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.

MVS_111021_HR		98.72 28	98.62 24	99.01 81	99.36 111	97.18 112	99.93 67	99.90 1	96.81 35	98.67 104	99.77 71	93.92 96	99.89 84	99.27 50	99.94 61	99.96 74

MVS_111021_LR		98.42 49	98.38 40	98.53 115	99.39 109	95.79 161	99.87 93	99.86 2	96.70 38	98.78 97	99.79 64	92.03 146	99.90 80	99.17 51	99.86 83	99.88 96

CHOSEN 1792x2688		96.81 117	96.53 116	97.64 153	98.91 135	93.07 228	99.65 162	99.80 3	95.64 71	95.39 186	98.86 174	84.35 232	99.90 80	96.98 141	99.16 124	99.95 82

HyFIR lowres test		96.66 127	96.43 119	97.36 167	99.05 121	93.91 209	99.70 153	99.80 3	90.54 239	96.26 172	98.08 205	92.15 144	98.23 224	96.84 146	95.46 202	99.93 85

test2506		97.53 90	97.19 94	98.58 108	98.66 149	96.90 123	98.81 269	99.77 5	94.93 89	97.95 131	98.96 159	92.51 135	99.20 160	94.93 167	98.15 146	99.64 127

thres100view900		96.74 122	95.92 138	99.18 57	98.90 136	98.77 40	99.74 143	99.71 6	92.59 183	95.84 178	98.86 174	89.25 185	99.50 150	93.84 196	94.57 209	99.27 183

tfpn200view9		96.79 118	95.99 128	99.19 56	98.94 129	98.82 35	99.78 129	99.71 6	92.86 165	96.02 175	98.87 172	89.33 183	99.50 150	93.84 196	94.57 209	99.27 183

thres600view7		96.69 125	95.87 141	99.14 66	98.90 136	98.78 39	99.74 143	99.71 6	92.59 183	95.84 178	98.86 174	89.25 185	99.50 150	93.44 209	94.50 212	99.16 190

thres400		96.78 119	95.99 128	99.16 62	98.94 129	98.82 35	99.78 129	99.71 6	92.86 165	96.02 175	98.87 172	89.33 183	99.50 150	93.84 196	94.57 209	99.16 190

thres200		96.96 111	96.21 123	99.22 53	98.97 127	98.84 34	99.85 107	99.71 6	93.17 160	96.26 172	98.88 170	89.87 177	99.51 148	94.26 190	94.91 208	99.31 180

PVSNet	91.05 13	97.13 106	96.69 111	98.45 120	99.52 102	95.81 160	99.95 43	99.65 11	94.73 98	99.04 86	99.21 140	84.48 230	99.95 65	94.92 168	98.74 133	99.58 144

PVSNet_0	88.03 19	91.80 244	90.27 256	96.38 199	98.27 167	90.46 286	99.94 61	99.61 12	93.99 132	86.26 311	97.39 222	71.13 324	99.89 84	98.77 77	67.05 358	98.79 208

MVS_0304		89.28 292	88.31 291	92.21 305	97.05 231	86.53 326	97.76 316	99.57 13	85.58 314	93.86 206	92.71 338	51.04 366	96.30 315	84.49 306	92.72 227	93.79 306

WTY-MVS		98.10 69	97.60 80	99.60 20	98.92 133	99.28 16	99.89 87	99.52 14	95.58 73	98.24 125	99.39 124	93.33 110	99.74 130	97.98 114	95.58 201	99.78 108

HY-MVS	92.50 7	97.79 82	97.17 97	99.63 15	98.98 126	99.32 8	97.49 318	99.52 14	95.69 70	98.32 120	97.41 220	93.32 111	99.77 120	98.08 108	95.75 198	99.81 103

EPNet		98.49 44	98.40 37	98.77 93	99.62 95	96.80 126	99.90 79	99.51 16	97.60 12	99.20 78	99.36 127	93.71 103	99.91 79	97.99 112	98.71 134	99.61 135
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PGM-MVS		98.34 55	98.13 57	98.99 82	99.92 36	97.00 118	99.75 140	99.50 17	93.90 138	99.37 68	99.76 75	93.24 117	100.00 1	97.75 125	99.96 52	99.98 55

ACMMP		97.74 84	97.44 85	98.66 100	99.92 36	96.13 151	99.18 226	99.45 18	94.84 95	96.41 169	99.71 91	91.40 153	99.99 40	97.99 112	98.03 154	99.87 98
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
MG-MVS		98.91 18	98.65 22	99.68 14	99.94 14	99.07 22	99.64 166	99.44 19	97.33 18	99.00 90	99.72 89	94.03 94	99.98 46	98.73 79	100.00 1	100.00 1

EPMVS		96.53 130	96.01 127	98.09 136	98.43 159	96.12 153	96.36 334	99.43 20	93.53 150	97.64 138	95.04 303	94.41 74	98.38 209	91.13 235	98.11 149	99.75 111

CHOSEN 280x420		99.01 13	99.03 10	98.95 86	99.38 110	98.87 31	98.46 290	99.42 21	97.03 28	99.02 87	99.09 144	99.35 1	98.21 225	99.73 32	99.78 93	99.77 109

D2MVS		92.76 221	92.59 215	93.27 290	95.13 283	89.54 302	99.69 154	99.38 22	92.26 196	87.59 289	94.61 318	85.05 227	97.79 244	91.59 230	88.01 254	92.47 335

sss		97.57 89	97.03 102	99.18 57	98.37 160	98.04 76	99.73 148	99.38 22	93.46 152	98.76 100	99.06 146	91.21 156	99.89 84	96.33 149	97.01 174	99.62 132

PAPM		98.60 34	98.42 33	99.14 66	96.05 259	98.96 24	99.90 79	99.35 24	96.68 39	98.35 119	99.66 102	96.45 29	98.51 193	99.45 42	99.89 78	99.96 74

UGNet		95.33 162	94.57 170	97.62 155	98.55 153	94.85 189	98.67 281	99.32 25	95.75 69	96.80 157	96.27 259	72.18 318	99.96 58	94.58 182	99.05 128	98.04 218
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
test_yl		97.83 78	97.37 87	99.21 54	99.18 114	97.98 79	99.64 166	99.27 26	91.43 222	97.88 134	98.99 153	95.84 39	99.84 108	98.82 72	95.32 205	99.79 105

DCV-MVSNet		97.83 78	97.37 87	99.21 54	99.18 114	97.98 79	99.64 166	99.27 26	91.43 222	97.88 134	98.99 153	95.84 39	99.84 108	98.82 72	95.32 205	99.79 105

VNet		97.21 105	96.57 115	99.13 71	98.97 127	97.82 85	99.03 244	99.21 28	94.31 118	99.18 82	98.88 170	86.26 215	99.89 84	98.93 64	94.32 213	99.69 118

PVSNet_BlendedMVS		96.05 144	95.82 142	96.72 186	99.59 96	96.99 119	99.95 43	99.10 29	94.06 129	98.27 122	95.80 268	89.00 190	99.95 65	99.12 52	87.53 260	93.24 323

PVSNet_Blended		97.94 73	97.64 77	98.83 91	99.59 96	96.99 119	100.00 1	99.10 29	95.38 77	98.27 122	99.08 145	89.00 190	99.95 65	99.12 52	99.25 121	99.57 145

UniMVSNet_NR-MVSNet		92.95 218	92.11 223	95.49 215	94.61 293	95.28 178	99.83 117	99.08 31	91.49 218	89.21 263	96.86 240	87.14 206	96.73 298	93.20 211	77.52 332	94.46 244

CSCG		97.10 107	97.04 101	97.27 170	99.89 50	91.92 256	99.90 79	99.07 32	88.67 270	95.26 189	99.82 55	93.17 119	99.98 46	98.15 103	99.47 114	99.90 93

PatchMatch-RL		96.04 145	95.40 149	97.95 140	99.59 96	95.22 182	99.52 183	99.07 32	93.96 134	96.49 165	98.35 200	82.28 243	99.82 110	90.15 256	99.22 123	98.81 207

VPA-MVSNet		92.70 223	91.55 235	96.16 203	95.09 284	96.20 148	98.88 259	99.00 34	91.02 232	91.82 223	95.29 297	76.05 298	97.96 238	95.62 159	81.19 303	94.30 259

CVMVSNet		94.68 178	94.94 163	93.89 276	96.80 245	86.92 324	99.06 238	98.98 35	94.45 108	94.23 201	99.02 148	85.60 219	95.31 335	90.91 243	95.39 204	99.43 167

UniMVSNet (Re)		93.07 215	92.13 222	95.88 209	94.84 288	96.24 147	99.88 90	98.98 35	92.49 191	89.25 261	95.40 287	87.09 207	97.14 273	93.13 215	78.16 327	94.26 262

h-mvs33		94.92 170	94.36 173	96.59 191	98.85 140	91.29 272	98.93 254	98.94 37	95.90 60	98.77 98	98.42 199	90.89 166	99.77 120	97.80 118	70.76 348	98.72 210

tfpnnormal		89.29 291	87.61 298	94.34 259	94.35 296	94.13 203	98.95 252	98.94 37	83.94 326	84.47 321	95.51 282	74.84 307	97.39 257	77.05 343	80.41 313	91.48 345

MVS		96.60 128	95.56 147	99.72 12	96.85 242	99.22 19	98.31 297	98.94 37	91.57 216	90.90 231	99.61 106	86.66 211	99.96 58	97.36 131	99.88 80	99.99 24

WR-MVS_H		91.30 250	90.35 253	94.15 263	94.17 299	92.62 242	99.17 227	98.94 37	88.87 266	86.48 306	94.46 323	84.36 231	96.61 304	88.19 273	78.51 325	93.21 324

FIs		94.10 193	93.43 196	96.11 204	94.70 291	96.82 125	99.58 173	98.93 41	92.54 187	89.34 259	97.31 223	87.62 201	97.10 277	94.22 192	86.58 265	94.40 251

EPNet_dtu		95.71 153	95.39 150	96.66 188	98.92 133	93.41 223	99.57 175	98.90 42	96.19 55	97.52 140	98.56 191	92.65 131	97.36 258	77.89 338	98.33 141	99.20 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
patch_mono-2		98.24 64	99.12 5	95.59 214	99.67 92	86.91 325	99.95 43	98.89 43	97.60 12	99.90 2	99.76 75	96.54 27	99.98 46	99.94 12	99.82 91	99.88 96

FC-MVSNet-test		93.81 198	93.15 205	95.80 212	94.30 297	96.20 148	99.42 198	98.89 43	92.33 195	89.03 268	97.27 225	87.39 204	96.83 294	93.20 211	86.48 266	94.36 254

baseline2		96.71 124	96.49 117	97.37 165	95.63 278	95.96 157	99.74 143	98.88 45	92.94 164	91.61 224	98.97 157	97.72 5	98.62 188	94.83 172	98.08 153	97.53 228

API-MVS		97.86 76	97.66 76	98.47 118	99.52 102	95.41 174	99.47 192	98.87 46	91.68 213	98.84 94	99.85 35	92.34 140	99.99 40	98.44 92	99.96 52	100.00 1

1314		96.84 116	95.96 135	99.48 36	96.74 249	98.52 59	98.31 297	98.86 47	95.82 62	89.91 243	98.98 155	87.49 202	99.96 58	97.80 118	99.73 96	99.96 74

MSLP-MVS++		99.13 8	99.01 11	99.49 34	99.94 14	98.46 63	99.98 10	98.86 47	97.10 26	99.80 17	99.94 4	95.92 37	100.00 1	99.51 39	100.00 1	100.00 1

