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.

test_vis1_n_1920		97.77 190	97.24 212	99.34 151	99.79 133	98.04 223	100.00 1	99.25 244	98.88 38	100.00 1	100.00 1	77.52 347	100.00 1	99.88 91	99.85 122	100.00 1

mvsany_test1		99.57 59	99.48 64	99.85 80	99.86 113	99.54 109	100.00 1	99.36 196	98.94 33	100.00 1	100.00 1	97.97 138	100.00 1	99.88 91	99.28 145	100.00 1

test_fmvs1		98.37 168	98.04 179	99.34 151	99.84 114	98.07 219	100.00 1	99.00 329	98.85 42	100.00 1	100.00 1	85.11 316	99.96 125	99.69 129	99.88 118	100.00 1

iter_conf_final		98.72 141	98.76 130	98.59 200	99.64 164	99.17 149	100.00 1	99.22 255	96.63 212	99.02 209	99.97 178	99.98 4	98.84 267	99.22 175	95.18 247	97.76 234

patch_mono-2		99.04 109	99.79 6	96.81 298	99.92 102	90.47 345	100.00 1	99.41 170	98.95 31	100.00 1	100.00 1	99.78 8	100.00 1	100.00 1	100.00 1	99.95 114

DVP-MVS++		99.81 11	99.75 14	100.00 1	100.00 1	99.99 5	100.00 1	99.42 124	98.79 50	100.00 1	100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

FOURS1					100.00 1	99.97 21	100.00 1	99.42 124	98.52 64	100.00 1

GeoE		98.06 180	97.65 195	99.29 159	99.47 214	98.41 191	100.00 1	99.19 268	94.85 270	98.88 218	100.00 1	91.21 252	99.59 193	97.02 262	98.19 188	99.88 157

test_method		91.04 320	91.10 317	90.85 337	98.34 293	77.63 363	100.00 1	98.93 336	76.69 365	96.25 322	98.52 331	70.44 359	97.98 333	89.02 348	91.74 296	96.92 340

SR-MVS-dyc-post		99.63 50	99.52 58	99.97 31	99.99 49	99.91 51	100.00 1	99.42 124	97.62 132	100.00 1	100.00 1	98.65 120	99.99 91	99.99 57	100.00 1	100.00 1

RE-MVS-def			99.55 54		99.99 49	99.91 51	100.00 1	99.42 124	97.62 132	100.00 1	100.00 1	98.94 100		99.99 57	100.00 1	100.00 1

SED-MVS		99.83 7	99.77 9	100.00 1	100.00 1	99.99 5	100.00 1	99.42 124	99.03 19	100.00 1	100.00 1	99.50 37	100.00 1	100.00 1	100.00 1	100.00 1

OPU-MVS				100.00 1	100.00 1	100.00 1	100.00 1				100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

SF-MVS		99.66 44	99.57 48	99.95 51	99.99 49	99.85 80	100.00 1	99.42 124	97.67 126	100.00 1	100.00 1	99.05 87	99.99 91	100.00 1	100.00 1	100.00 1

cl22		98.23 176	98.11 173	98.58 202	99.82 116	99.01 162	100.00 1	99.28 231	96.92 186	98.33 255	99.21 292	98.09 137	98.97 255	98.72 198	92.61 282	97.76 234

miper_enhance_ethall		98.33 169	98.27 164	98.51 203	99.66 157	99.04 158	100.00 1	99.22 255	97.53 143	98.51 246	99.38 284	99.49 39	98.75 278	98.02 231	92.61 282	97.76 234

ZNCC-MVS		99.71 29	99.62 40	99.97 31	99.99 49	99.90 58	100.00 1	99.79 44	97.97 102	99.97 104	100.00 1	98.97 94	100.00 1	99.94 84	100.00 1	100.00 1

cl____		97.54 203	97.32 206	98.18 225	99.47 214	98.14 215	100.00 1	99.10 301	94.16 294	97.60 292	99.63 258	97.52 155	98.65 285	96.47 276	91.97 294	97.76 234

DIV-MVS_self_test		97.52 205	97.35 205	98.05 244	99.46 216	98.11 216	100.00 1	99.10 301	94.21 291	97.62 290	99.63 258	97.65 149	98.29 311	96.47 276	91.98 293	97.76 234

9.14			99.57 48		99.99 49		100.00 1	99.42 124	97.54 141	100.00 1	100.00 1	99.15 82	99.99 91	100.00 1	100.00 1

save fliter					99.99 49	99.93 43	100.00 1	99.42 124	98.93 34

ET-MVSNet_ETH3D		96.41 253	95.48 282	99.20 168	99.81 120	99.75 90	100.00 1	99.02 326	97.30 166	78.33 367	100.00 1	97.73 146	97.94 335	99.70 122	87.41 334	99.92 131

EIA-MVS		99.26 93	99.19 93	99.45 136	99.63 166	98.75 175	100.00 1	99.27 239	96.93 184	99.95 130	100.00 1	97.47 158	99.79 173	99.74 114	99.72 131	99.82 176

miper_lstm_enhance		97.40 209	97.28 208	97.75 261	99.48 211	97.52 246	100.00 1	99.07 311	94.08 295	98.01 273	99.61 264	97.38 163	97.98 333	96.44 279	91.47 302	97.76 234

ETV-MVS		99.34 81	99.24 85	99.64 113	99.58 183	99.33 132	100.00 1	99.25 244	97.57 139	99.96 108	100.00 1	97.44 161	99.79 173	99.70 122	99.65 137	99.81 181

CS-MVS		99.33 83	99.27 79	99.50 131	99.99 49	99.00 164	100.00 1	99.13 291	97.26 167	99.96 108	100.00 1	97.79 145	99.64 190	99.64 138	99.67 135	99.87 165

D2MVS		97.63 199	97.83 186	97.05 283	98.83 280	94.60 309	100.00 1	99.82 39	96.89 189	98.28 259	99.03 304	94.05 219	99.47 223	98.58 210	94.97 258	97.09 336

DVP-MVS		99.83 7	99.78 7	100.00 1	100.00 1	99.99 5	100.00 1	99.42 124	99.04 14	100.00 1	100.00 1	99.53 29	100.00 1	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				100.00 1	99.99 49	99.99 5	100.00 1	99.42 124					100.00 1	100.00 1	100.00 1	100.00 1

test0726					100.00 1	99.99 5	100.00 1	99.42 124	99.04 14	100.00 1	100.00 1	99.53 29

SR-MVS		99.68 39	99.58 45	99.98 23	100.00 1	99.95 32	100.00 1	99.64 63	97.59 137	100.00 1	100.00 1	98.99 92	99.99 91	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 50	99.51 59	100.00 1	99.90 106	100.00 1	100.00 1	99.43 114	99.00 25	100.00 1	100.00 1	99.58 22	100.00 1	97.64 243	100.00 1	100.00 1

GST-MVS		99.64 47	99.53 56	99.95 51	100.00 1	99.86 77	100.00 1	99.79 44	97.72 121	99.95 130	100.00 1	98.39 129	100.00 1	99.96 77	99.99 97	100.00 1

test_yl		99.51 61	99.37 71	99.95 51	99.82 116	99.90 58	100.00 1	99.47 75	97.48 149	100.00 1	100.00 1	99.80 6	100.00 1	99.98 68	97.75 210	99.94 119

thisisatest0530		99.37 77	99.27 79	99.69 107	99.59 178	99.41 125	100.00 1	99.46 90	96.46 220	99.90 140	100.00 1	99.44 46	99.85 162	98.97 184	99.58 141	99.80 192

DCV-MVSNet		99.51 61	99.37 71	99.95 51	99.82 116	99.90 58	100.00 1	99.47 75	97.48 149	100.00 1	100.00 1	99.80 6	100.00 1	99.98 68	97.75 210	99.94 119

tttt0517		99.34 81	99.23 88	99.67 109	99.57 186	99.38 127	100.00 1	99.46 90	96.33 229	99.89 143	100.00 1	99.44 46	99.84 164	98.93 186	99.46 144	99.78 195

thisisatest0515		99.42 72	99.31 78	99.74 102	99.59 178	99.55 108	100.00 1	99.46 90	96.65 209	99.92 136	100.00 1	99.44 46	99.85 162	99.09 181	99.63 139	99.81 181

SMA-MVS		99.69 35	99.59 43	99.98 23	99.99 49	99.93 43	100.00 1	99.43 114	97.50 147	100.00 1	100.00 1	99.43 50	100.00 1	100.00 1	100.00 1	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
DPE-MVS		99.79 14	99.73 17	99.99 12	99.99 49	99.98 17	100.00 1	99.42 124	98.91 36	100.00 1	100.00 1	99.22 75	100.00 1	100.00 1	100.00 1	100.00 1
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
thres100view900		99.25 96	99.01 103	99.95 51	99.81 120	99.87 74	100.00 1	99.94 21	97.13 174	99.83 150	99.96 191	97.01 170	100.00 1	99.59 143	97.85 203	99.98 101

tfpn200view9		99.26 93	99.03 101	99.96 42	99.81 120	99.89 65	100.00 1	99.94 21	97.23 169	99.83 150	99.96 191	97.04 166	100.00 1	99.59 143	97.85 203	99.98 101

CANet		99.40 73	99.24 85	99.89 72	99.99 49	99.76 89	100.00 1	99.73 55	98.40 69	99.78 161	100.00 1	95.28 204	99.96 125	100.00 1	99.99 97	99.96 109

Fast-Effi-MVS+-dtu		98.38 167	98.56 149	97.82 258	99.58 183	94.44 313	100.00 1	99.16 281	96.75 196	99.51 176	99.63 258	95.03 209	99.60 191	97.71 241	99.67 135	99.42 217

Effi-MVS+-dtu		98.51 159	98.86 121	97.47 268	99.77 138	94.21 316	100.00 1	98.94 334	97.61 134	99.91 138	98.75 322	95.89 194	99.51 218	99.36 161	99.48 143	98.68 227

CANet_DTU		99.02 115	98.90 119	99.41 142	99.88 110	98.71 179	100.00 1	99.29 225	98.84 43	100.00 1	100.00 1	94.02 221	100.00 1	98.08 228	99.96 110	99.52 215

MP-MVS-pluss		99.61 54	99.50 60	99.97 31	99.98 84	99.92 48	100.00 1	99.42 124	97.53 143	99.77 162	100.00 1	98.77 115	100.00 1	99.99 57	100.00 1	99.99 99
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.81 11	99.77 9	99.94 63	100.00 1	99.86 77	100.00 1	99.42 124	98.87 41	100.00 1	100.00 1	99.65 15	99.96 125	100.00 1	100.00 1	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
TSAR-MVS + MP.		99.82 9	99.77 9	99.99 12	100.00 1	99.96 24	100.00 1	99.43 114	99.05 13	100.00 1	100.00 1	99.45 45	99.99 91	100.00 1	100.00 1	100.00 1
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
OPM-MVS		97.21 215	97.18 214	97.32 275	98.08 309	94.66 306	100.00 1	99.28 231	98.65 59	98.92 214	99.98 170	86.03 311	99.56 202	98.28 223	95.41 235	97.72 281
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		99.67 42	99.57 48	99.97 31	99.98 84	99.92 48	100.00 1	99.42 124	97.83 113	100.00 1	100.00 1	98.89 106	100.00 1	99.98 68	100.00 1	100.00 1

