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	91.17 155	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	90.86 166	82.48 2	84.60 95	93.20 88	69.35 101	95.22 90	71.39 242	90.88 118	93.07 148

MGCNet		87.69 24	87.55 29	88.12 13	89.45 141	71.76 54	91.47 57	89.54 212	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	84.22 337	69.48 103	91.05 64	85.27 342	81.30 6	76.83 260	91.65 137	66.09 154	95.56 70	76.00 188	93.85 68	93.38 125
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	60.40 246	96.75 31	77.20 169	93.73 70	95.29 7

TranMVSNet+NR-MVSNet		80.84 184	80.31 181	82.42 246	87.85 216	62.33 316	87.74 185	91.33 150	80.55 9	77.99 234	89.86 199	65.23 164	92.62 232	67.05 290	75.24 388	92.30 186

cashybrid2		86.09 56	86.04 63	86.24 67	88.17 198	68.05 149	89.44 104	92.79 71	80.30 10	84.71 88	92.78 103	72.83 51	95.05 101	82.81 94	90.57 122	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	93.05 151

fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 83	89.48 140	67.88 156	88.59 148	89.05 242	80.19 13	90.70 20	95.40 17	74.56 30	93.92 155	91.54 2	92.07 93	95.31 6

UniMVSNet_NR-MVSNet		81.88 158	81.54 157	82.92 225	88.46 187	63.46 288	87.13 207	92.37 90	80.19 13	78.38 223	89.14 224	71.66 68	93.05 217	70.05 258	76.46 361	92.25 188

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	88.18 197	67.85 157	87.66 186	89.73 206	80.05 16	82.95 134	89.59 213	70.74 80	94.82 112	80.66 121	84.72 242	93.28 131

ETV-MVS		84.90 90	84.67 90	85.59 88	89.39 145	68.66 129	88.74 141	92.64 80	79.97 17	84.10 107	85.71 325	69.32 102	95.38 84	80.82 116	91.37 107	92.72 164

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 125	87.76 225	65.62 217	89.20 115	92.21 106	79.94 18	89.74 28	94.86 26	68.63 118	94.20 140	90.83 5	91.39 106	94.38 64

EI-MVSNet-UG-set		83.81 108	83.38 119	85.09 106	87.87 215	67.53 169	87.44 199	89.66 207	79.74 19	82.23 147	89.41 222	70.24 86	94.74 118	79.95 129	83.92 257	92.99 156

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 214	87.08 266	65.21 230	89.09 124	90.21 189	79.67 20	89.98 25	95.02 24	73.17 44	91.71 276	91.30 3	91.60 101	92.34 183

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	95.02 103	84.24 78	93.46 73	95.13 11

casdiffmvs_mvg		85.99 60	86.09 62	85.70 83	87.65 233	67.22 183	88.69 144	93.04 48	79.64 22	85.33 78	92.54 106	73.30 41	94.50 128	83.49 84	91.14 111	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	93.23 133

EC-MVSNet		86.01 59	86.38 52	84.91 116	89.31 150	66.27 198	92.32 35	93.63 27	79.37 24	84.17 106	91.88 126	69.04 113	95.43 79	83.93 82	93.77 69	93.01 154

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 73	80.70 119	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	88.06 278	79.31 25	84.39 99	92.18 116	64.64 172	95.53 73	80.70 119	90.91 117	93.21 136

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		80.37 207	77.83 247	88.00 17	94.42 25	73.33 19	92.78 23	92.99 56	79.14 27	83.67 117	12.47 527	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	60.42 244	95.38 84	78.71 151	86.32 210	91.33 221

plane_prior2							91.25 60		79.12 29

IS-MVSNet		83.15 133	82.81 130	84.18 158	89.94 125	63.30 292	91.59 51	88.46 271	79.04 31	79.49 199	92.16 118	65.10 166	94.28 134	67.71 281	91.86 99	94.95 15

DU-MVS		81.12 179	80.52 176	82.90 226	87.80 219	63.46 288	87.02 212	91.87 125	79.01 32	78.38 223	89.07 226	65.02 167	93.05 217	70.05 258	76.46 361	92.20 191

NR-MVSNet		80.23 211	79.38 209	82.78 236	87.80 219	63.34 291	86.31 244	91.09 159	79.01 32	72.17 355	89.07 226	67.20 135	92.81 229	66.08 297	75.65 374	92.20 191

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 93	81.96 104	94.89 46	94.77 30

DELS-MVS		85.41 77	85.30 81	85.77 81	88.49 185	67.93 155	85.52 272	93.44 33	78.70 35	83.63 119	89.03 228	74.57 29	95.71 68	80.26 127	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		79.49 225	79.22 216	80.27 303	88.79 175	58.35 374	85.06 282	88.61 269	78.56 36	77.65 241	88.34 251	63.81 181	90.66 331	64.98 306	77.22 349	91.80 205

plane_prior3						68.60 130			78.44 37	78.92 210

UniMVSNet (Re)		81.60 166	81.11 163	83.09 214	88.38 191	64.41 261	87.60 187	93.02 52	78.42 38	78.56 218	88.16 257	69.78 95	93.26 199	69.58 265	76.49 360	91.60 211

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		75.12 326	75.19 308	74.91 401	90.40 111	45.09 487	80.29 389	78.42 438	78.37 41	76.54 270	87.75 267	44.36 414	87.28 389	57.04 394	83.49 269	92.37 182

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	87.47 245	65.68 215	88.53 152	92.38 89	77.91 43	84.27 103	92.48 107	72.19 58	93.88 160	80.37 122	90.97 114	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	93.78 163	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	87.20 257	65.77 214	87.75 184	92.83 67	77.84 45	84.36 102	92.38 109	72.15 59	93.93 154	81.27 112	90.48 124	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	87.08 266	67.56 168	88.06 172	91.65 138	77.80 46	82.21 148	91.79 129	57.27 271	94.07 146	77.77 162	89.89 137	94.56 55

BP-MVS1		84.32 93	83.71 110	86.17 70	87.84 217	67.85 157	89.38 110	89.64 209	77.73 47	83.98 110	92.12 121	56.89 276	95.43 79	84.03 81	91.75 100	95.24 8

CP-MVSNet		78.22 260	78.34 234	77.84 365	87.83 218	54.54 433	87.94 177	91.17 155	77.65 48	73.48 336	88.49 247	62.24 207	88.43 374	62.19 339	74.07 397	90.55 252

plane_prior						68.71 125	90.38 78		77.62 49						86.16 215

baseline		84.93 88	84.98 85	84.80 122	87.30 255	65.39 223	87.30 204	92.88 64	77.62 49	84.04 109	92.26 111	71.81 63	93.96 148	81.31 110	90.30 127	95.03 13

VDD-MVS		83.01 138	82.36 140	84.96 111	91.02 97	66.40 195	88.91 129	88.11 274	77.57 51	84.39 99	93.29 86	52.19 319	93.91 156	77.05 172	88.70 159	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		77.73 275	77.69 255	77.84 365	87.07 268	53.91 438	87.91 179	91.18 154	77.56 53	73.14 340	88.82 237	61.23 228	89.17 359	59.95 362	72.37 412	90.43 257

OPM-MVS		83.50 123	82.95 128	85.14 101	88.79 175	70.95 77	89.13 122	91.52 144	77.55 54	80.96 174	91.75 132	60.71 236	94.50 128	79.67 138	86.51 207	89.97 284
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		78.01 269	78.09 239	77.77 367	87.71 228	54.39 435	88.02 173	91.22 152	77.50 56	73.26 338	88.64 242	60.73 235	88.41 375	61.88 344	73.88 401	90.53 253

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	92.94 221	80.36 123	94.35 63	90.16 268

RRT-MVS		82.60 146	82.10 147	84.10 160	87.98 211	62.94 305	87.45 194	91.27 151	77.42 58	79.85 194	90.28 191	56.62 279	94.70 121	79.87 135	88.15 172	94.67 42

fmvsm_s_conf0.5_n_11		86.06 57	86.75 47	84.00 177	87.78 222	66.09 200	89.96 86	90.80 168	77.37 59	86.72 67	94.20 52	72.51 54	92.78 230	89.08 22	92.33 88	93.13 145

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	93.33 129
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	87.94 212	72.94 28	90.64 68	92.14 114	77.21 67	75.47 291	92.83 98	58.56 258	94.72 119	73.24 220	92.71 82	92.13 198

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		78.51 255	78.49 229	78.56 349	88.02 208	56.38 409	88.43 154	92.67 75	77.14 69	73.89 330	87.55 275	66.25 150	89.24 357	58.92 374	73.55 404	90.06 278

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		81.52 170	82.02 150	80.03 310	88.42 190	55.97 415	87.95 176	93.42 35	77.10 72	77.38 246	90.98 169	69.96 92	91.79 271	68.46 277	84.50 245	92.33 184

DTE-MVSNet		76.99 291	76.80 275	77.54 374	86.24 287	53.06 448	87.52 189	90.66 171	77.08 73	72.50 349	88.67 241	60.48 243	89.52 351	57.33 391	70.74 424	90.05 279

LFMVS		81.82 160	81.23 160	83.57 193	91.89 84	63.43 290	89.84 87	81.85 397	77.04 74	83.21 127	93.10 89	52.26 318	93.43 192	71.98 237	89.95 135	93.85 94

casdiffseed414692147		83.62 119	83.02 125	85.40 93	87.31 254	67.50 170	88.70 143	91.72 134	76.97 75	82.77 141	91.72 133	66.85 140	93.71 170	73.06 222	88.12 173	94.98 14

UGNet		80.83 185	79.59 204	84.54 129	88.04 207	68.09 146	89.42 107	88.16 273	76.95 76	76.22 277	89.46 218	49.30 370	93.94 151	68.48 276	90.31 126	91.60 211
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		82.07 154	82.42 137	81.04 284	88.80 174	58.34 375	88.26 165	93.49 32	76.93 77	78.47 222	91.04 164	69.92 93	92.34 250	69.87 262	84.97 237	92.44 181

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	93.38 125

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		78.69 250	78.66 226	78.76 344	88.31 193	55.72 419	84.45 302	86.63 323	76.79 81	78.26 226	90.55 183	59.30 252	89.70 349	66.63 292	77.05 351	90.88 237

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	90.69 120	93.23 133
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	93.91 156	81.50 107	88.80 155	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	93.91 156	81.50 107	88.80 155	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	93.23 133

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	80.82 116	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	93.13 145

E5new		84.22 94	84.12 97	84.51 132	87.60 235	65.36 225	87.45 194	92.31 93	76.51 91	83.53 120	92.26 111	69.25 106	93.50 183	79.88 131	88.26 166	94.69 37

E6new		84.22 94	84.12 97	84.52 130	87.60 235	65.36 225	87.45 194	92.30 95	76.51 91	83.53 120	92.26 111	69.26 104	93.49 185	79.88 131	88.26 166	94.69 37

E6		84.22 94	84.12 97	84.52 130	87.60 235	65.36 225	87.45 194	92.30 95	76.51 91	83.53 120	92.26 111	69.26 104	93.49 185	79.88 131	88.26 166	94.69 37

E5		84.22 94	84.12 97	84.51 132	87.60 235	65.36 225	87.45 194	92.31 93	76.51 91	83.53 120	92.26 111	69.25 106	93.50 183	79.88 131	88.26 166	94.69 37

MGCFI-Net		85.06 87	85.51 75	83.70 188	89.42 142	63.01 299	89.43 105	92.62 81	76.43 95	87.53 55	91.34 151	72.82 52	93.42 193	81.28 111	88.74 158	94.66 45

TSAR-MVS + GP.		85.71 70	85.33 79	86.84 57	91.34 90	72.50 36	89.07 125	87.28 300	76.41 96	85.80 73	90.22 195	74.15 37	95.37 87	81.82 105	91.88 96	92.65 169

HQP-NCC					89.33 147		89.17 117		76.41 96	77.23 251

ACMP_Plane					89.33 147		89.17 117		76.41 96	77.23 251

HQP-MVS		82.61 144	82.02 150	84.37 142	89.33 147	66.98 187	89.17 117	92.19 109	76.41 96	77.23 251	90.23 194	60.17 247	95.11 96	77.47 166	85.99 221	91.03 231

E4		84.10 100	83.99 103	84.45 137	87.58 243	64.99 239	86.54 234	92.25 100	76.38 100	83.37 125	92.09 122	69.88 94	93.58 172	79.78 136	88.03 177	94.77 30

CANet_DTU		80.61 196	79.87 194	82.83 229	85.60 303	63.17 297	87.36 201	88.65 267	76.37 101	75.88 284	88.44 249	53.51 307	93.07 215	73.30 218	89.74 139	92.25 188

VNet		82.21 151	82.41 138	81.62 265	90.82 102	60.93 343	84.47 299	89.78 201	76.36 102	84.07 108	91.88 126	64.71 171	90.26 337	70.68 250	88.89 153	93.66 107

Vis-MVSNet		83.46 124	82.80 131	85.43 92	90.25 114	68.74 123	90.30 80	90.13 192	76.33 103	80.87 177	92.89 96	61.00 233	94.20 140	72.45 234	90.97 114	93.35 128
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ME-MVS		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	87.33 251	67.30 178	89.50 101	90.98 160	76.25 106	90.56 23	94.75 29	68.38 121	94.24 139	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	93.78 163	81.51 106	88.95 152	94.63 48

MVS_111021_HR		85.14 83	84.75 89	86.32 66	91.65 87	72.70 30	85.98 254	90.33 184	76.11 108	82.08 150	91.61 142	71.36 72	94.17 143	81.02 113	92.58 83	92.08 199

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	89.16 236	76.02 110	84.67 90	91.39 150	61.54 219	95.50 75	82.71 98	75.48 378	91.72 210

hse-mvs2		81.72 161	80.94 167	84.07 166	88.72 178	67.68 163	85.87 258	87.26 305	76.02 110	84.67 90	88.22 256	61.54 219	93.48 188	82.71 98	73.44 406	91.06 229

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	87.70 230	64.95 240	86.40 241	92.23 101	75.85 113	83.21 127	91.78 130	70.09 89	93.55 177	79.52 140	88.05 175	94.66 45

E3		84.00 103	83.87 104	84.39 140	87.70 230	64.95 240	86.40 241	92.23 101	75.85 113	83.21 127	91.78 130	70.09 89	93.55 177	79.52 140	88.05 175	94.66 45

