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.

iter_conf_final		98.72 155	98.76 145	98.59 210	99.64 177	99.17 163	100.00 1	99.22 268	96.63 219	99.02 218	99.97 187	99.98 4	98.84 273	99.22 185	95.18 253	97.76 242

test_low_dy_conf_001		97.81 201	97.76 200	97.97 262	97.92 321	97.59 256	100.00 1	99.27 252	95.83 252	98.81 235	99.97 187	94.52 230	98.62 294	98.47 224	95.03 261	97.70 300

patch_mono-2		99.04 123	99.79 6	96.81 309	99.92 118	90.47 356	100.00 1	99.41 182	98.95 31	100.00 1	100.00 1	99.78 8	100.00 1	100.00 1	100.00 1	99.95 125

DVP-MVS++		99.81 11	99.75 15	100.00 1	100.00 1	99.99 5	100.00 1	99.42 130	98.79 48	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 22	100.00 1	99.42 130	98.52 62	100.00 1

GeoE		98.06 192	97.65 208	99.29 168	99.47 224	98.41 204	100.00 1	99.19 281	94.85 277	98.88 227	100.00 1	91.21 267	99.59 200	97.02 272	98.19 197	99.88 169

test_method		91.04 328	91.10 325	90.85 346	98.34 299	77.63 371	100.00 1	98.93 347	76.69 367	96.25 331	98.52 340	70.44 369	97.98 340	89.02 353	91.74 303	96.92 350

test1172		99.64 59	99.53 68	99.96 49	100.00 1	99.90 65	100.00 1	99.51 76	97.59 144	100.00 1	100.00 1	98.65 132	99.99 94	99.99 64	100.00 1	100.00 1

SR-MVS-dyc-post		99.63 63	99.52 72	99.97 34	99.99 59	99.91 57	100.00 1	99.42 130	97.62 138	100.00 1	100.00 1	98.65 132	99.99 94	99.99 64	100.00 1	100.00 1

RE-MVS-def			99.55 66		99.99 59	99.91 57	100.00 1	99.42 130	97.62 138	100.00 1	100.00 1	98.94 111		99.99 64	100.00 1	100.00 1

SED-MVS		99.83 7	99.77 10	100.00 1	100.00 1	99.99 5	100.00 1	99.42 130	99.03 19	100.00 1	100.00 1	99.50 38	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

xxxxxxxxxxxxxcwj		99.77 19	99.70 22	99.97 34	99.99 59	99.93 47	100.00 1	99.31 230	98.93 33	100.00 1	100.00 1	99.05 94	99.99 94	100.00 1	100.00 1	100.00 1

SF-MVS		99.66 55	99.57 59	99.95 61	99.99 59	99.85 92	100.00 1	99.42 130	97.67 132	100.00 1	100.00 1	99.05 94	99.99 94	100.00 1	100.00 1	100.00 1

ETH3D cwj APD-0.16		99.63 63	99.53 68	99.94 76	99.97 103	99.82 97	100.00 1	99.42 130	97.39 164	100.00 1	100.00 1	98.98 103	99.99 94	100.00 1	100.00 1	100.00 1

cl22		98.23 188	98.11 186	98.58 212	99.82 130	99.01 176	100.00 1	99.28 244	96.92 196	98.33 264	99.21 301	98.09 152	98.97 261	98.72 209	92.61 289	97.76 242

miper_enhance_ethall		98.33 181	98.27 177	98.51 213	99.66 171	99.04 172	100.00 1	99.22 268	97.53 152	98.51 255	99.38 294	99.49 40	98.75 284	98.02 242	92.61 289	97.76 242

ZNCC-MVS		99.71 36	99.62 48	99.97 34	99.99 59	99.90 65	100.00 1	99.79 44	97.97 105	99.97 114	100.00 1	98.97 105	100.00 1	99.94 98	100.00 1	100.00 1

ETH3 D test6400		99.68 48	99.59 52	99.97 34	99.99 59	99.93 47	100.00 1	99.42 130	97.97 105	100.00 1	100.00 1	98.90 116	99.99 94	100.00 1	100.00 1	100.00 1

cl____		97.54 215	97.32 219	98.18 235	99.47 224	98.14 228	100.00 1	99.10 314	94.16 301	97.60 301	99.63 268	97.52 168	98.65 292	96.47 286	91.97 301	97.76 242

DIV-MVS_self_test		97.52 217	97.35 218	98.05 255	99.46 226	98.11 229	100.00 1	99.10 314	94.21 298	97.62 299	99.63 268	97.65 162	98.29 319	96.47 286	91.98 300	97.76 242

9.14			99.57 59		99.99 59		100.00 1	99.42 130	97.54 150	100.00 1	100.00 1	99.15 88	99.99 94	100.00 1	100.00 1

testtj		99.70 40	99.61 49	99.99 12	100.00 1	99.98 17	100.00 1	99.53 71	97.71 128	100.00 1	100.00 1	99.23 78	100.00 1	100.00 1	100.00 1	100.00 1

ETH3D-3000-0.1		99.66 55	99.57 59	99.95 61	99.99 59	99.85 92	100.00 1	99.42 130	97.58 146	100.00 1	100.00 1	99.12 90	99.99 94	100.00 1	100.00 1	100.00 1

save fliter					99.99 59	99.93 47	100.00 1	99.42 130	98.93 33

ET-MVSNet_ETH3D		96.41 264	95.48 293	99.20 177	99.81 134	99.75 106	100.00 1	99.02 339	97.30 175	78.33 370	100.00 1	97.73 159	97.94 342	99.70 133	87.41 342	99.92 142

EIA-MVS		99.26 108	99.19 108	99.45 149	99.63 179	98.75 189	100.00 1	99.27 252	96.93 194	99.95 140	100.00 1	97.47 171	99.79 181	99.74 126	99.72 144	99.82 187

miper_lstm_enhance		97.40 221	97.28 221	97.75 273	99.48 221	97.52 258	100.00 1	99.07 324	94.08 302	98.01 282	99.61 274	97.38 176	97.98 340	96.44 289	91.47 309	97.76 242

ETV-MVS		99.34 96	99.24 100	99.64 126	99.58 194	99.33 147	100.00 1	99.25 258	97.57 148	99.96 118	100.00 1	97.44 174	99.79 181	99.70 133	99.65 148	99.81 192

CS-MVS		99.33 98	99.27 94	99.50 144	99.99 59	99.00 178	100.00 1	99.13 304	97.26 176	99.96 118	100.00 1	97.79 158	99.64 197	99.64 147	99.67 146	99.87 177

D2MVS		97.63 211	97.83 198	97.05 294	98.83 286	94.60 321	100.00 1	99.82 39	96.89 199	98.28 268	99.03 313	94.05 234	99.47 229	98.58 221	94.97 264	97.09 346

DVP-MVS		99.83 7	99.78 7	100.00 1	100.00 1	99.99 5	100.00 1	99.42 130	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 59	99.99 5	100.00 1	99.42 130					100.00 1	100.00 1	100.00 1	100.00 1

test0726					100.00 1	99.99 5	100.00 1	99.42 130	99.04 14	100.00 1	100.00 1	99.53 29

SR-MVS		99.68 48	99.58 56	99.98 25	100.00 1	99.95 34	100.00 1	99.64 66	97.59 144	100.00 1	100.00 1	98.99 102	99.99 94	100.00 1	100.00 1	100.00 1

DPM-MVS		99.63 63	99.51 73	100.00 1	99.90 122	100.00 1	100.00 1	99.43 122	99.00 25	100.00 1	100.00 1	99.58 22	100.00 1	97.64 255	100.00 1	100.00 1

GST-MVS		99.64 59	99.53 68	99.95 61	100.00 1	99.86 89	100.00 1	99.79 44	97.72 125	99.95 140	100.00 1	98.39 143	100.00 1	99.96 90	99.99 113	100.00 1

test_yl		99.51 76	99.37 86	99.95 61	99.82 130	99.90 65	100.00 1	99.47 80	97.48 157	100.00 1	100.00 1	99.80 6	100.00 1	99.98 81	97.75 217	99.94 130

thisisatest0530		99.37 92	99.27 94	99.69 121	99.59 189	99.41 140	100.00 1	99.46 95	96.46 227	99.90 151	100.00 1	99.44 48	99.85 170	98.97 195	99.58 152	99.80 202

DCV-MVSNet		99.51 76	99.37 86	99.95 61	99.82 130	99.90 65	100.00 1	99.47 80	97.48 157	100.00 1	100.00 1	99.80 6	100.00 1	99.98 81	97.75 217	99.94 130

tttt0517		99.34 96	99.23 103	99.67 123	99.57 197	99.38 142	100.00 1	99.46 95	96.33 237	99.89 154	100.00 1	99.44 48	99.84 172	98.93 197	99.46 155	99.78 204

thisisatest0515		99.42 87	99.31 93	99.74 117	99.59 189	99.55 125	100.00 1	99.46 95	96.65 216	99.92 148	100.00 1	99.44 48	99.85 170	99.09 192	99.63 150	99.81 192

SMA-MVS		99.69 44	99.59 52	99.98 25	99.99 59	99.93 47	100.00 1	99.43 122	97.50 156	100.00 1	100.00 1	99.43 52	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 15	99.73 18	99.99 12	99.99 59	99.98 17	100.00 1	99.42 130	98.91 36	100.00 1	100.00 1	99.22 80	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		99.25 111	99.01 118	99.95 61	99.81 134	99.87 86	100.00 1	99.94 21	97.13 184	99.83 161	99.96 201	97.01 183	100.00 1	99.59 152	97.85 210	99.98 112

tfpn200view9		99.26 108	99.03 116	99.96 49	99.81 134	99.89 73	100.00 1	99.94 21	97.23 178	99.83 161	99.96 201	97.04 179	100.00 1	99.59 152	97.85 210	99.98 112

CANet		99.40 88	99.24 100	99.89 88	99.99 59	99.76 104	100.00 1	99.73 57	98.40 68	99.78 172	100.00 1	95.28 218	99.96 137	100.00 1	99.99 113	99.96 120

Fast-Effi-MVS+-dtu		98.38 180	98.56 163	97.82 270	99.58 194	94.44 325	100.00 1	99.16 294	96.75 204	99.51 186	99.63 268	95.03 223	99.60 198	97.71 253	99.67 146	99.42 226

Effi-MVS+-dtu		98.51 172	98.86 136	97.47 280	99.77 152	94.21 328	100.00 1	98.94 344	97.61 140	99.91 150	98.75 331	95.89 207	99.51 224	99.36 170	99.48 154	98.68 235

CANet_DTU		99.02 129	98.90 134	99.41 155	99.88 126	98.71 193	100.00 1	99.29 238	98.84 41	100.00 1	100.00 1	94.02 236	100.00 1	98.08 239	99.96 126	99.52 224

