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 35	98.10 37	99.13 39	99.74 7	97.82 51	99.53 1	98.80 68	94.63 130	98.61 42	98.97 67	95.13 51	99.77 81	97.65 44	99.83 7	99.79 4

MVSFormer		97.57 65	97.49 57	97.84 113	98.07 156	95.76 139	99.47 2	98.40 152	94.98 116	98.79 32	98.83 83	92.34 88	98.41 253	96.91 69	99.59 54	99.34 90

test_djsdf		96.00 124	95.69 126	96.93 176	95.72 297	95.49 149	99.47 2	98.40 152	94.98 116	94.58 182	97.86 164	89.16 144	98.41 253	96.91 69	94.12 214	96.88 228

HPM-MVS_fast		98.38 33	98.13 36	99.12 41	99.75 3	97.86 48	99.44 4	98.82 58	94.46 137	98.94 23	99.20 37	95.16 50	99.74 87	97.58 47	99.85 2	99.77 14

nrg030		96.28 119	95.72 121	97.96 109	96.90 228	98.15 37	99.39 5	98.31 162	95.47 86	94.42 195	98.35 125	92.09 98	98.69 209	97.50 53	89.05 274	97.04 211

APDe-MVS		99.02 1	98.84 1	99.55 2	99.57 25	98.96 4	99.39 5	98.93 36	97.38 17	99.41 3	99.54 1	96.66 7	99.84 44	98.86 2	99.85 2	99.87 1

3Dnovator+	94.38 6	97.43 73	96.78 86	99.38 11	97.83 171	98.52 13	99.37 7	98.71 91	97.09 37	92.99 250	99.13 46	89.36 138	99.89 29	96.97 65	99.57 57	99.71 34

FIs		96.51 109	96.12 110	97.67 128	97.13 216	97.54 60	99.36 8	99.22 14	95.89 71	94.03 220	98.35 125	91.98 101	98.44 243	96.40 93	92.76 239	97.01 212

FC-MVSNet-test		96.42 112	96.05 111	97.53 140	96.95 223	97.27 68	99.36 8	99.23 12	95.83 73	93.93 222	98.37 123	92.00 100	98.32 262	96.02 103	92.72 240	97.00 213

3Dnovator	94.51 5	97.46 68	96.93 79	99.07 44	97.78 173	97.64 55	99.35 10	99.06 21	97.02 39	93.75 229	99.16 44	89.25 141	99.92 15	97.22 59	99.75 31	99.64 55

canonicalmvs		97.67 60	97.23 68	98.98 50	98.70 120	98.38 19	99.34 11	98.39 154	96.76 45	97.67 88	97.40 199	92.26 91	99.49 128	98.28 22	96.28 180	99.08 122

CP-MVS		98.57 21	98.36 19	99.19 29	99.66 19	97.86 48	99.34 11	98.87 49	95.96 70	98.60 43	99.13 46	96.05 24	99.94 3	97.77 39	99.86 1	99.77 14

EPP-MVSNet		97.46 68	97.28 65	97.99 107	98.64 126	95.38 152	99.33 13	98.31 162	93.61 175	97.19 101	99.07 57	94.05 72	99.23 146	96.89 71	98.43 119	99.37 89

XVS		98.70 5	98.49 12	99.34 14	99.70 15	98.35 24	99.29 14	98.88 47	97.40 14	98.46 47	99.20 37	95.90 31	99.89 29	97.85 34	99.74 34	99.78 7

X-MVStestdata		94.06 243	92.30 261	99.34 14	99.70 15	98.35 24	99.29 14	98.88 47	97.40 14	98.46 47	43.50 351	95.90 31	99.89 29	97.85 34	99.74 34	99.78 7

mPP-MVS		98.51 27	98.26 29	99.25 25	99.75 3	98.04 41	99.28 16	98.81 61	96.24 60	98.35 54	99.23 31	95.46 40	99.94 3	97.42 55	99.81 8	99.77 14

HSP-MVS		98.70 5	98.52 8	99.24 26	99.75 3	98.23 30	99.26 17	98.58 120	97.52 7	99.41 3	98.78 87	96.00 25	99.79 71	97.79 38	99.59 54	99.69 37

v7n		94.19 233	93.43 244	96.47 218	95.90 289	94.38 218	99.26 17	98.34 160	91.99 236	92.76 254	97.13 222	88.31 179	98.52 229	89.48 270	87.70 295	96.52 276

v748		93.75 249	93.06 249	95.82 248	95.73 296	92.64 255	99.25 19	98.24 175	91.60 245	92.22 269	96.52 272	87.60 202	98.46 238	90.64 241	85.72 313	96.36 285

tfpn1000		95.72 135	95.11 146	97.58 137	99.00 88	95.73 141	99.24 20	95.49 327	94.08 144	96.87 119	97.45 197	85.81 237	99.30 140	91.78 220	96.22 185	97.71 189

WR-MVS_H		95.05 182	94.46 184	96.81 181	96.86 230	95.82 138	99.24 20	99.24 10	93.87 156	92.53 260	96.84 259	90.37 127	98.24 270	93.24 177	87.93 291	96.38 284

HFP-MVS		98.63 13	98.40 14	99.32 17	99.72 11	98.29 27	99.23 22	98.96 31	96.10 67	98.94 23	99.17 41	96.06 22	99.92 15	97.62 45	99.78 14	99.75 22

region2R		98.61 14	98.38 17	99.29 19	99.74 7	98.16 36	99.23 22	98.93 36	96.15 62	98.94 23	99.17 41	95.91 30	99.94 3	97.55 50	99.79 10	99.78 7

v52		94.18 235	93.52 239	96.13 238	95.95 288	94.29 221	99.23 22	98.21 178	91.42 250	92.84 252	96.89 253	87.85 194	98.53 228	91.51 227	87.81 292	95.57 305

V4		94.18 235	93.52 239	96.13 238	95.89 290	94.31 220	99.23 22	98.22 177	91.42 250	92.82 253	96.89 253	87.93 190	98.52 229	91.51 227	87.81 292	95.58 304

ACMMPR		98.59 17	98.36 19	99.29 19	99.74 7	98.15 37	99.23 22	98.95 33	96.10 67	98.93 27	99.19 40	95.70 35	99.94 3	97.62 45	99.79 10	99.78 7

QAPM		96.29 117	95.40 130	98.96 52	97.85 170	97.60 58	99.23 22	98.93 36	89.76 287	93.11 247	99.02 60	89.11 145	99.93 9	91.99 214	99.62 49	99.34 90

MP-MVS		98.33 39	98.01 40	99.28 21	99.75 3	98.18 35	99.22 28	98.79 69	96.13 64	97.92 75	99.23 31	94.54 61	99.94 3	96.74 81	99.78 14	99.73 29

Vis-MVSNet		97.42 74	97.11 72	98.34 87	98.66 124	96.23 108	99.22 28	99.00 26	96.63 51	98.04 64	99.21 34	88.05 187	99.35 139	96.01 104	99.21 86	99.45 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CSCG		97.85 53	97.74 47	98.20 93	99.67 18	95.16 160	99.22 28	99.32 7	93.04 198	97.02 109	98.92 77	95.36 43	99.91 24	97.43 54	99.64 47	99.52 68

OpenMVS	93.04 13	95.83 131	95.00 150	98.32 88	97.18 213	97.32 66	99.21 31	98.97 29	89.96 280	91.14 278	99.05 59	86.64 217	99.92 15	93.38 173	99.47 71	97.73 187

DTE-MVSNet		93.98 245	93.26 248	96.14 237	96.06 283	94.39 217	99.20 32	98.86 52	93.06 197	91.78 273	97.81 172	85.87 236	97.58 297	90.53 243	86.17 310	96.46 282

Vis-MVSNet (Re-imp)		96.87 97	96.55 96	97.83 114	98.73 117	95.46 150	99.20 32	98.30 165	94.96 118	96.60 132	98.87 80	90.05 133	98.59 218	93.67 168	98.60 109	99.46 83

IS-MVSNet		97.22 83	96.88 81	98.25 91	98.85 111	96.36 103	99.19 34	97.97 220	95.39 90	97.23 100	98.99 66	91.11 117	98.93 189	94.60 143	98.59 110	99.47 79

conf0.01		95.56 148	94.84 164	97.72 120	98.90 98	95.93 124	99.17 35	95.70 319	93.42 181	96.50 144	97.16 214	86.12 225	99.22 148	90.51 244	96.06 189	98.02 176

conf0.002		95.56 148	94.84 164	97.72 120	98.90 98	95.93 124	99.17 35	95.70 319	93.42 181	96.50 144	97.16 214	86.12 225	99.22 148	90.51 244	96.06 189	98.02 176

thresconf0.02		95.50 151	94.84 164	97.51 141	98.90 98	95.93 124	99.17 35	95.70 319	93.42 181	96.50 144	97.16 214	86.12 225	99.22 148	90.51 244	96.06 189	97.37 199

tfpn_n400		95.50 151	94.84 164	97.51 141	98.90 98	95.93 124	99.17 35	95.70 319	93.42 181	96.50 144	97.16 214	86.12 225	99.22 148	90.51 244	96.06 189	97.37 199

tfpnconf		95.50 151	94.84 164	97.51 141	98.90 98	95.93 124	99.17 35	95.70 319	93.42 181	96.50 144	97.16 214	86.12 225	99.22 148	90.51 244	96.06 189	97.37 199

tfpnview11		95.50 151	94.84 164	97.51 141	98.90 98	95.93 124	99.17 35	95.70 319	93.42 181	96.50 144	97.16 214	86.12 225	99.22 148	90.51 244	96.06 189	97.37 199

PEN-MVS		94.42 222	93.73 228	96.49 216	96.28 270	94.84 188	99.17 35	99.00 26	93.51 177	92.23 268	97.83 170	86.10 232	97.90 287	92.55 201	86.92 305	96.74 241

PS-MVSNAJss		96.43 111	96.26 106	96.92 178	95.84 293	95.08 164	99.16 42	98.50 137	95.87 72	93.84 227	98.34 129	94.51 62	98.61 215	96.88 74	93.45 229	97.06 209

APD-MVS_3200maxsize		98.53 26	98.33 25	99.15 38	99.50 29	97.92 47	99.15 43	98.81 61	96.24 60	99.20 12	99.37 12	95.30 45	99.80 59	97.73 41	99.67 41	99.72 32

TSAR-MVS + MP.		98.78 3	98.62 4	99.24 26	99.69 17	98.28 29	99.14 44	98.66 107	96.84 43	99.56 2	99.31 21	96.34 12	99.70 93	98.32 20	99.73 36	99.73 29

anonymousdsp		95.42 161	94.91 160	96.94 175	95.10 310	95.90 134	99.14 44	98.41 150	93.75 161	93.16 243	97.46 195	87.50 205	98.41 253	95.63 119	94.03 216	96.50 279

jajsoiax		95.45 158	95.03 149	96.73 184	95.42 306	94.63 205	99.14 44	98.52 130	95.74 75	93.22 241	98.36 124	83.87 277	98.65 213	96.95 68	94.04 215	96.91 223

