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.

MM		89.16 7	89.23 9	88.97 4	90.79 104	73.65 10	92.66 28	91.17 156	86.57 1	87.39 59	94.97 25	71.70 66	97.68 1	92.19 1	95.63 32	95.57 2

UA-Net		85.08 86	84.96 86	85.45 91	92.07 81	68.07 147	89.78 91	90.86 167	82.48 2	84.60 95	93.20 88	69.35 101	95.22 91	71.39 243	90.88 119	93.07 149

MGCNet		87.69 24	87.55 29	88.12 13	89.45 142	71.76 54	91.47 57	89.54 213	82.14 3	86.65 68	94.28 46	68.28 124	97.46 6	90.81 6	95.31 38	95.15 9

CANet		86.45 48	86.10 61	87.51 42	90.09 117	70.94 78	89.70 94	92.59 82	81.78 4	81.32 164	91.43 149	70.34 83	97.23 16	84.26 76	93.36 74	94.37 65

NCCC		88.06 18	88.01 22	88.24 11	94.41 27	73.62 11	91.22 62	92.83 67	81.50 5	85.79 74	93.47 81	73.02 47	97.00 22	84.90 65	94.94 44	94.10 80

EPNet		83.72 114	82.92 129	86.14 74	84.22 338	69.48 103	91.05 64	85.27 343	81.30 6	76.83 260	91.65 137	66.09 154	95.56 71	76.00 189	93.85 68	93.38 126
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		88.93 12	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 53	80.90 7	88.06 45	94.06 59	76.43 21	96.84 26	88.48 37	95.99 20	94.34 67

3Dnovator+	77.84 4	85.48 74	84.47 94	88.51 7	91.08 95	73.49 16	93.18 16	93.78 24	80.79 8	76.66 265	93.37 84	60.40 247	96.75 31	77.20 170	93.73 70	95.29 7

TranMVSNet+NR-MVSNet		80.84 185	80.31 182	82.42 247	87.85 217	62.33 317	87.74 185	91.33 151	80.55 9	77.99 234	89.86 200	65.23 164	92.62 233	67.05 291	75.24 390	92.30 187

Casviewmamba		86.09 56	86.04 63	86.24 67	88.17 199	68.05 149	89.44 104	92.79 71	80.30 10	84.71 88	92.78 103	72.83 51	95.05 102	82.81 94	90.57 123	95.62 1

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 35	73.46 17	92.90 21	94.11 11	80.27 11	91.35 16	94.16 54	78.35 15	96.77 29	89.59 17	94.22 66	94.67 42
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
HPM-MVS++		89.02 10	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 66	80.26 12	87.78 50	94.27 47	75.89 24	96.81 28	87.45 48	96.44 9	93.05 152

fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 83	89.48 141	67.88 156	88.59 148	89.05 243	80.19 13	90.70 20	95.40 17	74.56 30	93.92 156	91.54 2	92.07 93	95.31 6

UniMVSNet_NR-MVSNet		81.88 159	81.54 158	82.92 225	88.46 188	63.46 289	87.13 207	92.37 90	80.19 13	78.38 223	89.14 225	71.66 68	93.05 218	70.05 259	76.46 363	92.25 189

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 80	72.96 25	93.73 5	93.67 26	80.19 13	88.10 44	94.80 27	73.76 39	97.11 18	87.51 47	95.82 25	94.90 18
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 98	83.81 107	85.31 96	88.18 198	67.85 157	87.66 186	89.73 207	80.05 16	82.95 134	89.59 214	70.74 80	94.82 113	80.66 122	84.72 243	93.28 132

ETV-MVS		84.90 90	84.67 90	85.59 88	89.39 146	68.66 129	88.74 141	92.64 80	79.97 17	84.10 107	85.71 326	69.32 102	95.38 85	80.82 117	91.37 108	92.72 165

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 125	87.76 226	65.62 217	89.20 115	92.21 106	79.94 18	89.74 28	94.86 26	68.63 118	94.20 141	90.83 5	91.39 107	94.38 64

EI-MVSNet-UG-set		83.81 108	83.38 119	85.09 106	87.87 216	67.53 169	87.44 199	89.66 208	79.74 19	82.23 147	89.41 223	70.24 86	94.74 119	79.95 130	83.92 258	92.99 157

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 214	87.08 267	65.21 231	89.09 124	90.21 190	79.67 20	89.98 25	95.02 24	73.17 44	91.71 277	91.30 3	91.60 101	92.34 184

CS-MVS		86.69 44	86.95 42	85.90 80	90.76 105	67.57 167	92.83 22	93.30 39	79.67 20	84.57 96	92.27 110	71.47 69	95.02 104	84.24 78	93.46 73	95.13 11

casdiffmvs_mvg		85.99 60	86.09 62	85.70 83	87.65 234	67.22 183	88.69 144	93.04 48	79.64 22	85.33 78	92.54 106	73.30 41	94.50 129	83.49 84	91.14 112	95.37 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MTAPA		87.23 36	87.00 39	87.90 22	94.18 40	74.25 5	86.58 232	92.02 115	79.45 23	85.88 72	94.80 27	68.07 126	96.21 52	86.69 53	95.34 36	93.23 134

EC-MVSNet		86.01 59	86.38 52	84.91 116	89.31 151	66.27 198	92.32 35	93.63 27	79.37 24	84.17 106	91.88 126	69.04 113	95.43 80	83.93 82	93.77 69	93.01 155

NormalMVS		86.29 54	85.88 66	87.52 41	93.26 57	72.47 38	91.65 47	92.19 109	79.31 25	84.39 99	92.18 116	64.64 172	95.53 74	80.70 120	94.65 52	94.56 55

SymmetryMVS		85.38 79	84.81 88	87.07 51	91.47 89	72.47 38	91.65 47	88.06 279	79.31 25	84.39 99	92.18 116	64.64 172	95.53 74	80.70 120	90.91 118	93.21 137

XVS		87.18 37	86.91 44	88.00 17	94.42 25	73.33 19	92.78 23	92.99 56	79.14 27	83.67 117	94.17 53	67.45 132	96.60 39	83.06 88	94.50 57	94.07 82

X-MVStestdata		80.37 208	77.83 248	88.00 17	94.42 25	73.33 19	92.78 23	92.99 56	79.14 27	83.67 117	12.47 532	67.45 132	96.60 39	83.06 88	94.50 57	94.07 82

HQP_MVS		83.64 117	83.14 122	85.14 101	90.08 118	68.71 125	91.25 60	92.44 85	79.12 29	78.92 210	91.00 167	60.42 245	95.38 85	78.71 152	86.32 211	91.33 222

plane_prior2							91.25 60		79.12 29

IS-MVSNet		83.15 133	82.81 130	84.18 158	89.94 125	63.30 293	91.59 51	88.46 272	79.04 31	79.49 199	92.16 118	65.10 166	94.28 135	67.71 282	91.86 99	94.95 15

DU-MVS		81.12 180	80.52 177	82.90 226	87.80 220	63.46 289	87.02 212	91.87 125	79.01 32	78.38 223	89.07 227	65.02 167	93.05 218	70.05 259	76.46 363	92.20 192

NR-MVSNet		80.23 212	79.38 210	82.78 236	87.80 220	63.34 292	86.31 244	91.09 160	79.01 32	72.17 356	89.07 227	67.20 135	92.81 230	66.08 298	75.65 376	92.20 192

SPE-MVS-test		86.29 54	86.48 51	85.71 82	91.02 97	67.21 184	92.36 34	93.78 24	78.97 34	83.51 124	91.20 157	70.65 82	95.15 94	81.96 104	94.89 46	94.77 30

DELS-MVS		85.41 77	85.30 81	85.77 81	88.49 186	67.93 155	85.52 272	93.44 33	78.70 35	83.63 119	89.03 229	74.57 29	95.71 68	80.26 128	94.04 67	93.66 107
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
WR-MVS		79.49 226	79.22 217	80.27 304	88.79 176	58.35 375	85.06 282	88.61 270	78.56 36	77.65 241	88.34 252	63.81 181	90.66 332	64.98 307	77.22 351	91.80 206

plane_prior3						68.60 130			78.44 37	78.92 210

UniMVSNet (Re)		81.60 167	81.11 164	83.09 214	88.38 192	64.41 262	87.60 187	93.02 52	78.42 38	78.56 218	88.16 258	69.78 95	93.26 200	69.58 266	76.49 362	91.60 212

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 32	71.25 66	95.06 1	94.23 6	78.38 39	92.78 4	95.74 8	82.45 3	97.49 4	89.42 19	96.68 2	94.95 15

test_0728_THIRD									78.38 39	92.12 11	95.78 6	81.46 8	97.40 9	89.42 19	96.57 7	94.67 42

testing3-2		75.12 327	75.19 309	74.91 403	90.40 111	45.09 489	80.29 390	78.42 440	78.37 41	76.54 270	87.75 268	44.36 416	87.28 390	57.04 396	83.49 270	92.37 183

test_one_0601					95.07 7	71.46 60		94.14 10	78.27 42	92.05 13	95.74 8	80.83 12

hybridcas		85.11 84	85.18 83	84.90 117	87.47 246	65.68 215	88.53 152	92.38 89	77.91 43	84.27 103	92.48 107	72.19 58	93.88 161	80.37 123	90.97 115	95.15 9

SD-MVS		88.06 18	88.50 18	86.71 61	92.60 77	72.71 29	91.81 46	93.19 42	77.87 44	90.32 24	94.00 63	74.83 28	93.78 164	87.63 46	94.27 65	93.65 111
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
casdiffmvs		85.11 84	85.14 84	85.01 109	87.20 258	65.77 214	87.75 184	92.83 67	77.84 45	84.36 102	92.38 109	72.15 59	93.93 155	81.27 112	90.48 125	95.33 5
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
KinetiMVS		83.31 131	82.61 135	85.39 94	87.08 267	67.56 168	88.06 172	91.65 138	77.80 46	82.21 148	91.79 129	57.27 272	94.07 147	77.77 163	89.89 138	94.56 55

BP-MVS1		84.32 93	83.71 110	86.17 70	87.84 218	67.85 157	89.38 110	89.64 210	77.73 47	83.98 110	92.12 121	56.89 277	95.43 80	84.03 81	91.75 100	95.24 8

CP-MVSNet		78.22 261	78.34 235	77.84 366	87.83 219	54.54 435	87.94 177	91.17 156	77.65 48	73.48 336	88.49 248	62.24 207	88.43 375	62.19 340	74.07 399	90.55 253

plane_prior						68.71 125	90.38 78		77.62 49						86.16 216

baseline		84.93 88	84.98 85	84.80 122	87.30 256	65.39 224	87.30 204	92.88 64	77.62 49	84.04 109	92.26 111	71.81 63	93.96 149	81.31 110	90.30 128	95.03 13

VDD-MVS		83.01 138	82.36 140	84.96 111	91.02 97	66.40 195	88.91 129	88.11 275	77.57 51	84.39 99	93.29 86	52.19 320	93.91 157	77.05 173	88.70 160	94.57 53

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 31	73.88 6	92.71 27	92.65 78	77.57 51	83.84 113	94.40 41	72.24 57	96.28 49	85.65 60	95.30 39	93.62 114
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.73 276	77.69 256	77.84 366	87.07 269	53.91 440	87.91 179	91.18 155	77.56 53	73.14 341	88.82 238	61.23 229	89.17 360	59.95 364	72.37 414	90.43 258

OPM-MVS		83.50 123	82.95 128	85.14 101	88.79 176	70.95 77	89.13 122	91.52 144	77.55 54	80.96 174	91.75 132	60.71 237	94.50 129	79.67 139	86.51 208	89.97 285
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 60	92.24 79	69.03 112	89.57 99	93.39 36	77.53 55	89.79 26	94.12 56	78.98 13	96.58 41	85.66 59	95.72 28	94.58 51

PS-CasMVS		78.01 270	78.09 240	77.77 368	87.71 229	54.39 437	88.02 173	91.22 153	77.50 56	73.26 338	88.64 243	60.73 236	88.41 376	61.88 346	73.88 403	90.53 254

MSLP-MVS++		85.43 76	85.76 70	84.45 137	91.93 83	70.24 87	90.71 67	92.86 65	77.46 57	84.22 104	92.81 100	67.16 136	92.94 222	80.36 124	94.35 63	90.16 269

RRT-MVS		82.60 146	82.10 147	84.10 160	87.98 212	62.94 306	87.45 194	91.27 152	77.42 58	79.85 194	90.28 192	56.62 280	94.70 122	79.87 136	88.15 173	94.67 42

fmvsm_s_conf0.5_n_11		86.06 57	86.75 47	84.00 177	87.78 223	66.09 200	89.96 86	90.80 169	77.37 59	86.72 67	94.20 52	72.51 54	92.78 231	89.08 22	92.33 88	93.13 146

DVP-MVS		89.60 4	90.35 4	87.33 45	95.27 5	71.25 66	93.49 10	92.73 72	77.33 60	92.12 11	95.78 6	80.98 10	97.40 9	89.08 22	96.41 12	93.33 130
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
test0726					95.27 5	71.25 66	93.60 7	94.11 11	77.33 60	92.81 3	95.79 5	80.98 10

MED-MVS		89.78 3	90.41 3	87.89 24	94.57 18	71.43 61	93.28 12	94.36 3	77.30 62	92.25 9	95.87 3	81.59 7	97.39 11	88.15 40	96.28 16	94.85 24

TestfortrainingZip a		88.83 13	89.21 11	87.68 37	94.57 18	71.25 66	93.28 12	93.91 20	77.30 62	91.13 18	95.87 3	77.62 17	96.95 23	86.12 58	93.07 76	94.85 24

BridgeMVS		86.78 42	86.99 40	86.15 72	91.24 92	67.61 165	90.51 70	92.90 63	77.26 64	87.44 58	91.63 139	71.27 73	96.06 56	85.62 61	95.01 41	94.78 29

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 74	93.57 8	94.06 15	77.24 65	93.10 1	95.72 10	82.99 1	97.44 7	89.07 25	96.63 4	94.88 19

test_241102_TWO								94.06 15	77.24 65	92.78 4	95.72 10	81.26 9	97.44 7	89.07 25	96.58 6	94.26 73

3Dnovator	76.31 5	83.38 127	82.31 141	86.59 62	87.94 213	72.94 28	90.64 68	92.14 114	77.21 67	75.47 291	92.83 98	58.56 259	94.72 120	73.24 221	92.71 82	92.13 199

test_241102_ONE					95.30 2	70.98 74		94.06 15	77.17 68	93.10 1	95.39 18	82.99 1	97.27 14

WR-MVS_H		78.51 256	78.49 230	78.56 350	88.02 209	56.38 411	88.43 154	92.67 75	77.14 69	73.89 330	87.55 276	66.25 150	89.24 358	58.92 376	73.55 406	90.06 279

lecture		88.09 17	88.59 16	86.58 63	93.26 57	69.77 98	93.70 6	94.16 8	77.13 70	89.76 27	95.52 16	72.26 56	96.27 50	86.87 51	94.65 52	93.70 105

DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 93	72.32 45	90.31 79	93.94 19	77.12 71	82.82 139	94.23 50	72.13 60	97.09 19	84.83 68	95.37 35	93.65 111
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		81.52 171	82.02 151	80.03 311	88.42 191	55.97 417	87.95 176	93.42 35	77.10 72	77.38 246	90.98 169	69.96 92	91.79 272	68.46 278	84.50 246	92.33 185

DTE-MVSNet		76.99 292	76.80 276	77.54 375	86.24 288	53.06 450	87.52 189	90.66 172	77.08 73	72.50 350	88.67 242	60.48 244	89.52 352	57.33 393	70.74 426	90.05 280

LFMVS		81.82 161	81.23 161	83.57 193	91.89 84	63.43 291	89.84 87	81.85 398	77.04 74	83.21 127	93.10 89	52.26 319	93.43 193	71.98 238	89.95 136	93.85 94

casdiffseed414692147		83.62 119	83.02 125	85.40 93	87.31 255	67.50 170	88.70 143	91.72 134	76.97 75	82.77 141	91.72 133	66.85 140	93.71 171	73.06 223	88.12 174	94.98 14

UGNet		80.83 186	79.59 205	84.54 129	88.04 208	68.09 146	89.42 107	88.16 274	76.95 76	76.22 277	89.46 219	49.30 371	93.94 152	68.48 277	90.31 127	91.60 212
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
FIs		82.07 155	82.42 137	81.04 285	88.80 175	58.34 376	88.26 165	93.49 32	76.93 77	78.47 222	91.04 164	69.92 93	92.34 251	69.87 263	84.97 238	92.44 182