MP-MVS-pluss		99.61 69	99.50 75	99.97 34	99.98 99	99.92 54	100.00 1	99.42 130	97.53 152	99.77 173	100.00 1	98.77 127	100.00 1	99.99 64	100.00 1	99.99 110
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MSP-MVS		99.81 11	99.77 10	99.94 76	100.00 1	99.86 89	100.00 1	99.42 130	98.87 40	100.00 1	100.00 1	99.65 15	99.96 137	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 10	99.99 12	100.00 1	99.96 25	100.00 1	99.43 122	99.05 13	100.00 1	100.00 1	99.45 47	99.99 94	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		97.21 227	97.18 227	97.32 286	98.08 316	94.66 318	100.00 1	99.28 244	98.65 57	98.92 223	99.98 179	86.03 327	99.56 208	98.28 234	95.41 241	97.72 290
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
ACMMP_NAP		99.67 53	99.57 59	99.97 34	99.98 99	99.92 54	100.00 1	99.42 130	97.83 118	100.00 1	100.00 1	98.89 118	100.00 1	99.98 81	100.00 1	100.00 1

zzz-MVS		99.68 48	99.59 52	99.97 34	99.99 59	99.91 57	100.00 1	99.42 130	98.32 79	99.94 142	100.00 1	98.65 132	100.00 1	99.96 90	100.00 1	100.00 1

mvs-test1		98.89 145	98.99 121	98.60 207	99.77 152	95.96 291	100.00 1	98.94 344	97.61 140	99.93 147	99.92 216	95.89 207	99.93 156	99.36 170	99.50 153	99.90 153

CS-MVS-test		99.31 101	99.27 94	99.43 152	99.99 59	98.77 188	100.00 1	99.19 281	97.24 177	99.96 118	100.00 1	97.56 167	99.70 194	99.68 140	99.81 140	99.82 187

pmmvs5		95.94 290	95.61 286	96.95 300	97.42 345	94.66 318	100.00 1	98.08 361	93.60 312	97.05 313	99.43 292	87.02 317	98.46 313	95.76 292	92.12 297	97.72 290

Fast-Effi-MVS+		98.40 179	98.02 193	99.55 141	99.63 179	99.06 170	100.00 1	99.15 295	95.07 272	99.42 192	99.95 209	93.26 245	99.73 191	97.44 261	98.24 193	99.87 177

MTAPA		99.68 48	99.59 52	99.97 34	99.99 59	99.91 57	100.00 1	99.42 130	98.32 79	99.94 142	100.00 1	98.65 132	100.00 1	99.96 90	100.00 1	100.00 1

MTMP							100.00 1	99.18 288

TEST9					100.00 1	99.95 34	100.00 1	99.42 130	97.65 134	100.00 1	100.00 1	99.53 29	99.97 123

train_agg		99.71 36	99.63 44	99.97 34	100.00 1	99.95 34	100.00 1	99.42 130	97.70 129	100.00 1	100.00 1	99.51 33	99.97 123	100.00 1	100.00 1	100.00 1

test_8					100.00 1	99.91 57	100.00 1	99.42 130	97.70 129	100.00 1	100.00 1	99.51 33	99.98 118

agg_prior1		99.71 36	99.63 44	99.95 61	100.00 1	99.88 83	100.00 1	99.42 130	97.72 125	100.00 1	100.00 1	99.51 33	99.97 123	100.00 1	100.00 1	100.00 1

canonicalmvs		99.03 125	98.73 148	99.94 76	99.75 156	99.95 34	100.00 1	99.30 233	97.64 136	100.00 1	100.00 1	95.22 220	99.97 123	99.76 124	96.90 231	99.91 144

alignmvs		99.38 90	99.21 104	99.91 82	99.73 157	99.92 54	100.00 1	99.51 76	97.61 140	100.00 1	100.00 1	99.06 93	99.93 156	99.83 112	97.12 225	99.90 153

nrg030		97.64 208	97.27 224	98.75 202	98.34 299	99.53 127	100.00 1	99.22 268	96.21 241	98.27 270	99.95 209	94.40 232	98.98 259	99.23 183	89.78 324	97.75 254

FIs		97.95 197	97.73 205	98.62 206	98.53 294	99.24 157	100.00 1	99.43 122	96.74 205	97.87 289	99.82 231	95.27 219	98.89 268	98.78 205	93.07 284	97.74 277

FC-MVSNet-test		97.84 199	97.63 209	98.45 216	98.30 304	99.05 171	100.00 1	99.43 122	96.63 219	97.61 300	99.82 231	95.19 221	98.57 303	98.64 214	93.05 285	97.73 283

HFP-MVS		99.74 27	99.67 32	99.96 49	100.00 1	99.89 73	100.00 1	99.76 48	97.95 110	100.00 1	100.00 1	99.31 65	100.00 1	99.99 64	100.00 1	100.00 1

v148		96.29 273	95.84 273	97.63 274	97.74 329	96.53 288	100.00 1	99.07 324	93.52 313	98.01 282	99.42 293	91.22 266	98.60 298	96.37 290	87.22 344	97.75 254

AllTest		98.55 168	98.40 171	98.99 187	99.93 115	97.35 264	100.00 1	99.40 185	97.08 188	99.09 211	99.98 179	93.37 242	99.95 143	96.94 274	99.84 137	99.68 216

region2R		99.72 33	99.64 40	99.97 34	100.00 1	99.90 65	100.00 1	99.74 55	97.86 117	100.00 1	100.00 1	99.19 84	100.00 1	99.99 64	100.00 1	100.00 1

iter_conf05		98.73 154	98.77 143	98.60 207	99.65 172	99.22 160	100.00 1	99.22 268	96.68 214	98.98 221	99.97 187	99.99 3	98.84 273	99.29 179	95.11 259	97.75 254

RRT_MVS		97.77 203	97.76 200	97.78 272	97.89 324	97.06 277	100.00 1	99.29 238	95.74 254	98.00 284	99.97 187	95.94 206	98.55 306	98.87 201	94.18 275	97.72 290

#test#		99.75 22	99.68 27	99.96 49	100.00 1	99.89 73	100.00 1	99.76 48	98.07 96	100.00 1	100.00 1	99.31 65	100.00 1	99.99 64	100.00 1	100.00 1

Regformer-3		99.73 30	99.65 36	99.96 49	100.00 1	99.89 73	100.00 1	99.44 112	98.40 68	100.00 1	100.00 1	99.03 100	99.97 123	99.99 64	100.00 1	100.00 1

Regformer-4		99.73 30	99.65 36	99.95 61	100.00 1	99.89 73	100.00 1	99.44 112	98.40 68	100.00 1	100.00 1	99.03 100	99.97 123	99.99 64	100.00 1	100.00 1

Regformer-1		99.75 22	99.68 27	99.98 25	100.00 1	99.94 42	100.00 1	99.44 112	98.43 65	100.00 1	100.00 1	99.23 78	99.99 94	99.99 64	100.00 1	100.00 1

Regformer-2		99.75 22	99.68 27	99.97 34	100.00 1	99.94 42	100.00 1	99.44 112	98.42 67	100.00 1	100.00 1	99.22 80	99.99 94	99.99 64	100.00 1	100.00 1

HPM-MVS++		99.82 9	99.76 13	99.99 12	99.99 59	99.98 17	100.00 1	99.83 38	98.88 38	99.96 118	100.00 1	99.21 82	100.00 1	100.00 1	100.00 1	99.99 110

test_prior4						99.93 47	100.00 1

XVS		99.79 15	99.73 18	99.98 25	100.00 1	99.94 42	100.00 1	99.75 52	98.67 55	100.00 1	100.00 1	99.16 86	100.00 1	100.00 1	100.00 1	100.00 1

test_prior3		99.81 11	99.78 7	99.90 85	100.00 1	99.75 106	100.00 1	99.73 57	98.82 44	100.00 1	100.00 1	99.47 44	99.97 123	100.00 1	100.00 1	100.00 1

test_prior2							100.00 1		98.82 44	100.00 1	100.00 1	99.47 44		100.00 1	100.00 1

X-MVStestdata		97.04 237	96.06 264	99.98 25	100.00 1	99.94 42	100.00 1	99.75 52	98.67 55	100.00 1	66.97 380	99.16 86	100.00 1	100.00 1	100.00 1	100.00 1

旧先验2							100.00 1		98.11 94	100.00 1			100.00 1	99.67 143

新几何2							100.00 1

无先验							100.00 1	99.80 42	97.98 103				100.00 1	99.33 174		100.00 1

原ACMM2							100.00 1

test222					99.99 59	99.90 65	100.00 1	99.69 64	97.66 133	100.00 1	100.00 1	99.30 71			100.00 1	100.00 1

testdata1							100.00 1		98.77 51

v2v482		96.70 252	96.18 259	98.27 228	98.04 317	98.39 207	100.00 1	99.13 304	94.19 300	98.58 249	99.08 307	90.48 278	98.67 290	95.69 294	90.44 320	97.75 254

SD-MVS		99.81 11	99.75 15	99.99 12	99.99 59	99.96 25	100.00 1	99.42 130	99.01 24	100.00 1	100.00 1	99.33 60	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		97.72 205	97.27 224	99.06 180	99.24 250	97.93 240	100.00 1	99.24 262	95.80 253	98.99 220	99.64 264	89.77 286	99.36 240	95.12 305	97.62 222	99.89 158

MSLP-MVS++		99.89 1	99.85 2	99.99 12	100.00 1	99.96 25	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	99.44 112	99.06 11	100.00 1	100.00 1	99.56 23	99.99 94	100.00 1	100.00 1	100.00 1

APD-MVS_3200maxsize		99.65 57	99.55 66	99.97 34	99.99 59	99.91 57	100.00 1	99.48 79	97.54 150	100.00 1	100.00 1	98.97 105	99.99 94	99.98 81	100.00 1	100.00 1

pmmvs4		97.17 229	96.80 233	98.27 228	97.68 331	98.64 198	100.00 1	99.18 288	94.22 297	98.55 250	99.71 246	93.67 239	98.47 312	95.66 295	92.57 292	97.71 299

test-LLR		99.03 125	98.91 131	99.40 157	99.40 237	99.28 151	100.00 1	99.45 102	96.70 210	99.42 192	99.12 304	99.31 65	99.01 256	96.82 279	99.99 113	99.91 144

TESTMET0.1,1		99.08 119	98.96 123	99.44 150	99.63 179	99.38 142	100.00 1	99.45 102	95.53 261	99.48 188	100.00 1	99.71 13	99.02 254	96.84 278	99.99 113	99.91 144

test-mter		98.96 137	98.82 138	99.40 157	99.40 237	99.28 151	100.00 1	99.45 102	95.44 270	99.42 192	99.12 304	99.70 14	99.01 256	96.82 279	99.99 113	99.91 144

ACMMPR		99.74 27	99.67 32	99.96 49	100.00 1	99.89 73	100.00 1	99.76 48	97.95 110	100.00 1	100.00 1	99.29 72	100.00 1	99.99 64	100.00 1	100.00 1

testgi		96.18 278	95.93 270	96.93 302	98.98 273	94.20 329	100.00 1	99.07 324	97.16 182	96.06 334	99.86 223	84.08 339	97.79 345	90.38 344	97.80 215	98.81 234

test20.03		93.11 316	92.85 318	93.88 341	95.19 363	91.83 347	100.00 1	98.87 349	93.68 309	92.76 354	98.88 326	89.20 294	92.71 372	77.88 371	89.19 330	97.09 346

thres600view7		99.24 113	99.00 119	99.95 61	99.81 134	99.87 86	100.00 1	99.94 21	97.13 184	99.83 161	99.96 201	97.01 183	100.00 1	99.54 160	97.77 216	99.97 117

