.46 38

ACMMP		63.27 58	67.85 48	57.93 76	62.64 62	72.30 106	68.23 43	48.77 59	66.50 50	43.05 92	62.07 38	57.84 67	49.98 119	66.58 91	66.46 101	74.93 130	83.17 94
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	57.39 119	72.73 100	63.38 94	45.16 132	59.37 62	46.49 60	58.93 49	57.68 68	56.31 69	71.12 33	70.37 40	76.23 96	85.88 67

E3new		63.04 60	65.16 75	60.56 46	61.92 75	78.50 34	67.67 54	48.17 74	49.34 102	47.42 55	49.85 88	52.98 92	58.75 38	68.79 61	69.33 54	79.43 22	87.30 41

E3		63.03 61	65.15 76	60.56 46	61.92 75	78.49 35	67.68 53	48.17 74	49.33 103	47.43 54	49.85 88	52.99 91	58.75 38	68.79 61	69.32 56	79.43 22	87.30 41

viewdifsd2359ckpt13		62.95 62	65.29 72	60.21 54	62.21 68	78.86 26	67.26 64	48.16 76	50.15 98	45.82 69	50.17 87	51.84 103	58.68 41	69.24 52	69.88 44	79.15 35	86.86 50

ETV-MVS		62.88 63	68.18 46	56.70 96	58.47 109	74.89 78	60.26 113	43.96 150	58.27 67	42.37 101	61.47 40	56.56 72	57.80 55	68.00 76	68.74 70	77.34 74	89.33 20

AdaColmap		62.79 64	62.63 96	62.98 29	70.82 26	72.90 97	67.84 49	54.09 31	65.14 53	50.71 36	41.78 133	47.64 137	60.17 25	67.41 84	66.83 92	74.28 147	76.69 149

3Dnovator+	55.76 7	62.70 65	65.10 77	59.90 61	65.89 44	72.15 107	62.94 97	49.82 53	62.77 56	49.06 41	43.62 122	61.47 58	58.60 42	68.51 64	66.75 93	73.08 177	80.40 129

OpenMVS	55.62 8	62.57 66	63.76 90	61.19 39	72.13 20	78.84 27	64.42 88	50.51 48	56.44 71	45.67 72	36.88 163	56.51 73	56.66 66	68.28 72	68.96 67	77.73 68	80.44 128

PVSNet_BlendedMVS		62.53 67	66.37 60	58.05 74	58.17 110	75.70 64	61.30 106	48.67 63	58.67 63	50.93 34	55.43 57	49.39 126	53.01 101	69.46 47	66.55 97	76.24 94	89.39 18

PVSNet_Blended		62.53 67	66.37 60	58.05 74	58.17 110	75.70 64	61.30 106	48.67 63	58.67 63	50.93 34	55.43 57	49.39 126	53.01 101	69.46 47	66.55 97	76.24 94	89.39 18

MVSTER		62.51 69	67.22 53	57.02 93	55.05 147	69.23 127	63.02 96	46.88 103	61.11 59	43.95 81	59.20 48	58.86 63	56.80 64	69.13 54	70.98 33	76.41 92	82.04 106

viewdifsd2359ckpt09		62.50 70	64.48 80	60.19 57	61.23 87	77.58 50	67.62 55	48.43 69	51.16 93	47.53 52	51.23 79	51.93 100	58.78 37	67.17 87	65.88 107	77.54 71	86.38 62

E5new		62.48 71	64.43 82	60.20 55	61.57 79	78.31 38	67.43 58	48.06 80	47.28 117	46.73 57	48.48 99	52.64 95	58.20 49	68.45 65	69.07 64	79.20 32	86.77 54

E5		62.48 71	64.43 82	60.20 55	61.57 79	78.31 38	67.43 58	48.06 80	47.28 117	46.73 57	48.48 99	52.64 95	58.20 49	68.45 65	69.07 64	79.20 32	86.77 54

CHOSEN 1792x2688		62.48 71	64.06 87	60.64 44	72.50 18	84.18 5	62.43 99	53.77 32	47.90 116	39.85 116	25.15 233	44.76 154	53.72 90	77.29 3	77.61 2	81.60 6	91.53 8

CostFormer		62.45 74	65.68 70	58.67 72	63.29 54	77.65 49	67.62 55	38.42 206	54.04 85	46.00 65	48.27 102	57.89 66	56.97 61	67.03 88	67.79 83	79.74 12	87.09 46

E4		62.36 75	64.27 85	60.14 58	61.58 78	78.25 43	67.38 61	47.91 83	46.78 122	46.58 59	48.07 103	52.52 97	58.23 48	68.32 70	68.96 67	79.19 34	86.98 48

PCF-MVS	55.99 6	62.31 76	66.60 58	57.32 85	59.12 108	73.68 87	67.53 57	48.71 61	61.35 58	42.83 94	51.33 78	63.48 45	53.48 96	65.64 99	64.87 122	72.22 182	85.83 68
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	57.08 121	75.60 70	66.90 69	47.06 101	55.45 80	43.37 89	53.45 65	55.60 79	57.21 60	66.57 92	68.00 79	75.89 102	87.70 34
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 90	78.01 44	68.04 45	46.83 105	46.59 124	45.11 76	47.34 105	52.79 93	57.50 59	68.43 67	69.54 52	79.08 38	87.01 47

E6new		61.98 79	63.77 88	59.90 61	61.52 84	78.01 44	67.09 66	47.57 90	45.71 128	45.97 66	46.87 107	51.47 106	58.17 51	68.20 74	69.31 58	79.07 39	86.81 52

E6		61.98 79	63.77 88	59.90 61	61.52 84	78.01 44	67.09 66	47.57 90	45.71 128	45.97 66	46.87 107	51.47 106	58.17 51	68.20 74	69.31 58	79.07 39	86.81 52

hybridnocas07		61.95 81	65.92 66	57.32 85	56.68 127	75.77 62	65.53 82	46.69 110	55.99 75	43.65 85	53.11 66	55.36 81	56.11 71	66.44 94	67.60 84	75.28 121	87.16 45

DI_MVS_pp		61.86 82	65.26 73	57.90 79	57.93 115	74.51 81	66.30 76	46.49 115	49.96 100	41.62 108	42.69 128	61.77 52	58.74 40	70.25 42	69.32 56	76.31 93	88.30 26

diffmvs_AUTHOR		61.85 83	65.54 71	57.54 81	56.64 128	75.64 69	66.65 73	46.55 114	53.31 88	42.72 98	51.70 75	55.51 80	56.91 62	66.66 89	68.09 75	75.77 111	87.89 31

MSLP-MVS++		61.81 84	62.19 101	61.37 38	68.33 37	63.08 189	70.75 32	38.89 202	63.96 55	57.51 15	48.59 96	61.66 55	53.67 93	62.04 152	59.92 191	79.03 41	76.08 152

hybrid		61.79 85	65.87 67	57.03 92	56.18 133	75.51 72	65.47 84	46.32 118	55.94 77	43.47 87	52.97 68	55.80 78	55.45 79	66.17 95	67.53 86	75.28 121	87.17 44