GST-MVS		87.42 31	87.26 34	87.89 24	94.12 41	72.97 24	92.39 31	93.43 34	76.89 78	84.68 89	93.99 65	70.67 81	96.82 27	84.18 80	95.01 41	93.90 92

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 27	73.55 13	92.74 25	92.22 104	76.87 79	82.81 140	94.25 49	66.44 147	96.24 51	82.88 93	94.28 64	93.38 126

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 29	73.33 19	93.03 19	93.81 23	76.81 80	85.24 79	94.32 44	71.76 64	96.93 24	85.53 62	95.79 26	94.32 69

VPNet		78.69 251	78.66 227	78.76 345	88.31 194	55.72 421	84.45 302	86.63 324	76.79 81	78.26 226	90.55 183	59.30 253	89.70 350	66.63 293	77.05 353	90.88 238

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 40	76.78 82	84.91 84	94.44 39	70.78 79	96.61 38	84.53 73	94.89 46	93.66 107

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 41	76.78 82	84.66 92	94.52 32	68.81 115	96.65 36	84.53 73	94.90 45	94.00 86

ACMMP		85.89 66	85.39 77	87.38 44	93.59 50	72.63 33	92.74 25	93.18 46	76.78 82	80.73 180	93.82 72	64.33 175	96.29 48	82.67 101	90.69 121	93.23 134
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
region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 47	76.73 85	84.45 97	94.52 32	69.09 109	96.70 32	84.37 75	94.83 49	94.03 84

sasdasda		85.91 64	85.87 68	86.04 76	89.84 127	69.44 107	90.45 76	93.00 53	76.70 86	88.01 47	91.23 153	73.28 42	93.91 157	81.50 107	88.80 156	94.77 30

canonicalmvs		85.91 64	85.87 68	86.04 76	89.84 127	69.44 107	90.45 76	93.00 53	76.70 86	88.01 47	91.23 153	73.28 42	93.91 157	81.50 107	88.80 156	94.77 30

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 39	73.86 7	93.98 3	92.82 70	76.62 88	83.68 116	94.46 36	67.93 127	95.95 64	84.20 79	94.39 61	93.23 134

DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 51	72.37 43	91.26 59	93.04 48	76.62 88	84.22 104	93.36 85	71.44 70	96.76 30	80.82 117	95.33 37	94.16 76
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TestfortrainingZip				87.28 46	92.85 69	72.05 50	93.28 12	93.32 38	76.52 90	88.91 33	93.52 77	77.30 18	96.67 34		91.98 95	93.13 146

E5new		84.22 94	84.12 97	84.51 132	87.60 236	65.36 226	87.45 194	92.31 93	76.51 91	83.53 120	92.26 111	69.25 106	93.50 184	79.88 132	88.26 167	94.69 37

E6new		84.22 94	84.12 97	84.52 130	87.60 236	65.36 226	87.45 194	92.30 95	76.51 91	83.53 120	92.26 111	69.26 104	93.49 186	79.88 132	88.26 167	94.69 37

E6		84.22 94	84.12 97	84.52 130	87.60 236	65.36 226	87.45 194	92.30 95	76.51 91	83.53 120	92.26 111	69.26 104	93.49 186	79.88 132	88.26 167	94.69 37

E5		84.22 94	84.12 97	84.51 132	87.60 236	65.36 226	87.45 194	92.31 93	76.51 91	83.53 120	92.26 111	69.25 106	93.50 184	79.88 132	88.26 167	94.69 37

MGCFI-Net		85.06 87	85.51 75	83.70 188	89.42 143	63.01 300	89.43 105	92.62 81	76.43 95	87.53 55	91.34 151	72.82 52	93.42 194	81.28 111	88.74 159	94.66 45

TSAR-MVS + GP.		85.71 70	85.33 79	86.84 57	91.34 90	72.50 36	89.07 125	87.28 301	76.41 96	85.80 73	90.22 196	74.15 37	95.37 88	81.82 105	91.88 96	92.65 170

HQP-NCC					89.33 148		89.17 117		76.41 96	77.23 251

ACMP_Plane					89.33 148		89.17 117		76.41 96	77.23 251

HQP-MVS		82.61 144	82.02 151	84.37 142	89.33 148	66.98 187	89.17 117	92.19 109	76.41 96	77.23 251	90.23 195	60.17 248	95.11 97	77.47 167	85.99 222	91.03 232

E4		84.10 100	83.99 103	84.45 137	87.58 244	64.99 240	86.54 234	92.25 100	76.38 100	83.37 125	92.09 122	69.88 94	93.58 173	79.78 137	88.03 178	94.77 30

CANet_DTU		80.61 197	79.87 195	82.83 229	85.60 304	63.17 298	87.36 201	88.65 268	76.37 101	75.88 284	88.44 250	53.51 308	93.07 216	73.30 219	89.74 140	92.25 189

VNet		82.21 151	82.41 138	81.62 266	90.82 102	60.93 344	84.47 299	89.78 202	76.36 102	84.07 108	91.88 126	64.71 171	90.26 338	70.68 251	88.89 154	93.66 107

Vis-MVSNet		83.46 124	82.80 131	85.43 92	90.25 114	68.74 123	90.30 80	90.13 193	76.33 103	80.87 177	92.89 96	61.00 234	94.20 141	72.45 235	90.97 115	93.35 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
aaEdge-Enhanced		88.98 11	89.39 8	87.75 30	94.54 21	71.43 61	91.61 49	94.25 5	76.30 104	90.62 22	95.03 22	78.06 16	97.07 20	88.15 40	95.96 21	94.75 35

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 46	73.05 22	90.86 65	93.59 29	76.27 105	88.14 43	95.09 21	71.06 76	96.67 34	87.67 45	96.37 14	94.09 81

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 98	87.33 252	67.30 178	89.50 101	90.98 161	76.25 106	90.56 23	94.75 29	68.38 121	94.24 140	90.80 7	92.32 90	94.19 75

alignmvs		85.48 74	85.32 80	85.96 79	89.51 137	69.47 104	89.74 92	92.47 84	76.17 107	87.73 54	91.46 148	70.32 84	93.78 164	81.51 106	88.95 153	94.63 48

MVS_111021_HR		85.14 83	84.75 89	86.32 66	91.65 87	72.70 30	85.98 254	90.33 185	76.11 108	82.08 150	91.61 142	71.36 72	94.17 144	81.02 114	92.58 83	92.08 200

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 44	72.16 47	92.19 38	93.33 37	76.07 109	83.81 114	93.95 68	69.77 96	96.01 60	85.15 63	94.66 51	94.32 69
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
h-mvs33		83.15 133	82.19 144	86.02 78	90.56 107	70.85 81	88.15 170	89.16 237	76.02 110	84.67 90	91.39 150	61.54 220	95.50 76	82.71 98	75.48 380	91.72 211

hse-mvs2		81.72 162	80.94 168	84.07 166	88.72 179	67.68 163	85.87 258	87.26 306	76.02 110	84.67 90	88.22 257	61.54 220	93.48 189	82.71 98	73.44 408	91.06 230

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 112	92.29 7	95.66 12	81.67 6	97.38 13	87.44 49	96.34 15	93.95 89
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
E2		84.00 103	83.87 104	84.39 140	87.70 231	64.95 241	86.40 241	92.23 101	75.85 113	83.21 127	91.78 130	70.09 89	93.55 178	79.52 141	88.05 176	94.66 45

E3		84.00 103	83.87 104	84.39 140	87.70 231	64.95 241	86.40 241	92.23 101	75.85 113	83.21 127	91.78 130	70.09 89	93.55 178	79.52 141	88.05 176	94.66 45

CLD-MVS		82.31 149	81.65 157	84.29 150	88.47 187	67.73 161	85.81 262	92.35 91	75.78 115	78.33 225	86.58 307	64.01 178	94.35 133	76.05 188	87.48 189	90.79 241
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
viewmacassd2359aftdt		83.76 112	83.66 112	84.07 166	86.59 282	64.56 254	86.88 219	91.82 128	75.72 116	83.34 126	92.15 120	68.24 125	92.88 225	79.05 144	89.15 151	94.77 30

SF-MVS		88.46 15	88.74 15	87.64 39	92.78 72	71.95 52	92.40 29	94.74 2	75.71 117	89.16 30	95.10 20	75.65 26	96.19 53	87.07 50	96.01 19	94.79 28

testdata1							84.14 314		75.71 117

APDe-MVS		89.15 8	89.63 7	87.73 31	94.49 23	71.69 55	93.83 4	93.96 18	75.70 119	91.06 19	96.03 1	76.84 19	97.03 21	89.09 21	95.65 31	94.47 60
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VPA-MVSNet		80.60 199	80.55 176	80.76 292	88.07 207	60.80 347	86.86 220	91.58 143	75.67 120	80.24 190	89.45 221	63.34 182	90.25 339	70.51 253	79.22 330	91.23 225

fmvsm_l_conf0.5_n_9		85.84 67	86.63 49	83.46 195	87.12 266	66.01 203	88.56 150	89.43 217	75.59 121	89.32 29	94.32 44	72.89 48	91.21 306	90.11 11	92.33 88	93.16 142

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 38	73.38 18	90.22 81	93.04 48	75.53 122	83.86 112	94.42 40	67.87 129	96.64 37	82.70 100	94.57 56	93.66 107

Effi-MVS+		83.62 119	83.08 123	85.24 98	88.38 192	67.45 171	88.89 130	89.15 239	75.50 123	82.27 146	88.28 254	69.61 98	94.45 132	77.81 162	87.84 181	93.84 96

viewcassd2359sk11		83.89 106	83.74 109	84.34 145	87.76 226	64.91 248	86.30 245	92.22 104	75.47 124	83.04 133	91.52 144	70.15 87	93.53 181	79.26 143	87.96 179	94.57 53

fmvsm_s_conf0.5_n_4		85.39 78	85.75 71	84.30 149	86.70 278	65.83 210	88.77 137	89.78 202	75.46 125	88.35 38	93.73 74	69.19 108	93.06 217	91.30 3	88.44 165	94.02 85

fmvsm_s_conf0.5_n_6		85.55 73	86.20 56	83.60 190	87.32 254	65.13 234	88.86 131	91.63 139	75.41 126	88.23 42	93.45 82	68.56 119	92.47 243	89.52 18	92.78 80	93.20 139

test_prior2							88.85 133		75.41 126	84.91 84	93.54 76	74.28 35		83.31 86	95.86 24

LPG-MVS_test		82.08 154	81.27 160	84.50 134	89.23 156	68.76 121	90.22 81	91.94 121	75.37 128	76.64 266	91.51 145	54.29 299	94.91 106	78.44 154	83.78 259	89.83 290

LGP-MVS_train				84.50 134	89.23 156	68.76 121		91.94 121	75.37 128	76.64 266	91.51 145	54.29 299	94.91 106	78.44 154	83.78 259	89.83 290

fmvsm_l_conf0.5_n_3		86.02 58	86.32 53	85.14 101	87.20 258	68.54 132	89.57 99	90.44 179	75.31 130	87.49 56	94.39 42	72.86 49	92.72 232	89.04 27	90.56 124	94.16 76

viewmamba		82.38 147	82.11 145	83.19 209	83.30 361	64.26 265	84.62 295	89.16 237	75.24 131	80.97 173	91.10 160	67.12 137	91.63 278	81.36 109	86.13 217	93.67 106

aaatest				87.86 27	94.57 18	71.43 61	93.28 12	94.36 3	75.24 131	92.25 9	95.03 22		97.39 11	88.15 40	95.96 21	94.75 35

viewdifsd2359ckpt07		82.83 141	82.78 133	82.99 221	86.51 284	62.58 310	85.09 281	90.83 168	75.22 133	82.28 145	91.63 139	69.43 100	92.03 260	77.71 164	86.32 211	94.34 67

MG-MVS		83.41 125	83.45 117	83.28 203	92.74 73	62.28 319	88.17 168	89.50 215	75.22 133	81.49 161	92.74 105	66.75 141	95.11 97	72.85 225	91.58 103	92.45 181

SSC-MVS3.2		73.35 351	73.39 333	73.23 421	85.30 313	49.01 474	74.58 454	81.57 400	75.21 135	73.68 333	85.58 332	52.53 313	82.05 439	54.33 415	77.69 347	88.63 333

LCM-MVSNet-Re		77.05 291	76.94 273	77.36 376	87.20 258	51.60 459	80.06 393	80.46 416	75.20 136	67.69 411	86.72 297	62.48 201	88.98 364	63.44 317	89.25 147	91.51 216

SDMVSNet		80.38 206	80.18 185	80.99 286	89.03 165	64.94 244	80.45 387	89.40 218	75.19 137	76.61 268	89.98 198	60.61 242	87.69 385	76.83 178	83.55 268	90.33 263

sd_testset		77.70 279	77.40 263	78.60 348	89.03 165	60.02 361	79.00 409	85.83 338	75.19 137	76.61 268	89.98 198	54.81 291	85.46 410	62.63 333	83.55 268	90.33 263

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 49	72.04 51	89.80 90	93.50 31	75.17 139	86.34 70	95.29 19	70.86 78	96.00 61	88.78 31	96.04 18	94.58 51
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
E3new		83.78 111	83.60 114	84.31 147	87.76 226	64.89 249	86.24 248	92.20 107	75.15 140	82.87 136	91.23 153	70.11 88	93.52 183	79.05 144	87.79 182	94.51 58

PRO-TEST		82.16 152	82.06 149	82.45 246	89.49 140	58.24 378	84.07 317	91.34 150	75.05 141	73.21 340	90.55 183	62.05 211	95.60 70	81.23 113	91.56 104	93.51 123

test1111		79.43 229	79.18 218	80.15 309	89.99 123	53.31 446	87.33 203	77.05 452	75.04 142	80.23 191	92.77 104	48.97 376	92.33 252	68.87 273	92.40 87	94.81 27

Effi-MVS+-dtu		80.03 217	78.57 229	84.42 139	85.13 319	68.74 123	88.77 137	88.10 276	74.99 143	74.97 315	83.49 384	57.27 272	93.36 195	73.53 215	80.88 305	91.18 226

reproduce-ours		87.47 27	87.61 27	87.07 51	93.27 55	71.60 56	91.56 54	93.19 42	74.98 144	88.96 31	95.54 14	71.20 74	96.54 42	86.28 55	93.49 71	93.06 150

our_new_method		87.47 27	87.61 27	87.07 51	93.27 55	71.60 56	91.56 54	93.19 42	74.98 144	88.96 31	95.54 14	71.20 74	96.54 42	86.28 55	93.49 71	93.06 150

fmvsm_s_conf0.5_n_7		83.34 128	84.03 102	81.28 277	85.73 300	65.13 234	85.40 273	89.90 200	74.96 146	82.13 149	93.89 69	66.65 142	87.92 381	86.56 54	91.05 113	90.80 240

OMC-MVS		82.69 142	81.97 153	84.85 119	88.75 178	67.42 172	87.98 174	90.87 166	74.92 147	79.72 196	91.65 137	62.19 208	93.96 149	75.26 200	86.42 209	93.16 142

viewmanbaseed2359cas		83.66 115	83.55 115	84.00 177	86.81 274	64.53 255	86.65 229	91.75 133	74.89 148	83.15 132	91.68 135	68.74 117	92.83 229	79.02 146	89.24 148	94.63 48

test2506		77.30 288	76.49 284	79.74 324	90.08 118	52.02 452	87.86 182	63.10 497	74.88 149	80.16 192	92.79 101	38.29 457	92.35 250	68.74 275	92.50 85	94.86 22

ECVR-MVS		79.61 222	79.26 215	80.67 294	90.08 118	54.69 433	87.89 180	77.44 448	74.88 149	80.27 189	92.79 101	48.96 377	92.45 244	68.55 276	92.50 85	94.86 22

MonoMVSNet		76.49 303	75.80 292	78.58 349	81.55 407	58.45 374	86.36 243	86.22 331	74.87 151	74.73 319	83.73 377	51.79 334	88.73 369	70.78 248	72.15 417	88.55 336

nrg030		83.88 107	83.53 116	84.96 111	86.77 276	69.28 111	90.46 75	92.67 75	74.79 152	82.95 134	91.33 152	72.70 53	93.09 215	80.79 119	79.28 329	92.50 177

SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 36	73.73 9	92.40 29	93.63 27	74.77 153	92.29 7	95.97 2	74.28 35	97.24 15	88.58 34	96.91 1	94.87 21
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
reproduce_model		87.28 35	87.39 33	86.95 55	93.10 63	71.24 71	91.60 50	93.19 42	74.69 154	88.80 35	95.61 13	70.29 85	96.44 45	86.20 57	93.08 75	93.16 142

