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.

bld_raw_dy_0_64		82.84 110	80.75 130	89.09 14	93.74 52	72.16 15	93.16 109	77.36 358	89.69 1	74.55 168	96.48 27	32.35 348	97.56 62	92.21 24	77.24 210	97.53 6

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 8	97.13 3	95.58 11	89.33 2	85.77 52	96.26 32	72.84 26	99.38 1	92.64 19	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 96	71.10 27	97.02 4	96.04 6	88.70 3	91.57 14	96.19 35	70.12 40	98.91 17	96.83 1	95.06 17	96.76 14

iter_conf05_11		86.99 35	86.27 43	89.15 13	93.74 52	72.45 13	97.56 1	87.04 307	88.32 4	92.60 5	96.57 23	32.61 346	97.45 66	92.21 24	95.80 10	97.53 6

DELS-MVS		90.05 7	90.09 11	89.94 4	93.14 71	73.88 7	97.01 5	94.40 50	88.32 4	85.71 53	94.91 70	74.11 19	98.91 17	87.26 61	95.94 8	97.03 12
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
MVS_0304		90.01 8	90.50 9	88.53 22	90.14 142	70.94 28	96.47 14	95.72 10	87.33 6	89.60 29	96.26 32	68.44 45	98.74 24	95.82 4	94.72 31	95.90 44

EPNet		87.84 23	88.38 19	86.23 79	93.30 65	66.05 136	95.26 33	94.84 29	87.09 7	88.06 35	94.53 79	66.79 59	97.34 75	83.89 91	91.68 73	95.29 66
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		89.61 12	89.99 12	88.46 23	94.39 39	69.71 49	96.53 13	93.78 66	86.89 8	89.68 28	95.78 42	65.94 66	99.10 9	92.99 16	93.91 41	96.58 20

patch_mono-2		89.71 11	90.99 6	85.85 89	96.04 24	63.70 199	95.04 41	95.19 19	86.74 9	91.53 15	95.15 64	73.86 20	97.58 59	93.38 14	92.00 68	96.28 34

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 130	93.00 74	58.16 302	96.72 9	94.41 48	86.50 10	90.25 22	97.83 1	75.46 14	98.67 25	92.78 18	95.49 13	97.32 8

CANet_DTU		84.09 87	83.52 81	85.81 90	90.30 139	66.82 118	91.87 165	89.01 263	85.27 11	86.09 49	93.74 103	47.71 266	96.98 100	77.90 137	89.78 96	93.65 137

CLD-MVS		82.73 112	82.35 111	83.86 158	87.90 201	67.65 97	95.45 29	92.18 133	85.06 12	72.58 191	92.27 136	52.46 222	95.78 146	84.18 87	79.06 190	88.16 242
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CNVR-MVS		90.32 6	90.89 7	88.61 21	96.76 8	70.65 31	96.47 14	94.83 30	84.83 13	89.07 32	96.80 19	70.86 36	99.06 15	92.64 19	95.71 11	96.12 37

NCCC		89.07 15	89.46 15	87.91 27	96.60 10	69.05 61	96.38 16	94.64 39	84.42 14	86.74 44	96.20 34	66.56 62	98.76 23	89.03 48	94.56 33	95.92 43

test_fmvsm_n_1920		87.69 25	88.50 18	85.27 110	87.05 222	63.55 206	93.69 88	91.08 183	84.18 15	90.17 24	97.04 8	67.58 54	97.99 39	95.72 5	90.03 93	94.26 111

PS-MVSNAJ		88.14 17	87.61 28	89.71 6	92.06 97	76.72 1	95.75 21	93.26 90	83.86 16	89.55 30	96.06 38	53.55 211	97.89 43	91.10 33	93.31 52	94.54 103

DeepC-MVS_fast	79.48 2	87.95 21	88.00 24	87.79 30	95.86 27	68.32 77	95.74 22	94.11 60	83.82 17	83.49 74	96.19 35	64.53 84	98.44 31	83.42 94	94.88 25	96.61 17
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_fmvsmconf_n		86.58 42	87.17 33	84.82 123	85.28 253	62.55 230	94.26 57	89.78 228	83.81 18	87.78 37	96.33 31	65.33 72	96.98 100	94.40 11	87.55 114	94.95 82

xiu_mvs_v2_base		87.92 22	87.38 32	89.55 11	91.41 120	76.43 3	95.74 22	93.12 98	83.53 19	89.55 30	95.95 40	53.45 215	97.68 50	91.07 34	92.62 59	94.54 103

test_fmvsmconf0.1_n		85.71 58	86.08 50	84.62 137	80.83 306	62.33 234	93.84 81	88.81 271	83.50 20	87.00 43	96.01 39	63.36 101	96.93 107	94.04 12	87.29 117	94.61 99

TSAR-MVS + MP.		88.11 19	88.64 17	86.54 69	91.73 109	68.04 87	90.36 225	93.55 79	82.89 21	91.29 16	92.89 121	72.27 31	96.03 140	87.99 52	94.77 26	95.54 54
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		90.70 3	90.52 8	91.24 1	89.68 151	76.68 2	97.29 2	95.35 15	82.87 22	91.58 13	97.22 3	79.93 5	99.10 9	83.12 95	97.64 2	97.94 1

iter_conf05		83.27 102	82.70 104	84.98 118	93.32 64	71.84 17	94.16 59	81.76 348	82.74 23	73.83 178	88.40 198	72.77 27	94.61 194	82.10 101	75.21 222	88.48 236

WTY-MVS		86.32 45	85.81 54	87.85 28	92.82 79	69.37 55	95.20 35	95.25 17	82.71 24	81.91 85	94.73 74	67.93 52	97.63 56	79.55 122	82.25 160	96.54 21

lupinMVS		87.74 24	87.77 26	87.63 37	89.24 166	71.18 24	96.57 12	92.90 106	82.70 25	87.13 40	95.27 58	64.99 75	95.80 145	89.34 43	91.80 71	95.93 42

fmvsm_s_conf0.5_n		86.39 44	86.91 37	84.82 123	87.36 215	63.54 207	94.74 48	90.02 222	82.52 26	90.14 25	96.92 13	62.93 109	97.84 46	95.28 8	82.26 159	93.07 154

HPM-MVS++		89.37 14	89.95 13	87.64 33	95.10 30	68.23 83	95.24 34	94.49 44	82.43 27	88.90 33	96.35 29	71.89 34	98.63 26	88.76 49	96.40 6	96.06 38

test_fmvsmconf0.01_n		83.70 97	83.52 81	84.25 151	75.26 357	61.72 248	92.17 147	87.24 306	82.36 28	84.91 62	95.41 50	55.60 187	96.83 111	92.85 17	85.87 132	94.21 113

PVSNet_Blended		86.73 40	86.86 39	86.31 78	93.76 49	67.53 101	96.33 17	93.61 76	82.34 29	81.00 95	93.08 115	63.19 104	97.29 78	87.08 63	91.38 79	94.13 118

MSP-MVS		90.38 5	91.87 1	85.88 86	92.83 77	64.03 189	93.06 112	94.33 54	82.19 30	93.65 3	96.15 37	85.89 1	97.19 84	91.02 35	97.75 1	96.43 28
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
PAPM		85.89 55	85.46 59	87.18 47	88.20 194	72.42 14	92.41 141	92.77 109	82.11 31	80.34 104	93.07 116	68.27 47	95.02 178	78.39 134	93.59 48	94.09 120

jason		86.40 43	86.17 47	87.11 49	86.16 238	70.54 33	95.71 25	92.19 132	82.00 32	84.58 65	94.34 89	61.86 118	95.53 165	87.76 54	90.89 85	95.27 69
jason: jason.
baseline1		81.84 127	81.03 127	84.28 150	91.60 112	66.62 124	91.08 203	91.66 158	81.87 33	74.86 165	91.67 150	69.98 41	94.92 184	71.76 182	64.75 299	91.29 199

CHOSEN 1792x2688		84.98 70	83.45 86	89.57 10	89.94 146	75.14 5	92.07 154	92.32 124	81.87 33	75.68 155	88.27 201	60.18 134	98.60 27	80.46 116	90.27 92	94.96 81

fmvsm_s_conf0.1_n		85.61 61	85.93 52	84.68 133	82.95 290	63.48 209	94.03 69	89.46 240	81.69 35	89.86 26	96.74 20	61.85 119	97.75 49	94.74 9	82.01 165	92.81 162

test_vis1_n_1920		81.66 129	82.01 114	80.64 239	82.24 295	55.09 330	94.76 47	86.87 309	81.67 36	84.40 67	94.63 77	38.17 315	94.67 193	91.98 28	83.34 150	92.16 183

casdiffmvs_mvg		85.66 60	85.18 63	87.09 50	88.22 193	69.35 56	93.74 87	91.89 144	81.47 37	80.10 106	91.45 153	64.80 80	96.35 126	87.23 62	87.69 112	95.58 52
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
h-mvs33		83.01 107	82.56 107	84.35 147	89.34 158	62.02 240	92.72 124	93.76 69	81.45 38	82.73 80	92.25 138	60.11 135	97.13 89	87.69 55	62.96 311	93.91 129

hse-mvs2		81.12 138	81.11 126	81.16 226	86.52 230	57.48 311	89.40 251	91.16 176	81.45 38	82.73 80	90.49 169	60.11 135	94.58 195	87.69 55	60.41 338	91.41 193

ET-MVSNet_ETH3D		84.01 88	83.15 96	86.58 67	90.78 132	70.89 29	94.74 48	94.62 40	81.44 40	58.19 327	93.64 106	73.64 23	92.35 280	82.66 97	78.66 195	96.50 26

fmvsm_s_conf0.5_n_a		85.75 57	86.09 49	84.72 130	85.73 247	63.58 204	93.79 84	89.32 246	81.42 41	90.21 23	96.91 14	62.41 113	97.67 51	94.48 10	80.56 178	92.90 160

test_fmvsmvis_n_1920		83.80 93	83.48 84	84.77 127	82.51 292	63.72 197	91.37 189	83.99 338	81.42 41	77.68 135	95.74 44	58.37 153	97.58 59	93.38 14	86.87 120	93.00 157

testing11		86.71 41	86.44 42	87.55 39	93.54 59	71.35 21	93.65 90	95.58 11	81.36 43	80.69 98	92.21 139	72.30 30	96.46 125	85.18 78	83.43 149	94.82 90

casdiffmvs		85.37 63	84.87 69	86.84 56	88.25 191	69.07 60	93.04 114	91.76 151	81.27 44	80.84 97	92.07 141	64.23 86	96.06 138	84.98 81	87.43 116	95.39 57
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ETV-MVS		86.01 51	86.11 48	85.70 96	90.21 141	67.02 115	93.43 103	91.92 141	81.21 45	84.13 71	94.07 98	60.93 128	95.63 156	89.28 44	89.81 94	94.46 109

DeepC-MVS	77.85 3	85.52 62	85.24 62	86.37 75	88.80 176	66.64 123	92.15 148	93.68 74	81.07 46	76.91 146	93.64 106	62.59 111	98.44 31	85.50 74	92.84 58	94.03 124
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		85.01 69	84.44 73	86.71 61	88.33 188	68.73 68	90.24 230	91.82 150	81.05 47	81.18 91	92.50 128	63.69 94	96.08 137	84.45 86	86.71 126	95.32 64

PC_three_1452									80.91 48	94.07 2	96.83 18	83.57 4	99.12 5	95.70 7	97.42 4	97.55 4

IU-MVS					96.46 11	69.91 42		95.18 20	80.75 49	95.28 1				92.34 21	95.36 14	96.47 27

diffmvs		84.28 80	83.83 78	85.61 98	87.40 213	68.02 88	90.88 209	89.24 249	80.54 50	81.64 87	92.52 127	59.83 139	94.52 202	87.32 60	85.11 136	94.29 110
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_l_conf0.5_n		87.49 27	88.19 22	85.39 104	86.95 223	64.37 179	94.30 55	88.45 284	80.51 51	92.70 4	96.86 15	69.98 41	97.15 88	95.83 3	88.08 109	94.65 97

fmvsm_s_conf0.1_n_a		84.76 72	84.84 70	84.53 139	80.23 316	63.50 208	92.79 121	88.73 275	80.46 52	89.84 27	96.65 22	60.96 127	97.57 61	93.80 13	80.14 180	92.53 169