SPE-MVS-test		61.68 86	65.97 65	56.67 97	57.77 116	72.59 103	57.63 129	45.54 126	58.53 66	47.11 56	59.45 46	56.34 75	55.15 81	64.52 116	65.03 120	76.80 86	85.34 76

OPM-MVS		61.59 87	62.30 100	60.76 43	66.53 42	73.35 90	71.41 27	54.18 30	40.82 159	41.57 109	45.70 114	54.84 84	54.43 86	69.92 44	69.19 61	76.45 91	82.25 103
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	57.74 117	75.65 68	64.78 87	47.08 100	55.24 82	42.97 93	52.09 72	54.80 85	56.05 73	65.74 97	67.28 88	74.63 140	85.53 75

viewdifsd2359ckpt07		61.43 89	63.33 93	59.20 71	61.66 77	77.47 52	66.75 71	46.85 104	45.54 130	45.32 75	48.59 96	51.61 105	56.09 72	67.46 80	68.01 78	78.54 51	84.67 84

MS-PatchMatch		61.41 90	61.88 105	60.85 41	70.57 27	75.98 61	66.29 77	46.91 102	50.56 97	48.28 47	36.30 166	51.64 104	50.95 114	72.89 20	70.65 38	82.13 3	75.17 163

onestephybrid01		61.26 91	65.06 78	56.82 94	57.98 113	75.52 71	64.18 91	46.76 108	54.24 83	43.46 88	52.35 70	55.20 82	55.00 82	65.65 98	66.25 102	73.56 164	86.21 65

casdiffseed414692147		60.92 92	62.03 102	59.63 66	62.33 65	76.41 60	67.31 62	47.59 87	48.83 110	43.83 83	41.47 134	47.12 142	58.26 47	67.43 83	68.40 74	78.47 52	84.57 87

viewmambaseed2359dif		60.68 93	63.59 92	57.29 87	56.93 123	75.24 74	65.36 85	45.82 124	49.89 101	43.57 86	49.83 90	51.89 102	56.33 68	64.86 111	65.71 109	75.75 112	87.72 32

EIA-MVS		60.56 94	64.29 84	56.20 102	59.14 107	72.68 102	59.55 119	43.56 157	51.78 91	41.01 111	55.47 56	51.93 100	55.87 75	65.01 107	66.57 96	78.06 62	86.60 60

dtuplus		60.37 95	63.09 94	57.19 89	57.03 122	75.16 75	65.19 86	45.85 123	48.37 113	43.35 90	48.48 99	52.00 99	55.90 74	64.94 110	65.48 113	75.85 107	87.18 43

ACMP	56.21 5	59.78 96	61.81 107	57.41 84	61.15 89	68.88 129	65.98 78	48.85 58	58.56 65	44.19 80	48.89 94	46.31 146	48.56 133	63.61 132	64.49 130	75.75 112	81.91 110
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 74	68.15 136	66.90 69	48.15 77	59.75 61	38.47 119	50.38 86	48.34 134	46.87 146	65.39 102	64.93 121	75.51 117	81.21 123

Effi-MVS+		59.63 98	61.78 108	57.12 90	61.56 81	71.63 112	63.61 92	47.59 87	47.18 119	37.79 120	45.29 115	49.93 122	56.27 70	67.45 81	67.06 90	75.91 100	83.93 91

CPTT-MVS		59.54 99	64.47 81	53.79 114	54.99 149	67.63 143	65.48 83	44.59 141	64.81 54	37.74 121	51.55 76	59.90 61	49.77 124	61.83 156	61.26 173	70.18 198	84.31 89

baseline2		59.20 100	61.72 109	56.27 101	59.61 103	74.12 82	58.65 124	49.42 55	48.10 114	40.12 115	49.10 93	44.15 157	51.24 111	66.65 90	67.88 82	78.56 48	82.06 105

MGCFI-Net		59.19 101	66.89 56	50.20 145	57.15 120	68.62 132	54.79 158	39.20 200	70.99 37	32.93 152	60.83 44	61.00 59	45.54 153	63.77 130	60.71 182	71.59 186	82.29 101

GeoE		58.97 102	60.94 110	56.67 97	61.27 86	72.71 101	61.35 105	45.69 125	49.19 107	41.22 110	39.55 150	49.58 125	52.79 105	64.79 112	65.89 106	77.73 68	84.87 81

baseline		58.65 103	61.99 103	54.75 109	54.70 151	71.85 110	60.20 114	43.91 151	55.99 75	40.13 114	53.50 64	50.91 119	55.76 76	61.29 164	61.73 165	73.83 158	78.68 140

PVSNet_Blended_VisFu		58.56 104	62.33 99	54.16 111	56.90 124	73.92 84	57.72 128	46.16 121	44.23 135	42.73 97	46.26 109	51.06 117	46.28 149	67.99 77	65.38 115	75.18 124	87.44 39

ACMM	53.73 9	57.91 105	58.27 130	57.49 82	63.10 55	66.45 153	65.65 80	49.02 57	53.69 86	42.67 99	36.41 165	46.07 149	50.38 117	64.74 114	64.63 127	74.14 153	75.91 153
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	52.18 158	70.20 118	58.14 127	37.32 213	56.49 70	31.06 162	57.38 51	50.05 121	53.67 93	64.98 109	65.04 119	74.57 142	81.29 122

ET-MVSNet_ETH3D		57.84 107	61.91 104	53.09 117	32.91 254	74.53 80	63.51 93	46.80 107	46.52 125	36.14 129	56.00 54	46.20 147	64.41 9	60.75 172	66.99 91	74.79 131	82.35 100

viewdifsd2359ckpt11		57.53 108	59.36 118	55.39 104	55.17 145	72.10 108	61.49 102	45.16 132	42.72 144	42.15 103	46.03 111	47.43 138	54.14 88	61.84 154	62.46 156	74.23 148	82.96 97

viewmsd2359difaftdt		57.53 108	59.36 118	55.39 104	55.17 145	72.10 108	61.49 102	45.16 132	42.72 144	42.15 103	46.03 111	47.42 139	54.15 87	61.84 154	62.46 156	74.23 148	82.96 97

tpm cat1		57.41 110	58.26 131	56.42 99	60.80 94	72.56 104	64.35 89	38.43 205	49.18 108	46.36 63	36.69 164	43.50 161	54.47 84	61.39 162	62.64 151	74.11 156	81.81 111

IB-MVS	53.15 10	57.33 111	59.02 122	55.37 106	60.83 93	77.11 55	54.51 159	50.10 51	43.22 141	42.82 96	40.50 140	37.61 182	44.67 163	59.27 187	69.81 45	79.29 29	85.59 73
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 99	70.65 116	60.71 109	35.38 224	47.91 115	42.58 100	39.78 145	45.45 151	54.44 85	62.19 149	62.82 148	77.37 72	84.73 83

baseline1		57.21 113	60.53 112	53.33 116	62.50 63	69.86 121	57.33 133	50.59 45	43.39 140	30.00 168	48.60 95	51.09 116	42.36 176	69.38 50	68.03 77	77.20 80	73.39 176

FA-MVS(training)		57.15 114	60.42 113	53.34 115	58.15 112	72.77 98	59.79 117	38.68 203	49.01 109	36.56 128	40.79 138	45.44 152	53.04 100	65.23 106	67.93 81	73.82 159	81.80 113