CS-MVS-test		99.31 86	99.27 79	99.43 139	99.99 49	98.77 174	100.00 1	99.19 268	97.24 168	99.96 108	100.00 1	97.56 154	99.70 187	99.68 130	99.81 126	99.82 176

pmmvs5		95.94 279	95.61 275	96.95 289	97.42 337	94.66 306	100.00 1	98.08 351	93.60 305	97.05 304	99.43 282	87.02 301	98.46 305	95.76 283	92.12 290	97.72 281

Fast-Effi-MVS+		98.40 166	98.02 181	99.55 128	99.63 166	99.06 156	100.00 1	99.15 282	95.07 265	99.42 182	99.95 198	93.26 230	99.73 184	97.44 250	98.24 184	99.87 165

MTAPA		99.68 39	99.59 43	99.97 31	99.99 49	99.91 51	100.00 1	99.42 124	98.32 78	99.94 132	100.00 1	98.65 120	100.00 1	99.96 77	100.00 1	100.00 1

MTMP							100.00 1	99.18 275

TEST9					100.00 1	99.95 32	100.00 1	99.42 124	97.65 128	100.00 1	100.00 1	99.53 29	99.97 114

train_agg		99.71 29	99.63 37	99.97 31	100.00 1	99.95 32	100.00 1	99.42 124	97.70 123	100.00 1	100.00 1	99.51 33	99.97 114	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 51	100.00 1	99.42 124	97.70 123	100.00 1	100.00 1	99.51 33	99.98 109

canonicalmvs		99.03 111	98.73 134	99.94 63	99.75 141	99.95 32	100.00 1	99.30 221	97.64 130	100.00 1	100.00 1	95.22 206	99.97 114	99.76 112	96.90 224	99.91 133

alignmvs		99.38 75	99.21 89	99.91 67	99.73 142	99.92 48	100.00 1	99.51 72	97.61 134	100.00 1	100.00 1	99.06 86	99.93 148	99.83 100	97.12 218	99.90 142

nrg030		97.64 196	97.27 210	98.75 193	98.34 293	99.53 111	100.00 1	99.22 255	96.21 233	98.27 261	99.95 198	94.40 217	98.98 253	99.23 173	89.78 317	97.75 245

FIs		97.95 185	97.73 192	98.62 197	98.53 288	99.24 142	100.00 1	99.43 114	96.74 198	97.87 280	99.82 220	95.27 205	98.89 262	98.78 194	93.07 277	97.74 268

FC-MVSNet-test		97.84 187	97.63 196	98.45 206	98.30 298	99.05 157	100.00 1	99.43 114	96.63 212	97.61 291	99.82 220	95.19 207	98.57 295	98.64 203	93.05 278	97.73 274

HFP-MVS		99.74 22	99.67 27	99.96 42	100.00 1	99.89 65	100.00 1	99.76 48	97.95 106	100.00 1	100.00 1	99.31 63	100.00 1	99.99 57	100.00 1	100.00 1

v148		96.29 262	95.84 261	97.63 262	97.74 321	96.53 277	100.00 1	99.07 311	93.52 307	98.01 273	99.42 283	91.22 251	98.60 290	96.37 280	87.22 336	97.75 245

AllTest		98.55 155	98.40 157	98.99 179	99.93 99	97.35 253	100.00 1	99.40 173	97.08 178	99.09 202	99.98 170	93.37 227	99.95 134	96.94 264	99.84 123	99.68 207

region2R		99.72 26	99.64 33	99.97 31	100.00 1	99.90 58	100.00 1	99.74 54	97.86 112	100.00 1	100.00 1	99.19 78	100.00 1	99.99 57	100.00 1	100.00 1

iter_conf05		98.73 140	98.77 128	98.60 198	99.65 158	99.22 145	100.00 1	99.22 255	96.68 207	98.98 212	99.97 178	99.99 3	98.84 267	99.29 168	95.11 253	97.75 245

RRT_MVS		97.77 190	97.76 188	97.78 260	97.89 316	97.06 266	100.00 1	99.29 225	95.74 246	98.00 275	99.97 178	95.94 193	98.55 298	98.87 190	94.18 268	97.72 281

HPM-MVS++		99.82 9	99.76 12	99.99 12	99.99 49	99.98 17	100.00 1	99.83 38	98.88 38	99.96 108	100.00 1	99.21 76	100.00 1	100.00 1	100.00 1	99.99 99

test_prior4						99.93 43	100.00 1

XVS		99.79 14	99.73 17	99.98 23	100.00 1	99.94 40	100.00 1	99.75 51	98.67 57	100.00 1	100.00 1	99.16 80	100.00 1	100.00 1	100.00 1	100.00 1

test_prior2							100.00 1		98.82 46	100.00 1	100.00 1	99.47 43		100.00 1	100.00 1

X-MVStestdata		97.04 225	96.06 252	99.98 23	100.00 1	99.94 40	100.00 1	99.75 51	98.67 57	100.00 1	66.97 378	99.16 80	100.00 1	100.00 1	100.00 1	100.00 1

旧先验2							100.00 1		98.11 92	100.00 1			100.00 1	99.67 133

新几何2							100.00 1

无先验							100.00 1	99.80 42	97.98 100				100.00 1	99.33 164		100.00 1

原ACMM2							100.00 1

test222					99.99 49	99.90 58	100.00 1	99.69 61	97.66 127	100.00 1	100.00 1	99.30 68			100.00 1	100.00 1

testdata1							100.00 1		98.77 53

v2v482		96.70 239	96.18 247	98.27 218	98.04 310	98.39 194	100.00 1	99.13 291	94.19 293	98.58 239	99.08 298	90.48 263	98.67 283	95.69 285	90.44 313	97.75 245

SD-MVS		99.81 11	99.75 14	99.99 12	99.99 49	99.96 24	100.00 1	99.42 124	99.01 24	100.00 1	100.00 1	99.33 58	100.00 1	100.00 1	100.00 1	100.00 1
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
GA-MVS		97.72 193	97.27 210	99.06 172	99.24 241	97.93 230	100.00 1	99.24 249	95.80 245	98.99 211	99.64 254	89.77 270	99.36 234	95.12 297	97.62 215	99.89 146

MSLP-MVS++		99.89 1	99.85 2	99.99 12	100.00 1	99.96 24	100.00 1	99.95 18	99.11 7	100.00 1	100.00 1	99.60 17	100.00 1	100.00 1	100.00 1	100.00 1

APDe-MVS		99.84 6	99.78 7	99.99 12	100.00 1	99.98 17	100.00 1	99.44 108	99.06 11	100.00 1	100.00 1	99.56 23	99.99 91	100.00 1	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 45	99.55 54	99.97 31	99.99 49	99.91 51	100.00 1	99.48 74	97.54 141	100.00 1	100.00 1	98.97 94	99.99 91	99.98 68	100.00 1	100.00 1

pmmvs4		97.17 217	96.80 221	98.27 218	97.68 323	98.64 184	100.00 1	99.18 275	94.22 290	98.55 241	99.71 235	93.67 224	98.47 304	95.66 286	92.57 285	97.71 289

test-LLR		99.03 111	98.91 116	99.40 144	99.40 227	99.28 136	100.00 1	99.45 97	96.70 203	99.42 182	99.12 295	99.31 63	99.01 250	96.82 269	99.99 97	99.91 133

TESTMET0.1,1		99.08 105	98.96 108	99.44 137	99.63 166	99.38 127	100.00 1	99.45 97	95.53 253	99.48 178	100.00 1	99.71 13	99.02 249	96.84 268	99.99 97	99.91 133

test-mter		98.96 124	98.82 123	99.40 144	99.40 227	99.28 136	100.00 1	99.45 97	95.44 262	99.42 182	99.12 295	99.70 14	99.01 250	96.82 269	99.99 97	99.91 133

ACMMPR		99.74 22	99.67 27	99.96 42	100.00 1	99.89 65	100.00 1	99.76 48	97.95 106	100.00 1	100.00 1	99.29 69	100.00 1	99.99 57	100.00 1	100.00 1

testgi		96.18 267	95.93 258	96.93 291	98.98 266	94.20 317	100.00 1	99.07 311	97.16 172	96.06 325	99.86 212	84.08 324	97.79 338	90.38 338	97.80 208	98.81 226

test20.03		93.11 306	92.85 310	93.88 331	95.19 357	91.83 336	100.00 1	98.87 339	93.68 302	92.76 345	98.88 317	89.20 278	92.71 369	77.88 367	89.19 323	97.09 336

thres600view7		99.24 98	99.00 104	99.95 51	99.81 120	99.87 74	100.00 1	99.94 21	97.13 174	99.83 150	99.96 191	97.01 170	100.00 1	99.54 151	97.77 209	99.97 106

MP-MVS		99.61 54	99.49 62	99.98 23	99.99 49	99.94 40	100.00 1	99.42 124	97.82 114	99.99 95	100.00 1	98.20 132	100.00 1	99.99 57	100.00 1	100.00 1
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
testmvs		80.17 332	81.95 335	74.80 355	58.54 382	59.58 379	100.00 1	87.14 381	76.09 366	99.61 173	100.00 1	67.06 363	74.19 378	98.84 191	50.30 372	90.64 367

thres400		99.26 93	99.03 101	99.95 51	99.81 120	99.89 65	100.00 1	99.94 21	97.23 169	99.83 150	99.96 191	97.04 166	100.00 1	99.59 143	97.85 203	99.97 106

test123		79.44 335	79.23 337	80.05 353	80.03 376	71.72 368	100.00 1	77.93 384	62.52 370	94.81 334	99.69 241	78.21 345	74.53 377	92.57 320	27.33 377	93.90 360

thres200		99.27 91	99.04 100	99.96 42	99.81 120	99.90 58	100.00 1	99.94 21	97.31 164	99.83 150	99.96 191	97.04 166	100.00 1	99.62 142	97.88 201	99.98 101

test0.0.03 1		98.12 179	98.03 180	98.39 209	99.11 246	98.07 219	100.00 1	99.93 29	96.70 203	96.91 308	99.95 198	99.31 63	98.19 315	91.93 325	98.44 169	98.91 225

pmmvs3		90.62 322	89.36 326	94.40 324	90.53 369	91.49 339	100.00 1	96.73 368	84.21 358	93.65 343	96.65 352	82.56 332	94.83 362	82.28 359	77.62 360	96.89 341

PGM-MVS		99.69 35	99.61 41	99.95 51	99.99 49	99.85 80	100.00 1	99.58 65	97.69 125	100.00 1	100.00 1	99.44 46	100.00 1	99.79 105	100.00 1	100.00 1

MCST-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.73 55	99.19 5	100.00 1	100.00 1	99.31 63	100.00 1	100.00 1	100.00 1	100.00 1

mvs_anonymous		98.80 137	98.60 146	99.38 148	99.57 186	99.24 142	100.00 1	99.21 264	95.87 239	98.92 214	99.82 220	96.39 190	99.03 248	99.13 179	98.50 165	99.88 157

