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	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 45					97.53 1	89.67 1	96.44 9	94.41 30

No_MVS				89.16 1	94.34 27	75.53 2		92.99 45					97.53 1	89.67 1	96.44 9	94.41 30

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 39	82.45 3	96.87 18	83.77 45	96.48 8	94.88 12

HPM-MVS++		89.02 8	89.15 8	88.63 4	95.01 9	76.03 1	92.38 26	92.85 54	80.26 9	87.78 27	94.27 30	75.89 19	96.81 21	87.45 19	96.44 9	93.05 86

SMA-MVS		89.08 7	89.23 7	88.61 5	94.25 31	73.73 9	92.40 23	93.63 21	74.77 103	92.29 7	95.97 2	74.28 29	97.24 11	88.58 13	96.91 1	94.87 14
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
3Dnovator+	77.84 4	85.48 50	84.47 62	88.51 6	91.08 81	73.49 15	93.18 11	93.78 18	80.79 6	76.66 180	93.37 50	60.40 174	96.75 24	77.20 103	93.73 60	95.29 4

CNVR-MVS		88.93 9	89.13 9	88.33 7	94.77 12	73.82 8	90.51 59	93.00 43	80.90 5	88.06 25	94.06 38	76.43 16	96.84 19	88.48 14	95.99 18	94.34 35

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 8	92.14 69	72.96 24	93.73 5	93.67 20	80.19 10	88.10 24	94.80 15	73.76 33	97.11 13	87.51 18	95.82 21	94.90 11
Skip Steuart: Steuart Systems R&D Blog.
MSP-MVS		89.51 4	89.91 5	88.30 9	94.28 30	73.46 16	92.90 16	94.11 6	80.27 8	91.35 14	94.16 34	78.35 13	96.77 22	89.59 3	94.22 56	94.67 22
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
NCCC		88.06 13	88.01 17	88.24 10	94.41 22	73.62 10	91.22 50	92.83 55	81.50 3	85.79 37	93.47 49	73.02 39	97.00 16	84.90 29	94.94 37	94.10 43

ZNCC-MVS		87.94 17	87.85 18	88.20 11	94.39 24	73.33 18	93.03 14	93.81 17	76.81 61	85.24 42	94.32 29	71.76 46	96.93 17	85.53 26	95.79 22	94.32 36

region2R		87.42 23	87.20 26	88.09 12	94.63 14	73.55 12	93.03 14	93.12 37	76.73 66	84.45 58	94.52 19	69.09 71	96.70 25	84.37 38	94.83 42	94.03 47

ACMMPR		87.44 21	87.23 25	88.08 13	94.64 13	73.59 11	93.04 12	93.20 34	76.78 63	84.66 54	94.52 19	68.81 75	96.65 28	84.53 36	94.90 38	94.00 48

DPE-MVS		89.48 5	89.98 4	88.01 14	94.80 11	72.69 30	91.59 41	94.10 8	75.90 83	92.29 7	95.66 10	81.67 6	97.38 9	87.44 20	96.34 15	93.95 49
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
XVS		87.18 27	86.91 31	88.00 15	94.42 20	73.33 18	92.78 18	92.99 45	79.14 19	83.67 72	94.17 33	67.45 83	96.60 31	83.06 50	94.50 48	94.07 45

X-MVStestdata		80.37 131	77.83 168	88.00 15	94.42 20	73.33 18	92.78 18	92.99 45	79.14 19	83.67 72	12.47 373	67.45 83	96.60 31	83.06 50	94.50 48	94.07 45

ACMMP_NAP		88.05 15	88.08 16	87.94 17	93.70 41	73.05 21	90.86 54	93.59 23	76.27 77	88.14 23	95.09 14	71.06 52	96.67 27	87.67 16	96.37 14	94.09 44

HFP-MVS		87.58 20	87.47 22	87.94 17	94.58 16	73.54 14	93.04 12	93.24 33	76.78 63	84.91 47	94.44 26	70.78 54	96.61 30	84.53 36	94.89 39	93.66 59

MP-MVS		87.71 18	87.64 20	87.93 19	94.36 26	73.88 6	92.71 22	92.65 64	77.57 39	83.84 69	94.40 28	72.24 42	96.28 38	85.65 25	95.30 33	93.62 66
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MTAPA		87.23 26	87.00 27	87.90 20	94.18 35	74.25 5	86.58 174	92.02 85	79.45 17	85.88 35	94.80 15	68.07 77	96.21 40	86.69 23	95.34 31	93.23 79

PGM-MVS		86.68 34	86.27 38	87.90 20	94.22 33	73.38 17	90.22 68	93.04 38	75.53 89	83.86 68	94.42 27	67.87 80	96.64 29	82.70 59	94.57 47	93.66 59

DVP-MVS++		90.23 1	91.01 1	87.89 22	94.34 27	71.25 55	95.06 1	94.23 3	78.38 31	92.78 4	95.74 6	82.45 3	97.49 3	89.42 4	96.68 2	94.95 8

GST-MVS		87.42 23	87.26 23	87.89 22	94.12 36	72.97 23	92.39 25	93.43 28	76.89 59	84.68 51	93.99 42	70.67 56	96.82 20	84.18 43	95.01 35	93.90 51

SED-MVS		90.08 2	90.85 2	87.77 24	95.30 2	70.98 61	93.57 7	94.06 10	77.24 48	93.10 1	95.72 8	82.99 1	97.44 5	89.07 9	96.63 4	94.88 12

DeepC-MVS_fast	79.65 3	86.91 31	86.62 34	87.76 25	93.52 46	72.37 40	91.26 46	93.04 38	76.62 68	84.22 62	93.36 51	71.44 50	96.76 23	80.82 72	95.33 32	94.16 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		89.15 6	89.63 6	87.73 26	94.49 18	71.69 50	93.83 4	93.96 13	75.70 87	91.06 16	96.03 1	76.84 14	97.03 15	89.09 6	95.65 27	94.47 29

MCST-MVS		87.37 25	87.25 24	87.73 26	94.53 17	72.46 37	89.82 75	93.82 16	73.07 140	84.86 50	92.89 61	76.22 17	96.33 36	84.89 31	95.13 34	94.40 32

TSAR-MVS + MP.		88.02 16	88.11 15	87.72 28	93.68 43	72.13 45	91.41 45	92.35 74	74.62 107	88.90 20	93.85 44	75.75 20	96.00 47	87.80 15	94.63 45	95.04 6
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
mPP-MVS		86.67 35	86.32 37	87.72 28	94.41 22	73.55 12	92.74 20	92.22 80	76.87 60	82.81 84	94.25 31	66.44 92	96.24 39	82.88 54	94.28 54	93.38 73

test_0728_SECOND				87.71 30	95.34 1	71.43 54	93.49 9	94.23 3					97.49 3	89.08 7	96.41 12	94.21 40

DeepC-MVS	79.81 2	87.08 30	86.88 32	87.69 31	91.16 80	72.32 42	90.31 66	93.94 14	77.12 53	82.82 83	94.23 32	72.13 44	97.09 14	84.83 32	95.37 30	93.65 63
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CP-MVS		87.11 28	86.92 30	87.68 32	94.20 34	73.86 7	93.98 3	92.82 58	76.62 68	83.68 71	94.46 23	67.93 78	95.95 50	84.20 42	94.39 51	93.23 79

SF-MVS		88.46 11	88.74 11	87.64 33	92.78 61	71.95 48	92.40 23	94.74 2	75.71 85	89.16 19	95.10 13	75.65 21	96.19 41	87.07 21	96.01 17	94.79 19

MP-MVS-pluss		87.67 19	87.72 19	87.54 34	93.64 44	72.04 47	89.80 77	93.50 25	75.17 96	86.34 33	95.29 12	70.86 53	96.00 47	88.78 12	96.04 16	94.58 25
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CANet		86.45 36	86.10 43	87.51 35	90.09 101	70.94 65	89.70 81	92.59 66	81.78 2	81.32 98	91.43 93	70.34 58	97.23 12	84.26 39	93.36 62	94.37 33

HPM-MVS		87.11 28	86.98 28	87.50 36	93.88 39	72.16 44	92.19 32	93.33 31	76.07 80	83.81 70	93.95 43	69.77 65	96.01 46	85.15 27	94.66 44	94.32 36
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		85.89 45	85.39 50	87.38 37	93.59 45	72.63 32	92.74 20	93.18 36	76.78 63	80.73 107	93.82 45	64.33 112	96.29 37	82.67 60	90.69 91	93.23 79
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
DVP-MVS		89.60 3	90.35 3	87.33 38	95.27 5	71.25 55	93.49 9	92.73 59	77.33 46	92.12 9	95.78 4	80.98 9	97.40 7	89.08 7	96.41 12	93.33 76
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
PHI-MVS		86.43 37	86.17 41	87.24 39	90.88 87	70.96 63	92.27 31	94.07 9	72.45 145	85.22 43	91.90 79	69.47 67	96.42 35	83.28 49	95.94 19	94.35 34

APD-MVS		87.44 21	87.52 21	87.19 40	94.24 32	72.39 38	91.86 39	92.83 55	73.01 142	88.58 21	94.52 19	73.36 34	96.49 34	84.26 39	95.01 35	92.70 95
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CDPH-MVS		85.76 47	85.29 54	87.17 41	93.49 47	71.08 59	88.58 113	92.42 72	68.32 224	84.61 55	93.48 47	72.32 41	96.15 43	79.00 85	95.43 29	94.28 38

train_agg		86.43 37	86.20 39	87.13 42	93.26 50	72.96 24	88.75 106	91.89 93	68.69 218	85.00 45	93.10 54	74.43 26	95.41 65	84.97 28	95.71 25	93.02 88

CSCG		86.41 39	86.19 40	87.07 43	92.91 58	72.48 36	90.81 55	93.56 24	73.95 119	83.16 78	91.07 103	75.94 18	95.19 73	79.94 81	94.38 52	93.55 69

SR-MVS		86.73 32	86.67 33	86.91 44	94.11 37	72.11 46	92.37 27	92.56 67	74.50 108	86.84 31	94.65 18	67.31 85	95.77 52	84.80 33	92.85 65	92.84 93

DPM-MVS		84.93 58	84.29 63	86.84 45	90.20 99	73.04 22	87.12 156	93.04 38	69.80 191	82.85 82	91.22 97	73.06 38	96.02 45	76.72 111	94.63 45	91.46 135

TSAR-MVS + GP.		85.71 48	85.33 51	86.84 45	91.34 78	72.50 35	89.07 95	87.28 220	76.41 70	85.80 36	90.22 121	74.15 31	95.37 70	81.82 64	91.88 76	92.65 99

test12				86.80 47	92.63 64	70.70 70		91.79 99		82.71 85		71.67 47	96.16 42		94.50 48	93.54 70

DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 48	92.24 68	69.03 93	89.57 83	93.39 30	77.53 43	89.79 18	94.12 35	78.98 12	96.58 33	85.66 24	95.72 24	94.58 25

SD-MVS		88.06 13	88.50 13	86.71 49	92.60 66	72.71 28	91.81 40	93.19 35	77.87 34	90.32 17	94.00 40	74.83 23	93.78 134	87.63 17	94.27 55	93.65 63
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
3Dnovator	76.31 5	83.38 73	82.31 82	86.59 50	87.94 177	72.94 27	90.64 57	92.14 84	77.21 50	75.47 203	92.83 63	58.56 181	94.72 97	73.24 143	92.71 67	92.13 117

