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.

patch_mono-2		99.26 66	99.62 1	98.16 283	99.81 42	94.59 344	99.52 129	99.64 33	99.33 2	99.73 49	99.90 11	99.00 25	99.99 1	99.69 1	99.98 2	99.89 2

h-mvs33		97.70 262	97.28 279	98.97 185	99.70 102	97.27 263	99.36 207	99.45 187	98.94 41	99.66 73	99.64 181	94.93 208	99.99 1	99.48 18	84.36 363	99.65 122

xiu_mvs_v1_base_debu		99.29 61	99.27 56	99.34 135	99.63 129	98.97 154	99.12 267	99.51 106	98.86 48	99.84 15	99.47 247	98.18 104	99.99 1	99.50 13	99.31 154	99.08 209

xiu_mvs_v1_base		99.29 61	99.27 56	99.34 135	99.63 129	98.97 154	99.12 267	99.51 106	98.86 48	99.84 15	99.47 247	98.18 104	99.99 1	99.50 13	99.31 154	99.08 209

xiu_mvs_v1_base_debi		99.29 61	99.27 56	99.34 135	99.63 129	98.97 154	99.12 267	99.51 106	98.86 48	99.84 15	99.47 247	98.18 104	99.99 1	99.50 13	99.31 154	99.08 209

EPNet		98.86 125	98.71 130	99.30 145	97.20 362	98.18 227	99.62 76	98.91 323	99.28 3	98.63 285	99.81 69	95.96 172	99.99 1	99.24 47	99.72 112	99.73 90
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
xiu_mvs_v2_base		99.26 66	99.25 60	99.29 148	99.53 159	98.91 170	99.02 291	99.45 187	98.80 56	99.71 55	99.26 301	98.94 35	99.98 7	99.34 34	99.23 159	98.98 223

PS-MVSNAJ		99.32 57	99.32 36	99.30 145	99.57 149	98.94 166	98.97 305	99.46 175	98.92 45	99.71 55	99.24 303	99.01 19	99.98 7	99.35 30	99.66 126	98.97 224

QAPM		98.67 154	98.30 171	99.80 43	99.20 248	99.67 57	99.77 32	99.72 11	94.74 333	98.73 266	99.90 11	95.78 182	99.98 7	96.96 275	99.88 38	99.76 77

3Dnovator	97.25 9	99.24 70	99.05 79	99.81 41	99.12 266	99.66 59	99.84 13	99.74 10	99.09 15	98.92 241	99.90 11	95.94 175	99.98 7	98.95 74	99.92 13	99.79 62

OpenMVS	96.50 16	98.47 163	98.12 181	99.52 110	99.04 283	99.53 85	99.82 17	99.72 11	94.56 336	98.08 316	99.88 21	94.73 224	99.98 7	97.47 242	99.76 104	99.06 215

CANet_DTU		98.97 116	98.87 109	99.25 154	99.33 215	98.42 220	99.08 276	99.30 269	99.16 6	99.43 129	99.75 121	95.27 199	99.97 12	98.56 141	99.95 8	99.36 188

zzz-MVS		99.49 17	99.36 27	99.89 4	99.90 4	99.86 13	99.36 207	99.47 165	98.79 57	99.68 62	99.81 69	98.43 88	99.97 12	98.88 83	99.90 25	99.83 33

MTAPA		99.52 14	99.39 22	99.89 4	99.90 4	99.86 13	99.66 58	99.47 165	98.79 57	99.68 62	99.81 69	98.43 88	99.97 12	98.88 83	99.90 25	99.83 33

PGM-MVS		99.45 29	99.31 43	99.86 21	99.87 16	99.78 40	99.58 98	99.65 32	97.84 154	99.71 55	99.80 84	99.12 13	99.97 12	98.33 165	99.87 42	99.83 33

mPP-MVS		99.44 33	99.30 46	99.86 21	99.88 12	99.79 33	99.69 46	99.48 147	98.12 121	99.50 116	99.75 121	98.78 52	99.97 12	98.57 138	99.89 35	99.83 33

CP-MVS		99.45 29	99.32 36	99.85 28	99.83 37	99.75 43	99.69 46	99.52 92	98.07 131	99.53 111	99.63 187	98.93 39	99.97 12	98.74 109	99.91 18	99.83 33

SteuartSystems-ACMMP		99.54 10	99.42 18	99.87 12	99.82 39	99.81 27	99.59 90	99.51 106	98.62 67	99.79 30	99.83 49	99.28 4	99.97 12	98.48 149	99.90 25	99.84 22
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator+	97.12 13	99.18 76	98.97 96	99.82 38	99.17 259	99.68 54	99.81 19	99.51 106	99.20 5	98.72 267	99.89 15	95.68 187	99.97 12	98.86 92	99.86 53	99.81 46

DVP-MVS++		99.59 3	99.50 10	99.88 6	99.51 163	99.88 8	99.87 9	99.51 106	98.99 31	99.88 6	99.81 69	99.27 5	99.96 20	98.85 94	99.80 90	99.81 46

MSC_two_6792asdad				99.87 12	99.51 163	99.76 41		99.33 252					99.96 20	98.87 87	99.84 68	99.89 2

No_MVS				99.87 12	99.51 163	99.76 41		99.33 252					99.96 20	98.87 87	99.84 68	99.89 2

ZD-MVS					99.71 95	99.79 33		99.61 36	96.84 252	99.56 104	99.54 221	98.58 75	99.96 20	96.93 278	99.75 105

SED-MVS		99.61 2	99.52 8	99.88 6	99.84 33	99.90 2	99.60 83	99.48 147	99.08 16	99.91 2	99.81 69	99.20 7	99.96 20	98.91 80	99.85 60	99.79 62

test_241102_TWO								99.48 147	99.08 16	99.88 6	99.81 69	98.94 35	99.96 20	98.91 80	99.84 68	99.88 8

ZNCC-MVS		99.47 25	99.33 34	99.87 12	99.87 16	99.81 27	99.64 68	99.67 22	98.08 130	99.55 108	99.64 181	98.91 40	99.96 20	98.72 113	99.90 25	99.82 40

testtj		99.12 90	98.87 109	99.86 21	99.72 89	99.79 33	99.44 168	99.51 106	97.29 212	99.59 98	99.74 127	98.15 107	99.96 20	96.74 286	99.69 118	99.81 46

DVP-MVS		99.57 8	99.47 14	99.88 6	99.85 26	99.89 4	99.57 104	99.37 235	99.10 12	99.81 25	99.80 84	98.94 35	99.96 20	98.93 77	99.86 53	99.81 46
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_THIRD									98.99 31	99.81 25	99.80 84	99.09 14	99.96 20	98.85 94	99.90 25	99.88 8

test_0728_SECOND				99.91 2	99.84 33	99.89 4	99.57 104	99.51 106					99.96 20	98.93 77	99.86 53	99.88 8

SR-MVS		99.43 37	99.29 50	99.86 21	99.75 68	99.83 17	99.59 90	99.62 34	98.21 108	99.73 49	99.79 96	98.68 67	99.96 20	98.44 155	99.77 101	99.79 62

DPE-MVS		99.46 27	99.32 36	99.91 2	99.78 48	99.88 8	99.36 207	99.51 106	98.73 61	99.88 6	99.84 45	98.72 64	99.96 20	98.16 179	99.87 42	99.88 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
UA-Net		99.42 42	99.29 50	99.80 43	99.62 135	99.55 80	99.50 140	99.70 15	98.79 57	99.77 38	99.96 1	97.45 123	99.96 20	98.92 79	99.90 25	99.89 2

HFP-MVS		99.49 17	99.37 25	99.86 21	99.87 16	99.80 29	99.66 58	99.67 22	98.15 116	99.68 62	99.69 153	99.06 16	99.96 20	98.69 118	99.87 42	99.84 22

region2R		99.48 22	99.35 30	99.87 12	99.88 12	99.80 29	99.65 65	99.66 27	98.13 119	99.66 73	99.68 161	98.96 29	99.96 20	98.62 127	99.87 42	99.84 22

#test#		99.43 37	99.29 50	99.86 21	99.87 16	99.80 29	99.55 119	99.67 22	97.83 155	99.68 62	99.69 153	99.06 16	99.96 20	98.39 157	99.87 42	99.84 22

HPM-MVS++		99.39 50	99.23 63	99.87 12	99.75 68	99.84 16	99.43 174	99.51 106	98.68 65	99.27 173	99.53 225	98.64 73	99.96 20	98.44 155	99.80 90	99.79 62

APDe-MVS		99.66 1	99.57 3	99.92 1	99.77 54	99.89 4	99.75 37	99.56 58	99.02 21	99.88 6	99.85 36	99.18 10	99.96 20	99.22 48	99.92 13	99.90 1

ACMMPR		99.49 17	99.36 27	99.86 21	99.87 16	99.79 33	99.66 58	99.67 22	98.15 116	99.67 68	99.69 153	98.95 32	99.96 20	98.69 118	99.87 42	99.84 22

MP-MVS		99.33 56	99.15 69	99.87 12	99.88 12	99.82 23	99.66 58	99.46 175	98.09 126	99.48 120	99.74 127	98.29 99	99.96 20	97.93 196	99.87 42	99.82 40
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
abl_6		99.44 33	99.31 43	99.83 36	99.85 26	99.75 43	99.66 58	99.59 44	98.13 119	99.82 23	99.81 69	98.60 74	99.96 20	98.46 153	99.88 38	99.79 62

CPTT-MVS		99.11 95	98.90 105	99.74 59	99.80 45	99.46 96	99.59 90	99.49 135	97.03 239	99.63 84	99.69 153	97.27 131	99.96 20	97.82 205	99.84 68	99.81 46

PVSNet_Blended_VisFu		99.36 53	99.28 54	99.61 85	99.86 22	99.07 142	99.47 160	99.93 2	97.66 176	99.71 55	99.86 31	97.73 118	99.96 20	99.47 20	99.82 83	99.79 62

UGNet		98.87 122	98.69 132	99.40 129	99.22 244	98.72 188	99.44 168	99.68 19	99.24 4	99.18 198	99.42 258	92.74 277	99.96 20	99.34 34	99.94 11	99.53 157
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
CSCG		99.32 57	99.32 36	99.32 140	99.85 26	98.29 223	99.71 43	99.66 27	98.11 123	99.41 136	99.80 84	98.37 95	99.96 20	98.99 70	99.96 7	99.72 96

ACMMP		99.45 29	99.32 36	99.82 38	99.89 9	99.67 57	99.62 76	99.69 18	98.12 121	99.63 84	99.84 45	98.73 63	99.96 20	98.55 144	99.83 77	99.81 46
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
test1172		99.43 37	99.29 50	99.85 28	99.75 68	99.82 23	99.60 83	99.56 58	98.28 99	99.74 47	99.79 96	98.53 79	99.95 47	98.55 144	99.78 97	99.79 62

SR-MVS-dyc-post		99.45 29	99.31 43	99.85 28	99.76 57	99.82 23	99.63 70	99.52 92	98.38 87	99.76 43	99.82 56	98.53 79	99.95 47	98.61 130	99.81 86	99.77 72

GST-MVS		99.40 49	99.24 61	99.85 28	99.86 22	99.79 33	99.60 83	99.67 22	97.97 142	99.63 84	99.68 161	98.52 81	99.95 47	98.38 159	99.86 53	99.81 46

CANet		99.25 69	99.14 70	99.59 87	99.41 195	99.16 128	99.35 213	99.57 53	98.82 52	99.51 115	99.61 196	96.46 158	99.95 47	99.59 6	99.98 2	99.65 122