MP-MVS		99.61 69	99.49 77	99.98 25	99.99 59	99.94 42	100.00 1	99.42 130	97.82 119	99.99 105	100.00 1	98.20 146	100.00 1	99.99 64	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		80.17 335	81.95 338	74.80 357	58.54 384	59.58 383	100.00 1	87.14 384	76.09 368	99.61 184	100.00 1	67.06 373	74.19 380	98.84 202	50.30 374	90.64 370

thres400		99.26 108	99.03 116	99.95 61	99.81 134	99.89 73	100.00 1	99.94 21	97.23 178	99.83 161	99.96 201	97.04 179	100.00 1	99.59 152	97.85 210	99.97 117

test123		79.44 337	79.23 340	80.05 355	80.03 378	71.72 373	100.00 1	77.93 386	62.52 372	94.81 343	99.69 251	78.21 359	74.53 379	92.57 327	27.33 379	93.90 365

thres200		99.27 106	99.04 115	99.96 49	99.81 134	99.90 65	100.00 1	99.94 21	97.31 173	99.83 161	99.96 201	97.04 179	100.00 1	99.62 151	97.88 208	99.98 112

test0.0.03 1		98.12 191	98.03 192	98.39 219	99.11 253	98.07 232	100.00 1	99.93 29	96.70 210	96.91 318	99.95 209	99.31 65	98.19 322	91.93 332	98.44 179	98.91 233

pmmvs3		90.62 330	89.36 334	94.40 334	90.53 372	91.49 350	100.00 1	96.73 373	84.21 362	93.65 352	96.65 358	82.56 347	94.83 365	82.28 363	77.62 362	96.89 351

PGM-MVS		99.69 44	99.61 49	99.95 61	99.99 59	99.85 92	100.00 1	99.58 68	97.69 131	100.00 1	100.00 1	99.44 48	100.00 1	99.79 118	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 57	99.19 5	100.00 1	100.00 1	99.31 65	100.00 1	100.00 1	100.00 1	100.00 1

mvs_anonymous		98.80 151	98.60 160	99.38 160	99.57 197	99.24 157	100.00 1	99.21 276	95.87 247	98.92 223	99.82 231	96.39 203	99.03 253	99.13 190	98.50 175	99.88 169

CDPH-MVS		99.73 30	99.64 40	99.99 12	100.00 1	99.97 22	100.00 1	99.42 130	98.02 99	100.00 1	100.00 1	99.32 63	99.99 94	100.00 1	100.00 1	100.00 1

casdiffmvs		98.65 161	98.38 172	99.46 148	99.52 210	98.74 192	100.00 1	99.15 295	96.91 197	99.05 216	100.00 1	92.75 254	99.83 173	99.70 133	98.38 185	99.81 192

diffmvs		98.96 137	98.73 148	99.63 127	99.54 200	99.16 164	100.00 1	99.18 288	97.33 170	99.96 118	100.00 1	94.60 229	99.91 160	99.66 145	98.33 190	99.82 187

baseline2		98.99 133	98.93 129	99.18 178	99.26 249	99.15 165	100.00 1	99.46 95	96.71 209	96.79 322	100.00 1	99.42 54	99.25 246	98.75 208	99.94 129	99.15 230

MDA-MVSNet_test_wron		92.61 318	91.09 326	97.19 292	96.71 352	97.26 270	100.00 1	99.14 300	88.61 351	67.90 375	98.32 347	89.03 295	96.57 353	90.47 343	89.59 325	97.74 277

HQP_MVS		97.71 206	97.82 199	97.37 282	99.00 269	94.80 312	100.00 1	99.40 185	99.00 25	99.08 213	99.97 187	88.58 304	99.55 213	99.79 118	95.57 239	97.76 242

plane_prior2							100.00 1		99.00 25

plane_prior						94.80 312	100.00 1		99.03 19						95.58 235

UniMVSNet_NR-MVSNet		97.16 230	96.80 233	98.22 232	98.38 298	98.41 204	100.00 1	99.45 102	96.14 242	97.76 291	99.64 264	95.05 222	98.50 309	97.98 244	86.84 345	97.75 254

DTE-MVSNet		95.52 296	94.99 302	97.08 293	97.49 342	96.45 289	100.00 1	99.25 258	93.82 305	96.17 332	99.57 280	87.81 310	97.18 348	94.57 309	86.26 350	97.62 324

DU-MVS		96.93 243	96.49 245	98.22 232	98.31 302	98.41 204	100.00 1	99.37 202	96.41 230	97.76 291	99.65 260	92.14 260	98.50 309	97.98 244	86.84 345	97.75 254

UniMVSNet (Re)		97.29 226	96.85 232	98.59 210	98.49 295	99.13 166	100.00 1	99.42 130	96.52 225	98.24 274	98.90 325	94.93 224	98.89 268	97.54 258	87.61 341	97.75 254

Baseline_NR-MVSNet		96.16 281	95.70 281	97.56 279	98.28 305	96.79 283	100.00 1	97.86 368	91.93 335	97.63 297	99.47 290	92.14 260	98.35 317	97.13 269	86.83 347	97.54 331

TranMVSNet+NR-MVSNet		96.45 263	96.01 266	97.79 271	98.00 319	97.62 255	100.00 1	99.35 216	95.98 244	97.31 308	99.64 264	90.09 283	98.00 339	96.89 277	86.80 348	97.75 254

TSAR-MVS + GP.		99.61 69	99.69 24	99.35 162	99.99 59	98.06 233	100.00 1	99.36 208	99.83 2	100.00 1	100.00 1	98.95 109	99.99 94	100.00 1	99.11 158	100.00 1

abl_6		99.53 75	99.40 80	99.92 81	100.00 1	99.76 104	100.00 1	99.42 130	97.21 181	100.00 1	100.00 1	98.12 149	100.00 1	99.82 117	100.00 1	100.00 1

mPP-MVS		99.69 44	99.60 51	99.97 34	100.00 1	99.91 57	100.00 1	99.42 130	97.91 112	100.00 1	100.00 1	99.04 97	100.00 1	100.00 1	100.00 1	100.00 1

XVG-OURS-SEG-HR		98.27 186	98.31 176	98.14 240	99.59 189	95.92 293	100.00 1	99.36 208	98.48 63	99.21 203	100.00 1	89.27 293	99.94 154	99.76 124	99.17 156	98.56 238

mvsmamba		98.13 190	98.06 190	98.32 225	98.22 309	98.50 203	100.00 1	99.22 268	96.41 230	98.91 225	99.96 201	95.69 213	98.73 286	99.19 187	94.95 266	97.73 283

MVSFormer		98.94 140	98.82 138	99.28 171	99.45 227	99.49 132	100.00 1	99.13 304	95.46 268	99.97 114	100.00 1	96.76 194	98.59 300	98.63 216	100.00 1	99.74 209

jason		99.11 117	98.96 123	99.59 133	99.17 251	99.31 149	100.00 1	99.13 304	97.38 165	99.83 161	100.00 1	95.54 216	99.72 192	99.57 156	99.97 124	99.74 209
jason: jason.
lupinMVS		99.29 104	99.16 111	99.69 121	99.45 227	99.49 132	100.00 1	99.15 295	97.45 160	99.97 114	100.00 1	96.76 194	99.76 186	99.67 143	100.00 1	99.81 192

test_djsdf		97.55 214	97.38 216	98.07 246	97.50 340	97.99 236	100.00 1	99.13 304	95.46 268	98.47 258	99.85 225	92.01 263	98.59 300	98.63 216	95.36 243	97.62 324

HPM-MVS_fast		99.60 72	99.49 77	99.91 82	99.99 59	99.78 101	100.00 1	99.42 130	97.09 186	100.00 1	100.00 1	98.95 109	99.96 137	99.98 81	100.00 1	100.00 1

HPM-MVS		99.59 73	99.50 75	99.89 88	100.00 1	99.70 116	100.00 1	99.42 130	97.46 159	100.00 1	100.00 1	98.60 136	99.96 137	99.99 64	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		98.30 182	98.36 175	98.13 243	99.58 194	95.91 294	100.00 1	99.36 208	98.69 53	99.23 202	100.00 1	91.20 268	99.92 159	99.34 173	97.82 213	98.56 238

LPG-MVS_test		97.31 224	97.32 219	97.28 288	98.85 284	94.60 321	100.00 1	99.37 202	97.35 167	98.85 230	99.98 179	86.66 320	99.56 208	99.55 157	95.26 247	97.70 300

baseline		98.69 159	98.45 169	99.41 155	99.52 210	98.67 196	100.00 1	99.17 293	97.03 191	99.13 209	100.00 1	93.17 247	99.74 189	99.70 133	98.34 187	99.81 192

CHOSEN 1792x2688		99.00 131	98.91 131	99.25 174	99.90 122	97.79 249	100.00 1	99.99 13	98.79 48	98.28 268	100.00 1	93.63 240	99.95 143	99.66 145	99.95 128	100.00 1

EPNet		99.62 67	99.69 24	99.42 154	99.99 59	98.37 210	100.00 1	99.89 35	98.83 42	100.00 1	100.00 1	98.97 105	100.00 1	99.90 102	99.61 151	99.89 158
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP-NCC					99.07 257		100.00 1		99.04 14	99.17 204

ACMP_Plane					99.07 257		100.00 1		99.04 14	99.17 204

APD-MVS		99.68 48	99.58 56	99.97 34	99.99 59	99.96 25	100.00 1	99.42 130	97.53 152	100.00 1	100.00 1	99.27 75	99.97 123	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 58	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 63	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 89	99.25 98	99.81 103	99.97 103	99.48 136	100.00 1	99.42 130	95.53 261	100.00 1	100.00 1	98.37 144	99.95 143	99.97 88	100.00 1	100.00 1

CP-MVS		99.67 53	99.58 56	99.95 61	100.00 1	99.84 95	100.00 1	99.42 130	97.77 122	100.00 1	100.00 1	99.07 92	100.00 1	100.00 1	100.00 1	100.00 1

SteuartSystems-ACMMP		99.78 17	99.71 21	99.98 25	99.76 154	99.95 34	100.00 1	99.42 130	98.69 53	100.00 1	100.00 1	99.52 32	99.99 94	100.00 1	100.00 1	100.00 1
Skip Steuart: Steuart Systems R&D Blog.
BH-w/o		98.82 150	98.81 140	98.88 195	99.62 182	96.71 284	100.00 1	99.28 244	97.09 186	98.81 235	100.00 1	94.91 225	99.96 137	99.54 160	100.00 1	99.96 120

DELS-MVS		99.62 67	99.56 65	99.82 100	99.92 118	99.45 137	100.00 1	99.78 46	98.92 35	99.73 177	100.00 1	97.70 161	100.00 1	99.93 99	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		98.64 162	98.65 154	98.60 207	99.59 189	96.17 290	100.00 1	99.28 244	96.67 215	98.41 260	100.00 1	94.52 230	99.83 173	99.41 168	100.00 1	99.81 192

MVSTER		98.58 165	98.52 165	98.77 201	99.65 172	99.68 118	100.00 1	99.29 238	95.63 257	98.65 244	99.80 237	99.78 8	98.88 271	98.59 220	95.31 245	97.73 283