HPM-MVS_fast		85.35 54	84.95 58	86.57 51	93.69 42	70.58 73	92.15 34	91.62 103	73.89 122	82.67 86	94.09 36	62.60 131	95.54 58	80.93 70	92.93 64	93.57 68

test_prior				86.33 52	92.61 65	69.59 85		92.97 50					95.48 60			93.91 50

MVS_111021_HR		85.14 55	84.75 59	86.32 53	91.65 76	72.70 29	85.98 189	90.33 138	76.11 79	82.08 89	91.61 87	71.36 51	94.17 118	81.02 69	92.58 68	92.08 118

SR-MVS-dyc-post		85.77 46	85.61 48	86.23 54	93.06 55	70.63 71	91.88 37	92.27 76	73.53 132	85.69 38	94.45 24	65.00 110	95.56 56	82.75 55	91.87 77	92.50 103

APD-MVS_3200maxsize		85.97 43	85.88 45	86.22 55	92.69 63	69.53 86	91.93 36	92.99 45	73.54 131	85.94 34	94.51 22	65.80 102	95.61 55	83.04 52	92.51 69	93.53 71

DP-MVS Recon		83.11 78	82.09 85	86.15 56	94.44 19	70.92 66	88.79 104	92.20 81	70.53 179	79.17 122	91.03 106	64.12 114	96.03 44	68.39 190	90.14 97	91.50 132

EPNet		83.72 65	82.92 74	86.14 57	84.22 246	69.48 87	91.05 53	85.27 245	81.30 4	76.83 175	91.65 84	66.09 97	95.56 56	76.00 116	93.85 58	93.38 73
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
canonicalmvs		85.91 44	85.87 46	86.04 58	89.84 111	69.44 91	90.45 64	93.00 43	76.70 67	88.01 26	91.23 96	73.28 36	93.91 129	81.50 66	88.80 111	94.77 20

h-mvs33		83.15 75	82.19 83	86.02 59	90.56 92	70.85 68	88.15 128	89.16 171	76.02 81	84.67 52	91.39 94	61.54 149	95.50 59	82.71 57	75.48 272	91.72 126

alignmvs		85.48 50	85.32 52	85.96 60	89.51 118	69.47 88	89.74 79	92.47 68	76.17 78	87.73 29	91.46 92	70.32 59	93.78 134	81.51 65	88.95 108	94.63 24

CS-MVS		86.69 33	86.95 29	85.90 61	90.76 90	67.57 129	92.83 17	93.30 32	79.67 15	84.57 57	92.27 73	71.47 49	95.02 84	84.24 41	93.46 61	95.13 5

DELS-MVS		85.41 53	85.30 53	85.77 62	88.49 158	67.93 121	85.52 206	93.44 27	78.70 27	83.63 74	89.03 149	74.57 24	95.71 54	80.26 79	94.04 57	93.66 59
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
CS-MVS-test		86.29 40	86.48 35	85.71 63	91.02 83	67.21 139	92.36 28	93.78 18	78.97 26	83.51 75	91.20 98	70.65 57	95.15 75	81.96 63	94.89 39	94.77 20

casdiffmvs_mvg		85.99 42	86.09 44	85.70 64	87.65 189	67.22 138	88.69 110	93.04 38	79.64 16	85.33 41	92.54 70	73.30 35	94.50 105	83.49 46	91.14 87	95.37 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ETV-MVS		84.90 60	84.67 60	85.59 65	89.39 123	68.66 108	88.74 108	92.64 65	79.97 13	84.10 65	85.71 238	69.32 69	95.38 67	80.82 72	91.37 84	92.72 94

UA-Net		85.08 57	84.96 57	85.45 66	92.07 70	68.07 119	89.78 78	90.86 125	82.48 1	84.60 56	93.20 53	69.35 68	95.22 72	71.39 158	90.88 90	93.07 85

Vis-MVSNet		83.46 70	82.80 76	85.43 67	90.25 98	68.74 102	90.30 67	90.13 144	76.33 76	80.87 106	92.89 61	61.00 163	94.20 116	72.45 152	90.97 88	93.35 75
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EI-MVSNet-Vis-set		84.19 61	83.81 64	85.31 68	88.18 168	67.85 122	87.66 142	89.73 155	80.05 12	82.95 79	89.59 133	70.74 55	94.82 93	80.66 76	84.72 159	93.28 78

MAR-MVS		81.84 94	80.70 105	85.27 69	91.32 79	71.53 52	89.82 75	90.92 121	69.77 192	78.50 136	86.21 230	62.36 137	94.52 104	65.36 214	92.05 75	89.77 202
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
Effi-MVS+		83.62 68	83.08 70	85.24 70	88.38 163	67.45 131	88.89 100	89.15 172	75.50 90	82.27 87	88.28 171	69.61 66	94.45 107	77.81 97	87.84 121	93.84 54

MVSFormer		82.85 81	82.05 86	85.24 70	87.35 197	70.21 75	90.50 60	90.38 134	68.55 220	81.32 98	89.47 136	61.68 146	93.46 151	78.98 86	90.26 95	92.05 119

OPM-MVS		83.50 69	82.95 73	85.14 72	88.79 148	70.95 64	89.13 94	91.52 106	77.55 42	80.96 105	91.75 82	60.71 166	94.50 105	79.67 82	86.51 140	89.97 194
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP_MVS		83.64 67	83.14 69	85.14 72	90.08 102	68.71 104	91.25 48	92.44 69	79.12 21	78.92 126	91.00 107	60.42 172	95.38 67	78.71 88	86.32 142	91.33 136

EI-MVSNet-UG-set		83.81 63	83.38 67	85.09 74	87.87 178	67.53 130	87.44 148	89.66 156	79.74 14	82.23 88	89.41 142	70.24 60	94.74 96	79.95 80	83.92 169	92.99 90

QAPM		80.88 113	79.50 128	85.03 75	88.01 176	68.97 96	91.59 41	92.00 87	66.63 240	75.15 218	92.16 75	57.70 188	95.45 61	63.52 224	88.76 112	90.66 161

casdiffmvs		85.11 56	85.14 55	85.01 76	87.20 204	65.77 166	87.75 140	92.83 55	77.84 35	84.36 61	92.38 72	72.15 43	93.93 128	81.27 68	90.48 92	95.33 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PCF-MVS	73.52 7	80.38 130	78.84 145	85.01 76	87.71 186	68.99 95	83.65 243	91.46 111	63.00 279	77.77 156	90.28 118	66.10 96	95.09 82	61.40 246	88.22 120	90.94 152
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
nrg030		83.88 62	83.53 65	84.96 78	86.77 212	69.28 92	90.46 63	92.67 61	74.79 102	82.95 79	91.33 95	72.70 40	93.09 169	80.79 74	79.28 229	92.50 103

VDD-MVS		83.01 80	82.36 81	84.96 78	91.02 83	66.40 150	88.91 99	88.11 200	77.57 39	84.39 60	93.29 52	52.19 230	93.91 129	77.05 105	88.70 113	94.57 27

PVSNet_Blended_VisFu		82.62 83	81.83 91	84.96 78	90.80 89	69.76 84	88.74 108	91.70 102	69.39 198	78.96 124	88.46 166	65.47 104	94.87 92	74.42 129	88.57 114	90.24 176

CPTT-MVS		83.73 64	83.33 68	84.92 81	93.28 49	70.86 67	92.09 35	90.38 134	68.75 217	79.57 118	92.83 63	60.60 170	93.04 173	80.92 71	91.56 82	90.86 154

DROMVSNet		86.01 41	86.38 36	84.91 82	89.31 129	66.27 153	92.32 29	93.63 21	79.37 18	84.17 64	91.88 80	69.04 74	95.43 63	83.93 44	93.77 59	93.01 89

OMC-MVS		82.69 82	81.97 89	84.85 83	88.75 150	67.42 132	87.98 131	90.87 124	74.92 99	79.72 116	91.65 84	62.19 141	93.96 122	75.26 124	86.42 141	93.16 83

EIA-MVS		83.31 74	82.80 76	84.82 84	89.59 114	65.59 168	88.21 124	92.68 60	74.66 105	78.96 124	86.42 226	69.06 72	95.26 71	75.54 122	90.09 98	93.62 66

PAPM_NR		83.02 79	82.41 79	84.82 84	92.47 67	66.37 151	87.93 135	91.80 98	73.82 123	77.32 164	90.66 112	67.90 79	94.90 89	70.37 167	89.48 105	93.19 82

baseline		84.93 58	84.98 56	84.80 86	87.30 202	65.39 175	87.30 152	92.88 52	77.62 37	84.04 67	92.26 74	71.81 45	93.96 122	81.31 67	90.30 94	95.03 7

lupinMVS		81.39 106	80.27 115	84.76 87	87.35 197	70.21 75	85.55 202	86.41 232	62.85 282	81.32 98	88.61 161	61.68 146	92.24 198	78.41 92	90.26 95	91.83 123

jason		81.39 106	80.29 114	84.70 88	86.63 214	69.90 82	85.95 190	86.77 228	63.24 275	81.07 104	89.47 136	61.08 162	92.15 200	78.33 93	90.07 100	92.05 119
jason: jason.
ET-MVSNet_ETH3D		78.63 172	76.63 200	84.64 89	86.73 213	69.47 88	85.01 212	84.61 253	69.54 196	66.51 310	86.59 219	50.16 256	91.75 213	76.26 112	84.24 167	92.69 97

EPP-MVSNet		83.40 72	83.02 72	84.57 90	90.13 100	64.47 193	92.32 29	90.73 126	74.45 111	79.35 121	91.10 101	69.05 73	95.12 76	72.78 147	87.22 129	94.13 42

mvsmamba		81.69 98	80.74 104	84.56 91	87.45 196	66.72 146	91.26 46	85.89 241	74.66 105	78.23 144	90.56 114	54.33 212	94.91 86	80.73 75	83.54 177	92.04 121

UGNet		80.83 115	79.59 126	84.54 92	88.04 174	68.09 118	89.42 84	88.16 199	76.95 57	76.22 190	89.46 138	49.30 267	93.94 125	68.48 188	90.31 93	91.60 127
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
LPG-MVS_test		82.08 89	81.27 95	84.50 93	89.23 133	68.76 100	90.22 68	91.94 91	75.37 92	76.64 181	91.51 89	54.29 213	94.91 86	78.44 90	83.78 170	89.83 199

LGP-MVS_train				84.50 93	89.23 133	68.76 100		91.94 91	75.37 92	76.64 181	91.51 89	54.29 213	94.91 86	78.44 90	83.78 170	89.83 199

iter_conf_final		80.63 123	79.35 132	84.46 95	89.36 125	67.70 126	89.85 73	84.49 255	73.19 138	78.30 142	88.94 150	45.98 291	94.56 100	79.59 83	84.48 163	91.11 143

MSLP-MVS++		85.43 52	85.76 47	84.45 96	91.93 72	70.24 74	90.71 56	92.86 53	77.46 45	84.22 62	92.81 65	67.16 87	92.94 175	80.36 77	94.35 53	90.16 178

Effi-MVS+-dtu		80.03 138	78.57 150	84.42 97	85.13 234	68.74 102	88.77 105	88.10 201	74.99 98	74.97 223	83.49 278	57.27 194	93.36 154	73.53 137	80.88 206	91.18 141

HQP-MVS		82.61 84	82.02 87	84.37 98	89.33 126	66.98 142	89.17 89	92.19 82	76.41 70	77.23 167	90.23 120	60.17 175	95.11 78	77.47 100	85.99 149	91.03 148

