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 29	86.92 31	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 6	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 10	88.86 10	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 53	84.47 68	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 21	87.47 23	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 22	87.23 26	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 18	87.85 19	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 24	87.20 27	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 34	86.95 30	85.90 63	90.76 90	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 28	86.91 32	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		80.37 144	77.83 181	88.00 17	94.42 20	73.33 19	92.78 18	92.99 45	79.14 21	83.67 85	12.47 396	67.45 95	96.60 33	83.06 63	94.50 50	94.07 47

mPP-MVS		86.67 36	86.32 38	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 47	85.39 53	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 19	87.64 21	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				88.97 4		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 11	88.74 11	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 7	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 24	87.26 24	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 8	89.15 8	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 33	86.67 34	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 41	86.48 36	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 42	86.38 37	84.91 88	89.31 130	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 83	83.02 83	84.57 96	90.13 100	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 38	86.17 42	87.24 41	90.88 87	70.96 65	92.27 32	94.07 9	72.45 152	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 13	88.08 16	88.08 14	89.67 113	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 29	86.98 29	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	32.83 401

HPM-MVS_fast		85.35 57	84.95 62	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 73	83.33 79	84.92 87	93.28 49	70.86 69	92.09 37	90.38 137	68.75 231	79.57 132	92.83 76	60.60 184	93.04 178	80.92 84	91.56 84	90.86 167

APD-MVS_3200maxsize		85.97 44	85.88 47	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

SR-MVS-dyc-post		85.77 48	85.61 51	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 52		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

APD-MVS		87.44 22	87.52 22	87.19 42	94.24 32	72.39 39	91.86 41	92.83 55	73.01 149	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 14	88.50 13	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		80.88 125	79.50 141	85.03 81	88.01 179	68.97 103	91.59 43	92.00 87	66.63 259	75.15 235	92.16 88	57.70 202	95.45 63	63.52 240	88.76 121	90.66 174

IS-MVSNet		83.15 87	82.81 87	84.18 116	89.94 109	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 14		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 17	88.11 15	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		81.69 110	80.74 116	84.56 97	87.45 199	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	83.54 191	92.04 134

DeepC-MVS_fast	79.65 3	86.91 32	86.62 35	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 76	83.14 80	85.14 77	90.08 102	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	91.33 149

plane_prior2							91.25 50		79.12 23

NCCC		88.06 14	88.01 18	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		81.99 104	81.23 108	84.26 114	90.94 85	70.18 82	91.10 53	89.32 169	71.51 169	78.66 147	88.28 186	65.26 118	95.10 83	64.74 236	91.23 88	87.51 272

RRT_MVS		80.35 145	79.22 150	83.74 140	87.63 193	65.46 185	91.08 54	88.92 191	73.82 127	76.44 203	90.03 136	49.05 290	94.25 117	76.84 121	79.20 246	91.51 143

EPNet		83.72 74	82.92 86	86.14 59	84.22 259	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 16	88.08 16	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 40	86.19 41	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 55	85.76 49	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	90.16 193

3Dnovator	76.31 5	83.38 84	82.31 94	86.59 52	87.94 180	72.94 28	90.64 59	92.14 84	77.21 52	75.47 220	92.83 76	58.56 195	94.72 99	73.24 159	92.71 69	92.13 130

OpenMVS	72.83 10	79.77 155	78.33 170	84.09 121	85.17 239	69.91 84	90.57 60	90.97 121	66.70 253	72.17 269	91.91 91	54.70 224	93.96 124	61.81 260	90.95 91	88.41 257

CNVR-MVS		88.93 9	89.13 9	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 93	82.05 98	85.24 75	87.35 200	70.21 77	90.50 62	90.38 137	68.55 234	81.32 112	89.47 151	61.68 159	93.46 153	78.98 99	90.26 100	92.05 132

test_djsdf		80.30 146	79.32 146	83.27 153	83.98 265	65.37 189	90.50 62	90.38 137	68.55 234	76.19 208	88.70 172	56.44 213	93.46 153	78.98 99	80.14 234	90.97 164

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

nrg030		83.88 70	83.53 74	84.96 84	86.77 216	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	79.28 244	92.50 114

canonicalmvs		85.91 46	85.87 48	86.04 60	89.84 111	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 31	86.88 33	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 81	82.80 88	85.43 71	90.25 98	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 35	86.27 39	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		82.08 101	81.27 107	84.50 99	89.23 134	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	83.78 181	89.83 214

Anonymous20231211		78.97 178	77.69 189	82.81 176	90.54 93	64.29 211	90.11 72	91.51 107	65.01 277	76.16 212	88.13 195	50.56 269	93.03 179	69.68 192	77.56 261	91.11 156

ACMM	73.20 8	80.78 133	79.84 134	83.58 143	89.31 130	68.37 121	89.99 73	91.60 104	70.28 193	77.25 180	89.66 144	53.37 237	93.53 149	74.24 148	82.85 200	88.85 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	74.13 6	81.51 117	80.57 119	84.36 106	89.42 122	68.69 115	89.97 74	91.50 110	74.46 114	75.04 239	90.41 130	53.82 233	94.54 104	77.56 113	82.91 199	89.86 213
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
iter_conf_final		80.63 135	79.35 145	84.46 102	89.36 126	67.70 137	89.85 75	84.49 267	73.19 145	78.30 157	88.94 165	45.98 311	94.56 102	79.59 96	84.48 174	91.11 156

LFMVS		81.82 107	81.23 108	83.57 144	91.89 73	63.43 230	89.84 76	81.85 307	77.04 58	83.21 89	93.10 67	52.26 245	93.43 155	71.98 169	89.95 107	93.85 57

MCST-MVS		87.37 26	87.25 25	87.73 28	94.53 17	72.46 38	89.82 77	93.82 16	73.07 147	84.86 62	92.89 74	76.22 17	96.33 38	84.89 44	95.13 36	94.40 34

MAR-MVS		81.84 106	80.70 117	85.27 74	91.32 79	71.53 54	89.82 77	90.92 122	69.77 206	78.50 151	86.21 245	62.36 150	94.52 106	65.36 230	92.05 77	89.77 217
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 20	87.72 20	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 61	84.96 61	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 53	85.32 56	85.96 62	89.51 119	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		81.52 115	80.67 118	84.05 128	90.44 95	64.13 214	89.73 82	85.91 250	71.11 175	83.18 90	93.48 58	50.54 270	93.49 150	73.40 156	88.25 128	94.54 30

CANet		86.45 37	86.10 44	87.51 37	90.09 101	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 52	85.65 50	85.50 69	82.99 290	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

114514_t		80.68 134	79.51 140	84.20 115	94.09 38	67.27 149	89.64 85	91.11 119	58.75 337	74.08 250	90.72 124	58.10 198	95.04 85	69.70 191	89.42 113	90.30 189

test_fmvsmconf_n		85.92 45	86.04 46	85.57 68	85.03 246	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

DeepPCF-MVS	80.84 1	88.10 12	88.56 12	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 65	84.52 67	85.34 72	80.25 330	69.03 99	89.47 88	89.65 161	73.24 144	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 72	83.71 73	84.07 123	86.69 218	67.31 147	89.46 89	83.07 292	71.09 176	86.96 41	93.70 55	69.02 83	91.47 233	88.79 18	84.62 170	93.44 80

fmvsm_s_conf0.5_n_a		83.63 77	83.41 76	84.28 111	86.14 224	68.12 127	89.43 90	82.87 296	70.27 194	87.27 37	93.80 54	69.09 78	91.58 224	88.21 26	83.65 187	93.14 93

UGNet		80.83 127	79.59 139	84.54 98	88.04 177	68.09 128	89.42 91	88.16 206	76.95 59	76.22 207	89.46 153	49.30 285	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		78.73 182	77.83 181	81.43 205	85.17 239	60.30 273	89.41 92	90.90 123	71.21 173	77.17 186	88.73 171	46.38 305	93.21 163	72.57 166	78.96 247	90.79 168

fmvsm_s_conf0.1_n		83.56 79	83.38 77	84.10 118	84.86 248	67.28 148	89.40 93	83.01 293	70.67 184	87.08 38	93.96 50	68.38 87	91.45 234	88.56 22	84.50 171	93.56 75

AdaColmap		80.58 139	79.42 142	84.06 125	93.09 54	68.91 104	89.36 94	88.97 188	69.27 215	75.70 217	89.69 143	57.20 209	95.77 54	63.06 245	88.41 127	87.50 273

fmvsm_s_conf0.1_n_a		83.32 85	82.99 84	84.28 111	83.79 268	68.07 129	89.34 95	82.85 297	69.80 204	87.36 36	94.06 42	68.34 88	91.56 226	87.95 27	83.46 193	93.21 90

PS-MVSNAJss		82.07 102	81.31 106	84.34 108	86.51 220	67.27 149	89.27 96	91.51 107	71.75 161	79.37 134	90.22 134	63.15 138	94.27 113	77.69 112	82.36 207	91.49 146

jajsoiax		79.29 169	77.96 176	83.27 153	84.68 251	66.57 162	89.25 97	90.16 147	69.20 219	75.46 222	89.49 150	45.75 316	93.13 172	76.84 121	80.80 224	90.11 197

mvs_tets		79.13 173	77.77 185	83.22 157	84.70 250	66.37 164	89.17 98	90.19 146	69.38 213	75.40 225	89.46 153	44.17 324	93.15 170	76.78 124	80.70 226	90.14 194