MP-MVS-pluss		99.37 52	99.20 65	99.88 6	99.90 4	99.87 12	99.30 223	99.52 92	97.18 222	99.60 95	99.79 96	98.79 51	99.95 47	98.83 100	99.91 18	99.83 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.42 42	99.27 56	99.88 6	99.89 9	99.80 29	99.67 54	99.50 127	98.70 63	99.77 38	99.49 238	98.21 102	99.95 47	98.46 153	99.77 101	99.88 8
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
mvs-test1		98.86 125	98.84 116	98.89 202	99.33 215	97.77 249	99.44 168	99.30 269	98.47 78	99.10 210	99.43 255	96.78 147	99.95 47	98.73 111	99.02 179	98.96 226

testdata2													99.95 47	96.67 291

APD-MVS_3200maxsize		99.48 22	99.35 30	99.85 28	99.76 57	99.83 17	99.63 70	99.54 75	98.36 91	99.79 30	99.82 56	98.86 44	99.95 47	98.62 127	99.81 86	99.78 70

RPMNet		96.72 299	95.90 309	99.19 160	99.18 253	98.49 212	99.22 253	99.52 92	88.72 363	99.56 104	97.38 360	94.08 249	99.95 47	86.87 370	98.58 201	99.14 201

sss		99.17 78	99.05 79	99.53 104	99.62 135	98.97 154	99.36 207	99.62 34	97.83 155	99.67 68	99.65 174	97.37 128	99.95 47	99.19 50	99.19 162	99.68 112

TSAR-MVS + MP.		99.58 5	99.50 10	99.81 41	99.91 1	99.66 59	99.63 70	99.39 221	98.91 46	99.78 35	99.85 36	99.36 2	99.94 58	98.84 97	99.88 38	99.82 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
Regformer-4		99.59 3	99.54 6	99.73 61	99.76 57	99.41 102	99.58 98	99.49 135	99.02 21	99.88 6	99.80 84	99.00 25	99.94 58	99.45 22	99.92 13	99.84 22

Regformer-2		99.54 10	99.47 14	99.75 54	99.71 95	99.52 88	99.49 150	99.49 135	98.94 41	99.83 20	99.76 116	99.01 19	99.94 58	99.15 56	99.87 42	99.80 56

XVS		99.53 12	99.42 18	99.87 12	99.85 26	99.83 17	99.69 46	99.68 19	98.98 34	99.37 150	99.74 127	98.81 49	99.94 58	98.79 105	99.86 53	99.84 22

X-MVStestdata		96.55 301	95.45 316	99.87 12	99.85 26	99.83 17	99.69 46	99.68 19	98.98 34	99.37 150	64.01 380	98.81 49	99.94 58	98.79 105	99.86 53	99.84 22

旧先验2							98.96 306		96.70 260	99.47 121			99.94 58	98.19 174

新几何1				99.75 54	99.75 68	99.59 73		99.54 75	96.76 256	99.29 169	99.64 181	98.43 88	99.94 58	96.92 280	99.66 126	99.72 96

testdata				99.54 96	99.75 68	98.95 163		99.51 106	97.07 234	99.43 129	99.70 143	98.87 43	99.94 58	97.76 211	99.64 129	99.72 96

HPM-MVS		99.42 42	99.28 54	99.83 36	99.90 4	99.72 47	99.81 19	99.54 75	97.59 180	99.68 62	99.63 187	98.91 40	99.94 58	98.58 136	99.91 18	99.84 22
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CHOSEN 1792x2688		99.19 74	99.10 74	99.45 122	99.89 9	98.52 208	99.39 195	99.94 1	98.73 61	99.11 207	99.89 15	95.50 191	99.94 58	99.50 13	99.97 5	99.89 2

APD-MVS		99.27 64	99.08 77	99.84 35	99.75 68	99.79 33	99.50 140	99.50 127	97.16 224	99.77 38	99.82 56	98.78 52	99.94 58	97.56 233	99.86 53	99.80 56
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DELS-MVS		99.48 22	99.42 18	99.65 75	99.72 89	99.40 104	99.05 282	99.66 27	99.14 7	99.57 103	99.80 84	98.46 86	99.94 58	99.57 8	99.84 68	99.60 139
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
WTY-MVS		99.06 103	98.88 108	99.61 85	99.62 135	99.16 128	99.37 203	99.56 58	98.04 137	99.53 111	99.62 192	96.84 145	99.94 58	98.85 94	98.49 208	99.72 96

DeepC-MVS	98.35 2	99.30 59	99.19 66	99.64 80	99.82 39	99.23 121	99.62 76	99.55 67	98.94 41	99.63 84	99.95 2	95.82 181	99.94 58	99.37 29	99.97 5	99.73 90
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LS3D		99.27 64	99.12 72	99.74 59	99.18 253	99.75 43	99.56 110	99.57 53	98.45 81	99.49 119	99.85 36	97.77 117	99.94 58	98.33 165	99.84 68	99.52 158

FE-MVS		98.48 162	98.17 176	99.40 129	99.54 158	98.96 158	99.68 51	98.81 333	95.54 319	99.62 88	99.70 143	93.82 256	99.93 73	97.35 250	99.46 141	99.32 193

xxxxxxxxxxxxxcwj		99.43 37	99.32 36	99.75 54	99.76 57	99.59 73	99.14 265	99.53 86	99.00 28	99.71 55	99.80 84	98.95 32	99.93 73	98.19 174	99.84 68	99.74 83

SF-MVS		99.38 51	99.24 61	99.79 46	99.79 46	99.68 54	99.57 104	99.54 75	97.82 160	99.71 55	99.80 84	98.95 32	99.93 73	98.19 174	99.84 68	99.74 83

dcpmvs_2		99.23 71	99.58 2	98.16 283	99.83 37	94.68 343	99.76 35	99.52 92	99.07 18	99.98 1	99.88 21	98.56 77	99.93 73	99.67 3	99.98 2	99.87 13

Anonymous20240529		98.09 197	97.68 231	99.34 135	99.66 119	98.44 217	99.40 191	99.43 204	93.67 343	99.22 186	99.89 15	90.23 328	99.93 73	99.26 46	98.33 211	99.66 118

ACMMP_NAP		99.47 25	99.34 32	99.88 6	99.87 16	99.86 13	99.47 160	99.48 147	98.05 136	99.76 43	99.86 31	98.82 48	99.93 73	98.82 104	99.91 18	99.84 22

EI-MVSNet-UG-set		99.58 5	99.57 3	99.64 80	99.78 48	99.14 134	99.60 83	99.45 187	99.01 24	99.90 4	99.83 49	98.98 27	99.93 73	99.59 6	99.95 8	99.86 15

Regformer-1		99.53 12	99.47 14	99.72 64	99.71 95	99.44 98	99.49 150	99.46 175	98.95 40	99.83 20	99.76 116	99.01 19	99.93 73	99.17 53	99.87 42	99.80 56

无先验							98.99 298	99.51 106	96.89 249				99.93 73	97.53 236		99.72 96

1121		99.09 99	98.87 109	99.75 54	99.74 76	99.60 70	99.27 234	99.48 147	96.82 255	99.25 180	99.65 174	98.38 93	99.93 73	97.53 236	99.67 125	99.73 90

VDDNet		97.55 274	97.02 290	99.16 163	99.49 175	98.12 232	99.38 200	99.30 269	95.35 322	99.68 62	99.90 11	82.62 367	99.93 73	99.31 37	98.13 226	99.42 182

ab-mvs		98.86 125	98.63 140	99.54 96	99.64 126	99.19 123	99.44 168	99.54 75	97.77 163	99.30 166	99.81 69	94.20 243	99.93 73	99.17 53	98.82 192	99.49 168

F-COLMAP		99.19 74	99.04 82	99.64 80	99.78 48	99.27 117	99.42 181	99.54 75	97.29 212	99.41 136	99.59 202	98.42 91	99.93 73	98.19 174	99.69 118	99.73 90

ETH3D cwj APD-0.16		99.06 103	98.84 116	99.72 64	99.51 163	99.60 70	99.23 248	99.44 196	97.04 237	99.39 144	99.67 167	98.30 98	99.92 86	97.27 253	99.69 118	99.64 129

Anonymous202405211		98.30 178	97.98 198	99.26 153	99.57 149	98.16 228	99.41 183	98.55 349	96.03 314	99.19 195	99.74 127	91.87 300	99.92 86	99.16 55	98.29 216	99.70 105

EI-MVSNet-Vis-set		99.58 5	99.56 5	99.64 80	99.78 48	99.15 133	99.61 82	99.45 187	99.01 24	99.89 5	99.82 56	99.01 19	99.92 86	99.56 9	99.95 8	99.85 18

VDD-MVS		97.73 256	97.35 270	98.88 205	99.47 184	97.12 269	99.34 216	98.85 329	98.19 110	99.67 68	99.85 36	82.98 365	99.92 86	99.49 17	98.32 215	99.60 139

VNet		99.11 95	98.90 105	99.73 61	99.52 161	99.56 78	99.41 183	99.39 221	99.01 24	99.74 47	99.78 103	95.56 189	99.92 86	99.52 11	98.18 222	99.72 96

XVG-OURS-SEG-HR		98.69 151	98.62 145	98.89 202	99.71 95	97.74 250	99.12 267	99.54 75	98.44 84	99.42 132	99.71 139	94.20 243	99.92 86	98.54 146	98.90 188	99.00 220

HPM-MVS_fast		99.51 15	99.40 21	99.85 28	99.91 1	99.79 33	99.76 35	99.56 58	97.72 169	99.76 43	99.75 121	99.13 12	99.92 86	99.07 64	99.92 13	99.85 18

HY-MVS	97.30 7	98.85 133	98.64 139	99.47 119	99.42 192	99.08 140	99.62 76	99.36 236	97.39 205	99.28 170	99.68 161	96.44 160	99.92 86	98.37 161	98.22 217	99.40 186

DP-MVS		99.16 80	98.95 100	99.78 48	99.77 54	99.53 85	99.41 183	99.50 127	97.03 239	99.04 222	99.88 21	97.39 124	99.92 86	98.66 123	99.90 25	99.87 13

IB-MVS	95.67 18	96.22 307	95.44 317	98.57 241	99.21 246	96.70 295	98.65 339	97.74 362	96.71 259	97.27 336	98.54 347	86.03 358	99.92 86	98.47 152	86.30 361	99.10 204
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
DeepC-MVS_fast	98.69 1	99.49 17	99.39 22	99.77 50	99.63 129	99.59 73	99.36 207	99.46 175	99.07 18	99.79 30	99.82 56	98.85 45	99.92 86	98.68 120	99.87 42	99.82 40
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETH3 D test6400		98.70 148	98.35 166	99.73 61	99.69 105	99.60 70	99.16 259	99.45 187	95.42 321	99.27 173	99.60 199	97.39 124	99.91 97	95.36 319	99.83 77	99.70 105

9.14			99.10 74		99.72 89		99.40 191	99.51 106	97.53 189	99.64 83	99.78 103	98.84 46	99.91 97	97.63 224	99.82 83

ETH3D-3000-0.1		99.21 72	99.02 87	99.77 50	99.73 84	99.69 52	99.38 200	99.51 106	97.45 196	99.61 91	99.75 121	98.51 82	99.91 97	97.45 245	99.83 77	99.71 103