ACMP	74.13 6	81.51 105	80.57 107	84.36 99	89.42 121	68.69 107	89.97 72	91.50 110	74.46 110	75.04 222	90.41 117	53.82 218	94.54 102	77.56 99	82.91 184	89.86 198
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
原ACMM1				84.35 100	93.01 57	68.79 98		92.44 69	63.96 273	81.09 103	91.57 88	66.06 98	95.45 61	67.19 200	94.82 43	88.81 232

PS-MVSNAJss		82.07 90	81.31 94	84.34 101	86.51 215	67.27 136	89.27 87	91.51 107	71.75 154	79.37 120	90.22 121	63.15 125	94.27 111	77.69 98	82.36 192	91.49 133

thisisatest0530		79.40 153	77.76 173	84.31 102	87.69 188	65.10 181	87.36 149	84.26 261	70.04 186	77.42 161	88.26 173	49.94 259	94.79 95	70.20 168	84.70 160	93.03 87

CLD-MVS		82.31 86	81.65 92	84.29 103	88.47 159	67.73 125	85.81 197	92.35 74	75.78 84	78.33 141	86.58 221	64.01 115	94.35 108	76.05 115	87.48 126	90.79 155
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		81.99 92	81.23 96	84.26 104	90.94 85	70.18 80	91.10 51	89.32 163	71.51 162	78.66 132	88.28 171	65.26 105	95.10 81	64.74 220	91.23 86	87.51 257

114514_t		80.68 122	79.51 127	84.20 105	94.09 38	67.27 136	89.64 82	91.11 118	58.75 316	74.08 233	90.72 111	58.10 184	95.04 83	69.70 175	89.42 106	90.30 174

IS-MVSNet		83.15 75	82.81 75	84.18 106	89.94 109	63.30 218	91.59 41	88.46 197	79.04 23	79.49 119	92.16 75	65.10 107	94.28 110	67.71 193	91.86 79	94.95 8

MVS_111021_LR		82.61 84	82.11 84	84.11 107	88.82 145	71.58 51	85.15 209	86.16 237	74.69 104	80.47 109	91.04 104	62.29 138	90.55 243	80.33 78	90.08 99	90.20 177

FA-MVS(test-final)		80.96 112	79.91 119	84.10 108	88.30 166	65.01 182	84.55 224	90.01 147	73.25 137	79.61 117	87.57 188	58.35 183	94.72 97	71.29 159	86.25 144	92.56 100

Anonymous20240529		80.19 136	78.89 144	84.10 108	90.60 91	64.75 187	88.95 98	90.90 122	65.97 248	80.59 108	91.17 100	49.97 258	93.73 140	69.16 181	82.70 189	93.81 55

OpenMVS	72.83 10	79.77 142	78.33 157	84.09 110	85.17 230	69.91 81	90.57 58	90.97 120	66.70 236	72.17 252	91.91 78	54.70 209	93.96 122	61.81 243	90.95 89	88.41 242

FE-MVS		77.78 194	75.68 210	84.08 111	88.09 172	66.00 157	83.13 253	87.79 210	68.42 223	78.01 151	85.23 250	45.50 298	95.12 76	59.11 264	85.83 152	91.11 143

hse-mvs2		81.72 96	80.94 102	84.07 112	88.72 151	67.68 127	85.87 193	87.26 221	76.02 81	84.67 52	88.22 174	61.54 149	93.48 149	82.71 57	73.44 299	91.06 146

dcpmvs_2		85.63 49	86.15 42	84.06 113	91.71 75	64.94 184	86.47 177	91.87 95	73.63 127	86.60 32	93.02 59	76.57 15	91.87 211	83.36 47	92.15 73	95.35 2

AdaColmap		80.58 127	79.42 129	84.06 113	93.09 54	68.91 97	89.36 86	88.97 181	69.27 201	75.70 200	89.69 128	57.20 195	95.77 52	63.06 229	88.41 118	87.50 258

AUN-MVS		79.21 158	77.60 178	84.05 115	88.71 152	67.61 128	85.84 195	87.26 221	69.08 209	77.23 167	88.14 179	53.20 223	93.47 150	75.50 123	73.45 298	91.06 146

VDDNet		81.52 103	80.67 106	84.05 115	90.44 95	64.13 200	89.73 80	85.91 240	71.11 168	83.18 77	93.48 47	50.54 253	93.49 148	73.40 140	88.25 119	94.54 28

xiu_mvs_v1_base_debu		80.80 118	79.72 123	84.03 117	87.35 197	70.19 77	85.56 199	88.77 187	69.06 210	81.83 91	88.16 175	50.91 247	92.85 177	78.29 94	87.56 123	89.06 217

xiu_mvs_v1_base		80.80 118	79.72 123	84.03 117	87.35 197	70.19 77	85.56 199	88.77 187	69.06 210	81.83 91	88.16 175	50.91 247	92.85 177	78.29 94	87.56 123	89.06 217

xiu_mvs_v1_base_debi		80.80 118	79.72 123	84.03 117	87.35 197	70.19 77	85.56 199	88.77 187	69.06 210	81.83 91	88.16 175	50.91 247	92.85 177	78.29 94	87.56 123	89.06 217

PAPR		81.66 101	80.89 103	83.99 120	90.27 97	64.00 201	86.76 170	91.77 101	68.84 216	77.13 173	89.50 134	67.63 81	94.88 91	67.55 195	88.52 116	93.09 84

XVG-OURS		80.41 129	79.23 136	83.97 121	85.64 224	69.02 94	83.03 257	90.39 133	71.09 169	77.63 158	91.49 91	54.62 211	91.35 224	75.71 118	83.47 178	91.54 129

XVG-OURS-SEG-HR		80.81 116	79.76 122	83.96 122	85.60 225	68.78 99	83.54 248	90.50 131	70.66 177	76.71 179	91.66 83	60.69 167	91.26 226	76.94 106	81.58 200	91.83 123

HyFIR lowres test		77.53 200	75.40 216	83.94 123	89.59 114	66.62 147	80.36 282	88.64 194	56.29 330	76.45 183	85.17 252	57.64 189	93.28 156	61.34 248	83.10 183	91.91 122

iter_conf05		80.00 140	78.70 146	83.91 124	87.84 180	65.83 162	88.84 103	84.92 250	71.61 159	78.70 129	88.94 150	43.88 304	94.56 100	79.28 84	84.28 166	91.33 136

tttt0517		79.40 153	77.91 165	83.90 125	88.10 171	63.84 204	88.37 120	84.05 263	71.45 163	76.78 177	89.12 146	49.93 261	94.89 90	70.18 169	83.18 182	92.96 91

GeoE		81.71 97	81.01 101	83.80 126	89.51 118	64.45 194	88.97 97	88.73 192	71.27 165	78.63 133	89.76 127	66.32 94	93.20 161	69.89 173	86.02 148	93.74 57

RRT_MVS		80.35 132	79.22 137	83.74 127	87.63 190	65.46 172	91.08 52	88.92 184	73.82 123	76.44 186	90.03 123	49.05 272	94.25 115	76.84 107	79.20 231	91.51 130

PS-MVSNAJ		81.69 98	81.02 100	83.70 128	89.51 118	68.21 117	84.28 233	90.09 145	70.79 173	81.26 102	85.62 242	63.15 125	94.29 109	75.62 120	88.87 110	88.59 238

xiu_mvs_v2_base		81.69 98	81.05 99	83.60 129	89.15 136	68.03 120	84.46 227	90.02 146	70.67 176	81.30 101	86.53 224	63.17 124	94.19 117	75.60 121	88.54 115	88.57 239

ACMM	73.20 8	80.78 121	79.84 121	83.58 130	89.31 129	68.37 112	89.99 71	91.60 104	70.28 183	77.25 165	89.66 129	53.37 221	93.53 147	74.24 132	82.85 185	88.85 230
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LFMVS		81.82 95	81.23 96	83.57 131	91.89 73	63.43 216	89.84 74	81.85 291	77.04 56	83.21 76	93.10 54	52.26 229	93.43 153	71.98 153	89.95 101	93.85 52

Fast-Effi-MVS+		80.81 116	79.92 118	83.47 132	88.85 142	64.51 190	85.53 204	89.39 161	70.79 173	78.49 137	85.06 255	67.54 82	93.58 142	67.03 203	86.58 138	92.32 109

CHOSEN 1792x2688		77.63 199	75.69 209	83.44 133	89.98 108	68.58 110	78.70 301	87.50 216	56.38 329	75.80 199	86.84 207	58.67 180	91.40 223	61.58 245	85.75 153	90.34 173

新几何1				83.42 134	93.13 52	70.71 69		85.48 244	57.43 324	81.80 94	91.98 77	63.28 120	92.27 196	64.60 221	92.99 63	87.27 263

DP-MVS		76.78 213	74.57 225	83.42 134	93.29 48	69.46 90	88.55 114	83.70 267	63.98 272	70.20 268	88.89 153	54.01 217	94.80 94	46.66 337	81.88 197	86.01 289

MVS_Test		83.15 75	83.06 71	83.41 136	86.86 208	63.21 220	86.11 187	92.00 87	74.31 112	82.87 81	89.44 141	70.03 61	93.21 158	77.39 102	88.50 117	93.81 55

LS3D		76.95 211	74.82 223	83.37 137	90.45 94	67.36 135	89.15 93	86.94 226	61.87 292	69.52 280	90.61 113	51.71 241	94.53 103	46.38 340	86.71 137	88.21 244

IB-MVS	68.01 15	75.85 227	73.36 239	83.31 138	84.76 238	66.03 155	83.38 249	85.06 247	70.21 185	69.40 281	81.05 303	45.76 295	94.66 99	65.10 217	75.49 271	89.25 214
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
MG-MVS		83.41 71	83.45 66	83.28 139	92.74 62	62.28 234	88.17 126	89.50 159	75.22 94	81.49 97	92.74 69	66.75 88	95.11 78	72.85 146	91.58 81	92.45 106

jajsoiax		79.29 156	77.96 163	83.27 140	84.68 240	66.57 149	89.25 88	90.16 143	69.20 205	75.46 205	89.49 135	45.75 296	93.13 167	76.84 107	80.80 208	90.11 182

test_djsdf		80.30 133	79.32 133	83.27 140	83.98 251	65.37 176	90.50 60	90.38 134	68.55 220	76.19 191	88.70 157	56.44 199	93.46 151	78.98 86	80.14 218	90.97 151

test_yl		81.17 108	80.47 110	83.24 142	89.13 137	63.62 207	86.21 184	89.95 149	72.43 148	81.78 95	89.61 131	57.50 191	93.58 142	70.75 162	86.90 133	92.52 101

DCV-MVSNet		81.17 108	80.47 110	83.24 142	89.13 137	63.62 207	86.21 184	89.95 149	72.43 148	81.78 95	89.61 131	57.50 191	93.58 142	70.75 162	86.90 133	92.52 101

mvs_tets		79.13 160	77.77 172	83.22 144	84.70 239	66.37 151	89.17 89	90.19 142	69.38 199	75.40 208	89.46 138	44.17 302	93.15 165	76.78 109	80.70 210	90.14 179

thisisatest0515		77.33 204	75.38 217	83.18 145	85.27 229	63.80 205	82.11 263	83.27 276	65.06 256	75.91 196	83.84 272	49.54 263	94.27 111	67.24 199	86.19 145	91.48 134

