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.

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 57	95.06 1	94.23 3	78.38 33	92.78 4	95.74 6	82.45 3	97.49 4	89.42 9	96.68 2	94.95 10

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 63	93.57 7	94.06 10	77.24 50	93.10 1	95.72 8	82.99 1	97.44 6	89.07 14	96.63 4	94.88 14

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 57	93.49 9	92.73 59	77.33 48	92.12 9	95.78 4	80.98 9	97.40 8	89.08 12	96.41 12	93.33 84
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
DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 85	92.29 7	95.66 10	81.67 6	97.38 10	87.44 33	96.34 15	93.95 52
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 37	78.35 13	96.77 24	89.59 8	94.22 58	94.67 24
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
APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 89	91.06 16	96.03 1	76.84 14	97.03 17	89.09 11	95.65 27	94.47 31
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MM		89.16 6	89.23 7	88.97 4	90.79 90	73.65 10	92.66 23	91.17 116	86.57 1	87.39 35	94.97 16	71.70 50	97.68 1	92.19 1	95.63 28	95.57 1

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 107	92.29 7	95.97 2	74.28 29	97.24 12	88.58 21	96.91 1	94.87 16
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 54	80.26 11	87.78 29	94.27 32	75.89 19	96.81 23	87.45 32	96.44 9	93.05 96

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 61	93.00 43	80.90 7	88.06 26	94.06 42	76.43 16	96.84 21	88.48 24	95.99 18	94.34 37

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 69	72.96 25	93.73 5	93.67 20	80.19 12	88.10 25	94.80 17	73.76 33	97.11 15	87.51 31	95.82 21	94.90 13
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 61	71.95 50	92.40 24	94.74 2	75.71 87	89.16 19	95.10 14	75.65 21	96.19 43	87.07 34	96.01 17	94.79 21

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 50	92.24 68	69.03 99	89.57 87	93.39 30	77.53 45	89.79 18	94.12 39	78.98 12	96.58 35	85.66 37	95.72 24	94.58 27

SD-MVS		88.06 15	88.50 14	86.71 51	92.60 66	72.71 29	91.81 42	93.19 35	77.87 36	90.32 17	94.00 46	74.83 23	93.78 136	87.63 30	94.27 57	93.65 69
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
9.14			88.26 15		92.84 60		91.52 46	94.75 1	73.93 125	88.57 22	94.67 19	75.57 22	95.79 53	86.77 35	95.76 23

TSAR-MVS + MP.		88.02 18	88.11 16	87.72 30	93.68 43	72.13 46	91.41 47	92.35 74	74.62 111	88.90 20	93.85 52	75.75 20	96.00 49	87.80 28	94.63 47	95.04 7
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MVS_0304		88.08 14	88.08 17	88.08 14	89.67 114	72.04 48	92.26 33	89.26 173	84.19 2	85.01 55	95.18 13	69.93 69	97.20 14	91.63 2	95.60 29	94.99 9

ACMMP_NAP		88.05 17	88.08 17	87.94 19	93.70 41	73.05 22	90.86 56	93.59 23	76.27 79	88.14 24	95.09 15	71.06 57	96.67 29	87.67 29	96.37 14	94.09 46

NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 52	92.83 55	81.50 5	85.79 48	93.47 60	73.02 39	97.00 18	84.90 42	94.94 39	94.10 45

ZNCC-MVS		87.94 19	87.85 20	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 63	85.24 53	94.32 31	71.76 48	96.93 19	85.53 39	95.79 22	94.32 38

MP-MVS-pluss		87.67 21	87.72 21	87.54 36	93.64 44	72.04 48	89.80 79	93.50 25	75.17 100	86.34 44	95.29 12	70.86 59	96.00 49	88.78 19	96.04 16	94.58 27
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		87.71 20	87.64 22	87.93 21	94.36 26	73.88 6	92.71 22	92.65 64	77.57 41	83.84 82	94.40 30	72.24 43	96.28 40	85.65 38	95.30 35	93.62 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		87.44 23	87.52 23	87.19 42	94.24 32	72.39 39	91.86 41	92.83 55	73.01 150	88.58 21	94.52 21	73.36 34	96.49 36	84.26 52	95.01 37	92.70 105
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		87.58 22	87.47 24	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 65	84.91 59	94.44 28	70.78 60	96.61 32	84.53 49	94.89 41	93.66 65

GST-MVS		87.42 25	87.26 25	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 61	84.68 63	93.99 48	70.67 62	96.82 22	84.18 56	95.01 37	93.90 55

MCST-MVS		87.37 27	87.25 26	87.73 28	94.53 17	72.46 38	89.82 77	93.82 16	73.07 148	84.86 62	92.89 74	76.22 17	96.33 38	84.89 44	95.13 36	94.40 34

ACMMPR		87.44 23	87.23 27	88.08 14	94.64 13	73.59 12	93.04 12	93.20 34	76.78 65	84.66 66	94.52 21	68.81 84	96.65 30	84.53 49	94.90 40	94.00 50

region2R		87.42 25	87.20 28	88.09 13	94.63 14	73.55 13	93.03 14	93.12 37	76.73 68	84.45 70	94.52 21	69.09 78	96.70 27	84.37 51	94.83 44	94.03 49

MTAPA		87.23 28	87.00 29	87.90 22	94.18 35	74.25 5	86.58 187	92.02 85	79.45 19	85.88 46	94.80 17	68.07 89	96.21 42	86.69 36	95.34 33	93.23 87

HPM-MVS		87.11 30	86.98 30	87.50 38	93.88 39	72.16 45	92.19 34	93.33 31	76.07 82	83.81 83	93.95 51	69.77 72	96.01 48	85.15 40	94.66 46	94.32 38
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS		86.69 35	86.95 31	85.90 63	90.76 91	67.57 140	92.83 17	93.30 32	79.67 17	84.57 69	92.27 86	71.47 53	95.02 86	84.24 54	93.46 63	95.13 6

CP-MVS		87.11 30	86.92 32	87.68 34	94.20 34	73.86 7	93.98 3	92.82 58	76.62 70	83.68 84	94.46 25	67.93 90	95.95 52	84.20 55	94.39 53	93.23 87

XVS		87.18 29	86.91 33	88.00 17	94.42 20	73.33 19	92.78 18	92.99 45	79.14 21	83.67 85	94.17 36	67.45 95	96.60 33	83.06 63	94.50 50	94.07 47

DeepC-MVS	79.81 2	87.08 32	86.88 34	87.69 33	91.16 80	72.32 43	90.31 68	93.94 14	77.12 55	82.82 96	94.23 35	72.13 45	97.09 16	84.83 45	95.37 32	93.65 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		86.73 34	86.67 35	86.91 46	94.11 37	72.11 47	92.37 28	92.56 67	74.50 112	86.84 42	94.65 20	67.31 97	95.77 54	84.80 46	92.85 67	92.84 103

DeepC-MVS_fast	79.65 3	86.91 33	86.62 36	87.76 27	93.52 46	72.37 41	91.26 48	93.04 38	76.62 70	84.22 74	93.36 63	71.44 54	96.76 25	80.82 85	95.33 34	94.16 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CS-MVS-test		86.29 42	86.48 37	85.71 65	91.02 83	67.21 152	92.36 29	93.78 18	78.97 28	83.51 88	91.20 111	70.65 63	95.15 77	81.96 76	94.89 41	94.77 22

EC-MVSNet		86.01 43	86.38 38	84.91 88	89.31 131	66.27 166	92.32 30	93.63 21	79.37 20	84.17 76	91.88 93	69.04 82	95.43 65	83.93 57	93.77 61	93.01 99

mPP-MVS		86.67 37	86.32 39	87.72 30	94.41 22	73.55 13	92.74 20	92.22 80	76.87 62	82.81 97	94.25 34	66.44 105	96.24 41	82.88 67	94.28 56	93.38 81

PGM-MVS		86.68 36	86.27 40	87.90 22	94.22 33	73.38 18	90.22 70	93.04 38	75.53 91	83.86 81	94.42 29	67.87 92	96.64 31	82.70 72	94.57 49	93.66 65

train_agg		86.43 39	86.20 41	87.13 44	93.26 50	72.96 25	88.75 115	91.89 93	68.69 235	85.00 57	93.10 67	74.43 26	95.41 67	84.97 41	95.71 25	93.02 98

CSCG		86.41 41	86.19 42	87.07 45	92.91 58	72.48 37	90.81 57	93.56 24	73.95 123	83.16 91	91.07 116	75.94 18	95.19 75	79.94 94	94.38 54	93.55 76

PHI-MVS		86.43 39	86.17 43	87.24 41	90.88 87	70.96 65	92.27 32	94.07 9	72.45 153	85.22 54	91.90 92	69.47 74	96.42 37	83.28 62	95.94 19	94.35 36

dcpmvs_2		85.63 52	86.15 44	84.06 125	91.71 75	64.94 197	86.47 190	91.87 95	73.63 131	86.60 43	93.02 72	76.57 15	91.87 216	83.36 60	92.15 75	95.35 3

CANet		86.45 38	86.10 45	87.51 37	90.09 102	70.94 67	89.70 83	92.59 66	81.78 4	81.32 112	91.43 106	70.34 64	97.23 13	84.26 52	93.36 64	94.37 35

casdiffmvs_mvg		85.99 44	86.09 46	85.70 66	87.65 196	67.22 151	88.69 119	93.04 38	79.64 18	85.33 52	92.54 83	73.30 35	94.50 107	83.49 59	91.14 89	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_fmvsmconf_n		85.92 46	86.04 47	85.57 68	85.03 251	69.51 90	89.62 86	90.58 131	73.42 138	87.75 31	94.02 44	72.85 40	93.24 160	90.37 3	90.75 93	93.96 51

APD-MVS_3200maxsize		85.97 45	85.88 48	86.22 57	92.69 63	69.53 89	91.93 38	92.99 45	73.54 135	85.94 45	94.51 24	65.80 115	95.61 57	83.04 65	92.51 71	93.53 78

canonicalmvs		85.91 47	85.87 49	86.04 60	89.84 112	69.44 95	90.45 66	93.00 43	76.70 69	88.01 28	91.23 109	73.28 36	93.91 131	81.50 79	88.80 120	94.77 22

MSLP-MVS++		85.43 56	85.76 50	84.45 103	91.93 72	70.24 76	90.71 58	92.86 53	77.46 47	84.22 74	92.81 78	67.16 99	92.94 180	80.36 90	94.35 55	90.16 195

test_fmvsmconf0.1_n		85.61 53	85.65 51	85.50 69	82.99 296	69.39 96	89.65 84	90.29 144	73.31 141	87.77 30	94.15 38	71.72 49	93.23 161	90.31 4	90.67 95	93.89 56

SR-MVS-dyc-post		85.77 49	85.61 52	86.23 56	93.06 55	70.63 73	91.88 39	92.27 76	73.53 136	85.69 49	94.45 26	65.00 123	95.56 58	82.75 68	91.87 79	92.50 114

RE-MVS-def			85.48 53		93.06 55	70.63 73	91.88 39	92.27 76	73.53 136	85.69 49	94.45 26	63.87 129		82.75 68	91.87 79	92.50 114

ACMMP		85.89 48	85.39 54	87.38 39	93.59 45	72.63 33	92.74 20	93.18 36	76.78 65	80.73 121	93.82 53	64.33 125	96.29 39	82.67 73	90.69 94	93.23 87
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
test_fmvsm_n_1920		85.29 59	85.34 55	85.13 79	86.12 230	69.93 83	88.65 121	90.78 127	69.97 202	88.27 23	93.98 49	71.39 55	91.54 228	88.49 23	90.45 97	93.91 53

TSAR-MVS + GP.		85.71 51	85.33 56	86.84 47	91.34 78	72.50 36	89.07 104	87.28 228	76.41 72	85.80 47	90.22 134	74.15 31	95.37 72	81.82 77	91.88 78	92.65 109

alignmvs		85.48 54	85.32 57	85.96 62	89.51 120	69.47 92	89.74 81	92.47 68	76.17 80	87.73 33	91.46 105	70.32 65	93.78 136	81.51 78	88.95 117	94.63 26