AdaColmap		97.23 104	96.80 108	98.51 116	99.99 1	95.60 170	99.09 231	98.84 49	93.32 155	96.74 158	99.72 89	86.04 216	100.00 1	98.01 110	99.43 117	99.94 84

IB-MVS	92.85 6	94.99 169	93.94 183	98.16 132	97.72 203	95.69 168	99.99 4	98.81 50	94.28 120	92.70 219	96.90 237	95.08 54	99.17 163	96.07 152	73.88 346	99.60 137
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
3Dnovator	91.47 12	96.28 141	95.34 152	99.08 74	96.82 244	97.47 102	99.45 195	98.81 50	95.52 75	89.39 257	99.00 152	81.97 245	99.95 65	97.27 133	99.83 85	99.84 100

PHI-MVS		98.41 50	98.21 51	99.03 78	99.86 59	97.10 116	99.98 10	98.80 52	90.78 237	99.62 44	99.78 69	95.30 50	100.00 1	99.80 23	99.93 67	99.99 24

MAR-MVS		97.43 93	97.19 94	98.15 135	99.47 106	94.79 193	99.05 242	98.76 53	92.65 179	98.66 105	99.82 55	88.52 196	99.98 46	98.12 104	99.63 102	99.67 121
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
DU-MVS		92.46 229	91.45 238	95.49 215	94.05 300	95.28 178	99.81 120	98.74 54	92.25 197	89.21 263	96.64 249	81.66 249	96.73 298	93.20 211	77.52 332	94.46 244

无先验							99.49 189	98.71 55	93.46 152				100.00 1	94.36 186		99.99 24

NR-MVSNet		91.56 249	90.22 257	95.60 213	94.05 300	95.76 163	98.25 300	98.70 56	91.16 228	80.78 339	96.64 249	83.23 240	96.57 305	91.41 231	77.73 331	94.46 244

CNVR-MVS		99.40 1	99.26 1	99.84 6	99.98 2	99.51 6	99.98 10	98.69 57	98.20 3	99.93 1	99.98 2	96.82 22	100.00 1	99.75 27	100.00 1	99.99 24

WR-MVS		92.31 232	91.25 240	95.48 218	94.45 294	95.29 177	99.60 171	98.68 58	90.10 246	88.07 284	96.89 238	80.68 261	96.80 296	93.14 214	79.67 319	94.36 254

ab-mvs		94.69 176	93.42 197	98.51 116	98.07 178	96.26 143	96.49 333	98.68 58	90.31 244	94.54 194	97.00 235	76.30 294	99.71 134	95.98 154	93.38 223	99.56 146

QAPM		95.40 161	94.17 177	99.10 72	96.92 236	97.71 87	99.40 199	98.68 58	89.31 255	88.94 269	98.89 168	82.48 242	99.96 58	93.12 216	99.83 85	99.62 132

Anonymous20240529		92.10 237	90.65 248	96.47 192	98.82 141	90.61 282	98.72 276	98.67 61	75.54 355	93.90 205	98.58 189	66.23 340	99.90 80	94.70 179	90.67 229	98.90 203

test_prior3		98.99 14	98.84 16	99.43 38	99.94 14	98.49 61	99.95 43	98.65 62	95.78 64	99.73 30	99.76 75	96.00 33	99.80 111	99.78 25	100.00 1	99.99 24

test_prior				99.43 38	99.94 14	98.49 61		98.65 62					99.80 111			99.99 24

TranMVSNet+NR-MVSNet		91.68 248	90.61 249	94.87 235	93.69 307	93.98 207	99.69 154	98.65 62	91.03 231	88.44 276	96.83 244	80.05 269	96.18 319	90.26 255	76.89 340	94.45 249

旧先验1					99.76 79	97.52 96		98.64 65			99.85 35	95.63 43			99.94 61	99.99 24

MCST-MVS		99.32 3	99.14 4	99.86 5	99.97 3	99.59 5	99.97 18	98.64 65	98.47 2	99.13 83	99.92 13	96.38 30	100.00 1	99.74 29	100.00 1	100.00 1

PVSNet_Blended_VisFu		97.27 102	96.81 107	98.66 100	98.81 142	96.67 128	99.92 71	98.64 65	94.51 107	96.38 170	98.49 193	89.05 189	99.88 90	97.10 138	98.34 140	99.43 167

新几何1				99.42 41	99.75 81	98.27 69		98.63 68	92.69 176	99.55 49	99.82 55	94.40 75	100.00 1	91.21 233	99.94 61	99.99 24

1121		98.03 71	97.57 82	99.40 44	99.74 82	98.21 70	98.31 297	98.62 69	92.78 171	99.53 51	99.83 51	95.08 54	100.00 1	94.36 186	99.92 71	99.99 24

NCCC		99.37 2	99.25 2	99.71 13	99.96 8	99.15 20	99.97 18	98.62 69	98.02 6	99.90 2	99.95 3	97.33 16	100.00 1	99.54 38	100.00 1	100.00 1

HFP-MVS		98.56 38	98.37 42	99.14 66	99.96 8	97.43 104	99.95 43	98.61 71	94.77 96	99.31 71	99.85 35	94.22 87	100.00 1	98.70 80	99.98 35	99.98 55

#test#		98.59 36	98.41 35	99.14 66	99.96 8	97.43 104	99.95 43	98.61 71	95.00 87	99.31 71	99.85 35	94.22 87	100.00 1	98.78 76	99.98 35	99.98 55

ACMMPR		98.50 43	98.32 46	99.05 76	99.96 8	97.18 112	99.95 43	98.60 73	94.77 96	99.31 71	99.84 48	93.73 102	100.00 1	98.70 80	99.98 35	99.98 55

VPNet		91.81 241	90.46 250	95.85 211	94.74 290	95.54 171	98.98 248	98.59 74	92.14 199	90.77 233	97.44 219	68.73 331	97.54 252	94.89 171	77.89 329	94.46 244

test0.0.03 1		93.86 195	93.61 188	94.64 243	95.02 287	92.18 250	99.93 67	98.58 75	94.07 127	87.96 285	98.50 192	93.90 98	94.96 339	81.33 324	93.17 224	96.78 230

DELS-MVS		98.54 40	98.22 50	99.50 32	99.15 117	98.65 52	100.00 1	98.58 75	97.70 9	98.21 126	99.24 138	92.58 133	99.94 73	98.63 87	99.94 61	99.92 91
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
CP-MVSNet		91.23 253	90.22 257	94.26 260	93.96 302	92.39 246	99.09 231	98.57 77	88.95 264	86.42 307	96.57 251	79.19 274	96.37 311	90.29 254	78.95 322	94.02 286

OpenMVS	90.15 15	94.77 174	93.59 191	98.33 127	96.07 258	97.48 101	99.56 177	98.57 77	90.46 240	86.51 304	98.95 163	78.57 279	99.94 73	93.86 195	99.74 95	97.57 227

hse-mvs2		94.38 187	94.08 180	95.31 222	98.27 167	90.02 294	99.29 218	98.56 79	95.90 60	98.77 98	98.00 208	90.89 166	98.26 223	97.80 118	69.20 354	97.64 225

AUN-MVS		93.28 210	92.60 212	95.34 220	98.29 163	90.09 293	99.31 213	98.56 79	91.80 211	96.35 171	98.00 208	89.38 182	98.28 219	92.46 220	69.22 353	97.64 225

HPM-MVS++		99.07 10	98.88 15	99.63 15	99.90 47	99.02 23	99.95 43	98.56 79	97.56 14	99.44 59	99.85 35	95.38 49	100.00 1	99.31 48	99.99 22	99.87 98

testdata				98.42 123	99.47 106	95.33 176		98.56 79	93.78 143	99.79 24	99.85 35	93.64 105	99.94 73	94.97 166	99.94 61	100.00 1

EPP-MVSNet		96.69 125	96.60 113	96.96 178	97.74 199	93.05 230	99.37 206	98.56 79	88.75 268	95.83 180	99.01 150	96.01 32	98.56 190	96.92 144	97.20 170	99.25 185

DeepPCF-MVS	95.94 2	97.71 86	98.98 12	93.92 274	99.63 94	81.76 351	99.96 25	98.56 79	99.47 1	99.19 81	99.99 1	94.16 91	100.00 1	99.92 13	99.93 67	100.00 1

testtj		98.89 19	98.69 20	99.52 29	99.94 14	98.56 57	99.90 79	98.55 85	95.14 83	99.72 33	99.84 48	95.46 47	100.00 1	99.65 37	99.99 22	99.99 24

region2R		98.54 40	98.37 42	99.05 76	99.96 8	97.18 112	99.96 25	98.55 85	94.87 94	99.45 58	99.85 35	94.07 93	100.00 1	98.67 82	100.00 1	99.98 55

test222					99.55 100	97.41 107	99.34 209	98.55 85	91.86 207	99.27 76	99.83 51	93.84 100			99.95 55	99.99 24

tpmvs		94.28 192	93.57 192	96.40 197	98.55 153	91.50 270	95.70 345	98.55 85	87.47 285	92.15 221	94.26 325	91.42 152	98.95 170	88.15 274	95.85 194	98.76 209

thisisatest0530		97.10 107	96.72 110	98.22 131	97.60 208	96.70 127	99.92 71	98.54 89	91.11 229	97.07 150	98.97 157	97.47 11	99.03 166	93.73 204	96.09 188	98.92 200

tttt0517		96.85 115	96.49 117	97.92 142	97.48 214	95.89 159	99.85 107	98.54 89	90.72 238	96.63 160	98.93 167	97.47 11	99.02 167	93.03 217	95.76 197	98.85 204

thisisatest0515		97.41 97	97.02 103	98.59 107	97.71 205	97.52 96	99.97 18	98.54 89	91.83 208	97.45 142	99.04 147	97.50 8	99.10 165	94.75 176	96.37 185	99.16 190

ZD-MVS					99.92 36	98.57 55		98.52 92	92.34 194	99.31 71	99.83 51	95.06 56	99.80 111	99.70 35	99.97 48

GG-mvs-BLEND				98.54 113	98.21 171	98.01 77	93.87 350	98.52 92		97.92 132	97.92 212	99.02 2	97.94 241	98.17 101	99.58 108	99.67 121

Regformer-3		98.58 37	98.41 35	99.10 72	99.84 65	97.57 93	99.66 159	98.52 92	95.79 63	99.01 88	99.77 71	94.40 75	99.75 126	98.82 72	99.83 85	99.98 55

Regformer-4		98.56 38	98.39 39	99.08 74	99.84 65	97.52 96	99.66 159	98.52 92	95.76 66	99.01 88	99.77 71	94.33 83	99.75 126	98.80 75	99.83 85	99.98 55

Regformer-1		98.79 25	98.60 25	99.36 48	99.85 60	98.34 66	99.87 93	98.52 92	96.05 57	99.41 62	99.79 64	94.93 64	99.76 123	99.07 54	99.90 76	99.99 24

Regformer-2		98.78 26	98.59 26	99.36 48	99.85 60	98.32 67	99.87 93	98.52 92	96.04 58	99.41 62	99.79 64	94.92 65	99.76 123	99.05 55	99.90 76	99.98 55

PS-CasMVS		90.63 266	89.51 271	93.99 272	93.83 304	91.70 265	98.98 248	98.52 92	88.48 274	86.15 312	96.53 253	75.46 300	96.31 314	88.83 266	78.86 324	93.95 294