CLD-MVS		82.31 149	81.65 156	84.29 150	88.47 186	67.73 161	85.81 262	92.35 91	75.78 115	78.33 225	86.58 306	64.01 178	94.35 132	76.05 187	87.48 188	90.79 240
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	86.59 281	64.56 253	86.88 219	91.82 128	75.72 116	83.34 126	92.15 120	68.24 125	92.88 224	79.05 143	89.15 150	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		80.60 198	80.55 175	80.76 291	88.07 206	60.80 346	86.86 220	91.58 143	75.67 120	80.24 190	89.45 220	63.34 182	90.25 338	70.51 252	79.22 328	91.23 224

fmvsm_l_conf0.5_n_9		85.84 67	86.63 49	83.46 195	87.12 265	66.01 203	88.56 150	89.43 216	75.59 121	89.32 29	94.32 44	72.89 48	91.21 305	90.11 11	92.33 88	93.16 141

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	88.38 191	67.45 171	88.89 130	89.15 238	75.50 123	82.27 146	88.28 253	69.61 98	94.45 131	77.81 161	87.84 180	93.84 96

viewcassd2359sk11		83.89 106	83.74 109	84.34 145	87.76 225	64.91 247	86.30 245	92.22 104	75.47 124	83.04 133	91.52 144	70.15 87	93.53 180	79.26 142	87.96 178	94.57 53

fmvsm_s_conf0.5_n_4		85.39 78	85.75 71	84.30 149	86.70 277	65.83 210	88.77 137	89.78 201	75.46 125	88.35 38	93.73 74	69.19 108	93.06 216	91.30 3	88.44 164	94.02 85

fmvsm_s_conf0.5_n_6		85.55 73	86.20 56	83.60 190	87.32 253	65.13 233	88.86 131	91.63 139	75.41 126	88.23 42	93.45 82	68.56 119	92.47 242	89.52 18	92.78 80	93.20 138

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		82.08 153	81.27 159	84.50 134	89.23 155	68.76 121	90.22 81	91.94 121	75.37 128	76.64 266	91.51 145	54.29 298	94.91 105	78.44 153	83.78 258	89.83 289

LGP-MVS_train				84.50 134	89.23 155	68.76 121		91.94 121	75.37 128	76.64 266	91.51 145	54.29 298	94.91 105	78.44 153	83.78 258	89.83 289

fmvsm_l_conf0.5_n_3		86.02 58	86.32 53	85.14 101	87.20 257	68.54 132	89.57 99	90.44 178	75.31 130	87.49 56	94.39 42	72.86 49	92.72 231	89.04 27	90.56 123	94.16 76

nocashy02		82.38 147	82.11 145	83.19 209	83.30 360	64.26 264	84.62 295	89.16 236	75.24 131	80.97 173	91.10 160	67.12 137	91.63 277	81.36 109	86.13 216	93.67 106

MED-MVS test				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	86.51 283	62.58 309	85.09 281	90.83 167	75.22 133	82.28 145	91.63 139	69.43 100	92.03 259	77.71 163	86.32 210	94.34 67

MG-MVS		83.41 125	83.45 117	83.28 203	92.74 73	62.28 318	88.17 168	89.50 214	75.22 133	81.49 161	92.74 105	66.75 141	95.11 96	72.85 224	91.58 103	92.45 180

SSC-MVS3.2		73.35 350	73.39 332	73.23 419	85.30 312	49.01 472	74.58 453	81.57 399	75.21 135	73.68 333	85.58 331	52.53 312	82.05 438	54.33 413	77.69 345	88.63 332

LCM-MVSNet-Re		77.05 290	76.94 272	77.36 375	87.20 257	51.60 457	80.06 392	80.46 415	75.20 136	67.69 409	86.72 296	62.48 201	88.98 363	63.44 316	89.25 146	91.51 215

SDMVSNet		80.38 205	80.18 184	80.99 285	89.03 164	64.94 243	80.45 386	89.40 217	75.19 137	76.61 268	89.98 197	60.61 241	87.69 384	76.83 177	83.55 267	90.33 262

sd_testset		77.70 278	77.40 262	78.60 347	89.03 164	60.02 360	79.00 408	85.83 337	75.19 137	76.61 268	89.98 197	54.81 290	85.46 409	62.63 332	83.55 267	90.33 262

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	87.76 225	64.89 248	86.24 248	92.20 107	75.15 140	82.87 136	91.23 153	70.11 88	93.52 182	79.05 143	87.79 181	94.51 58

test1111		79.43 228	79.18 217	80.15 308	89.99 123	53.31 444	87.33 203	77.05 450	75.04 141	80.23 191	92.77 104	48.97 375	92.33 251	68.87 272	92.40 87	94.81 27

Effi-MVS+-dtu		80.03 216	78.57 228	84.42 139	85.13 318	68.74 123	88.77 137	88.10 275	74.99 142	74.97 315	83.49 383	57.27 271	93.36 194	73.53 214	80.88 303	91.18 225

reproduce-ours		87.47 27	87.61 27	87.07 51	93.27 55	71.60 56	91.56 54	93.19 42	74.98 143	88.96 31	95.54 14	71.20 74	96.54 42	86.28 55	93.49 71	93.06 149

our_new_method		87.47 27	87.61 27	87.07 51	93.27 55	71.60 56	91.56 54	93.19 42	74.98 143	88.96 31	95.54 14	71.20 74	96.54 42	86.28 55	93.49 71	93.06 149

fmvsm_s_conf0.5_n_7		83.34 128	84.03 102	81.28 276	85.73 299	65.13 233	85.40 273	89.90 199	74.96 145	82.13 149	93.89 69	66.65 142	87.92 380	86.56 54	91.05 112	90.80 239

OMC-MVS		82.69 142	81.97 152	84.85 119	88.75 177	67.42 172	87.98 174	90.87 165	74.92 146	79.72 196	91.65 137	62.19 208	93.96 148	75.26 199	86.42 208	93.16 141

viewmanbaseed2359cas		83.66 115	83.55 115	84.00 177	86.81 273	64.53 254	86.65 229	91.75 133	74.89 147	83.15 132	91.68 135	68.74 117	92.83 228	79.02 145	89.24 147	94.63 48

test2506		77.30 287	76.49 283	79.74 323	90.08 118	52.02 450	87.86 182	63.10 494	74.88 148	80.16 192	92.79 101	38.29 455	92.35 249	68.74 274	92.50 85	94.86 22

ECVR-MVS		79.61 221	79.26 214	80.67 293	90.08 118	54.69 431	87.89 180	77.44 446	74.88 148	80.27 189	92.79 101	48.96 376	92.45 243	68.55 275	92.50 85	94.86 22

MonoMVSNet		76.49 302	75.80 291	78.58 348	81.55 405	58.45 373	86.36 243	86.22 330	74.87 150	74.73 319	83.73 376	51.79 333	88.73 368	70.78 247	72.15 415	88.55 335

nrg030		83.88 107	83.53 116	84.96 111	86.77 275	69.28 111	90.46 75	92.67 75	74.79 151	82.95 134	91.33 152	72.70 53	93.09 214	80.79 118	79.28 327	92.50 176

SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 36	73.73 9	92.40 29	93.63 27	74.77 152	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	74.69 153	88.80 35	95.61 13	70.29 85	96.44 45	86.20 57	93.08 75	93.16 141

MVS_111021_LR		82.61 144	82.11 145	84.11 159	88.82 169	71.58 58	85.15 278	86.16 332	74.69 153	80.47 187	91.04 164	62.29 205	90.55 332	80.33 125	90.08 132	90.20 267

EIA-MVS		83.31 131	82.80 131	84.82 120	89.59 133	65.59 218	88.21 166	92.68 74	74.66 155	78.96 208	86.42 311	69.06 111	95.26 89	75.54 195	90.09 131	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	74.62 156	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	74.50 157	86.84 66	94.65 31	67.31 134	95.77 66	84.80 69	92.85 79	92.84 163

FOURS1					95.00 10	72.39 41	95.06 1	93.84 21	74.49 158	91.30 17

ACMP	74.13 6	81.51 172	80.57 174	84.36 143	89.42 142	68.69 128	89.97 85	91.50 148	74.46 159	75.04 313	90.41 186	53.82 304	94.54 125	77.56 165	82.91 278	89.86 288
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	64.47 259	92.32 35	90.73 170	74.45 160	79.35 204	91.10 160	69.05 112	95.12 94	72.78 225	87.22 192	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	74.41 161	80.41 188	90.82 172	62.90 196	94.90 107	83.04 90	91.37 107	94.32 69

fmvsm_s_conf0.5_n_2		84.04 101	84.11 101	83.81 186	86.17 290	65.00 238	86.96 214	87.28 300	74.35 162	88.25 41	94.23 50	61.82 214	92.60 234	89.85 12	88.09 174	93.84 96

fmvsm_s_conf0.1_n_2		83.80 109	83.79 108	83.83 184	85.62 302	64.94 243	87.03 211	86.62 324	74.32 163	87.97 49	94.33 43	60.67 238	92.60 234	89.72 14	87.79 181	93.96 87

save fliter					93.80 45	72.35 44	90.47 74	91.17 155	74.31 164

MVS_Test		83.15 133	83.06 124	83.41 200	86.86 270	63.21 294	86.11 252	92.00 117	74.31 164	82.87 136	89.44 221	70.03 91	93.21 203	77.39 168	88.50 163	93.81 98

myMVS_eth3d28		73.62 341	73.53 331	73.90 415	88.20 196	47.41 477	78.06 423	79.37 430	74.29 166	73.98 329	84.29 360	44.67 410	83.54 426	51.47 427	87.39 189	90.74 244

UniMVSNet_ETH3D		79.10 239	78.24 237	81.70 264	86.85 271	60.24 358	87.28 205	88.79 254	74.25 167	76.84 259	90.53 184	49.48 365	91.56 283	67.98 279	82.15 287	93.29 130

IterMVS-LS		80.06 214	79.38 209	82.11 255	85.89 295	63.20 295	86.79 223	89.34 219	74.19 168	75.45 294	86.72 296	66.62 143	92.39 246	72.58 227	76.86 354	90.75 243
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 202	79.98 190	82.12 253	84.28 335	63.19 296	86.41 238	88.95 249	74.18 169	78.69 213	87.54 276	66.62 143	92.43 244	72.57 228	80.57 309	90.74 244

Vis-MVSNet (Re-imp)		78.36 258	78.45 230	78.07 361	88.64 181	51.78 456	86.70 227	79.63 428	74.14 170	75.11 310	90.83 171	61.29 227	89.75 347	58.10 384	91.60 101	92.69 167

v8		79.97 218	79.02 220	82.80 232	84.09 340	64.50 258	87.96 175	90.29 187	74.13 171	75.24 306	86.81 293	62.88 197	93.89 159	74.39 207	75.40 383	90.00 280

guyue		81.13 178	80.64 173	82.60 243	86.52 282	63.92 272	86.69 228	87.73 290	73.97 172	80.83 179	89.69 207	56.70 277	91.33 299	78.26 160	85.40 234	92.54 172

onestephybrid01		82.22 150	81.81 155	83.46 195	83.16 368	64.93 246	84.64 294	89.19 235	73.95 173	81.48 162	90.63 178	66.00 158	91.92 267	80.33 125	86.93 198	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	73.95 173	83.16 131	91.07 163	75.94 23	95.19 91	79.94 130	94.38 62	93.55 119

thres100view900		76.50 299	75.55 298	79.33 334	89.52 136	56.99 398	85.83 261	83.23 373	73.94 175	76.32 275	87.12 288	51.89 330	91.95 264	48.33 447	83.75 261	89.07 307

9.14			88.26 19		92.84 71		91.52 56	94.75 1	73.93 176	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	73.89 177	82.67 143	94.09 57	62.60 198	95.54 72	80.93 114	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	73.82 178	77.32 248	90.66 177	67.90 128	94.90 107	70.37 253	89.48 144	93.19 139

thres600view7		76.50 299	75.44 299	79.68 326	89.40 144	57.16 395	85.53 270	83.23 373	73.79 179	76.26 276	87.09 289	51.89 330	91.89 268	48.05 452	83.72 264	90.00 280

testing91		76.54 297	75.66 296	79.18 338	88.43 189	55.89 416	81.08 373	83.00 380	73.76 180	75.34 299	84.29 360	46.20 398	90.07 341	64.33 310	84.50 245	91.58 213

AstraMVS		80.81 186	80.14 187	82.80 232	86.05 294	63.96 269	86.46 237	85.90 336	73.71 181	80.85 178	90.56 182	54.06 302	91.57 282	79.72 137	83.97 256	92.86 161

v7n		78.97 243	77.58 258	83.14 212	83.45 357	65.51 219	88.32 162	91.21 153	73.69 182	72.41 351	86.32 314	57.93 262	93.81 162	69.18 268	75.65 374	90.11 272

dcpmvs_2		85.63 71	86.15 60	84.06 169	91.71 86	64.94 243	86.47 236	91.87 125	73.63 183	86.60 69	93.02 94	76.57 20	91.87 270	83.36 85	92.15 91	95.35 4

v2v482		80.23 211	79.29 213	83.05 218	83.62 353	64.14 266	87.04 210	89.97 196	73.61 184	78.18 229	87.22 284	61.10 231	93.82 161	76.11 185	76.78 357	91.18 225

Baseline_NR-MVSNet		78.15 264	78.33 235	77.61 371	85.79 297	56.21 413	86.78 224	85.76 338	73.60 185	77.93 235	87.57 273	65.02 167	88.99 362	67.14 289	75.33 385	87.63 355

BH-RMVSNet		79.61 221	78.44 231	83.14 212	89.38 146	65.93 206	84.95 285	87.15 308	73.56 186	78.19 228	89.79 205	56.67 278	93.36 194	59.53 367	86.74 203	90.13 270

APD-MVS_3200maxsize		85.97 62	85.88 66	86.22 69	92.69 74	69.53 101	91.93 42	92.99 56	73.54 187	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	73.53 188	85.69 75	94.45 37	65.00 169	95.56 70	82.75 96	91.87 97	92.50 176