PS-CasMVS		94.67 209	93.99 211	96.71 185	96.68 240	95.26 158	99.13 47	99.03 24	93.68 171	92.33 266	97.95 157	85.35 245	98.10 275	93.59 170	88.16 290	96.79 236

abl_6		98.30 41	98.03 39	99.13 39	99.56 26	97.76 53	99.13 47	98.82 58	96.14 63	99.26 8	99.37 12	93.33 78	99.93 9	96.96 67	99.67 41	99.69 37

CPTT-MVS		97.72 57	97.32 64	98.92 54	99.64 20	97.10 75	99.12 49	98.81 61	92.34 228	98.09 60	99.08 56	93.01 82	99.92 15	96.06 101	99.77 19	99.75 22

CP-MVSNet		94.94 190	94.30 191	96.83 180	96.72 238	95.56 146	99.11 50	98.95 33	93.89 154	92.42 265	97.90 161	87.19 208	98.12 274	94.32 151	88.21 288	96.82 235

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 13	99.22 73	98.43 18	99.10 51	98.87 49	97.38 17	99.35 5	99.40 6	97.78 1	99.87 37	97.77 39	99.85 2	99.78 7
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view600		95.60 144	94.93 156	97.62 131	99.05 83	94.85 179	99.09 52	97.01 285	95.36 95	96.52 139	97.37 200	84.55 257	99.59 109	89.07 276	96.39 168	98.40 161

view800		95.60 144	94.93 156	97.62 131	99.05 83	94.85 179	99.09 52	97.01 285	95.36 95	96.52 139	97.37 200	84.55 257	99.59 109	89.07 276	96.39 168	98.40 161

conf0.05thres1000		95.60 144	94.93 156	97.62 131	99.05 83	94.85 179	99.09 52	97.01 285	95.36 95	96.52 139	97.37 200	84.55 257	99.59 109	89.07 276	96.39 168	98.40 161

tfpn		95.60 144	94.93 156	97.62 131	99.05 83	94.85 179	99.09 52	97.01 285	95.36 95	96.52 139	97.37 200	84.55 257	99.59 109	89.07 276	96.39 168	98.40 161

K. test v3		92.55 265	91.91 266	94.48 292	95.64 299	89.24 297	99.07 56	94.88 333	94.04 146	86.78 305	97.59 189	77.64 312	97.64 295	92.08 209	89.43 270	96.57 270

tfpn_ndepth		95.53 150	94.90 161	97.39 155	98.96 95	95.88 136	99.05 57	95.27 328	93.80 160	96.95 110	96.93 250	85.53 241	99.40 134	91.54 226	96.10 188	96.89 226

v8		94.47 220	93.77 224	96.57 208	96.36 257	94.83 190	99.05 57	98.19 182	91.92 237	93.16 243	96.97 242	88.82 160	98.48 233	91.69 223	87.79 294	96.39 283

PHI-MVS		98.34 37	98.06 38	99.18 33	99.15 80	98.12 39	99.04 59	99.09 19	93.32 190	98.83 31	99.10 50	96.54 10	99.83 45	97.70 43	99.76 25	99.59 63

TranMVSNet+NR-MVSNet		95.14 180	94.48 182	97.11 165	96.45 250	96.36 103	99.03 60	99.03 24	95.04 114	93.58 231	97.93 159	88.27 180	98.03 280	94.13 156	86.90 306	96.95 217

ACMMP		98.23 42	97.95 42	99.09 43	99.74 7	97.62 57	99.03 60	99.41 6	95.98 69	97.60 93	99.36 16	94.45 66	99.93 9	97.14 61	98.85 99	99.70 36
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		95.41 163	95.00 150	96.65 196	95.58 301	94.42 215	99.00 62	98.55 124	95.73 76	93.21 242	98.38 122	83.45 280	98.63 214	97.09 63	94.00 217	96.91 223

v10		94.29 228	93.55 237	96.51 215	96.39 253	94.80 195	98.99 63	98.19 182	91.35 255	93.02 249	96.99 240	88.09 186	98.41 253	90.50 250	88.41 287	96.33 287

PGM-MVS		98.49 28	98.23 33	99.27 24	99.72 11	98.08 40	98.99 63	99.49 5	95.43 88	99.03 17	99.32 20	95.56 37	99.94 3	96.80 79	99.77 19	99.78 7

LPG-MVS_test		95.62 142	95.34 135	96.47 218	97.46 192	93.54 239	98.99 63	98.54 125	94.67 126	94.36 197	98.77 89	85.39 243	99.11 165	95.71 115	94.15 212	96.76 239

#test#		98.54 25	98.27 28	99.32 17	99.72 11	98.29 27	98.98 66	98.96 31	95.65 80	98.94 23	99.17 41	96.06 22	99.92 15	97.21 60	99.78 14	99.75 22

tfpnnormal		93.66 250	92.70 256	96.55 212	96.94 224	95.94 121	98.97 67	99.19 15	91.04 265	91.38 276	97.34 204	84.94 251	98.61 215	85.45 312	89.02 276	95.11 309

V42		94.78 198	94.14 200	96.70 187	96.33 264	95.22 159	98.97 67	98.09 211	92.32 230	94.31 201	97.06 230	88.39 178	98.55 221	92.90 191	88.87 280	96.34 286

pm-mvs1		93.94 246	93.06 249	96.59 204	96.49 248	95.16 160	98.95 69	98.03 219	92.32 230	91.08 279	97.84 167	84.54 261	98.41 253	92.16 207	86.13 312	96.19 290

VPA-MVSNet		95.75 134	95.11 146	97.69 126	97.24 206	97.27 68	98.94 70	99.23 12	95.13 109	95.51 165	97.32 206	85.73 238	98.91 191	97.33 58	89.55 268	96.89 226

LS3D		97.16 86	96.66 93	98.68 64	98.53 134	97.19 73	98.93 71	98.90 42	92.83 208	95.99 162	99.37 12	92.12 97	99.87 37	93.67 168	99.57 57	98.97 130

ACMM	93.85 9	95.69 139	95.38 134	96.61 202	97.61 181	93.84 232	98.91 72	98.44 146	95.25 104	94.28 205	98.47 115	86.04 235	99.12 161	95.50 122	93.95 219	96.87 229
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MTAPA		98.58 19	98.29 27	99.46 7	99.76 1	98.64 10	98.90 73	98.74 79	97.27 25	98.02 66	99.39 7	94.81 56	99.96 1	97.91 29	99.79 10	99.77 14

SD-MVS		98.64 11	98.68 3	98.53 74	99.33 44	98.36 23	98.90 73	98.85 53	97.28 21	99.72 1	99.39 7	96.63 9	97.60 296	98.17 23	99.85 2	99.64 55

TransMVSNet (Re)		92.67 264	91.51 268	96.15 236	96.58 243	94.65 203	98.90 73	96.73 301	90.86 267	89.46 293	97.86 164	85.62 240	98.09 277	86.45 304	81.12 323	95.71 301

EPNet		97.28 81	96.87 82	98.51 75	94.98 311	96.14 110	98.90 73	97.02 283	98.28 1	95.99 162	99.11 48	91.36 113	99.89 29	96.98 64	99.19 87	99.50 73
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UA-Net		97.96 46	97.62 49	98.98 50	98.86 109	97.47 62	98.89 77	99.08 20	96.67 49	98.72 37	99.54 1	93.15 81	99.81 52	94.87 136	98.83 100	99.65 52

OurMVSNet-221017-0		94.21 231	94.00 209	94.85 282	95.60 300	89.22 298	98.89 77	97.43 258	95.29 102	92.18 270	98.52 112	82.86 282	98.59 218	93.46 172	91.76 250	96.74 241

UGNet		96.78 100	96.30 104	98.19 95	98.24 144	95.89 135	98.88 79	98.93 36	97.39 16	96.81 123	97.84 167	82.60 283	99.90 27	96.53 88	99.49 69	98.79 141
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
tfpn111		95.43 159	94.74 172	97.51 141	98.98 91	94.92 173	98.87 80	96.90 293	95.38 91	96.61 129	96.88 255	84.29 264	99.59 109	88.43 286	96.32 174	98.02 176

conf200view11		95.40 164	94.70 174	97.50 146	98.98 91	94.92 173	98.87 80	96.90 293	95.38 91	96.61 129	96.88 255	84.29 264	99.56 118	88.11 292	96.29 176	98.02 176

thres100view900		95.38 165	94.70 174	97.41 150	98.98 91	94.92 173	98.87 80	96.90 293	95.38 91	96.61 129	96.88 255	84.29 264	99.56 118	88.11 292	96.29 176	97.76 184

v1neww		94.83 193	94.22 193	96.68 191	96.39 253	94.85 179	98.87 80	98.11 203	92.45 220	94.45 187	97.06 230	88.82 160	98.54 222	92.93 188	88.91 278	96.65 259

v7new		94.83 193	94.22 193	96.68 191	96.39 253	94.85 179	98.87 80	98.11 203	92.45 220	94.45 187	97.06 230	88.82 160	98.54 222	92.93 188	88.91 278	96.65 259

v6		94.83 193	94.21 195	96.69 188	96.36 257	94.85 179	98.87 80	98.11 203	92.46 215	94.44 193	97.05 234	88.76 166	98.57 220	92.95 187	88.92 277	96.65 259

XXY-MVS		95.20 178	94.45 186	97.46 147	96.75 236	96.56 95	98.86 86	98.65 111	93.30 192	93.27 240	98.27 136	84.85 253	98.87 197	94.82 138	91.26 256	96.96 215

VDDNet		95.36 168	94.53 181	97.86 112	98.10 155	95.13 162	98.85 87	97.75 228	90.46 269	98.36 53	99.39 7	73.27 328	99.64 102	97.98 27	96.58 161	98.81 140

thres600view7		95.49 155	94.77 170	97.67 128	98.98 91	95.02 165	98.85 87	96.90 293	95.38 91	96.63 128	96.90 252	84.29 264	99.59 109	88.65 285	96.33 173	98.40 161

114514_t		96.93 94	96.27 105	98.92 54	99.50 29	97.63 56	98.85 87	98.90 42	84.80 323	97.77 80	99.11 48	92.84 83	99.66 99	94.85 137	99.77 19	99.47 79

