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.

LCM-MVSNet		99.43 1	99.49 1	99.24 2	99.95 1	98.13 2	99.37 1	99.57 1	99.82 1	99.86 1	99.85 1	99.52 1	99.73 2	97.58 2	99.94 1	99.85 2

mamv4		98.21 2	97.86 3	99.26 1	98.24 74	99.36 1	96.10 63	99.32 2	98.75 2	99.58 2	98.70 20	91.78 133	99.88 1	98.60 1	99.67 20	98.54 123

mvs5depth		95.28 88	95.82 72	93.66 165	96.42 196	83.08 227	97.35 12	99.28 3	96.44 26	96.20 113	99.65 2	84.10 252	98.01 237	94.06 53	98.93 125	99.87 1

mmtdpeth		95.82 62	96.02 58	95.23 95	96.91 158	88.62 113	96.49 39	99.26 4	95.07 44	93.41 228	99.29 4	90.25 172	97.27 296	94.49 43	99.01 113	99.80 3

SPE-MVS-test		95.32 84	95.10 103	95.96 60	96.86 162	90.75 78	96.33 49	99.20 5	93.99 60	91.03 301	93.73 296	93.52 87	99.55 19	91.81 127	99.45 45	97.58 215

LCM-MVSNet-Re		94.20 137	94.58 127	93.04 188	95.91 241	83.13 226	93.79 158	99.19 6	92.00 103	98.84 6	98.04 49	93.64 84	99.02 110	81.28 315	98.54 176	96.96 252

EC-MVSNet		95.44 76	95.62 79	94.89 106	96.93 157	87.69 134	96.48 40	99.14 7	93.93 63	92.77 258	94.52 270	93.95 82	99.49 28	93.62 66	99.22 89	97.51 221

CS-MVS		95.77 64	95.58 81	96.37 54	96.84 164	91.72 65	96.73 28	99.06 8	94.23 56	92.48 267	94.79 259	93.56 85	99.49 28	93.47 74	99.05 106	97.89 187

LTVRE_ROB	93.87 1	97.93 3	98.16 2	97.26 30	98.81 27	93.86 35	99.07 2	98.98 9	97.01 15	98.92 5	98.78 16	95.22 42	98.61 176	96.85 7	99.77 9	99.31 30
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
dcpmvs_2		93.96 145	95.01 106	90.82 277	97.60 122	74.04 359	93.68 163	98.85 10	89.80 168	97.82 32	97.01 137	91.14 153	99.21 84	90.56 157	98.59 171	99.19 38

FOURS1					99.21 3	94.68 16	98.45 4	98.81 11	97.73 7	98.27 21

TDRefinement		97.68 4	97.60 5	97.93 3	99.02 12	95.95 9	98.61 3	98.81 11	97.41 11	97.28 59	98.46 33	94.62 66	98.84 134	94.64 41	99.53 37	98.99 58

test_fmvsmconf0.01_n		95.90 58	96.09 51	95.31 91	97.30 139	89.21 100	94.24 140	98.76 13	86.25 238	97.56 42	98.66 21	95.73 19	98.44 197	97.35 4	98.99 114	98.27 147

ANet_high		94.83 106	96.28 41	90.47 285	96.65 175	73.16 364	94.33 137	98.74 14	96.39 28	98.09 29	98.93 10	93.37 92	98.70 164	90.38 163	99.68 17	99.53 17

ACMH+	88.43 11	96.48 34	96.82 19	95.47 83	98.54 46	89.06 104	95.65 83	98.61 15	96.10 33	98.16 27	97.52 90	96.90 7	98.62 175	90.30 168	99.60 25	98.72 99

test_fmvsmconf0.1_n		95.61 70	95.72 76	95.26 92	96.85 163	89.20 101	93.51 167	98.60 16	85.68 252	97.42 52	98.30 38	95.34 35	98.39 198	96.85 7	98.98 115	98.19 153

SF-MVS		95.88 60	95.88 66	95.87 70	98.12 80	89.65 90	95.58 88	98.56 17	91.84 113	96.36 100	96.68 160	94.37 75	99.32 71	92.41 112	99.05 106	98.64 114

reproduce_model		97.35 5	97.24 12	97.70 5	98.44 58	95.08 12	95.88 74	98.50 18	96.62 22	98.27 21	97.93 57	94.57 68	99.50 22	95.57 24	99.35 59	98.52 126

test_fmvsmvis_n_1920		95.08 97	95.40 89	94.13 143	96.66 174	87.75 133	93.44 171	98.49 19	85.57 256	98.27 21	97.11 128	94.11 80	97.75 268	96.26 14	98.72 155	96.89 255

HPM-MVS		96.81 15	96.62 26	97.36 27	98.89 20	93.53 42	97.51 10	98.44 20	92.35 93	95.95 123	96.41 174	96.71 8	99.42 36	93.99 56	99.36 58	99.13 43
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
AllTest		94.88 104	94.51 128	96.00 58	98.02 90	92.17 54	95.26 102	98.43 21	90.48 155	95.04 178	96.74 155	92.54 118	97.86 254	85.11 274	98.98 115	97.98 174

TestCases				96.00 58	98.02 90	92.17 54		98.43 21	90.48 155	95.04 178	96.74 155	92.54 118	97.86 254	85.11 274	98.98 115	97.98 174

APDe-MVS		96.46 35	96.64 25	95.93 64	97.68 118	89.38 98	96.90 21	98.41 23	92.52 88	97.43 50	97.92 61	95.11 47	99.50 22	94.45 44	99.30 72	98.92 74
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
reproduce-ours		97.28 7	97.19 14	97.57 12	98.37 63	94.84 13	95.57 89	98.40 24	96.36 29	98.18 25	97.78 68	95.47 28	99.50 22	95.26 34	99.33 65	98.36 137

our_new_method		97.28 7	97.19 14	97.57 12	98.37 63	94.84 13	95.57 89	98.40 24	96.36 29	98.18 25	97.78 68	95.47 28	99.50 22	95.26 34	99.33 65	98.36 137

fmvsm_s_conf0.5_n_3		95.20 92	95.95 60	92.94 194	96.60 181	82.18 240	93.13 180	98.39 26	91.44 132	97.16 63	97.68 75	93.03 106	97.82 257	97.54 3	98.63 166	98.81 86

fmvsm_l_conf0.5_n_3		95.19 93	95.36 90	94.68 117	96.79 169	87.49 136	93.05 183	98.38 27	87.21 224	96.59 93	97.76 73	94.20 77	98.11 227	95.90 18	98.40 186	98.42 135

9.14			94.81 111		97.49 129		94.11 147	98.37 28	87.56 219	95.38 154	96.03 203	94.66 64	99.08 100	90.70 154	98.97 120

test_fmvsmconf_n		95.43 77	95.50 83	95.22 97	96.48 193	89.19 102	93.23 177	98.36 29	85.61 255	96.92 77	98.02 51	95.23 41	98.38 201	96.69 10	98.95 124	98.09 161

testf1		96.77 18	96.49 30	97.60 10	99.01 14	96.70 4	96.31 52	98.33 30	94.96 45	97.30 57	97.93 57	96.05 16	97.90 246	89.32 194	99.23 86	98.19 153

APD_test2		96.77 18	96.49 30	97.60 10	99.01 14	96.70 4	96.31 52	98.33 30	94.96 45	97.30 57	97.93 57	96.05 16	97.90 246	89.32 194	99.23 86	98.19 153

MP-MVS-pluss		96.08 52	95.92 64	96.57 48	99.06 10	91.21 69	93.25 175	98.32 32	87.89 209	96.86 79	97.38 100	95.55 26	99.39 52	95.47 27	99.47 41	99.11 46
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
FC-MVSNet-test		95.32 84	95.88 66	93.62 167	98.49 56	81.77 244	95.90 73	98.32 32	93.93 63	97.53 45	97.56 85	88.48 190	99.40 49	92.91 99	99.83 5	99.68 7

COLMAP_ROB	91.06 5	96.75 20	96.62 26	97.13 32	98.38 61	94.31 21	96.79 25	98.32 32	96.69 19	96.86 79	97.56 85	95.48 27	98.77 151	90.11 177	99.44 48	98.31 144
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
DPE-MVS		95.89 59	95.88 66	95.92 66	97.93 97	89.83 88	93.46 169	98.30 35	92.37 91	97.75 35	96.95 139	95.14 44	99.51 21	91.74 129	99.28 80	98.41 136
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
PGM-MVS		96.32 44	95.94 61	97.43 22	98.59 40	93.84 36	95.33 98	98.30 35	91.40 133	95.76 133	96.87 145	95.26 39	99.45 31	92.77 100	99.21 90	99.00 56

ACMH	88.36 12	96.59 31	97.43 6	94.07 145	98.56 41	85.33 193	96.33 49	98.30 35	94.66 49	98.72 9	98.30 38	97.51 5	98.00 239	94.87 38	99.59 27	98.86 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
nrg030		96.32 44	96.55 29	95.62 78	97.83 103	88.55 118	95.77 78	98.29 38	92.68 84	98.03 30	97.91 63	95.13 45	98.95 120	93.85 59	99.49 40	99.36 27

APD-MVS_3200maxsize		96.82 13	96.65 24	97.32 29	97.95 96	93.82 37	96.31 52	98.25 39	95.51 41	96.99 74	97.05 133	95.63 23	99.39 52	93.31 83	98.88 130	98.75 94

LPG-MVS_test		96.38 43	96.23 43	96.84 42	98.36 66	92.13 56	95.33 98	98.25 39	91.78 117	97.07 67	97.22 118	96.38 12	99.28 78	92.07 119	99.59 27	99.11 46

LGP-MVS_train				96.84 42	98.36 66	92.13 56		98.25 39	91.78 117	97.07 67	97.22 118	96.38 12	99.28 78	92.07 119	99.59 27	99.11 46

MGCFI-Net		94.44 122	94.67 123	93.75 161	95.56 264	85.47 190	95.25 103	98.24 42	91.53 129	95.04 178	92.21 333	94.94 57	98.54 186	91.56 137	97.66 251	97.24 239

sasdasda		94.59 115	94.69 119	94.30 137	95.60 262	87.03 147	95.59 85	98.24 42	91.56 127	95.21 169	92.04 338	94.95 55	98.66 170	91.45 139	97.57 255	97.20 241

Anonymous20231211		96.60 29	97.13 16	95.00 103	97.46 132	86.35 169	97.11 18	98.24 42	97.58 9	98.72 9	98.97 9	93.15 100	99.15 91	93.18 89	99.74 12	99.50 19

canonicalmvs		94.59 115	94.69 119	94.30 137	95.60 262	87.03 147	95.59 85	98.24 42	91.56 127	95.21 169	92.04 338	94.95 55	98.66 170	91.45 139	97.57 255	97.20 241

DVP-MVS++		95.93 56	96.34 38	94.70 115	96.54 186	86.66 159	98.45 4	98.22 46	93.26 78	97.54 43	97.36 104	93.12 101	99.38 58	93.88 57	98.68 161	98.04 165

test_0728_SECOND				94.88 107	98.55 44	86.72 156	95.20 106	98.22 46					99.38 58	93.44 77	99.31 70	98.53 125

Vis-MVSNet		95.50 74	95.48 84	95.56 81	98.11 81	89.40 97	95.35 96	98.22 46	92.36 92	94.11 205	98.07 46	92.02 127	99.44 32	93.38 82	97.67 250	97.85 193
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UA-Net		97.35 5	97.24 12	97.69 6	98.22 75	93.87 34	98.42 6	98.19 49	96.95 16	95.46 151	99.23 6	93.45 88	99.57 15	95.34 33	99.89 2	99.63 12

casdiffmvs_mvg		95.10 96	95.62 79	93.53 173	96.25 215	83.23 222	92.66 197	98.19 49	93.06 81	97.49 47	97.15 124	94.78 61	98.71 163	92.27 114	98.72 155	98.65 109
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_one_0601					98.26 71	87.14 144		98.18 51	94.25 55	96.99 74	97.36 104	95.13 45

test0726					98.51 49	86.69 157	95.34 97	98.18 51	91.85 110	97.63 38	97.37 101	95.58 24

MSP-MVS		95.34 83	94.63 125	97.48 18	98.67 32	94.05 27	96.41 45	98.18 51	91.26 136	95.12 173	95.15 241	86.60 228	99.50 22	93.43 80	96.81 286	98.89 77
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
ACMMP		96.61 28	96.34 38	97.43 22	98.61 37	93.88 33	96.95 20	98.18 51	92.26 96	96.33 101	96.84 148	95.10 48	99.40 49	93.47 74	99.33 65	99.02 55
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
EIA-MVS		92.35 196	92.03 199	93.30 182	95.81 248	83.97 211	92.80 192	98.17 55	87.71 214	89.79 326	87.56 388	91.17 152	99.18 89	87.97 229	97.27 267	96.77 261

HPM-MVS_fast		97.01 10	96.89 18	97.39 25	99.12 8	93.92 32	97.16 14	98.17 55	93.11 80	96.48 96	97.36 104	96.92 6	99.34 65	94.31 48	99.38 57	98.92 74

