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.

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 8	97.13 2	95.58 11	89.33 1	85.77 51	96.26 30	72.84 26	99.38 1	92.64 19	95.93 9	97.08 9

MM		90.87 2	91.52 2	88.92 13	92.12 95	71.10 25	97.02 3	96.04 6	88.70 2	91.57 13	96.19 33	70.12 40	98.91 17	96.83 1	95.06 16	96.76 12

DELS-MVS		90.05 7	90.09 11	89.94 4	93.14 69	73.88 7	97.01 4	94.40 50	88.32 3	85.71 52	94.91 68	74.11 19	98.91 17	87.26 59	95.94 8	97.03 10
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 20	90.14 141	70.94 26	96.47 13	95.72 10	87.33 4	89.60 28	96.26 30	68.44 45	98.74 24	95.82 4	94.72 30	95.90 42

EPNet		87.84 23	88.38 19	86.23 77	93.30 63	66.05 135	95.26 32	94.84 29	87.09 5	88.06 34	94.53 77	66.79 59	97.34 73	83.89 89	91.68 72	95.29 64
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 21	94.39 39	69.71 47	96.53 12	93.78 66	86.89 6	89.68 27	95.78 40	65.94 66	99.10 9	92.99 16	93.91 40	96.58 18

patch_mono-2		89.71 11	90.99 6	85.85 87	96.04 24	63.70 198	95.04 40	95.19 19	86.74 7	91.53 14	95.15 62	73.86 20	97.58 59	93.38 14	92.00 67	96.28 32

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 128	93.00 72	58.16 301	96.72 8	94.41 48	86.50 8	90.25 21	97.83 1	75.46 14	98.67 25	92.78 18	95.49 12	97.32 6

CANet_DTU		84.09 86	83.52 80	85.81 88	90.30 138	66.82 117	91.87 164	89.01 263	85.27 9	86.09 48	93.74 101	47.71 267	96.98 98	77.90 136	89.78 95	93.65 135

CLD-MVS		82.73 110	82.35 110	83.86 157	87.90 200	67.65 96	95.45 28	92.18 133	85.06 10	72.58 190	92.27 134	52.46 223	95.78 144	84.18 85	79.06 189	88.16 241
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 19	96.76 8	70.65 29	96.47 13	94.83 30	84.83 11	89.07 31	96.80 19	70.86 36	99.06 15	92.64 19	95.71 10	96.12 35

NCCC		89.07 15	89.46 15	87.91 25	96.60 10	69.05 60	96.38 15	94.64 39	84.42 12	86.74 43	96.20 32	66.56 62	98.76 23	89.03 46	94.56 32	95.92 41

test_fmvsm_n_1920		87.69 25	88.50 18	85.27 108	87.05 221	63.55 205	93.69 87	91.08 183	84.18 13	90.17 23	97.04 8	67.58 54	97.99 39	95.72 5	90.03 92	94.26 109

PS-MVSNAJ		88.14 17	87.61 28	89.71 6	92.06 96	76.72 1	95.75 20	93.26 90	83.86 14	89.55 29	96.06 36	53.55 212	97.89 43	91.10 31	93.31 51	94.54 101

DeepC-MVS_fast	79.48 2	87.95 21	88.00 24	87.79 28	95.86 27	68.32 76	95.74 21	94.11 60	83.82 15	83.49 73	96.19 33	64.53 84	98.44 31	83.42 92	94.88 24	96.61 15
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 41	87.17 33	84.82 121	85.28 252	62.55 229	94.26 56	89.78 228	83.81 16	87.78 36	96.33 29	65.33 72	96.98 98	94.40 11	87.55 113	94.95 80

xiu_mvs_v2_base		87.92 22	87.38 32	89.55 11	91.41 119	76.43 3	95.74 21	93.12 98	83.53 17	89.55 29	95.95 38	53.45 216	97.68 50	91.07 32	92.62 58	94.54 101

test_fmvsmconf0.1_n		85.71 57	86.08 49	84.62 135	80.83 305	62.33 233	93.84 80	88.81 271	83.50 18	87.00 42	96.01 37	63.36 101	96.93 105	94.04 12	87.29 116	94.61 97

TSAR-MVS + MP.		88.11 19	88.64 17	86.54 67	91.73 108	68.04 86	90.36 224	93.55 79	82.89 19	91.29 15	92.89 119	72.27 31	96.03 138	87.99 50	94.77 25	95.54 52
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	89.68 150	76.68 2	97.29 1	95.35 15	82.87 20	91.58 12	97.22 3	79.93 5	99.10 9	83.12 93	97.64 2	97.94 1

iter_conf05		83.27 101	82.70 103	84.98 116	93.32 62	71.84 15	94.16 58	81.76 348	82.74 21	73.83 177	88.40 196	72.77 27	94.61 192	82.10 99	75.21 221	88.48 235

WTY-MVS		86.32 44	85.81 53	87.85 26	92.82 77	69.37 53	95.20 34	95.25 17	82.71 22	81.91 84	94.73 72	67.93 52	97.63 56	79.55 120	82.25 159	96.54 19

lupinMVS		87.74 24	87.77 26	87.63 35	89.24 165	71.18 22	96.57 11	92.90 106	82.70 23	87.13 39	95.27 56	64.99 75	95.80 143	89.34 41	91.80 70	95.93 40

fmvsm_s_conf0.5_n		86.39 43	86.91 37	84.82 121	87.36 214	63.54 206	94.74 47	90.02 222	82.52 24	90.14 24	96.92 13	62.93 109	97.84 46	95.28 8	82.26 158	93.07 152

HPM-MVS++		89.37 14	89.95 13	87.64 31	95.10 30	68.23 82	95.24 33	94.49 44	82.43 25	88.90 32	96.35 27	71.89 34	98.63 26	88.76 47	96.40 6	96.06 36

test_fmvsmconf0.01_n		83.70 96	83.52 80	84.25 149	75.26 357	61.72 247	92.17 146	87.24 306	82.36 26	84.91 61	95.41 48	55.60 188	96.83 109	92.85 17	85.87 131	94.21 111

PVSNet_Blended		86.73 39	86.86 39	86.31 76	93.76 49	67.53 100	96.33 16	93.61 76	82.34 27	81.00 94	93.08 113	63.19 104	97.29 76	87.08 61	91.38 78	94.13 116

MSP-MVS		90.38 5	91.87 1	85.88 84	92.83 75	64.03 188	93.06 110	94.33 54	82.19 28	93.65 3	96.15 35	85.89 1	97.19 82	91.02 33	97.75 1	96.43 26
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 54	85.46 58	87.18 45	88.20 193	72.42 13	92.41 140	92.77 109	82.11 29	80.34 103	93.07 114	68.27 47	95.02 176	78.39 133	93.59 47	94.09 118

jason		86.40 42	86.17 46	87.11 47	86.16 237	70.54 31	95.71 24	92.19 132	82.00 30	84.58 64	94.34 87	61.86 118	95.53 163	87.76 52	90.89 84	95.27 67
jason: jason.
baseline1		81.84 125	81.03 126	84.28 148	91.60 111	66.62 123	91.08 202	91.66 158	81.87 31	74.86 164	91.67 148	69.98 41	94.92 182	71.76 181	64.75 299	91.29 197

CHOSEN 1792x2688		84.98 69	83.45 85	89.57 10	89.94 145	75.14 5	92.07 153	92.32 124	81.87 31	75.68 154	88.27 200	60.18 135	98.60 27	80.46 114	90.27 91	94.96 79

fmvsm_s_conf0.1_n		85.61 60	85.93 51	84.68 131	82.95 289	63.48 208	94.03 68	89.46 240	81.69 33	89.86 25	96.74 20	61.85 119	97.75 49	94.74 9	82.01 164	92.81 160

test_vis1_n_1920		81.66 128	82.01 113	80.64 238	82.24 294	55.09 330	94.76 46	86.87 308	81.67 34	84.40 66	94.63 75	38.17 316	94.67 191	91.98 26	83.34 149	92.16 181

casdiffmvs_mvg		85.66 59	85.18 62	87.09 48	88.22 192	69.35 54	93.74 86	91.89 144	81.47 35	80.10 105	91.45 151	64.80 80	96.35 124	87.23 60	87.69 111	95.58 50
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		83.01 106	82.56 106	84.35 145	89.34 157	62.02 239	92.72 123	93.76 69	81.45 36	82.73 79	92.25 136	60.11 136	97.13 87	87.69 53	62.96 311	93.91 127

hse-mvs2		81.12 137	81.11 125	81.16 225	86.52 229	57.48 311	89.40 250	91.16 176	81.45 36	82.73 79	90.49 167	60.11 136	94.58 194	87.69 53	60.41 338	91.41 191