LFMVS		95.86 130	94.98 152	98.47 79	98.87 108	96.32 105	98.84 90	96.02 313	93.40 187	98.62 41	99.20 37	74.99 321	99.63 105	97.72 42	97.20 150	99.46 83

alignmvs		97.56 66	97.07 75	99.01 47	98.66 124	98.37 22	98.83 91	98.06 215	96.74 46	98.00 70	97.65 184	90.80 123	99.48 132	98.37 19	96.56 162	99.19 108

DeepC-MVS	95.98 3	97.88 50	97.58 51	98.77 60	99.25 66	96.93 80	98.83 91	98.75 78	96.96 41	96.89 117	99.50 3	90.46 126	99.87 37	97.84 36	99.76 25	99.52 68
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 14	98.30 26	99.55 2	99.62 23	98.95 5	98.82 93	98.81 61	95.80 74	99.16 14	99.47 4	95.37 42	99.92 15	97.89 32	99.75 31	99.79 4

v18		92.10 271	90.97 271	95.50 257	96.34 260	94.85 179	98.82 93	97.52 241	89.99 279	85.31 316	93.26 315	88.90 154	96.92 308	88.82 281	79.77 327	94.73 315

GBi-Net		94.49 218	93.80 221	96.56 209	98.21 146	95.00 166	98.82 93	98.18 185	92.46 215	94.09 216	97.07 227	81.16 288	97.95 284	92.08 209	92.14 243	96.72 244

test1		94.49 218	93.80 221	96.56 209	98.21 146	95.00 166	98.82 93	98.18 185	92.46 215	94.09 216	97.07 227	81.16 288	97.95 284	92.08 209	92.14 243	96.72 244

FMVSNet1		93.19 260	92.07 263	96.56 209	97.54 187	95.00 166	98.82 93	98.18 185	90.38 272	92.27 267	97.07 227	73.68 327	97.95 284	89.36 272	91.30 254	96.72 244

API-MVS		97.41 75	97.25 66	97.91 110	98.70 120	96.80 85	98.82 93	98.69 94	94.53 132	98.11 59	98.28 133	94.50 65	99.57 116	94.12 157	99.49 69	97.37 199

v17		92.08 272	90.94 272	95.48 259	96.34 260	94.83 190	98.81 99	97.52 241	89.95 281	85.32 314	93.24 316	88.91 153	96.91 309	88.76 282	79.63 328	94.71 317

v16		92.08 272	90.94 272	95.49 258	96.38 256	94.84 188	98.81 99	97.51 244	89.94 282	85.25 317	93.28 314	88.86 155	96.91 309	88.70 283	79.78 326	94.72 316

ACMH	92.88 16	94.55 216	93.95 213	96.34 229	97.63 179	93.26 246	98.81 99	98.49 141	93.43 180	89.74 290	98.53 109	81.91 286	99.08 170	93.69 166	93.30 233	96.70 248
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Effi-MVS+-dtu		96.29 117	96.56 95	95.51 256	97.89 168	90.22 287	98.80 102	98.10 208	96.57 52	96.45 152	96.66 265	90.81 121	98.91 191	95.72 113	97.99 132	97.40 196

v11		91.85 279	90.68 281	95.36 269	96.34 260	94.74 202	98.80 102	97.43 258	89.60 293	85.09 319	93.03 321	88.53 175	96.75 316	87.37 299	79.96 325	94.58 323

HQP_MVS		96.14 122	95.90 116	96.85 179	97.42 196	94.60 210	98.80 102	98.56 122	97.28 21	95.34 166	98.28 133	87.09 209	99.03 177	96.07 99	94.27 206	96.92 218

plane_prior2							98.80 102		97.28 21

APD-MVS		98.35 36	98.00 41	99.42 10	99.51 28	98.72 9	98.80 102	98.82 58	94.52 133	99.23 10	99.25 30	95.54 39	99.80 59	96.52 89	99.77 19	99.74 27
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
UniMVSNet (Re)		95.78 133	95.19 144	97.58 137	96.99 222	97.47 62	98.79 107	99.18 16	95.60 81	93.92 223	97.04 235	91.68 105	98.48 233	95.80 111	87.66 296	96.79 236

FMVSNet2		94.47 220	93.61 234	97.04 168	98.21 146	96.43 100	98.79 107	98.27 168	92.46 215	93.50 236	97.09 225	81.16 288	98.00 282	91.09 232	91.93 247	96.70 248

v7		94.69 205	94.04 206	96.62 201	96.41 252	94.79 198	98.78 109	98.13 196	91.89 238	94.30 203	97.16 214	88.13 185	98.45 240	91.96 216	89.65 265	96.61 264

v15		91.94 274	90.77 276	95.43 264	96.31 268	94.83 190	98.77 110	97.50 247	89.92 283	85.13 318	93.08 319	88.76 166	96.86 311	88.40 287	79.10 330	94.61 321

V14		91.93 275	90.76 277	95.42 267	96.33 264	94.81 194	98.77 110	97.51 244	89.86 285	85.09 319	93.13 317	88.80 164	96.83 313	88.32 288	79.06 332	94.60 322

V9		91.91 276	90.73 278	95.45 261	96.32 267	94.80 195	98.77 110	97.50 247	89.81 286	85.03 321	93.08 319	88.76 166	96.86 311	88.24 289	79.03 333	94.69 318

v12		91.89 277	90.70 279	95.43 264	96.31 268	94.80 195	98.76 113	97.50 247	89.76 287	84.95 322	93.00 322	88.82 160	96.82 315	88.23 290	79.00 334	94.68 320

v13		91.88 278	90.69 280	95.43 264	96.33 264	94.78 200	98.75 114	97.50 247	89.68 290	84.93 323	92.98 323	88.84 158	96.83 313	88.14 291	79.09 331	94.69 318

testgi		93.06 262	92.45 259	94.88 281	96.43 251	89.90 288	98.75 114	97.54 240	95.60 81	91.63 275	97.91 160	74.46 325	97.02 306	86.10 306	93.67 222	97.72 188

LCM-MVSNet-Re		95.22 176	95.32 138	94.91 279	98.18 151	87.85 316	98.75 114	95.66 325	95.11 110	88.96 297	96.85 258	90.26 131	97.65 294	95.65 118	98.44 117	99.22 105

SixPastTwentyTwo		93.34 255	92.86 252	94.75 286	95.67 298	89.41 296	98.75 114	96.67 305	93.89 154	90.15 288	98.25 138	80.87 292	98.27 269	90.90 237	90.64 258	96.57 270

MVS_Test		97.28 81	97.00 77	98.13 98	98.33 140	95.97 117	98.74 118	98.07 213	94.27 140	98.44 51	98.07 148	92.48 87	99.26 143	96.43 92	98.19 127	99.16 113

UniMVSNet_NR-MVSNet		95.71 137	95.15 145	97.40 152	96.84 231	96.97 78	98.74 118	99.24 10	95.16 108	93.88 224	97.72 179	91.68 105	98.31 264	95.81 109	87.25 301	96.92 218

NR-MVSNet		94.98 186	94.16 198	97.44 148	96.53 245	97.22 72	98.74 118	98.95 33	94.96 118	89.25 295	97.69 180	89.32 139	98.18 272	94.59 144	87.40 298	96.92 218

MVSTER		96.06 123	95.72 121	97.08 167	98.23 145	95.93 124	98.73 121	98.27 168	94.86 122	95.07 170	98.09 147	88.21 181	98.54 222	96.59 85	93.46 227	96.79 236

ACMP	93.49 10	95.34 170	94.98 152	96.43 222	97.67 177	93.48 241	98.73 121	98.44 146	94.94 121	92.53 260	98.53 109	84.50 262	99.14 159	95.48 123	94.00 217	96.66 257
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS++		98.58 19	98.25 30	99.55 2	99.50 29	99.08 3	98.72 123	98.66 107	97.51 8	98.15 57	98.83 83	95.70 35	99.92 15	97.53 52	99.67 41	99.66 50

VPNet		94.99 184	94.19 197	97.40 152	97.16 214	96.57 94	98.71 124	98.97 29	95.67 78	94.84 175	98.24 139	80.36 298	98.67 212	96.46 90	87.32 299	96.96 215

MSLP-MVS++		98.56 22	98.57 5	98.55 72	99.26 65	96.80 85	98.71 124	99.05 23	97.28 21	98.84 29	99.28 27	96.47 11	99.40 134	98.52 14	99.70 39	99.47 79

ACMH+	92.99 14	94.30 227	93.77 224	95.88 246	97.81 172	92.04 262	98.71 124	98.37 157	93.99 149	90.60 285	98.47 115	80.86 293	99.05 172	92.75 195	92.40 242	96.55 273

DP-MVS		96.59 106	95.93 115	98.57 70	99.34 41	96.19 109	98.70 127	98.39 154	89.45 295	94.52 184	99.35 18	91.85 103	99.85 42	92.89 193	98.88 96	99.68 43

Fast-Effi-MVS+-dtu		95.87 129	95.85 117	95.91 244	97.74 175	91.74 268	98.69 128	98.15 193	95.56 83	94.92 173	97.68 183	88.98 150	98.79 206	93.19 179	97.78 141	97.20 207

v1141		94.75 201	94.11 204	96.67 194	96.27 272	94.86 178	98.69 128	98.12 198	92.43 223	94.31 201	96.94 246	88.78 165	98.48 233	92.63 198	88.85 282	96.67 254

divwei89l23v2f112		94.76 199	94.12 203	96.67 194	96.28 270	94.85 179	98.69 128	98.12 198	92.44 222	94.29 204	96.94 246	88.85 157	98.48 233	92.67 196	88.79 284	96.67 254

v1		94.75 201	94.11 204	96.69 188	96.27 272	94.87 177	98.69 128	98.12 198	92.43 223	94.32 200	96.94 246	88.71 169	98.54 222	92.66 197	88.84 283	96.67 254

tfpn200view9		95.32 172	94.62 177	97.43 149	98.94 96	94.98 169	98.68 132	96.93 291	95.33 99	96.55 135	96.53 270	84.23 269	99.56 118	88.11 292	96.29 176	97.76 184

VDD-MVS		95.82 132	95.23 142	97.61 136	98.84 112	93.98 228	98.68 132	97.40 261	95.02 115	97.95 72	99.34 19	74.37 326	99.78 76	98.64 4	96.80 156	99.08 122

thres400		95.38 165	94.62 177	97.65 130	98.94 96	94.98 169	98.68 132	96.93 291	95.33 99	96.55 135	96.53 270	84.23 269	99.56 118	88.11 292	96.29 176	98.40 161

pmmvs6		91.77 281	90.63 282	95.17 274	94.69 317	91.24 274	98.67 135	97.92 222	86.14 314	89.62 291	97.56 192	75.79 318	98.34 260	90.75 239	84.56 317	95.94 296

v2v482		94.69 205	94.03 207	96.65 196	96.17 277	94.79 198	98.67 135	98.08 212	92.72 209	94.00 221	97.16 214	87.69 200	98.45 240	92.91 190	88.87 280	96.72 244