VPNet		78.82 180	77.53 182	82.70 185	84.52 266	66.44 128	93.93 73	92.23 127	80.46 52	72.60 190	88.38 199	49.18 251	93.13 246	72.47 175	63.97 308	88.55 235

testing99		86.01 51	85.47 58	87.63 37	93.62 55	71.25 23	93.47 101	95.23 18	80.42 54	80.60 100	91.95 143	71.73 35	96.50 123	80.02 119	82.22 161	95.13 75

testing222		85.18 66	84.69 71	86.63 64	92.91 76	69.91 42	92.61 132	95.80 9	80.31 55	80.38 103	92.27 136	68.73 44	95.19 175	75.94 147	83.27 151	94.81 91

testing91		85.93 53	85.31 61	87.78 31	93.59 57	71.47 19	93.50 98	95.08 25	80.26 56	80.53 101	91.93 144	70.43 38	96.51 122	80.32 117	82.13 163	95.37 59

canonicalmvs		86.85 37	86.25 46	88.66 20	91.80 108	71.92 16	93.54 96	91.71 154	80.26 56	87.55 38	95.25 60	63.59 98	96.93 107	88.18 51	84.34 142	97.11 10

fmvsm_l_conf0.5_n_a		87.44 29	88.15 23	85.30 108	87.10 220	64.19 186	94.41 53	88.14 293	80.24 58	92.54 6	96.97 10	69.52 43	97.17 85	95.89 2	88.51 105	94.56 100

CS-MVS-test		86.14 49	87.01 35	83.52 167	92.63 85	59.36 290	95.49 28	91.92 141	80.09 59	85.46 57	95.53 49	61.82 120	95.77 148	86.77 67	93.37 51	95.41 56

CS-MVS		85.80 56	86.65 41	83.27 175	92.00 101	58.92 295	95.31 32	91.86 146	79.97 60	84.82 63	95.40 51	62.26 114	95.51 166	86.11 71	92.08 67	95.37 59

MVSTER		82.47 116	82.05 112	83.74 160	92.68 84	69.01 62	91.90 164	93.21 91	79.83 61	72.14 199	85.71 241	74.72 16	94.72 189	75.72 148	72.49 244	87.50 247

HQP-NCC					87.54 209		94.06 64		79.80 62	74.18 171

ACMP_Plane					87.54 209		94.06 64		79.80 62	74.18 171

HQP-MVS		81.14 136	80.64 134	82.64 187	87.54 209	63.66 202	94.06 64	91.70 156	79.80 62	74.18 171	90.30 173	51.63 229	95.61 158	77.63 138	78.90 191	88.63 232

baseline2		83.68 98	83.42 89	84.48 142	87.37 214	66.00 138	90.06 234	95.93 8	79.71 65	69.08 234	90.39 171	77.92 6	96.28 128	78.91 129	81.38 171	91.16 201

EI-MVSNet-Vis-set		83.77 94	83.67 80	84.06 154	92.79 82	63.56 205	91.76 172	94.81 31	79.65 66	77.87 133	94.09 96	63.35 102	97.90 42	79.35 123	79.36 187	90.74 205

ETVMVS		84.22 84	83.71 79	85.76 93	92.58 87	68.25 82	92.45 140	95.53 14	79.54 67	79.46 114	91.64 151	70.29 39	94.18 215	69.16 206	82.76 157	94.84 87

EIA-MVS		84.84 71	84.88 68	84.69 132	91.30 121	62.36 233	93.85 78	92.04 136	79.45 68	79.33 117	94.28 92	62.42 112	96.35 126	80.05 118	91.25 82	95.38 58

dmvs_re		76.93 212	75.36 215	81.61 216	87.78 206	60.71 268	80.00 343	87.99 297	79.42 69	69.02 236	89.47 186	46.77 269	94.32 206	63.38 260	74.45 227	89.81 217

plane_prior						62.42 231	93.85 78		79.38 70						78.80 193

dcpmvs_2		87.37 30	87.55 29	86.85 55	95.04 32	68.20 84	90.36 225	90.66 195	79.37 71	81.20 90	93.67 105	74.73 15	96.55 120	90.88 36	92.00 68	95.82 46

alignmvs		87.28 31	86.97 36	88.24 26	91.30 121	71.14 26	95.61 26	93.56 78	79.30 72	87.07 42	95.25 60	68.43 46	96.93 107	87.87 53	84.33 143	96.65 16

TESTMET0.1,1		82.41 117	81.98 115	83.72 163	88.08 195	63.74 195	92.70 126	93.77 68	79.30 72	77.61 137	87.57 216	58.19 156	94.08 219	73.91 163	86.68 127	93.33 146

EI-MVSNet-UG-set		83.14 105	82.96 97	83.67 165	92.28 91	63.19 215	91.38 188	94.68 37	79.22 74	76.60 148	93.75 102	62.64 110	97.76 48	78.07 136	78.01 198	90.05 214

PVSNet	73.49 8	80.05 158	78.63 165	84.31 148	90.92 128	64.97 164	92.47 139	91.05 186	79.18 75	72.43 196	90.51 168	37.05 330	94.06 221	68.06 215	86.00 131	93.90 131

HY-MVS	76.49 5	84.28 80	83.36 92	87.02 53	92.22 93	67.74 94	84.65 301	94.50 43	79.15 76	82.23 83	87.93 210	66.88 58	96.94 105	80.53 115	82.20 162	96.39 30

PVSNet_BlendedMVS		83.38 100	83.43 87	83.22 176	93.76 49	67.53 101	94.06 64	93.61 76	79.13 77	81.00 95	85.14 244	63.19 104	97.29 78	87.08 63	73.91 233	84.83 302

plane_prior3						61.95 243			79.09 78	72.53 192

MVS_111021_HR		86.19 48	85.80 55	87.37 43	93.17 70	69.79 46	93.99 70	93.76 69	79.08 79	78.88 124	93.99 99	62.25 115	98.15 36	85.93 73	91.15 83	94.15 117

test_cas_vis1_n_1920		80.45 150	80.61 135	79.97 258	78.25 342	57.01 318	94.04 68	88.33 287	79.06 80	82.81 79	93.70 104	38.65 310	91.63 295	90.82 37	79.81 182	91.27 200

MSLP-MVS++		86.27 46	85.91 53	87.35 44	92.01 100	68.97 64	95.04 41	92.70 111	79.04 81	81.50 88	96.50 26	58.98 150	96.78 112	83.49 93	93.93 40	96.29 32

IB-MVS	77.80 4	82.18 120	80.46 139	87.35 44	89.14 168	70.28 36	95.59 27	95.17 21	78.85 82	70.19 222	85.82 239	70.66 37	97.67 51	72.19 179	66.52 284	94.09 120
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
3Dnovator	73.91 6	82.69 115	80.82 129	88.31 25	89.57 153	71.26 22	92.60 133	94.39 51	78.84 83	67.89 255	92.48 131	48.42 257	98.52 28	68.80 211	94.40 35	95.15 74

HQP_MVS		80.34 152	79.75 148	82.12 205	86.94 224	62.42 231	93.13 110	91.31 170	78.81 84	72.53 192	89.14 191	50.66 236	95.55 163	76.74 141	78.53 196	88.39 239

plane_prior2							93.13 110		78.81 84

MG-MVS		87.11 33	86.27 43	89.62 7	97.79 1	76.27 4	94.96 44	94.49 44	78.74 86	83.87 73	92.94 119	64.34 85	96.94 105	75.19 152	94.09 37	95.66 49

gm-plane-assit					88.42 184	67.04 114			78.62 87		91.83 146		97.37 72	76.57 143

VNet		86.20 47	85.65 57	87.84 29	93.92 46	69.99 38	95.73 24	95.94 7	78.43 88	86.00 50	93.07 116	58.22 155	97.00 96	85.22 76	84.33 143	96.52 22

tpm		78.58 187	77.03 191	83.22 176	85.94 243	64.56 168	83.21 315	91.14 179	78.31 89	73.67 179	79.68 316	64.01 88	92.09 286	66.07 239	71.26 254	93.03 155

save fliter					93.84 48	67.89 91	95.05 40	92.66 114	78.19 90

TSAR-MVS + GP.		87.96 20	88.37 20	86.70 62	93.51 61	65.32 154	95.15 37	93.84 65	78.17 91	85.93 51	94.80 73	75.80 13	98.21 34	89.38 42	88.78 102	96.59 18

FIs		79.47 168	79.41 155	79.67 265	85.95 241	59.40 287	91.68 176	93.94 63	78.06 92	68.96 238	88.28 200	66.61 61	91.77 292	66.20 238	74.99 223	87.82 244

sss		82.71 114	82.38 110	83.73 162	89.25 163	59.58 285	92.24 145	94.89 28	77.96 93	79.86 109	92.38 133	56.70 174	97.05 91	77.26 140	80.86 175	94.55 101

PMMVS		81.98 126	82.04 113	81.78 212	89.76 150	56.17 322	91.13 202	90.69 192	77.96 93	80.09 107	93.57 108	46.33 276	94.99 180	81.41 108	87.46 115	94.17 115

EC-MVSNet		84.53 76	85.04 66	83.01 179	89.34 158	61.37 254	94.42 52	91.09 181	77.91 95	83.24 75	94.20 94	58.37 153	95.40 167	85.35 75	91.41 78	92.27 179

test1111		80.84 143	80.02 142	83.33 173	87.87 202	60.76 265	92.62 131	86.86 310	77.86 96	75.73 154	91.39 156	46.35 274	94.70 192	72.79 170	88.68 104	94.52 105

MVS_Test		84.16 86	83.20 93	87.05 52	91.56 114	69.82 45	89.99 239	92.05 135	77.77 97	82.84 78	86.57 229	63.93 90	96.09 134	74.91 157	89.18 100	95.25 72

SteuartSystems-ACMMP		86.82 39	86.90 38	86.58 67	90.42 136	66.38 129	96.09 18	93.87 64	77.73 98	84.01 72	95.66 45	63.39 100	97.94 40	87.40 59	93.55 49	95.42 55
Skip Steuart: Steuart Systems R&D Blog.
EPNet_dtu		78.80 181	79.26 159	77.43 294	88.06 196	49.71 354	91.96 162	91.95 140	77.67 99	76.56 149	91.28 158	58.51 152	90.20 316	56.37 296	80.95 174	92.39 171
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test2506		83.29 101	82.92 99	84.37 146	88.39 186	63.18 216	92.01 157	91.35 169	77.66 100	78.49 129	91.42 154	64.58 83	95.09 177	73.19 164	89.23 98	94.85 84

ECVR-MVS		81.29 134	80.38 140	84.01 156	88.39 186	61.96 242	92.56 138	86.79 311	77.66 100	76.63 147	91.42 154	46.34 275	95.24 174	74.36 161	89.23 98	94.85 84

tpmrst		80.57 146	79.14 161	84.84 122	90.10 143	68.28 79	81.70 325	89.72 235	77.63 102	75.96 152	79.54 318	64.94 77	92.71 263	75.43 150	77.28 209	93.55 139

testdata1							89.21 255		77.55 103

UniMVSNet_NR-MVSNet		78.15 194	77.55 181	79.98 256	84.46 268	60.26 275	92.25 144	93.20 93	77.50 104	68.88 239	86.61 228	66.10 64	92.13 284	66.38 235	62.55 315	87.54 246

UA-Net		80.02 159	79.65 149	81.11 228	89.33 160	57.72 306	86.33 294	89.00 266	77.44 105	81.01 94	89.15 190	59.33 146	95.90 143	61.01 276	84.28 145	89.73 220

PVSNet_Blended_VisFu		83.97 89	83.50 83	85.39 104	90.02 144	66.59 126	93.77 85	91.73 152	77.43 106	77.08 145	89.81 183	63.77 93	96.97 102	79.67 121	88.21 107	92.60 166

dmvs_testset		65.55 321	66.45 297	62.86 362	79.87 319	22.35 405	76.55 357	71.74 375	77.42 107	55.85 338	87.77 213	51.39 231	80.69 377	31.51 387	65.92 288	85.55 292

NR-MVSNet		76.05 227	74.59 223	80.44 241	82.96 288	62.18 238	90.83 211	91.73 152	77.12 108	60.96 312	86.35 231	59.28 147	91.80 291	60.74 277	61.34 330	87.35 253