CDS-MVSNet		79.07 162	77.70 175	83.17 146	87.60 191	68.23 116	84.40 231	86.20 236	67.49 230	76.36 187	86.54 223	61.54 149	90.79 239	61.86 242	87.33 127	90.49 168
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v7n		78.97 165	77.58 179	83.14 147	83.45 260	65.51 169	88.32 121	91.21 114	73.69 126	72.41 249	86.32 229	57.93 185	93.81 133	69.18 180	75.65 268	90.11 182

BH-RMVSNet		79.61 144	78.44 153	83.14 147	89.38 124	65.93 159	84.95 214	87.15 223	73.56 130	78.19 146	89.79 126	56.67 198	93.36 154	59.53 260	86.74 136	90.13 180

UniMVSNet (Re)		81.60 102	81.11 98	83.09 149	88.38 163	64.41 195	87.60 143	93.02 42	78.42 30	78.56 135	88.16 175	69.78 64	93.26 157	69.58 177	76.49 256	91.60 127

PLC	70.83 11	78.05 187	76.37 205	83.08 150	91.88 74	67.80 123	88.19 125	89.46 160	64.33 266	69.87 277	88.38 168	53.66 219	93.58 142	58.86 268	82.73 187	87.86 249
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1192		79.59 146	78.43 154	83.07 151	83.55 258	64.52 189	86.93 162	90.58 129	70.83 172	77.78 155	85.90 234	59.15 178	93.94 125	73.96 134	77.19 247	90.76 157

v2v482		80.23 134	79.29 134	83.05 152	83.62 256	64.14 199	87.04 158	89.97 148	73.61 128	78.18 147	87.22 199	61.10 161	93.82 132	76.11 113	76.78 254	91.18 141

TAMVS		78.89 167	77.51 180	83.03 153	87.80 182	67.79 124	84.72 218	85.05 248	67.63 227	76.75 178	87.70 184	62.25 139	90.82 238	58.53 272	87.13 130	90.49 168

v1144		80.03 138	79.03 141	83.01 154	83.78 254	64.51 190	87.11 157	90.57 130	71.96 153	78.08 150	86.20 231	61.41 153	93.94 125	74.93 125	77.23 245	90.60 164

cascas		76.72 214	74.64 224	82.99 155	85.78 222	65.88 161	82.33 261	89.21 169	60.85 298	72.74 244	81.02 304	47.28 281	93.75 138	67.48 196	85.02 155	89.34 211

anonymousdsp		78.60 173	77.15 185	82.98 156	80.51 312	67.08 140	87.24 154	89.53 158	65.66 251	75.16 217	87.19 201	52.52 224	92.25 197	77.17 104	79.34 228	89.61 206

v10		79.74 143	78.67 147	82.97 157	84.06 249	64.95 183	87.88 138	90.62 128	73.11 139	75.11 219	86.56 222	61.46 152	94.05 121	73.68 135	75.55 270	89.90 196

UniMVSNet_NR-MVSNet		81.88 93	81.54 93	82.92 158	88.46 160	63.46 214	87.13 155	92.37 73	80.19 10	78.38 139	89.14 145	71.66 48	93.05 171	70.05 170	76.46 257	92.25 112

DU-MVS		81.12 110	80.52 109	82.90 159	87.80 182	63.46 214	87.02 159	91.87 95	79.01 24	78.38 139	89.07 147	65.02 108	93.05 171	70.05 170	76.46 257	92.20 114

PVSNet_Blended		80.98 111	80.34 112	82.90 159	88.85 142	65.40 173	84.43 229	92.00 87	67.62 228	78.11 148	85.05 256	66.02 99	94.27 111	71.52 155	89.50 104	89.01 222

CANet_DTU		80.61 124	79.87 120	82.83 161	85.60 225	63.17 223	87.36 149	88.65 193	76.37 74	75.88 197	88.44 167	53.51 220	93.07 170	73.30 141	89.74 103	92.25 112

V42		79.38 155	78.24 159	82.83 161	81.10 306	65.50 170	85.55 202	89.82 151	71.57 161	78.21 145	86.12 232	60.66 168	93.18 164	75.64 119	75.46 274	89.81 201

Anonymous20231211		78.97 165	77.69 176	82.81 163	90.54 93	64.29 197	90.11 70	91.51 107	65.01 258	76.16 195	88.13 180	50.56 252	93.03 174	69.68 176	77.56 244	91.11 143

v1921920		79.22 157	78.03 162	82.80 164	83.30 263	63.94 203	86.80 166	90.33 138	69.91 189	77.48 160	85.53 243	58.44 182	93.75 138	73.60 136	76.85 252	90.71 160

v8		79.97 141	79.02 142	82.80 164	84.09 248	64.50 192	87.96 132	90.29 141	74.13 118	75.24 216	86.81 208	62.88 130	93.89 131	74.39 130	75.40 276	90.00 190

TAPA-MVS	73.13 9	79.15 159	77.94 164	82.79 166	89.59 114	62.99 227	88.16 127	91.51 107	65.77 249	77.14 172	91.09 102	60.91 164	93.21 158	50.26 320	87.05 131	92.17 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v144192		79.47 149	78.37 155	82.78 167	83.35 261	63.96 202	86.96 160	90.36 137	69.99 187	77.50 159	85.67 240	60.66 168	93.77 136	74.27 131	76.58 255	90.62 162

NR-MVSNet		80.23 134	79.38 130	82.78 167	87.80 182	63.34 217	86.31 181	91.09 119	79.01 24	72.17 252	89.07 147	67.20 86	92.81 180	66.08 209	75.65 268	92.20 114

diffmvs		82.10 88	81.88 90	82.76 169	83.00 273	63.78 206	83.68 242	89.76 153	72.94 143	82.02 90	89.85 125	65.96 101	90.79 239	82.38 61	87.30 128	93.71 58
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
v1240		78.99 164	77.78 171	82.64 170	83.21 265	63.54 211	86.62 173	90.30 140	69.74 195	77.33 163	85.68 239	57.04 196	93.76 137	73.13 144	76.92 249	90.62 162

Fast-Effi-MVS+-dtu		78.02 188	76.49 201	82.62 171	83.16 269	66.96 144	86.94 161	87.45 218	72.45 145	71.49 259	84.17 267	54.79 208	91.58 217	67.61 194	80.31 215	89.30 213

RPMNet		73.51 247	70.49 264	82.58 172	81.32 304	65.19 178	75.92 318	92.27 76	57.60 323	72.73 245	76.45 338	52.30 228	95.43 63	48.14 332	77.71 241	87.11 269

F-COLMAP		76.38 221	74.33 230	82.50 173	89.28 131	66.95 145	88.41 116	89.03 176	64.05 270	66.83 304	88.61 161	46.78 284	92.89 176	57.48 280	78.55 233	87.67 252

TranMVSNet+NR-MVSNet		80.84 114	80.31 113	82.42 174	87.85 179	62.33 232	87.74 141	91.33 112	80.55 7	77.99 152	89.86 124	65.23 106	92.62 181	67.05 202	75.24 281	92.30 110

MVSTER		79.01 163	77.88 167	82.38 175	83.07 270	64.80 186	84.08 239	88.95 182	69.01 213	78.69 130	87.17 202	54.70 209	92.43 188	74.69 126	80.57 212	89.89 197

PVSNet_BlendedMVS		80.60 125	80.02 116	82.36 176	88.85 142	65.40 173	86.16 186	92.00 87	69.34 200	78.11 148	86.09 233	66.02 99	94.27 111	71.52 155	82.06 194	87.39 259

EI-MVSNet		80.52 128	79.98 117	82.12 177	84.28 244	63.19 222	86.41 178	88.95 182	74.18 116	78.69 130	87.54 191	66.62 89	92.43 188	72.57 150	80.57 212	90.74 159

IterMVS-LS		80.06 137	79.38 130	82.11 178	85.89 220	63.20 221	86.79 167	89.34 162	74.19 115	75.45 206	86.72 211	66.62 89	92.39 190	72.58 149	76.86 251	90.75 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-untuned		79.47 149	78.60 149	82.05 179	89.19 135	65.91 160	86.07 188	88.52 196	72.18 150	75.42 207	87.69 185	61.15 160	93.54 146	60.38 253	86.83 135	86.70 277

ACMH+	68.96 14	76.01 225	74.01 232	82.03 180	88.60 155	65.31 177	88.86 101	87.55 214	70.25 184	67.75 292	87.47 193	41.27 319	93.19 163	58.37 273	75.94 265	87.60 254

Anonymous202405211		78.25 179	77.01 187	81.99 181	91.03 82	60.67 253	84.77 217	83.90 265	70.65 178	80.00 114	91.20 98	41.08 321	91.43 222	65.21 215	85.26 154	93.85 52

GA-MVS		76.87 212	75.17 221	81.97 182	82.75 278	62.58 229	81.44 272	86.35 235	72.16 152	74.74 226	82.89 285	46.20 290	92.02 204	68.85 185	81.09 204	91.30 139

CNLPA		78.08 185	76.79 194	81.97 182	90.40 96	71.07 60	87.59 144	84.55 254	66.03 247	72.38 250	89.64 130	57.56 190	86.04 294	59.61 259	83.35 179	88.79 233

MVS		78.19 183	76.99 189	81.78 184	85.66 223	66.99 141	84.66 219	90.47 132	55.08 334	72.02 254	85.27 248	63.83 117	94.11 120	66.10 208	89.80 102	84.24 311

ACMH	67.68 16	75.89 226	73.93 233	81.77 185	88.71 152	66.61 148	88.62 112	89.01 178	69.81 190	66.78 305	86.70 215	41.95 318	91.51 221	55.64 294	78.14 240	87.17 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet_ETH3D		79.10 161	78.24 159	81.70 186	86.85 209	60.24 260	87.28 153	88.79 186	74.25 114	76.84 174	90.53 116	49.48 264	91.56 218	67.98 191	82.15 193	93.29 77

VNet		82.21 87	82.41 79	81.62 187	90.82 88	60.93 248	84.47 225	89.78 152	76.36 75	84.07 66	91.88 80	64.71 111	90.26 245	70.68 164	88.89 109	93.66 59

XVG-ACMP-BASELINE		76.11 224	74.27 231	81.62 187	83.20 266	64.67 188	83.60 246	89.75 154	69.75 193	71.85 255	87.09 204	32.78 344	92.11 201	69.99 172	80.43 214	88.09 245

eth_miper_zixun_eth		77.92 191	76.69 198	81.61 189	83.00 273	61.98 237	83.15 252	89.20 170	69.52 197	74.86 225	84.35 265	61.76 145	92.56 184	71.50 157	72.89 303	90.28 175

PAPM		77.68 198	76.40 204	81.51 190	87.29 203	61.85 239	83.78 241	89.59 157	64.74 260	71.23 260	88.70 157	62.59 132	93.66 141	52.66 307	87.03 132	89.01 222

v148		78.72 170	77.80 170	81.47 191	82.73 279	61.96 238	86.30 182	88.08 202	73.26 136	76.18 192	85.47 245	62.46 135	92.36 192	71.92 154	73.82 295	90.09 184

tt0805		78.73 169	77.83 168	81.43 192	85.17 230	60.30 259	89.41 85	90.90 122	71.21 166	77.17 171	88.73 156	46.38 286	93.21 158	72.57 150	78.96 232	90.79 155