DU-MVS		95.42 161	94.76 171	97.40 152	96.53 245	96.97 78	98.66 137	98.99 28	95.43 88	93.88 224	97.69 180	88.57 172	98.31 264	95.81 109	87.25 301	96.92 218

MAR-MVS		96.91 95	96.40 101	98.45 80	98.69 122	96.90 82	98.66 137	98.68 97	92.40 226	97.07 106	97.96 156	91.54 111	99.75 85	93.68 167	98.92 94	98.69 146
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 55	97.40 62	98.96 52	98.88 107	97.55 59	98.63 139	98.93 36	96.74 46	99.02 18	98.84 82	90.33 129	99.83 45	98.53 10	96.66 158	99.50 73

PVSNet_Blended_VisFu		97.70 58	97.46 59	98.44 81	99.27 63	95.91 133	98.63 139	99.16 17	94.48 136	97.67 88	98.88 79	92.80 84	99.91 24	97.11 62	99.12 89	99.50 73

PAPM_NR		97.46 68	97.11 72	98.50 76	99.50 29	96.41 101	98.63 139	98.60 114	95.18 107	97.06 107	98.06 149	94.26 70	99.57 116	93.80 165	98.87 98	99.52 68

Baseline_NR-MVSNet		94.35 225	93.81 220	95.96 242	96.20 275	94.05 227	98.61 142	96.67 305	91.44 249	93.85 226	97.60 188	88.57 172	98.14 273	94.39 148	86.93 304	95.68 302

v1144		94.59 214	93.92 214	96.60 203	96.21 274	94.78 200	98.59 143	98.14 195	91.86 241	94.21 210	97.02 237	87.97 188	98.41 253	91.72 222	89.57 266	96.61 264

AllTest		95.24 175	94.65 176	96.99 170	99.25 66	93.21 248	98.59 143	98.18 185	91.36 253	93.52 234	98.77 89	84.67 254	99.72 88	89.70 265	97.87 136	98.02 176

Fast-Effi-MVS+		96.28 119	95.70 125	98.03 106	98.29 142	95.97 117	98.58 145	98.25 173	91.74 242	95.29 169	97.23 211	91.03 120	99.15 157	92.90 191	97.96 133	98.97 130

Anonymous20231206		91.66 282	91.10 270	93.33 304	94.02 321	87.35 318	98.58 145	97.26 273	90.48 268	90.16 287	96.31 277	83.83 278	96.53 323	79.36 325	89.90 263	96.12 291

v144192		94.39 224	93.70 229	96.48 217	96.06 283	94.35 219	98.58 145	98.16 192	91.45 248	94.33 199	97.02 237	87.50 205	98.45 240	91.08 233	89.11 273	96.63 262

v148		94.29 228	93.76 226	95.91 244	96.10 281	92.93 252	98.58 145	97.97 220	92.59 213	93.47 237	96.95 244	88.53 175	98.32 262	92.56 200	87.06 303	96.49 280

COLMAP_ROB	93.27 12	95.33 171	94.87 162	96.71 185	99.29 57	93.24 247	98.58 145	98.11 203	89.92 283	93.57 232	99.10 50	86.37 221	99.79 71	90.78 238	98.10 130	97.09 208
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mvs-test1		96.60 104	96.68 92	96.37 225	97.89 168	91.81 264	98.56 150	98.10 208	96.57 52	96.52 139	97.94 158	90.81 121	99.45 133	95.72 113	98.01 131	97.86 183

FMVSNet3		94.97 187	94.26 192	97.11 165	98.18 151	96.62 91	98.56 150	98.26 172	93.67 173	94.09 216	97.10 223	84.25 268	98.01 281	92.08 209	92.14 243	96.70 248

test_part3							98.55 152		96.40 57		99.31 21		99.93 9	96.37 95

ESAPD		98.70 5	98.39 15	99.62 1	99.63 21	99.18 1	98.55 152	98.84 54	96.40 57	99.27 6	99.31 21	97.38 2	99.93 9	96.37 95	99.78 14	99.76 20

MPTG		98.55 23	98.25 30	99.46 7	99.76 1	98.64 10	98.55 152	98.74 79	97.27 25	98.02 66	99.39 7	94.81 56	99.96 1	97.91 29	99.79 10	99.77 14

F-COLMAP		97.09 90	96.80 83	97.97 108	99.45 35	94.95 172	98.55 152	98.62 113	93.02 199	96.17 157	98.58 107	94.01 73	99.81 52	93.95 160	98.90 95	99.14 116

diffmvs		96.32 116	95.74 119	98.07 104	98.26 143	96.14 110	98.53 156	98.23 176	90.10 277	96.88 118	97.73 176	90.16 132	99.15 157	93.90 162	97.85 138	98.91 136

v1921920		94.20 232	93.47 243	96.40 224	95.98 286	94.08 226	98.52 157	98.15 193	91.33 256	94.25 207	97.20 213	86.41 220	98.42 246	90.04 258	89.39 271	96.69 253

EU-MVSNet		93.66 250	94.14 200	92.25 311	95.96 287	83.38 326	98.52 157	98.12 198	94.69 124	92.61 257	98.13 145	87.36 207	96.39 325	91.82 218	90.00 262	96.98 214

TAMVS		97.02 91	96.79 85	97.70 125	98.06 158	95.31 157	98.52 157	98.31 162	93.95 152	97.05 108	98.61 102	93.49 77	98.52 229	95.33 126	97.81 139	99.29 98

LTVRE_ROB	92.95 15	94.60 212	93.90 216	96.68 191	97.41 199	94.42 215	98.52 157	98.59 115	91.69 243	91.21 277	98.35 125	84.87 252	99.04 176	91.06 234	93.44 230	96.60 266
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		91.06 288	89.68 291	95.21 272	85.35 341	91.49 270	98.51 161	97.07 279	91.47 247	88.83 298	97.84 167	77.31 313	99.09 169	92.79 194	77.98 335	95.04 311

v1192		94.32 226	93.58 236	96.53 213	96.10 281	94.45 214	98.50 162	98.17 190	91.54 246	94.19 211	97.06 230	86.95 213	98.43 245	90.14 253	89.57 266	96.70 248

test_0402		91.32 284	90.27 286	94.48 292	96.60 242	91.12 275	98.50 162	97.22 275	86.10 315	88.30 300	96.98 241	77.65 311	97.99 283	78.13 329	92.94 238	94.34 325

DeepC-MVS_fast	96.70 1	98.55 23	98.34 22	99.18 33	99.25 66	98.04 41	98.50 162	98.78 71	97.72 4	98.92 28	99.28 27	95.27 46	99.82 50	97.55 50	99.77 19	99.69 37
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 9	99.32 47	98.87 7	98.47 165	98.81 61	97.72 4	98.76 35	99.16 44	97.05 4	99.78 76	98.06 25	99.66 44	99.69 37

NCCC		98.61 14	98.35 21	99.38 11	99.28 62	98.61 12	98.45 166	98.76 75	97.82 3	98.45 50	98.93 75	96.65 8	99.83 45	97.38 57	99.41 78	99.71 34

v1240		94.06 243	93.29 247	96.34 229	96.03 285	93.90 230	98.44 167	98.17 190	91.18 264	94.13 215	97.01 239	86.05 233	98.42 246	89.13 275	89.50 269	96.70 248

plane_prior						94.60 210	98.44 167		96.74 46						94.22 208

MP-MVS-pluss		98.31 40	97.92 43	99.49 5	99.72 11	98.88 6	98.43 169	98.78 71	94.10 143	97.69 87	99.42 5	95.25 47	99.92 15	98.09 24	99.80 9	99.67 48
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
OPM-MVS		95.69 139	95.33 137	96.76 183	96.16 280	94.63 205	98.43 169	98.39 154	96.64 50	95.02 172	98.78 87	85.15 248	99.05 172	95.21 133	94.20 209	96.60 266

MCST-MVS		98.65 10	98.37 18	99.48 6	99.60 24	98.87 7	98.41 171	98.68 97	97.04 38	98.52 46	98.80 86	96.78 6	99.83 45	97.93 28	99.61 50	99.74 27

Regformer-3		98.59 17	98.50 11	98.86 58	99.43 37	97.05 76	98.40 172	98.68 97	97.43 13	99.06 16	99.31 21	95.80 34	99.77 81	98.62 6	99.76 25	99.78 7

Regformer-4		98.64 11	98.53 7	98.99 48	99.43 37	97.37 65	98.40 172	98.79 69	97.46 12	99.09 15	99.31 21	95.86 33	99.80 59	98.64 4	99.76 25	99.79 4

Regformer-1		98.66 9	98.51 10	99.12 41	99.35 39	97.81 52	98.37 174	98.76 75	97.49 10	99.20 12	99.21 34	96.08 21	99.79 71	98.42 16	99.73 36	99.75 22

Regformer-2		98.69 8	98.52 8	99.19 29	99.35 39	98.01 43	98.37 174	98.81 61	97.48 11	99.21 11	99.21 34	96.13 18	99.80 59	98.40 18	99.73 36	99.75 22

CANet		98.05 44	97.76 46	98.90 56	98.73 117	97.27 68	98.35 176	98.78 71	97.37 19	97.72 85	98.96 71	91.53 112	99.92 15	98.79 3	99.65 45	99.51 71

DWT-MVSNet_test		94.82 196	94.36 189	96.20 235	97.35 201	90.79 278	98.34 177	96.57 308	92.91 204	95.33 168	96.44 275	82.00 285	99.12 161	94.52 146	95.78 199	98.70 145

test20.03		90.89 290	90.38 284	92.43 309	93.48 322	88.14 313	98.33 178	97.56 235	93.40 187	87.96 301	96.71 264	80.69 295	94.13 334	79.15 326	86.17 310	95.01 313

DP-MVS Recon		97.86 52	97.46 59	99.06 46	99.53 27	98.35 24	98.33 178	98.89 44	92.62 211	98.05 62	98.94 74	95.34 44	99.65 100	96.04 102	99.42 77	99.19 108

RPSCF		94.87 192	95.40 130	93.26 306	98.89 106	82.06 331	98.33 178	98.06 215	90.30 273	96.56 133	99.26 29	87.09 209	99.49 128	93.82 164	96.32 174	98.24 171

TAPA-MVS	93.98 7	95.35 169	94.56 180	97.74 119	99.13 81	94.83 190	98.33 178	98.64 112	86.62 311	96.29 155	98.61 102	94.00 74	99.29 142	80.00 323	99.41 78	99.09 119
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IterMVS-LS		95.46 157	95.21 143	96.22 234	98.12 154	93.72 237	98.32 182	98.13 196	93.71 166	94.26 206	97.31 207	92.24 92	98.10 275	94.63 141	90.12 260	96.84 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_anonymous		96.70 102	96.53 98	97.18 160	98.19 149	93.78 233	98.31 183	98.19 182	94.01 147	94.47 186	98.27 136	92.08 99	98.46 238	97.39 56	97.91 134	99.31 93

