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 34	98.10 36	99.13 38	99.74 7	97.82 50	99.53 1	98.80 68	94.63 127	98.61 41	98.97 65	95.13 50	99.77 79	97.65 44	99.83 7	99.79 4

MVSFormer		97.57 64	97.49 56	97.84 112	98.07 152	95.76 136	99.47 2	98.40 152	94.98 113	98.79 31	98.83 81	92.34 87	98.41 248	96.91 69	99.59 53	99.34 89

test_djsdf		96.00 123	95.69 125	96.93 172	95.72 293	95.49 146	99.47 2	98.40 152	94.98 113	94.58 178	97.86 162	89.16 143	98.41 248	96.91 69	94.12 210	96.88 222

HPM-MVS_fast		98.38 32	98.13 35	99.12 40	99.75 3	97.86 47	99.44 4	98.82 58	94.46 134	98.94 22	99.20 35	95.16 49	99.74 85	97.58 47	99.85 2	99.77 14

nrg030		96.28 118	95.72 120	97.96 108	96.90 224	98.15 36	99.39 5	98.31 162	95.47 84	94.42 191	98.35 123	92.09 97	98.69 204	97.50 53	89.05 270	97.04 205

APDe-MVS		99.02 1	98.84 1	99.55 1	99.57 24	98.96 3	99.39 5	98.93 36	97.38 17	99.41 3	99.54 1	96.66 6	99.84 42	98.86 2	99.85 2	99.87 1

3Dnovator+	94.38 6	97.43 72	96.78 85	99.38 10	97.83 167	98.52 12	99.37 7	98.71 91	97.09 37	92.99 246	99.13 44	89.36 137	99.89 27	96.97 65	99.57 56	99.71 33

FIs		96.51 108	96.12 109	97.67 125	97.13 212	97.54 59	99.36 8	99.22 14	95.89 69	94.03 216	98.35 123	91.98 100	98.44 238	96.40 93	92.76 235	97.01 206

FC-MVSNet-test		96.42 111	96.05 110	97.53 137	96.95 219	97.27 67	99.36 8	99.23 12	95.83 71	93.93 218	98.37 121	92.00 99	98.32 257	96.02 101	92.72 236	97.00 207

3Dnovator	94.51 5	97.46 67	96.93 78	99.07 43	97.78 169	97.64 54	99.35 10	99.06 21	97.02 39	93.75 225	99.16 42	89.25 140	99.92 13	97.22 59	99.75 30	99.64 54

canonicalmvs		97.67 59	97.23 67	98.98 49	98.70 116	98.38 18	99.34 11	98.39 154	96.76 45	97.67 87	97.40 197	92.26 90	99.49 125	98.28 22	96.28 178	99.08 119

CP-MVS		98.57 20	98.36 18	99.19 28	99.66 19	97.86 47	99.34 11	98.87 49	95.96 68	98.60 42	99.13 44	96.05 23	99.94 3	97.77 39	99.86 1	99.77 14

EPP-MVSNet		97.46 67	97.28 64	97.99 106	98.64 122	95.38 149	99.33 13	98.31 162	93.61 172	97.19 100	99.07 55	94.05 71	99.23 143	96.89 71	98.43 118	99.37 88

XVS		98.70 5	98.49 12	99.34 13	99.70 15	98.35 23	99.29 14	98.88 47	97.40 14	98.46 46	99.20 35	95.90 30	99.89 27	97.85 34	99.74 33	99.78 7

X-MVStestdata		94.06 239	92.30 257	99.34 13	99.70 15	98.35 23	99.29 14	98.88 47	97.40 14	98.46 46	43.50 346	95.90 30	99.89 27	97.85 34	99.74 33	99.78 7

mPP-MVS		98.51 26	98.26 28	99.25 24	99.75 3	98.04 40	99.28 16	98.81 61	96.24 58	98.35 53	99.23 29	95.46 39	99.94 3	97.42 55	99.81 8	99.77 14

HSP-MVS		98.70 5	98.52 8	99.24 25	99.75 3	98.23 29	99.26 17	98.58 120	97.52 7	99.41 3	98.78 85	96.00 24	99.79 69	97.79 38	99.59 53	99.69 36

v7n		94.19 229	93.43 240	96.47 214	95.90 285	94.38 214	99.26 17	98.34 160	91.99 231	92.76 250	97.13 218	88.31 178	98.52 224	89.48 266	87.70 291	96.52 270

v748		93.75 245	93.06 245	95.82 244	95.73 292	92.64 251	99.25 19	98.24 175	91.60 240	92.22 265	96.52 267	87.60 201	98.46 233	90.64 239	85.72 309	96.36 279

tfpn1000		95.72 134	95.11 145	97.58 134	99.00 87	95.73 138	99.24 20	95.49 324	94.08 141	96.87 118	97.45 195	85.81 234	99.30 137	91.78 218	96.22 183	97.71 183

WR-MVS_H		95.05 178	94.46 180	96.81 177	96.86 226	95.82 135	99.24 20	99.24 10	93.87 153	92.53 256	96.84 254	90.37 126	98.24 265	93.24 175	87.93 287	96.38 278

HFP-MVS		98.63 12	98.40 14	99.32 16	99.72 11	98.29 26	99.23 22	98.96 31	96.10 65	98.94 22	99.17 39	96.06 21	99.92 13	97.62 45	99.78 14	99.75 21

region2R		98.61 13	98.38 16	99.29 18	99.74 7	98.16 35	99.23 22	98.93 36	96.15 60	98.94 22	99.17 39	95.91 29	99.94 3	97.55 50	99.79 10	99.78 7

v52		94.18 231	93.52 235	96.13 234	95.95 284	94.29 217	99.23 22	98.21 178	91.42 245	92.84 248	96.89 249	87.85 193	98.53 223	91.51 225	87.81 288	95.57 299

V4		94.18 231	93.52 235	96.13 234	95.89 286	94.31 216	99.23 22	98.22 177	91.42 245	92.82 249	96.89 249	87.93 189	98.52 224	91.51 225	87.81 288	95.58 298

ACMMPR		98.59 16	98.36 18	99.29 18	99.74 7	98.15 36	99.23 22	98.95 33	96.10 65	98.93 26	99.19 38	95.70 34	99.94 3	97.62 45	99.79 10	99.78 7

QAPM		96.29 116	95.40 129	98.96 51	97.85 166	97.60 57	99.23 22	98.93 36	89.76 282	93.11 243	99.02 58	89.11 144	99.93 9	91.99 212	99.62 48	99.34 89

MP-MVS		98.33 38	98.01 39	99.28 20	99.75 3	98.18 34	99.22 28	98.79 69	96.13 62	97.92 74	99.23 29	94.54 60	99.94 3	96.74 81	99.78 14	99.73 28

Vis-MVSNet		97.42 73	97.11 71	98.34 86	98.66 120	96.23 107	99.22 28	99.00 26	96.63 51	98.04 63	99.21 32	88.05 186	99.35 136	96.01 102	99.21 85	99.45 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CSCG		97.85 52	97.74 46	98.20 92	99.67 18	95.16 157	99.22 28	99.32 7	93.04 193	97.02 108	98.92 75	95.36 42	99.91 22	97.43 54	99.64 46	99.52 67

OpenMVS	93.04 13	95.83 130	95.00 149	98.32 87	97.18 209	97.32 65	99.21 31	98.97 29	89.96 275	91.14 274	99.05 57	86.64 216	99.92 13	93.38 171	99.47 70	97.73 181

DTE-MVSNet		93.98 241	93.26 244	96.14 233	96.06 279	94.39 213	99.20 32	98.86 52	93.06 192	91.78 269	97.81 170	85.87 233	97.58 292	90.53 241	86.17 306	96.46 276

Vis-MVSNet (Re-imp)		96.87 96	96.55 95	97.83 113	98.73 113	95.46 147	99.20 32	98.30 165	94.96 115	96.60 130	98.87 78	90.05 132	98.59 213	93.67 166	98.60 108	99.46 82

IS-MVSNet		97.22 82	96.88 80	98.25 90	98.85 107	96.36 102	99.19 34	97.97 220	95.39 88	97.23 99	98.99 64	91.11 116	98.93 184	94.60 141	98.59 109	99.47 78

thresconf0.02		95.50 148	94.84 163	97.51 138	98.90 96	95.93 123	99.17 35	95.70 318	93.42 178	96.50 142	97.16 212	86.12 224	99.22 145	90.51 242	96.06 187	97.37 193

tfpn_n400		95.50 148	94.84 163	97.51 138	98.90 96	95.93 123	99.17 35	95.70 318	93.42 178	96.50 142	97.16 212	86.12 224	99.22 145	90.51 242	96.06 187	97.37 193

tfpnconf		95.50 148	94.84 163	97.51 138	98.90 96	95.93 123	99.17 35	95.70 318	93.42 178	96.50 142	97.16 212	86.12 224	99.22 145	90.51 242	96.06 187	97.37 193

tfpnview11		95.50 148	94.84 163	97.51 138	98.90 96	95.93 123	99.17 35	95.70 318	93.42 178	96.50 142	97.16 212	86.12 224	99.22 145	90.51 242	96.06 187	97.37 193

PEN-MVS		94.42 218	93.73 224	96.49 212	96.28 266	94.84 184	99.17 35	99.00 26	93.51 174	92.23 264	97.83 168	86.10 229	97.90 282	92.55 199	86.92 301	96.74 235

PS-MVSNAJss		96.43 110	96.26 105	96.92 174	95.84 289	95.08 161	99.16 40	98.50 137	95.87 70	93.84 223	98.34 127	94.51 61	98.61 210	96.88 74	93.45 225	97.06 203

APD-MVS_3200maxsize		98.53 25	98.33 24	99.15 37	99.50 28	97.92 46	99.15 41	98.81 61	96.24 58	99.20 11	99.37 12	95.30 44	99.80 57	97.73 41	99.67 40	99.72 31

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 25	99.69 17	98.28 28	99.14 42	98.66 107	96.84 43	99.56 2	99.31 21	96.34 11	99.70 91	98.32 20	99.73 35	99.73 28

anonymousdsp		95.42 157	94.91 159	96.94 171	95.10 306	95.90 131	99.14 42	98.41 150	93.75 158	93.16 239	97.46 193	87.50 204	98.41 248	95.63 117	94.03 212	96.50 273

jajsoiax		95.45 155	95.03 148	96.73 180	95.42 302	94.63 201	99.14 42	98.52 130	95.74 73	93.22 237	98.36 122	83.87 273	98.65 208	96.95 68	94.04 211	96.91 217