CPTT-MVS		99.49 81	99.38 83	99.85 96	100.00 1	99.54 126	100.00 1	99.42 130	97.58 146	99.98 110	100.00 1	97.43 175	100.00 1	99.99 64	100.00 1	100.00 1

PVSNet_Blended_VisFu		99.33 98	99.18 110	99.78 113	99.82 130	99.49 132	100.00 1	99.95 18	97.36 166	99.63 182	100.00 1	96.45 202	99.95 143	99.79 118	99.65 148	99.89 158

PVSNet_BlendedMVS		98.71 157	98.62 157	98.98 189	99.98 99	99.60 123	100.00 1	100.00 1	97.23 178	100.00 1	99.03 313	96.57 198	99.99 94	100.00 1	94.75 268	97.35 341

PVSNet_Blended		99.48 83	99.36 88	99.83 99	99.98 99	99.60 123	100.00 1	100.00 1	97.79 121	100.00 1	100.00 1	96.57 198	99.99 94	100.00 1	99.88 134	99.90 153

cascas		98.43 174	98.07 189	99.50 144	99.65 172	99.02 174	100.00 1	99.22 268	94.21 298	99.72 178	99.98 179	92.03 262	99.93 156	99.68 140	98.12 200	99.54 223

BH-RMVSNet		98.46 173	98.08 188	99.59 133	99.61 184	99.19 162	100.00 1	99.28 244	97.06 190	98.95 222	100.00 1	88.99 296	99.82 176	98.83 204	100.00 1	99.77 205

WTY-MVS		99.54 74	99.40 80	99.95 61	99.81 134	99.93 47	100.00 1	100.00 1	97.98 103	99.84 159	100.00 1	98.94 111	99.98 118	99.86 107	98.21 195	99.94 130

DROMVSNet		99.19 115	99.09 114	99.48 147	99.42 230	99.07 168	100.00 1	99.21 276	96.95 193	99.96 118	100.00 1	96.88 192	99.48 227	99.64 147	99.79 142	99.88 169

sss		99.45 85	99.34 92	99.80 107	99.76 154	99.50 129	100.00 1	99.91 34	97.72 125	99.98 110	99.94 213	98.45 141	100.00 1	99.53 162	98.75 169	99.89 158

DP-MVS Recon		99.76 20	99.69 24	99.98 25	100.00 1	99.95 34	100.00 1	99.52 72	97.99 101	99.99 105	100.00 1	99.72 12	100.00 1	99.96 90	100.00 1	100.00 1

MVS_111021_LR		99.70 40	99.65 36	99.88 92	99.96 109	99.70 116	100.00 1	99.97 17	98.96 28	100.00 1	100.00 1	97.93 154	99.95 143	99.99 64	100.00 1	100.00 1

HQP-MVS		97.73 204	97.85 197	97.39 281	99.07 257	94.82 309	100.00 1	99.40 185	99.04 14	99.17 204	99.97 187	88.61 302	99.57 204	99.79 118	95.58 235	97.77 240

HyFIR lowres test		99.32 100	99.24 100	99.58 137	99.95 110	99.26 153	100.00 1	99.99 13	96.72 208	99.29 200	99.91 217	99.49 40	99.47 229	99.74 126	98.08 202	100.00 1

PAPM_NR		99.74 27	99.66 35	99.99 12	100.00 1	99.96 25	100.00 1	99.47 80	97.87 116	100.00 1	100.00 1	99.60 17	100.00 1	100.00 1	100.00 1	100.00 1

PAPR		99.76 20	99.68 27	99.99 12	100.00 1	99.96 25	100.00 1	99.47 80	98.16 86	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	97.54 215	96.95 229	99.33 164	99.67 167	98.10 231	100.00 1	99.47 80	97.42 163	99.26 201	99.69 251	98.83 123	99.89 161	99.43 166	78.77 361	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 36	99.63 44	99.93 79	99.95 110	99.83 96	100.00 1	100.00 1	98.89 37	100.00 1	100.00 1	97.85 156	99.95 143	100.00 1	100.00 1	100.00 1

CSCG		99.28 105	99.35 90	99.05 182	99.99 59	97.15 273	100.00 1	99.47 80	97.44 161	99.42 192	100.00 1	97.83 157	100.00 1	99.99 64	100.00 1	100.00 1

PatchMatch-RL		99.02 129	98.78 142	99.74 117	99.99 59	99.29 150	100.00 1	100.00 1	98.38 71	99.89 154	99.81 234	93.14 249	99.99 94	97.85 250	99.98 121	99.95 125

USDC		95.90 291	95.70 281	96.50 314	98.60 291	92.56 344	100.00 1	98.30 355	97.77 122	96.92 316	99.94 213	81.25 352	99.45 234	93.54 322	94.96 265	97.49 334

PMMVS		99.12 116	98.97 122	99.58 137	99.57 197	98.98 180	100.00 1	99.30 233	97.14 183	99.96 118	100.00 1	96.53 201	99.82 176	99.70 133	98.49 176	99.94 130

PAPM		99.78 17	99.76 13	99.85 96	99.01 265	99.95 34	100.00 1	99.75 52	99.37 3	99.99 105	100.00 1	99.76 11	99.60 198	100.00 1	100.00 1	100.00 1

CNLPA		99.72 33	99.65 36	99.91 82	99.97 103	99.72 111	100.00 1	99.47 80	98.43 65	99.88 156	100.00 1	99.14 89	100.00 1	99.97 88	100.00 1	100.00 1

PHI-MVS		99.50 79	99.39 82	99.82 100	100.00 1	99.45 137	100.00 1	99.94 21	96.38 233	100.00 1	100.00 1	98.18 147	100.00 1	100.00 1	100.00 1	100.00 1

PVSNet	94.91 18	99.30 103	99.25 98	99.44 150	100.00 1	98.32 216	100.00 1	99.86 37	98.04 98	100.00 1	100.00 1	96.10 205	100.00 1	99.55 157	99.73 143	100.00 1

PVSNet_0	93.57 19	96.41 264	95.74 279	98.41 218	99.84 129	95.22 302	100.00 1	100.00 1	98.08 95	97.55 303	99.78 240	84.40 334	100.00 1	100.00 1	81.99 355	100.00 1

F-COLMAP		99.64 59	99.64 40	99.67 123	99.99 59	99.07 168	100.00 1	99.44 112	98.30 81	99.90 151	100.00 1	99.18 85	99.99 94	99.91 101	100.00 1	99.94 130

DeepPCF-MVS	98.03 4	98.54 169	99.72 20	94.98 329	99.99 59	84.94 366	100.00 1	99.42 130	99.98 1	100.00 1	100.00 1	98.11 150	100.00 1	100.00 1	100.00 1	100.00 1

OMC-MVS		99.27 106	99.38 83	98.96 190	99.95 110	97.06 277	100.00 1	99.40 185	98.83 42	99.88 156	100.00 1	97.01 183	99.86 165	99.47 165	99.84 137	99.97 117

DeepC-MVS	97.84 5	99.00 131	98.80 141	99.60 131	99.93 115	99.03 173	100.00 1	99.40 185	98.61 59	99.33 198	100.00 1	92.23 259	99.95 143	99.74 126	99.96 126	99.83 183
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 40	99.74 17	99.58 137	100.00 1	98.79 187	100.00 1	99.54 70	98.58 60	99.96 118	100.00 1	99.59 20	100.00 1	100.00 1	100.00 1	99.94 130
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMP	97.00 8	97.19 228	97.16 228	97.27 290	98.97 274	94.58 324	100.00 1	99.32 225	97.97 105	97.45 305	99.98 179	85.79 329	99.56 208	99.70 133	95.24 250	97.67 311
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
3Dnovator+	95.58 15	99.03 125	98.71 151	99.96 49	98.99 272	99.89 73	100.00 1	99.51 76	98.96 28	98.32 265	100.00 1	92.78 253	100.00 1	99.87 106	100.00 1	100.00 1

LF4IMVS		96.19 277	96.18 259	96.23 319	98.26 306	92.09 346	100.00 1	97.89 367	97.82 119	97.94 286	99.87 221	82.71 345	99.38 239	97.41 263	93.71 277	97.20 344

TAPA-MVS	96.40 10	97.64 208	97.37 217	98.45 216	99.94 113	95.70 297	100.00 1	99.40 185	97.65 134	99.53 185	100.00 1	99.31 65	99.66 196	80.48 367	100.00 1	100.00 1
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		98.90 144	98.63 156	99.70 120	99.92 118	99.25 155	100.00 1	99.37 202	95.71 255	99.40 197	100.00 1	96.58 197	99.95 143	96.80 281	99.94 129	99.91 144

ACMM	97.17 6	97.37 222	97.40 215	97.29 287	99.01 265	94.64 320	100.00 1	99.25 258	98.07 96	98.44 259	99.98 179	87.38 314	99.55 213	99.25 181	95.19 252	97.69 305
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CLD-MVS		97.64 208	97.74 203	97.36 283	99.01 265	94.76 317	100.00 1	99.34 221	99.30 4	99.00 219	99.97 187	87.49 313	99.57 204	99.96 90	95.58 235	97.75 254
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
miper_ehance_all_eth		97.81 201	97.66 207	98.23 231	99.49 219	98.37 210	99.99 199	99.11 312	94.78 278	98.25 272	99.21 301	98.18 147	98.57 303	97.35 266	92.61 289	97.76 242

ppachtmachnet_test		96.17 280	95.89 271	97.02 296	97.61 334	95.24 301	99.99 199	99.24 262	93.31 319	96.71 325	99.62 272	94.34 233	98.07 334	89.87 346	92.30 296	97.75 254

IterMVS-SCA-FT		96.72 251	96.42 249	97.62 276	99.40 237	96.83 282	99.99 199	99.14 300	94.65 284	97.55 303	99.72 244	89.65 288	98.31 318	95.62 297	92.05 298	97.73 283

SCA		98.30 182	97.98 195	99.23 175	99.41 232	98.25 220	99.99 199	99.45 102	96.91 197	99.76 175	99.58 277	89.65 288	99.54 216	98.31 231	98.79 165	99.91 144

v144192		96.40 267	95.81 274	98.17 237	97.89 324	98.11 229	99.99 199	99.06 332	93.39 316	98.75 239	99.09 306	90.43 279	98.66 291	93.10 324	90.55 319	97.75 254

v1921920		96.16 281	95.50 289	98.14 240	97.88 326	97.96 237	99.99 199	99.07 324	93.33 318	98.60 248	99.24 300	89.37 292	98.71 288	91.28 335	90.74 317	97.75 254

v1192		96.18 278	95.49 291	98.26 230	98.01 318	98.15 226	99.99 199	99.08 320	93.36 317	98.54 251	98.97 320	89.47 291	98.89 268	91.15 337	90.82 315	97.75 254

v1144		96.51 259	95.97 269	98.13 243	97.98 320	98.04 235	99.99 199	99.08 320	93.51 314	98.62 247	98.98 317	90.98 273	98.62 294	93.79 320	90.79 316	97.74 277

V42		96.65 253	96.16 261	98.11 245	98.17 313	98.23 221	99.99 199	99.09 319	93.97 303	98.74 240	99.05 310	91.09 270	98.82 276	95.46 299	89.90 322	97.27 343

