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

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

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

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
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.
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

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

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

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
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

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

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

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

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

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

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
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

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

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	12.47 403	67.45 95	96.60 33	83.06 63	94.50 50	94.07 47

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

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
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.
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

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

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
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

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

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

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

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

PHI-MVS		86.43 39	86.17 43	87.24 41	90.88 87	70.96 65	92.27 32	94.07 9	72.45 154	85.22 54	91.90 92	69.47 74	96.42 37	83.28 62	95.94 19	94.35 36

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

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
MTMP							92.18 35	32.83 408

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

CPTT-MVS		83.73 74	83.33 80	84.92 87	93.28 49	70.86 69	92.09 37	90.38 137	68.75 236	79.57 132	92.83 76	60.60 184	93.04 178	80.92 84	91.56 84	90.86 169

APD-MVS_3200maxsize		85.97 45	85.88 48	86.22 57	92.69 63	69.53 89	91.93 38	92.99 45	73.54 136	85.94 45	94.51 24	65.80 115	95.61 57	83.04 65	92.51 71	93.53 78

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	73.53 137	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	73.53 137	85.69 49	94.45 26	63.87 129		82.75 68	91.87 79	92.50 114

APD-MVS		87.44 23	87.52 23	87.19 42	94.24 32	72.39 39	91.86 41	92.83 55	73.01 151	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
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
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
QAPM		80.88 126	79.50 142	85.03 81	88.01 183	68.97 103	91.59 43	92.00 87	66.63 264	75.15 238	92.16 88	57.70 202	95.45 63	63.52 242	88.76 121	90.66 176

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

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
mvsmamba		81.69 111	80.74 117	84.56 97	87.45 205	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	83.54 197	92.04 134

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
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	91.33 151

plane_prior2							91.25 50		79.12 23

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

API-MVS		81.99 105	81.23 109	84.26 114	90.94 85	70.18 82	91.10 53	89.32 169	71.51 171	78.66 147	88.28 186	65.26 118	95.10 83	64.74 236	91.23 88	87.51 278

RRT_MVS		80.35 146	79.22 151	83.74 140	87.63 199	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	79.20 253	91.51 144

EPNet		83.72 75	82.92 87	86.14 59	84.22 267	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
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

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

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	90.16 196

3Dnovator	76.31 5	83.38 85	82.31 95	86.59 52	87.94 184	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

OpenMVS	72.83 10	79.77 156	78.33 171	84.09 121	85.17 246	69.91 84	90.57 60	90.97 121	66.70 258	72.17 276	91.91 91	54.70 224	93.96 124	61.81 263	90.95 91	88.41 263

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

MVSFormer		82.85 94	82.05 99	85.24 75	87.35 206	70.21 77	90.50 62	90.38 137	68.55 239	81.32 112	89.47 151	61.68 159	93.46 153	78.98 99	90.26 100	92.05 132

test_djsdf		80.30 147	79.32 147	83.27 153	83.98 273	65.37 189	90.50 62	90.38 137	68.55 239	76.19 208	88.70 172	56.44 213	93.46 153	78.98 99	80.14 241	90.97 166

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

nrg030		83.88 71	83.53 75	84.96 84	86.77 222	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	79.28 251	92.50 114

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

plane_prior						68.71 112	90.38 67		77.62 39						86.16 155

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
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
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

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	83.78 186	89.83 217

Anonymous20231211		78.97 179	77.69 190	82.81 176	90.54 94	64.29 211	90.11 72	91.51 107	65.01 282	76.16 212	88.13 195	50.56 270	93.03 179	69.68 192	77.56 268	91.11 158

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	70.28 197	77.25 180	89.66 144	53.37 238	93.53 149	74.24 148	82.85 206	88.85 250
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
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	75.04 242	90.41 130	53.82 233	94.54 104	77.56 113	82.91 205	89.86 216
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
iter_conf_final		80.63 136	79.35 146	84.46 102	89.36 127	67.70 137	89.85 75	84.49 267	73.19 147	78.30 157	88.94 165	45.98 313	94.56 102	79.59 96	84.48 177	91.11 158

LFMVS		81.82 108	81.23 109	83.57 144	91.89 73	63.43 230	89.84 76	81.85 308	77.04 58	83.21 89	93.10 67	52.26 246	93.43 155	71.98 169	89.95 107	93.85 57

MCST-MVS		87.37 27	87.25 26	87.73 28	94.53 17	72.46 38	89.82 77	93.82 16	73.07 149	84.86 62	92.89 74	76.22 17	96.33 38	84.89 44	95.13 36	94.40 34

MAR-MVS		81.84 107	80.70 118	85.27 74	91.32 79	71.53 54	89.82 77	90.92 122	69.77 210	78.50 151	86.21 245	62.36 150	94.52 106	65.36 230	92.05 77	89.77 220
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
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
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

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

VDDNet		81.52 116	80.67 119	84.05 128	90.44 96	64.13 214	89.73 82	85.91 250	71.11 178	83.18 90	93.48 58	50.54 271	93.49 150	73.40 156	88.25 128	94.54 30

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

test_fmvsmconf0.1_n		85.61 53	85.65 51	85.50 69	82.99 298	69.39 96	89.65 84	90.29 144	73.31 142	87.77 30	94.15 38	71.72 49	93.23 161	90.31 4	90.67 95	93.89 56

114514_t		80.68 135	79.51 141	84.20 115	94.09 38	67.27 149	89.64 85	91.11 119	58.75 344	74.08 254	90.72 124	58.10 198	95.04 85	69.70 191	89.42 113	90.30 192

test_fmvsmconf_n		85.92 46	86.04 47	85.57 68	85.03 253	69.51 90	89.62 86	90.58 131	73.42 139	87.75 31	94.02 44	72.85 40	93.24 160	90.37 3	90.75 93	93.96 51

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

test_fmvsmconf0.01_n		84.73 66	84.52 68	85.34 72	80.25 338	69.03 99	89.47 88	89.65 161	73.24 146	86.98 40	94.27 32	66.62 101	93.23 161	90.26 5	89.95 107	93.78 62

fmvsm_s_conf0.5_n		83.80 73	83.71 74	84.07 123	86.69 224	67.31 147	89.46 89	83.07 292	71.09 179	86.96 41	93.70 55	69.02 83	91.47 233	88.79 18	84.62 172	93.44 80

fmvsm_s_conf0.5_n_a		83.63 78	83.41 77	84.28 111	86.14 231	68.12 127	89.43 90	82.87 297	70.27 198	87.27 37	93.80 54	69.09 78	91.58 224	88.21 26	83.65 193	93.14 93

UGNet		80.83 128	79.59 140	84.54 98	88.04 181	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
tt0805		78.73 183	77.83 182	81.43 205	85.17 246	60.30 273	89.41 92	90.90 123	71.21 176	77.17 186	88.73 171	46.38 306	93.21 163	72.57 166	78.96 254	90.79 170

fmvsm_s_conf0.1_n		83.56 80	83.38 78	84.10 118	84.86 255	67.28 148	89.40 93	83.01 293	70.67 187	87.08 38	93.96 50	68.38 87	91.45 234	88.56 22	84.50 173	93.56 75

AdaColmap		80.58 140	79.42 143	84.06 125	93.09 54	68.91 104	89.36 94	88.97 188	69.27 219	75.70 217	89.69 143	57.20 209	95.77 54	63.06 247	88.41 127	87.50 279

fmvsm_s_conf0.1_n_a		83.32 86	82.99 85	84.28 111	83.79 276	68.07 129	89.34 95	82.85 298	69.80 208	87.36 36	94.06 42	68.34 88	91.56 226	87.95 27	83.46 199	93.21 90

PS-MVSNAJss		82.07 103	81.31 107	84.34 108	86.51 227	67.27 149	89.27 96	91.51 107	71.75 163	79.37 134	90.22 134	63.15 138	94.27 113	77.69 112	82.36 213	91.49 147

jajsoiax		79.29 170	77.96 177	83.27 153	84.68 258	66.57 162	89.25 97	90.16 147	69.20 224	75.46 223	89.49 150	45.75 318	93.13 172	76.84 121	80.80 231	90.11 200

mvs_tets		79.13 174	77.77 186	83.22 157	84.70 257	66.37 164	89.17 98	90.19 146	69.38 217	75.40 226	89.46 153	44.17 327	93.15 170	76.78 124	80.70 233	90.14 197

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

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	91.03 163

LS3D		76.95 226	74.82 241	83.37 150	90.45 95	67.36 146	89.15 102	86.94 235	61.87 319	69.52 304	90.61 126	51.71 259	94.53 105	46.38 362	86.71 146	88.21 265

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	89.97 212
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
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

test_prior4						72.60 34	89.01 105

GeoE		81.71 110	81.01 114	83.80 139	89.51 120	64.45 208	88.97 106	88.73 199	71.27 175	78.63 148	89.76 142	66.32 107	93.20 166	69.89 189	86.02 158	93.74 63

Anonymous20240529		80.19 150	78.89 158	84.10 118	90.60 92	64.75 201	88.95 107	90.90 123	65.97 272	80.59 122	91.17 113	49.97 276	93.73 142	69.16 197	82.70 210	93.81 60

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

Effi-MVS+		83.62 79	83.08 82	85.24 75	88.38 168	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