RE-MVS-def			85.48 76		93.06 65	70.63 84	91.88 43	92.27 97	73.53 188	85.69 75	94.45 37	63.87 179		82.75 96	91.87 97	92.50 176

reproduce_monomvs		75.40 322	74.38 320	78.46 354	83.92 345	57.80 386	83.78 319	86.94 314	73.47 190	72.25 354	84.47 354	38.74 451	89.27 356	75.32 198	70.53 425	88.31 339

test_fmvsmconf_n		85.92 63	86.04 63	85.57 89	85.03 321	69.51 102	89.62 98	90.58 173	73.42 191	87.75 52	94.02 61	72.85 50	93.24 200	90.37 8	90.75 119	93.96 87

tfpn200view9		76.42 305	75.37 303	79.55 331	89.13 159	57.65 389	85.17 276	83.60 365	73.41 192	76.45 271	86.39 312	52.12 320	91.95 264	48.33 447	83.75 261	89.07 307

thres400		76.50 299	75.37 303	79.86 316	89.13 159	57.65 389	85.17 276	83.60 365	73.41 192	76.45 271	86.39 312	52.12 320	91.95 264	48.33 447	83.75 261	90.00 280

diffmvs_AUTHOR		82.38 147	82.27 143	82.73 240	83.26 362	63.80 274	83.89 317	89.76 203	73.35 194	82.37 144	90.84 170	66.25 150	90.79 324	82.77 95	87.93 179	93.59 116

test_fmvsmconf0.1_n		85.61 72	85.65 72	85.50 90	82.99 378	69.39 109	89.65 95	90.29 187	73.31 195	87.77 51	94.15 55	71.72 65	93.23 201	90.31 9	90.67 121	93.89 93

testing99		76.09 311	75.12 310	79.00 339	88.16 199	55.50 422	80.79 377	81.40 402	73.30 196	75.17 307	84.27 363	44.48 413	90.02 342	64.28 311	84.22 254	91.48 218

v148		78.72 249	77.80 249	81.47 269	82.73 384	61.96 324	86.30 245	88.08 276	73.26 197	76.18 279	85.47 334	62.46 202	92.36 248	71.92 238	73.82 402	90.09 274

FA-MVS(test-final)		80.96 182	79.91 192	84.10 160	88.30 194	65.01 237	84.55 298	90.01 195	73.25 198	79.61 197	87.57 273	58.35 260	94.72 119	71.29 243	86.25 213	92.56 171

test_fmvsmconf0.01_n		84.73 91	84.52 93	85.34 95	80.25 422	69.03 112	89.47 102	89.65 208	73.24 199	86.98 64	94.27 47	66.62 143	93.23 201	90.26 10	89.95 135	93.78 102

viewdifsd2359ckpt11		80.37 207	79.73 198	82.30 250	83.70 351	62.39 313	84.20 311	86.67 320	73.22 200	80.90 175	90.62 179	63.00 194	91.56 283	76.81 178	78.44 334	92.95 158

viewmsd2359difaftdt		80.37 207	79.73 198	82.30 250	83.70 351	62.39 313	84.20 311	86.67 320	73.22 200	80.90 175	90.62 179	63.00 194	91.56 283	76.81 178	78.44 334	92.95 158

v10		79.74 220	78.67 225	82.97 224	84.06 341	64.95 240	87.88 181	90.62 172	73.11 202	75.11 310	86.56 307	61.46 222	94.05 147	73.68 212	75.55 376	89.90 286

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 22	72.46 40	89.82 88	93.82 22	73.07 203	84.86 87	92.89 96	76.22 22	96.33 47	84.89 67	95.13 40	94.40 63

baseline1		76.98 292	76.75 279	77.66 369	88.13 202	55.66 420	85.12 279	81.89 395	73.04 204	76.79 261	88.90 234	62.43 203	87.78 383	63.30 318	71.18 422	89.55 298

APD-MVS		87.44 29	87.52 30	87.19 48	94.24 37	72.39 41	91.86 45	92.83 67	73.01 205	88.58 36	94.52 32	73.36 40	96.49 44	84.26 76	95.01 41	92.70 165
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 152	81.88 153	82.76 238	83.00 374	63.78 276	83.68 322	89.76 203	72.94 206	82.02 151	89.85 200	65.96 159	90.79 324	82.38 102	87.30 191	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		71.19 376	68.51 389	79.21 337	83.04 373	57.78 387	84.35 308	76.91 451	72.90 207	62.99 456	82.86 395	39.27 447	91.09 311	61.65 348	52.66 485	88.75 327

MVSMamba_PlusPlus		85.99 60	85.96 65	86.05 75	91.09 94	67.64 164	89.63 97	92.65 78	72.89 208	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	88.14 201	68.45 134	89.13 122	92.69 73	72.82 209	83.71 115	91.86 128	55.69 285	95.35 88	80.03 128	89.74 139	94.69 37

hybridnocas07		81.44 173	81.13 162	82.37 248	82.13 395	63.11 298	83.45 331	88.74 261	72.54 210	80.71 181	90.73 173	65.14 165	90.74 329	80.35 124	86.41 209	93.27 132

fmvsm_s_conf0.5_n_5		85.22 82	85.55 74	84.25 156	86.26 286	67.40 174	89.18 116	89.31 225	72.50 211	88.31 39	93.86 70	69.66 97	91.96 263	89.81 13	91.05 112	93.38 125

Fast-Effi-MVS+-dtu		78.02 268	76.49 283	82.62 242	83.16 368	66.96 189	86.94 216	87.45 297	72.45 212	71.49 363	84.17 367	54.79 294	91.58 280	67.61 282	80.31 312	89.30 305

PHI-MVS		86.43 49	86.17 59	87.24 47	90.88 101	70.96 76	92.27 37	94.07 14	72.45 212	85.22 80	91.90 125	69.47 99	96.42 46	83.28 87	95.94 23	94.35 66

thres200		75.55 317	74.47 318	78.82 343	87.78 222	57.85 384	83.07 344	83.51 368	72.44 214	75.84 285	84.42 355	52.08 323	91.75 273	47.41 454	83.64 266	86.86 386

test_yl		81.17 176	80.47 178	83.24 206	89.13 159	63.62 277	86.21 249	89.95 197	72.43 215	81.78 156	89.61 211	57.50 268	93.58 172	70.75 248	86.90 199	92.52 174

DCV-MVSNet		81.17 176	80.47 178	83.24 206	89.13 159	63.62 277	86.21 249	89.95 197	72.43 215	81.78 156	89.61 211	57.50 268	93.58 172	70.75 248	86.90 199	92.52 174

viewdifsd2359ckpt13		82.91 139	82.29 142	84.77 123	86.96 269	66.90 191	87.47 191	91.62 140	72.19 217	81.68 158	90.71 175	66.92 139	93.28 196	75.90 189	87.15 194	94.12 79

BH-untuned		79.47 226	78.60 227	82.05 256	89.19 157	65.91 207	86.07 253	88.52 270	72.18 218	75.42 295	87.69 270	61.15 230	93.54 179	60.38 359	86.83 202	86.70 391

TransMVSNet (Re)		75.39 323	74.56 316	77.86 364	85.50 307	57.10 397	86.78 224	86.09 334	72.17 219	71.53 362	87.34 279	63.01 193	89.31 355	56.84 397	61.83 466	87.17 376

GA-MVS		76.87 294	75.17 309	81.97 259	82.75 383	62.58 309	81.44 368	86.35 329	72.16 220	74.74 318	82.89 394	46.20 398	92.02 261	68.85 273	81.09 300	91.30 223

VortexMVS		78.57 254	77.89 245	80.59 294	85.89 295	62.76 307	85.61 263	89.62 210	72.06 221	74.99 314	85.38 336	55.94 284	90.77 327	74.99 200	76.58 358	88.23 342

mmtdpeth		74.16 334	73.01 338	77.60 373	83.72 350	61.13 336	85.10 280	85.10 345	72.06 221	77.21 255	80.33 424	43.84 418	85.75 403	77.14 171	52.61 486	85.91 407

v1144		80.03 216	79.03 219	83.01 220	83.78 348	64.51 256	87.11 209	90.57 175	71.96 223	78.08 232	86.20 317	61.41 223	93.94 151	74.93 201	77.23 348	90.60 250

viewdifsd2359ckpt09		83.34 128	82.55 136	85.70 83	87.64 234	67.72 162	88.43 154	91.68 137	71.91 224	81.65 159	90.68 176	67.10 138	94.75 117	76.17 184	87.70 184	94.62 50

PS-MVSNAJss		82.07 154	81.31 158	84.34 145	86.51 283	67.27 180	89.27 113	91.51 145	71.75 225	79.37 203	90.22 195	63.15 189	94.27 135	77.69 164	82.36 286	91.49 217

EPNet_dtu		75.46 319	74.86 311	77.23 378	82.57 388	54.60 432	86.89 218	83.09 377	71.64 226	66.25 432	85.86 323	55.99 283	88.04 379	54.92 409	86.55 206	89.05 312
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 256	77.40 262	81.40 272	87.60 235	63.01 299	88.39 157	89.28 226	71.63 227	75.34 299	87.28 280	54.80 291	91.11 306	62.72 328	79.57 319	90.09 274

test1		78.40 256	77.40 262	81.40 272	87.60 235	63.01 299	88.39 157	89.28 226	71.63 227	75.34 299	87.28 280	54.80 291	91.11 306	62.72 328	79.57 319	90.09 274

FMVSNet2		78.20 262	77.21 266	81.20 279	87.60 235	62.89 306	87.47 191	89.02 244	71.63 227	75.29 305	87.28 280	54.80 291	91.10 309	62.38 336	79.38 325	89.61 296

patch_mono-2		83.65 116	84.54 91	80.99 285	90.06 122	65.83 210	84.21 310	88.74 261	71.60 230	85.01 81	92.44 108	74.51 31	83.50 427	82.15 103	92.15 91	93.64 113

V42		79.38 232	78.24 237	82.83 229	81.10 414	65.50 220	85.55 268	89.82 200	71.57 231	78.21 227	86.12 319	60.66 239	93.18 209	75.64 192	75.46 380	89.81 291

API-MVS		81.99 156	81.23 160	84.26 155	90.94 99	70.18 93	91.10 63	89.32 224	71.51 232	78.66 215	88.28 253	65.26 163	95.10 99	64.74 308	91.23 110	87.51 361

tttt0517		79.40 230	77.91 243	83.90 183	88.10 204	63.84 273	88.37 160	84.05 360	71.45 233	76.78 262	89.12 225	49.93 361	94.89 109	70.18 257	83.18 276	92.96 157

pm-mvs1		77.25 288	76.68 281	78.93 341	84.22 337	58.62 372	86.41 238	88.36 272	71.37 234	73.31 337	88.01 263	61.22 229	89.15 360	64.24 312	73.01 409	89.03 313

Elysia		81.53 168	80.16 185	85.62 86	85.51 305	68.25 141	88.84 134	92.19 109	71.31 235	80.50 185	89.83 201	46.89 387	94.82 112	76.85 174	89.57 141	93.80 100

StellarMVS		81.53 168	80.16 185	85.62 86	85.51 305	68.25 141	88.84 134	92.19 109	71.31 235	80.50 185	89.83 201	46.89 387	94.82 112	76.85 174	89.57 141	93.80 100

testing222		74.04 336	72.66 342	78.19 357	87.89 214	55.36 423	81.06 374	79.20 433	71.30 237	74.65 321	83.57 382	39.11 450	88.67 370	51.43 429	85.75 228	90.53 253

GeoE		81.71 162	81.01 166	83.80 187	89.51 137	64.45 260	88.97 127	88.73 263	71.27 238	78.63 216	89.76 206	66.32 149	93.20 206	69.89 261	86.02 220	93.74 103

tt0805		78.73 248	77.83 247	81.43 270	85.17 314	60.30 357	89.41 108	90.90 163	71.21 239	77.17 256	88.73 238	46.38 393	93.21 203	72.57 228	78.96 329	90.79 240

FMVSNet3		77.88 272	76.85 274	80.97 287	86.84 272	62.36 315	86.52 235	88.77 255	71.13 240	75.34 299	86.66 302	54.07 301	91.10 309	62.72 328	79.57 319	89.45 300

VDDNet		81.52 170	80.67 171	84.05 172	90.44 110	64.13 267	89.73 93	85.91 335	71.11 241	83.18 130	93.48 79	50.54 351	93.49 185	73.40 217	88.25 170	94.54 57

fmvsm_s_conf0.5_n		83.80 109	83.71 110	84.07 166	86.69 278	67.31 177	89.46 103	83.07 378	71.09 242	86.96 65	93.70 75	69.02 114	91.47 293	88.79 30	84.62 244	93.44 124

XVG-OURS		80.41 203	79.23 215	83.97 180	85.64 301	69.02 114	83.03 346	90.39 179	71.09 242	77.63 242	91.49 147	54.62 297	91.35 297	75.71 191	83.47 270	91.54 214

SSM_0407		81.58 167	80.48 177	84.87 118	88.81 170	67.96 152	87.37 200	89.25 230	71.06 244	79.48 200	90.39 188	59.57 249	94.48 130	72.45 234	85.93 223	92.18 193

SSM_0404		81.91 157	80.84 169	85.13 104	89.24 154	68.26 139	87.84 183	89.25 230	71.06 244	80.62 182	90.39 188	59.57 249	94.65 123	72.45 234	87.19 193	92.47 179

SixPastTwentyTwo		73.37 347	71.26 360	79.70 325	85.08 319	57.89 383	85.57 264	83.56 367	71.03 246	65.66 436	85.88 322	42.10 430	92.57 236	59.11 372	63.34 460	88.65 331

ZD-MVS					94.38 30	72.22 46		92.67 75	70.98 247	87.75 52	94.07 58	74.01 38	96.70 32	84.66 71	94.84 48

mamba_0408		79.37 233	77.52 259	84.93 114	88.81 170	67.96 152	65.03 490	88.66 265	70.96 248	79.48 200	89.80 203	58.69 255	94.65 123	70.35 254	85.93 223	92.18 193

SSM_04072		77.67 280	77.52 259	78.12 359	88.81 170	67.96 152	65.03 490	88.66 265	70.96 248	79.48 200	89.80 203	58.69 255	74.23 484	70.35 254	85.93 223	92.18 193

v1192		79.59 223	78.43 232	83.07 217	83.55 355	64.52 255	86.93 217	90.58 173	70.83 250	77.78 239	85.90 321	59.15 253	93.94 151	73.96 211	77.19 350	90.76 242

Fast-Effi-MVS+		80.81 186	79.92 191	83.47 194	88.85 166	64.51 256	85.53 270	89.39 218	70.79 251	78.49 220	85.06 345	67.54 131	93.58 172	67.03 291	86.58 205	92.32 185

PS-MVSNAJ		81.69 163	81.02 165	83.70 188	89.51 137	68.21 144	84.28 309	90.09 193	70.79 251	81.26 168	85.62 330	63.15 189	94.29 133	75.62 193	88.87 154	88.59 333

hybrid		81.05 180	80.66 172	82.22 252	81.97 397	62.99 303	83.42 332	88.68 264	70.76 253	80.56 184	90.40 187	64.49 174	90.48 333	79.57 139	86.06 218	93.19 139