XVG-OURS		94.72 110	94.12 143	96.50 51	98.00 92	94.23 22	91.48 250	98.17 55	90.72 148	95.30 160	96.47 169	87.94 203	96.98 313	91.41 141	97.61 254	98.30 145

ZNCC-MVS		96.42 39	96.20 45	97.07 34	98.80 29	92.79 50	96.08 65	98.16 58	91.74 121	95.34 158	96.36 182	95.68 21	99.44 32	94.41 46	99.28 80	98.97 64

FIs		94.90 103	95.35 91	93.55 170	98.28 69	81.76 245	95.33 98	98.14 59	93.05 82	97.07 67	97.18 122	87.65 206	99.29 74	91.72 130	99.69 14	99.61 14

XVG-OURS-SEG-HR		95.38 81	95.00 107	96.51 50	98.10 82	94.07 24	92.46 206	98.13 60	90.69 149	93.75 219	96.25 192	98.03 2	97.02 312	92.08 118	95.55 317	98.45 132

GDP-MVS		91.56 214	90.83 230	93.77 160	96.34 204	83.65 215	93.66 164	98.12 61	87.32 222	92.98 251	94.71 262	63.58 384	99.30 73	92.61 107	98.14 216	98.35 140

SR-MVS-dyc-post		96.84 11	96.60 28	97.56 14	98.07 84	95.27 10	96.37 46	98.12 61	95.66 39	97.00 72	97.03 134	94.85 60	99.42 36	93.49 71	98.84 135	98.00 170

RE-MVS-def			96.66 23		98.07 84	95.27 10	96.37 46	98.12 61	95.66 39	97.00 72	97.03 134	95.40 31		93.49 71	98.84 135	98.00 170

RPMNet		90.31 246	90.14 248	90.81 278	91.01 376	78.93 295	92.52 202	98.12 61	91.91 107	89.10 334	96.89 144	68.84 353	99.41 42	90.17 175	92.70 382	94.08 356

SED-MVS		96.00 55	96.41 36	94.76 112	98.51 49	86.97 149	95.21 104	98.10 65	91.95 104	97.63 38	97.25 114	96.48 10	99.35 62	93.29 84	99.29 75	97.95 178

test_241102_TWO								98.10 65	91.95 104	97.54 43	97.25 114	95.37 32	99.35 62	93.29 84	99.25 83	98.49 129

test_241102_ONE					98.51 49	86.97 149		98.10 65	91.85 110	97.63 38	97.03 134	96.48 10	98.95 120

WR-MVS_H		96.60 29	97.05 17	95.24 94	99.02 12	86.44 165	96.78 26	98.08 68	97.42 10	98.48 17	97.86 66	91.76 136	99.63 8	94.23 50	99.84 3	99.66 9

CP-MVS		96.44 38	96.08 53	97.54 15	98.29 68	94.62 18	96.80 24	98.08 68	92.67 86	95.08 177	96.39 179	94.77 62	99.42 36	93.17 90	99.44 48	98.58 121

ACMP	88.15 13	95.71 67	95.43 87	96.54 49	98.17 78	91.73 64	94.24 140	98.08 68	89.46 173	96.61 92	96.47 169	95.85 18	99.12 96	90.45 160	99.56 34	98.77 93
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_fmvsm_n_1920		94.72 110	94.74 117	94.67 118	96.30 210	88.62 113	93.19 178	98.07 71	85.63 254	97.08 66	97.35 107	90.86 156	97.66 275	95.70 20	98.48 183	97.74 206

SR-MVS		96.70 23	96.42 33	97.54 15	98.05 86	94.69 15	96.13 62	98.07 71	95.17 43	96.82 82	96.73 157	95.09 49	99.43 35	92.99 97	98.71 157	98.50 127

v7n		96.82 13	97.31 11	95.33 88	98.54 46	86.81 153	96.83 22	98.07 71	96.59 23	98.46 18	98.43 35	92.91 109	99.52 20	96.25 15	99.76 10	99.65 11

UniMVSNet (Re)		95.32 84	95.15 100	95.80 72	97.79 107	88.91 107	92.91 188	98.07 71	93.46 74	96.31 103	95.97 206	90.14 174	99.34 65	92.11 116	99.64 23	99.16 40

SD-MVS		95.19 93	95.73 75	93.55 170	96.62 180	88.88 109	94.67 123	98.05 75	91.26 136	97.25 61	96.40 175	95.42 30	94.36 378	92.72 104	99.19 92	97.40 230
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
casdiffmvs		94.32 130	94.80 112	92.85 199	96.05 231	81.44 252	92.35 213	98.05 75	91.53 129	95.75 135	96.80 149	93.35 93	98.49 190	91.01 148	98.32 199	98.64 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PEN-MVS		96.69 24	97.39 9	94.61 121	99.16 4	84.50 200	96.54 34	98.05 75	98.06 5	98.64 14	98.25 40	95.01 53	99.65 5	92.95 98	99.83 5	99.68 7

XVG-ACMP-BASELINE		95.68 68	95.34 92	96.69 45	98.40 59	93.04 45	94.54 133	98.05 75	90.45 157	96.31 103	96.76 152	92.91 109	98.72 157	91.19 143	99.42 50	98.32 142

baseline		94.26 132	94.80 112	92.64 206	96.08 229	80.99 258	93.69 162	98.04 79	90.80 147	94.89 185	96.32 184	93.19 98	98.48 194	91.68 132	98.51 180	98.43 134

ACMMP_NAP		96.21 48	96.12 50	96.49 52	98.90 19	91.42 67	94.57 129	98.03 80	90.42 158	96.37 99	97.35 107	95.68 21	99.25 81	94.44 45	99.34 63	98.80 88

ACMM	88.83 9	96.30 46	96.07 54	96.97 38	98.39 60	92.95 48	94.74 121	98.03 80	90.82 146	97.15 64	96.85 146	96.25 14	99.00 112	93.10 92	99.33 65	98.95 67
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DeepC-MVS	91.39 4	95.43 77	95.33 93	95.71 76	97.67 119	90.17 84	93.86 156	98.02 82	87.35 220	96.22 111	97.99 54	94.48 73	99.05 105	92.73 103	99.68 17	97.93 181
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
GST-MVS		96.24 47	95.99 59	97.00 37	98.65 33	92.71 51	95.69 82	98.01 83	92.08 102	95.74 136	96.28 188	95.22 42	99.42 36	93.17 90	99.06 103	98.88 79

OurMVSNet-221017-0		96.80 16	96.75 21	96.96 39	99.03 11	91.85 61	97.98 7	98.01 83	94.15 58	98.93 4	99.07 7	88.07 199	99.57 15	95.86 19	99.69 14	99.46 20

SteuartSystems-ACMMP		96.40 41	96.30 40	96.71 44	98.63 34	91.96 59	95.70 80	98.01 83	93.34 77	96.64 90	96.57 166	94.99 54	99.36 61	93.48 73	99.34 63	98.82 84
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.39 42	96.17 48	97.04 35	98.51 49	93.37 43	96.30 56	97.98 86	92.35 93	95.63 141	96.47 169	95.37 32	99.27 80	93.78 61	99.14 99	98.48 130

LS3D		96.11 51	95.83 70	96.95 40	94.75 290	94.20 23	97.34 13	97.98 86	97.31 12	95.32 159	96.77 150	93.08 103	99.20 87	91.79 128	98.16 214	97.44 226

PS-CasMVS		96.69 24	97.43 6	94.49 131	99.13 6	84.09 210	96.61 32	97.97 88	97.91 6	98.64 14	98.13 43	95.24 40	99.65 5	93.39 81	99.84 3	99.72 4

region2R		96.41 40	96.09 51	97.38 26	98.62 35	93.81 39	96.32 51	97.96 89	92.26 96	95.28 163	96.57 166	95.02 52	99.41 42	93.63 65	99.11 101	98.94 68

ACMMPR		96.46 35	96.14 49	97.41 24	98.60 38	93.82 37	96.30 56	97.96 89	92.35 93	95.57 144	96.61 164	94.93 58	99.41 42	93.78 61	99.15 98	99.00 56

XVS		96.49 33	96.18 46	97.44 20	98.56 41	93.99 30	96.50 37	97.95 91	94.58 50	94.38 200	96.49 168	94.56 69	99.39 52	93.57 67	99.05 106	98.93 70

X-MVStestdata		90.70 229	88.45 277	97.44 20	98.56 41	93.99 30	96.50 37	97.95 91	94.58 50	94.38 200	26.89 425	94.56 69	99.39 52	93.57 67	99.05 106	98.93 70

Gipuma		95.31 87	95.80 73	93.81 159	97.99 95	90.91 74	96.42 44	97.95 91	96.69 19	91.78 288	98.85 14	91.77 134	95.49 357	91.72 130	99.08 102	95.02 333
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DTE-MVSNet		96.74 21	97.43 6	94.67 118	99.13 6	84.68 199	96.51 36	97.94 94	98.14 4	98.67 13	98.32 37	95.04 50	99.69 4	93.27 86	99.82 7	99.62 13

RRT-MVS		92.28 198	93.01 173	90.07 297	94.06 310	73.01 366	95.36 95	97.88 95	92.24 98	95.16 171	97.52 90	78.51 302	99.29 74	90.55 158	95.83 312	97.92 183

PS-MVSNAJss		96.01 54	96.04 56	95.89 69	98.82 25	88.51 119	95.57 89	97.88 95	88.72 190	98.81 7	98.86 12	90.77 159	99.60 10	95.43 29	99.53 37	99.57 16

pmmvs6		96.80 16	97.36 10	95.15 100	99.12 8	87.82 132	96.68 29	97.86 97	96.10 33	98.14 28	99.28 5	97.94 3	98.21 216	91.38 142	99.69 14	99.42 21

TranMVSNet+NR-MVSNet		96.07 53	96.26 42	95.50 82	98.26 71	87.69 134	93.75 159	97.86 97	95.96 38	97.48 48	97.14 125	95.33 36	99.44 32	90.79 151	99.76 10	99.38 25

PHI-MVS		94.34 129	93.80 150	95.95 61	95.65 258	91.67 66	94.82 119	97.86 97	87.86 210	93.04 248	94.16 281	91.58 138	98.78 148	90.27 170	98.96 122	97.41 227

ETV-MVS		92.99 173	92.74 181	93.72 164	95.86 243	86.30 170	92.33 214	97.84 100	91.70 124	92.81 255	86.17 398	92.22 123	99.19 88	88.03 228	97.73 245	95.66 313

UniMVSNet_NR-MVSNet		95.35 82	95.21 98	95.76 73	97.69 117	88.59 116	92.26 220	97.84 100	94.91 47	96.80 83	95.78 217	90.42 168	99.41 42	91.60 134	99.58 31	99.29 31

3Dnovator+	92.74 2	95.86 61	95.77 74	96.13 56	96.81 167	90.79 77	96.30 56	97.82 102	96.13 32	94.74 191	97.23 116	91.33 143	99.16 90	93.25 87	98.30 200	98.46 131

HQP_MVS		94.26 132	93.93 146	95.23 95	97.71 114	88.12 125	94.56 130	97.81 103	91.74 121	93.31 233	95.59 224	86.93 221	98.95 120	89.26 200	98.51 180	98.60 119

plane_prior5								97.81 103					98.95 120	89.26 200	98.51 180	98.60 119

DU-MVS		95.28 88	95.12 102	95.75 74	97.75 109	88.59 116	92.58 200	97.81 103	93.99 60	96.80 83	95.90 207	90.10 177	99.41 42	91.60 134	99.58 31	99.26 32

APD-MVS		95.00 99	94.69 119	95.93 64	97.38 134	90.88 75	94.59 126	97.81 103	89.22 180	95.46 151	96.17 197	93.42 91	99.34 65	89.30 196	98.87 133	97.56 218
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SMA-MVS		95.77 64	95.54 82	96.47 53	98.27 70	91.19 70	95.09 109	97.79 107	86.48 234	97.42 52	97.51 94	94.47 74	99.29 74	93.55 69	99.29 75	98.93 70
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
test_vis1_n_1920		89.45 266	89.85 253	88.28 333	93.59 320	76.71 332	90.67 272	97.78 108	79.67 328	90.30 315	96.11 199	76.62 323	92.17 393	90.31 167	93.57 366	95.96 297

MP-MVS		96.14 50	95.68 77	97.51 17	98.81 27	94.06 25	96.10 63	97.78 108	92.73 83	93.48 226	96.72 158	94.23 76	99.42 36	91.99 121	99.29 75	99.05 53
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MSLP-MVS++		93.25 166	93.88 147	91.37 252	96.34 204	82.81 232	93.11 181	97.74 110	89.37 176	94.08 207	95.29 239	90.40 170	96.35 338	90.35 165	98.25 205	94.96 334

mPP-MVS		96.46 35	96.05 55	97.69 6	98.62 35	94.65 17	96.45 41	97.74 110	92.59 87	95.47 149	96.68 160	94.50 71	99.42 36	93.10 92	99.26 82	98.99 58

test_vis3_rt		90.40 238	90.03 249	91.52 249	92.58 338	88.95 106	90.38 282	97.72 112	73.30 376	97.79 33	97.51 94	77.05 316	87.10 414	89.03 207	94.89 336	98.50 127