HyFIR lowres test		57.12 115	59.11 120	54.80 108	61.55 82	77.55 51	59.02 122	45.00 136	41.84 156	33.93 146	22.44 240	49.16 129	51.02 113	68.39 69	68.71 71	78.26 58	85.70 71

MVS_111021_LR		57.06 116	60.60 111	52.93 118	56.25 131	65.14 166	55.16 156	41.21 185	52.32 89	44.89 78	53.92 62	49.27 128	52.16 107	61.46 160	60.54 183	67.92 210	81.53 117

DCV-MVSNet		56.80 117	58.96 123	54.28 110	59.96 98	66.74 151	60.37 112	44.87 138	41.01 158	36.81 126	47.57 104	47.87 136	48.23 136	64.41 118	65.17 117	75.45 118	79.95 133

Anonymous20231211		56.40 118	57.00 143	55.70 103	59.78 102	72.49 105	61.29 108	46.83 105	40.50 162	40.46 113	22.12 242	49.73 123	51.07 112	64.39 119	65.30 116	74.74 134	84.44 88

0.4-1-1-0.2		56.13 119	60.14 114	51.44 129	45.97 199	73.09 92	56.79 143	45.39 127	47.03 120	34.23 138	43.14 127	51.20 115	47.33 142	63.12 136	63.30 141	78.95 43	80.11 131

0.3-1-1-0.015		56.04 120	60.09 115	51.32 130	46.02 197	73.04 96	56.64 144	45.36 128	46.70 123	34.01 142	43.24 125	51.25 111	46.98 145	63.12 136	63.20 144	78.90 45	80.11 131

PMMVS		55.74 121	62.68 95	47.64 166	44.34 212	65.58 163	47.22 204	37.96 209	56.43 72	34.11 140	61.51 39	47.41 140	54.55 83	65.88 96	62.49 155	67.67 212	79.48 135

Fast-Effi-MVS+		55.73 122	58.26 131	52.76 119	54.33 152	68.19 135	57.05 134	34.66 226	46.92 121	38.96 118	40.53 139	41.55 170	55.69 77	65.31 104	65.99 103	75.90 101	79.34 136

FC-MVSNet-train		55.68 123	57.00 143	54.13 112	63.37 52	66.16 155	46.77 208	52.14 37	42.36 150	37.67 122	48.50 98	41.42 172	51.28 110	61.58 159	63.22 143	73.56 164	75.76 156

FMVSNet3		55.66 124	59.68 117	50.96 134	50.59 172	66.49 152	57.57 130	46.61 111	49.30 104	28.77 173	39.61 146	51.42 108	43.85 168	68.29 71	68.80 69	78.35 57	73.86 166

0.4-1-1-0.1		55.63 125	59.73 116	50.85 135	45.99 198	72.77 98	56.11 150	45.23 130	45.84 127	33.32 150	42.60 129	51.06 117	45.68 152	62.99 141	62.97 147	78.76 46	79.90 134

OMC-MVS		55.48 126	61.85 106	48.04 165	41.55 221	60.32 207	56.80 138	31.78 247	75.67 25	42.30 102	51.52 77	54.15 86	49.91 121	60.28 178	57.59 203	65.91 221	73.42 174

tpm		54.94 127	57.86 136	51.54 128	59.48 105	67.04 147	58.34 126	34.60 229	41.93 155	34.41 136	42.40 130	47.14 141	49.07 131	61.46 160	61.67 169	73.31 172	83.39 93

GBi-Net		54.66 128	58.42 128	50.26 143	49.36 181	65.81 160	56.80 138	46.61 111	49.30 104	28.77 173	39.61 146	51.42 108	42.71 172	64.25 122	65.54 110	77.32 76	73.03 179

test1		54.66 128	58.42 128	50.26 143	49.36 181	65.81 160	56.80 138	46.61 111	49.30 104	28.77 173	39.61 146	51.42 108	42.71 172	64.25 122	65.54 110	77.32 76	73.03 179

test-LLR		54.62 130	58.66 126	49.89 150	51.68 164	65.89 157	47.88 198	46.35 116	42.51 147	29.84 169	41.41 135	48.87 130	45.20 156	62.91 143	64.43 131	78.43 54	84.62 85

dmvs_re		54.51 131	57.04 142	51.56 127	56.51 129	62.63 193	55.56 152	50.45 49	45.31 131	24.75 191	43.94 121	39.99 177	42.74 171	66.53 93	65.44 114	79.33 28	75.46 158

TSAR-MVS + COLMAP		54.37 132	62.43 98	44.98 184	34.33 244	58.94 215	54.11 164	34.15 238	74.06 28	34.57 135	71.63 20	42.03 169	47.88 138	61.26 165	57.33 208	64.83 224	71.74 190

EPMVS		54.07 133	56.06 149	51.75 126	56.74 126	70.80 114	55.32 154	34.20 235	46.46 126	36.59 127	40.38 142	42.55 164	49.77 124	61.25 166	60.90 178	77.86 65	70.08 201

v2v482		54.00 134	55.12 156	52.69 121	51.73 163	69.42 126	60.65 110	45.09 135	34.56 194	33.73 149	35.29 170	35.36 193	49.92 120	64.05 128	65.16 118	75.00 128	81.98 108

CNLPA		54.00 134	57.08 141	50.40 142	49.83 178	61.75 198	53.47 168	37.27 214	74.55 27	44.85 79	33.58 182	45.42 153	52.94 104	58.89 189	53.66 229	64.06 228	71.68 191

FMVSNet2		53.94 136	57.29 138	50.03 147	49.36 181	65.81 160	56.80 138	45.95 122	43.13 142	28.04 177	35.68 167	48.18 135	42.71 172	67.23 86	67.95 80	77.32 76	73.03 179

v8		53.77 137	54.82 161	52.54 122	52.12 159	66.95 150	60.56 111	43.23 163	37.17 183	35.35 131	34.96 173	37.50 184	49.51 127	63.67 131	64.59 128	74.48 144	78.91 139

GA-MVS		53.77 137	56.41 148	50.70 137	51.63 166	69.96 120	57.55 131	44.39 142	34.31 195	27.15 179	40.99 137	36.40 188	47.65 141	67.45 81	67.16 89	75.83 108	78.60 141

Effi-MVS+-dtu		53.63 139	54.85 160	52.20 124	59.32 106	61.33 201	56.42 147	40.24 193	43.84 137	34.22 139	39.49 151	46.18 148	53.00 103	58.72 193	57.49 207	69.99 201	76.91 147

thisisatest0530		53.61 140	57.22 139	49.40 155	51.30 168	68.22 134	52.72 176	43.34 161	42.72 144	35.31 132	43.57 124	44.14 158	44.37 166	63.00 140	64.86 123	69.34 204	74.00 165

v1144		53.47 141	54.65 162	52.10 125	51.93 161	69.81 122	59.32 120	44.77 140	33.21 201	32.52 154	33.55 183	34.34 202	49.29 129	64.58 115	64.81 125	74.74 134	82.27 102

blend_shiyan4		53.44 142	57.17 140	49.10 158	46.19 195	65.49 164	58.38 125	42.54 173	48.56 112	34.01 142	44.21 118	51.25 111	36.84 189	57.58 197	57.87 198	76.63 89	75.23 160

v10		53.44 142	54.40 163	52.31 123	52.08 160	66.99 148	59.68 118	43.41 158	35.90 189	34.30 137	33.98 180	35.56 190	50.10 118	64.39 119	64.67 126	74.32 145	79.30 137