ACMH+	68.96 14	76.01 242	74.01 250	82.03 193	88.60 159	65.31 190	88.86 110	87.55 222	70.25 199	67.75 318	87.47 208	41.27 345	93.19 168	58.37 292	75.94 291	87.60 275

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

iter_conf05		80.00 154	78.70 160	83.91 137	87.84 188	65.83 175	88.84 112	84.92 262	71.61 168	78.70 144	88.94 165	43.88 329	94.56 102	79.28 97	84.28 181	91.33 151

DP-MVS Recon		83.11 91	82.09 98	86.15 58	94.44 19	70.92 68	88.79 113	92.20 81	70.53 192	79.17 137	91.03 119	64.12 127	96.03 46	68.39 206	90.14 102	91.50 146

Effi-MVS+-dtu		80.03 152	78.57 164	84.42 104	85.13 250	68.74 110	88.77 114	88.10 208	74.99 102	74.97 243	83.49 301	57.27 208	93.36 156	73.53 153	80.88 229	91.18 156

TEST9					93.26 50	72.96 25	88.75 115	91.89 93	68.44 242	85.00 57	93.10 67	74.36 28	95.41 67

train_agg		86.43 39	86.20 41	87.13 44	93.26 50	72.96 25	88.75 115	91.89 93	68.69 237	85.00 57	93.10 67	74.43 26	95.41 67	84.97 41	95.71 25	93.02 98

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

PVSNet_Blended_VisFu		82.62 96	81.83 104	84.96 84	90.80 89	69.76 87	88.74 117	91.70 102	69.39 216	78.96 139	88.46 181	65.47 117	94.87 94	74.42 145	88.57 123	90.24 194

casdiffmvs_mvg		85.99 44	86.09 46	85.70 66	87.65 198	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_8					93.13 52	72.57 35	88.68 120	91.84 97	68.69 237	84.87 61	93.10 67	74.43 26	95.16 76

test_fmvsm_n_1920		85.29 59	85.34 55	85.13 79	86.12 232	69.93 83	88.65 121	90.78 127	69.97 204	88.27 23	93.98 49	71.39 55	91.54 228	88.49 23	90.45 97	93.91 53

ACMH	67.68 16	75.89 243	73.93 252	81.77 198	88.71 156	66.61 161	88.62 122	89.01 185	69.81 207	66.78 330	86.70 230	41.95 344	91.51 231	55.64 313	78.14 263	87.17 286
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDPH-MVS		85.76 50	85.29 59	87.17 43	93.49 47	71.08 61	88.58 123	92.42 72	68.32 244	84.61 67	93.48 58	72.32 42	96.15 45	79.00 98	95.43 31	94.28 40

DP-MVS		76.78 228	74.57 243	83.42 147	93.29 48	69.46 94	88.55 124	83.70 279	63.98 296	70.20 292	88.89 168	54.01 232	94.80 96	46.66 359	81.88 219	86.01 311

fmvsm_l_conf0.5_n		84.47 67	84.54 66	84.27 113	85.42 242	68.81 105	88.49 125	87.26 229	68.08 246	88.03 27	93.49 57	72.04 46	91.77 218	88.90 17	89.14 116	92.24 125

WR-MVS_H		78.51 189	78.49 165	78.56 267	88.02 182	56.38 320	88.43 126	92.67 61	77.14 54	73.89 255	87.55 205	66.25 108	89.24 277	58.92 286	73.55 324	90.06 206

F-COLMAP		76.38 237	74.33 248	82.50 186	89.28 133	66.95 158	88.41 127	89.03 183	64.05 294	66.83 329	88.61 176	46.78 304	92.89 181	57.48 299	78.55 256	87.67 273

GBi-Net		78.40 190	77.40 195	81.40 207	87.60 200	63.01 238	88.39 128	89.28 170	71.63 165	75.34 228	87.28 210	54.80 220	91.11 242	62.72 249	79.57 245	90.09 202

test1		78.40 190	77.40 195	81.40 207	87.60 200	63.01 238	88.39 128	89.28 170	71.63 165	75.34 228	87.28 210	54.80 220	91.11 242	62.72 249	79.57 245	90.09 202

FMVSNet1		77.44 216	76.12 222	81.40 207	86.81 221	63.01 238	88.39 128	89.28 170	70.49 193	74.39 251	87.28 210	49.06 290	91.11 242	60.91 270	78.52 257	90.09 202

tttt0517		79.40 167	77.91 179	83.90 138	88.10 178	63.84 218	88.37 131	84.05 275	71.45 172	76.78 192	89.12 161	49.93 279	94.89 92	70.18 185	83.18 203	92.96 101

fmvsm_l_conf0.5_n_a		84.13 69	84.16 71	84.06 125	85.38 243	68.40 120	88.34 132	86.85 237	67.48 253	87.48 34	93.40 61	70.89 58	91.61 222	88.38 25	89.22 115	92.16 129

v7n		78.97 179	77.58 193	83.14 160	83.45 283	65.51 182	88.32 133	91.21 114	73.69 131	72.41 273	86.32 244	57.93 199	93.81 135	69.18 196	75.65 294	90.11 200

COLMAP_ROB	66.92 17	73.01 275	70.41 290	80.81 225	87.13 216	65.63 180	88.30 134	84.19 274	62.96 305	63.80 355	87.69 200	38.04 361	92.56 189	46.66 359	74.91 311	84.24 335
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
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

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

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	64.33 290	69.87 301	88.38 183	53.66 234	93.58 144	58.86 287	82.73 208	87.86 270
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
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

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	65.77 273	77.14 187	91.09 115	60.91 177	93.21 163	50.26 341	87.05 140	92.17 128
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_fmvsmvis_n_1920		84.02 70	83.87 72	84.49 101	84.12 269	69.37 97	88.15 140	87.96 212	70.01 202	83.95 80	93.23 65	68.80 85	91.51 231	88.61 20	89.96 106	92.57 110

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	75.48 298	91.72 139

bld_raw_dy_0_64		77.29 221	75.98 223	81.22 213	85.04 252	65.47 184	88.14 142	77.56 345	69.20 224	73.77 256	89.40 159	42.24 341	88.85 287	76.78 124	81.64 221	89.33 232

PS-CasMVS		78.01 203	78.09 175	77.77 281	87.71 195	54.39 344	88.02 143	91.22 113	77.50 46	73.26 262	88.64 175	60.73 178	88.41 293	61.88 261	73.88 321	90.53 182

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

v8		79.97 155	79.02 156	82.80 177	84.09 270	64.50 206	87.96 145	90.29 144	74.13 122	75.24 235	86.81 223	62.88 143	93.89 133	74.39 146	75.40 303	90.00 208

FC-MVSNet-test		81.52 116	82.02 100	80.03 240	88.42 167	55.97 326	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

CP-MVSNet		78.22 194	78.34 170	77.84 279	87.83 189	54.54 342	87.94 147	91.17 116	77.65 38	73.48 260	88.49 180	62.24 153	88.43 292	62.19 257	74.07 317	90.55 181

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

PEN-MVS		77.73 209	77.69 190	77.84 279	87.07 217	53.91 347	87.91 149	91.18 115	77.56 43	73.14 264	88.82 170	61.23 171	89.17 278	59.95 276	72.37 332	90.43 186

ECVR-MVS		79.61 158	79.26 149	80.67 228	90.08 103	54.69 340	87.89 150	77.44 348	74.88 104	80.27 124	92.79 79	48.96 293	92.45 192	68.55 203	92.50 72	94.86 17

v10		79.74 157	78.67 161	82.97 170	84.06 271	64.95 196	87.88 151	90.62 130	73.11 148	75.11 239	86.56 237	61.46 165	94.05 123	73.68 151	75.55 296	89.90 214

test2506		77.30 220	76.49 216	79.74 246	90.08 103	52.02 356	87.86 152	63.10 391	74.88 104	80.16 127	92.79 79	38.29 360	92.35 198	68.74 202	92.50 72	94.86 17

casdiffmvs		85.11 61	85.14 60	85.01 82	87.20 214	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
TranMVSNet+NR-MVSNet		80.84 127	80.31 126	82.42 187	87.85 187	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	75.24 308	92.30 121

EI-MVSNet-Vis-set		84.19 68	83.81 73	85.31 73	88.18 173	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

UniMVSNet (Re)		81.60 115	81.11 111	83.09 162	88.38 168	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	76.49 280	91.60 140

CNLPA		78.08 199	76.79 209	81.97 195	90.40 97	71.07 62	87.59 157	84.55 266	66.03 271	72.38 274	89.64 145	57.56 204	86.04 312	59.61 279	83.35 200	88.79 253

DTE-MVSNet		76.99 224	76.80 208	77.54 286	86.24 229	53.06 355	87.52 158	90.66 129	77.08 57	72.50 271	88.67 174	60.48 185	89.52 272	57.33 302	70.74 343	90.05 207

无先验							87.48 159	88.98 186	60.00 331				94.12 121	67.28 214		88.97 245

FMVSNet2		78.20 196	77.21 199	81.20 214	87.60 200	62.89 242	87.47 160	89.02 184	71.63 165	75.29 234	87.28 210	54.80 220	91.10 245	62.38 254	79.38 249	89.61 224

EI-MVSNet-UG-set		83.81 72	83.38 78	85.09 80	87.87 186	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	83.92 185	92.99 100

thisisatest0530		79.40 167	77.76 187	84.31 109	87.69 197	65.10 194	87.36 162	84.26 273	70.04 201	77.42 176	88.26 188	49.94 277	94.79 97	70.20 184	84.70 171	93.03 97