ET-MVSNet_ETH3D		84.01 87	83.15 95	86.58 65	90.78 131	70.89 27	94.74 47	94.62 40	81.44 38	58.19 327	93.64 104	73.64 23	92.35 279	82.66 95	78.66 194	96.50 24

fmvsm_s_conf0.5_n_a		85.75 56	86.09 48	84.72 128	85.73 246	63.58 203	93.79 83	89.32 246	81.42 39	90.21 22	96.91 14	62.41 113	97.67 51	94.48 10	80.56 177	92.90 158

test_fmvsmvis_n_1920		83.80 92	83.48 83	84.77 125	82.51 291	63.72 196	91.37 188	83.99 337	81.42 39	77.68 134	95.74 42	58.37 154	97.58 59	93.38 14	86.87 119	93.00 155

testing11		86.71 40	86.44 42	87.55 37	93.54 57	71.35 19	93.65 89	95.58 11	81.36 41	80.69 97	92.21 137	72.30 30	96.46 123	85.18 76	83.43 148	94.82 88

casdiffmvs		85.37 62	84.87 68	86.84 54	88.25 190	69.07 59	93.04 112	91.76 151	81.27 42	80.84 96	92.07 139	64.23 86	96.06 136	84.98 79	87.43 115	95.39 55
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 50	86.11 47	85.70 94	90.21 140	67.02 114	93.43 102	91.92 141	81.21 43	84.13 70	94.07 96	60.93 129	95.63 154	89.28 42	89.81 93	94.46 107

DeepC-MVS	77.85 3	85.52 61	85.24 61	86.37 73	88.80 175	66.64 122	92.15 147	93.68 74	81.07 44	76.91 145	93.64 104	62.59 111	98.44 31	85.50 72	92.84 57	94.03 122
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 68	84.44 72	86.71 59	88.33 187	68.73 67	90.24 229	91.82 150	81.05 45	81.18 90	92.50 126	63.69 94	96.08 135	84.45 84	86.71 125	95.32 62

PC_three_1452									80.91 46	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 40		95.18 20	80.75 47	95.28 1				92.34 21	95.36 13	96.47 25

diffmvs		84.28 79	83.83 77	85.61 96	87.40 212	68.02 87	90.88 208	89.24 249	80.54 48	81.64 86	92.52 125	59.83 140	94.52 201	87.32 58	85.11 135	94.29 108
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	85.39 102	86.95 222	64.37 178	94.30 54	88.45 284	80.51 49	92.70 4	96.86 15	69.98 41	97.15 86	95.83 3	88.08 108	94.65 95

fmvsm_s_conf0.1_n_a		84.76 71	84.84 69	84.53 137	80.23 315	63.50 207	92.79 120	88.73 275	80.46 50	89.84 26	96.65 22	60.96 128	97.57 61	93.80 13	80.14 179	92.53 167

VPNet		78.82 179	77.53 181	82.70 184	84.52 265	66.44 127	93.93 72	92.23 127	80.46 50	72.60 189	88.38 198	49.18 252	93.13 245	72.47 174	63.97 308	88.55 234

testing99		86.01 50	85.47 57	87.63 35	93.62 53	71.25 21	93.47 100	95.23 18	80.42 52	80.60 99	91.95 141	71.73 35	96.50 121	80.02 117	82.22 160	95.13 73

testing222		85.18 65	84.69 70	86.63 62	92.91 74	69.91 40	92.61 131	95.80 9	80.31 53	80.38 102	92.27 134	68.73 44	95.19 173	75.94 146	83.27 150	94.81 89

testing91		85.93 52	85.31 60	87.78 29	93.59 55	71.47 17	93.50 97	95.08 25	80.26 54	80.53 100	91.93 142	70.43 38	96.51 120	80.32 115	82.13 162	95.37 57

canonicalmvs		86.85 36	86.25 45	88.66 18	91.80 107	71.92 14	93.54 95	91.71 154	80.26 54	87.55 37	95.25 58	63.59 98	96.93 105	88.18 49	84.34 141	97.11 8

fmvsm_l_conf0.5_n_a		87.44 29	88.15 23	85.30 106	87.10 219	64.19 185	94.41 52	88.14 293	80.24 56	92.54 5	96.97 10	69.52 43	97.17 83	95.89 2	88.51 104	94.56 98

CS-MVS-test		86.14 48	87.01 35	83.52 166	92.63 84	59.36 289	95.49 27	91.92 141	80.09 57	85.46 56	95.53 47	61.82 121	95.77 146	86.77 65	93.37 50	95.41 54

CS-MVS		85.80 55	86.65 41	83.27 174	92.00 100	58.92 294	95.31 31	91.86 146	79.97 58	84.82 62	95.40 49	62.26 114	95.51 164	86.11 69	92.08 66	95.37 57

MVSTER		82.47 114	82.05 111	83.74 159	92.68 82	69.01 61	91.90 163	93.21 91	79.83 59	72.14 198	85.71 240	74.72 16	94.72 187	75.72 147	72.49 243	87.50 246

HQP-NCC					87.54 208		94.06 63		79.80 60	74.18 170

ACMP_Plane					87.54 208		94.06 63		79.80 60	74.18 170

HQP-MVS		81.14 135	80.64 133	82.64 186	87.54 208	63.66 201	94.06 63	91.70 156	79.80 60	74.18 170	90.30 171	51.63 230	95.61 156	77.63 137	78.90 190	88.63 231

baseline2		83.68 97	83.42 88	84.48 140	87.37 213	66.00 137	90.06 233	95.93 8	79.71 63	69.08 233	90.39 169	77.92 6	96.28 126	78.91 128	81.38 170	91.16 199

EI-MVSNet-Vis-set		83.77 93	83.67 79	84.06 153	92.79 80	63.56 204	91.76 171	94.81 31	79.65 64	77.87 132	94.09 94	63.35 102	97.90 42	79.35 122	79.36 186	90.74 203

ETVMVS		84.22 83	83.71 78	85.76 91	92.58 86	68.25 81	92.45 139	95.53 14	79.54 65	79.46 113	91.64 149	70.29 39	94.18 214	69.16 206	82.76 156	94.84 85

EIA-MVS		84.84 70	84.88 67	84.69 130	91.30 120	62.36 232	93.85 77	92.04 136	79.45 66	79.33 116	94.28 90	62.42 112	96.35 124	80.05 116	91.25 81	95.38 56

dmvs_re		76.93 211	75.36 214	81.61 215	87.78 205	60.71 267	80.00 343	87.99 297	79.42 67	69.02 235	89.47 184	46.77 270	94.32 205	63.38 260	74.45 226	89.81 215

plane_prior						62.42 230	93.85 77		79.38 68						78.80 192

dcpmvs_2		87.37 30	87.55 29	86.85 53	95.04 32	68.20 83	90.36 224	90.66 195	79.37 69	81.20 89	93.67 103	74.73 15	96.55 118	90.88 34	92.00 67	95.82 44

alignmvs		87.28 31	86.97 36	88.24 24	91.30 120	71.14 24	95.61 25	93.56 78	79.30 70	87.07 41	95.25 58	68.43 46	96.93 105	87.87 51	84.33 142	96.65 14

TESTMET0.1,1		82.41 115	81.98 114	83.72 162	88.08 194	63.74 194	92.70 125	93.77 68	79.30 70	77.61 136	87.57 215	58.19 157	94.08 218	73.91 162	86.68 126	93.33 144

EI-MVSNet-UG-set		83.14 104	82.96 96	83.67 164	92.28 90	63.19 214	91.38 187	94.68 37	79.22 72	76.60 147	93.75 100	62.64 110	97.76 48	78.07 135	78.01 197	90.05 212

PVSNet	73.49 8	80.05 157	78.63 164	84.31 146	90.92 127	64.97 163	92.47 138	91.05 186	79.18 73	72.43 195	90.51 166	37.05 331	94.06 220	68.06 215	86.00 130	93.90 129

HY-MVS	76.49 5	84.28 79	83.36 91	87.02 51	92.22 92	67.74 93	84.65 301	94.50 43	79.15 74	82.23 82	87.93 209	66.88 58	96.94 103	80.53 113	82.20 161	96.39 28

PVSNet_BlendedMVS		83.38 99	83.43 86	83.22 175	93.76 49	67.53 100	94.06 63	93.61 76	79.13 75	81.00 94	85.14 243	63.19 104	97.29 76	87.08 61	73.91 232	84.83 302

plane_prior3						61.95 242			79.09 76	72.53 191

MVS_111021_HR		86.19 47	85.80 54	87.37 41	93.17 68	69.79 44	93.99 69	93.76 69	79.08 77	78.88 123	93.99 97	62.25 115	98.15 36	85.93 71	91.15 82	94.15 115

test_cas_vis1_n_1920		80.45 149	80.61 134	79.97 257	78.25 341	57.01 318	94.04 67	88.33 287	79.06 78	82.81 78	93.70 102	38.65 311	91.63 294	90.82 35	79.81 181	91.27 198