PatchmatchNet		53.37 144	55.62 154	50.75 136	55.93 139	70.54 117	51.39 181	36.41 217	44.85 133	37.26 124	39.40 153	42.54 165	47.83 139	60.29 177	60.88 180	75.69 115	70.87 195
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	55.53 141	68.11 137	54.31 161	46.25 119	43.54 138	22.21 203	40.19 143	43.69 160	36.56 193	64.15 126	65.94 104	77.20 80	75.91 153

IterMVS-LS		53.36 145	55.65 153	50.68 139	55.34 143	59.04 213	55.00 157	39.98 194	38.72 171	33.22 151	44.52 117	47.05 143	49.63 126	61.82 157	61.77 164	70.92 193	76.61 151
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	44.94 206	65.89 157	47.88 198	35.95 220	42.51 147	29.84 169	41.41 135	48.87 130	45.20 156	62.91 143	64.43 131	78.43 54	84.62 85

tttt0517		53.05 148	56.73 147	48.76 159	50.35 174	67.51 144	51.96 180	43.34 161	42.00 154	33.88 147	43.19 126	43.49 162	44.37 166	62.58 148	64.86 123	68.67 206	73.46 173

MDTV_nov1_ep13		52.99 149	55.59 155	49.95 149	54.08 153	70.69 115	56.47 146	38.42 206	42.78 143	30.19 167	39.56 149	43.31 163	45.78 151	60.07 182	62.11 161	74.74 134	70.62 196

EPP-MVSNet		52.91 150	58.91 124	45.91 175	54.99 149	68.84 130	49.27 188	42.71 171	37.53 177	20.20 215	46.09 110	56.19 76	36.90 188	61.37 163	60.90 178	71.41 187	81.41 119

dps		52.84 151	52.92 178	52.74 120	59.89 100	69.49 125	54.47 160	37.38 212	42.49 149	39.53 117	35.33 169	32.71 212	51.83 109	60.45 174	61.12 175	73.33 171	68.86 210

v1192		52.69 152	53.86 168	51.31 131	51.22 169	69.76 123	57.37 132	44.39 142	32.21 204	31.39 161	32.41 191	32.44 215	49.19 130	64.25 122	64.17 133	74.31 146	81.81 111

V42		52.63 153	55.08 157	49.76 152	44.93 207	67.49 146	60.19 115	42.13 181	37.21 182	34.08 141	34.57 176	37.30 185	47.29 143	63.48 134	64.15 134	69.96 202	81.38 120

MSDG		52.58 154	51.40 191	53.95 113	65.48 46	64.31 176	61.44 104	44.02 148	44.17 136	32.92 153	30.40 204	31.81 219	46.35 148	62.13 150	62.55 153	73.49 167	64.41 218

ECVR-MVS		52.52 155	55.88 151	48.60 162	55.53 141	68.11 137	54.31 161	46.25 119	43.54 138	21.75 207	32.76 188	39.83 180	36.56 193	64.15 126	65.94 104	77.20 80	76.81 148

Fast-Effi-MVS+-dtu		52.47 156	55.89 150	48.48 163	56.25 131	65.07 167	58.75 123	23.79 260	41.27 157	27.07 181	37.95 158	41.34 173	50.85 115	62.90 145	62.34 159	74.17 152	80.37 130

v144192		52.43 157	53.63 172	51.03 133	51.06 170	69.60 124	56.94 136	44.84 139	32.15 205	30.88 163	32.45 190	32.71 212	48.36 134	62.98 142	63.52 139	74.10 157	82.02 107

thres100view900		52.33 158	53.91 167	50.48 141	56.10 134	67.79 140	56.18 149	49.18 56	35.86 191	25.22 188	34.74 174	34.10 204	42.41 175	64.45 117	62.62 152	73.81 160	77.85 142

v1921920		51.95 159	53.19 174	50.51 140	50.82 171	69.14 128	55.45 153	44.34 146	31.53 209	30.53 165	31.96 193	31.67 220	48.31 135	63.12 136	63.28 142	73.59 163	81.60 116

v148		51.72 160	53.15 175	50.05 146	50.15 176	67.51 144	56.98 135	42.85 168	32.60 203	32.41 156	33.88 181	34.71 198	44.45 164	61.06 167	63.00 146	73.45 168	79.24 138

TAPA-MVS	47.92 11	51.66 161	57.88 135	44.40 188	36.46 237	58.42 218	53.82 166	30.83 250	69.51 43	34.97 134	46.90 106	49.67 124	46.99 144	58.00 196	54.64 224	63.33 235	68.00 212
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	55.96 138	66.16 155	47.65 200	42.84 170	39.82 166	19.09 224	44.97 116	50.28 120	27.20 232	63.43 135	63.84 135	71.33 189	77.33 144

v1240		51.42 163	52.66 180	49.97 148	50.31 175	68.70 131	54.05 165	43.85 152	30.78 213	30.22 166	31.43 197	31.03 227	47.98 137	62.62 147	63.16 145	73.40 169	80.93 125

pmmvs4		51.28 164	52.50 182	49.85 151	49.54 180	63.02 190	52.83 175	43.41 158	44.65 134	35.71 130	34.38 177	32.25 216	45.14 159	60.21 181	60.03 188	72.44 181	72.98 182

Vis-MVSNet		51.13 165	58.04 133	43.06 198	47.68 188	67.71 141	49.10 189	39.09 201	37.75 175	22.57 200	51.03 82	48.78 132	32.42 217	62.12 151	61.80 163	67.49 215	77.12 145
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	40.59 223	65.32 165	46.65 210	39.26 198	39.90 165	27.30 178	54.12 61	52.03 98	30.93 221	59.85 184	59.62 193	67.23 217	80.70 126
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	56.10 134	64.53 171	53.06 172	47.31 96	35.86 191	25.22 188	34.74 174	34.10 204	41.08 178	60.84 169	61.37 171	71.90 185	75.70 157

SCA		50.88 168	53.70 170	47.59 167	55.99 136	55.81 229	43.14 223	33.45 241	45.16 132	37.14 125	41.83 132	43.82 159	44.43 165	60.37 175	60.02 189	71.38 188	68.90 209

gg-mvs-nofinetune		50.82 169	55.83 152	44.97 185	60.63 95	75.69 66	53.40 169	34.48 231	20.05 257	6.93 256	18.27 251	52.70 94	33.57 206	70.50 38	72.93 21	80.84 8	80.68 127

thres200		50.76 170	52.52 181	48.70 160	55.98 137	64.60 169	55.29 155	47.34 94	33.91 198	24.36 192	34.33 178	33.90 206	37.27 186	60.84 169	62.41 158	71.99 183	77.63 143

test1111		50.62 171	54.98 159	45.55 179	53.84 155	68.48 133	48.99 190	47.25 97	40.60 161	15.64 234	31.51 196	38.32 181	33.01 213	64.34 121	66.62 95	74.55 143	74.95 164

usedtu_blend_shiyan5		50.54 172	53.98 165	46.52 171	33.34 247	64.26 178	56.80 138	42.26 175	28.39 223	34.01 142	44.21 118	51.25 111	36.84 189	56.84 207	57.68 199	75.86 103	75.23 160

thres400		50.39 173	52.22 184	48.26 164	55.02 148	66.32 154	52.97 173	48.33 71	32.68 202	22.94 198	33.21 185	33.38 211	37.27 186	62.74 146	61.38 170	73.04 178	75.81 155