MVS_111021_LR		82.61 144	82.11 145	84.11 159	88.82 170	71.58 58	85.15 278	86.16 333	74.69 154	80.47 187	91.04 164	62.29 205	90.55 333	80.33 126	90.08 133	90.20 268

EIA-MVS		83.31 131	82.80 131	84.82 120	89.59 133	65.59 218	88.21 166	92.68 74	74.66 156	78.96 208	86.42 312	69.06 111	95.26 90	75.54 196	90.09 132	93.62 114

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 48	72.13 48	91.41 58	92.35 91	74.62 157	88.90 34	93.85 71	75.75 25	96.00 61	87.80 44	94.63 54	95.04 12
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS		86.73 43	86.67 48	86.91 56	94.11 42	72.11 49	92.37 33	92.56 83	74.50 158	86.84 66	94.65 31	67.31 134	95.77 66	84.80 69	92.85 79	92.84 164

FOURS1					95.00 10	72.39 41	95.06 1	93.84 21	74.49 159	91.30 17

ACMP	74.13 6	81.51 173	80.57 175	84.36 143	89.42 143	68.69 128	89.97 85	91.50 148	74.46 160	75.04 313	90.41 187	53.82 305	94.54 126	77.56 166	82.91 279	89.86 289
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 126	83.02 125	84.57 128	90.13 116	64.47 260	92.32 35	90.73 171	74.45 161	79.35 204	91.10 160	69.05 112	95.12 95	72.78 226	87.22 193	94.13 78

balanced_ft_v1		83.98 105	83.64 113	85.03 107	89.76 130	65.86 209	88.31 163	91.71 135	74.41 162	80.41 188	90.82 172	62.90 196	94.90 108	83.04 90	91.37 108	94.32 69

fmvsm_s_conf0.5_n_2		84.04 101	84.11 101	83.81 186	86.17 291	65.00 239	86.96 214	87.28 301	74.35 163	88.25 41	94.23 50	61.82 215	92.60 235	89.85 12	88.09 175	93.84 96

fmvsm_s_conf0.1_n_2		83.80 109	83.79 108	83.83 184	85.62 303	64.94 244	87.03 211	86.62 325	74.32 164	87.97 49	94.33 43	60.67 239	92.60 235	89.72 14	87.79 182	93.96 87

save fliter					93.80 45	72.35 44	90.47 74	91.17 156	74.31 165

MVS_Test		83.15 133	83.06 124	83.41 200	86.86 271	63.21 295	86.11 252	92.00 117	74.31 165	82.87 136	89.44 222	70.03 91	93.21 204	77.39 169	88.50 164	93.81 98

myMVS_eth3d28		73.62 342	73.53 332	73.90 417	88.20 197	47.41 479	78.06 424	79.37 432	74.29 167	73.98 329	84.29 361	44.67 412	83.54 427	51.47 429	87.39 190	90.74 245

UniMVSNet_ETH3D		79.10 240	78.24 238	81.70 265	86.85 272	60.24 359	87.28 205	88.79 255	74.25 168	76.84 259	90.53 185	49.48 366	91.56 284	67.98 280	82.15 288	93.29 131

IterMVS-LS		80.06 215	79.38 210	82.11 256	85.89 296	63.20 296	86.79 223	89.34 220	74.19 169	75.45 294	86.72 297	66.62 143	92.39 247	72.58 228	76.86 356	90.75 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 203	79.98 191	82.12 254	84.28 336	63.19 297	86.41 238	88.95 250	74.18 170	78.69 213	87.54 277	66.62 143	92.43 245	72.57 229	80.57 311	90.74 245

Vis-MVSNet (Re-imp)		78.36 259	78.45 231	78.07 362	88.64 182	51.78 458	86.70 227	79.63 430	74.14 171	75.11 310	90.83 171	61.29 228	89.75 348	58.10 386	91.60 101	92.69 168

v8		79.97 219	79.02 221	82.80 232	84.09 341	64.50 259	87.96 175	90.29 188	74.13 172	75.24 306	86.81 294	62.88 197	93.89 160	74.39 208	75.40 385	90.00 281

guyue		81.13 179	80.64 174	82.60 243	86.52 283	63.92 273	86.69 228	87.73 291	73.97 173	80.83 179	89.69 208	56.70 278	91.33 300	78.26 161	85.40 235	92.54 173

onestephybrid01		82.22 150	81.81 156	83.46 195	83.16 369	64.93 247	84.64 294	89.19 236	73.95 174	81.48 162	90.63 178	66.00 158	91.92 268	80.33 126	86.93 199	93.53 121

CSCG		86.41 51	86.19 58	87.07 51	92.91 68	72.48 37	90.81 66	93.56 30	73.95 174	83.16 131	91.07 163	75.94 23	95.19 92	79.94 131	94.38 62	93.55 119

thres100view900		76.50 300	75.55 299	79.33 335	89.52 136	56.99 400	85.83 261	83.23 374	73.94 176	76.32 275	87.12 289	51.89 331	91.95 265	48.33 449	83.75 262	89.07 308

9.14			88.26 19		92.84 71		91.52 56	94.75 1	73.93 177	88.57 37	94.67 30	75.57 27	95.79 65	86.77 52	95.76 27

HPM-MVS_fast		85.35 80	84.95 87	86.57 64	93.69 47	70.58 86	92.15 40	91.62 140	73.89 178	82.67 143	94.09 57	62.60 198	95.54 73	80.93 115	92.93 78	93.57 117

PAPM_NR		83.02 137	82.41 138	84.82 120	92.47 78	66.37 196	87.93 178	91.80 129	73.82 179	77.32 248	90.66 177	67.90 128	94.90 108	70.37 254	89.48 145	93.19 140

thres600view7		76.50 300	75.44 300	79.68 327	89.40 145	57.16 397	85.53 270	83.23 374	73.79 180	76.26 276	87.09 290	51.89 331	91.89 269	48.05 454	83.72 265	90.00 281

testing91		76.54 298	75.66 297	79.18 339	88.43 190	55.89 418	81.08 374	83.00 381	73.76 181	75.34 299	84.29 361	46.20 400	90.07 342	64.33 311	84.50 246	91.58 214

AstraMVS		80.81 187	80.14 188	82.80 232	86.05 295	63.96 270	86.46 237	85.90 337	73.71 182	80.85 178	90.56 182	54.06 303	91.57 283	79.72 138	83.97 257	92.86 162

v7n		78.97 244	77.58 259	83.14 212	83.45 358	65.51 219	88.32 162	91.21 154	73.69 183	72.41 352	86.32 315	57.93 263	93.81 163	69.18 269	75.65 376	90.11 273

dcpmvs_2		85.63 71	86.15 60	84.06 169	91.71 86	64.94 244	86.47 236	91.87 125	73.63 184	86.60 69	93.02 94	76.57 20	91.87 271	83.36 85	92.15 91	95.35 4

v2v482		80.23 212	79.29 214	83.05 218	83.62 354	64.14 267	87.04 210	89.97 197	73.61 185	78.18 229	87.22 285	61.10 232	93.82 162	76.11 186	76.78 359	91.18 226

Baseline_NR-MVSNet		78.15 265	78.33 236	77.61 372	85.79 298	56.21 415	86.78 224	85.76 339	73.60 186	77.93 235	87.57 274	65.02 167	88.99 363	67.14 290	75.33 387	87.63 356

BH-RMVSNet		79.61 222	78.44 232	83.14 212	89.38 147	65.93 206	84.95 285	87.15 309	73.56 187	78.19 228	89.79 206	56.67 279	93.36 195	59.53 369	86.74 204	90.13 271

APD-MVS_3200maxsize		85.97 62	85.88 66	86.22 69	92.69 74	69.53 101	91.93 42	92.99 56	73.54 188	85.94 71	94.51 35	65.80 160	95.61 69	83.04 90	92.51 84	93.53 121

SR-MVS-dyc-post		85.77 68	85.61 73	86.23 68	93.06 65	70.63 84	91.88 43	92.27 97	73.53 189	85.69 75	94.45 37	65.00 169	95.56 71	82.75 96	91.87 97	92.50 177

RE-MVS-def			85.48 76		93.06 65	70.63 84	91.88 43	92.27 97	73.53 189	85.69 75	94.45 37	63.87 179		82.75 96	91.87 97	92.50 177

reproduce_monomvs		75.40 323	74.38 321	78.46 355	83.92 346	57.80 388	83.78 320	86.94 315	73.47 191	72.25 355	84.47 355	38.74 453	89.27 357	75.32 199	70.53 427	88.31 340

test_fmvsmconf_n		85.92 63	86.04 63	85.57 89	85.03 322	69.51 102	89.62 98	90.58 174	73.42 192	87.75 52	94.02 61	72.85 50	93.24 201	90.37 8	90.75 120	93.96 87

tfpn200view9		76.42 306	75.37 304	79.55 332	89.13 160	57.65 391	85.17 276	83.60 366	73.41 193	76.45 271	86.39 313	52.12 321	91.95 265	48.33 449	83.75 262	89.07 308

thres400		76.50 300	75.37 304	79.86 317	89.13 160	57.65 391	85.17 276	83.60 366	73.41 193	76.45 271	86.39 313	52.12 321	91.95 265	48.33 449	83.75 262	90.00 281

diffmvs_AUTHOR		82.38 147	82.27 143	82.73 240	83.26 363	63.80 275	83.89 318	89.76 204	73.35 195	82.37 144	90.84 170	66.25 150	90.79 325	82.77 95	87.93 180	93.59 116

test_fmvsmconf0.1_n		85.61 72	85.65 72	85.50 90	82.99 379	69.39 109	89.65 95	90.29 188	73.31 196	87.77 51	94.15 55	71.72 65	93.23 202	90.31 9	90.67 122	93.89 93

testing99		76.09 312	75.12 311	79.00 340	88.16 200	55.50 424	80.79 378	81.40 403	73.30 197	75.17 307	84.27 364	44.48 415	90.02 343	64.28 312	84.22 255	91.48 219

v148		78.72 250	77.80 250	81.47 270	82.73 385	61.96 325	86.30 245	88.08 277	73.26 198	76.18 279	85.47 335	62.46 202	92.36 249	71.92 239	73.82 404	90.09 275

FA-MVS(test-final)		80.96 183	79.91 193	84.10 160	88.30 195	65.01 238	84.55 298	90.01 196	73.25 199	79.61 197	87.57 274	58.35 261	94.72 120	71.29 244	86.25 214	92.56 172

test_fmvsmconf0.01_n		84.73 91	84.52 93	85.34 95	80.25 424	69.03 112	89.47 102	89.65 209	73.24 200	86.98 64	94.27 47	66.62 143	93.23 202	90.26 10	89.95 136	93.78 102

viewdifsd2359ckpt11		80.37 208	79.73 199	82.30 251	83.70 352	62.39 314	84.20 311	86.67 321	73.22 201	80.90 175	90.62 179	63.00 194	91.56 284	76.81 179	78.44 336	92.95 159

viewmsd2359difaftdt		80.37 208	79.73 199	82.30 251	83.70 352	62.39 314	84.20 311	86.67 321	73.22 201	80.90 175	90.62 179	63.00 194	91.56 284	76.81 179	78.44 336	92.95 159

v10		79.74 221	78.67 226	82.97 224	84.06 342	64.95 241	87.88 181	90.62 173	73.11 203	75.11 310	86.56 308	61.46 223	94.05 148	73.68 213	75.55 378	89.90 287

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 22	72.46 40	89.82 88	93.82 22	73.07 204	84.86 87	92.89 96	76.22 22	96.33 47	84.89 67	95.13 40	94.40 63

baseline1		76.98 293	76.75 280	77.66 370	88.13 203	55.66 422	85.12 279	81.89 396	73.04 205	76.79 261	88.90 235	62.43 203	87.78 384	63.30 319	71.18 424	89.55 299

APD-MVS		87.44 29	87.52 30	87.19 48	94.24 37	72.39 41	91.86 45	92.83 67	73.01 206	88.58 36	94.52 32	73.36 40	96.49 44	84.26 76	95.01 41	92.70 166
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 153	81.88 154	82.76 238	83.00 375	63.78 277	83.68 323	89.76 204	72.94 207	82.02 151	89.85 201	65.96 159	90.79 325	82.38 102	87.30 192	93.71 104
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
K. test v3		71.19 378	68.51 391	79.21 338	83.04 374	57.78 389	84.35 308	76.91 453	72.90 208	62.99 458	82.86 396	39.27 449	91.09 312	61.65 350	52.66 488	88.75 328

MVSMamba_PlusPlus		85.99 60	85.96 65	86.05 75	91.09 94	67.64 164	89.63 97	92.65 78	72.89 209	84.64 93	91.71 134	71.85 62	96.03 57	84.77 70	94.45 60	94.49 59

GDP-MVS		83.52 122	82.64 134	86.16 71	88.14 202	68.45 134	89.13 122	92.69 73	72.82 210	83.71 115	91.86 128	55.69 286	95.35 89	80.03 129	89.74 140	94.69 37

hybridnocas07		81.44 174	81.13 163	82.37 249	82.13 397	63.11 299	83.45 332	88.74 262	72.54 211	80.71 181	90.73 173	65.14 165	90.74 330	80.35 125	86.41 210	93.27 133

fmvsm_s_conf0.5_n_5		85.22 82	85.55 74	84.25 156	86.26 287	67.40 174	89.18 116	89.31 226	72.50 212	88.31 39	93.86 70	69.66 97	91.96 264	89.81 13	91.05 113	93.38 126

Fast-Effi-MVS+-dtu		78.02 269	76.49 284	82.62 242	83.16 369	66.96 189	86.94 216	87.45 298	72.45 213	71.49 365	84.17 368	54.79 295	91.58 281	67.61 283	80.31 314	89.30 306

PHI-MVS		86.43 49	86.17 59	87.24 47	90.88 101	70.96 76	92.27 37	94.07 14	72.45 213	85.22 80	91.90 125	69.47 99	96.42 46	83.28 87	95.94 23	94.35 66

thres200		75.55 318	74.47 319	78.82 344	87.78 223	57.85 386	83.07 345	83.51 369	72.44 215	75.84 285	84.42 356	52.08 324	91.75 274	47.41 456	83.64 267	86.86 387

test_yl		81.17 177	80.47 179	83.24 206	89.13 160	63.62 278	86.21 249	89.95 198	72.43 216	81.78 156	89.61 212	57.50 269	93.58 173	70.75 249	86.90 200	92.52 175

DCV-MVSNet		81.17 177	80.47 179	83.24 206	89.13 160	63.62 278	86.21 249	89.95 198	72.43 216	81.78 156	89.61 212	57.50 269	93.58 173	70.75 249	86.90 200	92.52 175

viewdifsd2359ckpt13		82.91 139	82.29 142	84.77 123	86.96 270	66.90 191	87.47 191	91.62 140	72.19 218	81.68 158	90.71 175	66.92 139	93.28 197	75.90 190	87.15 195	94.12 79

BH-untuned		79.47 227	78.60 228	82.05 257	89.19 158	65.91 207	86.07 253	88.52 271	72.18 219	75.42 295	87.69 271	61.15 231	93.54 180	60.38 361	86.83 203	86.70 392

TransMVSNet (Re)		75.39 324	74.56 317	77.86 365	85.50 308	57.10 399	86.78 224	86.09 335	72.17 220	71.53 364	87.34 280	63.01 193	89.31 356	56.84 399	61.83 469	87.17 377

GA-MVS		76.87 295	75.17 310	81.97 260	82.75 384	62.58 310	81.44 369	86.35 330	72.16 221	74.74 318	82.89 395	46.20 400	92.02 262	68.85 274	81.09 302	91.30 224

VortexMVS		78.57 255	77.89 246	80.59 295	85.89 296	62.76 308	85.61 263	89.62 211	72.06 222	74.99 314	85.38 337	55.94 285	90.77 328	74.99 201	76.58 360	88.23 343

mmtdpeth		74.16 335	73.01 339	77.60 374	83.72 351	61.13 337	85.10 280	85.10 346	72.06 222	77.21 255	80.33 426	43.84 420	85.75 404	77.14 172	52.61 489	85.91 408

v1144		80.03 217	79.03 220	83.01 220	83.78 349	64.51 257	87.11 209	90.57 176	71.96 224	78.08 232	86.20 318	61.41 224	93.94 152	74.93 202	77.23 350	90.60 251

viewdifsd2359ckpt09		83.34 128	82.55 136	85.70 83	87.64 235	67.72 162	88.43 154	91.68 137	71.91 225	81.65 159	90.68 176	67.10 138	94.75 118	76.17 185	87.70 185	94.62 50