LTVRE_ROB	69.57 13	76.25 222	74.54 227	81.41 193	88.60 155	64.38 196	79.24 294	89.12 175	70.76 175	69.79 279	87.86 182	49.09 270	93.20 161	56.21 293	80.16 216	86.65 278
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
GBi-Net		78.40 176	77.40 181	81.40 194	87.60 191	63.01 224	88.39 117	89.28 164	71.63 156	75.34 210	87.28 195	54.80 205	91.11 229	62.72 231	79.57 223	90.09 184

test1		78.40 176	77.40 181	81.40 194	87.60 191	63.01 224	88.39 117	89.28 164	71.63 156	75.34 210	87.28 195	54.80 205	91.11 229	62.72 231	79.57 223	90.09 184

FMVSNet1		77.44 201	76.12 207	81.40 194	86.81 211	63.01 224	88.39 117	89.28 164	70.49 180	74.39 230	87.28 195	49.06 271	91.11 229	60.91 250	78.52 234	90.09 184

baseline2		75.70 228	73.83 236	81.30 197	83.26 264	61.79 241	82.57 260	80.65 300	66.81 233	66.88 302	83.42 279	57.86 187	92.19 199	63.47 225	79.57 223	89.91 195

c3_l		78.75 168	77.91 165	81.26 198	82.89 276	61.56 243	84.09 238	89.13 174	69.97 188	75.56 201	84.29 266	66.36 93	92.09 202	73.47 139	75.48 272	90.12 181

cl22		78.07 186	77.01 187	81.23 199	82.37 288	61.83 240	83.55 247	87.98 204	68.96 214	75.06 221	83.87 270	61.40 154	91.88 210	73.53 137	76.39 259	89.98 193

bld_raw_dy_0_64		77.29 206	75.98 208	81.22 200	85.04 236	65.47 171	88.14 129	77.56 323	69.20 205	73.77 235	89.40 144	42.24 315	88.85 271	76.78 109	81.64 199	89.33 212

FMVSNet2		78.20 182	77.21 184	81.20 201	87.60 191	62.89 228	87.47 147	89.02 177	71.63 156	75.29 215	87.28 195	54.80 205	91.10 232	62.38 235	79.38 227	89.61 206

TR-MVS		77.44 201	76.18 206	81.20 201	88.24 167	63.24 219	84.61 222	86.40 233	67.55 229	77.81 154	86.48 225	54.10 215	93.15 165	57.75 279	82.72 188	87.20 264

ab-mvs		79.51 147	78.97 143	81.14 203	88.46 160	60.91 249	83.84 240	89.24 168	70.36 181	79.03 123	88.87 154	63.23 123	90.21 247	65.12 216	82.57 190	92.28 111

MVP-Stereo		76.12 223	74.46 229	81.13 204	85.37 228	69.79 83	84.42 230	87.95 205	65.03 257	67.46 296	85.33 247	53.28 222	91.73 215	58.01 277	83.27 180	81.85 332
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
miper_ehance_all_eth		78.59 174	77.76 173	81.08 205	82.66 281	61.56 243	83.65 243	89.15 172	68.87 215	75.55 202	83.79 274	66.49 91	92.03 203	73.25 142	76.39 259	89.64 205

FIs		82.07 90	82.42 78	81.04 206	88.80 147	58.34 272	88.26 123	93.49 26	76.93 58	78.47 138	91.04 104	69.92 63	92.34 194	69.87 174	84.97 156	92.44 107

patch_mono-2		83.65 66	84.54 61	80.99 207	90.06 106	65.83 162	84.21 234	88.74 191	71.60 160	85.01 44	92.44 71	74.51 25	83.50 312	82.15 62	92.15 73	93.64 65

FMVSNet3		77.88 192	76.85 192	80.97 208	86.84 210	62.36 231	86.52 176	88.77 187	71.13 167	75.34 210	86.66 217	54.07 216	91.10 232	62.72 231	79.57 223	89.45 209

miper_enhance_ethall		77.87 193	76.86 191	80.92 209	81.65 295	61.38 245	82.68 258	88.98 179	65.52 253	75.47 203	82.30 293	65.76 103	92.00 205	72.95 145	76.39 259	89.39 210

BH-w/o		78.21 181	77.33 183	80.84 210	88.81 146	65.13 180	84.87 215	87.85 209	69.75 193	74.52 229	84.74 260	61.34 155	93.11 168	58.24 275	85.84 151	84.27 310

COLMAP_ROB	66.92 17	73.01 254	70.41 266	80.81 211	87.13 206	65.63 167	88.30 122	84.19 262	62.96 280	63.80 328	87.69 185	38.04 332	92.56 184	46.66 337	74.91 284	84.24 311
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VPA-MVSNet		80.60 125	80.55 108	80.76 212	88.07 173	60.80 251	86.86 164	91.58 105	75.67 88	80.24 111	89.45 140	63.34 119	90.25 246	70.51 166	79.22 230	91.23 140

EG-PatchMatch MVS		74.04 243	71.82 251	80.71 213	84.92 237	67.42 132	85.86 194	88.08 202	66.04 246	64.22 324	83.85 271	35.10 340	92.56 184	57.44 281	80.83 207	82.16 331

ECVR-MVS		79.61 144	79.26 135	80.67 214	90.08 102	54.69 319	87.89 137	77.44 326	74.88 100	80.27 110	92.79 66	48.96 274	92.45 187	68.55 187	92.50 70	94.86 15

cl____		77.72 196	76.76 195	80.58 215	82.49 285	60.48 256	83.09 254	87.87 207	69.22 203	74.38 231	85.22 251	62.10 142	91.53 219	71.09 160	75.41 275	89.73 204

DIV-MVS_self_test		77.72 196	76.76 195	80.58 215	82.48 286	60.48 256	83.09 254	87.86 208	69.22 203	74.38 231	85.24 249	62.10 142	91.53 219	71.09 160	75.40 276	89.74 203

MSDG		73.36 250	70.99 259	80.49 217	84.51 242	65.80 164	80.71 278	86.13 238	65.70 250	65.46 315	83.74 275	44.60 300	90.91 237	51.13 313	76.89 250	84.74 305

pmmvs4		74.03 244	71.91 250	80.39 218	81.96 292	68.32 113	81.45 271	82.14 287	59.32 310	69.87 277	85.13 253	52.40 227	88.13 279	60.21 255	74.74 286	84.73 306

HY-MVS	69.67 12	77.95 190	77.15 185	80.36 219	87.57 195	60.21 261	83.37 250	87.78 211	66.11 244	75.37 209	87.06 206	63.27 121	90.48 244	61.38 247	82.43 191	90.40 172

mvs_anonymous		79.42 152	79.11 140	80.34 220	84.45 243	57.97 278	82.59 259	87.62 213	67.40 231	76.17 194	88.56 164	68.47 76	89.59 255	70.65 165	86.05 147	93.47 72

1112_ss		77.40 203	76.43 203	80.32 221	89.11 141	60.41 258	83.65 243	87.72 212	62.13 290	73.05 242	86.72 211	62.58 133	89.97 249	62.11 240	80.80 208	90.59 165

WR-MVS		79.49 148	79.22 137	80.27 222	88.79 148	58.35 271	85.06 211	88.61 195	78.56 28	77.65 157	88.34 169	63.81 118	90.66 242	64.98 218	77.22 246	91.80 125

1314		76.53 215	75.30 220	80.21 223	83.93 252	62.32 233	84.66 219	88.81 185	60.23 302	70.16 271	84.07 269	55.30 203	90.73 241	67.37 197	83.21 181	87.59 256

test1111		79.43 151	79.18 139	80.15 224	89.99 107	53.31 332	87.33 151	77.05 329	75.04 97	80.23 112	92.77 68	48.97 273	92.33 195	68.87 184	92.40 72	94.81 18

IterMVS-SCA-FT		75.43 232	73.87 235	80.11 225	82.69 280	64.85 185	81.57 269	83.47 273	69.16 207	70.49 265	84.15 268	51.95 236	88.15 278	69.23 179	72.14 308	87.34 261

FC-MVSNet-test		81.52 103	82.02 87	80.03 226	88.42 162	55.97 308	87.95 133	93.42 29	77.10 54	77.38 162	90.98 109	69.96 62	91.79 212	68.46 189	84.50 161	92.33 108

testdata				79.97 227	90.90 86	64.21 198		84.71 251	59.27 311	85.40 40	92.91 60	62.02 144	89.08 264	68.95 183	91.37 84	86.63 279

SCA		74.22 241	72.33 248	79.91 228	84.05 250	62.17 235	79.96 288	79.29 315	66.30 243	72.38 250	80.13 313	51.95 236	88.60 273	59.25 262	77.67 243	88.96 226

thres400		76.50 216	75.37 218	79.86 229	89.13 137	57.65 284	85.17 207	83.60 268	73.41 134	76.45 183	86.39 227	52.12 231	91.95 206	48.33 328	83.75 172	90.00 190

test_0402		72.79 257	70.44 265	79.84 230	88.13 169	65.99 158	85.93 191	84.29 259	65.57 252	67.40 298	85.49 244	46.92 283	92.61 182	35.88 358	74.38 289	80.94 337

OurMVSNet-221017-0		74.26 240	72.42 247	79.80 231	83.76 255	59.59 265	85.92 192	86.64 229	66.39 242	66.96 301	87.58 187	39.46 325	91.60 216	65.76 212	69.27 321	88.22 243

test2506		77.30 205	76.49 201	79.74 232	90.08 102	52.02 335	87.86 139	63.10 363	74.88 100	80.16 113	92.79 66	38.29 331	92.35 193	68.74 186	92.50 70	94.86 15

SixPastTwentyTwo		73.37 248	71.26 258	79.70 233	85.08 235	57.89 280	85.57 198	83.56 270	71.03 170	65.66 314	85.88 235	42.10 316	92.57 183	59.11 264	63.34 339	88.65 237

thres600view7		76.50 216	75.44 214	79.68 234	89.40 122	57.16 289	85.53 204	83.23 277	73.79 125	76.26 189	87.09 204	51.89 238	91.89 209	48.05 333	83.72 175	90.00 190

CR-MVSNet		73.37 248	71.27 257	79.67 235	81.32 304	65.19 178	75.92 318	80.30 306	59.92 305	72.73 245	81.19 301	52.50 225	86.69 289	59.84 257	77.71 241	87.11 269

D2MVS		74.82 236	73.21 240	79.64 236	79.81 319	62.56 230	80.34 283	87.35 219	64.37 265	68.86 284	82.66 289	46.37 287	90.10 248	67.91 192	81.24 203	86.25 282

AllTest		70.96 268	68.09 283	79.58 237	85.15 232	63.62 207	84.58 223	79.83 310	62.31 288	60.32 338	86.73 209	32.02 345	88.96 268	50.28 318	71.57 312	86.15 285

TestCases				79.58 237	85.15 232	63.62 207		79.83 310	62.31 288	60.32 338	86.73 209	32.02 345	88.96 268	50.28 318	71.57 312	86.15 285

tfpn200view9		76.42 219	75.37 218	79.55 239	89.13 137	57.65 284	85.17 207	83.60 268	73.41 134	76.45 183	86.39 227	52.12 231	91.95 206	48.33 328	83.75 172	89.07 215

thres100view900		76.50 216	75.55 213	79.33 240	89.52 117	56.99 292	85.83 196	83.23 277	73.94 120	76.32 188	87.12 203	51.89 238	91.95 206	48.33 328	83.75 172	89.07 215