EG-PatchMatch MVS		50.23 174	50.89 194	49.47 153	59.54 104	70.88 113	52.46 177	44.01 149	26.22 241	31.91 157	24.97 234	31.45 223	33.48 208	64.79 112	66.51 100	75.40 119	71.39 193

IterMVS		50.23 174	53.27 173	46.68 170	47.59 190	60.58 205	53.10 171	36.62 216	36.07 187	25.89 184	39.42 152	40.05 176	43.65 169	60.22 180	61.35 172	73.23 173	75.23 160
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	42.23 219	60.12 208	53.78 167	34.65 227	40.73 160	25.46 186	35.49 168	44.55 155	49.82 122	60.74 173	60.21 187	67.50 214	76.92 146

FMVSNet1		50.14 177	52.78 179	47.06 168	45.56 203	63.56 186	54.22 163	43.74 155	34.10 197	25.37 187	29.79 211	42.06 168	38.70 182	64.25 122	65.54 110	74.75 132	70.18 200

ACMH	47.82 13	50.10 178	49.60 200	50.69 138	63.36 53	66.99 148	56.83 137	52.13 38	31.06 212	17.74 231	28.22 221	26.24 243	45.17 158	60.88 168	63.80 136	68.91 205	70.00 203
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	52.79 156	57.06 222	41.44 228	43.13 164	56.13 73	19.24 223	52.11 71	48.38 133	22.14 240	58.19 195	58.38 196	70.35 196	68.71 211
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	33.34 247	64.26 178	48.12 193	42.26 175	28.39 223	34.01 142	44.21 118	51.25 111	36.84 189	56.84 207	57.68 199	75.86 103	73.60 170

LS3D		49.59 181	49.75 199	49.40 155	55.88 140	59.86 210	56.31 148	45.33 129	48.57 111	28.32 176	31.54 195	36.81 187	46.27 150	57.17 202	55.88 219	64.29 227	58.42 238

usedtu_dtu_shiyan1		49.57 182	53.64 171	44.82 186	42.15 220	67.70 142	49.68 186	46.75 109	40.11 164	18.63 228	29.92 208	34.46 201	35.01 198	65.00 108	66.55 97	76.72 87	71.76 189

UniMVSNet_NR-MVSNet		49.56 183	53.04 176	45.49 180	51.59 167	64.42 175	46.97 205	51.01 41	37.87 173	16.42 232	39.87 144	34.91 197	33.43 210	59.59 185	62.70 149	73.52 166	71.94 185

CDS-MVSNet		49.25 184	53.97 166	43.75 194	47.53 191	64.53 171	48.59 191	42.27 174	33.77 199	26.64 182	40.46 141	42.26 167	30.01 224	61.77 158	61.71 166	67.48 216	73.28 178
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	42.78 217	55.60 232	53.11 170	34.46 232	55.69 79	32.47 155	34.16 179	41.45 171	48.91 132	57.13 203	54.09 226	64.84 223	64.10 219
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	56.75 125	62.01 197	56.62 145	47.55 92	37.49 178	23.98 193	26.68 227	29.46 234	43.12 170	57.45 201	58.85 195	68.62 207	70.05 202

NR-MVSNet		48.84 187	51.76 186	45.44 181	57.66 118	60.64 203	47.39 201	47.63 85	37.26 179	13.31 237	37.31 160	29.64 233	33.53 207	63.52 133	62.09 162	73.10 176	71.89 188

CR-MVSNet		48.82 188	51.85 185	45.29 182	46.74 193	55.95 227	52.06 178	34.21 233	42.17 151	31.74 158	32.92 187	42.53 166	45.00 160	58.80 190	61.11 176	61.99 241	69.47 205

thres600view7		48.44 189	50.23 197	46.35 173	54.05 154	64.60 169	50.18 184	47.34 94	31.73 208	20.74 213	32.28 192	32.62 214	33.79 205	60.84 169	56.11 217	71.99 183	73.40 175

test-mter		48.31 190	55.04 158	40.45 216	34.12 245	59.02 214	41.77 227	28.05 254	38.43 172	22.67 199	39.35 154	44.40 156	41.88 177	60.30 176	61.68 168	74.20 150	82.12 104

PatchT		48.11 191	51.27 193	44.43 187	50.13 177	61.58 199	33.59 243	32.92 243	40.38 163	31.74 158	30.60 203	36.93 186	45.00 160	58.80 190	61.11 176	73.19 174	69.47 205

TranMVSNet+NR-MVSNet		48.06 192	51.36 192	44.21 190	50.38 173	62.09 196	47.28 202	50.88 44	36.11 186	13.25 238	37.51 159	31.60 222	30.70 222	59.34 186	62.53 154	72.81 179	70.31 198

TransMVSNet (Re)		47.46 193	48.94 207	45.74 177	57.96 114	64.29 177	48.26 192	48.47 66	26.33 240	19.33 221	29.45 214	31.28 226	25.31 236	63.05 139	62.70 149	75.10 127	65.47 216

DU-MVS		47.33 194	50.86 195	43.20 197	44.43 210	60.64 203	46.97 205	47.63 85	37.26 179	16.42 232	37.31 160	31.39 224	33.43 210	57.53 199	59.98 190	70.35 196	71.94 185

v7n		47.22 195	48.38 212	45.87 176	48.20 187	63.58 185	50.69 182	40.93 189	26.60 239	26.44 183	26.52 228	29.65 232	38.19 184	58.22 194	60.23 186	70.79 194	73.83 167

UA-Net		47.19 196	53.02 177	40.38 217	55.31 144	60.02 209	38.41 234	38.68 203	36.42 185	22.47 202	51.95 74	58.72 65	25.62 235	54.11 223	53.40 230	61.79 242	56.51 242

Baseline_NR-MVSNet		47.14 197	50.83 196	42.84 200	44.43 210	63.31 188	44.50 219	50.36 50	37.71 176	11.25 243	30.84 200	32.09 217	30.96 220	57.53 199	63.73 137	75.53 116	70.60 197

pmmvs5		47.02 198	50.02 198	43.51 196	43.48 215	62.65 192	47.24 203	37.78 211	30.59 214	24.80 190	35.26 171	30.43 228	34.36 201	59.05 188	60.28 185	73.40 169	71.92 187