MSLP-MVS++		86.27 45	85.91 52	87.35 42	92.01 99	68.97 63	95.04 40	92.70 111	79.04 79	81.50 87	96.50 25	58.98 151	96.78 110	83.49 91	93.93 39	96.29 30

iter_conf_final		81.74 127	80.93 128	84.18 150	92.66 83	69.10 58	92.94 116	82.80 346	79.01 80	74.85 165	88.40 196	61.83 120	94.61 192	79.36 121	76.52 214	88.83 226

IB-MVS	77.80 4	82.18 118	80.46 138	87.35 42	89.14 167	70.28 34	95.59 26	95.17 21	78.85 81	70.19 221	85.82 238	70.66 37	97.67 51	72.19 178	66.52 284	94.09 118
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	82.69 113	80.82 129	88.31 23	89.57 152	71.26 20	92.60 132	94.39 51	78.84 82	67.89 254	92.48 129	48.42 258	98.52 28	68.80 211	94.40 34	95.15 72

HQP_MVS		80.34 151	79.75 147	82.12 204	86.94 223	62.42 230	93.13 108	91.31 170	78.81 83	72.53 191	89.14 189	50.66 237	95.55 161	76.74 140	78.53 195	88.39 238

plane_prior2							93.13 108		78.81 83

MG-MVS		87.11 33	86.27 43	89.62 7	97.79 1	76.27 4	94.96 43	94.49 44	78.74 85	83.87 72	92.94 117	64.34 85	96.94 103	75.19 151	94.09 36	95.66 47

gm-plane-assit					88.42 183	67.04 113			78.62 86		91.83 144		97.37 70	76.57 142

VNet		86.20 46	85.65 56	87.84 27	93.92 46	69.99 36	95.73 23	95.94 7	78.43 87	86.00 49	93.07 114	58.22 156	97.00 94	85.22 74	84.33 142	96.52 20

tpm		78.58 186	77.03 190	83.22 175	85.94 242	64.56 167	83.21 315	91.14 179	78.31 88	73.67 178	79.68 316	64.01 88	92.09 285	66.07 239	71.26 253	93.03 153

save fliter					93.84 48	67.89 90	95.05 39	92.66 114	78.19 89

TSAR-MVS + GP.		87.96 20	88.37 20	86.70 60	93.51 59	65.32 153	95.15 36	93.84 65	78.17 90	85.93 50	94.80 71	75.80 13	98.21 34	89.38 40	88.78 101	96.59 16

FIs		79.47 167	79.41 154	79.67 264	85.95 240	59.40 286	91.68 175	93.94 63	78.06 91	68.96 237	88.28 199	66.61 61	91.77 291	66.20 238	74.99 222	87.82 243

sss		82.71 112	82.38 109	83.73 161	89.25 162	59.58 284	92.24 144	94.89 28	77.96 92	79.86 108	92.38 131	56.70 175	97.05 89	77.26 139	80.86 174	94.55 99

PMMVS		81.98 124	82.04 112	81.78 211	89.76 149	56.17 322	91.13 201	90.69 192	77.96 92	80.09 106	93.57 106	46.33 277	94.99 178	81.41 106	87.46 114	94.17 113

EC-MVSNet		84.53 75	85.04 65	83.01 178	89.34 157	61.37 253	94.42 51	91.09 181	77.91 94	83.24 74	94.20 92	58.37 154	95.40 165	85.35 73	91.41 77	92.27 177

test1111		80.84 142	80.02 141	83.33 172	87.87 201	60.76 264	92.62 130	86.86 309	77.86 95	75.73 153	91.39 154	46.35 275	94.70 190	72.79 169	88.68 103	94.52 103

MVS_Test		84.16 85	83.20 92	87.05 50	91.56 113	69.82 43	89.99 238	92.05 135	77.77 96	82.84 77	86.57 228	63.93 90	96.09 132	74.91 156	89.18 99	95.25 70

SteuartSystems-ACMMP		86.82 38	86.90 38	86.58 65	90.42 135	66.38 128	96.09 17	93.87 64	77.73 97	84.01 71	95.66 43	63.39 100	97.94 40	87.40 57	93.55 48	95.42 53
Skip Steuart: Steuart Systems R&D Blog.
EPNet_dtu		78.80 180	79.26 158	77.43 293	88.06 195	49.71 354	91.96 161	91.95 140	77.67 98	76.56 148	91.28 156	58.51 153	90.20 315	56.37 296	80.95 173	92.39 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test2506		83.29 100	82.92 98	84.37 144	88.39 185	63.18 215	92.01 156	91.35 169	77.66 99	78.49 128	91.42 152	64.58 83	95.09 175	73.19 163	89.23 97	94.85 82

ECVR-MVS		81.29 133	80.38 139	84.01 155	88.39 185	61.96 241	92.56 137	86.79 310	77.66 99	76.63 146	91.42 152	46.34 276	95.24 172	74.36 160	89.23 97	94.85 82

tpmrst		80.57 145	79.14 160	84.84 120	90.10 142	68.28 78	81.70 325	89.72 235	77.63 101	75.96 151	79.54 318	64.94 77	92.71 262	75.43 149	77.28 208	93.55 137

testdata1							89.21 254		77.55 102

UniMVSNet_NR-MVSNet		78.15 193	77.55 180	79.98 255	84.46 267	60.26 274	92.25 143	93.20 93	77.50 103	68.88 238	86.61 227	66.10 64	92.13 283	66.38 235	62.55 315	87.54 245

UA-Net		80.02 158	79.65 148	81.11 227	89.33 159	57.72 306	86.33 294	89.00 266	77.44 104	81.01 93	89.15 188	59.33 147	95.90 141	61.01 276	84.28 144	89.73 218

PVSNet_Blended_VisFu		83.97 88	83.50 82	85.39 102	90.02 143	66.59 125	93.77 84	91.73 152	77.43 105	77.08 144	89.81 181	63.77 93	96.97 100	79.67 119	88.21 106	92.60 164

dmvs_testset		65.55 321	66.45 297	62.86 362	79.87 318	22.35 405	76.55 357	71.74 375	77.42 106	55.85 338	87.77 212	51.39 232	80.69 377	31.51 387	65.92 288	85.55 291

NR-MVSNet		76.05 226	74.59 222	80.44 240	82.96 287	62.18 237	90.83 210	91.73 152	77.12 107	60.96 312	86.35 230	59.28 148	91.80 290	60.74 277	61.34 330	87.35 252

FC-MVSNet-test		77.99 195	78.08 172	77.70 288	84.89 260	55.51 327	90.27 227	93.75 72	76.87 108	66.80 270	87.59 214	65.71 69	90.23 314	62.89 266	73.94 231	87.37 250

SD-MVS		87.49 27	87.49 30	87.50 39	93.60 54	68.82 66	93.90 74	92.63 117	76.86 109	87.90 35	95.76 41	66.17 63	97.63 56	89.06 45	91.48 76	96.05 37
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		76.85 214	75.71 210	80.25 246	83.07 286	59.16 291	91.44 179	80.64 353	76.84 110	67.95 250	86.33 232	46.17 280	94.24 212	76.06 145	72.92 239	87.36 251

UGNet		79.87 161	78.68 163	83.45 171	89.96 144	61.51 250	92.13 148	90.79 190	76.83 111	78.85 125	86.33 232	38.16 317	96.17 129	67.93 218	87.17 117	92.67 162
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		82.02 123	81.52 118	83.51 168	88.42 183	62.88 224	89.77 242	88.93 267	76.78 112	75.55 158	93.10 111	50.31 240	95.38 167	83.82 90	87.02 118	92.26 178

SDMVSNet		80.26 152	78.88 162	84.40 142	89.25 162	67.63 97	85.35 297	93.02 100	76.77 113	70.84 212	87.12 222	47.95 264	96.09 132	85.04 77	74.55 223	89.48 222

sd_testset		77.08 210	75.37 213	82.20 200	89.25 162	62.11 238	82.06 322	89.09 259	76.77 113	70.84 212	87.12 222	41.43 301	95.01 177	67.23 225	74.55 223	89.48 222

TranMVSNet+NR-MVSNet		75.86 231	74.52 225	79.89 259	82.44 292	60.64 270	91.37 188	91.37 168	76.63 115	67.65 257	86.21 234	52.37 224	91.55 296	61.84 272	60.81 333	87.48 247

PAPR		85.15 66	84.47 71	87.18 45	96.02 25	68.29 77	91.85 166	93.00 103	76.59 116	79.03 119	95.00 63	61.59 122	97.61 58	78.16 134	89.00 100	95.63 48