HQP-NCC					89.33 127		89.17 98		76.41 72	77.23 182

ACMP_Plane					89.33 127		89.17 98		76.41 72	77.23 182

HQP-MVS		82.61 96	82.02 99	84.37 105	89.33 127	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	85.99 158	91.03 161

LS3D		76.95 225	74.82 238	83.37 150	90.45 94	67.36 146	89.15 102	86.94 235	61.87 312	69.52 297	90.61 126	51.71 258	94.53 105	46.38 357	86.71 146	88.21 259

OPM-MVS		83.50 80	82.95 85	85.14 77	88.79 151	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	89.97 209
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
TSAR-MVS + GP.		85.71 50	85.33 55	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		81.71 109	81.01 113	83.80 139	89.51 119	64.45 208	88.97 106	88.73 199	71.27 172	78.63 148	89.76 142	66.32 107	93.20 166	69.89 189	86.02 157	93.74 63

Anonymous20240529		80.19 149	78.89 157	84.10 118	90.60 91	64.75 201	88.95 107	90.90 123	65.97 267	80.59 122	91.17 113	49.97 275	93.73 142	69.16 197	82.70 204	93.81 60

VDD-MVS		83.01 92	82.36 93	84.96 84	91.02 83	66.40 163	88.91 108	88.11 207	77.57 41	84.39 72	93.29 64	52.19 246	93.91 131	77.05 119	88.70 122	94.57 29

Effi-MVS+		83.62 78	83.08 81	85.24 75	88.38 166	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	76.01 239	74.01 247	82.03 193	88.60 158	65.31 190	88.86 110	87.55 222	70.25 195	67.75 311	87.47 208	41.27 341	93.19 168	58.37 289	75.94 284	87.60 269

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		80.00 153	78.70 159	83.91 137	87.84 183	65.83 175	88.84 112	84.92 262	71.61 166	78.70 144	88.94 165	43.88 326	94.56 102	79.28 97	84.28 177	91.33 149

DP-MVS Recon		83.11 90	82.09 97	86.15 58	94.44 19	70.92 68	88.79 113	92.20 81	70.53 188	79.17 137	91.03 119	64.12 127	96.03 46	68.39 206	90.14 102	91.50 145

Effi-MVS+-dtu		80.03 151	78.57 163	84.42 104	85.13 243	68.74 110	88.77 114	88.10 208	74.99 102	74.97 240	83.49 296	57.27 208	93.36 156	73.53 153	80.88 222	91.18 154

TEST9					93.26 50	72.96 25	88.75 115	91.89 93	68.44 237	85.00 57	93.10 67	74.36 28	95.41 67

train_agg		86.43 38	86.20 40	87.13 44	93.26 50	72.96 25	88.75 115	91.89 93	68.69 232	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 64	84.67 64	85.59 67	89.39 124	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 95	81.83 103	84.96 84	90.80 89	69.76 87	88.74 117	91.70 102	69.39 212	78.96 139	88.46 181	65.47 117	94.87 94	74.42 145	88.57 123	90.24 191

casdiffmvs_mvg		85.99 43	86.09 45	85.70 66	87.65 192	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	68.69 232	84.87 61	93.10 67	74.43 26	95.16 76

test_fmvsm_n_1920		85.29 58	85.34 54	85.13 79	86.12 225	69.93 83	88.65 121	90.78 127	69.97 200	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	75.89 240	73.93 248	81.77 198	88.71 155	66.61 161	88.62 122	89.01 185	69.81 203	66.78 323	86.70 230	41.95 340	91.51 231	55.64 310	78.14 256	87.17 280
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDPH-MVS		85.76 49	85.29 58	87.17 43	93.49 47	71.08 61	88.58 123	92.42 72	68.32 239	84.61 67	93.48 58	72.32 42	96.15 45	79.00 98	95.43 31	94.28 40

DP-MVS		76.78 227	74.57 240	83.42 147	93.29 48	69.46 94	88.55 124	83.70 279	63.98 291	70.20 285	88.89 168	54.01 232	94.80 96	46.66 354	81.88 212	86.01 305

fmvsm_l_conf0.5_n		84.47 66	84.54 65	84.27 113	85.42 235	68.81 105	88.49 125	87.26 229	68.08 241	88.03 27	93.49 57	72.04 46	91.77 218	88.90 17	89.14 116	92.24 125

WR-MVS_H		78.51 188	78.49 164	78.56 265	88.02 178	56.38 320	88.43 126	92.67 61	77.14 54	73.89 251	87.55 205	66.25 108	89.24 274	58.92 283	73.55 317	90.06 203

F-COLMAP		76.38 235	74.33 245	82.50 186	89.28 132	66.95 158	88.41 127	89.03 183	64.05 289	66.83 322	88.61 176	46.78 303	92.89 181	57.48 296	78.55 249	87.67 267

GBi-Net		78.40 189	77.40 194	81.40 207	87.60 194	63.01 238	88.39 128	89.28 170	71.63 163	75.34 227	87.28 210	54.80 220	91.11 242	62.72 247	79.57 238	90.09 199

test1		78.40 189	77.40 194	81.40 207	87.60 194	63.01 238	88.39 128	89.28 170	71.63 163	75.34 227	87.28 210	54.80 220	91.11 242	62.72 247	79.57 238	90.09 199

FMVSNet1		77.44 215	76.12 221	81.40 207	86.81 215	63.01 238	88.39 128	89.28 170	70.49 189	74.39 247	87.28 210	49.06 289	91.11 242	60.91 267	78.52 250	90.09 199

tttt0517		79.40 166	77.91 178	83.90 138	88.10 174	63.84 218	88.37 131	84.05 275	71.45 170	76.78 192	89.12 161	49.93 278	94.89 92	70.18 185	83.18 197	92.96 101

fmvsm_l_conf0.5_n_a		84.13 68	84.16 70	84.06 125	85.38 236	68.40 120	88.34 132	86.85 237	67.48 248	87.48 34	93.40 61	70.89 58	91.61 222	88.38 25	89.22 115	92.16 129

v7n		78.97 178	77.58 192	83.14 160	83.45 275	65.51 182	88.32 133	91.21 114	73.69 130	72.41 266	86.32 244	57.93 199	93.81 135	69.18 196	75.65 287	90.11 197

COLMAP_ROB	66.92 17	73.01 270	70.41 282	80.81 225	87.13 210	65.63 180	88.30 134	84.19 274	62.96 299	63.80 348	87.69 200	38.04 354	92.56 189	46.66 354	74.91 304	84.24 328
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FIs		82.07 102	82.42 90	81.04 219	88.80 150	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	84.97 165	92.44 118

EIA-MVS		83.31 86	82.80 88	84.82 90	89.59 115	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	78.05 200	76.37 219	83.08 163	91.88 74	67.80 134	88.19 137	89.46 165	64.33 285	69.87 294	88.38 183	53.66 234	93.58 144	58.86 284	82.73 202	87.86 264
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MG-MVS		83.41 82	83.45 75	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	79.15 172	77.94 177	82.79 179	89.59 115	62.99 241	88.16 139	91.51 107	65.77 268	77.14 187	91.09 115	60.91 177	93.21 163	50.26 337	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 69	83.87 71	84.49 101	84.12 261	69.37 97	88.15 140	87.96 212	70.01 198	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 87	82.19 95	86.02 61	90.56 92	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	75.48 291	91.72 139

bld_raw_dy_0_64		77.29 220	75.98 222	81.22 213	85.04 245	65.47 184	88.14 142	77.56 340	69.20 219	73.77 252	89.40 159	42.24 337	88.85 284	76.78 124	81.64 214	89.33 227

PS-CasMVS		78.01 202	78.09 174	77.77 277	87.71 189	54.39 340	88.02 143	91.22 113	77.50 46	73.26 256	88.64 175	60.73 178	88.41 289	61.88 258	73.88 314	90.53 180

OMC-MVS		82.69 94	81.97 101	84.85 89	88.75 153	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		79.97 154	79.02 155	82.80 177	84.09 262	64.50 206	87.96 145	90.29 144	74.13 122	75.24 233	86.81 223	62.88 143	93.89 133	74.39 146	75.40 296	90.00 205

FC-MVSNet-test		81.52 115	82.02 99	80.03 240	88.42 165	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	84.50 171	92.33 119

CP-MVSNet		78.22 193	78.34 169	77.84 275	87.83 184	54.54 338	87.94 147	91.17 116	77.65 38	73.48 254	88.49 180	62.24 153	88.43 288	62.19 254	74.07 310	90.55 179

PAPM_NR		83.02 91	82.41 91	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		77.73 208	77.69 189	77.84 275	87.07 211	53.91 343	87.91 149	91.18 115	77.56 43	73.14 258	88.82 170	61.23 171	89.17 275	59.95 273	72.37 325	90.43 183

ECVR-MVS		79.61 157	79.26 148	80.67 228	90.08 102	54.69 336	87.89 150	77.44 343	74.88 104	80.27 124	92.79 79	48.96 292	92.45 192	68.55 203	92.50 72	94.86 17

v10		79.74 156	78.67 160	82.97 170	84.06 263	64.95 196	87.88 151	90.62 130	73.11 146	75.11 236	86.56 237	61.46 165	94.05 123	73.68 151	75.55 289	89.90 211

test2506		77.30 219	76.49 215	79.74 246	90.08 102	52.02 352	87.86 152	63.10 384	74.88 104	80.16 127	92.79 79	38.29 353	92.35 198	68.74 202	92.50 72	94.86 17

casdiffmvs		85.11 60	85.14 59	85.01 82	87.20 208	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		80.84 126	80.31 125	82.42 187	87.85 182	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	75.24 301	92.30 121