PS-CasMVS		94.67 205	93.99 207	96.71 181	96.68 236	95.26 155	99.13 45	99.03 24	93.68 168	92.33 262	97.95 155	85.35 242	98.10 270	93.59 168	88.16 286	96.79 230

abl_6		98.30 40	98.03 38	99.13 38	99.56 25	97.76 52	99.13 45	98.82 58	96.14 61	99.26 7	99.37 12	93.33 77	99.93 9	96.96 67	99.67 40	99.69 36

CPTT-MVS		97.72 56	97.32 63	98.92 53	99.64 20	97.10 74	99.12 47	98.81 61	92.34 223	98.09 59	99.08 54	93.01 81	99.92 13	96.06 99	99.77 18	99.75 21

CP-MVSNet		94.94 186	94.30 187	96.83 176	96.72 234	95.56 143	99.11 48	98.95 33	93.89 151	92.42 261	97.90 159	87.19 207	98.12 269	94.32 149	88.21 284	96.82 229

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 12	99.22 72	98.43 17	99.10 49	98.87 49	97.38 17	99.35 5	99.40 6	97.78 1	99.87 35	97.77 39	99.85 2	99.78 7
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view600		95.60 143	94.93 155	97.62 128	99.05 82	94.85 175	99.09 50	97.01 285	95.36 92	96.52 137	97.37 198	84.55 254	99.59 107	89.07 272	96.39 167	98.40 158

view800		95.60 143	94.93 155	97.62 128	99.05 82	94.85 175	99.09 50	97.01 285	95.36 92	96.52 137	97.37 198	84.55 254	99.59 107	89.07 272	96.39 167	98.40 158

conf0.05thres1000		95.60 143	94.93 155	97.62 128	99.05 82	94.85 175	99.09 50	97.01 285	95.36 92	96.52 137	97.37 198	84.55 254	99.59 107	89.07 272	96.39 167	98.40 158

tfpn		95.60 143	94.93 155	97.62 128	99.05 82	94.85 175	99.09 50	97.01 285	95.36 92	96.52 137	97.37 198	84.55 254	99.59 107	89.07 272	96.39 167	98.40 158

K. test v3		92.55 261	91.91 262	94.48 288	95.64 295	89.24 293	99.07 54	94.88 330	94.04 143	86.78 301	97.59 187	77.64 308	97.64 290	92.08 207	89.43 266	96.57 264

tfpn_ndepth		95.53 147	94.90 160	97.39 151	98.96 93	95.88 133	99.05 55	95.27 325	93.80 157	96.95 109	96.93 246	85.53 238	99.40 131	91.54 224	96.10 186	96.89 220

v8		94.47 216	93.77 220	96.57 204	96.36 253	94.83 186	99.05 55	98.19 182	91.92 232	93.16 239	96.97 238	88.82 159	98.48 228	91.69 221	87.79 290	96.39 277

PHI-MVS		98.34 36	98.06 37	99.18 32	99.15 79	98.12 38	99.04 57	99.09 19	93.32 185	98.83 30	99.10 48	96.54 9	99.83 43	97.70 43	99.76 24	99.59 62

TranMVSNet+NR-MVSNet		95.14 176	94.48 178	97.11 161	96.45 246	96.36 102	99.03 58	99.03 24	95.04 111	93.58 227	97.93 157	88.27 179	98.03 275	94.13 154	86.90 302	96.95 211

ACMMP		98.23 41	97.95 41	99.09 42	99.74 7	97.62 56	99.03 58	99.41 6	95.98 67	97.60 92	99.36 16	94.45 65	99.93 9	97.14 61	98.85 98	99.70 35
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
mvs_tets		95.41 159	95.00 149	96.65 192	95.58 297	94.42 211	99.00 60	98.55 124	95.73 74	93.21 238	98.38 120	83.45 276	98.63 209	97.09 63	94.00 213	96.91 217

v10		94.29 224	93.55 233	96.51 211	96.39 249	94.80 191	98.99 61	98.19 182	91.35 250	93.02 245	96.99 236	88.09 185	98.41 248	90.50 246	88.41 283	96.33 281

PGM-MVS		98.49 27	98.23 32	99.27 23	99.72 11	98.08 39	98.99 61	99.49 5	95.43 86	99.03 16	99.32 20	95.56 36	99.94 3	96.80 79	99.77 18	99.78 7

LPG-MVS_test		95.62 141	95.34 134	96.47 214	97.46 188	93.54 235	98.99 61	98.54 125	94.67 123	94.36 193	98.77 87	85.39 240	99.11 160	95.71 113	94.15 208	96.76 233

#test#		98.54 24	98.27 27	99.32 16	99.72 11	98.29 26	98.98 64	98.96 31	95.65 78	98.94 22	99.17 39	96.06 21	99.92 13	97.21 60	99.78 14	99.75 21

tfpnnormal		93.66 246	92.70 252	96.55 208	96.94 220	95.94 120	98.97 65	99.19 15	91.04 260	91.38 272	97.34 202	84.94 248	98.61 210	85.45 307	89.02 272	95.11 303

V42		94.78 194	94.14 196	96.70 183	96.33 260	95.22 156	98.97 65	98.09 211	92.32 225	94.31 197	97.06 226	88.39 177	98.55 216	92.90 189	88.87 276	96.34 280

pm-mvs1		93.94 242	93.06 245	96.59 200	96.49 244	95.16 157	98.95 67	98.03 219	92.32 225	91.08 275	97.84 165	84.54 258	98.41 248	92.16 205	86.13 308	96.19 284

VPA-MVSNet		95.75 133	95.11 145	97.69 123	97.24 202	97.27 67	98.94 68	99.23 12	95.13 106	95.51 161	97.32 204	85.73 235	98.91 186	97.33 58	89.55 264	96.89 220

LS3D		97.16 85	96.66 92	98.68 63	98.53 130	97.19 72	98.93 69	98.90 42	92.83 203	95.99 158	99.37 12	92.12 96	99.87 35	93.67 166	99.57 56	98.97 127

ACMM	93.85 9	95.69 138	95.38 133	96.61 198	97.61 177	93.84 228	98.91 70	98.44 146	95.25 101	94.28 201	98.47 113	86.04 232	99.12 156	95.50 120	93.95 215	96.87 223
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MTAPA		98.58 18	98.29 26	99.46 6	99.76 1	98.64 9	98.90 71	98.74 79	97.27 25	98.02 65	99.39 7	94.81 55	99.96 1	97.91 29	99.79 10	99.77 14

SD-MVS		98.64 10	98.68 3	98.53 73	99.33 43	98.36 22	98.90 71	98.85 53	97.28 21	99.72 1	99.39 7	96.63 8	97.60 291	98.17 23	99.85 2	99.64 54

TransMVSNet (Re)		92.67 260	91.51 264	96.15 232	96.58 239	94.65 199	98.90 71	96.73 300	90.86 262	89.46 289	97.86 162	85.62 237	98.09 272	86.45 299	81.12 319	95.71 295

EPNet		97.28 80	96.87 81	98.51 74	94.98 307	96.14 109	98.90 71	97.02 283	98.28 1	95.99 158	99.11 46	91.36 112	99.89 27	96.98 64	99.19 86	99.50 72
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UA-Net		97.96 45	97.62 48	98.98 49	98.86 105	97.47 61	98.89 75	99.08 20	96.67 49	98.72 36	99.54 1	93.15 80	99.81 50	94.87 134	98.83 99	99.65 51

OurMVSNet-221017-0		94.21 227	94.00 205	94.85 278	95.60 296	89.22 294	98.89 75	97.43 258	95.29 99	92.18 266	98.52 110	82.86 278	98.59 213	93.46 170	91.76 246	96.74 235

UGNet		96.78 99	96.30 103	98.19 94	98.24 140	95.89 132	98.88 77	98.93 36	97.39 16	96.81 122	97.84 165	82.60 279	99.90 25	96.53 88	99.49 68	98.79 138
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
conf200view11		95.40 160	94.70 170	97.50 142	98.98 90	94.92 170	98.87 78	96.90 293	95.38 89	96.61 128	96.88 251	84.29 261	99.56 115	88.11 287	96.29 174	98.02 173

thres100view900		95.38 161	94.70 170	97.41 146	98.98 90	94.92 170	98.87 78	96.90 293	95.38 89	96.61 128	96.88 251	84.29 261	99.56 115	88.11 287	96.29 174	97.76 178

v1neww		94.83 189	94.22 189	96.68 187	96.39 249	94.85 175	98.87 78	98.11 203	92.45 215	94.45 183	97.06 226	88.82 159	98.54 217	92.93 186	88.91 274	96.65 253

v7new		94.83 189	94.22 189	96.68 187	96.39 249	94.85 175	98.87 78	98.11 203	92.45 215	94.45 183	97.06 226	88.82 159	98.54 217	92.93 186	88.91 274	96.65 253

v6		94.83 189	94.21 191	96.69 184	96.36 253	94.85 175	98.87 78	98.11 203	92.46 210	94.44 189	97.05 230	88.76 165	98.57 215	92.95 185	88.92 273	96.65 253

XXY-MVS		95.20 174	94.45 182	97.46 143	96.75 232	96.56 94	98.86 83	98.65 111	93.30 187	93.27 236	98.27 134	84.85 250	98.87 192	94.82 136	91.26 252	96.96 209

VDDNet		95.36 164	94.53 177	97.86 111	98.10 151	95.13 159	98.85 84	97.75 228	90.46 264	98.36 52	99.39 7	73.27 324	99.64 100	97.98 27	96.58 160	98.81 137

thres600view7		95.49 152	94.77 167	97.67 125	98.98 90	95.02 162	98.85 84	96.90 293	95.38 89	96.63 127	96.90 248	84.29 261	99.59 107	88.65 281	96.33 172	98.40 158

114514_t		96.93 93	96.27 104	98.92 53	99.50 28	97.63 55	98.85 84	98.90 42	84.80 318	97.77 79	99.11 46	92.84 82	99.66 97	94.85 135	99.77 18	99.47 78

LFMVS		95.86 129	94.98 151	98.47 78	98.87 104	96.32 104	98.84 87	96.02 312	93.40 182	98.62 40	99.20 35	74.99 317	99.63 103	97.72 42	97.20 149	99.46 82

alignmvs		97.56 65	97.07 74	99.01 46	98.66 120	98.37 21	98.83 88	98.06 215	96.74 46	98.00 69	97.65 182	90.80 122	99.48 129	98.37 19	96.56 161	99.19 105