DELS-MVS		85.41 57	85.30 58	85.77 64	88.49 162	67.93 132	85.52 219	93.44 27	78.70 29	83.63 87	89.03 164	74.57 24	95.71 56	80.26 92	94.04 59	93.66 65
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
CDPH-MVS		85.76 50	85.29 59	87.17 43	93.49 47	71.08 61	88.58 123	92.42 72	68.32 242	84.61 67	93.48 58	72.32 42	96.15 45	79.00 98	95.43 31	94.28 40

casdiffmvs		85.11 61	85.14 60	85.01 82	87.20 212	65.77 179	87.75 153	92.83 55	77.84 37	84.36 73	92.38 85	72.15 44	93.93 130	81.27 81	90.48 96	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		84.93 63	84.98 61	84.80 92	87.30 210	65.39 188	87.30 165	92.88 52	77.62 39	84.04 79	92.26 87	71.81 47	93.96 124	81.31 80	90.30 99	95.03 8

UA-Net		85.08 62	84.96 62	85.45 70	92.07 70	68.07 129	89.78 80	90.86 126	82.48 3	84.60 68	93.20 66	69.35 75	95.22 74	71.39 174	90.88 92	93.07 95

HPM-MVS_fast		85.35 58	84.95 63	86.57 53	93.69 42	70.58 75	92.15 36	91.62 103	73.89 126	82.67 99	94.09 40	62.60 144	95.54 60	80.93 83	92.93 66	93.57 74

MVS_111021_HR		85.14 60	84.75 64	86.32 55	91.65 76	72.70 30	85.98 202	90.33 141	76.11 81	82.08 102	91.61 100	71.36 56	94.17 120	81.02 82	92.58 70	92.08 131

ETV-MVS		84.90 65	84.67 65	85.59 67	89.39 125	68.66 116	88.74 117	92.64 65	79.97 15	84.10 77	85.71 254	69.32 76	95.38 69	80.82 85	91.37 86	92.72 104

fmvsm_l_conf0.5_n		84.47 67	84.54 66	84.27 113	85.42 240	68.81 105	88.49 125	87.26 229	68.08 244	88.03 27	93.49 57	72.04 46	91.77 218	88.90 17	89.14 116	92.24 125

patch_mono-2		83.65 76	84.54 66	80.99 220	90.06 107	65.83 175	84.21 247	88.74 198	71.60 168	85.01 55	92.44 84	74.51 25	83.50 331	82.15 75	92.15 75	93.64 71

test_fmvsmconf0.01_n		84.73 66	84.52 68	85.34 72	80.25 336	69.03 99	89.47 88	89.65 161	73.24 145	86.98 40	94.27 32	66.62 101	93.23 161	90.26 5	89.95 107	93.78 62

3Dnovator+	77.84 4	85.48 54	84.47 69	88.51 7	91.08 81	73.49 16	93.18 11	93.78 18	80.79 8	76.66 195	93.37 62	60.40 188	96.75 26	77.20 117	93.73 62	95.29 5

DPM-MVS		84.93 63	84.29 70	86.84 47	90.20 100	73.04 23	87.12 169	93.04 38	69.80 206	82.85 95	91.22 110	73.06 38	96.02 47	76.72 126	94.63 47	91.46 149

fmvsm_l_conf0.5_n_a		84.13 69	84.16 71	84.06 125	85.38 241	68.40 120	88.34 132	86.85 237	67.48 251	87.48 34	93.40 61	70.89 58	91.61 222	88.38 25	89.22 115	92.16 129

test_fmvsmvis_n_1920		84.02 70	83.87 72	84.49 101	84.12 267	69.37 97	88.15 140	87.96 212	70.01 200	83.95 80	93.23 65	68.80 85	91.51 231	88.61 20	89.96 106	92.57 110

EI-MVSNet-Vis-set		84.19 68	83.81 73	85.31 73	88.18 172	67.85 133	87.66 155	89.73 159	80.05 14	82.95 92	89.59 148	70.74 61	94.82 95	80.66 89	84.72 170	93.28 86

fmvsm_s_conf0.5_n		83.80 73	83.71 74	84.07 123	86.69 222	67.31 147	89.46 89	83.07 292	71.09 178	86.96 41	93.70 55	69.02 83	91.47 233	88.79 18	84.62 172	93.44 80

nrg030		83.88 71	83.53 75	84.96 84	86.77 220	69.28 98	90.46 65	92.67 61	74.79 106	82.95 92	91.33 108	72.70 41	93.09 174	80.79 87	79.28 249	92.50 114

MG-MVS		83.41 83	83.45 76	83.28 152	92.74 62	62.28 248	88.17 138	89.50 164	75.22 96	81.49 111	92.74 82	66.75 100	95.11 80	72.85 162	91.58 83	92.45 117

fmvsm_s_conf0.5_n_a		83.63 78	83.41 77	84.28 111	86.14 229	68.12 127	89.43 90	82.87 296	70.27 196	87.27 37	93.80 54	69.09 78	91.58 224	88.21 26	83.65 191	93.14 93

fmvsm_s_conf0.1_n		83.56 80	83.38 78	84.10 118	84.86 253	67.28 148	89.40 93	83.01 293	70.67 186	87.08 38	93.96 50	68.38 87	91.45 234	88.56 22	84.50 173	93.56 75

EI-MVSNet-UG-set		83.81 72	83.38 78	85.09 80	87.87 184	67.53 141	87.44 161	89.66 160	79.74 16	82.23 101	89.41 157	70.24 66	94.74 98	79.95 93	83.92 184	92.99 100

CPTT-MVS		83.73 74	83.33 80	84.92 87	93.28 49	70.86 69	92.09 37	90.38 137	68.75 234	79.57 132	92.83 76	60.60 184	93.04 178	80.92 84	91.56 84	90.86 168

HQP_MVS		83.64 77	83.14 81	85.14 77	90.08 103	68.71 112	91.25 50	92.44 69	79.12 23	78.92 141	91.00 120	60.42 186	95.38 69	78.71 102	86.32 151	91.33 150

Effi-MVS+		83.62 79	83.08 82	85.24 75	88.38 167	67.45 142	88.89 109	89.15 179	75.50 92	82.27 100	88.28 186	69.61 73	94.45 109	77.81 111	87.84 130	93.84 59

MVS_Test		83.15 88	83.06 83	83.41 149	86.86 216	63.21 234	86.11 200	92.00 87	74.31 116	82.87 94	89.44 156	70.03 67	93.21 163	77.39 116	88.50 126	93.81 60

EPP-MVSNet		83.40 84	83.02 84	84.57 96	90.13 101	64.47 207	92.32 30	90.73 128	74.45 115	79.35 135	91.10 114	69.05 81	95.12 78	72.78 163	87.22 138	94.13 44

fmvsm_s_conf0.1_n_a		83.32 86	82.99 85	84.28 111	83.79 274	68.07 129	89.34 95	82.85 297	69.80 206	87.36 36	94.06 42	68.34 88	91.56 226	87.95 27	83.46 197	93.21 90

OPM-MVS		83.50 81	82.95 86	85.14 77	88.79 152	70.95 66	89.13 103	91.52 106	77.55 44	80.96 119	91.75 95	60.71 179	94.50 107	79.67 95	86.51 149	89.97 211
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EPNet		83.72 75	82.92 87	86.14 59	84.22 265	69.48 91	91.05 55	85.27 257	81.30 6	76.83 190	91.65 97	66.09 110	95.56 58	76.00 132	93.85 60	93.38 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		83.15 88	82.81 88	84.18 116	89.94 110	63.30 232	91.59 43	88.46 204	79.04 25	79.49 133	92.16 88	65.10 120	94.28 112	67.71 209	91.86 81	94.95 10

EIA-MVS		83.31 87	82.80 89	84.82 90	89.59 116	65.59 181	88.21 136	92.68 60	74.66 109	78.96 139	86.42 241	69.06 80	95.26 73	75.54 138	90.09 103	93.62 72

Vis-MVSNet		83.46 82	82.80 89	85.43 71	90.25 99	68.74 110	90.30 69	90.13 148	76.33 78	80.87 120	92.89 74	61.00 176	94.20 118	72.45 168	90.97 90	93.35 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FIs		82.07 103	82.42 91	81.04 219	88.80 151	58.34 288	88.26 135	93.49 26	76.93 60	78.47 153	91.04 117	69.92 70	92.34 199	69.87 190	84.97 167	92.44 118

VNet		82.21 100	82.41 92	81.62 200	90.82 88	60.93 262	84.47 238	89.78 156	76.36 77	84.07 78	91.88 93	64.71 124	90.26 258	70.68 180	88.89 118	93.66 65

PAPM_NR		83.02 92	82.41 92	84.82 90	92.47 67	66.37 164	87.93 148	91.80 98	73.82 127	77.32 179	90.66 125	67.90 91	94.90 91	70.37 183	89.48 112	93.19 91

VDD-MVS		83.01 93	82.36 94	84.96 84	91.02 83	66.40 163	88.91 108	88.11 207	77.57 41	84.39 72	93.29 64	52.19 247	93.91 131	77.05 119	88.70 122	94.57 29

3Dnovator	76.31 5	83.38 85	82.31 95	86.59 52	87.94 182	72.94 28	90.64 59	92.14 84	77.21 52	75.47 221	92.83 76	58.56 195	94.72 99	73.24 159	92.71 69	92.13 130

h-mvs33		83.15 88	82.19 96	86.02 61	90.56 93	70.85 70	88.15 140	89.16 178	76.02 83	84.67 64	91.39 107	61.54 162	95.50 61	82.71 70	75.48 296	91.72 139

MVS_111021_LR		82.61 97	82.11 97	84.11 117	88.82 149	71.58 53	85.15 222	86.16 247	74.69 108	80.47 123	91.04 117	62.29 151	90.55 256	80.33 91	90.08 104	90.20 194

DP-MVS Recon		83.11 91	82.09 98	86.15 58	94.44 19	70.92 68	88.79 113	92.20 81	70.53 190	79.17 137	91.03 119	64.12 127	96.03 46	68.39 206	90.14 102	91.50 145

MVSFormer		82.85 94	82.05 99	85.24 75	87.35 204	70.21 77	90.50 62	90.38 137	68.55 237	81.32 112	89.47 151	61.68 159	93.46 153	78.98 99	90.26 100	92.05 132

FC-MVSNet-test		81.52 116	82.02 100	80.03 240	88.42 166	55.97 325	87.95 146	93.42 29	77.10 56	77.38 177	90.98 122	69.96 68	91.79 217	68.46 205	84.50 173	92.33 119

HQP-MVS		82.61 97	82.02 100	84.37 105	89.33 128	66.98 155	89.17 98	92.19 82	76.41 72	77.23 182	90.23 133	60.17 189	95.11 80	77.47 114	85.99 159	91.03 162

OMC-MVS		82.69 95	81.97 102	84.85 89	88.75 154	67.42 143	87.98 144	90.87 125	74.92 103	79.72 130	91.65 97	62.19 154	93.96 124	75.26 140	86.42 150	93.16 92

diffmvs		82.10 101	81.88 103	82.76 182	83.00 294	63.78 220	83.68 254	89.76 157	72.94 151	82.02 103	89.85 140	65.96 114	90.79 252	82.38 74	87.30 137	93.71 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu		82.62 96	81.83 104	84.96 84	90.80 89	69.76 87	88.74 117	91.70 102	69.39 214	78.96 139	88.46 181	65.47 117	94.87 94	74.42 145	88.57 123	90.24 193

CLD-MVS		82.31 99	81.65 105	84.29 110	88.47 163	67.73 136	85.81 210	92.35 74	75.78 86	78.33 156	86.58 236	64.01 128	94.35 110	76.05 131	87.48 135	90.79 169
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_NR-MVSNet		81.88 106	81.54 106	82.92 171	88.46 164	63.46 228	87.13 168	92.37 73	80.19 12	78.38 154	89.14 160	71.66 52	93.05 176	70.05 186	76.46 279	92.25 123