MVS_Test		98.93 141	98.65 154	99.77 115	99.62 182	99.50 129	99.99 199	99.19 281	95.52 263	99.96 118	99.86 223	96.54 200	99.98 118	98.65 213	98.48 177	99.82 187

YYNet1		92.44 319	90.92 327	97.03 295	96.20 354	97.06 277	99.99 199	99.14 300	88.21 354	67.93 374	98.43 344	88.63 301	96.28 357	90.64 339	89.08 331	97.74 277

K. test v3		95.46 298	95.14 299	96.40 315	97.53 339	93.40 335	99.99 199	99.23 266	95.49 266	92.70 356	99.73 243	84.26 335	98.12 327	93.94 319	93.38 282	97.68 307

XVG-ACMP-BASELINE		96.60 256	96.52 244	96.84 307	98.41 297	93.29 336	99.99 199	99.32 225	97.76 124	98.51 255	99.29 298	81.95 348	99.54 216	98.40 226	95.03 261	97.68 307

Test_1112_low_res		98.83 149	98.60 160	99.51 142	99.69 161	98.75 189	99.99 199	99.14 300	96.81 201	98.84 232	99.06 308	97.45 172	99.89 161	98.66 211	97.75 217	99.89 158

1112_ss		98.91 142	98.71 151	99.51 142	99.69 161	98.75 189	99.99 199	99.15 295	96.82 200	98.84 232	100.00 1	97.45 172	99.89 161	98.66 211	97.75 217	99.89 158

IterMVS		96.76 248	96.46 247	97.63 274	99.41 232	96.89 280	99.99 199	99.13 304	94.74 281	97.59 302	99.66 258	89.63 290	98.28 320	95.71 293	92.31 295	97.72 290
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPSCF		97.37 222	98.24 179	94.76 332	99.80 144	84.57 367	99.99 199	99.05 334	94.95 275	99.82 169	100.00 1	94.03 235	100.00 1	98.15 238	98.38 185	99.70 214

OpenMVS	95.20 17	98.76 152	98.41 170	99.78 113	98.89 279	99.81 98	99.99 199	99.76 48	98.02 99	98.02 281	100.00 1	91.44 265	100.00 1	99.63 150	99.97 124	99.55 222

our_test_3		96.51 259	96.35 253	96.98 299	97.61 334	95.05 304	99.98 217	99.01 341	94.68 282	96.77 324	99.06 308	95.87 209	98.14 325	91.81 333	92.37 294	97.75 254

Anonymous20231206		93.45 314	93.17 314	94.30 336	95.00 364	89.69 358	99.98 217	98.43 354	93.30 320	94.50 348	98.59 337	90.52 276	95.73 363	77.46 373	90.73 318	97.48 336

EI-MVSNet-Vis-set		99.70 40	99.64 40	99.87 93	100.00 1	99.64 121	99.98 217	99.44 112	98.35 77	99.99 105	100.00 1	99.04 97	99.96 137	99.98 81	100.00 1	100.00 1

Vis-MVSNet (Re-imp)		98.99 133	98.89 135	99.29 168	99.64 177	98.89 184	99.98 217	99.31 230	96.74 205	99.48 188	100.00 1	98.11 150	99.10 250	98.39 227	98.34 187	99.89 158

MG-MVS		99.75 22	99.68 27	99.97 34	100.00 1	99.91 57	99.98 217	99.47 80	99.09 8	100.00 1	100.00 1	98.59 137	100.00 1	99.95 96	100.00 1	100.00 1

3Dnovator	95.63 14	99.06 121	98.76 145	99.96 49	98.86 283	99.90 65	99.98 217	99.93 29	98.95 31	98.49 257	100.00 1	92.91 252	100.00 1	99.71 131	100.00 1	100.00 1

Anonymous20240521		93.29 315	92.76 319	94.90 331	95.64 360	91.27 352	99.97 223	98.82 351	87.04 357	94.71 344	98.19 349	83.86 340	96.80 350	84.04 360	92.56 293	96.64 354

CHOSEN 280x420		99.85 3	99.87 1	99.80 107	99.99 59	99.97 22	99.97 223	99.98 16	98.96 28	100.00 1	100.00 1	99.96 5	99.42 237	100.00 1	100.00 1	100.00 1

WR-MVS		97.09 233	96.64 238	98.46 215	98.43 296	99.09 167	99.97 223	99.33 223	95.62 258	97.76 291	99.67 256	91.17 269	98.56 305	98.49 223	89.28 329	97.74 277

new_pmnet		94.11 311	93.47 312	96.04 321	96.60 353	92.82 340	99.97 223	98.91 348	90.21 345	95.26 339	98.05 350	85.89 328	98.14 325	84.28 359	92.01 299	97.16 345

c3_l		97.58 212	97.42 213	98.06 251	99.48 221	98.16 224	99.96 227	99.10 314	94.54 287	98.13 276	99.20 303	97.87 155	98.25 321	97.28 267	91.20 312	97.75 254

v1240		95.96 289	95.25 297	98.07 246	97.91 323	97.87 245	99.96 227	99.07 324	93.24 321	98.64 246	98.96 321	88.98 297	98.61 296	89.58 349	90.92 314	97.75 254

EMVS		69.88 343	69.09 346	72.24 361	84.70 374	65.82 379	99.96 227	87.08 385	49.82 378	71.51 373	84.74 376	49.30 376	75.32 378	50.97 379	43.71 376	75.59 376

EPNet_dtu		98.53 170	98.23 181	99.43 152	99.92 118	99.01 176	99.96 227	99.47 80	98.80 46	99.96 118	99.96 201	98.56 138	99.30 244	87.78 354	99.68 145	100.00 1
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
API-MVS		99.72 33	99.70 22	99.79 109	99.97 103	99.37 145	99.96 227	99.94 21	98.48 63	100.00 1	100.00 1	98.92 114	100.00 1	100.00 1	100.00 1	100.00 1

bld_raw_dy_0_64		97.71 206	97.56 210	98.15 239	97.83 327	98.16 224	99.95 232	99.12 310	95.95 246	98.73 241	99.97 187	93.19 246	98.63 293	98.64 214	94.69 270	97.66 312

PS-MVSNAJ		99.64 59	99.57 59	99.85 96	99.78 149	99.81 98	99.95 232	99.42 130	98.38 71	100.00 1	100.00 1	98.75 128	100.00 1	99.88 105	99.99 113	99.74 209

jajsoiax		97.07 235	96.79 235	97.89 268	97.28 348	97.12 274	99.95 232	99.19 281	96.55 222	97.31 308	99.69 251	87.35 316	98.91 265	98.70 210	95.12 258	97.66 312

EI-MVSNet-UG-set		99.69 44	99.63 44	99.87 93	99.99 59	99.64 121	99.95 232	99.44 112	98.35 77	100.00 1	100.00 1	98.98 103	99.97 123	99.98 81	100.00 1	100.00 1

E-PMN		70.72 342	70.06 345	72.69 360	83.92 375	65.48 380	99.95 232	92.72 380	49.88 377	72.30 372	86.26 375	47.17 378	77.43 377	53.83 378	44.49 375	75.17 377

dcpmvs_2		98.87 146	99.53 68	96.90 303	99.87 128	90.88 355	99.94 237	99.07 324	98.20 84	100.00 1	100.00 1	98.69 131	99.86 165	100.00 1	100.00 1	99.95 125

v8		96.35 270	95.73 280	98.21 234	98.11 315	98.23 221	99.94 237	99.07 324	92.66 331	98.29 267	99.00 316	91.46 264	98.77 282	94.17 314	88.83 334	97.62 324

CDS-MVSNet		98.96 137	98.95 127	99.01 186	99.48 221	98.36 212	99.93 239	99.37 202	96.79 202	99.31 199	99.83 228	99.77 10	98.91 265	98.07 240	97.98 204	99.77 205
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		99.51 76	99.41 79	99.82 100	99.70 160	99.73 110	99.92 240	99.40 185	98.15 88	100.00 1	100.00 1	98.50 140	100.00 1	99.85 109	99.13 157	99.74 209

OurMVSNet-221017-0		96.14 283	95.98 268	96.62 312	97.49 342	93.44 334	99.92 240	98.16 357	95.86 249	97.65 296	99.95 209	85.71 330	98.78 279	94.93 307	94.18 275	97.64 321

EPP-MVSNet		99.10 118	99.00 119	99.40 157	99.51 215	98.68 195	99.92 240	99.43 122	95.47 267	99.65 181	100.00 1	99.51 33	99.76 186	99.53 162	98.00 203	99.75 208

pmmvs-eth3d		91.73 324	90.67 328	94.92 330	91.63 370	92.71 342	99.90 243	98.54 353	91.19 339	88.08 363	95.50 360	79.31 357	96.13 359	90.55 342	81.32 358	95.91 362

PEN-MVS		96.01 288	95.48 293	97.58 278	97.74 329	97.26 270	99.90 243	99.29 238	94.55 286	96.79 322	99.55 282	87.38 314	97.84 344	96.92 276	87.24 343	97.65 318

N_pmnet		91.88 323	93.37 313	87.40 350	97.24 349	66.33 378	99.90 243	91.05 381	89.77 348	95.65 338	98.58 338	90.05 284	98.11 329	85.39 357	92.72 288	97.75 254

PS-MVSNAJss		98.03 194	98.06 190	97.94 264	97.63 332	97.33 267	99.89 246	99.23 266	96.27 239	98.03 279	99.59 276	98.75 128	98.78 279	98.52 222	94.61 272	97.70 300

IterMVS-LS		97.56 213	97.44 212	97.92 267	99.38 241	97.90 241	99.89 246	99.10 314	94.41 293	98.32 265	99.54 284	97.21 177	98.11 329	97.50 259	91.62 305	97.75 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
QAPM		98.99 133	98.66 153	99.96 49	99.01 265	99.87 86	99.88 248	99.93 29	97.99 101	98.68 243	100.00 1	93.17 247	100.00 1	99.32 176	100.00 1	100.00 1

v7n		96.06 287	95.42 296	97.99 261	97.58 337	97.35 264	99.86 249	99.11 312	92.81 329	97.91 288	99.49 288	90.99 272	98.92 264	92.51 328	88.49 335	97.70 300

LCM-MVSNet-Re		96.52 258	97.21 226	94.44 333	99.27 247	85.80 365	99.85 250	96.61 375	95.98 244	92.75 355	98.48 341	93.97 237	97.55 347	99.58 155	98.43 180	99.98 112

SixPastTwentyTwo		95.71 294	95.49 291	96.38 316	97.42 345	93.01 337	99.84 251	98.23 356	94.75 279	95.98 335	99.97 187	85.35 331	98.43 314	94.71 308	93.17 283	97.69 305

new-patchmatchnet		90.30 331	89.46 333	92.84 344	90.77 371	88.55 362	99.83 252	98.80 352	90.07 347	87.86 364	95.00 363	78.77 358	94.30 368	84.86 358	79.15 360	95.68 364

v10		96.14 283	95.50 289	98.07 246	98.19 311	97.96 237	99.83 252	99.07 324	92.10 334	98.07 278	98.94 322	91.07 271	98.61 296	92.41 331	89.82 323	97.63 322