DeepC-MVS	95.98 3	97.88 49	97.58 50	98.77 59	99.25 65	96.93 79	98.83 88	98.75 78	96.96 41	96.89 116	99.50 3	90.46 125	99.87 35	97.84 36	99.76 24	99.52 67
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP_Plus		98.61 13	98.30 25	99.55 1	99.62 22	98.95 4	98.82 90	98.81 61	95.80 72	99.16 13	99.47 4	95.37 41	99.92 13	97.89 32	99.75 30	99.79 4

v18		92.10 267	90.97 267	95.50 253	96.34 256	94.85 175	98.82 90	97.52 241	89.99 274	85.31 312	93.26 310	88.90 153	96.92 303	88.82 277	79.77 323	94.73 309

GBi-Net		94.49 214	93.80 217	96.56 205	98.21 142	95.00 163	98.82 90	98.18 185	92.46 210	94.09 212	97.07 223	81.16 284	97.95 279	92.08 207	92.14 239	96.72 238

test1		94.49 214	93.80 217	96.56 205	98.21 142	95.00 163	98.82 90	98.18 185	92.46 210	94.09 212	97.07 223	81.16 284	97.95 279	92.08 207	92.14 239	96.72 238

FMVSNet1		93.19 256	92.07 259	96.56 205	97.54 183	95.00 163	98.82 90	98.18 185	90.38 267	92.27 263	97.07 223	73.68 323	97.95 279	89.36 268	91.30 250	96.72 238

API-MVS		97.41 74	97.25 65	97.91 109	98.70 116	96.80 84	98.82 90	98.69 94	94.53 129	98.11 58	98.28 131	94.50 64	99.57 113	94.12 155	99.49 68	97.37 193

v17		92.08 268	90.94 268	95.48 255	96.34 256	94.83 186	98.81 96	97.52 241	89.95 276	85.32 310	93.24 311	88.91 152	96.91 304	88.76 278	79.63 324	94.71 311

v16		92.08 268	90.94 268	95.49 254	96.38 252	94.84 184	98.81 96	97.51 244	89.94 277	85.25 313	93.28 309	88.86 154	96.91 304	88.70 279	79.78 322	94.72 310

ACMH	92.88 16	94.55 212	93.95 209	96.34 225	97.63 175	93.26 242	98.81 96	98.49 141	93.43 177	89.74 286	98.53 107	81.91 282	99.08 165	93.69 164	93.30 229	96.70 242
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		96.29 116	96.56 94	95.51 252	97.89 164	90.22 283	98.80 99	98.10 208	96.57 52	96.45 148	96.66 260	90.81 120	98.91 186	95.72 111	97.99 131	97.40 190

v11		91.85 275	90.68 277	95.36 265	96.34 256	94.74 198	98.80 99	97.43 258	89.60 288	85.09 315	93.03 316	88.53 174	96.75 311	87.37 294	79.96 321	94.58 317

HQP_MVS		96.14 121	95.90 115	96.85 175	97.42 192	94.60 206	98.80 99	98.56 122	97.28 21	95.34 162	98.28 131	87.09 208	99.03 172	96.07 97	94.27 202	96.92 212

plane_prior2							98.80 99		97.28 21

APD-MVS		98.35 35	98.00 40	99.42 9	99.51 27	98.72 8	98.80 99	98.82 58	94.52 130	99.23 9	99.25 28	95.54 38	99.80 57	96.52 89	99.77 18	99.74 26
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
UniMVSNet (Re)		95.78 132	95.19 143	97.58 134	96.99 218	97.47 61	98.79 104	99.18 16	95.60 79	93.92 219	97.04 231	91.68 104	98.48 228	95.80 109	87.66 292	96.79 230

FMVSNet2		94.47 216	93.61 230	97.04 164	98.21 142	96.43 99	98.79 104	98.27 168	92.46 210	93.50 232	97.09 221	81.16 284	98.00 277	91.09 230	91.93 243	96.70 242

v7		94.69 201	94.04 202	96.62 197	96.41 248	94.79 194	98.78 106	98.13 196	91.89 233	94.30 199	97.16 212	88.13 184	98.45 235	91.96 214	89.65 261	96.61 258

v15		91.94 270	90.77 272	95.43 260	96.31 264	94.83 186	98.77 107	97.50 247	89.92 278	85.13 314	93.08 314	88.76 165	96.86 306	88.40 282	79.10 326	94.61 315

V14		91.93 271	90.76 273	95.42 263	96.33 260	94.81 190	98.77 107	97.51 244	89.86 280	85.09 315	93.13 312	88.80 163	96.83 308	88.32 283	79.06 328	94.60 316

V9		91.91 272	90.73 274	95.45 257	96.32 263	94.80 191	98.77 107	97.50 247	89.81 281	85.03 317	93.08 314	88.76 165	96.86 306	88.24 284	79.03 329	94.69 312

v12		91.89 273	90.70 275	95.43 260	96.31 264	94.80 191	98.76 110	97.50 247	89.76 282	84.95 318	93.00 317	88.82 159	96.82 310	88.23 285	79.00 330	94.68 314

v13		91.88 274	90.69 276	95.43 260	96.33 260	94.78 196	98.75 111	97.50 247	89.68 285	84.93 319	92.98 318	88.84 157	96.83 308	88.14 286	79.09 327	94.69 312

testgi		93.06 258	92.45 255	94.88 277	96.43 247	89.90 284	98.75 111	97.54 240	95.60 79	91.63 271	97.91 158	74.46 321	97.02 301	86.10 301	93.67 218	97.72 182

LCM-MVSNet-Re		95.22 172	95.32 137	94.91 275	98.18 147	87.85 312	98.75 111	95.66 322	95.11 107	88.96 293	96.85 253	90.26 130	97.65 289	95.65 116	98.44 116	99.22 104

SixPastTwentyTwo		93.34 251	92.86 248	94.75 282	95.67 294	89.41 292	98.75 111	96.67 304	93.89 151	90.15 284	98.25 136	80.87 288	98.27 264	90.90 235	90.64 254	96.57 264

MVS_Test		97.28 80	97.00 76	98.13 97	98.33 136	95.97 116	98.74 115	98.07 213	94.27 137	98.44 50	98.07 146	92.48 86	99.26 140	96.43 92	98.19 126	99.16 110

UniMVSNet_NR-MVSNet		95.71 136	95.15 144	97.40 148	96.84 227	96.97 77	98.74 115	99.24 10	95.16 105	93.88 220	97.72 177	91.68 104	98.31 259	95.81 107	87.25 297	96.92 212

NR-MVSNet		94.98 182	94.16 194	97.44 144	96.53 241	97.22 71	98.74 115	98.95 33	94.96 115	89.25 291	97.69 178	89.32 138	98.18 267	94.59 142	87.40 294	96.92 212

MVSTER		96.06 122	95.72 120	97.08 163	98.23 141	95.93 123	98.73 118	98.27 168	94.86 119	95.07 166	98.09 145	88.21 180	98.54 217	96.59 85	93.46 223	96.79 230

ACMP	93.49 10	95.34 166	94.98 151	96.43 218	97.67 173	93.48 237	98.73 118	98.44 146	94.94 118	92.53 256	98.53 107	84.50 259	99.14 154	95.48 121	94.00 213	96.66 251
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS++		98.58 18	98.25 29	99.55 1	99.50 28	99.08 2	98.72 120	98.66 107	97.51 8	98.15 56	98.83 81	95.70 34	99.92 13	97.53 52	99.67 40	99.66 49

VPNet		94.99 180	94.19 193	97.40 148	97.16 210	96.57 93	98.71 121	98.97 29	95.67 76	94.84 171	98.24 137	80.36 294	98.67 207	96.46 90	87.32 295	96.96 209

MSLP-MVS++		98.56 21	98.57 5	98.55 71	99.26 64	96.80 84	98.71 121	99.05 23	97.28 21	98.84 28	99.28 25	96.47 10	99.40 131	98.52 14	99.70 38	99.47 78

ACMH+	92.99 14	94.30 223	93.77 220	95.88 242	97.81 168	92.04 258	98.71 121	98.37 157	93.99 146	90.60 281	98.47 113	80.86 289	99.05 167	92.75 193	92.40 238	96.55 267

DP-MVS		96.59 105	95.93 114	98.57 69	99.34 40	96.19 108	98.70 124	98.39 154	89.45 290	94.52 180	99.35 18	91.85 102	99.85 40	92.89 191	98.88 95	99.68 42

Fast-Effi-MVS+-dtu		95.87 128	95.85 116	95.91 240	97.74 171	91.74 264	98.69 125	98.15 193	95.56 81	94.92 169	97.68 181	88.98 149	98.79 201	93.19 177	97.78 140	97.20 201

v1141		94.75 197	94.11 200	96.67 190	96.27 268	94.86 174	98.69 125	98.12 198	92.43 218	94.31 197	96.94 242	88.78 164	98.48 228	92.63 196	88.85 278	96.67 248

divwei89l23v2f112		94.76 195	94.12 199	96.67 190	96.28 266	94.85 175	98.69 125	98.12 198	92.44 217	94.29 200	96.94 242	88.85 156	98.48 228	92.67 194	88.79 280	96.67 248

v1		94.75 197	94.11 200	96.69 184	96.27 268	94.87 173	98.69 125	98.12 198	92.43 218	94.32 196	96.94 242	88.71 168	98.54 217	92.66 195	88.84 279	96.67 248

tfpn200view9		95.32 168	94.62 173	97.43 145	98.94 94	94.98 166	98.68 129	96.93 291	95.33 96	96.55 133	96.53 265	84.23 265	99.56 115	88.11 287	96.29 174	97.76 178

VDD-MVS		95.82 131	95.23 141	97.61 133	98.84 108	93.98 224	98.68 129	97.40 261	95.02 112	97.95 71	99.34 19	74.37 322	99.78 74	98.64 4	96.80 155	99.08 119

thres400		95.38 161	94.62 173	97.65 127	98.94 94	94.98 166	98.68 129	96.93 291	95.33 96	96.55 133	96.53 265	84.23 265	99.56 115	88.11 287	96.29 174	98.40 158

pmmvs6		91.77 277	90.63 278	95.17 270	94.69 313	91.24 270	98.67 132	97.92 222	86.14 309	89.62 287	97.56 190	75.79 314	98.34 255	90.75 237	84.56 313	95.94 290

v2v482		94.69 201	94.03 203	96.65 192	96.17 273	94.79 194	98.67 132	98.08 212	92.72 204	94.00 217	97.16 212	87.69 199	98.45 235	92.91 188	88.87 276	96.72 238

DU-MVS		95.42 157	94.76 168	97.40 148	96.53 241	96.97 77	98.66 134	98.99 28	95.43 86	93.88 220	97.69 178	88.57 171	98.31 259	95.81 107	87.25 297	96.92 212