CostFormer		75.24 235	73.90 234	79.27 241	82.65 282	58.27 273	80.80 275	82.73 284	61.57 293	75.33 213	83.13 283	55.52 201	91.07 235	64.98 218	78.34 239	88.45 240

Test_1112_low_res		76.40 220	75.44 214	79.27 241	89.28 131	58.09 274	81.69 267	87.07 224	59.53 309	72.48 248	86.67 216	61.30 156	89.33 259	60.81 252	80.15 217	90.41 171

K. test v3		71.19 266	68.51 277	79.21 243	83.04 272	57.78 283	84.35 232	76.91 330	72.90 144	62.99 331	82.86 286	39.27 326	91.09 234	61.65 244	52.66 357	88.75 234

lessismore_v0				78.97 244	81.01 307	57.15 290		65.99 358		61.16 336	82.82 287	39.12 327	91.34 225	59.67 258	46.92 363	88.43 241

pm-mvs1		77.25 207	76.68 199	78.93 245	84.22 246	58.62 270	86.41 178	88.36 198	71.37 164	73.31 238	88.01 181	61.22 159	89.15 263	64.24 222	73.01 302	89.03 221

thres200		75.55 230	74.47 228	78.82 246	87.78 185	57.85 281	83.07 256	83.51 271	72.44 147	75.84 198	84.42 262	52.08 233	91.75 213	47.41 335	83.64 176	86.86 273

VPNet		78.69 171	78.66 148	78.76 247	88.31 165	55.72 310	84.45 228	86.63 230	76.79 62	78.26 143	90.55 115	59.30 177	89.70 254	66.63 204	77.05 248	90.88 153

tpm2		73.26 251	71.46 253	78.63 248	83.34 262	56.71 297	80.65 279	80.40 305	56.63 328	73.55 236	82.02 298	51.80 240	91.24 227	56.35 292	78.42 237	87.95 246

pmmvs6		74.69 237	73.39 238	78.61 249	81.38 301	57.48 287	86.64 172	87.95 205	64.99 259	70.18 269	86.61 218	50.43 254	89.52 256	62.12 239	70.18 318	88.83 231

WR-MVS_H		78.51 175	78.49 151	78.56 250	88.02 175	56.38 303	88.43 115	92.67 61	77.14 52	73.89 234	87.55 190	66.25 95	89.24 261	58.92 267	73.55 297	90.06 188

RPSCF		73.23 252	71.46 253	78.54 251	82.50 284	59.85 262	82.18 262	82.84 283	58.96 313	71.15 262	89.41 142	45.48 299	84.77 304	58.82 269	71.83 310	91.02 150

pmmvs-eth3d		70.50 275	67.83 287	78.52 252	77.37 334	66.18 154	81.82 264	81.51 293	58.90 314	63.90 327	80.42 311	42.69 310	86.28 293	58.56 271	65.30 335	83.11 324

PatchmatchNet		73.12 253	71.33 256	78.49 253	83.18 267	60.85 250	79.63 290	78.57 318	64.13 267	71.73 256	79.81 318	51.20 245	85.97 295	57.40 282	76.36 262	88.66 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS		74.29 239	72.94 243	78.35 254	81.53 298	63.49 213	81.58 268	82.49 285	68.06 226	69.99 274	83.69 276	51.66 242	85.54 297	65.85 211	71.64 311	86.01 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ITE_SJBPF				78.22 255	81.77 294	60.57 254		83.30 275	69.25 202	67.54 294	87.20 200	36.33 337	87.28 287	54.34 299	74.62 287	86.80 274

ppachtmachnet_test		70.04 279	67.34 294	78.14 256	79.80 320	61.13 246	79.19 296	80.59 301	59.16 312	65.27 317	79.29 319	46.75 285	87.29 286	49.33 324	66.72 328	86.00 291

tfpnnormal		74.39 238	73.16 241	78.08 257	86.10 219	58.05 275	84.65 221	87.53 215	70.32 182	71.22 261	85.63 241	54.97 204	89.86 250	43.03 349	75.02 283	86.32 281

Vis-MVSNet (Re-imp)		78.36 178	78.45 152	78.07 258	88.64 154	51.78 339	86.70 171	79.63 313	74.14 117	75.11 219	90.83 110	61.29 157	89.75 252	58.10 276	91.60 80	92.69 97

TransMVSNet (Re)		75.39 234	74.56 226	77.86 259	85.50 227	57.10 291	86.78 168	86.09 239	72.17 151	71.53 258	87.34 194	63.01 129	89.31 260	56.84 288	61.83 341	87.17 265

PEN-MVS		77.73 195	77.69 176	77.84 260	87.07 207	53.91 326	87.91 136	91.18 115	77.56 41	73.14 241	88.82 155	61.23 158	89.17 262	59.95 256	72.37 305	90.43 170

CP-MVSNet		78.22 180	78.34 156	77.84 260	87.83 181	54.54 321	87.94 134	91.17 116	77.65 36	73.48 237	88.49 165	62.24 140	88.43 275	62.19 237	74.07 290	90.55 166

PS-CasMVS		78.01 189	78.09 161	77.77 262	87.71 186	54.39 323	88.02 130	91.22 113	77.50 44	73.26 239	88.64 160	60.73 165	88.41 276	61.88 241	73.88 294	90.53 167

baseline1		76.98 210	76.75 197	77.66 263	88.13 169	55.66 311	85.12 210	81.89 289	73.04 141	76.79 176	88.90 152	62.43 136	87.78 283	63.30 228	71.18 314	89.55 208

OpenMVS_ROB	64.09 19	70.56 274	68.19 280	77.65 264	80.26 313	59.41 267	85.01 212	82.96 282	58.76 315	65.43 316	82.33 292	37.63 334	91.23 228	45.34 345	76.03 264	82.32 329

Patchmatch-RL test		70.24 277	67.78 289	77.61 265	77.43 333	59.57 266	71.16 335	70.33 347	62.94 281	68.65 286	72.77 347	50.62 251	85.49 298	69.58 177	66.58 330	87.77 251

Baseline_NR-MVSNet		78.15 184	78.33 157	77.61 265	85.79 221	56.21 306	86.78 168	85.76 242	73.60 129	77.93 153	87.57 188	65.02 108	88.99 265	67.14 201	75.33 278	87.63 253

DTE-MVSNet		76.99 209	76.80 193	77.54 267	86.24 217	53.06 334	87.52 145	90.66 127	77.08 55	72.50 247	88.67 159	60.48 171	89.52 256	57.33 283	70.74 316	90.05 189

LCM-MVSNet-Re		77.05 208	76.94 190	77.36 268	87.20 204	51.60 340	80.06 285	80.46 304	75.20 95	67.69 293	86.72 211	62.48 134	88.98 266	63.44 226	89.25 107	91.51 130

tpm cat1		70.57 273	68.31 279	77.35 269	82.41 287	57.95 279	78.08 306	80.22 308	52.04 340	68.54 288	77.66 333	52.00 235	87.84 282	51.77 309	72.07 309	86.25 282

MS-PatchMatch		73.83 245	72.67 244	77.30 270	83.87 253	66.02 156	81.82 264	84.66 252	61.37 296	68.61 287	82.82 287	47.29 280	88.21 277	59.27 261	84.32 165	77.68 346

MVS_0304		72.48 258	70.89 261	77.24 271	82.20 289	59.68 263	84.11 237	83.49 272	67.10 232	66.87 303	80.59 309	35.00 341	87.40 285	59.07 266	79.58 222	84.63 307

EPNet_dtu		75.46 231	74.86 222	77.23 272	82.57 283	54.60 320	86.89 163	83.09 280	71.64 155	66.25 312	85.86 236	55.99 200	88.04 280	54.92 296	86.55 139	89.05 220
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_lstm_enhance		74.11 242	73.11 242	77.13 273	80.11 315	59.62 264	72.23 332	86.92 227	66.76 235	70.40 266	82.92 284	56.93 197	82.92 316	69.06 182	72.63 304	88.87 229

TDRefinement		67.49 295	64.34 304	76.92 274	73.47 350	61.07 247	84.86 216	82.98 281	59.77 306	58.30 345	85.13 253	26.06 353	87.89 281	47.92 334	60.59 346	81.81 333

JIA-IIPM		66.32 304	62.82 314	76.82 275	77.09 335	61.72 242	65.34 356	75.38 335	58.04 320	64.51 322	62.32 357	42.05 317	86.51 291	51.45 312	69.22 322	82.21 330

PatchMatch-RL		72.38 260	70.90 260	76.80 276	88.60 155	67.38 134	79.53 291	76.17 334	62.75 284	69.36 282	82.00 299	45.51 297	84.89 303	53.62 302	80.58 211	78.12 345

tpmvs		71.09 267	69.29 272	76.49 277	82.04 291	56.04 307	78.92 299	81.37 295	64.05 270	67.18 300	78.28 328	49.74 262	89.77 251	49.67 323	72.37 305	83.67 318

CMPMVS	51.72 21	70.19 278	68.16 281	76.28 278	73.15 352	57.55 286	79.47 292	83.92 264	48.02 348	56.48 351	84.81 258	43.13 307	86.42 292	62.67 234	81.81 198	84.89 303
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		70.33 276	68.37 278	76.21 279	80.60 310	56.23 305	79.19 296	86.49 231	60.89 297	61.29 335	85.47 245	31.78 347	89.47 258	53.37 304	76.21 263	82.94 328

gg-mvs-nofinetune		69.95 280	67.96 284	75.94 280	83.07 270	54.51 322	77.23 313	70.29 348	63.11 277	70.32 267	62.33 356	43.62 305	88.69 272	53.88 301	87.76 122	84.62 308

MDA-MVSNet-bldmvs		66.68 300	63.66 308	75.75 281	79.28 327	60.56 255	73.92 329	78.35 319	64.43 263	50.13 358	79.87 317	44.02 303	83.67 310	46.10 341	56.86 349	83.03 326

PVSNet	64.34 18	72.08 263	70.87 262	75.69 282	86.21 218	56.44 301	74.37 328	80.73 299	62.06 291	70.17 270	82.23 295	42.86 309	83.31 314	54.77 297	84.45 164	87.32 262

pmmvs5		71.55 264	70.20 269	75.61 283	77.83 331	56.39 302	81.74 266	80.89 296	57.76 321	67.46 296	84.49 261	49.26 268	85.32 300	57.08 285	75.29 279	85.11 302

our_test_3		69.14 285	67.00 296	75.57 284	79.80 320	58.80 268	77.96 307	77.81 321	59.55 308	62.90 332	78.25 329	47.43 279	83.97 308	51.71 310	67.58 327	83.93 316

WTY-MVS		75.65 229	75.68 210	75.57 284	86.40 216	56.82 294	77.92 309	82.40 286	65.10 255	76.18 192	87.72 183	63.13 128	80.90 324	60.31 254	81.96 195	89.00 224

Patchmtry		70.74 271	69.16 274	75.49 286	80.72 308	54.07 325	74.94 327	80.30 306	58.34 317	70.01 272	81.19 301	52.50 225	86.54 290	53.37 304	71.09 315	85.87 293

GG-mvs-BLEND				75.38 287	81.59 297	55.80 309	79.32 293	69.63 350		67.19 299	73.67 346	43.24 306	88.90 270	50.41 315	84.50 161	81.45 334

ambc				75.24 288	73.16 351	50.51 347	63.05 361	87.47 217		64.28 323	77.81 332	17.80 363	89.73 253	57.88 278	60.64 345	85.49 295

