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 59	95.06 1	94.23 3	78.38 35	92.78 4	95.74 6	82.45 3	97.49 4	89.42 16	96.68 2	94.95 11

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 130	91.30 15

CP-MVS		87.11 33	86.92 38	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 78	83.68 101	94.46 29	67.93 104	95.95 57	84.20 69	94.39 55	93.23 101

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 98	91.06 16	96.03 1	76.84 14	97.03 17	89.09 18	95.65 27	94.47 39
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 72	72.96 25	93.73 5	93.67 20	80.19 12	88.10 34	94.80 21	73.76 33	97.11 15	87.51 39	95.82 21	94.90 14
Skip Steuart: Steuart Systems R&D Blog.
test0726					95.27 5	71.25 59	93.60 6	94.11 6	77.33 54	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 66	93.57 7	94.06 10	77.24 57	93.10 1	95.72 8	82.99 1	97.44 7	89.07 21	96.63 4	94.88 15

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 52	82.45 3	96.87 20	83.77 73	96.48 8	94.88 15

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 59	93.49 9	92.73 64	77.33 54	92.12 9	95.78 4	80.98 9	97.40 9	89.08 19	96.41 12	93.33 98
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 58	93.49 9	94.23 3					97.49 4	89.08 19	96.41 12	94.21 51

3Dnovator+	77.84 4	85.48 64	84.47 82	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 216	93.37 74	60.40 205	96.75 26	77.20 138	93.73 64	95.29 5

HFP-MVS		87.58 22	87.47 26	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 72	84.91 73	94.44 32	70.78 68	96.61 32	84.53 63	94.89 42	93.66 78

ACMMPR		87.44 25	87.23 31	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 72	84.66 80	94.52 25	68.81 94	96.65 30	84.53 63	94.90 41	94.00 61

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 70	85.24 68	94.32 37	71.76 53	96.93 19	85.53 52	95.79 22	94.32 47

region2R		87.42 27	87.20 32	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 75	84.45 85	94.52 25	69.09 88	96.70 27	84.37 65	94.83 45	94.03 59

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 45	78.35 13	96.77 24	89.59 14	94.22 60	94.67 28
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 39	86.95 37	85.90 71	90.76 96	67.57 149	92.83 17	93.30 32	79.67 18	84.57 84	92.27 98	71.47 58	95.02 93	84.24 68	93.46 67	95.13 8

XVS		87.18 32	86.91 39	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 23	83.67 102	94.17 44	67.45 109	96.60 33	83.06 78	94.50 51	94.07 57

X-MVStestdata		80.37 167	77.83 204	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 23	83.67 102	12.47 441	67.45 109	96.60 33	83.06 78	94.50 51	94.07 57

mPP-MVS		86.67 41	86.32 45	87.72 30	94.41 22	73.55 13	92.74 20	92.22 88	76.87 69	82.81 114	94.25 41	66.44 120	96.24 44	82.88 83	94.28 58	93.38 94

ACMMP		85.89 57	85.39 67	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 72	80.73 143	93.82 63	64.33 140	96.29 42	82.67 89	90.69 105	93.23 101
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 70	77.57 46	83.84 98	94.40 34	72.24 47	96.28 43	85.65 50	95.30 35	93.62 85
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 95	73.65 10	92.66 23	91.17 128	86.57 1	87.39 49	94.97 19	71.70 55	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 64	71.95 49	92.40 24	94.74 2	75.71 96	89.16 21	95.10 16	75.65 21	96.19 46	87.07 42	96.01 17	94.79 22

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 124	92.29 7	95.97 2	74.28 29	97.24 13	88.58 29	96.91 1	94.87 17
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
GST-MVS		87.42 27	87.26 29	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 68	84.68 77	93.99 56	70.67 70	96.82 22	84.18 70	95.01 37	93.90 67

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 59	80.26 11	87.78 40	94.27 39	75.89 19	96.81 23	87.45 40	96.44 9	93.05 114

SR-MVS		86.73 38	86.67 41	86.91 49	94.11 37	72.11 47	92.37 28	92.56 75	74.50 129	86.84 56	94.65 24	67.31 111	95.77 59	84.80 59	92.85 72	92.84 123

SPE-MVS-test		86.29 48	86.48 43	85.71 73	91.02 88	67.21 164	92.36 29	93.78 18	78.97 30	83.51 105	91.20 130	70.65 71	95.15 84	81.96 92	94.89 42	94.77 24

EC-MVSNet		86.01 50	86.38 44	84.91 98	89.31 139	66.27 177	92.32 30	93.63 21	79.37 22	84.17 91	91.88 106	69.04 92	95.43 70	83.93 72	93.77 63	93.01 117

EPP-MVSNet		83.40 101	83.02 101	84.57 107	90.13 107	64.47 223	92.32 30	90.73 140	74.45 132	79.35 158	91.10 133	69.05 91	95.12 85	72.78 186	87.22 160	94.13 54

PHI-MVS		86.43 44	86.17 51	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 178	85.22 69	91.90 105	69.47 83	96.42 40	83.28 77	95.94 19	94.35 45

HPM-MVS		87.11 33	86.98 36	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 91	83.81 99	93.95 59	69.77 80	96.01 53	85.15 53	94.66 47	94.32 47
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MTMP							92.18 34	32.83 446

HPM-MVS_fast		85.35 69	84.95 76	86.57 56	93.69 42	70.58 78	92.15 35	91.62 114	73.89 147	82.67 116	94.09 48	62.60 159	95.54 65	80.93 101	92.93 71	93.57 87

CPTT-MVS		83.73 90	83.33 97	84.92 97	93.28 49	70.86 72	92.09 36	90.38 150	68.75 262	79.57 155	92.83 88	60.60 201	93.04 187	80.92 102	91.56 92	90.86 189

APD-MVS_3200maxsize		85.97 53	85.88 57	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 157	85.94 60	94.51 28	65.80 130	95.61 62	83.04 80	92.51 77	93.53 91

SR-MVS-dyc-post		85.77 58	85.61 63	86.23 59	93.06 58	70.63 76	91.88 38	92.27 84	73.53 158	85.69 64	94.45 30	65.00 138	95.56 63	82.75 84	91.87 85	92.50 135

RE-MVS-def			85.48 66		93.06 58	70.63 76	91.88 38	92.27 84	73.53 158	85.69 64	94.45 30	63.87 144		82.75 84	91.87 85	92.50 135

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 172	88.58 26	94.52 25	73.36 34	96.49 38	84.26 66	95.01 37	92.70 125
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		88.06 14	88.50 14	86.71 54	92.60 69	72.71 29	91.81 41	93.19 35	77.87 39	90.32 17	94.00 54	74.83 23	93.78 143	87.63 38	94.27 59	93.65 82
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
reproduce_model		87.28 30	87.39 28	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 125	88.80 25	95.61 11	70.29 74	96.44 39	86.20 48	93.08 69	93.16 107

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 94	92.29 7	95.66 10	81.67 6	97.38 11	87.44 41	96.34 15	93.95 64
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
QAPM		80.88 146	79.50 164	85.03 91	88.01 194	68.97 107	91.59 43	92.00 96	66.63 291	75.15 260	92.16 100	57.70 219	95.45 68	63.52 266	88.76 137	90.66 198

IS-MVSNet		83.15 107	82.81 105	84.18 128	89.94 116	63.30 250	91.59 43	88.46 223	79.04 27	79.49 156	92.16 100	65.10 135	94.28 117	67.71 233	91.86 87	94.95 11

reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 116	88.96 22	95.54 12	71.20 63	96.54 36	86.28 46	93.49 65	93.06 112

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 116	88.96 22	95.54 12	71.20 63	96.54 36	86.28 46	93.49 65	93.06 112

9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 146	88.57 27	94.67 23	75.57 22	95.79 58	86.77 43	95.76 23

MVS_0304		87.69 20	87.55 24	88.12 13	89.45 130	71.76 51	91.47 49	89.54 181	82.14 3	86.65 57	94.28 38	68.28 101	97.46 6	90.81 5	95.31 34	95.15 7

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 82	74.62 128	88.90 24	93.85 62	75.75 20	96.00 54	87.80 36	94.63 48	95.04 9
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DeepC-MVS_fast	79.65 3	86.91 36	86.62 42	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 78	84.22 89	93.36 75	71.44 59	96.76 25	80.82 103	95.33 33	94.16 52
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 93	83.14 98	85.14 86	90.08 109	68.71 116	91.25 52	92.44 77	79.12 25	78.92 164	91.00 140	60.42 203	95.38 75	78.71 121	86.32 174	91.33 172

plane_prior2							91.25 52		79.12 25

NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 54	92.83 60	81.50 5	85.79 63	93.47 71	73.02 41	97.00 18	84.90 55	94.94 40	94.10 55

API-MVS		81.99 125	81.23 129	84.26 125	90.94 90	70.18 85	91.10 55	89.32 188	71.51 196	78.66 169	88.28 205	65.26 133	95.10 90	64.74 260	91.23 97	87.51 306

EPNet		83.72 91	82.92 104	86.14 65	84.22 296	69.48 94	91.05 56	85.27 282	81.30 6	76.83 211	91.65 113	66.09 125	95.56 63	76.00 152	93.85 62	93.38 94
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 57	93.59 23	76.27 88	88.14 33	95.09 17	71.06 65	96.67 29	87.67 37	96.37 14	94.09 56

CSCG		86.41 46	86.19 50	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 144	83.16 108	91.07 135	75.94 18	95.19 82	79.94 112	94.38 56	93.55 89

MSLP-MVS++		85.43 66	85.76 60	84.45 112	91.93 75	70.24 79	90.71 59	92.86 58	77.46 52	84.22 89	92.81 90	67.16 113	92.94 189	80.36 107	94.35 57	90.16 219

3Dnovator	76.31 5	83.38 102	82.31 114	86.59 55	87.94 196	72.94 28	90.64 60	92.14 93	77.21 59	75.47 242	92.83 88	58.56 212	94.72 105	73.24 182	92.71 75	92.13 153

OpenMVS	72.83 10	79.77 176	78.33 191	84.09 134	85.17 273	69.91 87	90.57 61	90.97 133	66.70 285	72.17 305	91.91 104	54.70 247	93.96 130	61.81 287	90.95 102	88.41 288

balanced_conf03		86.78 37	86.99 35	86.15 63	91.24 83	67.61 147	90.51 62	92.90 56	77.26 56	87.44 48	91.63 115	71.27 62	96.06 49	85.62 51	95.01 37	94.78 23

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 62	93.00 46	80.90 7	88.06 35	94.06 50	76.43 16	96.84 21	88.48 32	95.99 18	94.34 46

MVSFormer		82.85 113	82.05 119	85.24 84	87.35 218	70.21 80	90.50 64	90.38 150	68.55 265	81.32 132	89.47 172	61.68 175	93.46 160	78.98 118	90.26 112	92.05 155

test_djsdf		80.30 168	79.32 169	83.27 169	83.98 302	65.37 200	90.50 64	90.38 150	68.55 265	76.19 229	88.70 191	56.44 234	93.46 160	78.98 118	80.14 265	90.97 185

save fliter					93.80 40	72.35 42	90.47 66	91.17 128	74.31 135

nrg030		83.88 86	83.53 92	84.96 94	86.77 238	69.28 102	90.46 67	92.67 67	74.79 123	82.95 109	91.33 126	72.70 45	93.09 182	80.79 105	79.28 275	92.50 135

sasdasda		85.91 55	85.87 58	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 76	88.01 37	91.23 127	73.28 36	93.91 137	81.50 95	88.80 135	94.77 24

canonicalmvs		85.91 55	85.87 58	86.04 67	89.84 118	69.44 98	90.45 68	93.00 46	76.70 76	88.01 37	91.23 127	73.28 36	93.91 137	81.50 95	88.80 135	94.77 24

plane_prior						68.71 116	90.38 70		77.62 44						86.16 178

DeepC-MVS	79.81 2	87.08 35	86.88 40	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 62	82.82 113	94.23 42	72.13 49	97.09 16	84.83 58	95.37 31	93.65 82
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 99	82.80 106	85.43 79	90.25 105	68.74 114	90.30 72	90.13 162	76.33 87	80.87 140	92.89 86	61.00 192	94.20 122	72.45 191	90.97 101	93.35 97
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PGM-MVS		86.68 40	86.27 47	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 100	83.86 97	94.42 33	67.87 106	96.64 31	82.70 88	94.57 50	93.66 78

LPG-MVS_test		82.08 122	81.27 128	84.50 109	89.23 143	68.76 112	90.22 73	91.94 100	75.37 105	76.64 217	91.51 119	54.29 250	94.91 95	78.44 123	83.78 210	89.83 240

Anonymous20231211		78.97 199	77.69 212	82.81 193	90.54 99	64.29 227	90.11 75	91.51 118	65.01 311	76.16 233	88.13 214	50.56 296	93.03 188	69.68 216	77.56 294	91.11 178