PS-MVSNAJss		82.07 155	81.31 159	84.34 145	86.51 284	67.27 180	89.27 113	91.51 145	71.75 226	79.37 203	90.22 196	63.15 189	94.27 136	77.69 165	82.36 287	91.49 218

EPNet_dtu		75.46 320	74.86 312	77.23 379	82.57 390	54.60 434	86.89 218	83.09 378	71.64 227	66.25 434	85.86 324	55.99 284	88.04 380	54.92 411	86.55 207	89.05 313
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 257	77.40 263	81.40 273	87.60 236	63.01 300	88.39 157	89.28 227	71.63 228	75.34 299	87.28 281	54.80 292	91.11 307	62.72 329	79.57 321	90.09 275

test1		78.40 257	77.40 263	81.40 273	87.60 236	63.01 300	88.39 157	89.28 227	71.63 228	75.34 299	87.28 281	54.80 292	91.11 307	62.72 329	79.57 321	90.09 275

FMVSNet2		78.20 263	77.21 267	81.20 280	87.60 236	62.89 307	87.47 191	89.02 245	71.63 228	75.29 305	87.28 281	54.80 292	91.10 310	62.38 337	79.38 327	89.61 297

patch_mono-2		83.65 116	84.54 91	80.99 286	90.06 122	65.83 210	84.21 310	88.74 262	71.60 231	85.01 81	92.44 108	74.51 31	83.50 428	82.15 103	92.15 91	93.64 113

V42		79.38 233	78.24 238	82.83 229	81.10 416	65.50 220	85.55 268	89.82 201	71.57 232	78.21 227	86.12 320	60.66 240	93.18 210	75.64 193	75.46 382	89.81 292

API-MVS		81.99 157	81.23 161	84.26 155	90.94 99	70.18 93	91.10 63	89.32 225	71.51 233	78.66 215	88.28 254	65.26 163	95.10 100	64.74 309	91.23 111	87.51 362

tttt0517		79.40 231	77.91 244	83.90 183	88.10 205	63.84 274	88.37 160	84.05 361	71.45 234	76.78 262	89.12 226	49.93 362	94.89 110	70.18 258	83.18 277	92.96 158

pm-mvs1		77.25 289	76.68 282	78.93 342	84.22 338	58.62 373	86.41 238	88.36 273	71.37 235	73.31 337	88.01 264	61.22 230	89.15 361	64.24 313	73.01 411	89.03 314

Elysia		81.53 169	80.16 186	85.62 86	85.51 306	68.25 141	88.84 134	92.19 109	71.31 236	80.50 185	89.83 202	46.89 388	94.82 113	76.85 175	89.57 142	93.80 100

StellarMVS		81.53 169	80.16 186	85.62 86	85.51 306	68.25 141	88.84 134	92.19 109	71.31 236	80.50 185	89.83 202	46.89 388	94.82 113	76.85 175	89.57 142	93.80 100

testing222		74.04 337	72.66 343	78.19 358	87.89 215	55.36 425	81.06 375	79.20 435	71.30 238	74.65 321	83.57 383	39.11 452	88.67 371	51.43 431	85.75 229	90.53 254

GeoE		81.71 163	81.01 167	83.80 187	89.51 137	64.45 261	88.97 127	88.73 264	71.27 239	78.63 216	89.76 207	66.32 149	93.20 207	69.89 262	86.02 221	93.74 103

tt0805		78.73 249	77.83 248	81.43 271	85.17 315	60.30 358	89.41 108	90.90 164	71.21 240	77.17 256	88.73 239	46.38 395	93.21 204	72.57 229	78.96 331	90.79 241

FMVSNet3		77.88 273	76.85 275	80.97 288	86.84 273	62.36 316	86.52 235	88.77 256	71.13 241	75.34 299	86.66 303	54.07 302	91.10 310	62.72 329	79.57 321	89.45 301

VDDNet		81.52 171	80.67 172	84.05 172	90.44 110	64.13 268	89.73 93	85.91 336	71.11 242	83.18 130	93.48 79	50.54 352	93.49 186	73.40 218	88.25 171	94.54 57

fmvsm_s_conf0.5_n		83.80 109	83.71 110	84.07 166	86.69 279	67.31 177	89.46 103	83.07 379	71.09 243	86.96 65	93.70 75	69.02 114	91.47 294	88.79 30	84.62 245	93.44 125

XVG-OURS		80.41 204	79.23 216	83.97 180	85.64 302	69.02 114	83.03 347	90.39 180	71.09 243	77.63 242	91.49 147	54.62 298	91.35 298	75.71 192	83.47 271	91.54 215

SSM_0407		81.58 168	80.48 178	84.87 118	88.81 171	67.96 152	87.37 200	89.25 231	71.06 245	79.48 200	90.39 189	59.57 250	94.48 131	72.45 235	85.93 224	92.18 194

SSM_0404		81.91 158	80.84 170	85.13 104	89.24 155	68.26 139	87.84 183	89.25 231	71.06 245	80.62 182	90.39 189	59.57 250	94.65 124	72.45 235	87.19 194	92.47 180

SixPastTwentyTwo		73.37 348	71.26 362	79.70 326	85.08 320	57.89 385	85.57 264	83.56 368	71.03 247	65.66 438	85.88 323	42.10 432	92.57 237	59.11 374	63.34 463	88.65 332

ZD-MVS					94.38 30	72.22 46		92.67 75	70.98 248	87.75 52	94.07 58	74.01 38	96.70 32	84.66 71	94.84 48

mamba_0408		79.37 234	77.52 260	84.93 114	88.81 171	67.96 152	65.03 493	88.66 266	70.96 249	79.48 200	89.80 204	58.69 256	94.65 124	70.35 255	85.93 224	92.18 194

SSM_04072		77.67 281	77.52 260	78.12 360	88.81 171	67.96 152	65.03 493	88.66 266	70.96 249	79.48 200	89.80 204	58.69 256	74.23 486	70.35 255	85.93 224	92.18 194

v1192		79.59 224	78.43 233	83.07 217	83.55 356	64.52 256	86.93 217	90.58 174	70.83 251	77.78 239	85.90 322	59.15 254	93.94 152	73.96 212	77.19 352	90.76 243

Fast-Effi-MVS+		80.81 187	79.92 192	83.47 194	88.85 167	64.51 257	85.53 270	89.39 219	70.79 252	78.49 220	85.06 346	67.54 131	93.58 173	67.03 292	86.58 206	92.32 186

PS-MVSNAJ		81.69 164	81.02 166	83.70 188	89.51 137	68.21 144	84.28 309	90.09 194	70.79 252	81.26 168	85.62 331	63.15 189	94.29 134	75.62 194	88.87 155	88.59 334

hybrid		81.05 181	80.66 173	82.22 253	81.97 399	62.99 304	83.42 333	88.68 265	70.76 254	80.56 184	90.40 188	64.49 174	90.48 334	79.57 140	86.06 219	93.19 140

LTVRE_ROB	69.57 13	76.25 309	74.54 318	81.41 272	88.60 183	64.38 263	79.24 404	89.12 242	70.76 254	69.79 386	87.86 267	49.09 374	93.20 207	56.21 405	80.16 315	86.65 394
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
testing11		75.14 326	74.01 324	78.53 352	88.16 200	56.38 411	80.74 381	80.42 418	70.67 256	72.69 349	83.72 378	43.61 422	89.86 345	62.29 339	83.76 261	89.36 304

fmvsm_s_conf0.1_n		83.56 121	83.38 119	84.10 160	84.86 324	67.28 179	89.40 109	83.01 380	70.67 256	87.08 62	93.96 67	68.38 121	91.45 295	88.56 35	84.50 246	93.56 118

xiu_mvs_v2_base		81.69 164	81.05 165	83.60 190	89.15 159	68.03 150	84.46 301	90.02 195	70.67 256	81.30 167	86.53 310	63.17 188	94.19 143	75.60 195	88.54 162	88.57 335

XVG-OURS-SEG-HR		80.81 187	79.76 198	83.96 181	85.60 304	68.78 120	83.54 331	90.50 177	70.66 259	76.71 264	91.66 136	60.69 238	91.26 301	76.94 174	81.58 297	91.83 204

Anonymous202405211		78.25 260	77.01 270	81.99 259	91.03 96	60.67 351	84.77 288	83.90 363	70.65 260	80.00 193	91.20 157	41.08 439	91.43 296	65.21 304	85.26 236	93.85 94

DP-MVS Recon		83.11 136	82.09 148	86.15 72	94.44 24	70.92 79	88.79 136	92.20 107	70.53 261	79.17 206	91.03 166	64.12 177	96.03 57	68.39 279	90.14 131	91.50 217

icg_test_0407_2		78.92 246	78.93 223	78.90 343	87.13 261	63.59 282	76.58 437	89.33 221	70.51 262	77.82 236	89.03 229	61.84 213	81.38 445	72.56 231	85.56 231	91.74 207

IMVS_0407		80.61 197	79.90 194	82.75 239	87.13 261	63.59 282	85.33 274	89.33 221	70.51 262	77.82 236	89.03 229	61.84 213	92.91 223	72.56 231	85.56 231	91.74 207

IMVS_0404		77.16 290	76.42 287	79.37 334	87.13 261	63.59 282	77.12 434	89.33 221	70.51 262	66.22 435	89.03 229	50.36 354	82.78 433	72.56 231	85.56 231	91.74 207

IMVS_0403		80.80 190	80.12 189	82.87 228	87.13 261	63.59 282	85.19 275	89.33 221	70.51 262	78.49 220	89.03 229	63.26 185	93.27 199	72.56 231	85.56 231	91.74 207

FMVSNet1		77.44 284	76.12 291	81.40 273	86.81 274	63.01 300	88.39 157	89.28 227	70.49 266	74.39 325	87.28 281	49.06 375	91.11 307	60.91 357	78.52 334	90.09 275

LuminaMVS		80.68 195	79.62 204	83.83 184	85.07 321	68.01 151	86.99 213	88.83 253	70.36 267	81.38 163	87.99 265	50.11 357	92.51 242	79.02 146	86.89 202	90.97 235

testing3		68.56 411	67.67 408	71.22 442	87.33 252	42.87 494	83.06 346	71.54 474	70.36 267	69.08 393	84.38 358	30.33 480	85.69 406	37.50 489	75.45 383	85.09 425

ab-mvs		79.51 225	78.97 222	81.14 282	88.46 188	60.91 345	83.84 319	89.24 233	70.36 267	79.03 207	88.87 237	63.23 187	90.21 340	65.12 305	82.57 285	92.28 188

tfpnnormal		74.39 331	73.16 337	78.08 361	86.10 294	58.05 380	84.65 293	87.53 295	70.32 270	71.22 368	85.63 330	54.97 290	89.86 345	43.03 475	75.02 392	86.32 397

ACMM	73.20 8	80.78 194	79.84 196	83.58 192	89.31 151	68.37 136	89.99 84	91.60 142	70.28 271	77.25 249	89.66 210	53.37 310	93.53 181	74.24 210	82.85 280	88.85 323
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_a		83.63 118	83.41 118	84.28 151	86.14 292	68.12 145	89.43 105	82.87 384	70.27 272	87.27 61	93.80 73	69.09 109	91.58 281	88.21 39	83.65 266	93.14 145

ACMH+	68.96 14	76.01 313	74.01 324	82.03 258	88.60 183	65.31 230	88.86 131	87.55 294	70.25 273	67.75 410	87.47 279	41.27 437	93.19 209	58.37 383	75.94 373	87.60 357

IB-MVS	68.01 15	75.85 315	73.36 335	83.31 202	84.76 327	66.03 201	83.38 335	85.06 347	70.21 274	69.40 388	81.05 416	45.76 405	94.66 123	65.10 306	75.49 379	89.25 307
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
thisisatest0530		79.40 231	77.76 253	84.31 147	87.69 233	65.10 237	87.36 201	84.26 359	70.04 275	77.42 245	88.26 256	49.94 360	94.79 117	70.20 257	84.70 244	93.03 153

mvsmamba		80.60 199	79.38 210	84.27 153	89.74 131	67.24 182	87.47 191	86.95 314	70.02 276	75.38 297	88.93 234	51.24 342	92.56 238	75.47 198	89.22 149	93.00 156

test_fmvsmvis_n_1920		84.02 102	83.87 104	84.49 136	84.12 340	69.37 110	88.15 170	87.96 283	70.01 277	83.95 111	93.23 87	68.80 116	91.51 291	88.61 32	89.96 135	92.57 171

v144192		79.47 227	78.37 234	82.78 236	83.35 359	63.96 270	86.96 214	90.36 184	69.99 278	77.50 243	85.67 329	60.66 240	93.77 166	74.27 209	76.58 360	90.62 249

test_fmvsm_n_1920		85.29 81	85.34 78	85.13 104	86.12 293	69.93 94	88.65 146	90.78 170	69.97 279	88.27 40	93.98 66	71.39 71	91.54 288	88.49 36	90.45 126	93.91 90

c3_l		78.75 248	77.91 244	81.26 278	82.89 382	61.56 331	84.09 315	89.13 241	69.97 279	75.56 289	84.29 361	66.36 148	92.09 259	73.47 217	75.48 380	90.12 272

v1921920		79.22 236	78.03 241	82.80 232	83.30 361	63.94 272	86.80 222	90.33 185	69.91 281	77.48 244	85.53 333	58.44 260	93.75 168	73.60 214	76.85 357	90.71 247

ACMH	67.68 16	75.89 314	73.93 326	81.77 264	88.71 180	66.61 193	88.62 147	89.01 246	69.81 282	66.78 425	86.70 301	41.95 434	91.51 291	55.64 406	78.14 342	87.17 377
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.1_n_a		83.32 130	82.99 127	84.28 151	83.79 348	68.07 147	89.34 112	82.85 385	69.80 283	87.36 60	94.06 59	68.34 123	91.56 284	87.95 43	83.46 272	93.21 137

DPM-MVS		84.93 88	84.29 95	86.84 57	90.20 115	73.04 23	87.12 208	93.04 48	69.80 283	82.85 138	91.22 156	73.06 46	96.02 59	76.72 182	94.63 54	91.46 221

MAR-MVS		81.84 160	80.70 171	85.27 97	91.32 91	71.53 59	89.82 88	90.92 163	69.77 285	78.50 219	86.21 317	62.36 204	94.52 128	65.36 303	92.05 94	89.77 293
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
XVG-ACMP-BASELINE		76.11 311	74.27 323	81.62 266	83.20 366	64.67 253	83.60 328	89.75 206	69.75 286	71.85 360	87.09 290	32.78 473	92.11 258	69.99 261	80.43 313	88.09 347

BH-w/o		78.21 262	77.33 266	80.84 290	88.81 171	65.13 234	84.87 286	87.85 288	69.75 286	74.52 323	84.74 353	61.34 226	93.11 214	58.24 385	85.84 227	84.27 434

v1240		78.99 243	77.78 251	82.64 241	83.21 365	63.54 286	86.62 231	90.30 187	69.74 288	77.33 247	85.68 328	57.04 275	93.76 167	73.13 222	76.92 354	90.62 249

FE-MVSNET2		72.88 364	71.28 360	77.67 369	78.30 449	57.78 389	84.43 304	88.92 252	69.56 289	64.61 447	81.67 411	46.73 392	88.54 374	59.33 370	67.99 441	86.69 393

ET-MVSNet_ETH3D		78.63 252	76.63 283	84.64 127	86.73 277	69.47 104	85.01 283	84.61 352	69.54 290	66.51 432	86.59 305	50.16 356	91.75 274	76.26 184	84.24 254	92.69 168

eth_miper_zixun_eth		77.92 272	76.69 281	81.61 268	83.00 375	61.98 324	83.15 340	89.20 235	69.52 291	74.86 317	84.35 360	61.76 216	92.56 238	71.50 242	72.89 412	90.28 266

PVSNet_Blended_VisFu		82.62 143	81.83 155	84.96 111	90.80 103	69.76 99	88.74 141	91.70 136	69.39 292	78.96 208	88.46 249	65.47 162	94.87 112	74.42 207	88.57 161	90.24 267

mvs_tets		79.13 239	77.77 252	83.22 208	84.70 328	66.37 196	89.17 117	90.19 191	69.38 293	75.40 296	89.46 219	44.17 418	93.15 211	76.78 181	80.70 309	90.14 270