CDPH-MVS		99.73 25	99.64 33	99.99 12	100.00 1	99.97 21	100.00 1	99.42 124	98.02 96	100.00 1	100.00 1	99.32 61	99.99 91	100.00 1	100.00 1	100.00 1

casdiffmvs		98.65 147	98.38 159	99.46 135	99.52 199	98.74 178	100.00 1	99.15 282	96.91 187	99.05 207	100.00 1	92.75 238	99.83 165	99.70 122	98.38 175	99.81 181
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
diffmvs		98.96 124	98.73 134	99.63 114	99.54 189	99.16 150	100.00 1	99.18 275	97.33 161	99.96 108	100.00 1	94.60 215	99.91 151	99.66 136	98.33 181	99.82 176
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline2		98.99 120	98.93 114	99.18 169	99.26 240	99.15 151	100.00 1	99.46 90	96.71 202	96.79 312	100.00 1	99.42 52	99.25 241	98.75 197	99.94 113	99.15 221

MDA-MVSNet_test_wron		92.61 310	91.09 318	97.19 281	96.71 344	97.26 259	100.00 1	99.14 287	88.61 346	67.90 373	98.32 338	89.03 279	96.57 350	90.47 337	89.59 318	97.74 268

HQP_MVS		97.71 194	97.82 187	97.37 271	99.00 262	94.80 300	100.00 1	99.40 173	99.00 25	99.08 204	99.97 178	88.58 288	99.55 207	99.79 105	95.57 233	97.76 234

plane_prior2							100.00 1		99.00 25

plane_prior						94.80 300	100.00 1		99.03 19						95.58 229

UniMVSNet_NR-MVSNet		97.16 218	96.80 221	98.22 222	98.38 292	98.41 191	100.00 1	99.45 97	96.14 234	97.76 282	99.64 254	95.05 208	98.50 301	97.98 232	86.84 337	97.75 245

DTE-MVSNet		95.52 285	94.99 292	97.08 282	97.49 334	96.45 278	100.00 1	99.25 244	93.82 298	96.17 323	99.57 271	87.81 294	97.18 343	94.57 301	86.26 342	97.62 313

DU-MVS		96.93 230	96.49 233	98.22 222	98.31 296	98.41 191	100.00 1	99.37 190	96.41 223	97.76 282	99.65 250	92.14 245	98.50 301	97.98 232	86.84 337	97.75 245

UniMVSNet (Re)		97.29 214	96.85 220	98.59 200	98.49 289	99.13 152	100.00 1	99.42 124	96.52 218	98.24 265	98.90 316	94.93 210	98.89 262	97.54 247	87.61 333	97.75 245

Baseline_NR-MVSNet		96.16 270	95.70 270	97.56 267	98.28 299	96.79 272	100.00 1	97.86 358	91.93 329	97.63 288	99.47 280	92.14 245	98.35 309	97.13 259	86.83 339	97.54 320

TranMVSNet+NR-MVSNet		96.45 252	96.01 254	97.79 259	98.00 312	97.62 244	100.00 1	99.35 205	95.98 236	97.31 299	99.64 254	90.09 267	98.00 332	96.89 267	86.80 340	97.75 245

TSAR-MVS + GP.		99.61 54	99.69 22	99.35 150	99.99 49	98.06 221	100.00 1	99.36 196	99.83 2	100.00 1	100.00 1	98.95 98	99.99 91	100.00 1	99.11 148	100.00 1

mPP-MVS		99.69 35	99.60 42	99.97 31	100.00 1	99.91 51	100.00 1	99.42 124	97.91 108	100.00 1	100.00 1	99.04 89	100.00 1	100.00 1	100.00 1	100.00 1

XVG-OURS-SEG-HR		98.27 174	98.31 163	98.14 230	99.59 178	95.92 281	100.00 1	99.36 196	98.48 65	99.21 193	100.00 1	89.27 277	99.94 146	99.76 112	99.17 146	98.56 230

mvsmamba		98.13 178	98.06 177	98.32 215	98.22 303	98.50 190	100.00 1	99.22 255	96.41 223	98.91 216	99.96 191	95.69 199	98.73 280	99.19 177	94.95 260	97.73 274

MVSFormer		98.94 127	98.82 123	99.28 162	99.45 217	99.49 116	100.00 1	99.13 291	95.46 260	99.97 104	100.00 1	96.76 181	98.59 292	98.63 205	100.00 1	99.74 200

jason		99.11 103	98.96 108	99.59 120	99.17 243	99.31 134	100.00 1	99.13 291	97.38 156	99.83 150	100.00 1	95.54 202	99.72 185	99.57 147	99.97 108	99.74 200
jason: jason.
lupinMVS		99.29 89	99.16 96	99.69 107	99.45 217	99.49 116	100.00 1	99.15 282	97.45 152	99.97 104	100.00 1	96.76 181	99.76 179	99.67 133	100.00 1	99.81 181

test_djsdf		97.55 202	97.38 203	98.07 236	97.50 332	97.99 225	100.00 1	99.13 291	95.46 260	98.47 249	99.85 214	92.01 248	98.59 292	98.63 205	95.36 237	97.62 313

HPM-MVS_fast		99.60 57	99.49 62	99.91 67	99.99 49	99.78 87	100.00 1	99.42 124	97.09 176	100.00 1	100.00 1	98.95 98	99.96 125	99.98 68	100.00 1	100.00 1

HPM-MVS		99.59 58	99.50 60	99.89 72	100.00 1	99.70 99	100.00 1	99.42 124	97.46 151	100.00 1	100.00 1	98.60 122	99.96 125	99.99 57	100.00 1	100.00 1
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVG-OURS		98.30 170	98.36 162	98.13 233	99.58 183	95.91 282	100.00 1	99.36 196	98.69 55	99.23 192	100.00 1	91.20 253	99.92 150	99.34 163	97.82 206	98.56 230

casdiffmvs_mvg		98.64 148	98.39 158	99.40 144	99.50 207	98.60 185	100.00 1	99.22 255	96.85 190	99.10 201	100.00 1	92.75 238	99.78 177	99.71 119	98.35 177	99.81 181
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LPG-MVS_test		97.31 212	97.32 206	97.28 277	98.85 278	94.60 309	100.00 1	99.37 190	97.35 158	98.85 221	99.98 170	86.66 304	99.56 202	99.55 148	95.26 241	97.70 290

baseline		98.69 145	98.45 155	99.41 142	99.52 199	98.67 182	100.00 1	99.17 280	97.03 181	99.13 199	100.00 1	93.17 232	99.74 182	99.70 122	98.34 178	99.81 181

CHOSEN 1792x2688		99.00 117	98.91 116	99.25 165	99.90 106	97.79 239	100.00 1	99.99 13	98.79 50	98.28 259	100.00 1	93.63 225	99.95 134	99.66 136	99.95 112	100.00 1

EPNet		99.62 52	99.69 22	99.42 141	99.99 49	98.37 197	100.00 1	99.89 35	98.83 44	100.00 1	100.00 1	98.97 94	100.00 1	99.90 88	99.61 140	99.89 146
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP-NCC					99.07 250		100.00 1		99.04 14	99.17 194

ACMP_Plane					99.07 250		100.00 1		99.04 14	99.17 194

APD-MVS		99.68 39	99.58 45	99.97 31	99.99 49	99.96 24	100.00 1	99.42 124	97.53 143	100.00 1	100.00 1	99.27 72	99.97 114	100.00 1	100.00 1	100.00 1
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		99.85 3	99.80 4	100.00 1	100.00 1	99.99 5	100.00 1	99.77 47	99.07 9	100.00 1	100.00 1	99.39 56	100.00 1	100.00 1	100.00 1	100.00 1

NCCC		99.86 2	99.82 3	100.00 1	100.00 1	99.99 5	100.00 1	99.71 60	99.07 9	100.00 1	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	100.00 1

114514_t		99.39 74	99.25 83	99.81 88	99.97 88	99.48 120	100.00 1	99.42 124	95.53 253	100.00 1	100.00 1	98.37 130	99.95 134	99.97 75	100.00 1	100.00 1

CP-MVS		99.67 42	99.58 45	99.95 51	100.00 1	99.84 82	100.00 1	99.42 124	97.77 118	100.00 1	100.00 1	99.07 85	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 16	99.71 20	99.98 23	99.76 139	99.95 32	100.00 1	99.42 124	98.69 55	100.00 1	100.00 1	99.52 32	99.99 91	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
BH-w/o		98.82 136	98.81 125	98.88 187	99.62 171	96.71 273	100.00 1	99.28 231	97.09 176	98.81 226	100.00 1	94.91 211	99.96 125	99.54 151	100.00 1	99.96 109

DELS-MVS		99.62 52	99.56 53	99.82 85	99.92 102	99.45 121	100.00 1	99.78 46	98.92 35	99.73 166	100.00 1	97.70 148	100.00 1	99.93 85	100.00 1	100.00 1
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
BH-untuned		98.64 148	98.65 140	98.60 198	99.59 178	96.17 279	100.00 1	99.28 231	96.67 208	98.41 251	100.00 1	94.52 216	99.83 165	99.41 159	100.00 1	99.81 181

MVSTER		98.58 152	98.52 151	98.77 192	99.65 158	99.68 101	100.00 1	99.29 225	95.63 249	98.65 234	99.80 226	99.78 8	98.88 265	98.59 209	95.31 239	97.73 274

CPTT-MVS		99.49 66	99.38 68	99.85 80	100.00 1	99.54 109	100.00 1	99.42 124	97.58 138	99.98 100	100.00 1	97.43 162	100.00 1	99.99 57	100.00 1	100.00 1

PVSNet_Blended_VisFu		99.33 83	99.18 95	99.78 98	99.82 116	99.49 116	100.00 1	99.95 18	97.36 157	99.63 171	100.00 1	96.45 189	99.95 134	99.79 105	99.65 137	99.89 146

PVSNet_BlendedMVS		98.71 143	98.62 143	98.98 181	99.98 84	99.60 106	100.00 1	100.00 1	97.23 169	100.00 1	99.03 304	96.57 185	99.99 91	100.00 1	94.75 261	97.35 330

PVSNet_Blended		99.48 68	99.36 73	99.83 84	99.98 84	99.60 106	100.00 1	100.00 1	97.79 116	100.00 1	100.00 1	96.57 185	99.99 91	100.00 1	99.88 118	99.90 142

cascas		98.43 161	98.07 176	99.50 131	99.65 158	99.02 160	100.00 1	99.22 255	94.21 291	99.72 167	99.98 170	92.03 247	99.93 148	99.68 130	98.12 191	99.54 214

BH-RMVSNet		98.46 160	98.08 175	99.59 120	99.61 173	99.19 147	100.00 1	99.28 231	97.06 180	98.95 213	100.00 1	88.99 280	99.82 168	98.83 193	100.00 1	99.77 196

WTY-MVS		99.54 60	99.40 66	99.95 51	99.81 120	99.93 43	100.00 1	100.00 1	97.98 100	99.84 148	100.00 1	98.94 100	99.98 109	99.86 95	98.21 186	99.94 119

DROMVSNet		99.19 100	99.09 99	99.48 134	99.42 220	99.07 154	100.00 1	99.21 264	96.95 183	99.96 108	100.00 1	96.88 179	99.48 221	99.64 138	99.79 129	99.88 157

sss		99.45 70	99.34 77	99.80 92	99.76 139	99.50 113	100.00 1	99.91 34	97.72 121	99.98 100	99.94 202	98.45 127	100.00 1	99.53 153	98.75 159	99.89 146

DP-MVS Recon		99.76 18	99.69 22	99.98 23	100.00 1	99.95 32	100.00 1	99.52 68	97.99 98	99.99 95	100.00 1	99.72 12	100.00 1	99.96 77	100.00 1	100.00 1