LTVRE_ROB	69.57 13	76.25 308	74.54 317	81.41 271	88.60 182	64.38 262	79.24 403	89.12 241	70.76 253	69.79 384	87.86 266	49.09 373	93.20 206	56.21 403	80.16 313	86.65 393
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		75.14 325	74.01 323	78.53 351	88.16 199	56.38 409	80.74 380	80.42 417	70.67 255	72.69 348	83.72 377	43.61 420	89.86 344	62.29 338	83.76 260	89.36 303

fmvsm_s_conf0.1_n		83.56 121	83.38 119	84.10 160	84.86 323	67.28 179	89.40 109	83.01 379	70.67 255	87.08 62	93.96 67	68.38 121	91.45 294	88.56 35	84.50 245	93.56 118

xiu_mvs_v2_base		81.69 163	81.05 164	83.60 190	89.15 158	68.03 150	84.46 301	90.02 194	70.67 255	81.30 167	86.53 309	63.17 188	94.19 142	75.60 194	88.54 161	88.57 334

XVG-OURS-SEG-HR		80.81 186	79.76 197	83.96 181	85.60 303	68.78 120	83.54 330	90.50 176	70.66 258	76.71 264	91.66 136	60.69 237	91.26 300	76.94 173	81.58 295	91.83 203

Anonymous202405211		78.25 259	77.01 269	81.99 258	91.03 96	60.67 350	84.77 288	83.90 362	70.65 259	80.00 193	91.20 157	41.08 437	91.43 295	65.21 303	85.26 235	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	70.53 260	79.17 206	91.03 166	64.12 177	96.03 57	68.39 278	90.14 130	91.50 216

icg_test_0407_2		78.92 245	78.93 222	78.90 342	87.13 260	63.59 281	76.58 436	89.33 220	70.51 261	77.82 236	89.03 228	61.84 212	81.38 443	72.56 230	85.56 230	91.74 206

IMVS_0407		80.61 196	79.90 193	82.75 239	87.13 260	63.59 281	85.33 274	89.33 220	70.51 261	77.82 236	89.03 228	61.84 212	92.91 222	72.56 230	85.56 230	91.74 206

IMVS_0404		77.16 289	76.42 286	79.37 333	87.13 260	63.59 281	77.12 433	89.33 220	70.51 261	66.22 433	89.03 228	50.36 353	82.78 432	72.56 230	85.56 230	91.74 206

IMVS_0403		80.80 189	80.12 188	82.87 228	87.13 260	63.59 281	85.19 275	89.33 220	70.51 261	78.49 220	89.03 228	63.26 185	93.27 198	72.56 230	85.56 230	91.74 206

FMVSNet1		77.44 283	76.12 290	81.40 272	86.81 273	63.01 299	88.39 157	89.28 226	70.49 265	74.39 325	87.28 280	49.06 374	91.11 306	60.91 355	78.52 332	90.09 274

LuminaMVS		80.68 194	79.62 203	83.83 184	85.07 320	68.01 151	86.99 213	88.83 252	70.36 266	81.38 163	87.99 264	50.11 356	92.51 241	79.02 145	86.89 201	90.97 234

testing3		68.56 409	67.67 406	71.22 440	87.33 251	42.87 492	83.06 345	71.54 472	70.36 266	69.08 391	84.38 357	30.33 478	85.69 405	37.50 486	75.45 381	85.09 424

ab-mvs		79.51 224	78.97 221	81.14 281	88.46 187	60.91 344	83.84 318	89.24 232	70.36 266	79.03 207	88.87 236	63.23 187	90.21 339	65.12 304	82.57 284	92.28 187

tfpnnormal		74.39 330	73.16 336	78.08 360	86.10 293	58.05 378	84.65 293	87.53 294	70.32 269	71.22 366	85.63 329	54.97 289	89.86 344	43.03 473	75.02 390	86.32 396

ACMM	73.20 8	80.78 193	79.84 195	83.58 192	89.31 150	68.37 136	89.99 84	91.60 142	70.28 270	77.25 249	89.66 209	53.37 309	93.53 180	74.24 209	82.85 279	88.85 322
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	86.14 291	68.12 145	89.43 105	82.87 383	70.27 271	87.27 61	93.80 73	69.09 109	91.58 280	88.21 39	83.65 265	93.14 144

ACMH+	68.96 14	76.01 312	74.01 323	82.03 257	88.60 182	65.31 229	88.86 131	87.55 293	70.25 272	67.75 408	87.47 278	41.27 435	93.19 208	58.37 381	75.94 371	87.60 356

IB-MVS	68.01 15	75.85 314	73.36 334	83.31 202	84.76 326	66.03 201	83.38 334	85.06 346	70.21 273	69.40 386	81.05 414	45.76 403	94.66 122	65.10 305	75.49 377	89.25 306
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		79.40 230	77.76 252	84.31 147	87.69 232	65.10 236	87.36 201	84.26 358	70.04 274	77.42 245	88.26 255	49.94 359	94.79 116	70.20 256	84.70 243	93.03 152

mvsmamba		80.60 198	79.38 209	84.27 153	89.74 131	67.24 182	87.47 191	86.95 313	70.02 275	75.38 297	88.93 233	51.24 341	92.56 237	75.47 197	89.22 148	93.00 155

test_fmvsmvis_n_1920		84.02 102	83.87 104	84.49 136	84.12 339	69.37 110	88.15 170	87.96 282	70.01 276	83.95 111	93.23 87	68.80 116	91.51 290	88.61 32	89.96 134	92.57 170

v144192		79.47 226	78.37 233	82.78 236	83.35 358	63.96 269	86.96 214	90.36 183	69.99 277	77.50 243	85.67 328	60.66 239	93.77 165	74.27 208	76.58 358	90.62 248

test_fmvsm_n_1920		85.29 81	85.34 78	85.13 104	86.12 292	69.93 94	88.65 146	90.78 169	69.97 278	88.27 40	93.98 66	71.39 71	91.54 287	88.49 36	90.45 125	93.91 90

c3_l		78.75 247	77.91 243	81.26 277	82.89 381	61.56 330	84.09 315	89.13 240	69.97 278	75.56 289	84.29 360	66.36 148	92.09 258	73.47 216	75.48 378	90.12 271

v1921920		79.22 235	78.03 240	82.80 232	83.30 360	63.94 271	86.80 222	90.33 184	69.91 280	77.48 244	85.53 332	58.44 259	93.75 167	73.60 213	76.85 355	90.71 246

ACMH	67.68 16	75.89 313	73.93 325	81.77 263	88.71 179	66.61 193	88.62 147	89.01 245	69.81 281	66.78 423	86.70 300	41.95 432	91.51 290	55.64 404	78.14 340	87.17 376
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	83.79 347	68.07 147	89.34 112	82.85 384	69.80 282	87.36 60	94.06 59	68.34 123	91.56 283	87.95 43	83.46 271	93.21 136

DPM-MVS		84.93 88	84.29 95	86.84 57	90.20 115	73.04 23	87.12 208	93.04 48	69.80 282	82.85 138	91.22 156	73.06 46	96.02 59	76.72 181	94.63 54	91.46 220

MAR-MVS		81.84 159	80.70 170	85.27 97	91.32 91	71.53 59	89.82 88	90.92 162	69.77 284	78.50 219	86.21 316	62.36 204	94.52 127	65.36 302	92.05 94	89.77 292
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		76.11 310	74.27 322	81.62 265	83.20 365	64.67 252	83.60 327	89.75 205	69.75 285	71.85 358	87.09 289	32.78 471	92.11 257	69.99 260	80.43 311	88.09 346

BH-w/o		78.21 261	77.33 265	80.84 289	88.81 170	65.13 233	84.87 286	87.85 287	69.75 285	74.52 323	84.74 352	61.34 225	93.11 213	58.24 383	85.84 226	84.27 432

v1240		78.99 242	77.78 250	82.64 241	83.21 364	63.54 285	86.62 231	90.30 186	69.74 287	77.33 247	85.68 327	57.04 274	93.76 166	73.13 221	76.92 352	90.62 248

FE-MVSNET2		72.88 362	71.28 358	77.67 368	78.30 447	57.78 387	84.43 304	88.92 251	69.56 288	64.61 445	81.67 410	46.73 391	88.54 373	59.33 368	67.99 439	86.69 392

ET-MVSNet_ETH3D		78.63 251	76.63 282	84.64 127	86.73 276	69.47 104	85.01 283	84.61 351	69.54 289	66.51 430	86.59 304	50.16 355	91.75 273	76.26 183	84.24 253	92.69 167

eth_miper_zixun_eth		77.92 271	76.69 280	81.61 267	83.00 374	61.98 323	83.15 339	89.20 234	69.52 290	74.86 317	84.35 359	61.76 215	92.56 237	71.50 241	72.89 410	90.28 265

PVSNet_Blended_VisFu		82.62 143	81.83 154	84.96 111	90.80 103	69.76 99	88.74 141	91.70 136	69.39 291	78.96 208	88.46 248	65.47 162	94.87 111	74.42 206	88.57 160	90.24 266

mvs_tets		79.13 238	77.77 251	83.22 208	84.70 327	66.37 196	89.17 117	90.19 190	69.38 292	75.40 296	89.46 218	44.17 416	93.15 210	76.78 180	80.70 307	90.14 269

PVSNet_BlendedMVS		80.60 198	80.02 189	82.36 249	88.85 166	65.40 221	86.16 251	92.00 117	69.34 293	78.11 230	86.09 320	66.02 156	94.27 135	71.52 239	82.06 289	87.39 364

SD_0403		74.65 329	74.77 313	74.29 409	86.20 289	47.42 476	83.71 321	85.12 344	69.30 294	68.50 399	87.95 265	59.40 251	86.05 400	49.38 441	83.35 272	89.40 301

AdaColmap		80.58 201	79.42 207	84.06 169	93.09 64	68.91 117	89.36 111	88.97 248	69.27 295	75.70 287	89.69 207	57.20 273	95.77 66	63.06 323	88.41 165	87.50 362

ETVMVS		72.25 369	71.05 363	75.84 387	87.77 224	51.91 453	79.39 401	74.98 460	69.26 296	73.71 332	82.95 392	40.82 439	86.14 399	46.17 460	84.43 250	89.47 299

ITE_SJBPF				78.22 356	81.77 401	60.57 352		83.30 371	69.25 297	67.54 410	87.20 285	36.33 464	87.28 389	54.34 412	74.62 394	86.80 388

cl____		77.72 276	76.76 277	80.58 295	82.49 390	60.48 354	83.09 342	87.87 285	69.22 298	74.38 326	85.22 341	62.10 209	91.53 288	71.09 245	75.41 382	89.73 294

DIV-MVS_self_test		77.72 276	76.76 277	80.58 295	82.48 391	60.48 354	83.09 342	87.86 286	69.22 298	74.38 326	85.24 339	62.10 209	91.53 288	71.09 245	75.40 383	89.74 293

jajsoiax		79.29 234	77.96 241	83.27 204	84.68 328	66.57 194	89.25 114	90.16 191	69.20 300	75.46 293	89.49 215	45.75 404	93.13 212	76.84 176	80.80 305	90.11 272

IterMVS-SCA-FT		75.43 320	73.87 327	80.11 309	82.69 385	64.85 249	81.57 365	83.47 369	69.16 301	70.49 370	84.15 368	51.95 326	88.15 377	69.23 267	72.14 416	87.34 369

CL-MVSNet_self_test		72.37 366	71.46 354	75.09 399	79.49 436	53.53 440	80.76 379	85.01 348	69.12 302	70.51 369	82.05 407	57.92 263	84.13 420	52.27 423	66.00 447	87.60 356

AUN-MVS		79.21 236	77.60 257	84.05 172	88.71 179	67.61 165	85.84 260	87.26 305	69.08 303	77.23 251	88.14 261	53.20 311	93.47 189	75.50 196	73.45 405	91.06 229

xiu_mvs_v1_base_debu		80.80 189	79.72 200	84.03 174	87.35 246	70.19 90	85.56 265	88.77 255	69.06 304	81.83 152	88.16 257	50.91 344	92.85 225	78.29 157	87.56 185	89.06 309

xiu_mvs_v1_base		80.80 189	79.72 200	84.03 174	87.35 246	70.19 90	85.56 265	88.77 255	69.06 304	81.83 152	88.16 257	50.91 344	92.85 225	78.29 157	87.56 185	89.06 309

xiu_mvs_v1_base_debi		80.80 189	79.72 200	84.03 174	87.35 246	70.19 90	85.56 265	88.77 255	69.06 304	81.83 152	88.16 257	50.91 344	92.85 225	78.29 157	87.56 185	89.06 309

MVSTER		79.01 241	77.88 246	82.38 247	83.07 371	64.80 250	84.08 316	88.95 249	69.01 307	78.69 213	87.17 287	54.70 295	92.43 244	74.69 202	80.57 309	89.89 287

usedtu_dtu_shiyan1		76.43 303	75.32 305	79.76 321	83.00 374	60.72 347	81.74 360	88.76 259	68.99 308	72.98 342	84.19 365	56.41 281	90.27 335	62.39 334	79.40 323	88.31 339

FE-MVSNET3		76.43 303	75.32 305	79.76 321	83.00 374	60.72 347	81.74 360	88.76 259	68.99 308	72.98 342	84.19 365	56.41 281	90.27 335	62.39 334	79.40 323	88.31 339

cl22		78.07 266	77.01 269	81.23 278	82.37 393	61.83 326	83.55 328	87.98 280	68.96 310	75.06 312	83.87 370	61.40 224	91.88 269	73.53 214	76.39 363	89.98 283

miper_ehance_all_eth		78.59 253	77.76 252	81.08 283	82.66 386	61.56 330	83.65 323	89.15 238	68.87 311	75.55 290	83.79 374	66.49 146	92.03 259	73.25 219	76.39 363	89.64 295

PAPR		81.66 165	80.89 168	83.99 179	90.27 113	64.00 268	86.76 226	91.77 132	68.84 312	77.13 258	89.50 214	67.63 130	94.88 110	67.55 283	88.52 162	93.09 147

CPTT-MVS		83.73 113	83.33 121	84.92 115	93.28 54	70.86 80	92.09 41	90.38 180	68.75 313	79.57 198	92.83 98	60.60 242	93.04 219	80.92 115	91.56 104	90.86 238

train_agg		86.43 49	86.20 56	87.13 50	93.26 57	72.96 25	88.75 139	91.89 123	68.69 314	85.00 82	93.10 89	74.43 32	95.41 82	84.97 64	95.71 29	93.02 153

test_8					93.13 61	72.57 35	88.68 145	91.84 127	68.69 314	84.87 86	93.10 89	74.43 32	95.16 92

dmvs_re		71.14 377	70.58 371	72.80 426	81.96 398	59.68 363	75.60 444	79.34 431	68.55 316	69.27 390	80.72 420	49.42 366	76.54 465	52.56 422	77.79 342	82.19 456