CANet		98.27 60	97.82 73	99.63 15	99.72 88	99.10 21	99.98 10	98.51 99	97.00 29	98.52 110	99.71 91	87.80 199	99.95 65	99.75 27	99.38 118	99.83 101

gg-mvs-nofinetune		93.51 206	91.86 230	98.47 118	97.72 203	97.96 81	92.62 354	98.51 99	74.70 357	97.33 144	69.59 368	98.91 3	97.79 244	97.77 123	99.56 109	99.67 121

EI-MVSNet-Vis-set		98.27 60	98.11 59	98.75 95	99.83 68	96.59 132	99.40 199	98.51 99	95.29 80	98.51 111	99.76 75	93.60 106	99.71 134	98.53 90	99.52 111	99.95 82

原ACMM1				98.96 85	99.73 86	96.99 119		98.51 99	94.06 129	99.62 44	99.85 35	94.97 63	99.96 58	95.11 163	99.95 55	99.92 91

EI-MVSNet-UG-set		98.14 67	97.99 65	98.60 105	99.80 74	96.27 142	99.36 208	98.50 103	95.21 82	98.30 121	99.75 81	93.29 113	99.73 133	98.37 94	99.30 120	99.81 103

LS3D		95.84 149	95.11 159	98.02 139	99.85 60	95.10 184	98.74 274	98.50 103	87.22 290	93.66 207	99.86 31	87.45 203	99.95 65	90.94 242	99.81 92	99.02 198

PEN-MVS		90.19 278	89.06 279	93.57 285	93.06 319	90.90 277	99.06 238	98.47 105	88.11 278	85.91 314	96.30 258	76.67 289	95.94 328	87.07 287	76.91 339	93.89 299

DeepC-MVS_fast	96.59 1	98.81 23	98.54 28	99.62 18	99.90 47	98.85 33	99.24 222	98.47 105	98.14 4	99.08 84	99.91 15	93.09 120	100.00 1	99.04 59	99.99 22	100.00 1
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ETH3 D test6400		98.81 23	98.54 28	99.59 21	99.93 27	98.93 26	99.93 67	98.46 107	94.56 105	99.84 9	99.92 13	94.32 84	99.86 95	99.96 9	99.98 35	100.00 1

PLC	95.54 3	97.93 74	97.89 72	98.05 138	99.82 70	94.77 194	99.92 71	98.46 107	93.93 136	97.20 146	99.27 132	95.44 48	99.97 56	97.41 130	99.51 113	99.41 169
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test1111		95.57 157	94.98 162	97.37 165	98.56 151	93.37 225	98.86 263	98.45 109	94.95 88	96.63 160	98.95 163	75.21 305	99.11 164	95.02 165	98.14 148	99.64 127

ECVR-MVS		95.66 155	95.05 160	97.51 158	98.66 149	93.71 213	98.85 266	98.45 109	94.93 89	96.86 154	98.96 159	75.22 304	99.20 160	95.34 160	98.15 146	99.64 127

UA-Net		96.54 129	95.96 135	98.27 129	98.23 170	95.71 166	98.00 311	98.45 109	93.72 146	98.41 115	99.27 132	88.71 194	99.66 142	91.19 234	97.69 157	99.44 166

ZNCC-MVS		98.31 57	98.03 62	99.17 60	99.88 54	97.59 92	99.94 61	98.44 112	94.31 118	98.50 112	99.82 55	93.06 122	99.99 40	98.30 99	99.99 22	99.93 85

DPM-MVS		98.83 22	98.46 32	99.97 1	99.33 112	99.92 1	99.96 25	98.44 112	97.96 7	99.55 49	99.94 4	97.18 20	100.00 1	93.81 199	99.94 61	99.98 55

DPE-MVS		99.26 6	99.10 8	99.74 10	99.89 50	99.24 18	99.87 93	98.44 112	97.48 16	99.64 40	99.94 4	96.68 25	99.99 40	99.99 5	100.00 1	99.99 24
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
alignmvs		97.81 80	97.33 90	99.25 52	98.77 145	98.66 50	99.99 4	98.44 112	94.40 114	98.41 115	99.47 116	93.65 104	99.42 156	98.57 88	94.26 214	99.67 121

test11								98.44 112

SteuartSystems-ACMMP		99.02 12	98.97 13	99.18 57	98.72 146	97.71 87	99.98 10	98.44 112	96.85 31	99.80 17	99.91 15	97.57 6	99.85 99	99.44 43	99.99 22	99.99 24
Skip Steuart: Steuart Systems R&D Blog.
MDTV_nov1_ep13			95.69 144		97.90 186	94.15 202	95.98 341	98.44 112	93.12 161	97.98 130	95.74 270	95.10 53	98.58 189	90.02 257	96.92 176

DP-MVS Recon		98.41 50	98.02 63	99.56 24	99.97 3	98.70 47	99.92 71	98.44 112	92.06 203	98.40 117	99.84 48	95.68 42	100.00 1	98.19 100	99.71 98	99.97 67

DVP-MVS++		99.26 6	99.09 9	99.77 8	99.91 44	99.31 9	99.95 43	98.43 120	96.48 43	99.80 17	99.93 11	97.44 13	100.00 1	99.92 13	99.98 35	100.00 1

SED-MVS		99.28 5	99.11 7	99.77 8	99.93 27	99.30 11	99.96 25	98.43 120	97.27 21	99.80 17	99.94 4	96.71 23	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_TWO								98.43 120	97.27 21	99.80 17	99.94 4	97.18 20	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					99.93 27	99.30 11		98.43 120	97.26 23	99.80 17	99.88 24	96.71 23	100.00 1

test_0728_SECOND				99.82 7	99.94 14	99.47 7	99.95 43	98.43 120					100.00 1	99.99 5	100.00 1	100.00 1

TEST9					99.92 36	98.92 27	99.96 25	98.43 120	93.90 138	99.71 34	99.86 31	95.88 38	99.85 99

train_agg		98.88 20	98.65 22	99.59 21	99.92 36	98.92 27	99.96 25	98.43 120	94.35 115	99.71 34	99.86 31	95.94 35	99.85 99	99.69 36	99.98 35	99.99 24

test_8					99.92 36	98.88 30	99.96 25	98.43 120	94.35 115	99.69 36	99.85 35	95.94 35	99.85 99

agg_prior1		98.88 20	98.66 21	99.54 26	99.93 27	98.77 40	99.96 25	98.43 120	94.63 103	99.63 41	99.85 35	95.79 41	99.85 99	99.72 33	99.99 22	99.99 24

agg_prior					99.93 27	98.77 40		98.43 120		99.63 41			99.85 99

PAPM_NR		98.12 68	97.93 70	98.70 97	99.94 14	96.13 151	99.82 118	98.43 120	94.56 105	97.52 140	99.70 93	94.40 75	99.98 46	97.00 140	99.98 35	99.99 24

PAPR		98.52 42	98.16 55	99.58 23	99.97 3	98.77 40	99.95 43	98.43 120	95.35 78	98.03 129	99.75 81	94.03 94	99.98 46	98.11 105	99.83 85	99.99 24

test0726					99.93 27	99.29 14	99.96 25	98.42 132	97.28 19	99.86 5	99.94 4	97.22 18

MSP-MVS		99.09 9	99.12 5	98.98 83	99.93 27	97.24 109	99.95 43	98.42 132	97.50 15	99.52 54	99.88 24	97.43 15	99.71 134	99.50 40	99.98 35	100.00 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
XVS		98.70 29	98.55 27	99.15 64	99.94 14	97.50 99	99.94 61	98.42 132	96.22 53	99.41 62	99.78 69	94.34 80	99.96 58	98.92 65	99.95 55	99.99 24

X-MVStestdata		93.83 196	92.06 225	99.15 64	99.94 14	97.50 99	99.94 61	98.42 132	96.22 53	99.41 62	41.37 376	94.34 80	99.96 58	98.92 65	99.95 55	99.99 24

MSC_two_6792asdad				99.93 2	99.91 44	99.80 2		98.41 136					100.00 1	99.96 9	100.00 1	100.00 1

No_MVS				99.93 2	99.91 44	99.80 2		98.41 136					100.00 1	99.96 9	100.00 1	100.00 1

test_one_0601					99.94 14	99.30 11		98.41 136	96.63 40	99.75 28	99.93 11	97.49 9

IU-MVS					99.93 27	99.31 9		98.41 136	97.71 8	99.84 9				100.00 1	100.00 1	100.00 1

save fliter					99.82 70	98.79 37	99.96 25	98.40 140	97.66 10

test12				99.43 38	99.74 82	98.56 57		98.40 140		99.65 39		94.76 67	99.75 126		99.98 35	99.99 24

PatchmatchNet		95.94 147	95.45 148	97.39 164	97.83 192	94.41 199	96.05 340	98.40 140	92.86 165	97.09 149	95.28 298	94.21 90	98.07 232	89.26 263	98.11 149	99.70 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
GST-MVS		98.27 60	97.97 66	99.17 60	99.92 36	97.57 93	99.93 67	98.39 143	94.04 131	98.80 96	99.74 86	92.98 123	100.00 1	98.16 102	99.76 94	99.93 85

APDe-MVS		99.06 11	98.91 14	99.51 31	99.94 14	98.76 44	99.91 75	98.39 143	97.20 25	99.46 57	99.85 35	95.53 46	99.79 114	99.86 17	100.00 1	99.99 24

MP-MVS		98.23 65	97.97 66	99.03 78	99.94 14	97.17 115	99.95 43	98.39 143	94.70 99	98.26 124	99.81 59	91.84 150	100.00 1	98.85 71	99.97 48	99.93 85
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		98.45 47	98.32 46	98.87 89	99.96 8	96.62 130	99.97 18	98.39 143	94.43 110	98.90 93	99.87 28	94.30 85	100.00 1	99.04 59	99.99 22	99.99 24

SMA-MVS		98.76 27	98.48 31	99.62 18	99.87 57	98.87 31	99.86 104	98.38 147	93.19 159	99.77 26	99.94 4	95.54 44	100.00 1	99.74 29	99.99 22	100.00 1
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
TSAR-MVS + MP.		98.93 16	98.77 18	99.41 42	99.74 82	98.67 48	99.77 132	98.38 147	96.73 37	99.88 4	99.74 86	94.89 66	99.59 145	99.80 23	99.98 35	99.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
mPP-MVS		98.39 53	98.20 52	98.97 84	99.97 3	96.92 122	99.95 43	98.38 147	95.04 86	98.61 108	99.80 60	93.39 108	100.00 1	98.64 86	100.00 1	99.98 55

ETH3D-3000-0.1		98.68 30	98.42 33	99.47 37	99.83 68	98.57 55	99.90 79	98.37 150	93.81 141	99.81 13	99.90 19	94.34 80	99.86 95	99.84 18	99.98 35	99.97 67

ACMMP_NAP		98.49 44	98.14 56	99.54 26	99.66 93	98.62 54	99.85 107	98.37 150	94.68 100	99.53 51	99.83 51	92.87 125	100.00 1	98.66 85	99.84 84	99.99 24

FOURS1					99.92 36	97.66 91	99.95 43	98.36 152	95.58 73	99.52 54

test1172		98.38 54	98.25 49	98.77 93	99.88 54	96.56 133	99.80 125	98.36 152	94.68 100	99.20 78	99.80 60	93.28 114	99.78 116	99.34 47	99.92 71	99.98 55

