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.

HPM-MVS		98.36 45	98.10 48	99.13 54	99.74 7	97.82 72	99.53 1	98.80 86	94.63 150	98.61 63	98.97 87	95.13 70	99.77 100	97.65 59	99.83 9	99.79 10
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MVSFormer		97.57 83	97.49 75	97.84 135	98.07 181	95.76 160	99.47 2	98.40 182	94.98 133	98.79 49	98.83 107	92.34 113	98.41 265	96.91 92	99.59 71	99.34 111

test_djsdf		96.00 147	95.69 148	96.93 191	95.72 312	95.49 170	99.47 2	98.40 182	94.98 133	94.58 203	97.86 198	89.16 180	98.41 265	96.91 92	94.12 227	96.88 243

HPM-MVS_fast		98.38 43	98.13 46	99.12 57	99.75 3	97.86 68	99.44 4	98.82 70	94.46 157	98.94 39	99.20 52	95.16 69	99.74 106	97.58 65	99.85 3	99.77 20

nrg030		96.28 139	95.72 143	97.96 131	96.90 264	98.15 56	99.39 5	98.31 196	95.47 105	94.42 213	98.35 155	92.09 123	98.69 229	97.50 72	89.05 298	97.04 227

APDe-MVS		99.02 3	98.84 2	99.55 6	99.57 33	98.96 12	99.39 5	98.93 37	97.38 24	99.41 11	99.54 1	96.66 13	99.84 52	98.86 1	99.85 3	99.87 1

3Dnovator+	94.38 6	97.43 92	96.78 107	99.38 17	97.83 196	98.52 27	99.37 7	98.71 113	97.09 45	92.99 271	99.13 64	89.36 174	99.89 35	96.97 88	99.57 75	99.71 44

FIs		96.51 130	96.12 133	97.67 150	97.13 250	97.54 81	99.36 8	99.22 14	95.89 86	94.03 233	98.35 155	91.98 126	98.44 255	96.40 119	92.76 253	97.01 228

FC-MVSNet-test		96.42 133	96.05 134	97.53 160	96.95 259	97.27 90	99.36 8	99.23 12	95.83 89	93.93 235	98.37 153	92.00 125	98.32 274	96.02 131	92.72 254	97.00 229

3Dnovator	94.51 5	97.46 87	96.93 100	99.07 60	97.78 198	97.64 76	99.35 10	99.06 22	97.02 47	93.75 245	99.16 61	89.25 177	99.92 21	97.22 80	99.75 38	99.64 70

canonicalmvs		97.67 74	97.23 87	98.98 65	98.70 133	98.38 35	99.34 11	98.39 184	96.76 54	97.67 116	97.40 238	92.26 116	99.49 145	98.28 27	96.28 202	99.08 147

CP-MVS		98.57 26	98.36 22	99.19 43	99.66 27	97.86 68	99.34 11	98.87 55	95.96 85	98.60 64	99.13 64	96.05 32	99.94 3	97.77 49	99.86 1	99.77 20

EPP-MVSNet		97.46 87	97.28 85	97.99 128	98.64 139	95.38 173	99.33 13	98.31 196	93.61 198	97.19 132	99.07 77	94.05 95	99.23 167	96.89 95	98.43 143	99.37 110

XVS		98.70 9	98.49 16	99.34 23	99.70 23	98.35 43	99.29 14	98.88 49	97.40 21	98.46 68	99.20 52	95.90 40	99.89 35	97.85 44	99.74 41	99.78 13

X-MVStestdata		94.06 260	92.30 280	99.34 23	99.70 23	98.35 43	99.29 14	98.88 49	97.40 21	98.46 68	43.50 357	95.90 40	99.89 35	97.85 44	99.74 41	99.78 13

tttt0517		96.07 143	95.51 153	97.78 139	98.41 153	94.84 197	99.28 16	94.33 346	94.26 162	97.64 120	98.64 126	84.05 282	99.47 150	95.34 154	97.60 170	99.03 150

mPP-MVS		98.51 35	98.26 37	99.25 39	99.75 3	98.04 60	99.28 16	98.81 76	96.24 73	98.35 77	99.23 45	95.46 51	99.94 3	97.42 74	99.81 10	99.77 20

MSP-MVS		98.74 8	98.55 10	99.29 31	99.75 3	98.23 49	99.26 18	98.88 49	97.52 15	99.41 11	98.78 112	96.00 34	99.79 91	97.79 48	99.59 71	99.85 2

v7n		94.19 249	93.43 260	96.47 228	95.90 307	94.38 219	99.26 18	98.34 192	91.99 255	92.76 276	97.13 253	88.31 202	98.52 247	89.48 297	87.70 312	96.52 291

WR-MVS_H		95.05 197	94.46 201	96.81 198	96.86 266	95.82 158	99.24 20	99.24 10	93.87 178	92.53 284	96.84 286	90.37 160	98.24 284	93.24 219	87.93 310	96.38 303

HFP-MVS		98.63 16	98.40 18	99.32 28	99.72 12	98.29 46	99.23 21	98.96 32	96.10 82	98.94 39	99.17 56	96.06 30	99.92 21	97.62 61	99.78 23	99.75 28

region2R		98.61 17	98.38 20	99.29 31	99.74 7	98.16 55	99.23 21	98.93 37	96.15 77	98.94 39	99.17 56	95.91 39	99.94 3	97.55 69	99.79 19	99.78 13

ACMMPR		98.59 20	98.36 22	99.29 31	99.74 7	98.15 56	99.23 21	98.95 34	96.10 82	98.93 43	99.19 55	95.70 44	99.94 3	97.62 61	99.79 19	99.78 13

QAPM		96.29 137	95.40 154	98.96 67	97.85 195	97.60 79	99.23 21	98.93 37	89.76 308	93.11 268	99.02 80	89.11 182	99.93 15	91.99 255	99.62 66	99.34 111

MP-MVS		98.33 50	98.01 52	99.28 35	99.75 3	98.18 53	99.22 25	98.79 91	96.13 79	97.92 103	99.23 45	94.54 84	99.94 3	96.74 109	99.78 23	99.73 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
Vis-MVSNet		97.42 93	97.11 91	98.34 106	98.66 137	96.23 135	99.22 25	99.00 27	96.63 60	98.04 88	99.21 48	88.05 211	99.35 158	96.01 132	99.21 106	99.45 104
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CSCG		97.85 66	97.74 62	98.20 115	99.67 26	95.16 181	99.22 25	99.32 7	93.04 218	97.02 141	98.92 99	95.36 58	99.91 30	97.43 73	99.64 62	99.52 85

OpenMVS	93.04 13	95.83 155	95.00 176	98.32 107	97.18 247	97.32 87	99.21 28	98.97 30	89.96 304	91.14 304	99.05 79	86.64 237	99.92 21	93.38 214	99.47 90	97.73 209

DTE-MVSNet		93.98 262	93.26 265	96.14 248	96.06 302	94.39 218	99.20 29	98.86 61	93.06 217	91.78 298	97.81 206	85.87 251	97.58 319	90.53 277	86.17 328	96.46 300

Vis-MVSNet (Re-imp)		96.87 118	96.55 119	97.83 136	98.73 128	95.46 171	99.20 29	98.30 202	94.96 135	96.60 160	98.87 103	90.05 165	98.59 241	93.67 208	98.60 132	99.46 102

ZNCC-MVS		98.49 36	98.20 44	99.35 22	99.73 11	98.39 34	99.19 31	98.86 61	95.77 91	98.31 80	99.10 69	95.46 51	99.93 15	97.57 68	99.81 10	99.74 33

IS-MVSNet		97.22 102	96.88 102	98.25 112	98.85 122	96.36 130	99.19 31	97.97 253	95.39 109	97.23 131	98.99 86	91.11 147	98.93 206	94.60 175	98.59 133	99.47 98

PEN-MVS		94.42 236	93.73 247	96.49 226	96.28 293	94.84 197	99.17 33	99.00 27	93.51 200	92.23 292	97.83 204	86.10 247	97.90 308	92.55 241	86.92 323	96.74 259

PS-MVSNAJss		96.43 132	96.26 129	96.92 193	95.84 310	95.08 186	99.16 34	98.50 166	95.87 88	93.84 241	98.34 159	94.51 85	98.61 236	96.88 98	93.45 243	97.06 226

APD-MVS_3200maxsize		98.53 34	98.33 32	99.15 53	99.50 41	97.92 67	99.15 35	98.81 76	96.24 73	99.20 22	99.37 22	95.30 62	99.80 79	97.73 51	99.67 54	99.72 40

TSAR-MVS + MP.		98.78 6	98.62 7	99.24 40	99.69 25	98.28 48	99.14 36	98.66 131	96.84 51	99.56 5	99.31 35	96.34 19	99.70 114	98.32 25	99.73 43	99.73 36
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
anonymousdsp		95.42 173	94.91 181	96.94 190	95.10 325	95.90 156	99.14 36	98.41 180	93.75 183	93.16 264	97.46 232	87.50 224	98.41 265	95.63 148	94.03 229	96.50 296

jajsoiax		95.45 171	95.03 175	96.73 201	95.42 323	94.63 206	99.14 36	98.52 158	95.74 92	93.22 262	98.36 154	83.87 287	98.65 234	96.95 91	94.04 228	96.91 239

PS-CasMVS		94.67 219	93.99 229	96.71 202	96.68 276	95.26 179	99.13 39	99.03 25	93.68 193	92.33 290	97.95 190	85.35 259	98.10 292	93.59 210	88.16 309	96.79 253

abl_6		98.30 53	98.03 51	99.13 54	99.56 34	97.76 74	99.13 39	98.82 70	96.14 78	99.26 18	99.37 22	93.33 102	99.93 15	96.96 90	99.67 54	99.69 51

CPTT-MVS		97.72 72	97.32 84	98.92 69	99.64 28	97.10 99	99.12 41	98.81 76	92.34 244	98.09 84	99.08 76	93.01 106	99.92 21	96.06 129	99.77 26	99.75 28

SR-MVS-dyc-post		98.54 32	98.35 24	99.13 54	99.49 45	97.86 68	99.11 42	98.80 86	96.49 65	99.17 24	99.35 28	95.34 59	99.82 63	97.72 52	99.65 58	99.71 44

RE-MVS-def			98.34 28		99.49 45	97.86 68	99.11 42	98.80 86	96.49 65	99.17 24	99.35 28	95.29 63		97.72 52	99.65 58	99.71 44

CP-MVSNet		94.94 206	94.30 210	96.83 197	96.72 274	95.56 166	99.11 42	98.95 34	93.89 176	92.42 289	97.90 194	87.19 228	98.12 291	94.32 187	88.21 307	96.82 252

SteuartSystems-ACMMP		98.90 5	98.75 4	99.36 21	99.22 94	98.43 33	99.10 45	98.87 55	97.38 24	99.35 14	99.40 13	97.78 3	99.87 44	97.77 49	99.85 3	99.78 13
Skip Steuart: Steuart Systems R&D Blog.
test1172		98.56 28	98.35 24	99.16 50	99.53 36	97.94 66	99.09 46	98.83 68	96.52 64	99.05 32	99.34 31	95.34 59	99.82 63	97.86 43	99.64 62	99.73 36