FC-MVSNet-test		77.99 196	78.08 173	77.70 289	84.89 261	55.51 327	90.27 228	93.75 72	76.87 109	66.80 271	87.59 215	65.71 69	90.23 315	62.89 266	73.94 232	87.37 251

SD-MVS		87.49 27	87.49 30	87.50 41	93.60 56	68.82 67	93.90 75	92.63 117	76.86 110	87.90 36	95.76 43	66.17 63	97.63 56	89.06 47	91.48 77	96.05 39
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
mvsmamba		76.85 215	75.71 211	80.25 247	83.07 287	59.16 292	91.44 180	80.64 353	76.84 111	67.95 251	86.33 233	46.17 279	94.24 213	76.06 146	72.92 240	87.36 252

UGNet		79.87 162	78.68 164	83.45 172	89.96 145	61.51 251	92.13 149	90.79 190	76.83 112	78.85 126	86.33 233	38.16 316	96.17 131	67.93 218	87.17 118	92.67 164
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
MVS_111021_LR		82.02 125	81.52 119	83.51 169	88.42 184	62.88 225	89.77 243	88.93 267	76.78 113	75.55 159	93.10 113	50.31 239	95.38 169	83.82 92	87.02 119	92.26 180

SDMVSNet		80.26 153	78.88 163	84.40 144	89.25 163	67.63 98	85.35 297	93.02 100	76.77 114	70.84 213	87.12 223	47.95 263	96.09 134	85.04 79	74.55 224	89.48 224

sd_testset		77.08 211	75.37 214	82.20 201	89.25 163	62.11 239	82.06 322	89.09 259	76.77 114	70.84 213	87.12 223	41.43 300	95.01 179	67.23 225	74.55 224	89.48 224

TranMVSNet+NR-MVSNet		75.86 232	74.52 226	79.89 260	82.44 293	60.64 271	91.37 189	91.37 168	76.63 116	67.65 258	86.21 235	52.37 223	91.55 297	61.84 272	60.81 333	87.48 248

PAPR		85.15 67	84.47 72	87.18 47	96.02 25	68.29 78	91.85 167	93.00 103	76.59 117	79.03 120	95.00 65	61.59 121	97.61 58	78.16 135	89.00 101	95.63 50

UniMVSNet (Re)		77.58 203	76.78 195	79.98 256	84.11 274	60.80 262	91.76 172	93.17 95	76.56 118	69.93 228	84.78 249	63.32 103	92.36 279	64.89 251	62.51 317	86.78 263

DU-MVS		76.86 213	75.84 208	79.91 259	82.96 288	60.26 275	91.26 195	91.54 161	76.46 119	68.88 239	86.35 231	56.16 180	92.13 284	66.38 235	62.55 315	87.35 253

OPM-MVS		79.00 175	78.09 172	81.73 213	83.52 282	63.83 192	91.64 178	90.30 209	76.36 120	71.97 201	89.93 182	46.30 277	95.17 176	75.10 153	77.70 201	86.19 275
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
WR-MVS		76.76 218	75.74 210	79.82 262	84.60 264	62.27 237	92.60 133	92.51 121	76.06 121	67.87 256	85.34 242	56.76 172	90.24 314	62.20 270	63.69 310	86.94 261

GA-MVS		78.33 192	76.23 202	84.65 134	83.65 280	66.30 132	91.44 180	90.14 216	76.01 122	70.32 220	84.02 257	42.50 296	94.72 189	70.98 187	77.00 212	92.94 158

PVSNet_0	68.08 15	71.81 275	68.32 291	82.27 197	84.68 262	62.31 236	88.68 264	90.31 208	75.84 123	57.93 332	80.65 303	37.85 321	94.19 214	69.94 197	29.05 395	90.31 211

CDS-MVSNet		81.43 132	80.74 131	83.52 167	86.26 235	64.45 173	92.09 152	90.65 196	75.83 124	73.95 177	89.81 183	63.97 89	92.91 256	71.27 185	82.82 154	93.20 149
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UWE-MVS		80.81 144	81.01 128	80.20 249	89.33 160	57.05 316	91.91 163	94.71 35	75.67 125	75.01 164	89.37 187	63.13 106	91.44 303	67.19 226	82.80 156	92.12 184

CostFormer		82.33 118	81.15 122	85.86 88	89.01 171	68.46 74	82.39 321	93.01 101	75.59 126	80.25 105	81.57 286	72.03 33	94.96 181	79.06 127	77.48 206	94.16 116

nrg030		80.93 141	79.86 146	84.13 153	83.69 279	68.83 66	93.23 107	91.20 174	75.55 127	75.06 163	88.22 205	63.04 108	94.74 188	81.88 103	66.88 281	88.82 230

VDD-MVS		83.06 106	81.81 117	86.81 58	90.86 130	67.70 95	95.40 30	91.50 164	75.46 128	81.78 86	92.34 135	40.09 304	97.13 89	86.85 66	82.04 164	95.60 51

Effi-MVS+-dtu		76.14 223	75.28 217	78.72 280	83.22 284	55.17 329	89.87 240	87.78 300	75.42 129	67.98 250	81.43 288	45.08 287	92.52 273	75.08 154	71.63 249	88.48 236

test_prior2							95.10 39		75.40 130	85.25 61	95.61 47	67.94 51		87.47 58	94.77 26

MTAPA		83.91 90	83.38 91	85.50 100	91.89 106	65.16 159	81.75 324	92.23 127	75.32 131	80.53 101	95.21 62	56.06 183	97.16 87	84.86 83	92.55 61	94.18 114

EPMVS		78.49 189	75.98 206	86.02 82	91.21 123	69.68 50	80.23 339	91.20 174	75.25 132	72.48 194	78.11 326	54.65 197	93.69 237	57.66 293	83.04 152	94.69 93

miper_enhance_ethall		78.86 179	77.97 175	81.54 218	88.00 199	65.17 158	91.41 182	89.15 255	75.19 133	68.79 241	83.98 258	67.17 56	92.82 258	72.73 171	65.30 290	86.62 268

v2v482		77.42 205	75.65 212	82.73 184	80.38 312	67.13 111	91.85 167	90.23 213	75.09 134	69.37 230	83.39 264	53.79 209	94.44 204	71.77 181	65.00 296	86.63 267

VPA-MVSNet		79.03 174	78.00 174	82.11 208	85.95 241	64.48 172	93.22 108	94.66 38	75.05 135	74.04 176	84.95 246	52.17 224	93.52 240	74.90 158	67.04 280	88.32 241

ACMMP_NAP		86.05 50	85.80 55	86.80 59	91.58 113	67.53 101	91.79 169	93.49 83	74.93 136	84.61 64	95.30 55	59.42 144	97.92 41	86.13 70	94.92 20	94.94 83

thres200		79.66 164	78.33 168	83.66 166	92.54 88	65.82 144	93.06 112	96.31 3	74.90 137	73.30 182	88.66 193	59.67 141	95.61 158	47.84 330	78.67 194	89.56 223

TAMVS		80.37 151	79.45 154	83.13 178	85.14 256	63.37 210	91.23 197	90.76 191	74.81 138	72.65 189	88.49 195	60.63 130	92.95 251	69.41 202	81.95 166	93.08 153

MP-MVS-pluss		85.24 65	85.13 64	85.56 99	91.42 118	65.59 148	91.54 179	92.51 121	74.56 139	80.62 99	95.64 46	59.15 148	97.00 96	86.94 65	93.80 42	94.07 122
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mvs_anonymous		81.36 133	79.99 144	85.46 101	90.39 138	68.40 75	86.88 291	90.61 197	74.41 140	70.31 221	84.67 250	63.79 92	92.32 281	73.13 165	85.70 133	95.67 48

MAR-MVS		84.18 85	83.43 87	86.44 72	96.25 21	65.93 141	94.28 56	94.27 56	74.41 140	79.16 119	95.61 47	53.99 206	98.88 21	69.62 200	93.26 53	94.50 107
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
BH-w/o		80.49 149	79.30 158	84.05 155	90.83 131	64.36 181	93.60 93	89.42 243	74.35 142	69.09 233	90.15 178	55.23 191	95.61 158	64.61 252	86.43 130	92.17 182

thisisatest0515		83.41 99	82.49 108	86.16 80	89.46 157	68.26 80	93.54 96	94.70 36	74.31 143	75.75 153	90.92 161	72.62 28	96.52 121	69.64 198	81.50 170	93.71 135

Vis-MVSNet (Re-imp)		79.24 171	79.57 150	78.24 286	88.46 182	52.29 341	90.41 223	89.12 257	74.24 144	69.13 232	91.91 145	65.77 68	90.09 318	59.00 288	88.09 108	92.33 173

SMA-MVS		88.14 17	88.29 21	87.67 32	93.21 68	68.72 69	93.85 78	94.03 62	74.18 145	91.74 12	96.67 21	65.61 70	98.42 33	89.24 45	96.08 7	95.88 45
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
AUN-MVS		78.37 190	77.43 183	81.17 225	86.60 229	57.45 312	89.46 250	91.16 176	74.11 146	74.40 170	90.49 169	55.52 188	94.57 197	74.73 160	60.43 337	91.48 191

3Dnovator+	73.60 7	82.10 124	80.60 136	86.60 65	90.89 129	66.80 120	95.20 35	93.44 85	74.05 147	67.42 261	92.49 130	49.46 247	97.65 55	70.80 189	91.68 73	95.33 62

XVS		83.87 91	83.47 85	85.05 115	93.22 66	63.78 193	92.92 118	92.66 114	73.99 148	78.18 130	94.31 91	55.25 189	97.41 70	79.16 125	91.58 75	93.95 127

X-MVStestdata		76.86 213	74.13 233	85.05 115	93.22 66	63.78 193	92.92 118	92.66 114	73.99 148	78.18 130	10.19 405	55.25 189	97.41 70	79.16 125	91.58 75	93.95 127

MS-PatchMatch		77.90 200	76.50 198	82.12 205	85.99 240	69.95 41	91.75 174	92.70 111	73.97 150	62.58 306	84.44 254	41.11 301	95.78 146	63.76 258	92.17 65	80.62 349

LCM-MVSNet-Re		72.93 264	71.84 263	76.18 308	88.49 180	48.02 361	80.07 342	70.17 378	73.96 151	52.25 351	80.09 312	49.98 242	88.24 330	67.35 222	84.23 146	92.28 176

Vis-MVSNet		80.92 142	79.98 145	83.74 160	88.48 181	61.80 244	93.44 102	88.26 292	73.96 151	77.73 134	91.76 147	49.94 243	94.76 186	65.84 241	90.37 91	94.65 97
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test-mter		79.96 160	79.38 157	81.72 214	86.93 226	61.17 255	92.70 126	91.54 161	73.85 153	75.62 156	86.94 225	49.84 245	92.38 277	72.21 177	84.76 140	91.60 188

OMC-MVS		78.67 186	77.91 177	80.95 235	85.76 246	57.40 313	88.49 267	88.67 278	73.85 153	72.43 196	92.10 140	49.29 250	94.55 200	72.73 171	77.89 199	90.91 204

Fast-Effi-MVS+		81.14 136	80.01 143	84.51 141	90.24 140	65.86 142	94.12 63	89.15 255	73.81 155	75.37 161	88.26 202	57.26 163	94.53 201	66.97 229	84.92 137	93.15 150

ZNCC-MVS		85.33 64	85.08 65	86.06 81	93.09 73	65.65 146	93.89 76	93.41 87	73.75 156	79.94 108	94.68 76	60.61 131	98.03 38	82.63 98	93.72 45	94.52 105

V42		76.46 221	74.55 225	82.19 202	79.14 330	67.82 92	90.26 229	89.42 243	73.75 156	68.63 244	81.89 279	51.31 232	94.09 218	71.69 183	64.84 297	84.66 303

v1144		76.73 219	74.88 219	82.27 197	80.23 316	66.60 125	91.68 176	90.21 215	73.69 158	69.06 235	81.89 279	52.73 220	94.40 205	69.21 205	65.23 293	85.80 286

v148		76.19 222	74.47 227	81.36 221	80.05 318	64.44 174	91.75 174	90.23 213	73.68 159	67.13 265	80.84 299	55.92 185	93.86 235	68.95 209	61.73 326	85.76 289

CR-MVSNet		73.79 257	70.82 272	82.70 185	83.15 285	67.96 89	70.25 369	84.00 336	73.67 160	69.97 226	72.41 355	57.82 159	89.48 322	52.99 310	73.13 237	90.64 207