MAR-MVS		96.91 94	96.40 100	98.45 79	98.69 118	96.90 81	98.66 134	98.68 97	92.40 221	97.07 105	97.96 154	91.54 110	99.75 83	93.68 165	98.92 93	98.69 143
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 54	97.40 61	98.96 51	98.88 103	97.55 58	98.63 136	98.93 36	96.74 46	99.02 17	98.84 80	90.33 128	99.83 43	98.53 10	96.66 157	99.50 72

PVSNet_Blended_VisFu		97.70 57	97.46 58	98.44 80	99.27 62	95.91 130	98.63 136	99.16 17	94.48 133	97.67 87	98.88 77	92.80 83	99.91 22	97.11 62	99.12 88	99.50 72

PAPM_NR		97.46 67	97.11 71	98.50 75	99.50 28	96.41 100	98.63 136	98.60 114	95.18 104	97.06 106	98.06 147	94.26 69	99.57 113	93.80 163	98.87 97	99.52 67

Baseline_NR-MVSNet		94.35 221	93.81 216	95.96 238	96.20 271	94.05 223	98.61 139	96.67 304	91.44 244	93.85 222	97.60 186	88.57 171	98.14 268	94.39 146	86.93 300	95.68 296

v1144		94.59 210	93.92 210	96.60 199	96.21 270	94.78 196	98.59 140	98.14 195	91.86 236	94.21 206	97.02 233	87.97 187	98.41 248	91.72 220	89.57 262	96.61 258

AllTest		95.24 171	94.65 172	96.99 166	99.25 65	93.21 244	98.59 140	98.18 185	91.36 248	93.52 230	98.77 87	84.67 251	99.72 86	89.70 261	97.87 135	98.02 173

Fast-Effi-MVS+		96.28 118	95.70 124	98.03 105	98.29 138	95.97 116	98.58 142	98.25 173	91.74 237	95.29 165	97.23 209	91.03 119	99.15 152	92.90 189	97.96 132	98.97 127

Anonymous20231206		91.66 278	91.10 266	93.33 300	94.02 317	87.35 314	98.58 142	97.26 273	90.48 263	90.16 283	96.31 272	83.83 274	96.53 318	79.36 320	89.90 259	96.12 285

v144192		94.39 220	93.70 225	96.48 213	96.06 279	94.35 215	98.58 142	98.16 192	91.45 243	94.33 195	97.02 233	87.50 204	98.45 235	91.08 231	89.11 269	96.63 256

v148		94.29 224	93.76 222	95.91 240	96.10 277	92.93 248	98.58 142	97.97 220	92.59 208	93.47 233	96.95 240	88.53 174	98.32 257	92.56 198	87.06 299	96.49 274

COLMAP_ROB	93.27 12	95.33 167	94.87 161	96.71 181	99.29 56	93.24 243	98.58 142	98.11 203	89.92 278	93.57 228	99.10 48	86.37 220	99.79 69	90.78 236	98.10 129	97.09 202
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mvs-test1		96.60 103	96.68 91	96.37 221	97.89 164	91.81 260	98.56 147	98.10 208	96.57 52	96.52 137	97.94 156	90.81 120	99.45 130	95.72 111	98.01 130	97.86 177

FMVSNet3		94.97 183	94.26 188	97.11 161	98.18 147	96.62 90	98.56 147	98.26 172	93.67 170	94.09 212	97.10 219	84.25 264	98.01 276	92.08 207	92.14 239	96.70 242

MPTG		98.55 22	98.25 29	99.46 6	99.76 1	98.64 9	98.55 149	98.74 79	97.27 25	98.02 65	99.39 7	94.81 55	99.96 1	97.91 29	99.79 10	99.77 14

F-COLMAP		97.09 89	96.80 82	97.97 107	99.45 34	94.95 169	98.55 149	98.62 113	93.02 194	96.17 153	98.58 105	94.01 72	99.81 50	93.95 158	98.90 94	99.14 113

diffmvs		96.32 115	95.74 118	98.07 103	98.26 139	96.14 109	98.53 151	98.23 176	90.10 272	96.88 117	97.73 174	90.16 131	99.15 152	93.90 160	97.85 137	98.91 133

v1921920		94.20 228	93.47 239	96.40 220	95.98 282	94.08 222	98.52 152	98.15 193	91.33 251	94.25 203	97.20 211	86.41 219	98.42 241	90.04 254	89.39 267	96.69 247

EU-MVSNet		93.66 246	94.14 196	92.25 307	95.96 283	83.38 322	98.52 152	98.12 198	94.69 121	92.61 253	98.13 143	87.36 206	96.39 320	91.82 216	90.00 258	96.98 208

TAMVS		97.02 90	96.79 84	97.70 122	98.06 154	95.31 154	98.52 152	98.31 162	93.95 149	97.05 107	98.61 100	93.49 76	98.52 224	95.33 124	97.81 138	99.29 97

LTVRE_ROB	92.95 15	94.60 208	93.90 212	96.68 187	97.41 195	94.42 211	98.52 152	98.59 115	91.69 238	91.21 273	98.35 123	84.87 249	99.04 171	91.06 232	93.44 226	96.60 260
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		91.06 284	89.68 287	95.21 268	85.35 337	91.49 266	98.51 156	97.07 279	91.47 242	88.83 294	97.84 165	77.31 309	99.09 164	92.79 192	77.98 331	95.04 305

v1192		94.32 222	93.58 232	96.53 209	96.10 277	94.45 210	98.50 157	98.17 190	91.54 241	94.19 207	97.06 226	86.95 212	98.43 240	90.14 249	89.57 262	96.70 242

test_0402		91.32 280	90.27 282	94.48 288	96.60 238	91.12 271	98.50 157	97.22 275	86.10 310	88.30 296	96.98 237	77.65 307	97.99 278	78.13 324	92.94 234	94.34 319

DeepC-MVS_fast	96.70 1	98.55 22	98.34 21	99.18 32	99.25 65	98.04 40	98.50 157	98.78 71	97.72 4	98.92 27	99.28 25	95.27 45	99.82 48	97.55 50	99.77 18	99.69 36
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 3	98.56 6	99.45 8	99.32 46	98.87 6	98.47 160	98.81 61	97.72 4	98.76 34	99.16 42	97.05 3	99.78 74	98.06 25	99.66 43	99.69 36

NCCC		98.61 13	98.35 20	99.38 10	99.28 61	98.61 11	98.45 161	98.76 75	97.82 3	98.45 49	98.93 73	96.65 7	99.83 43	97.38 57	99.41 77	99.71 33

v1240		94.06 239	93.29 243	96.34 225	96.03 281	93.90 226	98.44 162	98.17 190	91.18 259	94.13 211	97.01 235	86.05 230	98.42 241	89.13 271	89.50 265	96.70 242

plane_prior						94.60 206	98.44 162		96.74 46						94.22 204

MP-MVS-pluss		98.31 39	97.92 42	99.49 4	99.72 11	98.88 5	98.43 164	98.78 71	94.10 140	97.69 86	99.42 5	95.25 46	99.92 13	98.09 24	99.80 9	99.67 47
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		95.69 138	95.33 136	96.76 179	96.16 276	94.63 201	98.43 164	98.39 154	96.64 50	95.02 168	98.78 85	85.15 245	99.05 167	95.21 131	94.20 205	96.60 260

MCST-MVS		98.65 9	98.37 17	99.48 5	99.60 23	98.87 6	98.41 166	98.68 97	97.04 38	98.52 45	98.80 84	96.78 5	99.83 43	97.93 28	99.61 49	99.74 26

Regformer-3		98.59 16	98.50 11	98.86 57	99.43 36	97.05 75	98.40 167	98.68 97	97.43 13	99.06 15	99.31 21	95.80 33	99.77 79	98.62 6	99.76 24	99.78 7

Regformer-4		98.64 10	98.53 7	98.99 47	99.43 36	97.37 64	98.40 167	98.79 69	97.46 12	99.09 14	99.31 21	95.86 32	99.80 57	98.64 4	99.76 24	99.79 4

Regformer-1		98.66 8	98.51 10	99.12 40	99.35 38	97.81 51	98.37 169	98.76 75	97.49 10	99.20 11	99.21 32	96.08 20	99.79 69	98.42 16	99.73 35	99.75 21

Regformer-2		98.69 7	98.52 8	99.19 28	99.35 38	98.01 42	98.37 169	98.81 61	97.48 11	99.21 10	99.21 32	96.13 17	99.80 57	98.40 18	99.73 35	99.75 21

CANet		98.05 43	97.76 45	98.90 55	98.73 113	97.27 67	98.35 171	98.78 71	97.37 19	97.72 84	98.96 69	91.53 111	99.92 13	98.79 3	99.65 44	99.51 70

DWT-MVSNet_test		94.82 192	94.36 185	96.20 231	97.35 197	90.79 274	98.34 172	96.57 307	92.91 199	95.33 164	96.44 270	82.00 281	99.12 156	94.52 144	95.78 195	98.70 142

test20.03		90.89 286	90.38 280	92.43 305	93.48 318	88.14 309	98.33 173	97.56 235	93.40 182	87.96 297	96.71 259	80.69 291	94.13 329	79.15 321	86.17 306	95.01 307

DP-MVS Recon		97.86 51	97.46 58	99.06 45	99.53 26	98.35 23	98.33 173	98.89 44	92.62 206	98.05 61	98.94 72	95.34 43	99.65 98	96.04 100	99.42 76	99.19 105

RPSCF		94.87 188	95.40 129	93.26 302	98.89 102	82.06 327	98.33 173	98.06 215	90.30 268	96.56 131	99.26 27	87.09 208	99.49 125	93.82 162	96.32 173	98.24 168

TAPA-MVS	93.98 7	95.35 165	94.56 176	97.74 118	99.13 80	94.83 186	98.33 173	98.64 112	86.62 306	96.29 151	98.61 100	94.00 73	99.29 139	80.00 318	99.41 77	99.09 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IterMVS-LS		95.46 154	95.21 142	96.22 230	98.12 150	93.72 233	98.32 177	98.13 196	93.71 163	94.26 202	97.31 205	92.24 91	98.10 270	94.63 139	90.12 256	96.84 226
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_anonymous		96.70 101	96.53 97	97.18 156	98.19 145	93.78 229	98.31 178	98.19 182	94.01 144	94.47 182	98.27 134	92.08 98	98.46 233	97.39 56	97.91 133	99.31 92

WTY-MVS		97.37 77	96.92 79	98.72 61	98.86 105	96.89 83	98.31 178	98.71 91	95.26 100	97.67 87	98.56 106	92.21 93	99.78 74	95.89 104	96.85 154	99.48 77