UniMVSNet (Re)		46.89 199	51.65 189	41.34 213	45.60 202	62.71 191	44.05 220	47.10 99	37.24 181	13.55 236	36.90 162	34.54 200	26.76 233	57.56 198	59.90 192	70.98 192	72.69 183

thisisatest0515		46.88 200	49.57 201	43.74 195	45.33 205	60.46 206	46.19 212	41.06 188	30.34 215	29.73 171	32.50 189	31.63 221	35.43 196	58.75 192	61.71 166	64.70 226	71.59 192

tfpnnormal		46.61 201	46.82 219	46.37 172	52.70 157	62.31 194	50.39 183	47.17 98	25.74 243	21.80 204	23.13 238	24.15 251	33.45 209	60.28 178	60.77 181	72.70 180	71.39 193

pm-mvs1		46.14 202	49.34 204	42.41 205	48.93 184	62.22 195	44.98 217	42.68 172	27.66 233	20.76 212	29.88 210	34.96 196	26.41 234	60.03 183	60.42 184	70.70 195	70.20 199

wanda-best-256-512		46.10 203	49.06 205	42.66 201	33.34 247	64.26 178	48.12 193	42.26 175	28.39 223	21.80 204	28.97 216	33.62 207	34.55 199	56.84 207	57.68 199	75.86 103	73.66 169

FE-blended-shiyan7		46.10 203	49.06 205	42.66 201	33.34 247	64.26 178	48.12 193	42.26 175	28.39 223	21.80 204	28.96 217	33.62 207	34.55 199	56.84 207	57.68 199	75.86 103	73.67 168

blended_shiyan6		45.97 205	48.94 207	42.50 204	33.34 247	64.17 182	47.94 197	42.22 179	28.07 230	21.66 209	28.80 218	33.57 209	34.06 202	56.79 212	57.55 205	75.79 109	73.60 170

blended_shiyan8		45.96 206	48.92 209	42.51 203	33.32 252	64.17 182	47.95 196	42.22 179	28.06 231	21.70 208	28.77 219	33.56 210	34.06 202	56.76 213	57.55 205	75.79 109	73.59 172

IterMVS-SCA-FT		45.87 207	51.55 190	39.24 221	46.22 194	59.43 211	52.89 174	31.93 244	36.01 188	23.68 194	38.86 155	39.88 179	39.05 180	56.25 216	58.17 197	41.70 263	72.25 184

MIMVSNet		45.62 208	49.56 202	41.02 214	38.17 228	64.43 174	49.48 187	35.43 223	36.53 184	20.06 217	22.58 239	35.16 195	28.75 229	61.97 153	62.20 160	74.20 150	64.07 220

gm-plane-assit		45.41 209	48.03 214	42.34 206	56.49 130	40.48 260	24.54 265	34.15 238	14.44 265	6.59 257	17.82 254	35.32 194	49.82 122	72.93 18	74.11 13	82.47 2	81.12 124

ADS-MVSNet		45.39 210	46.42 220	44.19 191	48.74 186	57.52 220	43.91 221	31.93 244	35.89 190	27.11 180	30.12 205	32.06 218	45.30 154	53.13 229	55.19 221	68.15 209	61.07 230

gbinet_0.2-2-1-0.02		45.07 211	48.55 211	41.00 215	30.59 257	63.61 184	46.97 205	41.88 182	25.18 245	18.93 227	27.74 224	34.25 203	32.89 214	56.40 215	57.32 209	74.75 132	75.37 159

GG-mvs-BLEND		44.87 212	64.59 79	21.86 257	0.01 280	73.70 86	55.99 151	0.01 275	50.70 96	0.01 281	49.18 92	63.61 44	0.01 277	63.83 129	64.50 129	75.13 126	86.62 58

pmmvs-eth3d		44.67 213	45.27 225	43.98 193	42.56 218	55.72 231	44.97 218	40.81 191	31.96 207	29.13 172	26.09 230	25.27 248	36.69 192	55.13 220	56.62 214	69.68 203	66.12 215

MDTV_nov1_ep13_2view		44.44 214	45.75 223	42.91 199	46.13 196	63.43 187	46.53 211	34.20 235	29.08 221	19.95 218	26.23 229	27.89 238	35.88 195	53.36 228	56.43 215	74.74 134	63.86 221

CMPMVS	33.64 16	44.39 215	46.41 221	42.03 207	44.21 213	56.50 225	46.73 209	26.48 259	34.20 196	35.14 133	24.22 235	34.64 199	40.52 179	56.50 214	56.07 218	59.12 246	62.74 226
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	51.82 162	55.24 233	34.57 242	41.63 183	39.10 169	8.84 251	45.93 113	46.63 145	14.45 253	54.09 224	57.03 211	63.00 236	63.65 223

TAMVS		44.27 217	49.35 203	38.35 225	44.74 208	61.04 202	39.07 232	31.82 246	29.95 217	18.34 229	33.55 183	39.94 178	30.01 224	56.85 206	57.58 204	66.13 220	66.54 213

MVS-HIRNet		43.98 218	43.63 229	44.39 189	47.66 189	59.31 212	32.66 249	33.88 240	30.15 216	33.75 148	16.82 259	28.39 237	45.25 155	53.92 227	55.00 223	73.16 175	61.80 227

UniMVSNet_ETH3D		43.97 219	46.01 222	41.59 209	38.31 227	56.20 226	49.69 185	38.18 208	28.18 227	19.88 220	27.82 223	30.20 229	33.41 212	54.18 222	56.30 216	70.05 200	69.17 207

RPMNet		43.70 220	48.17 213	38.48 224	45.52 204	55.95 227	37.66 236	26.63 258	42.17 151	25.47 185	29.59 213	37.61 182	33.87 204	50.85 234	52.02 234	61.75 243	69.00 208

PatchMatch-RL		43.37 221	44.93 226	41.56 210	37.94 229	51.70 235	40.02 230	35.75 221	39.04 170	30.71 164	35.14 172	27.43 240	46.58 147	51.99 230	50.55 238	58.38 248	58.64 236

FMVSNet5		43.29 222	47.07 217	38.87 222	30.46 258	50.99 237	45.87 214	37.19 215	42.17 151	19.32 222	26.77 226	40.51 174	30.26 223	56.82 211	55.81 220	70.10 199	56.46 243

test0.0.03 1		43.07 223	46.95 218	38.54 223	51.68 164	58.77 216	35.28 237	46.35 116	32.05 206	12.44 239	28.53 220	35.52 191	14.40 254	57.12 204	56.93 212	71.11 191	59.69 232

anonymousdsp		43.03 224	47.19 216	38.18 226	36.00 239	56.92 223	38.44 233	34.56 230	24.22 247	22.53 201	29.69 212	29.92 230	35.21 197	53.96 226	58.98 194	62.32 240	76.66 150

USDC		42.80 225	45.57 224	39.58 218	34.55 243	51.13 236	42.61 224	36.21 218	39.59 167	23.65 195	33.13 186	20.87 258	37.86 185	55.35 219	57.16 210	62.61 238	61.75 228

pmnet_mix02		42.41 226	43.24 232	41.44 212	45.80 201	57.46 221	42.19 225	41.57 184	29.38 219	23.39 196	26.08 231	23.96 252	27.31 231	51.50 231	53.76 228	68.36 208	60.58 231

CHOSEN 280x420		42.39 227	47.40 215	36.54 229	33.56 246	39.66 263	40.67 229	26.88 257	34.66 193	18.03 230	30.09 206	45.59 150	44.82 162	54.46 221	54.00 227	55.28 256	73.32 177

pmmvs6		41.90 228	44.01 228	39.43 219	44.45 209	58.77 216	41.92 226	39.22 199	21.74 250	19.08 225	17.40 257	31.33 225	24.28 238	55.94 217	56.67 213	67.60 213	66.24 214