ACMM	73.20 8	80.78 155	79.84 156	83.58 159	89.31 139	68.37 127	89.99 76	91.60 115	70.28 223	77.25 200	89.66 165	53.37 261	93.53 156	74.24 171	82.85 230	88.85 272
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	74.13 6	81.51 137	80.57 140	84.36 115	89.42 131	68.69 119	89.97 77	91.50 121	74.46 131	75.04 264	90.41 150	53.82 256	94.54 109	77.56 134	82.91 229	89.86 239
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LFMVS		81.82 128	81.23 129	83.57 160	91.89 76	63.43 248	89.84 78	81.85 334	77.04 65	83.21 106	93.10 79	52.26 270	93.43 162	71.98 192	89.95 119	93.85 69

MCST-MVS		87.37 29	87.25 30	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 170	84.86 76	92.89 86	76.22 17	96.33 41	84.89 57	95.13 36	94.40 42

MAR-MVS		81.84 127	80.70 137	85.27 83	91.32 82	71.53 56	89.82 79	90.92 134	69.77 237	78.50 173	86.21 266	62.36 165	94.52 111	65.36 254	92.05 83	89.77 243
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 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 113	86.34 59	95.29 15	70.86 67	96.00 54	88.78 27	96.04 16	94.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
UA-Net		85.08 74	84.96 75	85.45 78	92.07 73	68.07 135	89.78 82	90.86 138	82.48 2	84.60 83	93.20 78	69.35 84	95.22 81	71.39 197	90.88 103	93.07 111

alignmvs		85.48 64	85.32 70	85.96 70	89.51 127	69.47 95	89.74 83	92.47 76	76.17 89	87.73 44	91.46 122	70.32 73	93.78 143	81.51 94	88.95 132	94.63 32

VDDNet		81.52 135	80.67 138	84.05 141	90.44 101	64.13 230	89.73 84	85.91 275	71.11 203	83.18 107	93.48 69	50.54 297	93.49 157	73.40 179	88.25 146	94.54 37

CANet		86.45 43	86.10 53	87.51 37	90.09 108	70.94 70	89.70 85	92.59 74	81.78 4	81.32 132	91.43 123	70.34 72	97.23 14	84.26 66	93.36 68	94.37 44

test_fmvsmconf0.1_n		85.61 62	85.65 62	85.50 77	82.99 329	69.39 100	89.65 86	90.29 157	73.31 164	87.77 41	94.15 46	71.72 54	93.23 169	90.31 7	90.67 106	93.89 68

114514_t		80.68 156	79.51 163	84.20 127	94.09 38	67.27 160	89.64 87	91.11 131	58.75 377	74.08 279	90.72 144	58.10 215	95.04 92	69.70 215	89.42 127	90.30 215

MVSMamba_PlusPlus		85.99 51	85.96 56	86.05 66	91.09 85	67.64 146	89.63 88	92.65 70	72.89 175	84.64 81	91.71 111	71.85 51	96.03 50	84.77 60	94.45 54	94.49 38

test_fmvsmconf_n		85.92 54	86.04 55	85.57 76	85.03 280	69.51 93	89.62 89	90.58 143	73.42 161	87.75 42	94.02 52	72.85 43	93.24 168	90.37 6	90.75 104	93.96 62

fmvsm_l_conf0.5_n_3		86.02 49	86.32 45	85.14 86	87.20 227	68.54 123	89.57 90	90.44 148	75.31 107	87.49 46	94.39 35	72.86 42	92.72 195	89.04 23	90.56 107	94.16 52

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 53	92.24 71	69.03 103	89.57 90	93.39 30	77.53 50	89.79 19	94.12 47	78.98 12	96.58 35	85.66 49	95.72 24	94.58 33

test_fmvsmconf0.01_n		84.73 79	84.52 81	85.34 81	80.25 370	69.03 103	89.47 92	89.65 177	73.24 168	86.98 54	94.27 39	66.62 116	93.23 169	90.26 8	89.95 119	93.78 75

fmvsm_s_conf0.5_n		83.80 88	83.71 90	84.07 136	86.69 241	67.31 158	89.46 93	83.07 317	71.09 204	86.96 55	93.70 66	69.02 93	91.47 251	88.79 26	84.62 196	93.44 93

MGCFI-Net		85.06 75	85.51 65	83.70 155	89.42 131	63.01 256	89.43 94	92.62 73	76.43 80	87.53 45	91.34 125	72.82 44	93.42 163	81.28 98	88.74 138	94.66 31

fmvsm_s_conf0.5_n_a		83.63 94	83.41 94	84.28 121	86.14 251	68.12 133	89.43 94	82.87 322	70.27 224	87.27 51	93.80 64	69.09 88	91.58 241	88.21 34	83.65 217	93.14 109

UGNet		80.83 148	79.59 162	84.54 108	88.04 191	68.09 134	89.42 96	88.16 225	76.95 66	76.22 228	89.46 174	49.30 313	93.94 133	68.48 228	90.31 110	91.60 162
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		78.73 203	77.83 204	81.43 224	85.17 273	60.30 296	89.41 97	90.90 135	71.21 201	77.17 207	88.73 190	46.38 333	93.21 171	72.57 189	78.96 277	90.79 191

fmvsm_s_conf0.1_n		83.56 96	83.38 95	84.10 130	84.86 282	67.28 159	89.40 98	83.01 318	70.67 212	87.08 52	93.96 58	68.38 99	91.45 252	88.56 30	84.50 197	93.56 88

BP-MVS1		84.32 81	83.71 90	86.17 61	87.84 201	67.85 140	89.38 99	89.64 178	77.73 42	83.98 95	92.12 102	56.89 230	95.43 70	84.03 71	91.75 88	95.24 6

AdaColmap		80.58 162	79.42 165	84.06 138	93.09 57	68.91 108	89.36 100	88.97 207	69.27 246	75.70 238	89.69 163	57.20 227	95.77 59	63.06 271	88.41 145	87.50 307

fmvsm_s_conf0.1_n_a		83.32 104	82.99 102	84.28 121	83.79 306	68.07 135	89.34 101	82.85 323	69.80 235	87.36 50	94.06 50	68.34 100	91.56 244	87.95 35	83.46 223	93.21 104

PS-MVSNAJss		82.07 123	81.31 127	84.34 117	86.51 245	67.27 160	89.27 102	91.51 118	71.75 189	79.37 157	90.22 155	63.15 153	94.27 118	77.69 133	82.36 237	91.49 168

jajsoiax		79.29 190	77.96 198	83.27 169	84.68 287	66.57 173	89.25 103	90.16 161	69.20 251	75.46 244	89.49 171	45.75 344	93.13 180	76.84 143	80.80 255	90.11 223

fmvsm_s_conf0.5_n_8		86.56 42	87.17 33	84.73 104	87.76 208	65.62 193	89.20 104	92.21 89	79.94 16	89.74 20	94.86 20	68.63 96	94.20 122	90.83 4	91.39 94	94.38 43

fmvsm_s_conf0.5_n_5		85.22 71	85.55 64	84.25 126	86.26 247	67.40 155	89.18 105	89.31 189	72.50 177	88.31 29	93.86 61	69.66 81	91.96 226	89.81 10	91.05 99	93.38 94

mvs_tets		79.13 194	77.77 208	83.22 173	84.70 286	66.37 175	89.17 106	90.19 160	69.38 244	75.40 247	89.46 174	44.17 356	93.15 178	76.78 145	80.70 257	90.14 220

HQP-NCC					89.33 136		89.17 106		76.41 81	77.23 202

ACMP_Plane					89.33 136		89.17 106		76.41 81	77.23 202

HQP-MVS		82.61 116	82.02 120	84.37 114	89.33 136	66.98 167	89.17 106	92.19 91	76.41 81	77.23 202	90.23 154	60.17 206	95.11 87	77.47 135	85.99 182	91.03 182

LS3D		76.95 246	74.82 264	83.37 166	90.45 100	67.36 157	89.15 110	86.94 257	61.87 350	69.52 335	90.61 146	51.71 284	94.53 110	46.38 395	86.71 169	88.21 292

GDP-MVS		83.52 97	82.64 108	86.16 62	88.14 185	68.45 125	89.13 111	92.69 65	72.82 176	83.71 100	91.86 108	55.69 237	95.35 79	80.03 110	89.74 123	94.69 27

OPM-MVS		83.50 98	82.95 103	85.14 86	88.79 160	70.95 69	89.13 111	91.52 117	77.55 49	80.96 139	91.75 110	60.71 195	94.50 112	79.67 115	86.51 172	89.97 235
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
fmvsm_s_conf0.5_n_3		86.36 47	87.46 27	83.09 178	87.08 231	65.21 202	89.09 113	90.21 159	79.67 18	89.98 18	95.02 18	73.17 38	91.71 238	91.30 2	91.60 89	92.34 141

TSAR-MVS + GP.		85.71 60	85.33 69	86.84 50	91.34 81	72.50 36	89.07 114	87.28 248	76.41 81	85.80 62	90.22 155	74.15 31	95.37 78	81.82 93	91.88 84	92.65 129

test_prior4						72.60 34	89.01 115

GeoE		81.71 130	81.01 134	83.80 154	89.51 127	64.45 224	88.97 116	88.73 218	71.27 200	78.63 170	89.76 162	66.32 122	93.20 174	69.89 213	86.02 181	93.74 76

Anonymous20240529		80.19 171	78.89 179	84.10 130	90.60 97	64.75 217	88.95 117	90.90 135	65.97 299	80.59 144	91.17 132	49.97 303	93.73 149	69.16 221	82.70 234	93.81 73

VDD-MVS		83.01 112	82.36 113	84.96 94	91.02 88	66.40 174	88.91 118	88.11 226	77.57 46	84.39 87	93.29 76	52.19 271	93.91 137	77.05 141	88.70 139	94.57 35

Effi-MVS+		83.62 95	83.08 99	85.24 84	88.38 176	67.45 152	88.89 119	89.15 198	75.50 101	82.27 117	88.28 205	69.61 82	94.45 114	77.81 131	87.84 150	93.84 71

fmvsm_s_conf0.5_n_6		85.55 63	86.20 48	83.60 157	87.32 224	65.13 205	88.86 120	91.63 113	75.41 103	88.23 32	93.45 72	68.56 97	92.47 206	89.52 15	92.78 73	93.20 105

ACMH+	68.96 14	76.01 264	74.01 274	82.03 212	88.60 167	65.31 201	88.86 120	87.55 242	70.25 225	67.75 349	87.47 229	41.27 374	93.19 176	58.37 319	75.94 318	87.60 303

test_prior2							88.85 122		75.41 103	84.91 73	93.54 67	74.28 29		83.31 76	95.86 20

DP-MVS Recon		83.11 110	82.09 118	86.15 63	94.44 19	70.92 71	88.79 123	92.20 90	70.53 217	79.17 160	91.03 138	64.12 142	96.03 50	68.39 230	90.14 114	91.50 167

fmvsm_s_conf0.5_n_4		85.39 68	85.75 61	84.30 119	86.70 240	65.83 186	88.77 124	89.78 171	75.46 102	88.35 28	93.73 65	69.19 87	93.06 184	91.30 2	88.44 144	94.02 60

Effi-MVS+-dtu		80.03 173	78.57 184	84.42 113	85.13 277	68.74 114	88.77 124	88.10 227	74.99 115	74.97 266	83.49 331	57.27 225	93.36 164	73.53 176	80.88 253	91.18 176

TEST9					93.26 52	72.96 25	88.75 126	91.89 102	68.44 268	85.00 71	93.10 79	74.36 28	95.41 73

train_agg		86.43 44	86.20 48	87.13 44	93.26 52	72.96 25	88.75 126	91.89 102	68.69 263	85.00 71	93.10 79	74.43 26	95.41 73	84.97 54	95.71 25	93.02 116

ETV-MVS		84.90 78	84.67 78	85.59 75	89.39 134	68.66 120	88.74 128	92.64 72	79.97 15	84.10 92	85.71 275	69.32 85	95.38 75	80.82 103	91.37 95	92.72 124

PVSNet_Blended_VisFu		82.62 115	81.83 124	84.96 94	90.80 94	69.76 90	88.74 128	91.70 111	69.39 243	78.96 162	88.46 200	65.47 132	94.87 100	74.42 168	88.57 140	90.24 217

casdiffmvs_mvg		85.99 51	86.09 54	85.70 74	87.65 212	67.22 163	88.69 130	93.04 41	79.64 20	85.33 67	92.54 95	73.30 35	94.50 112	83.49 74	91.14 98	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 54	72.57 35	88.68 131	91.84 106	68.69 263	84.87 75	93.10 79	74.43 26	95.16 83

test_fmvsm_n_1920		85.29 70	85.34 68	85.13 89	86.12 252	69.93 86	88.65 132	90.78 139	69.97 231	88.27 30	93.98 57	71.39 60	91.54 246	88.49 31	90.45 109	93.91 65

ACMH	67.68 16	75.89 265	73.93 276	81.77 217	88.71 164	66.61 172	88.62 133	89.01 204	69.81 234	66.78 363	86.70 251	41.95 372	91.51 249	55.64 342	78.14 286	87.17 315
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CDPH-MVS		85.76 59	85.29 72	87.17 43	93.49 47	71.08 64	88.58 134	92.42 80	68.32 270	84.61 82	93.48 69	72.32 46	96.15 48	79.00 117	95.43 30	94.28 49