WTY-MVS		97.37 78	96.92 80	98.72 62	98.86 109	96.89 84	98.31 183	98.71 91	95.26 103	97.67 88	98.56 108	92.21 94	99.78 76	95.89 106	96.85 155	99.48 78

EI-MVSNet-Vis-set		98.47 29	98.39 15	98.69 63	99.46 34	96.49 98	98.30 185	98.69 94	97.21 28	98.84 29	99.36 16	95.41 41	99.78 76	98.62 6	99.65 45	99.80 3

DSMNet-mixed		92.52 266	92.58 257	92.33 310	94.15 319	82.65 329	98.30 185	94.26 339	89.08 300	92.65 256	95.73 294	85.01 250	95.76 328	86.24 305	97.76 142	98.59 153

EI-MVSNet-UG-set		98.41 31	98.34 22	98.61 68	99.45 35	96.32 105	98.28 187	98.68 97	97.17 31	98.74 36	99.37 12	95.25 47	99.79 71	98.57 8	99.54 66	99.73 29

OMC-MVS		97.55 67	97.34 63	98.20 93	99.33 44	95.92 131	98.28 187	98.59 115	95.52 85	97.97 71	99.10 50	93.28 80	99.49 128	95.09 134	98.88 96	99.19 108

PVSNet_BlendedMVS		96.73 101	96.60 94	97.12 164	99.25 66	95.35 155	98.26 189	99.26 8	94.28 139	97.94 73	97.46 195	92.74 85	99.81 52	96.88 74	93.32 232	96.20 289

BH-untuned		95.95 126	95.72 121	96.65 196	98.55 133	92.26 258	98.23 190	97.79 226	93.73 164	94.62 181	98.01 153	88.97 151	99.00 180	93.04 184	98.51 113	98.68 147

sss		97.39 76	96.98 78	98.61 68	98.60 130	96.61 93	98.22 191	98.93 36	93.97 151	98.01 68	98.48 114	91.98 101	99.85 42	96.45 91	98.15 128	99.39 88

WR-MVS		95.15 179	94.46 184	97.22 157	96.67 241	96.45 99	98.21 192	98.81 61	94.15 141	93.16 243	97.69 180	87.51 203	98.30 266	95.29 129	88.62 285	96.90 225

MVS_0304		97.70 58	97.25 66	99.07 44	98.90 98	97.83 50	98.20 193	98.74 79	97.51 8	98.03 65	99.06 58	86.12 225	99.93 9	99.02 1	99.64 47	99.44 86

pmmvs5		93.65 252	92.97 251	95.68 253	95.49 304	92.37 257	98.20 193	97.28 271	89.66 291	92.58 258	97.26 209	82.14 284	98.09 277	93.18 180	90.95 257	96.58 268

thres200		95.25 174	94.57 179	97.28 156	98.81 113	94.92 173	98.20 193	97.11 277	95.24 106	96.54 137	96.22 283	84.58 256	99.53 125	87.93 296	96.50 165	97.39 197

CDS-MVSNet		96.99 92	96.69 90	97.90 111	98.05 159	95.98 113	98.20 193	98.33 161	93.67 173	96.95 110	98.49 113	93.54 76	98.42 246	95.24 132	97.74 143	99.31 93
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
1314		96.25 121	95.73 120	97.79 117	97.13 216	95.55 148	98.19 197	98.59 115	93.47 179	92.03 272	97.82 171	91.33 114	99.49 128	94.62 142	98.44 117	98.32 170

1121		97.37 78	96.77 88	99.16 36	99.34 41	97.99 46	98.19 197	98.68 97	90.14 276	98.01 68	98.97 67	94.80 58	99.87 37	93.36 174	99.46 74	99.61 58

MVS		94.67 209	93.54 238	98.08 102	96.88 229	96.56 95	98.19 197	98.50 137	78.05 337	92.69 255	98.02 151	91.07 119	99.63 105	90.09 254	98.36 121	98.04 175

BH-RMVSNet		95.92 128	95.32 138	97.69 126	98.32 141	94.64 204	98.19 197	97.45 256	94.56 131	96.03 160	98.61 102	85.02 249	99.12 161	90.68 240	99.06 90	99.30 96

1112_ss		96.63 103	96.00 114	98.50 76	98.56 131	96.37 102	98.18 201	98.10 208	92.92 203	94.84 175	98.43 117	92.14 96	99.58 115	94.35 150	96.51 164	99.56 67

EPNet_dtu		95.21 177	94.95 155	95.99 241	96.17 277	90.45 285	98.16 202	97.27 272	96.77 44	93.14 246	98.33 130	90.34 128	98.42 246	85.57 310	98.81 102	99.09 119
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HY-MVS	93.96 8	96.82 99	96.23 108	98.57 70	98.46 135	97.00 77	98.14 203	98.21 178	93.95 152	96.72 126	97.99 155	91.58 107	99.76 83	94.51 147	96.54 163	98.95 134

PLC	95.07 4	97.20 84	96.78 86	98.44 81	99.29 57	96.31 107	98.14 203	98.76 75	92.41 225	96.39 153	98.31 132	94.92 55	99.78 76	94.06 158	98.77 103	99.23 104
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EG-PatchMatch MVS		91.13 286	90.12 287	94.17 299	94.73 316	89.00 302	98.13 205	97.81 225	89.22 299	85.32 314	96.46 273	67.71 336	98.42 246	87.89 297	93.82 221	95.08 310

EI-MVSNet		95.96 125	95.83 118	96.36 226	97.93 165	93.70 238	98.12 206	98.27 168	93.70 168	95.07 170	99.02 60	92.23 93	98.54 222	94.68 140	93.46 227	96.84 232

CVMVSNet		95.43 159	96.04 112	93.57 302	97.93 165	83.62 325	98.12 206	98.59 115	95.68 77	96.56 133	99.02 60	87.51 203	97.51 299	93.56 171	97.44 147	99.60 61

TSAR-MVS + GP.		98.38 33	98.24 32	98.81 59	99.22 73	97.25 71	98.11 208	98.29 167	97.19 30	98.99 22	99.02 60	96.22 13	99.67 98	98.52 14	98.56 112	99.51 71

XVG-ACMP-BASELINE		94.54 217	94.14 200	95.75 252	96.55 244	91.65 269	98.11 208	98.44 146	94.96 118	94.22 209	97.90 161	79.18 304	99.11 165	94.05 159	93.85 220	96.48 281

PatchFormer-LS_test		95.47 156	95.27 141	96.08 240	97.59 183	90.66 281	98.10 210	97.34 265	93.98 150	96.08 158	96.15 285	87.65 201	99.12 161	95.27 130	95.24 202	98.44 160

DI_MVS_plusplus_test		94.74 203	93.62 233	98.09 101	95.34 307	95.92 131	98.09 211	97.34 265	94.66 128	85.89 309	95.91 290	80.49 297	99.38 137	96.66 83	98.22 125	98.97 130

CNLPA		97.45 71	97.03 76	98.73 61	99.05 83	97.44 64	98.07 212	98.53 128	95.32 101	96.80 124	98.53 109	93.32 79	99.72 88	94.31 152	99.31 84	99.02 125

CHOSEN 1792x2688		97.12 88	96.80 83	98.08 102	99.30 54	94.56 212	98.05 213	99.71 1	93.57 176	97.09 103	98.91 78	88.17 182	99.89 29	96.87 77	99.56 63	99.81 2

HQP-NCC					97.20 210		98.05 213		96.43 54	94.45 187

ACMP_Plane					97.20 210		98.05 213		96.43 54	94.45 187

HQP-MVS		95.72 135	95.40 130	96.69 188	97.20 210	94.25 223	98.05 213	98.46 142	96.43 54	94.45 187	97.73 176	86.75 215	98.96 184	95.30 127	94.18 210	96.86 231

MIMVSNet1		89.67 298	88.28 303	93.82 300	92.81 326	91.08 276	98.01 217	97.45 256	87.95 305	87.90 302	95.87 292	67.63 337	94.56 333	78.73 328	88.18 289	95.83 298

AdaColmap		97.15 87	96.70 89	98.48 78	99.16 78	96.69 90	98.01 217	98.89 44	94.44 138	96.83 120	98.68 96	90.69 124	99.76 83	94.36 149	99.29 85	98.98 129

FMVSNet5		91.81 280	90.92 274	94.49 291	97.21 209	92.09 260	98.00 219	97.55 239	89.31 298	90.86 282	95.61 299	74.48 324	95.32 330	85.57 310	89.70 264	96.07 293

CANet_DTU		96.96 93	96.55 96	98.21 92	98.17 153	96.07 112	97.98 220	98.21 178	97.24 27	97.13 102	98.93 75	86.88 214	99.91 24	95.00 135	99.37 82	98.66 149

Anonymous20231211		83.69 311	81.50 313	90.26 315	89.23 335	80.10 333	97.97 221	97.06 281	72.79 341	82.05 329	92.57 329	50.28 345	96.32 326	76.15 333	75.38 339	94.37 324

MVP-Stereo		94.28 230	93.92 214	95.35 270	94.95 312	92.60 256	97.97 221	97.65 232	91.61 244	90.68 284	97.09 225	86.32 222	98.42 246	89.70 265	99.34 83	95.02 312
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS_111021_LR		98.34 37	98.23 33	98.67 65	99.27 63	96.90 82	97.95 223	99.58 3	97.14 33	98.44 51	99.01 64	95.03 53	99.62 107	97.91 29	99.75 31	99.50 73

Test4		92.21 269	90.34 285	97.82 116	92.83 325	95.87 137	97.94 224	98.05 218	94.50 134	82.12 328	94.48 307	59.54 343	98.54 222	95.39 125	98.22 125	99.06 124

TEST9					99.31 49	98.50 14	97.92 225	98.73 84	92.63 210	97.74 83	98.68 96	96.20 14	99.80 59

train_agg		97.97 45	97.52 55	99.33 16	99.31 49	98.50 14	97.92 225	98.73 84	92.98 201	97.74 83	98.68 96	96.20 14	99.80 59	96.59 85	99.57 57	99.68 43

agg_prior3		97.87 51	97.42 61	99.23 28	99.29 57	98.23 30	97.92 225	98.72 86	92.38 227	97.59 94	98.64 101	96.09 20	99.79 71	96.59 85	99.57 57	99.68 43

test_normal		94.72 204	93.59 235	98.11 100	95.30 308	95.95 120	97.91 228	97.39 263	94.64 129	85.70 312	95.88 291	80.52 296	99.36 138	96.69 82	98.30 124	99.01 128