EI-MVSNet-Vis-set		84.19 67	83.81 72	85.31 73	88.18 171	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	84.72 168	93.28 86

UniMVSNet (Re)		81.60 114	81.11 110	83.09 162	88.38 166	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	76.49 273	91.60 140

CNLPA		78.08 198	76.79 208	81.97 195	90.40 96	71.07 62	87.59 157	84.55 266	66.03 266	72.38 267	89.64 145	57.56 204	86.04 307	59.61 276	83.35 194	88.79 248

DTE-MVSNet		76.99 223	76.80 207	77.54 282	86.24 222	53.06 351	87.52 158	90.66 129	77.08 57	72.50 264	88.67 174	60.48 185	89.52 269	57.33 299	70.74 336	90.05 204

无先验							87.48 159	88.98 186	60.00 324				94.12 121	67.28 214		88.97 240

FMVSNet2		78.20 195	77.21 198	81.20 214	87.60 194	62.89 242	87.47 160	89.02 184	71.63 163	75.29 232	87.28 210	54.80 220	91.10 245	62.38 252	79.38 242	89.61 221

EI-MVSNet-UG-set		83.81 71	83.38 77	85.09 80	87.87 181	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	83.92 180	92.99 100

thisisatest0530		79.40 166	77.76 186	84.31 109	87.69 191	65.10 194	87.36 162	84.26 273	70.04 197	77.42 176	88.26 188	49.94 276	94.79 97	70.20 184	84.70 169	93.03 97

CANet_DTU		80.61 136	79.87 133	82.83 174	85.60 232	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		79.43 164	79.18 152	80.15 238	89.99 107	53.31 349	87.33 164	77.05 346	75.04 101	80.23 126	92.77 81	48.97 291	92.33 200	68.87 200	92.40 74	94.81 20

baseline		84.93 62	84.98 60	84.80 92	87.30 206	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		79.10 174	78.24 172	81.70 199	86.85 213	60.24 274	87.28 166	88.79 193	74.25 118	76.84 189	90.53 129	49.48 281	91.56 226	67.98 207	82.15 208	93.29 85

anonymousdsp		78.60 186	77.15 199	82.98 169	80.51 328	67.08 153	87.24 167	89.53 163	65.66 270	75.16 234	87.19 216	52.52 240	92.25 202	77.17 118	79.34 243	89.61 221

UniMVSNet_NR-MVSNet		81.88 105	81.54 105	82.92 171	88.46 163	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	76.46 274	92.25 123

DPM-MVS		84.93 62	84.29 69	86.84 47	90.20 99	73.04 23	87.12 169	93.04 38	69.80 204	82.85 95	91.22 110	73.06 38	96.02 47	76.72 126	94.63 47	91.46 148

v1144		80.03 151	79.03 154	83.01 167	83.78 269	64.51 204	87.11 170	90.57 133	71.96 160	78.08 165	86.20 246	61.41 166	93.94 127	74.93 141	77.23 262	90.60 177

v2v482		80.23 147	79.29 147	83.05 165	83.62 271	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	76.78 271	91.18 154

DU-MVS		81.12 122	80.52 121	82.90 172	87.80 185	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	76.46 274	92.20 126

v144192		79.47 162	78.37 168	82.78 180	83.35 276	63.96 216	86.96 173	90.36 140	69.99 199	77.50 174	85.67 257	60.66 181	93.77 138	74.27 147	76.58 272	90.62 175

Fast-Effi-MVS+-dtu		78.02 201	76.49 215	82.62 184	83.16 284	66.96 157	86.94 174	87.45 226	72.45 152	71.49 276	84.17 285	54.79 223	91.58 224	67.61 210	80.31 231	89.30 228

v1192		79.59 159	78.43 167	83.07 164	83.55 273	64.52 203	86.93 175	90.58 131	70.83 180	77.78 170	85.90 250	59.15 192	93.94 127	73.96 150	77.19 264	90.76 170

EPNet_dtu		75.46 246	74.86 237	77.23 286	82.57 299	54.60 337	86.89 176	83.09 291	71.64 162	66.25 332	85.86 252	55.99 214	88.04 293	54.92 312	86.55 148	89.05 235
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM2							86.86 177

VPA-MVSNet		80.60 137	80.55 120	80.76 226	88.07 176	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	79.22 245	91.23 153

v1921920		79.22 170	78.03 175	82.80 177	83.30 278	63.94 217	86.80 179	90.33 141	69.91 202	77.48 175	85.53 260	58.44 196	93.75 140	73.60 152	76.85 269	90.71 173

IterMVS-LS		80.06 150	79.38 143	82.11 191	85.89 227	63.20 235	86.79 180	89.34 168	74.19 119	75.45 223	86.72 226	66.62 101	92.39 195	72.58 165	76.86 268	90.75 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		75.39 249	74.56 241	77.86 274	85.50 234	57.10 308	86.78 181	86.09 249	72.17 158	71.53 275	87.34 209	63.01 142	89.31 273	56.84 304	61.83 362	87.17 280

Baseline_NR-MVSNet		78.15 197	78.33 170	77.61 280	85.79 228	56.21 323	86.78 181	85.76 253	73.60 133	77.93 168	87.57 203	65.02 121	88.99 278	67.14 217	75.33 298	87.63 268

PAPR		81.66 113	80.89 115	83.99 133	90.27 97	64.00 215	86.76 183	91.77 101	68.84 230	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)		78.36 191	78.45 165	78.07 273	88.64 157	51.78 356	86.70 184	79.63 329	74.14 121	75.11 236	90.83 123	61.29 170	89.75 265	58.10 292	91.60 82	92.69 107

pmmvs6		74.69 252	73.39 254	78.61 263	81.38 317	57.48 303	86.64 185	87.95 213	64.99 278	70.18 286	86.61 233	50.43 271	89.52 269	62.12 256	70.18 338	88.83 246

v1240		78.99 177	77.78 184	82.64 183	83.21 280	63.54 225	86.62 186	90.30 143	69.74 209	77.33 178	85.68 256	57.04 210	93.76 139	73.13 160	76.92 266	90.62 175

MTAPA		87.23 27	87.00 28	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		58.10 341	87.04 39			88.98 279	74.07 149

FMVSNet3		77.88 205	76.85 206	80.97 222	86.84 214	62.36 245	86.52 189	88.77 194	71.13 174	75.34 227	86.66 232	54.07 231	91.10 245	62.72 247	79.57 238	89.45 224

dcpmvs_2		85.63 51	86.15 43	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

pm-mvs1		77.25 221	76.68 213	78.93 259	84.22 259	58.62 286	86.41 191	88.36 205	71.37 171	73.31 255	88.01 196	61.22 172	89.15 276	64.24 238	73.01 322	89.03 236

EI-MVSNet		80.52 140	79.98 130	82.12 190	84.28 257	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	80.57 228	90.74 172

CVMVSNet		72.99 271	72.58 261	74.25 312	84.28 257	50.85 362	86.41 191	83.45 285	44.56 374	73.23 257	87.54 206	49.38 283	85.70 309	65.90 226	78.44 252	86.19 300

NR-MVSNet		80.23 147	79.38 143	82.78 180	87.80 185	63.34 231	86.31 194	91.09 120	79.01 26	72.17 269	89.07 162	67.20 98	92.81 185	66.08 225	75.65 287	92.20 126

v148		78.72 183	77.80 183	81.47 204	82.73 295	61.96 252	86.30 195	88.08 209	73.26 142	76.18 209	85.47 262	62.46 148	92.36 197	71.92 170	73.82 315	90.09 199

新几何2							86.29 196

test_yl		81.17 120	80.47 122	83.24 155	89.13 138	63.62 221	86.21 197	89.95 153	72.43 155	81.78 108	89.61 146	57.50 205	93.58 144	70.75 178	86.90 142	92.52 112

DCV-MVSNet		81.17 120	80.47 122	83.24 155	89.13 138	63.62 221	86.21 197	89.95 153	72.43 155	81.78 108	89.61 146	57.50 205	93.58 144	70.75 178	86.90 142	92.52 112

PVSNet_BlendedMVS		80.60 137	80.02 129	82.36 189	88.85 145	65.40 186	86.16 199	92.00 87	69.34 214	78.11 163	86.09 249	66.02 112	94.27 113	71.52 171	82.06 209	87.39 274

MVS_Test		83.15 87	83.06 82	83.41 149	86.86 212	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		79.47 162	78.60 162	82.05 192	89.19 136	65.91 173	86.07 201	88.52 203	72.18 157	75.42 224	87.69 200	61.15 173	93.54 148	60.38 270	86.83 144	86.70 293

MVS_111021_HR		85.14 59	84.75 63	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		81.39 118	80.29 126	84.70 94	86.63 219	69.90 85	85.95 203	86.77 238	63.24 294	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		72.79 273	70.44 281	79.84 244	88.13 172	65.99 171	85.93 204	84.29 271	65.57 271	67.40 317	85.49 261	46.92 302	92.61 187	35.88 378	74.38 309	80.94 357

OurMVSNet-221017-0		74.26 255	72.42 263	79.80 245	83.76 270	59.59 281	85.92 205	86.64 239	66.39 261	66.96 320	87.58 202	39.46 347	91.60 223	65.76 228	69.27 341	88.22 258

hse-mvs2		81.72 108	80.94 114	84.07 123	88.72 154	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	73.44 319	91.06 159

EG-PatchMatch MVS		74.04 258	71.82 267	80.71 227	84.92 247	67.42 143	85.86 207	88.08 209	66.04 265	64.22 344	83.85 289	35.10 362	92.56 189	57.44 297	80.83 223	82.16 351