CS-MVS		97.81 67	97.61 65	98.41 102	98.52 148	97.15 98	99.09 46	98.55 151	96.18 76	97.61 122	97.20 250	94.59 83	99.39 155	97.62 61	99.10 111	98.70 172

SR-MVS		98.57 26	98.35 24	99.24 40	99.53 36	98.18 53	99.09 46	98.82 70	96.58 61	99.10 29	99.32 33	95.39 55	99.82 63	97.70 57	99.63 64	99.72 40

GST-MVS		98.43 40	98.12 47	99.34 23	99.72 12	98.38 35	99.09 46	98.82 70	95.71 94	98.73 55	99.06 78	95.27 64	99.93 15	97.07 85	99.63 64	99.72 40

K. test v3		92.55 285	91.91 286	94.48 303	95.64 314	89.24 316	99.07 50	94.88 340	94.04 167	86.78 332	97.59 224	77.64 327	97.64 317	92.08 250	89.43 293	96.57 281

test0726					99.72 12	99.25 2	99.06 51	98.88 49	97.62 11	99.56 5	99.50 4	97.42 6

v8		94.47 234	93.77 243	96.57 218	96.36 290	94.83 199	99.05 52	98.19 215	91.92 257	93.16 264	96.97 274	88.82 193	98.48 249	91.69 262	87.79 311	96.39 302

SF-MVS		98.59 20	98.32 33	99.41 16	99.54 35	98.71 18	99.04 53	98.81 76	95.12 126	99.32 15	99.39 14	96.22 20	99.84 52	97.72 52	99.73 43	99.67 61

test_part1		92.87 281	91.72 287	96.32 241	97.55 217	93.50 247	99.04 53	98.74 102	83.31 340	90.81 308	97.70 213	76.61 330	98.60 240	94.43 182	87.30 318	96.85 248

PHI-MVS		98.34 48	98.06 49	99.18 47	99.15 101	98.12 58	99.04 53	99.09 20	93.32 208	98.83 48	99.10 69	96.54 16	99.83 55	97.70 57	99.76 32	99.59 80

TranMVSNet+NR-MVSNet		95.14 192	94.48 199	97.11 179	96.45 287	96.36 130	99.03 56	99.03 25	95.04 131	93.58 248	97.93 192	88.27 203	98.03 299	94.13 193	86.90 324	96.95 233

ACMMP		98.23 54	97.95 55	99.09 59	99.74 7	97.62 78	99.03 56	99.41 6	95.98 84	97.60 124	99.36 26	94.45 89	99.93 15	97.14 82	98.85 122	99.70 48
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
SED-MVS		99.09 1	98.91 1	99.63 3	99.71 20	99.24 4	99.02 58	98.87 55	97.65 9	99.73 1	99.48 6	97.53 4	99.94 3	98.43 18	99.81 10	99.70 48

OPU-MVS				99.37 20	99.24 92	99.05 10	99.02 58				99.16 61	97.81 2	99.37 157	97.24 79	99.73 43	99.70 48

EIA-MVS		97.75 70	97.58 67	98.27 109	98.38 154	96.44 126	99.01 60	98.60 139	95.88 87	97.26 130	97.53 229	94.97 74	99.33 160	97.38 76	99.20 107	99.05 149

Anonymous20231211		94.10 256	93.26 265	96.61 212	99.11 104	94.28 221	99.01 60	98.88 49	86.43 328	92.81 274	97.57 226	81.66 298	98.68 232	94.83 168	89.02 300	96.88 243

mvs_tets		95.41 175	95.00 176	96.65 207	95.58 316	94.42 216	99.00 62	98.55 151	95.73 93	93.21 263	98.38 152	83.45 291	98.63 235	97.09 84	94.00 230	96.91 239

baseline		97.64 76	97.44 79	98.25 112	98.35 156	96.20 136	99.00 62	98.32 194	96.33 72	98.03 89	99.17 56	91.35 141	99.16 173	98.10 31	98.29 149	99.39 108

v10		94.29 243	93.55 255	96.51 225	96.39 289	94.80 201	98.99 64	98.19 215	91.35 275	93.02 270	96.99 272	88.09 209	98.41 265	90.50 278	88.41 306	96.33 306

PGM-MVS		98.49 36	98.23 42	99.27 38	99.72 12	98.08 59	98.99 64	99.49 5	95.43 107	99.03 33	99.32 33	95.56 47	99.94 3	96.80 105	99.77 26	99.78 13

LPG-MVS_test		95.62 165	95.34 160	96.47 228	97.46 224	93.54 244	98.99 64	98.54 154	94.67 147	94.36 215	98.77 114	85.39 257	99.11 182	95.71 144	94.15 225	96.76 257

#test#		98.54 32	98.27 36	99.32 28	99.72 12	98.29 46	98.98 67	98.96 32	95.65 98	98.94 39	99.17 56	96.06 30	99.92 21	97.21 81	99.78 23	99.75 28

DVP-MVS		99.03 2	98.83 3	99.63 3	99.72 12	99.25 2	98.97 68	98.58 146	97.62 11	99.45 9	99.46 9	97.42 6	99.94 3	98.47 15	99.81 10	99.69 51

test_0728_SECOND				99.71 1	99.72 12	99.35 1	98.97 68	98.88 49					99.94 3	98.47 15	99.81 10	99.84 4

tfpnnormal		93.66 265	92.70 274	96.55 222	96.94 260	95.94 150	98.97 68	99.19 15	91.04 287	91.38 302	97.34 239	84.94 265	98.61 236	85.45 325	89.02 300	95.11 330

V42		94.78 212	94.14 219	96.70 204	96.33 292	95.22 180	98.97 68	98.09 238	92.32 246	94.31 218	97.06 264	88.39 201	98.55 244	92.90 230	88.87 302	96.34 304

SMA-MVS		98.58 23	98.25 38	99.56 5	99.51 39	99.04 11	98.95 72	98.80 86	93.67 195	99.37 13	99.52 3	96.52 17	99.89 35	98.06 33	99.81 10	99.76 26
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
pm-mvs1		93.94 263	93.06 267	96.59 215	96.49 285	95.16 181	98.95 72	98.03 250	92.32 246	91.08 305	97.84 201	84.54 273	98.41 265	92.16 248	86.13 330	96.19 311

VPA-MVSNet		95.75 158	95.11 172	97.69 148	97.24 239	97.27 90	98.94 74	99.23 12	95.13 125	95.51 185	97.32 241	85.73 252	98.91 208	97.33 78	89.55 291	96.89 242

RRT_test8_iter05		94.56 226	94.19 214	95.67 267	97.60 210	91.34 286	98.93 75	98.42 179	94.75 142	93.39 257	97.87 197	79.00 315	98.61 236	96.78 107	90.99 274	97.07 225

LS3D		97.16 107	96.66 116	98.68 79	98.53 147	97.19 96	98.93 75	98.90 44	92.83 229	95.99 181	99.37 22	92.12 122	99.87 44	93.67 208	99.57 75	98.97 156

ACMM	93.85 9	95.69 162	95.38 158	96.61 212	97.61 209	93.84 233	98.91 77	98.44 175	95.25 119	94.28 219	98.47 142	86.04 250	99.12 179	95.50 151	93.95 232	96.87 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MTAPA		98.58 23	98.29 35	99.46 12	99.76 1	98.64 22	98.90 78	98.74 102	97.27 33	98.02 90	99.39 14	94.81 77	99.96 1	97.91 38	99.79 19	99.77 20

SD-MVS		98.64 14	98.68 5	98.53 91	99.33 65	98.36 42	98.90 78	98.85 64	97.28 29	99.72 3	99.39 14	96.63 15	97.60 318	98.17 28	99.85 3	99.64 70
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
TransMVSNet (Re)		92.67 284	91.51 289	96.15 247	96.58 280	94.65 204	98.90 78	96.73 321	90.86 289	89.46 320	97.86 198	85.62 254	98.09 294	86.45 317	81.12 338	95.71 320

EPNet		97.28 100	96.87 103	98.51 92	94.98 326	96.14 139	98.90 78	97.02 309	98.28 1	95.99 181	99.11 67	91.36 140	99.89 35	96.98 87	99.19 108	99.50 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MTMP							98.89 82	94.14 349

UA-Net		97.96 58	97.62 64	98.98 65	98.86 120	97.47 83	98.89 82	99.08 21	96.67 58	98.72 56	99.54 1	93.15 105	99.81 70	94.87 166	98.83 123	99.65 67

OurMVSNet-221017-0		94.21 247	94.00 227	94.85 291	95.60 315	89.22 317	98.89 82	97.43 290	95.29 116	92.18 293	98.52 139	82.86 292	98.59 241	93.46 213	91.76 263	96.74 259

thisisatest0530		96.01 146	95.36 159	97.97 129	98.38 154	95.52 169	98.88 85	94.19 348	94.04 167	97.64 120	98.31 162	83.82 289	99.46 151	95.29 158	97.70 167	98.93 160

UGNet		96.78 121	96.30 127	98.19 117	98.24 166	95.89 157	98.88 85	98.93 37	97.39 23	96.81 152	97.84 201	82.60 293	99.90 33	96.53 113	99.49 88	98.79 167
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
Anonymous20240529		95.10 194	94.22 212	97.75 142	99.01 108	94.26 223	98.87 87	98.83 68	85.79 334	96.64 157	98.97 87	78.73 316	99.85 49	96.27 121	94.89 216	99.12 142

thres100view900		95.38 176	94.70 189	97.41 164	98.98 112	94.92 195	98.87 87	96.90 315	95.38 110	96.61 159	96.88 282	84.29 275	99.56 136	88.11 306	96.29 199	97.76 206

XXY-MVS		95.20 189	94.45 203	97.46 161	96.75 272	96.56 121	98.86 89	98.65 135	93.30 210	93.27 261	98.27 167	84.85 267	98.87 215	94.82 169	91.26 270	96.96 231

VDDNet		95.36 179	94.53 196	97.86 134	98.10 180	95.13 184	98.85 90	97.75 265	90.46 294	98.36 76	99.39 14	73.27 343	99.64 125	97.98 36	96.58 189	98.81 166

thres600view7		95.49 168	94.77 185	97.67 150	98.98 112	95.02 187	98.85 90	96.90 315	95.38 110	96.63 158	96.90 281	84.29 275	99.59 132	88.65 305	96.33 197	98.40 189

114514_t		96.93 115	96.27 128	98.92 69	99.50 41	97.63 77	98.85 90	98.90 44	84.80 337	97.77 108	99.11 67	92.84 107	99.66 122	94.85 167	99.77 26	99.47 98