PS-MVSNAJss		82.07 103	81.31 107	84.34 108	86.51 225	67.27 149	89.27 96	91.51 107	71.75 162	79.37 134	90.22 134	63.15 138	94.27 113	77.69 112	82.36 211	91.49 146

LPG-MVS_test		82.08 102	81.27 108	84.50 99	89.23 135	68.76 108	90.22 70	91.94 91	75.37 94	76.64 196	91.51 102	54.29 228	94.91 88	78.44 104	83.78 185	89.83 216

LFMVS		81.82 108	81.23 109	83.57 144	91.89 73	63.43 230	89.84 76	81.85 307	77.04 58	83.21 89	93.10 67	52.26 246	93.43 155	71.98 169	89.95 107	93.85 57

API-MVS		81.99 105	81.23 109	84.26 114	90.94 85	70.18 82	91.10 53	89.32 169	71.51 170	78.66 147	88.28 186	65.26 118	95.10 83	64.74 236	91.23 88	87.51 276

UniMVSNet (Re)		81.60 115	81.11 111	83.09 162	88.38 167	64.41 209	87.60 156	93.02 42	78.42 32	78.56 150	88.16 190	69.78 71	93.26 159	69.58 193	76.49 278	91.60 140

xiu_mvs_v2_base		81.69 111	81.05 112	83.60 142	89.15 138	68.03 131	84.46 240	90.02 150	70.67 186	81.30 115	86.53 239	63.17 137	94.19 119	75.60 137	88.54 124	88.57 258

PS-MVSNAJ		81.69 111	81.02 113	83.70 141	89.51 120	68.21 126	84.28 246	90.09 149	70.79 183	81.26 116	85.62 259	63.15 138	94.29 111	75.62 136	88.87 119	88.59 257

GeoE		81.71 110	81.01 114	83.80 139	89.51 120	64.45 208	88.97 106	88.73 199	71.27 174	78.63 148	89.76 142	66.32 107	93.20 166	69.89 189	86.02 158	93.74 63

hse-mvs2		81.72 109	80.94 115	84.07 123	88.72 155	67.68 138	85.87 206	87.26 229	76.02 83	84.67 64	88.22 189	61.54 162	93.48 151	82.71 70	73.44 324	91.06 160

PAPR		81.66 114	80.89 116	83.99 133	90.27 98	64.00 215	86.76 183	91.77 101	68.84 233	77.13 188	89.50 149	67.63 93	94.88 93	67.55 211	88.52 125	93.09 94

mvsmamba		81.69 111	80.74 117	84.56 97	87.45 203	66.72 159	91.26 48	85.89 251	74.66 109	78.23 159	90.56 127	54.33 227	94.91 88	80.73 88	83.54 195	92.04 134

MAR-MVS		81.84 107	80.70 118	85.27 74	91.32 79	71.53 54	89.82 77	90.92 122	69.77 208	78.50 151	86.21 245	62.36 150	94.52 106	65.36 230	92.05 77	89.77 219
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
VDDNet		81.52 116	80.67 119	84.05 128	90.44 96	64.13 214	89.73 82	85.91 250	71.11 177	83.18 90	93.48 58	50.54 271	93.49 150	73.40 156	88.25 128	94.54 30

ACMP	74.13 6	81.51 118	80.57 120	84.36 106	89.42 123	68.69 115	89.97 74	91.50 110	74.46 114	75.04 241	90.41 130	53.82 233	94.54 104	77.56 113	82.91 203	89.86 215
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		80.60 138	80.55 121	80.76 226	88.07 178	60.80 265	86.86 177	91.58 105	75.67 90	80.24 125	89.45 155	63.34 132	90.25 259	70.51 182	79.22 250	91.23 154

DU-MVS		81.12 123	80.52 122	82.90 172	87.80 188	63.46 228	87.02 172	91.87 95	79.01 26	78.38 154	89.07 162	65.02 121	93.05 176	70.05 186	76.46 279	92.20 126

test_yl		81.17 121	80.47 123	83.24 155	89.13 139	63.62 221	86.21 197	89.95 153	72.43 156	81.78 108	89.61 146	57.50 205	93.58 144	70.75 178	86.90 142	92.52 112

DCV-MVSNet		81.17 121	80.47 123	83.24 155	89.13 139	63.62 221	86.21 197	89.95 153	72.43 156	81.78 108	89.61 146	57.50 205	93.58 144	70.75 178	86.90 142	92.52 112

PVSNet_Blended		80.98 124	80.34 125	82.90 172	88.85 146	65.40 186	84.43 242	92.00 87	67.62 248	78.11 163	85.05 273	66.02 112	94.27 113	71.52 171	89.50 111	89.01 240

TranMVSNet+NR-MVSNet		80.84 127	80.31 126	82.42 187	87.85 185	62.33 246	87.74 154	91.33 112	80.55 9	77.99 167	89.86 139	65.23 119	92.62 186	67.05 218	75.24 306	92.30 121

jason		81.39 119	80.29 127	84.70 94	86.63 224	69.90 85	85.95 203	86.77 238	63.24 298	81.07 118	89.47 151	61.08 175	92.15 205	78.33 107	90.07 105	92.05 132
jason: jason.
lupinMVS		81.39 119	80.27 128	84.76 93	87.35 204	70.21 77	85.55 215	86.41 242	62.85 305	81.32 112	88.61 176	61.68 159	92.24 203	78.41 106	90.26 100	91.83 136

SDMVSNet		80.38 143	80.18 129	80.99 220	89.03 144	64.94 197	80.45 301	89.40 166	75.19 98	76.61 198	89.98 137	60.61 183	87.69 299	76.83 123	83.55 193	90.33 189

PVSNet_BlendedMVS		80.60 138	80.02 130	82.36 189	88.85 146	65.40 186	86.16 199	92.00 87	69.34 216	78.11 163	86.09 249	66.02 112	94.27 113	71.52 171	82.06 214	87.39 278

EI-MVSNet		80.52 141	79.98 131	82.12 190	84.28 263	63.19 236	86.41 191	88.95 189	74.18 120	78.69 145	87.54 206	66.62 101	92.43 193	72.57 166	80.57 233	90.74 173

Fast-Effi-MVS+		80.81 129	79.92 132	83.47 145	88.85 146	64.51 204	85.53 217	89.39 167	70.79 183	78.49 152	85.06 272	67.54 94	93.58 144	67.03 219	86.58 147	92.32 120

FA-MVS(test-final)		80.96 125	79.91 133	84.10 118	88.30 170	65.01 195	84.55 237	90.01 151	73.25 144	79.61 131	87.57 203	58.35 197	94.72 99	71.29 175	86.25 153	92.56 111

CANet_DTU		80.61 137	79.87 134	82.83 174	85.60 237	63.17 237	87.36 162	88.65 200	76.37 76	75.88 214	88.44 182	53.51 236	93.07 175	73.30 157	89.74 110	92.25 123

ACMM	73.20 8	80.78 134	79.84 135	83.58 143	89.31 131	68.37 121	89.99 73	91.60 104	70.28 195	77.25 180	89.66 144	53.37 238	93.53 149	74.24 148	82.85 204	88.85 248
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-OURS-SEG-HR		80.81 129	79.76 136	83.96 135	85.60 237	68.78 107	83.54 260	90.50 134	70.66 188	76.71 194	91.66 96	60.69 180	91.26 239	76.94 120	81.58 220	91.83 136

xiu_mvs_v1_base_debu		80.80 131	79.72 137	84.03 130	87.35 204	70.19 79	85.56 212	88.77 194	69.06 227	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 235

xiu_mvs_v1_base		80.80 131	79.72 137	84.03 130	87.35 204	70.19 79	85.56 212	88.77 194	69.06 227	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 235

xiu_mvs_v1_base_debi		80.80 131	79.72 137	84.03 130	87.35 204	70.19 79	85.56 212	88.77 194	69.06 227	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 235

UGNet		80.83 128	79.59 140	84.54 98	88.04 179	68.09 128	89.42 91	88.16 206	76.95 59	76.22 207	89.46 153	49.30 286	93.94 127	68.48 204	90.31 98	91.60 140
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
114514_t		80.68 135	79.51 141	84.20 115	94.09 38	67.27 149	89.64 85	91.11 119	58.75 342	74.08 253	90.72 124	58.10 198	95.04 85	69.70 191	89.42 113	90.30 191

QAPM		80.88 126	79.50 142	85.03 81	88.01 181	68.97 103	91.59 43	92.00 87	66.63 262	75.15 237	92.16 88	57.70 202	95.45 63	63.52 241	88.76 121	90.66 175

AdaColmap		80.58 140	79.42 143	84.06 125	93.09 54	68.91 104	89.36 94	88.97 188	69.27 217	75.70 217	89.69 143	57.20 209	95.77 54	63.06 246	88.41 127	87.50 277

NR-MVSNet		80.23 148	79.38 144	82.78 180	87.80 188	63.34 231	86.31 194	91.09 120	79.01 26	72.17 274	89.07 162	67.20 98	92.81 185	66.08 225	75.65 292	92.20 126

IterMVS-LS		80.06 151	79.38 144	82.11 191	85.89 232	63.20 235	86.79 180	89.34 168	74.19 119	75.45 224	86.72 226	66.62 101	92.39 195	72.58 165	76.86 273	90.75 172
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
iter_conf_final		80.63 136	79.35 146	84.46 102	89.36 127	67.70 137	89.85 75	84.49 267	73.19 146	78.30 157	88.94 165	45.98 312	94.56 102	79.59 96	84.48 176	91.11 157

test_djsdf		80.30 147	79.32 147	83.27 153	83.98 271	65.37 189	90.50 62	90.38 137	68.55 237	76.19 208	88.70 172	56.44 213	93.46 153	78.98 99	80.14 239	90.97 165

v2v482		80.23 148	79.29 148	83.05 165	83.62 277	64.14 213	87.04 171	89.97 152	73.61 132	78.18 162	87.22 214	61.10 174	93.82 134	76.11 129	76.78 276	91.18 155

ECVR-MVS		79.61 158	79.26 149	80.67 228	90.08 103	54.69 338	87.89 150	77.44 346	74.88 104	80.27 124	92.79 79	48.96 293	92.45 192	68.55 203	92.50 72	94.86 17

XVG-OURS		80.41 142	79.23 150	83.97 134	85.64 236	69.02 101	83.03 271	90.39 136	71.09 178	77.63 173	91.49 104	54.62 226	91.35 237	75.71 134	83.47 196	91.54 142

RRT_MVS		80.35 146	79.22 151	83.74 140	87.63 197	65.46 185	91.08 54	88.92 191	73.82 127	76.44 203	90.03 136	49.05 291	94.25 117	76.84 121	79.20 251	91.51 143

WR-MVS		79.49 162	79.22 151	80.27 236	88.79 152	58.35 287	85.06 224	88.61 202	78.56 30	77.65 172	88.34 184	63.81 131	90.66 255	64.98 234	77.22 268	91.80 138

test1111		79.43 165	79.18 153	80.15 238	89.99 108	53.31 351	87.33 164	77.05 349	75.04 101	80.23 126	92.77 81	48.97 292	92.33 200	68.87 200	92.40 74	94.81 20

mvs_anonymous		79.42 166	79.11 154	80.34 234	84.45 262	57.97 294	82.59 273	87.62 221	67.40 252	76.17 211	88.56 179	68.47 86	89.59 269	70.65 181	86.05 157	93.47 79

v1144		80.03 152	79.03 155	83.01 167	83.78 275	64.51 204	87.11 170	90.57 133	71.96 161	78.08 165	86.20 246	61.41 166	93.94 127	74.93 141	77.23 267	90.60 178

v8		79.97 155	79.02 156	82.80 177	84.09 268	64.50 206	87.96 145	90.29 144	74.13 122	75.24 234	86.81 223	62.88 143	93.89 133	74.39 146	75.40 301	90.00 207

ab-mvs		79.51 161	78.97 157	81.14 216	88.46 164	60.91 263	83.84 252	89.24 175	70.36 192	79.03 138	88.87 169	63.23 136	90.21 260	65.12 232	82.57 209	92.28 122