AUN-MVS		79.21 171	77.60 191	84.05 128	88.71 155	67.61 139	85.84 208	87.26 229	69.08 223	77.23 182	88.14 194	53.20 239	93.47 152	75.50 139	73.45 318	91.06 159

thres100view900		76.50 230	75.55 228	79.33 254	89.52 118	56.99 309	85.83 209	83.23 288	73.94 124	76.32 205	87.12 218	51.89 255	91.95 211	48.33 345	83.75 183	89.07 230

CLD-MVS		82.31 98	81.65 104	84.29 110	88.47 162	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	90.79 168
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SixPastTwentyTwo		73.37 264	71.26 274	79.70 247	85.08 244	57.89 296	85.57 211	83.56 282	71.03 178	65.66 334	85.88 251	42.10 338	92.57 188	59.11 281	63.34 360	88.65 252

xiu_mvs_v1_base_debu		80.80 130	79.72 136	84.03 130	87.35 200	70.19 79	85.56 212	88.77 194	69.06 224	81.83 104	88.16 190	50.91 264	92.85 182	78.29 108	87.56 132	89.06 232

xiu_mvs_v1_base		80.80 130	79.72 136	84.03 130	87.35 200	70.19 79	85.56 212	88.77 194	69.06 224	81.83 104	88.16 190	50.91 264	92.85 182	78.29 108	87.56 132	89.06 232

xiu_mvs_v1_base_debi		80.80 130	79.72 136	84.03 130	87.35 200	70.19 79	85.56 212	88.77 194	69.06 224	81.83 104	88.16 190	50.91 264	92.85 182	78.29 108	87.56 132	89.06 232

V42		79.38 168	78.24 172	82.83 174	81.10 322	65.50 183	85.55 215	89.82 155	71.57 168	78.21 160	86.12 248	60.66 181	93.18 169	75.64 135	75.46 293	89.81 216

lupinMVS		81.39 118	80.27 127	84.76 93	87.35 200	70.21 77	85.55 215	86.41 242	62.85 301	81.32 112	88.61 176	61.68 159	92.24 203	78.41 106	90.26 100	91.83 136

Fast-Effi-MVS+		80.81 128	79.92 131	83.47 145	88.85 145	64.51 204	85.53 217	89.39 167	70.79 181	78.49 152	85.06 272	67.54 94	93.58 144	67.03 219	86.58 147	92.32 120

thres600view7		76.50 230	75.44 229	79.68 248	89.40 123	57.16 306	85.53 217	83.23 288	73.79 129	76.26 206	87.09 219	51.89 255	91.89 214	48.05 350	83.72 186	90.00 205

DELS-MVS		85.41 56	85.30 57	85.77 64	88.49 161	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		76.42 233	75.37 233	79.55 253	89.13 138	57.65 300	85.17 220	83.60 280	73.41 139	76.45 200	86.39 242	52.12 247	91.95 211	48.33 345	83.75 183	89.07 230

thres400		76.50 230	75.37 233	79.86 243	89.13 138	57.65 300	85.17 220	83.60 280	73.41 139	76.45 200	86.39 242	52.12 247	91.95 211	48.33 345	83.75 183	90.00 205

MVS_111021_LR		82.61 96	82.11 96	84.11 117	88.82 148	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	90.20 192

baseline1		76.98 224	76.75 211	77.66 278	88.13 172	55.66 328	85.12 223	81.89 305	73.04 148	76.79 191	88.90 167	62.43 149	87.78 296	63.30 244	71.18 334	89.55 223

WR-MVS		79.49 161	79.22 150	80.27 236	88.79 151	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	77.22 263	91.80 138

ET-MVSNet_ETH3D		78.63 185	76.63 214	84.64 95	86.73 217	69.47 92	85.01 225	84.61 265	69.54 210	66.51 330	86.59 234	50.16 273	91.75 219	76.26 128	84.24 178	92.69 107

OpenMVS_ROB	64.09 19	70.56 290	68.19 296	77.65 279	80.26 329	59.41 283	85.01 225	82.96 295	58.76 336	65.43 336	82.33 310	37.63 356	91.23 241	45.34 362	76.03 283	82.32 348

BH-RMVSNet		79.61 157	78.44 166	83.14 160	89.38 125	65.93 172	84.95 227	87.15 232	73.56 134	78.19 161	89.79 141	56.67 212	93.36 156	59.53 277	86.74 145	90.13 195

BH-w/o		78.21 194	77.33 197	80.84 224	88.81 149	65.13 193	84.87 228	87.85 217	69.75 207	74.52 246	84.74 277	61.34 168	93.11 173	58.24 291	85.84 160	84.27 327

TDRefinement		67.49 313	64.34 323	76.92 288	73.47 371	61.07 261	84.86 229	82.98 294	59.77 326	58.30 366	85.13 270	26.06 376	87.89 294	47.92 351	60.59 367	81.81 353

Anonymous202405211		78.25 192	77.01 201	81.99 194	91.03 82	60.67 267	84.77 230	83.90 277	70.65 187	80.00 128	91.20 111	41.08 343	91.43 235	65.21 231	85.26 163	93.85 57

TAMVS		78.89 180	77.51 193	83.03 166	87.80 185	67.79 135	84.72 231	85.05 260	67.63 244	76.75 193	87.70 199	62.25 152	90.82 251	58.53 288	87.13 139	90.49 181

1314		76.53 229	75.30 235	80.21 237	83.93 266	62.32 247	84.66 232	88.81 192	60.23 322	70.16 288	84.07 287	55.30 217	90.73 254	67.37 213	83.21 196	87.59 271

MVS		78.19 196	76.99 203	81.78 197	85.66 230	66.99 154	84.66 232	90.47 135	55.08 357	72.02 271	85.27 265	63.83 130	94.11 122	66.10 224	89.80 109	84.24 328

tfpnnormal		74.39 253	73.16 257	78.08 272	86.10 226	58.05 291	84.65 234	87.53 223	70.32 192	71.22 278	85.63 258	54.97 218	89.86 263	43.03 366	75.02 303	86.32 297

TR-MVS		77.44 215	76.18 220	81.20 214	88.24 170	63.24 233	84.61 235	86.40 243	67.55 246	77.81 169	86.48 240	54.10 230	93.15 170	57.75 295	82.72 203	87.20 279

AllTest		70.96 284	68.09 299	79.58 251	85.15 241	63.62 221	84.58 236	79.83 326	62.31 308	60.32 359	86.73 224	32.02 366	88.96 281	50.28 335	71.57 332	86.15 301

FA-MVS(test-final)		80.96 124	79.91 132	84.10 118	88.30 169	65.01 195	84.55 237	90.01 151	73.25 143	79.61 131	87.57 203	58.35 197	94.72 99	71.29 175	86.25 153	92.56 111

EU-MVSNet		68.53 308	67.61 309	71.31 334	78.51 349	47.01 372	84.47 238	84.27 272	42.27 377	66.44 331	84.79 276	40.44 345	83.76 324	58.76 286	68.54 346	83.17 339

VNet		82.21 99	82.41 91	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		81.69 110	81.05 111	83.60 142	89.15 137	68.03 131	84.46 240	90.02 150	70.67 184	81.30 115	86.53 239	63.17 137	94.19 119	75.60 137	88.54 124	88.57 254

VPNet		78.69 184	78.66 161	78.76 261	88.31 168	55.72 327	84.45 241	86.63 240	76.79 64	78.26 158	90.55 128	59.30 191	89.70 267	66.63 220	77.05 265	90.88 166

PVSNet_Blended		80.98 123	80.34 124	82.90 172	88.85 145	65.40 186	84.43 242	92.00 87	67.62 245	78.11 163	85.05 273	66.02 112	94.27 113	71.52 171	89.50 111	89.01 237

MVP-Stereo		76.12 237	74.46 244	81.13 217	85.37 237	69.79 86	84.42 243	87.95 213	65.03 276	67.46 315	85.33 264	53.28 238	91.73 221	58.01 293	83.27 195	81.85 352
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CDS-MVSNet		79.07 175	77.70 188	83.17 159	87.60 194	68.23 125	84.40 244	86.20 246	67.49 247	76.36 204	86.54 238	61.54 162	90.79 252	61.86 259	87.33 136	90.49 181
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
K. test v3		71.19 281	68.51 293	79.21 257	83.04 287	57.78 299	84.35 245	76.91 347	72.90 151	62.99 351	82.86 304	39.27 348	91.09 247	61.65 261	52.66 378	88.75 249

PS-MVSNAJ		81.69 110	81.02 112	83.70 141	89.51 119	68.21 126	84.28 246	90.09 149	70.79 181	81.26 116	85.62 259	63.15 138	94.29 111	75.62 136	88.87 119	88.59 253

patch_mono-2		83.65 75	84.54 65	80.99 220	90.06 106	65.83 175	84.21 247	88.74 198	71.60 167	85.01 55	92.44 84	74.51 25	83.50 327	82.15 75	92.15 75	93.64 71

test222					91.50 77	68.26 124	84.16 248	83.20 290	54.63 358	79.74 129	91.63 99	58.97 193			91.42 85	86.77 291

testdata1							84.14 249		75.71 87

c3_l		78.75 181	77.91 178	81.26 211	82.89 292	61.56 257	84.09 250	89.13 181	69.97 200	75.56 218	84.29 284	66.36 106	92.09 207	73.47 155	75.48 291	90.12 196