CDPH-MVS		97.94 48	97.49 57	99.28 21	99.47 33	98.44 16	97.91 228	98.67 104	92.57 214	98.77 34	98.85 81	95.93 29	99.72 88	95.56 120	99.69 40	99.68 43

MVS_111021_HR		98.47 29	98.34 22	98.88 57	99.22 73	97.32 66	97.91 228	99.58 3	97.20 29	98.33 55	99.00 65	95.99 26	99.64 102	98.05 26	99.76 25	99.69 37

PatchMatch-RL		96.59 106	96.03 113	98.27 89	99.31 49	96.51 97	97.91 228	99.06 21	93.72 165	96.92 115	98.06 149	88.50 177	99.65 100	91.77 221	99.00 92	98.66 149

OpenMVS_ROB	86.42 20	89.00 300	87.43 306	93.69 301	93.08 324	89.42 295	97.91 228	96.89 297	78.58 336	85.86 310	94.69 306	69.48 333	98.29 268	77.13 330	93.29 234	93.36 334

test_8					99.29 57	98.44 16	97.89 233	98.72 86	92.98 201	97.70 86	98.66 99	96.20 14	99.80 59

ab-mvs		96.42 112	95.71 124	98.55 72	98.63 127	96.75 88	97.88 234	98.74 79	93.84 157	96.54 137	98.18 142	85.34 246	99.75 85	95.93 105	96.35 172	99.15 114

jason		97.32 80	97.08 74	98.06 105	97.45 195	95.59 143	97.87 235	97.91 223	94.79 123	98.55 45	98.83 83	91.12 116	99.23 146	97.58 47	99.60 51	99.34 90
jason: jason.
xiu_mvs_v1_base_debu		97.60 62	97.56 52	97.72 120	98.35 136	95.98 113	97.86 236	98.51 132	97.13 34	99.01 19	98.40 119	91.56 108	99.80 59	98.53 10	98.68 104	97.37 199

xiu_mvs_v1_base		97.60 62	97.56 52	97.72 120	98.35 136	95.98 113	97.86 236	98.51 132	97.13 34	99.01 19	98.40 119	91.56 108	99.80 59	98.53 10	98.68 104	97.37 199

xiu_mvs_v1_base_debi		97.60 62	97.56 52	97.72 120	98.35 136	95.98 113	97.86 236	98.51 132	97.13 34	99.01 19	98.40 119	91.56 108	99.80 59	98.53 10	98.68 104	97.37 199

test_prior4						98.01 43	97.86 236

agg_prior1		97.95 47	97.51 56	99.28 21	99.30 54	98.38 19	97.81 240	98.72 86	93.16 195	97.57 95	98.66 99	96.14 17	99.81 52	96.63 84	99.56 63	99.66 50

test_prior3		98.22 43	97.90 44	99.19 29	99.31 49	98.22 32	97.80 241	98.84 54	96.12 65	97.89 77	98.69 94	95.96 27	99.70 93	96.89 71	99.60 51	99.65 52

test_prior2							97.80 241		96.12 65	97.89 77	98.69 94	95.96 27		96.89 71	99.60 51

XVG-OURS-SEG-HR		96.51 109	96.34 102	97.02 169	98.77 115	93.76 234	97.79 243	98.50 137	95.45 87	96.94 112	99.09 54	87.87 193	99.55 124	96.76 80	95.83 198	97.74 186

MS-PatchMatch		93.84 248	93.63 232	94.46 294	96.18 276	89.45 294	97.76 244	98.27 168	92.23 233	92.13 271	97.49 193	79.50 301	98.69 209	89.75 263	99.38 81	95.25 307

DELS-MVS		98.40 32	98.20 35	98.99 48	99.00 88	97.66 54	97.75 245	98.89 44	97.71 6	98.33 55	98.97 67	94.97 54	99.88 36	98.42 16	99.76 25	99.42 87
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 54	97.60 50	98.44 81	99.12 82	95.97 117	97.75 245	98.78 71	96.89 42	98.46 47	99.22 33	93.90 75	99.68 97	94.81 139	99.52 68	99.67 48

Test_1112_low_res		96.34 115	95.66 128	98.36 86	98.56 131	95.94 121	97.71 247	98.07 213	92.10 234	94.79 179	97.29 208	91.75 104	99.56 118	94.17 155	96.50 165	99.58 65

BH-w/o		95.38 165	95.08 148	96.26 233	98.34 139	91.79 265	97.70 248	97.43 258	92.87 206	94.24 208	97.22 212	88.66 170	98.84 200	91.55 225	97.70 144	98.16 173

testing_2		90.61 293	88.50 300	96.95 174	90.08 333	95.57 145	97.69 249	98.06 215	93.02 199	76.55 335	92.48 331	61.18 342	98.44 243	95.45 124	91.98 246	96.84 232

lupinMVS		97.44 72	97.22 69	98.12 99	98.07 156	95.76 139	97.68 250	97.76 227	94.50 134	98.79 32	98.61 102	92.34 88	99.30 140	97.58 47	99.59 54	99.31 93

原ACMM2							97.67 251

LF4IMVS		93.14 261	92.79 254	94.20 297	95.88 291	88.67 306	97.66 252	97.07 279	93.81 159	91.71 274	97.65 184	77.96 308	98.81 204	91.47 229	91.92 248	95.12 308

新几何2							97.64 253

MDA-MVSNet-bldmvs		89.97 296	88.35 302	94.83 284	95.21 309	91.34 271	97.64 253	97.51 244	88.36 304	71.17 341	96.13 286	79.22 303	96.63 322	83.65 315	86.27 309	96.52 276

pmmvs-eth3d		90.36 294	89.05 297	94.32 296	91.10 330	92.12 259	97.63 255	96.95 290	88.86 301	84.91 324	93.13 317	78.32 306	96.74 317	88.70 283	81.81 322	94.09 329

TR-MVS		94.94 190	94.20 196	97.17 161	97.75 174	94.14 225	97.59 256	97.02 283	92.28 232	95.75 164	97.64 186	83.88 276	98.96 184	89.77 261	96.15 186	98.40 161

无先验							97.58 257	98.72 86	91.38 252				99.87 37	93.36 174		99.60 61

旧先验2							97.57 258		91.30 258	98.67 38			99.80 59	95.70 117

CostFormer		94.95 188	94.73 173	95.60 255	97.28 204	89.06 300	97.53 259	96.89 297	89.66 291	96.82 122	96.72 263	86.05 233	98.95 188	95.53 121	96.13 187	98.79 141

tpmp4_e23		93.91 247	93.42 246	95.38 268	97.62 180	88.59 308	97.52 260	97.34 265	87.94 306	94.17 213	96.79 261	82.91 281	99.05 172	90.62 242	95.91 196	98.50 156

XVG-OURS		96.55 108	96.41 100	96.99 170	98.75 116	93.76 234	97.50 261	98.52 130	95.67 78	96.83 120	99.30 26	88.95 152	99.53 125	95.88 107	96.26 181	97.69 190

xiu_mvs_v2_base		97.66 61	97.70 48	97.56 139	98.61 129	95.46 150	97.44 262	98.46 142	97.15 32	98.65 40	98.15 143	94.33 68	99.80 59	97.84 36	98.66 108	97.41 195

tpm		94.13 239	93.80 221	95.12 275	96.50 247	87.91 315	97.44 262	95.89 318	92.62 211	96.37 154	96.30 278	84.13 272	98.30 266	93.24 177	91.66 252	99.14 116

DeepPCF-MVS	96.37 2	97.93 49	98.48 13	96.30 231	99.00 88	89.54 293	97.43 264	98.87 49	98.16 2	99.26 8	99.38 11	96.12 19	99.64 102	98.30 21	99.77 19	99.72 32

test222					99.23 72	97.17 74	97.40 265	98.66 107	88.68 302	98.05 62	98.96 71	94.14 71			99.53 67	99.61 58

pmmvs4		94.69 205	93.99 211	96.81 181	95.74 295	95.94 121	97.40 265	97.67 231	90.42 271	93.37 238	97.59 189	89.08 146	98.20 271	92.97 186	91.67 251	96.30 288

test0.0.03 1		94.08 241	93.51 241	95.80 249	95.53 303	92.89 253	97.38 267	95.97 315	95.11 110	92.51 262	96.66 265	87.71 197	96.94 307	87.03 301	93.67 222	97.57 192

HyFIR lowres test		96.90 96	96.49 99	98.14 96	99.33 44	95.56 146	97.38 267	99.65 2	92.34 228	97.61 92	98.20 141	89.29 140	99.10 168	96.97 65	97.60 146	99.77 14

Effi-MVS+		97.12 88	96.69 90	98.39 85	98.19 149	96.72 89	97.37 269	98.43 149	93.71 166	97.65 91	98.02 151	92.20 95	99.25 144	96.87 77	97.79 140	99.19 108

N_pmnet		87.12 307	87.77 304	85.17 327	95.46 305	61.92 350	97.37 269	70.66 357	85.83 318	88.73 299	96.04 288	85.33 247	97.76 293	80.02 322	90.48 259	95.84 297

PAPR		96.84 98	96.24 107	98.65 66	98.72 119	96.92 81	97.36 271	98.57 121	93.33 189	96.67 127	97.57 191	94.30 69	99.56 118	91.05 236	98.59 110	99.47 79

PMMVS		96.60 104	96.33 103	97.41 150	97.90 167	93.93 229	97.35 272	98.41 150	92.84 207	97.76 81	97.45 197	91.10 118	99.20 154	96.26 97	97.91 134	99.11 118

PS-MVSNAJ		97.73 56	97.77 45	97.62 131	98.68 123	95.58 144	97.34 273	98.51 132	97.29 20	98.66 39	97.88 163	94.51 62	99.90 27	97.87 33	99.17 88	97.39 197

Patchmatch-test1		95.32 172	94.97 154	96.35 227	97.67 177	91.29 273	97.33 274	97.60 233	94.68 125	96.92 115	96.95 244	83.97 274	98.50 232	91.33 231	98.32 123	99.25 102

testdata1							97.32 275		96.34 59

tpm2		94.19 233	93.76 226	95.46 260	97.23 207	89.04 301	97.31 276	96.85 300	87.08 310	96.21 156	96.79 261	83.75 279	98.74 208	92.43 205	96.23 183	98.59 153

PVSNet_Blended		97.38 77	97.12 71	98.14 96	99.25 66	95.35 155	97.28 277	99.26 8	93.13 196	97.94 73	98.21 140	92.74 85	99.81 52	96.88 74	99.40 80	99.27 100