Anonymous20240529		80.19 150	78.89 158	84.10 118	90.60 92	64.75 201	88.95 107	90.90 123	65.97 270	80.59 122	91.17 113	49.97 276	93.73 142	69.16 197	82.70 208	93.81 60

PCF-MVS	73.52 7	80.38 143	78.84 159	85.01 82	87.71 193	68.99 102	83.65 255	91.46 111	63.00 302	77.77 171	90.28 131	66.10 109	95.09 84	61.40 264	88.22 129	90.94 166
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
iter_conf05		80.00 154	78.70 160	83.91 137	87.84 186	65.83 175	88.84 112	84.92 262	71.61 167	78.70 144	88.94 165	43.88 328	94.56 102	79.28 97	84.28 180	91.33 150

v10		79.74 157	78.67 161	82.97 170	84.06 269	64.95 196	87.88 151	90.62 130	73.11 147	75.11 238	86.56 237	61.46 165	94.05 123	73.68 151	75.55 294	89.90 213

VPNet		78.69 185	78.66 162	78.76 262	88.31 169	55.72 327	84.45 241	86.63 240	76.79 64	78.26 158	90.55 128	59.30 191	89.70 268	66.63 220	77.05 270	90.88 167

BH-untuned		79.47 163	78.60 163	82.05 192	89.19 137	65.91 173	86.07 201	88.52 203	72.18 158	75.42 225	87.69 200	61.15 173	93.54 148	60.38 271	86.83 144	86.70 297

Effi-MVS+-dtu		80.03 152	78.57 164	84.42 104	85.13 248	68.74 110	88.77 114	88.10 208	74.99 102	74.97 242	83.49 299	57.27 208	93.36 156	73.53 153	80.88 227	91.18 155

WR-MVS_H		78.51 189	78.49 165	78.56 266	88.02 180	56.38 320	88.43 126	92.67 61	77.14 54	73.89 254	87.55 205	66.25 108	89.24 275	58.92 284	73.55 322	90.06 205

Vis-MVSNet (Re-imp)		78.36 192	78.45 166	78.07 275	88.64 158	51.78 360	86.70 184	79.63 330	74.14 121	75.11 238	90.83 123	61.29 170	89.75 266	58.10 293	91.60 82	92.69 107

BH-RMVSNet		79.61 158	78.44 167	83.14 160	89.38 126	65.93 172	84.95 227	87.15 232	73.56 134	78.19 161	89.79 141	56.67 212	93.36 156	59.53 278	86.74 145	90.13 197

v1192		79.59 160	78.43 168	83.07 164	83.55 279	64.52 203	86.93 175	90.58 131	70.83 182	77.78 170	85.90 250	59.15 192	93.94 127	73.96 150	77.19 269	90.76 171

v144192		79.47 163	78.37 169	82.78 180	83.35 282	63.96 216	86.96 173	90.36 140	69.99 201	77.50 174	85.67 257	60.66 181	93.77 138	74.27 147	76.58 277	90.62 176

CP-MVSNet		78.22 194	78.34 170	77.84 277	87.83 187	54.54 340	87.94 147	91.17 116	77.65 38	73.48 259	88.49 180	62.24 153	88.43 290	62.19 255	74.07 315	90.55 180

Baseline_NR-MVSNet		78.15 198	78.33 171	77.61 282	85.79 233	56.21 323	86.78 181	85.76 253	73.60 133	77.93 168	87.57 203	65.02 121	88.99 279	67.14 217	75.33 303	87.63 272

OpenMVS	72.83 10	79.77 156	78.33 171	84.09 121	85.17 244	69.91 84	90.57 60	90.97 121	66.70 256	72.17 274	91.91 91	54.70 224	93.96 124	61.81 261	90.95 91	88.41 261

UniMVSNet_ETH3D		79.10 175	78.24 173	81.70 199	86.85 217	60.24 274	87.28 166	88.79 193	74.25 118	76.84 189	90.53 129	49.48 282	91.56 226	67.98 207	82.15 212	93.29 85

V42		79.38 169	78.24 173	82.83 174	81.10 328	65.50 183	85.55 215	89.82 155	71.57 169	78.21 160	86.12 248	60.66 181	93.18 169	75.64 135	75.46 298	89.81 218

PS-CasMVS		78.01 203	78.09 175	77.77 279	87.71 193	54.39 342	88.02 143	91.22 113	77.50 46	73.26 261	88.64 175	60.73 178	88.41 291	61.88 259	73.88 319	90.53 181

v1921920		79.22 171	78.03 176	82.80 177	83.30 284	63.94 217	86.80 179	90.33 141	69.91 204	77.48 175	85.53 260	58.44 196	93.75 140	73.60 152	76.85 274	90.71 174

jajsoiax		79.29 170	77.96 177	83.27 153	84.68 256	66.57 162	89.25 97	90.16 147	69.20 222	75.46 223	89.49 150	45.75 317	93.13 172	76.84 121	80.80 229	90.11 199

TAPA-MVS	73.13 9	79.15 173	77.94 178	82.79 179	89.59 116	62.99 241	88.16 139	91.51 107	65.77 271	77.14 187	91.09 115	60.91 177	93.21 163	50.26 339	87.05 140	92.17 128
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tttt0517		79.40 167	77.91 179	83.90 138	88.10 176	63.84 218	88.37 131	84.05 275	71.45 171	76.78 192	89.12 161	49.93 279	94.89 92	70.18 185	83.18 201	92.96 101

c3_l		78.75 182	77.91 179	81.26 211	82.89 298	61.56 257	84.09 250	89.13 181	69.97 202	75.56 219	84.29 284	66.36 106	92.09 207	73.47 155	75.48 296	90.12 198

MVSTER		79.01 177	77.88 181	82.38 188	83.07 291	64.80 200	84.08 251	88.95 189	69.01 230	78.69 145	87.17 217	54.70 224	92.43 193	74.69 142	80.57 233	89.89 214

tt0805		78.73 183	77.83 182	81.43 205	85.17 244	60.30 273	89.41 92	90.90 123	71.21 175	77.17 186	88.73 171	46.38 306	93.21 163	72.57 166	78.96 252	90.79 169

X-MVStestdata		80.37 145	77.83 182	88.00 17	94.42 20	73.33 19	92.78 18	92.99 45	79.14 21	83.67 85	12.47 401	67.45 95	96.60 33	83.06 63	94.50 50	94.07 47

v148		78.72 184	77.80 184	81.47 204	82.73 301	61.96 252	86.30 195	88.08 209	73.26 143	76.18 209	85.47 262	62.46 148	92.36 197	71.92 170	73.82 320	90.09 201

v1240		78.99 178	77.78 185	82.64 183	83.21 286	63.54 225	86.62 186	90.30 143	69.74 211	77.33 178	85.68 256	57.04 210	93.76 139	73.13 160	76.92 271	90.62 176

mvs_tets		79.13 174	77.77 186	83.22 157	84.70 255	66.37 164	89.17 98	90.19 146	69.38 215	75.40 226	89.46 153	44.17 326	93.15 170	76.78 124	80.70 231	90.14 196

miper_ehance_all_eth		78.59 188	77.76 187	81.08 218	82.66 303	61.56 257	83.65 255	89.15 179	68.87 232	75.55 220	83.79 294	66.49 104	92.03 208	73.25 158	76.39 281	89.64 222

thisisatest0530		79.40 167	77.76 187	84.31 109	87.69 195	65.10 194	87.36 162	84.26 273	70.04 199	77.42 176	88.26 188	49.94 277	94.79 97	70.20 184	84.70 171	93.03 97

CDS-MVSNet		79.07 176	77.70 189	83.17 159	87.60 198	68.23 125	84.40 244	86.20 246	67.49 250	76.36 204	86.54 238	61.54 162	90.79 252	61.86 260	87.33 136	90.49 183
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Anonymous20231211		78.97 179	77.69 190	82.81 176	90.54 94	64.29 211	90.11 72	91.51 107	65.01 280	76.16 212	88.13 195	50.56 270	93.03 179	69.68 192	77.56 266	91.11 157

PEN-MVS		77.73 209	77.69 190	77.84 277	87.07 215	53.91 345	87.91 149	91.18 115	77.56 43	73.14 263	88.82 170	61.23 171	89.17 276	59.95 274	72.37 330	90.43 185

AUN-MVS		79.21 172	77.60 192	84.05 128	88.71 156	67.61 139	85.84 208	87.26 229	69.08 226	77.23 182	88.14 194	53.20 240	93.47 152	75.50 139	73.45 323	91.06 160

v7n		78.97 179	77.58 193	83.14 160	83.45 281	65.51 182	88.32 133	91.21 114	73.69 130	72.41 271	86.32 244	57.93 199	93.81 135	69.18 196	75.65 292	90.11 199

TAMVS		78.89 181	77.51 194	83.03 166	87.80 188	67.79 135	84.72 231	85.05 260	67.63 247	76.75 193	87.70 199	62.25 152	90.82 251	58.53 289	87.13 139	90.49 183

sd_testset		77.70 212	77.40 195	78.60 265	89.03 144	60.02 276	79.00 319	85.83 252	75.19 98	76.61 198	89.98 137	54.81 219	85.46 317	62.63 252	83.55 193	90.33 189

GBi-Net		78.40 190	77.40 195	81.40 207	87.60 198	63.01 238	88.39 128	89.28 170	71.63 164	75.34 228	87.28 210	54.80 220	91.11 242	62.72 248	79.57 243	90.09 201

test1		78.40 190	77.40 195	81.40 207	87.60 198	63.01 238	88.39 128	89.28 170	71.63 164	75.34 228	87.28 210	54.80 220	91.11 242	62.72 248	79.57 243	90.09 201

BH-w/o		78.21 195	77.33 198	80.84 224	88.81 150	65.13 193	84.87 228	87.85 217	69.75 209	74.52 249	84.74 277	61.34 168	93.11 173	58.24 292	85.84 161	84.27 332

FMVSNet2		78.20 196	77.21 199	81.20 214	87.60 198	62.89 242	87.47 160	89.02 184	71.63 164	75.29 233	87.28 210	54.80 220	91.10 245	62.38 253	79.38 247	89.61 223

anonymousdsp		78.60 187	77.15 200	82.98 169	80.51 334	67.08 153	87.24 167	89.53 163	65.66 273	75.16 236	87.19 216	52.52 241	92.25 202	77.17 118	79.34 248	89.61 223

HY-MVS	69.67 12	77.95 204	77.15 200	80.36 233	87.57 202	60.21 275	83.37 262	87.78 219	66.11 266	75.37 227	87.06 221	63.27 134	90.48 257	61.38 265	82.43 210	90.40 187

cl22		78.07 200	77.01 202	81.23 212	82.37 310	61.83 254	83.55 259	87.98 211	68.96 231	75.06 240	83.87 290	61.40 167	91.88 215	73.53 153	76.39 281	89.98 210

Anonymous202405211		78.25 193	77.01 202	81.99 194	91.03 82	60.67 267	84.77 230	83.90 277	70.65 189	80.00 128	91.20 111	41.08 345	91.43 235	65.21 231	85.26 165	93.85 57

MVS		78.19 197	76.99 204	81.78 197	85.66 235	66.99 154	84.66 232	90.47 135	55.08 362	72.02 276	85.27 265	63.83 130	94.11 122	66.10 224	89.80 109	84.24 333

LCM-MVSNet-Re		77.05 223	76.94 205	77.36 285	87.20 212	51.60 361	80.06 305	80.46 321	75.20 97	67.69 317	86.72 226	62.48 147	88.98 280	63.44 243	89.25 114	91.51 143

miper_enhance_ethall		77.87 207	76.86 206	80.92 223	81.65 317	61.38 259	82.68 272	88.98 186	65.52 275	75.47 221	82.30 316	65.76 116	92.00 210	72.95 161	76.39 281	89.39 228

FMVSNet3		77.88 206	76.85 207	80.97 222	86.84 218	62.36 245	86.52 189	88.77 194	71.13 176	75.34 228	86.66 232	54.07 231	91.10 245	62.72 248	79.57 243	89.45 227