EI-MVSNet-Vis-set		98.47 28	98.39 15	98.69 62	99.46 33	96.49 97	98.30 180	98.69 94	97.21 28	98.84 28	99.36 16	95.41 40	99.78 74	98.62 6	99.65 44	99.80 3

DSMNet-mixed		92.52 262	92.58 253	92.33 306	94.15 315	82.65 325	98.30 180	94.26 336	89.08 295	92.65 252	95.73 289	85.01 247	95.76 323	86.24 300	97.76 141	98.59 150

EI-MVSNet-UG-set		98.41 30	98.34 21	98.61 67	99.45 34	96.32 104	98.28 182	98.68 97	97.17 31	98.74 35	99.37 12	95.25 46	99.79 69	98.57 8	99.54 65	99.73 28

OMC-MVS		97.55 66	97.34 62	98.20 92	99.33 43	95.92 128	98.28 182	98.59 115	95.52 83	97.97 70	99.10 48	93.28 79	99.49 125	95.09 132	98.88 95	99.19 105

PVSNet_BlendedMVS		96.73 100	96.60 93	97.12 160	99.25 65	95.35 152	98.26 184	99.26 8	94.28 136	97.94 72	97.46 193	92.74 84	99.81 50	96.88 74	93.32 228	96.20 283

BH-untuned		95.95 125	95.72 120	96.65 192	98.55 129	92.26 254	98.23 185	97.79 226	93.73 161	94.62 177	98.01 151	88.97 150	99.00 175	93.04 182	98.51 112	98.68 144

sss		97.39 75	96.98 77	98.61 67	98.60 126	96.61 92	98.22 186	98.93 36	93.97 148	98.01 67	98.48 112	91.98 100	99.85 40	96.45 91	98.15 127	99.39 87

WR-MVS		95.15 175	94.46 180	97.22 153	96.67 237	96.45 98	98.21 187	98.81 61	94.15 138	93.16 239	97.69 178	87.51 202	98.30 261	95.29 127	88.62 281	96.90 219

MVS_0304		97.70 57	97.25 65	99.07 43	98.90 96	97.83 49	98.20 188	98.74 79	97.51 8	98.03 64	99.06 56	86.12 224	99.93 9	99.02 1	99.64 46	99.44 85

pmmvs5		93.65 248	92.97 247	95.68 249	95.49 300	92.37 253	98.20 188	97.28 271	89.66 286	92.58 254	97.26 207	82.14 280	98.09 272	93.18 178	90.95 253	96.58 262

thres200		95.25 170	94.57 175	97.28 152	98.81 109	94.92 170	98.20 188	97.11 277	95.24 103	96.54 135	96.22 278	84.58 253	99.53 122	87.93 291	96.50 164	97.39 191

CDS-MVSNet		96.99 91	96.69 89	97.90 110	98.05 155	95.98 112	98.20 188	98.33 161	93.67 170	96.95 109	98.49 111	93.54 75	98.42 241	95.24 130	97.74 142	99.31 92
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1314		96.25 120	95.73 119	97.79 116	97.13 212	95.55 145	98.19 192	98.59 115	93.47 176	92.03 268	97.82 169	91.33 113	99.49 125	94.62 140	98.44 116	98.32 167

1121		97.37 77	96.77 87	99.16 35	99.34 40	97.99 45	98.19 192	98.68 97	90.14 271	98.01 67	98.97 65	94.80 57	99.87 35	93.36 172	99.46 73	99.61 57

MVS		94.67 205	93.54 234	98.08 101	96.88 225	96.56 94	98.19 192	98.50 137	78.05 332	92.69 251	98.02 149	91.07 118	99.63 103	90.09 250	98.36 120	98.04 172

BH-RMVSNet		95.92 127	95.32 137	97.69 123	98.32 137	94.64 200	98.19 192	97.45 256	94.56 128	96.03 156	98.61 100	85.02 246	99.12 156	90.68 238	99.06 89	99.30 95

1112_ss		96.63 102	96.00 113	98.50 75	98.56 127	96.37 101	98.18 196	98.10 208	92.92 198	94.84 171	98.43 115	92.14 95	99.58 112	94.35 148	96.51 163	99.56 66

EPNet_dtu		95.21 173	94.95 154	95.99 237	96.17 273	90.45 281	98.16 197	97.27 272	96.77 44	93.14 242	98.33 128	90.34 127	98.42 241	85.57 305	98.81 101	99.09 116
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HY-MVS	93.96 8	96.82 98	96.23 107	98.57 69	98.46 131	97.00 76	98.14 198	98.21 178	93.95 149	96.72 125	97.99 153	91.58 106	99.76 81	94.51 145	96.54 162	98.95 131

PLC	95.07 4	97.20 83	96.78 85	98.44 80	99.29 56	96.31 106	98.14 198	98.76 75	92.41 220	96.39 149	98.31 130	94.92 54	99.78 74	94.06 156	98.77 102	99.23 103
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EG-PatchMatch MVS		91.13 282	90.12 283	94.17 295	94.73 312	89.00 298	98.13 200	97.81 225	89.22 294	85.32 310	96.46 268	67.71 332	98.42 241	87.89 292	93.82 217	95.08 304

EI-MVSNet		95.96 124	95.83 117	96.36 222	97.93 161	93.70 234	98.12 201	98.27 168	93.70 165	95.07 166	99.02 58	92.23 92	98.54 217	94.68 138	93.46 223	96.84 226

CVMVSNet		95.43 156	96.04 111	93.57 298	97.93 161	83.62 321	98.12 201	98.59 115	95.68 75	96.56 131	99.02 58	87.51 202	97.51 294	93.56 169	97.44 146	99.60 60

TSAR-MVS + GP.		98.38 32	98.24 31	98.81 58	99.22 72	97.25 70	98.11 203	98.29 167	97.19 30	98.99 21	99.02 58	96.22 12	99.67 96	98.52 14	98.56 111	99.51 70

XVG-ACMP-BASELINE		94.54 213	94.14 196	95.75 248	96.55 240	91.65 265	98.11 203	98.44 146	94.96 115	94.22 205	97.90 159	79.18 300	99.11 160	94.05 157	93.85 216	96.48 275

PatchFormer-LS_test		95.47 153	95.27 140	96.08 236	97.59 179	90.66 277	98.10 205	97.34 265	93.98 147	96.08 154	96.15 280	87.65 200	99.12 156	95.27 128	95.24 198	98.44 157

DI_MVS_plusplus_test		94.74 199	93.62 229	98.09 100	95.34 303	95.92 128	98.09 206	97.34 265	94.66 125	85.89 305	95.91 285	80.49 293	99.38 134	96.66 83	98.22 124	98.97 127

CNLPA		97.45 70	97.03 75	98.73 60	99.05 82	97.44 63	98.07 207	98.53 128	95.32 98	96.80 123	98.53 107	93.32 78	99.72 86	94.31 150	99.31 83	99.02 122

CHOSEN 1792x2688		97.12 87	96.80 82	98.08 101	99.30 53	94.56 208	98.05 208	99.71 1	93.57 173	97.09 102	98.91 76	88.17 181	99.89 27	96.87 77	99.56 62	99.81 2

HQP-NCC					97.20 206		98.05 208		96.43 54	94.45 183

ACMP_Plane					97.20 206		98.05 208		96.43 54	94.45 183

HQP-MVS		95.72 134	95.40 129	96.69 184	97.20 206	94.25 219	98.05 208	98.46 142	96.43 54	94.45 183	97.73 174	86.75 214	98.96 179	95.30 125	94.18 206	96.86 225

MIMVSNet1		89.67 294	88.28 299	93.82 296	92.81 322	91.08 272	98.01 212	97.45 256	87.95 300	87.90 298	95.87 287	67.63 333	94.56 328	78.73 323	88.18 285	95.83 292

AdaColmap		97.15 86	96.70 88	98.48 77	99.16 77	96.69 89	98.01 212	98.89 44	94.44 135	96.83 119	98.68 94	90.69 123	99.76 81	94.36 147	99.29 84	98.98 126

FMVSNet5		91.81 276	90.92 270	94.49 287	97.21 205	92.09 256	98.00 214	97.55 239	89.31 293	90.86 278	95.61 294	74.48 320	95.32 325	85.57 305	89.70 260	96.07 287

CANet_DTU		96.96 92	96.55 95	98.21 91	98.17 149	96.07 111	97.98 215	98.21 178	97.24 27	97.13 101	98.93 73	86.88 213	99.91 22	95.00 133	99.37 81	98.66 146

Anonymous20231211		83.69 307	81.50 309	90.26 311	89.23 331	80.10 329	97.97 216	97.06 281	72.79 336	82.05 325	92.57 324	50.28 341	96.32 321	76.15 328	75.38 335	94.37 318

MVP-Stereo		94.28 226	93.92 210	95.35 266	94.95 308	92.60 252	97.97 216	97.65 232	91.61 239	90.68 280	97.09 221	86.32 221	98.42 241	89.70 261	99.34 82	95.02 306
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS_111021_LR		98.34 36	98.23 32	98.67 64	99.27 62	96.90 81	97.95 218	99.58 3	97.14 33	98.44 50	99.01 62	95.03 52	99.62 105	97.91 29	99.75 30	99.50 72

Test4		92.21 265	90.34 281	97.82 115	92.83 321	95.87 134	97.94 219	98.05 218	94.50 131	82.12 324	94.48 302	59.54 339	98.54 217	95.39 123	98.22 124	99.06 121

TEST9					99.31 48	98.50 13	97.92 220	98.73 84	92.63 205	97.74 82	98.68 94	96.20 13	99.80 57

train_agg		97.97 44	97.52 54	99.33 15	99.31 48	98.50 13	97.92 220	98.73 84	92.98 196	97.74 82	98.68 94	96.20 13	99.80 57	96.59 85	99.57 56	99.68 42

agg_prior3		97.87 50	97.42 60	99.23 27	99.29 56	98.23 29	97.92 220	98.72 86	92.38 222	97.59 93	98.64 99	96.09 19	99.79 69	96.59 85	99.57 56	99.68 42

test_normal		94.72 200	93.59 231	98.11 99	95.30 304	95.95 119	97.91 223	97.39 263	94.64 126	85.70 308	95.88 286	80.52 292	99.36 135	96.69 82	98.30 123	99.01 125

CDPH-MVS		97.94 47	97.49 56	99.28 20	99.47 32	98.44 15	97.91 223	98.67 104	92.57 209	98.77 33	98.85 79	95.93 28	99.72 86	95.56 118	99.69 39	99.68 42

MVS_111021_HR		98.47 28	98.34 21	98.88 56	99.22 72	97.32 65	97.91 223	99.58 3	97.20 29	98.33 54	99.00 63	95.99 25	99.64 100	98.05 26	99.76 24	99.69 36