PVSNet_BlendedMVS		80.60 199	80.02 190	82.36 250	88.85 167	65.40 222	86.16 251	92.00 117	69.34 294	78.11 230	86.09 321	66.02 156	94.27 136	71.52 240	82.06 290	87.39 365

SD_0403		74.65 330	74.77 314	74.29 411	86.20 290	47.42 478	83.71 322	85.12 345	69.30 295	68.50 401	87.95 266	59.40 252	86.05 401	49.38 443	83.35 273	89.40 302

AdaColmap		80.58 202	79.42 208	84.06 169	93.09 64	68.91 117	89.36 111	88.97 249	69.27 296	75.70 287	89.69 208	57.20 274	95.77 66	63.06 324	88.41 166	87.50 363

ETVMVS		72.25 371	71.05 365	75.84 389	87.77 225	51.91 455	79.39 402	74.98 462	69.26 297	73.71 332	82.95 393	40.82 441	86.14 400	46.17 462	84.43 251	89.47 300

ITE_SJBPF				78.22 357	81.77 403	60.57 353		83.30 372	69.25 298	67.54 412	87.20 286	36.33 466	87.28 390	54.34 414	74.62 396	86.80 389

cl____		77.72 277	76.76 278	80.58 296	82.49 392	60.48 355	83.09 343	87.87 286	69.22 299	74.38 326	85.22 342	62.10 209	91.53 289	71.09 246	75.41 384	89.73 295

DIV-MVS_self_test		77.72 277	76.76 278	80.58 296	82.48 393	60.48 355	83.09 343	87.86 287	69.22 299	74.38 326	85.24 340	62.10 209	91.53 289	71.09 246	75.40 385	89.74 294

jajsoiax		79.29 235	77.96 242	83.27 204	84.68 329	66.57 194	89.25 114	90.16 192	69.20 301	75.46 293	89.49 216	45.75 406	93.13 213	76.84 177	80.80 307	90.11 273

IterMVS-SCA-FT		75.43 321	73.87 328	80.11 310	82.69 386	64.85 250	81.57 366	83.47 370	69.16 302	70.49 372	84.15 369	51.95 327	88.15 378	69.23 268	72.14 418	87.34 370

CL-MVSNet_self_test		72.37 368	71.46 356	75.09 401	79.49 438	53.53 442	80.76 380	85.01 349	69.12 303	70.51 371	82.05 408	57.92 264	84.13 421	52.27 425	66.00 449	87.60 357

AUN-MVS		79.21 237	77.60 258	84.05 172	88.71 180	67.61 165	85.84 260	87.26 306	69.08 304	77.23 251	88.14 262	53.20 312	93.47 190	75.50 197	73.45 407	91.06 230

xiu_mvs_v1_base_debu		80.80 190	79.72 201	84.03 174	87.35 247	70.19 90	85.56 265	88.77 256	69.06 305	81.83 152	88.16 258	50.91 345	92.85 226	78.29 158	87.56 186	89.06 310

xiu_mvs_v1_base		80.80 190	79.72 201	84.03 174	87.35 247	70.19 90	85.56 265	88.77 256	69.06 305	81.83 152	88.16 258	50.91 345	92.85 226	78.29 158	87.56 186	89.06 310

xiu_mvs_v1_base_debi		80.80 190	79.72 201	84.03 174	87.35 247	70.19 90	85.56 265	88.77 256	69.06 305	81.83 152	88.16 258	50.91 345	92.85 226	78.29 158	87.56 186	89.06 310

MVSTER		79.01 242	77.88 247	82.38 248	83.07 372	64.80 251	84.08 316	88.95 250	69.01 308	78.69 213	87.17 288	54.70 296	92.43 245	74.69 203	80.57 311	89.89 288

usedtu_dtu_shiyan1		76.43 304	75.32 306	79.76 322	83.00 375	60.72 348	81.74 361	88.76 260	68.99 309	72.98 343	84.19 366	56.41 282	90.27 336	62.39 335	79.40 325	88.31 340

FE-MVSNET3		76.43 304	75.32 306	79.76 322	83.00 375	60.72 348	81.74 361	88.76 260	68.99 309	72.98 343	84.19 366	56.41 282	90.27 336	62.39 335	79.40 325	88.31 340

cl22		78.07 267	77.01 270	81.23 279	82.37 395	61.83 327	83.55 329	87.98 281	68.96 311	75.06 312	83.87 371	61.40 225	91.88 270	73.53 215	76.39 365	89.98 284

miper_ehance_all_eth		78.59 254	77.76 253	81.08 284	82.66 387	61.56 331	83.65 324	89.15 239	68.87 312	75.55 290	83.79 375	66.49 146	92.03 260	73.25 220	76.39 365	89.64 296

PAPR		81.66 166	80.89 169	83.99 179	90.27 113	64.00 269	86.76 226	91.77 132	68.84 313	77.13 258	89.50 215	67.63 130	94.88 111	67.55 284	88.52 163	93.09 148

CPTT-MVS		83.73 113	83.33 121	84.92 115	93.28 54	70.86 80	92.09 41	90.38 181	68.75 314	79.57 198	92.83 98	60.60 243	93.04 220	80.92 116	91.56 104	90.86 239

train_agg		86.43 49	86.20 56	87.13 50	93.26 57	72.96 25	88.75 139	91.89 123	68.69 315	85.00 82	93.10 89	74.43 32	95.41 83	84.97 64	95.71 29	93.02 154

test_8					93.13 61	72.57 35	88.68 145	91.84 127	68.69 315	84.87 86	93.10 89	74.43 32	95.16 93

dmvs_re		71.14 379	70.58 373	72.80 428	81.96 400	59.68 364	75.60 445	79.34 433	68.55 317	69.27 392	80.72 422	49.42 367	76.54 467	52.56 424	77.79 344	82.19 458

MVSFormer		82.85 140	82.05 150	85.24 98	87.35 247	70.21 88	90.50 72	90.38 181	68.55 317	81.32 164	89.47 217	61.68 217	93.46 191	78.98 149	90.26 129	92.05 201

test_djsdf		80.30 211	79.32 213	83.27 204	83.98 344	65.37 225	90.50 72	90.38 181	68.55 317	76.19 278	88.70 240	56.44 281	93.46 191	78.98 149	80.14 317	90.97 235

TEST9					93.26 57	72.96 25	88.75 139	91.89 123	68.44 320	85.00 82	93.10 89	74.36 34	95.41 83

FE-MVS		77.78 275	75.68 295	84.08 165	88.09 206	66.00 204	83.13 341	87.79 289	68.42 321	78.01 233	85.23 341	45.50 409	95.12 95	59.11 374	85.83 228	91.11 228

CDPH-MVS		85.76 69	85.29 82	87.17 49	93.49 52	71.08 72	88.58 149	92.42 88	68.32 322	84.61 94	93.48 79	72.32 55	96.15 55	79.00 148	95.43 34	94.28 72

PC_three_1452									68.21 323	92.02 14	94.00 63	82.09 5	95.98 63	84.58 72	96.68 2	94.95 15

fmvsm_l_conf0.5_n		84.47 92	84.54 91	84.27 153	85.42 309	68.81 118	88.49 153	87.26 306	68.08 324	88.03 46	93.49 78	72.04 61	91.77 273	88.90 29	89.14 152	92.24 191

IterMVS		74.29 332	72.94 340	78.35 356	81.53 408	63.49 288	81.58 365	82.49 388	68.06 325	69.99 381	83.69 379	51.66 336	85.54 408	65.85 300	71.64 421	86.01 405
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_testset		62.63 442	64.11 432	58.19 474	78.55 445	24.76 517	75.28 446	65.94 491	67.91 326	60.34 468	76.01 467	53.56 307	73.94 489	31.79 495	67.65 442	75.88 482

TAMVS		78.89 247	77.51 262	83.03 219	87.80 220	67.79 160	84.72 289	85.05 348	67.63 327	76.75 263	87.70 270	62.25 206	90.82 324	58.53 381	87.13 196	90.49 256

PVSNet_Blended		80.98 182	80.34 181	82.90 226	88.85 167	65.40 222	84.43 304	92.00 117	67.62 328	78.11 230	85.05 347	66.02 156	94.27 136	71.52 240	89.50 144	89.01 315

TR-MVS		77.44 284	76.18 290	81.20 280	88.24 196	63.24 294	84.61 296	86.40 328	67.55 329	77.81 238	86.48 311	54.10 301	93.15 211	57.75 389	82.72 283	87.20 375

CDS-MVSNet		79.07 241	77.70 255	83.17 211	87.60 236	68.23 143	84.40 307	86.20 332	67.49 330	76.36 274	86.54 309	61.54 220	90.79 325	61.86 347	87.33 191	90.49 256
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
fmvsm_l_conf0.5_n_a		84.13 99	84.16 96	84.06 169	85.38 310	68.40 135	88.34 161	86.85 318	67.48 331	87.48 57	93.40 83	70.89 77	91.61 279	88.38 38	89.22 149	92.16 198

dtuplus		80.04 216	79.40 209	81.97 260	83.08 371	62.61 309	83.63 327	87.98 281	67.47 332	81.02 171	90.50 186	64.86 170	90.77 328	71.28 245	84.76 242	92.53 174

mvs_anonymous		79.42 230	79.11 219	80.34 302	84.45 335	57.97 383	82.59 349	87.62 293	67.40 333	76.17 281	88.56 247	68.47 120	89.59 351	70.65 252	86.05 220	93.47 124

viewmambaseed2359dif		80.41 204	79.84 196	82.12 254	82.95 381	62.50 313	83.39 334	88.06 279	67.11 334	80.98 172	90.31 191	66.20 152	91.01 315	74.62 204	84.90 239	92.86 162

mvs5depth		69.45 403	67.45 412	75.46 397	73.93 474	55.83 419	79.19 406	83.23 374	66.89 335	71.63 363	83.32 386	33.69 472	85.09 413	59.81 366	55.34 485	85.46 416

IU-MVS					95.30 2	71.25 66		92.95 62	66.81 336	92.39 6				88.94 28	96.63 4	94.85 24

baseline2		75.70 316	73.83 329	81.30 276	83.26 363	61.79 328	82.57 350	80.65 411	66.81 336	66.88 423	83.42 385	57.86 265	92.19 256	63.47 316	79.57 321	89.91 286

miper_lstm_enhance		74.11 336	73.11 338	77.13 380	80.11 427	59.62 365	72.23 463	86.92 317	66.76 338	70.40 373	82.92 394	56.93 276	82.92 432	69.06 271	72.63 413	88.87 322

OpenMVS	72.83 10	79.77 220	78.33 236	84.09 164	85.17 315	69.91 95	90.57 69	90.97 162	66.70 339	72.17 356	91.91 124	54.70 296	93.96 149	61.81 348	90.95 117	88.41 339

test-LLR		72.94 361	72.43 345	74.48 408	81.35 412	58.04 381	78.38 418	77.46 446	66.66 340	69.95 382	79.00 441	48.06 380	79.24 453	66.13 295	84.83 240	86.15 401

test20.03		67.45 419	66.95 418	68.94 452	75.48 469	44.84 490	77.50 430	77.67 444	66.66 340	63.01 457	83.80 374	47.02 386	78.40 457	42.53 479	68.86 436	83.58 443

test0.0.03 1		68.00 417	67.69 407	68.90 453	77.55 458	47.43 477	75.70 444	72.95 473	66.66 340	66.56 428	82.29 405	48.06 380	75.87 476	44.97 471	74.51 397	83.41 444

Syy-MVS		68.05 416	67.85 402	68.67 456	84.68 329	40.97 500	78.62 415	73.08 471	66.65 343	66.74 426	79.46 436	52.11 323	82.30 437	32.89 494	76.38 368	82.75 453

myMVS_eth3d		67.02 423	66.29 423	69.21 451	84.68 329	42.58 495	78.62 415	73.08 471	66.65 343	66.74 426	79.46 436	31.53 477	82.30 437	39.43 485	76.38 368	82.75 453

QAPM		80.88 184	79.50 207	85.03 107	88.01 211	68.97 116	91.59 51	92.00 117	66.63 345	75.15 309	92.16 118	57.70 266	95.45 78	63.52 315	88.76 158	90.66 248

XXY-MVS		75.41 322	75.56 298	74.96 402	83.59 355	57.82 387	80.59 384	83.87 364	66.54 346	74.93 316	88.31 253	63.24 186	80.09 451	62.16 341	76.85 357	86.97 385

OurMVSNet-221017-0		74.26 333	72.42 346	79.80 319	83.76 350	59.59 366	85.92 257	86.64 323	66.39 347	66.96 422	87.58 273	39.46 448	91.60 280	65.76 301	69.27 432	88.22 344

SCA		74.22 334	72.33 347	79.91 315	84.05 343	62.17 320	79.96 396	79.29 434	66.30 348	72.38 353	80.13 429	51.95 327	88.60 372	59.25 372	77.67 348	88.96 319

nomal-1		73.10 357	71.76 352	77.13 380	82.58 389	65.50 220	73.53 460	79.64 429	66.14 349	72.17 356	81.27 413	46.45 393	81.47 444	62.08 344	81.93 293	84.42 433

testgi		66.67 426	66.53 422	67.08 463	75.62 468	41.69 499	75.93 440	76.50 455	66.11 350	65.20 445	86.59 305	35.72 468	74.71 483	43.71 472	73.38 409	84.84 428

HY-MVS	69.67 12	77.95 271	77.15 268	80.36 301	87.57 245	60.21 360	83.37 336	87.78 290	66.11 350	75.37 298	87.06 292	63.27 184	90.48 334	61.38 354	82.43 286	90.40 260

EG-PatchMatch MVS		74.04 337	71.82 351	80.71 293	84.92 323	67.42 172	85.86 259	88.08 277	66.04 352	64.22 450	83.85 372	35.10 469	92.56 238	57.44 391	80.83 306	82.16 459

CNLPA		78.08 266	76.79 277	81.97 260	90.40 111	71.07 73	87.59 188	84.55 353	66.03 353	72.38 353	89.64 211	57.56 268	86.04 402	59.61 368	83.35 273	88.79 326

gbinet_0.2-2-1-0.02		73.24 354	70.86 370	80.39 299	78.03 452	61.62 330	83.10 342	86.69 320	65.98 354	69.29 391	76.15 466	49.77 363	91.51 291	62.75 328	66.00 449	88.03 348

Anonymous20240529		80.19 214	78.89 224	84.10 160	90.60 106	64.75 252	88.95 128	90.90 164	65.97 355	80.59 183	91.17 159	49.97 359	93.73 170	69.16 270	82.70 284	93.81 98

TAPA-MVS	73.13 9	79.15 238	77.94 243	82.79 235	89.59 133	62.99 304	88.16 169	91.51 145	65.77 356	77.14 257	91.09 162	60.91 235	93.21 204	50.26 439	87.05 197	92.17 197
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 350	70.99 366	80.49 298	84.51 334	65.80 212	80.71 382	86.13 334	65.70 357	65.46 440	83.74 376	44.60 413	90.91 321	51.13 432	76.89 355	84.74 429

anonymousdsp		78.60 253	77.15 268	82.98 223	80.51 422	67.08 185	87.24 206	89.53 214	65.66 358	75.16 308	87.19 287	52.52 314	92.25 254	77.17 171	79.34 328	89.61 297

test_0402		72.79 365	70.44 376	79.84 318	88.13 203	65.99 205	85.93 256	84.29 357	65.57 359	67.40 418	85.49 334	46.92 387	92.61 234	35.88 491	74.38 398	80.94 466

UBG		73.08 358	72.27 348	75.51 395	88.02 209	51.29 463	78.35 421	77.38 449	65.52 360	73.87 331	82.36 402	45.55 407	86.48 397	55.02 410	84.39 252	88.75 328

miper_enhance_ethall		77.87 274	76.86 274	80.92 289	81.65 404	61.38 335	82.68 348	88.98 247	65.52 360	75.47 291	82.30 404	65.76 161	92.00 263	72.95 224	76.39 365	89.39 303

WBMVS		73.43 345	72.81 341	75.28 399	87.91 214	50.99 465	78.59 417	81.31 405	65.51 362	74.47 324	84.83 350	46.39 394	86.68 394	58.41 382	77.86 343	88.17 346

blend_shiyan4		72.29 370	69.65 383	80.21 307	78.24 450	62.16 321	82.29 354	87.27 304	65.41 363	68.43 403	76.42 462	39.91 446	91.23 303	63.21 322	65.66 456	87.22 374

blended_shiyan8		73.38 346	71.17 363	80.02 312	78.36 447	61.51 333	82.43 351	87.28 301	65.40 364	68.61 397	77.53 454	51.91 330	91.00 318	63.28 320	65.76 451	87.53 361

blended_shiyan6		73.38 346	71.17 363	80.01 313	78.36 447	61.48 334	82.43 351	87.27 304	65.40 364	68.56 399	77.55 453	51.94 329	91.01 315	63.27 321	65.76 451	87.55 360