APD-MVS		98.62 33	98.35 45	99.41 42	99.90 47	98.51 60	99.87 93	98.36 152	94.08 126	99.74 29	99.73 88	94.08 92	99.74 130	99.42 44	99.99 22	99.99 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SR-MVS		98.46 46	98.30 48	98.93 87	99.88 54	97.04 117	99.84 111	98.35 155	94.92 91	99.32 70	99.80 60	93.35 109	99.78 116	99.30 49	99.95 55	99.96 74

CPTT-MVS		97.64 88	97.32 91	98.58 108	99.97 3	95.77 162	99.96 25	98.35 155	89.90 250	98.36 118	99.79 64	91.18 160	99.99 40	98.37 94	99.99 22	99.99 24

SD-MVS		98.92 17	98.70 19	99.56 24	99.70 90	98.73 45	99.94 61	98.34 157	96.38 48	99.81 13	99.76 75	94.59 71	99.98 46	99.84 18	99.96 52	99.97 67
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
9.14			98.38 40		99.87 57		99.91 75	98.33 158	93.22 158	99.78 25	99.89 21	94.57 72	99.85 99	99.84 18	99.97 48

CDPH-MVS		98.65 32	98.36 44	99.49 34	99.94 14	98.73 45	99.87 93	98.33 158	93.97 133	99.76 27	99.87 28	94.99 62	99.75 126	98.55 89	100.00 1	99.98 55

DVP-MVS		99.30 4	99.16 3	99.73 11	99.93 27	99.29 14	99.95 43	98.32 160	97.28 19	99.83 11	99.91 15	97.22 18	100.00 1	99.99 5	100.00 1	99.89 94
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
SCA		94.69 176	93.81 187	97.33 169	97.10 228	94.44 197	98.86 263	98.32 160	93.30 156	96.17 174	95.59 277	76.48 292	97.95 239	91.06 237	97.43 162	99.59 138

SR-MVS-dyc-post		98.31 57	98.17 54	98.71 96	99.79 75	96.37 140	99.76 137	98.31 162	94.43 110	99.40 66	99.75 81	93.28 114	99.78 116	98.90 68	99.92 71	99.97 67

RE-MVS-def			98.13 57		99.79 75	96.37 140	99.76 137	98.31 162	94.43 110	99.40 66	99.75 81	92.95 124		98.90 68	99.92 71	99.97 67

RPMNet		89.76 285	87.28 300	97.19 171	96.29 254	92.66 239	92.01 357	98.31 162	70.19 362	96.94 151	85.87 361	87.25 205	99.78 116	62.69 364	95.96 191	99.13 194

APD-MVS_3200maxsize		98.25 63	98.08 60	98.78 92	99.81 73	96.60 131	99.82 118	98.30 165	93.95 135	99.37 68	99.77 71	92.84 126	99.76 123	98.95 62	99.92 71	99.97 67

TESTMET0.1,1		96.74 122	96.26 122	98.16 132	97.36 218	96.48 134	99.96 25	98.29 166	91.93 205	95.77 181	98.07 206	95.54 44	98.29 217	90.55 248	98.89 129	99.70 116

zzz-MVS		98.33 56	98.00 64	99.30 50	99.85 60	97.93 82	99.80 125	98.28 167	95.76 66	97.18 147	99.88 24	92.74 129	100.00 1	98.67 82	99.88 80	99.99 24

MTGPA								98.28 167

MTAPA		98.29 59	97.96 69	99.30 50	99.85 60	97.93 82	99.39 203	98.28 167	95.76 66	97.18 147	99.88 24	92.74 129	100.00 1	98.67 82	99.88 80	99.99 24

114514_t		97.41 97	96.83 106	99.14 66	99.51 104	97.83 84	99.89 87	98.27 170	88.48 274	99.06 85	99.66 102	90.30 172	99.64 144	96.32 150	99.97 48	99.96 74

test_part1		92.15 236	90.72 246	96.44 196	98.87 139	97.46 103	98.99 247	98.26 171	85.89 306	86.34 309	96.34 257	81.71 247	97.48 254	91.06 237	78.99 321	94.37 253

Anonymous20231211		89.86 283	88.44 289	94.13 265	98.93 131	90.68 280	98.54 287	98.26 171	76.28 351	86.73 300	95.54 279	70.60 325	97.56 251	90.82 245	80.27 316	94.15 276

ETH3D cwj APD-0.16		98.40 52	98.07 61	99.40 44	99.59 96	98.41 64	99.86 104	98.24 173	92.18 198	99.73 30	99.87 28	93.47 107	99.85 99	99.74 29	99.95 55	99.93 85

Vis-MVSNet		95.72 151	95.15 158	97.45 160	97.62 207	94.28 201	99.28 219	98.24 173	94.27 121	96.84 155	98.94 165	79.39 272	98.76 179	93.25 210	98.49 137	99.30 181
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
3Dnovator+	91.53 11	96.31 138	95.24 154	99.52 29	96.88 241	98.64 53	99.72 151	98.24 173	95.27 81	88.42 280	98.98 155	82.76 241	99.94 73	97.10 138	99.83 85	99.96 74

DTE-MVSNet		89.40 289	88.24 293	92.88 298	92.66 327	89.95 296	99.10 230	98.22 176	87.29 288	85.12 319	96.22 260	76.27 295	95.30 336	83.56 313	75.74 343	93.41 317

SF-MVS		98.67 31	98.40 37	99.50 32	99.77 78	98.67 48	99.90 79	98.21 177	93.53 150	99.81 13	99.89 21	94.70 69	99.86 95	99.84 18	99.93 67	99.96 74

VDDNet		93.12 213	91.91 228	96.76 184	96.67 252	92.65 241	98.69 279	98.21 177	82.81 334	97.75 137	99.28 129	61.57 353	99.48 154	98.09 107	94.09 216	98.15 216

test-LLR		96.47 131	96.04 126	97.78 146	97.02 233	95.44 172	99.96 25	98.21 177	94.07 127	95.55 183	96.38 254	93.90 98	98.27 221	90.42 251	98.83 131	99.64 127

test-mter		96.39 135	95.93 137	97.78 146	97.02 233	95.44 172	99.96 25	98.21 177	91.81 210	95.55 183	96.38 254	95.17 51	98.27 221	90.42 251	98.83 131	99.64 127

DWT-MVSNet_test		97.31 100	97.19 94	97.66 152	98.24 169	94.67 195	98.86 263	98.20 181	93.60 149	98.09 127	98.89 168	97.51 7	98.78 176	94.04 193	97.28 167	99.55 147

MP-MVS-pluss		98.07 70	97.64 77	99.38 47	99.74 82	98.41 64	99.74 143	98.18 182	93.35 154	96.45 166	99.85 35	92.64 132	99.97 56	98.91 67	99.89 78	99.77 109
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PS-MVSNAJ		98.44 48	98.20 52	99.16 62	98.80 143	98.92 27	99.54 181	98.17 183	97.34 17	99.85 7	99.85 35	91.20 157	99.89 84	99.41 45	99.67 100	98.69 211

HPM-MVS		97.96 72	97.72 75	98.68 98	99.84 65	96.39 139	99.90 79	98.17 183	92.61 181	98.62 107	99.57 109	91.87 149	99.67 141	98.87 70	99.99 22	99.99 24
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
tpmrst		96.27 142	95.98 130	97.13 174	97.96 183	93.15 227	96.34 335	98.17 183	92.07 201	98.71 103	95.12 301	93.91 97	98.73 181	94.91 170	96.62 179	99.50 159

ADS-MVSNet		94.79 172	94.02 181	97.11 176	97.87 189	93.79 210	94.24 346	98.16 186	90.07 247	96.43 167	94.48 321	90.29 173	98.19 226	87.44 281	97.23 168	99.36 174

HPM-MVS_fast		97.80 81	97.50 83	98.68 98	99.79 75	96.42 136	99.88 90	98.16 186	91.75 212	98.94 92	99.54 112	91.82 151	99.65 143	97.62 127	99.99 22	99.99 24

Vis-MVSNet (Re-imp)		96.32 137	95.98 130	97.35 168	97.93 185	94.82 191	99.47 192	98.15 188	91.83 208	95.09 190	99.11 143	91.37 154	97.47 255	93.47 208	97.43 162	99.74 112

abl_6		97.67 87	97.34 89	98.66 100	99.68 91	96.11 154	99.68 156	98.14 189	93.80 142	99.27 76	99.70 93	88.65 195	99.98 46	97.46 129	99.72 97	99.89 94

CNLPA		97.76 83	97.38 86	98.92 88	99.53 101	96.84 124	99.87 93	98.14 189	93.78 143	96.55 164	99.69 96	92.28 141	99.98 46	97.13 136	99.44 116	99.93 85

JIA-IIPM		91.76 247	90.70 247	94.94 233	96.11 257	87.51 321	93.16 353	98.13 191	75.79 354	97.58 139	77.68 365	92.84 126	97.97 236	88.47 271	96.54 180	99.33 178

cl22		93.77 200	93.25 204	95.33 221	99.49 105	94.43 198	99.61 170	98.09 192	90.38 241	89.16 266	95.61 275	90.56 170	97.34 260	91.93 225	84.45 281	94.21 267

cdsmvs_eth3d_5k		23.43 343	31.24 346	0.00 360	0.00 383	0.00 384	0.00 371	98.09 192	0.00 378	0.00 379	99.67 100	83.37 238	0.00 379	0.00 377	0.00 377	0.00 375

xiu_mvs_v2_base		98.23 65	97.97 66	99.02 80	98.69 147	98.66 50	99.52 183	98.08 194	97.05 27	99.86 5	99.86 31	90.65 168	99.71 134	99.39 46	98.63 135	98.69 211

tpm cat1		93.51 206	92.52 217	96.47 192	97.77 196	91.47 271	96.13 338	98.06 195	80.98 341	92.91 216	93.78 329	89.66 178	98.87 171	87.03 289	96.39 184	99.09 196

DeepC-MVS	94.51 4	96.92 114	96.40 120	98.45 120	99.16 116	95.90 158	99.66 159	98.06 195	96.37 51	94.37 198	99.49 115	83.29 239	99.90 80	97.63 126	99.61 106	99.55 147
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EU-MVSNet		90.14 280	90.34 254	89.54 327	92.55 328	81.06 355	98.69 279	98.04 197	91.41 224	86.59 303	96.84 243	80.83 259	93.31 355	86.20 295	81.91 298	94.26 262

TAPA-MVS	92.12 8	94.42 186	93.60 190	96.90 180	99.33 112	91.78 260	99.78 129	98.00 198	89.89 251	94.52 195	99.47 116	91.97 147	99.18 162	69.90 354	99.52 111	99.73 113
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
baseline1		95.78 150	94.86 164	98.54 113	98.47 158	98.07 74	99.06 238	97.99 199	92.68 177	94.13 202	98.62 186	93.28 114	98.69 185	93.79 201	85.76 269	98.84 205

UnsupCasMVSNet_eth		85.52 309	83.99 310	90.10 323	89.36 355	83.51 340	96.65 331	97.99 199	89.14 256	75.89 354	93.83 328	63.25 350	93.92 348	81.92 322	67.90 357	92.88 329

LFMVS		94.75 175	93.56 193	98.30 128	99.03 122	95.70 167	98.74 274	97.98 201	87.81 283	98.47 113	99.39 124	67.43 337	99.53 146	98.01 110	95.20 207	99.67 121

dp		95.05 167	94.43 172	96.91 179	97.99 182	92.73 237	96.29 336	97.98 201	89.70 253	95.93 177	94.67 316	93.83 101	98.45 198	86.91 293	96.53 181	99.54 151