CANet_DTU		80.61 137	79.87 134	82.83 174	85.60 239	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

test1111		79.43 165	79.18 153	80.15 238	89.99 108	53.31 353	87.33 164	77.05 351	75.04 101	80.23 126	92.77 81	48.97 292	92.33 200	68.87 200	92.40 74	94.81 20

baseline		84.93 63	84.98 61	84.80 92	87.30 212	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

UniMVSNet_ETH3D		79.10 175	78.24 173	81.70 199	86.85 219	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	82.15 214	93.29 85

anonymousdsp		78.60 187	77.15 200	82.98 169	80.51 336	67.08 153	87.24 167	89.53 163	65.66 275	75.16 237	87.19 216	52.52 241	92.25 202	77.17 118	79.34 250	89.61 224

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	76.46 281	92.25 123

DPM-MVS		84.93 63	84.29 70	86.84 47	90.20 100	73.04 23	87.12 169	93.04 38	69.80 208	82.85 95	91.22 110	73.06 38	96.02 47	76.72 126	94.63 47	91.46 150

v1144		80.03 152	79.03 155	83.01 167	83.78 277	64.51 204	87.11 170	90.57 133	71.96 162	78.08 165	86.20 246	61.41 166	93.94 127	74.93 141	77.23 269	90.60 179

v2v482		80.23 148	79.29 148	83.05 165	83.62 279	64.14 213	87.04 171	89.97 152	73.61 133	78.18 162	87.22 214	61.10 174	93.82 134	76.11 129	76.78 278	91.18 156

DU-MVS		81.12 123	80.52 122	82.90 172	87.80 190	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	76.46 281	92.20 126

v144192		79.47 163	78.37 169	82.78 180	83.35 284	63.96 216	86.96 173	90.36 140	69.99 203	77.50 174	85.67 257	60.66 181	93.77 138	74.27 147	76.58 279	90.62 177

Fast-Effi-MVS+-dtu		78.02 202	76.49 216	82.62 184	83.16 292	66.96 157	86.94 174	87.45 226	72.45 154	71.49 283	84.17 288	54.79 223	91.58 224	67.61 210	80.31 238	89.30 233

v1192		79.59 160	78.43 168	83.07 164	83.55 281	64.52 203	86.93 175	90.58 131	70.83 183	77.78 170	85.90 250	59.15 192	93.94 127	73.96 150	77.19 271	90.76 172

EPNet_dtu		75.46 249	74.86 240	77.23 290	82.57 307	54.60 341	86.89 176	83.09 291	71.64 164	66.25 339	85.86 252	55.99 214	88.04 297	54.92 315	86.55 148	89.05 240
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM2							86.86 177

VPA-MVSNet		80.60 138	80.55 121	80.76 226	88.07 180	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	79.22 252	91.23 155

v1921920		79.22 171	78.03 176	82.80 177	83.30 286	63.94 217	86.80 179	90.33 141	69.91 206	77.48 175	85.53 260	58.44 196	93.75 140	73.60 152	76.85 276	90.71 175

IterMVS-LS		80.06 151	79.38 144	82.11 191	85.89 234	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	76.86 275	90.75 173
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		75.39 252	74.56 244	77.86 278	85.50 241	57.10 308	86.78 181	86.09 249	72.17 160	71.53 282	87.34 209	63.01 142	89.31 276	56.84 307	61.83 369	87.17 286

Baseline_NR-MVSNet		78.15 198	78.33 171	77.61 284	85.79 235	56.21 324	86.78 181	85.76 253	73.60 134	77.93 168	87.57 203	65.02 121	88.99 281	67.14 217	75.33 305	87.63 274

PAPR		81.66 114	80.89 116	83.99 133	90.27 98	64.00 215	86.76 183	91.77 101	68.84 235	77.13 188	89.50 149	67.63 93	94.88 93	67.55 211	88.52 125	93.09 94

Vis-MVSNet (Re-imp)		78.36 192	78.45 166	78.07 277	88.64 158	51.78 362	86.70 184	79.63 332	74.14 121	75.11 239	90.83 123	61.29 170	89.75 268	58.10 295	91.60 82	92.69 107

pmmvs6		74.69 256	73.39 258	78.61 265	81.38 325	57.48 303	86.64 185	87.95 213	64.99 283	70.18 293	86.61 233	50.43 272	89.52 272	62.12 259	70.18 345	88.83 251

v1240		78.99 178	77.78 185	82.64 183	83.21 288	63.54 225	86.62 186	90.30 143	69.74 213	77.33 178	85.68 256	57.04 210	93.76 139	73.13 160	76.92 273	90.62 177

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

旧先验2							86.56 188		58.10 348	87.04 39			88.98 282	74.07 149

FMVSNet3		77.88 206	76.85 207	80.97 222	86.84 220	62.36 245	86.52 189	88.77 194	71.13 177	75.34 228	86.66 232	54.07 231	91.10 245	62.72 249	79.57 245	89.45 228

dcpmvs_2		85.63 52	86.15 44	84.06 125	91.71 75	64.94 197	86.47 190	91.87 95	73.63 132	86.60 43	93.02 72	76.57 15	91.87 216	83.36 60	92.15 75	95.35 3

pm-mvs1		77.25 222	76.68 214	78.93 261	84.22 267	58.62 286	86.41 191	88.36 205	71.37 173	73.31 261	88.01 196	61.22 172	89.15 279	64.24 240	73.01 329	89.03 241

EI-MVSNet		80.52 141	79.98 131	82.12 190	84.28 265	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	80.57 235	90.74 174

CVMVSNet		72.99 276	72.58 266	74.25 317	84.28 265	50.85 368	86.41 191	83.45 285	44.56 381	73.23 263	87.54 206	49.38 284	85.70 314	65.90 226	78.44 259	86.19 306

NR-MVSNet		80.23 148	79.38 144	82.78 180	87.80 190	63.34 231	86.31 194	91.09 120	79.01 26	72.17 276	89.07 162	67.20 98	92.81 185	66.08 225	75.65 294	92.20 126

v148		78.72 184	77.80 184	81.47 204	82.73 303	61.96 252	86.30 195	88.08 209	73.26 144	76.18 209	85.47 262	62.46 148	92.36 197	71.92 170	73.82 322	90.09 202

新几何2							86.29 196

test_yl		81.17 121	80.47 123	83.24 155	89.13 139	63.62 221	86.21 197	89.95 153	72.43 157	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	72.43 157	81.78 108	89.61 146	57.50 205	93.58 144	70.75 178	86.90 142	92.52 112

PVSNet_BlendedMVS		80.60 138	80.02 130	82.36 189	88.85 146	65.40 186	86.16 199	92.00 87	69.34 218	78.11 163	86.09 249	66.02 112	94.27 113	71.52 171	82.06 216	87.39 280

MVS_Test		83.15 88	83.06 83	83.41 149	86.86 218	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

BH-untuned		79.47 163	78.60 163	82.05 192	89.19 137	65.91 173	86.07 201	88.52 203	72.18 159	75.42 225	87.69 200	61.15 173	93.54 148	60.38 273	86.83 144	86.70 299

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

jason		81.39 119	80.29 127	84.70 94	86.63 226	69.90 85	85.95 203	86.77 238	63.24 300	81.07 118	89.47 151	61.08 175	92.15 205	78.33 107	90.07 105	92.05 132
jason: jason.
test_0402		72.79 278	70.44 289	79.84 244	88.13 176	65.99 171	85.93 204	84.29 271	65.57 276	67.40 324	85.49 261	46.92 303	92.61 187	35.88 385	74.38 316	80.94 364

OurMVSNet-221017-0		74.26 259	72.42 268	79.80 245	83.76 278	59.59 281	85.92 205	86.64 239	66.39 266	66.96 327	87.58 202	39.46 353	91.60 223	65.76 228	69.27 348	88.22 264

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	73.44 326	91.06 161

EG-PatchMatch MVS		74.04 262	71.82 272	80.71 227	84.92 254	67.42 143	85.86 207	88.08 209	66.04 270	64.22 351	83.85 292	35.10 369	92.56 189	57.44 300	80.83 230	82.16 358

AUN-MVS		79.21 172	77.60 192	84.05 128	88.71 156	67.61 139	85.84 208	87.26 229	69.08 228	77.23 182	88.14 194	53.20 240	93.47 152	75.50 139	73.45 325	91.06 161

thres100view900		76.50 232	75.55 230	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	48.33 350	83.75 189	89.07 235

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	90.79 170
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SixPastTwentyTwo		73.37 269	71.26 281	79.70 247	85.08 251	57.89 296	85.57 211	83.56 282	71.03 181	65.66 341	85.88 251	42.10 342	92.57 188	59.11 284	63.34 367	88.65 258

xiu_mvs_v1_base_debu		80.80 131	79.72 137	84.03 130	87.35 206	70.19 79	85.56 212	88.77 194	69.06 229	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 237

xiu_mvs_v1_base		80.80 131	79.72 137	84.03 130	87.35 206	70.19 79	85.56 212	88.77 194	69.06 229	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 237

xiu_mvs_v1_base_debi		80.80 131	79.72 137	84.03 130	87.35 206	70.19 79	85.56 212	88.77 194	69.06 229	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 237