PatchMatch-RL		96.59 105	96.03 112	98.27 88	99.31 48	96.51 96	97.91 223	99.06 21	93.72 162	96.92 114	98.06 147	88.50 176	99.65 98	91.77 219	99.00 91	98.66 146

OpenMVS_ROB	86.42 20	89.00 296	87.43 302	93.69 297	93.08 320	89.42 291	97.91 223	96.89 296	78.58 331	85.86 306	94.69 301	69.48 329	98.29 263	77.13 325	93.29 230	93.36 328

test_8					99.29 56	98.44 15	97.89 228	98.72 86	92.98 196	97.70 85	98.66 97	96.20 13	99.80 57

ab-mvs		96.42 111	95.71 123	98.55 71	98.63 123	96.75 87	97.88 229	98.74 79	93.84 154	96.54 135	98.18 140	85.34 243	99.75 83	95.93 103	96.35 171	99.15 111

jason		97.32 79	97.08 73	98.06 104	97.45 191	95.59 140	97.87 230	97.91 223	94.79 120	98.55 44	98.83 81	91.12 115	99.23 143	97.58 47	99.60 50	99.34 89
jason: jason.
xiu_mvs_v1_base_debu		97.60 61	97.56 51	97.72 119	98.35 132	95.98 112	97.86 231	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

xiu_mvs_v1_base		97.60 61	97.56 51	97.72 119	98.35 132	95.98 112	97.86 231	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

xiu_mvs_v1_base_debi		97.60 61	97.56 51	97.72 119	98.35 132	95.98 112	97.86 231	98.51 132	97.13 34	99.01 18	98.40 117	91.56 107	99.80 57	98.53 10	98.68 103	97.37 193

test_prior4						98.01 42	97.86 231

agg_prior1		97.95 46	97.51 55	99.28 20	99.30 53	98.38 18	97.81 235	98.72 86	93.16 190	97.57 94	98.66 97	96.14 16	99.81 50	96.63 84	99.56 62	99.66 49

test_prior3		98.22 42	97.90 43	99.19 28	99.31 48	98.22 31	97.80 236	98.84 54	96.12 63	97.89 76	98.69 92	95.96 26	99.70 91	96.89 71	99.60 50	99.65 51

test_prior2							97.80 236		96.12 63	97.89 76	98.69 92	95.96 26		96.89 71	99.60 50

XVG-OURS-SEG-HR		96.51 108	96.34 101	97.02 165	98.77 111	93.76 230	97.79 238	98.50 137	95.45 85	96.94 111	99.09 52	87.87 192	99.55 121	96.76 80	95.83 194	97.74 180

MS-PatchMatch		93.84 244	93.63 228	94.46 290	96.18 272	89.45 290	97.76 239	98.27 168	92.23 228	92.13 267	97.49 191	79.50 297	98.69 204	89.75 259	99.38 80	95.25 301

DELS-MVS		98.40 31	98.20 34	98.99 47	99.00 87	97.66 53	97.75 240	98.89 44	97.71 6	98.33 54	98.97 65	94.97 53	99.88 34	98.42 16	99.76 24	99.42 86
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 53	97.60 49	98.44 80	99.12 81	95.97 116	97.75 240	98.78 71	96.89 42	98.46 46	99.22 31	93.90 74	99.68 95	94.81 137	99.52 67	99.67 47

Test_1112_low_res		96.34 114	95.66 127	98.36 85	98.56 127	95.94 120	97.71 242	98.07 213	92.10 229	94.79 175	97.29 206	91.75 103	99.56 115	94.17 153	96.50 164	99.58 64

BH-w/o		95.38 161	95.08 147	96.26 229	98.34 135	91.79 261	97.70 243	97.43 258	92.87 201	94.24 204	97.22 210	88.66 169	98.84 195	91.55 223	97.70 143	98.16 170

testing_2		90.61 289	88.50 296	96.95 170	90.08 329	95.57 142	97.69 244	98.06 215	93.02 194	76.55 331	92.48 326	61.18 338	98.44 238	95.45 122	91.98 242	96.84 226

lupinMVS		97.44 71	97.22 68	98.12 98	98.07 152	95.76 136	97.68 245	97.76 227	94.50 131	98.79 31	98.61 100	92.34 87	99.30 137	97.58 47	99.59 53	99.31 92

原ACMM2							97.67 246

LF4IMVS		93.14 257	92.79 250	94.20 293	95.88 287	88.67 302	97.66 247	97.07 279	93.81 156	91.71 270	97.65 182	77.96 304	98.81 199	91.47 227	91.92 244	95.12 302

新几何2							97.64 248

MDA-MVSNet-bldmvs		89.97 292	88.35 298	94.83 280	95.21 305	91.34 267	97.64 248	97.51 244	88.36 299	71.17 337	96.13 281	79.22 299	96.63 317	83.65 310	86.27 305	96.52 270

pmmvs-eth3d		90.36 290	89.05 293	94.32 292	91.10 326	92.12 255	97.63 250	96.95 290	88.86 296	84.91 320	93.13 312	78.32 302	96.74 312	88.70 279	81.81 318	94.09 323

TR-MVS		94.94 186	94.20 192	97.17 157	97.75 170	94.14 221	97.59 251	97.02 283	92.28 227	95.75 160	97.64 184	83.88 272	98.96 179	89.77 257	96.15 184	98.40 158

无先验							97.58 252	98.72 86	91.38 247				99.87 35	93.36 172		99.60 60

旧先验2							97.57 253		91.30 253	98.67 37			99.80 57	95.70 115

CostFormer		94.95 184	94.73 169	95.60 251	97.28 200	89.06 296	97.53 254	96.89 296	89.66 286	96.82 121	96.72 258	86.05 230	98.95 183	95.53 119	96.13 185	98.79 138

tpmp4_e23		93.91 243	93.42 242	95.38 264	97.62 176	88.59 304	97.52 255	97.34 265	87.94 301	94.17 209	96.79 256	82.91 277	99.05 167	90.62 240	95.91 192	98.50 153

XVG-OURS		96.55 107	96.41 99	96.99 166	98.75 112	93.76 230	97.50 256	98.52 130	95.67 76	96.83 119	99.30 24	88.95 151	99.53 122	95.88 105	96.26 179	97.69 184

xiu_mvs_v2_base		97.66 60	97.70 47	97.56 136	98.61 125	95.46 147	97.44 257	98.46 142	97.15 32	98.65 39	98.15 141	94.33 67	99.80 57	97.84 36	98.66 107	97.41 189

tpm		94.13 235	93.80 217	95.12 271	96.50 243	87.91 311	97.44 257	95.89 317	92.62 206	96.37 150	96.30 273	84.13 268	98.30 261	93.24 175	91.66 248	99.14 113

DeepPCF-MVS	96.37 2	97.93 48	98.48 13	96.30 227	99.00 87	89.54 289	97.43 259	98.87 49	98.16 2	99.26 7	99.38 11	96.12 18	99.64 100	98.30 21	99.77 18	99.72 31

test222					99.23 71	97.17 73	97.40 260	98.66 107	88.68 297	98.05 61	98.96 69	94.14 70			99.53 66	99.61 57

pmmvs4		94.69 201	93.99 207	96.81 177	95.74 291	95.94 120	97.40 260	97.67 231	90.42 266	93.37 234	97.59 187	89.08 145	98.20 266	92.97 184	91.67 247	96.30 282

test0.0.03 1		94.08 237	93.51 237	95.80 245	95.53 299	92.89 249	97.38 262	95.97 314	95.11 107	92.51 258	96.66 260	87.71 196	96.94 302	87.03 296	93.67 218	97.57 186

HyFIR lowres test		96.90 95	96.49 98	98.14 95	99.33 43	95.56 143	97.38 262	99.65 2	92.34 223	97.61 91	98.20 139	89.29 139	99.10 163	96.97 65	97.60 145	99.77 14

Effi-MVS+		97.12 87	96.69 89	98.39 84	98.19 145	96.72 88	97.37 264	98.43 149	93.71 163	97.65 90	98.02 149	92.20 94	99.25 141	96.87 77	97.79 139	99.19 105

N_pmnet		87.12 303	87.77 300	85.17 323	95.46 301	61.92 346	97.37 264	70.66 354	85.83 313	88.73 295	96.04 283	85.33 244	97.76 288	80.02 317	90.48 255	95.84 291

PAPR		96.84 97	96.24 106	98.65 65	98.72 115	96.92 80	97.36 266	98.57 121	93.33 184	96.67 126	97.57 189	94.30 68	99.56 115	91.05 234	98.59 109	99.47 78

PMMVS		96.60 103	96.33 102	97.41 146	97.90 163	93.93 225	97.35 267	98.41 150	92.84 202	97.76 80	97.45 195	91.10 117	99.20 149	96.26 95	97.91 133	99.11 115

PS-MVSNAJ		97.73 55	97.77 44	97.62 128	98.68 119	95.58 141	97.34 268	98.51 132	97.29 20	98.66 38	97.88 161	94.51 61	99.90 25	97.87 33	99.17 87	97.39 191

Patchmatch-test1		95.32 168	94.97 153	96.35 223	97.67 173	91.29 269	97.33 269	97.60 233	94.68 122	96.92 114	96.95 240	83.97 270	98.50 227	91.33 229	98.32 122	99.25 101

testdata1							97.32 270		96.34 57

tpm2		94.19 229	93.76 222	95.46 256	97.23 203	89.04 297	97.31 271	96.85 299	87.08 305	96.21 152	96.79 256	83.75 275	98.74 203	92.43 203	96.23 181	98.59 150

PVSNet_Blended		97.38 76	97.12 70	98.14 95	99.25 65	95.35 152	97.28 272	99.26 8	93.13 191	97.94 72	98.21 138	92.74 84	99.81 50	96.88 74	99.40 79	99.27 99

CLD-MVS		95.62 141	95.34 134	96.46 217	97.52 185	93.75 232	97.27 273	98.46 142	95.53 82	94.42 191	98.00 152	86.21 222	98.97 176	96.25 96	94.37 200	96.66 251
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPMVS		94.99 180	94.48 178	96.52 210	97.22 204	91.75 263	97.23 274	91.66 343	94.11 139	97.28 98	96.81 255	85.70 236	98.84 195	93.04 182	97.28 148	98.97 127

YYNet1		90.70 288	89.39 289	94.62 285	94.79 311	90.65 278	97.20 275	97.46 254	87.54 303	72.54 335	95.74 288	86.51 217	96.66 316	86.00 302	86.76 304	96.54 268