LFMVS		95.86 153	94.98 178	98.47 96	98.87 119	96.32 132	98.84 93	96.02 328	93.40 205	98.62 62	99.20 52	74.99 337	99.63 128	97.72 52	97.20 176	99.46 102

testtj		98.33 50	97.95 55	99.47 11	99.49 45	98.70 19	98.83 94	98.86 61	95.48 104	98.91 45	99.17 56	95.48 50	99.93 15	95.80 139	99.53 85	99.76 26

alignmvs		97.56 84	97.07 94	99.01 62	98.66 137	98.37 41	98.83 94	98.06 247	96.74 55	98.00 96	97.65 218	90.80 153	99.48 149	98.37 23	96.56 190	99.19 132

DeepC-MVS	95.98 3	97.88 64	97.58 67	98.77 75	99.25 86	96.93 104	98.83 94	98.75 101	96.96 49	96.89 148	99.50 4	90.46 159	99.87 44	97.84 46	99.76 32	99.52 85
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_NAP		98.61 17	98.30 34	99.55 6	99.62 30	98.95 13	98.82 97	98.81 76	95.80 90	99.16 26	99.47 8	95.37 57	99.92 21	97.89 41	99.75 38	99.79 10

casdiffmvs		97.63 77	97.41 80	98.28 108	98.33 161	96.14 139	98.82 97	98.32 194	96.38 70	97.95 98	99.21 48	91.23 145	99.23 167	98.12 30	98.37 144	99.48 96

GBi-Net		94.49 232	93.80 240	96.56 219	98.21 169	95.00 188	98.82 97	98.18 218	92.46 237	94.09 229	97.07 261	81.16 299	97.95 304	92.08 250	92.14 257	96.72 262

test1		94.49 232	93.80 240	96.56 219	98.21 169	95.00 188	98.82 97	98.18 218	92.46 237	94.09 229	97.07 261	81.16 299	97.95 304	92.08 250	92.14 257	96.72 262

FMVSNet1		93.19 277	92.07 282	96.56 219	97.54 218	95.00 188	98.82 97	98.18 218	90.38 297	92.27 291	97.07 261	73.68 342	97.95 304	89.36 299	91.30 268	96.72 262

API-MVS		97.41 94	97.25 86	97.91 132	98.70 133	96.80 109	98.82 97	98.69 117	94.53 152	98.11 83	98.28 164	94.50 88	99.57 134	94.12 194	99.49 88	97.37 219

ACMH	92.88 16	94.55 227	93.95 231	96.34 239	97.63 208	93.26 257	98.81 103	98.49 170	93.43 204	89.74 317	98.53 136	81.91 296	99.08 187	93.69 205	93.30 247	96.70 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		96.29 137	96.56 118	95.51 270	97.89 193	90.22 305	98.80 104	98.10 234	96.57 62	96.45 171	96.66 292	90.81 151	98.91 208	95.72 142	97.99 155	97.40 216

HQP_MVS		96.14 142	95.90 139	96.85 196	97.42 229	94.60 211	98.80 104	98.56 149	97.28 29	95.34 186	98.28 164	87.09 229	99.03 193	96.07 126	94.27 219	96.92 234

plane_prior2							98.80 104		97.28 29

APD-MVS		98.35 46	98.00 53	99.42 15	99.51 39	98.72 17	98.80 104	98.82 70	94.52 154	99.23 20	99.25 43	95.54 49	99.80 79	96.52 114	99.77 26	99.74 33
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
UniMVSNet (Re)		95.78 157	95.19 168	97.58 156	96.99 258	97.47 83	98.79 108	99.18 16	95.60 99	93.92 236	97.04 267	91.68 131	98.48 249	95.80 139	87.66 313	96.79 253

FMVSNet2		94.47 234	93.61 253	97.04 182	98.21 169	96.43 127	98.79 108	98.27 205	92.46 237	93.50 254	97.09 258	81.16 299	98.00 302	91.09 267	91.93 261	96.70 266

testgi		93.06 279	92.45 278	94.88 290	96.43 288	89.90 306	98.75 110	97.54 280	95.60 99	91.63 301	97.91 193	74.46 340	97.02 328	86.10 319	93.67 236	97.72 210

LCM-MVSNet-Re		95.22 187	95.32 163	94.91 288	98.18 174	87.85 335	98.75 110	95.66 334	95.11 127	88.96 322	96.85 285	90.26 164	97.65 316	95.65 147	98.44 141	99.22 128

SixPastTwentyTwo		93.34 271	92.86 270	94.75 295	95.67 313	89.41 315	98.75 110	96.67 325	93.89 176	90.15 315	98.25 169	80.87 303	98.27 283	90.90 272	90.64 277	96.57 281

UniMVSNet_ETH3D		94.24 246	93.33 262	96.97 187	97.19 246	93.38 253	98.74 113	98.57 147	91.21 284	93.81 242	98.58 132	72.85 344	98.77 226	95.05 164	93.93 233	98.77 169

MVS_Test		97.28 100	97.00 97	98.13 120	98.33 161	95.97 147	98.74 113	98.07 242	94.27 161	98.44 72	98.07 180	92.48 111	99.26 163	96.43 118	98.19 150	99.16 137

UniMVSNet_NR-MVSNet		95.71 160	95.15 169	97.40 166	96.84 267	96.97 102	98.74 113	99.24 10	95.16 123	93.88 238	97.72 212	91.68 131	98.31 276	95.81 137	87.25 319	96.92 234

NR-MVSNet		94.98 202	94.16 217	97.44 162	96.53 282	97.22 95	98.74 113	98.95 34	94.96 135	89.25 321	97.69 214	89.32 175	98.18 286	94.59 177	87.40 316	96.92 234

ETV-MVS		97.96 58	97.81 59	98.40 103	98.42 152	97.27 90	98.73 117	98.55 151	96.84 51	98.38 75	97.44 235	95.39 55	99.35 158	97.62 61	98.89 118	98.58 184

baseline1		95.84 154	95.12 171	98.01 127	98.49 150	95.98 142	98.73 117	97.03 307	95.37 112	96.22 175	98.19 173	89.96 167	99.16 173	94.60 175	87.48 314	98.90 162

MVSTER		96.06 144	95.72 143	97.08 181	98.23 167	95.93 153	98.73 117	98.27 205	94.86 139	95.07 190	98.09 179	88.21 204	98.54 245	96.59 111	93.46 241	96.79 253

ACMP	93.49 10	95.34 181	94.98 178	96.43 233	97.67 205	93.48 248	98.73 117	98.44 175	94.94 138	92.53 284	98.53 136	84.50 274	99.14 177	95.48 152	94.00 230	96.66 272
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS++		98.58 23	98.25 38	99.55 6	99.50 41	99.08 9	98.72 121	98.66 131	97.51 16	98.15 81	98.83 107	95.70 44	99.92 21	97.53 71	99.67 54	99.66 65

9.14			98.06 49		99.47 48		98.71 122	98.82 70	94.36 159	99.16 26	99.29 39	96.05 32	99.81 70	97.00 86	99.71 50

VPNet		94.99 200	94.19 214	97.40 166	97.16 248	96.57 120	98.71 122	98.97 30	95.67 96	94.84 196	98.24 170	80.36 307	98.67 233	96.46 115	87.32 317	96.96 231

MSLP-MVS++		98.56 28	98.57 8	98.55 87	99.26 85	96.80 109	98.71 122	99.05 24	97.28 29	98.84 46	99.28 40	96.47 18	99.40 154	98.52 13	99.70 51	99.47 98

ACMH+	92.99 14	94.30 242	93.77 243	95.88 260	97.81 197	92.04 274	98.71 122	98.37 187	93.99 172	90.60 311	98.47 142	80.86 304	99.05 189	92.75 234	92.40 256	96.55 285

Anonymous202405211		95.28 184	94.49 198	97.67 150	99.00 109	93.75 237	98.70 126	97.04 306	90.66 290	96.49 168	98.80 110	78.13 321	99.83 55	96.21 124	95.36 215	99.44 105

DP-MVS		96.59 127	95.93 138	98.57 85	99.34 62	96.19 138	98.70 126	98.39 184	89.45 313	94.52 205	99.35 28	91.85 128	99.85 49	92.89 232	98.88 119	99.68 57

Fast-Effi-MVS+-dtu		95.87 152	95.85 140	95.91 257	97.74 202	91.74 280	98.69 128	98.15 226	95.56 101	94.92 194	97.68 217	88.98 188	98.79 224	93.19 221	97.78 163	97.20 223

tfpn200view9		95.32 183	94.62 192	97.43 163	98.94 114	94.98 191	98.68 129	96.93 313	95.33 113	96.55 163	96.53 298	84.23 278	99.56 136	88.11 306	96.29 199	97.76 206

VDD-MVS		95.82 156	95.23 166	97.61 155	98.84 123	93.98 229	98.68 129	97.40 292	95.02 132	97.95 98	99.34 31	74.37 341	99.78 95	98.64 3	96.80 182	99.08 147

thres400		95.38 176	94.62 192	97.65 153	98.94 114	94.98 191	98.68 129	96.93 313	95.33 113	96.55 163	96.53 298	84.23 278	99.56 136	88.11 306	96.29 199	98.40 189

ETH3D-3000-0.1		98.35 46	98.00 53	99.38 17	99.47 48	98.68 21	98.67 132	98.84 65	94.66 149	99.11 28	99.25 43	95.46 51	99.81 70	96.80 105	99.73 43	99.63 73

pmmvs6		91.77 290	90.63 294	95.17 281	94.69 332	91.24 291	98.67 132	97.92 258	86.14 330	89.62 318	97.56 228	75.79 334	98.34 272	90.75 275	84.56 332	95.94 317

v2v482		94.69 214	94.03 223	96.65 207	96.17 297	94.79 202	98.67 132	98.08 240	92.72 230	94.00 234	97.16 252	87.69 221	98.45 253	92.91 229	88.87 302	96.72 262

DU-MVS		95.42 173	94.76 186	97.40 166	96.53 282	96.97 102	98.66 135	98.99 29	95.43 107	93.88 238	97.69 214	88.57 196	98.31 276	95.81 137	87.25 319	96.92 234

MAR-MVS		96.91 116	96.40 124	98.45 97	98.69 135	96.90 106	98.66 135	98.68 120	92.40 243	97.07 138	97.96 189	91.54 137	99.75 104	93.68 206	98.92 116	98.69 174
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
VNet		97.79 69	97.40 81	98.96 67	98.88 118	97.55 80	98.63 137	98.93 37	96.74 55	99.02 34	98.84 106	90.33 162	99.83 55	98.53 9	96.66 186	99.50 91

PVSNet_Blended_VisFu		97.70 73	97.46 77	98.44 98	99.27 83	95.91 155	98.63 137	99.16 17	94.48 156	97.67 116	98.88 102	92.80 108	99.91 30	97.11 83	99.12 110	99.50 91

PAPM_NR		97.46 87	97.11 91	98.50 93	99.50 41	96.41 128	98.63 137	98.60 139	95.18 122	97.06 139	98.06 181	94.26 93	99.57 134	93.80 204	98.87 121	99.52 85