PMMVS		96.76 120	96.76 109	96.76 184	98.28 165	92.10 251	99.91 75	97.98 201	94.12 124	99.53 51	99.39 124	86.93 209	98.73 181	96.95 143	97.73 156	99.45 164

F-COLMAP		96.93 113	96.95 104	96.87 181	99.71 89	91.74 261	99.85 107	97.95 204	93.11 162	95.72 182	99.16 142	92.35 139	99.94 73	95.32 161	99.35 119	98.92 200

OMC-MVS		97.28 101	97.23 93	97.41 162	99.76 79	93.36 226	99.65 162	97.95 204	96.03 59	97.41 143	99.70 93	89.61 179	99.51 148	96.73 147	98.25 145	99.38 171

Anonymous202405211		93.10 214	91.99 226	96.40 197	99.10 118	89.65 300	98.88 259	97.93 206	83.71 329	94.00 203	98.75 179	68.79 329	99.88 90	95.08 164	91.71 228	99.68 119

tpm2		95.47 160	95.18 157	96.35 200	96.91 237	91.70 265	96.96 329	97.93 206	88.04 280	98.44 114	95.40 287	93.32 111	97.97 236	94.00 194	95.61 200	99.38 171

TSAR-MVS + GP.		98.60 34	98.51 30	98.86 90	99.73 86	96.63 129	99.97 18	97.92 208	98.07 5	98.76 100	99.55 110	95.00 61	99.94 73	99.91 16	97.68 158	99.99 24

CDS-MVSNet		96.34 136	96.07 125	97.13 174	97.37 217	94.96 187	99.53 182	97.91 209	91.55 217	95.37 187	98.32 201	95.05 57	97.13 274	93.80 200	95.75 198	99.30 181
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
HQP3-MVS								97.89 210							89.60 230

HQP-MVS		94.61 180	94.50 171	94.92 234	95.78 265	91.85 257	99.87 93	97.89 210	96.82 32	93.37 209	98.65 183	80.65 262	98.39 205	97.92 116	89.60 230	94.53 239

HQP_MVS		94.49 185	94.36 173	94.87 235	95.71 274	91.74 261	99.84 111	97.87 212	96.38 48	93.01 213	98.59 187	80.47 266	98.37 210	97.79 121	89.55 233	94.52 241

plane_prior5								97.87 212					98.37 210	97.79 121	89.55 233	94.52 241

xiu_mvs_v1_base_debu		97.43 93	97.06 98	98.55 110	97.74 199	98.14 71	99.31 213	97.86 214	96.43 45	99.62 44	99.69 96	85.56 220	99.68 138	99.05 55	98.31 142	97.83 220

xiu_mvs_v1_base		97.43 93	97.06 98	98.55 110	97.74 199	98.14 71	99.31 213	97.86 214	96.43 45	99.62 44	99.69 96	85.56 220	99.68 138	99.05 55	98.31 142	97.83 220

xiu_mvs_v1_base_debi		97.43 93	97.06 98	98.55 110	97.74 199	98.14 71	99.31 213	97.86 214	96.43 45	99.62 44	99.69 96	85.56 220	99.68 138	99.05 55	98.31 142	97.83 220

CostFormer		96.10 143	95.88 140	96.78 183	97.03 232	92.55 243	97.08 326	97.83 217	90.04 249	98.72 102	94.89 310	95.01 60	98.29 217	96.54 148	95.77 196	99.50 159

TAMVS		95.85 148	95.58 146	96.65 189	97.07 229	93.50 220	99.17 227	97.82 218	91.39 225	95.02 191	98.01 207	92.20 142	97.30 263	93.75 203	95.83 195	99.14 193

VDD-MVS		93.77 200	92.94 206	96.27 201	98.55 153	90.22 290	98.77 273	97.79 219	90.85 235	96.82 156	99.42 120	61.18 355	99.77 120	98.95 62	94.13 215	98.82 206

cascas		94.64 179	93.61 188	97.74 151	97.82 193	96.26 143	99.96 25	97.78 220	85.76 309	94.00 203	97.54 217	76.95 287	99.21 159	97.23 134	95.43 203	97.76 224

CLD-MVS		94.06 194	93.90 184	94.55 248	96.02 260	90.69 279	99.98 10	97.72 221	96.62 42	91.05 230	98.85 177	77.21 284	98.47 194	98.11 105	89.51 235	94.48 243
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MS-PatchMatch		90.65 264	90.30 255	91.71 311	94.22 298	85.50 332	98.24 301	97.70 222	88.67 270	86.42 307	96.37 256	67.82 335	98.03 234	83.62 312	99.62 103	91.60 343

XXY-MVS		91.82 240	90.46 250	95.88 209	93.91 303	95.40 175	98.87 262	97.69 223	88.63 272	87.87 286	97.08 230	74.38 311	97.89 242	91.66 229	84.07 286	94.35 257

EI-MVSNet		93.73 202	93.40 200	94.74 239	96.80 245	92.69 238	99.06 238	97.67 224	88.96 263	91.39 226	99.02 148	88.75 193	97.30 263	91.07 236	87.85 255	94.22 265

MVSTER		95.53 158	95.22 155	96.45 194	98.56 151	97.72 86	99.91 75	97.67 224	92.38 193	91.39 226	97.14 227	97.24 17	97.30 263	94.80 173	87.85 255	94.34 258

ETV-MVS		97.92 75	97.80 74	98.25 130	98.14 176	96.48 134	99.98 10	97.63 226	95.61 72	99.29 75	99.46 118	92.55 134	98.82 173	99.02 61	98.54 136	99.46 162

CANet_DTU		96.76 120	96.15 124	98.60 105	98.78 144	97.53 95	99.84 111	97.63 226	97.25 24	99.20 78	99.64 104	81.36 253	99.98 46	92.77 219	98.89 129	98.28 214

LPG-MVS_test		92.96 217	92.71 210	93.71 280	95.43 280	88.67 309	99.75 140	97.62 228	92.81 168	90.05 238	98.49 193	75.24 302	98.40 203	95.84 157	89.12 237	94.07 283

LGP-MVS_train				93.71 280	95.43 280	88.67 309		97.62 228	92.81 168	90.05 238	98.49 193	75.24 302	98.40 203	95.84 157	89.12 237	94.07 283

FMVSNet3		92.69 224	91.58 233	95.99 206	98.29 163	97.42 106	99.26 221	97.62 228	89.80 252	89.68 249	95.32 293	81.62 251	96.27 316	87.01 290	85.65 270	94.29 260

ET-MVSNet_ETH3D		94.37 188	93.28 203	97.64 153	98.30 162	97.99 78	99.99 4	97.61 231	94.35 115	71.57 359	99.45 119	96.23 31	95.34 334	96.91 145	85.14 276	99.59 138

EIA-MVS		97.53 90	97.46 84	97.76 149	98.04 180	94.84 190	99.98 10	97.61 231	94.41 113	97.90 133	99.59 107	92.40 138	98.87 171	98.04 109	99.13 125	99.59 138

OPM-MVS		93.21 211	92.80 208	94.44 255	93.12 317	90.85 278	99.77 132	97.61 231	96.19 55	91.56 225	98.65 183	75.16 306	98.47 194	93.78 202	89.39 236	93.99 291
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
IS-MVSNet		96.29 140	95.90 139	97.45 160	98.13 177	94.80 192	99.08 233	97.61 231	92.02 204	95.54 185	98.96 159	90.64 169	98.08 230	93.73 204	97.41 165	99.47 161

CMPMVS	61.59 21	84.75 315	85.14 309	83.57 343	90.32 349	62.54 369	96.98 328	97.59 235	74.33 358	69.95 361	96.66 247	64.17 347	98.32 213	87.88 278	88.41 251	89.84 356
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UniMVSNet_ETH3D		90.06 281	88.58 287	94.49 252	94.67 292	88.09 318	97.81 315	97.57 236	83.91 328	88.44 276	97.41 220	57.44 359	97.62 250	91.41 231	88.59 248	97.77 223

lupinMVS		97.85 77	97.60 80	98.62 103	97.28 225	97.70 89	99.99 4	97.55 237	95.50 76	99.43 60	99.67 100	90.92 164	98.71 183	98.40 93	99.62 103	99.45 164

XVG-OURS		94.82 171	94.74 168	95.06 229	98.00 181	89.19 303	99.08 233	97.55 237	94.10 125	94.71 193	99.62 105	80.51 264	99.74 130	96.04 153	93.06 226	96.25 233

XVG-OURS-SEG-HR		94.79 172	94.70 169	95.08 228	98.05 179	89.19 303	99.08 233	97.54 239	93.66 147	94.87 192	99.58 108	78.78 277	99.79 114	97.31 132	93.40 222	96.25 233

PatchT		90.38 271	88.75 285	95.25 225	95.99 261	90.16 291	91.22 361	97.54 239	76.80 350	97.26 145	86.01 360	91.88 148	96.07 324	66.16 361	95.91 193	99.51 157

BH-RMVSNet		95.18 164	94.31 175	97.80 144	98.17 174	95.23 181	99.76 137	97.53 241	92.52 188	94.27 200	99.25 136	76.84 288	98.80 174	90.89 244	99.54 110	99.35 176

ACMP	92.05 9	92.74 222	92.42 219	93.73 278	95.91 264	88.72 308	99.81 120	97.53 241	94.13 123	87.00 298	98.23 202	74.07 312	98.47 194	96.22 151	88.86 242	93.99 291
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
xxxxxxxxxxxxxcwj		98.98 15	98.79 17	99.54 26	99.82 70	98.79 37	99.96 25	97.52 243	97.66 10	99.81 13	99.89 21	94.70 69	99.86 95	99.84 18	99.93 67	99.96 74

ACMM	91.95 10	92.88 219	92.52 217	93.98 273	95.75 270	89.08 306	99.77 132	97.52 243	93.00 163	89.95 242	97.99 210	76.17 296	98.46 197	93.63 207	88.87 241	94.39 252
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TR-MVS		94.54 182	93.56 193	97.49 159	97.96 183	94.34 200	98.71 277	97.51 245	90.30 245	94.51 196	98.69 181	75.56 299	98.77 178	92.82 218	95.99 190	99.35 176

BH-w/o		95.71 153	95.38 151	96.68 187	98.49 157	92.28 247	99.84 111	97.50 246	92.12 200	92.06 222	98.79 178	84.69 228	98.67 186	95.29 162	99.66 101	99.09 196

mvs_anonymous		95.65 156	95.03 161	97.53 156	98.19 172	95.74 164	99.33 210	97.49 247	90.87 234	90.47 236	97.10 229	88.23 197	97.16 271	95.92 155	97.66 159	99.68 119

DP-MVS		94.54 182	93.42 197	97.91 143	99.46 108	94.04 204	98.93 254	97.48 248	81.15 340	90.04 240	99.55 110	87.02 208	99.95 65	88.97 265	98.11 149	99.73 113

RRT_test8_iter05		94.58 181	94.11 178	95.98 207	97.88 187	96.11 154	99.89 87	97.45 249	91.66 214	88.28 281	96.71 245	96.53 28	97.40 256	94.73 178	83.85 289	94.45 249