TAPA-MVS	88.58 10	92.49 191	91.75 208	94.73 113	96.50 190	89.69 89	92.91 188	97.68 113	78.02 345	92.79 257	94.10 282	90.85 157	97.96 243	84.76 280	98.16 214	96.54 266
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CPTT-MVS		94.74 109	94.12 143	96.60 47	98.15 79	93.01 46	95.84 76	97.66 114	89.21 181	93.28 236	95.46 230	88.89 188	98.98 113	89.80 184	98.82 141	97.80 199

APD_test1		95.91 57	95.42 88	97.36 27	98.82 25	96.62 7	95.64 84	97.64 115	93.38 76	95.89 128	97.23 116	93.35 93	97.66 275	88.20 220	98.66 165	97.79 200

DP-MVS		95.62 69	95.84 69	94.97 104	97.16 146	88.62 113	94.54 133	97.64 115	96.94 17	96.58 94	97.32 111	93.07 104	98.72 157	90.45 160	98.84 135	97.57 216

MTGPA								97.62 117

MTAPA		96.65 26	96.38 37	97.47 19	98.95 18	94.05 27	95.88 74	97.62 117	94.46 54	96.29 105	96.94 140	93.56 85	99.37 60	94.29 49	99.42 50	98.99 58

anonymousdsp		96.74 21	96.42 33	97.68 8	98.00 92	94.03 29	96.97 19	97.61 119	87.68 216	98.45 19	98.77 17	94.20 77	99.50 22	96.70 9	99.40 55	99.53 17

mvs_tets		96.83 12	96.71 22	97.17 31	98.83 24	92.51 52	96.58 33	97.61 119	87.57 218	98.80 8	98.90 11	96.50 9	99.59 14	96.15 16	99.47 41	99.40 24

MVSMamba_PlusPlus		94.82 107	95.89 65	91.62 244	97.82 104	78.88 299	96.52 35	97.60 121	97.14 14	94.23 203	98.48 32	87.01 218	99.71 3	95.43 29	98.80 145	96.28 282

VPA-MVSNet		95.14 95	95.67 78	93.58 169	97.76 108	83.15 225	94.58 128	97.58 122	93.39 75	97.05 70	98.04 49	93.25 96	98.51 189	89.75 187	99.59 27	99.08 50

v10		94.68 113	95.27 97	92.90 197	96.57 183	80.15 265	94.65 125	97.57 123	90.68 150	97.43 50	98.00 52	88.18 196	99.15 91	94.84 39	99.55 35	99.41 23

CSCG		94.69 112	94.75 115	94.52 128	97.55 126	87.87 130	95.01 114	97.57 123	92.68 84	96.20 113	93.44 304	91.92 130	98.78 148	89.11 205	99.24 85	96.92 253

ZD-MVS					97.23 141	90.32 82		97.54 125	84.40 277	94.78 189	95.79 214	92.76 114	99.39 52	88.72 215	98.40 186

UniMVSNet_ETH3D		97.13 9	97.72 4	95.35 86	99.51 2	87.38 138	97.70 8	97.54 125	98.16 3	98.94 3	99.33 3	97.84 4	99.08 100	90.73 153	99.73 13	99.59 15

Effi-MVS+		92.79 181	92.74 181	92.94 194	95.10 278	83.30 220	94.00 151	97.53 127	91.36 134	89.35 333	90.65 361	94.01 81	98.66 170	87.40 239	95.30 326	96.88 257

CP-MVSNet		96.19 49	96.80 20	94.38 136	98.99 16	83.82 213	96.31 52	97.53 127	97.60 8	98.34 20	97.52 90	91.98 129	99.63 8	93.08 94	99.81 8	99.70 5

RPSCF		95.58 72	94.89 109	97.62 9	97.58 124	96.30 8	95.97 70	97.53 127	92.42 89	93.41 228	97.78 68	91.21 148	97.77 265	91.06 145	97.06 274	98.80 88

fmvsm_s_conf0.1_n_2		94.38 125	94.78 114	93.19 185	97.07 150	81.72 247	91.97 229	97.51 130	87.05 229	97.31 56	97.92 61	88.29 194	98.15 223	97.10 5	98.81 143	99.70 5

diffmvs		91.74 209	91.93 203	91.15 265	93.06 328	78.17 310	88.77 330	97.51 130	86.28 237	92.42 271	93.96 289	88.04 200	97.46 285	90.69 155	96.67 292	97.82 197
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
balanced_conf03		93.45 158	94.17 141	91.28 258	95.81 248	78.40 306	96.20 60	97.48 132	88.56 196	95.29 162	97.20 121	85.56 241	99.21 84	92.52 110	98.91 127	96.24 285

PVSNet_Blended_VisFu		91.63 212	91.20 220	92.94 194	97.73 112	83.95 212	92.14 223	97.46 133	78.85 341	92.35 275	94.98 249	84.16 251	99.08 100	86.36 258	96.77 288	95.79 306

DeepPCF-MVS	90.46 6	94.20 137	93.56 162	96.14 55	95.96 238	92.96 47	89.48 310	97.46 133	85.14 264	96.23 110	95.42 233	93.19 98	98.08 230	90.37 164	98.76 151	97.38 233

jajsoiax		96.59 31	96.42 33	97.12 33	98.76 30	92.49 53	96.44 43	97.42 135	86.96 230	98.71 11	98.72 19	95.36 34	99.56 18	95.92 17	99.45 45	99.32 29

OMC-MVS		94.22 136	93.69 155	95.81 71	97.25 140	91.27 68	92.27 219	97.40 136	87.10 228	94.56 195	95.42 233	93.74 83	98.11 227	86.62 251	98.85 134	98.06 162

v1240		93.29 162	93.71 154	92.06 230	96.01 236	77.89 314	91.81 242	97.37 137	85.12 265	96.69 88	96.40 175	86.67 226	99.07 104	94.51 42	98.76 151	99.22 35

NR-MVSNet		95.28 88	95.28 96	95.26 92	97.75 109	87.21 142	95.08 110	97.37 137	93.92 65	97.65 37	95.90 207	90.10 177	99.33 70	90.11 177	99.66 21	99.26 32

MVSFormer		92.18 202	92.23 194	92.04 231	94.74 291	80.06 269	97.15 15	97.37 137	88.98 184	88.83 337	92.79 320	77.02 317	99.60 10	96.41 12	96.75 289	96.46 274

test_djsdf		96.62 27	96.49 30	97.01 36	98.55 44	91.77 63	97.15 15	97.37 137	88.98 184	98.26 24	98.86 12	93.35 93	99.60 10	96.41 12	99.45 45	99.66 9

DP-MVS Recon		92.31 197	91.88 204	93.60 168	97.18 145	86.87 152	91.10 260	97.37 137	84.92 270	92.08 284	94.08 283	88.59 189	98.20 217	83.50 289	98.14 216	95.73 308

test_prior				94.61 121	95.95 239	87.23 141		97.36 142					98.68 168			97.93 181

QAPM		92.88 177	92.77 179	93.22 184	95.82 246	83.31 219	96.45 41	97.35 143	83.91 281	93.75 219	96.77 150	89.25 186	98.88 127	84.56 282	97.02 276	97.49 222

GeoE		94.55 118	94.68 122	94.15 141	97.23 141	85.11 195	94.14 146	97.34 144	88.71 191	95.26 164	95.50 229	94.65 65	99.12 96	90.94 149	98.40 186	98.23 149

OPM-MVS		95.61 70	95.45 85	96.08 57	98.49 56	91.00 72	92.65 198	97.33 145	90.05 163	96.77 85	96.85 146	95.04 50	98.56 183	92.77 100	99.06 103	98.70 103
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
HQP3-MVS								97.31 146							97.73 245

HQP-MVS		92.09 203	91.49 214	93.88 154	96.36 200	84.89 197	91.37 251	97.31 146	87.16 225	88.81 339	93.40 305	84.76 247	98.60 178	86.55 254	97.73 245	98.14 158

PCF-MVS	84.52 17	89.12 272	87.71 296	93.34 180	96.06 230	85.84 182	86.58 369	97.31 146	68.46 404	93.61 223	93.89 292	87.51 209	98.52 188	67.85 403	98.11 219	95.66 313
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
114514_t		90.51 234	89.80 254	92.63 209	98.00 92	82.24 239	93.40 172	97.29 149	65.84 411	89.40 332	94.80 258	86.99 219	98.75 152	83.88 288	98.61 168	96.89 255

CLD-MVS		91.82 206	91.41 216	93.04 188	96.37 198	83.65 215	86.82 361	97.29 149	84.65 274	92.27 279	89.67 370	92.20 125	97.85 256	83.95 287	99.47 41	97.62 213
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
3Dnovator	92.54 3	94.80 108	94.90 108	94.47 132	95.47 268	87.06 146	96.63 31	97.28 151	91.82 116	94.34 202	97.41 98	90.60 166	98.65 173	92.47 111	98.11 219	97.70 208

fmvsm_s_conf0.5_n_2		94.25 135	94.63 125	93.10 187	96.65 175	81.75 246	91.72 245	97.25 152	86.93 233	97.20 62	97.67 77	88.44 192	98.14 226	97.06 6	98.77 149	99.42 21

DELS-MVS		92.05 204	92.16 195	91.72 239	94.44 300	80.13 267	87.62 342	97.25 152	87.34 221	92.22 280	93.18 312	89.54 184	98.73 156	89.67 188	98.20 212	96.30 280
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
v1921920		93.26 164	93.61 159	92.19 223	96.04 235	78.31 308	91.88 237	97.24 154	85.17 263	96.19 116	96.19 194	86.76 225	99.05 105	94.18 51	98.84 135	99.22 35

test_0402		95.73 66	96.22 44	94.26 139	98.19 77	85.77 183	93.24 176	97.24 154	96.88 18	97.69 36	97.77 72	94.12 79	99.13 95	91.54 138	99.29 75	97.88 188

v1192		93.49 156	93.78 151	92.62 210	96.16 221	79.62 282	91.83 241	97.22 156	86.07 243	96.10 119	96.38 180	87.22 213	99.02 110	94.14 52	98.88 130	99.22 35

F-COLMAP		92.28 198	91.06 225	95.95 61	97.52 127	91.90 60	93.53 166	97.18 157	83.98 280	88.70 345	94.04 284	88.41 193	98.55 185	80.17 327	95.99 307	97.39 231

patch_mono-2		92.46 192	92.72 184	91.71 240	96.65 175	78.91 298	88.85 327	97.17 158	83.89 282	92.45 269	96.76 152	89.86 181	97.09 308	90.24 172	98.59 171	99.12 45

v8		94.65 114	95.29 95	92.74 202	96.65 175	79.77 280	94.59 126	97.17 158	91.86 109	97.47 49	97.93 57	88.16 197	99.08 100	94.32 47	99.47 41	99.38 25

v144192		93.20 169	93.54 163	92.16 227	96.05 231	78.26 309	91.95 230	97.14 160	84.98 269	95.96 122	96.11 199	87.08 217	99.04 108	93.79 60	98.84 135	99.17 39

DeepC-MVS_fast	89.96 7	93.73 151	93.44 165	94.60 124	96.14 224	87.90 129	93.36 174	97.14 160	85.53 257	93.90 217	95.45 231	91.30 145	98.59 180	89.51 190	98.62 167	97.31 236
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MCST-MVS		92.91 175	92.51 188	94.10 144	97.52 127	85.72 185	91.36 254	97.13 162	80.33 321	92.91 254	94.24 277	91.23 147	98.72 157	89.99 181	97.93 236	97.86 191

KD-MVS_self_test		94.10 140	94.73 118	92.19 223	97.66 120	79.49 286	94.86 118	97.12 163	89.59 172	96.87 78	97.65 79	90.40 170	98.34 206	89.08 206	99.35 59	98.75 94

pm-mvs1		95.43 77	95.94 61	93.93 152	98.38 61	85.08 196	95.46 94	97.12 163	91.84 113	97.28 59	98.46 33	95.30 38	97.71 272	90.17 175	99.42 50	98.99 58

save fliter					97.46 132	88.05 127	92.04 226	97.08 165	87.63 217

CDPH-MVS		92.67 186	91.83 206	95.18 99	96.94 155	88.46 121	90.70 271	97.07 166	77.38 348	92.34 277	95.08 246	92.67 116	98.88 127	85.74 264	98.57 173	98.20 152

test_fmvs3		92.42 193	92.40 192	92.46 218	93.80 318	87.28 140	93.86 156	97.05 167	76.86 354	96.25 108	98.66 21	82.87 263	91.26 397	95.44 28	96.83 285	98.82 84

OpenMVS	89.45 8	92.27 200	92.13 198	92.68 205	94.53 299	84.10 209	95.70 80	97.03 168	82.44 302	91.14 300	96.42 173	88.47 191	98.38 201	85.95 262	97.47 260	95.55 318