SMA-MVS		99.44 33	99.30 46	99.85 28	99.73 84	99.83 17	99.56 110	99.47 165	97.45 196	99.78 35	99.82 56	99.18 10	99.91 97	98.79 105	99.89 35	99.81 46
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
TEST9					99.67 110	99.65 62	99.05 282	99.41 209	96.22 298	98.95 236	99.49 238	98.77 55	99.91 97

train_agg		99.02 109	98.77 124	99.77 50	99.67 110	99.65 62	99.05 282	99.41 209	96.28 291	98.95 236	99.49 238	98.76 57	99.91 97	97.63 224	99.72 112	99.75 78

test_8					99.67 110	99.61 68	99.03 288	99.41 209	96.28 291	98.93 240	99.48 244	98.76 57	99.91 97

agg_prior1		99.01 112	98.76 126	99.76 53	99.67 110	99.62 66	98.99 298	99.40 217	96.26 294	98.87 249	99.49 238	98.77 55	99.91 97	97.69 221	99.72 112	99.75 78

agg_prior					99.67 110	99.62 66		99.40 217		98.87 249			99.91 97

Regformer-3		99.57 8	99.53 7	99.68 68	99.76 57	99.29 114	99.58 98	99.44 196	99.01 24	99.87 12	99.80 84	98.97 28	99.91 97	99.44 24	99.92 13	99.83 33

原ACMM1				99.65 75	99.73 84	99.33 107		99.47 165	97.46 193	99.12 205	99.66 173	98.67 70	99.91 97	97.70 220	99.69 118	99.71 103

LFMVS		97.90 227	97.35 270	99.54 96	99.52 161	99.01 149	99.39 195	98.24 353	97.10 232	99.65 79	99.79 96	84.79 362	99.91 97	99.28 42	98.38 210	99.69 108

XVG-OURS		98.73 146	98.68 133	98.88 205	99.70 102	97.73 251	98.92 313	99.55 67	98.52 75	99.45 124	99.84 45	95.27 199	99.91 97	98.08 186	98.84 191	99.00 220

PLC	97.94 4	99.02 109	98.85 115	99.53 104	99.66 119	99.01 149	99.24 247	99.52 92	96.85 251	99.27 173	99.48 244	98.25 101	99.91 97	97.76 211	99.62 132	99.65 122
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PCF-MVS	97.08 14	97.66 269	97.06 289	99.47 119	99.61 139	99.09 139	98.04 364	99.25 281	91.24 357	98.51 294	99.70 143	94.55 233	99.91 97	92.76 350	99.85 60	99.42 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
FA-MVS(test-final)		98.75 144	98.53 157	99.41 128	99.55 157	99.05 145	99.80 23	99.01 310	96.59 272	99.58 100	99.59 202	95.39 194	99.90 112	97.78 208	99.49 140	99.28 196

MCST-MVS		99.43 37	99.30 46	99.82 38	99.79 46	99.74 46	99.29 227	99.40 217	98.79 57	99.52 113	99.62 192	98.91 40	99.90 112	98.64 125	99.75 105	99.82 40

CDPH-MVS		99.13 84	98.91 104	99.80 43	99.75 68	99.71 49	99.15 263	99.41 209	96.60 270	99.60 95	99.55 216	98.83 47	99.90 112	97.48 240	99.83 77	99.78 70

NCCC		99.34 55	99.19 66	99.79 46	99.61 139	99.65 62	99.30 223	99.48 147	98.86 48	99.21 189	99.63 187	98.72 64	99.90 112	98.25 170	99.63 131	99.80 56

114514_t		98.93 118	98.67 134	99.72 64	99.85 26	99.53 85	99.62 76	99.59 44	92.65 352	99.71 55	99.78 103	98.06 110	99.90 112	98.84 97	99.91 18	99.74 83

1112_ss		98.98 114	98.77 124	99.59 87	99.68 109	99.02 147	99.25 245	99.48 147	97.23 219	99.13 203	99.58 206	96.93 144	99.90 112	98.87 87	98.78 195	99.84 22

PHI-MVS		99.30 59	99.17 68	99.70 67	99.56 153	99.52 88	99.58 98	99.80 8	97.12 228	99.62 88	99.73 134	98.58 75	99.90 112	98.61 130	99.91 18	99.68 112

AdaColmap		99.01 112	98.80 121	99.66 71	99.56 153	99.54 82	99.18 257	99.70 15	98.18 114	99.35 157	99.63 187	96.32 163	99.90 112	97.48 240	99.77 101	99.55 150

COLMAP_ROB	97.56 6	98.86 125	98.75 127	99.17 162	99.88 12	98.53 204	99.34 216	99.59 44	97.55 185	98.70 274	99.89 15	95.83 180	99.90 112	98.10 181	99.90 25	99.08 209
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
thisisatest0530		98.35 174	98.03 193	99.31 141	99.63 129	98.56 201	99.54 123	96.75 369	97.53 189	99.73 49	99.65 174	91.25 316	99.89 121	98.62 127	99.56 135	99.48 169

tttt0517		98.42 167	98.14 179	99.28 151	99.66 119	98.38 221	99.74 40	96.85 367	97.68 173	99.79 30	99.74 127	91.39 313	99.89 121	98.83 100	99.56 135	99.57 148

test12				99.75 54	99.64 126	99.61 68		99.29 274		99.21 189		98.38 93	99.89 121		99.74 108	99.74 83

Test_1112_low_res		98.89 121	98.66 137	99.57 92	99.69 105	98.95 163	99.03 288	99.47 165	96.98 241	99.15 201	99.23 304	96.77 149	99.89 121	98.83 100	98.78 195	99.86 15

CNLPA		99.14 82	98.99 92	99.59 87	99.58 147	99.41 102	99.16 259	99.44 196	98.45 81	99.19 195	99.49 238	98.08 109	99.89 121	97.73 215	99.75 105	99.48 169

diffmvs		99.14 82	99.02 87	99.51 112	99.61 139	98.96 158	99.28 229	99.49 135	98.46 80	99.72 54	99.71 139	96.50 157	99.88 126	99.31 37	99.11 168	99.67 115

PVSNet_BlendedMVS		98.86 125	98.80 121	99.03 175	99.76 57	98.79 184	99.28 229	99.91 3	97.42 202	99.67 68	99.37 273	97.53 121	99.88 126	98.98 71	97.29 265	98.42 327

PVSNet_Blended		99.08 101	98.97 96	99.42 127	99.76 57	98.79 184	98.78 327	99.91 3	96.74 257	99.67 68	99.49 238	97.53 121	99.88 126	98.98 71	99.85 60	99.60 139

MVS		97.28 288	96.55 297	99.48 116	98.78 316	98.95 163	99.27 234	99.39 221	83.53 367	98.08 316	99.54 221	96.97 142	99.87 129	94.23 333	99.16 163	99.63 133

MG-MVS		99.13 84	99.02 87	99.45 122	99.57 149	98.63 195	99.07 277	99.34 245	98.99 31	99.61 91	99.82 56	97.98 112	99.87 129	97.00 271	99.80 90	99.85 18

MSDG		98.98 114	98.80 121	99.53 104	99.76 57	99.19 123	98.75 330	99.55 67	97.25 216	99.47 121	99.77 110	97.82 115	99.87 129	96.93 278	99.90 25	99.54 152

ETV-MVS		99.26 66	99.21 64	99.40 129	99.46 185	99.30 112	99.56 110	99.52 92	98.52 75	99.44 128	99.27 299	98.41 92	99.86 132	99.10 60	99.59 134	99.04 216

thisisatest0515		98.14 192	97.79 216	99.19 160	99.50 173	98.50 211	98.61 341	96.82 368	96.95 245	99.54 109	99.43 255	91.66 309	99.86 132	98.08 186	99.51 139	99.22 199

thres600view7		97.86 232	97.51 247	98.92 193	99.72 89	97.95 241	99.59 90	98.74 338	97.94 144	99.27 173	98.62 344	91.75 303	99.86 132	93.73 338	98.19 221	98.96 226

lupinMVS		99.13 84	99.01 91	99.46 121	99.51 163	98.94 166	99.05 282	99.16 294	97.86 150	99.80 28	99.56 213	97.39 124	99.86 132	98.94 75	99.85 60	99.58 147

PVSNet	96.02 17	98.85 133	98.84 116	98.89 202	99.73 84	97.28 262	98.32 357	99.60 41	97.86 150	99.50 116	99.57 210	96.75 150	99.86 132	98.56 141	99.70 117	99.54 152

MAR-MVS		98.86 125	98.63 140	99.54 96	99.37 206	99.66 59	99.45 164	99.54 75	96.61 268	99.01 225	99.40 265	97.09 136	99.86 132	97.68 223	99.53 138	99.10 204
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
test2506		96.81 297	96.65 295	97.29 325	99.74 76	92.21 365	99.60 83	85.06 384	99.13 8	99.77 38	99.93 4	87.82 354	99.85 138	99.38 27	99.38 146	99.80 56

AllTest		98.87 122	98.72 128	99.31 141	99.86 22	98.48 214	99.56 110	99.61 36	97.85 152	99.36 154	99.85 36	95.95 173	99.85 138	96.66 292	99.83 77	99.59 143

TestCases				99.31 141	99.86 22	98.48 214		99.61 36	97.85 152	99.36 154	99.85 36	95.95 173	99.85 138	96.66 292	99.83 77	99.59 143

jason		99.13 84	99.03 84	99.45 122	99.46 185	98.87 173	99.12 267	99.26 279	98.03 139	99.79 30	99.65 174	97.02 139	99.85 138	99.02 68	99.90 25	99.65 122
jason: jason.
CNVR-MVS		99.42 42	99.30 46	99.78 48	99.62 135	99.71 49	99.26 243	99.52 92	98.82 52	99.39 144	99.71 139	98.96 29	99.85 138	98.59 135	99.80 90	99.77 72

PAPM_NR		99.04 106	98.84 116	99.66 71	99.74 76	99.44 98	99.39 195	99.38 227	97.70 171	99.28 170	99.28 296	98.34 96	99.85 138	96.96 275	99.45 142	99.69 108

test1111		98.04 206	98.11 182	97.83 305	99.74 76	93.82 352	99.58 98	95.40 374	99.12 10	99.65 79	99.93 4	90.73 321	99.84 144	99.43 25	99.38 146	99.82 40

ECVR-MVS		98.04 206	98.05 191	98.00 295	99.74 76	94.37 347	99.59 90	94.98 375	99.13 8	99.66 73	99.93 4	90.67 322	99.84 144	99.40 26	99.38 146	99.80 56

test_yl		98.86 125	98.63 140	99.54 96	99.49 175	99.18 125	99.50 140	99.07 305	98.22 106	99.61 91	99.51 232	95.37 195	99.84 144	98.60 133	98.33 211	99.59 143

DCV-MVSNet		98.86 125	98.63 140	99.54 96	99.49 175	99.18 125	99.50 140	99.07 305	98.22 106	99.61 91	99.51 232	95.37 195	99.84 144	98.60 133	98.33 211	99.59 143

Fast-Effi-MVS+		98.70 148	98.43 161	99.51 112	99.51 163	99.28 115	99.52 129	99.47 165	96.11 309	99.01 225	99.34 282	96.20 167	99.84 144	97.88 199	98.82 192	99.39 187

TSAR-MVS + GP.		99.36 53	99.36 27	99.36 134	99.67 110	98.61 198	99.07 277	99.33 252	99.00 28	99.82 23	99.81 69	99.06 16	99.84 144	99.09 61	99.42 144	99.65 122

tpmrst		98.33 175	98.48 159	97.90 301	99.16 261	94.78 341	99.31 221	99.11 299	97.27 214	99.45 124	99.59 202	95.33 197	99.84 144	98.48 149	98.61 198	99.09 208