ACMH	89.72 17	90.64 265	89.63 266	93.66 284	95.64 277	88.64 311	98.55 285	97.45 249	89.03 259	81.62 334	97.61 216	69.75 327	98.41 201	89.37 261	87.62 259	93.92 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-ACMP-BASELINE		91.22 254	90.75 245	92.63 301	93.73 306	85.61 330	98.52 289	97.44 251	92.77 172	89.90 244	96.85 241	66.64 339	98.39 205	92.29 222	88.61 246	93.89 299

mvs_tets		91.81 241	91.08 242	94.00 271	91.63 339	90.58 283	98.67 281	97.43 252	92.43 192	87.37 295	97.05 233	71.76 319	97.32 262	94.75 176	88.68 245	94.11 281

LTVRE_ROB	88.28 18	90.29 275	89.05 280	94.02 269	95.08 285	90.15 292	97.19 323	97.43 252	84.91 322	83.99 323	97.06 232	74.00 313	98.28 219	84.08 307	87.71 257	93.62 314
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
jajsoiax		91.92 239	91.18 241	94.15 263	91.35 341	90.95 276	99.00 246	97.42 254	92.61 181	87.38 294	97.08 230	72.46 317	97.36 258	94.53 183	88.77 243	94.13 280

K. test v3		88.05 299	87.24 301	90.47 320	91.82 337	82.23 347	98.96 251	97.42 254	89.05 258	76.93 350	95.60 276	68.49 332	95.42 332	85.87 299	81.01 309	93.75 308

RRT_MVS		95.23 163	94.77 167	96.61 190	98.28 165	98.32 67	99.81 120	97.41 256	92.59 183	91.28 228	97.76 214	95.02 58	97.23 269	93.65 206	87.14 262	94.28 261

FMVSNet2		91.02 256	89.56 268	95.41 219	97.53 210	95.74 164	98.98 248	97.41 256	87.05 291	88.43 278	95.00 306	71.34 321	96.24 318	85.12 302	85.21 275	94.25 264

jason		97.24 103	96.86 105	98.38 126	95.73 271	97.32 108	99.97 18	97.40 258	95.34 79	98.60 109	99.54 112	87.70 200	98.56 190	97.94 115	99.47 114	99.25 185
jason: jason.
PS-MVSNAJss		93.64 205	93.31 202	94.61 244	92.11 332	92.19 249	99.12 229	97.38 259	92.51 189	88.45 275	96.99 236	91.20 157	97.29 266	94.36 186	87.71 257	94.36 254

MSDG		94.37 188	93.36 201	97.40 163	98.88 138	93.95 208	99.37 206	97.38 259	85.75 311	90.80 232	99.17 141	84.11 234	99.88 90	86.35 294	98.43 139	98.36 213

CL-MVSNet_self_test		84.50 317	83.15 319	88.53 334	86.00 362	81.79 350	98.82 268	97.35 261	85.12 318	83.62 326	90.91 350	76.66 290	91.40 360	69.53 355	60.36 363	92.40 336

canonicalmvs		97.09 109	96.32 121	99.39 46	98.93 131	98.95 25	99.72 151	97.35 261	94.45 108	97.88 134	99.42 120	86.71 210	99.52 147	98.48 91	93.97 218	99.72 115

UnsupCasMVSNet_bld		79.97 329	77.03 332	88.78 332	85.62 363	81.98 348	93.66 351	97.35 261	75.51 356	70.79 360	83.05 362	48.70 367	94.91 340	78.31 337	60.29 364	89.46 359

MVS-HIRNet		86.22 306	83.19 318	95.31 222	96.71 251	90.29 289	92.12 356	97.33 264	62.85 363	86.82 299	70.37 367	69.37 328	97.49 253	75.12 347	97.99 155	98.15 216

BH-untuned		95.18 164	94.83 165	96.22 202	98.36 161	91.22 273	99.80 125	97.32 265	90.91 233	91.08 229	98.67 182	83.51 236	98.54 192	94.23 191	99.61 106	98.92 200

PCF-MVS	94.20 5	95.18 164	94.10 179	98.43 122	98.55 153	95.99 156	97.91 313	97.31 266	90.35 243	89.48 256	99.22 139	85.19 225	99.89 84	90.40 253	98.47 138	99.41 169
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
miper_enhance_ethall		94.36 190	93.98 182	95.49 215	98.68 148	95.24 180	99.73 148	97.29 267	93.28 157	89.86 245	95.97 266	94.37 79	97.05 280	92.20 223	84.45 281	94.19 268

bset_n11_16_dypcd		93.05 216	92.30 220	95.31 222	90.23 351	95.05 185	99.44 197	97.28 268	92.51 189	90.65 234	96.68 246	85.30 224	96.71 300	94.49 184	84.14 284	94.16 274

MVSFormer		96.94 112	96.60 113	97.95 140	97.28 225	97.70 89	99.55 179	97.27 269	91.17 226	99.43 60	99.54 112	90.92 164	96.89 290	94.67 180	99.62 103	99.25 185

test_djsdf		92.83 220	92.29 221	94.47 253	91.90 335	92.46 244	99.55 179	97.27 269	91.17 226	89.96 241	96.07 265	81.10 255	96.89 290	94.67 180	88.91 239	94.05 285

GA-MVS		93.83 196	92.84 207	96.80 182	95.73 271	93.57 217	99.88 90	97.24 271	92.57 186	92.92 215	96.66 247	78.73 278	97.67 248	87.75 279	94.06 217	99.17 189

Effi-MVS+		96.30 139	95.69 144	98.16 132	97.85 191	96.26 143	97.41 319	97.21 272	90.37 242	98.65 106	98.58 189	86.61 212	98.70 184	97.11 137	97.37 166	99.52 156

Patchmatch-test		92.65 226	91.50 236	96.10 205	96.85 242	90.49 285	91.50 359	97.19 273	82.76 335	90.23 237	95.59 277	95.02 58	98.00 235	77.41 340	96.98 175	99.82 102

diffmvs		97.00 110	96.64 112	98.09 136	97.64 206	96.17 150	99.81 120	97.19 273	94.67 102	98.95 91	99.28 129	86.43 213	98.76 179	98.37 94	97.42 164	99.33 178

ACMH+	89.98 16	90.35 272	89.54 269	92.78 300	95.99 261	86.12 328	98.81 269	97.18 275	89.38 254	83.14 327	97.76 214	68.42 333	98.43 199	89.11 264	86.05 268	93.78 307

anonymousdsp		91.79 246	90.92 244	94.41 258	90.76 346	92.93 232	98.93 254	97.17 276	89.08 257	87.46 293	95.30 294	78.43 282	96.92 289	92.38 221	88.73 244	93.39 319

baseline		96.43 133	95.98 130	97.76 149	97.34 219	95.17 183	99.51 185	97.17 276	93.92 137	96.90 153	99.28 129	85.37 223	98.64 187	97.50 128	96.86 178	99.46 162

nrg030		93.51 206	92.53 216	96.45 194	94.36 295	97.20 111	99.81 120	97.16 278	91.60 215	89.86 245	97.46 218	86.37 214	97.68 247	95.88 156	80.31 315	94.46 244

MVS_Test		96.46 132	95.74 143	98.61 104	98.18 173	97.23 110	99.31 213	97.15 279	91.07 230	98.84 94	97.05 233	88.17 198	98.97 169	94.39 185	97.50 161	99.61 135

MIMVSNet		90.30 274	88.67 286	95.17 227	96.45 253	91.64 267	92.39 355	97.15 279	85.99 305	90.50 235	93.19 336	66.95 338	94.86 341	82.01 321	93.43 221	99.01 199

CS-MVS		97.73 85	97.92 71	97.18 172	99.09 119	93.69 214	99.99 4	97.14 281	95.06 85	99.67 37	99.75 81	93.09 120	98.31 214	98.32 97	99.12 126	99.54 151

KD-MVS_2432*1600		88.00 300	86.10 304	93.70 282	96.91 237	94.04 204	97.17 324	97.12 282	84.93 320	81.96 331	92.41 341	92.48 136	94.51 344	79.23 331	52.68 366	92.56 332

miper_refine_blended		88.00 300	86.10 304	93.70 282	96.91 237	94.04 204	97.17 324	97.12 282	84.93 320	81.96 331	92.41 341	92.48 136	94.51 344	79.23 331	52.68 366	92.56 332

v7n		89.65 287	88.29 292	93.72 279	92.22 331	90.56 284	99.07 237	97.10 284	85.42 317	86.73 300	94.72 312	80.06 268	97.13 274	81.14 325	78.12 328	93.49 316

casdiffmvs		96.42 134	95.97 133	97.77 148	97.30 223	94.98 186	99.84 111	97.09 285	93.75 145	96.58 162	99.26 135	85.07 226	98.78 176	97.77 123	97.04 173	99.54 151

Fast-Effi-MVS+		95.02 168	94.19 176	97.52 157	97.88 187	94.55 196	99.97 18	97.08 286	88.85 267	94.47 197	97.96 211	84.59 229	98.41 201	89.84 259	97.10 171	99.59 138

miper_ehance_all_eth		93.16 212	92.60 212	94.82 238	97.57 209	93.56 218	99.50 187	97.07 287	88.75 268	88.85 270	95.52 281	90.97 163	96.74 297	90.77 246	84.45 281	94.17 269

Effi-MVS+-dtu		94.53 184	95.30 153	92.22 304	97.77 196	82.54 344	99.59 172	97.06 288	94.92 91	95.29 188	95.37 291	85.81 217	97.89 242	94.80 173	97.07 172	96.23 235

mvs-test1		95.53 158	95.97 133	94.20 262	97.77 196	85.44 333	99.95 43	97.06 288	94.92 91	96.58 162	98.72 180	85.81 217	98.98 168	94.80 173	98.11 149	98.18 215

DROMVSNet		97.38 99	97.24 92	97.80 144	97.41 215	95.64 169	99.99 4	97.06 288	94.59 104	99.63 41	99.32 128	89.20 188	98.14 227	98.76 78	99.23 122	99.62 132

IterMVS		90.91 258	90.17 259	93.12 293	96.78 248	90.42 288	98.89 257	97.05 291	89.03 259	86.49 305	95.42 286	76.59 291	95.02 337	87.22 286	84.09 285	93.93 296
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CS-MVS-test		97.53 90	97.64 77	97.18 172	99.09 119	93.69 214	100.00 1	97.04 292	95.07 84	99.67 37	99.25 136	91.22 155	98.31 214	98.32 97	99.12 126	99.54 151

v1192		90.62 267	89.25 275	94.72 241	93.13 315	93.07 228	99.50 187	97.02 293	86.33 302	89.56 255	95.01 304	79.22 273	97.09 279	82.34 319	81.16 304	94.01 288

v2v482		91.30 250	90.07 262	95.01 230	93.13 315	93.79 210	99.77 132	97.02 293	88.05 279	89.25 261	95.37 291	80.73 260	97.15 272	87.28 285	80.04 318	94.09 282

V42		91.28 252	90.12 261	94.74 239	93.42 312	93.46 221	99.68 156	97.02 293	87.36 287	89.85 247	95.05 302	81.31 254	97.34 260	87.34 284	80.07 317	93.40 318