原ACMM1				92.87 198	96.91 158	84.22 206		97.01 169	76.84 355	89.64 329	94.46 271	88.00 201	98.70 164	81.53 313	98.01 230	95.70 311

DVP-MVS		95.82 62	96.18 46	94.72 114	98.51 49	86.69 157	95.20 106	97.00 170	91.85 110	97.40 54	97.35 107	95.58 24	99.34 65	93.44 77	99.31 70	98.13 159
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
CANet		92.38 195	91.99 201	93.52 175	93.82 317	83.46 217	91.14 258	97.00 170	89.81 167	86.47 370	94.04 284	87.90 204	99.21 84	89.50 191	98.27 202	97.90 185

HPM-MVS++		95.02 98	94.39 130	96.91 41	97.88 100	93.58 41	94.09 149	96.99 172	91.05 141	92.40 272	95.22 240	91.03 155	99.25 81	92.11 116	98.69 160	97.90 185

v1144		93.50 155	93.81 148	92.57 213	96.28 211	79.61 283	91.86 240	96.96 173	86.95 231	95.91 126	96.32 184	87.65 206	98.96 118	93.51 70	98.88 130	99.13 43

MVS_Test		92.57 190	93.29 167	90.40 288	93.53 321	75.85 341	92.52 202	96.96 173	88.73 189	92.35 275	96.70 159	90.77 159	98.37 205	92.53 109	95.49 319	96.99 251

PVSNet_BlendedMVS		90.35 243	89.96 250	91.54 248	94.81 286	78.80 303	90.14 290	96.93 175	79.43 331	88.68 346	95.06 247	86.27 231	98.15 223	80.27 323	98.04 226	97.68 210

PVSNet_Blended		88.74 285	88.16 291	90.46 287	94.81 286	78.80 303	86.64 365	96.93 175	74.67 367	88.68 346	89.18 377	86.27 231	98.15 223	80.27 323	96.00 306	94.44 351

TEST9					96.45 194	89.46 93	90.60 274	96.92 177	79.09 337	90.49 309	94.39 273	91.31 144	98.88 127

train_agg		92.71 185	91.83 206	95.35 86	96.45 194	89.46 93	90.60 274	96.92 177	79.37 332	90.49 309	94.39 273	91.20 149	98.88 127	88.66 216	98.43 185	97.72 207

NCCC		94.08 141	93.54 163	95.70 77	96.49 191	89.90 87	92.39 212	96.91 179	90.64 151	92.33 278	94.60 267	90.58 167	98.96 118	90.21 174	97.70 248	98.23 149

test_8					96.37 198	89.14 103	90.51 277	96.89 180	79.37 332	90.42 311	94.36 275	91.20 149	98.82 136

agg_prior					96.20 218	88.89 108		96.88 181		90.21 316			98.78 148

MSC_two_6792asdad				95.90 67	96.54 186	89.57 91		96.87 182					99.41 42	94.06 53	99.30 72	98.72 99

No_MVS				95.90 67	96.54 186	89.57 91		96.87 182					99.41 42	94.06 53	99.30 72	98.72 99

MIMVSNet1		95.52 73	95.45 85	95.72 75	99.14 5	89.02 105	96.23 59	96.87 182	93.73 67	97.87 31	98.49 31	90.73 163	99.05 105	86.43 257	99.60 25	99.10 49

IU-MVS					98.51 49	86.66 159		96.83 185	72.74 381	95.83 130				93.00 96	99.29 75	98.64 114

TSAR-MVS + MP.		94.96 101	94.75 115	95.57 80	98.86 22	88.69 110	96.37 46	96.81 186	85.23 261	94.75 190	97.12 127	91.85 131	99.40 49	93.45 76	98.33 197	98.62 118
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CNVR-MVS		94.58 117	94.29 135	95.46 84	96.94 155	89.35 99	91.81 242	96.80 187	89.66 170	93.90 217	95.44 232	92.80 113	98.72 157	92.74 102	98.52 178	98.32 142

cascas		87.02 323	86.28 326	89.25 315	91.56 370	76.45 335	84.33 394	96.78 188	71.01 391	86.89 369	85.91 399	81.35 280	96.94 315	83.09 293	95.60 316	94.35 353

IterMVS-LS		93.78 150	94.28 136	92.27 220	96.27 212	79.21 293	91.87 238	96.78 188	91.77 119	96.57 95	97.07 131	87.15 215	98.74 155	91.99 121	99.03 112	98.86 80
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous20240529		95.50 74	95.83 70	94.50 129	97.33 138	85.93 179	95.19 108	96.77 190	96.64 21	97.61 41	98.05 47	93.23 97	98.79 145	88.60 217	99.04 111	98.78 90

TransMVSNet (Re)		95.27 91	96.04 56	92.97 191	98.37 63	81.92 243	95.07 111	96.76 191	93.97 62	97.77 34	98.57 26	95.72 20	97.90 246	88.89 211	99.23 86	99.08 50

EG-PatchMatch MVS		94.54 119	94.67 123	94.14 142	97.87 102	86.50 161	92.00 228	96.74 192	88.16 205	96.93 76	97.61 82	93.04 105	97.90 246	91.60 134	98.12 218	98.03 168

fmvsm_l_conf0.5_n		93.79 149	93.81 148	93.73 163	96.16 221	86.26 171	92.46 206	96.72 193	81.69 310	95.77 132	97.11 128	90.83 158	97.82 257	95.58 23	97.99 231	97.11 244

1112_ss		88.42 291	87.41 300	91.45 250	96.69 172	80.99 258	89.72 304	96.72 193	73.37 375	87.00 368	90.69 359	77.38 312	98.20 217	81.38 314	93.72 364	95.15 326

Baseline_NR-MVSNet		94.47 121	95.09 104	92.60 212	98.50 55	80.82 261	92.08 224	96.68 195	93.82 66	96.29 105	98.56 27	90.10 177	97.75 268	90.10 179	99.66 21	99.24 34

eth_miper_zixun_eth		90.72 228	90.61 236	91.05 266	92.04 356	76.84 330	86.91 357	96.67 196	85.21 262	94.41 198	93.92 290	79.53 292	98.26 213	89.76 186	97.02 276	98.06 162

Fast-Effi-MVS+-dtu		92.77 183	92.16 195	94.58 127	94.66 296	88.25 123	92.05 225	96.65 197	89.62 171	90.08 318	91.23 349	92.56 117	98.60 178	86.30 259	96.27 302	96.90 254

test11								96.65 197

EGC-MVSNET		80.97 373	75.73 390	96.67 46	98.85 23	94.55 19	96.83 22	96.60 199	2.44 427	5.32 428	98.25 40	92.24 122	98.02 236	91.85 126	99.21 90	97.45 224

LF4IMVS		92.72 184	92.02 200	94.84 109	95.65 258	91.99 58	92.92 187	96.60 199	85.08 267	92.44 270	93.62 299	86.80 224	96.35 338	86.81 246	98.25 205	96.18 288

test_fmvs1_n		88.73 286	88.38 279	89.76 304	92.06 355	82.53 234	92.30 218	96.59 201	71.14 389	92.58 264	95.41 236	68.55 354	89.57 408	91.12 144	95.66 315	97.18 243

fmvsm_l_conf0.5_n_a		93.59 154	93.63 157	93.49 177	96.10 227	85.66 187	92.32 215	96.57 202	81.32 313	95.63 141	97.14 125	90.19 173	97.73 271	95.37 32	98.03 227	97.07 245

GBi-Net		93.21 167	92.96 174	93.97 148	95.40 270	84.29 203	95.99 67	96.56 203	88.63 192	95.10 174	98.53 28	81.31 281	98.98 113	86.74 247	98.38 191	98.65 109

test1		93.21 167	92.96 174	93.97 148	95.40 270	84.29 203	95.99 67	96.56 203	88.63 192	95.10 174	98.53 28	81.31 281	98.98 113	86.74 247	98.38 191	98.65 109

FMVSNet1		94.84 105	95.13 101	93.97 148	97.60 122	84.29 203	95.99 67	96.56 203	92.38 90	97.03 71	98.53 28	90.12 175	98.98 113	88.78 213	99.16 97	98.65 109

ITE_SJBPF				95.95 61	97.34 137	93.36 44		96.55 206	91.93 106	94.82 187	95.39 237	91.99 128	97.08 309	85.53 267	97.96 234	97.41 227

Fast-Effi-MVS+		91.28 222	90.86 228	92.53 215	95.45 269	82.53 234	89.25 320	96.52 207	85.00 268	89.91 322	88.55 382	92.94 107	98.84 134	84.72 281	95.44 321	96.22 286

V42		93.43 159	93.58 160	92.97 191	95.34 274	81.22 255	92.67 196	96.49 208	87.25 223	96.20 113	96.37 181	87.32 212	98.85 133	92.39 113	98.21 210	98.85 83

test_fmvs2		90.62 233	90.40 242	91.29 257	91.93 360	85.46 191	92.70 195	96.48 209	74.44 369	94.91 184	97.59 83	75.52 328	90.57 400	93.44 77	96.56 294	97.84 194

PLC	85.34 15	90.40 238	88.92 269	94.85 108	96.53 189	90.02 85	91.58 247	96.48 209	80.16 322	86.14 372	92.18 334	85.73 236	98.25 214	76.87 356	94.61 345	96.30 280
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
c3_l		91.32 221	91.42 215	91.00 270	92.29 346	76.79 331	87.52 348	96.42 211	85.76 250	94.72 193	93.89 292	82.73 266	98.16 222	90.93 150	98.55 174	98.04 165

USDC		89.02 275	89.08 264	88.84 321	95.07 279	74.50 353	88.97 323	96.39 212	73.21 377	93.27 237	96.28 188	82.16 273	96.39 335	77.55 350	98.80 145	95.62 316

ambc				92.98 190	96.88 160	83.01 229	95.92 72	96.38 213		96.41 98	97.48 96	88.26 195	97.80 260	89.96 182	98.93 125	98.12 160

PAPM_NR		91.03 224	90.81 231	91.68 242	96.73 170	81.10 257	93.72 161	96.35 214	88.19 203	88.77 343	92.12 337	85.09 245	97.25 297	82.40 303	93.90 361	96.68 264

v2v482		93.29 162	93.63 157	92.29 219	96.35 203	78.82 301	91.77 244	96.28 215	88.45 197	95.70 140	96.26 191	86.02 234	98.90 124	93.02 95	98.81 143	99.14 42

AdaColmap		91.63 212	91.36 217	92.47 217	95.56 264	86.36 168	92.24 222	96.27 216	88.88 188	89.90 323	92.69 323	91.65 137	98.32 207	77.38 353	97.64 252	92.72 385

Test_1112_low_res		87.50 310	86.58 318	90.25 292	96.80 168	77.75 316	87.53 347	96.25 217	69.73 400	86.47 370	93.61 300	75.67 327	97.88 250	79.95 329	93.20 374	95.11 330

test12				94.43 134	95.95 239	86.75 155		96.24 218		89.76 327		89.79 182	98.79 145		97.95 235	97.75 205

PAPR		87.65 305	86.77 316	90.27 291	92.85 335	77.38 321	88.56 335	96.23 219	76.82 356	84.98 381	89.75 369	86.08 233	97.16 305	72.33 385	93.35 371	96.26 284

MVS_111021_HR		93.63 153	93.42 166	94.26 139	96.65 175	86.96 151	89.30 317	96.23 219	88.36 201	93.57 224	94.60 267	93.45 88	97.77 265	90.23 173	98.38 191	98.03 168

XXY-MVS		92.58 188	93.16 172	90.84 276	97.75 109	79.84 276	91.87 238	96.22 221	85.94 245	95.53 145	97.68 75	92.69 115	94.48 374	83.21 292	97.51 257	98.21 151

MSDG		90.82 225	90.67 235	91.26 259	94.16 305	83.08 227	86.63 366	96.19 222	90.60 153	91.94 286	91.89 340	89.16 187	95.75 352	80.96 320	94.51 346	94.95 335

miper_ehance_all_eth		90.48 235	90.42 241	90.69 280	91.62 368	76.57 334	86.83 360	96.18 223	83.38 285	94.06 209	92.66 325	82.20 272	98.04 232	89.79 185	97.02 276	97.45 224

TinyColmap		92.00 205	92.76 180	89.71 306	95.62 261	77.02 325	90.72 270	96.17 224	87.70 215	95.26 164	96.29 186	92.54 118	96.45 333	81.77 308	98.77 149	95.66 313

DPM-MVS		89.35 268	88.40 278	92.18 226	96.13 226	84.20 207	86.96 356	96.15 225	75.40 363	87.36 365	91.55 347	83.30 257	98.01 237	82.17 306	96.62 293	94.32 354

test_vis1_n		89.01 277	89.01 267	89.03 317	92.57 339	82.46 236	92.62 199	96.06 226	73.02 379	90.40 312	95.77 218	74.86 330	89.68 406	90.78 152	94.98 334	94.95 335