V42		79.38 169	78.24 173	82.83 174	81.10 330	65.50 183	85.55 215	89.82 155	71.57 170	78.21 160	86.12 248	60.66 181	93.18 169	75.64 135	75.46 300	89.81 219

lupinMVS		81.39 119	80.27 128	84.76 93	87.35 206	70.21 77	85.55 215	86.41 242	62.85 307	81.32 112	88.61 176	61.68 159	92.24 203	78.41 106	90.26 100	91.83 136

Fast-Effi-MVS+		80.81 129	79.92 132	83.47 145	88.85 146	64.51 204	85.53 217	89.39 167	70.79 184	78.49 152	85.06 272	67.54 94	93.58 144	67.03 219	86.58 147	92.32 120

thres600view7		76.50 232	75.44 231	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	48.05 355	83.72 192	90.00 208

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
tfpn200view9		76.42 235	75.37 235	79.55 253	89.13 139	57.65 300	85.17 220	83.60 280	73.41 140	76.45 200	86.39 242	52.12 248	91.95 211	48.33 350	83.75 189	89.07 235

thres400		76.50 232	75.37 235	79.86 243	89.13 139	57.65 300	85.17 220	83.60 280	73.41 140	76.45 200	86.39 242	52.12 248	91.95 211	48.33 350	83.75 189	90.00 208

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	90.20 195

baseline1		76.98 225	76.75 212	77.66 282	88.13 176	55.66 330	85.12 223	81.89 306	73.04 150	76.79 191	88.90 167	62.43 149	87.78 300	63.30 246	71.18 341	89.55 226

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	77.22 270	91.80 138

ET-MVSNet_ETH3D		78.63 186	76.63 215	84.64 95	86.73 223	69.47 92	85.01 225	84.61 265	69.54 214	66.51 337	86.59 234	50.16 274	91.75 219	76.26 128	84.24 182	92.69 107

OpenMVS_ROB	64.09 19	70.56 298	68.19 304	77.65 283	80.26 337	59.41 283	85.01 225	82.96 296	58.76 343	65.43 343	82.33 317	37.63 363	91.23 241	45.34 369	76.03 290	82.32 355

BH-RMVSNet		79.61 158	78.44 167	83.14 160	89.38 126	65.93 172	84.95 227	87.15 232	73.56 135	78.19 161	89.79 141	56.67 212	93.36 156	59.53 280	86.74 145	90.13 198

BH-w/o		78.21 195	77.33 198	80.84 224	88.81 150	65.13 193	84.87 228	87.85 217	69.75 211	74.52 250	84.74 277	61.34 168	93.11 173	58.24 294	85.84 161	84.27 334

TDRefinement		67.49 321	64.34 331	76.92 292	73.47 379	61.07 261	84.86 229	82.98 295	59.77 333	58.30 373	85.13 270	26.06 383	87.89 298	47.92 356	60.59 374	81.81 360

Anonymous202405211		78.25 193	77.01 202	81.99 194	91.03 82	60.67 267	84.77 230	83.90 277	70.65 191	80.00 128	91.20 111	41.08 347	91.43 235	65.21 231	85.26 165	93.85 57

TAMVS		78.89 181	77.51 194	83.03 166	87.80 190	67.79 135	84.72 231	85.05 260	67.63 249	76.75 193	87.70 199	62.25 152	90.82 251	58.53 291	87.13 139	90.49 184

1314		76.53 231	75.30 237	80.21 237	83.93 274	62.32 247	84.66 232	88.81 192	60.23 329	70.16 295	84.07 290	55.30 217	90.73 254	67.37 213	83.21 202	87.59 277

MVS		78.19 197	76.99 204	81.78 197	85.66 237	66.99 154	84.66 232	90.47 135	55.08 364	72.02 278	85.27 265	63.83 130	94.11 122	66.10 224	89.80 109	84.24 335

tfpnnormal		74.39 257	73.16 261	78.08 276	86.10 233	58.05 291	84.65 234	87.53 223	70.32 196	71.22 285	85.63 258	54.97 218	89.86 265	43.03 373	75.02 310	86.32 303

TR-MVS		77.44 216	76.18 221	81.20 214	88.24 172	63.24 233	84.61 235	86.40 243	67.55 251	77.81 169	86.48 240	54.10 230	93.15 170	57.75 298	82.72 209	87.20 285

AllTest		70.96 292	68.09 307	79.58 251	85.15 248	63.62 221	84.58 236	79.83 329	62.31 314	60.32 366	86.73 224	32.02 373	88.96 284	50.28 339	71.57 339	86.15 307

FA-MVS(test-final)		80.96 125	79.91 133	84.10 118	88.30 171	65.01 195	84.55 237	90.01 151	73.25 145	79.61 131	87.57 203	58.35 197	94.72 99	71.29 175	86.25 153	92.56 111

EU-MVSNet		68.53 316	67.61 317	71.31 341	78.51 357	47.01 379	84.47 238	84.27 272	42.27 384	66.44 338	84.79 276	40.44 350	83.76 330	58.76 289	68.54 353	83.17 346

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

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	70.67 187	81.30 115	86.53 239	63.17 137	94.19 119	75.60 137	88.54 124	88.57 260

VPNet		78.69 185	78.66 162	78.76 263	88.31 170	55.72 329	84.45 241	86.63 240	76.79 64	78.26 158	90.55 128	59.30 191	89.70 270	66.63 220	77.05 272	90.88 168

PVSNet_Blended		80.98 124	80.34 125	82.90 172	88.85 146	65.40 186	84.43 242	92.00 87	67.62 250	78.11 163	85.05 273	66.02 112	94.27 113	71.52 171	89.50 111	89.01 242

MVP-Stereo		76.12 239	74.46 247	81.13 217	85.37 244	69.79 86	84.42 243	87.95 213	65.03 281	67.46 322	85.33 264	53.28 239	91.73 221	58.01 296	83.27 201	81.85 359
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CDS-MVSNet		79.07 176	77.70 189	83.17 159	87.60 200	68.23 125	84.40 244	86.20 246	67.49 252	76.36 204	86.54 238	61.54 162	90.79 252	61.86 262	87.33 136	90.49 184
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
K. test v3		71.19 289	68.51 301	79.21 257	83.04 295	57.78 299	84.35 245	76.91 352	72.90 153	62.99 358	82.86 311	39.27 354	91.09 247	61.65 264	52.66 385	88.75 255

PS-MVSNAJ		81.69 111	81.02 113	83.70 141	89.51 120	68.21 126	84.28 246	90.09 149	70.79 184	81.26 116	85.62 259	63.15 138	94.29 111	75.62 136	88.87 119	88.59 259

patch_mono-2		83.65 76	84.54 66	80.99 220	90.06 107	65.83 175	84.21 247	88.74 198	71.60 169	85.01 55	92.44 84	74.51 25	83.50 333	82.15 75	92.15 75	93.64 71

test222					91.50 77	68.26 124	84.16 248	83.20 290	54.63 365	79.74 129	91.63 99	58.97 193			91.42 85	86.77 297

testdata1							84.14 249		75.71 87

c3_l		78.75 182	77.91 179	81.26 211	82.89 300	61.56 257	84.09 250	89.13 181	69.97 204	75.56 219	84.29 284	66.36 106	92.09 207	73.47 155	75.48 298	90.12 199

MVSTER		79.01 177	77.88 181	82.38 188	83.07 293	64.80 200	84.08 251	88.95 189	69.01 232	78.69 145	87.17 217	54.70 224	92.43 193	74.69 142	80.57 235	89.89 215

ab-mvs		79.51 161	78.97 157	81.14 216	88.46 164	60.91 263	83.84 252	89.24 175	70.36 194	79.03 138	88.87 169	63.23 136	90.21 260	65.12 232	82.57 211	92.28 122

PAPM		77.68 213	76.40 219	81.51 203	87.29 213	61.85 253	83.78 253	89.59 162	64.74 284	71.23 284	88.70 172	62.59 145	93.66 143	52.66 326	87.03 141	89.01 242

diffmvs		82.10 101	81.88 103	82.76 182	83.00 296	63.78 220	83.68 254	89.76 157	72.94 152	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
miper_ehance_all_eth		78.59 188	77.76 187	81.08 218	82.66 305	61.56 257	83.65 255	89.15 179	68.87 234	75.55 220	83.79 295	66.49 104	92.03 208	73.25 158	76.39 283	89.64 223

1112_ss		77.40 218	76.43 218	80.32 235	89.11 143	60.41 272	83.65 255	87.72 220	62.13 317	73.05 265	86.72 226	62.58 146	89.97 264	62.11 260	80.80 231	90.59 180

PCF-MVS	73.52 7	80.38 143	78.84 159	85.01 82	87.71 195	68.99 102	83.65 255	91.46 111	63.00 304	77.77 171	90.28 131	66.10 109	95.09 84	61.40 266	88.22 129	90.94 167
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
XVG-ACMP-BASELINE		76.11 240	74.27 249	81.62 200	83.20 289	64.67 202	83.60 258	89.75 158	69.75 211	71.85 279	87.09 219	32.78 372	92.11 206	69.99 188	80.43 237	88.09 266

cl22		78.07 200	77.01 202	81.23 212	82.37 312	61.83 254	83.55 259	87.98 211	68.96 233	75.06 241	83.87 291	61.40 167	91.88 215	73.53 153	76.39 283	89.98 211