XXY-MVS		77.94 198	76.44 199	82.43 191	82.60 291	64.44 174	92.01 157	91.83 149	73.59 161	70.00 225	85.82 239	54.43 202	94.76 186	69.63 199	68.02 274	88.10 243

tfpn200view9		78.79 182	77.43 183	82.88 181	92.21 94	64.49 170	92.05 155	96.28 4	73.48 162	71.75 204	88.26 202	60.07 137	95.32 170	45.16 341	77.58 203	88.83 228

thres400		78.68 184	77.43 183	82.43 191	92.21 94	64.49 170	92.05 155	96.28 4	73.48 162	71.75 204	88.26 202	60.07 137	95.32 170	45.16 341	77.58 203	87.48 248

FMVSNet3		77.73 201	76.04 205	82.80 182	91.20 124	68.99 63	91.87 165	91.99 138	73.35 164	67.04 266	83.19 266	56.62 176	92.14 283	59.80 284	69.34 261	87.28 255

GST-MVS		84.63 75	84.29 75	85.66 97	92.82 79	65.27 155	93.04 114	93.13 97	73.20 165	78.89 121	94.18 95	59.41 145	97.85 45	81.45 107	92.48 62	93.86 132

USDC		67.43 312	64.51 313	76.19 307	77.94 346	55.29 328	78.38 350	85.00 327	73.17 166	48.36 366	80.37 306	21.23 376	92.48 275	52.15 311	64.02 307	80.81 347

MP-MVS		85.02 68	84.97 67	85.17 114	92.60 86	64.27 184	93.24 106	92.27 126	73.13 167	79.63 112	94.43 82	61.90 117	97.17 85	85.00 80	92.56 60	94.06 123
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
xiu_mvs_v1_base_debu		82.16 121	81.12 123	85.26 111	86.42 231	68.72 69	92.59 135	90.44 202	73.12 168	84.20 68	94.36 84	38.04 318	95.73 150	84.12 88	86.81 121	91.33 194

xiu_mvs_v1_base		82.16 121	81.12 123	85.26 111	86.42 231	68.72 69	92.59 135	90.44 202	73.12 168	84.20 68	94.36 84	38.04 318	95.73 150	84.12 88	86.81 121	91.33 194

xiu_mvs_v1_base_debi		82.16 121	81.12 123	85.26 111	86.42 231	68.72 69	92.59 135	90.44 202	73.12 168	84.20 68	94.36 84	38.04 318	95.73 150	84.12 88	86.81 121	91.33 194

D2MVS		73.80 256	72.02 261	79.15 276	79.15 329	62.97 219	88.58 266	90.07 218	72.94 171	59.22 321	78.30 323	42.31 298	92.70 265	65.59 245	72.00 247	81.79 338

BH-RMVSNet		79.46 169	77.65 179	84.89 120	91.68 111	65.66 145	93.55 95	88.09 295	72.93 172	73.37 181	91.12 160	46.20 278	96.12 133	56.28 297	85.61 135	92.91 159

Syy-MVS		69.65 291	69.52 283	70.03 346	87.87 202	43.21 379	88.07 272	89.01 263	72.91 173	63.11 299	88.10 206	45.28 285	85.54 349	22.07 392	69.23 264	81.32 341

myMVS_eth3d		72.58 273	72.74 251	72.10 339	87.87 202	49.45 356	88.07 272	89.01 263	72.91 173	63.11 299	88.10 206	63.63 95	85.54 349	32.73 381	69.23 264	81.32 341

IS-MVSNet		80.14 156	79.41 155	82.33 195	87.91 200	60.08 279	91.97 161	88.27 290	72.90 175	71.44 209	91.73 149	61.44 122	93.66 238	62.47 269	86.53 128	93.24 147

PS-MVSNAJss		77.26 207	76.31 201	80.13 251	80.64 310	59.16 292	90.63 220	91.06 185	72.80 176	68.58 245	84.57 252	53.55 211	93.96 229	72.97 166	71.96 248	87.27 256

9.14			87.63 27		93.86 47		94.41 53	94.18 57	72.76 177	86.21 47	96.51 25	66.64 60	97.88 44	90.08 40	94.04 38

v1192		75.98 229	73.92 236	82.15 203	79.73 320	66.24 134	91.22 198	89.75 230	72.67 178	68.49 246	81.42 289	49.86 244	94.27 210	67.08 227	65.02 295	85.95 283

Effi-MVS+		83.82 92	82.76 102	86.99 54	89.56 154	69.40 52	91.35 191	86.12 318	72.59 179	83.22 76	92.81 125	59.60 142	96.01 142	81.76 104	87.80 111	95.56 53

UnsupCasMVSNet_eth		65.79 319	63.10 321	73.88 323	70.71 372	50.29 352	81.09 331	89.88 226	72.58 180	49.25 364	74.77 350	32.57 347	87.43 341	55.96 298	41.04 380	83.90 309

1112_ss		80.56 147	79.83 147	82.77 183	88.65 178	60.78 263	92.29 143	88.36 286	72.58 180	72.46 195	94.95 66	65.09 74	93.42 243	66.38 235	77.71 200	94.10 119

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 42	93.96 71	94.37 52	72.48 182	92.07 9	96.85 16	83.82 2	99.15 2	91.53 31	97.42 4	97.55 4

test_0728_THIRD									72.48 182	90.55 20	96.93 11	76.24 11	99.08 11	91.53 31	94.99 18	96.43 28

cl22		77.94 198	76.78 195	81.42 220	87.57 208	64.93 166	90.67 216	88.86 270	72.45 184	67.63 259	82.68 271	64.07 87	92.91 256	71.79 180	65.30 290	86.44 269

thres600view7		78.00 195	76.66 197	82.03 210	91.93 103	63.69 200	91.30 194	96.33 1	72.43 185	70.46 217	87.89 211	60.31 132	94.92 184	42.64 353	76.64 214	87.48 248

IterMVS-LS		76.49 220	75.18 218	80.43 242	84.49 267	62.74 227	90.64 218	88.80 272	72.40 186	65.16 279	81.72 282	60.98 126	92.27 282	67.74 219	64.65 301	86.29 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		78.97 176	78.22 171	81.25 223	85.33 251	62.73 228	89.53 248	93.21 91	72.39 187	72.14 199	90.13 179	60.99 125	94.72 189	67.73 220	72.49 244	86.29 271

miper_ehance_all_eth		77.60 202	76.44 199	81.09 232	85.70 248	64.41 177	90.65 217	88.64 280	72.31 188	67.37 264	82.52 272	64.77 81	92.64 270	70.67 191	65.30 290	86.24 273

v144192		76.05 227	74.03 234	82.12 205	79.50 324	66.55 127	91.39 186	89.71 236	72.30 189	68.17 248	81.33 291	51.75 227	94.03 226	67.94 217	64.19 303	85.77 287

thres100view900		78.37 190	77.01 192	82.46 190	91.89 106	63.21 214	91.19 201	96.33 1	72.28 190	70.45 218	87.89 211	60.31 132	95.32 170	45.16 341	77.58 203	88.83 228

PatchmatchNet		77.46 204	74.63 222	85.96 84	89.55 155	70.35 35	79.97 344	89.55 238	72.23 191	70.94 211	76.91 337	57.03 166	92.79 261	54.27 304	81.17 172	94.74 92
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
HFP-MVS		84.73 73	84.40 74	85.72 95	93.75 51	65.01 163	93.50 98	93.19 94	72.19 192	79.22 118	94.93 68	59.04 149	97.67 51	81.55 105	92.21 63	94.49 108

ACMMPR		84.37 77	84.06 76	85.28 109	93.56 58	64.37 179	93.50 98	93.15 96	72.19 192	78.85 126	94.86 71	56.69 175	97.45 66	81.55 105	92.20 64	94.02 125

1314		80.70 145	78.95 162	85.94 85	87.77 207	67.56 99	87.91 276	92.55 120	72.17 194	67.44 260	93.09 114	50.27 240	97.04 94	71.68 184	87.64 113	93.23 148

region2R		84.36 78	84.03 77	85.36 106	93.54 59	64.31 182	93.43 103	92.95 104	72.16 195	78.86 125	94.84 72	56.97 170	97.53 64	81.38 109	92.11 66	94.24 112

Test_1112_low_res		79.56 166	78.60 166	82.43 191	88.24 192	60.39 274	92.09 152	87.99 297	72.10 196	71.84 202	87.42 218	64.62 82	93.04 247	65.80 242	77.30 208	93.85 133

v1921920		75.63 237	73.49 242	82.06 209	79.38 325	66.35 130	91.07 205	89.48 239	71.98 197	67.99 249	81.22 294	49.16 253	93.90 232	66.56 231	64.56 302	85.92 285

DVP-MVS		89.41 13	89.73 14	88.45 24	96.40 15	69.99 38	96.64 10	94.52 42	71.92 198	90.55 20	96.93 11	73.77 21	99.08 11	91.91 29	94.90 22	96.29 32
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					96.40 15	69.99 38	96.76 8	94.33 54	71.92 198	91.89 11	97.11 6	73.77 21

Fast-Effi-MVS+-dtu		75.04 243	73.37 243	80.07 252	80.86 305	59.52 286	91.20 200	85.38 323	71.90 200	65.20 278	84.84 248	41.46 299	92.97 250	66.50 234	72.96 239	87.73 245

LFMVS		84.34 79	82.73 103	89.18 12	94.76 33	73.25 9	94.99 43	91.89 144	71.90 200	82.16 84	93.49 110	47.98 262	97.05 91	82.55 99	84.82 138	97.25 9

eth_miper_zixun_eth		75.96 231	74.40 228	80.66 238	84.66 263	63.02 218	89.28 253	88.27 290	71.88 202	65.73 274	81.65 283	59.45 143	92.81 259	68.13 214	60.53 335	86.14 276

train_agg		87.21 32	87.42 31	86.60 65	94.18 41	67.28 106	94.16 59	93.51 80	71.87 203	85.52 55	95.33 53	68.19 48	97.27 82	89.09 46	94.90 22	95.25 72

test_8					94.19 40	67.19 108	94.15 62	93.42 86	71.87 203	85.38 58	95.35 52	68.19 48	96.95 104

MDTV_nov1_ep13			72.61 254		89.06 169	68.48 73	80.33 337	90.11 217	71.84 205	71.81 203	75.92 345	53.01 217	93.92 231	48.04 327	73.38 235

ab-mvs		80.18 155	78.31 169	85.80 91	88.44 183	65.49 153	83.00 318	92.67 113	71.82 206	77.36 140	85.01 245	54.50 198	96.59 116	76.35 145	75.63 220	95.32 64

ACMMP		81.49 131	80.67 133	83.93 157	91.71 110	62.90 224	92.13 149	92.22 130	71.79 207	71.68 206	93.49 110	50.32 238	96.96 103	78.47 133	84.22 147	91.93 186
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
PHI-MVS		86.83 38	86.85 40	86.78 60	93.47 62	65.55 150	95.39 31	95.10 22	71.77 208	85.69 54	96.52 24	62.07 116	98.77 22	86.06 72	95.60 12	96.03 40

TEST9					94.18 41	67.28 106	94.16 59	93.51 80	71.75 209	85.52 55	95.33 53	68.01 50	97.27 82

WB-MVSnew		77.14 209	76.18 204	80.01 255	86.18 237	63.24 213	91.26 195	94.11 60	71.72 210	73.52 180	87.29 221	45.14 286	93.00 249	56.98 294	79.42 185	83.80 310

c3_l		76.83 217	75.47 213	80.93 236	85.02 259	64.18 187	90.39 224	88.11 294	71.66 211	66.65 272	81.64 284	63.58 99	92.56 271	69.31 204	62.86 312	86.04 280

SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 53	96.89 6	94.44 46	71.65 212	92.11 7	97.21 4	76.79 9	99.11 6	92.34 21	95.36 14	97.62 2

test_241102_TWO								94.41 48	71.65 212	92.07 9	97.21 4	74.58 17	99.11 6	92.34 21	95.36 14	96.59 18

test_241102_ONE					96.45 12	69.38 53		94.44 46	71.65 212	92.11 7	97.05 7	76.79 9	99.11 6

v8		75.35 239	73.26 244	81.61 216	80.67 309	66.82 118	89.54 247	89.27 248	71.65 212	63.30 298	80.30 308	54.99 195	94.06 221	67.33 224	62.33 318	83.94 308

v1240		75.21 242	72.98 247	81.88 211	79.20 327	66.00 138	90.75 214	89.11 258	71.63 216	67.41 262	81.22 294	47.36 267	93.87 233	65.46 247	64.72 300	85.77 287