DP-MVS		76.78 249	74.57 266	83.42 163	93.29 48	69.46 97	88.55 135	83.70 303	63.98 326	70.20 323	88.89 187	54.01 255	94.80 102	46.66 392	81.88 243	86.01 341

fmvsm_l_conf0.5_n		84.47 80	84.54 79	84.27 123	85.42 267	68.81 109	88.49 136	87.26 250	68.08 272	88.03 36	93.49 68	72.04 50	91.77 234	88.90 25	89.14 131	92.24 148

WR-MVS_H		78.51 210	78.49 185	78.56 289	88.02 192	56.38 347	88.43 137	92.67 67	77.14 61	73.89 281	87.55 226	66.25 123	89.24 298	58.92 312	73.55 351	90.06 229

F-COLMAP		76.38 259	74.33 272	82.50 205	89.28 141	66.95 170	88.41 138	89.03 202	64.05 324	66.83 362	88.61 195	46.78 330	92.89 190	57.48 326	78.55 279	87.67 301

GBi-Net		78.40 211	77.40 217	81.40 226	87.60 213	63.01 256	88.39 139	89.28 190	71.63 191	75.34 250	87.28 231	54.80 243	91.11 261	62.72 273	79.57 269	90.09 225

test1		78.40 211	77.40 217	81.40 226	87.60 213	63.01 256	88.39 139	89.28 190	71.63 191	75.34 250	87.28 231	54.80 243	91.11 261	62.72 273	79.57 269	90.09 225

FMVSNet1		77.44 237	76.12 244	81.40 226	86.81 237	63.01 256	88.39 139	89.28 190	70.49 218	74.39 276	87.28 231	49.06 317	91.11 261	60.91 294	78.52 280	90.09 225

tttt0517		79.40 187	77.91 200	83.90 150	88.10 188	63.84 236	88.37 142	84.05 299	71.45 197	76.78 213	89.12 181	49.93 306	94.89 98	70.18 209	83.18 227	92.96 120

fmvsm_l_conf0.5_n_a		84.13 83	84.16 84	84.06 138	85.38 268	68.40 126	88.34 143	86.85 260	67.48 279	87.48 47	93.40 73	70.89 66	91.61 239	88.38 33	89.22 129	92.16 152

v7n		78.97 199	77.58 215	83.14 176	83.45 314	65.51 195	88.32 144	91.21 126	73.69 152	72.41 301	86.32 265	57.93 216	93.81 142	69.18 220	75.65 321	90.11 223

COLMAP_ROB	66.92 17	73.01 304	70.41 319	80.81 244	87.13 230	65.63 192	88.30 145	84.19 298	62.96 335	63.80 389	87.69 221	38.04 392	92.56 201	46.66 392	74.91 338	84.24 368
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FIs		82.07 123	82.42 110	81.04 238	88.80 159	58.34 314	88.26 146	93.49 26	76.93 67	78.47 175	91.04 136	69.92 78	92.34 214	69.87 214	84.97 191	92.44 139

EIA-MVS		83.31 105	82.80 106	84.82 100	89.59 123	65.59 194	88.21 147	92.68 66	74.66 127	78.96 162	86.42 262	69.06 90	95.26 80	75.54 158	90.09 115	93.62 85

PLC	70.83 11	78.05 222	76.37 242	83.08 180	91.88 77	67.80 142	88.19 148	89.46 184	64.33 319	69.87 332	88.38 202	53.66 257	93.58 151	58.86 313	82.73 232	87.86 298
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MG-MVS		83.41 100	83.45 93	83.28 168	92.74 65	62.28 269	88.17 149	89.50 183	75.22 108	81.49 130	92.74 94	66.75 114	95.11 87	72.85 185	91.58 91	92.45 138

TAPA-MVS	73.13 9	79.15 193	77.94 199	82.79 197	89.59 123	62.99 260	88.16 150	91.51 118	65.77 300	77.14 208	91.09 134	60.91 193	93.21 171	50.26 374	87.05 162	92.17 151
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_fmvsmvis_n_1920		84.02 85	83.87 87	84.49 111	84.12 298	69.37 101	88.15 151	87.96 231	70.01 229	83.95 96	93.23 77	68.80 95	91.51 249	88.61 28	89.96 118	92.57 130

h-mvs33		83.15 107	82.19 115	86.02 69	90.56 98	70.85 73	88.15 151	89.16 197	76.02 92	84.67 78	91.39 124	61.54 178	95.50 66	82.71 86	75.48 325	91.72 161

KinetiMVS		83.31 105	82.61 109	85.39 80	87.08 231	67.56 150	88.06 153	91.65 112	77.80 41	82.21 119	91.79 109	57.27 225	94.07 128	77.77 132	89.89 121	94.56 36

PS-CasMVS		78.01 224	78.09 196	77.77 306	87.71 209	54.39 372	88.02 154	91.22 125	77.50 51	73.26 289	88.64 194	60.73 194	88.41 315	61.88 285	73.88 348	90.53 204

OMC-MVS		82.69 114	81.97 122	84.85 99	88.75 162	67.42 153	87.98 155	90.87 137	74.92 119	79.72 153	91.65 113	62.19 169	93.96 130	75.26 162	86.42 173	93.16 107

v8		79.97 175	79.02 177	82.80 194	84.09 299	64.50 222	87.96 156	90.29 157	74.13 142	75.24 257	86.81 244	62.88 158	93.89 140	74.39 169	75.40 330	90.00 231

FC-MVSNet-test		81.52 135	82.02 120	80.03 261	88.42 175	55.97 353	87.95 157	93.42 29	77.10 63	77.38 197	90.98 142	69.96 77	91.79 233	68.46 229	84.50 197	92.33 142

CP-MVSNet		78.22 215	78.34 190	77.84 304	87.83 202	54.54 370	87.94 158	91.17 128	77.65 43	73.48 287	88.49 199	62.24 168	88.43 314	62.19 281	74.07 344	90.55 203

PAPM_NR		83.02 111	82.41 111	84.82 100	92.47 70	66.37 175	87.93 159	91.80 107	73.82 148	77.32 199	90.66 145	67.90 105	94.90 97	70.37 207	89.48 126	93.19 106

PEN-MVS		77.73 230	77.69 212	77.84 304	87.07 233	53.91 375	87.91 160	91.18 127	77.56 48	73.14 291	88.82 189	61.23 187	89.17 300	59.95 301	72.37 359	90.43 208

ECVR-MVS		79.61 178	79.26 171	80.67 247	90.08 109	54.69 368	87.89 161	77.44 380	74.88 120	80.27 146	92.79 91	48.96 319	92.45 207	68.55 227	92.50 78	94.86 18

v10		79.74 177	78.67 181	82.97 187	84.06 300	64.95 211	87.88 162	90.62 142	73.11 169	75.11 261	86.56 258	61.46 181	94.05 129	73.68 174	75.55 323	89.90 237

test2506		77.30 241	76.49 238	79.74 267	90.08 109	52.02 385	87.86 163	63.10 427	74.88 120	80.16 149	92.79 91	38.29 391	92.35 213	68.74 226	92.50 78	94.86 18

casdiffmvs		85.11 73	85.14 73	85.01 92	87.20 227	65.77 190	87.75 164	92.83 60	77.84 40	84.36 88	92.38 97	72.15 48	93.93 136	81.27 99	90.48 108	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		80.84 147	80.31 146	82.42 206	87.85 200	62.33 267	87.74 165	91.33 123	80.55 9	77.99 187	89.86 159	65.23 134	92.62 196	67.05 242	75.24 335	92.30 144

EI-MVSNet-Vis-set		84.19 82	83.81 88	85.31 82	88.18 182	67.85 140	87.66 166	89.73 175	80.05 14	82.95 109	89.59 169	70.74 69	94.82 101	80.66 106	84.72 194	93.28 100

UniMVSNet (Re)		81.60 134	81.11 131	83.09 178	88.38 176	64.41 225	87.60 167	93.02 45	78.42 34	78.56 172	88.16 209	69.78 79	93.26 167	69.58 217	76.49 307	91.60 162

CNLPA		78.08 220	76.79 231	81.97 214	90.40 102	71.07 65	87.59 168	84.55 291	66.03 298	72.38 302	89.64 166	57.56 221	86.04 340	59.61 305	83.35 224	88.79 275

DTE-MVSNet		76.99 244	76.80 230	77.54 312	86.24 248	53.06 384	87.52 169	90.66 141	77.08 64	72.50 299	88.67 193	60.48 202	89.52 292	57.33 329	70.74 371	90.05 230

无先验							87.48 170	88.98 205	60.00 363				94.12 126	67.28 238		88.97 267

mvsmamba		80.60 159	79.38 166	84.27 123	89.74 121	67.24 162	87.47 171	86.95 256	70.02 228	75.38 248	88.93 185	51.24 288	92.56 201	75.47 160	89.22 129	93.00 118

FMVSNet2		78.20 217	77.21 221	81.20 233	87.60 213	62.89 262	87.47 171	89.02 203	71.63 191	75.29 256	87.28 231	54.80 243	91.10 264	62.38 278	79.38 273	89.61 247

RRT-MVS		82.60 118	82.10 117	84.10 130	87.98 195	62.94 261	87.45 173	91.27 124	77.42 53	79.85 151	90.28 151	56.62 233	94.70 107	79.87 113	88.15 148	94.67 28

EI-MVSNet-UG-set		83.81 87	83.38 95	85.09 90	87.87 199	67.53 151	87.44 174	89.66 176	79.74 17	82.23 118	89.41 178	70.24 75	94.74 104	79.95 111	83.92 209	92.99 119

thisisatest0530		79.40 187	77.76 209	84.31 118	87.69 211	65.10 208	87.36 175	84.26 297	70.04 227	77.42 196	88.26 207	49.94 304	94.79 103	70.20 208	84.70 195	93.03 115

CANet_DTU		80.61 158	79.87 155	82.83 191	85.60 264	63.17 255	87.36 175	88.65 219	76.37 85	75.88 235	88.44 201	53.51 259	93.07 183	73.30 180	89.74 123	92.25 146

test1111		79.43 185	79.18 174	80.15 259	89.99 114	53.31 381	87.33 177	77.05 384	75.04 114	80.23 148	92.77 93	48.97 318	92.33 215	68.87 224	92.40 80	94.81 21

baseline		84.93 76	84.98 74	84.80 102	87.30 225	65.39 199	87.30 178	92.88 57	77.62 44	84.04 94	92.26 99	71.81 52	93.96 130	81.31 97	90.30 111	95.03 10

UniMVSNet_ETH3D		79.10 195	78.24 193	81.70 218	86.85 235	60.24 297	87.28 179	88.79 212	74.25 138	76.84 210	90.53 149	49.48 309	91.56 244	67.98 231	82.15 238	93.29 99

anonymousdsp		78.60 207	77.15 222	82.98 186	80.51 368	67.08 165	87.24 180	89.53 182	65.66 302	75.16 259	87.19 237	52.52 265	92.25 217	77.17 139	79.34 274	89.61 247

UniMVSNet_NR-MVSNet		81.88 126	81.54 126	82.92 188	88.46 172	63.46 246	87.13 181	92.37 81	80.19 12	78.38 176	89.14 180	71.66 57	93.05 185	70.05 210	76.46 308	92.25 146

DPM-MVS		84.93 76	84.29 83	86.84 50	90.20 106	73.04 23	87.12 182	93.04 41	69.80 235	82.85 112	91.22 129	73.06 40	96.02 52	76.72 146	94.63 48	91.46 171

v1144		80.03 173	79.03 176	83.01 184	83.78 307	64.51 220	87.11 183	90.57 145	71.96 188	78.08 185	86.20 267	61.41 182	93.94 133	74.93 164	77.23 295	90.60 201

v2v482		80.23 169	79.29 170	83.05 182	83.62 310	64.14 229	87.04 184	89.97 166	73.61 154	78.18 182	87.22 235	61.10 190	93.82 141	76.11 149	76.78 304	91.18 176

fmvsm_s_conf0.1_n_2		83.80 88	83.79 89	83.83 151	85.62 263	64.94 212	87.03 185	86.62 264	74.32 134	87.97 39	94.33 36	60.67 197	92.60 198	89.72 11	87.79 151	93.96 62

DU-MVS		81.12 143	80.52 142	82.90 189	87.80 203	63.46 246	87.02 186	91.87 104	79.01 28	78.38 176	89.07 182	65.02 136	93.05 185	70.05 210	76.46 308	92.20 149

LuminaMVS		80.68 156	79.62 161	83.83 151	85.07 279	68.01 138	86.99 187	88.83 210	70.36 219	81.38 131	87.99 216	50.11 301	92.51 205	79.02 116	86.89 166	90.97 185

fmvsm_s_conf0.5_n_2		84.04 84	84.11 85	83.81 153	86.17 250	65.00 210	86.96 188	87.28 248	74.35 133	88.25 31	94.23 42	61.82 173	92.60 198	89.85 9	88.09 149	93.84 71

v144192		79.47 183	78.37 189	82.78 198	83.35 315	63.96 232	86.96 188	90.36 153	69.99 230	77.50 194	85.67 278	60.66 198	93.77 145	74.27 170	76.58 305	90.62 199