CLD-MVS		95.62 142	95.34 135	96.46 221	97.52 189	93.75 236	97.27 278	98.46 142	95.53 84	94.42 195	98.00 154	86.21 223	98.97 181	96.25 98	94.37 204	96.66 257
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPMVS		94.99 184	94.48 182	96.52 214	97.22 208	91.75 267	97.23 279	91.66 346	94.11 142	97.28 99	96.81 260	85.70 239	98.84 200	93.04 184	97.28 149	98.97 130

YYNet1		90.70 292	89.39 293	94.62 289	94.79 315	90.65 282	97.20 280	97.46 254	87.54 308	72.54 339	95.74 293	86.51 218	96.66 321	86.00 307	86.76 308	96.54 274

MDA-MVSNet_test_wron		90.71 291	89.38 294	94.68 287	94.83 314	90.78 279	97.19 281	97.46 254	87.60 307	72.41 340	95.72 296	86.51 218	96.71 320	85.92 308	86.80 307	96.56 272

IterMVS		94.09 240	93.85 219	94.80 285	97.99 162	90.35 286	97.18 282	98.12 198	93.68 171	92.46 264	97.34 204	84.05 273	97.41 301	92.51 203	91.33 253	96.62 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
new-patchmatchnet		88.50 304	87.45 305	91.67 313	90.31 332	85.89 322	97.16 283	97.33 268	89.47 294	83.63 326	92.77 327	76.38 315	95.06 332	82.70 317	77.29 336	94.06 330

UnsupCasMVSNet_eth		90.99 289	89.92 290	94.19 298	94.08 320	89.83 289	97.13 284	98.67 104	93.69 169	85.83 311	96.19 284	75.15 320	96.74 317	89.14 274	79.41 329	96.00 294

IB-MVS	91.98 17	93.27 257	91.97 264	97.19 159	97.47 191	93.41 244	97.09 285	95.99 314	93.32 190	92.47 263	95.73 294	78.06 307	99.53 125	94.59 144	82.98 318	98.62 152
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	89.70 297	89.67 292	89.78 316	93.19 323	76.56 336	97.00 286	98.35 159	80.97 333	81.57 330	97.75 175	74.75 323	98.61 215	89.85 260	93.63 224	94.17 327
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tpmrst		95.63 141	95.69 126	95.44 262	97.54 187	88.54 309	96.97 287	97.56 235	93.50 178	97.52 97	96.93 250	89.49 135	99.16 156	95.25 131	96.42 167	98.64 151

dp		94.15 238	93.90 216	94.90 280	97.31 203	86.82 321	96.97 287	97.19 276	91.22 263	96.02 161	96.61 269	85.51 242	99.02 179	90.00 259	94.30 205	98.85 137

PM-MVS		87.77 305	86.55 307	91.40 314	91.03 331	83.36 327	96.92 289	95.18 331	91.28 260	86.48 308	93.42 313	53.27 344	96.74 317	89.43 271	81.97 321	94.11 328

TinyColmap		92.31 268	91.53 267	94.65 288	96.92 225	89.75 290	96.92 289	96.68 304	90.45 270	89.62 291	97.85 166	76.06 317	98.81 204	86.74 302	92.51 241	95.41 306

test-LLR		95.10 181	94.87 162	95.80 249	96.77 233	89.70 291	96.91 291	95.21 329	95.11 110	94.83 177	95.72 296	87.71 197	98.97 181	93.06 182	98.50 114	98.72 143

TESTMET0.1,1		94.18 235	93.69 230	95.63 254	96.92 225	89.12 299	96.91 291	94.78 334	93.17 194	94.88 174	96.45 274	78.52 305	98.92 190	93.09 181	98.50 114	98.85 137

test-mter		94.08 241	93.51 241	95.80 249	96.77 233	89.70 291	96.91 291	95.21 329	92.89 205	94.83 177	95.72 296	77.69 309	98.97 181	93.06 182	98.50 114	98.72 143

USDC		93.33 256	92.71 255	95.21 272	96.83 232	90.83 277	96.91 291	97.50 247	93.84 157	90.72 283	98.14 144	77.69 309	98.82 203	89.51 269	93.21 236	95.97 295

MDTV_nov1_ep13_2view						84.26 324	96.89 295		90.97 266	97.90 76		89.89 134		93.91 161		99.18 112

tpmvs		94.60 212	94.36 189	95.33 271	97.46 192	88.60 307	96.88 296	97.68 230	91.29 259	93.80 228	96.42 276	88.58 171	99.24 145	91.06 234	96.04 195	98.17 172

MDTV_nov1_ep13			95.40 130		97.48 190	88.34 311	96.85 297	97.29 270	93.74 163	97.48 98	97.26 209	89.18 143	99.05 172	91.92 217	97.43 148

PatchmatchNet		95.71 137	95.52 129	96.29 232	97.58 184	90.72 280	96.84 298	97.52 241	94.06 145	97.08 104	96.96 243	89.24 142	98.90 194	92.03 213	98.37 120	99.26 101
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MSDG		95.93 127	95.30 140	97.83 114	98.90 98	95.36 153	96.83 299	98.37 157	91.32 257	94.43 194	98.73 93	90.27 130	99.60 108	90.05 257	98.82 101	98.52 155

GA-MVS		94.81 197	94.03 207	97.14 162	97.15 215	93.86 231	96.76 300	97.58 234	94.00 148	94.76 180	97.04 235	80.91 291	98.48 233	91.79 219	96.25 182	99.09 119

tpm cat1		93.36 253	92.80 253	95.07 277	97.58 184	87.97 314	96.76 300	97.86 224	82.17 331	93.53 233	96.04 288	86.13 224	99.13 160	89.24 273	95.87 197	98.10 174

test_post1							96.68 302				30.43 355	87.85 194	98.69 209	92.59 199

1111		84.94 310	84.30 311	86.86 322	87.59 337	75.10 339	96.63 303	96.43 310	82.53 328	80.75 332	92.91 325	68.94 334	93.79 335	68.24 341	84.66 316	91.70 336

.test1245		73.05 319	76.31 317	63.27 340	87.59 337	75.10 339	96.63 303	96.43 310	82.53 328	80.75 332	92.91 325	68.94 334	93.79 335	68.24 341	12.72 353	20.91 353

pmmvs3		86.67 308	84.86 310	92.11 312	88.16 336	87.19 320	96.63 303	94.75 335	79.88 335	87.22 304	92.75 328	66.56 338	95.20 331	81.24 321	76.56 338	93.96 331

testmvs		21.48 331	24.95 332	11.09 344	14.89 357	6.47 359	96.56 306	9.87 359	7.55 353	17.93 353	39.02 352	9.43 362	5.90 357	16.56 354	12.72 353	20.91 353

testus		88.91 301	89.08 296	88.40 319	91.39 328	76.05 337	96.56 306	96.48 309	89.38 297	89.39 294	95.17 302	70.94 331	93.56 337	77.04 331	95.41 201	95.61 303

test123		20.95 332	23.72 333	12.64 343	13.54 358	8.19 358	96.55 308	6.13 360	7.48 354	16.74 354	37.98 353	12.97 358	6.05 356	16.69 353	5.43 355	23.68 352

test1235678		86.26 309	85.81 308	87.62 321	86.97 339	75.00 341	96.55 308	96.32 312	86.08 316	81.32 331	92.98 323	73.10 330	92.05 342	71.64 338	87.32 299	95.81 299

GG-mvs-BLEND				96.59 204	96.34 260	94.98 169	96.51 310	88.58 350		93.10 248	94.34 310	80.34 299	98.05 279	89.53 268	96.99 153	96.74 241

new_pmnet		90.06 295	89.00 298	93.22 307	94.18 318	88.32 312	96.42 311	96.89 297	86.19 313	85.67 313	93.62 312	77.18 314	97.10 305	81.61 320	89.29 272	94.23 326

PVSNet	91.96 18	96.35 114	96.15 109	96.96 173	99.17 77	92.05 261	96.08 312	98.68 97	93.69 169	97.75 82	97.80 173	88.86 155	99.69 96	94.26 154	99.01 91	99.15 114

ADS-MVSNet2		94.58 215	94.40 188	95.11 276	98.00 160	88.74 304	96.04 313	97.30 269	90.15 274	96.47 150	96.64 267	87.89 191	97.56 298	90.08 255	97.06 151	99.02 125

ADS-MVSNet		95.00 183	94.45 186	96.63 199	98.00 160	91.91 263	96.04 313	97.74 229	90.15 274	96.47 150	96.64 267	87.89 191	98.96 184	90.08 255	97.06 151	99.02 125

PAPM		94.95 188	94.00 209	97.78 118	97.04 219	95.65 142	96.03 315	98.25 173	91.23 262	94.19 211	97.80 173	91.27 115	98.86 199	82.61 318	97.61 145	98.84 139

cascas		94.63 211	93.86 218	96.93 176	96.91 227	94.27 222	96.00 316	98.51 132	85.55 319	94.54 183	96.23 281	84.20 271	98.87 197	95.80 111	96.98 154	97.66 191

testmv		78.74 313	77.35 314	82.89 330	78.16 350	69.30 347	95.87 317	94.65 336	81.11 332	70.98 342	87.11 339	46.31 346	90.42 345	65.28 344	76.72 337	88.95 339

gg-mvs-nofinetune		92.21 269	90.58 283	97.13 163	96.75 236	95.09 163	95.85 318	89.40 349	85.43 320	94.50 185	81.98 342	80.80 294	98.40 259	92.16 207	98.33 122	97.88 182

FPMVS		77.62 317	77.14 315	79.05 332	79.25 347	60.97 351	95.79 319	95.94 316	65.96 342	67.93 343	94.40 308	37.73 351	88.88 347	68.83 340	88.46 286	87.29 340

CHOSEN 280x420		97.18 85	97.18 70	97.20 158	98.81 113	93.27 245	95.78 320	99.15 18	95.25 104	96.79 125	98.11 146	92.29 90	99.07 171	98.56 9	99.85 2	99.25 102

MIMVSNet		93.26 258	92.21 262	96.41 223	97.73 176	93.13 250	95.65 321	97.03 282	91.27 261	94.04 219	96.06 287	75.33 319	97.19 304	86.56 303	96.23 183	98.92 135

PCF-MVS	93.45 11	94.68 208	93.43 244	98.42 84	98.62 128	96.77 87	95.48 322	98.20 181	84.63 324	93.34 239	98.32 131	88.55 174	99.81 52	84.80 314	98.96 93	98.68 147
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test12356		83.47 312	83.37 312	83.78 328	84.43 342	70.09 346	95.12 323	95.60 326	82.98 326	78.89 334	92.43 332	64.99 339	91.41 344	70.36 339	85.55 315	89.82 338

test2356		88.68 303	88.61 299	88.87 318	89.90 334	78.23 334	95.11 324	96.66 307	88.66 303	89.06 296	94.33 311	73.14 329	92.56 341	75.56 334	95.11 203	95.81 299