MDA-MVSNet_test_wron		90.71 287	89.38 290	94.68 283	94.83 310	90.78 275	97.19 276	97.46 254	87.60 302	72.41 336	95.72 291	86.51 217	96.71 315	85.92 303	86.80 303	96.56 266

IterMVS		94.09 236	93.85 215	94.80 281	97.99 158	90.35 282	97.18 277	98.12 198	93.68 168	92.46 260	97.34 202	84.05 269	97.41 296	92.51 201	91.33 249	96.62 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
new-patchmatchnet		88.50 300	87.45 301	91.67 309	90.31 328	85.89 318	97.16 278	97.33 268	89.47 289	83.63 322	92.77 322	76.38 311	95.06 327	82.70 312	77.29 332	94.06 324

UnsupCasMVSNet_eth		90.99 285	89.92 286	94.19 294	94.08 316	89.83 285	97.13 279	98.67 104	93.69 166	85.83 307	96.19 279	75.15 316	96.74 312	89.14 270	79.41 325	96.00 288

IB-MVS	91.98 17	93.27 253	91.97 260	97.19 155	97.47 187	93.41 240	97.09 280	95.99 313	93.32 185	92.47 259	95.73 289	78.06 303	99.53 122	94.59 142	82.98 314	98.62 149
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
CMPMVS	66.06 21	89.70 293	89.67 288	89.78 312	93.19 319	76.56 332	97.00 281	98.35 159	80.97 328	81.57 326	97.75 173	74.75 319	98.61 210	89.85 256	93.63 220	94.17 321
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tpmrst		95.63 140	95.69 125	95.44 258	97.54 183	88.54 305	96.97 282	97.56 235	93.50 175	97.52 96	96.93 246	89.49 134	99.16 151	95.25 129	96.42 166	98.64 148

dp		94.15 234	93.90 212	94.90 276	97.31 199	86.82 317	96.97 282	97.19 276	91.22 258	96.02 157	96.61 264	85.51 239	99.02 174	90.00 255	94.30 201	98.85 134

PM-MVS		87.77 301	86.55 303	91.40 310	91.03 327	83.36 323	96.92 284	95.18 328	91.28 255	86.48 304	93.42 308	53.27 340	96.74 312	89.43 267	81.97 317	94.11 322

TinyColmap		92.31 264	91.53 263	94.65 284	96.92 221	89.75 286	96.92 284	96.68 303	90.45 265	89.62 287	97.85 164	76.06 313	98.81 199	86.74 297	92.51 237	95.41 300

test-LLR		95.10 177	94.87 161	95.80 245	96.77 229	89.70 287	96.91 286	95.21 326	95.11 107	94.83 173	95.72 291	87.71 196	98.97 176	93.06 180	98.50 113	98.72 140

TESTMET0.1,1		94.18 231	93.69 226	95.63 250	96.92 221	89.12 295	96.91 286	94.78 331	93.17 189	94.88 170	96.45 269	78.52 301	98.92 185	93.09 179	98.50 113	98.85 134

test-mter		94.08 237	93.51 237	95.80 245	96.77 229	89.70 287	96.91 286	95.21 326	92.89 200	94.83 173	95.72 291	77.69 305	98.97 176	93.06 180	98.50 113	98.72 140

USDC		93.33 252	92.71 251	95.21 268	96.83 228	90.83 273	96.91 286	97.50 247	93.84 154	90.72 279	98.14 142	77.69 305	98.82 198	89.51 265	93.21 232	95.97 289

MDTV_nov1_ep13_2view						84.26 320	96.89 290		90.97 261	97.90 75		89.89 133		93.91 159		99.18 109

tpmvs		94.60 208	94.36 185	95.33 267	97.46 188	88.60 303	96.88 291	97.68 230	91.29 254	93.80 224	96.42 271	88.58 170	99.24 142	91.06 232	96.04 191	98.17 169

MDTV_nov1_ep13			95.40 129		97.48 186	88.34 307	96.85 292	97.29 270	93.74 160	97.48 97	97.26 207	89.18 142	99.05 167	91.92 215	97.43 147

PatchmatchNet		95.71 136	95.52 128	96.29 228	97.58 180	90.72 276	96.84 293	97.52 241	94.06 142	97.08 103	96.96 239	89.24 141	98.90 189	92.03 211	98.37 119	99.26 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MSDG		95.93 126	95.30 139	97.83 113	98.90 96	95.36 150	96.83 294	98.37 157	91.32 252	94.43 190	98.73 91	90.27 129	99.60 106	90.05 253	98.82 100	98.52 152

GA-MVS		94.81 193	94.03 203	97.14 158	97.15 211	93.86 227	96.76 295	97.58 234	94.00 145	94.76 176	97.04 231	80.91 287	98.48 228	91.79 217	96.25 180	99.09 116

tpm cat1		93.36 249	92.80 249	95.07 273	97.58 180	87.97 310	96.76 295	97.86 224	82.17 326	93.53 229	96.04 283	86.13 223	99.13 155	89.24 269	95.87 193	98.10 171

test_post1							96.68 297				30.43 350	87.85 193	98.69 204	92.59 197

1111		84.94 306	84.30 307	86.86 318	87.59 333	75.10 335	96.63 298	96.43 309	82.53 323	80.75 328	92.91 320	68.94 330	93.79 330	68.24 336	84.66 312	91.70 330

.test1245		73.05 315	76.31 313	63.27 336	87.59 333	75.10 335	96.63 298	96.43 309	82.53 323	80.75 328	92.91 320	68.94 330	93.79 330	68.24 336	12.72 349	20.91 347

pmmvs3		86.67 304	84.86 306	92.11 308	88.16 332	87.19 316	96.63 298	94.75 332	79.88 330	87.22 300	92.75 323	66.56 334	95.20 326	81.24 316	76.56 334	93.96 325

testmvs		21.48 327	24.95 328	11.09 340	14.89 353	6.47 355	96.56 301	9.87 356	7.55 348	17.93 349	39.02 347	9.43 358	5.90 352	16.56 349	12.72 349	20.91 347

testus		88.91 297	89.08 292	88.40 315	91.39 324	76.05 333	96.56 301	96.48 308	89.38 292	89.39 290	95.17 297	70.94 327	93.56 332	77.04 326	95.41 197	95.61 297

test123		20.95 328	23.72 329	12.64 339	13.54 354	8.19 354	96.55 303	6.13 357	7.48 349	16.74 350	37.98 348	12.97 354	6.05 351	16.69 348	5.43 351	23.68 346

test1235678		86.26 305	85.81 304	87.62 317	86.97 335	75.00 337	96.55 303	96.32 311	86.08 311	81.32 327	92.98 318	73.10 326	92.05 337	71.64 333	87.32 295	95.81 293

GG-mvs-BLEND				96.59 200	96.34 256	94.98 166	96.51 305	88.58 347		93.10 244	94.34 305	80.34 295	98.05 274	89.53 264	96.99 152	96.74 235

new_pmnet		90.06 291	89.00 294	93.22 303	94.18 314	88.32 308	96.42 306	96.89 296	86.19 308	85.67 309	93.62 307	77.18 310	97.10 300	81.61 315	89.29 268	94.23 320

PVSNet	91.96 18	96.35 113	96.15 108	96.96 169	99.17 76	92.05 257	96.08 307	98.68 97	93.69 166	97.75 81	97.80 171	88.86 154	99.69 94	94.26 152	99.01 90	99.15 111

ADS-MVSNet2		94.58 211	94.40 184	95.11 272	98.00 156	88.74 300	96.04 308	97.30 269	90.15 269	96.47 146	96.64 262	87.89 190	97.56 293	90.08 251	97.06 150	99.02 122

ADS-MVSNet		95.00 179	94.45 182	96.63 195	98.00 156	91.91 259	96.04 308	97.74 229	90.15 269	96.47 146	96.64 262	87.89 190	98.96 179	90.08 251	97.06 150	99.02 122

PAPM		94.95 184	94.00 205	97.78 117	97.04 215	95.65 139	96.03 310	98.25 173	91.23 257	94.19 207	97.80 171	91.27 114	98.86 194	82.61 313	97.61 144	98.84 136

cascas		94.63 207	93.86 214	96.93 172	96.91 223	94.27 218	96.00 311	98.51 132	85.55 314	94.54 179	96.23 276	84.20 267	98.87 192	95.80 109	96.98 153	97.66 185

testmv		78.74 309	77.35 310	82.89 326	78.16 346	69.30 343	95.87 312	94.65 333	81.11 327	70.98 338	87.11 334	46.31 342	90.42 340	65.28 339	76.72 333	88.95 333

gg-mvs-nofinetune		92.21 265	90.58 279	97.13 159	96.75 232	95.09 160	95.85 313	89.40 346	85.43 315	94.50 181	81.98 337	80.80 290	98.40 254	92.16 205	98.33 121	97.88 176

FPMVS		77.62 313	77.14 311	79.05 328	79.25 343	60.97 347	95.79 314	95.94 315	65.96 337	67.93 339	94.40 303	37.73 347	88.88 342	68.83 335	88.46 282	87.29 334

CHOSEN 280x420		97.18 84	97.18 69	97.20 154	98.81 109	93.27 241	95.78 315	99.15 18	95.25 101	96.79 124	98.11 144	92.29 89	99.07 166	98.56 9	99.85 2	99.25 101

MIMVSNet		93.26 254	92.21 258	96.41 219	97.73 172	93.13 246	95.65 316	97.03 282	91.27 256	94.04 215	96.06 282	75.33 315	97.19 299	86.56 298	96.23 181	98.92 132

PCF-MVS	93.45 11	94.68 204	93.43 240	98.42 83	98.62 124	96.77 86	95.48 317	98.20 181	84.63 319	93.34 235	98.32 129	88.55 173	99.81 50	84.80 309	98.96 92	98.68 144
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test12356		83.47 308	83.37 308	83.78 324	84.43 338	70.09 342	95.12 318	95.60 323	82.98 321	78.89 330	92.43 327	64.99 335	91.41 339	70.36 334	85.55 311	89.82 332

test2356		88.68 299	88.61 295	88.87 314	89.90 330	78.23 330	95.11 319	96.66 306	88.66 298	89.06 292	94.33 306	73.14 325	92.56 336	75.56 329	95.11 199	95.81 293

JIA-IIPM		93.35 250	92.49 254	95.92 239	96.48 245	90.65 278	95.01 320	96.96 289	85.93 312	96.08 154	87.33 333	87.70 198	98.78 202	91.35 228	95.58 196	98.34 165

CR-MVSNet		94.76 195	94.15 195	96.59 200	97.00 216	93.43 238	94.96 321	97.56 235	92.46 210	96.93 112	96.24 274	88.15 182	97.88 286	87.38 293	96.65 158	98.46 155