Fast-Effi-MVS+-dtu		78.02 223	76.49 238	82.62 202	83.16 323	66.96 169	86.94 190	87.45 246	72.45 178	71.49 313	84.17 315	54.79 246	91.58 241	67.61 234	80.31 262	89.30 255

v1192		79.59 180	78.43 188	83.07 181	83.55 312	64.52 219	86.93 191	90.58 143	70.83 208	77.78 190	85.90 271	59.15 209	93.94 133	73.96 173	77.19 297	90.76 193

EPNet_dtu		75.46 271	74.86 263	77.23 316	82.57 338	54.60 369	86.89 192	83.09 316	71.64 190	66.25 372	85.86 273	55.99 235	88.04 319	54.92 346	86.55 171	89.05 262
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM2							86.86 193

VPA-MVSNet		80.60 159	80.55 141	80.76 245	88.07 190	60.80 288	86.86 193	91.58 116	75.67 99	80.24 147	89.45 176	63.34 147	90.25 279	70.51 206	79.22 276	91.23 175

v1921920		79.22 191	78.03 197	82.80 194	83.30 317	63.94 234	86.80 195	90.33 154	69.91 233	77.48 195	85.53 282	58.44 213	93.75 147	73.60 175	76.85 302	90.71 197

IterMVS-LS		80.06 172	79.38 166	82.11 210	85.89 256	63.20 253	86.79 196	89.34 187	74.19 139	75.45 245	86.72 247	66.62 116	92.39 210	72.58 188	76.86 301	90.75 194
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		75.39 275	74.56 267	77.86 303	85.50 266	57.10 335	86.78 197	86.09 274	72.17 184	71.53 312	87.34 230	63.01 157	89.31 296	56.84 335	61.83 400	87.17 315

Baseline_NR-MVSNet		78.15 219	78.33 191	77.61 309	85.79 258	56.21 351	86.78 197	85.76 278	73.60 155	77.93 188	87.57 224	65.02 136	88.99 303	67.14 241	75.33 332	87.63 302

PAPR		81.66 133	80.89 136	83.99 146	90.27 104	64.00 231	86.76 199	91.77 110	68.84 261	77.13 209	89.50 170	67.63 107	94.88 99	67.55 235	88.52 142	93.09 110

Vis-MVSNet (Re-imp)		78.36 213	78.45 186	78.07 300	88.64 166	51.78 391	86.70 200	79.63 362	74.14 141	75.11 261	90.83 143	61.29 186	89.75 288	58.10 322	91.60 89	92.69 127

guyue		81.13 142	80.64 139	82.60 203	86.52 244	63.92 235	86.69 201	87.73 239	73.97 143	80.83 142	89.69 163	56.70 231	91.33 257	78.26 130	85.40 188	92.54 132

pmmvs6		74.69 280	73.39 283	78.61 286	81.38 357	57.48 330	86.64 202	87.95 232	64.99 312	70.18 324	86.61 254	50.43 298	89.52 292	62.12 283	70.18 374	88.83 273

v1240		78.99 198	77.78 207	82.64 201	83.21 319	63.54 243	86.62 203	90.30 156	69.74 240	77.33 198	85.68 277	57.04 228	93.76 146	73.13 183	76.92 299	90.62 199

MTAPA		87.23 31	87.00 34	87.90 22	94.18 35	74.25 5	86.58 204	92.02 94	79.45 21	85.88 61	94.80 21	68.07 102	96.21 45	86.69 44	95.34 32	93.23 101

旧先验2							86.56 205		58.10 382	87.04 53			88.98 304	74.07 172

FMVSNet3		77.88 227	76.85 229	80.97 241	86.84 236	62.36 266	86.52 206	88.77 213	71.13 202	75.34 250	86.66 253	54.07 253	91.10 264	62.72 273	79.57 269	89.45 251

dcpmvs_2		85.63 61	86.15 52	84.06 138	91.71 78	64.94 212	86.47 207	91.87 104	73.63 153	86.60 58	93.02 84	76.57 15	91.87 232	83.36 75	92.15 81	95.35 3

AstraMVS		80.81 149	80.14 150	82.80 194	86.05 255	63.96 232	86.46 208	85.90 276	73.71 151	80.85 141	90.56 147	54.06 254	91.57 243	79.72 114	83.97 208	92.86 122

pm-mvs1		77.25 242	76.68 236	78.93 282	84.22 296	58.62 311	86.41 209	88.36 224	71.37 198	73.31 288	88.01 215	61.22 188	89.15 301	64.24 264	73.01 356	89.03 263

EI-MVSNet		80.52 163	79.98 152	82.12 209	84.28 294	63.19 254	86.41 209	88.95 208	74.18 140	78.69 167	87.54 227	66.62 116	92.43 208	72.57 189	80.57 259	90.74 195

CVMVSNet		72.99 305	72.58 294	74.25 347	84.28 294	50.85 399	86.41 209	83.45 309	44.56 418	73.23 290	87.54 227	49.38 311	85.70 343	65.90 250	78.44 282	86.19 336

MonoMVSNet		76.49 256	75.80 245	78.58 288	81.55 353	58.45 312	86.36 212	86.22 270	74.87 122	74.73 270	83.73 324	51.79 283	88.73 309	70.78 201	72.15 362	88.55 285

NR-MVSNet		80.23 169	79.38 166	82.78 198	87.80 203	63.34 249	86.31 213	91.09 132	79.01 28	72.17 305	89.07 182	67.20 112	92.81 194	66.08 249	75.65 321	92.20 149

v148		78.72 204	77.80 206	81.47 223	82.73 334	61.96 273	86.30 214	88.08 228	73.26 166	76.18 230	85.47 284	62.46 163	92.36 212	71.92 193	73.82 349	90.09 225

新几何2							86.29 215

test_yl		81.17 140	80.47 143	83.24 171	89.13 147	63.62 239	86.21 216	89.95 167	72.43 181	81.78 127	89.61 167	57.50 222	93.58 151	70.75 202	86.90 164	92.52 133

DCV-MVSNet		81.17 140	80.47 143	83.24 171	89.13 147	63.62 239	86.21 216	89.95 167	72.43 181	81.78 127	89.61 167	57.50 222	93.58 151	70.75 202	86.90 164	92.52 133

PVSNet_BlendedMVS		80.60 159	80.02 151	82.36 208	88.85 154	65.40 197	86.16 218	92.00 96	69.34 245	78.11 183	86.09 270	66.02 127	94.27 118	71.52 194	82.06 240	87.39 308

MVS_Test		83.15 107	83.06 100	83.41 165	86.86 234	63.21 252	86.11 219	92.00 96	74.31 135	82.87 111	89.44 177	70.03 76	93.21 171	77.39 137	88.50 143	93.81 73

BH-untuned		79.47 183	78.60 183	82.05 211	89.19 145	65.91 184	86.07 220	88.52 222	72.18 183	75.42 246	87.69 221	61.15 189	93.54 155	60.38 298	86.83 167	86.70 329

MVS_111021_HR		85.14 72	84.75 77	86.32 58	91.65 79	72.70 30	85.98 221	90.33 154	76.11 90	82.08 121	91.61 117	71.36 61	94.17 125	81.02 100	92.58 76	92.08 154

jason		81.39 138	80.29 147	84.70 105	86.63 243	69.90 88	85.95 222	86.77 261	63.24 330	81.07 138	89.47 172	61.08 191	92.15 220	78.33 126	90.07 117	92.05 155
jason: jason.
test_0402		72.79 307	70.44 318	79.84 265	88.13 186	65.99 182	85.93 223	84.29 295	65.57 303	67.40 356	85.49 283	46.92 329	92.61 197	35.88 420	74.38 343	80.94 399

OurMVSNet-221017-0		74.26 283	72.42 296	79.80 266	83.76 308	59.59 304	85.92 224	86.64 262	66.39 293	66.96 360	87.58 223	39.46 382	91.60 240	65.76 252	69.27 377	88.22 291

hse-mvs2		81.72 129	80.94 135	84.07 136	88.72 163	67.68 145	85.87 225	87.26 250	76.02 92	84.67 78	88.22 208	61.54 178	93.48 158	82.71 86	73.44 353	91.06 180

EG-PatchMatch MVS		74.04 287	71.82 301	80.71 246	84.92 281	67.42 153	85.86 226	88.08 228	66.04 297	64.22 384	83.85 319	35.10 402	92.56 201	57.44 327	80.83 254	82.16 393

AUN-MVS		79.21 192	77.60 214	84.05 141	88.71 164	67.61 147	85.84 227	87.26 250	69.08 254	77.23 202	88.14 213	53.20 263	93.47 159	75.50 159	73.45 352	91.06 180

thres100view900		76.50 253	75.55 252	79.33 275	89.52 126	56.99 336	85.83 228	83.23 312	73.94 145	76.32 226	87.12 239	51.89 280	91.95 227	48.33 383	83.75 213	89.07 257

CLD-MVS		82.31 119	81.65 125	84.29 120	88.47 171	67.73 144	85.81 229	92.35 82	75.78 95	78.33 178	86.58 257	64.01 143	94.35 115	76.05 151	87.48 156	90.79 191
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VortexMVS		78.57 209	77.89 202	80.59 248	85.89 256	62.76 263	85.61 230	89.62 179	72.06 186	74.99 265	85.38 286	55.94 236	90.77 273	74.99 163	76.58 305	88.23 290

SixPastTwentyTwo		73.37 296	71.26 310	79.70 268	85.08 278	57.89 322	85.57 231	83.56 306	71.03 206	65.66 374	85.88 272	42.10 370	92.57 200	59.11 310	63.34 396	88.65 281

xiu_mvs_v1_base_debu		80.80 152	79.72 158	84.03 143	87.35 218	70.19 82	85.56 232	88.77 213	69.06 255	81.83 123	88.16 209	50.91 291	92.85 191	78.29 127	87.56 153	89.06 259

xiu_mvs_v1_base		80.80 152	79.72 158	84.03 143	87.35 218	70.19 82	85.56 232	88.77 213	69.06 255	81.83 123	88.16 209	50.91 291	92.85 191	78.29 127	87.56 153	89.06 259

xiu_mvs_v1_base_debi		80.80 152	79.72 158	84.03 143	87.35 218	70.19 82	85.56 232	88.77 213	69.06 255	81.83 123	88.16 209	50.91 291	92.85 191	78.29 127	87.56 153	89.06 259

V42		79.38 189	78.24 193	82.83 191	81.10 362	65.50 196	85.55 235	89.82 170	71.57 195	78.21 180	86.12 269	60.66 198	93.18 177	75.64 155	75.46 327	89.81 242

lupinMVS		81.39 138	80.27 148	84.76 103	87.35 218	70.21 80	85.55 235	86.41 266	62.85 337	81.32 132	88.61 195	61.68 175	92.24 218	78.41 125	90.26 112	91.83 158

Fast-Effi-MVS+		80.81 149	79.92 153	83.47 161	88.85 154	64.51 220	85.53 237	89.39 186	70.79 209	78.49 174	85.06 295	67.54 108	93.58 151	67.03 243	86.58 170	92.32 143

thres600view7		76.50 253	75.44 253	79.68 269	89.40 133	57.16 333	85.53 237	83.23 312	73.79 149	76.26 227	87.09 240	51.89 280	91.89 230	48.05 388	83.72 216	90.00 231

DELS-MVS		85.41 67	85.30 71	85.77 72	88.49 170	67.93 139	85.52 239	93.44 27	78.70 31	83.63 104	89.03 184	74.57 24	95.71 61	80.26 109	94.04 61	93.66 78
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
fmvsm_s_conf0.5_n_7		83.34 103	84.03 86	81.28 230	85.73 260	65.13 205	85.40 240	89.90 169	74.96 118	82.13 120	93.89 60	66.65 115	87.92 320	86.56 45	91.05 99	90.80 190

tfpn200view9		76.42 257	75.37 257	79.55 274	89.13 147	57.65 327	85.17 241	83.60 304	73.41 162	76.45 222	86.39 263	52.12 272	91.95 227	48.33 383	83.75 213	89.07 257

thres400		76.50 253	75.37 257	79.86 264	89.13 147	57.65 327	85.17 241	83.60 304	73.41 162	76.45 222	86.39 263	52.12 272	91.95 227	48.33 383	83.75 213	90.00 231

MVS_111021_LR		82.61 116	82.11 116	84.11 129	88.82 157	71.58 55	85.15 243	86.16 272	74.69 125	80.47 145	91.04 136	62.29 166	90.55 276	80.33 108	90.08 116	90.20 218

baseline1		76.98 245	76.75 234	77.66 307	88.13 186	55.66 358	85.12 244	81.89 332	73.04 171	76.79 212	88.90 186	62.43 164	87.78 323	63.30 270	71.18 369	89.55 249

mmtdpeth		74.16 285	73.01 289	77.60 311	83.72 309	61.13 281	85.10 245	85.10 284	72.06 186	77.21 206	80.33 371	43.84 358	85.75 342	77.14 140	52.61 419	85.91 344