Vis-MVSNet		99.12 90	98.97 96	99.56 94	99.78 48	99.10 138	99.68 51	99.66 27	98.49 77	99.86 13	99.87 27	94.77 221	99.84 144	99.19 50	99.41 145	99.74 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PAPR		98.63 158	98.34 167	99.51 112	99.40 200	99.03 146	98.80 325	99.36 236	96.33 288	99.00 230	99.12 318	98.46 86	99.84 144	95.23 321	99.37 153	99.66 118

PatchMatch-RL		98.84 136	98.62 145	99.52 110	99.71 95	99.28 115	99.06 280	99.77 9	97.74 168	99.50 116	99.53 225	95.41 193	99.84 144	97.17 264	99.64 129	99.44 180

EPP-MVSNet		99.13 84	98.99 92	99.53 104	99.65 124	99.06 143	99.81 19	99.33 252	97.43 200	99.60 95	99.88 21	97.14 133	99.84 144	99.13 57	98.94 183	99.69 108

thres100view900		97.76 248	97.45 254	98.69 232	99.72 89	97.86 246	99.59 90	98.74 338	97.93 145	99.26 178	98.62 344	91.75 303	99.83 155	93.22 343	98.18 222	98.37 333

tfpn200view9		97.72 258	97.38 266	98.72 230	99.69 105	97.96 239	99.50 140	98.73 343	97.83 155	99.17 199	98.45 349	91.67 307	99.83 155	93.22 343	98.18 222	98.37 333

test_prior3		99.21 72	99.05 79	99.68 68	99.67 110	99.48 93	98.96 306	99.56 58	98.34 93	99.01 225	99.52 228	98.68 67	99.83 155	97.96 193	99.74 108	99.74 83

test_prior				99.68 68	99.67 110	99.48 93		99.56 58					99.83 155			99.74 83

1314		98.68 153	98.54 156	99.11 167	98.89 300	98.65 193	99.27 234	99.49 135	96.89 249	97.99 321	99.56 213	97.72 119	99.83 155	97.74 214	99.27 157	98.84 233

thres400		97.77 247	97.38 266	98.92 193	99.69 105	97.96 239	99.50 140	98.73 343	97.83 155	99.17 199	98.45 349	91.67 307	99.83 155	93.22 343	98.18 222	98.96 226

casdiffmvs		99.13 84	98.98 95	99.56 94	99.65 124	99.16 128	99.56 110	99.50 127	98.33 96	99.41 136	99.86 31	95.92 176	99.83 155	99.45 22	99.16 163	99.70 105

CS-MVS-test		99.49 17	99.48 12	99.54 96	99.78 48	99.30 112	99.89 2	99.58 50	98.56 71	99.73 49	99.69 153	98.55 78	99.82 162	99.69 1	99.85 60	99.48 169

MVS_Test		99.10 98	98.97 96	99.48 116	99.49 175	99.14 134	99.67 54	99.34 245	97.31 210	99.58 100	99.76 116	97.65 120	99.82 162	98.87 87	99.07 174	99.46 177

dp		97.75 252	97.80 215	97.59 316	99.10 271	93.71 355	99.32 219	98.88 327	96.48 280	99.08 215	99.55 216	92.67 283	99.82 162	96.52 294	98.58 201	99.24 198

RPSCF		98.22 182	98.62 145	96.99 330	99.82 39	91.58 367	99.72 41	99.44 196	96.61 268	99.66 73	99.89 15	95.92 176	99.82 162	97.46 243	99.10 171	99.57 148

PMMVS		98.80 140	98.62 145	99.34 135	99.27 233	98.70 189	98.76 329	99.31 265	97.34 207	99.21 189	99.07 320	97.20 132	99.82 162	98.56 141	98.87 189	99.52 158

EIA-MVS		99.18 76	99.09 76	99.45 122	99.49 175	99.18 125	99.67 54	99.53 86	97.66 176	99.40 141	99.44 253	98.10 108	99.81 167	98.94 75	99.62 132	99.35 189

Effi-MVS+		98.81 137	98.59 152	99.48 116	99.46 185	99.12 137	98.08 363	99.50 127	97.50 192	99.38 148	99.41 262	96.37 162	99.81 167	99.11 59	98.54 205	99.51 164

thres200		97.61 272	97.28 279	98.62 235	99.64 126	98.03 233	99.26 243	98.74 338	97.68 173	99.09 214	98.32 353	91.66 309	99.81 167	92.88 347	98.22 217	98.03 347

tpmvs		97.98 217	98.02 195	97.84 304	99.04 283	94.73 342	99.31 221	99.20 289	96.10 313	98.76 264	99.42 258	94.94 207	99.81 167	96.97 274	98.45 209	98.97 224

DeepPCF-MVS	98.18 3	98.81 137	99.37 25	97.12 329	99.60 143	91.75 366	98.61 341	99.44 196	99.35 1	99.83 20	99.85 36	98.70 66	99.81 167	99.02 68	99.91 18	99.81 46

DPM-MVS		98.95 117	98.71 130	99.66 71	99.63 129	99.55 80	98.64 340	99.10 300	97.93 145	99.42 132	99.55 216	98.67 70	99.80 172	95.80 308	99.68 123	99.61 137

DP-MVS Recon		99.12 90	98.95 100	99.65 75	99.74 76	99.70 51	99.27 234	99.57 53	96.40 287	99.42 132	99.68 161	98.75 60	99.80 172	97.98 192	99.72 112	99.44 180

MVS_111021_LR		99.41 46	99.33 34	99.65 75	99.77 54	99.51 90	98.94 312	99.85 6	98.82 52	99.65 79	99.74 127	98.51 82	99.80 172	98.83 100	99.89 35	99.64 129

CS-MVS		99.50 16	99.48 12	99.54 96	99.76 57	99.42 100	99.90 1	99.55 67	98.56 71	99.78 35	99.70 143	98.65 72	99.79 175	99.65 4	99.78 97	99.41 184

Fast-Effi-MVS+-dtu		98.77 143	98.83 120	98.60 236	99.41 195	96.99 283	99.52 129	99.49 135	98.11 123	99.24 181	99.34 282	96.96 143	99.79 175	97.95 195	99.45 142	99.02 219

baseline1		98.31 176	97.95 202	99.38 133	99.50 173	98.74 186	99.59 90	98.93 318	98.41 85	99.14 202	99.60 199	94.59 230	99.79 175	98.48 149	93.29 340	99.61 137

baseline		99.15 81	99.02 87	99.53 104	99.66 119	99.14 134	99.72 41	99.48 147	98.35 92	99.42 132	99.84 45	96.07 169	99.79 175	99.51 12	99.14 166	99.67 115

PVSNet_0	94.43 19	96.09 312	95.47 315	97.94 298	99.31 223	94.34 349	97.81 365	99.70 15	97.12 228	97.46 332	98.75 341	89.71 333	99.79 175	97.69 221	81.69 367	99.68 112

API-MVS		99.04 106	99.03 84	99.06 171	99.40 200	99.31 111	99.55 119	99.56 58	98.54 73	99.33 161	99.39 269	98.76 57	99.78 180	96.98 273	99.78 97	98.07 344

OMC-MVS		99.08 101	99.04 82	99.20 159	99.67 110	98.22 226	99.28 229	99.52 92	98.07 131	99.66 73	99.81 69	97.79 116	99.78 180	97.79 207	99.81 86	99.60 139

GeoE		98.85 133	98.62 145	99.53 104	99.61 139	99.08 140	99.80 23	99.51 106	97.10 232	99.31 164	99.78 103	95.23 203	99.77 182	98.21 172	99.03 177	99.75 78

alignmvs		98.81 137	98.56 155	99.58 90	99.43 191	99.42 100	99.51 134	98.96 316	98.61 68	99.35 157	98.92 335	94.78 218	99.77 182	99.35 30	98.11 227	99.54 152

tpm cat1		97.39 285	97.36 268	97.50 320	99.17 259	93.73 354	99.43 174	99.31 265	91.27 356	98.71 268	99.08 319	94.31 241	99.77 182	96.41 298	98.50 207	99.00 220

CostFormer		97.72 258	97.73 227	97.71 312	99.15 264	94.02 351	99.54 123	99.02 309	94.67 334	99.04 222	99.35 279	92.35 295	99.77 182	98.50 148	97.94 230	99.34 191

test_241102_ONE					99.84 33	99.90 2		99.48 147	99.07 18	99.91 2	99.74 127	99.20 7	99.76 186

MDTV_nov1_ep13			98.32 169		99.11 268	94.44 346	99.27 234	98.74 338	97.51 191	99.40 141	99.62 192	94.78 218	99.76 186	97.59 227	98.81 194

canonicalmvs		99.02 109	98.86 114	99.51 112	99.42 192	99.32 108	99.80 23	99.48 147	98.63 66	99.31 164	98.81 338	97.09 136	99.75 188	99.27 45	97.90 231	99.47 175

Effi-MVS+-dtu		98.78 141	98.89 107	98.47 255	99.33 215	96.91 289	99.57 104	99.30 269	98.47 78	99.41 136	98.99 329	96.78 147	99.74 189	98.73 111	99.38 146	98.74 248

patchmatchnet-post											98.70 342	94.79 217	99.74 189

SCA		98.19 186	98.16 177	98.27 278	99.30 224	95.55 323	99.07 277	98.97 314	97.57 183	99.43 129	99.57 210	92.72 278	99.74 189	97.58 228	99.20 161	99.52 158

BH-untuned		98.42 167	98.36 164	98.59 237	99.49 175	96.70 295	99.27 234	99.13 298	97.24 218	98.80 259	99.38 270	95.75 183	99.74 189	97.07 269	99.16 163	99.33 192

BH-RMVSNet		98.41 169	98.08 187	99.40 129	99.41 195	98.83 180	99.30 223	98.77 334	97.70 171	98.94 238	99.65 174	92.91 273	99.74 189	96.52 294	99.55 137	99.64 129

MVS_111021_HR		99.41 46	99.32 36	99.66 71	99.72 89	99.47 95	98.95 310	99.85 6	98.82 52	99.54 109	99.73 134	98.51 82	99.74 189	98.91 80	99.88 38	99.77 72

test_post											65.99 378	94.65 229	99.73 195

XVG-ACMP-BASELINE		97.83 238	97.71 229	98.20 280	99.11 268	96.33 308	99.41 183	99.52 92	98.06 135	99.05 221	99.50 235	89.64 335	99.73 195	97.73 215	97.38 263	98.53 314

HyFIR lowres test		99.11 95	98.92 102	99.65 75	99.90 4	99.37 105	99.02 291	99.91 3	97.67 175	99.59 98	99.75 121	95.90 178	99.73 195	99.53 10	99.02 179	99.86 15

DeepMVS_CX				93.34 347	99.29 228	82.27 373		99.22 285	85.15 365	96.33 349	99.05 323	90.97 319	99.73 195	93.57 340	97.77 234	98.01 348

Patchmatch-test		97.93 222	97.65 234	98.77 227	99.18 253	97.07 274	99.03 288	99.14 297	96.16 304	98.74 265	99.57 210	94.56 232	99.72 199	93.36 342	99.11 168	99.52 158

LPG-MVS_test		98.22 182	98.13 180	98.49 249	99.33 215	97.05 276	99.58 98	99.55 67	97.46 193	99.24 181	99.83 49	92.58 285	99.72 199	98.09 182	97.51 248	98.68 267

LGP-MVS_train				98.49 249	99.33 215	97.05 276		99.55 67	97.46 193	99.24 181	99.83 49	92.58 285	99.72 199	98.09 182	97.51 248	98.68 267