MVS_111021_LR		99.70 32	99.65 31	99.88 76	99.96 93	99.70 99	100.00 1	99.97 17	98.96 28	100.00 1	100.00 1	97.93 140	99.95 134	99.99 57	100.00 1	100.00 1

HQP-MVS		97.73 192	97.85 185	97.39 270	99.07 250	94.82 297	100.00 1	99.40 173	99.04 14	99.17 194	99.97 178	88.61 286	99.57 198	99.79 105	95.58 229	97.77 232

HyFIR lowres test		99.32 85	99.24 85	99.58 124	99.95 94	99.26 138	100.00 1	99.99 13	96.72 201	99.29 190	99.91 206	99.49 39	99.47 223	99.74 114	98.08 193	100.00 1

PAPM_NR		99.74 22	99.66 30	99.99 12	100.00 1	99.96 24	100.00 1	99.47 75	97.87 111	100.00 1	100.00 1	99.60 17	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 18	99.68 25	99.99 12	100.00 1	99.96 24	100.00 1	99.47 75	98.16 84	100.00 1	100.00 1	99.51 33	100.00 1	100.00 1	100.00 1	100.00 1

IB-MVS	96.24 12	97.54 203	96.95 216	99.33 154	99.67 153	98.10 218	100.00 1	99.47 75	97.42 155	99.26 191	99.69 241	98.83 111	99.89 153	99.43 157	78.77 359	100.00 1
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
MVS_111021_HR		99.71 29	99.63 37	99.93 65	99.95 94	99.83 83	100.00 1	100.00 1	98.89 37	100.00 1	100.00 1	97.85 142	99.95 134	100.00 1	100.00 1	100.00 1

CSCG		99.28 90	99.35 75	99.05 174	99.99 49	97.15 262	100.00 1	99.47 75	97.44 153	99.42 182	100.00 1	97.83 144	100.00 1	99.99 57	100.00 1	100.00 1

PatchMatch-RL		99.02 115	98.78 127	99.74 102	99.99 49	99.29 135	100.00 1	100.00 1	98.38 70	99.89 143	99.81 223	93.14 234	99.99 91	97.85 238	99.98 105	99.95 114

USDC		95.90 280	95.70 270	96.50 303	98.60 285	92.56 333	100.00 1	98.30 345	97.77 118	96.92 306	99.94 202	81.25 337	99.45 228	93.54 314	94.96 259	97.49 323

PMMVS		99.12 102	98.97 107	99.58 124	99.57 186	98.98 166	100.00 1	99.30 221	97.14 173	99.96 108	100.00 1	96.53 188	99.82 168	99.70 122	98.49 166	99.94 119

PAPM		99.78 16	99.76 12	99.85 80	99.01 258	99.95 32	100.00 1	99.75 51	99.37 3	99.99 95	100.00 1	99.76 11	99.60 191	100.00 1	100.00 1	100.00 1

CNLPA		99.72 26	99.65 31	99.91 67	99.97 88	99.72 94	100.00 1	99.47 75	98.43 68	99.88 145	100.00 1	99.14 83	100.00 1	99.97 75	100.00 1	100.00 1

PHI-MVS		99.50 64	99.39 67	99.82 85	100.00 1	99.45 121	100.00 1	99.94 21	96.38 226	100.00 1	100.00 1	98.18 133	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 88	99.25 83	99.44 137	100.00 1	98.32 203	100.00 1	99.86 37	98.04 95	100.00 1	100.00 1	96.10 192	100.00 1	99.55 148	99.73 130	100.00 1

PVSNet_0	93.57 19	96.41 253	95.74 268	98.41 208	99.84 114	95.22 290	100.00 1	100.00 1	98.08 93	97.55 294	99.78 229	84.40 319	100.00 1	100.00 1	81.99 352	100.00 1

F-COLMAP		99.64 47	99.64 33	99.67 109	99.99 49	99.07 154	100.00 1	99.44 108	98.30 79	99.90 140	100.00 1	99.18 79	99.99 91	99.91 87	100.00 1	99.94 119

DeepPCF-MVS	98.03 4	98.54 156	99.72 19	94.98 319	99.99 49	84.94 357	100.00 1	99.42 124	99.98 1	100.00 1	100.00 1	98.11 135	100.00 1	100.00 1	100.00 1	100.00 1

OMC-MVS		99.27 91	99.38 68	98.96 182	99.95 94	97.06 266	100.00 1	99.40 173	98.83 44	99.88 145	100.00 1	97.01 170	99.86 157	99.47 156	99.84 123	99.97 106

DeepC-MVS	97.84 5	99.00 117	98.80 126	99.60 118	99.93 99	99.03 159	100.00 1	99.40 173	98.61 61	99.33 188	100.00 1	92.23 244	99.95 134	99.74 114	99.96 110	99.83 171
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PLC	98.56 2	99.70 32	99.74 16	99.58 124	100.00 1	98.79 173	100.00 1	99.54 67	98.58 62	99.96 108	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	99.94 119
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMP	97.00 8	97.19 216	97.16 215	97.27 279	98.97 267	94.58 312	100.00 1	99.32 214	97.97 102	97.45 296	99.98 170	85.79 313	99.56 202	99.70 122	95.24 244	97.67 300
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator+	95.58 15	99.03 111	98.71 137	99.96 42	98.99 265	99.89 65	100.00 1	99.51 72	98.96 28	98.32 256	100.00 1	92.78 237	100.00 1	99.87 94	100.00 1	100.00 1

LF4IMVS		96.19 266	96.18 247	96.23 308	98.26 300	92.09 335	100.00 1	97.89 357	97.82 114	97.94 276	99.87 210	82.71 330	99.38 233	97.41 252	93.71 270	97.20 333

TAPA-MVS	96.40 10	97.64 196	97.37 204	98.45 206	99.94 97	95.70 285	100.00 1	99.40 173	97.65 128	99.53 174	100.00 1	99.31 63	99.66 189	80.48 363	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		98.90 131	98.63 142	99.70 106	99.92 102	99.25 140	100.00 1	99.37 190	95.71 247	99.40 187	100.00 1	96.58 184	99.95 134	96.80 271	99.94 113	99.91 133

ACMM	97.17 6	97.37 210	97.40 202	97.29 276	99.01 258	94.64 308	100.00 1	99.25 244	98.07 94	98.44 250	99.98 170	87.38 298	99.55 207	99.25 170	95.19 246	97.69 294
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		97.64 196	97.74 190	97.36 272	99.01 258	94.76 305	100.00 1	99.34 210	99.30 4	99.00 210	99.97 178	87.49 297	99.57 198	99.96 77	95.58 229	97.75 245
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
test_fmvs1_n		97.43 207	96.86 219	99.15 170	99.68 149	97.48 248	99.99 186	98.98 332	98.82 46	100.00 1	100.00 1	74.85 352	99.96 125	99.67 133	99.70 132	100.00 1

test_vis1_rt		93.10 307	92.93 308	93.58 332	99.63 166	85.07 356	99.99 186	93.71 375	97.49 148	90.96 350	97.10 349	60.40 366	99.95 134	99.24 172	97.90 200	95.72 354

test_fmvs2		95.17 292	95.23 287	95.01 317	98.95 270	88.99 350	99.99 186	97.77 359	97.79 116	98.58 239	99.70 238	73.36 354	99.34 237	95.88 282	95.03 255	96.70 344

miper_ehance_all_eth		97.81 189	97.66 194	98.23 221	99.49 209	98.37 197	99.99 186	99.11 299	94.78 271	98.25 263	99.21 292	98.18 133	98.57 295	97.35 256	92.61 282	97.76 234

ppachtmachnet_test		96.17 269	95.89 259	97.02 285	97.61 326	95.24 289	99.99 186	99.24 249	93.31 313	96.71 315	99.62 262	94.34 218	98.07 327	89.87 340	92.30 289	97.75 245

IterMVS-SCA-FT		96.72 238	96.42 237	97.62 264	99.40 227	96.83 271	99.99 186	99.14 287	94.65 277	97.55 294	99.72 233	89.65 272	98.31 310	95.62 288	92.05 291	97.73 274

SCA		98.30 170	97.98 183	99.23 166	99.41 222	98.25 207	99.99 186	99.45 97	96.91 187	99.76 164	99.58 268	89.65 272	99.54 210	98.31 219	98.79 155	99.91 133

v144192		96.40 256	95.81 262	98.17 227	97.89 316	98.11 216	99.99 186	99.06 319	93.39 310	98.75 229	99.09 297	90.43 264	98.66 284	93.10 317	90.55 312	97.75 245

v1921920		96.16 270	95.50 278	98.14 230	97.88 318	97.96 227	99.99 186	99.07 311	93.33 312	98.60 238	99.24 291	89.37 276	98.71 281	91.28 329	90.74 310	97.75 245

v1192		96.18 267	95.49 280	98.26 220	98.01 311	98.15 213	99.99 186	99.08 307	93.36 311	98.54 242	98.97 311	89.47 275	98.89 262	91.15 331	90.82 308	97.75 245

v1144		96.51 248	95.97 257	98.13 233	97.98 313	98.04 223	99.99 186	99.08 307	93.51 308	98.62 237	98.98 308	90.98 258	98.62 287	93.79 312	90.79 309	97.74 268

V42		96.65 241	96.16 249	98.11 235	98.17 307	98.23 208	99.99 186	99.09 306	93.97 296	98.74 230	99.05 301	91.09 255	98.82 270	95.46 290	89.90 315	97.27 332

MVS_Test		98.93 128	98.65 140	99.77 100	99.62 171	99.50 113	99.99 186	99.19 268	95.52 255	99.96 108	99.86 212	96.54 187	99.98 109	98.65 202	98.48 167	99.82 176

YYNet1		92.44 311	90.92 319	97.03 284	96.20 346	97.06 266	99.99 186	99.14 287	88.21 349	67.93 372	98.43 335	88.63 285	96.28 354	90.64 333	89.08 324	97.74 268

K. test v3		95.46 287	95.14 289	96.40 304	97.53 331	93.40 324	99.99 186	99.23 253	95.49 258	92.70 347	99.73 232	84.26 320	98.12 320	93.94 311	93.38 275	97.68 296

XVG-ACMP-BASELINE		96.60 244	96.52 232	96.84 296	98.41 291	93.29 325	99.99 186	99.32 214	97.76 120	98.51 246	99.29 289	81.95 333	99.54 210	98.40 214	95.03 255	97.68 296

Test_1112_low_res		98.83 135	98.60 146	99.51 129	99.69 146	98.75 175	99.99 186	99.14 287	96.81 193	98.84 223	99.06 299	97.45 159	99.89 153	98.66 200	97.75 210	99.89 146

1112_ss		98.91 129	98.71 137	99.51 129	99.69 146	98.75 175	99.99 186	99.15 282	96.82 192	98.84 223	100.00 1	97.45 159	99.89 153	98.66 200	97.75 210	99.89 146

IterMVS		96.76 235	96.46 235	97.63 262	99.41 222	96.89 269	99.99 186	99.13 291	94.74 274	97.59 293	99.66 248	89.63 274	98.28 312	95.71 284	92.31 288	97.72 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPSCF		97.37 210	98.24 166	94.76 322	99.80 130	84.57 358	99.99 186	99.05 321	94.95 268	99.82 158	100.00 1	94.03 220	100.00 1	98.15 227	98.38 175	99.70 205