WR-MVS		79.49 182	79.22 173	80.27 256	88.79 160	58.35 313	85.06 246	88.61 221	78.56 32	77.65 192	88.34 203	63.81 146	90.66 275	64.98 258	77.22 296	91.80 160

ET-MVSNet_ETH3D		78.63 206	76.63 237	84.64 106	86.73 239	69.47 95	85.01 247	84.61 290	69.54 241	66.51 370	86.59 255	50.16 300	91.75 235	76.26 148	84.24 205	92.69 127

OpenMVS_ROB	64.09 19	70.56 328	68.19 334	77.65 308	80.26 369	59.41 307	85.01 247	82.96 321	58.76 376	65.43 376	82.33 350	37.63 394	91.23 260	45.34 402	76.03 317	82.32 390

BH-RMVSNet		79.61 178	78.44 187	83.14 176	89.38 135	65.93 183	84.95 249	87.15 253	73.56 156	78.19 181	89.79 161	56.67 232	93.36 164	59.53 306	86.74 168	90.13 221

BH-w/o		78.21 216	77.33 220	80.84 243	88.81 158	65.13 205	84.87 250	87.85 236	69.75 238	74.52 274	84.74 302	61.34 184	93.11 181	58.24 321	85.84 184	84.27 367

TDRefinement		67.49 354	64.34 365	76.92 318	73.47 413	61.07 284	84.86 251	82.98 320	59.77 365	58.30 408	85.13 293	26.06 417	87.89 321	47.92 389	60.59 405	81.81 395

Anonymous202405211		78.25 214	77.01 224	81.99 213	91.03 87	60.67 290	84.77 252	83.90 301	70.65 216	80.00 150	91.20 130	41.08 376	91.43 253	65.21 255	85.26 189	93.85 69

TAMVS		78.89 201	77.51 216	83.03 183	87.80 203	67.79 143	84.72 253	85.05 286	67.63 275	76.75 214	87.70 220	62.25 167	90.82 270	58.53 317	87.13 161	90.49 206

sc_t1		72.19 313	69.51 324	80.23 257	84.81 283	61.09 283	84.68 254	80.22 356	60.70 357	71.27 314	83.58 329	36.59 397	89.24 298	60.41 297	63.31 397	90.37 211

1314		76.53 252	75.30 259	80.21 258	83.93 303	62.32 268	84.66 255	88.81 211	60.23 361	70.16 326	84.07 317	55.30 240	90.73 274	67.37 237	83.21 226	87.59 305

MVS		78.19 218	76.99 226	81.78 216	85.66 261	66.99 166	84.66 255	90.47 147	55.08 398	72.02 307	85.27 288	63.83 145	94.11 127	66.10 248	89.80 122	84.24 368

tfpnnormal		74.39 281	73.16 287	78.08 299	86.10 254	58.05 317	84.65 257	87.53 243	70.32 222	71.22 316	85.63 279	54.97 241	89.86 285	43.03 406	75.02 337	86.32 333

TR-MVS		77.44 237	76.18 243	81.20 233	88.24 180	63.24 251	84.61 258	86.40 267	67.55 277	77.81 189	86.48 261	54.10 252	93.15 178	57.75 325	82.72 233	87.20 314

AllTest		70.96 322	68.09 337	79.58 272	85.15 275	63.62 239	84.58 259	79.83 359	62.31 344	60.32 401	86.73 245	32.02 407	88.96 306	50.28 372	71.57 367	86.15 337

FA-MVS(test-final)		80.96 145	79.91 154	84.10 130	88.30 179	65.01 209	84.55 260	90.01 165	73.25 167	79.61 154	87.57 224	58.35 214	94.72 105	71.29 198	86.25 176	92.56 131

EU-MVSNet		68.53 349	67.61 348	71.31 374	78.51 390	47.01 412	84.47 261	84.27 296	42.27 421	66.44 371	84.79 301	40.44 379	83.76 360	58.76 315	68.54 382	83.17 380

VNet		82.21 120	82.41 111	81.62 219	90.82 93	60.93 285	84.47 261	89.78 171	76.36 86	84.07 93	91.88 106	64.71 139	90.26 278	70.68 204	88.89 133	93.66 78

xiu_mvs_v2_base		81.69 131	81.05 132	83.60 157	89.15 146	68.03 137	84.46 263	90.02 164	70.67 212	81.30 135	86.53 260	63.17 152	94.19 124	75.60 157	88.54 141	88.57 284

VPNet		78.69 205	78.66 182	78.76 284	88.31 178	55.72 357	84.45 264	86.63 263	76.79 71	78.26 179	90.55 148	59.30 208	89.70 290	66.63 244	77.05 298	90.88 188

PVSNet_Blended		80.98 144	80.34 145	82.90 189	88.85 154	65.40 197	84.43 265	92.00 96	67.62 276	78.11 183	85.05 296	66.02 127	94.27 118	71.52 194	89.50 125	89.01 264

MVP-Stereo		76.12 261	74.46 270	81.13 236	85.37 269	69.79 89	84.42 266	87.95 232	65.03 310	67.46 353	85.33 287	53.28 262	91.73 237	58.01 323	83.27 225	81.85 394
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CDS-MVSNet		79.07 196	77.70 211	83.17 175	87.60 213	68.23 131	84.40 267	86.20 271	67.49 278	76.36 225	86.54 259	61.54 178	90.79 271	61.86 286	87.33 158	90.49 206
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
K. test v3		71.19 319	68.51 331	79.21 278	83.04 326	57.78 326	84.35 268	76.91 385	72.90 174	62.99 392	82.86 343	39.27 383	91.09 266	61.65 288	52.66 418	88.75 277

PS-MVSNAJ		81.69 131	81.02 133	83.70 155	89.51 127	68.21 132	84.28 269	90.09 163	70.79 209	81.26 136	85.62 280	63.15 153	94.29 116	75.62 156	88.87 134	88.59 283

patch_mono-2		83.65 92	84.54 79	80.99 239	90.06 113	65.83 186	84.21 270	88.74 217	71.60 194	85.01 70	92.44 96	74.51 25	83.50 364	82.15 91	92.15 81	93.64 84

test222					91.50 80	68.26 130	84.16 271	83.20 315	54.63 399	79.74 152	91.63 115	58.97 210			91.42 93	86.77 327

testdata1							84.14 272		75.71 96

c3_l		78.75 202	77.91 200	81.26 231	82.89 331	61.56 278	84.09 273	89.13 200	69.97 231	75.56 240	84.29 310	66.36 121	92.09 222	73.47 178	75.48 325	90.12 222

MVSTER		79.01 197	77.88 203	82.38 207	83.07 324	64.80 216	84.08 274	88.95 208	69.01 258	78.69 167	87.17 238	54.70 247	92.43 208	74.69 165	80.57 259	89.89 238

ab-mvs		79.51 181	78.97 178	81.14 235	88.46 172	60.91 286	83.84 275	89.24 194	70.36 219	79.03 161	88.87 188	63.23 151	90.21 280	65.12 256	82.57 235	92.28 145

reproduce_monomvs		75.40 274	74.38 271	78.46 294	83.92 304	57.80 325	83.78 276	86.94 257	73.47 160	72.25 304	84.47 304	38.74 387	89.27 297	75.32 161	70.53 372	88.31 289

PAPM		77.68 234	76.40 241	81.51 222	87.29 226	61.85 274	83.78 276	89.59 180	64.74 313	71.23 315	88.70 191	62.59 160	93.66 150	52.66 358	87.03 163	89.01 264

diffmvs		82.10 121	81.88 123	82.76 200	83.00 327	63.78 238	83.68 278	89.76 173	72.94 173	82.02 122	89.85 160	65.96 129	90.79 271	82.38 90	87.30 159	93.71 77
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		78.59 208	77.76 209	81.08 237	82.66 336	61.56 278	83.65 279	89.15 198	68.87 260	75.55 241	83.79 322	66.49 119	92.03 223	73.25 181	76.39 310	89.64 246

1112_ss		77.40 239	76.43 240	80.32 255	89.11 151	60.41 295	83.65 279	87.72 240	62.13 347	73.05 292	86.72 247	62.58 161	89.97 284	62.11 284	80.80 255	90.59 202

PCF-MVS	73.52 7	80.38 165	78.84 180	85.01 92	87.71 209	68.99 106	83.65 279	91.46 122	63.00 334	77.77 191	90.28 151	66.10 124	95.09 91	61.40 290	88.22 147	90.94 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
XVG-ACMP-BASELINE		76.11 262	74.27 273	81.62 219	83.20 320	64.67 218	83.60 282	89.75 174	69.75 238	71.85 308	87.09 240	32.78 406	92.11 221	69.99 212	80.43 261	88.09 294

tt0320		70.49 330	68.03 338	77.89 302	84.78 284	59.12 308	83.55 283	80.44 351	58.13 381	67.43 355	80.41 370	39.26 384	87.54 326	55.12 344	63.18 398	86.99 322

cl22		78.07 221	77.01 224	81.23 232	82.37 343	61.83 275	83.55 283	87.98 230	68.96 259	75.06 263	83.87 318	61.40 183	91.88 231	73.53 176	76.39 310	89.98 234

XVG-OURS-SEG-HR		80.81 149	79.76 157	83.96 148	85.60 264	68.78 111	83.54 285	90.50 146	70.66 215	76.71 215	91.66 112	60.69 196	91.26 258	76.94 142	81.58 245	91.83 158

IB-MVS	68.01 15	75.85 266	73.36 285	83.31 167	84.76 285	66.03 179	83.38 286	85.06 285	70.21 226	69.40 336	81.05 361	45.76 343	94.66 108	65.10 257	75.49 324	89.25 256
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	77.95 225	77.15 222	80.36 253	87.57 217	60.21 298	83.37 287	87.78 238	66.11 295	75.37 249	87.06 242	63.27 149	90.48 277	61.38 291	82.43 236	90.40 210

tt0320-xc		70.11 334	67.45 351	78.07 300	85.33 270	59.51 306	83.28 288	78.96 369	58.77 375	67.10 359	80.28 372	36.73 396	87.42 327	56.83 336	59.77 407	87.29 312

test_vis1_n_1920		75.52 270	75.78 246	74.75 343	79.84 376	57.44 331	83.26 289	85.52 280	62.83 338	79.34 159	86.17 268	45.10 349	79.71 384	78.75 120	81.21 249	87.10 321

Anonymous20240521		68.80 345	67.22 354	73.55 353	74.33 405	54.11 373	83.18 290	85.61 279	58.15 380	61.68 396	80.94 364	30.71 412	81.27 378	57.00 333	73.34 355	85.28 353

eth_miper_zixun_eth		77.92 226	76.69 235	81.61 221	83.00 327	61.98 272	83.15 291	89.20 196	69.52 242	74.86 268	84.35 309	61.76 174	92.56 201	71.50 196	72.89 357	90.28 216

FE-MVS		77.78 229	75.68 248	84.08 135	88.09 189	66.00 181	83.13 292	87.79 237	68.42 269	78.01 186	85.23 290	45.50 347	95.12 85	59.11 310	85.83 185	91.11 178

cl____		77.72 231	76.76 232	80.58 249	82.49 340	60.48 293	83.09 293	87.87 234	69.22 249	74.38 277	85.22 291	62.10 170	91.53 247	71.09 199	75.41 329	89.73 245

DIV-MVS_self_test		77.72 231	76.76 232	80.58 249	82.48 341	60.48 293	83.09 293	87.86 235	69.22 249	74.38 277	85.24 289	62.10 170	91.53 247	71.09 199	75.40 330	89.74 244

thres200		75.55 269	74.47 269	78.82 283	87.78 206	57.85 323	83.07 295	83.51 307	72.44 180	75.84 236	84.42 305	52.08 275	91.75 235	47.41 390	83.64 218	86.86 325

testing3		68.56 348	67.67 347	71.22 375	87.33 223	42.87 425	83.06 296	71.54 405	70.36 219	69.08 340	84.38 307	30.33 413	85.69 344	37.50 418	75.45 328	85.09 359

XVG-OURS		80.41 164	79.23 172	83.97 147	85.64 262	69.02 105	83.03 297	90.39 149	71.09 204	77.63 193	91.49 121	54.62 249	91.35 255	75.71 154	83.47 222	91.54 165

miper_enhance_ethall		77.87 228	76.86 228	80.92 242	81.65 350	61.38 280	82.68 298	88.98 205	65.52 304	75.47 242	82.30 351	65.76 131	92.00 225	72.95 184	76.39 310	89.39 252

mvs_anonymous		79.42 186	79.11 175	80.34 254	84.45 293	57.97 320	82.59 299	87.62 241	67.40 280	76.17 232	88.56 198	68.47 98	89.59 291	70.65 205	86.05 180	93.47 92

baseline2		75.70 267	73.83 279	81.30 229	83.26 318	61.79 276	82.57 300	80.65 346	66.81 282	66.88 361	83.42 332	57.86 218	92.19 219	63.47 267	79.57 269	89.91 236

cascas		76.72 250	74.64 265	82.99 185	85.78 259	65.88 185	82.33 301	89.21 195	60.85 356	72.74 295	81.02 362	47.28 326	93.75 147	67.48 236	85.02 190	89.34 254