SCA		75.82 233	72.76 250	85.01 117	86.63 228	70.08 37	81.06 332	89.19 252	71.60 217	70.01 224	77.09 335	45.53 282	90.25 311	60.43 279	73.27 236	94.68 94

BH-untuned		78.68 184	77.08 190	83.48 171	89.84 147	63.74 195	92.70 126	88.59 281	71.57 218	66.83 270	88.65 194	51.75 227	95.39 168	59.03 287	84.77 139	91.32 197

IterMVS		72.65 272	70.83 270	78.09 287	82.17 296	62.96 220	87.64 281	86.28 314	71.56 219	60.44 314	78.85 321	45.42 284	86.66 344	63.30 262	61.83 323	84.65 304
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mPP-MVS		82.96 109	82.44 109	84.52 140	92.83 77	62.92 223	92.76 122	91.85 148	71.52 220	75.61 158	94.24 93	53.48 214	96.99 99	78.97 128	90.73 86	93.64 138

test-LLR		80.10 157	79.56 151	81.72 214	86.93 226	61.17 255	92.70 126	91.54 161	71.51 221	75.62 156	86.94 225	53.83 207	92.38 277	72.21 177	84.76 140	91.60 188

test0.0.03 1		72.76 267	72.71 253	72.88 331	80.25 315	47.99 362	91.22 198	89.45 241	71.51 221	62.51 307	87.66 214	53.83 207	85.06 353	50.16 317	67.84 277	85.58 290

test_one_0601					96.32 18	69.74 48		94.18 57	71.42 223	90.67 19	96.85 16	74.45 18

PGM-MVS		83.25 103	82.70 104	84.92 119	92.81 81	64.07 188	90.44 221	92.20 131	71.28 224	77.23 142	94.43 82	55.17 193	97.31 77	79.33 124	91.38 79	93.37 143

thisisatest0530		81.15 135	80.07 141	84.39 145	88.26 190	65.63 147	91.40 184	94.62 40	71.27 225	70.93 212	89.18 189	72.47 29	96.04 139	65.62 244	76.89 213	91.49 190

cl____		76.07 224	74.67 220	80.28 245	85.15 255	61.76 246	90.12 232	88.73 275	71.16 226	65.43 276	81.57 286	61.15 123	92.95 251	66.54 232	62.17 319	86.13 278

DIV-MVS_self_test		76.07 224	74.67 220	80.28 245	85.14 256	61.75 247	90.12 232	88.73 275	71.16 226	65.42 277	81.60 285	61.15 123	92.94 255	66.54 232	62.16 321	86.14 276

dp		75.01 244	72.09 260	83.76 159	89.28 162	66.22 135	79.96 345	89.75 230	71.16 226	67.80 257	77.19 334	51.81 226	92.54 272	50.39 315	71.44 253	92.51 170

FA-MVS(test-final)		79.12 173	77.23 189	84.81 126	90.54 134	63.98 190	81.35 330	91.71 154	71.09 229	74.85 166	82.94 267	52.85 218	97.05 91	67.97 216	81.73 169	93.41 142

CP-MVS		83.71 96	83.40 90	84.65 134	93.14 71	63.84 191	94.59 50	92.28 125	71.03 230	77.41 139	94.92 69	55.21 192	96.19 130	81.32 110	90.70 87	93.91 129

v10		74.77 246	72.54 256	81.46 219	80.33 314	66.71 122	89.15 257	89.08 260	70.94 231	63.08 301	79.86 313	52.52 221	94.04 224	65.70 243	62.17 319	83.64 311

CDPH-MVS		85.71 58	85.46 59	86.46 71	94.75 34	67.19 108	93.89 76	92.83 108	70.90 232	83.09 77	95.28 56	63.62 96	97.36 73	80.63 114	94.18 36	94.84 87

GBi-Net		75.65 235	73.83 237	81.10 229	88.85 173	65.11 160	90.01 236	90.32 205	70.84 233	67.04 266	80.25 309	48.03 259	91.54 298	59.80 284	69.34 261	86.64 264

test1		75.65 235	73.83 237	81.10 229	88.85 173	65.11 160	90.01 236	90.32 205	70.84 233	67.04 266	80.25 309	48.03 259	91.54 298	59.80 284	69.34 261	86.64 264

FMVSNet2		76.07 224	74.01 235	82.26 199	88.85 173	67.66 96	91.33 192	91.61 159	70.84 233	65.98 273	82.25 275	48.03 259	92.00 288	58.46 289	68.73 269	87.10 258

SF-MVS		87.03 34	87.09 34	86.84 56	92.70 83	67.45 104	93.64 91	93.76 69	70.78 236	86.25 46	96.44 28	66.98 57	97.79 47	88.68 50	94.56 33	95.28 68

ZD-MVS					96.63 9	65.50 152		93.50 82	70.74 237	85.26 60	95.19 63	64.92 78	97.29 78	87.51 57	93.01 55

HyFIR lowres test		81.03 140	79.56 151	85.43 102	87.81 205	68.11 86	90.18 231	90.01 223	70.65 238	72.95 185	86.06 237	63.61 97	94.50 203	75.01 155	79.75 184	93.67 136

RRT_MVS		74.44 248	72.97 248	78.84 279	82.36 294	57.66 308	89.83 242	88.79 274	70.61 239	64.58 284	84.89 247	39.24 306	92.65 269	70.11 196	66.34 285	86.21 274

MVP-Stereo		77.12 210	76.23 202	79.79 263	81.72 300	66.34 131	89.29 252	90.88 189	70.56 240	62.01 309	82.88 268	49.34 248	94.13 216	65.55 246	93.80 42	78.88 363
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMP	71.68 10	75.58 238	74.23 231	79.62 267	84.97 260	59.64 283	90.80 212	89.07 261	70.39 241	62.95 302	87.30 220	38.28 314	93.87 233	72.89 167	71.45 252	85.36 296
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		83.25 103	82.95 98	84.17 152	92.25 92	62.88 225	90.91 206	91.86 146	70.30 242	77.12 143	93.96 100	56.75 173	96.28 128	82.04 102	91.34 81	93.34 144
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
GeoE		78.90 178	77.43 183	83.29 174	88.95 172	62.02 240	92.31 142	86.23 316	70.24 243	71.34 210	89.27 188	54.43 202	94.04 224	63.31 261	80.81 177	93.81 134

tpm2		79.80 163	77.95 176	85.34 107	88.28 189	68.26 80	81.56 327	91.42 167	70.11 244	77.59 138	80.50 304	67.40 55	94.26 212	67.34 223	77.35 207	93.51 140

TR-MVS		78.77 183	77.37 188	82.95 180	90.49 135	60.88 261	93.67 89	90.07 218	70.08 245	74.51 169	91.37 157	45.69 281	95.70 155	60.12 282	80.32 179	92.29 175

CL-MVSNet_self_test		69.92 288	68.09 292	75.41 311	73.25 364	55.90 325	90.05 235	89.90 225	69.96 246	61.96 310	76.54 338	51.05 234	87.64 337	49.51 321	50.59 365	82.70 329

PAPM_NR		82.97 108	81.84 116	86.37 75	94.10 44	66.76 121	87.66 280	92.84 107	69.96 246	74.07 175	93.57 108	63.10 107	97.50 65	70.66 192	90.58 89	94.85 84

PCF-MVS	73.15 9	79.29 170	77.63 180	84.29 149	86.06 239	65.96 140	87.03 287	91.10 180	69.86 248	69.79 229	90.64 164	57.54 162	96.59 116	64.37 254	82.29 158	90.32 210
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
miper_lstm_enhance		73.05 262	71.73 265	77.03 299	83.80 277	58.32 301	81.76 323	88.88 268	69.80 249	61.01 311	78.23 325	57.19 164	87.51 340	65.34 248	59.53 340	85.27 299

MIMVSNet		71.64 276	68.44 289	81.23 224	81.97 299	64.44 174	73.05 365	88.80 272	69.67 250	64.59 283	74.79 349	32.79 344	87.82 334	53.99 305	76.35 216	91.42 192

LPG-MVS_test		75.82 233	74.58 224	79.56 269	84.31 271	59.37 288	90.44 221	89.73 233	69.49 251	64.86 280	88.42 196	38.65 310	94.30 208	72.56 173	72.76 241	85.01 300

LGP-MVS_train				79.56 269	84.31 271	59.37 288		89.73 233	69.49 251	64.86 280	88.42 196	38.65 310	94.30 208	72.56 173	72.76 241	85.01 300

APDe-MVS		87.54 26	87.84 25	86.65 63	96.07 23	66.30 132	94.84 46	93.78 66	69.35 253	88.39 34	96.34 30	67.74 53	97.66 54	90.62 38	93.44 50	96.01 41
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
tttt0517		79.50 167	78.53 167	82.41 194	87.22 217	61.43 253	89.75 244	94.76 32	69.29 254	67.91 253	88.06 209	72.92 25	95.63 156	62.91 265	73.90 234	90.16 212

Patchmatch-RL test		68.17 304	64.49 314	79.19 273	71.22 369	53.93 335	70.07 371	71.54 377	69.22 255	56.79 336	62.89 377	56.58 177	88.61 325	69.53 201	52.61 360	95.03 80

test_yl		84.28 80	83.16 94	87.64 33	94.52 37	69.24 57	95.78 19	95.09 23	69.19 256	81.09 92	92.88 122	57.00 168	97.44 68	81.11 112	81.76 167	96.23 35

DCV-MVSNet		84.28 80	83.16 94	87.64 33	94.52 37	69.24 57	95.78 19	95.09 23	69.19 256	81.09 92	92.88 122	57.00 168	97.44 68	81.11 112	81.76 167	96.23 35

jajsoiax		73.05 262	71.51 267	77.67 290	77.46 348	54.83 331	88.81 262	90.04 221	69.13 258	62.85 304	83.51 262	31.16 355	92.75 262	70.83 188	69.80 257	85.43 295

DP-MVS Recon		82.73 112	81.65 118	85.98 83	97.31 4	67.06 112	95.15 37	91.99 138	69.08 259	76.50 150	93.89 101	54.48 201	98.20 35	70.76 190	85.66 134	92.69 163

Baseline_NR-MVSNet		73.99 254	72.83 249	77.48 293	80.78 307	59.29 291	91.79 169	84.55 331	68.85 260	68.99 237	80.70 300	56.16 180	92.04 287	62.67 267	60.98 332	81.11 343

CHOSEN 280x420		77.35 206	76.95 194	78.55 281	87.07 221	62.68 229	69.71 372	82.95 345	68.80 261	71.48 208	87.27 222	66.03 65	84.00 359	76.47 144	82.81 155	88.95 227

DPE-MVS		88.77 16	89.21 16	87.45 42	96.26 20	67.56 99	94.17 58	94.15 59	68.77 262	90.74 18	97.27 2	76.09 12	98.49 29	90.58 39	94.91 21	96.30 31
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
mvs_tets		72.71 269	71.11 268	77.52 291	77.41 349	54.52 333	88.45 268	89.76 229	68.76 263	62.70 305	83.26 265	29.49 359	92.71 263	70.51 194	69.62 259	85.34 297

MVS		84.66 74	82.86 101	90.06 2	90.93 127	74.56 6	87.91 276	95.54 13	68.55 264	72.35 198	94.71 75	59.78 140	98.90 19	81.29 111	94.69 32	96.74 15

EPP-MVSNet		81.79 128	81.52 119	82.61 188	88.77 177	60.21 277	93.02 116	93.66 75	68.52 265	72.90 186	90.39 171	72.19 32	94.96 181	74.93 156	79.29 189	92.67 164

CSCG		86.87 36	86.26 45	88.72 17	95.05 31	70.79 30	93.83 83	95.33 16	68.48 266	77.63 136	94.35 88	73.04 24	98.45 30	84.92 82	93.71 46	96.92 13

testing3		70.38 285	70.83 270	69.03 350	85.82 245	43.93 378	90.72 215	90.56 198	68.06 267	60.24 315	86.82 227	64.83 79	84.12 355	26.33 388	64.10 305	79.04 362