DTE-MVSNet		76.99 224	76.80 208	77.54 284	86.24 227	53.06 353	87.52 158	90.66 129	77.08 57	72.50 269	88.67 174	60.48 185	89.52 270	57.33 300	70.74 341	90.05 206

CNLPA		78.08 199	76.79 209	81.97 195	90.40 97	71.07 62	87.59 157	84.55 266	66.03 269	72.38 272	89.64 145	57.56 204	86.04 310	59.61 277	83.35 198	88.79 251

cl____		77.72 210	76.76 210	80.58 229	82.49 307	60.48 270	83.09 267	87.87 215	69.22 220	74.38 251	85.22 268	62.10 155	91.53 229	71.09 176	75.41 300	89.73 221

DIV-MVS_self_test		77.72 210	76.76 210	80.58 229	82.48 308	60.48 270	83.09 267	87.86 216	69.22 220	74.38 251	85.24 266	62.10 155	91.53 229	71.09 176	75.40 301	89.74 220

baseline1		76.98 225	76.75 212	77.66 280	88.13 174	55.66 328	85.12 223	81.89 305	73.04 149	76.79 191	88.90 167	62.43 149	87.78 298	63.30 245	71.18 339	89.55 225

eth_miper_zixun_eth		77.92 205	76.69 213	81.61 202	83.00 294	61.98 251	83.15 265	89.20 177	69.52 213	74.86 244	84.35 283	61.76 158	92.56 189	71.50 173	72.89 328	90.28 192

pm-mvs1		77.25 222	76.68 214	78.93 260	84.22 265	58.62 286	86.41 191	88.36 205	71.37 172	73.31 260	88.01 196	61.22 172	89.15 277	64.24 239	73.01 327	89.03 239

ET-MVSNet_ETH3D		78.63 186	76.63 215	84.64 95	86.73 221	69.47 92	85.01 225	84.61 265	69.54 212	66.51 335	86.59 234	50.16 274	91.75 219	76.26 128	84.24 181	92.69 107

test2506		77.30 220	76.49 216	79.74 246	90.08 103	52.02 354	87.86 152	63.10 389	74.88 104	80.16 127	92.79 79	38.29 358	92.35 198	68.74 202	92.50 72	94.86 17

Fast-Effi-MVS+-dtu		78.02 202	76.49 216	82.62 184	83.16 290	66.96 157	86.94 174	87.45 226	72.45 153	71.49 281	84.17 287	54.79 223	91.58 224	67.61 210	80.31 236	89.30 231

1112_ss		77.40 218	76.43 218	80.32 235	89.11 143	60.41 272	83.65 255	87.72 220	62.13 315	73.05 264	86.72 226	62.58 146	89.97 263	62.11 258	80.80 229	90.59 179

PAPM		77.68 213	76.40 219	81.51 203	87.29 211	61.85 253	83.78 253	89.59 162	64.74 282	71.23 282	88.70 172	62.59 145	93.66 143	52.66 324	87.03 141	89.01 240

PLC	70.83 11	78.05 201	76.37 220	83.08 163	91.88 74	67.80 134	88.19 137	89.46 165	64.33 288	69.87 299	88.38 183	53.66 234	93.58 144	58.86 285	82.73 206	87.86 268
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 216	76.18 221	81.20 214	88.24 171	63.24 233	84.61 235	86.40 243	67.55 249	77.81 169	86.48 240	54.10 230	93.15 170	57.75 296	82.72 207	87.20 283

FMVSNet1		77.44 216	76.12 222	81.40 207	86.81 219	63.01 238	88.39 128	89.28 170	70.49 191	74.39 250	87.28 210	49.06 290	91.11 242	60.91 268	78.52 255	90.09 201

bld_raw_dy_0_64		77.29 221	75.98 223	81.22 213	85.04 250	65.47 184	88.14 142	77.56 343	69.20 222	73.77 255	89.40 159	42.24 339	88.85 285	76.78 124	81.64 219	89.33 230

test_vis1_n_1920		75.52 247	75.78 224	74.75 311	79.84 342	57.44 304	83.26 263	85.52 255	62.83 306	79.34 136	86.17 247	45.10 321	79.71 350	78.75 101	81.21 224	87.10 290

CHOSEN 1792x2688		77.63 214	75.69 225	83.44 146	89.98 109	68.58 118	78.70 323	87.50 224	56.38 357	75.80 216	86.84 222	58.67 194	91.40 236	61.58 263	85.75 163	90.34 188

FE-MVS		77.78 208	75.68 226	84.08 122	88.09 177	66.00 170	83.13 266	87.79 218	68.42 241	78.01 166	85.23 267	45.50 319	95.12 78	59.11 282	85.83 162	91.11 157

WTY-MVS		75.65 245	75.68 226	75.57 301	86.40 226	56.82 311	77.92 333	82.40 301	65.10 277	76.18 209	87.72 198	63.13 141	80.90 346	60.31 272	81.96 215	89.00 242

XXY-MVS		75.41 250	75.56 228	74.96 307	83.59 278	57.82 298	80.59 298	83.87 278	66.54 263	74.93 243	88.31 185	63.24 135	80.09 349	62.16 256	76.85 274	86.97 291

thres100view900		76.50 231	75.55 229	79.33 254	89.52 119	56.99 309	85.83 209	83.23 288	73.94 124	76.32 205	87.12 218	51.89 256	91.95 211	48.33 348	83.75 187	89.07 233

thres600view7		76.50 231	75.44 230	79.68 248	89.40 124	57.16 306	85.53 217	83.23 288	73.79 129	76.26 206	87.09 219	51.89 256	91.89 214	48.05 353	83.72 190	90.00 207

Test_1112_low_res		76.40 235	75.44 230	79.27 255	89.28 133	58.09 290	81.69 282	87.07 233	59.53 334	72.48 270	86.67 231	61.30 169	89.33 273	60.81 270	80.15 238	90.41 186

HyFIR lowres test		77.53 215	75.40 232	83.94 136	89.59 116	66.62 160	80.36 302	88.64 201	56.29 358	76.45 200	85.17 269	57.64 203	93.28 158	61.34 266	83.10 202	91.91 135

thisisatest0515		77.33 219	75.38 233	83.18 158	85.27 243	63.80 219	82.11 278	83.27 287	65.06 278	75.91 213	83.84 292	49.54 281	94.27 113	67.24 215	86.19 154	91.48 147

tfpn200view9		76.42 234	75.37 234	79.55 253	89.13 139	57.65 300	85.17 220	83.60 280	73.41 139	76.45 200	86.39 242	52.12 248	91.95 211	48.33 348	83.75 187	89.07 233

thres400		76.50 231	75.37 234	79.86 243	89.13 139	57.65 300	85.17 220	83.60 280	73.41 139	76.45 200	86.39 242	52.12 248	91.95 211	48.33 348	83.75 187	90.00 207

1314		76.53 230	75.30 236	80.21 237	83.93 272	62.32 247	84.66 232	88.81 192	60.23 327	70.16 293	84.07 289	55.30 217	90.73 254	67.37 213	83.21 200	87.59 275

GA-MVS		76.87 227	75.17 237	81.97 195	82.75 300	62.58 243	81.44 287	86.35 245	72.16 160	74.74 245	82.89 308	46.20 311	92.02 209	68.85 201	81.09 225	91.30 153

testing99		76.09 240	75.12 238	79.00 258	88.16 173	55.50 330	80.79 293	81.40 311	73.30 142	75.17 235	84.27 285	44.48 324	90.02 262	64.28 238	84.22 182	91.48 147

EPNet_dtu		75.46 248	74.86 239	77.23 288	82.57 305	54.60 339	86.89 176	83.09 291	71.64 163	66.25 337	85.86 252	55.99 214	88.04 295	54.92 313	86.55 148	89.05 238
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		76.95 226	74.82 240	83.37 150	90.45 95	67.36 146	89.15 102	86.94 235	61.87 317	69.52 302	90.61 126	51.71 259	94.53 105	46.38 360	86.71 146	88.21 263

cascas		76.72 229	74.64 241	82.99 168	85.78 234	65.88 174	82.33 275	89.21 176	60.85 323	72.74 266	81.02 327	47.28 300	93.75 140	67.48 212	85.02 166	89.34 229

DP-MVS		76.78 228	74.57 242	83.42 147	93.29 48	69.46 94	88.55 124	83.70 279	63.98 294	70.20 290	88.89 168	54.01 232	94.80 96	46.66 357	81.88 217	86.01 309

TransMVSNet (Re)		75.39 251	74.56 243	77.86 276	85.50 239	57.10 308	86.78 181	86.09 249	72.17 159	71.53 280	87.34 209	63.01 142	89.31 274	56.84 305	61.83 367	87.17 284

LTVRE_ROB	69.57 13	76.25 237	74.54 244	81.41 206	88.60 159	64.38 210	79.24 315	89.12 182	70.76 185	69.79 301	87.86 197	49.09 289	93.20 166	56.21 310	80.16 237	86.65 298
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
thres200		75.55 246	74.47 245	78.82 261	87.78 191	57.85 297	83.07 269	83.51 283	72.44 155	75.84 215	84.42 279	52.08 251	91.75 219	47.41 355	83.64 192	86.86 293

MVP-Stereo		76.12 238	74.46 246	81.13 217	85.37 242	69.79 86	84.42 243	87.95 213	65.03 279	67.46 320	85.33 264	53.28 239	91.73 221	58.01 294	83.27 199	81.85 357
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
F-COLMAP		76.38 236	74.33 247	82.50 186	89.28 133	66.95 158	88.41 127	89.03 183	64.05 292	66.83 327	88.61 176	46.78 304	92.89 181	57.48 297	78.55 254	87.67 271

XVG-ACMP-BASELINE		76.11 239	74.27 248	81.62 200	83.20 287	64.67 202	83.60 258	89.75 158	69.75 209	71.85 277	87.09 219	32.78 370	92.11 206	69.99 188	80.43 235	88.09 264

ACMH+	68.96 14	76.01 241	74.01 249	82.03 193	88.60 159	65.31 190	88.86 110	87.55 222	70.25 197	67.75 316	87.47 208	41.27 343	93.19 168	58.37 290	75.94 289	87.60 273

ACMH	67.68 16	75.89 242	73.93 250	81.77 198	88.71 156	66.61 161	88.62 122	89.01 185	69.81 205	66.78 328	86.70 230	41.95 342	91.51 231	55.64 311	78.14 261	87.17 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		75.24 252	73.90 251	79.27 255	82.65 304	58.27 289	80.80 292	82.73 299	61.57 318	75.33 231	83.13 304	55.52 215	91.07 248	64.98 234	78.34 260	88.45 259

IterMVS-SCA-FT		75.43 249	73.87 252	80.11 239	82.69 302	64.85 199	81.57 284	83.47 284	69.16 224	70.49 287	84.15 288	51.95 254	88.15 293	69.23 195	72.14 333	87.34 280

baseline2		75.70 244	73.83 253	81.30 210	83.26 285	61.79 255	82.57 274	80.65 317	66.81 253	66.88 326	83.42 300	57.86 201	92.19 204	63.47 242	79.57 243	89.91 212

test_cas_vis1_n_1920		73.76 264	73.74 254	73.81 319	75.90 363	59.77 278	80.51 299	82.40 301	58.30 344	81.62 110	85.69 255	44.35 325	76.41 368	76.29 127	78.61 253	85.23 320

sss		73.60 265	73.64 255	73.51 321	82.80 299	55.01 336	76.12 340	81.69 308	62.47 311	74.68 246	85.85 253	57.32 207	78.11 357	60.86 269	80.93 226	87.39 278

pmmvs6		74.69 254	73.39 256	78.61 264	81.38 323	57.48 303	86.64 185	87.95 213	64.99 281	70.18 291	86.61 233	50.43 272	89.52 270	62.12 257	70.18 343	88.83 249