BH-w/o		98.00 215	97.89 211	98.32 271	99.35 209	96.20 312	99.01 296	98.90 325	96.42 285	98.38 303	99.00 328	95.26 201	99.72 199	96.06 302	98.61 198	99.03 217

ACMP	97.20 11	98.06 200	97.94 204	98.45 257	99.37 206	97.01 281	99.44 168	99.49 135	97.54 188	98.45 298	99.79 96	91.95 299	99.72 199	97.91 197	97.49 253	98.62 297
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	97.16 12	98.02 210	97.90 207	98.40 264	99.23 241	96.80 293	99.70 44	99.60 41	97.12 228	98.18 313	99.70 143	91.73 305	99.72 199	98.39 157	97.45 256	98.68 267
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
test_post1							99.23 248				65.14 379	94.18 246	99.71 205	97.58 228

ADS-MVSNet		98.20 185	98.08 187	98.56 243	99.33 215	96.48 303	99.23 248	99.15 295	96.24 296	99.10 210	99.67 167	94.11 247	99.71 205	96.81 283	99.05 175	99.48 169

JIA-IIPM		97.50 279	97.02 290	98.93 191	98.73 322	97.80 248	99.30 223	98.97 314	91.73 355	98.91 242	94.86 368	95.10 205	99.71 205	97.58 228	97.98 229	99.28 196

EPMVS		97.82 241	97.65 234	98.35 268	98.88 301	95.98 315	99.49 150	94.71 377	97.57 183	99.26 178	99.48 244	92.46 292	99.71 205	97.87 200	99.08 173	99.35 189

TDRefinement		95.42 318	94.57 324	97.97 297	89.83 377	96.11 314	99.48 155	98.75 335	96.74 257	96.68 346	99.88 21	88.65 343	99.71 205	98.37 161	82.74 366	98.09 343

ACMM	97.58 5	98.37 173	98.34 167	98.48 251	99.41 195	97.10 270	99.56 110	99.45 187	98.53 74	99.04 222	99.85 36	93.00 269	99.71 205	98.74 109	97.45 256	98.64 286
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		99.12 90	99.13 71	99.08 168	99.66 119	97.89 243	98.43 351	99.71 13	98.88 47	99.62 88	99.76 116	96.63 153	99.70 211	99.46 21	99.99 1	99.66 118

DROMVSNet		99.44 33	99.39 22	99.58 90	99.56 153	99.49 91	99.88 4	99.58 50	98.38 87	99.73 49	99.69 153	98.20 103	99.70 211	99.64 5	99.82 83	99.54 152

PatchmatchNet		98.31 176	98.36 164	98.19 281	99.16 261	95.32 331	99.27 234	98.92 320	97.37 206	99.37 150	99.58 206	94.90 211	99.70 211	97.43 247	99.21 160	99.54 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH	97.28 8	98.10 196	97.99 197	98.44 260	99.41 195	96.96 287	99.60 83	99.56 58	98.09 126	98.15 314	99.91 9	90.87 320	99.70 211	98.88 83	97.45 256	98.67 274
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP_MVS		98.27 181	98.22 175	98.44 260	99.29 228	96.97 285	99.39 195	99.47 165	98.97 37	99.11 207	99.61 196	92.71 280	99.69 215	97.78 208	97.63 236	98.67 274

plane_prior5								99.47 165					99.69 215	97.78 208	97.63 236	98.67 274

D2MVS		98.41 169	98.50 158	98.15 286	99.26 235	96.62 299	99.40 191	99.61 36	97.71 170	98.98 232	99.36 276	96.04 170	99.67 217	98.70 115	97.41 260	98.15 342

IS-MVSNet		99.05 105	98.87 109	99.57 92	99.73 84	99.32 108	99.75 37	99.20 289	98.02 140	99.56 104	99.86 31	96.54 156	99.67 217	98.09 182	99.13 167	99.73 90

CLD-MVS		98.16 190	98.10 183	98.33 269	99.29 228	96.82 292	98.75 330	99.44 196	97.83 155	99.13 203	99.55 216	92.92 271	99.67 217	98.32 167	97.69 235	98.48 318
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
AUN-MVS		96.88 295	96.31 301	98.59 237	99.48 183	97.04 279	99.27 234	99.22 285	97.44 199	98.51 294	99.41 262	91.97 298	99.66 220	97.71 218	83.83 364	99.07 214

UniMVSNet_ETH3D		97.32 287	96.81 293	98.87 209	99.40 200	97.46 258	99.51 134	99.53 86	95.86 316	98.54 293	99.77 110	82.44 368	99.66 220	98.68 120	97.52 246	99.50 167

OPM-MVS		98.19 186	98.10 183	98.45 257	98.88 301	97.07 274	99.28 229	99.38 227	98.57 70	99.22 186	99.81 69	92.12 296	99.66 220	98.08 186	97.54 245	98.61 306
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMH+	97.24 10	97.92 225	97.78 219	98.32 271	99.46 185	96.68 297	99.56 110	99.54 75	98.41 85	97.79 328	99.87 27	90.18 329	99.66 220	98.05 190	97.18 270	98.62 297

hse-mvs2		97.50 279	97.14 286	98.59 237	99.49 175	97.05 276	99.28 229	99.22 285	98.94 41	99.66 73	99.42 258	94.93 208	99.65 224	99.48 18	83.80 365	99.08 209

VPA-MVSNet		98.29 179	97.95 202	99.30 145	99.16 261	99.54 82	99.50 140	99.58 50	98.27 101	99.35 157	99.37 273	92.53 287	99.65 224	99.35 30	94.46 324	98.72 251

TR-MVS		97.76 248	97.41 264	98.82 220	99.06 279	97.87 244	98.87 319	98.56 348	96.63 267	98.68 276	99.22 305	92.49 288	99.65 224	95.40 317	97.79 233	98.95 229

gm-plane-assit					98.54 341	92.96 361			94.65 335		99.15 313		99.64 227	97.56 233

HQP4-MVS										98.66 277			99.64 227			98.64 286

HQP-MVS		98.02 210	97.90 207	98.37 267	99.19 250	96.83 290	98.98 302	99.39 221	98.24 102	98.66 277	99.40 265	92.47 289	99.64 227	97.19 261	97.58 241	98.64 286

PAPM		97.59 273	97.09 288	99.07 170	99.06 279	98.26 225	98.30 358	99.10 300	94.88 330	98.08 316	99.34 282	96.27 165	99.64 227	89.87 359	98.92 186	99.31 194

TAPA-MVS	97.07 15	97.74 255	97.34 273	98.94 189	99.70 102	97.53 256	99.25 245	99.51 106	91.90 354	99.30 166	99.63 187	98.78 52	99.64 227	88.09 366	99.87 42	99.65 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
XXY-MVS		98.38 172	98.09 186	99.24 156	99.26 235	99.32 108	99.56 110	99.55 67	97.45 196	98.71 268	99.83 49	93.23 265	99.63 232	98.88 83	96.32 285	98.76 243

ITE_SJBPF				98.08 288	99.29 228	96.37 306		98.92 320	98.34 93	98.83 255	99.75 121	91.09 317	99.62 233	95.82 306	97.40 261	98.25 338

LF4IMVS		97.52 276	97.46 253	97.70 313	98.98 291	95.55 323	99.29 227	98.82 332	98.07 131	98.66 277	99.64 181	89.97 330	99.61 234	97.01 270	96.68 275	97.94 354

tpm		97.67 268	97.55 241	98.03 290	99.02 285	95.01 337	99.43 174	98.54 350	96.44 283	99.12 205	99.34 282	91.83 302	99.60 235	97.75 213	96.46 281	99.48 169

tpm2		97.44 284	97.34 273	97.74 311	99.15 264	94.36 348	99.45 164	98.94 317	93.45 348	98.90 244	99.44 253	91.35 314	99.59 236	97.31 251	98.07 228	99.29 195

baseline2		97.87 230	97.55 241	98.82 220	99.18 253	98.02 234	99.41 183	96.58 371	96.97 242	96.51 347	99.17 310	93.43 262	99.57 237	97.71 218	99.03 177	98.86 231

MS-PatchMatch		97.24 290	97.32 276	96.99 330	98.45 344	93.51 359	98.82 323	99.32 262	97.41 203	98.13 315	99.30 292	88.99 339	99.56 238	95.68 311	99.80 90	97.90 357

TinyColmap		97.12 292	96.89 292	97.83 305	99.07 276	95.52 326	98.57 344	98.74 338	97.58 182	97.81 327	99.79 96	88.16 349	99.56 238	95.10 322	97.21 268	98.39 331

USDC		97.34 286	97.20 284	97.75 310	99.07 276	95.20 333	98.51 348	99.04 308	97.99 141	98.31 307	99.86 31	89.02 338	99.55 240	95.67 312	97.36 264	98.49 317

MSLP-MVS++		99.46 27	99.47 14	99.44 126	99.60 143	99.16 128	99.41 183	99.71 13	98.98 34	99.45 124	99.78 103	99.19 9	99.54 241	99.28 42	99.84 68	99.63 133

TAMVS		99.12 90	99.08 77	99.24 156	99.46 185	98.55 202	99.51 134	99.46 175	98.09 126	99.45 124	99.82 56	98.34 96	99.51 242	98.70 115	98.93 184	99.67 115

EPNet_dtu		98.03 208	97.96 200	98.23 279	98.27 346	95.54 325	99.23 248	98.75 335	99.02 21	97.82 326	99.71 139	96.11 168	99.48 243	93.04 346	99.65 128	99.69 108
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_part1		97.75 252	97.24 283	99.29 148	99.59 145	99.63 65	99.65 65	99.49 135	96.17 302	98.44 299	99.69 153	89.80 332	99.47 244	98.68 120	93.66 336	98.78 237

EG-PatchMatch MVS		95.97 313	95.69 313	96.81 336	97.78 353	92.79 362	99.16 259	98.93 318	96.16 304	94.08 360	99.22 305	82.72 366	99.47 244	95.67 312	97.50 250	98.17 341

MVP-Stereo		97.81 243	97.75 225	97.99 296	97.53 355	96.60 300	98.96 306	98.85 329	97.22 220	97.23 337	99.36 276	95.28 198	99.46 246	95.51 314	99.78 97	97.92 356
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CVMVSNet		98.57 159	98.67 134	98.30 273	99.35 209	95.59 322	99.50 140	99.55 67	98.60 69	99.39 144	99.83 49	94.48 235	99.45 247	98.75 108	98.56 204	99.85 18

test-LLR		98.06 200	97.90 207	98.55 245	98.79 313	97.10 270	98.67 336	97.75 360	97.34 207	98.61 288	98.85 336	94.45 236	99.45 247	97.25 255	99.38 146	99.10 204

TESTMET0.1,1		97.55 274	97.27 282	98.40 264	98.93 296	96.53 301	98.67 336	97.61 363	96.96 243	98.64 284	99.28 296	88.63 344	99.45 247	97.30 252	99.38 146	99.21 200

test-mter		97.49 282	97.13 287	98.55 245	98.79 313	97.10 270	98.67 336	97.75 360	96.65 264	98.61 288	98.85 336	88.23 348	99.45 247	97.25 255	99.38 146	99.10 204

mvs_anonymous		99.03 108	98.99 92	99.16 163	99.38 204	98.52 208	99.51 134	99.38 227	97.79 161	99.38 148	99.81 69	97.30 129	99.45 247	99.35 30	98.99 181	99.51 164

tfpnnormal		97.84 236	97.47 251	98.98 183	99.20 248	99.22 122	99.64 68	99.61 36	96.32 289	98.27 310	99.70 143	93.35 264	99.44 252	95.69 310	95.40 308	98.27 336

v7n		97.87 230	97.52 245	98.92 193	98.76 320	98.58 200	99.84 13	99.46 175	96.20 299	98.91 242	99.70 143	94.89 212	99.44 252	96.03 303	93.89 334	98.75 245

jajsoiax		98.43 166	98.28 172	98.88 205	98.60 337	98.43 218	99.82 17	99.53 86	98.19 110	98.63 285	99.80 84	93.22 267	99.44 252	99.22 48	97.50 250	98.77 241

mvs_tets		98.40 171	98.23 174	98.91 197	98.67 330	98.51 210	99.66 58	99.53 86	98.19 110	98.65 283	99.81 69	92.75 275	99.44 252	99.31 37	97.48 254	98.77 241