Anonymous20231206		40.63 229	43.29 231	37.53 227	48.88 185	55.81 229	34.99 238	44.98 137	28.16 228	10.16 248	17.26 258	27.50 239	18.28 245	54.00 225	55.07 222	67.85 211	65.23 217

dtuonlycased		40.33 230	41.14 235	39.39 220	35.91 240	57.86 219	46.10 213	31.75 248	28.52 222	19.94 219	18.33 250	35.38 192	38.79 181	41.36 255	42.69 255	63.84 230	63.79 222

FE-MVSNET2		39.87 231	43.46 230	35.69 232	30.82 256	56.74 224	37.91 235	42.85 168	24.70 246	8.15 253	18.01 252	23.67 253	23.12 239	56.86 205	61.26 173	71.25 190	62.95 224

CVMVSNet		38.91 232	44.49 227	32.40 241	34.57 242	47.20 248	34.81 239	34.20 235	31.45 210	8.95 250	38.86 155	36.38 189	24.30 237	47.77 239	46.94 251	57.59 250	62.85 225

COLMAP_ROB	34.79 15	38.65 233	40.72 236	36.23 230	36.41 238	49.22 244	45.51 216	27.60 256	37.81 174	20.54 214	23.37 237	24.25 250	28.11 230	51.02 233	48.55 241	59.22 245	50.82 254
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	40.56 224	50.07 240	33.17 246	44.35 145	27.64 235	5.54 263	30.84 200	26.67 241	14.99 251	45.64 242	52.38 233	66.29 219	58.83 235

WR-MVS		37.61 235	42.15 233	32.31 243	43.64 214	51.85 234	29.39 256	43.35 160	27.65 234	4.40 265	29.90 209	29.80 231	10.46 258	46.73 241	51.98 235	62.60 239	57.16 240

TinyColmap		37.18 236	37.37 249	36.95 228	31.17 255	45.21 254	39.71 231	34.65 227	29.83 218	20.20 215	18.54 249	13.72 269	38.27 183	50.33 235	51.57 236	57.71 249	52.42 251

CP-MVSNet		37.09 237	40.62 237	32.99 236	37.56 231	48.25 245	32.75 247	43.05 165	27.88 232	5.93 259	31.27 198	25.82 246	15.09 249	43.37 250	48.82 239	63.54 233	58.90 233

DTE-MVSNet		36.91 238	40.44 238	32.79 239	40.74 222	47.55 247	30.71 254	44.39 142	27.03 237	4.32 266	30.88 199	25.99 244	12.73 256	45.58 243	50.80 237	63.86 229	55.23 246

PS-CasMVS		36.84 239	40.23 241	32.89 237	37.44 232	48.09 246	32.68 248	42.97 167	27.36 236	5.89 260	30.08 207	25.48 247	14.96 252	43.28 251	48.71 240	63.39 234	58.63 237

WR-MVS_H		36.29 240	40.35 240	31.55 245	37.80 230	49.94 242	30.57 255	41.11 187	26.90 238	4.14 267	30.72 202	28.85 235	10.45 259	42.47 253	47.99 245	65.24 222	55.54 244

SixPastTwentyTwo		36.11 241	37.80 245	34.13 235	37.13 235	46.72 251	34.58 241	34.96 225	21.20 253	11.66 240	29.15 215	19.88 259	29.77 226	44.93 244	48.34 242	56.67 253	54.41 248

test20.03		36.00 242	38.92 242	32.60 240	45.92 200	50.99 237	28.05 261	43.69 156	21.62 251	6.03 258	17.61 256	25.91 245	8.34 265	51.26 232	52.60 232	63.58 231	52.46 250

TDRefinement		35.76 243	38.23 243	32.88 238	19.09 268	46.04 253	43.29 222	29.49 251	33.49 200	19.04 226	22.29 241	17.82 264	29.69 228	48.60 237	47.24 248	56.65 254	52.12 252

LTVRE_ROB	32.83 17	35.10 244	37.46 246	32.35 242	43.12 216	49.99 241	28.52 258	33.23 242	12.73 267	8.18 252	27.71 225	21.34 256	32.64 216	46.92 240	48.11 243	48.41 260	55.45 245
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	22.78 263	40.82 259	33.56 244	23.61 261	29.16 220	21.43 211	28.00 222	21.43 255	31.90 218	44.33 248	42.12 256	54.07 258	61.34 229

testgi		34.51 246	37.42 247	31.12 247	47.37 192	50.34 239	24.38 266	41.21 185	20.32 255	5.64 262	20.56 243	26.55 242	8.06 266	49.28 236	52.65 231	60.05 244	42.23 261

MDA-MVSNet-bldmvs		34.31 247	34.11 255	34.54 233	24.73 260	49.66 243	33.42 245	43.03 166	21.59 252	11.10 244	19.81 247	12.68 270	31.41 219	35.59 260	48.05 244	63.56 232	51.39 253

N_pmnet		34.09 248	35.74 252	32.17 244	37.25 234	43.17 257	32.26 251	35.57 222	26.22 241	10.60 247	20.44 245	19.38 263	20.20 243	44.59 246	47.00 249	57.13 252	49.35 257

RPSCF		33.61 249	40.43 239	25.65 253	16.00 270	32.41 265	31.73 253	13.33 268	50.13 99	23.12 197	31.56 194	40.09 175	32.73 215	41.14 258	37.05 259	36.99 266	50.63 255

FE-MVSNET		33.52 250	37.02 250	29.45 248	23.65 261	47.19 250	28.15 260	40.92 190	20.01 258	3.42 270	16.28 260	19.67 261	17.80 246	47.90 238	54.52 225	62.73 237	53.53 249

EU-MVSNet		33.00 251	36.49 251	28.92 249	33.10 253	42.86 258	29.32 257	35.99 219	22.94 248	5.83 261	25.29 232	24.43 249	15.21 248	41.22 257	41.65 258	54.08 257	57.01 241

pmmvs3		31.22 252	33.62 256	28.43 250	22.82 262	40.26 262	26.40 262	22.05 263	16.89 262	10.99 245	14.72 262	16.26 265	29.70 227	44.82 245	47.39 247	58.61 247	54.98 247

usedtu_dtu_shiyan2		31.12 253	34.28 254	27.44 252	11.70 271	47.20 248	32.04 252	31.41 249	14.11 266	8.15 253	13.22 264	19.80 260	16.49 247	42.54 252	45.42 253	64.82 225	57.66 239

FC-MVSNet-test		30.97 254	37.38 248	23.49 256	37.42 233	33.68 264	19.43 268	39.27 197	31.37 211	1.67 274	38.56 157	28.85 235	6.06 269	41.40 254	43.80 254	37.10 265	44.03 260

new-patchmatchnet		30.47 255	32.80 258	27.75 251	36.81 236	43.98 255	24.85 264	39.29 196	20.52 254	4.06 268	15.94 261	16.05 266	9.57 260	41.32 256	42.05 257	51.94 259	49.74 256