XVG-OURS-SEG-HR		80.81 129	79.76 136	83.96 135	85.60 239	68.78 107	83.54 260	90.50 134	70.66 190	76.71 194	91.66 96	60.69 180	91.26 239	76.94 120	81.58 222	91.83 136

IB-MVS	68.01 15	75.85 244	73.36 259	83.31 151	84.76 256	66.03 168	83.38 261	85.06 259	70.21 200	69.40 305	81.05 328	45.76 317	94.66 101	65.10 233	75.49 297	89.25 234
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
HY-MVS	69.67 12	77.95 204	77.15 200	80.36 233	87.57 204	60.21 275	83.37 262	87.78 219	66.11 268	75.37 227	87.06 221	63.27 134	90.48 257	61.38 267	82.43 212	90.40 188

test_vis1_n_1920		75.52 248	75.78 224	74.75 313	79.84 344	57.44 304	83.26 263	85.52 255	62.83 308	79.34 136	86.17 247	45.10 322	79.71 352	78.75 101	81.21 226	87.10 292

Anonymous20240521		68.80 312	67.22 321	73.55 322	74.33 372	54.11 345	83.18 264	85.61 254	58.15 347	61.68 361	80.94 331	30.71 378	81.27 346	57.00 305	73.34 328	85.28 321

eth_miper_zixun_eth		77.92 205	76.69 213	81.61 202	83.00 296	61.98 251	83.15 265	89.20 177	69.52 215	74.86 245	84.35 283	61.76 158	92.56 189	71.50 173	72.89 330	90.28 193

FE-MVS		77.78 208	75.68 226	84.08 122	88.09 179	66.00 170	83.13 266	87.79 218	68.42 243	78.01 166	85.23 267	45.50 320	95.12 78	59.11 284	85.83 162	91.11 158

cl____		77.72 210	76.76 210	80.58 229	82.49 309	60.48 270	83.09 267	87.87 215	69.22 222	74.38 252	85.22 268	62.10 155	91.53 229	71.09 176	75.41 302	89.73 222

DIV-MVS_self_test		77.72 210	76.76 210	80.58 229	82.48 310	60.48 270	83.09 267	87.86 216	69.22 222	74.38 252	85.24 266	62.10 155	91.53 229	71.09 176	75.40 303	89.74 221

thres200		75.55 247	74.47 246	78.82 262	87.78 193	57.85 297	83.07 269	83.51 283	72.44 156	75.84 215	84.42 279	52.08 251	91.75 219	47.41 357	83.64 194	86.86 295

testing3		68.56 315	67.67 316	71.22 342	87.33 211	42.87 390	83.06 270	71.54 372	70.36 194	69.08 309	84.38 281	30.33 379	85.69 315	37.50 384	75.45 301	85.09 327

XVG-OURS		80.41 142	79.23 150	83.97 134	85.64 238	69.02 101	83.03 271	90.39 136	71.09 179	77.63 173	91.49 104	54.62 226	91.35 237	75.71 134	83.47 198	91.54 143

miper_enhance_ethall		77.87 207	76.86 206	80.92 223	81.65 319	61.38 259	82.68 272	88.98 186	65.52 277	75.47 221	82.30 318	65.76 116	92.00 210	72.95 161	76.39 283	89.39 229

mvs_anonymous		79.42 166	79.11 154	80.34 234	84.45 264	57.97 294	82.59 273	87.62 221	67.40 254	76.17 211	88.56 179	68.47 86	89.59 271	70.65 181	86.05 157	93.47 79

baseline2		75.70 245	73.83 255	81.30 210	83.26 287	61.79 255	82.57 274	80.65 318	66.81 255	66.88 328	83.42 302	57.86 201	92.19 204	63.47 243	79.57 245	89.91 213

cascas		76.72 229	74.64 242	82.99 168	85.78 236	65.88 174	82.33 275	89.21 176	60.85 325	72.74 267	81.02 329	47.28 300	93.75 140	67.48 212	85.02 166	89.34 231

WB-MVSnew		71.96 286	71.65 274	72.89 328	84.67 261	51.88 360	82.29 276	77.57 344	62.31 314	73.67 258	83.00 307	53.49 237	81.10 347	45.75 366	82.13 215	85.70 316

RPSCF		73.23 273	71.46 276	78.54 268	82.50 308	59.85 277	82.18 277	82.84 299	58.96 341	71.15 286	89.41 157	45.48 321	84.77 325	58.82 288	71.83 337	91.02 165

thisisatest0515		77.33 219	75.38 234	83.18 158	85.27 245	63.80 219	82.11 278	83.27 287	65.06 280	75.91 213	83.84 293	49.54 281	94.27 113	67.24 215	86.19 154	91.48 148

pmmvs-eth3d		70.50 299	67.83 312	78.52 270	77.37 361	66.18 167	81.82 279	81.51 310	58.90 342	63.90 354	80.42 336	42.69 336	86.28 310	58.56 290	65.30 363	83.11 348

MS-PatchMatch		73.83 265	72.67 264	77.30 289	83.87 275	66.02 169	81.82 279	84.66 264	61.37 323	68.61 313	82.82 312	47.29 299	88.21 294	59.27 281	84.32 180	77.68 373

pmmvs5		71.55 287	70.20 293	75.61 302	77.83 358	56.39 319	81.74 281	80.89 314	57.76 350	67.46 322	84.49 278	49.26 287	85.32 321	57.08 304	75.29 306	85.11 326

Test_1112_low_res		76.40 236	75.44 231	79.27 255	89.28 133	58.09 290	81.69 282	87.07 233	59.53 336	72.48 272	86.67 231	61.30 169	89.33 275	60.81 272	80.15 240	90.41 187

IterMVS		74.29 258	72.94 263	78.35 272	81.53 322	63.49 227	81.58 283	82.49 301	68.06 247	69.99 298	83.69 298	51.66 260	85.54 317	65.85 227	71.64 338	86.01 311
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		75.43 250	73.87 254	80.11 239	82.69 304	64.85 199	81.57 284	83.47 284	69.16 226	70.49 289	84.15 289	51.95 254	88.15 295	69.23 195	72.14 335	87.34 282

test_vis1_n		69.85 306	69.21 297	71.77 335	72.66 384	55.27 336	81.48 285	76.21 356	52.03 371	75.30 233	83.20 305	28.97 380	76.22 372	74.60 143	78.41 261	83.81 341

pmmvs4		74.03 264	71.91 271	80.39 232	81.96 315	68.32 122	81.45 286	82.14 304	59.32 337	69.87 301	85.13 270	52.40 244	88.13 296	60.21 275	74.74 313	84.73 331

GA-MVS		76.87 227	75.17 238	81.97 195	82.75 302	62.58 243	81.44 287	86.35 245	72.16 161	74.74 246	82.89 310	46.20 311	92.02 209	68.85 201	81.09 227	91.30 154

UWE-MVS		72.13 284	71.49 275	74.03 319	86.66 225	47.70 376	81.40 288	76.89 353	63.60 299	75.59 218	84.22 287	39.94 352	85.62 316	48.98 347	86.13 156	88.77 254

test_fmvs1_n		70.86 294	70.24 292	72.73 330	72.51 385	55.28 335	81.27 289	79.71 331	51.49 374	78.73 143	84.87 274	27.54 382	77.02 364	76.06 130	79.97 243	85.88 314

testing91		76.54 230	75.66 228	79.18 258	88.43 166	55.89 327	81.08 290	83.00 294	73.76 130	75.34 228	84.29 284	46.20 311	90.07 262	64.33 238	84.50 173	91.58 142

testing222		74.04 262	72.66 265	78.19 274	87.89 185	55.36 333	81.06 291	79.20 336	71.30 174	74.65 248	83.57 300	39.11 356	88.67 289	51.43 333	85.75 163	90.53 182

test_fmvs1		70.93 293	70.52 287	72.16 333	73.71 375	55.05 337	80.82 292	78.77 338	51.21 375	78.58 149	84.41 280	31.20 377	76.94 365	75.88 133	80.12 242	84.47 333

CostFormer		75.24 253	73.90 253	79.27 255	82.65 306	58.27 289	80.80 293	82.73 300	61.57 320	75.33 232	83.13 306	55.52 215	91.07 248	64.98 234	78.34 262	88.45 261

testing99		76.09 241	75.12 239	79.00 259	88.16 174	55.50 332	80.79 294	81.40 312	73.30 143	75.17 236	84.27 286	44.48 325	90.02 263	64.28 239	84.22 183	91.48 148

MIMVSNet1		68.58 314	66.78 324	73.98 320	80.07 341	51.82 361	80.77 295	84.37 268	64.40 288	59.75 369	82.16 321	36.47 365	83.63 332	42.73 374	70.33 344	86.48 302

CL-MVSNet_self_test		72.37 281	71.46 276	75.09 308	79.49 351	53.53 349	80.76 296	85.01 261	69.12 227	70.51 288	82.05 322	57.92 200	84.13 328	52.27 328	66.00 361	87.60 275

testing11		75.14 254	74.01 250	78.53 269	88.16 174	56.38 320	80.74 297	80.42 323	70.67 187	72.69 270	83.72 297	43.61 331	89.86 265	62.29 256	83.76 188	89.36 230