CP-MVSNet		70.50 283	69.91 280	72.26 336	80.71 308	51.00 348	87.23 286	90.30 209	67.84 268	59.64 318	82.69 270	50.23 241	82.30 371	51.28 312	59.28 341	83.46 316

pmmvs5		73.35 259	71.52 266	78.86 278	78.64 338	60.61 272	91.08 203	86.90 308	67.69 269	63.32 297	83.64 260	44.33 290	90.53 308	62.04 271	66.02 287	85.46 294

pm-mvs1		72.89 265	71.09 269	78.26 285	79.10 331	57.62 309	90.80 212	89.30 247	67.66 270	62.91 303	81.78 281	49.11 254	92.95 251	60.29 281	58.89 343	84.22 306

MDTV_nov1_ep13_2view						59.90 281	80.13 341		67.65 271	72.79 187		54.33 204		59.83 283		92.58 167

pmmvs4		73.92 255	71.81 264	80.25 247	79.17 328	65.24 156	87.43 283	87.26 305	67.64 272	63.46 296	83.91 259	48.96 255	91.53 301	62.94 264	65.49 289	83.96 307

WR-MVS_H		70.59 282	69.94 279	72.53 333	81.03 304	51.43 345	87.35 284	92.03 137	67.38 273	60.23 316	80.70 300	55.84 186	83.45 363	46.33 337	58.58 345	82.72 327

KD-MVS_2432*1600		69.03 296	66.37 299	77.01 300	85.56 249	61.06 258	81.44 328	90.25 211	67.27 274	58.00 330	76.53 339	54.49 199	87.63 338	48.04 327	35.77 387	82.34 333

miper_refine_blended		69.03 296	66.37 299	77.01 300	85.56 249	61.06 258	81.44 328	90.25 211	67.27 274	58.00 330	76.53 339	54.49 199	87.63 338	48.04 327	35.77 387	82.34 333

PS-CasMVS		69.86 290	69.13 285	72.07 340	80.35 313	50.57 350	87.02 288	89.75 230	67.27 274	59.19 322	82.28 274	46.58 272	82.24 372	50.69 314	59.02 342	83.39 318

PEN-MVS		69.46 293	68.56 287	72.17 338	79.27 326	49.71 354	86.90 290	89.24 249	67.24 277	59.08 323	82.51 273	47.23 268	83.54 362	48.42 325	57.12 346	83.25 319

cascas		78.18 193	75.77 209	85.41 103	87.14 219	69.11 59	92.96 117	91.15 178	66.71 278	70.47 216	86.07 236	37.49 324	96.48 124	70.15 195	79.80 183	90.65 206

APD-MVS		85.93 53	85.99 51	85.76 93	95.98 26	65.21 157	93.59 94	92.58 119	66.54 279	86.17 48	95.88 41	63.83 91	97.00 96	86.39 69	92.94 56	95.06 77
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	70.45 11	78.54 188	75.92 207	86.41 74	85.93 244	71.68 18	92.74 123	92.51 121	66.49 280	64.56 285	91.96 142	43.88 291	98.10 37	54.61 302	90.65 88	89.44 226

DTE-MVSNet		68.46 302	67.33 295	71.87 342	77.94 346	49.00 359	86.16 295	88.58 282	66.36 281	58.19 327	82.21 276	46.36 273	83.87 360	44.97 344	55.17 353	82.73 326

IterMVS-SCA-FT		71.55 278	69.97 278	76.32 306	81.48 301	60.67 270	87.64 281	85.99 319	66.17 282	59.50 319	78.88 320	45.53 282	83.65 361	62.58 268	61.93 322	84.63 305

TransMVSNet (Re)		70.07 287	67.66 293	77.31 297	80.62 311	59.13 294	91.78 171	84.94 328	65.97 283	60.08 317	80.44 305	50.78 235	91.87 289	48.84 323	45.46 373	80.94 345

MVSFormer		83.75 95	82.88 100	86.37 75	89.24 166	71.18 24	89.07 258	90.69 192	65.80 284	87.13 40	94.34 89	64.99 75	92.67 266	72.83 168	91.80 71	95.27 69

test_djsdf		73.76 258	72.56 255	77.39 295	77.00 351	53.93 335	89.07 258	90.69 192	65.80 284	63.92 291	82.03 278	43.14 295	92.67 266	72.83 168	68.53 270	85.57 291

API-MVS		82.28 119	80.53 137	87.54 40	96.13 22	70.59 32	93.63 92	91.04 187	65.72 286	75.45 160	92.83 124	56.11 182	98.89 20	64.10 255	89.75 97	93.15 150

原ACMM1				84.42 143	93.21 68	64.27 184		93.40 88	65.39 287	79.51 113	92.50 128	58.11 157	96.69 114	65.27 249	93.96 39	92.32 174

testgi		64.48 326	62.87 324	69.31 349	71.24 368	40.62 384	85.49 296	79.92 355	65.36 288	54.18 344	83.49 263	23.74 372	84.55 354	41.60 355	60.79 334	82.77 325

QAPM		79.95 161	77.39 187	87.64 33	89.63 152	71.41 20	93.30 105	93.70 73	65.34 289	67.39 263	91.75 148	47.83 264	98.96 16	57.71 292	89.81 94	92.54 168

HPM-MVS_fast		80.25 154	79.55 153	82.33 195	91.55 115	59.95 280	91.32 193	89.16 254	65.23 290	74.71 167	93.07 116	47.81 265	95.74 149	74.87 159	88.23 106	91.31 198

tfpnnormal		70.10 286	67.36 294	78.32 283	83.45 283	60.97 260	88.85 261	92.77 109	64.85 291	60.83 313	78.53 322	43.52 293	93.48 241	31.73 384	61.70 327	80.52 350

FE-MVS		75.97 230	73.02 246	84.82 123	89.78 148	65.56 149	77.44 355	91.07 184	64.55 292	72.66 188	79.85 314	46.05 280	96.69 114	54.97 301	80.82 176	92.21 181

SR-MVS		82.81 111	82.58 106	83.50 170	93.35 63	61.16 257	92.23 146	91.28 173	64.48 293	81.27 89	95.28 56	53.71 210	95.86 144	82.87 96	88.77 103	93.49 141

K. test v3		63.09 332	59.61 336	73.53 326	76.26 354	49.38 358	83.27 312	77.15 360	64.35 294	47.77 368	72.32 357	28.73 361	87.79 335	49.93 319	36.69 386	83.41 317

v7n		71.31 279	68.65 286	79.28 272	76.40 353	60.77 264	86.71 292	89.45 241	64.17 295	58.77 326	78.24 324	44.59 289	93.54 239	57.76 291	61.75 325	83.52 314

FMVSNet1		72.71 269	69.91 280	81.10 229	83.60 281	65.11 160	90.01 236	90.32 205	63.92 296	63.56 295	80.25 309	36.35 333	91.54 298	54.46 303	66.75 282	86.64 264

XVG-OURS		74.25 251	72.46 257	79.63 266	78.45 340	57.59 310	80.33 337	87.39 302	63.86 297	68.76 242	89.62 185	40.50 303	91.72 293	69.00 208	74.25 229	89.58 221

UniMVSNet_ETH3D		72.74 268	70.53 275	79.36 271	78.62 339	56.64 320	85.01 299	89.20 251	63.77 298	64.84 282	84.44 254	34.05 341	91.86 290	63.94 256	70.89 256	89.57 222

test_fmvs1		74.07 252	73.69 239	75.22 312	78.91 334	47.34 366	89.06 260	74.69 368	63.68 299	79.41 115	91.59 152	24.36 369	87.77 336	85.22 76	76.26 217	90.55 209

114514_t		79.17 172	77.67 178	83.68 164	95.32 29	65.53 151	92.85 120	91.60 160	63.49 300	67.92 252	90.63 166	46.65 271	95.72 154	67.01 228	83.54 148	89.79 218

test_fmvs1_n		72.69 271	71.92 262	74.99 315	71.15 370	47.08 368	87.34 285	75.67 363	63.48 301	78.08 132	91.17 159	20.16 380	87.87 333	84.65 84	75.57 221	90.01 215

APD-MVS_3200maxsize		81.64 130	81.32 121	82.59 189	92.36 89	58.74 297	91.39 186	91.01 188	63.35 302	79.72 111	94.62 78	51.82 225	96.14 132	79.71 120	87.93 110	92.89 161

test20.03		63.83 329	62.65 325	67.38 357	70.58 374	39.94 385	86.57 293	84.17 333	63.29 303	51.86 352	77.30 331	37.09 329	82.47 369	38.87 366	54.13 357	79.73 356

XVG-OURS-SEG-HR		74.70 247	73.08 245	79.57 268	78.25 342	57.33 314	80.49 335	87.32 303	63.22 304	68.76 242	90.12 181	44.89 288	91.59 296	70.55 193	74.09 231	89.79 218

test_vis1_n		71.63 277	70.73 273	74.31 322	69.63 376	47.29 367	86.91 289	72.11 373	63.21 305	75.18 162	90.17 177	20.40 378	85.76 348	84.59 85	74.42 228	89.87 216

ACMM	69.62 13	74.34 249	72.73 252	79.17 274	84.25 273	57.87 304	90.36 225	89.93 224	63.17 306	65.64 275	86.04 238	37.79 322	94.10 217	65.89 240	71.52 251	85.55 292
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmvs		72.88 266	69.76 282	82.22 200	90.98 126	67.05 113	78.22 352	88.30 288	63.10 307	64.35 290	74.98 348	55.09 194	94.27 210	43.25 347	69.57 260	85.34 297

SixPastTwentyTwo		64.92 323	61.78 330	74.34 321	78.74 336	49.76 353	83.42 311	79.51 357	62.86 308	50.27 359	77.35 330	30.92 357	90.49 309	45.89 339	47.06 370	82.78 324

SR-MVS-dyc-post		81.06 139	80.70 132	82.15 203	92.02 98	58.56 299	90.90 207	90.45 199	62.76 309	78.89 121	94.46 80	51.26 233	95.61 158	78.77 131	86.77 124	92.28 176

RE-MVS-def			80.48 138		92.02 98	58.56 299	90.90 207	90.45 199	62.76 309	78.89 121	94.46 80	49.30 249		78.77 131	86.77 124	92.28 176

TAPA-MVS	70.22 12	74.94 245	73.53 241	79.17 274	90.40 137	52.07 342	89.19 256	89.61 237	62.69 311	70.07 223	92.67 126	48.89 256	94.32 206	38.26 367	79.97 181	91.12 202
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Anonymous202405211		77.96 197	75.33 216	85.87 87	93.73 54	64.52 169	94.85 45	85.36 324	62.52 312	76.11 151	90.18 176	29.43 360	97.29 78	68.51 213	77.24 210	95.81 47

pmmvs-eth3d		65.53 322	62.32 327	75.19 313	69.39 377	59.59 284	82.80 319	83.43 341	62.52 312	51.30 356	72.49 353	32.86 343	87.16 343	55.32 300	50.73 364	78.83 364

AdaColmap		78.94 177	77.00 193	84.76 128	96.34 17	65.86 142	92.66 130	87.97 299	62.18 314	70.56 215	92.37 134	43.53 292	97.35 74	64.50 253	82.86 153	91.05 203

FOURS1					93.95 45	61.77 245	93.96 71	91.92 141	62.14 315	86.57 45

无先验							92.71 125	92.61 118	62.03 316				97.01 95	66.63 230		93.97 126

XVG-ACMP-BASELINE		68.04 305	65.53 305	75.56 310	74.06 362	52.37 340	78.43 349	85.88 320	62.03 316	58.91 325	81.21 296	20.38 379	91.15 305	60.69 278	68.18 272	83.16 321

anonymousdsp		71.14 280	69.37 284	76.45 305	72.95 365	54.71 332	84.19 303	88.88 268	61.92 318	62.15 308	79.77 315	38.14 317	91.44 303	68.90 210	67.45 278	83.21 320

tpm cat1		75.30 240	72.21 259	84.58 138	88.52 179	67.77 93	78.16 353	88.02 296	61.88 319	68.45 247	76.37 341	60.65 129	94.03 226	53.77 307	74.11 230	91.93 186