WB-MVSnew		71.96 316	71.65 303	72.89 360	84.67 290	51.88 389	82.29 302	77.57 377	62.31 344	73.67 285	83.00 339	53.49 260	81.10 379	45.75 399	82.13 239	85.70 347

RPSCF		73.23 301	71.46 305	78.54 290	82.50 339	59.85 300	82.18 303	82.84 324	58.96 373	71.15 317	89.41 178	45.48 348	84.77 355	58.82 314	71.83 365	91.02 184

thisisatest0515		77.33 240	75.38 256	83.18 174	85.27 272	63.80 237	82.11 304	83.27 311	65.06 309	75.91 234	83.84 320	49.54 308	94.27 118	67.24 239	86.19 177	91.48 169

pmmvs-eth3d		70.50 329	67.83 343	78.52 292	77.37 394	66.18 178	81.82 305	81.51 337	58.90 374	63.90 388	80.42 369	42.69 365	86.28 338	58.56 316	65.30 392	83.11 382

MS-PatchMatch		73.83 290	72.67 292	77.30 315	83.87 305	66.02 180	81.82 305	84.66 289	61.37 354	68.61 344	82.82 344	47.29 325	88.21 316	59.27 307	84.32 204	77.68 409

pmmvs5		71.55 317	70.20 322	75.61 328	77.83 391	56.39 346	81.74 307	80.89 342	57.76 384	67.46 353	84.49 303	49.26 314	85.32 350	57.08 331	75.29 333	85.11 358

Test_1112_low_res		76.40 258	75.44 253	79.27 276	89.28 141	58.09 316	81.69 308	87.07 254	59.53 368	72.48 300	86.67 252	61.30 185	89.33 295	60.81 296	80.15 264	90.41 209

IterMVS		74.29 282	72.94 290	78.35 295	81.53 354	63.49 245	81.58 309	82.49 326	68.06 273	69.99 329	83.69 326	51.66 285	85.54 346	65.85 251	71.64 366	86.01 341
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		75.43 272	73.87 278	80.11 260	82.69 335	64.85 215	81.57 310	83.47 308	69.16 252	70.49 320	84.15 316	51.95 278	88.15 317	69.23 219	72.14 363	87.34 310

test_vis1_n		69.85 338	69.21 327	71.77 368	72.66 419	55.27 364	81.48 311	76.21 389	52.03 406	75.30 255	83.20 336	28.97 414	76.22 404	74.60 166	78.41 284	83.81 374

pmmvs4		74.03 289	71.91 300	80.39 252	81.96 346	68.32 128	81.45 312	82.14 329	59.32 369	69.87 332	85.13 293	52.40 268	88.13 318	60.21 300	74.74 340	84.73 364

GA-MVS		76.87 247	75.17 261	81.97 214	82.75 333	62.58 264	81.44 313	86.35 269	72.16 185	74.74 269	82.89 342	46.20 338	92.02 224	68.85 225	81.09 250	91.30 174

UWE-MVS		72.13 314	71.49 304	74.03 349	86.66 242	47.70 408	81.40 314	76.89 386	63.60 329	75.59 239	84.22 314	39.94 381	85.62 345	48.98 380	86.13 179	88.77 276

test_fmvs1_n		70.86 324	70.24 321	72.73 362	72.51 420	55.28 363	81.27 315	79.71 361	51.49 409	78.73 166	84.87 298	27.54 416	77.02 396	76.06 150	79.97 267	85.88 345

testing91		76.54 251	75.66 250	79.18 279	88.43 174	55.89 354	81.08 316	83.00 319	73.76 150	75.34 250	84.29 310	46.20 338	90.07 282	64.33 262	84.50 197	91.58 164

testing222		74.04 287	72.66 293	78.19 297	87.89 198	55.36 361	81.06 317	79.20 367	71.30 199	74.65 272	83.57 330	39.11 386	88.67 311	51.43 366	85.75 186	90.53 204

test_fmvs1		70.93 323	70.52 316	72.16 366	73.71 409	55.05 365	80.82 318	78.77 370	51.21 410	78.58 171	84.41 306	31.20 411	76.94 397	75.88 153	80.12 266	84.47 366

CostFormer		75.24 276	73.90 277	79.27 276	82.65 337	58.27 315	80.80 319	82.73 325	61.57 351	75.33 254	83.13 337	55.52 238	91.07 267	64.98 258	78.34 285	88.45 286

testing99		76.09 263	75.12 262	79.00 280	88.16 183	55.50 360	80.79 320	81.40 339	73.30 165	75.17 258	84.27 313	44.48 353	90.02 283	64.28 263	84.22 206	91.48 169

MIMVSNet1		68.58 347	66.78 357	73.98 350	80.07 373	51.82 390	80.77 321	84.37 292	64.40 317	59.75 404	82.16 354	36.47 398	83.63 362	42.73 407	70.33 373	86.48 332

CL-MVSNet_self_test		72.37 310	71.46 305	75.09 337	79.49 383	53.53 377	80.76 322	85.01 287	69.12 253	70.51 319	82.05 355	57.92 217	84.13 358	52.27 360	66.00 390	87.60 303

testing11		75.14 277	74.01 274	78.53 291	88.16 183	56.38 347	80.74 323	80.42 352	70.67 212	72.69 298	83.72 325	43.61 360	89.86 285	62.29 280	83.76 212	89.36 253

MSDG		73.36 298	70.99 312	80.49 251	84.51 292	65.80 188	80.71 324	86.13 273	65.70 301	65.46 375	83.74 323	44.60 351	90.91 269	51.13 367	76.89 300	84.74 363

tpm2		73.26 300	71.46 305	78.63 285	83.34 316	56.71 341	80.65 325	80.40 353	56.63 392	73.55 286	82.02 356	51.80 282	91.24 259	56.35 340	78.42 283	87.95 295

XXY-MVS		75.41 273	75.56 251	74.96 338	83.59 311	57.82 324	80.59 326	83.87 302	66.54 292	74.93 267	88.31 204	63.24 150	80.09 383	62.16 282	76.85 302	86.97 323

test_cas_vis1_n_1920		73.76 291	73.74 280	73.81 352	75.90 398	59.77 301	80.51 327	82.40 327	58.30 379	81.62 129	85.69 276	44.35 355	76.41 402	76.29 147	78.61 278	85.23 354

EGC-MVSNET		52.07 394	47.05 398	67.14 394	83.51 313	60.71 289	80.50 328	67.75 416	0.07 444	0.43 445	75.85 406	24.26 422	81.54 376	28.82 427	62.25 399	59.16 427

SDMVSNet		80.38 165	80.18 149	80.99 239	89.03 152	64.94 212	80.45 329	89.40 185	75.19 111	76.61 219	89.98 157	60.61 200	87.69 324	76.83 144	83.55 219	90.33 213

HyFIR lowres test		77.53 236	75.40 255	83.94 149	89.59 123	66.62 171	80.36 330	88.64 220	56.29 394	76.45 222	85.17 292	57.64 220	93.28 166	61.34 292	83.10 228	91.91 157

D2MVS		74.82 279	73.21 286	79.64 271	79.81 377	62.56 265	80.34 331	87.35 247	64.37 318	68.86 341	82.66 346	46.37 334	90.10 281	67.91 232	81.24 248	86.25 334

testing3-2		75.12 278	75.19 260	74.91 339	90.40 102	45.09 420	80.29 332	78.42 372	78.37 37	76.54 221	87.75 218	44.36 354	87.28 329	57.04 332	83.49 221	92.37 140

TinyColmap		67.30 357	64.81 363	74.76 342	81.92 348	56.68 342	80.29 332	81.49 338	60.33 359	56.27 415	83.22 334	24.77 421	87.66 325	45.52 400	69.47 376	79.95 404

LCM-MVSNet-Re		77.05 243	76.94 227	77.36 313	87.20 227	51.60 392	80.06 334	80.46 350	75.20 110	67.69 350	86.72 247	62.48 162	88.98 304	63.44 268	89.25 128	91.51 166

test_fmvs2		68.35 351	67.48 350	70.98 377	69.50 423	51.95 387	80.05 335	76.38 388	49.33 412	74.65 272	84.38 307	23.30 425	75.40 413	74.51 167	75.17 336	85.60 348

FMVSNet5		69.50 339	67.96 339	74.15 348	82.97 330	55.35 362	80.01 336	82.12 330	62.56 342	63.02 390	81.53 358	36.92 395	81.92 374	48.42 382	74.06 345	85.17 357

SCA		74.22 284	72.33 297	79.91 263	84.05 301	62.17 270	79.96 337	79.29 366	66.30 294	72.38 302	80.13 374	51.95 278	88.60 312	59.25 308	77.67 293	88.96 268

tpmrst		72.39 308	72.13 299	73.18 359	80.54 367	49.91 403	79.91 338	79.08 368	63.11 332	71.69 310	79.95 376	55.32 239	82.77 369	65.66 253	73.89 347	86.87 324

PatchmatchNet		73.12 302	71.33 308	78.49 293	83.18 321	60.85 287	79.63 339	78.57 371	64.13 320	71.73 309	79.81 379	51.20 289	85.97 341	57.40 328	76.36 315	88.66 280
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.38 309	70.90 313	76.80 320	88.60 167	67.38 156	79.53 340	76.17 390	62.75 340	69.36 337	82.00 357	45.51 346	84.89 354	53.62 353	80.58 258	78.12 408

CMPMVS	51.72 21	70.19 333	68.16 335	76.28 322	73.15 416	57.55 329	79.47 341	83.92 300	48.02 414	56.48 414	84.81 300	43.13 362	86.42 337	62.67 276	81.81 244	84.89 361
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ETVMVS		72.25 312	71.05 311	75.84 325	87.77 207	51.91 388	79.39 342	74.98 393	69.26 247	73.71 283	82.95 340	40.82 378	86.14 339	46.17 396	84.43 202	89.47 250

GG-mvs-BLEND				75.38 334	81.59 352	55.80 356	79.32 343	69.63 410		67.19 357	73.67 411	43.24 361	88.90 308	50.41 369	84.50 197	81.45 396

LTVRE_ROB	69.57 13	76.25 260	74.54 268	81.41 225	88.60 167	64.38 226	79.24 344	89.12 201	70.76 211	69.79 334	87.86 217	49.09 316	93.20 174	56.21 341	80.16 263	86.65 330
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		72.37 310	71.71 302	74.35 346	82.19 344	52.00 386	79.22 345	77.29 382	64.56 315	72.95 294	83.68 327	51.35 286	83.26 367	58.33 320	75.80 319	87.81 299

mvs5depth		69.45 340	67.45 351	75.46 333	73.93 407	55.83 355	79.19 346	83.23 312	66.89 281	71.63 311	83.32 333	33.69 405	85.09 351	59.81 303	55.34 415	85.46 350

ppachtmachnet_test		70.04 335	67.34 353	78.14 298	79.80 378	61.13 281	79.19 346	80.59 347	59.16 371	65.27 377	79.29 382	46.75 331	87.29 328	49.33 378	66.72 385	86.00 343

USDC		70.33 331	68.37 332	76.21 323	80.60 366	56.23 350	79.19 346	86.49 265	60.89 355	61.29 397	85.47 284	31.78 409	89.47 294	53.37 355	76.21 316	82.94 386

sd_testset		77.70 233	77.40 217	78.60 287	89.03 152	60.02 299	79.00 349	85.83 277	75.19 111	76.61 219	89.98 157	54.81 242	85.46 348	62.63 277	83.55 219	90.33 213

PM-MVS		66.41 363	64.14 366	73.20 358	73.92 408	56.45 344	78.97 350	64.96 424	63.88 328	64.72 381	80.24 373	19.84 429	83.44 365	66.24 245	64.52 394	79.71 405

tpmvs		71.09 321	69.29 326	76.49 321	82.04 345	56.04 352	78.92 351	81.37 340	64.05 324	67.18 358	78.28 391	49.74 307	89.77 287	49.67 377	72.37 359	83.67 376

test_post1							78.90 352				5.43 443	48.81 321	85.44 349	59.25 308

mamv4		76.81 248	78.23 195	72.54 364	86.12 252	65.75 191	78.76 353	82.07 331	64.12 321	72.97 293	91.02 139	67.97 103	68.08 429	83.04 80	78.02 287	83.80 375

CHOSEN 1792x2688		77.63 235	75.69 247	83.44 162	89.98 115	68.58 122	78.70 354	87.50 244	56.38 393	75.80 237	86.84 243	58.67 211	91.40 254	61.58 289	85.75 186	90.34 212

Syy-MVS		68.05 352	67.85 341	68.67 388	84.68 287	40.97 431	78.62 355	73.08 402	66.65 289	66.74 364	79.46 380	52.11 274	82.30 371	32.89 423	76.38 313	82.75 387

myMVS_eth3d		67.02 358	66.29 359	69.21 383	84.68 287	42.58 426	78.62 355	73.08 402	66.65 289	66.74 364	79.46 380	31.53 410	82.30 371	39.43 415	76.38 313	82.75 387