MVSTER		79.01 176	77.88 180	82.38 188	83.07 285	64.80 200	84.08 251	88.95 189	69.01 227	78.69 145	87.17 217	54.70 224	92.43 193	74.69 142	80.57 228	89.89 212

ab-mvs		79.51 160	78.97 156	81.14 216	88.46 163	60.91 263	83.84 252	89.24 175	70.36 190	79.03 138	88.87 169	63.23 136	90.21 260	65.12 232	82.57 205	92.28 122

PAPM		77.68 212	76.40 218	81.51 203	87.29 207	61.85 253	83.78 253	89.59 162	64.74 279	71.23 277	88.70 172	62.59 145	93.66 143	52.66 323	87.03 141	89.01 237

diffmvs		82.10 100	81.88 102	82.76 182	83.00 288	63.78 220	83.68 254	89.76 157	72.94 150	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		78.59 187	77.76 186	81.08 218	82.66 297	61.56 257	83.65 255	89.15 179	68.87 229	75.55 219	83.79 292	66.49 104	92.03 208	73.25 158	76.39 276	89.64 220

1112_ss		77.40 217	76.43 217	80.32 235	89.11 142	60.41 272	83.65 255	87.72 220	62.13 310	73.05 259	86.72 226	62.58 146	89.97 262	62.11 257	80.80 224	90.59 178

PCF-MVS	73.52 7	80.38 142	78.84 158	85.01 82	87.71 189	68.99 102	83.65 255	91.46 111	63.00 298	77.77 171	90.28 131	66.10 109	95.09 84	61.40 263	88.22 129	90.94 165
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
XVG-ACMP-BASELINE		76.11 238	74.27 246	81.62 200	83.20 281	64.67 202	83.60 258	89.75 158	69.75 207	71.85 272	87.09 219	32.78 365	92.11 206	69.99 188	80.43 230	88.09 260

cl22		78.07 199	77.01 201	81.23 212	82.37 304	61.83 254	83.55 259	87.98 211	68.96 228	75.06 238	83.87 288	61.40 167	91.88 215	73.53 153	76.39 276	89.98 208

XVG-OURS-SEG-HR		80.81 128	79.76 135	83.96 135	85.60 232	68.78 107	83.54 260	90.50 134	70.66 186	76.71 194	91.66 96	60.69 180	91.26 239	76.94 120	81.58 215	91.83 136

IB-MVS	68.01 15	75.85 241	73.36 255	83.31 151	84.76 249	66.03 168	83.38 261	85.06 259	70.21 196	69.40 298	81.05 321	45.76 315	94.66 101	65.10 233	75.49 290	89.25 229
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	77.95 203	77.15 199	80.36 233	87.57 198	60.21 275	83.37 262	87.78 219	66.11 263	75.37 226	87.06 221	63.27 134	90.48 257	61.38 264	82.43 206	90.40 185

test_vis1_n_1920		75.52 245	75.78 223	74.75 308	79.84 336	57.44 304	83.26 263	85.52 255	62.83 302	79.34 136	86.17 247	45.10 320	79.71 345	78.75 101	81.21 219	87.10 286

Anonymous20240521		68.80 304	67.22 313	73.55 316	74.33 364	54.11 341	83.18 264	85.61 254	58.15 340	61.68 354	80.94 324	30.71 371	81.27 340	57.00 302	73.34 321	85.28 314

eth_miper_zixun_eth		77.92 204	76.69 212	81.61 202	83.00 288	61.98 251	83.15 265	89.20 177	69.52 211	74.86 242	84.35 283	61.76 158	92.56 189	71.50 173	72.89 323	90.28 190

FE-MVS		77.78 207	75.68 225	84.08 122	88.09 175	66.00 170	83.13 266	87.79 218	68.42 238	78.01 166	85.23 267	45.50 318	95.12 78	59.11 281	85.83 161	91.11 156

cl____		77.72 209	76.76 209	80.58 229	82.49 301	60.48 270	83.09 267	87.87 215	69.22 217	74.38 248	85.22 268	62.10 155	91.53 229	71.09 176	75.41 295	89.73 219

DIV-MVS_self_test		77.72 209	76.76 209	80.58 229	82.48 302	60.48 270	83.09 267	87.86 216	69.22 217	74.38 248	85.24 266	62.10 155	91.53 229	71.09 176	75.40 296	89.74 218

thres200		75.55 244	74.47 243	78.82 260	87.78 188	57.85 297	83.07 269	83.51 283	72.44 154	75.84 215	84.42 279	52.08 250	91.75 219	47.41 352	83.64 188	86.86 289

testing3		68.56 307	67.67 308	71.22 335	87.33 205	42.87 383	83.06 270	71.54 365	70.36 190	69.08 302	84.38 281	30.33 372	85.69 310	37.50 377	75.45 294	85.09 320

XVG-OURS		80.41 141	79.23 149	83.97 134	85.64 231	69.02 101	83.03 271	90.39 136	71.09 176	77.63 173	91.49 104	54.62 226	91.35 237	75.71 134	83.47 192	91.54 142

miper_enhance_ethall		77.87 206	76.86 205	80.92 223	81.65 311	61.38 259	82.68 272	88.98 186	65.52 272	75.47 220	82.30 311	65.76 116	92.00 210	72.95 161	76.39 276	89.39 225

mvs_anonymous		79.42 165	79.11 153	80.34 234	84.45 256	57.97 294	82.59 273	87.62 221	67.40 249	76.17 211	88.56 179	68.47 86	89.59 268	70.65 181	86.05 156	93.47 79

baseline2		75.70 242	73.83 251	81.30 210	83.26 279	61.79 255	82.57 274	80.65 316	66.81 250	66.88 321	83.42 297	57.86 201	92.19 204	63.47 241	79.57 238	89.91 210

cascas		76.72 228	74.64 239	82.99 168	85.78 229	65.88 174	82.33 275	89.21 176	60.85 318	72.74 261	81.02 322	47.28 299	93.75 140	67.48 212	85.02 164	89.34 226

RPSCF		73.23 268	71.46 269	78.54 266	82.50 300	59.85 277	82.18 276	82.84 298	58.96 334	71.15 279	89.41 157	45.48 319	84.77 319	58.82 285	71.83 330	91.02 163

thisisatest0515		77.33 218	75.38 232	83.18 158	85.27 238	63.80 219	82.11 277	83.27 287	65.06 275	75.91 213	83.84 290	49.54 280	94.27 113	67.24 215	86.19 154	91.48 147

pmmvs-eth3d		70.50 291	67.83 304	78.52 267	77.37 353	66.18 167	81.82 278	81.51 309	58.90 335	63.90 347	80.42 329	42.69 332	86.28 306	58.56 287	65.30 356	83.11 341

MS-PatchMatch		73.83 260	72.67 260	77.30 285	83.87 267	66.02 169	81.82 278	84.66 264	61.37 316	68.61 306	82.82 305	47.29 298	88.21 290	59.27 278	84.32 176	77.68 366

pmmvs5		71.55 279	70.20 285	75.61 297	77.83 350	56.39 319	81.74 280	80.89 312	57.76 343	67.46 315	84.49 278	49.26 286	85.32 315	57.08 301	75.29 299	85.11 319

Test_1112_low_res		76.40 234	75.44 229	79.27 255	89.28 132	58.09 290	81.69 281	87.07 233	59.53 329	72.48 265	86.67 231	61.30 169	89.33 272	60.81 269	80.15 233	90.41 184

IterMVS		74.29 254	72.94 259	78.35 269	81.53 314	63.49 227	81.58 282	82.49 300	68.06 242	69.99 291	83.69 294	51.66 259	85.54 311	65.85 227	71.64 331	86.01 305
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		75.43 247	73.87 250	80.11 239	82.69 296	64.85 199	81.57 283	83.47 284	69.16 221	70.49 282	84.15 286	51.95 253	88.15 291	69.23 195	72.14 328	87.34 276

test_vis1_n		69.85 298	69.21 289	71.77 328	72.66 376	55.27 332	81.48 284	76.21 350	52.03 364	75.30 231	83.20 300	28.97 373	76.22 365	74.60 143	78.41 254	83.81 334

pmmvs4		74.03 259	71.91 266	80.39 232	81.96 307	68.32 122	81.45 285	82.14 303	59.32 330	69.87 294	85.13 270	52.40 243	88.13 292	60.21 272	74.74 306	84.73 324

GA-MVS		76.87 226	75.17 236	81.97 195	82.75 294	62.58 243	81.44 286	86.35 245	72.16 159	74.74 243	82.89 303	46.20 310	92.02 209	68.85 201	81.09 220	91.30 152

test_fmvs1_n		70.86 286	70.24 284	72.73 323	72.51 377	55.28 331	81.27 287	79.71 328	51.49 367	78.73 143	84.87 274	27.54 375	77.02 357	76.06 130	79.97 236	85.88 308

test_fmvs1		70.93 285	70.52 279	72.16 326	73.71 367	55.05 333	80.82 288	78.77 334	51.21 368	78.58 149	84.41 280	31.20 370	76.94 358	75.88 133	80.12 235	84.47 326

CostFormer		75.24 250	73.90 249	79.27 255	82.65 298	58.27 289	80.80 289	82.73 299	61.57 313	75.33 230	83.13 301	55.52 215	91.07 248	64.98 234	78.34 255	88.45 255

MIMVSNet1		68.58 306	66.78 316	73.98 314	80.07 333	51.82 355	80.77 290	84.37 268	64.40 283	59.75 362	82.16 314	36.47 358	83.63 326	42.73 367	70.33 337	86.48 296