MVSFormer		82.85 140	82.05 149	85.24 98	87.35 246	70.21 88	90.50 72	90.38 180	68.55 316	81.32 164	89.47 216	61.68 216	93.46 190	78.98 148	90.26 128	92.05 200

test_djsdf		80.30 210	79.32 212	83.27 204	83.98 343	65.37 224	90.50 72	90.38 180	68.55 316	76.19 278	88.70 239	56.44 280	93.46 190	78.98 148	80.14 315	90.97 234

TEST9					93.26 57	72.96 25	88.75 139	91.89 123	68.44 319	85.00 82	93.10 89	74.36 34	95.41 82

FE-MVS		77.78 274	75.68 294	84.08 165	88.09 205	66.00 204	83.13 340	87.79 288	68.42 320	78.01 233	85.23 340	45.50 407	95.12 94	59.11 372	85.83 227	91.11 227

CDPH-MVS		85.76 69	85.29 82	87.17 49	93.49 52	71.08 72	88.58 149	92.42 88	68.32 321	84.61 94	93.48 79	72.32 55	96.15 55	79.00 147	95.43 34	94.28 72

PC_three_1452									68.21 322	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	85.42 308	68.81 118	88.49 153	87.26 305	68.08 323	88.03 46	93.49 78	72.04 61	91.77 272	88.90 29	89.14 151	92.24 190

IterMVS		74.29 331	72.94 339	78.35 355	81.53 406	63.49 287	81.58 364	82.49 387	68.06 324	69.99 379	83.69 378	51.66 335	85.54 407	65.85 299	71.64 419	86.01 404
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_testset		62.63 440	64.11 430	58.19 472	78.55 443	24.76 514	75.28 445	65.94 488	67.91 325	60.34 466	76.01 465	53.56 306	73.94 487	31.79 492	67.65 440	75.88 479

TAMVS		78.89 246	77.51 261	83.03 219	87.80 219	67.79 160	84.72 289	85.05 347	67.63 326	76.75 263	87.70 269	62.25 206	90.82 323	58.53 379	87.13 195	90.49 255

PVSNet_Blended		80.98 181	80.34 180	82.90 226	88.85 166	65.40 221	84.43 304	92.00 117	67.62 327	78.11 230	85.05 346	66.02 156	94.27 135	71.52 239	89.50 143	89.01 314

TR-MVS		77.44 283	76.18 289	81.20 279	88.24 195	63.24 293	84.61 296	86.40 327	67.55 328	77.81 238	86.48 310	54.10 300	93.15 210	57.75 387	82.72 282	87.20 374

CDS-MVSNet		79.07 240	77.70 254	83.17 211	87.60 235	68.23 143	84.40 307	86.20 331	67.49 329	76.36 274	86.54 308	61.54 219	90.79 324	61.86 345	87.33 190	90.49 255
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	85.38 309	68.40 135	88.34 161	86.85 317	67.48 330	87.48 57	93.40 83	70.89 77	91.61 278	88.38 38	89.22 148	92.16 197

dtuplus		80.04 215	79.40 208	81.97 259	83.08 370	62.61 308	83.63 326	87.98 280	67.47 331	81.02 171	90.50 185	64.86 170	90.77 327	71.28 244	84.76 241	92.53 173

mvs_anonymous		79.42 229	79.11 218	80.34 301	84.45 334	57.97 381	82.59 348	87.62 292	67.40 332	76.17 281	88.56 246	68.47 120	89.59 350	70.65 251	86.05 219	93.47 123

viewmambaseed2359dif		80.41 203	79.84 195	82.12 253	82.95 380	62.50 312	83.39 333	88.06 278	67.11 333	80.98 172	90.31 190	66.20 152	91.01 314	74.62 203	84.90 238	92.86 161

mvs5depth		69.45 401	67.45 410	75.46 395	73.93 472	55.83 417	79.19 405	83.23 373	66.89 334	71.63 361	83.32 385	33.69 470	85.09 412	59.81 364	55.34 482	85.46 415

IU-MVS					95.30 2	71.25 66		92.95 62	66.81 335	92.39 6				88.94 28	96.63 4	94.85 24

baseline2		75.70 315	73.83 328	81.30 275	83.26 362	61.79 327	82.57 349	80.65 410	66.81 335	66.88 421	83.42 384	57.86 264	92.19 255	63.47 315	79.57 319	89.91 285

miper_lstm_enhance		74.11 335	73.11 337	77.13 379	80.11 425	59.62 364	72.23 461	86.92 316	66.76 337	70.40 371	82.92 393	56.93 275	82.92 431	69.06 270	72.63 411	88.87 321

OpenMVS	72.83 10	79.77 219	78.33 235	84.09 164	85.17 314	69.91 95	90.57 69	90.97 161	66.70 338	72.17 355	91.91 124	54.70 295	93.96 148	61.81 346	90.95 116	88.41 338

test-LLR		72.94 359	72.43 344	74.48 406	81.35 410	58.04 379	78.38 417	77.46 444	66.66 339	69.95 380	79.00 439	48.06 379	79.24 451	66.13 294	84.83 239	86.15 400

test20.03		67.45 417	66.95 416	68.94 450	75.48 467	44.84 488	77.50 429	77.67 442	66.66 339	63.01 455	83.80 373	47.02 385	78.40 455	42.53 477	68.86 434	83.58 441

test0.0.03 1		68.00 415	67.69 405	68.90 451	77.55 456	47.43 475	75.70 443	72.95 471	66.66 339	66.56 426	82.29 404	48.06 379	75.87 474	44.97 469	74.51 395	83.41 442

Syy-MVS		68.05 414	67.85 400	68.67 454	84.68 328	40.97 498	78.62 414	73.08 469	66.65 342	66.74 424	79.46 434	52.11 322	82.30 436	32.89 491	76.38 366	82.75 451

myMVS_eth3d		67.02 421	66.29 421	69.21 449	84.68 328	42.58 493	78.62 414	73.08 469	66.65 342	66.74 424	79.46 434	31.53 475	82.30 436	39.43 483	76.38 366	82.75 451

QAPM		80.88 183	79.50 206	85.03 107	88.01 210	68.97 116	91.59 51	92.00 117	66.63 344	75.15 309	92.16 118	57.70 265	95.45 77	63.52 314	88.76 157	90.66 247

XXY-MVS		75.41 321	75.56 297	74.96 400	83.59 354	57.82 385	80.59 383	83.87 363	66.54 345	74.93 316	88.31 252	63.24 186	80.09 449	62.16 340	76.85 355	86.97 384

OurMVSNet-221017-0		74.26 332	72.42 345	79.80 318	83.76 349	59.59 365	85.92 257	86.64 322	66.39 346	66.96 420	87.58 272	39.46 446	91.60 279	65.76 300	69.27 430	88.22 343

SCA		74.22 333	72.33 346	79.91 314	84.05 342	62.17 319	79.96 395	79.29 432	66.30 347	72.38 352	80.13 427	51.95 326	88.60 371	59.25 370	77.67 346	88.96 318

testgi		66.67 424	66.53 420	67.08 461	75.62 466	41.69 497	75.93 439	76.50 453	66.11 348	65.20 443	86.59 304	35.72 466	74.71 481	43.71 470	73.38 407	84.84 427

HY-MVS	69.67 12	77.95 270	77.15 267	80.36 300	87.57 244	60.21 359	83.37 335	87.78 289	66.11 348	75.37 298	87.06 291	63.27 184	90.48 333	61.38 352	82.43 285	90.40 259

EG-PatchMatch MVS		74.04 336	71.82 350	80.71 292	84.92 322	67.42 172	85.86 259	88.08 276	66.04 350	64.22 448	83.85 371	35.10 467	92.56 237	57.44 389	80.83 304	82.16 457

CNLPA		78.08 265	76.79 276	81.97 259	90.40 111	71.07 73	87.59 188	84.55 352	66.03 351	72.38 352	89.64 210	57.56 267	86.04 401	59.61 366	83.35 272	88.79 325

gbinet_0.2-2-1-0.02		73.24 353	70.86 368	80.39 298	78.03 450	61.62 329	83.10 341	86.69 319	65.98 352	69.29 389	76.15 464	49.77 362	91.51 290	62.75 327	66.00 447	88.03 347

Anonymous20240529		80.19 213	78.89 223	84.10 160	90.60 106	64.75 251	88.95 128	90.90 163	65.97 353	80.59 183	91.17 159	49.97 358	93.73 169	69.16 269	82.70 283	93.81 98

TAPA-MVS	73.13 9	79.15 237	77.94 242	82.79 235	89.59 133	62.99 303	88.16 169	91.51 145	65.77 354	77.14 257	91.09 162	60.91 234	93.21 203	50.26 437	87.05 196	92.17 196
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 349	70.99 364	80.49 297	84.51 333	65.80 212	80.71 381	86.13 333	65.70 355	65.46 438	83.74 375	44.60 411	90.91 320	51.13 430	76.89 353	84.74 428

anonymousdsp		78.60 252	77.15 267	82.98 223	80.51 420	67.08 185	87.24 206	89.53 213	65.66 356	75.16 308	87.19 286	52.52 313	92.25 253	77.17 170	79.34 326	89.61 296

test_0402		72.79 363	70.44 374	79.84 317	88.13 202	65.99 205	85.93 256	84.29 356	65.57 357	67.40 416	85.49 333	46.92 386	92.61 233	35.88 488	74.38 396	80.94 464

UBG		73.08 356	72.27 347	75.51 393	88.02 208	51.29 461	78.35 420	77.38 447	65.52 358	73.87 331	82.36 401	45.55 405	86.48 396	55.02 408	84.39 251	88.75 327

miper_enhance_ethall		77.87 273	76.86 273	80.92 288	81.65 402	61.38 334	82.68 347	88.98 246	65.52 358	75.47 291	82.30 403	65.76 161	92.00 262	72.95 223	76.39 363	89.39 302

WBMVS		73.43 344	72.81 340	75.28 397	87.91 213	50.99 463	78.59 416	81.31 404	65.51 360	74.47 324	84.83 349	46.39 392	86.68 393	58.41 380	77.86 341	88.17 345

blend_shiyan4		72.29 368	69.65 381	80.21 306	78.24 448	62.16 320	82.29 353	87.27 303	65.41 361	68.43 401	76.42 460	39.91 444	91.23 302	63.21 321	65.66 454	87.22 373

blended_shiyan8		73.38 345	71.17 361	80.02 311	78.36 445	61.51 332	82.43 350	87.28 300	65.40 362	68.61 395	77.53 452	51.91 329	91.00 317	63.28 319	65.76 449	87.53 360

blended_shiyan6		73.38 345	71.17 361	80.01 312	78.36 445	61.48 333	82.43 350	87.27 303	65.40 362	68.56 397	77.55 451	51.94 328	91.01 314	63.27 320	65.76 449	87.55 359

UnsupCasMVSNet_eth		67.33 418	65.99 422	71.37 436	73.48 477	51.47 459	75.16 447	85.19 343	65.20 364	60.78 464	80.93 419	42.35 426	77.20 461	57.12 392	53.69 484	85.44 416

wanda-best-256-512		72.94 359	70.66 369	79.79 319	77.80 452	61.03 341	81.31 370	87.15 308	65.18 365	68.09 402	76.28 461	51.32 337	90.97 318	63.06 323	65.76 449	87.35 366

FE-blended-shiyan7		72.94 359	70.66 369	79.79 319	77.80 452	61.03 341	81.31 370	87.15 308	65.18 365	68.09 402	76.28 461	51.32 337	90.97 318	63.06 323	65.76 449	87.35 366

WTY-MVS		75.65 316	75.68 294	75.57 391	86.40 285	56.82 400	77.92 426	82.40 388	65.10 367	76.18 279	87.72 268	63.13 192	80.90 446	60.31 360	81.96 290	89.00 316

thisisatest0515		77.33 286	75.38 302	83.18 210	85.27 313	63.80 274	82.11 356	83.27 372	65.06 368	75.91 283	83.84 372	49.54 364	94.27 135	67.24 287	86.19 214	91.48 218

MVP-Stereo		76.12 309	74.46 319	81.13 282	85.37 310	69.79 97	84.42 306	87.95 283	65.03 369	67.46 413	85.33 337	53.28 310	91.73 275	58.01 385	83.27 274	81.85 459
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 243	77.69 255	82.81 231	90.54 108	64.29 263	90.11 83	91.51 145	65.01 370	76.16 282	88.13 262	50.56 350	93.03 220	69.68 264	77.56 347	91.11 227

pmmvs6		74.69 328	73.39 332	78.61 346	81.38 409	57.48 392	86.64 230	87.95 283	64.99 371	70.18 374	86.61 303	50.43 352	89.52 351	62.12 341	70.18 427	88.83 323

PAPM		77.68 279	76.40 287	81.51 268	87.29 256	61.85 325	83.78 319	89.59 211	64.74 372	71.23 365	88.70 239	62.59 199	93.66 171	52.66 421	87.03 197	89.01 314

MIMVSNet		70.69 384	69.30 383	74.88 402	84.52 332	56.35 411	75.87 442	79.42 429	64.59 373	67.76 407	82.41 400	41.10 436	81.54 441	46.64 458	81.34 296	86.75 390

tpm		72.37 366	71.71 351	74.35 408	82.19 394	52.00 451	79.22 404	77.29 448	64.56 374	72.95 344	83.68 379	51.35 336	83.26 430	58.33 382	75.80 372	87.81 352

MDA-MVSNet-bldmvs		66.68 423	63.66 433	75.75 388	79.28 439	60.56 353	73.92 457	78.35 439	64.43 375	50.13 490	79.87 431	44.02 417	83.67 423	46.10 461	56.86 476	83.03 448

usedtu_blend_shiyan5		73.29 351	70.96 365	80.25 304	77.80 452	62.16 320	84.44 303	87.38 298	64.41 376	68.09 402	76.28 461	51.32 337	91.23 302	63.21 321	65.76 449	87.35 366

MIMVSNet1		68.58 408	66.78 419	73.98 414	80.07 426	51.82 455	80.77 378	84.37 353	64.40 377	59.75 470	82.16 406	36.47 463	83.63 424	42.73 474	70.33 426	86.48 395

D2MVS		74.82 327	73.21 335	79.64 328	79.81 430	62.56 311	80.34 388	87.35 299	64.37 378	68.86 392	82.66 398	46.37 394	90.10 340	67.91 280	81.24 298	86.25 397