HyFIR lowres test		87.19 318	85.51 331	92.24 221	97.12 149	80.51 262	85.03 386	96.06 226	66.11 410	91.66 290	92.98 316	70.12 350	99.14 93	75.29 368	95.23 328	97.07 245

xiu_mvs_v1_base_debu		91.47 217	91.52 211	91.33 254	95.69 255	81.56 249	89.92 297	96.05 228	83.22 289	91.26 296	90.74 356	91.55 139	98.82 136	89.29 197	95.91 308	93.62 371

xiu_mvs_v1_base		91.47 217	91.52 211	91.33 254	95.69 255	81.56 249	89.92 297	96.05 228	83.22 289	91.26 296	90.74 356	91.55 139	98.82 136	89.29 197	95.91 308	93.62 371

xiu_mvs_v1_base_debi		91.47 217	91.52 211	91.33 254	95.69 255	81.56 249	89.92 297	96.05 228	83.22 289	91.26 296	90.74 356	91.55 139	98.82 136	89.29 197	95.91 308	93.62 371

SDMVSNet		94.43 123	95.02 105	92.69 204	97.93 97	82.88 231	91.92 234	95.99 231	93.65 72	95.51 146	98.63 23	94.60 67	96.48 331	87.57 235	99.35 59	98.70 103

UnsupCasMVSNet_eth		90.33 244	90.34 243	90.28 290	94.64 297	80.24 263	89.69 305	95.88 232	85.77 249	93.94 216	95.69 221	81.99 275	92.98 390	84.21 285	91.30 393	97.62 213

CANet_DTU		89.85 260	89.17 263	91.87 233	92.20 350	80.02 272	90.79 267	95.87 233	86.02 244	82.53 402	91.77 342	80.01 289	98.57 182	85.66 266	97.70 248	97.01 250

PMVS	87.21 14	94.97 100	95.33 93	93.91 153	98.97 17	97.16 3	95.54 92	95.85 234	96.47 25	93.40 231	97.46 97	95.31 37	95.47 358	86.18 261	98.78 148	89.11 403
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
alignmvs		93.26 164	92.85 178	94.50 129	95.70 254	87.45 137	93.45 170	95.76 235	91.58 126	95.25 166	92.42 331	81.96 276	98.72 157	91.61 133	97.87 240	97.33 235

无先验							89.94 296	95.75 236	70.81 393				98.59 180	81.17 318		94.81 340

test_fmvs1		87.59 307	87.27 303	88.54 327	88.32 406	81.26 254	90.43 281	95.72 237	70.55 395	91.70 289	94.63 265	68.13 355	89.42 409	90.59 156	95.34 325	94.94 337

WR-MVS		93.49 156	93.72 153	92.80 201	97.57 125	80.03 271	90.14 290	95.68 238	93.70 68	96.62 91	95.39 237	87.21 214	99.04 108	87.50 236	99.64 23	99.33 28

VPNet		93.08 170	93.76 152	91.03 267	98.60 38	75.83 343	91.51 248	95.62 239	91.84 113	95.74 136	97.10 130	89.31 185	98.32 207	85.07 276	99.06 103	98.93 70

Anonymous20240521		92.86 180	93.57 161	90.74 279	96.57 183	75.50 345	94.15 144	95.60 240	89.38 175	95.90 127	97.90 65	80.39 288	97.96 243	92.60 108	99.68 17	98.75 94

xiu_mvs_v2_base		89.00 278	89.19 262	88.46 331	94.86 284	74.63 350	86.97 355	95.60 240	80.88 317	87.83 358	88.62 381	91.04 154	98.81 141	82.51 301	94.38 348	91.93 391

PS-MVSNAJ		88.86 282	88.99 268	88.48 330	94.88 282	74.71 348	86.69 364	95.60 240	80.88 317	87.83 358	87.37 391	90.77 159	98.82 136	82.52 300	94.37 349	91.93 391

CHOSEN 1792x2688		87.19 318	85.92 329	91.00 270	97.13 148	79.41 287	84.51 392	95.60 240	64.14 414	90.07 319	94.81 256	78.26 304	97.14 306	73.34 379	95.38 324	96.46 274

miper_enhance_ethall		88.42 291	87.87 294	90.07 297	88.67 405	75.52 344	85.10 385	95.59 244	75.68 359	92.49 266	89.45 373	78.96 295	97.88 250	87.86 232	97.02 276	96.81 259

MVP-Stereo		90.07 255	88.92 269	93.54 172	96.31 208	86.49 162	90.93 264	95.59 244	79.80 324	91.48 292	95.59 224	80.79 285	97.39 291	78.57 344	91.19 394	96.76 262
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
cdsmvs_eth3d_5k		23.35 394	31.13 397	0.00 412	0.00 435	0.00 437	0.00 423	95.58 246	0.00 430	0.00 431	91.15 350	93.43 90	0.00 431	0.00 430	0.00 429	0.00 427

CNLPA		91.72 210	91.20 220	93.26 183	96.17 220	91.02 71	91.14 258	95.55 247	90.16 162	90.87 302	93.56 302	86.31 230	94.40 377	79.92 333	97.12 272	94.37 352

FMVSNet2		92.78 182	92.73 183	92.95 193	95.40 270	81.98 242	94.18 143	95.53 248	88.63 192	96.05 120	97.37 101	81.31 281	98.81 141	87.38 240	98.67 163	98.06 162

ab-mvs		92.40 194	92.62 186	91.74 238	97.02 151	81.65 248	95.84 76	95.50 249	86.95 231	92.95 253	97.56 85	90.70 164	97.50 282	79.63 334	97.43 262	96.06 293

fmvsm_s_conf0.1_n_a		94.26 132	94.37 132	93.95 151	97.36 136	85.72 185	94.15 144	95.44 250	83.25 288	95.51 146	98.05 47	92.54 118	97.19 302	95.55 25	97.46 261	98.94 68

test_cas_vis1_n_1920		88.25 294	88.27 284	88.20 335	92.19 351	78.92 297	89.45 311	95.44 250	75.29 366	93.23 241	95.65 223	71.58 344	90.23 404	88.05 226	93.55 368	95.44 320

MVS_111021_LR		93.66 152	93.28 169	94.80 110	96.25 215	90.95 73	90.21 287	95.43 252	87.91 207	93.74 221	94.40 272	92.88 111	96.38 336	90.39 162	98.28 201	97.07 245

tfpnnormal		94.27 131	94.87 110	92.48 216	97.71 114	80.88 260	94.55 132	95.41 253	93.70 68	96.67 89	97.72 74	91.40 142	98.18 220	87.45 237	99.18 94	98.36 137

Effi-MVS+-dtu		93.90 148	92.60 187	97.77 4	94.74 291	96.67 6	94.00 151	95.41 253	89.94 164	91.93 287	92.13 336	90.12 175	98.97 117	87.68 234	97.48 259	97.67 211

cl____		90.65 231	90.56 238	90.91 274	91.85 361	76.98 328	86.75 362	95.36 255	85.53 257	94.06 209	94.89 252	77.36 314	97.98 242	90.27 170	98.98 115	97.76 203

DIV-MVS_self_test		90.65 231	90.56 238	90.91 274	91.85 361	76.99 327	86.75 362	95.36 255	85.52 259	94.06 209	94.89 252	77.37 313	97.99 241	90.28 169	98.97 120	97.76 203

fmvsm_s_conf0.1_n		94.19 139	94.41 129	93.52 175	97.22 143	84.37 201	93.73 160	95.26 257	84.45 276	95.76 133	98.00 52	91.85 131	97.21 299	95.62 21	97.82 242	98.98 62

fmvsm_s_conf0.5_n_a		94.02 143	94.08 145	93.84 157	96.72 171	85.73 184	93.65 165	95.23 258	83.30 286	95.13 172	97.56 85	92.22 123	97.17 303	95.51 26	97.41 263	98.64 114

testgi		90.38 241	91.34 218	87.50 346	97.49 129	71.54 375	89.43 312	95.16 259	88.38 199	94.54 196	94.68 264	92.88 111	93.09 389	71.60 390	97.85 241	97.88 188

fmvsm_s_conf0.5_n		94.00 144	94.20 140	93.42 179	96.69 172	84.37 201	93.38 173	95.13 260	84.50 275	95.40 153	97.55 89	91.77 134	97.20 300	95.59 22	97.79 243	98.69 106

v148		92.87 179	93.29 167	91.62 244	96.25 215	77.72 317	91.28 255	95.05 261	89.69 169	95.93 125	96.04 202	87.34 211	98.38 201	90.05 180	97.99 231	98.78 90

sd_testset		93.94 146	94.39 130	92.61 211	97.93 97	83.24 221	93.17 179	95.04 262	93.65 72	95.51 146	98.63 23	94.49 72	95.89 350	81.72 310	99.35 59	98.70 103

miper_lstm_enhance		89.90 259	89.80 254	90.19 296	91.37 372	77.50 319	83.82 399	95.00 263	84.84 272	93.05 247	94.96 250	76.53 325	95.20 366	89.96 182	98.67 163	97.86 191

VNet		92.67 186	92.96 174	91.79 236	96.27 212	80.15 265	91.95 230	94.98 264	92.19 100	94.52 197	96.07 201	87.43 210	97.39 291	84.83 278	98.38 191	97.83 195

FMVSNet3		90.78 227	90.32 244	92.16 227	93.03 330	79.92 275	92.54 201	94.95 265	86.17 242	95.10 174	96.01 204	69.97 351	98.75 152	86.74 247	98.38 191	97.82 197

BH-untuned		90.68 230	90.90 226	90.05 300	95.98 237	79.57 284	90.04 293	94.94 266	87.91 207	94.07 208	93.00 314	87.76 205	97.78 264	79.19 340	95.17 330	92.80 384

D2MVS		89.93 258	89.60 259	90.92 272	94.03 311	78.40 306	88.69 332	94.85 267	78.96 339	93.08 245	95.09 245	74.57 331	96.94 315	88.19 221	98.96 122	97.41 227

SixPastTwentyTwo		94.91 102	95.21 98	93.98 147	98.52 48	83.19 224	95.93 71	94.84 268	94.86 48	98.49 16	98.74 18	81.45 279	99.60 10	94.69 40	99.39 56	99.15 41

旧先验1					96.20 218	84.17 208		94.82 269			95.57 228	89.57 183			97.89 238	96.32 279

API-MVS		91.52 216	91.61 209	91.26 259	94.16 305	86.26 171	94.66 124	94.82 269	91.17 139	92.13 283	91.08 352	90.03 180	97.06 311	79.09 341	97.35 266	90.45 401

MonoMVSNet		88.46 290	89.28 261	85.98 366	90.52 383	70.07 385	95.31 101	94.81 271	88.38 199	93.47 227	96.13 198	73.21 336	95.07 367	82.61 298	89.12 402	92.81 383

FMVSNet5		87.82 301	86.56 320	91.62 244	92.31 345	79.81 279	93.49 168	94.81 271	83.26 287	91.36 294	96.93 141	52.77 409	97.49 284	76.07 363	98.03 227	97.55 219

MAR-MVS		90.32 245	88.87 272	94.66 120	94.82 285	91.85 61	94.22 142	94.75 273	80.91 316	87.52 364	88.07 386	86.63 227	97.87 253	76.67 357	96.21 303	94.25 355
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
mvs_anonymous		90.37 242	91.30 219	87.58 345	92.17 352	68.00 392	89.84 300	94.73 274	83.82 283	93.22 242	97.40 99	87.54 208	97.40 290	87.94 230	95.05 333	97.34 234

EI-MVSNet-UG-set		94.35 128	94.27 138	94.59 125	92.46 343	85.87 181	92.42 210	94.69 275	93.67 71	96.13 117	95.84 211	91.20 149	98.86 131	93.78 61	98.23 207	99.03 54

EI-MVSNet-Vis-set		94.36 127	94.28 136	94.61 121	92.55 340	85.98 178	92.44 208	94.69 275	93.70 68	96.12 118	95.81 213	91.24 146	98.86 131	93.76 64	98.22 209	98.98 62

EI-MVSNet		92.99 173	93.26 171	92.19 223	92.12 353	79.21 293	92.32 215	94.67 277	91.77 119	95.24 167	95.85 209	87.14 216	98.49 190	91.99 121	98.26 203	98.86 80

MVSTER		89.32 269	88.75 273	91.03 267	90.10 390	76.62 333	90.85 265	94.67 277	82.27 303	95.24 167	95.79 214	61.09 394	98.49 190	90.49 159	98.26 203	97.97 177

新几何1				93.17 186	97.16 146	87.29 139		94.43 279	67.95 405	91.29 295	94.94 251	86.97 220	98.23 215	81.06 319	97.75 244	93.98 361

CMPMVS	68.83 22	87.28 314	85.67 330	92.09 229	88.77 404	85.42 192	90.31 285	94.38 280	70.02 398	88.00 355	93.30 307	73.78 335	94.03 382	75.96 365	96.54 295	96.83 258
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
IS-MVSNet		94.49 120	94.35 134	94.92 105	98.25 73	86.46 164	97.13 17	94.31 281	96.24 31	96.28 107	96.36 182	82.88 262	99.35 62	88.19 221	99.52 39	98.96 66

tt0805		95.42 80	95.93 63	93.86 156	98.75 31	88.47 120	97.68 9	94.29 282	96.48 24	95.38 154	93.63 298	94.89 59	97.94 245	95.38 31	96.92 282	95.17 324

testdata				91.03 267	96.87 161	82.01 241		94.28 283	71.55 386	92.46 268	95.42 233	85.65 238	97.38 293	82.64 297	97.27 267	93.70 368