Baseline_NR-MVSNet		94.35 239	93.81 239	95.96 255	96.20 295	94.05 228	98.61 140	96.67 325	91.44 271	93.85 240	97.60 223	88.57 196	98.14 289	94.39 183	86.93 322	95.68 321

v1144		94.59 224	93.92 232	96.60 214	96.21 294	94.78 203	98.59 141	98.14 228	91.86 260	94.21 224	97.02 269	87.97 212	98.41 265	91.72 261	89.57 289	96.61 276

AllTest		95.24 186	94.65 191	96.99 184	99.25 86	93.21 259	98.59 141	98.18 218	91.36 273	93.52 251	98.77 114	84.67 270	99.72 108	89.70 292	97.87 159	98.02 201

Fast-Effi-MVS+		96.28 139	95.70 147	98.03 126	98.29 165	95.97 147	98.58 143	98.25 210	91.74 261	95.29 189	97.23 247	91.03 150	99.15 176	92.90 230	97.96 156	98.97 156

Anonymous20231206		91.66 291	91.10 291	93.33 316	94.02 338	87.35 337	98.58 143	97.26 299	90.48 293	90.16 314	96.31 304	83.83 288	96.53 338	79.36 343	89.90 285	96.12 312

v144192		94.39 238	93.70 249	96.48 227	96.06 302	94.35 220	98.58 143	98.16 225	91.45 270	94.33 217	97.02 269	87.50 224	98.45 253	91.08 268	89.11 297	96.63 274

v148		94.29 243	93.76 245	95.91 257	96.10 300	92.93 264	98.58 143	97.97 253	92.59 235	93.47 255	96.95 278	88.53 199	98.32 274	92.56 240	87.06 321	96.49 297

COLMAP_ROB	93.27 12	95.33 182	94.87 183	96.71 202	99.29 78	93.24 258	98.58 143	98.11 232	89.92 305	93.57 249	99.10 69	86.37 243	99.79 91	90.78 274	98.10 153	97.09 224
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RRT_MVS		96.04 145	95.53 151	97.56 158	97.07 254	97.32 87	98.57 148	98.09 238	95.15 124	95.02 192	98.44 144	88.20 205	98.58 243	96.17 125	93.09 250	96.79 253

mvs-test1		96.60 125	96.68 115	96.37 236	97.89 193	91.81 276	98.56 149	98.10 234	96.57 62	96.52 167	97.94 191	90.81 151	99.45 152	95.72 142	98.01 154	97.86 205

FMVSNet3		94.97 203	94.26 211	97.11 179	98.18 174	96.62 115	98.56 149	98.26 209	93.67 195	94.09 229	97.10 254	84.25 277	98.01 300	92.08 250	92.14 257	96.70 266

zzz-MVS		98.55 30	98.25 38	99.46 12	99.76 1	98.64 22	98.55 151	98.74 102	97.27 33	98.02 90	99.39 14	94.81 77	99.96 1	97.91 38	99.79 19	99.77 20

F-COLMAP		97.09 111	96.80 104	97.97 129	99.45 55	94.95 194	98.55 151	98.62 138	93.02 219	96.17 177	98.58 132	94.01 96	99.81 70	93.95 199	98.90 117	99.14 140

v1921920		94.20 248	93.47 259	96.40 235	95.98 305	94.08 227	98.52 153	98.15 226	91.33 276	94.25 221	97.20 250	86.41 242	98.42 258	90.04 286	89.39 294	96.69 271

EU-MVSNet		93.66 265	94.14 219	92.25 324	95.96 306	83.38 345	98.52 153	98.12 230	94.69 145	92.61 281	98.13 177	87.36 227	96.39 340	91.82 258	90.00 284	96.98 230

TAMVS		97.02 112	96.79 106	97.70 147	98.06 183	95.31 178	98.52 153	98.31 196	93.95 174	97.05 140	98.61 127	93.49 101	98.52 247	95.33 155	97.81 161	99.29 122

LTVRE_ROB	92.95 15	94.60 222	93.90 234	96.68 206	97.41 232	94.42 216	98.52 153	98.59 141	91.69 264	91.21 303	98.35 155	84.87 266	99.04 192	91.06 269	93.44 244	96.60 277
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
TDRefinement		91.06 296	89.68 301	95.21 279	85.35 353	91.49 285	98.51 157	97.07 304	91.47 269	88.83 325	97.84 201	77.31 328	99.09 186	92.79 233	77.98 342	95.04 332

v1192		94.32 241	93.58 254	96.53 223	96.10 300	94.45 215	98.50 158	98.17 223	91.54 268	94.19 225	97.06 264	86.95 233	98.43 257	90.14 281	89.57 289	96.70 266

test_0402		91.32 293	90.27 297	94.48 303	96.60 279	91.12 292	98.50 158	97.22 300	86.10 331	88.30 327	96.98 273	77.65 326	97.99 303	78.13 347	92.94 252	94.34 336

DeepC-MVS_fast	96.70 1	98.55 30	98.34 28	99.18 47	99.25 86	98.04 60	98.50 158	98.78 94	97.72 6	98.92 44	99.28 40	95.27 64	99.82 63	97.55 69	99.77 26	99.69 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CNVR-MVS		98.78 6	98.56 9	99.45 14	99.32 68	98.87 15	98.47 161	98.81 76	97.72 6	98.76 52	99.16 61	97.05 10	99.78 95	98.06 33	99.66 57	99.69 51

test_yl		97.22 102	96.78 107	98.54 89	98.73 128	96.60 118	98.45 162	98.31 196	94.70 143	98.02 90	98.42 147	90.80 153	99.70 114	96.81 103	96.79 183	99.34 111

DCV-MVSNet		97.22 102	96.78 107	98.54 89	98.73 128	96.60 118	98.45 162	98.31 196	94.70 143	98.02 90	98.42 147	90.80 153	99.70 114	96.81 103	96.79 183	99.34 111

NCCC		98.61 17	98.35 24	99.38 17	99.28 82	98.61 24	98.45 162	98.76 98	97.82 5	98.45 71	98.93 97	96.65 14	99.83 55	97.38 76	99.41 97	99.71 44

v1240		94.06 260	93.29 264	96.34 239	96.03 304	93.90 231	98.44 165	98.17 223	91.18 285	94.13 228	97.01 271	86.05 248	98.42 258	89.13 302	89.50 292	96.70 266

plane_prior						94.60 211	98.44 165		96.74 55						94.22 221

MP-MVS-pluss		98.31 52	97.92 57	99.49 9	99.72 12	98.88 14	98.43 167	98.78 94	94.10 165	97.69 115	99.42 12	95.25 66	99.92 21	98.09 32	99.80 17	99.67 61
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		95.69 162	95.33 162	96.76 200	96.16 299	94.63 206	98.43 167	98.39 184	96.64 59	95.02 192	98.78 112	85.15 262	99.05 189	95.21 162	94.20 222	96.60 277

DPE-MVS		98.92 4	98.67 6	99.65 2	99.58 32	99.20 7	98.42 169	98.91 43	97.58 14	99.54 7	99.46 9	97.10 9	99.94 3	97.64 60	99.84 8	99.83 5

MCST-MVS		98.65 13	98.37 21	99.48 10	99.60 31	98.87 15	98.41 170	98.68 120	97.04 46	98.52 67	98.80 110	96.78 12	99.83 55	97.93 37	99.61 67	99.74 33

Regformer-3		98.59 20	98.50 14	98.86 73	99.43 57	97.05 100	98.40 171	98.68 120	97.43 20	99.06 31	99.31 35	95.80 43	99.77 100	98.62 5	99.76 32	99.78 13

Regformer-4		98.64 14	98.53 11	98.99 63	99.43 57	97.37 86	98.40 171	98.79 91	97.46 19	99.09 30	99.31 35	95.86 42	99.80 79	98.64 3	99.76 32	99.79 10

Regformer-1		98.66 12	98.51 13	99.12 57	99.35 60	97.81 73	98.37 173	98.76 98	97.49 17	99.20 22	99.21 48	96.08 29	99.79 91	98.42 20	99.73 43	99.75 28

Regformer-2		98.69 11	98.52 12	99.19 43	99.35 60	98.01 62	98.37 173	98.81 76	97.48 18	99.21 21	99.21 48	96.13 27	99.80 79	98.40 22	99.73 43	99.75 28

CANet		98.05 56	97.76 61	98.90 71	98.73 128	97.27 90	98.35 175	98.78 94	97.37 26	97.72 113	98.96 93	91.53 138	99.92 21	98.79 2	99.65 58	99.51 89

AUN-MVS		94.53 229	93.73 247	96.92 193	98.50 149	93.52 246	98.34 176	98.10 234	93.83 181	95.94 183	97.98 188	85.59 255	99.03 193	94.35 185	80.94 340	98.22 196

DWT-MVSNet_test		94.82 209	94.36 208	96.20 246	97.35 234	90.79 297	98.34 176	96.57 327	92.91 225	95.33 188	96.44 302	82.00 295	99.12 179	94.52 179	95.78 213	98.70 172

ETH3D cwj APD-0.16		97.96 58	97.52 72	99.29 31	99.05 105	98.52 27	98.33 178	98.68 120	93.18 213	98.68 57	99.13 64	94.62 81	99.83 55	96.45 116	99.55 83	99.52 85

test20.03		90.89 298	90.38 296	92.43 322	93.48 339	88.14 332	98.33 178	97.56 275	93.40 205	87.96 328	96.71 291	80.69 306	94.13 349	79.15 344	86.17 328	95.01 334

DP-MVS Recon		97.86 65	97.46 77	99.06 61	99.53 36	98.35 43	98.33 178	98.89 46	92.62 233	98.05 86	98.94 96	95.34 59	99.65 123	96.04 130	99.42 96	99.19 132

RPSCF		94.87 208	95.40 154	93.26 318	98.89 117	82.06 349	98.33 178	98.06 247	90.30 299	96.56 161	99.26 42	87.09 229	99.49 145	93.82 203	96.32 198	98.24 195

TAPA-MVS	93.98 7	95.35 180	94.56 195	97.74 143	99.13 102	94.83 199	98.33 178	98.64 136	86.62 326	96.29 174	98.61 127	94.00 97	99.29 162	80.00 341	99.41 97	99.09 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IterMVS-LS		95.46 169	95.21 167	96.22 245	98.12 178	93.72 240	98.32 183	98.13 229	93.71 188	94.26 220	97.31 242	92.24 117	98.10 292	94.63 172	90.12 282	96.84 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_anonymous		96.70 123	96.53 121	97.18 174	98.19 172	93.78 234	98.31 184	98.19 215	94.01 170	94.47 207	98.27 167	92.08 124	98.46 252	97.39 75	97.91 157	99.31 117

WTY-MVS		97.37 97	96.92 101	98.72 77	98.86 120	96.89 108	98.31 184	98.71 113	95.26 118	97.67 116	98.56 135	92.21 119	99.78 95	95.89 134	96.85 181	99.48 96