PLC	70.83 11	78.05 267	76.37 288	83.08 216	91.88 85	67.80 159	88.19 167	89.46 215	64.33 379	69.87 382	88.38 250	53.66 305	93.58 172	58.86 375	82.73 281	87.86 351
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 355	71.33 357	78.49 353	83.18 366	60.85 345	79.63 398	78.57 437	64.13 380	71.73 359	79.81 432	51.20 342	85.97 402	57.40 390	76.36 368	88.66 330
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_2432*1600		66.22 428	63.89 431	73.21 420	75.47 468	53.42 442	70.76 468	84.35 354	64.10 381	66.52 428	78.52 443	34.55 468	84.98 413	50.40 433	50.33 489	81.23 462

miper_refine_blended		66.22 428	63.89 431	73.21 420	75.47 468	53.42 442	70.76 468	84.35 354	64.10 381	66.52 428	78.52 443	34.55 468	84.98 413	50.40 433	50.33 489	81.23 462

tpmvs		71.09 378	69.29 384	76.49 383	82.04 396	56.04 414	78.92 411	81.37 403	64.05 383	67.18 418	78.28 445	49.74 363	89.77 346	49.67 440	72.37 412	83.67 440

F-COLMAP		76.38 307	74.33 321	82.50 245	89.28 152	66.95 190	88.41 156	89.03 243	64.05 383	66.83 422	88.61 243	46.78 389	92.89 223	57.48 388	78.55 331	87.67 354

DP-MVS		76.78 295	74.57 315	83.42 198	93.29 53	69.46 106	88.55 151	83.70 364	63.98 385	70.20 373	88.89 235	54.01 303	94.80 115	46.66 456	81.88 292	86.01 404

原ACMM1				84.35 144	93.01 67	68.79 119		92.44 85	63.96 386	81.09 169	91.57 143	66.06 155	95.45 77	67.19 288	94.82 50	88.81 324

PM-MVS		66.41 426	64.14 429	73.20 422	73.92 473	56.45 406	78.97 409	64.96 491	63.88 387	64.72 444	80.24 426	19.84 495	83.44 428	66.24 293	64.52 458	79.71 470

FE-MVSNET		67.25 420	65.33 424	73.02 424	75.86 463	52.54 449	80.26 391	80.56 412	63.80 388	60.39 465	79.70 433	41.41 434	84.66 418	43.34 472	62.62 464	81.86 458

UWE-MVS		72.13 371	71.49 353	74.03 413	86.66 279	47.70 474	81.40 369	76.89 452	63.60 389	75.59 288	84.22 364	39.94 443	85.62 406	48.98 444	86.13 216	88.77 326

0.4-1-1-0.1		70.93 380	67.94 399	79.91 314	79.35 438	61.27 335	78.95 410	82.19 392	63.36 390	67.50 411	69.40 485	39.83 445	91.04 313	62.44 333	68.40 436	87.40 363

jason		81.39 174	80.29 182	84.70 126	86.63 280	69.90 96	85.95 255	86.77 318	63.24 391	81.07 170	89.47 216	61.08 232	92.15 256	78.33 156	90.07 133	92.05 200
jason: jason.
KD-MVS_self_test		68.81 405	67.59 408	72.46 429	74.29 471	45.45 482	77.93 425	87.00 312	63.12 392	63.99 451	78.99 441	42.32 427	84.77 416	56.55 401	64.09 459	87.16 378

gg-mvs-nofinetune		69.95 396	67.96 397	75.94 386	83.07 371	54.51 434	77.23 432	70.29 475	63.11 393	70.32 372	62.33 490	43.62 419	88.69 369	53.88 415	87.76 183	84.62 430

tpmrst		72.39 364	72.13 348	73.18 423	80.54 419	49.91 468	79.91 396	79.08 434	63.11 393	71.69 360	79.95 429	55.32 287	82.77 433	65.66 301	73.89 400	86.87 385

PCF-MVS	73.52 7	80.38 205	78.84 224	85.01 109	87.71 228	68.99 115	83.65 323	91.46 149	63.00 395	77.77 240	90.28 191	66.10 153	95.09 100	61.40 351	88.22 171	90.94 236
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 357	70.41 375	80.81 290	87.13 260	65.63 216	88.30 164	84.19 359	62.96 396	63.80 453	87.69 270	38.04 456	92.56 237	46.66 456	74.91 391	84.24 433
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 390	67.78 404	77.61 371	77.43 457	59.57 366	71.16 465	70.33 474	62.94 397	68.65 394	72.77 476	50.62 349	85.49 408	69.58 265	66.58 444	87.77 353

lupinMVS		81.39 174	80.27 183	84.76 124	87.35 246	70.21 88	85.55 268	86.41 326	62.85 398	81.32 164	88.61 243	61.68 216	92.24 254	78.41 155	90.26 128	91.83 203

test_vis1_n_1920		75.52 318	75.78 292	74.75 405	79.84 429	57.44 393	83.26 337	85.52 340	62.83 399	79.34 205	86.17 318	45.10 409	79.71 450	78.75 150	81.21 299	87.10 382

EPMVS		69.02 404	68.16 393	71.59 434	79.61 434	49.80 470	77.40 430	66.93 485	62.82 400	70.01 377	79.05 437	45.79 402	77.86 459	56.58 400	75.26 387	87.13 379

PatchMatch-RL		72.38 365	70.90 366	76.80 382	88.60 182	67.38 175	79.53 399	76.17 457	62.75 401	69.36 387	82.00 409	45.51 406	84.89 415	53.62 416	80.58 308	78.12 474

gm-plane-assit					81.40 408	53.83 439			62.72 402		80.94 417		92.39 246	63.40 317

0.3-1-1-0.015		70.03 394	66.80 418	79.72 324	78.18 449	61.07 339	77.63 428	82.32 391	62.65 403	65.50 437	67.29 486	37.62 459	90.91 320	61.99 343	68.04 438	87.19 375

FMVSNet5		69.50 400	67.96 397	74.15 411	82.97 379	55.35 424	80.01 394	82.12 394	62.56 404	63.02 454	81.53 411	36.92 460	81.92 439	48.42 446	74.06 398	85.17 422

sss		73.60 342	73.64 330	73.51 418	82.80 382	55.01 428	76.12 438	81.69 398	62.47 405	74.68 320	85.85 324	57.32 270	78.11 457	60.86 356	80.93 301	87.39 364

0.4-1-1-0.2		70.01 395	66.86 417	79.44 332	77.61 455	60.64 351	76.77 435	82.34 390	62.40 406	65.91 435	66.65 487	40.05 442	90.83 322	61.77 347	68.24 437	86.86 386

WB-MVSnew		71.96 373	71.65 352	72.89 425	84.67 331	51.88 454	82.29 353	77.57 443	62.31 407	73.67 334	83.00 391	53.49 308	81.10 445	45.75 464	82.13 288	85.70 411

AllTest		70.96 379	68.09 395	79.58 329	85.15 316	63.62 277	84.58 297	79.83 425	62.31 407	60.32 467	86.73 294	32.02 472	88.96 365	50.28 435	71.57 420	86.15 400

TestCases				79.58 329	85.15 316	63.62 277		79.83 425	62.31 407	60.32 467	86.73 294	32.02 472	88.96 365	50.28 435	71.57 420	86.15 400

1112_ss		77.40 285	76.43 285	80.32 302	89.11 163	60.41 356	83.65 323	87.72 291	62.13 410	73.05 341	86.72 296	62.58 200	89.97 343	62.11 342	80.80 305	90.59 251

PVSNet	64.34 18	72.08 372	70.87 367	75.69 389	86.21 288	56.44 407	74.37 455	80.73 409	62.06 411	70.17 375	82.23 405	42.86 424	83.31 429	54.77 410	84.45 249	87.32 370

UWE-MVS-28		65.32 431	64.93 425	66.49 462	78.70 442	38.55 500	77.86 427	64.39 492	62.00 412	64.13 449	83.60 380	41.44 433	76.00 472	31.39 493	80.89 302	84.92 425

LS3D		76.95 293	74.82 312	83.37 201	90.45 109	67.36 176	89.15 121	86.94 314	61.87 413	69.52 385	90.61 181	51.71 334	94.53 126	46.38 459	86.71 204	88.21 344

CostFormer		75.24 324	73.90 326	79.27 335	82.65 387	58.27 376	80.80 376	82.73 386	61.57 414	75.33 303	83.13 389	55.52 286	91.07 312	64.98 306	78.34 339	88.45 336

new-patchmatchnet		61.73 442	61.73 442	61.70 468	72.74 484	24.50 515	69.16 475	78.03 440	61.40 415	56.72 479	75.53 469	38.42 453	76.48 467	45.95 462	57.67 475	84.13 435

ANet_high		50.57 460	46.10 464	63.99 465	48.67 513	39.13 499	70.99 467	80.85 407	61.39 416	31.18 502	57.70 499	17.02 498	73.65 488	31.22 494	15.89 512	79.18 471

MS-PatchMatch		73.83 339	72.67 341	77.30 377	83.87 346	66.02 202	81.82 358	84.66 350	61.37 417	68.61 395	82.82 396	47.29 382	88.21 376	59.27 369	84.32 252	77.68 475

USDC		70.33 389	68.37 390	76.21 385	80.60 418	56.23 412	79.19 405	86.49 325	60.89 418	61.29 462	85.47 334	31.78 474	89.47 353	53.37 418	76.21 369	82.94 450

cascas		76.72 296	74.64 314	82.99 221	85.78 298	65.88 208	82.33 352	89.21 233	60.85 419	72.74 345	81.02 415	47.28 383	93.75 167	67.48 284	85.02 236	89.34 304

sc_t1		72.19 370	69.51 382	80.23 305	84.81 324	61.09 338	84.68 290	80.22 422	60.70 420	71.27 364	83.58 381	36.59 462	89.24 357	60.41 358	63.31 461	90.37 260

MDTV_nov1_ep13			69.97 380		83.18 366	53.48 441	77.10 434	80.18 424	60.45 421	69.33 388	80.44 421	48.89 377	86.90 391	51.60 426	78.51 333

TinyColmap		67.30 419	64.81 426	74.76 404	81.92 400	56.68 404	80.29 389	81.49 401	60.33 422	56.27 482	83.22 386	24.77 487	87.66 385	45.52 465	69.47 429	79.95 469

test-mter		71.41 375	70.39 376	74.48 406	81.35 410	58.04 379	78.38 417	77.46 444	60.32 423	69.95 380	79.00 439	36.08 465	79.24 451	66.13 294	84.83 239	86.15 400

1314		76.53 298	75.30 307	80.21 306	83.93 344	62.32 317	84.66 291	88.81 253	60.23 424	70.16 376	84.07 369	55.30 288	90.73 330	67.37 285	83.21 275	87.59 358

PatchT		68.46 411	67.85 400	70.29 444	80.70 417	43.93 490	72.47 460	74.88 461	60.15 425	70.55 368	76.57 456	49.94 359	81.59 440	50.58 431	74.83 392	85.34 417

dtuonlycased		68.45 412	67.29 413	71.92 431	80.18 424	54.90 429	79.76 397	80.38 419	60.11 426	62.57 459	76.44 459	49.34 368	82.31 435	55.05 407	61.77 467	78.53 473

无先验							87.48 190	88.98 246	60.00 427				94.12 144	67.28 286		88.97 317

CR-MVSNet		73.37 347	71.27 359	79.67 327	81.32 412	65.19 231	75.92 440	80.30 420	59.92 428	72.73 346	81.19 412	52.50 314	86.69 392	59.84 363	77.71 343	87.11 380

TDRefinement		67.49 416	64.34 428	76.92 380	73.47 478	61.07 339	84.86 287	82.98 381	59.77 429	58.30 474	85.13 343	26.06 483	87.89 381	47.92 453	60.59 472	81.81 460

dp		66.80 422	65.43 423	70.90 443	79.74 433	48.82 473	75.12 449	74.77 462	59.61 430	64.08 450	77.23 453	42.89 423	80.72 447	48.86 445	66.58 444	83.16 445

our_test_3		69.14 403	67.00 415	75.57 391	79.80 431	58.80 370	77.96 424	77.81 441	59.55 431	62.90 457	78.25 446	47.43 381	83.97 421	51.71 425	67.58 441	83.93 438

Test_1112_low_res		76.40 306	75.44 299	79.27 335	89.28 152	58.09 377	81.69 363	87.07 311	59.53 432	72.48 350	86.67 301	61.30 226	89.33 354	60.81 357	80.15 314	90.41 258

pmmvs4		74.03 338	71.91 349	80.39 298	81.96 398	68.32 137	81.45 367	82.14 393	59.32 433	69.87 382	85.13 343	52.40 316	88.13 378	60.21 361	74.74 393	84.73 429

testdata				79.97 313	90.90 100	64.21 265		84.71 349	59.27 434	85.40 77	92.91 95	62.02 211	89.08 361	68.95 271	91.37 107	86.63 394

WB-MVS		54.94 450	54.72 451	55.60 479	73.50 476	20.90 517	74.27 456	61.19 496	59.16 435	50.61 488	74.15 472	47.19 384	75.78 475	17.31 511	35.07 498	70.12 487

ppachtmachnet_test		70.04 393	67.34 412	78.14 358	79.80 431	61.13 336	79.19 405	80.59 411	59.16 435	65.27 440	79.29 436	46.75 390	87.29 388	49.33 442	66.72 442	86.00 406

RPSCF		73.23 354	71.46 354	78.54 350	82.50 389	59.85 361	82.18 355	82.84 385	58.96 437	71.15 367	89.41 222	45.48 408	84.77 416	58.82 376	71.83 418	91.02 233

pmmvs-eth3d		70.50 387	67.83 402	78.52 352	77.37 458	66.18 199	81.82 358	81.51 400	58.90 438	63.90 452	80.42 422	42.69 425	86.28 398	58.56 378	65.30 456	83.11 446

tt0320-xc		70.11 392	67.45 410	78.07 361	85.33 311	59.51 367	83.28 336	78.96 435	58.77 439	67.10 419	80.28 425	36.73 461	87.42 387	56.83 398	59.77 474	87.29 371

OpenMVS_ROB	64.09 19	70.56 386	68.19 392	77.65 370	80.26 421	59.41 368	85.01 283	82.96 382	58.76 440	65.43 439	82.33 402	37.63 458	91.23 302	45.34 468	76.03 370	82.32 454

114514_t		80.68 194	79.51 205	84.20 157	94.09 43	67.27 180	89.64 96	91.11 158	58.75 441	74.08 328	90.72 174	58.10 261	95.04 102	69.70 263	89.42 145	90.30 264