FMVSNet5		68.04 305	65.66 304	75.18 314	84.43 269	57.89 303	83.54 307	86.26 315	61.83 320	53.64 347	73.30 352	37.15 328	85.08 352	48.99 322	61.77 324	82.56 332

Anonymous20231206		67.53 310	65.78 301	72.79 332	74.95 358	47.59 364	88.23 270	87.32 303	61.75 321	58.07 329	77.29 332	37.79 322	87.29 342	42.91 349	63.71 309	83.48 315

PatchMatch-RL		72.06 274	69.98 277	78.28 284	89.51 156	55.70 326	83.49 308	83.39 343	61.24 322	63.72 294	82.76 269	34.77 338	93.03 248	53.37 309	77.59 202	86.12 279

tt0805		73.07 261	70.73 273	80.07 252	78.37 341	57.05 316	87.78 278	92.18 133	61.23 323	67.04 266	86.49 230	31.35 354	94.58 195	65.06 250	67.12 279	88.57 234

PLC	68.80 14	75.23 241	73.68 240	79.86 261	92.93 75	58.68 298	90.64 218	88.30 288	60.90 324	64.43 289	90.53 167	42.38 297	94.57 197	56.52 295	76.54 215	86.33 270
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH	63.93 17	68.62 299	64.81 309	80.03 254	85.22 254	63.25 212	87.72 279	84.66 330	60.83 325	51.57 354	79.43 319	27.29 365	94.96 181	41.76 354	64.84 297	81.88 337
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 300	65.41 306	77.96 288	78.69 337	62.93 221	89.86 241	89.17 253	60.55 326	50.27 359	77.73 329	22.60 374	94.06 221	47.18 333	72.65 243	76.88 371

VDDNet		80.50 148	78.26 170	87.21 46	86.19 236	69.79 46	94.48 51	91.31 170	60.42 327	79.34 116	90.91 162	38.48 313	96.56 119	82.16 100	81.05 173	95.27 69

CPTT-MVS		79.59 165	79.16 160	80.89 237	91.54 116	59.80 282	92.10 151	88.54 283	60.42 327	72.96 184	93.28 112	48.27 258	92.80 260	78.89 130	86.50 129	90.06 213

our_test_3		68.29 303	64.69 311	79.11 277	78.92 332	64.85 167	88.40 269	85.06 326	60.32 329	52.68 349	76.12 343	40.81 302	89.80 321	44.25 346	55.65 351	82.67 331

ITE_SJBPF				70.43 345	74.44 360	47.06 369		77.32 359	60.16 330	54.04 345	83.53 261	23.30 373	84.01 358	43.07 348	61.58 329	80.21 355

ppachtmachnet_test		67.72 307	63.70 318	79.77 264	78.92 332	66.04 137	88.68 264	82.90 346	60.11 331	55.45 339	75.96 344	39.19 307	90.55 307	39.53 362	52.55 361	82.71 328

new-patchmatchnet		59.30 343	56.48 345	67.79 354	65.86 383	44.19 375	82.47 320	81.77 347	59.94 332	43.65 380	66.20 372	27.67 364	81.68 374	39.34 363	41.40 379	77.50 370

mvsany_test1		68.77 298	68.56 287	69.39 348	73.57 363	45.88 373	80.93 333	60.88 391	59.65 333	71.56 207	90.26 175	43.22 294	75.05 381	74.26 162	62.70 314	87.25 257

新几何1				84.73 129	92.32 90	64.28 183		91.46 166	59.56 334	79.77 110	92.90 120	56.95 171	96.57 118	63.40 259	92.91 57	93.34 144

旧先验2							92.00 160		59.37 335	87.54 39			93.47 242	75.39 151

PM-MVS		59.40 342	56.59 344	67.84 353	63.63 384	41.86 380	76.76 356	63.22 388	59.01 336	51.07 357	72.27 358	11.72 391	83.25 365	61.34 274	50.28 366	78.39 367

LTVRE_ROB	59.60 19	66.27 316	63.54 319	74.45 319	84.00 276	51.55 344	67.08 379	83.53 340	58.78 337	54.94 341	80.31 307	34.54 339	93.23 245	40.64 360	68.03 273	78.58 366
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
testdata				81.34 222	89.02 170	57.72 306		89.84 227	58.65 338	85.32 59	94.09 96	57.03 166	93.28 244	69.34 203	90.56 90	93.03 155

ACMH+	65.35 16	67.65 308	64.55 312	76.96 302	84.59 265	57.10 315	88.08 271	80.79 351	58.59 339	53.00 348	81.09 298	26.63 367	92.95 251	46.51 335	61.69 328	80.82 346

ADS-MVSNet2		66.90 313	63.44 320	77.26 298	88.06 196	60.70 269	68.01 376	75.56 365	57.57 340	64.48 286	69.87 365	38.68 308	84.10 356	40.87 358	67.89 275	86.97 259

ADS-MVSNet		68.54 301	64.38 316	81.03 233	88.06 196	66.90 117	68.01 376	84.02 335	57.57 340	64.48 286	69.87 365	38.68 308	89.21 324	40.87 358	67.89 275	86.97 259

MDA-MVSNet-bldmvs		61.54 337	57.70 341	73.05 329	79.53 323	57.00 319	83.08 316	81.23 349	57.57 340	34.91 388	72.45 354	32.79 344	86.26 347	35.81 371	41.95 378	75.89 373

KD-MVS_self_test		60.87 338	58.60 338	67.68 355	66.13 382	39.93 386	75.63 362	84.70 329	57.32 343	49.57 362	68.45 368	29.55 358	82.87 367	48.09 326	47.94 369	80.25 354

UnsupCasMVSNet_bld		61.60 336	57.71 340	73.29 328	68.73 378	51.64 343	78.61 348	89.05 262	57.20 344	46.11 369	61.96 380	28.70 362	88.60 326	50.08 318	38.90 384	79.63 357

MSDG		69.54 292	65.73 302	80.96 234	85.11 258	63.71 198	84.19 303	83.28 344	56.95 345	54.50 342	84.03 256	31.50 352	96.03 140	42.87 351	69.13 266	83.14 322

F-COLMAP		70.66 281	68.44 289	77.32 296	86.37 234	55.91 324	88.00 274	86.32 313	56.94 346	57.28 335	88.07 208	33.58 342	92.49 274	51.02 313	68.37 271	83.55 312

test222					89.77 149	61.60 250	89.55 246	89.42 243	56.83 347	77.28 141	92.43 132	52.76 219			91.14 84	93.09 152

CNLPA		74.31 250	72.30 258	80.32 243	91.49 117	61.66 249	90.85 210	80.72 352	56.67 348	63.85 293	90.64 164	46.75 270	90.84 306	53.79 306	75.99 219	88.47 238

OurMVSNet-221017-0		64.68 324	62.17 328	72.21 337	76.08 356	47.35 365	80.67 334	81.02 350	56.19 349	51.60 353	79.66 317	27.05 366	88.56 327	53.60 308	53.63 358	80.71 348

YYNet1		63.76 331	60.14 334	74.62 318	78.06 345	60.19 278	83.46 310	83.99 338	56.18 350	39.25 384	71.56 362	37.18 327	83.34 364	42.90 350	48.70 368	80.32 352

MDA-MVSNet_test_wron		63.78 330	60.16 333	74.64 317	78.15 344	60.41 273	83.49 308	84.03 334	56.17 351	39.17 385	71.59 361	37.22 326	83.24 366	42.87 351	48.73 367	80.26 353

OpenMVS_ROB	61.12 18	66.39 315	62.92 323	76.80 304	76.51 352	57.77 305	89.22 254	83.41 342	55.48 352	53.86 346	77.84 328	26.28 368	93.95 230	34.90 374	68.76 268	78.68 365

MIMVSNet1		60.16 341	57.33 342	68.67 351	69.71 375	44.13 376	78.92 347	84.21 332	55.05 353	44.63 377	71.85 359	23.91 371	81.54 375	32.63 382	55.03 354	80.35 351

test_fmvs2		65.78 320	64.84 308	68.60 352	66.54 381	41.71 381	83.27 312	69.81 379	54.38 354	67.91 253	84.54 253	15.35 385	81.22 376	75.65 149	66.16 286	82.88 323

CVMVSNet		74.04 253	74.27 230	73.33 327	85.33 251	43.94 377	89.53 248	88.39 285	54.33 355	70.37 219	90.13 179	49.17 252	84.05 357	61.83 273	79.36 187	91.99 185

Anonymous20240529		76.84 216	74.15 232	84.88 121	91.02 125	64.95 165	93.84 81	91.09 181	53.57 356	73.00 183	87.42 218	35.91 334	97.32 76	69.14 207	72.41 246	92.36 172

pmmvs6		67.57 309	64.76 310	76.00 309	72.82 367	53.37 337	88.71 263	86.78 312	53.19 357	57.58 334	78.03 327	35.33 337	92.41 276	55.56 299	54.88 355	82.21 335

TinyColmap		60.32 339	56.42 346	72.00 341	78.78 335	53.18 338	78.36 351	75.64 364	52.30 358	41.59 383	75.82 346	14.76 388	88.35 329	35.84 370	54.71 356	74.46 375

test_0402		64.54 325	61.09 331	74.92 316	84.10 275	60.75 266	87.95 275	79.71 356	52.03 359	52.41 350	77.20 333	32.21 350	91.64 294	23.14 390	61.03 331	72.36 379

test_vis1_rt		59.09 344	57.31 343	64.43 360	68.44 379	46.02 372	83.05 317	48.63 400	51.96 360	49.57 362	63.86 376	16.30 383	80.20 378	71.21 186	62.79 313	67.07 385

Anonymous20231211		73.08 260	70.39 276	81.13 227	90.62 133	63.33 211	91.40 184	90.06 220	51.84 361	64.46 288	80.67 302	36.49 332	94.07 220	63.83 257	64.17 304	85.98 282

AllTest		61.66 335	58.06 339	72.46 334	79.57 321	51.42 346	80.17 340	68.61 381	51.25 362	45.88 370	81.23 292	19.86 381	86.58 345	38.98 364	57.01 348	79.39 358

TestCases				72.46 334	79.57 321	51.42 346		68.61 381	51.25 362	45.88 370	81.23 292	19.86 381	86.58 345	38.98 364	57.01 348	79.39 358

PatchT		69.11 295	65.37 307	80.32 243	82.07 298	63.68 201	67.96 378	87.62 301	50.86 364	69.37 230	65.18 373	57.09 165	88.53 328	41.59 356	66.60 283	88.74 231

Anonymous20240521		62.09 334	59.08 337	71.10 343	67.19 380	48.72 360	83.91 305	85.23 325	50.38 365	47.84 367	71.22 364	20.74 377	85.51 351	46.47 336	58.75 344	79.06 361

DP-MVS		69.90 289	66.48 296	80.14 250	95.36 28	62.93 221	89.56 245	76.11 361	50.27 366	57.69 333	85.23 243	39.68 305	95.73 150	33.35 377	71.05 255	81.78 339

gg-mvs-nofinetune		77.18 208	74.31 229	85.80 91	91.42 118	68.36 76	71.78 366	94.72 34	49.61 367	77.12 143	45.92 390	77.41 8	93.98 228	67.62 221	93.16 54	95.05 78

JIA-IIPM		66.06 317	62.45 326	76.88 303	81.42 303	54.45 334	57.49 391	88.67 278	49.36 368	63.86 292	46.86 389	56.06 183	90.25 311	49.53 320	68.83 267	85.95 283

N_pmnet		50.55 350	49.11 353	54.88 370	77.17 350	4.02 413	84.36 302	2.00 411	48.59 369	45.86 372	68.82 367	32.22 349	82.80 368	31.58 385	51.38 363	77.81 369

ANet_high		40.27 361	35.20 364	55.47 368	34.74 408	34.47 393	63.84 383	71.56 376	48.42 370	18.80 397	41.08 396	9.52 395	64.45 397	20.18 393	8.66 404	67.49 384

COLMAP_ROB	57.96 20	62.98 333	59.65 335	72.98 330	81.44 302	53.00 339	83.75 306	75.53 366	48.34 371	48.81 365	81.40 290	24.14 370	90.30 310	32.95 379	60.52 336	75.65 374
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmtry		67.53 310	63.93 317	78.34 282	82.12 297	64.38 178	68.72 373	84.00 336	48.23 372	59.24 320	72.41 355	57.82 159	89.27 323	46.10 338	56.68 350	81.36 340