UnsupCasMVSNet_eth		67.33 420	65.99 424	71.37 438	73.48 479	51.47 461	75.16 448	85.19 344	65.20 366	60.78 466	80.93 421	42.35 428	77.20 463	57.12 394	53.69 487	85.44 417

wanda-best-256-512		72.94 361	70.66 371	79.79 320	77.80 454	61.03 342	81.31 371	87.15 309	65.18 367	68.09 404	76.28 463	51.32 338	90.97 319	63.06 324	65.76 451	87.35 367

FE-blended-shiyan7		72.94 361	70.66 371	79.79 320	77.80 454	61.03 342	81.31 371	87.15 309	65.18 367	68.09 404	76.28 463	51.32 338	90.97 319	63.06 324	65.76 451	87.35 367

WTY-MVS		75.65 317	75.68 295	75.57 393	86.40 286	56.82 402	77.92 427	82.40 389	65.10 369	76.18 279	87.72 269	63.13 192	80.90 448	60.31 362	81.96 291	89.00 317

thisisatest0515		77.33 287	75.38 303	83.18 210	85.27 314	63.80 275	82.11 357	83.27 373	65.06 370	75.91 283	83.84 373	49.54 365	94.27 136	67.24 288	86.19 215	91.48 219

MVP-Stereo		76.12 310	74.46 320	81.13 283	85.37 311	69.79 97	84.42 306	87.95 284	65.03 371	67.46 415	85.33 338	53.28 311	91.73 276	58.01 387	83.27 275	81.85 461
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 244	77.69 256	82.81 231	90.54 108	64.29 264	90.11 83	91.51 145	65.01 372	76.16 282	88.13 263	50.56 351	93.03 221	69.68 265	77.56 349	91.11 228

pmmvs6		74.69 329	73.39 333	78.61 347	81.38 411	57.48 394	86.64 230	87.95 284	64.99 373	70.18 376	86.61 304	50.43 353	89.52 352	62.12 342	70.18 429	88.83 324

PAPM		77.68 280	76.40 288	81.51 269	87.29 257	61.85 326	83.78 320	89.59 212	64.74 374	71.23 367	88.70 240	62.59 199	93.66 172	52.66 423	87.03 198	89.01 315

MIMVSNet		70.69 386	69.30 385	74.88 404	84.52 333	56.35 413	75.87 443	79.42 431	64.59 375	67.76 409	82.41 401	41.10 438	81.54 442	46.64 460	81.34 298	86.75 391

tpm		72.37 368	71.71 353	74.35 410	82.19 396	52.00 453	79.22 405	77.29 450	64.56 376	72.95 345	83.68 380	51.35 337	83.26 431	58.33 384	75.80 374	87.81 353

MDA-MVSNet-bldmvs		66.68 425	63.66 435	75.75 390	79.28 441	60.56 354	73.92 458	78.35 441	64.43 377	50.13 492	79.87 433	44.02 419	83.67 424	46.10 463	56.86 479	83.03 450

usedtu_blend_shiyan5		73.29 352	70.96 367	80.25 305	77.80 454	62.16 321	84.44 303	87.38 299	64.41 378	68.09 404	76.28 463	51.32 338	91.23 303	63.21 322	65.76 451	87.35 367

MIMVSNet1		68.58 410	66.78 421	73.98 416	80.07 428	51.82 457	80.77 379	84.37 354	64.40 379	59.75 472	82.16 407	36.47 465	83.63 425	42.73 476	70.33 428	86.48 396

D2MVS		74.82 328	73.21 336	79.64 329	79.81 432	62.56 312	80.34 389	87.35 300	64.37 380	68.86 394	82.66 399	46.37 396	90.10 341	67.91 281	81.24 300	86.25 398

PLC	70.83 11	78.05 268	76.37 289	83.08 216	91.88 85	67.80 159	88.19 167	89.46 216	64.33 381	69.87 384	88.38 251	53.66 306	93.58 173	58.86 377	82.73 282	87.86 352
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 356	71.33 359	78.49 354	83.18 367	60.85 346	79.63 399	78.57 439	64.13 382	71.73 361	79.81 434	51.20 343	85.97 403	57.40 392	76.36 370	88.66 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_2432*1600		66.22 430	63.89 433	73.21 422	75.47 470	53.42 444	70.76 470	84.35 355	64.10 383	66.52 430	78.52 445	34.55 470	84.98 414	50.40 435	50.33 492	81.23 464

miper_refine_blended		66.22 430	63.89 433	73.21 422	75.47 470	53.42 444	70.76 470	84.35 355	64.10 383	66.52 430	78.52 445	34.55 470	84.98 414	50.40 435	50.33 492	81.23 464

tpmvs		71.09 380	69.29 386	76.49 385	82.04 398	56.04 416	78.92 412	81.37 404	64.05 385	67.18 420	78.28 447	49.74 364	89.77 347	49.67 442	72.37 414	83.67 442

F-COLMAP		76.38 308	74.33 322	82.50 245	89.28 153	66.95 190	88.41 156	89.03 244	64.05 385	66.83 424	88.61 244	46.78 390	92.89 224	57.48 390	78.55 333	87.67 355

DP-MVS		76.78 296	74.57 316	83.42 198	93.29 53	69.46 106	88.55 151	83.70 365	63.98 387	70.20 375	88.89 236	54.01 304	94.80 116	46.66 458	81.88 294	86.01 405

原ACMM1				84.35 144	93.01 67	68.79 119		92.44 85	63.96 388	81.09 169	91.57 143	66.06 155	95.45 78	67.19 289	94.82 50	88.81 325

PM-MVS		66.41 428	64.14 431	73.20 424	73.92 475	56.45 408	78.97 410	64.96 494	63.88 389	64.72 446	80.24 428	19.84 497	83.44 429	66.24 294	64.52 461	79.71 473

FE-MVSNET		67.25 422	65.33 426	73.02 426	75.86 465	52.54 451	80.26 392	80.56 413	63.80 390	60.39 467	79.70 435	41.41 436	84.66 419	43.34 474	62.62 467	81.86 460

UWE-MVS		72.13 373	71.49 355	74.03 415	86.66 280	47.70 476	81.40 370	76.89 454	63.60 391	75.59 288	84.22 365	39.94 445	85.62 407	48.98 446	86.13 217	88.77 327

0.4-1-1-0.1		70.93 382	67.94 401	79.91 315	79.35 440	61.27 336	78.95 411	82.19 393	63.36 392	67.50 413	69.40 487	39.83 447	91.04 314	62.44 334	68.40 438	87.40 364

jason		81.39 175	80.29 183	84.70 126	86.63 281	69.90 96	85.95 255	86.77 319	63.24 393	81.07 170	89.47 217	61.08 233	92.15 257	78.33 157	90.07 134	92.05 201
jason: jason.
KD-MVS_self_test		68.81 407	67.59 410	72.46 431	74.29 473	45.45 484	77.93 426	87.00 313	63.12 394	63.99 453	78.99 443	42.32 429	84.77 417	56.55 403	64.09 462	87.16 379

gg-mvs-nofinetune		69.95 398	67.96 399	75.94 388	83.07 372	54.51 436	77.23 433	70.29 477	63.11 395	70.32 374	62.33 492	43.62 421	88.69 370	53.88 417	87.76 184	84.62 431

tpmrst		72.39 366	72.13 349	73.18 425	80.54 421	49.91 470	79.91 397	79.08 436	63.11 395	71.69 362	79.95 431	55.32 288	82.77 434	65.66 302	73.89 402	86.87 386

PCF-MVS	73.52 7	80.38 206	78.84 225	85.01 109	87.71 229	68.99 115	83.65 324	91.46 149	63.00 397	77.77 240	90.28 192	66.10 153	95.09 101	61.40 353	88.22 172	90.94 237
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 359	70.41 377	80.81 291	87.13 261	65.63 216	88.30 164	84.19 360	62.96 398	63.80 455	87.69 271	38.04 458	92.56 238	46.66 458	74.91 393	84.24 435
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 392	67.78 406	77.61 372	77.43 459	59.57 367	71.16 467	70.33 476	62.94 399	68.65 396	72.77 478	50.62 350	85.49 409	69.58 266	66.58 446	87.77 354

lupinMVS		81.39 175	80.27 184	84.76 124	87.35 247	70.21 88	85.55 268	86.41 327	62.85 400	81.32 164	88.61 244	61.68 217	92.24 255	78.41 156	90.26 129	91.83 204

test_vis1_n_1920		75.52 319	75.78 293	74.75 407	79.84 431	57.44 395	83.26 338	85.52 341	62.83 401	79.34 205	86.17 319	45.10 411	79.71 452	78.75 151	81.21 301	87.10 383

EPMVS		69.02 406	68.16 395	71.59 436	79.61 436	49.80 472	77.40 431	66.93 488	62.82 402	70.01 379	79.05 439	45.79 404	77.86 461	56.58 402	75.26 389	87.13 380

PatchMatch-RL		72.38 367	70.90 368	76.80 384	88.60 183	67.38 175	79.53 400	76.17 459	62.75 403	69.36 389	82.00 410	45.51 408	84.89 416	53.62 418	80.58 310	78.12 477

gm-plane-assit					81.40 410	53.83 441			62.72 404		80.94 419		92.39 247	63.40 318

0.3-1-1-0.015		70.03 396	66.80 420	79.72 325	78.18 451	61.07 340	77.63 429	82.32 392	62.65 405	65.50 439	67.29 488	37.62 461	90.91 321	61.99 345	68.04 440	87.19 376

FMVSNet5		69.50 402	67.96 399	74.15 413	82.97 380	55.35 426	80.01 395	82.12 395	62.56 406	63.02 456	81.53 412	36.92 462	81.92 440	48.42 448	74.06 400	85.17 423

sss		73.60 343	73.64 331	73.51 420	82.80 383	55.01 430	76.12 439	81.69 399	62.47 407	74.68 320	85.85 325	57.32 271	78.11 459	60.86 358	80.93 303	87.39 365

0.4-1-1-0.2		70.01 397	66.86 419	79.44 333	77.61 457	60.64 352	76.77 436	82.34 391	62.40 408	65.91 437	66.65 489	40.05 444	90.83 323	61.77 349	68.24 439	86.86 387

WB-MVSnew		71.96 375	71.65 354	72.89 427	84.67 332	51.88 456	82.29 354	77.57 445	62.31 409	73.67 334	83.00 392	53.49 309	81.10 447	45.75 466	82.13 289	85.70 412

AllTest		70.96 381	68.09 397	79.58 330	85.15 317	63.62 278	84.58 297	79.83 426	62.31 409	60.32 469	86.73 295	32.02 474	88.96 366	50.28 437	71.57 422	86.15 401

TestCases				79.58 330	85.15 317	63.62 278		79.83 426	62.31 409	60.32 469	86.73 295	32.02 474	88.96 366	50.28 437	71.57 422	86.15 401

1112_ss		77.40 286	76.43 286	80.32 303	89.11 164	60.41 357	83.65 324	87.72 292	62.13 412	73.05 342	86.72 297	62.58 200	89.97 344	62.11 343	80.80 307	90.59 252

PVSNet	64.34 18	72.08 374	70.87 369	75.69 391	86.21 289	56.44 409	74.37 456	80.73 410	62.06 413	70.17 377	82.23 406	42.86 426	83.31 430	54.77 412	84.45 250	87.32 371

UWE-MVS-28		65.32 433	64.93 427	66.49 464	78.70 444	38.55 502	77.86 428	64.39 495	62.00 414	64.13 451	83.60 381	41.44 435	76.00 474	31.39 496	80.89 304	84.92 426

LS3D		76.95 294	74.82 313	83.37 201	90.45 109	67.36 176	89.15 121	86.94 315	61.87 415	69.52 387	90.61 181	51.71 335	94.53 127	46.38 461	86.71 205	88.21 345

CostFormer		75.24 325	73.90 327	79.27 336	82.65 388	58.27 377	80.80 377	82.73 387	61.57 416	75.33 303	83.13 390	55.52 287	91.07 313	64.98 307	78.34 341	88.45 337

new-patchmatchnet		61.73 444	61.73 444	61.70 470	72.74 486	24.50 518	69.16 477	78.03 442	61.40 417	56.72 481	75.53 471	38.42 455	76.48 469	45.95 464	57.67 478	84.13 437

ANet_high		50.57 462	46.10 466	63.99 467	48.67 515	39.13 501	70.99 469	80.85 408	61.39 418	31.18 504	57.70 501	17.02 500	73.65 490	31.22 497	15.89 516	79.18 474

MS-PatchMatch		73.83 340	72.67 342	77.30 378	83.87 347	66.02 202	81.82 359	84.66 351	61.37 419	68.61 397	82.82 397	47.29 383	88.21 377	59.27 371	84.32 253	77.68 478

USDC		70.33 391	68.37 392	76.21 387	80.60 420	56.23 414	79.19 406	86.49 326	60.89 420	61.29 464	85.47 335	31.78 476	89.47 354	53.37 420	76.21 371	82.94 452

cascas		76.72 297	74.64 315	82.99 221	85.78 299	65.88 208	82.33 353	89.21 234	60.85 421	72.74 346	81.02 417	47.28 384	93.75 168	67.48 285	85.02 237	89.34 305

sc_t1		72.19 372	69.51 384	80.23 306	84.81 325	61.09 339	84.68 290	80.22 423	60.70 422	71.27 366	83.58 382	36.59 464	89.24 358	60.41 360	63.31 464	90.37 261

MDTV_nov1_ep13			69.97 382		83.18 367	53.48 443	77.10 435	80.18 425	60.45 423	69.33 390	80.44 423	48.89 378	86.90 392	51.60 428	78.51 335

TinyColmap		67.30 421	64.81 428	74.76 406	81.92 402	56.68 406	80.29 390	81.49 402	60.33 424	56.27 484	83.22 387	24.77 489	87.66 386	45.52 467	69.47 431	79.95 472

test-mter		71.41 377	70.39 378	74.48 408	81.35 412	58.04 381	78.38 418	77.46 446	60.32 425	69.95 382	79.00 441	36.08 467	79.24 453	66.13 295	84.83 240	86.15 401

1314		76.53 299	75.30 308	80.21 307	83.93 345	62.32 318	84.66 291	88.81 254	60.23 426	70.16 378	84.07 370	55.30 289	90.73 331	67.37 286	83.21 276	87.59 359

PatchT		68.46 413	67.85 402	70.29 446	80.70 419	43.93 492	72.47 462	74.88 463	60.15 427	70.55 370	76.57 458	49.94 360	81.59 441	50.58 433	74.83 394	85.34 418

dtuonlycased		68.45 414	67.29 415	71.92 433	80.18 426	54.90 431	79.76 398	80.38 420	60.11 428	62.57 461	76.44 461	49.34 369	82.31 436	55.05 409	61.77 470	78.53 476

无先验							87.48 190	88.98 247	60.00 429				94.12 145	67.28 287		88.97 318

CR-MVSNet		73.37 348	71.27 361	79.67 328	81.32 414	65.19 232	75.92 441	80.30 421	59.92 430	72.73 347	81.19 414	52.50 315	86.69 393	59.84 365	77.71 345	87.11 381

TDRefinement		67.49 418	64.34 430	76.92 382	73.47 480	61.07 340	84.86 287	82.98 382	59.77 431	58.30 476	85.13 344	26.06 485	87.89 382	47.92 455	60.59 475	81.81 462

dp		66.80 424	65.43 425	70.90 445	79.74 435	48.82 475	75.12 450	74.77 464	59.61 432	64.08 452	77.23 455	42.89 425	80.72 449	48.86 447	66.58 446	83.16 447

our_test_3		69.14 405	67.00 417	75.57 393	79.80 433	58.80 371	77.96 425	77.81 443	59.55 433	62.90 459	78.25 448	47.43 382	83.97 422	51.71 427	67.58 443	83.93 440

Test_1112_low_res		76.40 307	75.44 300	79.27 336	89.28 153	58.09 379	81.69 364	87.07 312	59.53 434	72.48 351	86.67 302	61.30 227	89.33 355	60.81 359	80.15 316	90.41 259

pmmvs4		74.03 339	71.91 350	80.39 299	81.96 400	68.32 137	81.45 368	82.14 394	59.32 435	69.87 384	85.13 344	52.40 317	88.13 379	60.21 363	74.74 395	84.73 430

testdata				79.97 314	90.90 100	64.21 266		84.71 350	59.27 436	85.40 77	92.91 95	62.02 212	89.08 362	68.95 272	91.37 108	86.63 395