JIA-IIPM		93.35 254	92.49 258	95.92 243	96.48 249	90.65 282	95.01 325	96.96 289	85.93 317	96.08 158	87.33 338	87.70 199	98.78 207	91.35 230	95.58 200	98.34 168

CR-MVSNet		94.76 199	94.15 199	96.59 204	97.00 220	93.43 242	94.96 326	97.56 235	92.46 215	96.93 113	96.24 279	88.15 183	97.88 291	87.38 298	96.65 159	98.46 158

RPMNet		92.52 266	91.17 269	96.59 204	97.00 220	93.43 242	94.96 326	97.26 273	82.27 330	96.93 113	92.12 334	86.98 212	97.88 291	76.32 332	96.65 159	98.46 158

UnsupCasMVSNet_bld		87.17 306	85.12 309	93.31 305	91.94 327	88.77 303	94.92 328	98.30 165	84.30 325	82.30 327	90.04 335	63.96 341	97.25 303	85.85 309	74.47 341	93.93 332

PVSNet_0	88.72 19	91.28 285	90.03 288	95.00 278	97.99 162	87.29 319	94.84 329	98.50 137	92.06 235	89.86 289	95.19 300	79.81 300	99.39 136	92.27 206	69.79 342	98.33 169

Patchmatch-test		94.42 222	93.68 231	96.63 199	97.60 182	91.76 266	94.83 330	97.49 253	89.45 295	94.14 214	97.10 223	88.99 147	98.83 202	85.37 313	98.13 129	99.29 98

no-one		74.41 318	70.76 320	85.35 326	79.88 346	76.83 335	94.68 331	94.22 340	80.33 334	63.81 344	79.73 345	35.45 353	93.36 338	71.78 337	36.99 350	85.86 343

Patchmtry		93.22 259	92.35 260	95.84 247	96.77 233	93.09 251	94.66 332	97.56 235	87.37 309	92.90 251	96.24 279	88.15 183	97.90 287	87.37 299	90.10 261	96.53 275

PatchT		93.06 262	91.97 264	96.35 227	96.69 239	92.67 254	94.48 333	97.08 278	86.62 311	97.08 104	92.23 333	87.94 189	97.90 287	78.89 327	96.69 157	98.49 157

LCM-MVSNet		78.70 314	76.24 318	86.08 324	77.26 351	71.99 344	94.34 334	96.72 302	61.62 345	76.53 336	89.33 336	33.91 354	92.78 340	81.85 319	74.60 340	93.46 333

PMMVS2		77.95 316	75.44 319	85.46 325	82.54 343	74.95 342	94.23 335	93.08 344	72.80 340	74.68 337	87.38 337	36.36 352	91.56 343	73.95 336	63.94 343	89.87 337

MVS-HIRNet		89.46 299	88.40 301	92.64 308	97.58 184	82.15 330	94.16 336	93.05 345	75.73 339	90.90 281	82.52 341	79.42 302	98.33 261	83.53 316	98.68 104	97.43 194

LP		91.12 287	89.99 289	94.53 290	96.35 259	88.70 305	93.86 337	97.35 264	84.88 322	90.98 280	94.77 305	84.40 263	97.43 300	75.41 335	91.89 249	97.47 193

Patchmatch-RL test		91.49 283	90.85 275	93.41 303	91.37 329	84.40 323	92.81 338	95.93 317	91.87 240	87.25 303	94.87 304	88.99 147	96.53 323	92.54 202	82.00 320	99.30 96

ambc				89.49 317	86.66 340	75.78 338	92.66 339	96.72 302		86.55 307	92.50 330	46.01 347	97.90 287	90.32 251	82.09 319	94.80 314

EMVS		64.07 325	63.26 326	66.53 339	81.73 345	58.81 355	91.85 340	84.75 354	51.93 350	59.09 347	75.13 348	43.32 349	79.09 353	42.03 351	39.47 348	61.69 350

E-PMN		64.94 324	64.25 324	67.02 338	82.28 344	59.36 354	91.83 341	85.63 353	52.69 348	60.22 346	77.28 347	41.06 350	80.12 352	46.15 350	41.14 347	61.57 351

ANet_high		69.08 320	65.37 322	80.22 331	65.99 354	71.96 345	90.91 342	90.09 348	82.62 327	49.93 350	78.39 346	29.36 355	81.75 350	62.49 347	38.52 349	86.95 342

PNet_i23d		67.70 322	65.07 323	75.60 334	78.61 348	59.61 353	89.14 343	88.24 351	61.83 344	52.37 348	80.89 343	18.91 356	84.91 349	62.70 346	52.93 345	82.28 345

tmp_tt		68.90 321	66.97 321	74.68 336	50.78 356	59.95 352	87.13 344	83.47 355	38.80 351	62.21 345	96.23 281	64.70 340	76.91 354	88.91 280	30.49 351	87.19 341

wuykxyi23d		63.73 326	58.86 328	78.35 333	67.62 353	67.90 348	86.56 345	87.81 352	58.26 346	42.49 352	70.28 350	11.55 359	85.05 348	63.66 345	41.50 346	82.11 346

MVE	62.14 22	63.28 327	59.38 327	74.99 335	74.33 352	65.47 349	85.55 346	80.50 356	52.02 349	51.10 349	75.00 349	10.91 361	80.50 351	51.60 349	53.40 344	78.99 347
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	61.03 23	65.95 323	63.57 325	73.09 337	57.90 355	51.22 356	85.05 347	93.93 343	54.45 347	44.32 351	83.57 340	13.22 357	89.15 346	58.68 348	81.00 324	78.91 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testpf		88.74 302	89.09 295	87.69 320	95.78 294	83.16 328	84.05 348	94.13 342	85.22 321	90.30 286	94.39 309	74.92 322	95.80 327	89.77 261	93.28 235	84.10 344

Gipuma		78.40 315	76.75 316	83.38 329	95.54 302	80.43 332	79.42 349	97.40 261	64.67 343	73.46 338	80.82 344	45.65 348	93.14 339	66.32 343	87.43 297	76.56 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuyk23d		30.17 329	30.18 331	30.16 342	78.61 348	43.29 357	66.79 350	14.21 358	17.31 352	14.82 355	11.93 356	11.55 359	41.43 355	37.08 352	19.30 352	5.76 355

cdsmvs_eth3d_5k		23.98 330	31.98 330	0.00 345	0.00 359	0.00 360	0.00 351	98.59 115	0.00 355	0.00 356	98.61 102	90.60 125	0.00 358	0.00 355	0.00 356	0.00 356

pcd_1.5k_mvsjas		7.88 334	10.50 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	94.51 62	0.00 358	0.00 355	0.00 356	0.00 356

pcd1.5k->3k		39.42 328	41.78 329	32.35 341	96.17 277	0.00 360	0.00 351	98.54 125	0.00 355	0.00 356	0.00 357	87.78 196	0.00 358	0.00 355	93.56 226	97.06 209

sosnet-low-res		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uncertanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

Regformer		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

ab-mvs-re		8.20 333	10.94 334	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	98.43 117	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

GSMVS																99.20 106

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

sam_mvs1												89.45 136				99.20 106

sam_mvs												88.99 147

semantic-postprocess				94.85 282	97.98 164	90.56 284		98.11 203	93.75 161	92.58 258	97.48 194	83.91 275	97.41 301	92.48 204	91.30 254	96.58 268

MTGPA								98.74 79

test_post											31.83 354	88.83 159	98.91 191

patchmatchnet-post											95.10 303	89.42 137	98.89 195

MTMP								94.14 341

gm-plane-assit					95.88 291	87.47 317			89.74 289		96.94 246		99.19 155	93.32 176

test9_res														96.39 94	99.57 57	99.69 37

agg_prior2														95.87 108	99.57 57	99.68 43

agg_prior					99.30 54	98.38 19		98.72 86		97.57 95			99.81 52

TestCases				96.99 170	99.25 66	93.21 248		98.18 185	91.36 253	93.52 234	98.77 89	84.67 254	99.72 88	89.70 265	97.87 136	98.02 176

test_prior				99.19 29	99.31 49	98.22 32		98.84 54					99.70 93			99.65 52

新几何1				99.16 36	99.34 41	98.01 43		98.69 94	90.06 278	98.13 58	98.95 73	94.60 60	99.89 29	91.97 215	99.47 71	99.59 63

旧先验1					99.29 57	97.48 61		98.70 93			99.09 54	95.56 37			99.47 71	99.61 58

原ACMM1				98.65 66	99.32 47	96.62 91		98.67 104	93.27 193	97.81 79	98.97 67	95.18 49	99.83 45	93.84 163	99.46 74	99.50 73

testdata2													99.89 29	91.65 224

segment_acmp												96.85 5

testdata				98.26 90	99.20 76	95.36 153		98.68 97	91.89 238	98.60 43	99.10 50	94.44 67	99.82 50	94.27 153	99.44 76	99.58 65

test12				99.18 33	99.16 78	98.19 34		98.53 128		98.07 61		95.13 51	99.72 88		99.56 63	99.63 57

plane_prior7					97.42 196	94.63 205

plane_prior6					97.35 201	94.61 208						87.09 209

plane_prior5								98.56 122					99.03 177	96.07 99	94.27 206	96.92 218

plane_prior4											98.28 133

plane_prior3						94.61 208			97.02 39	95.34 166

plane_prior1					97.37 200

n2								0.00 361

nn								0.00 361

door-mid								94.37 338

lessismore_v0				94.45 295	94.93 313	88.44 310		91.03 347		86.77 306	97.64 186	76.23 316	98.42 246	90.31 252	85.64 314	96.51 278

LGP-MVS_train				96.47 218	97.46 192	93.54 239		98.54 125	94.67 126	94.36 197	98.77 89	85.39 243	99.11 165	95.71 115	94.15 212	96.76 239

test11								98.66 107

door								94.64 337

HQP5-MVS						94.25 223

BP-MVS														95.30 127

HQP4-MVS										94.45 187			98.96 184			96.87 229

HQP3-MVS								98.46 142							94.18 210

HQP2-MVS												86.75 215

NP-MVS					97.28 204	94.51 213					97.73 176

ACMMP++_ref															92.97 237

ACMMP++															93.61 225

Test By Simon												94.64 59

ITE_SJBPF				95.44 262	97.42 196	91.32 272		97.50 247	95.09 113	93.59 230	98.35 125	81.70 287	98.88 196	89.71 264	93.39 231	96.12 291

DeepMVS_CX				86.78 323	97.09 218	72.30 343		95.17 332	75.92 338	84.34 325	95.19 300	70.58 332	95.35 329	79.98 324	89.04 275	92.68 335

High-resolution multi-view benchmark