UniMVSNet (Re)		77.58 202	76.78 194	79.98 255	84.11 273	60.80 261	91.76 171	93.17 95	76.56 117	69.93 227	84.78 248	63.32 103	92.36 278	64.89 251	62.51 317	86.78 262

DU-MVS		76.86 212	75.84 207	79.91 258	82.96 287	60.26 274	91.26 194	91.54 161	76.46 118	68.88 238	86.35 230	56.16 181	92.13 283	66.38 235	62.55 315	87.35 252

OPM-MVS		79.00 174	78.09 171	81.73 212	83.52 281	63.83 191	91.64 177	90.30 209	76.36 119	71.97 200	89.93 180	46.30 278	95.17 174	75.10 152	77.70 200	86.19 274
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
WR-MVS		76.76 217	75.74 209	79.82 261	84.60 263	62.27 236	92.60 132	92.51 121	76.06 120	67.87 255	85.34 241	56.76 173	90.24 313	62.20 270	63.69 310	86.94 260

GA-MVS		78.33 191	76.23 201	84.65 132	83.65 279	66.30 131	91.44 179	90.14 216	76.01 121	70.32 219	84.02 257	42.50 297	94.72 187	70.98 186	77.00 210	92.94 156

PVSNet_0	68.08 15	71.81 274	68.32 291	82.27 196	84.68 261	62.31 235	88.68 263	90.31 208	75.84 122	57.93 332	80.65 303	37.85 322	94.19 213	69.94 196	29.05 395	90.31 209

CDS-MVSNet		81.43 131	80.74 130	83.52 166	86.26 234	64.45 172	92.09 151	90.65 196	75.83 123	73.95 176	89.81 181	63.97 89	92.91 255	71.27 184	82.82 153	93.20 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UWE-MVS		80.81 143	81.01 127	80.20 248	89.33 159	57.05 316	91.91 162	94.71 35	75.67 124	75.01 163	89.37 185	63.13 106	91.44 302	67.19 226	82.80 155	92.12 182

CostFormer		82.33 116	81.15 121	85.86 86	89.01 170	68.46 73	82.39 321	93.01 101	75.59 125	80.25 104	81.57 286	72.03 33	94.96 179	79.06 126	77.48 205	94.16 114

nrg030		80.93 140	79.86 145	84.13 152	83.69 278	68.83 65	93.23 106	91.20 174	75.55 126	75.06 162	88.22 204	63.04 108	94.74 186	81.88 101	66.88 281	88.82 229

VDD-MVS		83.06 105	81.81 116	86.81 56	90.86 129	67.70 94	95.40 29	91.50 164	75.46 127	81.78 85	92.34 133	40.09 305	97.13 87	86.85 64	82.04 163	95.60 49

Effi-MVS+-dtu		76.14 222	75.28 216	78.72 279	83.22 283	55.17 329	89.87 239	87.78 300	75.42 128	67.98 249	81.43 288	45.08 288	92.52 272	75.08 153	71.63 248	88.48 235

test_prior2							95.10 38		75.40 129	85.25 60	95.61 45	67.94 51		87.47 56	94.77 25

MTAPA		83.91 89	83.38 90	85.50 98	91.89 105	65.16 158	81.75 324	92.23 127	75.32 130	80.53 100	95.21 60	56.06 184	97.16 85	84.86 81	92.55 60	94.18 112

EPMVS		78.49 188	75.98 205	86.02 80	91.21 122	69.68 48	80.23 339	91.20 174	75.25 131	72.48 193	78.11 326	54.65 198	93.69 236	57.66 293	83.04 151	94.69 91

miper_enhance_ethall		78.86 178	77.97 174	81.54 217	88.00 198	65.17 157	91.41 181	89.15 255	75.19 132	68.79 240	83.98 258	67.17 56	92.82 257	72.73 170	65.30 290	86.62 267

v2v482		77.42 204	75.65 211	82.73 183	80.38 311	67.13 110	91.85 166	90.23 213	75.09 133	69.37 229	83.39 264	53.79 210	94.44 203	71.77 180	65.00 296	86.63 266

VPA-MVSNet		79.03 173	78.00 173	82.11 207	85.95 240	64.48 171	93.22 107	94.66 38	75.05 134	74.04 175	84.95 245	52.17 225	93.52 239	74.90 157	67.04 280	88.32 240

ACMMP_NAP		86.05 49	85.80 54	86.80 57	91.58 112	67.53 100	91.79 168	93.49 83	74.93 135	84.61 63	95.30 53	59.42 145	97.92 41	86.13 68	94.92 19	94.94 81

thres200		79.66 163	78.33 167	83.66 165	92.54 87	65.82 143	93.06 110	96.31 3	74.90 136	73.30 181	88.66 191	59.67 142	95.61 156	47.84 330	78.67 193	89.56 221

TAMVS		80.37 150	79.45 153	83.13 177	85.14 255	63.37 209	91.23 196	90.76 191	74.81 137	72.65 188	88.49 193	60.63 131	92.95 250	69.41 202	81.95 165	93.08 151

MP-MVS-pluss		85.24 64	85.13 63	85.56 97	91.42 117	65.59 147	91.54 178	92.51 121	74.56 138	80.62 98	95.64 44	59.15 149	97.00 94	86.94 63	93.80 41	94.07 120
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mvs_anonymous		81.36 132	79.99 143	85.46 99	90.39 137	68.40 74	86.88 291	90.61 197	74.41 139	70.31 220	84.67 249	63.79 92	92.32 280	73.13 164	85.70 132	95.67 46

MAR-MVS		84.18 84	83.43 86	86.44 70	96.25 21	65.93 140	94.28 55	94.27 56	74.41 139	79.16 118	95.61 45	53.99 207	98.88 21	69.62 200	93.26 52	94.50 105
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		80.49 148	79.30 157	84.05 154	90.83 130	64.36 180	93.60 92	89.42 243	74.35 141	69.09 232	90.15 176	55.23 192	95.61 156	64.61 252	86.43 129	92.17 180

thisisatest0515		83.41 98	82.49 107	86.16 78	89.46 156	68.26 79	93.54 95	94.70 36	74.31 142	75.75 152	90.92 159	72.62 28	96.52 119	69.64 198	81.50 169	93.71 133

Vis-MVSNet (Re-imp)		79.24 170	79.57 149	78.24 285	88.46 181	52.29 341	90.41 222	89.12 257	74.24 143	69.13 231	91.91 143	65.77 68	90.09 317	59.00 288	88.09 107	92.33 171

SMA-MVS		88.14 17	88.29 21	87.67 30	93.21 66	68.72 68	93.85 77	94.03 62	74.18 144	91.74 11	96.67 21	65.61 70	98.42 33	89.24 43	96.08 7	95.88 43
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		78.37 189	77.43 182	81.17 224	86.60 228	57.45 312	89.46 249	91.16 176	74.11 145	74.40 169	90.49 167	55.52 189	94.57 196	74.73 159	60.43 337	91.48 189

3Dnovator+	73.60 7	82.10 122	80.60 135	86.60 63	90.89 128	66.80 119	95.20 34	93.44 85	74.05 146	67.42 260	92.49 128	49.46 248	97.65 55	70.80 188	91.68 72	95.33 60

XVS		83.87 90	83.47 84	85.05 113	93.22 64	63.78 192	92.92 117	92.66 114	73.99 147	78.18 129	94.31 89	55.25 190	97.41 68	79.16 124	91.58 74	93.95 125

X-MVStestdata		76.86 212	74.13 232	85.05 113	93.22 64	63.78 192	92.92 117	92.66 114	73.99 147	78.18 129	10.19 405	55.25 190	97.41 68	79.16 124	91.58 74	93.95 125

MS-PatchMatch		77.90 199	76.50 197	82.12 204	85.99 239	69.95 39	91.75 173	92.70 111	73.97 149	62.58 305	84.44 253	41.11 302	95.78 144	63.76 258	92.17 64	80.62 349

LCM-MVSNet-Re		72.93 263	71.84 262	76.18 308	88.49 179	48.02 361	80.07 342	70.17 378	73.96 150	52.25 351	80.09 312	49.98 243	88.24 330	67.35 222	84.23 145	92.28 174

Vis-MVSNet		80.92 141	79.98 144	83.74 159	88.48 180	61.80 243	93.44 101	88.26 292	73.96 150	77.73 133	91.76 145	49.94 244	94.76 184	65.84 241	90.37 90	94.65 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test-mter		79.96 159	79.38 156	81.72 213	86.93 225	61.17 254	92.70 125	91.54 161	73.85 152	75.62 155	86.94 224	49.84 246	92.38 276	72.21 176	84.76 139	91.60 186

OMC-MVS		78.67 185	77.91 176	80.95 234	85.76 245	57.40 313	88.49 266	88.67 278	73.85 152	72.43 195	92.10 138	49.29 251	94.55 199	72.73 170	77.89 198	90.91 202