OpenMVS	95.20 17	98.76 138	98.41 156	99.78 98	98.89 273	99.81 84	99.99 186	99.76 48	98.02 96	98.02 272	100.00 1	91.44 250	100.00 1	99.63 141	99.97 108	99.55 213

our_test_3		96.51 248	96.35 240	96.98 288	97.61 326	95.05 292	99.98 207	99.01 328	94.68 275	96.77 314	99.06 299	95.87 195	98.14 318	91.81 326	92.37 287	97.75 245

Anonymous20231206		93.45 304	93.17 305	94.30 326	95.00 358	89.69 347	99.98 207	98.43 344	93.30 314	94.50 339	98.59 328	90.52 261	95.73 360	77.46 369	90.73 311	97.48 325

EI-MVSNet-Vis-set		99.70 32	99.64 33	99.87 77	100.00 1	99.64 104	99.98 207	99.44 108	98.35 76	99.99 95	100.00 1	99.04 89	99.96 125	99.98 68	100.00 1	100.00 1

Vis-MVSNet (Re-imp)		98.99 120	98.89 120	99.29 159	99.64 164	98.89 170	99.98 207	99.31 219	96.74 198	99.48 178	100.00 1	98.11 135	99.10 245	98.39 215	98.34 178	99.89 146

MG-MVS		99.75 20	99.68 25	99.97 31	100.00 1	99.91 51	99.98 207	99.47 75	99.09 8	100.00 1	100.00 1	98.59 123	100.00 1	99.95 82	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 107	98.76 130	99.96 42	98.86 277	99.90 58	99.98 207	99.93 29	98.95 31	98.49 248	100.00 1	92.91 236	100.00 1	99.71 119	100.00 1	100.00 1

test_vis1_n		96.69 240	95.81 262	99.32 156	99.14 244	97.98 226	99.97 213	98.98 332	98.45 67	100.00 1	100.00 1	66.44 364	99.99 91	99.78 111	99.57 142	100.00 1

Anonymous20240521		93.29 305	92.76 311	94.90 321	95.64 354	91.27 341	99.97 213	98.82 341	87.04 352	94.71 335	98.19 340	83.86 325	96.80 346	84.04 356	92.56 286	96.64 345

CHOSEN 280x420		99.85 3	99.87 1	99.80 92	99.99 49	99.97 21	99.97 213	99.98 16	98.96 28	100.00 1	100.00 1	99.96 5	99.42 231	100.00 1	100.00 1	100.00 1

WR-MVS		97.09 221	96.64 226	98.46 205	98.43 290	99.09 153	99.97 213	99.33 212	95.62 250	97.76 282	99.67 246	91.17 254	98.56 297	98.49 212	89.28 322	97.74 268

new_pmnet		94.11 301	93.47 303	96.04 310	96.60 345	92.82 329	99.97 213	98.91 337	90.21 340	95.26 330	98.05 343	85.89 312	98.14 318	84.28 355	92.01 292	97.16 334

test_fmvs3		87.19 327	87.02 330	87.71 344	92.69 362	76.64 364	99.96 218	97.27 364	93.55 306	90.82 352	94.03 362	38.00 376	92.19 370	93.49 315	83.35 350	94.32 359

c3_l		97.58 200	97.42 200	98.06 240	99.48 211	98.16 211	99.96 218	99.10 301	94.54 280	98.13 267	99.20 294	97.87 141	98.25 314	97.28 257	91.20 305	97.75 245

v1240		95.96 278	95.25 286	98.07 236	97.91 315	97.87 235	99.96 218	99.07 311	93.24 315	98.64 236	98.96 312	88.98 281	98.61 288	89.58 343	90.92 307	97.75 245

EMVS		69.88 341	69.09 344	72.24 359	84.70 372	65.82 375	99.96 218	87.08 382	49.82 376	71.51 370	84.74 373	49.30 369	75.32 376	50.97 377	43.71 374	75.59 374

EPNet_dtu		98.53 157	98.23 168	99.43 139	99.92 102	99.01 162	99.96 218	99.47 75	98.80 48	99.96 108	99.96 191	98.56 124	99.30 239	87.78 349	99.68 133	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
API-MVS		99.72 26	99.70 21	99.79 94	99.97 88	99.37 130	99.96 218	99.94 21	98.48 65	100.00 1	100.00 1	98.92 103	100.00 1	100.00 1	100.00 1	100.00 1

bld_raw_dy_0_64		97.71 194	97.56 197	98.15 229	97.83 319	98.16 211	99.95 224	99.12 297	95.95 238	98.73 231	99.97 178	93.19 231	98.63 286	98.64 203	94.69 263	97.66 301

PS-MVSNAJ		99.64 47	99.57 48	99.85 80	99.78 136	99.81 84	99.95 224	99.42 124	98.38 70	100.00 1	100.00 1	98.75 116	100.00 1	99.88 91	99.99 97	99.74 200

jajsoiax		97.07 223	96.79 223	97.89 256	97.28 340	97.12 263	99.95 224	99.19 268	96.55 215	97.31 299	99.69 241	87.35 300	98.91 259	98.70 199	95.12 252	97.66 301

EI-MVSNet-UG-set		99.69 35	99.63 37	99.87 77	99.99 49	99.64 104	99.95 224	99.44 108	98.35 76	100.00 1	100.00 1	98.98 93	99.97 114	99.98 68	100.00 1	100.00 1

E-PMN		70.72 340	70.06 343	72.69 358	83.92 373	65.48 376	99.95 224	92.72 377	49.88 375	72.30 369	86.26 372	47.17 371	77.43 375	53.83 376	44.49 373	75.17 375

dcpmvs_2		98.87 132	99.53 56	96.90 292	99.87 112	90.88 344	99.94 229	99.07 311	98.20 82	100.00 1	100.00 1	98.69 119	99.86 157	100.00 1	100.00 1	99.95 114

v8		96.35 259	95.73 269	98.21 224	98.11 308	98.23 208	99.94 229	99.07 311	92.66 325	98.29 258	99.00 307	91.46 249	98.77 276	94.17 306	88.83 327	97.62 313

CDS-MVSNet		98.96 124	98.95 112	99.01 178	99.48 211	98.36 199	99.93 231	99.37 190	96.79 194	99.31 189	99.83 217	99.77 10	98.91 259	98.07 229	97.98 195	99.77 196
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		99.51 61	99.41 65	99.82 85	99.70 145	99.73 93	99.92 232	99.40 173	98.15 86	100.00 1	100.00 1	98.50 126	100.00 1	99.85 97	99.13 147	99.74 200

OurMVSNet-221017-0		96.14 272	95.98 256	96.62 301	97.49 334	93.44 323	99.92 232	98.16 347	95.86 241	97.65 287	99.95 198	85.71 314	98.78 273	94.93 299	94.18 268	97.64 310

EPP-MVSNet		99.10 104	99.00 104	99.40 144	99.51 204	98.68 181	99.92 232	99.43 114	95.47 259	99.65 170	100.00 1	99.51 33	99.76 179	99.53 153	98.00 194	99.75 199

pmmvs-eth3d		91.73 316	90.67 320	94.92 320	91.63 366	92.71 331	99.90 235	98.54 343	91.19 333	88.08 358	95.50 355	79.31 343	96.13 356	90.55 336	81.32 355	95.91 353

PEN-MVS		96.01 277	95.48 282	97.58 266	97.74 321	97.26 259	99.90 235	99.29 225	94.55 279	96.79 312	99.55 273	87.38 298	97.84 337	96.92 266	87.24 335	97.65 307

N_pmnet		91.88 315	93.37 304	87.40 345	97.24 341	66.33 374	99.90 235	91.05 378	89.77 343	95.65 329	98.58 329	90.05 268	98.11 322	85.39 353	92.72 281	97.75 245

PS-MVSNAJss		98.03 182	98.06 177	97.94 252	97.63 324	97.33 256	99.89 238	99.23 253	96.27 231	98.03 270	99.59 266	98.75 116	98.78 273	98.52 211	94.61 265	97.70 290

IterMVS-LS		97.56 201	97.44 199	97.92 255	99.38 231	97.90 231	99.89 238	99.10 301	94.41 286	98.32 256	99.54 275	97.21 164	98.11 322	97.50 248	91.62 298	97.75 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
QAPM		98.99 120	98.66 139	99.96 42	99.01 258	99.87 74	99.88 240	99.93 29	97.99 98	98.68 233	100.00 1	93.17 232	100.00 1	99.32 165	100.00 1	100.00 1

test_f		86.87 328	86.06 331	89.28 341	91.45 367	76.37 365	99.87 241	97.11 365	91.10 334	88.46 357	93.05 364	38.31 375	96.66 349	91.77 327	83.46 349	94.82 358

v7n		96.06 276	95.42 285	97.99 250	97.58 329	97.35 253	99.86 242	99.11 299	92.81 324	97.91 278	99.49 278	90.99 257	98.92 258	92.51 321	88.49 328	97.70 290

LCM-MVSNet-Re		96.52 246	97.21 213	94.44 323	99.27 238	85.80 355	99.85 243	96.61 370	95.98 236	92.75 346	98.48 332	93.97 222	97.55 342	99.58 146	98.43 170	99.98 101

SixPastTwentyTwo		95.71 283	95.49 280	96.38 305	97.42 337	93.01 326	99.84 244	98.23 346	94.75 272	95.98 326	99.97 178	85.35 315	98.43 306	94.71 300	93.17 276	97.69 294

FA-MVS(test-final)		99.00 117	98.75 132	99.73 105	99.63 166	99.43 124	99.83 245	99.43 114	95.84 244	99.52 175	99.37 285	97.84 143	99.96 125	97.63 244	99.68 133	99.79 194

new-patchmatchnet		90.30 323	89.46 325	92.84 335	90.77 368	88.55 352	99.83 245	98.80 342	90.07 342	87.86 359	95.00 358	78.77 344	94.30 365	84.86 354	79.15 357	95.68 356

v10		96.14 272	95.50 278	98.07 236	98.19 305	97.96 227	99.83 245	99.07 311	92.10 328	98.07 269	98.94 313	91.07 256	98.61 288	92.41 324	89.82 316	97.63 311

AUN-MVS		96.26 264	95.67 274	98.06 240	99.68 149	95.60 286	99.82 248	99.42 124	96.78 195	99.88 145	99.80 226	94.84 212	99.47 223	97.48 249	73.29 363	99.12 222

test_vis3_rt		79.61 333	78.19 338	83.86 349	88.68 370	69.56 371	99.81 249	82.19 383	86.78 353	68.57 371	84.51 374	25.06 380	98.26 313	89.18 347	78.94 358	83.75 371

hse-mvs2		96.79 233	96.38 238	98.04 246	99.68 149	95.54 287	99.81 249	99.42 124	98.21 80	100.00 1	99.80 226	97.49 156	99.46 227	99.72 117	73.27 364	99.12 222

anonymousdsp		97.16 218	96.88 218	98.00 248	97.08 342	98.06 221	99.81 249	99.15 282	94.58 278	97.84 281	99.62 262	90.49 262	98.60 290	97.98 232	95.32 238	97.33 331

JIA-IIPM		97.09 221	96.34 241	99.36 149	98.88 274	98.59 186	99.81 249	99.43 114	84.81 357	99.96 108	90.34 367	98.55 125	99.52 216	97.00 263	98.28 183	99.98 101