UGNet		93.08 170	92.50 189	94.79 111	93.87 315	87.99 128	95.07 111	94.26 284	90.64 151	87.33 366	97.67 77	86.89 223	98.49 190	88.10 224	98.71 157	97.91 184
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
MVS		84.98 338	84.30 339	87.01 350	91.03 375	77.69 318	91.94 232	94.16 285	59.36 419	84.23 388	87.50 390	85.66 237	96.80 323	71.79 387	93.05 379	86.54 411

1314		86.46 328	86.33 325	86.87 355	91.65 367	74.54 351	91.94 232	94.10 286	74.28 370	84.78 383	87.33 392	83.03 261	95.00 368	78.72 342	91.16 395	91.06 398

cl22		89.02 275	88.50 276	90.59 283	89.76 392	76.45 335	86.62 367	94.03 287	82.98 295	92.65 261	92.49 326	72.05 342	97.53 280	88.93 208	97.02 276	97.78 201

EPP-MVSNet		93.91 147	93.68 156	94.59 125	98.08 83	85.55 189	97.44 11	94.03 287	94.22 57	94.94 182	96.19 194	82.07 274	99.57 15	87.28 241	98.89 128	98.65 109

UnsupCasMVSNet_bld		88.50 289	88.03 292	89.90 302	95.52 266	78.88 299	87.39 349	94.02 289	79.32 335	93.06 246	94.02 286	80.72 286	94.27 379	75.16 369	93.08 378	96.54 266

h-mvs33		92.89 176	91.99 201	95.58 79	96.97 153	90.55 80	93.94 154	94.01 290	89.23 178	93.95 214	96.19 194	76.88 320	99.14 93	91.02 146	95.71 314	97.04 249

pmmvs-eth3d		91.54 215	90.73 234	93.99 146	95.76 252	87.86 131	90.83 266	93.98 291	78.23 344	94.02 212	96.22 193	82.62 269	96.83 322	86.57 252	98.33 197	97.29 237

BH-RMVSNet		90.47 236	90.44 240	90.56 284	95.21 277	78.65 305	89.15 321	93.94 292	88.21 202	92.74 259	94.22 278	86.38 229	97.88 250	78.67 343	95.39 323	95.14 327

reproduce_monomvs		87.13 320	86.90 312	87.84 343	90.92 378	68.15 391	91.19 257	93.75 293	85.84 247	94.21 204	95.83 212	42.99 422	97.10 307	89.46 192	97.88 239	98.26 148

test222					96.95 154	85.27 194	88.83 328	93.61 294	65.09 413	90.74 305	94.85 254	84.62 249			97.36 265	93.91 362

test_vis1_rt		85.58 333	84.58 336	88.60 326	87.97 407	86.76 154	85.45 383	93.59 295	66.43 408	87.64 361	89.20 376	79.33 293	85.38 418	81.59 311	89.98 401	93.66 369

CDS-MVSNet		89.55 263	88.22 288	93.53 173	95.37 273	86.49 162	89.26 318	93.59 295	79.76 326	91.15 299	92.31 332	77.12 315	98.38 201	77.51 351	97.92 237	95.71 309
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
new-patchmatchnet		88.97 279	90.79 232	83.50 389	94.28 304	55.83 424	85.34 384	93.56 297	86.18 241	95.47 149	95.73 220	83.10 259	96.51 330	85.40 268	98.06 224	98.16 156

IterMVS-SCA-FT		91.65 211	91.55 210	91.94 232	93.89 314	79.22 292	87.56 345	93.51 298	91.53 129	95.37 156	96.62 163	78.65 298	98.90 124	91.89 125	94.95 335	97.70 208

Anonymous20231206		88.77 284	88.29 282	90.20 295	96.31 208	78.81 302	89.56 308	93.49 299	74.26 371	92.38 273	95.58 227	82.21 271	95.43 360	72.07 386	98.75 153	96.34 278

FA-MVS(test-final)		91.81 207	91.85 205	91.68 242	94.95 281	79.99 273	96.00 66	93.44 300	87.80 211	94.02 212	97.29 112	77.60 308	98.45 196	88.04 227	97.49 258	96.61 265

OpenMVS_ROB	85.12 16	89.52 265	89.05 265	90.92 272	94.58 298	81.21 256	91.10 260	93.41 301	77.03 353	93.41 228	93.99 288	83.23 258	97.80 260	79.93 331	94.80 340	93.74 367

VDD-MVS		94.37 126	94.37 132	94.40 135	97.49 129	86.07 176	93.97 153	93.28 302	94.49 52	96.24 109	97.78 68	87.99 202	98.79 145	88.92 209	99.14 99	98.34 141

jason		89.17 271	88.32 280	91.70 241	95.73 253	80.07 268	88.10 338	93.22 303	71.98 384	90.09 317	92.79 320	78.53 301	98.56 183	87.43 238	97.06 274	96.46 274
jason: jason.
PAPM		81.91 367	80.11 377	87.31 348	93.87 315	72.32 373	84.02 396	93.22 303	69.47 401	76.13 419	89.84 364	72.15 341	97.23 298	53.27 421	89.02 403	92.37 388

BH-w/o		87.21 316	87.02 311	87.79 344	94.77 289	77.27 323	87.90 340	93.21 305	81.74 309	89.99 321	88.39 384	83.47 255	96.93 317	71.29 391	92.43 386	89.15 402

ppachtmachnet_test		88.61 288	88.64 274	88.50 329	91.76 363	70.99 379	84.59 391	92.98 306	79.30 336	92.38 273	93.53 303	79.57 291	97.45 286	86.50 256	97.17 271	97.07 245

IterMVS		90.18 248	90.16 245	90.21 294	93.15 326	75.98 340	87.56 345	92.97 307	86.43 236	94.09 206	96.40 175	78.32 303	97.43 287	87.87 231	94.69 343	97.23 240
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test20.03		90.80 226	90.85 229	90.63 282	95.63 260	79.24 291	89.81 301	92.87 308	89.90 165	94.39 199	96.40 175	85.77 235	95.27 365	73.86 377	99.05 106	97.39 231

CR-MVSNet		87.89 298	87.12 309	90.22 293	91.01 376	78.93 295	92.52 202	92.81 309	73.08 378	89.10 334	96.93 141	67.11 360	97.64 277	88.80 212	92.70 382	94.08 356

Patchmtry		90.11 252	89.92 251	90.66 281	90.35 387	77.00 326	92.96 186	92.81 309	90.25 161	94.74 191	96.93 141	67.11 360	97.52 281	85.17 269	98.98 115	97.46 223

GA-MVS		87.70 302	86.82 314	90.31 289	93.27 324	77.22 324	84.72 390	92.79 311	85.11 266	89.82 324	90.07 362	66.80 363	97.76 267	84.56 282	94.27 352	95.96 297

sss		87.23 315	86.82 314	88.46 331	93.96 312	77.94 311	86.84 359	92.78 312	77.59 347	87.61 363	91.83 341	78.75 297	91.92 394	77.84 347	94.20 354	95.52 319

Patchmatch-RL test		88.81 283	88.52 275	89.69 307	95.33 275	79.94 274	86.22 374	92.71 313	78.46 342	95.80 131	94.18 280	66.25 368	95.33 363	89.22 202	98.53 177	93.78 365

test_yl		90.11 252	89.73 257	91.26 259	94.09 308	79.82 277	90.44 278	92.65 314	90.90 142	93.19 243	93.30 307	73.90 333	98.03 233	82.23 304	96.87 283	95.93 299

DCV-MVSNet		90.11 252	89.73 257	91.26 259	94.09 308	79.82 277	90.44 278	92.65 314	90.90 142	93.19 243	93.30 307	73.90 333	98.03 233	82.23 304	96.87 283	95.93 299

CL-MVSNet_self_test		90.04 257	89.90 252	90.47 285	95.24 276	77.81 315	86.60 368	92.62 316	85.64 253	93.25 240	93.92 290	83.84 253	96.06 345	79.93 331	98.03 227	97.53 220

TSAR-MVS + GP.		93.07 172	92.41 191	95.06 102	95.82 246	90.87 76	90.97 263	92.61 317	88.04 206	94.61 194	93.79 295	88.08 198	97.81 259	89.41 193	98.39 190	96.50 271

TAMVS		90.16 249	89.05 265	93.49 177	96.49 191	86.37 167	90.34 284	92.55 318	80.84 319	92.99 249	94.57 269	81.94 277	98.20 217	73.51 378	98.21 210	95.90 302

MS-PatchMatch		88.05 297	87.75 295	88.95 318	93.28 323	77.93 312	87.88 341	92.49 319	75.42 362	92.57 265	93.59 301	80.44 287	94.24 381	81.28 315	92.75 381	94.69 347

MG-MVS		89.54 264	89.80 254	88.76 322	94.88 282	72.47 372	89.60 306	92.44 320	85.82 248	89.48 330	95.98 205	82.85 264	97.74 270	81.87 307	95.27 327	96.08 292

mvsmamba		90.24 247	89.43 260	92.64 206	95.52 266	82.36 237	96.64 30	92.29 321	81.77 308	92.14 282	96.28 188	70.59 348	99.10 99	84.44 284	95.22 329	96.47 273

lupinMVS		88.34 293	87.31 301	91.45 250	94.74 291	80.06 269	87.23 350	92.27 322	71.10 390	88.83 337	91.15 350	77.02 317	98.53 187	86.67 250	96.75 289	95.76 307

pmmvs5		87.87 299	87.14 307	90.07 297	93.26 325	76.97 329	88.89 325	92.18 323	73.71 374	88.36 350	93.89 292	76.86 322	96.73 325	80.32 322	96.81 286	96.51 268

PM-MVS		93.33 161	92.67 185	95.33 88	96.58 182	94.06 25	92.26 220	92.18 323	85.92 246	96.22 111	96.61 164	85.64 239	95.99 348	90.35 165	98.23 207	95.93 299

pmmvs4		88.95 280	87.70 297	92.70 203	94.30 303	85.60 188	87.22 351	92.16 325	74.62 368	89.75 328	94.19 279	77.97 306	96.41 334	82.71 296	96.36 299	96.09 291

MDA-MVSNet-bldmvs		91.04 223	90.88 227	91.55 247	94.68 295	80.16 264	85.49 382	92.14 326	90.41 159	94.93 183	95.79 214	85.10 244	96.93 317	85.15 271	94.19 356	97.57 216

door-mid								92.13 327

WTY-MVS		86.93 324	86.50 324	88.24 334	94.96 280	74.64 349	87.19 352	92.07 328	78.29 343	88.32 351	91.59 346	78.06 305	94.27 379	74.88 370	93.15 376	95.80 305

AUN-MVS		90.05 256	88.30 281	95.32 90	96.09 228	90.52 81	92.42 210	92.05 329	82.08 306	88.45 349	92.86 317	65.76 370	98.69 166	88.91 210	96.07 304	96.75 263

hse-mvs2		92.24 201	91.20 220	95.38 85	96.16 221	90.65 79	92.52 202	92.01 330	89.23 178	93.95 214	92.99 315	76.88 320	98.69 166	91.02 146	96.03 305	96.81 259

BP-MVS1		91.77 208	91.10 224	93.75 161	96.42 196	83.40 218	94.10 148	91.89 331	91.27 135	93.36 232	94.85 254	64.43 378	99.29 74	94.88 37	98.74 154	98.56 122

TR-MVS		87.70 302	87.17 306	89.27 314	94.11 307	79.26 290	88.69 332	91.86 332	81.94 307	90.69 307	89.79 367	82.82 265	97.42 288	72.65 384	91.98 390	91.14 397

VDDNet		94.03 142	94.27 138	93.31 181	98.87 21	82.36 237	95.51 93	91.78 333	97.19 13	96.32 102	98.60 25	84.24 250	98.75 152	87.09 244	98.83 140	98.81 86

test_f		86.65 327	87.13 308	85.19 374	90.28 388	86.11 175	86.52 370	91.66 334	69.76 399	95.73 138	97.21 120	69.51 352	81.28 421	89.15 204	94.40 347	88.17 407

Anonymous202405211		92.58 188	92.50 189	92.83 200	96.55 185	83.22 223	92.43 209	91.64 335	94.10 59	95.59 143	96.64 162	81.88 278	97.50 282	85.12 273	98.52 178	97.77 202

HY-MVS	82.50 18	86.81 326	85.93 328	89.47 308	93.63 319	77.93 312	94.02 150	91.58 336	75.68 359	83.64 392	93.64 297	77.40 311	97.42 288	71.70 389	92.07 389	93.05 380

door								91.26 337

PatchMatch-RL		89.18 270	88.02 293	92.64 206	95.90 242	92.87 49	88.67 334	91.06 338	80.34 320	90.03 320	91.67 344	83.34 256	94.42 376	76.35 361	94.84 339	90.64 400