WB-MVS		54.94 452	54.72 453	55.60 481	73.50 478	20.90 520	74.27 457	61.19 499	59.16 437	50.61 490	74.15 474	47.19 385	75.78 477	17.31 514	35.07 501	70.12 490

ppachtmachnet_test		70.04 395	67.34 414	78.14 359	79.80 433	61.13 337	79.19 406	80.59 412	59.16 437	65.27 442	79.29 438	46.75 391	87.29 389	49.33 444	66.72 444	86.00 407

RPSCF		73.23 355	71.46 356	78.54 351	82.50 391	59.85 362	82.18 356	82.84 386	58.96 439	71.15 369	89.41 223	45.48 410	84.77 417	58.82 378	71.83 420	91.02 234

pmmvs-eth3d		70.50 389	67.83 404	78.52 353	77.37 460	66.18 199	81.82 359	81.51 401	58.90 440	63.90 454	80.42 424	42.69 427	86.28 399	58.56 380	65.30 458	83.11 448

tt0320-xc		70.11 394	67.45 412	78.07 362	85.33 312	59.51 368	83.28 337	78.96 437	58.77 441	67.10 421	80.28 427	36.73 463	87.42 388	56.83 400	59.77 477	87.29 372

OpenMVS_ROB	64.09 19	70.56 388	68.19 394	77.65 371	80.26 423	59.41 369	85.01 283	82.96 383	58.76 442	65.43 441	82.33 403	37.63 460	91.23 303	45.34 470	76.03 372	82.32 456

114514_t		80.68 195	79.51 206	84.20 157	94.09 43	67.27 180	89.64 96	91.11 159	58.75 443	74.08 328	90.72 174	58.10 262	95.04 103	69.70 264	89.42 146	90.30 265

Patchmtry		70.74 385	69.16 388	75.49 396	80.72 418	54.07 439	74.94 452	80.30 421	58.34 444	70.01 379	81.19 414	52.50 315	86.54 395	53.37 420	71.09 425	85.87 410

test_cas_vis1_n_1920		73.76 341	73.74 330	73.81 418	75.90 464	59.77 363	80.51 385	82.40 389	58.30 445	81.62 160	85.69 327	44.35 417	76.41 470	76.29 183	78.61 332	85.23 420

Anonymous20240521		68.80 408	67.22 416	73.55 419	74.33 472	54.11 438	83.18 339	85.61 340	58.15 446	61.68 463	80.94 419	30.71 479	81.27 446	57.00 397	73.34 410	85.28 419

tt0320		70.49 390	68.03 398	77.89 364	84.78 326	59.12 370	83.55 329	80.44 417	58.13 447	67.43 417	80.41 425	39.26 450	87.54 387	55.12 408	63.18 465	86.99 384

旧先验2							86.56 233		58.10 448	87.04 63			88.98 364	74.07 211

JIA-IIPM		66.32 429	62.82 441	76.82 383	77.09 461	61.72 329	65.34 491	75.38 460	58.04 449	64.51 448	62.32 493	42.05 433	86.51 396	51.45 430	69.22 433	82.21 457

pmmvs5		71.55 376	70.20 380	75.61 392	77.83 453	56.39 410	81.74 361	80.89 407	57.76 450	67.46 415	84.49 354	49.26 372	85.32 412	57.08 395	75.29 388	85.11 424

TESTMET0.1,1		69.89 400	69.00 389	72.55 430	79.27 442	56.85 401	78.38 418	74.71 466	57.64 451	68.09 404	77.19 456	37.75 459	76.70 466	63.92 314	84.09 256	84.10 438

RPMNet		73.51 344	70.49 375	82.58 244	81.32 414	65.19 232	75.92 441	92.27 97	57.60 452	72.73 347	76.45 459	52.30 318	95.43 80	48.14 453	77.71 345	87.11 381

SSC-MVS		53.88 455	53.59 455	54.75 484	72.87 485	19.59 521	73.84 459	60.53 501	57.58 453	49.18 494	73.45 477	46.34 398	75.47 480	16.20 517	32.28 503	69.20 491

dtuonly		69.95 398	69.98 381	69.85 448	73.09 484	49.46 473	74.55 455	76.40 456	57.56 454	67.82 408	86.31 316	50.89 349	74.23 486	61.46 352	81.71 296	85.86 411

新几何1				83.42 198	93.13 61	70.71 82		85.48 342	57.43 455	81.80 155	91.98 123	63.28 183	92.27 253	64.60 310	92.99 77	87.27 373

YYNet1		65.03 434	62.91 439	71.38 437	75.85 466	56.60 407	69.12 478	74.66 467	57.28 456	54.12 486	77.87 450	45.85 403	74.48 484	49.95 440	61.52 472	83.05 449

MDA-MVSNet_test_wron		65.03 434	62.92 438	71.37 438	75.93 463	56.73 403	69.09 479	74.73 465	57.28 456	54.03 487	77.89 449	45.88 402	74.39 485	49.89 441	61.55 471	82.99 451

Anonymous20231206		68.60 409	67.80 405	71.02 443	80.23 425	50.75 467	78.30 422	80.47 415	56.79 458	66.11 436	82.63 400	46.35 397	78.95 455	43.62 473	75.70 375	83.36 445

tpm2		73.26 353	71.46 356	78.63 346	83.34 360	56.71 405	80.65 383	80.40 419	56.63 459	73.55 335	82.02 409	51.80 333	91.24 302	56.35 404	78.42 339	87.95 349

CHOSEN 1792x2688		77.63 282	75.69 294	83.44 197	89.98 124	68.58 131	78.70 414	87.50 296	56.38 460	75.80 286	86.84 293	58.67 258	91.40 297	61.58 351	85.75 229	90.34 262

HyFIR lowres test		77.53 283	75.40 302	83.94 182	89.59 133	66.62 192	80.36 388	88.64 269	56.29 461	76.45 271	85.17 343	57.64 267	93.28 197	61.34 355	83.10 278	91.91 203

usedtu_dtu_shiyan2		64.75 437	61.63 445	74.10 414	70.64 490	53.18 449	82.10 358	81.27 406	56.22 462	56.39 483	74.67 473	27.94 483	83.56 426	42.71 477	62.73 466	85.57 414

PVSNet_0	57.27 20	61.67 445	59.27 448	68.85 454	79.61 436	57.44 395	68.01 480	73.44 470	55.93 463	58.54 475	70.41 484	44.58 414	77.55 462	47.01 457	35.91 500	71.55 489

UnsupCasMVSNet_bld		63.70 440	61.53 446	70.21 447	73.69 477	51.39 462	72.82 461	81.89 396	55.63 464	57.81 478	71.80 480	38.67 454	78.61 456	49.26 445	52.21 490	80.63 468

MDTV_nov1_ep13_2view						37.79 503	75.16 448		55.10 465	66.53 429		49.34 369		53.98 416		87.94 350

MVS		78.19 264	76.99 272	81.78 263	85.66 301	66.99 186	84.66 291	90.47 178	55.08 466	72.02 359	85.27 339	63.83 180	94.11 146	66.10 297	89.80 139	84.24 435

test222					91.50 88	68.26 139	84.16 313	83.20 377	54.63 467	79.74 195	91.63 139	58.97 255			91.42 106	86.77 390

dongtai		45.42 466	45.38 467	45.55 488	73.36 481	26.85 515	67.72 481	34.19 514	54.15 468	49.65 493	56.41 504	25.43 486	62.94 504	19.45 512	28.09 505	46.86 509

CHOSEN 280x420		66.51 427	64.71 429	71.90 434	81.45 409	63.52 287	57.98 502	68.95 483	53.57 469	62.59 460	76.70 457	46.22 399	75.29 482	55.25 407	79.68 320	76.88 480

ADS-MVSNet2		66.20 432	63.33 436	74.82 405	79.92 429	58.75 372	67.55 482	75.19 461	53.37 470	65.25 443	75.86 468	42.32 429	80.53 450	41.57 480	68.91 434	85.18 421

ADS-MVSNet		64.36 438	62.88 440	68.78 455	79.92 429	47.17 480	67.55 482	71.18 475	53.37 470	65.25 443	75.86 468	42.32 429	73.99 488	41.57 480	68.91 434	85.18 421

LF4IMVS		64.02 439	62.19 442	69.50 450	70.90 489	53.29 447	76.13 438	77.18 451	52.65 472	58.59 474	80.98 418	23.55 492	76.52 468	53.06 422	66.66 445	78.68 475

tpm cat1		70.57 387	68.31 393	77.35 377	82.41 394	57.95 384	78.08 423	80.22 423	52.04 473	68.54 400	77.66 452	52.00 326	87.84 383	51.77 426	72.07 419	86.25 398

test_vis1_n		69.85 401	69.21 387	71.77 435	72.66 487	55.27 428	81.48 367	76.21 458	52.03 474	75.30 304	83.20 389	28.97 481	76.22 472	74.60 205	78.41 340	83.81 441

Patchmatch-test		64.82 436	63.24 437	69.57 449	79.42 439	49.82 471	63.49 497	69.05 482	51.98 475	59.95 471	80.13 429	50.91 345	70.98 492	40.66 482	73.57 405	87.90 351

N_pmnet		52.79 458	53.26 456	51.40 486	78.99 443	7.68 534	69.52 474	3.89 534	51.63 476	57.01 480	74.98 472	40.83 440	65.96 500	37.78 487	64.67 460	80.56 471

test_fmvs1_n		70.86 384	70.24 379	72.73 429	72.51 488	55.28 427	81.27 373	79.71 428	51.49 477	78.73 212	84.87 349	27.54 484	77.02 464	76.06 187	79.97 319	85.88 409

test_fmvs1		70.93 382	70.52 374	72.16 432	73.71 476	55.05 429	80.82 376	78.77 438	51.21 478	78.58 217	84.41 357	31.20 478	76.94 465	75.88 191	80.12 318	84.47 432

PatchmatchNet2					0.00 564	30.51 510	67.30 484	67.46 486	50.92 479
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PMMVS		69.34 404	68.67 390	71.35 440	75.67 467	62.03 323	75.17 447	73.46 469	50.00 480	68.68 395	79.05 439	52.07 325	78.13 458	61.16 356	82.77 281	73.90 485

test_fmvs2		68.35 415	67.48 411	70.98 444	69.50 492	51.95 454	80.05 394	76.38 457	49.33 481	74.65 321	84.38 358	23.30 493	75.40 481	74.51 206	75.17 391	85.60 413

ttmdpeth		59.91 447	57.10 451	68.34 458	67.13 496	46.65 483	74.64 453	67.41 487	48.30 482	62.52 462	85.04 348	20.40 495	75.93 475	42.55 478	45.90 498	82.44 455

CMPMVS	51.72 21	70.19 393	68.16 395	76.28 386	73.15 483	57.55 393	79.47 401	83.92 362	48.02 483	56.48 482	84.81 351	43.13 424	86.42 398	62.67 332	81.81 295	84.89 427
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvsany_test1		62.30 443	61.26 447	65.41 466	69.52 491	54.86 432	66.86 485	49.78 508	46.65 484	68.50 401	83.21 388	49.15 373	66.28 499	56.93 398	60.77 473	75.11 483

kuosan		39.70 472	40.40 471	37.58 493	64.52 499	26.98 513	65.62 490	33.02 515	46.12 485	42.79 498	48.99 511	24.10 491	46.56 514	12.16 522	26.30 506	39.20 513

test_fmvs3		63.36 441	61.82 443	67.98 460	62.51 501	46.96 482	77.37 432	74.03 468	45.24 486	67.50 413	78.79 444	12.16 505	72.98 491	72.77 227	66.02 448	83.99 439

CVMVSNet		72.99 360	72.58 344	74.25 412	84.28 336	50.85 466	86.41 238	83.45 371	44.56 487	73.23 339	87.54 277	49.38 368	85.70 405	65.90 299	78.44 336	86.19 400

test_vis1_rt		60.28 446	58.42 449	65.84 465	67.25 495	55.60 423	70.44 472	60.94 500	44.33 488	59.00 473	66.64 490	24.91 488	68.67 497	62.80 327	69.48 430	73.25 486

mvsany_test3		53.99 454	51.45 459	61.61 471	55.51 506	44.74 491	63.52 496	45.41 512	43.69 489	58.11 477	76.45 459	17.99 498	63.76 503	54.77 412	47.59 494	76.34 481

EU-MVSNet		68.53 412	67.61 409	71.31 441	78.51 446	47.01 481	84.47 299	84.27 358	42.27 490	66.44 433	84.79 352	40.44 442	83.76 423	58.76 379	68.54 437	83.17 446

FPMVS		53.68 456	51.64 458	59.81 473	65.08 498	51.03 464	69.48 475	69.58 480	41.46 491	40.67 500	72.32 479	16.46 501	70.00 496	24.24 507	65.42 457	58.40 500

pmmvs3		57.79 449	54.26 454	68.37 457	64.02 500	56.72 404	75.12 450	65.17 492	40.20 492	52.93 488	69.86 486	20.36 496	75.48 479	45.45 468	55.25 486	72.90 487

new_pmnet		50.91 461	50.29 461	52.78 485	68.58 493	34.94 508	63.71 495	56.63 505	39.73 493	44.95 495	65.47 491	21.93 494	58.48 506	34.98 492	56.62 480	64.92 494

MVS-HIRNet		59.14 448	57.67 450	63.57 468	81.65 404	43.50 493	71.73 464	65.06 493	39.59 494	51.43 489	57.73 500	38.34 456	82.58 435	39.53 483	73.95 401	64.62 495

MVStest1		56.63 451	52.76 457	68.25 459	61.67 502	53.25 448	71.67 465	68.90 484	38.59 495	50.59 491	83.05 391	25.08 487	70.66 493	36.76 490	38.56 499	80.83 467

PMMVS2		40.82 471	38.86 475	46.69 487	53.84 508	16.45 525	48.61 505	49.92 507	37.49 496	31.67 503	60.97 495	8.14 511	56.42 508	28.42 499	30.72 504	67.19 493

test_vis3_rt		49.26 463	47.02 465	56.00 478	54.30 507	45.27 488	66.76 487	48.08 509	36.83 497	44.38 496	53.20 507	7.17 512	64.07 502	56.77 401	55.66 482	58.65 499

test_f		52.09 459	50.82 460	55.90 479	53.82 509	42.31 498	59.42 501	58.31 504	36.45 498	56.12 485	70.96 483	12.18 504	57.79 507	53.51 419	56.57 481	67.60 492

LCM-MVSNet		54.25 453	49.68 463	67.97 461	53.73 510	45.28 487	66.85 486	80.78 409	35.96 499	39.45 502	62.23 494	8.70 509	78.06 460	48.24 452	51.20 491	80.57 470

ArgMatch-Sym		43.72 470	39.92 473	55.10 483	52.36 512	37.56 504	61.93 499	23.00 520	35.80 500	43.62 497	70.22 485	3.22 518	55.93 509	45.35 469	23.80 509	71.81 488

APD_test1		53.31 457	49.93 462	63.42 469	65.68 497	50.13 469	71.59 466	66.90 489	34.43 501	40.58 501	71.56 481	8.65 510	76.27 471	34.64 493	55.36 484	63.86 496

PMVS	37.38 22	44.16 468	40.28 472	55.82 480	40.82 518	42.54 497	65.12 492	63.99 496	34.43 501	24.48 510	57.12 502	3.92 517	76.17 473	17.10 515	55.52 483	48.75 506
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ArgMatch-SfM		44.04 469	39.87 474	56.58 477	50.92 514	36.22 505	59.86 500	27.68 518	33.67 503	42.15 499	71.07 482	3.10 520	59.10 505	45.79 465	24.54 507	74.41 484

Gipuma		45.18 467	41.86 470	55.16 482	77.03 462	51.52 460	32.50 512	80.52 414	32.46 504	27.12 508	35.02 520	9.52 508	75.50 478	22.31 509	60.21 476	38.45 514
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 450	56.90 452	60.38 472	67.70 494	35.61 506	69.18 476	53.97 506	32.30 505	57.49 479	79.88 432	40.39 443	68.57 498	38.78 486	72.37 414	76.97 479

testf1		45.72 464	41.96 468	57.00 475	56.90 504	45.32 485	66.14 488	59.26 502	26.19 506	30.89 505	60.96 496	4.14 515	70.64 494	26.39 505	46.73 496	55.04 502