Vis-MVSNet (Re-imp)		98.87 122	98.72 128	99.31 141	99.71 95	98.88 172	99.80 23	99.44 196	97.91 147	99.36 154	99.78 103	95.49 192	99.43 256	97.91 197	99.11 168	99.62 135

OPU-MVS				99.64 80	99.56 153	99.72 47	99.60 83				99.70 143	99.27 5	99.42 257	98.24 171	99.80 90	99.79 62

Anonymous20231211		97.88 228	97.54 244	98.90 199	99.71 95	98.53 204	99.48 155	99.57 53	94.16 339	98.81 257	99.68 161	93.23 265	99.42 257	98.84 97	94.42 326	98.76 243

MVS_0304		96.79 298	96.52 298	97.59 316	99.22 244	94.92 340	99.04 287	99.59 44	96.49 276	98.43 300	98.99 329	80.48 371	99.39 259	97.15 265	99.27 157	98.47 320

VPNet		97.84 236	97.44 259	99.01 177	99.21 246	98.94 166	99.48 155	99.57 53	98.38 87	99.28 170	99.73 134	88.89 340	99.39 259	99.19 50	93.27 341	98.71 253

iter_conf_final		98.71 147	98.61 151	98.99 181	99.49 175	98.96 158	99.63 70	99.41 209	98.19 110	99.39 144	99.77 110	94.82 214	99.38 261	99.30 40	97.52 246	98.64 286

nrg030		98.64 157	98.42 162	99.28 151	99.05 282	99.69 52	99.81 19	99.46 175	98.04 137	99.01 225	99.82 56	96.69 152	99.38 261	99.34 34	94.59 323	98.78 237

iter_conf05		98.55 160	98.44 160	98.87 209	99.34 213	98.60 199	99.55 119	99.42 206	98.21 108	99.37 150	99.77 110	93.55 261	99.38 261	99.30 40	97.48 254	98.63 294

GA-MVS		97.85 233	97.47 251	99.00 179	99.38 204	97.99 236	98.57 344	99.15 295	97.04 237	98.90 244	99.30 292	89.83 331	99.38 261	96.70 289	98.33 211	99.62 135

UniMVSNet (Re)		98.29 179	98.00 196	99.13 166	99.00 287	99.36 106	99.49 150	99.51 106	97.95 143	98.97 234	99.13 315	96.30 164	99.38 261	98.36 163	93.34 339	98.66 282

FIs		98.78 141	98.63 140	99.23 158	99.18 253	99.54 82	99.83 16	99.59 44	98.28 99	98.79 261	99.81 69	96.75 150	99.37 266	99.08 63	96.38 283	98.78 237

PS-MVSNAJss		98.92 119	98.92 102	98.90 199	98.78 316	98.53 204	99.78 30	99.54 75	98.07 131	99.00 230	99.76 116	99.01 19	99.37 266	99.13 57	97.23 267	98.81 234

CDS-MVSNet		99.09 99	99.03 84	99.25 154	99.42 192	98.73 187	99.45 164	99.46 175	98.11 123	99.46 123	99.77 110	98.01 111	99.37 266	98.70 115	98.92 186	99.66 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS-HIRNet		95.75 315	95.16 319	97.51 319	99.30 224	93.69 356	98.88 317	95.78 372	85.09 366	98.78 262	92.65 370	91.29 315	99.37 266	94.85 326	99.85 60	99.46 177

v1192		97.81 243	97.44 259	98.91 197	98.88 301	98.68 190	99.51 134	99.34 245	96.18 301	99.20 192	99.34 282	94.03 250	99.36 270	95.32 320	95.18 312	98.69 262

EI-MVSNet		98.67 154	98.67 134	98.68 233	99.35 209	97.97 237	99.50 140	99.38 227	96.93 248	99.20 192	99.83 49	97.87 113	99.36 270	98.38 159	97.56 243	98.71 253

MVSTER		98.49 161	98.32 169	99.00 179	99.35 209	99.02 147	99.54 123	99.38 227	97.41 203	99.20 192	99.73 134	93.86 255	99.36 270	98.87 87	97.56 243	98.62 297

gg-mvs-nofinetune		96.17 310	95.32 318	98.73 229	98.79 313	98.14 230	99.38 200	94.09 378	91.07 359	98.07 319	91.04 373	89.62 336	99.35 273	96.75 285	99.09 172	98.68 267

pm-mvs1		97.68 265	97.28 279	98.88 205	99.06 279	98.62 196	99.50 140	99.45 187	96.32 289	97.87 324	99.79 96	92.47 289	99.35 273	97.54 235	93.54 338	98.67 274

OurMVSNet-221017-0		97.88 228	97.77 221	98.19 281	98.71 326	96.53 301	99.88 4	99.00 311	97.79 161	98.78 262	99.94 3	91.68 306	99.35 273	97.21 257	96.99 274	98.69 262

EGC-MVSNET		82.80 338	77.86 344	97.62 314	97.91 350	96.12 313	99.33 218	99.28 276	8.40 381	25.05 382	99.27 299	84.11 363	99.33 276	89.20 361	98.22 217	97.42 363

pmmvs6		96.53 302	96.09 305	97.82 307	98.69 328	95.47 327	99.37 203	99.47 165	93.46 347	97.41 333	99.78 103	87.06 356	99.33 276	96.92 280	92.70 348	98.65 284

mvsmamba		98.92 119	98.87 109	99.08 168	99.07 276	99.16 128	99.88 4	99.51 106	98.15 116	99.40 141	99.89 15	97.12 134	99.33 276	99.38 27	97.40 261	98.73 250

V42		98.06 200	97.79 216	98.86 213	98.98 291	98.84 177	99.69 46	99.34 245	96.53 274	99.30 166	99.37 273	94.67 227	99.32 279	97.57 232	94.66 321	98.42 327

lessismore_v0				97.79 309	98.69 328	95.44 329		94.75 376		95.71 355	99.87 27	88.69 342	99.32 279	95.89 305	94.93 319	98.62 297

OpenMVS_ROB	92.34 20	94.38 328	93.70 332	96.41 341	97.38 357	93.17 360	99.06 280	98.75 335	86.58 364	94.84 359	98.26 354	81.53 369	99.32 279	89.01 362	97.87 232	96.76 364

bld_raw_dy_0_64		98.69 151	98.58 153	98.99 181	98.88 301	98.96 158	99.80 23	99.41 209	97.91 147	99.32 162	99.87 27	95.70 186	99.31 282	99.09 61	97.27 266	98.71 253

v8		97.95 221	97.63 237	98.93 191	98.95 295	98.81 183	99.80 23	99.41 209	96.03 314	99.10 210	99.42 258	94.92 210	99.30 283	96.94 277	94.08 332	98.66 282

v1921920		97.80 245	97.45 254	98.84 217	98.80 312	98.53 204	99.52 129	99.34 245	96.15 306	99.24 181	99.47 247	93.98 251	99.29 284	95.40 317	95.13 314	98.69 262

anonymousdsp		98.44 165	98.28 172	98.94 189	98.50 342	98.96 158	99.77 32	99.50 127	97.07 234	98.87 249	99.77 110	94.76 222	99.28 285	98.66 123	97.60 239	98.57 312

MVSFormer		99.17 78	99.12 72	99.29 148	99.51 163	98.94 166	99.88 4	99.46 175	97.55 185	99.80 28	99.65 174	97.39 124	99.28 285	99.03 66	99.85 60	99.65 122

test_djsdf		98.67 154	98.57 154	98.98 183	98.70 327	98.91 170	99.88 4	99.46 175	97.55 185	99.22 186	99.88 21	95.73 184	99.28 285	99.03 66	97.62 238	98.75 245

cascas		97.69 263	97.43 262	98.48 251	98.60 337	97.30 261	98.18 362	99.39 221	92.96 351	98.41 301	98.78 340	93.77 258	99.27 288	98.16 179	98.61 198	98.86 231

v144192		97.92 225	97.60 239	98.87 209	98.83 311	98.65 193	99.55 119	99.34 245	96.20 299	99.32 162	99.40 265	94.36 238	99.26 289	96.37 299	95.03 316	98.70 258

RRT_MVS		98.70 148	98.66 137	98.83 219	98.90 298	98.45 216	99.89 2	99.28 276	97.76 164	98.94 238	99.92 8	96.98 141	99.25 290	99.28 42	97.00 273	98.80 235

v2v482		98.06 200	97.77 221	98.92 193	98.90 298	98.82 181	99.57 104	99.36 236	96.65 264	99.19 195	99.35 279	94.20 243	99.25 290	97.72 217	94.97 317	98.69 262

v1240		97.69 263	97.32 276	98.79 225	98.85 309	98.43 218	99.48 155	99.36 236	96.11 309	99.27 173	99.36 276	93.76 259	99.24 292	94.46 330	95.23 311	98.70 258

v1144		97.98 217	97.69 230	98.85 216	98.87 305	98.66 192	99.54 123	99.35 241	96.27 293	99.23 185	99.35 279	94.67 227	99.23 293	96.73 287	95.16 313	98.68 267

v10		97.85 233	97.52 245	98.86 213	98.99 288	98.67 191	99.75 37	99.41 209	95.70 317	98.98 232	99.41 262	94.75 223	99.23 293	96.01 304	94.63 322	98.67 274

WR-MVS_H		98.13 193	97.87 212	98.90 199	99.02 285	98.84 177	99.70 44	99.59 44	97.27 214	98.40 302	99.19 309	95.53 190	99.23 293	98.34 164	93.78 335	98.61 306

miper_enhance_ethall		98.16 190	98.08 187	98.41 262	98.96 294	97.72 252	98.45 350	99.32 262	96.95 245	98.97 234	99.17 310	97.06 138	99.22 296	97.86 201	95.99 292	98.29 335

GG-mvs-BLEND				98.45 257	98.55 340	98.16 228	99.43 174	93.68 379		97.23 337	98.46 348	89.30 337	99.22 296	95.43 316	98.22 217	97.98 352

FC-MVSNet-test		98.75 144	98.62 145	99.15 165	99.08 275	99.45 97	99.86 12	99.60 41	98.23 105	98.70 274	99.82 56	96.80 146	99.22 296	99.07 64	96.38 283	98.79 236

UniMVSNet_NR-MVSNet		98.22 182	97.97 199	98.96 186	98.92 297	98.98 151	99.48 155	99.53 86	97.76 164	98.71 268	99.46 251	96.43 161	99.22 296	98.57 138	92.87 346	98.69 262

DU-MVS		98.08 199	97.79 216	98.96 186	98.87 305	98.98 151	99.41 183	99.45 187	97.87 149	98.71 268	99.50 235	94.82 214	99.22 296	98.57 138	92.87 346	98.68 267

cl____		98.01 213	97.84 214	98.55 245	99.25 239	97.97 237	98.71 334	99.34 245	96.47 282	98.59 291	99.54 221	95.65 188	99.21 301	97.21 257	95.77 298	98.46 324