CL-MVSNet_self_test		72.37 276	71.46 269	75.09 303	79.49 343	53.53 345	80.76 291	85.01 261	69.12 222	70.51 281	82.05 315	57.92 200	84.13 322	52.27 325	66.00 354	87.60 269

MSDG		73.36 266	70.99 275	80.49 231	84.51 255	65.80 177	80.71 292	86.13 248	65.70 269	65.46 335	83.74 293	44.60 321	90.91 250	51.13 330	76.89 267	84.74 323

tpm2		73.26 267	71.46 269	78.63 262	83.34 277	56.71 314	80.65 293	80.40 321	56.63 351	73.55 253	82.02 316	51.80 257	91.24 240	56.35 308	78.42 253	87.95 261

XXY-MVS		75.41 248	75.56 227	74.96 304	83.59 272	57.82 298	80.59 294	83.87 278	66.54 260	74.93 241	88.31 185	63.24 135	80.09 344	62.16 255	76.85 269	86.97 287

test_cas_vis1_n_1920		73.76 261	73.74 252	73.81 315	75.90 357	59.77 278	80.51 295	82.40 301	58.30 339	81.62 110	85.69 255	44.35 323	76.41 363	76.29 127	78.61 248	85.23 315

EGC-MVSNET		52.07 350	47.05 354	67.14 352	83.51 274	60.71 266	80.50 296	67.75 375	0.07 399	0.43 400	75.85 363	24.26 379	81.54 338	28.82 384	62.25 361	59.16 384

SDMVSNet		80.38 142	80.18 128	80.99 220	89.03 143	64.94 197	80.45 297	89.40 166	75.19 98	76.61 198	89.98 137	60.61 183	87.69 297	76.83 123	83.55 189	90.33 187

HyFIR lowres test		77.53 214	75.40 231	83.94 136	89.59 115	66.62 160	80.36 298	88.64 201	56.29 353	76.45 200	85.17 269	57.64 203	93.28 158	61.34 265	83.10 198	91.91 135

D2MVS		74.82 251	73.21 256	79.64 250	79.81 337	62.56 244	80.34 299	87.35 227	64.37 284	68.86 303	82.66 307	46.37 306	90.10 261	67.91 208	81.24 218	86.25 298

TinyColmap		67.30 316	64.81 321	74.76 307	81.92 309	56.68 315	80.29 300	81.49 310	60.33 320	56.27 373	83.22 298	24.77 378	87.66 298	45.52 360	69.47 340	79.95 361

LCM-MVSNet-Re		77.05 222	76.94 204	77.36 283	87.20 208	51.60 357	80.06 301	80.46 320	75.20 97	67.69 312	86.72 226	62.48 147	88.98 279	63.44 242	89.25 114	91.51 143

test_fmvs2		68.35 310	67.48 311	70.98 337	69.50 380	51.95 354	80.05 302	76.38 349	49.33 370	74.65 245	84.38 281	23.30 381	75.40 372	74.51 144	75.17 302	85.60 310

FMVSNet5		69.50 299	67.96 300	74.15 313	82.97 291	55.35 330	80.01 303	82.12 304	62.56 306	63.02 349	81.53 318	36.92 357	81.92 336	48.42 344	74.06 311	85.17 318

SCA		74.22 256	72.33 264	79.91 242	84.05 264	62.17 249	79.96 304	79.29 332	66.30 262	72.38 267	80.13 331	51.95 253	88.60 286	59.25 279	77.67 260	88.96 241

tpmrst		72.39 274	72.13 265	73.18 321	80.54 327	49.91 366	79.91 305	79.08 333	63.11 296	71.69 274	79.95 333	55.32 216	82.77 332	65.66 229	73.89 313	86.87 288

PatchmatchNet		73.12 269	71.33 272	78.49 268	83.18 282	60.85 264	79.63 306	78.57 335	64.13 286	71.73 273	79.81 336	51.20 262	85.97 308	57.40 298	76.36 281	88.66 251
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.38 275	70.90 276	76.80 290	88.60 158	67.38 145	79.53 307	76.17 351	62.75 304	69.36 299	82.00 317	45.51 317	84.89 318	53.62 318	80.58 227	78.12 365

CMPMVS	51.72 21	70.19 294	68.16 297	76.28 292	73.15 373	57.55 302	79.47 308	83.92 276	48.02 371	56.48 372	84.81 275	43.13 329	86.42 305	62.67 250	81.81 213	84.89 321
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
GG-mvs-BLEND				75.38 301	81.59 313	55.80 326	79.32 309	69.63 370		67.19 318	73.67 368	43.24 328	88.90 283	50.41 332	84.50 171	81.45 354

LTVRE_ROB	69.57 13	76.25 236	74.54 242	81.41 206	88.60 158	64.38 210	79.24 310	89.12 182	70.76 183	69.79 296	87.86 197	49.09 288	93.20 166	56.21 309	80.16 232	86.65 294
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		72.37 276	71.71 268	74.35 311	82.19 305	52.00 353	79.22 311	77.29 344	64.56 281	72.95 260	83.68 295	51.35 260	83.26 330	58.33 290	75.80 285	87.81 265

ppachtmachnet_test		70.04 295	67.34 312	78.14 271	79.80 338	61.13 260	79.19 312	80.59 317	59.16 332	65.27 337	79.29 339	46.75 304	87.29 299	49.33 341	66.72 349	86.00 307

USDC		70.33 292	68.37 294	76.21 293	80.60 326	56.23 322	79.19 312	86.49 241	60.89 317	61.29 355	85.47 262	31.78 368	89.47 271	53.37 320	76.21 282	82.94 345

sd_testset		77.70 211	77.40 194	78.60 264	89.03 143	60.02 276	79.00 314	85.83 252	75.19 98	76.61 198	89.98 137	54.81 219	85.46 313	62.63 251	83.55 189	90.33 187

PM-MVS		66.41 322	64.14 324	73.20 320	73.92 366	56.45 317	78.97 315	64.96 382	63.88 293	64.72 341	80.24 330	19.84 384	83.44 328	66.24 221	64.52 358	79.71 362

tpmvs		71.09 283	69.29 288	76.49 291	82.04 306	56.04 324	78.92 316	81.37 311	64.05 289	67.18 319	78.28 348	49.74 279	89.77 264	49.67 340	72.37 325	83.67 335

test_post1							78.90 317				5.43 398	48.81 294	85.44 314	59.25 279

CHOSEN 1792x2688		77.63 213	75.69 224	83.44 146	89.98 108	68.58 118	78.70 318	87.50 224	56.38 352	75.80 216	86.84 222	58.67 194	91.40 236	61.58 262	85.75 162	90.34 186

Syy-MVS		68.05 311	67.85 302	68.67 348	84.68 251	40.97 389	78.62 319	73.08 362	66.65 257	66.74 324	79.46 337	52.11 249	82.30 334	32.89 381	76.38 279	82.75 346

myMVS_eth3d		67.02 317	66.29 318	69.21 343	84.68 251	42.58 384	78.62 319	73.08 362	66.65 257	66.74 324	79.46 337	31.53 369	82.30 334	39.43 374	76.38 279	82.75 346

test-LLR		72.94 272	72.43 262	74.48 309	81.35 318	58.04 292	78.38 321	77.46 341	66.66 254	69.95 292	79.00 342	48.06 295	79.24 346	66.13 222	84.83 166	86.15 301

TESTMET0.1,1		69.89 297	69.00 291	72.55 324	79.27 346	56.85 310	78.38 321	74.71 357	57.64 344	68.09 309	77.19 355	37.75 355	76.70 359	63.92 239	84.09 179	84.10 331

test-mter		71.41 280	70.39 283	74.48 309	81.35 318	58.04 292	78.38 321	77.46 341	60.32 321	69.95 292	79.00 342	36.08 360	79.24 346	66.13 222	84.83 166	86.15 301

Anonymous20231206		68.60 305	67.80 305	71.02 336	80.23 331	50.75 363	78.30 324	80.47 319	56.79 350	66.11 333	82.63 308	46.35 307	78.95 348	43.62 365	75.70 286	83.36 338

tpm cat1		70.57 289	68.31 295	77.35 284	82.41 303	57.95 295	78.08 325	80.22 324	52.04 363	68.54 307	77.66 353	52.00 252	87.84 295	51.77 326	72.07 329	86.25 298

our_test_3		69.14 301	67.00 314	75.57 298	79.80 338	58.80 284	77.96 326	77.81 338	59.55 328	62.90 352	78.25 349	47.43 297	83.97 323	51.71 327	67.58 348	83.93 333

KD-MVS_self_test		68.81 303	67.59 310	72.46 325	74.29 365	45.45 374	77.93 327	87.00 234	63.12 295	63.99 346	78.99 344	42.32 334	84.77 319	56.55 307	64.09 359	87.16 282

WTY-MVS		75.65 243	75.68 225	75.57 298	86.40 221	56.82 311	77.92 328	82.40 301	65.10 274	76.18 209	87.72 198	63.13 141	80.90 341	60.31 271	81.96 210	89.00 239

test20.03		67.45 314	66.95 315	68.94 344	75.48 361	44.84 379	77.50 329	77.67 339	66.66 254	63.01 350	83.80 291	47.02 301	78.40 350	42.53 368	68.86 345	83.58 336

EPMVS		69.02 302	68.16 297	71.59 329	79.61 341	49.80 368	77.40 330	66.93 376	62.82 303	70.01 289	79.05 340	45.79 314	77.86 354	56.58 306	75.26 300	87.13 283

test_fmvs3		63.36 333	61.82 336	67.98 350	62.51 387	46.96 373	77.37 331	74.03 359	45.24 373	67.50 314	78.79 345	12.16 392	72.98 380	72.77 164	66.02 353	83.99 332

gg-mvs-nofinetune		69.95 296	67.96 300	75.94 294	83.07 285	54.51 339	77.23 332	70.29 368	63.11 296	70.32 284	62.33 379	43.62 327	88.69 285	53.88 317	87.76 131	84.62 325