APD_test2		45.72 464	41.96 468	57.00 475	56.90 504	45.32 485	66.14 488	59.26 502	26.19 506	30.89 505	60.96 496	4.14 515	70.64 494	26.39 505	46.73 496	55.04 502

DenseAffine		31.97 473	28.22 479	43.21 490	43.10 517	27.10 512	46.21 506	11.36 524	24.92 508	27.70 507	58.81 499	1.09 524	46.50 515	26.95 502	13.85 520	56.02 501

E-PMN		31.77 474	30.64 476	35.15 495	52.87 511	27.67 511	57.09 503	47.86 510	24.64 509	16.40 524	33.05 521	11.23 506	54.90 510	14.46 518	18.15 514	22.87 521

EMVS		30.81 476	29.65 477	34.27 496	50.96 513	25.95 516	56.58 504	46.80 511	24.01 510	15.53 525	30.68 524	12.47 503	54.43 511	12.81 521	17.05 515	22.43 522

RoMa-SfM		28.67 478	25.38 482	38.54 491	32.61 522	22.48 519	40.24 507	7.23 528	21.81 511	26.66 509	60.46 498	0.96 525	41.72 516	26.47 504	11.95 521	51.40 505

DKM		25.67 480	23.01 484	33.64 497	32.08 523	19.25 523	37.50 509	5.52 530	18.67 512	23.58 513	55.44 505	0.64 531	34.02 518	23.95 508	9.73 523	47.66 508

PDCNetPlus		24.75 481	22.46 485	31.64 498	35.53 520	17.00 524	32.00 513	9.46 525	18.43 513	18.56 522	51.31 509	1.65 522	33.00 520	26.51 503	8.70 525	44.91 510

MVE	26.22 23	30.37 477	25.89 481	43.81 489	44.55 516	35.46 507	28.87 517	39.07 513	18.20 514	18.58 521	40.18 516	2.68 521	47.37 513	17.07 516	23.78 510	48.60 507
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 501	40.17 519	26.90 514		24.59 519	17.44 515	23.95 511	48.61 513	9.77 507	26.48 522	18.06 513	24.47 508	28.83 519

RoMa-HiRes		21.63 483	19.64 488	27.59 500	22.40 527	14.25 527	29.71 515	4.10 532	15.42 516	21.09 517	54.77 506	0.72 529	28.87 521	21.01 510	7.52 529	39.65 512

DKM-HiRes		20.87 484	19.15 489	26.02 502	25.34 526	14.13 528	29.63 516	3.62 537	14.53 517	20.13 518	50.55 510	0.47 539	24.22 525	20.96 511	7.15 530	39.70 511

LoFTR		27.52 479	24.27 483	37.29 494	34.75 521	19.27 522	33.78 511	21.60 521	12.42 518	21.61 516	56.59 503	0.91 526	40.37 517	13.94 519	22.80 511	52.22 504

MatchFormer		22.13 482	19.86 487	28.93 499	28.66 524	15.74 526	31.91 514	17.10 523	7.75 519	18.87 520	47.50 514	0.62 533	33.92 519	7.49 529	18.87 513	37.14 515

wuyk23d		16.82 487	15.94 491	19.46 505	58.74 503	31.45 509	39.22 508	3.74 536	6.84 520	6.04 532	2.70 555	1.27 523	24.29 524	10.54 527	14.40 519	2.63 539

PMatch-SfM		14.15 490	12.67 494	18.59 506	12.84 533	7.03 536	17.41 519	2.28 539	6.63 521	12.96 526	43.56 515	0.09 556	16.11 528	13.90 520	4.38 540	32.63 518

ELoFTR		14.23 489	11.56 495	22.24 503	11.02 535	6.56 538	13.59 524	7.57 527	5.55 522	11.96 528	39.09 517	0.21 544	24.93 523	9.43 528	5.66 534	35.22 516

PMatch-Up-SfM		10.76 494	9.99 497	13.09 507	9.50 541	4.83 541	12.94 526	1.40 548	4.65 523	10.16 529	37.54 518	0.07 559	10.94 530	10.71 526	2.92 551	23.50 520

MASt3R-SfM		13.55 491	13.93 492	12.41 508	10.54 538	5.97 540	16.61 520	6.07 529	4.50 524	16.53 523	48.67 512	0.73 528	9.44 531	11.56 525	10.18 522	21.81 523

GLUNet-SfM		12.90 492	10.00 496	21.62 504	13.58 532	8.30 532	10.19 528	9.30 526	4.31 525	12.18 527	30.90 523	0.50 537	22.76 526	4.89 530	4.14 541	33.79 517

test_method		31.52 475	29.28 478	38.23 492	27.03 525	6.50 539	20.94 518	62.21 498	4.05 526	22.35 514	52.50 508	13.33 502	47.58 512	27.04 501	34.04 502	60.62 497

VLMVS_CLIP		15.14 488	16.11 490	12.23 509	12.32 534	7.35 535	15.53 521	20.73 522	4.02 527	22.32 515	31.59 522	4.37 514	21.02 527	11.59 524	22.52 512	8.32 525

ALIKED-LG		8.61 495	8.70 499	8.33 511	20.63 528	8.70 531	15.50 522	4.61 531	2.19 528	5.84 533	18.70 526	0.80 527	8.06 532	1.03 540	8.97 524	8.25 526

MVS_clip		11.37 493	13.03 493	6.40 514	15.78 531	6.79 537	11.98 527	1.47 547	1.89 529	19.38 519	35.95 519	3.13 519	3.09 537	12.10 523	15.54 517	9.34 524

ALIKED-MNN		7.86 496	7.83 502	7.97 512	19.40 529	8.86 530	14.48 523	3.90 533	1.59 530	4.74 538	16.49 527	0.59 534	7.65 533	0.91 541	8.34 527	7.39 529

ALIKED-NN		7.51 497	7.61 503	7.21 513	18.26 530	8.10 533	13.45 525	3.88 535	1.50 531	4.87 536	16.47 528	0.64 531	7.00 534	0.88 542	8.50 526	6.52 534

tmp_tt		18.61 486	21.40 486	10.23 510	4.82 557	10.11 529	34.70 510	30.74 517	1.48 532	23.91 512	26.07 525	28.42 482	13.41 529	27.12 500	15.35 518	7.17 532

SP-DiffGlue		4.29 504	4.46 507	3.77 519	3.68 558	2.12 549	5.97 533	2.22 540	1.10 533	4.89 535	13.93 531	0.66 530	1.95 543	2.47 531	5.24 535	7.22 531

XFeat-MNN		4.39 503	4.49 506	4.10 515	2.88 560	1.91 555	5.86 534	2.57 538	1.06 534	5.04 534	13.99 530	0.43 541	4.47 535	2.00 533	6.55 532	5.92 535

SP-SuperGlue		4.24 506	4.38 509	3.81 518	10.75 537	2.00 551	8.18 530	2.09 541	1.00 535	2.41 539	8.29 535	0.56 535	2.05 542	1.27 536	4.91 537	7.39 529

SP-LightGlue		4.27 505	4.41 508	3.86 516	10.99 536	1.99 552	8.19 529	2.06 542	0.98 536	2.37 540	8.29 535	0.56 535	2.10 540	1.27 536	4.99 536	7.48 528

SP-NN		4.00 508	4.12 511	3.63 520	9.92 540	1.81 557	7.94 532	1.90 545	0.86 537	2.15 542	8.00 538	0.50 537	2.09 541	1.20 538	4.63 539	6.98 533

SP-MNN		4.14 507	4.24 510	3.82 517	10.32 539	1.83 556	8.11 531	1.99 543	0.82 538	2.23 541	8.27 537	0.47 539	2.14 539	1.20 538	4.77 538	7.49 527

XFeat-NN		3.78 509	3.96 513	3.23 522	2.65 561	1.53 560	4.99 535	1.92 544	0.81 539	4.77 537	12.37 533	0.38 542	3.39 536	1.64 534	6.13 533	4.77 537

SIFT-NN		2.77 511	2.92 514	2.34 523	8.70 542	3.08 542	4.46 536	1.01 550	0.68 540	1.46 543	5.49 539	0.16 545	1.65 544	0.26 543	4.04 542	2.27 540

SIFT-MNN		2.63 512	2.75 515	2.25 524	8.10 543	2.84 543	4.08 537	1.02 549	0.68 540	1.28 544	5.34 542	0.15 546	1.64 545	0.26 543	3.88 544	2.27 540

SIFT-NN-UMatch		2.26 516	2.39 519	1.89 529	6.21 551	2.08 550	3.76 539	0.83 553	0.66 542	1.04 548	5.09 543	0.14 547	1.52 548	0.23 546	3.51 546	2.07 544

SIFT-NCM-Cal		2.40 514	2.52 517	2.05 526	7.74 544	2.54 545	3.75 540	0.84 552	0.65 543	0.89 551	4.78 548	0.13 550	1.60 546	0.19 554	3.71 545	2.01 546

SIFT-NN-NCMNet		2.52 513	2.64 516	2.14 525	7.53 545	2.74 544	4.00 538	0.98 551	0.65 543	1.24 546	5.08 545	0.14 547	1.60 546	0.23 546	3.94 543	2.07 544

SIFT-NN-CMatch		2.31 515	2.41 518	2.00 527	6.59 549	2.34 547	3.48 541	0.83 553	0.65 543	1.28 544	5.09 543	0.14 547	1.52 548	0.23 546	3.41 547	2.14 542

SIFT-ConvMatch		2.25 517	2.37 520	1.90 528	7.29 546	2.37 546	3.21 545	0.75 555	0.65 543	1.03 549	4.91 546	0.12 553	1.51 550	0.22 549	3.13 549	1.81 547

SIFT-UMatch		2.16 518	2.30 521	1.72 531	6.99 547	1.97 554	3.32 543	0.70 557	0.64 547	0.91 550	4.86 547	0.12 553	1.49 551	0.22 549	2.97 550	1.72 549

SIFT-CM-Cal		2.02 520	2.13 523	1.67 532	6.79 548	1.99 552	2.79 547	0.64 558	0.63 548	0.87 552	4.48 551	0.13 550	1.41 553	0.19 554	2.70 552	1.61 551

SIFT-UM-Cal		1.97 521	2.12 524	1.52 533	6.57 550	1.67 558	2.93 546	0.57 560	0.62 549	0.83 553	4.55 550	0.11 555	1.37 554	0.20 553	2.69 553	1.53 552

SIFT-NN-PointCN		2.07 519	2.18 522	1.74 530	5.75 552	1.65 559	3.27 544	0.73 556	0.60 550	1.07 547	4.62 549	0.13 550	1.43 552	0.21 551	3.22 548	2.12 543

SIFT-NCMNet		1.44 524	1.56 527	1.08 537	5.14 555	1.07 563	1.97 550	0.32 562	0.56 551	0.64 556	3.23 554	0.07 559	1.01 557	0.14 558	1.95 556	1.15 553

SIFT-PCN-Cal		1.72 522	1.82 526	1.39 534	5.64 553	1.19 562	2.39 549	0.53 561	0.55 552	0.72 554	3.90 552	0.09 556	1.22 556	0.17 556	2.42 555	1.76 548

SIFT-PointCN		1.72 522	1.83 525	1.36 535	5.55 554	1.22 561	2.59 548	0.59 559	0.55 552	0.71 555	3.77 553	0.08 558	1.24 555	0.17 556	2.48 554	1.63 550

VLMVS		4.54 502	4.93 505	3.37 521	4.86 556	2.23 548	3.38 542	1.77 546	0.23 554	7.94 530	11.34 534	4.62 513	2.44 538	2.43 532	7.76 528	5.44 536

EGC-MVSNET		52.07 460	47.05 464	67.14 462	83.51 357	60.71 350	80.50 386	67.75 485	0.07 555	0.43 557	75.85 470	24.26 490	81.54 442	28.82 498	62.25 468	59.16 498

testmvs		6.04 500	8.02 501	0.10 539	0.08 562	0.03 566	69.74 473	0.04 564	0.05 556	0.31 558	1.68 556	0.02 562	0.04 558	0.24 545	0.02 557	0.25 555

MVS_baseline		3.29 510	4.00 512	1.16 536	3.08 559	0.09 564	1.26 551	0.24 563	0.04 557	6.52 531	16.19 529	0.30 543	0.00 560	1.53 535	6.83 531	3.39 538

test123		6.12 499	8.11 500	0.14 538	0.06 563	0.09 564	71.05 468	0.03 565	0.04 557	0.25 559	1.30 557	0.05 561	0.03 559	0.21 551	0.01 558	0.29 554

mmdepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

monomultidepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

test_blank		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

uanet_test		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

DCPMVS		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

cdsmvs_eth3d_5k		19.96 485	26.61 480	0.00 540	0.00 564	0.00 567	0.00 552	89.26 230	0.00 559	0.00 560	88.61 244	61.62 219	0.00 560	0.00 559	0.00 559	0.00 556

pcd_1.5k_mvsjas		5.26 501	7.02 504	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	63.15 189	0.00 560	0.00 559	0.00 559	0.00 556

sosnet-low-res		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

sosnet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

uncertanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

Regformer		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

ab-mvs-re		7.23 498	9.64 498	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	86.72 297	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

uanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

PatchmatchNet1														37.67 488	64.79 459	80.58 469
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													65.90 501
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
test-260524					94.58 16	71.43 61		94.16 8		90.64 21		78.62 14	97.13 17	88.60 33	96.28 16

WAC-MVS						42.58 495								39.46 484

MSC_two_6792asdad				89.16 1	94.34 32	75.53 2		92.99 56					97.53 2	89.67 15	96.44 9	94.41 61

No_MVS				89.16 1	94.34 32	75.53 2		92.99 56					97.53 2	89.67 15	96.44 9	94.41 61

eth-test2					0.00 564

eth-test					0.00 564

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 61	82.45 3	96.87 25	83.77 83	96.48 8	94.88 19

test_0728_SECOND				87.71 35	95.34 1	71.43 61	93.49 10	94.23 6					97.49 4	89.08 22	96.41 12	94.21 74

GSMVS																88.96 319

test_part2					95.06 8	72.65 32				91.80 15

sam_mvs1												51.32 338				88.96 319

sam_mvs												50.01 358

ambc				75.24 400	73.16 482	50.51 468	63.05 498	87.47 297		64.28 449	77.81 451	17.80 499	89.73 349	57.88 388	60.64 474	85.49 415

MTGPA								92.02 115

test_post1							78.90 413				5.43 541	48.81 379	85.44 411	59.25 372

test_post											5.46 540	50.36 354	84.24 420

patchmatchnet-post											74.00 475	51.12 344	88.60 372

GG-mvs-BLEND				75.38 398	81.59 406	55.80 420	79.32 403	69.63 479		67.19 419	73.67 476	43.24 423	88.90 368	50.41 434	84.50 246	81.45 463

MTMP							92.18 39	32.83 516

test9_res														84.90 65	95.70 30	92.87 161

agg_prior2														82.91 92	95.45 33	92.70 166

agg_prior					92.85 69	71.94 53		91.78 131		84.41 98			94.93 105

test_prior4						72.60 34	89.01 126

test_prior				86.33 65	92.61 76	69.59 100		92.97 61					95.48 77			93.91 90

新几何2							86.29 247

旧先验1					91.96 82	65.79 213		86.37 329			93.08 93	69.31 103			92.74 81	88.74 330

原ACMM2							86.86 220

testdata2													91.01 315	62.37 338

segment_acmp												73.08 45

test12				86.80 59	92.63 75	70.70 83		91.79 130		82.71 142		71.67 67	96.16 54		94.50 57	93.54 120

plane_prior7					90.08 118	68.51 133

plane_prior6					89.84 127	68.70 127						60.42 245

plane_prior5								92.44 85					95.38 85	78.71 152	86.32 211	91.33 222

plane_prior4											91.00 167

plane_prior1					89.90 126

n2								0.00 566

nn								0.00 566

door-mid								69.98 478

lessismore_v0				78.97 341	81.01 417	57.15 398		65.99 490		61.16 465	82.82 397	39.12 451	91.34 299	59.67 367	46.92 495	88.43 338

test11								92.23 101

door								69.44 481

HQP5-MVS						66.98 187

BP-MVS														77.47 167

HQP4-MVS										77.24 250			95.11 97			91.03 232

HQP3-MVS								92.19 109							85.99 222

HQP2-MVS												60.17 248

NP-MVS					89.62 132	68.32 137					90.24 194

ACMMP++_ref															81.95 292

ACMMP++															81.25 299

Test By Simon												64.33 175

High-resolution multi-view benchmark