MSDG		73.36 271	70.99 283	80.49 231	84.51 263	65.80 177	80.71 298	86.13 248	65.70 274	65.46 342	83.74 296	44.60 323	90.91 250	51.13 334	76.89 274	84.74 330

tpm2		73.26 272	71.46 276	78.63 264	83.34 285	56.71 314	80.65 299	80.40 324	56.63 358	73.55 259	82.02 323	51.80 258	91.24 240	56.35 311	78.42 260	87.95 267

XXY-MVS		75.41 251	75.56 229	74.96 309	83.59 280	57.82 298	80.59 300	83.87 278	66.54 265	74.93 244	88.31 185	63.24 135	80.09 351	62.16 258	76.85 276	86.97 293

test_cas_vis1_n_1920		73.76 266	73.74 256	73.81 321	75.90 365	59.77 278	80.51 301	82.40 302	58.30 346	81.62 110	85.69 255	44.35 326	76.41 370	76.29 127	78.61 255	85.23 322

EGC-MVSNET		52.07 358	47.05 362	67.14 359	83.51 282	60.71 266	80.50 302	67.75 382	0.07 406	0.43 407	75.85 370	24.26 386	81.54 344	28.82 391	62.25 368	59.16 391

SDMVSNet		80.38 143	80.18 129	80.99 220	89.03 144	64.94 197	80.45 303	89.40 166	75.19 98	76.61 198	89.98 137	60.61 183	87.69 301	76.83 123	83.55 195	90.33 190

HyFIR lowres test		77.53 215	75.40 233	83.94 136	89.59 116	66.62 160	80.36 304	88.64 201	56.29 360	76.45 200	85.17 269	57.64 203	93.28 158	61.34 268	83.10 204	91.91 135

D2MVS		74.82 255	73.21 260	79.64 250	79.81 345	62.56 244	80.34 305	87.35 227	64.37 289	68.86 310	82.66 314	46.37 307	90.10 261	67.91 208	81.24 225	86.25 304

TinyColmap		67.30 324	64.81 329	74.76 312	81.92 317	56.68 315	80.29 306	81.49 311	60.33 327	56.27 380	83.22 303	24.77 385	87.66 302	45.52 367	69.47 347	79.95 368

LCM-MVSNet-Re		77.05 223	76.94 205	77.36 287	87.20 214	51.60 363	80.06 307	80.46 322	75.20 97	67.69 319	86.72 226	62.48 147	88.98 282	63.44 244	89.25 114	91.51 144

test_fmvs2		68.35 318	67.48 319	70.98 344	69.50 388	51.95 358	80.05 308	76.38 355	49.33 377	74.65 248	84.38 281	23.30 388	75.40 379	74.51 144	75.17 309	85.60 317

FMVSNet5		69.50 307	67.96 308	74.15 318	82.97 299	55.35 334	80.01 309	82.12 305	62.56 312	63.02 356	81.53 325	36.92 364	81.92 342	48.42 349	74.06 318	85.17 325

SCA		74.22 260	72.33 269	79.91 242	84.05 272	62.17 249	79.96 310	79.29 335	66.30 267	72.38 274	80.13 338	51.95 254	88.60 290	59.25 282	77.67 267	88.96 246

tpmrst		72.39 279	72.13 270	73.18 327	80.54 335	49.91 372	79.91 311	79.08 337	63.11 302	71.69 281	79.95 340	55.32 216	82.77 338	65.66 229	73.89 320	86.87 294

PatchmatchNet		73.12 274	71.33 279	78.49 271	83.18 290	60.85 264	79.63 312	78.57 339	64.13 291	71.73 280	79.81 343	51.20 263	85.97 313	57.40 301	76.36 288	88.66 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.38 280	70.90 284	76.80 294	88.60 159	67.38 145	79.53 313	76.17 357	62.75 310	69.36 306	82.00 324	45.51 319	84.89 324	53.62 321	80.58 234	78.12 372

CMPMVS	51.72 21	70.19 302	68.16 305	76.28 296	73.15 381	57.55 302	79.47 314	83.92 276	48.02 378	56.48 379	84.81 275	43.13 333	86.42 309	62.67 252	81.81 220	84.89 328
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ETVMVS		72.25 283	71.05 282	75.84 299	87.77 194	51.91 359	79.39 315	74.98 360	69.26 220	73.71 257	82.95 308	40.82 349	86.14 311	46.17 363	84.43 179	89.47 227

GG-mvs-BLEND				75.38 306	81.59 321	55.80 328	79.32 316	69.63 377		67.19 325	73.67 375	43.24 332	88.90 286	50.41 336	84.50 173	81.45 361

LTVRE_ROB	69.57 13	76.25 238	74.54 245	81.41 206	88.60 159	64.38 210	79.24 317	89.12 182	70.76 186	69.79 303	87.86 197	49.09 289	93.20 166	56.21 312	80.16 239	86.65 300
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
tpm		72.37 281	71.71 273	74.35 316	82.19 313	52.00 357	79.22 318	77.29 349	64.56 286	72.95 266	83.68 299	51.35 261	83.26 336	58.33 293	75.80 292	87.81 271

ppachtmachnet_test		70.04 303	67.34 320	78.14 275	79.80 346	61.13 260	79.19 319	80.59 319	59.16 339	65.27 344	79.29 346	46.75 305	87.29 303	49.33 345	66.72 356	86.00 313

USDC		70.33 300	68.37 302	76.21 297	80.60 334	56.23 323	79.19 319	86.49 241	60.89 324	61.29 362	85.47 262	31.78 375	89.47 274	53.37 323	76.21 289	82.94 352

sd_testset		77.70 212	77.40 195	78.60 266	89.03 144	60.02 276	79.00 321	85.83 252	75.19 98	76.61 198	89.98 137	54.81 219	85.46 319	62.63 253	83.55 195	90.33 190

PM-MVS		66.41 330	64.14 332	73.20 326	73.92 374	56.45 317	78.97 322	64.96 389	63.88 298	64.72 348	80.24 337	19.84 391	83.44 334	66.24 221	64.52 365	79.71 369

tpmvs		71.09 291	69.29 296	76.49 295	82.04 314	56.04 325	78.92 323	81.37 313	64.05 294	67.18 326	78.28 355	49.74 280	89.77 267	49.67 344	72.37 332	83.67 342

test_post1							78.90 324				5.43 405	48.81 295	85.44 320	59.25 282

CHOSEN 1792x2688		77.63 214	75.69 225	83.44 146	89.98 109	68.58 118	78.70 325	87.50 224	56.38 359	75.80 216	86.84 222	58.67 194	91.40 236	61.58 265	85.75 163	90.34 189

Syy-MVS		68.05 319	67.85 310	68.67 355	84.68 258	40.97 396	78.62 326	73.08 369	66.65 262	66.74 331	79.46 344	52.11 250	82.30 340	32.89 388	76.38 286	82.75 353

myMVS_eth3d		67.02 325	66.29 326	69.21 350	84.68 258	42.58 391	78.62 326	73.08 369	66.65 262	66.74 331	79.46 344	31.53 376	82.30 340	39.43 381	76.38 286	82.75 353

test-LLR		72.94 277	72.43 267	74.48 314	81.35 326	58.04 292	78.38 328	77.46 346	66.66 259	69.95 299	79.00 349	48.06 296	79.24 353	66.13 222	84.83 168	86.15 307

TESTMET0.1,1		69.89 305	69.00 299	72.55 331	79.27 354	56.85 310	78.38 328	74.71 364	57.64 351	68.09 316	77.19 362	37.75 362	76.70 366	63.92 241	84.09 184	84.10 338

test-mter		71.41 288	70.39 291	74.48 314	81.35 326	58.04 292	78.38 328	77.46 346	60.32 328	69.95 299	79.00 349	36.08 367	79.24 353	66.13 222	84.83 168	86.15 307

Anonymous20231206		68.60 313	67.80 313	71.02 343	80.23 339	50.75 369	78.30 331	80.47 321	56.79 357	66.11 340	82.63 315	46.35 308	78.95 355	43.62 372	75.70 293	83.36 345

tpm cat1		70.57 297	68.31 303	77.35 288	82.41 311	57.95 295	78.08 332	80.22 327	52.04 370	68.54 314	77.66 360	52.00 253	87.84 299	51.77 329	72.07 336	86.25 304

our_test_3		69.14 309	67.00 322	75.57 303	79.80 346	58.80 284	77.96 333	77.81 342	59.55 335	62.90 359	78.25 356	47.43 298	83.97 329	51.71 330	67.58 355	83.93 340

KD-MVS_self_test		68.81 311	67.59 318	72.46 332	74.29 373	45.45 381	77.93 334	87.00 234	63.12 301	63.99 353	78.99 351	42.32 338	84.77 325	56.55 310	64.09 366	87.16 288

WTY-MVS		75.65 246	75.68 226	75.57 303	86.40 228	56.82 311	77.92 335	82.40 302	65.10 279	76.18 209	87.72 198	63.13 141	80.90 348	60.31 274	81.96 217	89.00 244

test20.03		67.45 322	66.95 323	68.94 351	75.48 369	44.84 386	77.50 336	77.67 343	66.66 259	63.01 357	83.80 294	47.02 302	78.40 357	42.53 375	68.86 352	83.58 343