MDTV_nov1_ep13			69.97 286		83.18 282	53.48 346	77.10 333	80.18 325	60.45 319	69.33 300	80.44 328	48.89 293	86.90 301	51.60 328	78.51 251

LF4IMVS		64.02 331	62.19 335	69.50 342	70.90 378	53.29 350	76.13 334	77.18 345	52.65 362	58.59 364	80.98 323	23.55 380	76.52 361	53.06 322	66.66 350	78.68 364

sss		73.60 262	73.64 253	73.51 317	82.80 293	55.01 334	76.12 335	81.69 308	62.47 307	74.68 244	85.85 253	57.32 207	78.11 352	60.86 268	80.93 221	87.39 274

testgi		66.67 320	66.53 317	67.08 353	75.62 360	41.69 388	75.93 336	76.50 348	66.11 263	65.20 340	86.59 234	35.72 361	74.71 374	43.71 364	73.38 320	84.84 322

CR-MVSNet		73.37 264	71.27 273	79.67 249	81.32 320	65.19 191	75.92 337	80.30 322	59.92 325	72.73 262	81.19 319	52.50 241	86.69 302	59.84 274	77.71 258	87.11 284

RPMNet		73.51 263	70.49 280	82.58 185	81.32 320	65.19 191	75.92 337	92.27 76	57.60 345	72.73 262	76.45 358	52.30 244	95.43 65	48.14 349	77.71 258	87.11 284

MIMVSNet		70.69 288	69.30 287	74.88 305	84.52 254	56.35 321	75.87 339	79.42 330	64.59 280	67.76 310	82.41 309	41.10 342	81.54 338	46.64 356	81.34 216	86.75 292

test0.0.03 1		68.00 312	67.69 307	68.90 345	77.55 351	47.43 370	75.70 340	72.95 364	66.66 254	66.56 326	82.29 312	48.06 295	75.87 367	44.97 363	74.51 308	83.41 337

dmvs_re		71.14 282	70.58 278	72.80 322	81.96 307	59.68 279	75.60 341	79.34 331	68.55 234	69.27 301	80.72 327	49.42 282	76.54 360	52.56 324	77.79 257	82.19 350

dmvs_testset		62.63 334	64.11 325	58.19 363	78.55 348	24.76 399	75.28 342	65.94 379	67.91 243	60.34 358	76.01 360	53.56 235	73.94 378	31.79 382	67.65 347	75.88 370

PMMVS		69.34 300	68.67 292	71.35 333	75.67 359	62.03 250	75.17 343	73.46 360	50.00 369	68.68 304	79.05 340	52.07 251	78.13 351	61.16 266	82.77 201	73.90 372

UnsupCasMVSNet_eth		67.33 315	65.99 319	71.37 331	73.48 370	51.47 359	75.16 344	85.19 258	65.20 273	60.78 357	80.93 326	42.35 333	77.20 356	57.12 300	53.69 377	85.44 312

MDTV_nov1_ep13_2view						37.79 391	75.16 344		55.10 356	66.53 327		49.34 284		53.98 316		87.94 262

pmmvs3		57.79 340	54.26 345	68.37 349	64.02 386	56.72 313	75.12 346	65.17 380	40.20 379	52.93 377	69.86 376	20.36 383	75.48 370	45.45 361	55.25 376	72.90 374

dp		66.80 318	65.43 320	70.90 338	79.74 340	48.82 369	75.12 346	74.77 355	59.61 327	64.08 345	77.23 354	42.89 330	80.72 342	48.86 343	66.58 351	83.16 340

Patchmtry		70.74 287	69.16 290	75.49 300	80.72 324	54.07 342	74.94 348	80.30 322	58.34 338	70.01 289	81.19 319	52.50 241	86.54 303	53.37 320	71.09 335	85.87 309

PVSNet	64.34 18	72.08 278	70.87 277	75.69 296	86.21 223	56.44 318	74.37 349	80.73 315	62.06 311	70.17 287	82.23 313	42.86 331	83.31 329	54.77 313	84.45 175	87.32 277

WB-MVS		54.94 342	54.72 344	55.60 369	73.50 369	20.90 401	74.27 350	61.19 386	59.16 332	50.61 379	74.15 366	47.19 300	75.78 368	17.31 393	35.07 388	70.12 376

MDA-MVSNet-bldmvs		66.68 319	63.66 328	75.75 295	79.28 345	60.56 269	73.92 351	78.35 336	64.43 282	50.13 380	79.87 335	44.02 325	83.67 325	46.10 358	56.86 370	83.03 343

SSC-MVS		53.88 345	53.59 346	54.75 371	72.87 374	19.59 402	73.84 352	60.53 388	57.58 346	49.18 381	73.45 369	46.34 308	75.47 371	16.20 396	32.28 390	69.20 377

UnsupCasMVSNet_bld		63.70 332	61.53 338	70.21 340	73.69 368	51.39 360	72.82 353	81.89 305	55.63 355	57.81 368	71.80 372	38.67 350	78.61 349	49.26 342	52.21 379	80.63 358

PatchT		68.46 309	67.85 302	70.29 339	80.70 325	43.93 381	72.47 354	74.88 354	60.15 323	70.55 280	76.57 357	49.94 276	81.59 337	50.58 331	74.83 305	85.34 313

miper_lstm_enhance		74.11 257	73.11 258	77.13 287	80.11 332	59.62 280	72.23 355	86.92 236	66.76 252	70.40 283	82.92 302	56.93 211	82.92 331	69.06 198	72.63 324	88.87 244

MVS-HIRNet		59.14 339	57.67 342	63.57 357	81.65 311	43.50 382	71.73 356	65.06 381	39.59 381	51.43 378	57.73 385	38.34 352	82.58 333	39.53 372	73.95 312	64.62 381

APD_test1		53.31 347	49.93 352	63.42 358	65.68 384	50.13 365	71.59 357	66.90 377	34.43 386	40.58 385	71.56 373	8.65 397	76.27 364	34.64 380	55.36 375	63.86 382

Patchmatch-RL test		70.24 293	67.78 306	77.61 280	77.43 352	59.57 282	71.16 358	70.33 367	62.94 300	68.65 305	72.77 370	50.62 268	85.49 312	69.58 193	66.58 351	87.77 266

test123		6.12 367	8.11 370	0.14 382	0.06 405	0.09 407	71.05 359	0.03 407	0.04 401	0.25 402	1.30 401	0.05 405	0.03 402	0.21 401	0.01 400	0.29 397

ANet_high		50.57 352	46.10 356	63.99 356	48.67 398	39.13 390	70.99 360	80.85 313	61.39 315	31.18 388	57.70 386	17.02 387	73.65 379	31.22 383	15.89 396	79.18 363

KD-MVS_2432*1600		66.22 324	63.89 326	73.21 318	75.47 362	53.42 347	70.76 361	84.35 269	64.10 287	66.52 328	78.52 346	34.55 363	84.98 316	50.40 333	50.33 381	81.23 355

miper_refine_blended		66.22 324	63.89 326	73.21 318	75.47 362	53.42 347	70.76 361	84.35 269	64.10 287	66.52 328	78.52 346	34.55 363	84.98 316	50.40 333	50.33 381	81.23 355

test_vis1_rt		60.28 338	58.42 341	65.84 354	67.25 383	55.60 329	70.44 363	60.94 387	44.33 375	59.00 363	66.64 377	24.91 377	68.67 385	62.80 246	69.48 339	73.25 373

testmvs		6.04 368	8.02 371	0.10 383	0.08 404	0.03 408	69.74 364	0.04 406	0.05 400	0.31 401	1.68 400	0.02 406	0.04 401	0.24 400	0.02 399	0.25 398

N_pmnet		52.79 348	53.26 347	51.40 373	78.99 347	7.68 405	69.52 365	3.89 404	51.63 366	57.01 370	74.98 365	40.83 344	65.96 388	37.78 376	64.67 357	80.56 360

FPMVS		53.68 346	51.64 348	59.81 362	65.08 385	51.03 361	69.48 366	69.58 371	41.46 378	40.67 384	72.32 371	16.46 388	70.00 384	24.24 390	65.42 355	58.40 386

DSMNet-mixed		57.77 341	56.90 343	60.38 361	67.70 382	35.61 392	69.18 367	53.97 393	32.30 389	57.49 369	79.88 334	40.39 346	68.57 386	38.78 375	72.37 325	76.97 367

new-patchmatchnet		61.73 336	61.73 337	61.70 359	72.74 375	24.50 400	69.16 368	78.03 337	61.40 314	56.72 371	75.53 364	38.42 351	76.48 362	45.95 359	57.67 369	84.13 330

YYNet1		65.03 327	62.91 332	71.38 330	75.85 358	56.60 316	69.12 369	74.66 358	57.28 348	54.12 375	77.87 351	45.85 313	74.48 375	49.95 338	61.52 364	83.05 342

MDA-MVSNet_test_wron		65.03 327	62.92 331	71.37 331	75.93 356	56.73 312	69.09 370	74.73 356	57.28 348	54.03 376	77.89 350	45.88 312	74.39 376	49.89 339	61.55 363	82.99 344