CL-MVSNet_self_test		72.37 261	71.46 253	75.09 289	79.49 325	53.53 328	80.76 277	85.01 249	69.12 208	70.51 264	82.05 297	57.92 186	84.13 307	52.27 308	66.00 333	87.60 254

XXY-MVS		75.41 233	75.56 212	74.96 290	83.59 257	57.82 282	80.59 280	83.87 266	66.54 241	74.93 224	88.31 170	63.24 122	80.09 327	62.16 238	76.85 252	86.97 271

MIMVSNet		70.69 272	69.30 271	74.88 291	84.52 241	56.35 304	75.87 320	79.42 314	64.59 261	67.76 291	82.41 291	41.10 320	81.54 321	46.64 339	81.34 201	86.75 276

ADS-MVSNet2		66.20 307	63.33 309	74.82 292	79.92 317	58.75 269	67.55 349	75.19 336	53.37 337	65.25 318	75.86 340	42.32 312	80.53 326	41.57 352	68.91 323	85.18 299

TinyColmap		67.30 298	64.81 302	74.76 293	81.92 293	56.68 298	80.29 284	81.49 294	60.33 300	56.27 352	83.22 280	24.77 355	87.66 284	45.52 343	69.47 320	79.95 341

test-LLR		72.94 256	72.43 246	74.48 294	81.35 302	58.04 276	78.38 302	77.46 324	66.66 237	69.95 275	79.00 322	48.06 277	79.24 328	66.13 206	84.83 157	86.15 285

test-mter		71.41 265	70.39 267	74.48 294	81.35 302	58.04 276	78.38 302	77.46 324	60.32 301	69.95 275	79.00 322	36.08 338	79.24 328	66.13 206	84.83 157	86.15 285

tpm		72.37 261	71.71 252	74.35 296	82.19 290	52.00 336	79.22 295	77.29 327	64.56 262	72.95 243	83.68 277	51.35 243	83.26 315	58.33 274	75.80 266	87.81 250

CVMVSNet		72.99 255	72.58 245	74.25 297	84.28 244	50.85 345	86.41 178	83.45 274	44.56 351	73.23 240	87.54 191	49.38 265	85.70 296	65.90 210	78.44 236	86.19 284

FMVSNet5		69.50 283	67.96 284	74.15 298	82.97 275	55.35 313	80.01 287	82.12 288	62.56 286	63.02 329	81.53 300	36.92 335	81.92 319	48.42 327	74.06 291	85.17 301

MIMVSNet1		68.58 290	66.78 298	73.98 299	80.07 316	51.82 338	80.77 276	84.37 256	64.40 264	59.75 341	82.16 296	36.47 336	83.63 311	42.73 350	70.33 317	86.48 280

Anonymous20240521		68.80 288	67.22 295	73.55 300	74.33 344	54.11 324	83.18 251	85.61 243	58.15 318	61.68 334	80.94 306	30.71 349	81.27 323	57.00 286	73.34 301	85.28 298

sss		73.60 246	73.64 237	73.51 301	82.80 277	55.01 317	76.12 316	81.69 292	62.47 287	74.68 227	85.85 237	57.32 193	78.11 334	60.86 251	80.93 205	87.39 259

KD-MVS_2432*1600		66.22 305	63.89 306	73.21 302	75.47 342	53.42 330	70.76 338	84.35 257	64.10 268	66.52 308	78.52 326	34.55 342	84.98 301	50.40 316	50.33 360	81.23 335

miper_refine_blended		66.22 305	63.89 306	73.21 302	75.47 342	53.42 330	70.76 338	84.35 257	64.10 268	66.52 308	78.52 326	34.55 342	84.98 301	50.40 316	50.33 360	81.23 335

PM-MVS		66.41 303	64.14 305	73.20 304	73.92 346	56.45 300	78.97 298	64.96 361	63.88 274	64.72 321	80.24 312	19.84 361	83.44 313	66.24 205	64.52 337	79.71 342

tpmrst		72.39 259	72.13 249	73.18 305	80.54 311	49.91 349	79.91 289	79.08 316	63.11 277	71.69 257	79.95 315	55.32 202	82.77 317	65.66 213	73.89 293	86.87 272

test_fmvs1_n		70.86 270	70.24 268	72.73 306	72.51 355	55.28 314	81.27 273	79.71 312	51.49 344	78.73 128	84.87 257	27.54 352	77.02 339	76.06 114	79.97 220	85.88 292

TESTMET0.1,1		69.89 281	69.00 275	72.55 307	79.27 328	56.85 293	78.38 302	74.71 340	57.64 322	68.09 290	77.19 335	37.75 333	76.70 341	63.92 223	84.09 168	84.10 314

KD-MVS_self_test		68.81 287	67.59 292	72.46 308	74.29 345	45.45 357	77.93 308	87.00 225	63.12 276	63.99 326	78.99 324	42.32 312	84.77 304	56.55 291	64.09 338	87.16 267

test_fmvs1		70.93 269	70.52 263	72.16 309	73.71 347	55.05 316	80.82 274	78.77 317	51.21 345	78.58 134	84.41 263	31.20 348	76.94 340	75.88 117	80.12 219	84.47 309

CHOSEN 280x420		66.51 302	64.71 303	71.90 310	81.45 299	63.52 212	57.98 363	68.95 354	53.57 336	62.59 333	76.70 336	46.22 289	75.29 351	55.25 295	79.68 221	76.88 348

test_vis1_n		69.85 282	69.21 273	71.77 311	72.66 354	55.27 315	81.48 270	76.21 333	52.03 341	75.30 214	83.20 282	28.97 350	76.22 345	74.60 127	78.41 238	83.81 317

EPMVS		69.02 286	68.16 281	71.59 312	79.61 323	49.80 351	77.40 311	66.93 356	62.82 283	70.01 272	79.05 320	45.79 294	77.86 336	56.58 290	75.26 280	87.13 268

YYNet1		65.03 308	62.91 312	71.38 313	75.85 338	56.60 299	69.12 346	74.66 341	57.28 325	54.12 354	77.87 331	45.85 293	74.48 353	49.95 321	61.52 343	83.05 325

MDA-MVSNet_test_wron		65.03 308	62.92 311	71.37 314	75.93 337	56.73 295	69.09 347	74.73 339	57.28 325	54.03 355	77.89 330	45.88 292	74.39 354	49.89 322	61.55 342	82.99 327

UnsupCasMVSNet_eth		67.33 297	65.99 300	71.37 314	73.48 349	51.47 342	75.16 323	85.19 246	65.20 254	60.78 337	80.93 308	42.35 311	77.20 338	57.12 284	53.69 356	85.44 296

PMMVS		69.34 284	68.67 276	71.35 316	75.67 339	62.03 236	75.17 322	73.46 343	50.00 346	68.68 285	79.05 320	52.07 234	78.13 333	61.16 249	82.77 186	73.90 351

EU-MVSNet		68.53 291	67.61 291	71.31 317	78.51 330	47.01 355	84.47 225	84.27 260	42.27 354	66.44 311	84.79 259	40.44 323	83.76 309	58.76 270	68.54 326	83.17 322

Anonymous20231206		68.60 289	67.80 288	71.02 318	80.23 314	50.75 346	78.30 305	80.47 303	56.79 327	66.11 313	82.63 290	46.35 288	78.95 330	43.62 348	75.70 267	83.36 321

test_fmvs2		68.35 293	67.48 293	70.98 319	69.50 358	51.95 337	80.05 286	76.38 332	49.33 347	74.65 228	84.38 264	23.30 358	75.40 350	74.51 128	75.17 282	85.60 294

dp		66.80 299	65.43 301	70.90 320	79.74 322	48.82 352	75.12 325	74.77 338	59.61 307	64.08 325	77.23 334	42.89 308	80.72 325	48.86 326	66.58 330	83.16 323

PatchT		68.46 292	67.85 286	70.29 321	80.70 309	43.93 364	72.47 331	74.88 337	60.15 303	70.55 263	76.57 337	49.94 259	81.59 320	50.58 314	74.83 285	85.34 297

UnsupCasMVSNet_bld		63.70 313	61.53 318	70.21 322	73.69 348	51.39 343	72.82 330	81.89 289	55.63 332	57.81 347	71.80 349	38.67 328	78.61 331	49.26 325	52.21 358	80.63 338

Patchmatch-test		64.82 310	63.24 310	69.57 323	79.42 326	49.82 350	63.49 360	69.05 353	51.98 342	59.95 340	80.13 313	50.91 247	70.98 358	40.66 354	73.57 296	87.90 248

LF4IMVS		64.02 312	62.19 315	69.50 324	70.90 356	53.29 333	76.13 315	77.18 328	52.65 339	58.59 343	80.98 305	23.55 357	76.52 342	53.06 306	66.66 329	78.68 344

test20.03		67.45 296	66.95 297	68.94 325	75.48 341	44.84 362	77.50 310	77.67 322	66.66 237	63.01 330	83.80 273	47.02 282	78.40 332	42.53 351	68.86 325	83.58 319

test0.0.03 1		68.00 294	67.69 290	68.90 326	77.55 332	47.43 353	75.70 321	72.95 345	66.66 237	66.56 306	82.29 294	48.06 277	75.87 347	44.97 346	74.51 288	83.41 320

PVSNet_0	57.27 20	61.67 317	59.27 320	68.85 327	79.61 323	57.44 288	68.01 348	73.44 344	55.93 331	58.54 344	70.41 352	44.58 301	77.55 337	47.01 336	35.91 366	71.55 354

ADS-MVSNet		64.36 311	62.88 313	68.78 328	79.92 317	47.17 354	67.55 349	71.18 346	53.37 337	65.25 318	75.86 340	42.32 312	73.99 355	41.57 352	68.91 323	85.18 299

pmmvs3		57.79 320	54.26 324	68.37 329	64.02 364	56.72 296	75.12 325	65.17 359	40.20 356	52.93 356	69.86 353	20.36 360	75.48 349	45.45 344	55.25 355	72.90 353

test_fmvs3		63.36 314	61.82 316	67.98 330	62.51 365	46.96 356	77.37 312	74.03 342	45.24 350	67.50 295	78.79 325	12.16 369	72.98 357	72.77 148	66.02 332	83.99 315

LCM-MVSNet		54.25 322	49.68 331	67.97 331	53.73 373	45.28 360	66.85 352	80.78 298	35.96 362	39.45 363	62.23 358	8.70 373	78.06 335	48.24 331	51.20 359	80.57 339

EGC-MVSNET		52.07 328	47.05 332	67.14 332	83.51 259	60.71 252	80.50 281	67.75 355	0.07 376	0.43 377	75.85 342	24.26 356	81.54 321	28.82 362	62.25 340	59.16 361

testgi		66.67 301	66.53 299	67.08 333	75.62 340	41.69 368	75.93 317	76.50 331	66.11 244	65.20 320	86.59 219	35.72 339	74.71 352	43.71 347	73.38 300	84.84 304

test_vis1_rt		60.28 318	58.42 321	65.84 334	67.25 361	55.60 312	70.44 340	60.94 365	44.33 352	59.00 342	66.64 354	24.91 354	68.67 362	62.80 230	69.48 319	73.25 352

mvsany_test1		62.30 315	61.26 319	65.41 335	69.52 357	54.86 318	66.86 351	49.78 372	46.65 349	68.50 289	83.21 281	49.15 269	66.28 364	56.93 287	60.77 344	75.11 350