AUN-MVS		96.26 275	95.67 285	98.06 251	99.68 164	95.60 298	99.82 254	99.42 130	96.78 203	99.88 156	99.80 237	94.84 226	99.47 229	97.48 260	73.29 365	99.12 231

hse-mvs2		96.79 246	96.38 250	98.04 257	99.68 164	95.54 299	99.81 255	99.42 130	98.21 82	100.00 1	99.80 237	97.49 169	99.46 233	99.72 129	73.27 366	99.12 231

anonymousdsp		97.16 230	96.88 231	98.00 259	97.08 350	98.06 233	99.81 255	99.15 295	94.58 285	97.84 290	99.62 272	90.49 277	98.60 298	97.98 244	95.32 244	97.33 342

JIA-IIPM		97.09 233	96.34 254	99.36 161	98.88 280	98.59 199	99.81 255	99.43 122	84.81 361	99.96 118	90.34 370	98.55 139	99.52 222	97.00 273	98.28 192	99.98 112

ACMH+	96.20 13	96.49 262	96.33 255	97.00 297	99.06 261	93.80 331	99.81 255	99.31 230	97.32 171	95.89 337	99.97 187	82.62 346	99.54 216	98.34 230	94.63 271	97.65 318

PM-MVS		88.39 333	87.41 336	91.31 345	91.73 369	82.02 370	99.79 259	96.62 374	91.06 340	90.71 360	95.73 359	48.60 377	95.96 360	90.56 341	81.91 357	95.97 361

xiu_mvs_v1_base_debu		99.35 93	99.21 104	99.79 109	99.67 167	99.71 112	99.78 260	99.36 208	98.13 90	100.00 1	100.00 1	97.00 186	100.00 1	99.83 112	99.07 159	99.66 218

xiu_mvs_v1_base		99.35 93	99.21 104	99.79 109	99.67 167	99.71 112	99.78 260	99.36 208	98.13 90	100.00 1	100.00 1	97.00 186	100.00 1	99.83 112	99.07 159	99.66 218

xiu_mvs_v1_base_debi		99.35 93	99.21 104	99.79 109	99.67 167	99.71 112	99.78 260	99.36 208	98.13 90	100.00 1	100.00 1	97.00 186	100.00 1	99.83 112	99.07 159	99.66 218

baseline1		98.91 142	98.61 158	99.81 103	99.71 158	99.77 102	99.78 260	99.44 112	97.51 155	98.81 235	99.99 176	98.25 145	99.76 186	98.60 219	95.41 241	99.89 158

IS-MVSNet		99.08 119	98.91 131	99.59 133	99.65 172	99.38 142	99.78 260	99.24 262	96.70 210	99.51 186	100.00 1	98.44 142	99.52 222	98.47 224	98.39 184	99.88 169

CP-MVSNet		96.73 249	96.25 257	98.18 235	98.21 310	98.67 196	99.77 265	99.32 225	95.06 273	97.20 311	99.65 260	90.10 282	98.19 322	98.06 241	88.90 332	97.66 312

tpm cat1		98.05 193	97.76 200	98.92 192	99.50 218	97.10 276	99.77 265	99.30 233	90.20 346	99.72 178	98.71 332	97.71 160	99.86 165	96.75 285	98.20 196	99.81 192

EU-MVSNet		96.63 254	96.53 242	96.94 301	97.59 336	96.87 281	99.76 267	99.47 80	96.35 235	96.85 320	99.78 240	92.57 257	96.27 358	95.33 300	91.08 313	97.68 307

ACMMP		99.65 57	99.57 59	99.89 88	99.99 59	99.66 119	99.75 268	99.73 57	98.16 86	99.75 176	100.00 1	98.90 116	100.00 1	99.96 90	99.88 134	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
KD-MVS_self_test		91.16 326	90.09 331	94.35 335	94.44 366	91.27 352	99.74 269	99.08 320	90.82 342	94.53 347	94.91 365	86.11 324	94.78 366	82.67 362	68.52 368	96.99 348

EI-MVSNet		97.98 196	97.93 196	98.16 238	99.11 253	97.84 246	99.74 269	99.29 238	94.39 294	98.65 244	100.00 1	97.21 177	98.88 271	97.62 257	95.31 245	97.75 254

CVMVSNet		98.56 167	98.47 168	98.82 198	99.11 253	97.67 253	99.74 269	99.47 80	97.57 148	99.06 215	100.00 1	95.72 212	98.97 261	98.21 236	97.33 224	99.83 183

MAR-MVS		99.49 81	99.36 88	99.89 88	99.97 103	99.66 119	99.74 269	99.95 18	97.89 113	100.00 1	100.00 1	96.71 196	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		94.25 307	93.89 306	95.34 325	97.63 332	92.13 345	99.73 273	99.36 208	94.88 276	92.78 353	98.63 336	82.72 344	96.53 354	94.57 309	84.73 352	97.36 340

DeepC-MVS_fast	98.92 1	99.75 22	99.67 32	99.99 12	99.99 59	99.96 25	99.73 273	99.52 72	99.06 11	100.00 1	100.00 1	98.80 126	100.00 1	99.95 96	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		96.73 249	96.32 256	97.95 263	98.26 306	97.88 243	99.72 275	99.43 122	95.06 273	96.99 315	98.68 334	93.02 251	98.53 307	97.43 262	88.33 336	97.43 337

PS-CasMVS		96.34 271	95.78 277	98.03 258	98.18 312	98.27 219	99.71 276	99.32 225	94.75 279	96.82 321	99.65 260	86.98 319	98.15 324	97.74 252	88.85 333	97.66 312

FMVSNet3		97.30 225	96.95 229	98.37 221	99.65 172	99.25 155	99.71 276	99.28 244	94.23 296	98.53 252	98.91 324	93.30 244	98.11 329	95.31 301	93.60 278	97.73 283

PMMVS2		79.15 338	77.28 341	84.76 353	82.34 376	72.66 372	99.70 278	95.11 378	71.68 370	84.78 368	90.87 368	32.05 381	89.99 373	75.53 374	63.45 372	91.64 368

AdaColmap		99.44 86	99.26 97	99.95 61	100.00 1	99.86 89	99.70 278	99.99 13	98.53 61	99.90 151	100.00 1	95.34 217	100.00 1	99.92 100	100.00 1	100.00 1

tfpnnormal		96.36 269	95.69 284	98.37 221	98.55 292	98.71 193	99.69 280	99.45 102	93.16 323	96.69 326	99.71 246	88.44 306	98.99 258	94.17 314	91.38 310	97.41 338

tpm2		98.64 162	98.58 162	98.81 200	99.42 230	97.12 274	99.69 280	99.37 202	93.63 311	99.94 142	99.67 256	98.96 108	99.47 229	98.62 218	97.95 205	99.83 183

Vis-MVSNet		98.52 171	98.25 178	99.34 163	99.68 164	98.55 200	99.68 282	99.41 182	97.34 169	99.94 142	100.00 1	90.38 280	99.70 194	99.03 194	98.84 164	99.76 207
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CostFormer		98.84 148	98.77 143	99.04 184	99.41 232	97.58 257	99.67 283	99.35 216	94.66 283	99.96 118	99.36 295	99.28 74	99.74 189	99.41 168	97.81 214	99.81 192

DSMNet-mixed		95.18 302	95.21 298	95.08 326	96.03 356	90.21 357	99.65 284	93.64 379	92.91 326	98.34 263	97.40 355	90.05 284	95.51 364	91.02 338	97.86 209	99.51 225

HY-MVS	96.53 9	99.50 79	99.35 90	99.96 49	99.81 134	99.93 47	99.64 285	100.00 1	97.97 105	99.84 159	99.85 225	98.94 111	99.99 94	99.86 107	98.23 194	99.95 125

ACMH	96.25 11	96.77 247	96.62 240	97.21 291	98.96 275	94.43 326	99.64 285	99.33 223	97.43 162	96.55 327	99.97 187	83.52 341	99.54 216	99.07 193	95.13 257	97.66 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
KD-MVS_2432*1600		94.15 308	93.08 315	97.35 284	99.53 203	97.83 247	99.63 287	99.19 281	92.88 327	96.29 329	97.68 352	98.84 121	96.70 351	89.73 347	63.92 370	97.53 332

miper_refine_blended		94.15 308	93.08 315	97.35 284	99.53 203	97.83 247	99.63 287	99.19 281	92.88 327	96.29 329	97.68 352	98.84 121	96.70 351	89.73 347	63.92 370	97.53 332

test_part1		96.98 241	96.36 252	98.85 197	99.78 149	99.77 102	99.63 287	99.30 233	92.74 330	97.05 313	99.52 285	93.10 250	99.02 254	98.02 242	88.10 337	97.76 242

eth_miper_zixun_eth		97.47 219	97.28 221	98.06 251	99.41 232	97.94 239	99.62 290	99.08 320	94.46 292	98.19 275	99.56 281	96.91 191	98.50 309	96.78 282	91.49 307	97.74 277

test_0402		94.35 306	93.70 309	96.32 317	97.92 321	93.60 332	99.61 291	98.85 350	88.19 355	94.68 345	99.48 289	80.01 354	98.58 302	89.39 350	95.15 256	96.77 352

mvs_tets		97.00 240	96.69 237	97.94 264	97.41 347	97.27 269	99.60 292	99.18 288	96.51 226	97.35 307	99.69 251	86.53 322	98.91 265	98.84 202	95.09 260	97.65 318

EPMVS		99.25 111	99.13 112	99.60 131	99.60 186	99.20 161	99.60 292	100.00 1	96.93 194	99.92 148	99.36 295	99.05 94	99.71 193	98.77 206	98.94 163	99.90 153

TAMVS		98.76 152	98.73 148	98.86 196	99.44 229	97.69 252	99.57 294	99.34 221	96.57 221	99.12 210	99.81 234	98.83 123	99.16 248	97.97 247	97.91 207	99.73 213

MDTV_nov1_ep13_2view						99.24 157	99.56 295		96.31 238	99.96 118		98.86 119		98.92 198		99.89 158

CL-MVSNet_self_test		91.07 327	90.35 330	93.24 342	93.27 367	89.16 360	99.55 296	99.25 258	92.34 332	95.23 340	97.05 356	88.86 300	93.59 370	80.67 366	66.95 369	96.96 349

bld_raw_conf005		97.48 218	97.28 221	98.07 246	98.12 314	97.75 250	99.55 296	99.20 280	96.37 234	98.00 284	99.96 201	90.29 281	98.73 286	99.14 188	94.93 267	97.72 290

LS3D		99.31 101	99.13 112	99.87 93	99.99 59	99.71 112	99.55 296	99.46 95	97.32 171	99.82 169	100.00 1	96.85 193	99.97 123	99.14 188	100.00 1	99.92 142

XXY-MVS		97.14 232	96.63 239	98.67 204	98.65 288	98.92 183	99.54 299	99.29 238	95.57 260	97.63 297	99.83 228	87.79 311	99.35 242	98.39 227	92.95 286	97.75 254

tpm		98.24 187	98.22 182	98.32 225	99.13 252	95.79 296	99.53 300	99.12 310	95.20 271	99.96 118	99.36 295	97.58 164	99.28 245	97.41 263	96.67 232	99.88 169

tpmrst		98.98 136	98.93 129	99.14 179	99.61 184	97.74 251	99.52 301	99.36 208	96.05 243	99.98 110	99.64 264	99.04 97	99.86 165	98.94 196	98.19 197	99.82 187