IterMVS-SCA-FT		90.85 261	90.16 260	92.93 297	96.72 250	89.96 295	98.89 257	96.99 296	88.95 264	86.63 302	95.67 273	76.48 292	95.00 338	87.04 288	84.04 288	93.84 303

v144192		90.79 262	89.52 270	94.59 245	93.11 318	92.77 233	99.56 177	96.99 296	86.38 301	89.82 248	94.95 309	80.50 265	97.10 277	83.98 309	80.41 313	93.90 298

v1921920		90.46 269	89.12 277	94.50 251	92.96 322	92.46 244	99.49 189	96.98 298	86.10 304	89.61 254	95.30 294	78.55 280	97.03 284	82.17 320	80.89 311	94.01 288

v1144		91.09 255	89.83 263	94.87 235	93.25 314	93.69 214	99.62 169	96.98 298	86.83 297	89.64 253	94.99 307	80.94 257	97.05 280	85.08 303	81.16 304	93.87 301

eth_miper_zixun_eth		92.41 230	91.93 227	93.84 277	97.28 225	90.68 280	98.83 267	96.97 300	88.57 273	89.19 265	95.73 272	89.24 187	96.69 301	89.97 258	81.55 300	94.15 276

GBi-Net		90.88 259	89.82 264	94.08 266	97.53 210	91.97 252	98.43 292	96.95 301	87.05 291	89.68 249	94.72 312	71.34 321	96.11 320	87.01 290	85.65 270	94.17 269

test1		90.88 259	89.82 264	94.08 266	97.53 210	91.97 252	98.43 292	96.95 301	87.05 291	89.68 249	94.72 312	71.34 321	96.11 320	87.01 290	85.65 270	94.17 269

FMVSNet1		88.50 296	86.64 302	94.08 266	95.62 279	91.97 252	98.43 292	96.95 301	83.00 332	86.08 313	94.72 312	59.09 357	96.11 320	81.82 323	84.07 286	94.17 269

v8		90.54 268	89.17 276	94.66 242	93.43 311	93.40 224	99.20 224	96.94 304	85.76 309	87.56 290	94.51 319	81.96 246	97.19 270	84.94 304	78.25 326	93.38 320

c3_l		92.53 227	91.87 229	94.52 249	97.40 216	92.99 231	99.40 199	96.93 305	87.86 281	88.69 273	95.44 285	89.95 176	96.44 309	90.45 250	80.69 312	94.14 279

v1240		90.20 277	88.79 284	94.44 255	93.05 320	92.27 248	99.38 204	96.92 306	85.89 306	89.36 258	94.87 311	77.89 283	97.03 284	80.66 327	81.08 307	94.01 288

tpm		93.70 204	93.41 199	94.58 246	95.36 282	87.41 322	97.01 327	96.90 307	90.85 235	96.72 159	94.14 326	90.40 171	96.84 293	90.75 247	88.54 249	99.51 157

v148		90.70 263	89.63 266	93.92 274	92.97 321	90.97 275	99.75 140	96.89 308	87.51 284	88.27 282	95.01 304	81.67 248	97.04 282	87.40 283	77.17 337	93.75 308

IterMVS-LS		92.69 224	92.11 223	94.43 257	96.80 245	92.74 235	99.45 195	96.89 308	88.98 261	89.65 252	95.38 290	88.77 192	96.34 313	90.98 241	82.04 297	94.22 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		90.25 276	88.82 283	94.57 247	93.53 309	93.43 222	99.08 233	96.87 310	85.00 319	87.34 296	94.51 319	80.93 258	97.02 286	82.85 316	79.23 320	93.26 322

ADS-MVSNet2		93.80 199	93.88 185	93.55 286	97.87 189	85.94 329	94.24 346	96.84 311	90.07 247	96.43 167	94.48 321	90.29 173	95.37 333	87.44 281	97.23 168	99.36 174

Fast-Effi-MVS+-dtu		93.72 203	93.86 186	93.29 289	97.06 230	86.16 327	99.80 125	96.83 312	92.66 178	92.58 220	97.83 213	81.39 252	97.67 248	89.75 260	96.87 177	96.05 237

pmmvs4		92.10 237	91.07 243	95.18 226	92.82 325	94.96 187	99.48 191	96.83 312	87.45 286	88.66 274	96.56 252	83.78 235	96.83 294	89.29 262	84.77 279	93.75 308

AllTest		92.48 228	91.64 231	95.00 231	99.01 123	88.43 313	98.94 253	96.82 314	86.50 299	88.71 271	98.47 197	74.73 308	99.88 90	85.39 300	96.18 186	96.71 231

TestCases				95.00 231	99.01 123	88.43 313		96.82 314	86.50 299	88.71 271	98.47 197	74.73 308	99.88 90	85.39 300	96.18 186	96.71 231

miper_lstm_enhance		91.81 241	91.39 239	93.06 296	97.34 219	89.18 305	99.38 204	96.79 316	86.70 298	87.47 292	95.22 299	90.00 175	95.86 329	88.26 272	81.37 302	94.15 276

cl____		92.31 232	91.58 233	94.52 249	97.33 221	92.77 233	99.57 175	96.78 317	86.97 295	87.56 290	95.51 282	89.43 181	96.62 303	88.60 267	82.44 294	94.16 274

DIV-MVS_self_test		92.32 231	91.60 232	94.47 253	97.31 222	92.74 235	99.58 173	96.75 318	86.99 294	87.64 288	95.54 279	89.55 180	96.50 307	88.58 268	82.44 294	94.17 269

ppachtmachnet_test		89.58 288	88.35 290	93.25 291	92.40 329	90.44 287	99.33 210	96.73 319	85.49 315	85.90 315	95.77 269	81.09 256	96.00 327	76.00 346	82.49 293	93.30 321

GeoE		94.36 190	93.48 195	96.99 177	97.29 224	93.54 219	99.96 25	96.72 320	88.35 277	93.43 208	98.94 165	82.05 244	98.05 233	88.12 276	96.48 183	99.37 173

COLMAP_ROB	90.47 14	92.18 235	91.49 237	94.25 261	99.00 125	88.04 319	98.42 295	96.70 321	82.30 337	88.43 278	99.01 150	76.97 286	99.85 99	86.11 297	96.50 182	94.86 238
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
1112_ss		96.01 146	95.20 156	98.42 123	97.80 194	96.41 137	99.65 162	96.66 322	92.71 174	92.88 217	99.40 122	92.16 143	99.30 157	91.92 226	93.66 219	99.55 147

Test_1112_low_res		95.72 151	94.83 165	98.42 123	97.79 195	96.41 137	99.65 162	96.65 323	92.70 175	92.86 218	96.13 263	92.15 144	99.30 157	91.88 227	93.64 220	99.55 147

RPSCF		91.80 244	92.79 209	88.83 331	98.15 175	69.87 365	98.11 307	96.60 324	83.93 327	94.33 199	99.27 132	79.60 271	99.46 155	91.99 224	93.16 225	97.18 229

YYNet1		85.50 311	83.33 316	92.00 307	90.89 345	88.38 316	99.22 223	96.55 325	79.60 346	57.26 366	92.72 337	79.09 276	93.78 351	77.25 341	77.37 335	93.84 303

MDA-MVSNet_test_wron		85.51 310	83.32 317	92.10 306	90.96 344	88.58 312	99.20 224	96.52 326	79.70 345	57.12 367	92.69 339	79.11 275	93.86 350	77.10 342	77.46 334	93.86 302

MTMP							99.87 93	96.49 327

pm-mvs1		89.36 290	87.81 297	94.01 270	93.40 313	91.93 255	98.62 284	96.48 328	86.25 303	83.86 324	96.14 262	73.68 314	97.04 282	86.16 296	75.73 344	93.04 327

KD-MVS_self_test		83.59 322	82.06 322	88.20 336	86.93 360	80.70 357	97.21 322	96.38 329	82.87 333	82.49 329	88.97 353	67.63 336	92.32 357	73.75 349	62.30 362	91.58 344

our_test_3		90.39 270	89.48 273	93.12 293	92.40 329	89.57 301	99.33 210	96.35 330	87.84 282	85.30 317	94.99 307	84.14 233	96.09 323	80.38 328	84.56 280	93.71 313

CR-MVSNet		93.45 209	92.62 211	95.94 208	96.29 254	92.66 239	92.01 357	96.23 331	92.62 180	96.94 151	93.31 334	91.04 161	96.03 325	79.23 331	95.96 191	99.13 194

Patchmtry		89.70 286	88.49 288	93.33 288	96.24 256	89.94 298	91.37 360	96.23 331	78.22 348	87.69 287	93.31 334	91.04 161	96.03 325	80.18 330	82.10 296	94.02 286

MVP-Stereo		90.93 257	90.45 252	92.37 303	91.25 343	88.76 307	98.05 310	96.17 333	87.27 289	84.04 322	95.30 294	78.46 281	97.27 268	83.78 311	99.70 99	91.09 346
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
pmmvs6		85.69 307	83.84 313	91.26 314	90.00 353	84.41 338	97.82 314	96.15 334	75.86 353	81.29 336	95.39 289	61.21 354	96.87 292	83.52 314	73.29 347	92.50 334

EG-PatchMatch MVS		85.35 312	83.81 314	89.99 325	90.39 348	81.89 349	98.21 304	96.09 335	81.78 339	74.73 356	93.72 330	51.56 365	97.12 276	79.16 334	88.61 246	90.96 348

DeepMVS_CX				82.92 345	95.98 263	58.66 371		96.01 336	92.72 173	78.34 347	95.51 282	58.29 358	98.08 230	82.57 317	85.29 273	92.03 340

test20.03		84.72 316	83.99 310	86.91 338	88.19 359	80.62 358	98.88 259	95.94 337	88.36 276	78.87 344	94.62 317	68.75 330	89.11 365	66.52 360	75.82 342	91.00 347

MDA-MVSNet-bldmvs		84.09 319	81.52 325	91.81 310	91.32 342	88.00 320	98.67 281	95.92 338	80.22 343	55.60 368	93.32 333	68.29 334	93.60 353	73.76 348	76.61 341	93.82 305

lessismore_v0				90.53 318	90.58 347	80.90 356		95.80 339		77.01 349	95.84 267	66.15 341	96.95 287	83.03 315	75.05 345	93.74 311

Anonymous20240521		85.15 313	83.81 314	89.16 329	88.32 357	82.69 342	98.80 271	95.74 340	79.72 344	81.53 335	90.99 348	65.38 344	94.16 346	72.69 350	81.11 306	90.63 351

ITE_SJBPF				92.38 302	95.69 276	85.14 334		95.71 341	92.81 168	89.33 260	98.11 204	70.23 326	98.42 200	85.91 298	88.16 253	93.59 315

FMVSNet5		88.32 297	87.47 299	90.88 315	96.90 240	88.39 315	97.28 321	95.68 342	82.60 336	84.67 320	92.40 343	79.83 270	91.16 361	76.39 345	81.51 301	93.09 325

testgi		89.01 294	88.04 295	91.90 309	93.49 310	84.89 336	99.73 148	95.66 343	93.89 140	85.14 318	98.17 203	59.68 356	94.66 343	77.73 339	88.88 240	96.16 236

new_pmnet		84.49 318	82.92 320	89.21 328	90.03 352	82.60 343	96.89 330	95.62 344	80.59 342	75.77 355	89.17 352	65.04 346	94.79 342	72.12 351	81.02 308	90.23 353

pmmvs5		90.17 279	89.09 278	93.40 287	92.10 333	89.77 299	99.74 143	95.58 345	85.88 308	87.24 297	95.74 270	73.41 315	96.48 308	88.54 269	83.56 290	93.95 294

USDC		90.00 282	88.96 281	93.10 295	94.81 289	88.16 317	98.71 277	95.54 346	93.66 147	83.75 325	97.20 226	65.58 342	98.31 214	83.96 310	87.49 261	92.85 330

test_method		80.79 325	79.70 328	84.08 342	92.83 324	67.06 367	99.51 185	95.42 347	54.34 365	81.07 338	93.53 331	44.48 368	92.22 358	78.90 335	77.23 336	92.94 328