IB-MVS	68.01 15	75.85 243	73.36 257	83.31 151	84.76 254	66.03 168	83.38 261	85.06 259	70.21 198	69.40 303	81.05 326	45.76 316	94.66 101	65.10 233	75.49 295	89.25 232
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
D2MVS		74.82 253	73.21 258	79.64 250	79.81 343	62.56 244	80.34 303	87.35 227	64.37 287	68.86 308	82.66 312	46.37 307	90.10 261	67.91 208	81.24 223	86.25 302

tfpnnormal		74.39 255	73.16 259	78.08 274	86.10 231	58.05 291	84.65 234	87.53 223	70.32 194	71.22 283	85.63 258	54.97 218	89.86 264	43.03 371	75.02 308	86.32 301

miper_lstm_enhance		74.11 259	73.11 260	77.13 289	80.11 338	59.62 280	72.23 360	86.92 236	66.76 255	70.40 288	82.92 307	56.93 211	82.92 335	69.06 198	72.63 329	88.87 247

IterMVS		74.29 256	72.94 261	78.35 270	81.53 320	63.49 227	81.58 283	82.49 300	68.06 245	69.99 296	83.69 296	51.66 260	85.54 315	65.85 227	71.64 336	86.01 309
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MS-PatchMatch		73.83 263	72.67 262	77.30 287	83.87 273	66.02 169	81.82 279	84.66 264	61.37 321	68.61 311	82.82 310	47.29 299	88.21 292	59.27 279	84.32 179	77.68 371

testing222		74.04 260	72.66 263	78.19 272	87.89 183	55.36 331	81.06 290	79.20 334	71.30 173	74.65 247	83.57 298	39.11 354	88.67 287	51.43 331	85.75 163	90.53 181

CVMVSNet		72.99 274	72.58 264	74.25 315	84.28 263	50.85 366	86.41 191	83.45 285	44.56 379	73.23 262	87.54 206	49.38 284	85.70 312	65.90 226	78.44 257	86.19 304

test-LLR		72.94 275	72.43 265	74.48 312	81.35 324	58.04 292	78.38 326	77.46 344	66.66 257	69.95 297	79.00 347	48.06 296	79.24 351	66.13 222	84.83 168	86.15 305

OurMVSNet-221017-0		74.26 257	72.42 266	79.80 245	83.76 276	59.59 281	85.92 205	86.64 239	66.39 264	66.96 325	87.58 202	39.46 351	91.60 223	65.76 228	69.27 346	88.22 262

SCA		74.22 258	72.33 267	79.91 242	84.05 270	62.17 249	79.96 308	79.29 333	66.30 265	72.38 272	80.13 336	51.95 254	88.60 288	59.25 280	77.67 265	88.96 244

tpmrst		72.39 277	72.13 268	73.18 325	80.54 333	49.91 370	79.91 309	79.08 335	63.11 300	71.69 279	79.95 338	55.32 216	82.77 336	65.66 229	73.89 318	86.87 292

pmmvs4		74.03 262	71.91 269	80.39 232	81.96 313	68.32 122	81.45 286	82.14 303	59.32 335	69.87 299	85.13 270	52.40 244	88.13 294	60.21 273	74.74 311	84.73 329

EG-PatchMatch MVS		74.04 260	71.82 270	80.71 227	84.92 252	67.42 143	85.86 207	88.08 209	66.04 268	64.22 349	83.85 291	35.10 367	92.56 189	57.44 298	80.83 228	82.16 356

tpm		72.37 279	71.71 271	74.35 314	82.19 311	52.00 355	79.22 316	77.29 347	64.56 284	72.95 265	83.68 297	51.35 261	83.26 334	58.33 291	75.80 290	87.81 269

WB-MVSnew		71.96 284	71.65 272	72.89 326	84.67 259	51.88 358	82.29 276	77.57 342	62.31 312	73.67 257	83.00 305	53.49 237	81.10 345	45.75 364	82.13 213	85.70 314

UWE-MVS		72.13 282	71.49 273	74.03 317	86.66 223	47.70 374	81.40 288	76.89 351	63.60 297	75.59 218	84.22 286	39.94 350	85.62 314	48.98 345	86.13 156	88.77 252

CL-MVSNet_self_test		72.37 279	71.46 274	75.09 306	79.49 349	53.53 347	80.76 295	85.01 261	69.12 225	70.51 286	82.05 320	57.92 200	84.13 326	52.27 326	66.00 359	87.60 273

tpm2		73.26 270	71.46 274	78.63 263	83.34 283	56.71 314	80.65 297	80.40 322	56.63 356	73.55 258	82.02 321	51.80 258	91.24 240	56.35 309	78.42 258	87.95 265

RPSCF		73.23 271	71.46 274	78.54 267	82.50 306	59.85 277	82.18 277	82.84 298	58.96 339	71.15 284	89.41 157	45.48 320	84.77 323	58.82 286	71.83 335	91.02 164

PatchmatchNet		73.12 272	71.33 277	78.49 269	83.18 288	60.85 264	79.63 310	78.57 337	64.13 289	71.73 278	79.81 341	51.20 263	85.97 311	57.40 299	76.36 286	88.66 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		73.37 267	71.27 278	79.67 249	81.32 326	65.19 191	75.92 342	80.30 323	59.92 330	72.73 267	81.19 324	52.50 242	86.69 304	59.84 275	77.71 263	87.11 288

SixPastTwentyTwo		73.37 267	71.26 279	79.70 247	85.08 249	57.89 296	85.57 211	83.56 282	71.03 180	65.66 339	85.88 251	42.10 340	92.57 188	59.11 282	63.34 365	88.65 256

ETVMVS		72.25 281	71.05 280	75.84 297	87.77 192	51.91 357	79.39 313	74.98 358	69.26 218	73.71 256	82.95 306	40.82 347	86.14 309	46.17 361	84.43 178	89.47 226

MSDG		73.36 269	70.99 281	80.49 231	84.51 261	65.80 177	80.71 296	86.13 248	65.70 272	65.46 340	83.74 295	44.60 322	90.91 250	51.13 332	76.89 272	84.74 328

PatchMatch-RL		72.38 278	70.90 282	76.80 292	88.60 159	67.38 145	79.53 311	76.17 355	62.75 308	69.36 304	82.00 322	45.51 318	84.89 322	53.62 319	80.58 232	78.12 370

PVSNet	64.34 18	72.08 283	70.87 283	75.69 299	86.21 228	56.44 318	74.37 354	80.73 316	62.06 316	70.17 292	82.23 318	42.86 333	83.31 333	54.77 314	84.45 177	87.32 281

dmvs_re		71.14 288	70.58 284	72.80 327	81.96 313	59.68 279	75.60 346	79.34 332	68.55 237	69.27 306	80.72 332	49.42 283	76.54 365	52.56 325	77.79 262	82.19 355

test_fmvs1		70.93 291	70.52 285	72.16 331	73.71 373	55.05 335	80.82 291	78.77 336	51.21 373	78.58 149	84.41 280	31.20 375	76.94 363	75.88 133	80.12 240	84.47 331

RPMNet		73.51 266	70.49 286	82.58 185	81.32 326	65.19 191	75.92 342	92.27 76	57.60 350	72.73 267	76.45 363	52.30 245	95.43 65	48.14 352	77.71 263	87.11 288

test_0402		72.79 276	70.44 287	79.84 244	88.13 174	65.99 171	85.93 204	84.29 271	65.57 274	67.40 322	85.49 261	46.92 303	92.61 187	35.88 383	74.38 314	80.94 362

COLMAP_ROB	66.92 17	73.01 273	70.41 288	80.81 225	87.13 214	65.63 180	88.30 134	84.19 274	62.96 303	63.80 353	87.69 200	38.04 359	92.56 189	46.66 357	74.91 309	84.24 333
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test-mter		71.41 286	70.39 289	74.48 312	81.35 324	58.04 292	78.38 326	77.46 344	60.32 326	69.95 297	79.00 347	36.08 365	79.24 351	66.13 222	84.83 168	86.15 305

test_fmvs1_n		70.86 292	70.24 290	72.73 328	72.51 383	55.28 333	81.27 289	79.71 329	51.49 372	78.73 143	84.87 274	27.54 380	77.02 362	76.06 130	79.97 241	85.88 312

pmmvs5		71.55 285	70.20 291	75.61 300	77.83 356	56.39 319	81.74 281	80.89 313	57.76 348	67.46 320	84.49 278	49.26 287	85.32 319	57.08 302	75.29 304	85.11 324

MDTV_nov1_ep13			69.97 292		83.18 288	53.48 348	77.10 338	80.18 326	60.45 324	69.33 305	80.44 333	48.89 294	86.90 303	51.60 329	78.51 256

MIMVSNet		70.69 294	69.30 293	74.88 308	84.52 260	56.35 321	75.87 344	79.42 331	64.59 283	67.76 315	82.41 314	41.10 344	81.54 342	46.64 359	81.34 221	86.75 296

tpmvs		71.09 289	69.29 294	76.49 293	82.04 312	56.04 324	78.92 321	81.37 312	64.05 292	67.18 324	78.28 353	49.74 280	89.77 265	49.67 342	72.37 330	83.67 340

test_vis1_n		69.85 304	69.21 295	71.77 333	72.66 382	55.27 334	81.48 285	76.21 354	52.03 369	75.30 232	83.20 303	28.97 378	76.22 370	74.60 143	78.41 259	83.81 339

Patchmtry		70.74 293	69.16 296	75.49 303	80.72 330	54.07 344	74.94 353	80.30 323	58.34 343	70.01 294	81.19 324	52.50 242	86.54 305	53.37 321	71.09 340	85.87 313

TESTMET0.1,1		69.89 303	69.00 297	72.55 329	79.27 352	56.85 310	78.38 326	74.71 362	57.64 349	68.09 314	77.19 360	37.75 360	76.70 364	63.92 240	84.09 183	84.10 336

PMMVS		69.34 306	68.67 298	71.35 338	75.67 365	62.03 250	75.17 348	73.46 365	50.00 374	68.68 309	79.05 345	52.07 252	78.13 356	61.16 267	82.77 205	73.90 377

K. test v3		71.19 287	68.51 299	79.21 257	83.04 293	57.78 299	84.35 245	76.91 350	72.90 152	62.99 356	82.86 309	39.27 352	91.09 247	61.65 262	52.66 383	88.75 253

USDC		70.33 298	68.37 300	76.21 295	80.60 332	56.23 322	79.19 317	86.49 241	60.89 322	61.29 360	85.47 262	31.78 373	89.47 272	53.37 321	76.21 287	82.94 350

tpm cat1		70.57 295	68.31 301	77.35 286	82.41 309	57.95 295	78.08 330	80.22 325	52.04 368	68.54 312	77.66 358	52.00 253	87.84 297	51.77 327	72.07 334	86.25 302

OpenMVS_ROB	64.09 19	70.56 296	68.19 302	77.65 281	80.26 335	59.41 283	85.01 225	82.96 295	58.76 341	65.43 341	82.33 315	37.63 361	91.23 241	45.34 367	76.03 288	82.32 353

EPMVS		69.02 308	68.16 303	71.59 334	79.61 347	49.80 372	77.40 335	66.93 381	62.82 307	70.01 294	79.05 345	45.79 315	77.86 359	56.58 307	75.26 305	87.13 287

CMPMVS	51.72 21	70.19 300	68.16 303	76.28 294	73.15 379	57.55 302	79.47 312	83.92 276	48.02 376	56.48 377	84.81 275	43.13 331	86.42 307	62.67 251	81.81 218	84.89 326
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
AllTest		70.96 290	68.09 305	79.58 251	85.15 246	63.62 221	84.58 236	79.83 327	62.31 312	60.32 364	86.73 224	32.02 371	88.96 282	50.28 337	71.57 337	86.15 305

gg-mvs-nofinetune		69.95 302	67.96 306	75.94 296	83.07 291	54.51 341	77.23 337	70.29 373	63.11 300	70.32 289	62.33 384	43.62 329	88.69 286	53.88 318	87.76 131	84.62 330

FMVSNet5		69.50 305	67.96 306	74.15 316	82.97 297	55.35 332	80.01 307	82.12 304	62.56 310	63.02 354	81.53 323	36.92 362	81.92 340	48.42 347	74.06 316	85.17 323