Fast-Effi-MVS+		81.14 135	80.01 142	84.51 139	90.24 139	65.86 141	94.12 62	89.15 255	73.81 154	75.37 160	88.26 201	57.26 164	94.53 200	66.97 229	84.92 136	93.15 148

ZNCC-MVS		85.33 63	85.08 64	86.06 79	93.09 71	65.65 145	93.89 75	93.41 87	73.75 155	79.94 107	94.68 74	60.61 132	98.03 38	82.63 96	93.72 44	94.52 103

V42		76.46 220	74.55 224	82.19 201	79.14 329	67.82 91	90.26 228	89.42 243	73.75 155	68.63 243	81.89 279	51.31 233	94.09 217	71.69 182	64.84 297	84.66 303

v1144		76.73 218	74.88 218	82.27 196	80.23 315	66.60 124	91.68 175	90.21 215	73.69 157	69.06 234	81.89 279	52.73 221	94.40 204	69.21 205	65.23 293	85.80 285

v148		76.19 221	74.47 226	81.36 220	80.05 317	64.44 173	91.75 173	90.23 213	73.68 158	67.13 264	80.84 299	55.92 186	93.86 234	68.95 209	61.73 326	85.76 288

CR-MVSNet		73.79 256	70.82 271	82.70 184	83.15 284	67.96 88	70.25 369	84.00 335	73.67 159	69.97 225	72.41 355	57.82 160	89.48 321	52.99 310	73.13 236	90.64 205

XXY-MVS		77.94 197	76.44 198	82.43 190	82.60 290	64.44 173	92.01 156	91.83 149	73.59 160	70.00 224	85.82 238	54.43 203	94.76 184	69.63 199	68.02 274	88.10 242

tfpn200view9		78.79 181	77.43 182	82.88 180	92.21 93	64.49 169	92.05 154	96.28 4	73.48 161	71.75 203	88.26 201	60.07 138	95.32 168	45.16 341	77.58 202	88.83 226

thres400		78.68 183	77.43 182	82.43 190	92.21 93	64.49 169	92.05 154	96.28 4	73.48 161	71.75 203	88.26 201	60.07 138	95.32 168	45.16 341	77.58 202	87.48 247

FMVSNet3		77.73 200	76.04 204	82.80 181	91.20 123	68.99 62	91.87 164	91.99 138	73.35 163	67.04 265	83.19 266	56.62 177	92.14 282	59.80 284	69.34 261	87.28 254

GST-MVS		84.63 74	84.29 74	85.66 95	92.82 77	65.27 154	93.04 112	93.13 97	73.20 164	78.89 120	94.18 93	59.41 146	97.85 45	81.45 105	92.48 61	93.86 130

USDC		67.43 312	64.51 313	76.19 307	77.94 345	55.29 328	78.38 350	85.00 326	73.17 165	48.36 366	80.37 306	21.23 376	92.48 274	52.15 311	64.02 307	80.81 347

MP-MVS		85.02 67	84.97 66	85.17 112	92.60 85	64.27 183	93.24 105	92.27 126	73.13 166	79.63 111	94.43 80	61.90 117	97.17 83	85.00 78	92.56 59	94.06 121
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
xiu_mvs_v1_base_debu		82.16 119	81.12 122	85.26 109	86.42 230	68.72 68	92.59 134	90.44 202	73.12 167	84.20 67	94.36 82	38.04 319	95.73 148	84.12 86	86.81 120	91.33 192

xiu_mvs_v1_base		82.16 119	81.12 122	85.26 109	86.42 230	68.72 68	92.59 134	90.44 202	73.12 167	84.20 67	94.36 82	38.04 319	95.73 148	84.12 86	86.81 120	91.33 192

xiu_mvs_v1_base_debi		82.16 119	81.12 122	85.26 109	86.42 230	68.72 68	92.59 134	90.44 202	73.12 167	84.20 67	94.36 82	38.04 319	95.73 148	84.12 86	86.81 120	91.33 192

D2MVS		73.80 255	72.02 260	79.15 275	79.15 328	62.97 218	88.58 265	90.07 218	72.94 170	59.22 321	78.30 323	42.31 299	92.70 264	65.59 245	72.00 246	81.79 338

BH-RMVSNet		79.46 168	77.65 178	84.89 118	91.68 110	65.66 144	93.55 94	88.09 295	72.93 171	73.37 180	91.12 158	46.20 279	96.12 131	56.28 297	85.61 134	92.91 157

Syy-MVS		69.65 291	69.52 283	70.03 346	87.87 201	43.21 379	88.07 271	89.01 263	72.91 172	63.11 298	88.10 205	45.28 286	85.54 349	22.07 392	69.23 264	81.32 341

myMVS_eth3d		72.58 272	72.74 250	72.10 339	87.87 201	49.45 356	88.07 271	89.01 263	72.91 172	63.11 298	88.10 205	63.63 95	85.54 349	32.73 381	69.23 264	81.32 341

IS-MVSNet		80.14 155	79.41 154	82.33 194	87.91 199	60.08 278	91.97 160	88.27 290	72.90 174	71.44 208	91.73 147	61.44 123	93.66 237	62.47 269	86.53 127	93.24 145

PS-MVSNAJss		77.26 206	76.31 200	80.13 250	80.64 309	59.16 291	90.63 219	91.06 185	72.80 175	68.58 244	84.57 251	53.55 212	93.96 228	72.97 165	71.96 247	87.27 255

9.14			87.63 27		93.86 47		94.41 52	94.18 57	72.76 176	86.21 46	96.51 24	66.64 60	97.88 44	90.08 38	94.04 37

v1192		75.98 228	73.92 235	82.15 202	79.73 319	66.24 133	91.22 197	89.75 230	72.67 177	68.49 245	81.42 289	49.86 245	94.27 209	67.08 227	65.02 295	85.95 282

Effi-MVS+		83.82 91	82.76 101	86.99 52	89.56 153	69.40 50	91.35 190	86.12 317	72.59 178	83.22 75	92.81 123	59.60 143	96.01 140	81.76 102	87.80 110	95.56 51

UnsupCasMVSNet_eth		65.79 319	63.10 321	73.88 323	70.71 372	50.29 352	81.09 331	89.88 226	72.58 179	49.25 364	74.77 350	32.57 348	87.43 341	55.96 298	41.04 380	83.90 309

1112_ss		80.56 146	79.83 146	82.77 182	88.65 177	60.78 262	92.29 142	88.36 286	72.58 179	72.46 194	94.95 64	65.09 74	93.42 242	66.38 235	77.71 199	94.10 117

DVP-MVS++		90.53 4	91.09 5	88.87 14	97.31 4	69.91 40	93.96 70	94.37 52	72.48 181	92.07 8	96.85 16	83.82 2	99.15 2	91.53 29	97.42 4	97.55 4

test_0728_THIRD									72.48 181	90.55 19	96.93 11	76.24 11	99.08 11	91.53 29	94.99 17	96.43 26

cl22		77.94 197	76.78 194	81.42 219	87.57 207	64.93 165	90.67 215	88.86 270	72.45 183	67.63 258	82.68 271	64.07 87	92.91 255	71.79 179	65.30 290	86.44 268

thres600view7		78.00 194	76.66 196	82.03 209	91.93 102	63.69 199	91.30 193	96.33 1	72.43 184	70.46 216	87.89 210	60.31 133	94.92 182	42.64 353	76.64 212	87.48 247

IterMVS-LS		76.49 219	75.18 217	80.43 241	84.49 266	62.74 226	90.64 217	88.80 272	72.40 185	65.16 278	81.72 282	60.98 127	92.27 281	67.74 219	64.65 301	86.29 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		78.97 175	78.22 170	81.25 222	85.33 250	62.73 227	89.53 247	93.21 91	72.39 186	72.14 198	90.13 177	60.99 126	94.72 187	67.73 220	72.49 243	86.29 270

miper_ehance_all_eth		77.60 201	76.44 198	81.09 231	85.70 247	64.41 176	90.65 216	88.64 280	72.31 187	67.37 263	82.52 272	64.77 81	92.64 269	70.67 190	65.30 290	86.24 272

v144192		76.05 226	74.03 233	82.12 204	79.50 323	66.55 126	91.39 185	89.71 236	72.30 188	68.17 247	81.33 291	51.75 228	94.03 225	67.94 217	64.19 303	85.77 286

thres100view900		78.37 189	77.01 191	82.46 189	91.89 105	63.21 213	91.19 200	96.33 1	72.28 189	70.45 217	87.89 210	60.31 133	95.32 168	45.16 341	77.58 202	88.83 226