FE-MVS		89.06 274	88.29 282	91.36 253	94.78 288	79.57 284	96.77 27	90.99 339	84.87 271	92.96 252	96.29 186	60.69 396	98.80 144	80.18 326	97.11 273	95.71 309

ADS-MVSNet2		84.01 347	82.20 359	89.41 310	89.04 401	76.37 337	87.57 343	90.98 340	72.71 382	84.46 384	92.45 327	68.08 356	96.48 331	70.58 397	83.97 412	95.38 321

MM		94.41 124	94.14 142	95.22 97	95.84 244	87.21 142	94.31 139	90.92 341	94.48 53	92.80 256	97.52 90	85.27 242	99.49 28	96.58 11	99.57 33	98.97 64

KD-MVS_2432*1600		82.17 363	80.75 370	86.42 361	82.04 425	70.09 383	81.75 406	90.80 342	82.56 298	90.37 313	89.30 374	42.90 423	96.11 343	74.47 372	92.55 384	93.06 378

miper_refine_blended		82.17 363	80.75 370	86.42 361	82.04 425	70.09 383	81.75 406	90.80 342	82.56 298	90.37 313	89.30 374	42.90 423	96.11 343	74.47 372	92.55 384	93.06 378

wuyk23d		87.83 300	90.79 232	78.96 400	90.46 386	88.63 112	92.72 193	90.67 344	91.65 125	98.68 12	97.64 80	96.06 15	77.53 422	59.84 416	99.41 54	70.73 420

our_test_3		87.55 308	87.59 298	87.44 347	91.76 363	70.48 380	83.83 398	90.55 345	79.79 325	92.06 285	92.17 335	78.63 300	95.63 353	84.77 279	94.73 341	96.22 286

test_method		50.44 391	48.94 394	54.93 405	39.68 431	12.38 434	28.59 422	90.09 346	6.82 425	41.10 427	78.41 418	54.41 405	70.69 425	50.12 422	51.26 424	81.72 418

EU-MVSNet		87.39 312	86.71 317	89.44 309	93.40 322	76.11 338	94.93 117	90.00 347	57.17 420	95.71 139	97.37 101	64.77 377	97.68 274	92.67 105	94.37 349	94.52 349

MVS_0304		92.88 177	92.27 193	94.69 116	92.35 344	86.03 177	92.88 190	89.68 348	90.53 154	91.52 291	96.43 172	82.52 270	99.32 71	95.01 36	99.54 36	98.71 102

CHOSEN 280x420		80.04 381	77.97 388	86.23 365	90.13 389	74.53 352	72.87 417	89.59 349	66.38 409	76.29 418	85.32 404	56.96 401	95.36 361	69.49 400	94.72 342	88.79 405

WBMVS		84.00 348	83.48 347	85.56 369	92.71 336	61.52 416	83.82 399	89.38 350	79.56 330	90.74 305	93.20 311	48.21 412	97.28 295	75.63 367	98.10 221	97.88 188

MDA-MVSNet_test_wron		88.16 296	88.23 287	87.93 339	92.22 348	73.71 360	80.71 410	88.84 351	82.52 300	94.88 186	95.14 242	82.70 267	93.61 384	83.28 291	93.80 363	96.46 274

YYNet1		88.17 295	88.24 286	87.93 339	92.21 349	73.62 361	80.75 409	88.77 352	82.51 301	94.99 181	95.11 244	82.70 267	93.70 383	83.33 290	93.83 362	96.48 272

PVSNet	76.22 20	82.89 358	82.37 357	84.48 380	93.96 312	64.38 411	78.60 412	88.61 353	71.50 387	84.43 386	86.36 397	74.27 332	94.60 373	69.87 399	93.69 365	94.46 350

MIMVSNet		87.13 320	86.54 321	88.89 320	96.05 231	76.11 338	94.39 135	88.51 354	81.37 312	88.27 352	96.75 154	72.38 340	95.52 355	65.71 408	95.47 320	95.03 332

tpmvs		84.22 345	83.97 343	84.94 376	87.09 413	65.18 406	91.21 256	88.35 355	82.87 296	85.21 376	90.96 354	65.24 375	96.75 324	79.60 337	85.25 411	92.90 382

EPNet_dtu		85.63 332	84.37 338	89.40 311	86.30 416	74.33 355	91.64 246	88.26 356	84.84 272	72.96 421	89.85 363	71.27 346	97.69 273	76.60 358	97.62 253	96.18 288
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm cat1		80.61 376	79.46 379	84.07 385	88.78 403	65.06 409	89.26 318	88.23 357	62.27 417	81.90 407	89.66 371	62.70 390	95.29 364	71.72 388	80.60 419	91.86 393

baseline1		87.62 306	87.31 301	88.54 327	94.71 294	74.27 356	93.10 182	88.20 358	86.20 240	92.18 281	93.04 313	73.21 336	95.52 355	79.32 338	85.82 410	95.83 304

CVMVSNet		85.16 336	84.72 334	86.48 359	92.12 353	70.19 381	92.32 215	88.17 359	56.15 421	90.64 308	95.85 209	67.97 358	96.69 326	88.78 213	90.52 398	92.56 386

SCA		87.43 311	87.21 305	88.10 337	92.01 357	71.98 374	89.43 312	88.11 360	82.26 304	88.71 344	92.83 318	78.65 298	97.59 278	79.61 335	93.30 372	94.75 344

testing91		83.56 352	82.45 356	86.91 354	92.92 333	67.29 393	86.33 372	88.07 361	86.22 239	84.26 387	85.76 400	48.15 413	97.17 303	76.27 362	94.08 360	96.27 283

WB-MVS		89.44 267	92.15 197	81.32 395	97.73 112	48.22 427	89.73 303	87.98 362	95.24 42	96.05 120	96.99 138	85.18 243	96.95 314	82.45 302	97.97 233	98.78 90

tpmrst		82.85 359	82.93 353	82.64 391	87.65 408	58.99 422	90.14 290	87.90 363	75.54 361	83.93 390	91.63 345	66.79 365	95.36 361	81.21 317	81.54 418	93.57 374

SSC-MVS		90.16 249	92.96 174	81.78 394	97.88 100	48.48 426	90.75 268	87.69 364	96.02 37	96.70 87	97.63 81	85.60 240	97.80 260	85.73 265	98.60 170	99.06 52

Vis-MVSNet (Re-imp)		90.42 237	90.16 245	91.20 263	97.66 120	77.32 322	94.33 137	87.66 365	91.20 138	92.99 249	95.13 243	75.40 329	98.28 209	77.86 346	99.19 92	97.99 173

MDTV_nov1_ep13			83.88 346		89.42 399	61.52 416	88.74 331	87.41 366	73.99 372	84.96 382	94.01 287	65.25 374	95.53 354	78.02 345	93.16 375

dmvs_re		84.69 342	83.94 344	86.95 353	92.24 347	82.93 230	89.51 309	87.37 367	84.38 278	85.37 375	85.08 405	72.44 339	86.59 415	68.05 402	91.03 397	91.33 395

PatchmatchNet		85.22 335	84.64 335	86.98 351	89.51 398	69.83 387	90.52 276	87.34 368	78.87 340	87.22 367	92.74 322	66.91 362	96.53 328	81.77 308	86.88 408	94.58 348
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ttmdpeth		86.91 325	86.57 319	87.91 341	89.68 394	74.24 357	91.49 249	87.09 369	79.84 323	89.46 331	97.86 66	65.42 372	91.04 398	81.57 312	96.74 291	98.44 133

N_pmnet		88.90 281	87.25 304	93.83 158	94.40 302	93.81 39	84.73 388	87.09 369	79.36 334	93.26 238	92.43 330	79.29 294	91.68 395	77.50 352	97.22 269	96.00 295

EPNet		89.80 262	88.25 285	94.45 133	83.91 423	86.18 173	93.87 155	87.07 371	91.16 140	80.64 412	94.72 261	78.83 296	98.89 126	85.17 269	98.89 128	98.28 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmatch-test		86.10 330	86.01 327	86.38 363	90.63 381	74.22 358	89.57 307	86.69 372	85.73 251	89.81 325	92.83 318	65.24 375	91.04 398	77.82 349	95.78 313	93.88 364

K. test v3		93.37 160	93.27 170	93.66 165	98.05 86	82.62 233	94.35 136	86.62 373	96.05 35	97.51 46	98.85 14	76.59 324	99.65 5	93.21 88	98.20 212	98.73 98

CostFormer		83.09 355	82.21 358	85.73 367	89.27 400	67.01 395	90.35 283	86.47 374	70.42 396	83.52 394	93.23 310	61.18 393	96.85 321	77.21 354	88.26 406	93.34 376

thres200		85.85 331	85.18 332	87.88 342	94.44 300	72.52 371	89.08 322	86.21 375	88.57 195	91.44 293	88.40 383	64.22 379	98.00 239	68.35 401	95.88 311	93.12 377

ET-MVSNet_ETH3D		86.15 329	84.27 340	91.79 236	93.04 329	81.28 253	87.17 353	86.14 376	79.57 329	83.65 391	88.66 379	57.10 400	98.18 220	87.74 233	95.40 322	95.90 302

PatchT		87.51 309	88.17 290	85.55 370	90.64 380	66.91 396	92.02 227	86.09 377	92.20 99	89.05 336	97.16 123	64.15 380	96.37 337	89.21 203	92.98 380	93.37 375

tfpn200view9		87.05 322	86.52 322	88.67 324	95.77 250	72.94 367	91.89 235	86.00 378	90.84 144	92.61 262	89.80 365	63.93 381	98.28 209	71.27 392	96.54 295	94.79 342

thres400		87.20 317	86.52 322	89.24 316	95.77 250	72.94 367	91.89 235	86.00 378	90.84 144	92.61 262	89.80 365	63.93 381	98.28 209	71.27 392	96.54 295	96.51 268

IB-MVS	77.21 19	83.11 354	81.05 366	89.29 313	91.15 374	75.85 341	85.66 381	86.00 378	79.70 327	82.02 406	86.61 394	48.26 411	98.39 198	77.84 347	92.22 387	93.63 370
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
testing99		82.94 357	81.72 360	86.59 357	92.55 340	66.53 399	86.08 376	85.70 381	85.47 260	83.95 389	85.70 401	45.87 415	97.07 310	76.58 359	93.56 367	96.17 290

PMMVS		83.00 356	81.11 365	88.66 325	83.81 424	86.44 165	82.24 405	85.65 382	61.75 418	82.07 404	85.64 402	79.75 290	91.59 396	75.99 364	93.09 377	87.94 408

MVStest1		84.79 340	84.06 342	86.98 351	77.73 428	74.76 347	91.08 262	85.63 383	77.70 346	96.86 79	97.97 55	41.05 427	88.24 412	92.22 115	96.28 301	97.94 180

tpm		84.38 344	84.08 341	85.30 373	90.47 385	63.43 413	89.34 315	85.63 383	77.24 352	87.62 362	95.03 248	61.00 395	97.30 294	79.26 339	91.09 396	95.16 325

LFMVS		91.33 220	91.16 223	91.82 235	96.27 212	79.36 288	95.01 114	85.61 385	96.04 36	94.82 187	97.06 132	72.03 343	98.46 195	84.96 277	98.70 159	97.65 212

FPMVS		84.50 343	83.28 349	88.16 336	96.32 207	94.49 20	85.76 380	85.47 386	83.09 292	85.20 377	94.26 276	63.79 383	86.58 416	63.72 412	91.88 392	83.40 414

tpm2		81.46 368	80.35 375	84.80 377	89.90 391	65.14 407	90.44 278	85.36 387	65.82 412	82.05 405	92.44 329	57.94 399	96.69 326	70.71 396	88.49 405	92.56 386

thres100view900		87.35 313	86.89 313	88.72 323	96.14 224	73.09 365	93.00 185	85.31 388	92.13 101	93.26 238	90.96 354	63.42 385	98.28 209	71.27 392	96.54 295	94.79 342

thres600view7		87.66 304	87.10 310	89.36 312	96.05 231	73.17 363	92.72 193	85.31 388	91.89 108	93.29 235	90.97 353	63.42 385	98.39 198	73.23 380	96.99 281	96.51 268

dp		79.28 384	78.62 384	81.24 396	85.97 418	56.45 423	86.91 357	85.26 390	72.97 380	81.45 410	89.17 378	56.01 404	95.45 359	73.19 381	76.68 420	91.82 394

PMMVS2		81.31 369	83.44 348	74.92 403	90.52 383	46.49 429	69.19 419	85.23 391	84.30 279	87.95 357	94.71 262	76.95 319	84.36 420	64.07 411	98.09 222	93.89 363