WBMVS		73.43 295	72.81 291	75.28 335	87.91 197	50.99 398	78.59 357	81.31 341	65.51 306	74.47 275	84.83 299	46.39 332	86.68 333	58.41 318	77.86 288	88.17 293

test-LLR		72.94 306	72.43 295	74.48 344	81.35 358	58.04 318	78.38 358	77.46 378	66.66 286	69.95 330	79.00 385	48.06 322	79.24 385	66.13 246	84.83 192	86.15 337

TESTMET0.1,1		69.89 337	69.00 329	72.55 363	79.27 386	56.85 337	78.38 358	74.71 397	57.64 385	68.09 347	77.19 398	37.75 393	76.70 398	63.92 265	84.09 207	84.10 371

test-mter		71.41 318	70.39 320	74.48 344	81.35 358	58.04 318	78.38 358	77.46 378	60.32 360	69.95 330	79.00 385	36.08 400	79.24 385	66.13 246	84.83 192	86.15 337

UBG		73.08 303	72.27 298	75.51 331	88.02 192	51.29 396	78.35 361	77.38 381	65.52 304	73.87 282	82.36 349	45.55 345	86.48 336	55.02 345	84.39 203	88.75 277

Anonymous20231206		68.60 346	67.80 344	71.02 376	80.23 371	50.75 400	78.30 362	80.47 349	56.79 391	66.11 373	82.63 347	46.35 335	78.95 387	43.62 405	75.70 320	83.36 379

tpm cat1		70.57 327	68.31 333	77.35 314	82.41 342	57.95 321	78.08 363	80.22 356	52.04 405	68.54 345	77.66 396	52.00 277	87.84 322	51.77 361	72.07 364	86.25 334

myMVS_eth3d28		73.62 292	73.53 282	73.90 351	88.20 181	47.41 410	78.06 364	79.37 364	74.29 137	73.98 280	84.29 310	44.67 350	83.54 363	51.47 364	87.39 157	90.74 195

our_test_3		69.14 342	67.00 355	75.57 329	79.80 378	58.80 309	77.96 365	77.81 375	59.55 367	62.90 393	78.25 392	47.43 324	83.97 359	51.71 362	67.58 384	83.93 373

KD-MVS_self_test		68.81 344	67.59 349	72.46 365	74.29 406	45.45 415	77.93 366	87.00 255	63.12 331	63.99 387	78.99 387	42.32 367	84.77 355	56.55 339	64.09 395	87.16 317

WTY-MVS		75.65 268	75.68 248	75.57 329	86.40 246	56.82 338	77.92 367	82.40 327	65.10 308	76.18 230	87.72 219	63.13 156	80.90 380	60.31 299	81.96 241	89.00 266

UWE-MVS-28		65.32 368	64.93 362	66.49 396	78.70 388	38.55 433	77.86 368	64.39 425	62.00 349	64.13 385	83.60 328	41.44 373	76.00 406	31.39 425	80.89 252	84.92 360

test20.03		67.45 355	66.95 356	68.94 384	75.48 402	44.84 421	77.50 369	77.67 376	66.66 286	63.01 391	83.80 321	47.02 328	78.40 389	42.53 409	68.86 381	83.58 377

EPMVS		69.02 343	68.16 335	71.59 369	79.61 381	49.80 405	77.40 370	66.93 418	62.82 339	70.01 327	79.05 383	45.79 342	77.86 393	56.58 338	75.26 334	87.13 318

test_fmvs3		63.36 375	61.82 378	67.98 392	62.51 432	46.96 413	77.37 371	74.03 399	45.24 417	67.50 352	78.79 388	12.16 437	72.98 421	72.77 187	66.02 389	83.99 372

gg-mvs-nofinetune		69.95 336	67.96 339	75.94 324	83.07 324	54.51 371	77.23 372	70.29 408	63.11 332	70.32 322	62.33 422	43.62 359	88.69 310	53.88 352	87.76 152	84.62 365

MDTV_nov1_ep13			69.97 323		83.18 321	53.48 378	77.10 373	80.18 358	60.45 358	69.33 338	80.44 368	48.89 320	86.90 331	51.60 363	78.51 281

LF4IMVS		64.02 373	62.19 377	69.50 382	70.90 421	53.29 382	76.13 374	77.18 383	52.65 404	58.59 406	80.98 363	23.55 424	76.52 400	53.06 357	66.66 386	78.68 407

sss		73.60 293	73.64 281	73.51 354	82.80 332	55.01 366	76.12 375	81.69 335	62.47 343	74.68 271	85.85 274	57.32 224	78.11 391	60.86 295	80.93 251	87.39 308

testgi		66.67 361	66.53 358	67.08 395	75.62 401	41.69 430	75.93 376	76.50 387	66.11 295	65.20 380	86.59 255	35.72 401	74.71 415	43.71 404	73.38 354	84.84 362

CR-MVSNet		73.37 296	71.27 309	79.67 270	81.32 360	65.19 203	75.92 377	80.30 354	59.92 364	72.73 296	81.19 359	52.50 266	86.69 332	59.84 302	77.71 290	87.11 319

RPMNet		73.51 294	70.49 317	82.58 204	81.32 360	65.19 203	75.92 377	92.27 84	57.60 386	72.73 296	76.45 401	52.30 269	95.43 70	48.14 387	77.71 290	87.11 319

MIMVSNet		70.69 326	69.30 325	74.88 340	84.52 291	56.35 349	75.87 379	79.42 363	64.59 314	67.76 348	82.41 348	41.10 375	81.54 376	46.64 394	81.34 246	86.75 328

test0.0.03 1		68.00 353	67.69 346	68.90 385	77.55 392	47.43 409	75.70 380	72.95 404	66.66 286	66.56 366	82.29 352	48.06 322	75.87 408	44.97 403	74.51 342	83.41 378

dmvs_re		71.14 320	70.58 315	72.80 361	81.96 346	59.68 302	75.60 381	79.34 365	68.55 265	69.27 339	80.72 367	49.42 310	76.54 399	52.56 359	77.79 289	82.19 392

dmvs_testset		62.63 376	64.11 367	58.19 406	78.55 389	24.76 444	75.28 382	65.94 421	67.91 274	60.34 400	76.01 403	53.56 258	73.94 419	31.79 424	67.65 383	75.88 413

PMMVS		69.34 341	68.67 330	71.35 373	75.67 400	62.03 271	75.17 383	73.46 400	50.00 411	68.68 342	79.05 383	52.07 276	78.13 390	61.16 293	82.77 231	73.90 415

UnsupCasMVSNet_eth		67.33 356	65.99 360	71.37 371	73.48 412	51.47 394	75.16 384	85.19 283	65.20 307	60.78 399	80.93 366	42.35 366	77.20 395	57.12 330	53.69 417	85.44 351

MDTV_nov1_ep13_2view						37.79 434	75.16 384		55.10 397	66.53 367		49.34 312		53.98 351		87.94 296

pmmvs3		57.79 383	54.26 388	68.37 389	64.02 431	56.72 340	75.12 386	65.17 422	40.20 423	52.93 419	69.86 419	20.36 428	75.48 411	45.45 401	55.25 416	72.90 417

dp		66.80 359	65.43 361	70.90 378	79.74 380	48.82 407	75.12 386	74.77 395	59.61 366	64.08 386	77.23 397	42.89 363	80.72 381	48.86 381	66.58 387	83.16 381

Patchmtry		70.74 325	69.16 328	75.49 332	80.72 364	54.07 374	74.94 388	80.30 354	58.34 378	70.01 327	81.19 359	52.50 266	86.54 334	53.37 355	71.09 370	85.87 346

ttmdpeth		59.91 381	57.10 385	68.34 390	67.13 427	46.65 414	74.64 389	67.41 417	48.30 413	62.52 395	85.04 297	20.40 427	75.93 407	42.55 408	45.90 428	82.44 389

SSC-MVS3.2		73.35 299	73.39 283	73.23 355	85.30 271	49.01 406	74.58 390	81.57 336	75.21 109	73.68 284	85.58 281	52.53 264	82.05 373	54.33 350	77.69 292	88.63 282

PVSNet	64.34 18	72.08 315	70.87 314	75.69 327	86.21 249	56.44 345	74.37 391	80.73 345	62.06 348	70.17 325	82.23 353	42.86 364	83.31 366	54.77 347	84.45 201	87.32 311

WB-MVS		54.94 386	54.72 387	55.60 412	73.50 411	20.90 446	74.27 392	61.19 429	59.16 371	50.61 421	74.15 409	47.19 327	75.78 409	17.31 437	35.07 431	70.12 419

MDA-MVSNet-bldmvs		66.68 360	63.66 370	75.75 326	79.28 385	60.56 292	73.92 393	78.35 373	64.43 316	50.13 423	79.87 378	44.02 357	83.67 361	46.10 397	56.86 409	83.03 384

SSC-MVS		53.88 389	53.59 389	54.75 414	72.87 417	19.59 447	73.84 394	60.53 431	57.58 387	49.18 425	73.45 412	46.34 336	75.47 412	16.20 440	32.28 433	69.20 420

UnsupCasMVSNet_bld		63.70 374	61.53 380	70.21 380	73.69 410	51.39 395	72.82 395	81.89 332	55.63 396	57.81 410	71.80 415	38.67 388	78.61 388	49.26 379	52.21 420	80.63 401

PatchT		68.46 350	67.85 341	70.29 379	80.70 365	43.93 423	72.47 396	74.88 394	60.15 362	70.55 318	76.57 400	49.94 304	81.59 375	50.58 368	74.83 339	85.34 352

miper_lstm_enhance		74.11 286	73.11 288	77.13 317	80.11 372	59.62 303	72.23 397	86.92 259	66.76 284	70.40 321	82.92 341	56.93 229	82.92 368	69.06 222	72.63 358	88.87 271

MVS-HIRNet		59.14 382	57.67 384	63.57 400	81.65 350	43.50 424	71.73 398	65.06 423	39.59 425	51.43 420	57.73 428	38.34 390	82.58 370	39.53 413	73.95 346	64.62 424

MVStest1		56.63 385	52.76 391	68.25 391	61.67 433	53.25 383	71.67 399	68.90 415	38.59 426	50.59 422	83.05 338	25.08 419	70.66 423	36.76 419	38.56 429	80.83 400

APD_test1		53.31 391	49.93 396	63.42 401	65.68 428	50.13 402	71.59 400	66.90 419	34.43 431	40.58 430	71.56 416	8.65 442	76.27 403	34.64 422	55.36 414	63.86 425

Patchmatch-RL test		70.24 332	67.78 345	77.61 309	77.43 393	59.57 305	71.16 401	70.33 407	62.94 336	68.65 343	72.77 413	50.62 295	85.49 347	69.58 217	66.58 387	87.77 300

test123		6.12 413	8.11 416	0.14 427	0.06 451	0.09 452	71.05 402	0.03 452	0.04 446	0.25 447	1.30 446	0.05 450	0.03 447	0.21 446	0.01 445	0.29 442

ANet_high		50.57 396	46.10 400	63.99 399	48.67 444	39.13 432	70.99 403	80.85 343	61.39 353	31.18 433	57.70 429	17.02 432	73.65 420	31.22 426	15.89 441	79.18 406

KD-MVS_2432*1600		66.22 365	63.89 368	73.21 356	75.47 403	53.42 379	70.76 404	84.35 293	64.10 322	66.52 368	78.52 389	34.55 403	84.98 352	50.40 370	50.33 422	81.23 397

miper_refine_blended		66.22 365	63.89 368	73.21 356	75.47 403	53.42 379	70.76 404	84.35 293	64.10 322	66.52 368	78.52 389	34.55 403	84.98 352	50.40 370	50.33 422	81.23 397

test_vis1_rt		60.28 380	58.42 383	65.84 397	67.25 426	55.60 359	70.44 406	60.94 430	44.33 419	59.00 405	66.64 420	24.91 420	68.67 427	62.80 272	69.48 375	73.25 416

testmvs		6.04 414	8.02 417	0.10 428	0.08 450	0.03 453	69.74 407	0.04 451	0.05 445	0.31 446	1.68 445	0.02 451	0.04 446	0.24 445	0.02 444	0.25 443

N_pmnet		52.79 392	53.26 390	51.40 416	78.99 387	7.68 450	69.52 408	3.89 449	51.63 408	57.01 412	74.98 408	40.83 377	65.96 431	37.78 417	64.67 393	80.56 403

FPMVS		53.68 390	51.64 392	59.81 405	65.08 429	51.03 397	69.48 409	69.58 411	41.46 422	40.67 429	72.32 414	16.46 433	70.00 426	24.24 433	65.42 391	58.40 429

DSMNet-mixed		57.77 384	56.90 386	60.38 404	67.70 425	35.61 435	69.18 410	53.97 436	32.30 434	57.49 411	79.88 377	40.39 380	68.57 428	38.78 416	72.37 359	76.97 410

new-patchmatchnet		61.73 378	61.73 379	61.70 402	72.74 418	24.50 445	69.16 411	78.03 374	61.40 352	56.72 413	75.53 407	38.42 389	76.48 401	45.95 398	57.67 408	84.13 370