PatchmatchNet		77.46 203	74.63 221	85.96 82	89.55 154	70.35 33	79.97 344	89.55 238	72.23 190	70.94 210	76.91 337	57.03 167	92.79 260	54.27 304	81.17 171	94.74 90
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
HFP-MVS		84.73 72	84.40 73	85.72 93	93.75 51	65.01 162	93.50 97	93.19 94	72.19 191	79.22 117	94.93 66	59.04 150	97.67 51	81.55 103	92.21 62	94.49 106

ACMMPR		84.37 76	84.06 75	85.28 107	93.56 56	64.37 178	93.50 97	93.15 96	72.19 191	78.85 125	94.86 69	56.69 176	97.45 65	81.55 103	92.20 63	94.02 123

1314		80.70 144	78.95 161	85.94 83	87.77 206	67.56 98	87.91 275	92.55 120	72.17 193	67.44 259	93.09 112	50.27 241	97.04 92	71.68 183	87.64 112	93.23 146

region2R		84.36 77	84.03 76	85.36 104	93.54 57	64.31 181	93.43 102	92.95 104	72.16 194	78.86 124	94.84 70	56.97 171	97.53 63	81.38 107	92.11 65	94.24 110

Test_1112_low_res		79.56 165	78.60 165	82.43 190	88.24 191	60.39 273	92.09 151	87.99 297	72.10 195	71.84 201	87.42 217	64.62 82	93.04 246	65.80 242	77.30 207	93.85 131

v1921920		75.63 236	73.49 241	82.06 208	79.38 324	66.35 129	91.07 204	89.48 239	71.98 196	67.99 248	81.22 294	49.16 254	93.90 231	66.56 231	64.56 302	85.92 284

DVP-MVS		89.41 13	89.73 14	88.45 22	96.40 15	69.99 36	96.64 9	94.52 42	71.92 197	90.55 19	96.93 11	73.77 21	99.08 11	91.91 27	94.90 21	96.29 30
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 36	96.76 7	94.33 54	71.92 197	91.89 10	97.11 6	73.77 21

Fast-Effi-MVS+-dtu		75.04 242	73.37 242	80.07 251	80.86 304	59.52 285	91.20 199	85.38 322	71.90 199	65.20 277	84.84 247	41.46 300	92.97 249	66.50 234	72.96 238	87.73 244

LFMVS		84.34 78	82.73 102	89.18 12	94.76 33	73.25 9	94.99 42	91.89 144	71.90 199	82.16 83	93.49 108	47.98 263	97.05 89	82.55 97	84.82 137	97.25 7

eth_miper_zixun_eth		75.96 230	74.40 227	80.66 237	84.66 262	63.02 217	89.28 252	88.27 290	71.88 201	65.73 273	81.65 283	59.45 144	92.81 258	68.13 214	60.53 335	86.14 275

train_agg		87.21 32	87.42 31	86.60 63	94.18 41	67.28 105	94.16 58	93.51 80	71.87 202	85.52 54	95.33 51	68.19 48	97.27 80	89.09 44	94.90 21	95.25 70

test_8					94.19 40	67.19 107	94.15 61	93.42 86	71.87 202	85.38 57	95.35 50	68.19 48	96.95 102

MDTV_nov1_ep13			72.61 253		89.06 168	68.48 72	80.33 337	90.11 217	71.84 204	71.81 202	75.92 345	53.01 218	93.92 230	48.04 327	73.38 234

ab-mvs		80.18 154	78.31 168	85.80 89	88.44 182	65.49 152	83.00 318	92.67 113	71.82 205	77.36 139	85.01 244	54.50 199	96.59 114	76.35 144	75.63 219	95.32 62

ACMMP		81.49 130	80.67 132	83.93 156	91.71 109	62.90 223	92.13 148	92.22 130	71.79 206	71.68 205	93.49 108	50.32 239	96.96 101	78.47 132	84.22 146	91.93 184
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 37	86.85 40	86.78 58	93.47 60	65.55 149	95.39 30	95.10 22	71.77 207	85.69 53	96.52 23	62.07 116	98.77 22	86.06 70	95.60 11	96.03 38

TEST9					94.18 41	67.28 105	94.16 58	93.51 80	71.75 208	85.52 54	95.33 51	68.01 50	97.27 80

WB-MVSnew		77.14 208	76.18 203	80.01 254	86.18 236	63.24 212	91.26 194	94.11 60	71.72 209	73.52 179	87.29 220	45.14 287	93.00 248	56.98 294	79.42 184	83.80 310

c3_l		76.83 216	75.47 212	80.93 235	85.02 258	64.18 186	90.39 223	88.11 294	71.66 210	66.65 271	81.64 284	63.58 99	92.56 270	69.31 204	62.86 312	86.04 279

SED-MVS		89.94 9	90.36 10	88.70 16	96.45 12	69.38 51	96.89 5	94.44 46	71.65 211	92.11 6	97.21 4	76.79 9	99.11 6	92.34 21	95.36 13	97.62 2

test_241102_TWO								94.41 48	71.65 211	92.07 8	97.21 4	74.58 17	99.11 6	92.34 21	95.36 13	96.59 16

test_241102_ONE					96.45 12	69.38 51		94.44 46	71.65 211	92.11 6	97.05 7	76.79 9	99.11 6

v8		75.35 238	73.26 243	81.61 215	80.67 308	66.82 117	89.54 246	89.27 248	71.65 211	63.30 297	80.30 308	54.99 196	94.06 220	67.33 224	62.33 318	83.94 308

v1240		75.21 241	72.98 246	81.88 210	79.20 326	66.00 137	90.75 213	89.11 258	71.63 215	67.41 261	81.22 294	47.36 268	93.87 232	65.46 247	64.72 300	85.77 286

SCA		75.82 232	72.76 249	85.01 115	86.63 227	70.08 35	81.06 332	89.19 252	71.60 216	70.01 223	77.09 335	45.53 283	90.25 310	60.43 279	73.27 235	94.68 92

BH-untuned		78.68 183	77.08 189	83.48 170	89.84 146	63.74 194	92.70 125	88.59 281	71.57 217	66.83 269	88.65 192	51.75 228	95.39 166	59.03 287	84.77 138	91.32 195

IterMVS		72.65 271	70.83 269	78.09 286	82.17 295	62.96 219	87.64 281	86.28 313	71.56 218	60.44 314	78.85 321	45.42 285	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		82.96 108	82.44 108	84.52 138	92.83 75	62.92 222	92.76 121	91.85 148	71.52 219	75.61 157	94.24 91	53.48 215	96.99 97	78.97 127	90.73 85	93.64 136

test-LLR		80.10 156	79.56 150	81.72 213	86.93 225	61.17 254	92.70 125	91.54 161	71.51 220	75.62 155	86.94 224	53.83 208	92.38 276	72.21 176	84.76 139	91.60 186

test0.0.03 1		72.76 266	72.71 252	72.88 331	80.25 314	47.99 362	91.22 197	89.45 241	71.51 220	62.51 306	87.66 213	53.83 208	85.06 353	50.16 317	67.84 277	85.58 289

test_one_0601					96.32 18	69.74 46		94.18 57	71.42 222	90.67 18	96.85 16	74.45 18

PGM-MVS		83.25 102	82.70 103	84.92 117	92.81 79	64.07 187	90.44 220	92.20 131	71.28 223	77.23 141	94.43 80	55.17 194	97.31 75	79.33 123	91.38 78	93.37 141

thisisatest0530		81.15 134	80.07 140	84.39 143	88.26 189	65.63 146	91.40 183	94.62 40	71.27 224	70.93 211	89.18 187	72.47 29	96.04 137	65.62 244	76.89 211	91.49 188

cl____		76.07 223	74.67 219	80.28 244	85.15 254	61.76 245	90.12 231	88.73 275	71.16 225	65.43 275	81.57 286	61.15 124	92.95 250	66.54 232	62.17 319	86.13 277

DIV-MVS_self_test		76.07 223	74.67 219	80.28 244	85.14 255	61.75 246	90.12 231	88.73 275	71.16 225	65.42 276	81.60 285	61.15 124	92.94 254	66.54 232	62.16 321	86.14 275

dp		75.01 243	72.09 259	83.76 158	89.28 161	66.22 134	79.96 345	89.75 230	71.16 225	67.80 256	77.19 334	51.81 227	92.54 271	50.39 315	71.44 252	92.51 168

FA-MVS(test-final)		79.12 172	77.23 188	84.81 124	90.54 133	63.98 189	81.35 330	91.71 154	71.09 228	74.85 165	82.94 267	52.85 219	97.05 89	67.97 216	81.73 168	93.41 140

CP-MVS		83.71 95	83.40 89	84.65 132	93.14 69	63.84 190	94.59 49	92.28 125	71.03 229	77.41 138	94.92 67	55.21 193	96.19 128	81.32 108	90.70 86	93.91 127

v10		74.77 245	72.54 255	81.46 218	80.33 313	66.71 121	89.15 256	89.08 260	70.94 230	63.08 300	79.86 313	52.52 222	94.04 223	65.70 243	62.17 319	83.64 311