Patchmtry		70.74 383	69.16 386	75.49 394	80.72 416	54.07 437	74.94 451	80.30 420	58.34 442	70.01 377	81.19 412	52.50 314	86.54 394	53.37 418	71.09 423	85.87 409

test_cas_vis1_n_1920		73.76 340	73.74 329	73.81 416	75.90 462	59.77 362	80.51 384	82.40 388	58.30 443	81.62 160	85.69 326	44.35 415	76.41 468	76.29 182	78.61 330	85.23 419

Anonymous20240521		68.80 406	67.22 414	73.55 417	74.33 470	54.11 436	83.18 338	85.61 339	58.15 444	61.68 461	80.94 417	30.71 477	81.27 444	57.00 395	73.34 408	85.28 418

tt0320		70.49 388	68.03 396	77.89 363	84.78 325	59.12 369	83.55 328	80.44 416	58.13 445	67.43 415	80.41 423	39.26 448	87.54 386	55.12 406	63.18 462	86.99 383

旧先验2							86.56 233		58.10 446	87.04 63			88.98 363	74.07 210

JIA-IIPM		66.32 427	62.82 439	76.82 381	77.09 459	61.72 328	65.34 488	75.38 458	58.04 447	64.51 446	62.32 491	42.05 431	86.51 395	51.45 428	69.22 431	82.21 455

pmmvs5		71.55 374	70.20 378	75.61 390	77.83 451	56.39 408	81.74 360	80.89 406	57.76 448	67.46 413	84.49 353	49.26 371	85.32 411	57.08 393	75.29 386	85.11 423

TESTMET0.1,1		69.89 398	69.00 387	72.55 428	79.27 440	56.85 399	78.38 417	74.71 464	57.64 449	68.09 402	77.19 454	37.75 457	76.70 464	63.92 313	84.09 255	84.10 436

RPMNet		73.51 343	70.49 373	82.58 244	81.32 412	65.19 231	75.92 440	92.27 97	57.60 450	72.73 346	76.45 457	52.30 317	95.43 79	48.14 451	77.71 343	87.11 380

SSC-MVS		53.88 453	53.59 453	54.75 482	72.87 483	19.59 518	73.84 458	60.53 498	57.58 451	49.18 492	73.45 475	46.34 396	75.47 478	16.20 514	32.28 500	69.20 488

dtuonly		69.95 396	69.98 379	69.85 446	73.09 482	49.46 471	74.55 454	76.40 454	57.56 452	67.82 406	86.31 315	50.89 348	74.23 484	61.46 350	81.71 294	85.86 410

新几何1				83.42 198	93.13 61	70.71 82		85.48 341	57.43 453	81.80 155	91.98 123	63.28 183	92.27 252	64.60 309	92.99 77	87.27 372

YYNet1		65.03 432	62.91 437	71.38 435	75.85 464	56.60 405	69.12 476	74.66 465	57.28 454	54.12 484	77.87 448	45.85 401	74.48 482	49.95 438	61.52 469	83.05 447

MDA-MVSNet_test_wron		65.03 432	62.92 436	71.37 436	75.93 461	56.73 401	69.09 477	74.73 463	57.28 454	54.03 485	77.89 447	45.88 400	74.39 483	49.89 439	61.55 468	82.99 449

Anonymous20231206		68.60 407	67.80 403	71.02 441	80.23 423	50.75 465	78.30 421	80.47 414	56.79 456	66.11 434	82.63 399	46.35 395	78.95 453	43.62 471	75.70 373	83.36 443

tpm2		73.26 352	71.46 354	78.63 345	83.34 359	56.71 403	80.65 382	80.40 418	56.63 457	73.55 335	82.02 408	51.80 332	91.24 301	56.35 402	78.42 337	87.95 348

CHOSEN 1792x2688		77.63 281	75.69 293	83.44 197	89.98 124	68.58 131	78.70 413	87.50 295	56.38 458	75.80 286	86.84 292	58.67 257	91.40 296	61.58 349	85.75 228	90.34 261

HyFIR lowres test		77.53 282	75.40 301	83.94 182	89.59 133	66.62 192	80.36 387	88.64 268	56.29 459	76.45 271	85.17 342	57.64 266	93.28 196	61.34 353	83.10 277	91.91 202

usedtu_dtu_shiyan2		64.75 435	61.63 443	74.10 412	70.64 488	53.18 447	82.10 357	81.27 405	56.22 460	56.39 481	74.67 471	27.94 481	83.56 425	42.71 475	62.73 463	85.57 413

PVSNet_0	57.27 20	61.67 443	59.27 446	68.85 452	79.61 434	57.44 393	68.01 478	73.44 468	55.93 461	58.54 473	70.41 482	44.58 412	77.55 460	47.01 455	35.91 497	71.55 486

UnsupCasMVSNet_bld		63.70 438	61.53 444	70.21 445	73.69 475	51.39 460	72.82 459	81.89 395	55.63 462	57.81 476	71.80 478	38.67 452	78.61 454	49.26 443	52.21 487	80.63 466

MDTV_nov1_ep13_2view						37.79 501	75.16 447		55.10 463	66.53 427		49.34 368		53.98 414		87.94 349

MVS		78.19 263	76.99 271	81.78 262	85.66 300	66.99 186	84.66 291	90.47 177	55.08 464	72.02 357	85.27 338	63.83 180	94.11 145	66.10 296	89.80 138	84.24 433

test222					91.50 88	68.26 139	84.16 313	83.20 376	54.63 465	79.74 195	91.63 139	58.97 254			91.42 105	86.77 389

dongtai		45.42 464	45.38 465	45.55 486	73.36 479	26.85 512	67.72 479	34.19 511	54.15 466	49.65 491	56.41 502	25.43 484	62.94 501	19.45 509	28.09 502	46.86 506

CHOSEN 280x420		66.51 425	64.71 427	71.90 432	81.45 407	63.52 286	57.98 499	68.95 481	53.57 467	62.59 458	76.70 455	46.22 397	75.29 480	55.25 405	79.68 318	76.88 477

ADS-MVSNet2		66.20 430	63.33 434	74.82 403	79.92 427	58.75 371	67.55 480	75.19 459	53.37 468	65.25 441	75.86 466	42.32 427	80.53 448	41.57 478	68.91 432	85.18 420

ADS-MVSNet		64.36 436	62.88 438	68.78 453	79.92 427	47.17 478	67.55 480	71.18 473	53.37 468	65.25 441	75.86 466	42.32 427	73.99 486	41.57 478	68.91 432	85.18 420

LF4IMVS		64.02 437	62.19 440	69.50 448	70.90 487	53.29 445	76.13 437	77.18 449	52.65 470	58.59 472	80.98 416	23.55 490	76.52 466	53.06 420	66.66 443	78.68 472

tpm cat1		70.57 385	68.31 391	77.35 376	82.41 392	57.95 382	78.08 422	80.22 422	52.04 471	68.54 398	77.66 450	52.00 325	87.84 382	51.77 424	72.07 417	86.25 397

test_vis1_n		69.85 399	69.21 385	71.77 433	72.66 485	55.27 426	81.48 366	76.21 456	52.03 472	75.30 304	83.20 388	28.97 479	76.22 470	74.60 204	78.41 338	83.81 439

Patchmatch-test		64.82 434	63.24 435	69.57 447	79.42 437	49.82 469	63.49 494	69.05 480	51.98 473	59.95 469	80.13 427	50.91 344	70.98 490	40.66 480	73.57 403	87.90 350

N_pmnet		52.79 456	53.26 454	51.40 484	78.99 441	7.68 531	69.52 472	3.89 530	51.63 474	57.01 478	74.98 470	40.83 438	65.96 498	37.78 485	64.67 457	80.56 468

test_fmvs1_n		70.86 382	70.24 377	72.73 427	72.51 486	55.28 425	81.27 372	79.71 427	51.49 475	78.73 212	84.87 348	27.54 482	77.02 462	76.06 186	79.97 317	85.88 408

test_fmvs1		70.93 380	70.52 372	72.16 430	73.71 474	55.05 427	80.82 375	78.77 436	51.21 476	78.58 217	84.41 356	31.20 476	76.94 463	75.88 190	80.12 316	84.47 431

PMMVS		69.34 402	68.67 388	71.35 438	75.67 465	62.03 322	75.17 446	73.46 467	50.00 477	68.68 393	79.05 437	52.07 324	78.13 456	61.16 354	82.77 280	73.90 482

test_fmvs2		68.35 413	67.48 409	70.98 442	69.50 490	51.95 452	80.05 393	76.38 455	49.33 478	74.65 321	84.38 357	23.30 491	75.40 479	74.51 205	75.17 389	85.60 412

ttmdpeth		59.91 445	57.10 449	68.34 456	67.13 494	46.65 481	74.64 452	67.41 484	48.30 479	62.52 460	85.04 347	20.40 493	75.93 473	42.55 476	45.90 495	82.44 453

CMPMVS	51.72 21	70.19 391	68.16 393	76.28 384	73.15 481	57.55 391	79.47 400	83.92 361	48.02 480	56.48 480	84.81 350	43.13 422	86.42 397	62.67 331	81.81 293	84.89 426
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvsany_test1		62.30 441	61.26 445	65.41 464	69.52 489	54.86 430	66.86 482	49.78 505	46.65 481	68.50 399	83.21 387	49.15 372	66.28 497	56.93 396	60.77 470	75.11 480

kuosan		39.70 470	40.40 469	37.58 491	64.52 497	26.98 510	65.62 487	33.02 512	46.12 482	42.79 496	48.99 509	24.10 489	46.56 511	12.16 519	26.30 503	39.20 510

test_fmvs3		63.36 439	61.82 441	67.98 458	62.51 499	46.96 480	77.37 431	74.03 466	45.24 483	67.50 411	78.79 442	12.16 503	72.98 489	72.77 226	66.02 446	83.99 437

CVMVSNet		72.99 358	72.58 343	74.25 410	84.28 335	50.85 464	86.41 238	83.45 370	44.56 484	73.23 339	87.54 276	49.38 367	85.70 404	65.90 298	78.44 334	86.19 399

test_vis1_rt		60.28 444	58.42 447	65.84 463	67.25 493	55.60 421	70.44 470	60.94 497	44.33 485	59.00 471	66.64 488	24.91 486	68.67 495	62.80 326	69.48 428	73.25 483

mvsany_test3		53.99 452	51.45 457	61.61 469	55.51 504	44.74 489	63.52 493	45.41 509	43.69 486	58.11 475	76.45 457	17.99 496	63.76 500	54.77 410	47.59 491	76.34 478

EU-MVSNet		68.53 410	67.61 407	71.31 439	78.51 444	47.01 479	84.47 299	84.27 357	42.27 487	66.44 431	84.79 351	40.44 440	83.76 422	58.76 377	68.54 435	83.17 444

FPMVS		53.68 454	51.64 456	59.81 471	65.08 496	51.03 462	69.48 473	69.58 478	41.46 488	40.67 498	72.32 477	16.46 499	70.00 494	24.24 504	65.42 455	58.40 497

pmmvs3		57.79 447	54.26 452	68.37 455	64.02 498	56.72 402	75.12 449	65.17 489	40.20 489	52.93 486	69.86 484	20.36 494	75.48 477	45.45 466	55.25 483	72.90 484

new_pmnet		50.91 459	50.29 459	52.78 483	68.58 491	34.94 506	63.71 492	56.63 502	39.73 490	44.95 493	65.47 489	21.93 492	58.48 503	34.98 489	56.62 477	64.92 491

MVS-HIRNet		59.14 446	57.67 448	63.57 466	81.65 402	43.50 491	71.73 462	65.06 490	39.59 491	51.43 487	57.73 498	38.34 454	82.58 434	39.53 481	73.95 399	64.62 492

MVStest1		56.63 449	52.76 455	68.25 457	61.67 500	53.25 446	71.67 463	68.90 482	38.59 492	50.59 489	83.05 390	25.08 485	70.66 491	36.76 487	38.56 496	80.83 465

PMMVS2		40.82 469	38.86 473	46.69 485	53.84 506	16.45 522	48.61 502	49.92 504	37.49 493	31.67 501	60.97 493	8.14 509	56.42 505	28.42 496	30.72 501	67.19 490

test_vis3_rt		49.26 461	47.02 463	56.00 476	54.30 505	45.27 486	66.76 484	48.08 506	36.83 494	44.38 494	53.20 505	7.17 510	64.07 499	56.77 399	55.66 479	58.65 496

test_f		52.09 457	50.82 458	55.90 477	53.82 507	42.31 496	59.42 498	58.31 501	36.45 495	56.12 483	70.96 481	12.18 502	57.79 504	53.51 417	56.57 478	67.60 489

LCM-MVSNet		54.25 451	49.68 461	67.97 459	53.73 508	45.28 485	66.85 483	80.78 408	35.96 496	39.45 500	62.23 492	8.70 507	78.06 458	48.24 450	51.20 488	80.57 467

ArgMatch-Sym		43.72 468	39.92 471	55.10 481	52.36 510	37.56 502	61.93 496	23.00 517	35.80 497	43.62 495	70.22 483	3.22 514	55.93 506	45.35 467	23.80 506	71.81 485

APD_test1		53.31 455	49.93 460	63.42 467	65.68 495	50.13 467	71.59 464	66.90 486	34.43 498	40.58 499	71.56 479	8.65 508	76.27 469	34.64 490	55.36 481	63.86 493

PMVS	37.38 22	44.16 466	40.28 470	55.82 478	40.82 516	42.54 495	65.12 489	63.99 493	34.43 498	24.48 508	57.12 500	3.92 513	76.17 471	17.10 512	55.52 480	48.75 503
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ArgMatch-SfM		44.04 467	39.87 472	56.58 475	50.92 512	36.22 503	59.86 497	27.68 515	33.67 500	42.15 497	71.07 480	3.10 515	59.10 502	45.79 463	24.54 504	74.41 481

Gipuma		45.18 465	41.86 468	55.16 480	77.03 460	51.52 458	32.50 509	80.52 413	32.46 501	27.12 506	35.02 517	9.52 506	75.50 476	22.31 506	60.21 473	38.45 511
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 448	56.90 450	60.38 470	67.70 492	35.61 504	69.18 474	53.97 503	32.30 502	57.49 477	79.88 430	40.39 441	68.57 496	38.78 484	72.37 412	76.97 476

testf1		45.72 462	41.96 466	57.00 473	56.90 502	45.32 483	66.14 485	59.26 499	26.19 503	30.89 503	60.96 494	4.14 511	70.64 492	26.39 502	46.73 493	55.04 499