YYNet1		65.03 369	62.91 374	71.38 370	75.85 399	56.60 343	69.12 412	74.66 398	57.28 389	54.12 417	77.87 394	45.85 341	74.48 416	49.95 375	61.52 402	83.05 383

MDA-MVSNet_test_wron		65.03 369	62.92 373	71.37 371	75.93 397	56.73 339	69.09 413	74.73 396	57.28 389	54.03 418	77.89 393	45.88 340	74.39 417	49.89 376	61.55 401	82.99 385

PVSNet_0	57.27 20	61.67 379	59.27 382	68.85 386	79.61 381	57.44 331	68.01 414	73.44 401	55.93 395	58.54 407	70.41 418	44.58 352	77.55 394	47.01 391	35.91 430	71.55 418

dongtai		45.42 400	45.38 401	45.55 418	73.36 414	26.85 442	67.72 415	34.19 444	54.15 400	49.65 424	56.41 431	25.43 418	62.94 434	19.45 435	28.09 435	46.86 434

ADS-MVSNet2		66.20 367	63.33 371	74.82 341	79.92 374	58.75 310	67.55 416	75.19 392	53.37 402	65.25 378	75.86 404	42.32 367	80.53 382	41.57 410	68.91 379	85.18 355

ADS-MVSNet		64.36 372	62.88 375	68.78 387	79.92 374	47.17 411	67.55 416	71.18 406	53.37 402	65.25 378	75.86 404	42.32 367	73.99 418	41.57 410	68.91 379	85.18 355

mvsany_test1		62.30 377	61.26 381	65.41 398	69.52 422	54.86 367	66.86 418	49.78 438	46.65 415	68.50 346	83.21 335	49.15 315	66.28 430	56.93 334	60.77 403	75.11 414

LCM-MVSNet		54.25 387	49.68 397	67.97 393	53.73 441	45.28 418	66.85 419	80.78 344	35.96 430	39.45 431	62.23 424	8.70 441	78.06 392	48.24 386	51.20 421	80.57 402

test_vis3_rt		49.26 397	47.02 399	56.00 409	54.30 438	45.27 419	66.76 420	48.08 439	36.83 428	44.38 427	53.20 432	7.17 444	64.07 432	56.77 337	55.66 412	58.65 428

testf1		45.72 398	41.96 402	57.00 407	56.90 435	45.32 416	66.14 421	59.26 432	26.19 435	30.89 434	60.96 426	4.14 445	70.64 424	26.39 431	46.73 426	55.04 430

APD_test2		45.72 398	41.96 402	57.00 407	56.90 435	45.32 416	66.14 421	59.26 432	26.19 435	30.89 434	60.96 426	4.14 445	70.64 424	26.39 431	46.73 426	55.04 430

kuosan		39.70 404	40.40 405	37.58 421	64.52 430	26.98 440	65.62 423	33.02 445	46.12 416	42.79 428	48.99 434	24.10 423	46.56 442	12.16 443	26.30 436	39.20 435

JIA-IIPM		66.32 364	62.82 376	76.82 319	77.09 395	61.72 277	65.34 424	75.38 391	58.04 383	64.51 382	62.32 423	42.05 371	86.51 335	51.45 365	69.22 378	82.21 391

PMVS	37.38 22	44.16 402	40.28 406	55.82 411	40.82 446	42.54 428	65.12 425	63.99 426	34.43 431	24.48 437	57.12 430	3.92 447	76.17 405	17.10 438	55.52 413	48.75 432
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		50.91 395	50.29 395	52.78 415	68.58 424	34.94 437	63.71 426	56.63 435	39.73 424	44.95 426	65.47 421	21.93 426	58.48 435	34.98 421	56.62 410	64.92 423

mvsany_test3		53.99 388	51.45 393	61.61 403	55.51 437	44.74 422	63.52 427	45.41 442	43.69 420	58.11 409	76.45 401	17.99 430	63.76 433	54.77 347	47.59 424	76.34 412

Patchmatch-test		64.82 371	63.24 372	69.57 381	79.42 384	49.82 404	63.49 428	69.05 413	51.98 407	59.95 403	80.13 374	50.91 291	70.98 422	40.66 412	73.57 350	87.90 297

ambc				75.24 336	73.16 415	50.51 401	63.05 429	87.47 245		64.28 383	77.81 395	17.80 431	89.73 289	57.88 324	60.64 404	85.49 349

test_f		52.09 393	50.82 394	55.90 410	53.82 440	42.31 429	59.42 430	58.31 434	36.45 429	56.12 416	70.96 417	12.18 436	57.79 436	53.51 354	56.57 411	67.60 421

CHOSEN 280x420		66.51 362	64.71 364	71.90 367	81.45 355	63.52 244	57.98 431	68.95 414	53.57 401	62.59 394	76.70 399	46.22 337	75.29 414	55.25 343	79.68 268	76.88 411

E-PMN		31.77 405	30.64 408	35.15 422	52.87 442	27.67 439	57.09 432	47.86 440	24.64 437	16.40 442	33.05 438	11.23 438	54.90 438	14.46 441	18.15 439	22.87 438

EMVS		30.81 407	29.65 409	34.27 423	50.96 443	25.95 443	56.58 433	46.80 441	24.01 438	15.53 443	30.68 439	12.47 435	54.43 439	12.81 442	17.05 440	22.43 439

PMMVS2		40.82 403	38.86 407	46.69 417	53.84 439	16.45 448	48.61 434	49.92 437	37.49 427	31.67 432	60.97 425	8.14 443	56.42 437	28.42 428	30.72 434	67.19 422

wuyk23d		16.82 411	15.94 414	19.46 425	58.74 434	31.45 438	39.22 435	3.74 450	6.84 441	6.04 444	2.70 444	1.27 449	24.29 444	10.54 444	14.40 443	2.63 441

tmp_tt		18.61 410	21.40 413	10.23 426	4.82 449	10.11 449	34.70 436	30.74 447	1.48 443	23.91 439	26.07 440	28.42 415	13.41 445	27.12 429	15.35 442	7.17 440

Gipuma		45.18 401	41.86 404	55.16 413	77.03 396	51.52 393	32.50 437	80.52 348	32.46 433	27.12 436	35.02 437	9.52 440	75.50 410	22.31 434	60.21 406	38.45 436
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	26.22 23	30.37 408	25.89 412	43.81 419	44.55 445	35.46 436	28.87 438	39.07 443	18.20 439	18.58 441	40.18 436	2.68 448	47.37 441	17.07 439	23.78 438	48.60 433
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		31.52 406	29.28 410	38.23 420	27.03 448	6.50 451	20.94 439	62.21 428	4.05 442	22.35 440	52.50 433	13.33 434	47.58 440	27.04 430	34.04 432	60.62 426

mmdepth		0.00 416	0.00 419	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	0.00 447	0.00 452	0.00 448	0.00 447	0.00 446	0.00 444

monomultidepth		0.00 416	0.00 419	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	0.00 447	0.00 452	0.00 448	0.00 447	0.00 446	0.00 444

test_blank		0.00 416	0.00 419	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	0.00 447	0.00 452	0.00 448	0.00 447	0.00 446	0.00 444

uanet_test		0.00 416	0.00 419	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	0.00 447	0.00 452	0.00 448	0.00 447	0.00 446	0.00 444

DCPMVS		0.00 416	0.00 419	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	0.00 447	0.00 452	0.00 448	0.00 447	0.00 446	0.00 444

cdsmvs_eth3d_5k		19.96 409	26.61 411	0.00 429	0.00 452	0.00 454	0.00 440	89.26 193	0.00 447	0.00 448	88.61 195	61.62 177	0.00 448	0.00 447	0.00 446	0.00 444

pcd_1.5k_mvsjas		5.26 415	7.02 418	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	0.00 447	63.15 153	0.00 448	0.00 447	0.00 446	0.00 444

sosnet-low-res		0.00 416	0.00 419	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	0.00 447	0.00 452	0.00 448	0.00 447	0.00 446	0.00 444

sosnet		0.00 416	0.00 419	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	0.00 447	0.00 452	0.00 448	0.00 447	0.00 446	0.00 444

uncertanet		0.00 416	0.00 419	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	0.00 447	0.00 452	0.00 448	0.00 447	0.00 446	0.00 444

Regformer		0.00 416	0.00 419	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	0.00 447	0.00 452	0.00 448	0.00 447	0.00 446	0.00 444

ab-mvs-re		7.23 412	9.64 415	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	86.72 247	0.00 452	0.00 448	0.00 447	0.00 446	0.00 444

uanet		0.00 416	0.00 419	0.00 429	0.00 452	0.00 454	0.00 440	0.00 453	0.00 447	0.00 448	0.00 447	0.00 452	0.00 448	0.00 447	0.00 446	0.00 444

WAC-MVS						42.58 426								39.46 414

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 12	96.44 9	94.41 40

PC_three_1452									68.21 271	92.02 12	94.00 54	82.09 5	95.98 56	84.58 62	96.68 2	94.95 11

No_MVS				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 12	96.44 9	94.41 40

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 38	92.05 11	95.74 6	80.83 11

eth-test2					0.00 452

eth-test					0.00 452

ZD-MVS					94.38 25	72.22 44		92.67 67	70.98 207	87.75 42	94.07 49	74.01 32	96.70 27	84.66 61	94.84 44

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 282	92.39 6				88.94 24	96.63 4	94.85 20

test_241102_TWO								94.06 10	77.24 57	92.78 4	95.72 8	81.26 8	97.44 7	89.07 21	96.58 6	94.26 50

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 60	93.10 1	95.39 14	82.99 1	97.27 12

test_0728_THIRD									78.38 35	92.12 9	95.78 4	81.46 7	97.40 9	89.42 16	96.57 7	94.67 28

GSMVS																88.96 268

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 287				88.96 268

sam_mvs												50.01 302

MTGPA								92.02 94

test_post											5.46 442	50.36 299	84.24 357

patchmatchnet-post											74.00 410	51.12 290	88.60 312

gm-plane-assit					81.40 356	53.83 376			62.72 341		80.94 364		92.39 210	63.40 269

test9_res														84.90 55	95.70 26	92.87 121

agg_prior2														82.91 82	95.45 29	92.70 125

agg_prior					92.85 62	71.94 50		91.78 109		84.41 86			94.93 94

TestCases				79.58 272	85.15 275	63.62 239		79.83 359	62.31 344	60.32 401	86.73 245	32.02 407	88.96 306	50.28 372	71.57 367	86.15 337

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 65

新几何1				83.42 163	93.13 54	70.71 74		85.48 281	57.43 388	81.80 126	91.98 103	63.28 148	92.27 216	64.60 261	92.99 70	87.27 313

旧先验1					91.96 74	65.79 189		86.37 268			93.08 83	69.31 86			92.74 74	88.74 279

原ACMM1				84.35 116	93.01 60	68.79 110		92.44 77	63.96 327	81.09 137	91.57 118	66.06 126	95.45 68	67.19 240	94.82 46	88.81 274

testdata2													91.01 268	62.37 279

segment_acmp												73.08 39

testdata				79.97 262	90.90 91	64.21 228		84.71 288	59.27 370	85.40 66	92.91 85	62.02 172	89.08 302	68.95 223	91.37 95	86.63 331

test12				86.80 52	92.63 67	70.70 75		91.79 108		82.71 115		71.67 56	96.16 47		94.50 51	93.54 90

plane_prior7					90.08 109	68.51 124

plane_prior6					89.84 118	68.70 118						60.42 203

plane_prior5								92.44 77					95.38 75	78.71 121	86.32 174	91.33 172

plane_prior4											91.00 140

plane_prior3						68.60 121			78.44 33	78.92 164

plane_prior1					89.90 117

n2								0.00 453

nn								0.00 453

door-mid								69.98 409

lessismore_v0				78.97 281	81.01 363	57.15 334		65.99 420		61.16 398	82.82 344	39.12 385	91.34 256	59.67 304	46.92 425	88.43 287

LGP-MVS_train				84.50 109	89.23 143	68.76 112		91.94 100	75.37 105	76.64 217	91.51 119	54.29 250	94.91 95	78.44 123	83.78 210	89.83 240

test11								92.23 87

door								69.44 412

HQP5-MVS						66.98 167

BP-MVS														77.47 135

HQP4-MVS										77.24 201			95.11 87			91.03 182

HQP3-MVS								92.19 91							85.99 182

HQP2-MVS												60.17 206

NP-MVS					89.62 122	68.32 128					90.24 153

ACMMP++_ref															81.95 242

ACMMP++															81.25 247

Test By Simon												64.33 140

ITE_SJBPF				78.22 296	81.77 349	60.57 291		83.30 310	69.25 248	67.54 351	87.20 236	36.33 399	87.28 329	54.34 349	74.62 341	86.80 326

DeepMVS_CX				27.40 424	40.17 447	26.90 441		24.59 448	17.44 440	23.95 438	48.61 435	9.77 439	26.48 443	18.06 436	24.47 437	28.83 437

High-resolution multi-view benchmark