MIMVSNet1		82.58 323	80.51 327	88.78 332	86.68 361	84.20 339	96.65 331	95.41 348	78.75 347	78.59 346	92.44 340	51.88 364	89.76 364	65.26 363	78.95 322	92.38 337

OurMVSNet-221017-0		89.81 284	89.48 273	90.83 317	91.64 338	81.21 353	98.17 305	95.38 349	91.48 219	85.65 316	97.31 223	72.66 316	97.29 266	88.15 274	84.83 278	93.97 293

Anonymous20231206		86.32 305	85.42 307	89.02 330	89.11 356	80.53 359	99.05 242	95.28 350	85.43 316	82.82 328	93.92 327	74.40 310	93.44 354	66.99 359	81.83 299	93.08 326

new-patchmatchnet		81.19 324	79.34 329	86.76 339	82.86 367	80.36 360	97.92 312	95.27 351	82.09 338	72.02 358	86.87 358	62.81 351	90.74 363	71.10 352	63.08 360	89.19 360

OpenMVS_ROB	79.82 20	83.77 321	81.68 324	90.03 324	88.30 358	82.82 341	98.46 290	95.22 352	73.92 359	76.00 353	91.29 347	55.00 361	96.94 288	68.40 357	88.51 250	90.34 352

test_0402		85.58 308	83.94 312	90.50 319	93.81 305	85.04 335	98.55 285	95.20 353	76.01 352	79.72 343	95.13 300	64.15 348	96.26 317	66.04 362	86.88 264	90.21 354

SixPastTwentyTwo		88.73 295	88.01 296	90.88 315	91.85 336	82.24 346	98.22 303	95.18 354	88.97 262	82.26 330	96.89 238	71.75 320	96.67 302	84.00 308	82.98 291	93.72 312

Gipuma		66.95 334	65.00 334	72.79 349	91.52 340	67.96 366	66.16 369	95.15 355	47.89 367	58.54 365	67.99 369	29.74 371	87.54 366	50.20 369	77.83 330	62.87 369
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LF4IMVS		89.25 293	88.85 282	90.45 321	92.81 326	81.19 354	98.12 306	94.79 356	91.44 221	86.29 310	97.11 228	65.30 345	98.11 229	88.53 270	85.25 274	92.07 338

FPMVS		68.72 331	68.72 333	68.71 351	65.95 374	44.27 379	95.97 342	94.74 357	51.13 366	53.26 369	90.50 351	25.11 374	83.00 369	60.80 365	80.97 310	78.87 365

pmmvs-eth3d		84.03 320	81.97 323	90.20 322	84.15 365	87.09 323	98.10 308	94.73 358	83.05 331	74.10 357	87.77 356	65.56 343	94.01 347	81.08 326	69.24 352	89.49 358

TDRefinement		84.76 314	82.56 321	91.38 313	74.58 370	84.80 337	97.36 320	94.56 359	84.73 323	80.21 341	96.12 264	63.56 349	98.39 205	87.92 277	63.97 359	90.95 349

ambc				83.23 344	77.17 369	62.61 368	87.38 364	94.55 360		76.72 351	86.65 359	30.16 370	96.36 312	84.85 305	69.86 349	90.73 350

TinyColmap		87.87 302	86.51 303	91.94 308	95.05 286	85.57 331	97.65 317	94.08 361	84.40 325	81.82 333	96.85 241	62.14 352	98.33 212	80.25 329	86.37 267	91.91 342

TransMVSNet (Re)		87.25 303	85.28 308	93.16 292	93.56 308	91.03 274	98.54 287	94.05 362	83.69 330	81.09 337	96.16 261	75.32 301	96.40 310	76.69 344	68.41 355	92.06 339

Baseline_NR-MVSNet		90.33 273	89.51 271	92.81 299	92.84 323	89.95 296	99.77 132	93.94 363	84.69 324	89.04 267	95.66 274	81.66 249	96.52 306	90.99 240	76.98 338	91.97 341

EGC-MVSNET		69.38 330	63.76 337	86.26 340	90.32 349	81.66 352	96.24 337	93.85 364	0.99 377	3.22 378	92.33 344	52.44 363	92.92 356	59.53 367	84.90 277	84.21 363

LCM-MVSNet		67.77 332	64.73 335	76.87 347	62.95 376	56.25 373	89.37 363	93.74 365	44.53 368	61.99 363	80.74 363	20.42 376	86.53 367	69.37 356	59.50 365	87.84 361

Patchmatch-RL test		86.90 304	85.98 306	89.67 326	84.45 364	75.59 362	89.71 362	92.43 366	86.89 296	77.83 348	90.94 349	94.22 87	93.63 352	87.75 279	69.61 350	99.79 105

pmmvs3		80.27 327	77.77 331	87.76 337	80.32 368	82.43 345	98.23 302	91.97 367	72.74 360	78.75 345	87.97 355	57.30 360	90.99 362	70.31 353	62.37 361	89.87 355

LCM-MVSNet-Re		92.31 232	92.60 212	91.43 312	97.53 210	79.27 361	99.02 245	91.83 368	92.07 201	80.31 340	94.38 324	83.50 237	95.48 331	97.22 135	97.58 160	99.54 151

PM-MVS		80.47 326	78.88 330	85.26 341	83.79 366	72.22 364	95.89 343	91.08 369	85.71 312	76.56 352	88.30 354	36.64 369	93.90 349	82.39 318	69.57 351	89.66 357

door								90.31 370

DSMNet-mixed		88.28 298	88.24 293	88.42 335	89.64 354	75.38 363	98.06 309	89.86 371	85.59 313	88.20 283	92.14 345	76.15 297	91.95 359	78.46 336	96.05 189	97.92 219

door-mid								89.69 372

PMVS	49.05 23	53.75 337	51.34 341	60.97 354	40.80 380	34.68 380	74.82 368	89.62 373	37.55 370	28.67 376	72.12 366	7.09 380	81.63 370	43.17 372	68.21 356	66.59 368
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		65.23 335	62.94 338	72.13 350	44.90 379	50.03 375	81.05 366	89.42 374	38.45 369	48.51 371	99.90 19	54.09 362	78.70 371	91.84 228	18.26 373	87.64 362

PMMVS2		67.15 333	64.15 336	76.14 348	70.56 373	62.07 370	93.89 349	87.52 375	58.09 364	60.02 364	78.32 364	22.38 375	84.54 368	59.56 366	47.03 368	81.80 364

ANet_high		56.10 336	52.24 339	67.66 352	49.27 378	56.82 372	83.94 365	82.02 376	70.47 361	33.28 375	64.54 370	17.23 378	69.16 373	45.59 371	23.85 372	77.02 366

MVE	53.74 22	51.54 339	47.86 343	62.60 353	59.56 377	50.93 374	79.41 367	77.69 377	35.69 372	36.27 374	61.76 373	5.79 382	69.63 372	37.97 373	36.61 369	67.24 367
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		52.30 338	52.18 340	52.67 355	71.51 371	45.40 376	93.62 352	76.60 378	36.01 371	43.50 372	64.13 371	27.11 373	67.31 374	31.06 374	26.06 370	45.30 373

EMVS		51.44 340	51.22 342	52.11 356	70.71 372	44.97 378	94.04 348	75.66 379	35.34 373	42.40 373	61.56 374	28.93 372	65.87 375	27.64 375	24.73 371	45.49 372

N_pmnet		80.06 328	80.78 326	77.89 346	91.94 334	45.28 377	98.80 271	56.82 380	78.10 349	80.08 342	93.33 332	77.03 285	95.76 330	68.14 358	82.81 292	92.64 331

testmvs		40.60 341	44.45 344	29.05 358	19.49 382	14.11 383	99.68 156	18.47 381	20.74 374	64.59 362	98.48 196	10.95 379	17.09 378	56.66 368	11.01 374	55.94 371

test123		37.68 342	39.14 345	33.31 357	19.94 381	24.83 382	98.36 296	9.75 382	15.53 375	51.31 370	87.14 357	19.62 377	17.74 377	47.10 370	3.47 376	57.36 370

wuyk23d		20.37 344	20.84 347	18.99 359	65.34 375	27.73 381	50.43 370	7.67 383	9.50 376	8.01 377	6.34 377	6.13 381	26.24 376	23.40 376	10.69 375	2.99 374

test_blank		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.02 378	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet_test		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

pcd_1.5k_mvsjas		7.60 346	10.13 349	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 379	91.20 157	0.00 379	0.00 377	0.00 377	0.00 375

sosnet-low-res		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

sosnet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uncertanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

Regformer		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

n2								0.00 384

nn								0.00 384

ab-mvs-re		8.28 345	11.04 348	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	99.40 122	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

uanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 384	0.00 371	0.00 384	0.00 378	0.00 379	0.00 379	0.00 383	0.00 379	0.00 377	0.00 377	0.00 375

PC_three_1452									96.96 30	99.80 17	99.79 64	97.49 9	100.00 1	99.99 5	99.98 35	100.00 1

eth-test2					0.00 383

eth-test					0.00 383

OPU-MVS				99.93 2	99.89 50	99.80 2	99.96 25				99.80 60	97.44 13	100.00 1	100.00 1	99.98 35	100.00 1

test_0728_THIRD									96.48 43	99.83 11	99.91 15	97.87 4	100.00 1	99.92 13	100.00 1	100.00 1

GSMVS																99.59 138

test_part2					99.89 50	99.25 17				99.49 56

sam_mvs1												94.72 68				99.59 138

sam_mvs												94.25 86

test_post1							95.78 344				59.23 375	93.20 118	97.74 246	91.06 237

test_post											63.35 372	94.43 73	98.13 228

patchmatchnet-post											91.70 346	95.12 52	97.95 239

gm-plane-assit					96.97 235	93.76 212			91.47 220		98.96 159		98.79 175	94.92 168

test9_res														99.71 34	99.99 22	100.00 1

agg_prior2														99.48 41	100.00 1	100.00 1

test_prior4						98.05 75	99.94 61

test_prior2							99.95 43		95.78 64	99.73 30	99.76 75	96.00 33		99.78 25	100.00 1

旧先验2							99.46 194		94.21 122	99.85 7			99.95 65	96.96 142

新几何2							99.40 199

原ACMM2							99.90 79

testdata2													99.99 40	90.54 249

segment_acmp												96.68 25

testdata1							99.28 219		96.35 52

plane_prior7					95.71 274	91.59 269

plane_prior6					95.76 269	91.72 264						80.47 266

plane_prior4											98.59 187

plane_prior3						91.64 267			96.63 40	93.01 213

plane_prior2							99.84 111		96.38 48

plane_prior1					95.73 271

plane_prior						91.74 261	99.86 104		96.76 36						89.59 232

HQP5-MVS						91.85 257

HQP-NCC					95.78 265		99.87 93		96.82 32	93.37 209

ACMP_Plane					95.78 265		99.87 93		96.82 32	93.37 209

BP-MVS														97.92 116

HQP4-MVS										93.37 209			98.39 205			94.53 239

HQP2-MVS												80.65 262

NP-MVS					95.77 268	91.79 259					98.65 183

MDTV_nov1_ep13_2view						96.26 143	96.11 339		91.89 206	98.06 128		94.40 75		94.30 189		99.67 121

ACMMP++_ref															87.04 263

ACMMP++															88.23 252

Test By Simon												92.82 128

High-resolution multi-view benchmark