DP-MVS		98.86 147	98.54 164	99.81 103	99.97 103	99.45 137	99.52 301	99.40 185	94.35 295	98.36 261	100.00 1	96.13 204	99.97 123	99.12 191	100.00 1	100.00 1

VNet		99.04 123	98.75 147	99.90 85	99.81 134	99.75 106	99.50 303	99.47 80	98.36 75	100.00 1	99.99 176	94.66 228	100.00 1	99.90 102	97.09 226	99.96 120

VPNet		96.41 264	95.76 278	98.33 224	98.61 290	98.30 217	99.48 304	99.45 102	96.98 192	98.87 229	99.88 220	81.57 349	98.93 263	99.22 185	87.82 340	97.76 242

test1111		98.42 176	98.12 185	99.29 168	99.88 126	98.15 226	99.46 305	100.00 1	98.36 75	99.42 192	100.00 1	87.91 307	99.79 181	99.31 177	98.78 166	99.94 130

ECVR-MVS		98.43 174	98.14 184	99.32 166	99.89 124	98.21 223	99.46 305	100.00 1	98.38 71	99.47 191	100.00 1	87.91 307	99.80 180	99.35 172	98.78 166	99.94 130

test2506		99.48 83	99.38 83	99.75 116	99.89 124	99.51 128	99.45 307	100.00 1	98.38 71	99.83 161	100.00 1	98.86 119	99.81 179	99.25 181	98.78 166	99.94 130

h-mvs33		97.03 238	96.53 242	98.51 213	99.79 147	95.90 295	99.45 307	99.45 102	98.21 82	100.00 1	99.78 240	97.49 169	99.99 94	99.72 129	74.92 363	99.65 221

dp		98.72 155	98.61 158	99.03 185	99.53 203	97.39 262	99.45 307	99.39 198	95.62 258	99.94 142	99.52 285	98.83 123	99.82 176	96.77 284	98.42 181	99.89 158

FMVSNet2		96.22 276	95.60 287	98.06 251	99.53 203	98.33 214	99.45 307	99.27 252	93.71 306	98.03 279	98.84 327	84.23 336	98.10 332	93.97 318	93.40 281	97.73 283

LTVRE_ROB	95.29 16	96.32 272	96.10 262	96.99 298	98.55 292	93.88 330	99.45 307	99.28 244	94.50 290	96.46 328	99.52 285	84.86 332	99.48 227	97.26 268	95.03 261	97.59 328
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		91.65 325	89.94 332	96.79 310	96.72 351	96.70 285	99.42 312	98.94 344	88.89 350	66.97 377	98.37 345	81.43 350	95.91 361	89.24 352	89.46 328	97.75 254

TinyColmap		95.50 297	95.12 300	96.64 311	98.69 287	93.00 338	99.40 313	97.75 369	96.40 232	96.14 333	99.87 221	79.47 355	99.50 225	93.62 321	94.72 269	97.40 339

MDTV_nov1_ep13			98.94 128		99.53 203	98.36 212	99.39 314	99.46 95	96.54 223	99.99 105	99.63 268	98.92 114	99.86 165	98.30 233	98.71 170

VPA-MVSNet		97.03 238	96.43 248	98.82 198	98.64 289	99.32 148	99.38 315	99.47 80	96.73 207	98.91 225	98.94 322	87.00 318	99.40 238	99.23 183	89.59 325	97.76 242

MVS-HIRNet		94.12 310	92.73 320	98.29 227	99.33 243	95.95 292	99.38 315	99.19 281	74.54 369	98.26 271	86.34 374	86.07 325	99.06 252	91.60 334	99.87 136	99.85 179

PatchmatchNet		99.03 125	98.96 123	99.26 173	99.49 219	98.33 214	99.38 315	99.45 102	96.64 217	99.96 118	99.58 277	99.49 40	99.50 225	97.63 256	99.00 162	99.93 140
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous202405211		97.87 198	97.53 211	98.90 193	99.81 134	96.70 285	99.35 318	99.46 95	92.98 325	98.83 234	99.99 176	90.63 275	100.00 1	99.70 133	97.03 227	100.00 1

MIMVSNet1		91.96 320	91.20 323	94.23 338	94.94 365	91.69 349	99.34 319	99.22 268	88.23 353	94.18 350	98.45 342	75.52 363	93.41 371	79.37 369	91.49 307	97.60 327

test_post1							99.32 320				88.24 373	99.33 60	99.59 200	98.31 231

tpmvs		98.59 164	98.38 172	99.23 175	99.69 161	97.90 241	99.31 321	99.47 80	94.52 288	99.68 180	99.28 299	97.64 163	99.89 161	97.71 253	98.17 199	99.89 158

COLMAP_ROB	97.10 7	98.29 184	98.17 183	98.65 205	99.94 113	97.39 262	99.30 322	99.40 185	95.64 256	97.75 294	100.00 1	92.69 256	99.95 143	98.89 199	99.92 131	98.62 237
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TR-MVS		98.14 189	97.74 203	99.33 164	99.59 189	98.28 218	99.27 323	99.21 276	96.42 229	99.15 208	99.94 213	88.87 299	99.79 181	98.88 200	98.29 191	99.93 140

OpenMVS_ROB	88.34 20	91.89 322	91.12 324	94.19 339	95.55 361	87.63 363	99.26 324	98.03 362	86.61 358	90.65 361	96.82 357	70.14 371	98.78 279	86.54 356	96.50 234	96.15 358

FMVSNet5		95.32 299	95.43 295	94.99 328	99.39 240	92.99 339	99.25 325	99.24 262	90.45 343	97.44 306	98.45 342	95.78 211	94.39 367	87.02 355	91.88 302	97.59 328

pm-mvs1		95.76 293	95.01 301	98.00 259	98.23 308	97.45 260	99.24 326	99.04 337	93.13 324	95.93 336	99.72 244	86.28 323	98.84 273	95.62 297	87.92 339	97.72 290

1314		99.38 90	99.19 108	99.96 49	98.88 280	99.89 73	99.24 326	99.93 29	98.88 38	98.79 238	100.00 1	97.02 182	100.00 1	100.00 1	100.00 1	100.00 1

1121		99.63 63	99.51 73	99.99 12	99.99 59	99.96 25	99.24 326	99.74 55	97.89 113	100.00 1	100.00 1	99.39 58	100.00 1	99.33 174	100.00 1	100.00 1

MVS		99.22 114	98.96 123	99.98 25	99.00 269	99.95 34	99.24 326	99.94 21	98.14 89	98.88 227	100.00 1	95.63 215	100.00 1	99.85 109	100.00 1	100.00 1

ADS-MVSNet2		98.28 185	98.51 166	97.62 276	99.51 215	95.03 305	99.24 326	99.41 182	95.52 263	99.96 118	99.70 249	97.57 165	97.94 342	97.11 270	98.54 173	99.88 169

ADS-MVSNet		98.70 158	98.51 166	99.28 171	99.51 215	98.39 207	99.24 326	99.44 112	95.52 263	99.96 118	99.70 249	97.57 165	99.58 203	97.11 270	98.54 173	99.88 169

TransMVSNet (Re)		94.78 304	93.72 308	97.93 266	98.34 299	97.88 243	99.23 332	97.98 365	91.60 336	94.55 346	99.71 246	87.89 309	98.36 316	89.30 351	84.92 351	97.56 330

PCF-MVS	98.23 3	98.69 159	98.37 174	99.62 128	99.78 149	99.02 174	99.23 332	99.06 332	96.43 228	98.08 277	100.00 1	94.72 227	99.95 143	98.16 237	99.91 132	99.90 153
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NR-MVSNet		96.63 254	96.04 265	98.38 220	98.31 302	98.98 180	99.22 334	99.35 216	95.87 247	94.43 349	99.65 260	92.73 255	98.40 315	96.78 282	88.05 338	97.75 254

VDD-MVS		96.58 257	95.99 267	98.34 223	99.52 210	95.33 300	99.18 335	99.38 200	96.64 217	99.77 173	100.00 1	72.51 367	100.00 1	100.00 1	96.94 230	99.70 214

GBi-Net		96.07 285	95.80 275	96.89 304	99.53 203	94.87 306	99.18 335	99.27 252	93.71 306	98.53 252	98.81 328	84.23 336	98.07 334	95.31 301	93.60 278	97.72 290

test1		96.07 285	95.80 275	96.89 304	99.53 203	94.87 306	99.18 335	99.27 252	93.71 306	98.53 252	98.81 328	84.23 336	98.07 334	95.31 301	93.60 278	97.72 290

FMVSNet1		94.45 305	93.63 310	96.89 304	98.87 282	94.87 306	99.18 335	99.27 252	90.95 341	97.31 308	98.81 328	72.89 366	98.07 334	92.61 326	92.81 287	97.72 290

UGNet		98.41 178	98.11 186	99.31 167	99.54 200	98.55 200	99.18 335	100.00 1	98.64 58	99.79 171	99.04 311	87.61 312	100.00 1	99.30 178	99.89 133	99.40 227
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				99.59 133	99.54 200	99.49 132	99.17 340	99.52 72		99.96 118	99.68 255	100.00 1	99.33 243	99.71 131	99.99 113	99.96 120

Anonymous20240529		96.93 243	96.22 258	99.05 182	99.79 147	97.30 268	99.16 341	99.47 80	88.51 352	98.69 242	100.00 1	83.50 342	100.00 1	99.83 112	97.02 228	99.83 183

LFMVS		97.42 220	96.62 240	99.81 103	99.80 144	99.50 129	99.16 341	99.56 69	94.48 291	100.00 1	100.00 1	79.35 356	100.00 1	99.89 104	97.37 223	99.94 130

Anonymous20231211		96.29 273	95.70 281	98.07 246	99.80 144	97.49 259	99.15 343	99.40 185	89.11 349	97.75 294	99.45 291	88.93 298	98.98 259	98.26 235	89.47 327	97.73 283

VDDNet		96.39 268	95.55 288	98.90 193	99.27 247	97.45 260	99.15 343	99.92 33	91.28 338	99.98 110	100.00 1	73.55 364	100.00 1	99.85 109	96.98 229	99.24 228

UniMVSNet_ETH3D		95.28 300	94.41 305	97.89 268	98.91 277	95.14 303	99.13 345	99.35 216	92.11 333	97.17 312	99.66 258	70.28 370	99.36 240	97.88 249	95.18 253	99.16 229

CR-MVSNet		98.02 195	97.71 206	98.93 191	99.31 244	98.86 185	99.13 345	99.00 342	96.53 224	99.96 118	98.98 317	96.94 189	98.10 332	91.18 336	98.40 182	99.84 180

RPMNet		95.26 301	93.82 307	99.56 140	99.31 244	98.86 185	99.13 345	99.42 130	79.82 366	99.96 118	95.13 362	95.69 213	99.98 118	77.54 372	98.40 182	99.84 180

ab-mvs		98.42 176	98.02 193	99.61 129	99.71 158	99.00 178	99.10 348	99.64 66	96.70 210	99.04 217	99.81 234	90.64 274	99.98 118	99.64 147	97.93 206	99.84 180