ANet_high		50.57 330	46.10 334	63.99 336	48.67 376	39.13 369	70.99 337	80.85 297	61.39 295	31.18 365	57.70 363	17.02 364	73.65 356	31.22 361	15.89 373	79.18 343

MVS-HIRNet		59.14 319	57.67 322	63.57 337	81.65 295	43.50 365	71.73 333	65.06 360	39.59 358	51.43 357	57.73 362	38.34 330	82.58 318	39.53 355	73.95 292	64.62 358

APD_test1		53.31 325	49.93 330	63.42 338	65.68 362	50.13 348	71.59 334	66.90 357	34.43 363	40.58 362	71.56 350	8.65 374	76.27 344	34.64 360	55.36 354	63.86 359

new-patchmatchnet		61.73 316	61.73 317	61.70 339	72.74 353	24.50 378	69.16 345	78.03 320	61.40 294	56.72 350	75.53 343	38.42 329	76.48 343	45.95 342	57.67 348	84.13 313

mvsany_test3		53.99 323	51.45 327	61.61 340	55.51 369	44.74 363	63.52 359	45.41 376	43.69 353	58.11 346	76.45 338	17.99 362	63.76 367	54.77 297	47.59 362	76.34 349

DSMNet-mixed		57.77 321	56.90 323	60.38 341	67.70 360	35.61 371	69.18 344	53.97 370	32.30 366	57.49 348	79.88 316	40.39 324	68.57 363	38.78 356	72.37 305	76.97 347

FPMVS		53.68 324	51.64 326	59.81 342	65.08 363	51.03 344	69.48 343	69.58 351	41.46 355	40.67 361	72.32 348	16.46 365	70.00 361	24.24 368	65.42 334	58.40 363

testf1		45.72 332	41.96 335	57.00 343	56.90 367	45.32 358	66.14 354	59.26 366	26.19 367	30.89 366	60.96 360	4.14 377	70.64 359	26.39 366	46.73 364	55.04 364

APD_test2		45.72 332	41.96 335	57.00 343	56.90 367	45.32 358	66.14 354	59.26 366	26.19 367	30.89 366	60.96 360	4.14 377	70.64 359	26.39 366	46.73 364	55.04 364

test_vis3_rt		49.26 331	47.02 333	56.00 345	54.30 370	45.27 361	66.76 353	48.08 373	36.83 360	44.38 360	53.20 365	7.17 376	64.07 366	56.77 289	55.66 352	58.65 362

test_f		52.09 327	50.82 328	55.90 346	53.82 372	42.31 367	59.42 362	58.31 368	36.45 361	56.12 353	70.96 351	12.18 368	57.79 369	53.51 303	56.57 351	67.60 355

PMVS	37.38 22	44.16 335	40.28 338	55.82 347	40.82 378	42.54 366	65.12 357	63.99 362	34.43 363	24.48 369	57.12 364	3.92 379	76.17 346	17.10 371	55.52 353	48.75 366
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 334	41.86 337	55.16 348	77.03 336	51.52 341	32.50 369	80.52 302	32.46 365	27.12 368	35.02 369	9.52 372	75.50 348	22.31 369	60.21 347	38.45 368
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new_pmnet		50.91 329	50.29 329	52.78 349	68.58 359	34.94 373	63.71 358	56.63 369	39.73 357	44.95 359	65.47 355	21.93 359	58.48 368	34.98 359	56.62 350	64.92 357

N_pmnet		52.79 326	53.26 325	51.40 350	78.99 329	7.68 381	69.52 342	3.89 381	51.63 343	57.01 349	74.98 344	40.83 322	65.96 365	37.78 357	64.67 336	80.56 340

PMMVS2		40.82 336	38.86 339	46.69 351	53.84 371	16.45 379	48.61 366	49.92 371	37.49 359	31.67 364	60.97 359	8.14 375	56.42 370	28.42 363	30.72 368	67.19 356

MVE	26.22 23	30.37 340	25.89 344	43.81 352	44.55 377	35.46 372	28.87 370	39.07 377	18.20 371	18.58 373	40.18 368	2.68 380	47.37 374	17.07 372	23.78 370	48.60 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		31.52 338	29.28 342	38.23 353	27.03 380	6.50 382	20.94 371	62.21 364	4.05 374	22.35 372	52.50 366	13.33 366	47.58 373	27.04 365	34.04 367	60.62 360

E-PMN		31.77 337	30.64 340	35.15 354	52.87 374	27.67 375	57.09 364	47.86 374	24.64 369	16.40 374	33.05 370	11.23 370	54.90 371	14.46 373	18.15 371	22.87 370

EMVS		30.81 339	29.65 341	34.27 355	50.96 375	25.95 377	56.58 365	46.80 375	24.01 370	15.53 375	30.68 371	12.47 367	54.43 372	12.81 374	17.05 372	22.43 371

DeepMVS_CX				27.40 356	40.17 379	26.90 376		24.59 380	17.44 372	23.95 370	48.61 367	9.77 371	26.48 375	18.06 370	24.47 369	28.83 369

wuyk23d		16.82 343	15.94 346	19.46 357	58.74 366	31.45 374	39.22 367	3.74 382	6.84 373	6.04 376	2.70 376	1.27 381	24.29 376	10.54 375	14.40 375	2.63 373

tmp_tt		18.61 342	21.40 345	10.23 358	4.82 381	10.11 380	34.70 368	30.74 379	1.48 375	23.91 371	26.07 372	28.42 351	13.41 377	27.12 364	15.35 374	7.17 372

test123		6.12 345	8.11 348	0.14 359	0.06 383	0.09 383	71.05 336	0.03 384	0.04 378	0.25 379	1.30 378	0.05 382	0.03 379	0.21 377	0.01 377	0.29 374

testmvs		6.04 346	8.02 349	0.10 360	0.08 382	0.03 384	69.74 341	0.04 383	0.05 377	0.31 378	1.68 377	0.02 383	0.04 378	0.24 376	0.02 376	0.25 375

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

cdsmvs_eth3d_5k		19.96 341	26.61 343	0.00 361	0.00 384	0.00 385	0.00 372	89.26 167	0.00 379	0.00 380	88.61 161	61.62 148	0.00 380	0.00 378	0.00 378	0.00 376

pcd_1.5k_mvsjas		5.26 347	7.02 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	63.15 125	0.00 380	0.00 378	0.00 378	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

ab-mvs-re		7.23 344	9.64 347	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	86.72 211	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 378	0.00 376

FOURS1					95.00 10	72.39 38	95.06 1	93.84 15	74.49 109	91.30 15

PC_three_1452									68.21 225	92.02 12	94.00 40	82.09 5	95.98 49	84.58 35	96.68 2	94.95 8

test_one_0601					95.07 7	71.46 53		94.14 5	78.27 33	92.05 11	95.74 6	80.83 11

eth-test2					0.00 384

eth-test					0.00 384

ZD-MVS					94.38 25	72.22 43		92.67 61	70.98 171	87.75 28	94.07 37	74.01 32	96.70 25	84.66 34	94.84 41

RE-MVS-def			85.48 49		93.06 55	70.63 71	91.88 37	92.27 76	73.53 132	85.69 38	94.45 24	63.87 116		82.75 55	91.87 77	92.50 103

IU-MVS					95.30 2	71.25 55		92.95 51	66.81 233	92.39 6				88.94 11	96.63 4	94.85 17

test_241102_TWO								94.06 10	77.24 48	92.78 4	95.72 8	81.26 8	97.44 5	89.07 9	96.58 6	94.26 39

test_241102_ONE					95.30 2	70.98 61		94.06 10	77.17 51	93.10 1	95.39 11	82.99 1	97.27 10

9.14			88.26 14		92.84 60		91.52 44	94.75 1	73.93 121	88.57 22	94.67 17	75.57 22	95.79 51	86.77 22	95.76 23

save fliter					93.80 40	72.35 41	90.47 62	91.17 116	74.31 112

test_0728_THIRD									78.38 31	92.12 9	95.78 4	81.46 7	97.40 7	89.42 4	96.57 7	94.67 22

test0726					95.27 5	71.25 55	93.60 6	94.11 6	77.33 46	92.81 3	95.79 3	80.98 9

GSMVS																88.96 226

test_part2					95.06 8	72.65 31				91.80 13

sam_mvs1												51.32 244				88.96 226

sam_mvs												50.01 257

MTGPA								92.02 85

test_post1							78.90 300				5.43 375	48.81 276	85.44 299	59.25 262

test_post											5.46 374	50.36 255	84.24 306

patchmatchnet-post											74.00 345	51.12 246	88.60 273

MTMP							92.18 33	32.83 378

gm-plane-assit					81.40 300	53.83 327			62.72 285		80.94 306		92.39 190	63.40 227

test9_res														84.90 29	95.70 26	92.87 92

TEST9					93.26 50	72.96 24	88.75 106	91.89 93	68.44 222	85.00 45	93.10 54	74.36 28	95.41 65

test_8					93.13 52	72.57 34	88.68 111	91.84 97	68.69 218	84.87 49	93.10 54	74.43 26	95.16 74

agg_prior2														82.91 53	95.45 28	92.70 95

agg_prior					92.85 59	71.94 49		91.78 100		84.41 59			94.93 85

test_prior4						72.60 33	89.01 96

test_prior2							88.85 102		75.41 91	84.91 47	93.54 46	74.28 29		83.31 48	95.86 20

旧先验2							86.56 175		58.10 319	87.04 30			88.98 266	74.07 133

新几何2							86.29 183

旧先验1					91.96 71	65.79 165		86.37 234			93.08 58	69.31 70			92.74 66	88.74 235

无先验							87.48 146	88.98 179	60.00 304				94.12 119	67.28 198		88.97 225

原ACMM2							86.86 164

test222					91.50 77	68.26 115	84.16 235	83.20 279	54.63 335	79.74 115	91.63 86	58.97 179			91.42 83	86.77 275

testdata2													91.01 236	62.37 236

segment_acmp												73.08 37

testdata1							84.14 236		75.71 85

plane_prior7					90.08 102	68.51 111

plane_prior6					89.84 111	68.70 106						60.42 172

plane_prior5								92.44 69					95.38 67	78.71 88	86.32 142	91.33 136

plane_prior4											91.00 107

plane_prior3						68.60 109			78.44 29	78.92 126

plane_prior2							91.25 48		79.12 21

plane_prior1					89.90 110

plane_prior						68.71 104	90.38 65		77.62 37						86.16 146

n2								0.00 385

nn								0.00 385

door-mid								69.98 349

test11								92.23 79

door								69.44 352

HQP5-MVS						66.98 142

HQP-NCC					89.33 126		89.17 89		76.41 70	77.23 167

ACMP_Plane					89.33 126		89.17 89		76.41 70	77.23 167

BP-MVS														77.47 100

HQP4-MVS										77.24 166			95.11 78			91.03 148

HQP3-MVS								92.19 82							85.99 149

HQP2-MVS												60.17 175

NP-MVS					89.62 113	68.32 113					90.24 119

MDTV_nov1_ep13_2view						37.79 370	75.16 323		55.10 333	66.53 307		49.34 266		53.98 300		87.94 247

MDTV_nov1_ep13			69.97 270		83.18 267	53.48 329	77.10 314	80.18 309	60.45 299	69.33 283	80.44 310	48.89 275	86.90 288	51.60 311	78.51 235

ACMMP++_ref															81.95 196

ACMMP++															81.25 202

Test By Simon												64.33 112

High-resolution multi-view benchmark