ACMH+	96.20 13	96.49 251	96.33 242	97.00 286	99.06 254	93.80 319	99.81 249	99.31 219	97.32 162	95.89 328	99.97 178	82.62 331	99.54 210	98.34 218	94.63 264	97.65 307

PM-MVS		88.39 326	87.41 329	91.31 336	91.73 365	82.02 361	99.79 254	96.62 369	91.06 335	90.71 353	95.73 354	48.60 370	95.96 357	90.56 335	81.91 354	95.97 352

xiu_mvs_v1_base_debu		99.35 78	99.21 89	99.79 94	99.67 153	99.71 95	99.78 255	99.36 196	98.13 88	100.00 1	100.00 1	97.00 173	100.00 1	99.83 100	99.07 149	99.66 209

xiu_mvs_v1_base		99.35 78	99.21 89	99.79 94	99.67 153	99.71 95	99.78 255	99.36 196	98.13 88	100.00 1	100.00 1	97.00 173	100.00 1	99.83 100	99.07 149	99.66 209

xiu_mvs_v1_base_debi		99.35 78	99.21 89	99.79 94	99.67 153	99.71 95	99.78 255	99.36 196	98.13 88	100.00 1	100.00 1	97.00 173	100.00 1	99.83 100	99.07 149	99.66 209

baseline1		98.91 129	98.61 144	99.81 88	99.71 143	99.77 88	99.78 255	99.44 108	97.51 146	98.81 226	99.99 166	98.25 131	99.76 179	98.60 208	95.41 235	99.89 146

IS-MVSNet		99.08 105	98.91 116	99.59 120	99.65 158	99.38 127	99.78 255	99.24 249	96.70 203	99.51 176	100.00 1	98.44 128	99.52 216	98.47 213	98.39 174	99.88 157

CP-MVSNet		96.73 236	96.25 244	98.18 225	98.21 304	98.67 182	99.77 260	99.32 214	95.06 266	97.20 302	99.65 250	90.10 266	98.19 315	98.06 230	88.90 325	97.66 301

tpm cat1		98.05 181	97.76 188	98.92 184	99.50 207	97.10 265	99.77 260	99.30 221	90.20 341	99.72 167	98.71 323	97.71 147	99.86 157	96.75 275	98.20 187	99.81 181

APD_test1		93.07 308	94.14 296	89.85 340	99.18 242	72.49 367	99.76 262	98.90 338	92.86 323	96.35 319	99.94 202	75.56 350	99.91 151	86.73 351	97.98 195	97.15 335

EU-MVSNet		96.63 242	96.53 230	96.94 290	97.59 328	96.87 270	99.76 262	99.47 75	96.35 227	96.85 310	99.78 229	92.57 242	96.27 355	95.33 291	91.08 306	97.68 296

ACMMP		99.65 45	99.57 48	99.89 72	99.99 49	99.66 102	99.75 264	99.73 55	98.16 84	99.75 165	100.00 1	98.90 105	100.00 1	99.96 77	99.88 118	100.00 1
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
mvsany_test3		89.36 325	88.96 327	90.56 338	91.95 363	78.97 362	99.74 265	96.59 371	96.84 191	89.25 355	96.07 353	52.59 368	97.11 344	95.17 296	82.44 351	95.58 357

KD-MVS_self_test		91.16 318	90.09 323	94.35 325	94.44 360	91.27 341	99.74 265	99.08 307	90.82 337	94.53 338	94.91 360	86.11 308	94.78 363	82.67 358	68.52 366	96.99 338

EI-MVSNet		97.98 184	97.93 184	98.16 228	99.11 246	97.84 236	99.74 265	99.29 225	94.39 287	98.65 234	100.00 1	97.21 164	98.88 265	97.62 246	95.31 239	97.75 245

CVMVSNet		98.56 154	98.47 154	98.82 189	99.11 246	97.67 242	99.74 265	99.47 75	97.57 139	99.06 206	100.00 1	95.72 198	98.97 255	98.21 225	97.33 217	99.83 171

MAR-MVS		99.49 66	99.36 73	99.89 72	99.97 88	99.66 102	99.74 265	99.95 18	97.89 109	100.00 1	100.00 1	96.71 183	100.00 1	100.00 1	100.00 1	100.00 1
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
UnsupCasMVSNet_eth		94.25 297	93.89 297	95.34 314	97.63 324	92.13 334	99.73 270	99.36 196	94.88 269	92.78 344	98.63 327	82.72 329	96.53 351	94.57 301	84.73 344	97.36 329

DeepC-MVS_fast	98.92 1	99.75 20	99.67 27	99.99 12	99.99 49	99.96 24	99.73 270	99.52 68	99.06 11	100.00 1	100.00 1	98.80 114	100.00 1	99.95 82	100.00 1	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
WR-MVS_H		96.73 236	96.32 243	97.95 251	98.26 300	97.88 233	99.72 272	99.43 114	95.06 266	96.99 305	98.68 325	93.02 235	98.53 299	97.43 251	88.33 329	97.43 326

PS-CasMVS		96.34 260	95.78 266	98.03 247	98.18 306	98.27 206	99.71 273	99.32 214	94.75 272	96.82 311	99.65 250	86.98 303	98.15 317	97.74 240	88.85 326	97.66 301

FMVSNet3		97.30 213	96.95 216	98.37 211	99.65 158	99.25 140	99.71 273	99.28 231	94.23 289	98.53 243	98.91 315	93.30 229	98.11 322	95.31 292	93.60 271	97.73 274

PMMVS2		79.15 336	77.28 339	84.76 348	82.34 374	72.66 366	99.70 275	95.11 374	71.68 368	84.78 365	90.87 365	32.05 378	89.99 371	75.53 372	63.45 370	91.64 365

AdaColmap		99.44 71	99.26 82	99.95 51	100.00 1	99.86 77	99.70 275	99.99 13	98.53 63	99.90 140	100.00 1	95.34 203	100.00 1	99.92 86	100.00 1	100.00 1

tfpnnormal		96.36 258	95.69 273	98.37 211	98.55 286	98.71 179	99.69 277	99.45 97	93.16 317	96.69 316	99.71 235	88.44 290	98.99 252	94.17 306	91.38 303	97.41 327

tpm2		98.64 148	98.58 148	98.81 191	99.42 220	97.12 263	99.69 277	99.37 190	93.63 304	99.94 132	99.67 246	98.96 97	99.47 223	98.62 207	97.95 197	99.83 171

Vis-MVSNet		98.52 158	98.25 165	99.34 151	99.68 149	98.55 187	99.68 279	99.41 170	97.34 160	99.94 132	100.00 1	90.38 265	99.70 187	99.03 183	98.84 154	99.76 198
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CostFormer		98.84 134	98.77 128	99.04 176	99.41 222	97.58 245	99.67 280	99.35 205	94.66 276	99.96 108	99.36 286	99.28 71	99.74 182	99.41 159	97.81 207	99.81 181

DSMNet-mixed		95.18 291	95.21 288	95.08 315	96.03 348	90.21 346	99.65 281	93.64 376	92.91 320	98.34 254	97.40 348	90.05 268	95.51 361	91.02 332	97.86 202	99.51 216

HY-MVS	96.53 9	99.50 64	99.35 75	99.96 42	99.81 120	99.93 43	99.64 282	100.00 1	97.97 102	99.84 148	99.85 214	98.94 100	99.99 91	99.86 95	98.23 185	99.95 114

ACMH	96.25 11	96.77 234	96.62 228	97.21 280	98.96 268	94.43 314	99.64 282	99.33 212	97.43 154	96.55 317	99.97 178	83.52 326	99.54 210	99.07 182	95.13 251	97.66 301
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
KD-MVS_2432*1600		94.15 298	93.08 306	97.35 273	99.53 192	97.83 237	99.63 284	99.19 268	92.88 321	96.29 320	97.68 345	98.84 109	96.70 347	89.73 341	63.92 368	97.53 321

miper_refine_blended		94.15 298	93.08 306	97.35 273	99.53 192	97.83 237	99.63 284	99.19 268	92.88 321	96.29 320	97.68 345	98.84 109	96.70 347	89.73 341	63.92 368	97.53 321

eth_miper_zixun_eth		97.47 206	97.28 208	98.06 240	99.41 222	97.94 229	99.62 286	99.08 307	94.46 285	98.19 266	99.56 272	96.91 178	98.50 301	96.78 272	91.49 300	97.74 268

test_0402		94.35 296	93.70 300	96.32 306	97.92 314	93.60 320	99.61 287	98.85 340	88.19 350	94.68 336	99.48 279	80.01 339	98.58 294	89.39 344	95.15 250	96.77 342

mvs_tets		97.00 228	96.69 225	97.94 252	97.41 339	97.27 258	99.60 288	99.18 275	96.51 219	97.35 298	99.69 241	86.53 306	98.91 259	98.84 191	95.09 254	97.65 307

EPMVS		99.25 96	99.13 97	99.60 118	99.60 175	99.20 146	99.60 288	100.00 1	96.93 184	99.92 136	99.36 286	99.05 87	99.71 186	98.77 195	98.94 153	99.90 142

FE-MVS		99.16 101	98.99 106	99.66 111	99.65 158	99.18 148	99.58 290	99.43 114	95.24 263	99.91 138	99.59 266	99.37 57	99.97 114	98.31 219	99.81 126	99.83 171

TAMVS		98.76 138	98.73 134	98.86 188	99.44 219	97.69 241	99.57 291	99.34 210	96.57 214	99.12 200	99.81 223	98.83 111	99.16 243	97.97 235	97.91 199	99.73 204

MDTV_nov1_ep13_2view						99.24 142	99.56 292		96.31 230	99.96 108		98.86 107		98.92 187		99.89 146

CL-MVSNet_self_test		91.07 319	90.35 322	93.24 333	93.27 361	89.16 349	99.55 293	99.25 244	92.34 326	95.23 331	97.05 350	88.86 284	93.59 367	80.67 362	66.95 367	96.96 339

LS3D		99.31 86	99.13 97	99.87 77	99.99 49	99.71 95	99.55 293	99.46 90	97.32 162	99.82 158	100.00 1	96.85 180	99.97 114	99.14 178	100.00 1	99.92 131

XXY-MVS		97.14 220	96.63 227	98.67 195	98.65 282	98.92 169	99.54 295	99.29 225	95.57 252	97.63 288	99.83 217	87.79 295	99.35 236	98.39 215	92.95 279	97.75 245

tpm		98.24 175	98.22 169	98.32 215	99.13 245	95.79 284	99.53 296	99.12 297	95.20 264	99.96 108	99.36 286	97.58 151	99.28 240	97.41 252	96.67 225	99.88 157

tpmrst		98.98 123	98.93 114	99.14 171	99.61 173	97.74 240	99.52 297	99.36 196	96.05 235	99.98 100	99.64 254	99.04 89	99.86 157	98.94 185	98.19 188	99.82 176

DP-MVS		98.86 133	98.54 150	99.81 88	99.97 88	99.45 121	99.52 297	99.40 173	94.35 288	98.36 252	100.00 1	96.13 191	99.97 114	99.12 180	100.00 1	100.00 1

VNet		99.04 109	98.75 132	99.90 70	99.81 120	99.75 90	99.50 299	99.47 75	98.36 74	100.00 1	99.99 166	94.66 214	100.00 1	99.90 88	97.09 219	99.96 109