WR-MVS		98.06 200	97.73 227	99.06 171	98.86 308	99.25 119	99.19 256	99.35 241	97.30 211	98.66 277	99.43 255	93.94 252	99.21 301	98.58 136	94.28 328	98.71 253

test_0402		96.64 300	96.24 302	97.85 303	98.85 309	96.43 305	99.44 168	99.26 279	93.52 345	96.98 344	99.52 228	88.52 345	99.20 303	92.58 352	97.50 250	97.93 355

SixPastTwentyTwo		97.50 279	97.33 275	98.03 290	98.65 331	96.23 311	99.77 32	98.68 346	97.14 225	97.90 323	99.93 4	90.45 323	99.18 304	97.00 271	96.43 282	98.67 274

cl22		97.85 233	97.64 236	98.48 251	99.09 273	97.87 244	98.60 343	99.33 252	97.11 231	98.87 249	99.22 305	92.38 294	99.17 305	98.21 172	95.99 292	98.42 327

IterMVS-SCA-FT		97.82 241	97.75 225	98.06 289	99.57 149	96.36 307	99.02 291	99.49 135	97.18 222	98.71 268	99.72 138	92.72 278	99.14 306	97.44 246	95.86 297	98.67 274

pmmvs5		97.52 276	97.30 278	98.16 283	98.57 339	96.73 294	99.27 234	98.90 325	96.14 307	98.37 304	99.53 225	91.54 312	99.14 306	97.51 238	95.87 296	98.63 294

v148		97.79 246	97.55 241	98.50 248	98.74 321	97.72 252	99.54 123	99.33 252	96.26 294	98.90 244	99.51 232	94.68 226	99.14 306	97.83 204	93.15 343	98.63 294

miper_ehance_all_eth		98.18 188	98.10 183	98.41 262	99.23 241	97.72 252	98.72 333	99.31 265	96.60 270	98.88 247	99.29 294	97.29 130	99.13 309	97.60 226	95.99 292	98.38 332

NR-MVSNet		97.97 220	97.61 238	99.02 176	98.87 305	99.26 118	99.47 160	99.42 206	97.63 178	97.08 342	99.50 235	95.07 206	99.13 309	97.86 201	93.59 337	98.68 267

IterMVS		97.83 238	97.77 221	98.02 292	99.58 147	96.27 310	99.02 291	99.48 147	97.22 220	98.71 268	99.70 143	92.75 275	99.13 309	97.46 243	96.00 291	98.67 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CMPMVS	69.68 23	94.13 329	94.90 321	91.84 350	97.24 361	80.01 375	98.52 347	99.48 147	89.01 361	91.99 365	99.67 167	85.67 360	99.13 309	95.44 315	97.03 272	96.39 366
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
eth_miper_zixun_eth		98.05 205	97.96 200	98.33 269	99.26 235	97.38 260	98.56 346	99.31 265	96.65 264	98.88 247	99.52 228	96.58 154	99.12 313	97.39 249	95.53 306	98.47 320

pmmvs4		98.13 193	97.90 207	98.81 222	98.61 336	98.87 173	98.99 298	99.21 288	96.44 283	99.06 220	99.58 206	95.90 178	99.11 314	97.18 263	96.11 289	98.46 324

TransMVSNet (Re)		97.15 291	96.58 296	98.86 213	99.12 266	98.85 176	99.49 150	98.91 323	95.48 320	97.16 340	99.80 84	93.38 263	99.11 314	94.16 335	91.73 351	98.62 297

ambc				93.06 348	92.68 373	82.36 372	98.47 349	98.73 343		95.09 357	97.41 359	55.55 377	99.10 316	96.42 297	91.32 352	97.71 358

Baseline_NR-MVSNet		97.76 248	97.45 254	98.68 233	99.09 273	98.29 223	99.41 183	98.85 329	95.65 318	98.63 285	99.67 167	94.82 214	99.10 316	98.07 189	92.89 345	98.64 286

CP-MVSNet		98.09 197	97.78 219	99.01 177	98.97 293	99.24 120	99.67 54	99.46 175	97.25 216	98.48 297	99.64 181	93.79 257	99.06 318	98.63 126	94.10 331	98.74 248

PS-CasMVS		97.93 222	97.59 240	98.95 188	98.99 288	99.06 143	99.68 51	99.52 92	97.13 226	98.31 307	99.68 161	92.44 293	99.05 319	98.51 147	94.08 332	98.75 245

K. test v3		97.10 293	96.79 294	98.01 293	98.72 324	96.33 308	99.87 9	97.05 366	97.59 180	96.16 351	99.80 84	88.71 341	99.04 320	96.69 290	96.55 280	98.65 284

new_pmnet		96.38 306	96.03 306	97.41 321	98.13 349	95.16 336	99.05 282	99.20 289	93.94 340	97.39 334	98.79 339	91.61 311	99.04 320	90.43 357	95.77 298	98.05 346

DIV-MVS_self_test		98.01 213	97.85 213	98.48 251	99.24 240	97.95 241	98.71 334	99.35 241	96.50 275	98.60 290	99.54 221	95.72 185	99.03 322	97.21 257	95.77 298	98.46 324

IterMVS-LS		98.46 164	98.42 162	98.58 240	99.59 145	98.00 235	99.37 203	99.43 204	96.94 247	99.07 216	99.59 202	97.87 113	99.03 322	98.32 167	95.62 303	98.71 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
our_test_3		97.65 270	97.68 231	97.55 318	98.62 334	94.97 338	98.84 321	99.30 269	96.83 254	98.19 312	99.34 282	97.01 140	99.02 324	95.00 325	96.01 290	98.64 286

Patchmtry		97.75 252	97.40 265	98.81 222	99.10 271	98.87 173	99.11 273	99.33 252	94.83 331	98.81 257	99.38 270	94.33 239	99.02 324	96.10 301	95.57 304	98.53 314

N_pmnet		94.95 323	95.83 311	92.31 349	98.47 343	79.33 376	99.12 267	92.81 382	93.87 341	97.68 329	99.13 315	93.87 254	99.01 326	91.38 354	96.19 287	98.59 310

CR-MVSNet		98.17 189	97.93 205	98.87 209	99.18 253	98.49 212	99.22 253	99.33 252	96.96 243	99.56 104	99.38 270	94.33 239	99.00 327	94.83 327	98.58 201	99.14 201

c3_l		98.12 195	98.04 192	98.38 266	99.30 224	97.69 255	98.81 324	99.33 252	96.67 262	98.83 255	99.34 282	97.11 135	98.99 328	97.58 228	95.34 309	98.48 318

test0.0.03 1		97.71 261	97.42 263	98.56 243	98.41 345	97.82 247	98.78 327	98.63 347	97.34 207	98.05 320	98.98 332	94.45 236	98.98 329	95.04 324	97.15 271	98.89 230

PatchT		97.03 294	96.44 299	98.79 225	98.99 288	98.34 222	99.16 259	99.07 305	92.13 353	99.52 113	97.31 363	94.54 234	98.98 329	88.54 364	98.73 197	99.03 217

GBi-Net		97.68 265	97.48 249	98.29 274	99.51 163	97.26 265	99.43 174	99.48 147	96.49 276	99.07 216	99.32 289	90.26 325	98.98 329	97.10 266	96.65 276	98.62 297

test1		97.68 265	97.48 249	98.29 274	99.51 163	97.26 265	99.43 174	99.48 147	96.49 276	99.07 216	99.32 289	90.26 325	98.98 329	97.10 266	96.65 276	98.62 297

FMVSNet3		98.03 208	97.76 224	98.84 217	99.39 203	98.98 151	99.40 191	99.38 227	96.67 262	99.07 216	99.28 296	92.93 270	98.98 329	97.10 266	96.65 276	98.56 313

FMVSNet2		97.72 258	97.36 268	98.80 224	99.51 163	98.84 177	99.45 164	99.42 206	96.49 276	98.86 254	99.29 294	90.26 325	98.98 329	96.44 296	96.56 279	98.58 311

FMVSNet1		96.84 296	96.36 300	98.29 274	99.32 222	97.26 265	99.43 174	99.48 147	95.11 325	98.55 292	99.32 289	83.95 364	98.98 329	95.81 307	96.26 286	98.62 297

ppachtmachnet_test		97.49 282	97.45 254	97.61 315	98.62 334	95.24 332	98.80 325	99.46 175	96.11 309	98.22 311	99.62 192	96.45 159	98.97 336	93.77 337	95.97 295	98.61 306

TranMVSNet+NR-MVSNet		97.93 222	97.66 233	98.76 228	98.78 316	98.62 196	99.65 65	99.49 135	97.76 164	98.49 296	99.60 199	94.23 242	98.97 336	98.00 191	92.90 344	98.70 258

test_method		91.10 333	91.36 336	90.31 353	95.85 367	73.72 381	94.89 370	99.25 281	68.39 373	95.82 354	99.02 327	80.50 370	98.95 338	93.64 339	94.89 320	98.25 338

ADS-MVSNet2		98.02 210	98.07 190	97.87 302	99.33 215	95.19 334	99.23 248	99.08 303	96.24 296	99.10 210	99.67 167	94.11 247	98.93 339	96.81 283	99.05 175	99.48 169

ET-MVSNet_ETH3D		96.49 303	95.64 314	99.05 173	99.53 159	98.82 181	98.84 321	97.51 364	97.63 178	84.77 368	99.21 308	92.09 297	98.91 340	98.98 71	92.21 350	99.41 184

miper_lstm_enhance		98.00 215	97.91 206	98.28 277	99.34 213	97.43 259	98.88 317	99.36 236	96.48 280	98.80 259	99.55 216	95.98 171	98.91 340	97.27 253	95.50 307	98.51 316

PEN-MVS		97.76 248	97.44 259	98.72 230	98.77 319	98.54 203	99.78 30	99.51 106	97.06 236	98.29 309	99.64 181	92.63 284	98.89 342	98.09 182	93.16 342	98.72 251

testgi		97.65 270	97.50 248	98.13 287	99.36 208	96.45 304	99.42 181	99.48 147	97.76 164	97.87 324	99.45 252	91.09 317	98.81 343	94.53 329	98.52 206	99.13 203

MIMVSNet		97.73 256	97.45 254	98.57 241	99.45 190	97.50 257	99.02 291	98.98 313	96.11 309	99.41 136	99.14 314	90.28 324	98.74 344	95.74 309	98.93 184	99.47 175

LCM-MVSNet-Re		97.83 238	98.15 178	96.87 335	99.30 224	92.25 364	99.59 90	98.26 352	97.43 200	96.20 350	99.13 315	96.27 165	98.73 345	98.17 178	98.99 181	99.64 129

DTE-MVSNet		97.51 278	97.19 285	98.46 256	98.63 333	98.13 231	99.84 13	99.48 147	96.68 261	97.97 322	99.67 167	92.92 271	98.56 346	96.88 282	92.60 349	98.70 258

PC_three_1452									98.18 114	99.84 15	99.70 143	99.31 3	98.52 347	98.30 169	99.80 90	99.81 46

UnsupCasMVSNet_bld		93.53 331	92.51 334	96.58 340	97.38 357	93.82 352	98.24 359	99.48 147	91.10 358	93.10 363	96.66 364	74.89 372	98.37 348	94.03 336	87.71 359	97.56 361

Anonymous20240521		96.20 309	95.89 310	97.13 328	97.72 354	94.96 339	99.79 29	99.29 274	93.01 350	97.20 339	99.03 325	89.69 334	98.36 349	91.16 355	96.13 288	98.07 344