ADS-MVSNet		82.25 361	81.55 362	84.34 382	89.04 401	65.30 405	87.57 343	85.13 392	72.71 382	84.46 384	92.45 327	68.08 356	92.33 392	70.58 397	83.97 412	95.38 321

testing11		81.98 366	80.52 373	86.38 363	92.69 337	67.13 394	85.79 379	84.80 393	82.16 305	81.19 411	85.41 403	45.24 416	96.88 320	74.14 375	93.24 373	95.14 327

test-LLR		83.58 351	83.17 350	84.79 378	89.68 394	66.86 397	83.08 401	84.52 394	83.07 293	82.85 398	84.78 406	62.86 388	93.49 385	82.85 294	94.86 337	94.03 359

test-mter		81.21 371	80.01 378	84.79 378	89.68 394	66.86 397	83.08 401	84.52 394	73.85 373	82.85 398	84.78 406	43.66 421	93.49 385	82.85 294	94.86 337	94.03 359

JIA-IIPM		85.08 337	83.04 351	91.19 264	87.56 409	86.14 174	89.40 314	84.44 396	88.98 184	82.20 403	97.95 56	56.82 402	96.15 341	76.55 360	83.45 414	91.30 396

thisisatest0530		88.69 287	87.52 299	92.20 222	96.33 206	79.36 288	92.81 191	84.01 397	86.44 235	93.67 222	92.68 324	53.62 408	99.25 81	89.65 189	98.45 184	98.00 170

tttt0517		89.81 261	88.90 271	92.55 214	97.00 152	79.73 281	95.03 113	83.65 398	89.88 166	95.30 160	94.79 259	53.64 407	99.39 52	91.99 121	98.79 147	98.54 123

thisisatest0515		84.72 341	82.99 352	89.90 302	92.96 332	75.33 346	84.36 393	83.42 399	77.37 349	88.27 352	86.65 393	53.94 406	98.72 157	82.56 299	97.40 264	95.67 312

PVSNet_0	70.34 21	74.58 388	72.96 391	79.47 399	90.63 381	66.24 401	73.26 415	83.40 400	63.67 416	78.02 416	78.35 419	72.53 338	89.59 407	56.68 418	60.05 423	82.57 417

UBG		80.28 380	78.94 383	84.31 383	92.86 334	61.77 415	83.87 397	83.31 401	77.33 350	82.78 400	83.72 410	47.60 414	96.06 345	65.47 409	93.48 369	95.11 330

testing222		80.54 377	78.53 385	86.58 358	92.54 342	68.60 390	86.24 373	82.72 402	83.78 284	82.68 401	84.24 408	39.25 428	95.94 349	60.25 415	95.09 332	95.20 323

pmmvs3		80.83 374	78.96 382	86.45 360	87.23 412	77.48 320	84.87 387	82.31 403	63.83 415	85.03 380	89.50 372	49.66 410	93.10 388	73.12 382	95.10 331	88.78 406

E-PMN		80.72 375	80.86 369	80.29 398	85.11 420	68.77 389	72.96 416	81.97 404	87.76 213	83.25 397	83.01 413	62.22 391	89.17 410	77.15 355	94.31 351	82.93 415

test0.0.03 1		82.48 360	81.47 364	85.48 371	89.70 393	73.57 362	84.73 388	81.64 405	83.07 293	88.13 354	86.61 394	62.86 388	89.10 411	66.24 407	90.29 399	93.77 366

Syy-MVS		84.81 339	84.93 333	84.42 381	91.71 365	63.36 414	85.89 377	81.49 406	81.03 314	85.13 378	81.64 415	77.44 310	95.00 368	85.94 263	94.12 357	94.91 338

myMVS_eth3d		79.62 383	78.26 386	83.72 387	91.71 365	61.25 418	85.89 377	81.49 406	81.03 314	85.13 378	81.64 415	32.12 429	95.00 368	71.17 395	94.12 357	94.91 338

baseline2		83.38 353	81.54 363	88.90 319	91.38 371	72.84 369	88.78 329	81.22 408	78.97 338	79.82 414	87.56 388	61.73 392	97.80 260	74.30 374	90.05 400	96.05 294

WB-MVSnew		84.20 346	83.89 345	85.16 375	91.62 368	66.15 403	88.44 337	81.00 409	76.23 358	87.98 356	87.77 387	84.98 246	93.35 387	62.85 414	94.10 359	95.98 296

ETVMVS		79.85 382	77.94 389	85.59 368	92.97 331	66.20 402	86.13 375	80.99 410	81.41 311	83.52 394	83.89 409	41.81 426	94.98 371	56.47 419	94.25 353	95.61 317

EMVS		80.35 378	80.28 376	80.54 397	84.73 422	69.07 388	72.54 418	80.73 411	87.80 211	81.66 408	81.73 414	62.89 387	89.84 405	75.79 366	94.65 344	82.71 416

TESTMET0.1,1		79.09 385	78.04 387	82.25 392	87.52 410	64.03 412	83.08 401	80.62 412	70.28 397	80.16 413	83.22 412	44.13 419	90.56 401	79.95 329	93.36 370	92.15 389

lessismore_v0				93.87 155	98.05 86	83.77 214		80.32 413		97.13 65	97.91 63	77.49 309	99.11 98	92.62 106	98.08 223	98.74 97

new_pmnet		81.22 370	81.01 368	81.86 393	90.92 378	70.15 382	84.03 395	80.25 414	70.83 392	85.97 373	89.78 368	67.93 359	84.65 419	67.44 404	91.90 391	90.78 399

test1111		90.39 240	90.61 236	89.74 305	98.04 89	71.50 376	95.59 85	79.72 415	89.41 174	95.94 124	98.14 42	70.79 347	98.81 141	88.52 218	99.32 69	98.90 76

mvsany_test3		89.11 273	88.21 289	91.83 234	91.30 373	90.25 83	88.09 339	78.76 416	76.37 357	96.43 97	98.39 36	83.79 254	90.43 403	86.57 252	94.20 354	94.80 341

dmvs_testset		78.23 387	78.99 381	75.94 402	91.99 358	55.34 425	88.86 326	78.70 417	82.69 297	81.64 409	79.46 417	75.93 326	85.74 417	48.78 423	82.85 416	86.76 410

ECVR-MVS		90.12 251	90.16 245	90.00 301	97.81 105	72.68 370	95.76 79	78.54 418	89.04 182	95.36 157	98.10 44	70.51 349	98.64 174	87.10 243	99.18 94	98.67 107

MVS-HIRNet		78.83 386	80.60 372	73.51 404	93.07 327	47.37 428	87.10 354	78.00 419	68.94 402	77.53 417	97.26 113	71.45 345	94.62 372	63.28 413	88.74 404	78.55 419

DSMNet-mixed		82.21 362	81.56 361	84.16 384	89.57 397	70.00 386	90.65 273	77.66 420	54.99 422	83.30 396	97.57 84	77.89 307	90.50 402	66.86 406	95.54 318	91.97 390

UWE-MVS		80.29 379	79.10 380	83.87 386	91.97 359	59.56 420	86.50 371	77.43 421	75.40 363	87.79 360	88.10 385	44.08 420	96.90 319	64.23 410	96.36 299	95.14 327

testing3		83.66 350	82.52 355	87.08 349	95.84 244	65.84 404	89.80 302	77.17 422	88.17 204	90.84 303	88.63 380	30.95 430	98.11 227	84.05 286	97.19 270	97.28 238

mvsany_test1		83.91 349	82.93 353	86.84 356	86.18 417	85.93 179	81.11 408	75.03 423	70.80 394	88.57 348	94.63 265	83.08 260	87.38 413	80.39 321	86.57 409	87.21 409

EPMVS		81.17 372	80.37 374	83.58 388	85.58 419	65.08 408	90.31 285	71.34 424	77.31 351	85.80 374	91.30 348	59.38 397	92.70 391	79.99 328	82.34 417	92.96 381

gg-mvs-nofinetune		82.10 365	81.02 367	85.34 372	87.46 411	71.04 377	94.74 121	67.56 425	96.44 26	79.43 415	98.99 8	45.24 416	96.15 341	67.18 405	92.17 388	88.85 404

GG-mvs-BLEND				83.24 390	85.06 421	71.03 378	94.99 116	65.55 426		74.09 420	75.51 420	44.57 418	94.46 375	59.57 417	87.54 407	84.24 413

MVE	59.87 23	73.86 389	72.65 392	77.47 401	87.00 415	74.35 354	61.37 421	60.93 427	67.27 406	69.69 422	86.49 396	81.24 284	72.33 424	56.45 420	83.45 414	85.74 412
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test2506		85.42 334	84.57 337	87.96 338	97.81 105	66.53 399	96.14 61	56.35 428	89.04 182	93.55 225	98.10 44	42.88 425	98.68 168	88.09 225	99.18 94	98.67 107

MTMP							94.82 119	54.62 429

DeepMVS_CX				53.83 406	70.38 429	64.56 410		48.52 430	33.01 424	65.50 424	74.21 421	56.19 403	46.64 427	38.45 425	70.07 421	50.30 422

tmp_tt		37.97 393	44.33 395	18.88 409	11.80 432	21.54 433	63.51 420	45.66 431	4.23 426	51.34 425	50.48 424	59.08 398	22.11 428	44.50 424	68.35 422	13.00 424

kuosan		43.63 392	44.25 396	41.78 408	66.04 430	34.37 432	75.56 414	32.62 432	53.25 423	50.46 426	51.18 423	25.28 432	49.13 426	13.44 427	30.41 426	41.84 423

dongtai		53.72 390	53.79 393	53.51 407	79.69 427	36.70 431	77.18 413	32.53 433	71.69 385	68.63 423	60.79 422	26.65 431	73.11 423	30.67 426	36.29 425	50.73 421

testmvs		9.02 396	11.42 399	1.81 411	2.77 434	1.13 436	79.44 411	1.90 434	1.18 429	2.65 430	6.80 426	1.95 434	0.87 430	2.62 429	3.45 428	3.44 426

test123		9.49 395	12.01 398	1.91 410	2.87 433	1.30 435	82.38 404	1.34 435	1.36 428	2.84 429	6.56 427	2.45 433	0.97 429	2.73 428	5.56 427	3.47 425

mmdepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

monomultidepth		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

test_blank		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet_test		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

DCPMVS		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

pcd_1.5k_mvsjas		7.56 397	10.09 400	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	90.77 159	0.00 431	0.00 430	0.00 429	0.00 427

sosnet-low-res		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

sosnet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uncertanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

Regformer		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

n2								0.00 436

nn								0.00 436

ab-mvs-re		7.56 397	10.08 401	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	90.69 359	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

uanet		0.00 399	0.00 402	0.00 412	0.00 435	0.00 437	0.00 423	0.00 436	0.00 430	0.00 431	0.00 430	0.00 435	0.00 431	0.00 430	0.00 429	0.00 427

WAC-MVS						61.25 418								74.55 371

PC_three_1452									75.31 365	95.87 129	95.75 219	92.93 108	96.34 340	87.18 242	98.68 161	98.04 165

eth-test2					0.00 435

eth-test					0.00 435

OPU-MVS				95.15 100	96.84 164	89.43 95	95.21 104				95.66 222	93.12 101	98.06 231	86.28 260	98.61 168	97.95 178

test_0728_THIRD									93.26 78	97.40 54	97.35 107	94.69 63	99.34 65	93.88 57	99.42 50	98.89 77

GSMVS																94.75 344

test_part2					98.21 76	89.41 96				96.72 86

sam_mvs1												66.64 366				94.75 344

sam_mvs												66.41 367

test_post1							90.21 287				5.85 429	65.36 373	96.00 347	79.61 335

test_post											6.07 428	65.74 371	95.84 351

patchmatchnet-post											91.71 343	66.22 369	97.59 278

gm-plane-assit					87.08 414	59.33 421			71.22 388		83.58 411		97.20 300	73.95 376

test9_res														88.16 223	98.40 186	97.83 195

agg_prior2														87.06 245	98.36 196	97.98 174

test_prior4						89.91 86	90.74 269

test_prior2							90.21 287		89.33 177	90.77 304	94.81 256	90.41 169		88.21 219	98.55 174

旧先验2							90.00 295		68.65 403	92.71 260			96.52 329	85.15 271

新几何2							90.02 294

原ACMM2							89.34 315

testdata2													98.03 233	80.24 325

segment_acmp												92.14 126

testdata1							88.96 324		88.44 198

plane_prior7					97.71 114	88.68 111

plane_prior6					97.21 144	88.23 124						86.93 221

plane_prior4											95.59 224

plane_prior3						88.43 122			90.35 160	93.31 233

plane_prior2							94.56 130		91.74 121

plane_prior1					97.38 134

plane_prior						88.12 125	93.01 184		88.98 184						98.06 224

HQP5-MVS						84.89 197

HQP-NCC					96.36 200		91.37 251		87.16 225	88.81 339

ACMP_Plane					96.36 200		91.37 251		87.16 225	88.81 339

BP-MVS														86.55 254

HQP4-MVS										88.81 339			98.61 176			98.15 157

HQP2-MVS												84.76 247

NP-MVS					96.82 166	87.10 145					93.40 305

MDTV_nov1_ep13_2view						42.48 430	88.45 336		67.22 407	83.56 393		66.80 363		72.86 383		94.06 358

ACMMP++_ref															98.82 141

ACMMP++															99.25 83

Test By Simon												90.61 165

High-resolution multi-view benchmark