LS3D		69.17 294	66.40 298	77.50 292	91.92 104	56.12 323	85.12 298	80.37 354	46.96 373	56.50 337	87.51 217	37.25 325	93.71 236	32.52 383	79.40 186	82.68 330

RPSCF		64.24 327	61.98 329	71.01 344	76.10 355	45.00 374	75.83 361	75.94 362	46.94 374	58.96 324	84.59 251	31.40 353	82.00 373	47.76 331	60.33 339	86.04 280

RPMNet		70.42 284	65.68 303	84.63 136	83.15 285	67.96 89	70.25 369	90.45 199	46.83 375	69.97 226	65.10 374	56.48 179	95.30 173	35.79 372	73.13 237	90.64 207

WB-MVS		46.23 354	44.94 356	50.11 374	62.13 388	21.23 407	76.48 358	55.49 393	45.89 376	35.78 386	61.44 382	35.54 335	72.83 385	9.96 401	21.75 396	56.27 389

CMPMVS	48.56 21	66.77 314	64.41 315	73.84 324	70.65 373	50.31 351	77.79 354	85.73 322	45.54 377	44.76 376	82.14 277	35.40 336	90.14 317	63.18 263	74.54 226	81.07 344
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		64.01 328	63.01 322	67.02 358	74.40 361	38.86 389	83.27 312	86.19 317	45.11 378	54.27 343	81.15 297	36.91 331	80.01 379	48.79 324	57.02 347	82.19 336

TDRefinement		55.28 348	51.58 351	66.39 359	59.53 391	46.15 371	76.23 359	72.80 371	44.60 379	42.49 381	76.28 342	15.29 386	82.39 370	33.20 378	43.75 375	70.62 381

Patchmatch-test		65.86 318	60.94 332	80.62 240	83.75 278	58.83 296	58.91 390	75.26 367	44.50 380	50.95 358	77.09 335	58.81 151	87.90 332	35.13 373	64.03 306	95.12 76

test_fmvs3		56.82 345	54.86 348	62.69 363	53.59 394	35.47 391	75.87 360	65.64 386	43.91 381	55.10 340	71.43 363	6.91 399	74.40 384	68.64 212	52.63 359	78.20 368

mvsany_test3		48.86 352	46.35 355	56.41 366	46.00 400	31.67 396	62.26 384	47.25 401	43.71 382	45.54 374	68.15 369	10.84 392	64.44 398	57.95 290	35.44 389	73.13 376

SSC-MVS		44.51 356	43.35 358	47.99 378	61.01 390	18.90 409	74.12 364	54.36 394	43.42 383	34.10 389	60.02 383	34.42 340	70.39 388	9.14 403	19.57 397	54.68 390

LF4IMVS		54.01 349	52.12 350	59.69 364	62.41 387	39.91 387	68.59 374	68.28 383	42.96 384	44.55 378	75.18 347	14.09 390	68.39 390	41.36 357	51.68 362	70.78 380

DSMNet-mixed		56.78 346	54.44 349	63.79 361	63.21 385	29.44 400	64.43 382	64.10 387	42.12 385	51.32 355	71.60 360	31.76 351	75.04 382	36.23 369	65.20 294	86.87 262

pmmvs3		55.51 347	51.50 352	67.53 356	57.90 392	50.93 349	80.37 336	73.66 370	40.63 386	44.15 379	64.75 375	16.30 383	78.97 380	44.77 345	40.98 382	72.69 377

new_pmnet		49.31 351	46.44 354	57.93 365	62.84 386	40.74 383	68.47 375	62.96 389	36.48 387	35.09 387	57.81 384	14.97 387	72.18 386	32.86 380	46.44 371	60.88 387

MVS-HIRNet		60.25 340	55.55 347	74.35 320	84.37 270	56.57 321	71.64 367	74.11 369	34.44 388	45.54 374	42.24 395	31.11 356	89.81 319	40.36 361	76.10 218	76.67 372

test_f		46.58 353	43.45 357	55.96 367	45.18 401	32.05 395	61.18 385	49.49 399	33.39 389	42.05 382	62.48 379	7.00 398	65.56 394	47.08 334	43.21 377	70.27 382

test_vis3_rt		40.46 360	37.79 361	48.47 377	44.49 402	33.35 394	66.56 380	32.84 408	32.39 390	29.65 390	39.13 398	3.91 406	68.65 389	50.17 316	40.99 381	43.40 393

DeepMVS_CX				34.71 384	51.45 396	24.73 404		28.48 410	31.46 391	17.49 400	52.75 386	5.80 401	42.60 405	18.18 394	19.42 398	36.81 397

FPMVS		45.64 355	43.10 359	53.23 372	51.42 397	36.46 390	64.97 381	71.91 374	29.13 392	27.53 392	61.55 381	9.83 394	65.01 396	16.00 398	55.58 352	58.22 388

PMMVS2		37.93 363	33.61 366	50.92 373	46.31 399	24.76 403	60.55 388	50.05 397	28.94 393	20.93 395	47.59 388	4.41 405	65.13 395	25.14 389	18.55 399	62.87 386

LCM-MVSNet		40.54 358	35.79 363	54.76 371	36.92 407	30.81 397	51.41 394	69.02 380	22.07 394	24.63 394	45.37 391	4.56 403	65.81 393	33.67 376	34.50 390	67.67 383

APD_test1		40.50 359	37.31 362	50.09 375	51.88 395	35.27 392	59.45 389	52.59 396	21.64 395	26.12 393	57.80 385	4.56 403	66.56 392	22.64 391	39.09 383	48.43 391

PMVS	26.43 22	31.84 367	28.16 370	42.89 380	25.87 410	27.58 401	50.92 395	49.78 398	21.37 396	14.17 402	40.81 397	2.01 409	66.62 391	9.61 402	38.88 385	34.49 398
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.91 364	31.44 367	45.30 379	70.99 371	39.64 388	19.85 401	72.56 372	20.10 397	16.16 401	21.47 402	5.08 402	71.16 387	13.07 399	43.70 376	25.08 399
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 365	29.47 368	42.67 381	41.89 404	30.81 397	52.07 392	43.45 402	15.45 398	18.52 398	44.82 392	2.12 407	58.38 399	16.05 396	30.87 393	38.83 394

APD_test2		32.77 365	29.47 368	42.67 381	41.89 404	30.81 397	52.07 392	43.45 402	15.45 398	18.52 398	44.82 392	2.12 407	58.38 399	16.05 396	30.87 393	38.83 394

E-PMN		24.61 368	24.00 372	26.45 385	43.74 403	18.44 410	60.86 386	39.66 404	15.11 400	9.53 404	22.10 401	6.52 400	46.94 403	8.31 404	10.14 401	13.98 401

EMVS		23.76 370	23.20 374	25.46 386	41.52 406	16.90 411	60.56 387	38.79 407	14.62 401	8.99 405	20.24 404	7.35 397	45.82 404	7.25 405	9.46 402	13.64 402

MVE	24.84 23	24.35 369	19.77 375	38.09 383	34.56 409	26.92 402	26.57 399	38.87 406	11.73 402	11.37 403	27.44 399	1.37 410	50.42 402	11.41 400	14.60 400	36.93 396
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		38.59 362	35.16 365	48.89 376	54.33 393	21.35 406	45.32 397	53.71 395	7.41 403	28.74 391	51.62 387	8.70 396	52.87 401	33.73 375	32.89 391	72.47 378

wuyk23d		11.30 373	10.95 376	12.33 388	48.05 398	19.89 408	25.89 400	1.92 412	3.58 404	3.12 406	1.37 406	0.64 411	15.77 407	6.23 406	7.77 405	1.35 403

tmp_tt		22.26 371	23.75 373	17.80 387	5.23 411	12.06 412	35.26 398	39.48 405	2.82 405	18.94 396	44.20 394	22.23 375	24.64 406	36.30 368	9.31 403	16.69 400

EGC-MVSNET		42.35 357	38.09 360	55.11 369	74.57 359	46.62 370	71.63 368	55.77 392	0.04 406	0.24 407	62.70 378	14.24 389	74.91 383	17.59 395	46.06 372	43.80 392

testmvs		7.23 375	9.62 378	0.06 390	0.04 412	0.02 415	84.98 300	0.02 413	0.03 407	0.18 408	1.21 407	0.01 413	0.02 408	0.14 407	0.01 406	0.13 405

test123		6.92 376	9.21 379	0.08 389	0.03 413	0.05 414	81.65 326	0.01 414	0.02 408	0.14 409	0.85 408	0.03 412	0.02 408	0.12 408	0.00 407	0.16 404

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 407	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 407	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 407	0.00 406

cdsmvs_eth3d_5k		19.86 372	26.47 371	0.00 391	0.00 414	0.00 416	0.00 402	93.45 84	0.00 409	0.00 410	95.27 58	49.56 246	0.00 410	0.00 409	0.00 407	0.00 406

pcd_1.5k_mvsjas		4.46 377	5.95 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	53.55 211	0.00 410	0.00 409	0.00 407	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 407	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 407	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 407	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 407	0.00 406

ab-mvs-re		7.91 374	10.55 377	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	94.95 66	0.00 414	0.00 410	0.00 409	0.00 407	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 407	0.00 406

WAC-MVS						49.45 356								31.56 386

MSC_two_6792asdad				89.60 8	97.31 4	73.22 10		95.05 26					99.07 13	92.01 26	94.77 26	96.51 23

No_MVS				89.60 8	97.31 4	73.22 10		95.05 26					99.07 13	92.01 26	94.77 26	96.51 23

eth-test2					0.00 414

eth-test					0.00 414

OPU-MVS				89.97 3	97.52 3	73.15 12	96.89 6				97.00 9	83.82 2	99.15 2	95.72 5	97.63 3	97.62 2

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 52					99.15 2	91.91 29	94.90 22	96.51 23

GSMVS																94.68 94

test_part2					96.29 19	68.16 85				90.78 17

sam_mvs1												57.85 158				94.68 94

sam_mvs												54.91 196

ambc				69.61 347	61.38 389	41.35 382	49.07 396	85.86 321		50.18 361	66.40 371	10.16 393	88.14 331	45.73 340	44.20 374	79.32 360

MTGPA								92.23 127

test_post1							78.95 346				20.70 403	53.05 216	91.50 302	60.43 279

test_post											23.01 400	56.49 178	92.67 266

patchmatchnet-post											67.62 370	57.62 161	90.25 311

GG-mvs-BLEND				86.53 70	91.91 105	69.67 51	75.02 363	94.75 33		78.67 128	90.85 163	77.91 7	94.56 199	72.25 176	93.74 44	95.36 61

MTMP							93.77 85	32.52 409

test9_res														89.41 41	94.96 19	95.29 66

agg_prior2														86.41 68	94.75 30	95.33 62

agg_prior					94.16 43	66.97 116		93.31 89		84.49 66			96.75 113

test_prior4						67.18 110	93.92 74

test_prior				86.42 73	94.71 35	67.35 105		93.10 99					96.84 110			95.05 78

新几何2							91.41 182

旧先验1					91.94 102	60.74 267		91.50 164			94.36 84	65.23 73			91.84 70	94.55 101

原ACMM2							92.01 157

testdata2													96.09 134	61.26 275

segment_acmp												65.94 66

test12				87.09 50	94.60 36	68.86 65		92.91 105		82.67 82		65.44 71	97.55 63		93.69 47	94.84 87

plane_prior7					86.94 224	61.51 251

plane_prior6					87.23 216	62.32 235						50.66 236

plane_prior5								91.31 170					95.55 163	76.74 141	78.53 196	88.39 239

plane_prior4											89.14 191

plane_prior1					87.15 218

n2								0.00 415

nn								0.00 415

door-mid								66.01 385

lessismore_v0				73.72 325	72.93 366	47.83 363		61.72 390		45.86 372	73.76 351	28.63 363	89.81 319	47.75 332	31.37 392	83.53 313

test11								93.01 101

door								66.57 384

HQP5-MVS						63.66 202

BP-MVS														77.63 138

HQP4-MVS										74.18 171			95.61 158			88.63 232

HQP3-MVS								91.70 156							78.90 191

HQP2-MVS												51.63 229

NP-MVS					87.41 212	63.04 217					90.30 173

ACMMP++_ref															71.63 249

ACMMP++															69.72 258

Test By Simon												54.21 205

High-resolution multi-view benchmark