MDA-MVSNet_test_wron		95.45 317	94.60 323	98.01 293	98.16 348	97.21 268	99.11 273	99.24 283	93.49 346	80.73 373	98.98 332	93.02 268	98.18 350	94.22 334	94.45 325	98.64 286

UnsupCasMVSNet_eth		96.44 304	96.12 304	97.40 322	98.65 331	95.65 320	99.36 207	99.51 106	97.13 226	96.04 353	98.99 329	88.40 346	98.17 351	96.71 288	90.27 354	98.40 330

KD-MVS_2432*1600		94.62 324	93.72 330	97.31 323	97.19 363	95.82 318	98.34 354	99.20 289	95.00 328	97.57 330	98.35 351	87.95 351	98.10 352	92.87 348	77.00 371	98.01 348

miper_refine_blended		94.62 324	93.72 330	97.31 323	97.19 363	95.82 318	98.34 354	99.20 289	95.00 328	97.57 330	98.35 351	87.95 351	98.10 352	92.87 348	77.00 371	98.01 348

YYNet1		95.36 319	94.51 325	97.92 299	97.89 351	97.10 270	99.10 275	99.23 284	93.26 349	80.77 372	99.04 324	92.81 274	98.02 354	94.30 331	94.18 330	98.64 286

EU-MVSNet		97.98 217	98.03 193	97.81 308	98.72 324	96.65 298	99.66 58	99.66 27	98.09 126	98.35 305	99.82 56	95.25 202	98.01 355	97.41 248	95.30 310	98.78 237

Gipuma		90.99 334	90.15 337	93.51 346	98.73 322	90.12 369	93.98 371	99.45 187	79.32 369	92.28 364	94.91 367	69.61 373	97.98 356	87.42 367	95.67 302	92.45 370
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
pmmvs-eth3d		95.34 320	94.73 322	97.15 326	95.53 370	95.94 316	99.35 213	99.10 300	95.13 323	93.55 361	97.54 358	88.15 350	97.91 357	94.58 328	89.69 357	97.61 359

PM-MVS		92.96 332	92.23 335	95.14 345	95.61 368	89.98 370	99.37 203	98.21 354	94.80 332	95.04 358	97.69 357	65.06 374	97.90 358	94.30 331	89.98 356	97.54 362

MDA-MVSNet-bldmvs		94.96 322	93.98 328	97.92 299	98.24 347	97.27 263	99.15 263	99.33 252	93.80 342	80.09 374	99.03 325	88.31 347	97.86 359	93.49 341	94.36 327	98.62 297

Patchmatch-RL test		95.84 314	95.81 312	95.95 343	95.61 368	90.57 368	98.24 359	98.39 351	95.10 327	95.20 356	98.67 343	94.78 218	97.77 360	96.28 300	90.02 355	99.51 164

Anonymous20231206		96.22 307	96.03 306	96.79 337	97.31 360	94.14 350	99.63 70	99.08 303	96.17 302	97.04 343	99.06 322	93.94 252	97.76 361	86.96 369	95.06 315	98.47 320

SD-MVS		99.41 46	99.52 8	99.05 173	99.74 76	99.68 54	99.46 163	99.52 92	99.11 11	99.88 6	99.91 9	99.43 1	97.70 362	98.72 113	99.93 12	99.77 72
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
DSMNet-mixed		97.25 289	97.35 270	96.95 333	97.84 352	93.61 358	99.57 104	96.63 370	96.13 308	98.87 249	98.61 346	94.59 230	97.70 362	95.08 323	98.86 190	99.55 150

pmmvs3		94.09 330	93.25 333	96.60 339	94.76 372	94.49 345	98.92 313	98.18 356	89.66 360	96.48 348	98.06 356	86.28 357	97.33 364	89.68 360	87.20 360	97.97 353

KD-MVS_self_test		95.00 321	94.34 326	96.96 332	97.07 365	95.39 330	99.56 110	99.44 196	95.11 325	97.13 341	97.32 362	91.86 301	97.27 365	90.35 358	81.23 368	98.23 340

FMVSNet5		96.43 305	96.19 303	97.15 326	99.11 268	95.89 317	99.32 219	99.52 92	94.47 338	98.34 306	99.07 320	87.54 355	97.07 366	92.61 351	95.72 301	98.47 320

new-patchmatchnet		94.48 327	94.08 327	95.67 344	95.08 371	92.41 363	99.18 257	99.28 276	94.55 337	93.49 362	97.37 361	87.86 353	97.01 367	91.57 353	88.36 358	97.61 359

LCM-MVSNet		86.80 336	85.22 340	91.53 351	87.81 378	80.96 374	98.23 361	98.99 312	71.05 371	90.13 367	96.51 365	48.45 380	96.88 368	90.51 356	85.30 362	96.76 364

CL-MVSNet_self_test		94.49 326	93.97 329	96.08 342	96.16 366	93.67 357	98.33 356	99.38 227	95.13 323	97.33 335	98.15 355	92.69 282	96.57 369	88.67 363	79.87 369	97.99 351

MIMVSNet1		95.51 316	95.04 320	96.92 334	97.38 357	95.60 321	99.52 129	99.50 127	93.65 344	96.97 345	99.17 310	85.28 361	96.56 370	88.36 365	95.55 305	98.60 309

test20.03		96.12 311	95.96 308	96.63 338	97.44 356	95.45 328	99.51 134	99.38 227	96.55 273	96.16 351	99.25 302	93.76 259	96.17 371	87.35 368	94.22 329	98.27 336

tmp_tt		82.80 338	81.52 341	86.66 354	66.61 384	68.44 382	92.79 373	97.92 358	68.96 372	80.04 375	99.85 36	85.77 359	96.15 372	97.86 201	43.89 377	95.39 368

PMMVS2		86.87 335	85.37 339	91.35 352	90.21 376	83.80 371	98.89 316	97.45 365	83.13 368	91.67 366	95.03 366	48.49 379	94.70 373	85.86 371	77.62 370	95.54 367

PMVS	70.75 22	75.98 344	74.97 345	79.01 360	70.98 383	55.18 384	93.37 372	98.21 354	65.08 377	61.78 378	93.83 369	21.74 385	92.53 374	78.59 373	91.12 353	89.34 373
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		84.93 337	85.65 338	82.75 358	86.77 379	63.39 383	98.35 353	98.92 320	74.11 370	83.39 370	98.98 332	50.85 378	92.40 375	84.54 372	94.97 317	92.46 369

MVE	76.82 21	76.91 343	74.31 347	84.70 355	85.38 381	76.05 380	96.88 369	93.17 380	67.39 374	71.28 376	89.01 375	21.66 386	87.69 376	71.74 375	72.29 373	90.35 372
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		80.61 340	79.88 342	82.81 357	90.75 375	76.38 379	97.69 366	95.76 373	66.44 375	83.52 369	92.25 371	62.54 376	87.16 377	68.53 376	61.40 374	84.89 375

EMVS		80.02 341	79.22 343	82.43 359	91.19 374	76.40 378	97.55 368	92.49 383	66.36 376	83.01 371	91.27 372	64.63 375	85.79 378	65.82 377	60.65 375	85.08 374

ANet_high		77.30 342	74.86 346	84.62 356	75.88 382	77.61 377	97.63 367	93.15 381	88.81 362	64.27 377	89.29 374	36.51 381	83.93 379	75.89 374	52.31 376	92.33 371

wuyk23d		40.18 345	41.29 350	36.84 361	86.18 380	49.12 385	79.73 374	22.81 386	27.64 378	25.46 381	28.45 381	21.98 384	48.89 380	55.80 378	23.56 380	12.51 378

test123		39.01 347	42.50 349	28.53 362	39.17 385	20.91 386	98.75 330	19.17 387	19.83 380	38.57 379	66.67 377	33.16 382	15.42 381	37.50 380	29.66 379	49.26 376

testmvs		39.17 346	43.78 348	25.37 363	36.04 386	16.84 387	98.36 352	26.56 385	20.06 379	38.51 380	67.32 376	29.64 383	15.30 382	37.59 379	39.90 378	43.98 377

test_blank		0.13 351	0.17 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	1.57 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.02 352	0.03 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.27 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.02 352	0.03 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.27 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

cdsmvs_eth3d_5k		24.64 348	32.85 351	0.00 364	0.00 387	0.00 388	0.00 375	99.51 106	0.00 382	0.00 383	99.56 213	96.58 154	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		8.27 350	11.03 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.27 383	99.01 19	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.02 352	0.03 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.27 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.02 352	0.03 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.27 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.02 352	0.03 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.27 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.02 352	0.03 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.27 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		8.30 349	11.06 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	99.58 206	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.02 352	0.03 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.27 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					99.91 1	99.93 1	99.87 9	99.56 58	99.10 12	99.81 25

test_one_0601					99.81 42	99.88 8		99.49 135	98.97 37	99.65 79	99.81 69	99.09 14

eth-test2					0.00 387

eth-test					0.00 387

RE-MVS-def			99.34 32		99.76 57	99.82 23	99.63 70	99.52 92	98.38 87	99.76 43	99.82 56	98.75 60		98.61 130	99.81 86	99.77 72

IU-MVS					99.84 33	99.88 8		99.32 262	98.30 98	99.84 15				98.86 92	99.85 60	99.89 2

save fliter					99.76 57	99.59 73	99.14 265	99.40 217	99.00 28

test0726					99.85 26	99.89 4	99.62 76	99.50 127	99.10 12	99.86 13	99.82 56	98.94 35

GSMVS																99.52 158

test_part2					99.81 42	99.83 17				99.77 38

sam_mvs1												94.86 213				99.52 158

sam_mvs												94.72 225

MTGPA								99.47 165

MTMP							99.54 123	98.88 327

test9_res														97.49 239	99.72 112	99.75 78

agg_prior2														97.21 257	99.73 111	99.75 78

test_prior4						99.56 78	98.99 298

test_prior2							98.96 306		98.34 93	99.01 225	99.52 228	98.68 67		97.96 193	99.74 108

新几何2							99.01 296

旧先验1					99.74 76	99.59 73		99.54 75			99.69 153	98.47 85			99.68 123	99.73 90

原ACMM2							98.95 310

test222					99.75 68	99.49 91	98.91 315	99.49 135	96.42 285	99.34 160	99.65 174	98.28 100			99.69 118	99.72 96

segment_acmp												98.96 29

testdata1							98.85 320		98.32 97

plane_prior7					99.29 228	97.03 280

plane_prior6					99.27 233	96.98 284						92.71 280

plane_prior4											99.61 196

plane_prior3						97.00 282			98.69 64	99.11 207

plane_prior2							99.39 195		98.97 37

plane_prior1					99.26 235

plane_prior						96.97 285	99.21 255		98.45 81						97.60 239

n2								0.00 388

nn								0.00 388

door-mid								98.05 357

test11								99.35 241

door								97.92 358

HQP5-MVS						96.83 290

HQP-NCC					99.19 250		98.98 302		98.24 102	98.66 277

ACMP_Plane					99.19 250		98.98 302		98.24 102	98.66 277

BP-MVS														97.19 261

HQP3-MVS								99.39 221							97.58 241

HQP2-MVS												92.47 289

NP-MVS					99.23 241	96.92 288					99.40 265

MDTV_nov1_ep13_2view						95.18 335	99.35 213		96.84 252	99.58 100		95.19 204		97.82 205		99.46 177

ACMMP++_ref															97.19 269

ACMMP++															97.43 259

Test By Simon												98.75 60

High-resolution multi-view benchmark