D2MVS		95.18 190	95.08 173	95.48 271	97.10 252	92.07 272	98.30 186	99.13 19	94.02 169	92.90 272	96.73 289	89.48 171	98.73 228	94.48 181	93.60 240	95.65 322

EI-MVSNet-Vis-set		98.47 38	98.39 19	98.69 78	99.46 51	96.49 124	98.30 186	98.69 117	97.21 36	98.84 46	99.36 26	95.41 54	99.78 95	98.62 5	99.65 58	99.80 9

DSMNet-mixed		92.52 286	92.58 276	92.33 323	94.15 334	82.65 347	98.30 186	94.26 347	89.08 317	92.65 280	95.73 318	85.01 264	95.76 342	86.24 318	97.76 164	98.59 182

EI-MVSNet-UG-set		98.41 41	98.34 28	98.61 83	99.45 55	96.32 132	98.28 189	98.68 120	97.17 39	98.74 53	99.37 22	95.25 66	99.79 91	98.57 7	99.54 84	99.73 36

OMC-MVS		97.55 85	97.34 83	98.20 115	99.33 65	95.92 154	98.28 189	98.59 141	95.52 103	97.97 97	99.10 69	93.28 104	99.49 145	95.09 163	98.88 119	99.19 132

baseline2		95.11 193	94.52 197	96.87 195	96.65 278	93.56 243	98.27 191	94.10 350	93.45 203	92.02 297	97.43 236	87.45 226	99.19 171	93.88 201	97.41 174	97.87 204

PVSNet_BlendedMVS		96.73 122	96.60 117	97.12 178	99.25 86	95.35 176	98.26 192	99.26 8	94.28 160	97.94 100	97.46 232	92.74 109	99.81 70	96.88 98	93.32 246	96.20 310

BH-untuned		95.95 149	95.72 143	96.65 207	98.55 146	92.26 269	98.23 193	97.79 263	93.73 186	94.62 202	98.01 185	88.97 189	99.00 197	93.04 226	98.51 137	98.68 175

sss		97.39 95	96.98 99	98.61 83	98.60 143	96.61 117	98.22 194	98.93 37	93.97 173	98.01 94	98.48 141	91.98 126	99.85 49	96.45 116	98.15 151	99.39 108

xxxxxxxxxxxxxcwj		98.70 9	98.50 14	99.30 30	99.46 51	98.38 35	98.21 195	98.52 158	97.95 3	99.32 15	99.39 14	96.22 20	99.84 52	97.72 52	99.73 43	99.67 61

save fliter					99.46 51	98.38 35	98.21 195	98.71 113	97.95 3

WR-MVS		95.15 191	94.46 201	97.22 171	96.67 277	96.45 125	98.21 195	98.81 76	94.15 163	93.16 264	97.69 214	87.51 222	98.30 278	95.29 158	88.62 304	96.90 241

ETH3 D test6400		97.59 81	97.01 96	99.34 23	99.40 59	98.56 25	98.20 198	98.81 76	91.63 266	98.44 72	98.85 104	93.98 98	99.82 63	94.11 195	99.69 52	99.64 70

pmmvs5		93.65 267	92.97 269	95.68 266	95.49 319	92.37 268	98.20 198	97.28 297	89.66 310	92.58 282	97.26 244	82.14 294	98.09 294	93.18 222	90.95 275	96.58 279

thres200		95.25 185	94.57 194	97.28 169	98.81 124	94.92 195	98.20 198	97.11 302	95.24 121	96.54 165	96.22 310	84.58 272	99.53 142	87.93 310	96.50 193	97.39 217

CDS-MVSNet		96.99 113	96.69 113	97.90 133	98.05 184	95.98 142	98.20 198	98.33 193	93.67 195	96.95 142	98.49 140	93.54 100	98.42 258	95.24 161	97.74 165	99.31 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1314		96.25 141	95.73 142	97.79 138	97.13 250	95.55 168	98.19 202	98.59 141	93.47 202	92.03 296	97.82 205	91.33 142	99.49 145	94.62 174	98.44 141	98.32 194

1121		97.37 97	96.77 111	99.16 50	99.34 62	97.99 65	98.19 202	98.68 120	90.14 302	98.01 94	98.97 87	94.80 79	99.87 44	93.36 216	99.46 93	99.61 75

MVS		94.67 219	93.54 256	98.08 123	96.88 265	96.56 121	98.19 202	98.50 166	78.05 347	92.69 279	98.02 183	91.07 149	99.63 128	90.09 282	98.36 146	98.04 200

BH-RMVSNet		95.92 151	95.32 163	97.69 148	98.32 163	94.64 205	98.19 202	97.45 288	94.56 151	96.03 179	98.61 127	85.02 263	99.12 179	90.68 276	99.06 112	99.30 120

1112_ss		96.63 124	96.00 137	98.50 93	98.56 144	96.37 129	98.18 206	98.10 234	92.92 224	94.84 196	98.43 145	92.14 121	99.58 133	94.35 185	96.51 192	99.56 84

MVS_0304		92.81 282	92.01 283	95.23 278	97.46 224	91.33 288	98.17 207	98.81 76	91.13 286	93.80 243	95.68 323	66.08 350	98.06 297	90.79 273	96.13 208	96.32 307

EPNet_dtu		95.21 188	94.95 180	95.99 252	96.17 297	90.45 303	98.16 208	97.27 298	96.77 53	93.14 267	98.33 160	90.34 161	98.42 258	85.57 323	98.81 125	99.09 144
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HY-MVS	93.96 8	96.82 120	96.23 131	98.57 85	98.46 151	97.00 101	98.14 209	98.21 212	93.95 174	96.72 155	97.99 187	91.58 133	99.76 102	94.51 180	96.54 191	98.95 159

PLC	95.07 4	97.20 105	96.78 107	98.44 98	99.29 78	96.31 134	98.14 209	98.76 98	92.41 242	96.39 172	98.31 162	94.92 76	99.78 95	94.06 197	98.77 126	99.23 127
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EG-PatchMatch MVS		91.13 295	90.12 298	94.17 310	94.73 331	89.00 321	98.13 211	97.81 262	89.22 316	85.32 339	96.46 300	67.71 347	98.42 258	87.89 311	93.82 235	95.08 331

EI-MVSNet		95.96 148	95.83 141	96.36 237	97.93 190	93.70 241	98.12 212	98.27 205	93.70 190	95.07 190	99.02 80	92.23 118	98.54 245	94.68 171	93.46 241	96.84 249

CVMVSNet		95.43 172	96.04 135	93.57 313	97.93 190	83.62 344	98.12 212	98.59 141	95.68 95	96.56 161	99.02 80	87.51 222	97.51 322	93.56 212	97.44 172	99.60 78

TSAR-MVS + GP.		98.38 43	98.24 41	98.81 74	99.22 94	97.25 94	98.11 214	98.29 204	97.19 38	98.99 38	99.02 80	96.22 20	99.67 121	98.52 13	98.56 135	99.51 89

XVG-ACMP-BASELINE		94.54 228	94.14 219	95.75 265	96.55 281	91.65 282	98.11 214	98.44 175	94.96 135	94.22 223	97.90 194	79.18 314	99.11 182	94.05 198	93.85 234	96.48 298

CNLPA		97.45 90	97.03 95	98.73 76	99.05 105	97.44 85	98.07 216	98.53 156	95.32 115	96.80 153	98.53 136	93.32 103	99.72 108	94.31 188	99.31 104	99.02 151

diffmvs		97.58 82	97.40 81	98.13 120	98.32 163	95.81 159	98.06 217	98.37 187	96.20 75	98.74 53	98.89 101	91.31 143	99.25 164	98.16 29	98.52 136	99.34 111

CHOSEN 1792x2688		97.12 109	96.80 104	98.08 123	99.30 75	94.56 213	98.05 218	99.71 1	93.57 199	97.09 135	98.91 100	88.17 206	99.89 35	96.87 101	99.56 80	99.81 8

HQP-NCC					97.20 243		98.05 218		96.43 67	94.45 208

ACMP_Plane					97.20 243		98.05 218		96.43 67	94.45 208

HQP-MVS		95.72 159	95.40 154	96.69 205	97.20 243	94.25 224	98.05 218	98.46 171	96.43 67	94.45 208	97.73 210	86.75 235	98.96 201	95.30 156	94.18 223	96.86 247

MIMVSNet1		89.67 308	88.28 312	93.82 311	92.81 343	91.08 293	98.01 222	97.45 288	87.95 321	87.90 329	95.87 316	67.63 348	94.56 348	78.73 346	88.18 308	95.83 319

AdaColmap		97.15 108	96.70 112	98.48 95	99.16 99	96.69 114	98.01 222	98.89 46	94.44 158	96.83 149	98.68 121	90.69 156	99.76 102	94.36 184	99.29 105	98.98 155

FMVSNet5		91.81 289	90.92 292	94.49 302	97.21 242	92.09 271	98.00 224	97.55 279	89.31 315	90.86 307	95.61 324	74.48 339	95.32 345	85.57 323	89.70 287	96.07 314

CANet_DTU		96.96 114	96.55 119	98.21 114	98.17 176	96.07 141	97.98 225	98.21 212	97.24 35	97.13 134	98.93 97	86.88 234	99.91 30	95.00 165	99.37 101	98.66 178

MVP-Stereo		94.28 245	93.92 232	95.35 276	94.95 327	92.60 267	97.97 226	97.65 269	91.61 267	90.68 310	97.09 258	86.32 244	98.42 258	89.70 292	99.34 102	95.02 333
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
DIV-MVS_2432*1600		90.38 302	89.38 304	93.40 315	92.85 342	88.94 322	97.95 227	97.94 256	90.35 298	90.25 313	93.96 335	79.82 309	95.94 341	84.62 331	76.69 344	95.33 325

MVS_111021_LR		98.34 48	98.23 42	98.67 80	99.27 83	96.90 106	97.95 227	99.58 3	97.14 41	98.44 72	99.01 84	95.03 73	99.62 130	97.91 38	99.75 38	99.50 91

TEST9					99.31 70	98.50 29	97.92 229	98.73 107	92.63 232	97.74 111	98.68 121	96.20 23	99.80 79

train_agg		97.97 57	97.52 72	99.33 27	99.31 70	98.50 29	97.92 229	98.73 107	92.98 221	97.74 111	98.68 121	96.20 23	99.80 79	96.59 111	99.57 75	99.68 57

CDPH-MVS		97.94 62	97.49 75	99.28 35	99.47 48	98.44 31	97.91 231	98.67 128	92.57 236	98.77 51	98.85 104	95.93 38	99.72 108	95.56 149	99.69 52	99.68 57

MVS_111021_HR		98.47 38	98.34 28	98.88 72	99.22 94	97.32 87	97.91 231	99.58 3	97.20 37	98.33 78	99.00 85	95.99 35	99.64 125	98.05 35	99.76 32	99.69 51