TDRefinement		91.93 321	90.48 329	96.27 318	81.60 377	92.65 343	99.10 348	97.61 370	93.96 304	93.77 351	99.85 225	80.03 353	99.53 221	97.82 251	70.59 367	96.63 355

EGC-MVSNET		79.46 336	74.04 344	95.72 324	96.00 357	92.73 341	99.09 350	99.04 337	5.08 381	16.72 381	98.71 332	73.03 365	98.74 285	82.05 364	96.64 233	95.69 363

UA-Net		99.06 121	98.83 137	99.74 117	99.52 210	99.40 141	99.08 351	99.45 102	97.64 136	99.83 161	100.00 1	95.80 210	99.94 154	98.35 229	99.80 141	99.88 169

PatchT		95.90 291	94.95 303	98.75 202	99.03 263	98.39 207	99.08 351	99.32 225	85.52 359	99.96 118	94.99 364	97.94 153	98.05 338	80.20 368	98.47 178	99.81 192

gg-mvs-nofinetune		96.95 242	96.10 262	99.50 144	99.41 232	99.36 146	99.07 353	99.52 72	83.69 363	99.96 118	83.60 377	100.00 1	99.20 247	99.68 140	99.99 113	99.96 120

EG-PatchMatch MVS		92.94 317	92.49 321	94.29 337	95.87 358	87.07 364	99.07 353	98.11 360	93.19 322	88.98 362	98.66 335	70.89 368	99.08 251	92.43 330	95.21 251	96.72 353

pmmvs6		93.64 312	92.87 317	95.94 322	97.47 344	91.41 351	98.92 355	99.02 339	87.84 356	95.01 342	99.61 274	77.24 361	98.77 282	94.33 312	86.41 349	97.63 322

Patchmtry		96.81 245	96.37 251	98.14 240	99.31 244	98.55 200	98.91 356	99.00 342	90.45 343	97.92 287	98.98 317	96.94 189	98.12 327	94.27 313	91.53 306	97.75 254

MS-PatchMatch		95.66 295	95.87 272	95.05 327	97.80 328	89.25 359	98.88 357	99.30 233	96.35 235	96.86 319	99.01 315	81.35 351	99.43 235	93.30 323	99.98 121	96.46 356

MVP-Stereo		96.51 259	96.48 246	96.60 313	95.65 359	94.25 327	98.84 358	98.16 357	95.85 251	95.23 340	99.04 311	92.54 258	99.13 249	92.98 325	99.98 121	96.43 357
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Patchmatch-test		97.83 200	97.42 213	99.06 180	99.08 256	97.66 254	98.66 359	99.21 276	93.65 310	98.25 272	99.58 277	99.47 44	99.57 204	90.25 345	98.59 172	99.95 125

MVS_0304		95.11 303	94.84 304	95.91 323	99.60 186	91.24 354	98.64 360	99.88 36	94.51 289	99.62 183	98.31 348	69.19 372	98.82 276	95.22 304	98.60 171	97.66 312

UnsupCasMVSNet_bld		89.50 332	88.00 335	93.99 340	95.30 362	88.86 361	98.52 361	99.28 244	85.50 360	87.80 365	94.11 366	61.63 374	96.96 349	90.63 340	79.26 359	96.15 358

MIMVSNet		97.06 236	96.73 236	98.05 255	99.38 241	96.64 287	98.47 362	99.35 216	93.41 315	99.48 188	98.53 339	89.66 287	97.70 346	94.16 316	98.11 201	99.80 202

FPMVS		77.92 340	79.45 339	73.34 359	76.87 380	46.81 385	98.24 363	99.05 334	59.89 374	73.55 371	98.34 346	36.81 380	86.55 374	80.96 365	91.35 311	86.65 372

Patchmatch-RL test		93.49 313	93.63 310	93.05 343	91.78 368	83.41 368	98.21 364	96.95 372	91.58 337	91.05 358	97.64 354	99.40 57	95.83 362	94.11 317	81.95 356	99.91 144

CMPMVS	66.12 22	90.65 329	92.04 322	86.46 351	96.18 355	66.87 377	98.03 365	99.38 200	83.38 364	85.49 367	99.55 282	77.59 360	98.80 278	94.44 311	94.31 274	93.72 367
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Effi-MVS+		98.58 165	98.24 179	99.61 129	99.60 186	99.26 153	97.85 366	99.10 314	96.22 240	99.97 114	99.89 219	93.75 238	99.77 185	99.43 166	98.34 187	99.81 192

ambc				88.45 348	86.84 373	70.76 375	97.79 367	98.02 364		90.91 359	95.14 361	38.69 379	98.51 308	94.97 306	84.23 353	96.09 360

LCM-MVSNet		79.01 339	76.93 342	85.27 352	78.28 379	68.01 376	96.57 368	98.03 362	55.10 375	82.03 369	93.27 367	31.99 382	93.95 369	82.72 361	74.37 364	93.84 366

MVE	68.59 21	67.22 344	64.68 348	74.84 356	74.67 382	62.32 382	95.84 369	90.87 382	50.98 376	58.72 378	81.05 378	12.20 386	78.95 376	61.06 377	56.75 373	83.24 374
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		66.05 345	63.44 349	73.88 358	61.14 383	63.45 381	95.68 370	87.18 383	79.93 365	47.35 379	80.68 379	22.35 383	72.33 381	61.24 376	35.42 377	85.88 373

Gipuma		84.73 334	83.50 337	88.40 349	97.50 340	82.21 369	88.87 371	99.05 334	65.81 371	85.71 366	90.49 369	53.70 375	96.31 356	78.64 370	91.74 303	86.67 371
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	60.66 23	65.98 346	65.05 347	68.75 362	55.06 385	38.40 386	88.19 372	96.98 371	48.30 379	44.82 380	88.52 372	12.22 385	86.49 375	67.58 375	83.79 354	81.35 375
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		75.80 341	74.26 343	80.43 354	52.91 386	53.67 384	87.42 373	97.98 365	61.80 373	67.04 376	100.00 1	76.43 362	96.40 355	96.47 286	28.26 378	91.23 369

wuyk23d		28.28 347	29.73 351	23.92 363	75.89 381	32.61 387	66.50 374	12.88 387	16.09 380	14.59 382	16.59 381	12.35 384	32.36 382	39.36 380	13.36 380	6.79 378

test_blank		0.07 351	0.09 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.79 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.01 352	0.02 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.14 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.01 352	0.02 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.14 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

cdsmvs_eth3d_5k		24.41 348	32.55 350	0.00 364	0.00 387	0.00 388	0.00 375	99.39 198	0.00 382	0.00 383	100.00 1	93.55 241	0.00 383	0.00 381	0.00 381	0.00 379

pcd_1.5k_mvsjas		8.24 350	10.99 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.14 383	98.75 128	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.01 352	0.02 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.14 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.01 352	0.02 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.14 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.01 352	0.02 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.14 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.01 352	0.02 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.14 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

ab-mvs-re		8.33 349	11.11 352	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	100.00 1	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.01 352	0.02 355	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.14 383	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

MSC_two_6792asdad				100.00 1	100.00 1	100.00 1		99.42 130					100.00 1	100.00 1	100.00 1	100.00 1

PC_three_1452									98.80 46	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		99.42 130					100.00 1	100.00 1	100.00 1	100.00 1

test_one_0601					100.00 1	99.99 5		99.42 130	98.72 52	100.00 1	100.00 1	99.60 17

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					100.00 1	99.98 17		99.80 42	97.31 173	100.00 1	100.00 1	99.32 63	99.99 94	100.00 1	100.00 1

IU-MVS					100.00 1	99.99 5		99.42 130	99.12 6	100.00 1				100.00 1	100.00 1	100.00 1

test_241102_TWO								99.42 130	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		99.42 130	99.03 19	100.00 1	100.00 1	99.50 38	100.00 1

test_0728_THIRD									98.79 48	100.00 1	100.00 1	99.61 16	100.00 1	100.00 1	100.00 1	100.00 1

GSMVS																99.91 144

test_part2					100.00 1	99.99 5				100.00 1

sam_mvs1												99.29 72				99.91 144

sam_mvs												99.33 60

MTGPA								99.42 130

test_post											89.05 371	99.49 40	99.59 200

patchmatchnet-post											97.79 351	99.41 56	99.54 216

gm-plane-assit					99.52 210	97.26 270			95.86 249		100.00 1		99.43 235	98.76 207

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 83		99.42 130		100.00 1			99.97 123

TestCases				98.99 187	99.93 115	97.35 264		99.40 185	97.08 188	99.09 211	99.98 179	93.37 242	99.95 143	96.94 274	99.84 137	99.68 216

test_prior				99.90 85	100.00 1	99.75 106		99.73 57					99.97 123			100.00 1

新几何1				99.99 12	100.00 1	99.96 25		99.81 41	97.89 113	100.00 1	100.00 1	99.20 83	100.00 1	97.91 248	100.00 1	100.00 1

旧先验1					99.99 59	99.88 83		99.82 39			100.00 1	99.27 75			100.00 1	100.00 1

原ACMM1				99.93 79	100.00 1	99.80 100		99.66 65	98.18 85	100.00 1	100.00 1	99.43 52	100.00 1	99.50 164	100.00 1	100.00 1

testdata2													100.00 1	97.36 265

segment_acmp												99.55 25

testdata				99.66 125	99.99 59	98.97 182		99.73 57	97.96 109	100.00 1	100.00 1	99.42 54	100.00 1	99.28 180	100.00 1	100.00 1

test12				99.95 61	99.99 59	99.89 73		99.42 130		100.00 1		99.24 77	99.97 123		100.00 1	100.00 1

plane_prior7					99.00 269	94.78 316

plane_prior6					99.06 261	94.80 312						88.58 304

plane_prior5								99.40 185					99.55 213	99.79 118	95.57 239	97.76 242

plane_prior4											99.97 187

plane_prior3						94.79 315			99.03 19	99.08 213

plane_prior1					99.02 264

n2								0.00 388

nn								0.00 388

door-mid								96.32 376

lessismore_v0				96.05 320	97.55 338	91.80 348		99.22 268		91.87 357	99.91 217	83.50 342	98.68 289	92.48 329	90.42 321	97.68 307

LGP-MVS_train				97.28 288	98.85 284	94.60 321		99.37 202	97.35 167	98.85 230	99.98 179	86.66 320	99.56 208	99.55 157	95.26 247	97.70 300

test11								99.42 130

door								96.13 377

HQP5-MVS						94.82 309

BP-MVS														99.79 118

HQP4-MVS										99.17 204			99.57 204			97.77 240

HQP3-MVS								99.40 185							95.58 235

HQP2-MVS												88.61 302

NP-MVS					99.07 257	94.81 311					99.97 187

ACMMP++_ref															94.58 273

ACMMP++															95.17 255

Test By Simon												99.10 91

ITE_SJBPF				96.84 307	98.96 275	93.49 333		98.12 359	98.12 93	98.35 262	99.97 187	84.45 333	99.56 208	95.63 296	95.25 249	97.49 334

DeepMVS_CX				89.98 347	98.90 278	71.46 374		99.18 288	97.61 140	96.92 316	99.83 228	86.07 325	99.83 173	96.02 291	97.65 221	98.65 236

High-resolution multi-view benchmark