VPNet		96.41 253	95.76 267	98.33 214	98.61 284	98.30 204	99.48 300	99.45 97	96.98 182	98.87 220	99.88 209	81.57 334	98.93 257	99.22 175	87.82 332	97.76 234

test1111		98.42 163	98.12 172	99.29 159	99.88 110	98.15 213	99.46 301	100.00 1	98.36 74	99.42 182	100.00 1	87.91 291	99.79 173	99.31 166	98.78 156	99.94 119

ECVR-MVS		98.43 161	98.14 171	99.32 156	99.89 108	98.21 210	99.46 301	100.00 1	98.38 70	99.47 181	100.00 1	87.91 291	99.80 172	99.35 162	98.78 156	99.94 119

test2506		99.48 68	99.38 68	99.75 101	99.89 108	99.51 112	99.45 303	100.00 1	98.38 70	99.83 150	100.00 1	98.86 107	99.81 171	99.25 170	98.78 156	99.94 119

h-mvs33		97.03 226	96.53 230	98.51 203	99.79 133	95.90 283	99.45 303	99.45 97	98.21 80	100.00 1	99.78 229	97.49 156	99.99 91	99.72 117	74.92 361	99.65 212

dp		98.72 141	98.61 144	99.03 177	99.53 192	97.39 251	99.45 303	99.39 186	95.62 250	99.94 132	99.52 276	98.83 111	99.82 168	96.77 274	98.42 171	99.89 146

FMVSNet2		96.22 265	95.60 276	98.06 240	99.53 192	98.33 201	99.45 303	99.27 239	93.71 299	98.03 270	98.84 318	84.23 321	98.10 325	93.97 310	93.40 274	97.73 274

LTVRE_ROB	95.29 16	96.32 261	96.10 250	96.99 287	98.55 286	93.88 318	99.45 303	99.28 231	94.50 283	96.46 318	99.52 276	84.86 317	99.48 221	97.26 258	95.03 255	97.59 317
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
MDA-MVSNet-bldmvs		91.65 317	89.94 324	96.79 299	96.72 343	96.70 274	99.42 308	98.94 334	88.89 345	66.97 375	98.37 336	81.43 335	95.91 358	89.24 346	89.46 321	97.75 245

TinyColmap		95.50 286	95.12 290	96.64 300	98.69 281	93.00 327	99.40 309	97.75 360	96.40 225	96.14 324	99.87 210	79.47 341	99.50 219	93.62 313	94.72 262	97.40 328

MDTV_nov1_ep13			98.94 113		99.53 192	98.36 199	99.39 310	99.46 90	96.54 216	99.99 95	99.63 258	98.92 103	99.86 157	98.30 222	98.71 160

VPA-MVSNet		97.03 226	96.43 236	98.82 189	98.64 283	99.32 133	99.38 311	99.47 75	96.73 200	98.91 216	98.94 313	87.00 302	99.40 232	99.23 173	89.59 318	97.76 234

MVS-HIRNet		94.12 300	92.73 312	98.29 217	99.33 233	95.95 280	99.38 311	99.19 268	74.54 367	98.26 262	86.34 371	86.07 309	99.06 247	91.60 328	99.87 121	99.85 167

PatchmatchNet		99.03 111	98.96 108	99.26 164	99.49 209	98.33 201	99.38 311	99.45 97	96.64 210	99.96 108	99.58 268	99.49 39	99.50 219	97.63 244	99.00 152	99.93 129
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous202405211		97.87 186	97.53 198	98.90 185	99.81 120	96.70 274	99.35 314	99.46 90	92.98 319	98.83 225	99.99 166	90.63 260	100.00 1	99.70 122	97.03 220	100.00 1

MIMVSNet1		91.96 312	91.20 315	94.23 328	94.94 359	91.69 338	99.34 315	99.22 255	88.23 348	94.18 341	98.45 333	75.52 351	93.41 368	79.37 365	91.49 300	97.60 316

tt0805		96.52 246	96.23 245	97.40 269	99.30 237	93.55 321	99.32 316	99.45 97	96.75 196	97.88 279	99.99 166	79.99 340	99.59 193	97.39 254	95.98 228	99.06 224

test_post1							99.32 316				88.24 370	99.33 58	99.59 193	98.31 219

tpmvs		98.59 151	98.38 159	99.23 166	99.69 146	97.90 231	99.31 318	99.47 75	94.52 281	99.68 169	99.28 290	97.64 150	99.89 153	97.71 241	98.17 190	99.89 146

COLMAP_ROB	97.10 7	98.29 172	98.17 170	98.65 196	99.94 97	97.39 251	99.30 319	99.40 173	95.64 248	97.75 285	100.00 1	92.69 241	99.95 134	98.89 188	99.92 115	98.62 229
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TR-MVS		98.14 177	97.74 190	99.33 154	99.59 178	98.28 205	99.27 320	99.21 264	96.42 222	99.15 198	99.94 202	88.87 283	99.79 173	98.88 189	98.29 182	99.93 129

OpenMVS_ROB	88.34 20	91.89 314	91.12 316	94.19 329	95.55 355	87.63 353	99.26 321	98.03 352	86.61 354	90.65 354	96.82 351	70.14 361	98.78 273	86.54 352	96.50 227	96.15 349

FMVSNet5		95.32 288	95.43 284	94.99 318	99.39 230	92.99 328	99.25 322	99.24 249	90.45 338	97.44 297	98.45 333	95.78 197	94.39 364	87.02 350	91.88 295	97.59 317

pm-mvs1		95.76 282	95.01 291	98.00 248	98.23 302	97.45 249	99.24 323	99.04 324	93.13 318	95.93 327	99.72 233	86.28 307	98.84 267	95.62 288	87.92 331	97.72 281

1314		99.38 75	99.19 93	99.96 42	98.88 274	99.89 65	99.24 323	99.93 29	98.88 38	98.79 228	100.00 1	97.02 169	100.00 1	100.00 1	100.00 1	100.00 1

MVS		99.22 99	98.96 108	99.98 23	99.00 262	99.95 32	99.24 323	99.94 21	98.14 87	98.88 218	100.00 1	95.63 201	100.00 1	99.85 97	100.00 1	100.00 1

ADS-MVSNet2		98.28 173	98.51 152	97.62 264	99.51 204	95.03 293	99.24 323	99.41 170	95.52 255	99.96 108	99.70 238	97.57 152	97.94 335	97.11 260	98.54 163	99.88 157

ADS-MVSNet		98.70 144	98.51 152	99.28 162	99.51 204	98.39 194	99.24 323	99.44 108	95.52 255	99.96 108	99.70 238	97.57 152	99.58 197	97.11 260	98.54 163	99.88 157

TransMVSNet (Re)		94.78 294	93.72 299	97.93 254	98.34 293	97.88 233	99.23 328	97.98 355	91.60 330	94.55 337	99.71 235	87.89 293	98.36 308	89.30 345	84.92 343	97.56 319

PCF-MVS	98.23 3	98.69 145	98.37 161	99.62 115	99.78 136	99.02 160	99.23 328	99.06 319	96.43 221	98.08 268	100.00 1	94.72 213	99.95 134	98.16 226	99.91 116	99.90 142
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NR-MVSNet		96.63 242	96.04 253	98.38 210	98.31 296	98.98 166	99.22 330	99.35 205	95.87 239	94.43 340	99.65 250	92.73 240	98.40 307	96.78 272	88.05 330	97.75 245

VDD-MVS		96.58 245	95.99 255	98.34 213	99.52 199	95.33 288	99.18 331	99.38 188	96.64 210	99.77 162	100.00 1	72.51 357	100.00 1	100.00 1	96.94 223	99.70 205

GBi-Net		96.07 274	95.80 264	96.89 293	99.53 192	94.87 294	99.18 331	99.27 239	93.71 299	98.53 243	98.81 319	84.23 321	98.07 327	95.31 292	93.60 271	97.72 281

test1		96.07 274	95.80 264	96.89 293	99.53 192	94.87 294	99.18 331	99.27 239	93.71 299	98.53 243	98.81 319	84.23 321	98.07 327	95.31 292	93.60 271	97.72 281

FMVSNet1		94.45 295	93.63 301	96.89 293	98.87 276	94.87 294	99.18 331	99.27 239	90.95 336	97.31 299	98.81 319	72.89 356	98.07 327	92.61 319	92.81 280	97.72 281

UGNet		98.41 165	98.11 173	99.31 158	99.54 189	98.55 187	99.18 331	100.00 1	98.64 60	99.79 160	99.04 302	87.61 296	100.00 1	99.30 167	99.89 117	99.40 218
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
GG-mvs-BLEND				99.59 120	99.54 189	99.49 116	99.17 336	99.52 68		99.96 108	99.68 245	100.00 1	99.33 238	99.71 119	99.99 97	99.96 109

Anonymous20240529		96.93 230	96.22 246	99.05 174	99.79 133	97.30 257	99.16 337	99.47 75	88.51 347	98.69 232	100.00 1	83.50 327	100.00 1	99.83 100	97.02 221	99.83 171

LFMVS		97.42 208	96.62 228	99.81 88	99.80 130	99.50 113	99.16 337	99.56 66	94.48 284	100.00 1	100.00 1	79.35 342	100.00 1	99.89 90	97.37 216	99.94 119

Anonymous20231211		96.29 262	95.70 270	98.07 236	99.80 130	97.49 247	99.15 339	99.40 173	89.11 344	97.75 285	99.45 281	88.93 282	98.98 253	98.26 224	89.47 320	97.73 274

VDDNet		96.39 257	95.55 277	98.90 185	99.27 238	97.45 249	99.15 339	99.92 33	91.28 332	99.98 100	100.00 1	73.55 353	100.00 1	99.85 97	96.98 222	99.24 219

UniMVSNet_ETH3D		95.28 289	94.41 295	97.89 256	98.91 271	95.14 291	99.13 341	99.35 205	92.11 327	97.17 303	99.66 248	70.28 360	99.36 234	97.88 237	95.18 247	99.16 220

CR-MVSNet		98.02 183	97.71 193	98.93 183	99.31 234	98.86 171	99.13 341	99.00 329	96.53 217	99.96 108	98.98 308	96.94 176	98.10 325	91.18 330	98.40 172	99.84 168

RPMNet		95.26 290	93.82 298	99.56 127	99.31 234	98.86 171	99.13 341	99.42 124	79.82 364	99.96 108	95.13 357	95.69 199	99.98 109	77.54 368	98.40 172	99.84 168

ab-mvs		98.42 163	98.02 181	99.61 116	99.71 143	99.00 164	99.10 344	99.64 63	96.70 203	99.04 208	99.81 223	90.64 259	99.98 109	99.64 138	97.93 198	99.84 168

TDRefinement		91.93 313	90.48 321	96.27 307	81.60 375	92.65 332	99.10 344	97.61 363	93.96 297	93.77 342	99.85 214	80.03 338	99.53 215	97.82 239	70.59 365	96.63 346

EGC-MVSNET		79.46 334	74.04 342	95.72 313	96.00 349	92.73 330	99.09 346	99.04 324	5.08 379	16.72 379	98.71 323	73.03 355	98.74 279	82.05 360	96.64 226	95.69 355

UA-Net		99.06 107	98.83 122	99.74 102	99.52 199	99.40 126	99.08 347	99.45 97	97.64 130	99.83 150	100.00 1	95.80 196	99.94 146	98.35 217	99.80 128	99.88 157