PatchMatch-RL		96.59 127	96.03 136	98.27 109	99.31 70	96.51 123	97.91 231	99.06 22	93.72 187	96.92 146	98.06 181	88.50 200	99.65 123	91.77 260	99.00 114	98.66 178

OpenMVS_ROB	86.42 20	89.00 312	87.43 317	93.69 312	93.08 341	89.42 314	97.91 231	96.89 317	78.58 346	85.86 336	94.69 329	69.48 346	98.29 281	77.13 348	93.29 248	93.36 345

test_8					99.29 78	98.44 31	97.89 235	98.72 109	92.98 221	97.70 114	98.66 124	96.20 23	99.80 79

ab-mvs		96.42 133	95.71 146	98.55 87	98.63 140	96.75 112	97.88 236	98.74 102	93.84 179	96.54 165	98.18 174	85.34 260	99.75 104	95.93 133	96.35 196	99.15 138

jason		97.32 99	97.08 93	98.06 125	97.45 228	95.59 163	97.87 237	97.91 259	94.79 141	98.55 66	98.83 107	91.12 146	99.23 167	97.58 65	99.60 68	99.34 111
jason: jason.
xiu_mvs_v1_base_debu		97.60 78	97.56 69	97.72 144	98.35 156	95.98 142	97.86 238	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

xiu_mvs_v1_base		97.60 78	97.56 69	97.72 144	98.35 156	95.98 142	97.86 238	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

xiu_mvs_v1_base_debi		97.60 78	97.56 69	97.72 144	98.35 156	95.98 142	97.86 238	98.51 161	97.13 42	99.01 35	98.40 149	91.56 134	99.80 79	98.53 9	98.68 127	97.37 219

test_prior4						98.01 62	97.86 238

agg_prior1		97.95 61	97.51 74	99.28 35	99.30 75	98.38 35	97.81 242	98.72 109	93.16 215	97.57 125	98.66 124	96.14 26	99.81 70	96.63 110	99.56 80	99.66 65

test_prior3		98.22 55	97.90 58	99.19 43	99.31 70	98.22 50	97.80 243	98.84 65	96.12 80	97.89 105	98.69 119	95.96 36	99.70 114	96.89 95	99.60 68	99.65 67

test_prior2							97.80 243		96.12 80	97.89 105	98.69 119	95.96 36		96.89 95	99.60 68

XVG-OURS-SEG-HR		96.51 130	96.34 125	97.02 183	98.77 126	93.76 235	97.79 245	98.50 166	95.45 106	96.94 143	99.09 74	87.87 216	99.55 141	96.76 108	95.83 212	97.74 208

MS-PatchMatch		93.84 264	93.63 252	94.46 305	96.18 296	89.45 313	97.76 246	98.27 205	92.23 249	92.13 294	97.49 230	79.50 311	98.69 229	89.75 290	99.38 100	95.25 326

DELS-MVS		98.40 42	98.20 44	98.99 63	99.00 109	97.66 75	97.75 247	98.89 46	97.71 8	98.33 78	98.97 87	94.97 74	99.88 43	98.42 20	99.76 32	99.42 107
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
MG-MVS		97.81 67	97.60 66	98.44 98	99.12 103	95.97 147	97.75 247	98.78 94	96.89 50	98.46 68	99.22 47	93.90 99	99.68 120	94.81 170	99.52 87	99.67 61

Test_1112_low_res		96.34 136	95.66 150	98.36 105	98.56 144	95.94 150	97.71 249	98.07 242	92.10 253	94.79 200	97.29 243	91.75 130	99.56 136	94.17 192	96.50 193	99.58 82

BH-w/o		95.38 176	95.08 173	96.26 244	98.34 160	91.79 277	97.70 250	97.43 290	92.87 227	94.24 222	97.22 248	88.66 194	98.84 218	91.55 264	97.70 167	98.16 198

testing_2		90.61 301	88.50 309	96.95 189	90.08 350	95.57 165	97.69 251	98.06 247	93.02 219	76.55 346	92.48 342	61.18 353	98.44 255	95.45 153	91.98 260	96.84 249

lupinMVS		97.44 91	97.22 88	98.12 122	98.07 181	95.76 160	97.68 252	97.76 264	94.50 155	98.79 49	98.61 127	92.34 113	99.30 161	97.58 65	99.59 71	99.31 117

原ACMM2							97.67 253

LF4IMVS		93.14 278	92.79 272	94.20 308	95.88 308	88.67 325	97.66 254	97.07 304	93.81 182	91.71 299	97.65 218	77.96 323	98.81 222	91.47 265	91.92 262	95.12 329

新几何2							97.64 255

MDA-MVSNet-bldmvs		89.97 306	88.35 311	94.83 293	95.21 324	91.34 286	97.64 255	97.51 282	88.36 320	71.17 352	96.13 312	79.22 313	96.63 337	83.65 332	86.27 327	96.52 291

pmmvs-eth3d		90.36 303	89.05 307	94.32 307	91.10 347	92.12 270	97.63 257	96.95 312	88.86 318	84.91 340	93.13 338	78.32 318	96.74 332	88.70 304	81.81 337	94.09 340

TR-MVS		94.94 206	94.20 213	97.17 175	97.75 199	94.14 226	97.59 258	97.02 309	92.28 248	95.75 184	97.64 220	83.88 286	98.96 201	89.77 289	96.15 207	98.40 189

无先验							97.58 259	98.72 109	91.38 272				99.87 44	93.36 216		99.60 78

旧先验2							97.57 260		91.30 278	98.67 58			99.80 79	95.70 146

CostFormer		94.95 204	94.73 188	95.60 269	97.28 237	89.06 319	97.53 261	96.89 317	89.66 310	96.82 151	96.72 290	86.05 248	98.95 205	95.53 150	96.13 208	98.79 167

XVG-OURS		96.55 129	96.41 123	96.99 184	98.75 127	93.76 235	97.50 262	98.52 158	95.67 96	96.83 149	99.30 38	88.95 190	99.53 142	95.88 135	96.26 203	97.69 211

xiu_mvs_v2_base		97.66 75	97.70 63	97.56 158	98.61 142	95.46 171	97.44 263	98.46 171	97.15 40	98.65 61	98.15 175	94.33 91	99.80 79	97.84 46	98.66 131	97.41 215

tpm		94.13 253	93.80 240	95.12 282	96.50 284	87.91 334	97.44 263	95.89 333	92.62 233	96.37 173	96.30 305	84.13 281	98.30 278	93.24 219	91.66 265	99.14 140

DeepPCF-MVS	96.37 2	97.93 63	98.48 17	96.30 242	99.00 109	89.54 312	97.43 265	98.87 55	98.16 2	99.26 18	99.38 21	96.12 28	99.64 125	98.30 26	99.77 26	99.72 40

test222					99.23 93	97.17 97	97.40 266	98.66 131	88.68 319	98.05 86	98.96 93	94.14 94			99.53 85	99.61 75

pmmvs4		94.69 214	93.99 229	96.81 198	95.74 311	95.94 150	97.40 266	97.67 268	90.42 296	93.37 258	97.59 224	89.08 183	98.20 285	92.97 228	91.67 264	96.30 308

test0.0.03 1		94.08 258	93.51 257	95.80 262	95.53 318	92.89 265	97.38 268	95.97 330	95.11 127	92.51 286	96.66 292	87.71 218	96.94 329	87.03 314	93.67 236	97.57 213

HyFIR lowres test		96.90 117	96.49 122	98.14 118	99.33 65	95.56 166	97.38 268	99.65 2	92.34 244	97.61 122	98.20 172	89.29 176	99.10 185	96.97 88	97.60 170	99.77 20

Effi-MVS+		97.12 109	96.69 113	98.39 104	98.19 172	96.72 113	97.37 270	98.43 178	93.71 188	97.65 119	98.02 183	92.20 120	99.25 164	96.87 101	97.79 162	99.19 132

N_pmnet		87.12 316	87.77 315	85.17 333	95.46 320	61.92 358	97.37 270	70.66 363	85.83 333	88.73 326	96.04 314	85.33 261	97.76 315	80.02 340	90.48 278	95.84 318

PAPR		96.84 119	96.24 130	98.65 81	98.72 132	96.92 105	97.36 272	98.57 147	93.33 207	96.67 156	97.57 226	94.30 92	99.56 136	91.05 271	98.59 133	99.47 98

PMMVS		96.60 125	96.33 126	97.41 164	97.90 192	93.93 230	97.35 273	98.41 180	92.84 228	97.76 109	97.45 234	91.10 148	99.20 170	96.26 122	97.91 157	99.11 143

PS-MVSNAJ		97.73 71	97.77 60	97.62 154	98.68 136	95.58 164	97.34 274	98.51 161	97.29 28	98.66 60	97.88 196	94.51 85	99.90 33	97.87 42	99.17 109	97.39 217

SCA		95.46 169	95.13 170	96.46 231	97.67 205	91.29 290	97.33 275	97.60 273	94.68 146	96.92 146	97.10 254	83.97 284	98.89 212	92.59 238	98.32 148	99.20 129

testdata1							97.32 276		96.34 71

ET-MVSNet_ETH3D		94.13 253	92.98 268	97.58 156	98.22 168	96.20 136	97.31 277	95.37 335	94.53 152	79.56 345	97.63 222	86.51 238	97.53 321	96.91 92	90.74 276	99.02 151

tpm2		94.19 249	93.76 245	95.46 273	97.23 240	89.04 320	97.31 277	96.85 320	87.08 325	96.21 176	96.79 288	83.75 290	98.74 227	92.43 246	96.23 205	98.59 182

PVSNet_Blended		97.38 96	97.12 90	98.14 118	99.25 86	95.35 176	97.28 279	99.26 8	93.13 216	97.94 100	98.21 171	92.74 109	99.81 70	96.88 98	99.40 99	99.27 124

CLD-MVS		95.62 165	95.34 160	96.46 231	97.52 221	93.75 237	97.27 280	98.46 171	95.53 102	94.42 213	98.00 186	86.21 245	98.97 198	96.25 123	94.37 217	96.66 272
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPMVS		94.99 200	94.48 199	96.52 224	97.22 241	91.75 279	97.23 281	91.66 354	94.11 164	97.28 129	96.81 287	85.70 253	98.84 218	93.04 226	97.28 175	98.97 156

miper_lstm_enhance		94.33 240	94.07 222	95.11 283	97.75 199	90.97 294	97.22 282	98.03 250	91.67 265	92.76 276	96.97 274	90.03 166	97.78 314	92.51 243	89.64 288	96.56 283

YYNet1		90.70 300	89.39 303	94.62 299	94.79 330	90.65 300	97.20 283	97.46 286	87.54 323	72.54 350	95.74 317	86.51 238	96.66 336	86.00 320	86.76 326	96.54 286

MDA-MVSNet_test_wron		90.71 299	89.38 304	94.68 297	94.83 329	90.78 298	97.19 284	97.46 286	87.60 322	72.41 351	95.72 320	86.51 238	96.71 335	85.92 321	86.80 325	96.56 283