Syy-MVS		68.05 317	67.85 308	68.67 353	84.68 256	40.97 394	78.62 324	73.08 367	66.65 260	66.74 329	79.46 342	52.11 250	82.30 338	32.89 386	76.38 284	82.75 351

PatchT		68.46 315	67.85 308	70.29 344	80.70 331	43.93 386	72.47 359	74.88 359	60.15 328	70.55 285	76.57 362	49.94 277	81.59 341	50.58 333	74.83 310	85.34 318

pmmvs-eth3d		70.50 297	67.83 310	78.52 268	77.37 359	66.18 167	81.82 279	81.51 309	58.90 340	63.90 352	80.42 334	42.69 334	86.28 308	58.56 288	65.30 361	83.11 346

Anonymous20231206		68.60 311	67.80 311	71.02 341	80.23 337	50.75 367	78.30 329	80.47 320	56.79 355	66.11 338	82.63 313	46.35 308	78.95 353	43.62 370	75.70 291	83.36 343

Patchmatch-RL test		70.24 299	67.78 312	77.61 282	77.43 358	59.57 282	71.16 363	70.33 372	62.94 304	68.65 310	72.77 375	50.62 269	85.49 316	69.58 193	66.58 356	87.77 270

test0.0.03 1		68.00 318	67.69 313	68.90 350	77.55 357	47.43 375	75.70 345	72.95 369	66.66 257	66.56 331	82.29 317	48.06 296	75.87 372	44.97 368	74.51 313	83.41 342

testing3		68.56 313	67.67 314	71.22 340	87.33 209	42.87 388	83.06 270	71.54 370	70.36 192	69.08 307	84.38 281	30.33 377	85.69 313	37.50 382	75.45 299	85.09 325

EU-MVSNet		68.53 314	67.61 315	71.31 339	78.51 355	47.01 377	84.47 238	84.27 272	42.27 382	66.44 336	84.79 276	40.44 348	83.76 328	58.76 287	68.54 351	83.17 344

KD-MVS_self_test		68.81 309	67.59 316	72.46 330	74.29 371	45.45 379	77.93 332	87.00 234	63.12 299	63.99 351	78.99 349	42.32 336	84.77 323	56.55 308	64.09 364	87.16 286

test_fmvs2		68.35 316	67.48 317	70.98 342	69.50 386	51.95 356	80.05 306	76.38 353	49.33 375	74.65 247	84.38 281	23.30 386	75.40 377	74.51 144	75.17 307	85.60 315

ppachtmachnet_test		70.04 301	67.34 318	78.14 273	79.80 344	61.13 260	79.19 317	80.59 318	59.16 337	65.27 342	79.29 344	46.75 305	87.29 301	49.33 343	66.72 354	86.00 311

Anonymous20240521		68.80 310	67.22 319	73.55 320	74.33 370	54.11 343	83.18 264	85.61 254	58.15 345	61.68 359	80.94 329	30.71 376	81.27 344	57.00 303	73.34 326	85.28 319

our_test_3		69.14 307	67.00 320	75.57 301	79.80 344	58.80 284	77.96 331	77.81 340	59.55 333	62.90 357	78.25 354	47.43 298	83.97 327	51.71 328	67.58 353	83.93 338

test20.03		67.45 320	66.95 321	68.94 349	75.48 367	44.84 384	77.50 334	77.67 341	66.66 257	63.01 355	83.80 293	47.02 302	78.40 355	42.53 373	68.86 350	83.58 341

MIMVSNet1		68.58 312	66.78 322	73.98 318	80.07 339	51.82 359	80.77 294	84.37 268	64.40 286	59.75 367	82.16 319	36.47 363	83.63 330	42.73 372	70.33 342	86.48 300

testgi		66.67 326	66.53 323	67.08 358	75.62 366	41.69 393	75.93 341	76.50 352	66.11 266	65.20 345	86.59 234	35.72 366	74.71 379	43.71 369	73.38 325	84.84 327

myMVS_eth3d		67.02 323	66.29 324	69.21 348	84.68 256	42.58 389	78.62 324	73.08 367	66.65 260	66.74 329	79.46 342	31.53 374	82.30 338	39.43 379	76.38 284	82.75 351

UnsupCasMVSNet_eth		67.33 321	65.99 325	71.37 336	73.48 376	51.47 363	75.16 349	85.19 258	65.20 276	60.78 362	80.93 331	42.35 335	77.20 361	57.12 301	53.69 382	85.44 317

dp		66.80 324	65.43 326	70.90 343	79.74 346	48.82 373	75.12 351	74.77 360	59.61 332	64.08 350	77.23 359	42.89 332	80.72 347	48.86 346	66.58 356	83.16 345

TinyColmap		67.30 322	64.81 327	74.76 310	81.92 315	56.68 315	80.29 304	81.49 310	60.33 325	56.27 378	83.22 301	24.77 383	87.66 300	45.52 365	69.47 345	79.95 366

CHOSEN 280x420		66.51 327	64.71 328	71.90 332	81.45 321	63.52 226	57.98 391	68.95 379	53.57 364	62.59 358	76.70 361	46.22 310	75.29 378	55.25 312	79.68 242	76.88 373

TDRefinement		67.49 319	64.34 329	76.92 290	73.47 377	61.07 261	84.86 229	82.98 294	59.77 331	58.30 371	85.13 270	26.06 381	87.89 296	47.92 354	60.59 372	81.81 358

PM-MVS		66.41 328	64.14 330	73.20 324	73.92 372	56.45 317	78.97 320	64.96 387	63.88 296	64.72 346	80.24 335	19.84 389	83.44 332	66.24 221	64.52 363	79.71 367

dmvs_testset		62.63 340	64.11 331	58.19 368	78.55 354	24.76 404	75.28 347	65.94 384	67.91 246	60.34 363	76.01 365	53.56 235	73.94 383	31.79 387	67.65 352	75.88 375

KD-MVS_2432*1600		66.22 330	63.89 332	73.21 322	75.47 368	53.42 349	70.76 366	84.35 269	64.10 290	66.52 333	78.52 351	34.55 368	84.98 320	50.40 335	50.33 386	81.23 360

miper_refine_blended		66.22 330	63.89 332	73.21 322	75.47 368	53.42 349	70.76 366	84.35 269	64.10 290	66.52 333	78.52 351	34.55 368	84.98 320	50.40 335	50.33 386	81.23 360

MDA-MVSNet-bldmvs		66.68 325	63.66 334	75.75 298	79.28 351	60.56 269	73.92 356	78.35 338	64.43 285	50.13 385	79.87 340	44.02 327	83.67 329	46.10 362	56.86 375	83.03 348

ADS-MVSNet2		66.20 332	63.33 335	74.82 309	79.92 340	58.75 285	67.55 377	75.19 357	53.37 365	65.25 343	75.86 366	42.32 336	80.53 348	41.57 374	68.91 348	85.18 321

Patchmatch-test		64.82 335	63.24 336	69.57 346	79.42 350	49.82 371	63.49 388	69.05 378	51.98 370	59.95 366	80.13 336	50.91 265	70.98 386	40.66 376	73.57 321	87.90 267

MDA-MVSNet_test_wron		65.03 333	62.92 337	71.37 336	75.93 362	56.73 312	69.09 375	74.73 361	57.28 353	54.03 381	77.89 355	45.88 313	74.39 381	49.89 341	61.55 368	82.99 349

YYNet1		65.03 333	62.91 338	71.38 335	75.85 364	56.60 316	69.12 374	74.66 363	57.28 353	54.12 380	77.87 356	45.85 314	74.48 380	49.95 340	61.52 369	83.05 347

ADS-MVSNet		64.36 336	62.88 339	68.78 352	79.92 340	47.17 376	67.55 377	71.18 371	53.37 365	65.25 343	75.86 366	42.32 336	73.99 382	41.57 374	68.91 348	85.18 321

JIA-IIPM		66.32 329	62.82 340	76.82 291	77.09 360	61.72 256	65.34 384	75.38 356	58.04 347	64.51 347	62.32 385	42.05 341	86.51 306	51.45 330	69.22 347	82.21 354

LF4IMVS		64.02 337	62.19 341	69.50 347	70.90 384	53.29 352	76.13 339	77.18 348	52.65 367	58.59 369	80.98 328	23.55 385	76.52 366	53.06 323	66.66 355	78.68 369

test_fmvs3		63.36 339	61.82 342	67.98 355	62.51 393	46.96 378	77.37 336	74.03 364	45.24 378	67.50 319	78.79 350	12.16 397	72.98 385	72.77 164	66.02 358	83.99 337

new-patchmatchnet		61.73 342	61.73 343	61.70 364	72.74 381	24.50 405	69.16 373	78.03 339	61.40 319	56.72 376	75.53 369	38.42 356	76.48 367	45.95 363	57.67 374	84.13 335

UnsupCasMVSNet_bld		63.70 338	61.53 344	70.21 345	73.69 374	51.39 364	72.82 358	81.89 305	55.63 360	57.81 373	71.80 377	38.67 355	78.61 354	49.26 344	52.21 384	80.63 363

mvsany_test1		62.30 341	61.26 345	65.41 360	69.52 385	54.86 337	66.86 379	49.78 400	46.65 377	68.50 313	83.21 302	49.15 288	66.28 392	56.93 304	60.77 370	75.11 376

PVSNet_0	57.27 20	61.67 343	59.27 346	68.85 351	79.61 347	57.44 304	68.01 376	73.44 366	55.93 359	58.54 370	70.41 380	44.58 323	77.55 360	47.01 356	35.91 392	71.55 380

test_vis1_rt		60.28 344	58.42 347	65.84 359	67.25 389	55.60 329	70.44 368	60.94 392	44.33 380	59.00 368	66.64 382	24.91 382	68.67 390	62.80 247	69.48 344	73.25 378

MVS-HIRNet		59.14 345	57.67 348	63.57 362	81.65 317	43.50 387	71.73 361	65.06 386	39.59 386	51.43 383	57.73 390	38.34 357	82.58 337	39.53 377	73.95 317	64.62 386

DSMNet-mixed		57.77 347	56.90 349	60.38 366	67.70 388	35.61 397	69.18 372	53.97 398	32.30 394	57.49 374	79.88 339	40.39 349	68.57 391	38.78 380	72.37 330	76.97 372

WB-MVS		54.94 348	54.72 350	55.60 374	73.50 375	20.90 406	74.27 355	61.19 391	59.16 337	50.61 384	74.15 371	47.19 301	75.78 373	17.31 398	35.07 393	70.12 381

pmmvs3		57.79 346	54.26 351	68.37 354	64.02 392	56.72 313	75.12 351	65.17 385	40.20 384	52.93 382	69.86 381	20.36 388	75.48 375	45.45 366	55.25 381	72.90 379

SSC-MVS		53.88 351	53.59 352	54.75 376	72.87 380	19.59 407	73.84 357	60.53 393	57.58 351	49.18 386	73.45 374	46.34 309	75.47 376	16.20 401	32.28 395	69.20 382

N_pmnet		52.79 354	53.26 353	51.40 378	78.99 353	7.68 410	69.52 370	3.89 409	51.63 371	57.01 375	74.98 370	40.83 346	65.96 393	37.78 381	64.67 362	80.56 365

FPMVS		53.68 352	51.64 354	59.81 367	65.08 391	51.03 365	69.48 371	69.58 376	41.46 383	40.67 389	72.32 376	16.46 393	70.00 389	24.24 395	65.42 360	58.40 391

mvsany_test3		53.99 350	51.45 355	61.61 365	55.51 397	44.74 385	63.52 387	45.41 404	43.69 381	58.11 372	76.45 363	17.99 390	63.76 395	54.77 314	47.59 388	76.34 374

test_f		52.09 355	50.82 356	55.90 372	53.82 400	42.31 392	59.42 390	58.31 396	36.45 389	56.12 379	70.96 379	12.18 396	57.79 397	53.51 320	56.57 377	67.60 383

new_pmnet		50.91 357	50.29 357	52.78 377	68.58 387	34.94 399	63.71 386	56.63 397	39.73 385	44.95 387	65.47 383	21.93 387	58.48 396	34.98 384	56.62 376	64.92 385