PatchT		95.90 280	94.95 293	98.75 193	99.03 256	98.39 194	99.08 347	99.32 214	85.52 355	99.96 108	94.99 359	97.94 139	98.05 331	80.20 364	98.47 168	99.81 181

gg-mvs-nofinetune		96.95 229	96.10 250	99.50 131	99.41 222	99.36 131	99.07 349	99.52 68	83.69 359	99.96 108	83.60 375	100.00 1	99.20 242	99.68 130	99.99 97	99.96 109

EG-PatchMatch MVS		92.94 309	92.49 313	94.29 327	95.87 350	87.07 354	99.07 349	98.11 350	93.19 316	88.98 356	98.66 326	70.89 358	99.08 246	92.43 323	95.21 245	96.72 343

pmmvs6		93.64 302	92.87 309	95.94 311	97.47 336	91.41 340	98.92 351	99.02 326	87.84 351	95.01 333	99.61 264	77.24 348	98.77 276	94.33 304	86.41 341	97.63 311

Patchmtry		96.81 232	96.37 239	98.14 230	99.31 234	98.55 187	98.91 352	99.00 329	90.45 338	97.92 277	98.98 308	96.94 176	98.12 320	94.27 305	91.53 299	97.75 245

MS-PatchMatch		95.66 284	95.87 260	95.05 316	97.80 320	89.25 348	98.88 353	99.30 221	96.35 227	96.86 309	99.01 306	81.35 336	99.43 229	93.30 316	99.98 105	96.46 347

MVP-Stereo		96.51 248	96.48 234	96.60 302	95.65 353	94.25 315	98.84 354	98.16 347	95.85 243	95.23 331	99.04 302	92.54 243	99.13 244	92.98 318	99.98 105	96.43 348
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
testf1		84.40 330	84.79 332	83.23 350	95.71 351	58.71 380	98.79 355	97.75 360	81.58 361	84.94 363	98.07 341	45.33 372	97.73 339	77.09 370	83.85 346	93.24 363

APD_test2		84.40 330	84.79 332	83.23 350	95.71 351	58.71 380	98.79 355	97.75 360	81.58 361	84.94 363	98.07 341	45.33 372	97.73 339	77.09 370	83.85 346	93.24 363

Patchmatch-test		97.83 188	97.42 200	99.06 172	99.08 249	97.66 243	98.66 357	99.21 264	93.65 303	98.25 263	99.58 268	99.47 43	99.57 198	90.25 339	98.59 162	99.95 114

MVS_0304		95.11 293	94.84 294	95.91 312	99.60 175	91.24 343	98.64 358	99.88 36	94.51 282	99.62 172	98.31 339	69.19 362	98.82 270	95.22 295	98.60 161	97.66 301

UnsupCasMVSNet_bld		89.50 324	88.00 328	93.99 330	95.30 356	88.86 351	98.52 359	99.28 231	85.50 356	87.80 360	94.11 361	61.63 365	96.96 345	90.63 334	79.26 356	96.15 349

MIMVSNet		97.06 224	96.73 224	98.05 244	99.38 231	96.64 276	98.47 360	99.35 205	93.41 309	99.48 178	98.53 330	89.66 271	97.70 341	94.16 308	98.11 192	99.80 192

FPMVS		77.92 338	79.45 336	73.34 357	76.87 378	46.81 383	98.24 361	99.05 321	59.89 372	73.55 368	98.34 337	36.81 377	86.55 372	80.96 361	91.35 304	86.65 369

Patchmatch-RL test		93.49 303	93.63 301	93.05 334	91.78 364	83.41 359	98.21 362	96.95 367	91.58 331	91.05 349	97.64 347	99.40 55	95.83 359	94.11 309	81.95 353	99.91 133

CMPMVS	66.12 22	90.65 321	92.04 314	86.46 346	96.18 347	66.87 373	98.03 363	99.38 188	83.38 360	85.49 362	99.55 273	77.59 346	98.80 272	94.44 303	94.31 267	93.72 362
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Effi-MVS+		98.58 152	98.24 166	99.61 116	99.60 175	99.26 138	97.85 364	99.10 301	96.22 232	99.97 104	99.89 208	93.75 223	99.77 178	99.43 157	98.34 178	99.81 181

ambc				88.45 342	86.84 371	70.76 370	97.79 365	98.02 354		90.91 351	95.14 356	38.69 374	98.51 300	94.97 298	84.23 345	96.09 351

LCM-MVSNet		79.01 337	76.93 340	85.27 347	78.28 377	68.01 372	96.57 366	98.03 352	55.10 373	82.03 366	93.27 363	31.99 379	93.95 366	82.72 357	74.37 362	93.84 361

MVE	68.59 21	67.22 342	64.68 346	74.84 354	74.67 380	62.32 378	95.84 367	90.87 379	50.98 374	58.72 376	81.05 376	12.20 384	78.95 374	61.06 375	56.75 371	83.24 372
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 343	63.44 347	73.88 356	61.14 381	63.45 377	95.68 368	87.18 380	79.93 363	47.35 377	80.68 377	22.35 381	72.33 379	61.24 374	35.42 375	85.88 370

Gipuma		84.73 329	83.50 334	88.40 343	97.50 332	82.21 360	88.87 369	99.05 321	65.81 369	85.71 361	90.49 366	53.70 367	96.31 353	78.64 366	91.74 296	86.67 368
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	60.66 23	65.98 344	65.05 345	68.75 360	55.06 383	38.40 384	88.19 370	96.98 366	48.30 377	44.82 378	88.52 369	12.22 383	86.49 373	67.58 373	83.79 348	81.35 373
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		75.80 339	74.26 341	80.43 352	52.91 384	53.67 382	87.42 371	97.98 355	61.80 371	67.04 374	100.00 1	76.43 349	96.40 352	96.47 276	28.26 376	91.23 366

wuyk23d		28.28 345	29.73 349	23.92 361	75.89 379	32.61 385	66.50 372	12.88 385	16.09 378	14.59 380	16.59 379	12.35 382	32.36 380	39.36 378	13.36 378	6.79 376

test_blank		0.07 349	0.09 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.79 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uanet_test		0.01 350	0.02 353	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.14 381	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

DCPMVS		0.01 350	0.02 353	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.14 381	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

cdsmvs_eth3d_5k		24.41 346	32.55 348	0.00 362	0.00 385	0.00 386	0.00 373	99.39 186	0.00 380	0.00 381	100.00 1	93.55 226	0.00 381	0.00 379	0.00 379	0.00 377

pcd_1.5k_mvsjas		8.24 348	10.99 351	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.14 381	98.75 116	0.00 381	0.00 379	0.00 379	0.00 377

sosnet-low-res		0.01 350	0.02 353	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.14 381	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

sosnet		0.01 350	0.02 353	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.14 381	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uncertanet		0.01 350	0.02 353	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.14 381	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

Regformer		0.01 350	0.02 353	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.14 381	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

ab-mvs-re		8.33 347	11.11 350	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	100.00 1	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uanet		0.01 350	0.02 353	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.14 381	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 124					100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 48	100.00 1	100.00 1	99.54 26	100.00 1	100.00 1	100.00 1	100.00 1

No_MVS				100.00 1	100.00 1	100.00 1		99.42 124					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 124	98.72 54	100.00 1	100.00 1	99.60 17

eth-test2					0.00 385

eth-test					0.00 385

ZD-MVS					100.00 1	99.98 17		99.80 42	97.31 164	100.00 1	100.00 1	99.32 61	99.99 91	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 124	99.12 6	100.00 1				100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 124	99.03 19	100.00 1	100.00 1	99.56 23	100.00 1	100.00 1	100.00 1	100.00 1

test_241102_ONE					100.00 1	99.99 5		99.42 124	99.03 19	100.00 1	100.00 1	99.50 37	100.00 1

test_0728_THIRD									98.79 50	100.00 1	100.00 1	99.61 16	100.00 1	100.00 1	100.00 1	100.00 1

GSMVS																99.91 133

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs1												99.29 69				99.91 133

sam_mvs												99.33 58

MTGPA								99.42 124

test_post											89.05 368	99.49 39	99.59 193

patchmatchnet-post											97.79 344	99.41 54	99.54 210

gm-plane-assit					99.52 199	97.26 259			95.86 241		100.00 1		99.43 229	98.76 196

test9_res														100.00 1	100.00 1	100.00 1

agg_prior2														100.00 1	100.00 1	100.00 1

agg_prior					100.00 1	99.88 72		99.42 124		100.00 1			99.97 114

TestCases				98.99 179	99.93 99	97.35 253		99.40 173	97.08 178	99.09 202	99.98 170	93.37 227	99.95 134	96.94 264	99.84 123	99.68 207

test_prior				99.90 70	100.00 1	99.75 90		99.73 55					99.97 114			100.00 1

新几何1				99.99 12	100.00 1	99.96 24		99.81 41	97.89 109	100.00 1	100.00 1	99.20 77	100.00 1	97.91 236	100.00 1	100.00 1

旧先验1					99.99 49	99.88 72		99.82 39			100.00 1	99.27 72			100.00 1	100.00 1

原ACMM1				99.93 65	100.00 1	99.80 86		99.66 62	98.18 83	100.00 1	100.00 1	99.43 50	100.00 1	99.50 155	100.00 1	100.00 1

testdata2													100.00 1	97.36 255

segment_acmp												99.55 25

testdata				99.66 111	99.99 49	98.97 168		99.73 55	97.96 105	100.00 1	100.00 1	99.42 52	100.00 1	99.28 169	100.00 1	100.00 1

test12				99.95 51	99.99 49	99.89 65		99.42 124		100.00 1		99.24 74	99.97 114		100.00 1	100.00 1

plane_prior7					99.00 262	94.78 304

plane_prior6					99.06 254	94.80 300						88.58 288

plane_prior5								99.40 173					99.55 207	99.79 105	95.57 233	97.76 234

plane_prior4											99.97 178

plane_prior3						94.79 303			99.03 19	99.08 204

plane_prior1					99.02 257

n2								0.00 386

nn								0.00 386

door-mid								96.32 372

lessismore_v0				96.05 309	97.55 330	91.80 337		99.22 255		91.87 348	99.91 206	83.50 327	98.68 282	92.48 322	90.42 314	97.68 296

LGP-MVS_train				97.28 277	98.85 278	94.60 309		99.37 190	97.35 158	98.85 221	99.98 170	86.66 304	99.56 202	99.55 148	95.26 241	97.70 290

test11								99.42 124

door								96.13 373

HQP5-MVS						94.82 297

BP-MVS														99.79 105

HQP4-MVS										99.17 194			99.57 198			97.77 232

HQP3-MVS								99.40 173							95.58 229

HQP2-MVS												88.61 286

NP-MVS					99.07 250	94.81 299					99.97 178

ACMMP++_ref															94.58 266

ACMMP++															95.17 249

Test By Simon												99.10 84

ITE_SJBPF				96.84 296	98.96 268	93.49 322		98.12 349	98.12 91	98.35 253	99.97 178	84.45 318	99.56 202	95.63 287	95.25 243	97.49 323

DeepMVS_CX				89.98 339	98.90 272	71.46 369		99.18 275	97.61 134	96.92 306	99.83 217	86.07 309	99.83 165	96.02 281	97.65 214	98.65 228

High-resolution multi-view benchmark