IterMVS-SCA-FT		94.11 255	93.87 236	94.85 291	97.98 189	90.56 302	97.18 285	98.11 232	93.75 183	92.58 282	97.48 231	83.97 284	97.41 323	92.48 245	91.30 268	96.58 279

IterMVS		94.09 257	93.85 238	94.80 294	97.99 187	90.35 304	97.18 285	98.12 230	93.68 193	92.46 288	97.34 239	84.05 282	97.41 323	92.51 243	91.33 267	96.62 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
DPM-MVS		97.55 85	96.99 98	99.23 42	99.04 107	98.55 26	97.17 287	98.35 190	94.85 140	97.93 102	98.58 132	95.07 72	99.71 113	92.60 236	99.34 102	99.43 106

cl_fuxian		94.79 211	94.43 205	95.89 259	97.75 199	93.12 262	97.16 288	98.03 250	92.23 249	93.46 256	97.05 266	91.39 139	98.01 300	93.58 211	89.21 296	96.53 288

new-patchmatchnet		88.50 313	87.45 316	91.67 326	90.31 349	85.89 341	97.16 288	97.33 294	89.47 312	83.63 342	92.77 339	76.38 331	95.06 347	82.70 334	77.29 343	94.06 341

UnsupCasMVSNet_eth		90.99 297	89.92 300	94.19 309	94.08 335	89.83 307	97.13 290	98.67 128	93.69 191	85.83 337	96.19 311	75.15 336	96.74 332	89.14 301	79.41 341	96.00 315

IB-MVS	91.98 17	93.27 273	91.97 284	97.19 173	97.47 223	93.41 251	97.09 291	95.99 329	93.32 208	92.47 287	95.73 318	78.06 322	99.53 142	94.59 177	82.98 333	98.62 181
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
cl-mvsnet_		94.51 231	94.01 226	96.02 251	97.58 212	93.40 252	97.05 292	97.96 255	91.73 263	92.76 276	97.08 260	89.06 184	98.13 290	92.61 235	90.29 281	96.52 291

cl-mvsnet1		94.52 230	94.03 223	95.99 252	97.57 216	93.38 253	97.05 292	97.94 256	91.74 261	92.81 274	97.10 254	89.12 181	98.07 296	92.60 236	90.30 280	96.53 288

miper_ehance_all_eth		95.01 198	94.69 190	95.97 254	97.70 204	93.31 255	97.02 294	98.07 242	92.23 249	93.51 253	96.96 276	91.85 128	98.15 288	93.68 206	91.16 271	96.44 301

CMPMVS	66.06 21	89.70 307	89.67 302	89.78 328	93.19 340	76.56 351	97.00 295	98.35 190	80.97 344	81.57 344	97.75 209	74.75 338	98.61 236	89.85 288	93.63 238	94.17 338
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tpmrst		95.63 164	95.69 148	95.44 274	97.54 218	88.54 327	96.97 296	97.56 275	93.50 201	97.52 127	96.93 280	89.49 170	99.16 173	95.25 160	96.42 195	98.64 180

dp		94.15 252	93.90 234	94.90 289	97.31 236	86.82 340	96.97 296	97.19 301	91.22 283	96.02 180	96.61 297	85.51 256	99.02 196	90.00 287	94.30 218	98.85 163

cl-mvsnet2		94.68 216	94.19 214	96.13 249	98.11 179	93.60 242	96.94 298	98.31 196	92.43 241	93.32 260	96.87 284	86.51 238	98.28 282	94.10 196	91.16 271	96.51 294

PM-MVS		87.77 314	86.55 318	91.40 327	91.03 348	83.36 346	96.92 299	95.18 338	91.28 280	86.48 335	93.42 337	53.27 354	96.74 332	89.43 298	81.97 336	94.11 339

TinyColmap		92.31 287	91.53 288	94.65 298	96.92 261	89.75 308	96.92 299	96.68 324	90.45 295	89.62 318	97.85 200	76.06 333	98.81 222	86.74 315	92.51 255	95.41 324

our_test_3		93.65 267	93.30 263	94.69 296	95.45 321	89.68 311	96.91 301	97.65 269	91.97 256	91.66 300	96.88 282	89.67 169	97.93 307	88.02 309	91.49 266	96.48 298

test-LLR		95.10 194	94.87 183	95.80 262	96.77 269	89.70 309	96.91 301	95.21 336	95.11 127	94.83 198	95.72 320	87.71 218	98.97 198	93.06 224	98.50 138	98.72 170

TESTMET0.1,1		94.18 251	93.69 250	95.63 268	96.92 261	89.12 318	96.91 301	94.78 341	93.17 214	94.88 195	96.45 301	78.52 317	98.92 207	93.09 223	98.50 138	98.85 163

test-mter		94.08 258	93.51 257	95.80 262	96.77 269	89.70 309	96.91 301	95.21 336	92.89 226	94.83 198	95.72 320	77.69 324	98.97 198	93.06 224	98.50 138	98.72 170

USDC		93.33 272	92.71 273	95.21 279	96.83 268	90.83 296	96.91 301	97.50 283	93.84 179	90.72 309	98.14 176	77.69 324	98.82 221	89.51 296	93.21 249	95.97 316

MDTV_nov1_ep13_2view						84.26 343	96.89 306		90.97 288	97.90 104		89.89 168		93.91 200		99.18 136

ppachtmachnet_test		93.22 275	92.63 275	94.97 287	95.45 321	90.84 295	96.88 307	97.88 260	90.60 291	92.08 295	97.26 244	88.08 210	97.86 313	85.12 327	90.33 279	96.22 309

tpmvs		94.60 222	94.36 208	95.33 277	97.46 224	88.60 326	96.88 307	97.68 267	91.29 279	93.80 243	96.42 303	88.58 195	99.24 166	91.06 269	96.04 210	98.17 197

MDTV_nov1_ep13			95.40 154		97.48 222	88.34 329	96.85 309	97.29 296	93.74 185	97.48 128	97.26 244	89.18 179	99.05 189	91.92 257	97.43 173

PatchmatchNet		95.71 160	95.52 152	96.29 243	97.58 212	90.72 299	96.84 310	97.52 281	94.06 166	97.08 136	96.96 276	89.24 178	98.90 211	92.03 254	98.37 144	99.26 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MSDG		95.93 150	95.30 165	97.83 136	98.90 116	95.36 174	96.83 311	98.37 187	91.32 277	94.43 212	98.73 118	90.27 163	99.60 131	90.05 285	98.82 124	98.52 185

thisisatest0515		95.61 167	94.89 182	97.76 141	98.15 177	95.15 183	96.77 312	94.41 344	92.95 223	97.18 133	97.43 236	84.78 268	99.45 152	94.63 172	97.73 166	98.68 175

GA-MVS		94.81 210	94.03 223	97.14 176	97.15 249	93.86 232	96.76 313	97.58 274	94.00 171	94.76 201	97.04 267	80.91 302	98.48 249	91.79 259	96.25 204	99.09 144

tpm cat1		93.36 269	92.80 271	95.07 285	97.58 212	87.97 333	96.76 313	97.86 261	82.17 343	93.53 250	96.04 314	86.13 246	99.13 178	89.24 300	95.87 211	98.10 199

eth_miper_zixun_eth		94.68 216	94.41 206	95.47 272	97.64 207	91.71 281	96.73 315	98.07 242	92.71 231	93.64 246	97.21 249	90.54 158	98.17 287	93.38 214	89.76 286	96.54 286

test_post1							96.68 316				30.43 361	87.85 217	98.69 229	92.59 238

pmmvs3		86.67 317	84.86 320	92.11 325	88.16 351	87.19 339	96.63 317	94.75 342	79.88 345	87.22 331	92.75 340	66.56 349	95.20 346	81.24 338	76.56 345	93.96 342

miper_enhance_ethall		95.10 194	94.75 187	96.12 250	97.53 220	93.73 239	96.61 318	98.08 240	92.20 252	93.89 237	96.65 294	92.44 112	98.30 278	94.21 191	91.16 271	96.34 304

testmvs		21.48 330	24.95 333	11.09 344	14.89 364	6.47 366	96.56 319	9.87 365	7.55 360	17.93 360	39.02 358	9.43 367	5.90 362	16.56 360	12.72 359	20.91 357

test123		20.95 331	23.72 334	12.64 343	13.54 365	8.19 365	96.55 320	6.13 366	7.48 361	16.74 361	37.98 359	12.97 364	6.05 361	16.69 359	5.43 360	23.68 356

CL-MVSNet_2432*1600		90.11 304	89.14 306	93.02 320	91.86 345	88.23 331	96.51 321	98.07 242	90.49 292	90.49 312	94.41 330	84.75 269	95.34 344	80.79 339	74.95 346	95.50 323

GG-mvs-BLEND				96.59 215	96.34 291	94.98 191	96.51 321	88.58 358		93.10 269	94.34 334	80.34 308	98.05 298	89.53 295	96.99 179	96.74 259

new_pmnet		90.06 305	89.00 308	93.22 319	94.18 333	88.32 330	96.42 323	96.89 317	86.19 329	85.67 338	93.62 336	77.18 329	97.10 327	81.61 337	89.29 295	94.23 337

PVSNet	91.96 18	96.35 135	96.15 132	96.96 188	99.17 98	92.05 273	96.08 324	98.68 120	93.69 191	97.75 110	97.80 207	88.86 191	99.69 119	94.26 190	99.01 113	99.15 138

ADS-MVSNet2		94.58 225	94.40 207	95.11 283	98.00 185	88.74 324	96.04 325	97.30 295	90.15 300	96.47 169	96.64 295	87.89 214	97.56 320	90.08 283	97.06 177	99.02 151

ADS-MVSNet		95.00 199	94.45 203	96.63 210	98.00 185	91.91 275	96.04 325	97.74 266	90.15 300	96.47 169	96.64 295	87.89 214	98.96 201	90.08 283	97.06 177	99.02 151

PAPM		94.95 204	94.00 227	97.78 139	97.04 255	95.65 162	96.03 327	98.25 210	91.23 282	94.19 225	97.80 207	91.27 144	98.86 217	82.61 335	97.61 169	98.84 165

cascas		94.63 221	93.86 237	96.93 191	96.91 263	94.27 222	96.00 328	98.51 161	85.55 335	94.54 204	96.23 308	84.20 280	98.87 215	95.80 139	96.98 180	97.66 212

gg-mvs-nofinetune		92.21 288	90.58 295	97.13 177	96.75 272	95.09 185	95.85 329	89.40 357	85.43 336	94.50 206	81.98 351	80.80 305	98.40 271	92.16 248	98.33 147	97.88 203

FPMVS		77.62 321	77.14 321	79.05 336	79.25 357	60.97 359	95.79 330	95.94 331	65.96 351	67.93 353	94.40 331	37.73 359	88.88 354	68.83 351	88.46 305	87.29 348