APD_test2		45.72 462	41.96 466	57.00 473	56.90 502	45.32 483	66.14 485	59.26 499	26.19 503	30.89 503	60.96 494	4.14 511	70.64 492	26.39 502	46.73 493	55.04 499

DenseAffine		31.97 471	28.22 477	43.21 488	43.10 515	27.10 509	46.21 503	11.36 520	24.92 505	27.70 505	58.81 497	1.09 519	46.50 512	26.95 499	13.85 515	56.02 498

E-PMN		31.77 472	30.64 474	35.15 493	52.87 509	27.67 508	57.09 500	47.86 507	24.64 506	16.40 520	33.05 518	11.23 504	54.90 507	14.46 515	18.15 510	22.87 518

EMVS		30.81 474	29.65 475	34.27 494	50.96 511	25.95 513	56.58 501	46.80 508	24.01 507	15.53 521	30.68 520	12.47 501	54.43 508	12.81 518	17.05 511	22.43 519

RoMa-SfM		28.67 476	25.38 480	38.54 489	32.61 520	22.48 516	40.24 504	7.23 524	21.81 508	26.66 507	60.46 496	0.96 520	41.72 513	26.47 501	11.95 516	51.40 502

DKM		25.67 478	23.01 482	33.64 495	32.08 521	19.25 520	37.50 506	5.52 526	18.67 509	23.58 511	55.44 503	0.64 526	34.02 515	23.95 505	9.73 518	47.66 505

PDCNetPlus		24.75 479	22.46 483	31.64 496	35.53 518	17.00 521	32.00 510	9.46 521	18.43 510	18.56 518	51.31 507	1.65 517	33.00 517	26.51 500	8.70 520	44.91 507

MVE	26.22 23	30.37 475	25.89 479	43.81 487	44.55 514	35.46 505	28.87 514	39.07 510	18.20 511	18.58 517	40.18 514	2.68 516	47.37 510	17.07 513	23.78 507	48.60 504
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 499	40.17 517	26.90 511		24.59 516	17.44 512	23.95 509	48.61 511	9.77 505	26.48 519	18.06 510	24.47 505	28.83 516

RoMa-HiRes		21.63 481	19.64 486	27.59 498	22.40 525	14.25 524	29.71 512	4.10 528	15.42 513	21.09 514	54.77 504	0.72 524	28.87 518	21.01 507	7.52 523	39.65 509

DKM-HiRes		20.87 482	19.15 487	26.02 500	25.34 524	14.13 525	29.63 513	3.62 533	14.53 514	20.13 515	50.55 508	0.47 534	24.22 522	20.96 508	7.15 524	39.70 508

LoFTR		27.52 477	24.27 481	37.29 492	34.75 519	19.27 519	33.78 508	21.60 518	12.42 515	21.61 513	56.59 501	0.91 521	40.37 514	13.94 516	22.80 508	52.22 501

MatchFormer		22.13 480	19.86 485	28.93 497	28.66 522	15.74 523	31.91 511	17.10 519	7.75 516	18.87 516	47.50 512	0.62 528	33.92 516	7.49 524	18.87 509	37.14 512

wuyk23d		16.82 485	15.94 488	19.46 503	58.74 501	31.45 507	39.22 505	3.74 532	6.84 517	6.04 526	2.70 549	1.27 518	24.29 521	10.54 522	14.40 514	2.63 532

PMatch-SfM		14.15 487	12.67 490	18.59 504	12.84 530	7.03 532	17.41 516	2.28 535	6.63 518	12.96 522	43.56 513	0.09 550	16.11 524	13.90 517	4.38 533	32.63 515

ELoFTR		14.23 486	11.56 491	22.24 501	11.02 531	6.56 533	13.59 520	7.57 523	5.55 519	11.96 524	39.09 515	0.21 538	24.93 520	9.43 523	5.66 527	35.22 513

PMatch-Up-SfM		10.76 490	9.99 493	13.09 505	9.50 537	4.83 536	12.94 522	1.40 542	4.65 520	10.16 525	37.54 516	0.07 553	10.94 526	10.71 521	2.92 544	23.50 517

MASt3R-SfM		13.55 488	13.93 489	12.41 506	10.54 534	5.97 535	16.61 517	6.07 525	4.50 521	16.53 519	48.67 510	0.73 523	9.44 527	11.56 520	10.18 517	21.81 520

GLUNet-SfM		12.90 489	10.00 492	21.62 502	13.58 529	8.30 529	10.19 523	9.30 522	4.31 522	12.18 523	30.90 519	0.50 532	22.76 523	4.89 525	4.14 534	33.79 514

test_method		31.52 473	29.28 476	38.23 490	27.03 523	6.50 534	20.94 515	62.21 495	4.05 523	22.35 512	52.50 506	13.33 500	47.58 509	27.04 498	34.04 499	60.62 494

ALIKED-LG		8.61 491	8.70 495	8.33 508	20.63 526	8.70 528	15.50 518	4.61 527	2.19 524	5.84 527	18.70 522	0.80 522	8.06 528	1.03 533	8.97 519	8.25 521

ALIKED-MNN		7.86 492	7.83 498	7.97 509	19.40 527	8.86 527	14.48 519	3.90 529	1.59 525	4.74 532	16.49 523	0.59 529	7.65 529	0.91 534	8.34 522	7.39 524

ALIKED-NN		7.51 493	7.61 499	7.21 510	18.26 528	8.10 530	13.45 521	3.88 531	1.50 526	4.87 530	16.47 524	0.64 526	7.00 530	0.88 535	8.50 521	6.52 529

tmp_tt		18.61 484	21.40 484	10.23 507	4.82 552	10.11 526	34.70 507	30.74 514	1.48 527	23.91 510	26.07 521	28.42 480	13.41 525	27.12 497	15.35 513	7.17 527

SP-DiffGlue		4.29 499	4.46 502	3.77 515	3.68 553	2.12 543	5.97 528	2.22 536	1.10 528	4.89 529	13.93 526	0.66 525	1.95 537	2.47 526	5.24 528	7.22 526

XFeat-MNN		4.39 498	4.49 501	4.10 511	2.88 554	1.91 549	5.86 529	2.57 534	1.06 529	5.04 528	13.99 525	0.43 536	4.47 531	2.00 527	6.55 525	5.92 530

SP-SuperGlue		4.24 501	4.38 504	3.81 514	10.75 533	2.00 545	8.18 525	2.09 537	1.00 530	2.41 533	8.29 529	0.56 530	2.05 536	1.27 529	4.91 530	7.39 524

SP-LightGlue		4.27 500	4.41 503	3.86 512	10.99 532	1.99 546	8.19 524	2.06 538	0.98 531	2.37 534	8.29 529	0.56 530	2.10 534	1.27 529	4.99 529	7.48 523

SP-NN		4.00 503	4.12 506	3.63 516	9.92 536	1.81 551	7.94 527	1.90 541	0.86 532	2.15 536	8.00 532	0.50 532	2.09 535	1.20 531	4.63 532	6.98 528

SP-MNN		4.14 502	4.24 505	3.82 513	10.32 535	1.83 550	8.11 526	1.99 539	0.82 533	2.23 535	8.27 531	0.47 534	2.14 533	1.20 531	4.77 531	7.49 522

XFeat-NN		3.78 504	3.96 507	3.23 517	2.65 555	1.53 554	4.99 530	1.92 540	0.81 534	4.77 531	12.37 528	0.38 537	3.39 532	1.64 528	6.13 526	4.77 531

SIFT-NN		2.77 505	2.92 508	2.34 518	8.70 538	3.08 537	4.46 531	1.01 544	0.68 535	1.46 537	5.49 533	0.16 539	1.65 538	0.26 536	4.04 535	2.27 533

SIFT-MNN		2.63 506	2.75 509	2.25 519	8.10 539	2.84 538	4.08 532	1.02 543	0.68 535	1.28 538	5.34 536	0.15 540	1.64 539	0.26 536	3.88 537	2.27 533

SIFT-NN-UMatch		2.26 510	2.39 513	1.89 524	6.21 547	2.08 544	3.76 534	0.83 547	0.66 537	1.04 542	5.09 537	0.14 541	1.52 542	0.23 539	3.51 539	2.07 537

SIFT-NCM-Cal		2.40 508	2.52 511	2.05 521	7.74 540	2.54 540	3.75 535	0.84 546	0.65 538	0.89 545	4.78 542	0.13 544	1.60 540	0.19 547	3.71 538	2.01 539

SIFT-NN-NCMNet		2.52 507	2.64 510	2.14 520	7.53 541	2.74 539	4.00 533	0.98 545	0.65 538	1.24 540	5.08 539	0.14 541	1.60 540	0.23 539	3.94 536	2.07 537

SIFT-NN-CMatch		2.31 509	2.41 512	2.00 522	6.59 545	2.34 542	3.48 536	0.83 547	0.65 538	1.28 538	5.09 537	0.14 541	1.52 542	0.23 539	3.41 540	2.14 535

SIFT-ConvMatch		2.25 511	2.37 514	1.90 523	7.29 542	2.37 541	3.21 539	0.75 549	0.65 538	1.03 543	4.91 540	0.12 547	1.51 544	0.22 542	3.13 542	1.81 540

SIFT-UMatch		2.16 512	2.30 515	1.72 526	6.99 543	1.97 548	3.32 537	0.70 551	0.64 542	0.91 544	4.86 541	0.12 547	1.49 545	0.22 542	2.97 543	1.72 542

SIFT-CM-Cal		2.02 514	2.13 517	1.67 527	6.79 544	1.99 546	2.79 541	0.64 552	0.63 543	0.87 546	4.48 545	0.13 544	1.41 547	0.19 547	2.70 545	1.61 544

SIFT-UM-Cal		1.97 515	2.12 518	1.52 528	6.57 546	1.67 552	2.93 540	0.57 554	0.62 544	0.83 547	4.55 544	0.11 549	1.37 548	0.20 546	2.69 546	1.53 545

SIFT-NN-PointCN		2.07 513	2.18 516	1.74 525	5.75 548	1.65 553	3.27 538	0.73 550	0.60 545	1.07 541	4.62 543	0.13 544	1.43 546	0.21 544	3.22 541	2.12 536

SIFT-NCMNet		1.44 518	1.56 521	1.08 531	5.14 551	1.07 557	1.97 544	0.32 556	0.56 546	0.64 550	3.23 548	0.07 553	1.01 551	0.14 551	1.95 549	1.15 546

SIFT-PCN-Cal		1.72 516	1.82 520	1.39 529	5.64 549	1.19 556	2.39 543	0.53 555	0.55 547	0.72 548	3.90 546	0.09 550	1.22 550	0.17 549	2.42 548	1.76 541

SIFT-PointCN		1.72 516	1.83 519	1.36 530	5.55 550	1.22 555	2.59 542	0.59 553	0.55 547	0.71 549	3.77 547	0.08 552	1.24 549	0.17 549	2.48 547	1.63 543

EGC-MVSNET		52.07 458	47.05 462	67.14 460	83.51 356	60.71 349	80.50 385	67.75 483	0.07 549	0.43 551	75.85 468	24.26 488	81.54 441	28.82 495	62.25 465	59.16 495

testmvs		6.04 496	8.02 497	0.10 533	0.08 556	0.03 559	69.74 471	0.04 557	0.05 550	0.31 552	1.68 550	0.02 556	0.04 552	0.24 538	0.02 550	0.25 548

test123		6.12 495	8.11 496	0.14 532	0.06 557	0.09 558	71.05 466	0.03 558	0.04 551	0.25 553	1.30 551	0.05 555	0.03 553	0.21 544	0.01 551	0.29 547

mmdepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

monomultidepth		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

test_blank		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet_test		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

DCPMVS		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

cdsmvs_eth3d_5k		19.96 483	26.61 478	0.00 534	0.00 558	0.00 560	0.00 545	89.26 229	0.00 552	0.00 554	88.61 243	61.62 218	0.00 554	0.00 552	0.00 552	0.00 549

pcd_1.5k_mvsjas		5.26 497	7.02 500	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	63.15 189	0.00 554	0.00 552	0.00 552	0.00 549

sosnet-low-res		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

sosnet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uncertanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

Regformer		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

ab-mvs-re		7.23 494	9.64 494	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	86.72 296	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

uanet		0.00 519	0.00 522	0.00 534	0.00 558	0.00 560	0.00 545	0.00 559	0.00 552	0.00 554	0.00 552	0.00 557	0.00 554	0.00 552	0.00 552	0.00 549

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						42.58 493								39.46 482

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					0.00 558

eth-test					0.00 558

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																88.96 318

test_part2					95.06 8	72.65 32				91.80 15

sam_mvs1												51.32 337				88.96 318

sam_mvs												50.01 357

ambc				75.24 398	73.16 480	50.51 466	63.05 495	87.47 296		64.28 447	77.81 449	17.80 497	89.73 348	57.88 386	60.64 471	85.49 414

MTGPA								92.02 115

test_post1							78.90 412				5.43 535	48.81 378	85.44 410	59.25 370

test_post											5.46 534	50.36 353	84.24 419

patchmatchnet-post											74.00 473	51.12 343	88.60 371

GG-mvs-BLEND				75.38 396	81.59 404	55.80 418	79.32 402	69.63 477		67.19 417	73.67 474	43.24 421	88.90 367	50.41 432	84.50 245	81.45 461

MTMP							92.18 39	32.83 513

test9_res														84.90 65	95.70 30	92.87 160

agg_prior2														82.91 92	95.45 33	92.70 165

agg_prior					92.85 69	71.94 53		91.78 131		84.41 98			94.93 104

test_prior4						72.60 34	89.01 126

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

新几何2							86.29 247

旧先验1					91.96 82	65.79 213		86.37 328			93.08 93	69.31 103			92.74 81	88.74 329

原ACMM2							86.86 220

testdata2													91.01 314	62.37 337

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						60.42 244

plane_prior5								92.44 85					95.38 84	78.71 151	86.32 210	91.33 221

plane_prior4											91.00 167

plane_prior1					89.90 126

n2								0.00 559

nn								0.00 559

door-mid								69.98 476

lessismore_v0				78.97 340	81.01 415	57.15 396		65.99 487		61.16 463	82.82 396	39.12 449	91.34 298	59.67 365	46.92 492	88.43 337

test11								92.23 101

door								69.44 479

HQP5-MVS						66.98 187

BP-MVS														77.47 166

HQP4-MVS										77.24 250			95.11 96			91.03 231

HQP3-MVS								92.19 109							85.99 221

HQP2-MVS												60.17 247

NP-MVS					89.62 132	68.32 137					90.24 193

ACMMP++_ref															81.95 291

ACMMP++															81.25 297

Test By Simon												64.33 175

High-resolution multi-view benchmark