EPMVS		69.02 310	68.16 305	71.59 336	79.61 349	49.80 374	77.40 337	66.93 383	62.82 309	70.01 296	79.05 347	45.79 316	77.86 361	56.58 309	75.26 307	87.13 289

test_fmvs3		63.36 341	61.82 344	67.98 357	62.51 395	46.96 380	77.37 338	74.03 366	45.24 380	67.50 321	78.79 352	12.16 399	72.98 387	72.77 164	66.02 360	83.99 339

gg-mvs-nofinetune		69.95 304	67.96 308	75.94 298	83.07 293	54.51 343	77.23 339	70.29 375	63.11 302	70.32 291	62.33 386	43.62 330	88.69 288	53.88 320	87.76 131	84.62 332

MDTV_nov1_ep13			69.97 294		83.18 290	53.48 350	77.10 340	80.18 328	60.45 326	69.33 307	80.44 335	48.89 294	86.90 305	51.60 331	78.51 258

LF4IMVS		64.02 339	62.19 343	69.50 349	70.90 386	53.29 354	76.13 341	77.18 350	52.65 369	58.59 371	80.98 330	23.55 387	76.52 368	53.06 325	66.66 357	78.68 371

sss		73.60 267	73.64 257	73.51 323	82.80 301	55.01 338	76.12 342	81.69 309	62.47 313	74.68 247	85.85 253	57.32 207	78.11 359	60.86 271	80.93 228	87.39 280

testgi		66.67 328	66.53 325	67.08 360	75.62 368	41.69 395	75.93 343	76.50 354	66.11 268	65.20 347	86.59 234	35.72 368	74.71 381	43.71 371	73.38 327	84.84 329

CR-MVSNet		73.37 269	71.27 280	79.67 249	81.32 328	65.19 191	75.92 344	80.30 325	59.92 332	72.73 268	81.19 326	52.50 242	86.69 306	59.84 277	77.71 265	87.11 290

RPMNet		73.51 268	70.49 288	82.58 185	81.32 328	65.19 191	75.92 344	92.27 76	57.60 352	72.73 268	76.45 365	52.30 245	95.43 65	48.14 354	77.71 265	87.11 290

MIMVSNet		70.69 296	69.30 295	74.88 310	84.52 262	56.35 322	75.87 346	79.42 333	64.59 285	67.76 317	82.41 316	41.10 346	81.54 344	46.64 361	81.34 223	86.75 298

test0.0.03 1		68.00 320	67.69 315	68.90 352	77.55 359	47.43 377	75.70 347	72.95 371	66.66 259	66.56 333	82.29 319	48.06 296	75.87 374	44.97 370	74.51 315	83.41 344

dmvs_re		71.14 290	70.58 286	72.80 329	81.96 315	59.68 279	75.60 348	79.34 334	68.55 239	69.27 308	80.72 334	49.42 283	76.54 367	52.56 327	77.79 264	82.19 357

dmvs_testset		62.63 342	64.11 333	58.19 370	78.55 356	24.76 406	75.28 349	65.94 386	67.91 248	60.34 365	76.01 367	53.56 235	73.94 385	31.79 389	67.65 354	75.88 377

PMMVS		69.34 308	68.67 300	71.35 340	75.67 367	62.03 250	75.17 350	73.46 367	50.00 376	68.68 311	79.05 347	52.07 252	78.13 358	61.16 269	82.77 207	73.90 379

UnsupCasMVSNet_eth		67.33 323	65.99 327	71.37 338	73.48 378	51.47 365	75.16 351	85.19 258	65.20 278	60.78 364	80.93 333	42.35 337	77.20 363	57.12 303	53.69 384	85.44 319

MDTV_nov1_ep13_2view						37.79 398	75.16 351		55.10 363	66.53 334		49.34 285		53.98 319		87.94 268

pmmvs3		57.79 348	54.26 353	68.37 356	64.02 394	56.72 313	75.12 353	65.17 387	40.20 386	52.93 384	69.86 383	20.36 390	75.48 377	45.45 368	55.25 383	72.90 381

dp		66.80 326	65.43 328	70.90 345	79.74 348	48.82 375	75.12 353	74.77 362	59.61 334	64.08 352	77.23 361	42.89 334	80.72 349	48.86 348	66.58 358	83.16 347

Patchmtry		70.74 295	69.16 298	75.49 305	80.72 332	54.07 346	74.94 355	80.30 325	58.34 345	70.01 296	81.19 326	52.50 242	86.54 307	53.37 323	71.09 342	85.87 315

PVSNet	64.34 18	72.08 285	70.87 285	75.69 301	86.21 230	56.44 318	74.37 356	80.73 317	62.06 318	70.17 294	82.23 320	42.86 335	83.31 335	54.77 316	84.45 178	87.32 283

WB-MVS		54.94 350	54.72 352	55.60 376	73.50 377	20.90 408	74.27 357	61.19 393	59.16 339	50.61 386	74.15 373	47.19 301	75.78 375	17.31 400	35.07 395	70.12 383

MDA-MVSNet-bldmvs		66.68 327	63.66 336	75.75 300	79.28 353	60.56 269	73.92 358	78.35 340	64.43 287	50.13 387	79.87 342	44.02 328	83.67 331	46.10 364	56.86 377	83.03 350

SSC-MVS		53.88 353	53.59 354	54.75 378	72.87 382	19.59 409	73.84 359	60.53 395	57.58 353	49.18 388	73.45 376	46.34 309	75.47 378	16.20 403	32.28 397	69.20 384

UnsupCasMVSNet_bld		63.70 340	61.53 346	70.21 347	73.69 376	51.39 366	72.82 360	81.89 306	55.63 362	57.81 375	71.80 379	38.67 357	78.61 356	49.26 346	52.21 386	80.63 365

PatchT		68.46 317	67.85 310	70.29 346	80.70 333	43.93 388	72.47 361	74.88 361	60.15 330	70.55 287	76.57 364	49.94 277	81.59 343	50.58 335	74.83 312	85.34 320

miper_lstm_enhance		74.11 261	73.11 262	77.13 291	80.11 340	59.62 280	72.23 362	86.92 236	66.76 257	70.40 290	82.92 309	56.93 211	82.92 337	69.06 198	72.63 331	88.87 249

MVS-HIRNet		59.14 347	57.67 350	63.57 364	81.65 319	43.50 389	71.73 363	65.06 388	39.59 388	51.43 385	57.73 392	38.34 359	82.58 339	39.53 379	73.95 319	64.62 388

APD_test1		53.31 355	49.93 360	63.42 365	65.68 392	50.13 371	71.59 364	66.90 384	34.43 393	40.58 392	71.56 380	8.65 404	76.27 371	34.64 387	55.36 382	63.86 389

Patchmatch-RL test		70.24 301	67.78 314	77.61 284	77.43 360	59.57 282	71.16 365	70.33 374	62.94 306	68.65 312	72.77 377	50.62 269	85.49 318	69.58 193	66.58 358	87.77 272

test123		6.12 375	8.11 378	0.14 389	0.06 413	0.09 414	71.05 366	0.03 414	0.04 408	0.25 409	1.30 408	0.05 412	0.03 409	0.21 408	0.01 407	0.29 404

ANet_high		50.57 360	46.10 364	63.99 363	48.67 406	39.13 397	70.99 367	80.85 315	61.39 322	31.18 395	57.70 393	17.02 394	73.65 386	31.22 390	15.89 403	79.18 370

KD-MVS_2432*1600		66.22 332	63.89 334	73.21 324	75.47 370	53.42 351	70.76 368	84.35 269	64.10 292	66.52 335	78.52 353	34.55 370	84.98 322	50.40 337	50.33 388	81.23 362

miper_refine_blended		66.22 332	63.89 334	73.21 324	75.47 370	53.42 351	70.76 368	84.35 269	64.10 292	66.52 335	78.52 353	34.55 370	84.98 322	50.40 337	50.33 388	81.23 362

test_vis1_rt		60.28 346	58.42 349	65.84 361	67.25 391	55.60 331	70.44 370	60.94 394	44.33 382	59.00 370	66.64 384	24.91 384	68.67 392	62.80 248	69.48 346	73.25 380

testmvs		6.04 376	8.02 379	0.10 390	0.08 412	0.03 415	69.74 371	0.04 413	0.05 407	0.31 408	1.68 407	0.02 413	0.04 408	0.24 407	0.02 406	0.25 405

N_pmnet		52.79 356	53.26 355	51.40 380	78.99 355	7.68 412	69.52 372	3.89 411	51.63 373	57.01 377	74.98 372	40.83 348	65.96 395	37.78 383	64.67 364	80.56 367

FPMVS		53.68 354	51.64 356	59.81 369	65.08 393	51.03 367	69.48 373	69.58 378	41.46 385	40.67 391	72.32 378	16.46 395	70.00 391	24.24 397	65.42 362	58.40 393

DSMNet-mixed		57.77 349	56.90 351	60.38 368	67.70 390	35.61 399	69.18 374	53.97 400	32.30 396	57.49 376	79.88 341	40.39 351	68.57 393	38.78 382	72.37 332	76.97 374

new-patchmatchnet		61.73 344	61.73 345	61.70 366	72.74 383	24.50 407	69.16 375	78.03 341	61.40 321	56.72 378	75.53 371	38.42 358	76.48 369	45.95 365	57.67 376	84.13 337

YYNet1		65.03 335	62.91 340	71.38 337	75.85 366	56.60 316	69.12 376	74.66 365	57.28 355	54.12 382	77.87 358	45.85 315	74.48 382	49.95 342	61.52 371	83.05 349