RPMNet		92.52 262	91.17 265	96.59 200	97.00 216	93.43 238	94.96 321	97.26 273	82.27 325	96.93 112	92.12 329	86.98 211	97.88 286	76.32 327	96.65 158	98.46 155

UnsupCasMVSNet_bld		87.17 302	85.12 305	93.31 301	91.94 323	88.77 299	94.92 323	98.30 165	84.30 320	82.30 323	90.04 330	63.96 337	97.25 298	85.85 304	74.47 337	93.93 326

PVSNet_0	88.72 19	91.28 281	90.03 284	95.00 274	97.99 158	87.29 315	94.84 324	98.50 137	92.06 230	89.86 285	95.19 295	79.81 296	99.39 133	92.27 204	69.79 338	98.33 166

Patchmatch-test		94.42 218	93.68 227	96.63 195	97.60 178	91.76 262	94.83 325	97.49 253	89.45 290	94.14 210	97.10 219	88.99 146	98.83 197	85.37 308	98.13 128	99.29 97

no-one		74.41 314	70.76 316	85.35 322	79.88 342	76.83 331	94.68 326	94.22 337	80.33 329	63.81 340	79.73 340	35.45 349	93.36 333	71.78 332	36.99 346	85.86 337

Patchmtry		93.22 255	92.35 256	95.84 243	96.77 229	93.09 247	94.66 327	97.56 235	87.37 304	92.90 247	96.24 274	88.15 182	97.90 282	87.37 294	90.10 257	96.53 269

PatchT		93.06 258	91.97 260	96.35 223	96.69 235	92.67 250	94.48 328	97.08 278	86.62 306	97.08 103	92.23 328	87.94 188	97.90 282	78.89 322	96.69 156	98.49 154

LCM-MVSNet		78.70 310	76.24 314	86.08 320	77.26 347	71.99 340	94.34 329	96.72 301	61.62 340	76.53 332	89.33 331	33.91 350	92.78 335	81.85 314	74.60 336	93.46 327

PMMVS2		77.95 312	75.44 315	85.46 321	82.54 339	74.95 338	94.23 330	93.08 341	72.80 335	74.68 333	87.38 332	36.36 348	91.56 338	73.95 331	63.94 339	89.87 331

MVS-HIRNet		89.46 295	88.40 297	92.64 304	97.58 180	82.15 326	94.16 331	93.05 342	75.73 334	90.90 277	82.52 336	79.42 298	98.33 256	83.53 311	98.68 103	97.43 188

LP		91.12 283	89.99 285	94.53 286	96.35 255	88.70 301	93.86 332	97.35 264	84.88 317	90.98 276	94.77 300	84.40 260	97.43 295	75.41 330	91.89 245	97.47 187

Patchmatch-RL test		91.49 279	90.85 271	93.41 299	91.37 325	84.40 319	92.81 333	95.93 316	91.87 235	87.25 299	94.87 299	88.99 146	96.53 318	92.54 200	82.00 316	99.30 95

ambc				89.49 313	86.66 336	75.78 334	92.66 334	96.72 301		86.55 303	92.50 325	46.01 343	97.90 282	90.32 247	82.09 315	94.80 308

EMVS		64.07 321	63.26 322	66.53 335	81.73 341	58.81 351	91.85 335	84.75 351	51.93 345	59.09 343	75.13 343	43.32 345	79.09 348	42.03 346	39.47 344	61.69 344

E-PMN		64.94 320	64.25 320	67.02 334	82.28 340	59.36 350	91.83 336	85.63 350	52.69 343	60.22 342	77.28 342	41.06 346	80.12 347	46.15 345	41.14 343	61.57 345

ANet_high		69.08 316	65.37 318	80.22 327	65.99 350	71.96 341	90.91 337	90.09 345	82.62 322	49.93 346	78.39 341	29.36 351	81.75 345	62.49 342	38.52 345	86.95 336

PNet_i23d		67.70 318	65.07 319	75.60 330	78.61 344	59.61 349	89.14 338	88.24 348	61.83 339	52.37 344	80.89 338	18.91 352	84.91 344	62.70 341	52.93 341	82.28 339

tmp_tt		68.90 317	66.97 317	74.68 332	50.78 352	59.95 348	87.13 339	83.47 352	38.80 346	62.21 341	96.23 276	64.70 336	76.91 349	88.91 276	30.49 347	87.19 335

wuykxyi23d		63.73 322	58.86 324	78.35 329	67.62 349	67.90 344	86.56 340	87.81 349	58.26 341	42.49 348	70.28 345	11.55 355	85.05 343	63.66 340	41.50 342	82.11 340

MVE	62.14 22	63.28 323	59.38 323	74.99 331	74.33 348	65.47 345	85.55 341	80.50 353	52.02 344	51.10 345	75.00 344	10.91 357	80.50 346	51.60 344	53.40 340	78.99 341
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	61.03 23	65.95 319	63.57 321	73.09 333	57.90 351	51.22 352	85.05 342	93.93 340	54.45 342	44.32 347	83.57 335	13.22 353	89.15 341	58.68 343	81.00 320	78.91 342
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testpf		88.74 298	89.09 291	87.69 316	95.78 290	83.16 324	84.05 343	94.13 339	85.22 316	90.30 282	94.39 304	74.92 318	95.80 322	89.77 257	93.28 231	84.10 338

Gipuma		78.40 311	76.75 312	83.38 325	95.54 298	80.43 328	79.42 344	97.40 261	64.67 338	73.46 334	80.82 339	45.65 344	93.14 334	66.32 338	87.43 293	76.56 343
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuyk23d		30.17 325	30.18 327	30.16 338	78.61 344	43.29 353	66.79 345	14.21 355	17.31 347	14.82 351	11.93 351	11.55 355	41.43 350	37.08 347	19.30 348	5.76 349

cdsmvs_eth3d_5k		23.98 326	31.98 326	0.00 341	0.00 355	0.00 356	0.00 346	98.59 115	0.00 350	0.00 352	98.61 100	90.60 124	0.00 353	0.00 350	0.00 352	0.00 350

pcd_1.5k_mvsjas		7.88 330	10.50 331	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	94.51 61	0.00 353	0.00 350	0.00 352	0.00 350

pcd1.5k->3k		39.42 324	41.78 325	32.35 337	96.17 273	0.00 356	0.00 346	98.54 125	0.00 350	0.00 352	0.00 352	87.78 195	0.00 353	0.00 350	93.56 222	97.06 203

sosnet-low-res		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

sosnet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uncertanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

Regformer		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

ab-mvs-re		8.20 329	10.94 330	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	98.43 115	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

uanet		0.00 331	0.00 332	0.00 341	0.00 355	0.00 356	0.00 346	0.00 358	0.00 350	0.00 352	0.00 352	0.00 359	0.00 353	0.00 350	0.00 352	0.00 350

test_part2					99.63 21	99.18 1				99.27 6

test_part1								98.84 54				97.38 2			99.78 14	99.76 20

test_all								98.84 54

sam_mvs1												89.45 135

sam_mvs												88.99 146

semantic-postprocess				94.85 278	97.98 160	90.56 280		98.11 203	93.75 158	92.58 254	97.48 192	83.91 271	97.41 296	92.48 202	91.30 250	96.58 262

MTGPA								98.74 79

test_post											31.83 349	88.83 158	98.91 186

patchmatchnet-post											95.10 298	89.42 136	98.89 190

MTMP								94.14 338

gm-plane-assit					95.88 287	87.47 313			89.74 284		96.94 242		99.19 150	93.32 174

test9_res														96.39 94	99.57 56	99.69 36

agg_prior2														95.87 106	99.57 56	99.68 42

agg_prior					99.30 53	98.38 18		98.72 86		97.57 94			99.81 50

TestCases				96.99 166	99.25 65	93.21 244		98.18 185	91.36 248	93.52 230	98.77 87	84.67 251	99.72 86	89.70 261	97.87 135	98.02 173

test_prior				99.19 28	99.31 48	98.22 31		98.84 54					99.70 91			99.65 51

新几何1				99.16 35	99.34 40	98.01 42		98.69 94	90.06 273	98.13 57	98.95 71	94.60 59	99.89 27	91.97 213	99.47 70	99.59 62

旧先验1					99.29 56	97.48 60		98.70 93			99.09 52	95.56 36			99.47 70	99.61 57

原ACMM1				98.65 65	99.32 46	96.62 90		98.67 104	93.27 188	97.81 78	98.97 65	95.18 48	99.83 43	93.84 161	99.46 73	99.50 72

testdata2													99.89 27	91.65 222

segment_acmp												96.85 4

testdata				98.26 89	99.20 75	95.36 150		98.68 97	91.89 233	98.60 42	99.10 48	94.44 66	99.82 48	94.27 151	99.44 75	99.58 64

test12				99.18 32	99.16 77	98.19 33		98.53 128		98.07 60		95.13 50	99.72 86		99.56 62	99.63 56

plane_prior7					97.42 192	94.63 201

plane_prior6					97.35 197	94.61 204						87.09 208

plane_prior5								98.56 122					99.03 172	96.07 97	94.27 202	96.92 212

plane_prior4											98.28 131

plane_prior3						94.61 204			97.02 39	95.34 162

plane_prior1					97.37 196

n2								0.00 358

nn								0.00 358

door-mid								94.37 335

lessismore_v0				94.45 291	94.93 309	88.44 306		91.03 344		86.77 302	97.64 184	76.23 312	98.42 241	90.31 248	85.64 310	96.51 272

LGP-MVS_train				96.47 214	97.46 188	93.54 235		98.54 125	94.67 123	94.36 193	98.77 87	85.39 240	99.11 160	95.71 113	94.15 208	96.76 233

test11								98.66 107

door								94.64 334

HQP5-MVS						94.25 219

BP-MVS														95.30 125

HQP4-MVS										94.45 183			98.96 179			96.87 223

HQP3-MVS								98.46 142							94.18 206

HQP2-MVS												86.75 214

NP-MVS					97.28 200	94.51 209					97.73 174

ACMMP++_ref															92.97 233

ACMMP++															93.61 221

Test By Simon												94.64 58

ITE_SJBPF				95.44 258	97.42 192	91.32 268		97.50 247	95.09 110	93.59 226	98.35 123	81.70 283	98.88 191	89.71 260	93.39 227	96.12 285

DeepMVS_CX				86.78 319	97.09 214	72.30 339		95.17 329	75.92 333	84.34 321	95.19 295	70.58 328	95.35 324	79.98 319	89.04 271	92.68 329

High-resolution multi-view benchmark