CDPH-MVS		85.71 57	85.46 58	86.46 69	94.75 34	67.19 107	93.89 75	92.83 108	70.90 231	83.09 76	95.28 54	63.62 96	97.36 71	80.63 112	94.18 35	94.84 85

GBi-Net		75.65 234	73.83 236	81.10 228	88.85 172	65.11 159	90.01 235	90.32 205	70.84 232	67.04 265	80.25 309	48.03 260	91.54 297	59.80 284	69.34 261	86.64 263

test1		75.65 234	73.83 236	81.10 228	88.85 172	65.11 159	90.01 235	90.32 205	70.84 232	67.04 265	80.25 309	48.03 260	91.54 297	59.80 284	69.34 261	86.64 263

FMVSNet2		76.07 223	74.01 234	82.26 198	88.85 172	67.66 95	91.33 191	91.61 159	70.84 232	65.98 272	82.25 275	48.03 260	92.00 287	58.46 289	68.73 269	87.10 257

SF-MVS		87.03 34	87.09 34	86.84 54	92.70 81	67.45 103	93.64 90	93.76 69	70.78 235	86.25 45	96.44 26	66.98 57	97.79 47	88.68 48	94.56 32	95.28 66

ZD-MVS					96.63 9	65.50 151		93.50 82	70.74 236	85.26 59	95.19 61	64.92 78	97.29 76	87.51 55	93.01 54

HyFIR lowres test		81.03 139	79.56 150	85.43 100	87.81 204	68.11 85	90.18 230	90.01 223	70.65 237	72.95 184	86.06 236	63.61 97	94.50 202	75.01 154	79.75 183	93.67 134

RRT_MVS		74.44 247	72.97 247	78.84 278	82.36 293	57.66 308	89.83 241	88.79 274	70.61 238	64.58 283	84.89 246	39.24 307	92.65 268	70.11 195	66.34 285	86.21 273

MVP-Stereo		77.12 209	76.23 201	79.79 262	81.72 299	66.34 130	89.29 251	90.88 189	70.56 239	62.01 308	82.88 268	49.34 249	94.13 215	65.55 246	93.80 41	78.88 363
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMP	71.68 10	75.58 237	74.23 230	79.62 266	84.97 259	59.64 282	90.80 211	89.07 261	70.39 240	62.95 301	87.30 219	38.28 315	93.87 232	72.89 166	71.45 251	85.36 295
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		83.25 102	82.95 97	84.17 151	92.25 91	62.88 224	90.91 205	91.86 146	70.30 241	77.12 142	93.96 98	56.75 174	96.28 126	82.04 100	91.34 80	93.34 142
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
GeoE		78.90 177	77.43 182	83.29 173	88.95 171	62.02 239	92.31 141	86.23 315	70.24 242	71.34 209	89.27 186	54.43 203	94.04 223	63.31 261	80.81 176	93.81 132

tpm2		79.80 162	77.95 175	85.34 105	88.28 188	68.26 79	81.56 327	91.42 167	70.11 243	77.59 137	80.50 304	67.40 55	94.26 211	67.34 223	77.35 206	93.51 138

TR-MVS		78.77 182	77.37 187	82.95 179	90.49 134	60.88 260	93.67 88	90.07 218	70.08 244	74.51 168	91.37 155	45.69 282	95.70 153	60.12 282	80.32 178	92.29 173

CL-MVSNet_self_test		69.92 288	68.09 292	75.41 311	73.25 364	55.90 325	90.05 234	89.90 225	69.96 245	61.96 309	76.54 338	51.05 235	87.64 337	49.51 321	50.59 365	82.70 329

PAPM_NR		82.97 107	81.84 115	86.37 73	94.10 44	66.76 120	87.66 280	92.84 107	69.96 245	74.07 174	93.57 106	63.10 107	97.50 64	70.66 191	90.58 88	94.85 82

PCF-MVS	73.15 9	79.29 169	77.63 179	84.29 147	86.06 238	65.96 139	87.03 287	91.10 180	69.86 247	69.79 228	90.64 162	57.54 163	96.59 114	64.37 254	82.29 157	90.32 208
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
miper_lstm_enhance		73.05 261	71.73 264	77.03 299	83.80 276	58.32 300	81.76 323	88.88 268	69.80 248	61.01 311	78.23 325	57.19 165	87.51 340	65.34 248	59.53 340	85.27 298

MIMVSNet		71.64 275	68.44 289	81.23 223	81.97 298	64.44 173	73.05 365	88.80 272	69.67 249	64.59 282	74.79 349	32.79 346	87.82 334	53.99 305	76.35 215	91.42 190

LPG-MVS_test		75.82 232	74.58 223	79.56 268	84.31 270	59.37 287	90.44 220	89.73 233	69.49 250	64.86 279	88.42 194	38.65 311	94.30 207	72.56 172	72.76 240	85.01 300

LGP-MVS_train				79.56 268	84.31 270	59.37 287		89.73 233	69.49 250	64.86 279	88.42 194	38.65 311	94.30 207	72.56 172	72.76 240	85.01 300

APDe-MVS		87.54 26	87.84 25	86.65 61	96.07 23	66.30 131	94.84 45	93.78 66	69.35 252	88.39 33	96.34 28	67.74 53	97.66 54	90.62 36	93.44 49	96.01 39
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
tttt0517		79.50 166	78.53 166	82.41 193	87.22 216	61.43 252	89.75 243	94.76 32	69.29 253	67.91 252	88.06 208	72.92 25	95.63 154	62.91 265	73.90 233	90.16 210

Patchmatch-RL test		68.17 304	64.49 314	79.19 272	71.22 369	53.93 335	70.07 371	71.54 377	69.22 254	56.79 336	62.89 377	56.58 178	88.61 324	69.53 201	52.61 360	95.03 78

test_yl		84.28 79	83.16 93	87.64 31	94.52 37	69.24 55	95.78 18	95.09 23	69.19 255	81.09 91	92.88 120	57.00 169	97.44 66	81.11 110	81.76 166	96.23 33

DCV-MVSNet		84.28 79	83.16 93	87.64 31	94.52 37	69.24 55	95.78 18	95.09 23	69.19 255	81.09 91	92.88 120	57.00 169	97.44 66	81.11 110	81.76 166	96.23 33

jajsoiax		73.05 261	71.51 266	77.67 289	77.46 348	54.83 331	88.81 261	90.04 221	69.13 257	62.85 303	83.51 262	31.16 355	92.75 261	70.83 187	69.80 257	85.43 294

DP-MVS Recon		82.73 110	81.65 117	85.98 81	97.31 4	67.06 111	95.15 36	91.99 138	69.08 258	76.50 149	93.89 99	54.48 202	98.20 35	70.76 189	85.66 133	92.69 161

Baseline_NR-MVSNet		73.99 253	72.83 248	77.48 292	80.78 306	59.29 290	91.79 168	84.55 330	68.85 259	68.99 236	80.70 300	56.16 181	92.04 286	62.67 267	60.98 332	81.11 343

CHOSEN 280x420		77.35 205	76.95 193	78.55 280	87.07 220	62.68 228	69.71 372	82.95 344	68.80 260	71.48 207	87.27 221	66.03 65	84.00 359	76.47 143	82.81 154	88.95 225

DPE-MVS		88.77 16	89.21 16	87.45 40	96.26 20	67.56 98	94.17 57	94.15 59	68.77 261	90.74 17	97.27 2	76.09 12	98.49 29	90.58 37	94.91 20	96.30 29
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
mvs_tets		72.71 268	71.11 267	77.52 290	77.41 349	54.52 333	88.45 267	89.76 229	68.76 262	62.70 304	83.26 265	29.49 359	92.71 262	70.51 193	69.62 259	85.34 296

MVS		84.66 73	82.86 100	90.06 2	90.93 126	74.56 6	87.91 275	95.54 13	68.55 263	72.35 197	94.71 73	59.78 141	98.90 19	81.29 109	94.69 31	96.74 13

EPP-MVSNet		81.79 126	81.52 118	82.61 187	88.77 176	60.21 276	93.02 114	93.66 75	68.52 264	72.90 185	90.39 169	72.19 32	94.96 179	74.93 155	79.29 188	92.67 162

CSCG		86.87 35	86.26 44	88.72 15	95.05 31	70.79 28	93.83 82	95.33 16	68.48 265	77.63 135	94.35 86	73.04 24	98.45 30	84.92 80	93.71 45	96.92 11

testing3		70.38 285	70.83 269	69.03 350	85.82 244	43.93 378	90.72 214	90.56 198	68.06 266	60.24 315	86.82 226	64.83 79	84.12 355	26.33 388	64.10 305	79.04 362

bld_raw_dy_0_64		71.59 277	69.71 282	77.22 298	77.82 347	58.12 302	87.71 279	73.66 369	68.01 267	61.90 310	84.29 255	33.68 343	88.43 328	69.91 197	70.43 256	85.11 299