MDA-MVSNet_test_wron		65.03 335	62.92 339	71.37 338	75.93 364	56.73 312	69.09 377	74.73 363	57.28 355	54.03 383	77.89 357	45.88 314	74.39 383	49.89 343	61.55 370	82.99 351

PVSNet_0	57.27 20	61.67 345	59.27 348	68.85 353	79.61 349	57.44 304	68.01 378	73.44 368	55.93 361	58.54 372	70.41 382	44.58 324	77.55 362	47.01 358	35.91 394	71.55 382

ADS-MVSNet2		66.20 334	63.33 337	74.82 311	79.92 342	58.75 285	67.55 379	75.19 359	53.37 367	65.25 345	75.86 368	42.32 338	80.53 350	41.57 376	68.91 350	85.18 323

ADS-MVSNet		64.36 338	62.88 341	68.78 354	79.92 342	47.17 378	67.55 379	71.18 373	53.37 367	65.25 345	75.86 368	42.32 338	73.99 384	41.57 376	68.91 350	85.18 323

mvsany_test1		62.30 343	61.26 347	65.41 362	69.52 387	54.86 339	66.86 381	49.78 402	46.65 379	68.50 315	83.21 304	49.15 288	66.28 394	56.93 306	60.77 372	75.11 378

LCM-MVSNet		54.25 351	49.68 361	67.97 358	53.73 403	45.28 384	66.85 382	80.78 316	35.96 392	39.45 393	62.23 388	8.70 403	78.06 360	48.24 353	51.20 387	80.57 366

test_vis3_rt		49.26 361	47.02 363	56.00 373	54.30 400	45.27 385	66.76 383	48.08 403	36.83 390	44.38 390	53.20 395	7.17 406	64.07 396	56.77 308	55.66 380	58.65 392

testf1		45.72 362	41.96 365	57.00 371	56.90 397	45.32 382	66.14 384	59.26 396	26.19 397	30.89 396	60.96 390	4.14 407	70.64 389	26.39 395	46.73 392	55.04 394

APD_test2		45.72 362	41.96 365	57.00 371	56.90 397	45.32 382	66.14 384	59.26 396	26.19 397	30.89 396	60.96 390	4.14 407	70.64 389	26.39 395	46.73 392	55.04 394

JIA-IIPM		66.32 331	62.82 342	76.82 293	77.09 362	61.72 256	65.34 386	75.38 358	58.04 349	64.51 349	62.32 387	42.05 343	86.51 308	51.45 332	69.22 349	82.21 356

PMVS	37.38 22	44.16 365	40.28 368	55.82 375	40.82 408	42.54 393	65.12 387	63.99 390	34.43 393	24.48 399	57.12 394	3.92 409	76.17 373	17.10 401	55.52 381	48.75 396
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		50.91 359	50.29 359	52.78 379	68.58 389	34.94 401	63.71 388	56.63 399	39.73 387	44.95 389	65.47 385	21.93 389	58.48 398	34.98 386	56.62 378	64.92 387

mvsany_test3		53.99 352	51.45 357	61.61 367	55.51 399	44.74 387	63.52 389	45.41 406	43.69 383	58.11 374	76.45 365	17.99 392	63.76 397	54.77 316	47.59 390	76.34 376

Patchmatch-test		64.82 337	63.24 338	69.57 348	79.42 352	49.82 373	63.49 390	69.05 380	51.98 372	59.95 368	80.13 338	50.91 265	70.98 388	40.66 378	73.57 323	87.90 269

ambc				75.24 307	73.16 380	50.51 370	63.05 391	87.47 225		64.28 350	77.81 359	17.80 393	89.73 269	57.88 297	60.64 373	85.49 318

test_f		52.09 357	50.82 358	55.90 374	53.82 402	42.31 394	59.42 392	58.31 398	36.45 391	56.12 381	70.96 381	12.18 398	57.79 399	53.51 322	56.57 379	67.60 385

CHOSEN 280x420		66.51 329	64.71 330	71.90 334	81.45 323	63.52 226	57.98 393	68.95 381	53.57 366	62.59 360	76.70 363	46.22 310	75.29 380	55.25 314	79.68 244	76.88 375

E-PMN		31.77 367	30.64 370	35.15 384	52.87 404	27.67 403	57.09 394	47.86 404	24.64 399	16.40 404	33.05 400	11.23 400	54.90 401	14.46 404	18.15 401	22.87 400

EMVS		30.81 369	29.65 371	34.27 385	50.96 405	25.95 405	56.58 395	46.80 405	24.01 400	15.53 405	30.68 401	12.47 397	54.43 402	12.81 405	17.05 402	22.43 401

PMMVS2		40.82 366	38.86 369	46.69 381	53.84 401	16.45 410	48.61 396	49.92 401	37.49 389	31.67 394	60.97 389	8.14 405	56.42 400	28.42 392	30.72 398	67.19 386

wuyk23d		16.82 373	15.94 376	19.46 387	58.74 396	31.45 402	39.22 397	3.74 412	6.84 403	6.04 406	2.70 406	1.27 411	24.29 406	10.54 406	14.40 405	2.63 403

tmp_tt		18.61 372	21.40 375	10.23 388	4.82 411	10.11 411	34.70 398	30.74 409	1.48 405	23.91 401	26.07 402	28.42 381	13.41 407	27.12 393	15.35 404	7.17 402

Gipuma		45.18 364	41.86 367	55.16 377	77.03 363	51.52 364	32.50 399	80.52 320	32.46 395	27.12 398	35.02 399	9.52 402	75.50 376	22.31 398	60.21 375	38.45 398
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	26.22 23	30.37 370	25.89 374	43.81 382	44.55 407	35.46 400	28.87 400	39.07 407	18.20 401	18.58 403	40.18 398	2.68 410	47.37 404	17.07 402	23.78 400	48.60 397
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		31.52 368	29.28 372	38.23 383	27.03 410	6.50 413	20.94 401	62.21 392	4.05 404	22.35 402	52.50 396	13.33 396	47.58 403	27.04 394	34.04 396	60.62 390

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

cdsmvs_eth3d_5k		19.96 371	26.61 373	0.00 391	0.00 414	0.00 416	0.00 402	89.26 173	0.00 409	0.00 410	88.61 176	61.62 161	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		5.26 377	7.02 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	63.15 138	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		7.23 374	9.64 377	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	86.72 226	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

WAC-MVS						42.58 391								39.46 380

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									68.21 245	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					0.00 414

eth-test					0.00 414

ZD-MVS					94.38 25	72.22 44		92.67 61	70.98 182	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	66.81 255	92.39 6				88.94 16	96.63 4	94.85 19

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

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

GSMVS																88.96 246

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 262				88.96 246

sam_mvs												50.01 275

MTGPA								92.02 85

test_post											5.46 404	50.36 273	84.24 327

patchmatchnet-post											74.00 374	51.12 264	88.60 290

gm-plane-assit					81.40 324	53.83 348			62.72 311		80.94 331		92.39 195	63.40 245

test9_res														84.90 42	95.70 26	92.87 102

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	85.15 248	63.62 221		79.83 329	62.31 314	60.32 366	86.73 224	32.02 373	88.96 284	50.28 339	71.57 339	86.15 307

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

新几何1				83.42 147	93.13 52	70.71 71		85.48 256	57.43 354	81.80 107	91.98 90	63.28 133	92.27 201	64.60 237	92.99 65	87.27 284

旧先验1					91.96 71	65.79 178		86.37 244			93.08 71	69.31 77			92.74 68	88.74 256

原ACMM1				84.35 107	93.01 57	68.79 106		92.44 69	63.96 297	81.09 117	91.57 101	66.06 111	95.45 63	67.19 216	94.82 45	88.81 252

testdata2													91.01 249	62.37 255

segment_acmp												73.08 37

testdata				79.97 241	90.90 86	64.21 212		84.71 263	59.27 338	85.40 51	92.91 73	62.02 157	89.08 280	68.95 199	91.37 86	86.63 301

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	91.33 151

plane_prior4											91.00 120

plane_prior3						68.60 117			78.44 31	78.92 141

plane_prior1					89.90 111

n2								0.00 415

nn								0.00 415

door-mid								69.98 376

lessismore_v0				78.97 260	81.01 331	57.15 307		65.99 385		61.16 363	82.82 312	39.12 355	91.34 238	59.67 278	46.92 391	88.43 262

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	83.78 186	89.83 217

test11								92.23 79

door								69.44 379

HQP5-MVS						66.98 155

BP-MVS														77.47 114

HQP4-MVS										77.24 181			95.11 80			91.03 163

HQP3-MVS								92.19 82							85.99 159

HQP2-MVS												60.17 189

NP-MVS					89.62 115	68.32 122					90.24 132

ACMMP++_ref															81.95 218

ACMMP++															81.25 224

Test By Simon												64.33 125

ITE_SJBPF				78.22 273	81.77 318	60.57 268		83.30 286	69.25 221	67.54 320	87.20 215	36.33 366	87.28 304	54.34 318	74.62 314	86.80 296

DeepMVS_CX				27.40 386	40.17 409	26.90 404		24.59 410	17.44 402	23.95 400	48.61 397	9.77 401	26.48 405	18.06 399	24.47 399	28.83 399

High-resolution multi-view benchmark