APD_test1		53.31 353	49.93 358	63.42 363	65.68 390	50.13 369	71.59 362	66.90 382	34.43 391	40.58 390	71.56 378	8.65 402	76.27 369	34.64 385	55.36 380	63.86 387

LCM-MVSNet		54.25 349	49.68 359	67.97 356	53.73 401	45.28 382	66.85 380	80.78 315	35.96 390	39.45 391	62.23 386	8.70 401	78.06 358	48.24 351	51.20 385	80.57 364

EGC-MVSNET		52.07 356	47.05 360	67.14 357	83.51 280	60.71 266	80.50 300	67.75 380	0.07 404	0.43 405	75.85 368	24.26 384	81.54 342	28.82 389	62.25 366	59.16 389

test_vis3_rt		49.26 359	47.02 361	56.00 371	54.30 398	45.27 383	66.76 381	48.08 401	36.83 388	44.38 388	53.20 393	7.17 404	64.07 394	56.77 306	55.66 378	58.65 390

ANet_high		50.57 358	46.10 362	63.99 361	48.67 404	39.13 395	70.99 365	80.85 314	61.39 320	31.18 393	57.70 391	17.02 392	73.65 384	31.22 388	15.89 401	79.18 368

testf1		45.72 360	41.96 363	57.00 369	56.90 395	45.32 380	66.14 382	59.26 394	26.19 395	30.89 394	60.96 388	4.14 405	70.64 387	26.39 393	46.73 390	55.04 392

APD_test2		45.72 360	41.96 363	57.00 369	56.90 395	45.32 380	66.14 382	59.26 394	26.19 395	30.89 394	60.96 388	4.14 405	70.64 387	26.39 393	46.73 390	55.04 392

Gipuma		45.18 362	41.86 365	55.16 375	77.03 361	51.52 362	32.50 397	80.52 319	32.46 393	27.12 396	35.02 397	9.52 400	75.50 374	22.31 396	60.21 373	38.45 396
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 363	40.28 366	55.82 373	40.82 406	42.54 391	65.12 385	63.99 388	34.43 391	24.48 397	57.12 392	3.92 407	76.17 371	17.10 399	55.52 379	48.75 394
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 364	38.86 367	46.69 379	53.84 399	16.45 408	48.61 394	49.92 399	37.49 387	31.67 392	60.97 387	8.14 403	56.42 398	28.42 390	30.72 396	67.19 384

E-PMN		31.77 365	30.64 368	35.15 382	52.87 402	27.67 401	57.09 392	47.86 402	24.64 397	16.40 402	33.05 398	11.23 398	54.90 399	14.46 402	18.15 399	22.87 398

EMVS		30.81 367	29.65 369	34.27 383	50.96 403	25.95 403	56.58 393	46.80 403	24.01 398	15.53 403	30.68 399	12.47 395	54.43 400	12.81 403	17.05 400	22.43 399

test_method		31.52 366	29.28 370	38.23 381	27.03 408	6.50 411	20.94 399	62.21 390	4.05 402	22.35 400	52.50 394	13.33 394	47.58 401	27.04 392	34.04 394	60.62 388

cdsmvs_eth3d_5k		19.96 369	26.61 371	0.00 389	0.00 412	0.00 414	0.00 400	89.26 173	0.00 407	0.00 408	88.61 176	61.62 161	0.00 408	0.00 407	0.00 406	0.00 404

MVE	26.22 23	30.37 368	25.89 372	43.81 380	44.55 405	35.46 398	28.87 398	39.07 405	18.20 399	18.58 401	40.18 396	2.68 408	47.37 402	17.07 400	23.78 398	48.60 395
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		18.61 370	21.40 373	10.23 386	4.82 409	10.11 409	34.70 396	30.74 407	1.48 403	23.91 399	26.07 400	28.42 379	13.41 405	27.12 391	15.35 402	7.17 400

wuyk23d		16.82 371	15.94 374	19.46 385	58.74 394	31.45 400	39.22 395	3.74 410	6.84 401	6.04 404	2.70 404	1.27 409	24.29 404	10.54 404	14.40 403	2.63 401

ab-mvs-re		7.23 372	9.64 375	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	86.72 226	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

test123		6.12 373	8.11 376	0.14 387	0.06 411	0.09 412	71.05 364	0.03 412	0.04 406	0.25 407	1.30 406	0.05 410	0.03 407	0.21 406	0.01 405	0.29 402

testmvs		6.04 374	8.02 377	0.10 388	0.08 410	0.03 413	69.74 369	0.04 411	0.05 405	0.31 406	1.68 405	0.02 411	0.04 406	0.24 405	0.02 404	0.25 403

pcd_1.5k_mvsjas		5.26 375	7.02 378	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	63.15 138	0.00 408	0.00 407	0.00 406	0.00 404

test_blank		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

uanet_test		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

DCPMVS		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

sosnet-low-res		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

sosnet		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

uncertanet		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

Regformer		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

uanet		0.00 376	0.00 379	0.00 389	0.00 412	0.00 414	0.00 400	0.00 413	0.00 407	0.00 408	0.00 407	0.00 412	0.00 408	0.00 407	0.00 406	0.00 404

WAC-MVS						42.58 389								39.46 378

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 113	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 45					97.53 2	89.67 6	96.44 9	94.41 32

PC_three_1452									68.21 243	92.02 12	94.00 46	82.09 5	95.98 51	84.58 48	96.68 2	94.95 10

No_MVS				89.16 1	94.34 27	75.53 2		92.99 45					97.53 2	89.67 6	96.44 9	94.41 32

test_one_0601					95.07 7	71.46 55		94.14 5	78.27 35	92.05 11	95.74 6	80.83 11

eth-test2					0.00 412

eth-test					0.00 412

ZD-MVS					94.38 25	72.22 44		92.67 61	70.98 181	87.75 31	94.07 41	74.01 32	96.70 27	84.66 47	94.84 43

IU-MVS					95.30 2	71.25 57		92.95 51	66.81 253	92.39 6				88.94 16	96.63 4	94.85 19

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 44	82.45 3	96.87 20	83.77 58	96.48 8	94.88 14

test_241102_TWO								94.06 10	77.24 50	92.78 4	95.72 8	81.26 8	97.44 6	89.07 14	96.58 6	94.26 41

test_241102_ONE					95.30 2	70.98 63		94.06 10	77.17 53	93.10 1	95.39 11	82.99 1	97.27 11

save fliter					93.80 40	72.35 42	90.47 64	91.17 116	74.31 116

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 8	89.42 9	96.57 7	94.67 24

test_0728_SECOND				87.71 32	95.34 1	71.43 56	93.49 9	94.23 3					97.49 4	89.08 12	96.41 12	94.21 42

test0726					95.27 5	71.25 57	93.60 6	94.11 6	77.33 48	92.81 3	95.79 3	80.98 9

GSMVS																88.96 244

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 262				88.96 244

sam_mvs												50.01 275

ambc				75.24 305	73.16 378	50.51 368	63.05 389	87.47 225		64.28 348	77.81 357	17.80 391	89.73 267	57.88 295	60.64 371	85.49 316

MTGPA								92.02 85

test_post1							78.90 322				5.43 403	48.81 295	85.44 318	59.25 280

test_post											5.46 402	50.36 273	84.24 325

patchmatchnet-post											74.00 372	51.12 264	88.60 288

GG-mvs-BLEND				75.38 304	81.59 319	55.80 326	79.32 314	69.63 375		67.19 323	73.67 373	43.24 330	88.90 284	50.41 334	84.50 173	81.45 359

MTMP							92.18 35	32.83 406

gm-plane-assit					81.40 322	53.83 346			62.72 309		80.94 329		92.39 195	63.40 244

test9_res														84.90 42	95.70 26	92.87 102

TEST9					93.26 50	72.96 25	88.75 115	91.89 93	68.44 240	85.00 57	93.10 67	74.36 28	95.41 67

test_8					93.13 52	72.57 35	88.68 120	91.84 97	68.69 235	84.87 61	93.10 67	74.43 26	95.16 76

agg_prior2														82.91 66	95.45 30	92.70 105

agg_prior					92.85 59	71.94 51		91.78 100		84.41 71			94.93 87

TestCases				79.58 251	85.15 246	63.62 221		79.83 327	62.31 312	60.32 364	86.73 224	32.02 371	88.96 282	50.28 337	71.57 337	86.15 305

test_prior4						72.60 34	89.01 105

test_prior2							88.85 111		75.41 93	84.91 59	93.54 56	74.28 29		83.31 61	95.86 20

test_prior				86.33 54	92.61 65	69.59 88		92.97 50					95.48 62			93.91 53

旧先验2							86.56 188		58.10 346	87.04 39			88.98 280	74.07 149

新几何2							86.29 196

新几何1				83.42 147	93.13 52	70.71 71		85.48 256	57.43 352	81.80 107	91.98 90	63.28 133	92.27 201	64.60 237	92.99 65	87.27 282

旧先验1					91.96 71	65.79 178		86.37 244			93.08 71	69.31 77			92.74 68	88.74 254

无先验							87.48 159	88.98 186	60.00 329				94.12 121	67.28 214		88.97 243

原ACMM2							86.86 177

原ACMM1				84.35 107	93.01 57	68.79 106		92.44 69	63.96 295	81.09 117	91.57 101	66.06 111	95.45 63	67.19 216	94.82 45	88.81 250

test222					91.50 77	68.26 124	84.16 248	83.20 290	54.63 363	79.74 129	91.63 99	58.97 193			91.42 85	86.77 295

testdata2													91.01 249	62.37 254

segment_acmp												73.08 37

testdata				79.97 241	90.90 86	64.21 212		84.71 263	59.27 336	85.40 51	92.91 73	62.02 157	89.08 278	68.95 199	91.37 86	86.63 299

testdata1							84.14 249		75.71 87

test12				86.80 49	92.63 64	70.70 72		91.79 99		82.71 98		71.67 51	96.16 44		94.50 50	93.54 77

plane_prior7					90.08 103	68.51 119

plane_prior6					89.84 112	68.70 114						60.42 186

plane_prior5								92.44 69					95.38 69	78.71 102	86.32 151	91.33 150

plane_prior4											91.00 120

plane_prior3						68.60 117			78.44 31	78.92 141

plane_prior2							91.25 50		79.12 23

plane_prior1					89.90 111

plane_prior						68.71 112	90.38 67		77.62 39						86.16 155

n2								0.00 413

nn								0.00 413

door-mid								69.98 374

lessismore_v0				78.97 259	81.01 329	57.15 307		65.99 383		61.16 361	82.82 310	39.12 353	91.34 238	59.67 276	46.92 389	88.43 260

LGP-MVS_train				84.50 99	89.23 135	68.76 108		91.94 91	75.37 94	76.64 196	91.51 102	54.29 228	94.91 88	78.44 104	83.78 185	89.83 216

test11								92.23 79

door								69.44 377

HQP5-MVS						66.98 155

HQP-NCC					89.33 128		89.17 98		76.41 72	77.23 182

ACMP_Plane					89.33 128		89.17 98		76.41 72	77.23 182

BP-MVS														77.47 114

HQP4-MVS										77.24 181			95.11 80			91.03 162

HQP3-MVS								92.19 82							85.99 159

HQP2-MVS												60.17 189

NP-MVS					89.62 115	68.32 122					90.24 132

MDTV_nov1_ep13_2view						37.79 396	75.16 349		55.10 361	66.53 332		49.34 285		53.98 317		87.94 266

ACMMP++_ref															81.95 216

ACMMP++															81.25 222

Test By Simon												64.33 125

ITE_SJBPF				78.22 271	81.77 316	60.57 268		83.30 286	69.25 219	67.54 318	87.20 215	36.33 364	87.28 302	54.34 316	74.62 312	86.80 294

DeepMVS_CX				27.40 384	40.17 407	26.90 402		24.59 408	17.44 400	23.95 398	48.61 395	9.77 399	26.48 403	18.06 397	24.47 397	28.83 397

High-resolution multi-view benchmark