MIMVSNet1		29.60 256	33.37 257	25.20 255	19.52 266	43.94 256	26.29 263	37.92 210	19.95 259	3.79 269	12.64 267	21.99 254	7.70 267	43.83 249	46.32 252	55.97 255	44.92 259

FPMVS		26.87 257	28.19 259	25.32 254	27.09 259	29.49 267	32.28 250	17.79 265	28.09 229	11.33 241	19.38 248	14.69 267	20.88 241	35.11 261	32.82 262	42.56 262	37.75 262

WB-MVS		22.51 258	25.28 260	19.27 259	35.74 241	31.57 266	11.45 271	40.75 192	15.01 264	0.98 277	20.48 244	12.53 271	1.77 272	36.11 259	35.01 261	24.91 269	26.27 265

PMVS	18.18 18	21.95 259	22.85 261	20.90 258	21.92 264	14.78 269	19.95 267	17.31 266	15.69 263	11.32 242	13.70 263	13.91 268	15.02 250	34.92 262	31.72 263	39.85 264	35.20 263
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		19.10 260	22.71 262	14.89 261	10.93 273	24.08 268	14.22 269	13.94 267	18.68 260	2.93 271	12.84 266	11.27 272	11.94 257	30.57 264	30.58 264	35.38 267	30.93 264

Gipuma		17.16 261	17.83 263	16.36 260	18.76 269	12.15 272	11.97 270	27.78 255	17.94 261	4.86 264	2.53 276	2.73 279	8.90 263	34.32 263	36.09 260	25.92 268	19.06 269
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	1.69 277	6.92 275	11.25 272	5.74 269	22.41 249	8.11 255	10.40 268	20.91 257	13.73 255	22.17 265	13.98 267	20.44 270	23.18 266

PMMVS2		12.25 263	14.17 265	10.00 265	11.39 272	14.35 270	8.21 273	19.29 264	9.31 269	0.19 278	7.38 271	6.19 275	1.10 275	19.26 266	21.13 266	19.85 271	21.56 268

E-PMN		10.66 264	8.30 268	13.42 262	19.91 265	7.87 273	4.30 276	29.47 252	8.37 272	1.70 273	3.67 273	1.29 283	9.12 262	8.98 271	13.59 268	16.03 272	14.30 273

EMVS		10.15 265	7.67 269	13.05 263	19.22 267	7.77 274	4.48 274	29.34 253	8.65 271	1.67 274	3.55 274	1.36 282	9.15 261	8.15 272	11.79 270	14.44 273	12.43 274

MVE	10.35 19	9.76 266	11.08 267	8.22 266	4.43 274	13.04 271	3.36 277	23.57 262	5.74 274	1.76 272	3.09 275	1.75 281	6.78 268	12.78 268	23.04 265	9.44 274	18.09 270
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
VLMVS_CLIP		7.02 267	11.44 266	1.87 268	2.24 276	3.07 277	0.81 279	0.27 271	10.75 268	0.07 279	17.95 253	5.68 276	4.56 270	11.47 269	9.13 271	3.26 277	22.06 267

VLMVS		3.53 268	6.13 270	0.48 269	0.40 278	0.82 279	0.27 281	0.02 274	6.70 273	0.05 280	9.77 269	5.39 277	1.43 274	3.00 273	2.55 272	0.74 278	14.71 271

MVS_clip		1.91 269	3.26 271	0.34 270	0.20 279	0.48 280	0.12 282	0.03 273	3.08 275	0.00 282	5.21 272	2.59 280	1.44 273	2.82 274	2.43 273	0.23 279	6.18 275

MVS_baseline		0.62 270	1.11 272	0.06 271	0.01 280	0.01 281	0.00 283	0.00 276	0.81 276	0.00 282	1.69 277	0.82 284	0.33 276	0.26 275	0.27 274	0.00 280	3.90 276

testmvs		0.01 271	0.01 273	0.00 272	0.00 282	0.00 282	0.00 283	0.00 276	0.01 277	0.00 282	0.02 278	0.00 285	0.00 279	0.01 276	0.01 275	0.00 280	0.03 277

test123		0.01 271	0.01 273	0.00 272	0.00 282	0.00 282	0.00 283	0.00 276	0.01 277	0.00 282	0.02 278	0.00 285	0.01 277	0.00 277	0.01 275	0.00 280	0.03 277

uanet_test		0.00 273	0.00 275	0.00 272	0.00 282	0.00 282	0.00 283	0.00 276	0.00 279	0.00 282	0.00 280	0.00 285	0.00 279	0.00 277	0.00 277	0.00 280	0.00 279

sosnet-low-res		0.00 273	0.00 275	0.00 272	0.00 282	0.00 282	0.00 283	0.00 276	0.00 279	0.00 282	0.00 280	0.00 285	0.00 279	0.00 277	0.00 277	0.00 280	0.00 279

sosnet		0.00 273	0.00 275	0.00 272	0.00 282	0.00 282	0.00 283	0.00 276	0.00 279	0.00 282	0.00 280	0.00 285	0.00 279	0.00 277	0.00 277	0.00 280	0.00 279

ACM-MVS					78.03 8	82.69 8	77.05 13		82.01 13	54.59 22	69.80 24	77.02 14	67.12 5			79.40 26	89.79 14

PatchmatchNet2					40.39 225	46.58 252	34.70 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1												19.42 262	20.22 242	44.40 247	46.98 250	57.14 251	49.28 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3										10.66 246	20.37 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
TestfortrainingZip							78.23 5	61.85 3		68.16 1						81.99 4

TPM-MVS					78.45 6	83.50 7	78.26 4			58.88 10	72.62 19	77.54 11	69.42 4			80.40 9	85.71 69
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										21.59 210

9.14												80.07 7

SR-MVS					63.74 50			48.51 65				73.80 23

Anonymous202405211			56.81 146		60.91 91	73.48 89	59.82 116	48.68 62	39.26 168		24.00 236	46.77 144	50.73 116	65.28 105	65.72 108	75.37 120	83.17 94

our_test_3					49.68 179	61.50 200	45.84 215

ambc			35.52 253		38.36 226	40.40 261	28.38 259		25.20 244	14.87 235	13.22 264	7.54 274	19.34 244	55.63 218	47.79 246	47.91 261	58.89 234

MTAPA										54.82 21		71.98 29

MTMP										50.64 37		68.31 34

Patchmatch-RL test							0.69 280

tmp_tt				4.41 267	2.56 275	1.81 278	2.61 278	0.27 271	20.12 256	9.81 249	17.69 255	9.04 273	1.96 271	12.88 267	12.11 269	9.23 275

XVS					62.70 60	73.06 93	61.80 100			42.02 105		63.42 46				74.68 138

X-MVStestdata					62.70 60	73.06 93	61.80 100			42.02 105		63.42 46				74.68 138

mPP-MVS					63.08 56							62.34 50

NP-MVS									72.62 32

Patchmtry						64.49 173	52.06 178	34.21 233		31.74 158

DeepMVS_CX						5.87 276	4.32 275	1.74 270	9.04 270	1.30 276	7.97 270	3.16 278	8.56 264	9.74 270		6.30 276	14.51 272

High-resolution multi-view benchmark