CHOSEN 280x420		97.18 106	97.18 89	97.20 172	98.81 124	93.27 256	95.78 331	99.15 18	95.25 119	96.79 154	98.11 178	92.29 115	99.07 188	98.56 8	99.85 3	99.25 126

MIMVSNet		93.26 274	92.21 281	96.41 234	97.73 203	93.13 261	95.65 332	97.03 307	91.27 281	94.04 232	96.06 313	75.33 335	97.19 326	86.56 316	96.23 205	98.92 161

KD-MVS_2432*1600		89.61 309	87.96 313	94.54 300	94.06 336	91.59 283	95.59 333	97.63 271	89.87 306	88.95 323	94.38 332	78.28 319	96.82 330	84.83 328	68.05 350	95.21 327

miper_refine_blended		89.61 309	87.96 313	94.54 300	94.06 336	91.59 283	95.59 333	97.63 271	89.87 306	88.95 323	94.38 332	78.28 319	96.82 330	84.83 328	68.05 350	95.21 327

PCF-MVS	93.45 11	94.68 216	93.43 260	98.42 101	98.62 141	96.77 111	95.48 335	98.20 214	84.63 338	93.34 259	98.32 161	88.55 198	99.81 70	84.80 330	98.96 115	98.68 175
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
JIA-IIPM		93.35 270	92.49 277	95.92 256	96.48 286	90.65 300	95.01 336	96.96 311	85.93 332	96.08 178	87.33 348	87.70 220	98.78 225	91.35 266	95.58 214	98.34 192

CR-MVSNet		94.76 213	94.15 218	96.59 215	97.00 256	93.43 249	94.96 337	97.56 275	92.46 237	96.93 144	96.24 306	88.15 207	97.88 312	87.38 312	96.65 187	98.46 187

RPMNet		92.81 282	91.34 290	97.24 170	97.00 256	93.43 249	94.96 337	98.80 86	82.27 342	96.93 144	92.12 344	86.98 232	99.82 63	76.32 349	96.65 187	98.46 187

UnsupCasMVSNet_bld		87.17 315	85.12 319	93.31 317	91.94 344	88.77 323	94.92 339	98.30 202	84.30 339	82.30 343	90.04 345	63.96 352	97.25 325	85.85 322	74.47 348	93.93 343

PVSNet_0	88.72 19	91.28 294	90.03 299	95.00 286	97.99 187	87.29 338	94.84 340	98.50 166	92.06 254	89.86 316	95.19 325	79.81 310	99.39 155	92.27 247	69.79 349	98.33 193

Patchmatch-test		94.42 236	93.68 251	96.63 210	97.60 210	91.76 278	94.83 341	97.49 285	89.45 313	94.14 227	97.10 254	88.99 185	98.83 220	85.37 326	98.13 152	99.29 122

Patchmtry		93.22 275	92.35 279	95.84 261	96.77 269	93.09 263	94.66 342	97.56 275	87.37 324	92.90 272	96.24 306	88.15 207	97.90 308	87.37 313	90.10 283	96.53 288

PatchT		93.06 279	91.97 284	96.35 238	96.69 275	92.67 266	94.48 343	97.08 303	86.62 326	97.08 136	92.23 343	87.94 213	97.90 308	78.89 345	96.69 185	98.49 186

LCM-MVSNet		78.70 318	76.24 323	86.08 331	77.26 359	71.99 355	94.34 344	96.72 322	61.62 353	76.53 347	89.33 346	33.91 361	92.78 351	81.85 336	74.60 347	93.46 344

PMMVS2		77.95 320	75.44 324	85.46 332	82.54 354	74.95 353	94.23 345	93.08 352	72.80 350	74.68 348	87.38 347	36.36 360	91.56 352	73.95 350	63.94 352	89.87 347

MVS-HIRNet		89.46 311	88.40 310	92.64 321	97.58 212	82.15 348	94.16 346	93.05 353	75.73 349	90.90 306	82.52 350	79.42 312	98.33 273	83.53 333	98.68 127	97.43 214

Patchmatch-RL test		91.49 292	90.85 293	93.41 314	91.37 346	84.40 342	92.81 347	95.93 332	91.87 259	87.25 330	94.87 328	88.99 185	96.53 338	92.54 242	82.00 335	99.30 120

ambc				89.49 329	86.66 352	75.78 352	92.66 348	96.72 322		86.55 334	92.50 341	46.01 355	97.90 308	90.32 279	82.09 334	94.80 335

EMVS		64.07 326	63.26 329	66.53 341	81.73 356	58.81 362	91.85 349	84.75 360	51.93 357	59.09 356	75.13 355	43.32 357	79.09 358	42.03 357	39.47 355	61.69 354

E-PMN		64.94 325	64.25 327	67.02 340	82.28 355	59.36 361	91.83 350	85.63 359	52.69 355	60.22 355	77.28 354	41.06 358	80.12 357	46.15 356	41.14 354	61.57 355

ANet_high		69.08 322	65.37 326	80.22 335	65.99 361	71.96 356	90.91 351	90.09 356	82.62 341	49.93 358	78.39 353	29.36 362	81.75 355	62.49 353	38.52 356	86.95 350

tmp_tt		68.90 323	66.97 325	74.68 338	50.78 363	59.95 360	87.13 352	83.47 361	38.80 358	62.21 354	96.23 308	64.70 351	76.91 359	88.91 303	30.49 357	87.19 349

MVE	62.14 22	63.28 327	59.38 330	74.99 337	74.33 360	65.47 357	85.55 353	80.50 362	52.02 356	51.10 357	75.00 356	10.91 366	80.50 356	51.60 355	53.40 353	78.99 351
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	61.03 23	65.95 324	63.57 328	73.09 339	57.90 362	51.22 363	85.05 354	93.93 351	54.45 354	44.32 359	83.57 349	13.22 363	89.15 353	58.68 354	81.00 339	78.91 352
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		78.40 319	76.75 322	83.38 334	95.54 317	80.43 350	79.42 355	97.40 292	64.67 352	73.46 349	80.82 352	45.65 356	93.14 350	66.32 352	87.43 315	76.56 353
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuyk23d		30.17 328	30.18 332	30.16 342	78.61 358	43.29 364	66.79 356	14.21 364	17.31 359	14.82 362	11.93 362	11.55 365	41.43 360	37.08 358	19.30 358	5.76 358

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

cdsmvs_eth3d_5k		23.98 329	31.98 331	0.00 345	0.00 366	0.00 367	0.00 357	98.59 141	0.00 362	0.00 363	98.61 127	90.60 157	0.00 363	0.00 361	0.00 361	0.00 359

pcd_1.5k_mvsjas		7.88 333	10.50 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	94.51 85	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ab-mvs-re		8.20 332	10.94 335	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	98.43 145	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ZD-MVS					99.46 51	98.70 19		98.79 91	93.21 212	98.67 58	98.97 87	95.70 44	99.83 55	96.07 126	99.58 74

IU-MVS					99.71 20	99.23 6		98.64 136	95.28 117	99.63 4				98.35 24	99.81 10	99.83 5

test_241102_TWO								98.87 55	97.65 9	99.53 8	99.48 6	97.34 8	99.94 3	98.43 18	99.80 17	99.83 5

test_241102_ONE					99.71 20	99.24 4		98.87 55	97.62 11	99.73 1	99.39 14	97.53 4	99.74 106

test_0728_THIRD									97.32 27	99.45 9	99.46 9	97.88 1	99.94 3	98.47 15	99.86 1	99.85 2

GSMVS																99.20 129

test_part2					99.63 29	99.18 8				99.27 17

sam_mvs1												89.45 172				99.20 129

sam_mvs												88.99 185

MTGPA								98.74 102

test_post											31.83 360	88.83 192	98.91 208

patchmatchnet-post											95.10 327	89.42 173	98.89 212

gm-plane-assit					95.88 308	87.47 336			89.74 309		96.94 279		99.19 171	93.32 218

test9_res														96.39 120	99.57 75	99.69 51

agg_prior2														95.87 136	99.57 75	99.68 57

agg_prior					99.30 75	98.38 35		98.72 109		97.57 125			99.81 70

TestCases				96.99 184	99.25 86	93.21 259		98.18 218	91.36 273	93.52 251	98.77 114	84.67 270	99.72 108	89.70 292	97.87 159	98.02 201

test_prior				99.19 43	99.31 70	98.22 50		98.84 65					99.70 114			99.65 67

新几何1				99.16 50	99.34 62	98.01 62		98.69 117	90.06 303	98.13 82	98.95 95	94.60 82	99.89 35	91.97 256	99.47 90	99.59 80

旧先验1					99.29 78	97.48 82		98.70 116			99.09 74	95.56 47			99.47 90	99.61 75

原ACMM1				98.65 81	99.32 68	96.62 115		98.67 128	93.27 211	97.81 107	98.97 87	95.18 68	99.83 55	93.84 202	99.46 93	99.50 91

testdata2													99.89 35	91.65 263

segment_acmp												96.85 11

testdata				98.26 111	99.20 97	95.36 174		98.68 120	91.89 258	98.60 64	99.10 69	94.44 90	99.82 63	94.27 189	99.44 95	99.58 82

test12				99.18 47	99.16 99	98.19 52		98.53 156		98.07 85		95.13 70	99.72 108		99.56 80	99.63 73

plane_prior7					97.42 229	94.63 206

plane_prior6					97.35 234	94.61 209						87.09 229

plane_prior5								98.56 149					99.03 193	96.07 126	94.27 219	96.92 234

plane_prior4											98.28 164

plane_prior3						94.61 209			97.02 47	95.34 186

plane_prior1					97.37 233

n2								0.00 367

nn								0.00 367

door-mid								94.37 345

lessismore_v0				94.45 306	94.93 328	88.44 328		91.03 355		86.77 333	97.64 220	76.23 332	98.42 258	90.31 280	85.64 331	96.51 294

LGP-MVS_train				96.47 228	97.46 224	93.54 244		98.54 154	94.67 147	94.36 215	98.77 114	85.39 257	99.11 182	95.71 144	94.15 225	96.76 257

test11								98.66 131

door								94.64 343

HQP5-MVS						94.25 224

BP-MVS														95.30 156

HQP4-MVS										94.45 208			98.96 201			96.87 245

HQP3-MVS								98.46 171							94.18 223

HQP2-MVS												86.75 235

NP-MVS					97.28 237	94.51 214					97.73 210

ACMMP++_ref															92.97 251

ACMMP++															93.61 239

Test By Simon												94.64 80

ITE_SJBPF				95.44 274	97.42 229	91.32 289		97.50 283	95.09 130	93.59 247	98.35 155	81.70 297	98.88 214	89.71 291	93.39 245	96.12 312

DeepMVS_CX				86.78 330	97.09 253	72.30 354		95.17 339	75.92 348	84.34 341	95.19 325	70.58 345	95.35 343	79.98 342	89.04 299	92.68 346

High-resolution multi-view benchmark