PVSNet_0	57.27 20	61.67 337	59.27 340	68.85 346	79.61 341	57.44 304	68.01 371	73.44 361	55.93 354	58.54 365	70.41 375	44.58 322	77.55 355	47.01 353	35.91 387	71.55 375

ADS-MVSNet2		66.20 326	63.33 329	74.82 306	79.92 334	58.75 285	67.55 372	75.19 353	53.37 360	65.25 338	75.86 361	42.32 334	80.53 343	41.57 369	68.91 343	85.18 316

ADS-MVSNet		64.36 330	62.88 333	68.78 347	79.92 334	47.17 371	67.55 372	71.18 366	53.37 360	65.25 338	75.86 361	42.32 334	73.99 377	41.57 369	68.91 343	85.18 316

mvsany_test1		62.30 335	61.26 339	65.41 355	69.52 379	54.86 335	66.86 374	49.78 395	46.65 372	68.50 308	83.21 299	49.15 287	66.28 387	56.93 303	60.77 365	75.11 371

LCM-MVSNet		54.25 343	49.68 353	67.97 351	53.73 395	45.28 377	66.85 375	80.78 314	35.96 385	39.45 386	62.23 381	8.70 396	78.06 353	48.24 348	51.20 380	80.57 359

test_vis3_rt		49.26 353	47.02 355	56.00 366	54.30 392	45.27 378	66.76 376	48.08 396	36.83 383	44.38 383	53.20 388	7.17 399	64.07 389	56.77 305	55.66 373	58.65 385

testf1		45.72 354	41.96 357	57.00 364	56.90 389	45.32 375	66.14 377	59.26 389	26.19 390	30.89 389	60.96 383	4.14 400	70.64 382	26.39 388	46.73 385	55.04 387

APD_test2		45.72 354	41.96 357	57.00 364	56.90 389	45.32 375	66.14 377	59.26 389	26.19 390	30.89 389	60.96 383	4.14 400	70.64 382	26.39 388	46.73 385	55.04 387

JIA-IIPM		66.32 323	62.82 334	76.82 289	77.09 354	61.72 256	65.34 379	75.38 352	58.04 342	64.51 342	62.32 380	42.05 339	86.51 304	51.45 329	69.22 342	82.21 349

PMVS	37.38 22	44.16 357	40.28 360	55.82 368	40.82 400	42.54 386	65.12 380	63.99 383	34.43 386	24.48 392	57.12 387	3.92 402	76.17 366	17.10 394	55.52 374	48.75 389
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		50.91 351	50.29 351	52.78 372	68.58 381	34.94 394	63.71 381	56.63 392	39.73 380	44.95 382	65.47 378	21.93 382	58.48 391	34.98 379	56.62 371	64.92 380

mvsany_test3		53.99 344	51.45 349	61.61 360	55.51 391	44.74 380	63.52 382	45.41 399	43.69 376	58.11 367	76.45 358	17.99 385	63.76 390	54.77 313	47.59 383	76.34 369

Patchmatch-test		64.82 329	63.24 330	69.57 341	79.42 344	49.82 367	63.49 383	69.05 373	51.98 365	59.95 361	80.13 331	50.91 264	70.98 381	40.66 371	73.57 316	87.90 263

ambc				75.24 302	73.16 372	50.51 364	63.05 384	87.47 225		64.28 343	77.81 352	17.80 386	89.73 266	57.88 294	60.64 366	85.49 311

test_f		52.09 349	50.82 350	55.90 367	53.82 394	42.31 387	59.42 385	58.31 391	36.45 384	56.12 374	70.96 374	12.18 391	57.79 392	53.51 319	56.57 372	67.60 378

CHOSEN 280x420		66.51 321	64.71 322	71.90 327	81.45 315	63.52 226	57.98 386	68.95 374	53.57 359	62.59 353	76.70 356	46.22 309	75.29 373	55.25 311	79.68 237	76.88 368

E-PMN		31.77 359	30.64 362	35.15 377	52.87 396	27.67 396	57.09 387	47.86 397	24.64 392	16.40 397	33.05 393	11.23 393	54.90 394	14.46 397	18.15 394	22.87 393

EMVS		30.81 361	29.65 363	34.27 378	50.96 397	25.95 398	56.58 388	46.80 398	24.01 393	15.53 398	30.68 394	12.47 390	54.43 395	12.81 398	17.05 395	22.43 394

PMMVS2		40.82 358	38.86 361	46.69 374	53.84 393	16.45 403	48.61 389	49.92 394	37.49 382	31.67 387	60.97 382	8.14 398	56.42 393	28.42 385	30.72 391	67.19 379

wuyk23d		16.82 365	15.94 368	19.46 380	58.74 388	31.45 395	39.22 390	3.74 405	6.84 396	6.04 399	2.70 399	1.27 404	24.29 399	10.54 399	14.40 398	2.63 396

tmp_tt		18.61 364	21.40 367	10.23 381	4.82 403	10.11 404	34.70 391	30.74 402	1.48 398	23.91 394	26.07 395	28.42 374	13.41 400	27.12 386	15.35 397	7.17 395

Gipuma		45.18 356	41.86 359	55.16 370	77.03 355	51.52 358	32.50 392	80.52 318	32.46 388	27.12 391	35.02 392	9.52 395	75.50 369	22.31 391	60.21 368	38.45 391
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	26.22 23	30.37 362	25.89 366	43.81 375	44.55 399	35.46 393	28.87 393	39.07 400	18.20 394	18.58 396	40.18 391	2.68 403	47.37 397	17.07 395	23.78 393	48.60 390
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		31.52 360	29.28 364	38.23 376	27.03 402	6.50 406	20.94 394	62.21 385	4.05 397	22.35 395	52.50 389	13.33 389	47.58 396	27.04 387	34.04 389	60.62 383

test_blank		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uanet_test		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

DCPMVS		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

cdsmvs_eth3d_5k		19.96 363	26.61 365	0.00 384	0.00 406	0.00 409	0.00 395	89.26 173	0.00 402	0.00 403	88.61 176	61.62 161	0.00 403	0.00 402	0.00 401	0.00 399

pcd_1.5k_mvsjas		5.26 369	7.02 372	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	63.15 138	0.00 403	0.00 402	0.00 401	0.00 399

sosnet-low-res		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

sosnet		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uncertanet		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

Regformer		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

ab-mvs-re		7.23 366	9.64 369	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	86.72 226	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

uanet		0.00 370	0.00 373	0.00 384	0.00 406	0.00 409	0.00 395	0.00 408	0.00 402	0.00 403	0.00 402	0.00 407	0.00 403	0.00 402	0.00 401	0.00 399

WAC-MVS						42.58 384								39.46 373

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									68.21 240	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					0.00 406

eth-test					0.00 406

ZD-MVS					94.38 25	72.22 44		92.67 61	70.98 179	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	66.81 250	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																88.96 241

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 261				88.96 241

sam_mvs												50.01 274

MTGPA								92.02 85

test_post											5.46 397	50.36 272	84.24 321

patchmatchnet-post											74.00 367	51.12 263	88.60 286

gm-plane-assit					81.40 316	53.83 344			62.72 305		80.94 324		92.39 195	63.40 243

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	85.15 241	63.62 221		79.83 326	62.31 308	60.32 359	86.73 224	32.02 366	88.96 281	50.28 335	71.57 332	86.15 301

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	57.43 347	81.80 107	91.98 90	63.28 133	92.27 201	64.60 237	92.99 65	87.27 278

旧先验1					91.96 71	65.79 178		86.37 244			93.08 71	69.31 77			92.74 68	88.74 250

原ACMM1				84.35 107	93.01 57	68.79 106		92.44 69	63.96 292	81.09 117	91.57 101	66.06 111	95.45 63	67.19 216	94.82 45	88.81 247

testdata2													91.01 249	62.37 253

segment_acmp												73.08 37

testdata				79.97 241	90.90 86	64.21 212		84.71 263	59.27 331	85.40 51	92.91 73	62.02 157	89.08 277	68.95 199	91.37 86	86.63 295

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					90.08 102	68.51 119

plane_prior6					89.84 111	68.70 114						60.42 186

plane_prior5								92.44 69					95.38 69	78.71 102	86.32 151	91.33 149

plane_prior4											91.00 120

plane_prior3						68.60 117			78.44 31	78.92 141

plane_prior1					89.90 110

n2								0.00 408

nn								0.00 408

door-mid								69.98 369

lessismore_v0				78.97 258	81.01 323	57.15 307		65.99 378		61.16 356	82.82 305	39.12 349	91.34 238	59.67 275	46.92 384	88.43 256

LGP-MVS_train				84.50 99	89.23 134	68.76 108		91.94 91	75.37 94	76.64 196	91.51 102	54.29 228	94.91 88	78.44 104	83.78 181	89.83 214

test11								92.23 79

door								69.44 372

HQP5-MVS						66.98 155

BP-MVS														77.47 114

HQP4-MVS										77.24 181			95.11 80			91.03 161

HQP3-MVS								92.19 82							85.99 158

HQP2-MVS												60.17 189

NP-MVS					89.62 114	68.32 122					90.24 132

ACMMP++_ref															81.95 211

ACMMP++															81.25 217

Test By Simon												64.33 125

ITE_SJBPF				78.22 270	81.77 310	60.57 268		83.30 286	69.25 216	67.54 313	87.20 215	36.33 359	87.28 300	54.34 315	74.62 307	86.80 290

DeepMVS_CX				27.40 379	40.17 401	26.90 397		24.59 403	17.44 395	23.95 393	48.61 390	9.77 394	26.48 398	18.06 392	24.47 392	28.83 392

High-resolution multi-view benchmark