CP-MVSNet		70.50 283	69.91 279	72.26 336	80.71 307	51.00 348	87.23 286	90.30 209	67.84 268	59.64 318	82.69 270	50.23 242	82.30 371	51.28 312	59.28 341	83.46 316

pmmvs5		73.35 258	71.52 265	78.86 277	78.64 337	60.61 271	91.08 202	86.90 307	67.69 269	63.32 296	83.64 260	44.33 291	90.53 307	62.04 271	66.02 287	85.46 293

pm-mvs1		72.89 264	71.09 268	78.26 284	79.10 330	57.62 309	90.80 211	89.30 247	67.66 270	62.91 302	81.78 281	49.11 255	92.95 250	60.29 281	58.89 343	84.22 306

MDTV_nov1_ep13_2view						59.90 280	80.13 341		67.65 271	72.79 186		54.33 205		59.83 283		92.58 165

pmmvs4		73.92 254	71.81 263	80.25 246	79.17 327	65.24 155	87.43 283	87.26 305	67.64 272	63.46 295	83.91 259	48.96 256	91.53 300	62.94 264	65.49 289	83.96 307

WR-MVS_H		70.59 282	69.94 278	72.53 333	81.03 303	51.43 345	87.35 284	92.03 137	67.38 273	60.23 316	80.70 300	55.84 187	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	85.56 248	61.06 257	81.44 328	90.25 211	67.27 274	58.00 330	76.53 339	54.49 200	87.63 338	48.04 327	35.77 387	82.34 333

miper_refine_blended		69.03 296	66.37 299	77.01 300	85.56 248	61.06 257	81.44 328	90.25 211	67.27 274	58.00 330	76.53 339	54.49 200	87.63 338	48.04 327	35.77 387	82.34 333

PS-CasMVS		69.86 290	69.13 285	72.07 340	80.35 312	50.57 350	87.02 288	89.75 230	67.27 274	59.19 322	82.28 274	46.58 273	82.24 372	50.69 314	59.02 342	83.39 318

PEN-MVS		69.46 293	68.56 287	72.17 338	79.27 325	49.71 354	86.90 290	89.24 249	67.24 277	59.08 323	82.51 273	47.23 269	83.54 362	48.42 325	57.12 346	83.25 319

cascas		78.18 192	75.77 208	85.41 101	87.14 218	69.11 57	92.96 115	91.15 178	66.71 278	70.47 215	86.07 235	37.49 325	96.48 122	70.15 194	79.80 182	90.65 204

APD-MVS		85.93 52	85.99 50	85.76 91	95.98 26	65.21 156	93.59 93	92.58 119	66.54 279	86.17 47	95.88 39	63.83 91	97.00 94	86.39 67	92.94 55	95.06 75
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	70.45 11	78.54 187	75.92 206	86.41 72	85.93 243	71.68 16	92.74 122	92.51 121	66.49 280	64.56 284	91.96 140	43.88 292	98.10 37	54.61 302	90.65 87	89.44 224

DTE-MVSNet		68.46 302	67.33 295	71.87 342	77.94 345	49.00 359	86.16 295	88.58 282	66.36 281	58.19 327	82.21 276	46.36 274	83.87 360	44.97 344	55.17 353	82.73 326

IterMVS-SCA-FT		71.55 278	69.97 277	76.32 306	81.48 300	60.67 269	87.64 281	85.99 318	66.17 282	59.50 319	78.88 320	45.53 283	83.65 361	62.58 268	61.93 322	84.63 305

TransMVSNet (Re)		70.07 287	67.66 293	77.31 296	80.62 310	59.13 293	91.78 170	84.94 327	65.97 283	60.08 317	80.44 305	50.78 236	91.87 288	48.84 323	45.46 373	80.94 345

MVSFormer		83.75 94	82.88 99	86.37 73	89.24 165	71.18 22	89.07 257	90.69 192	65.80 284	87.13 39	94.34 87	64.99 75	92.67 265	72.83 167	91.80 70	95.27 67

test_djsdf		73.76 257	72.56 254	77.39 294	77.00 351	53.93 335	89.07 257	90.69 192	65.80 284	63.92 290	82.03 278	43.14 296	92.67 265	72.83 167	68.53 270	85.57 290

API-MVS		82.28 117	80.53 136	87.54 38	96.13 22	70.59 30	93.63 91	91.04 187	65.72 286	75.45 159	92.83 122	56.11 183	98.89 20	64.10 255	89.75 96	93.15 148

原ACMM1				84.42 141	93.21 66	64.27 183		93.40 88	65.39 287	79.51 112	92.50 126	58.11 158	96.69 112	65.27 249	93.96 38	92.32 172

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		79.95 160	77.39 186	87.64 31	89.63 151	71.41 18	93.30 104	93.70 73	65.34 289	67.39 262	91.75 146	47.83 265	98.96 16	57.71 292	89.81 93	92.54 166

HPM-MVS_fast		80.25 153	79.55 152	82.33 194	91.55 114	59.95 279	91.32 192	89.16 254	65.23 290	74.71 167	93.07 114	47.81 266	95.74 147	74.87 158	88.23 105	91.31 196

tfpnnormal		70.10 286	67.36 294	78.32 282	83.45 282	60.97 259	88.85 260	92.77 109	64.85 291	60.83 313	78.53 322	43.52 294	93.48 240	31.73 384	61.70 327	80.52 350

FE-MVS		75.97 229	73.02 245	84.82 121	89.78 147	65.56 148	77.44 355	91.07 184	64.55 292	72.66 187	79.85 314	46.05 281	96.69 112	54.97 301	80.82 175	92.21 179

SR-MVS		82.81 109	82.58 105	83.50 169	93.35 61	61.16 256	92.23 145	91.28 173	64.48 293	81.27 88	95.28 54	53.71 211	95.86 142	82.87 94	88.77 102	93.49 139

K. test v3		63.09 332	59.61 336	73.53 326	76.26 354	49.38 358	83.27 312	77.15 359	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	79.28 271	76.40 353	60.77 263	86.71 292	89.45 241	64.17 295	58.77 326	78.24 324	44.59 290	93.54 238	57.76 291	61.75 325	83.52 314

FMVSNet1		72.71 268	69.91 279	81.10 228	83.60 280	65.11 159	90.01 235	90.32 205	63.92 296	63.56 294	80.25 309	36.35 334	91.54 297	54.46 303	66.75 282	86.64 263

XVG-OURS		74.25 250	72.46 256	79.63 265	78.45 339	57.59 310	80.33 337	87.39 302	63.86 297	68.76 241	89.62 183	40.50 304	91.72 292	69.00 208	74.25 228	89.58 219

UniMVSNet_ETH3D		72.74 267	70.53 274	79.36 270	78.62 338	56.64 320	85.01 299	89.20 251	63.77 298	64.84 281	84.44 253	34.05 342	91.86 289	63.94 256	70.89 255	89.57 220

test_fmvs1		74.07 251	73.69 238	75.22 312	78.91 333	47.34 366	89.06 259	74.69 367	63.68 299	79.41 114	91.59 150	24.36 369	87.77 336	85.22 74	76.26 216	90.55 207

114514_t		79.17 171	77.67 177	83.68 163	95.32 29	65.53 150	92.85 119	91.60 160	63.49 300	67.92 251	90.63 164	46.65 272	95.72 152	67.01 228	83.54 147	89.79 216

test_fmvs1_n		72.69 270	71.92 261	74.99 315	71.15 370	47.08 368	87.34 285	75.67 362	63.48 301	78.08 131	91.17 157	20.16 380	87.87 333	84.65 82	75.57 220	90.01 213

APD-MVS_3200maxsize		81.64 129	81.32 120	82.59 188	92.36 88	58.74 296	91.39 185	91.01 188	63.35 302	79.72 110	94.62 76	51.82 226	96.14 130	79.71 118	87.93 109	92.89 159

test20.03		63.83 329	62.65 325	67.38 357	70.58 374	39.94 385	86.57 293	84.17 332	63.29 303	51.86 352	77.30 331	37.09 330	82.47 369	38.87 366	54.13 357	79.73 356

XVG-OURS-SEG-HR		74.70 246	73.08 244	79.57 267	78.25 341	57.33 314	80.49 335	87.32 303	63.22 304	68.76 241	90.12 179	44.89 289	91.59 295	70.55 192	74.09 230	89.79 216

test_vis1_n		71.63 276	70.73 272	74.31 322	69.63 376	47.29 367	86.91 289	72.11 373	63.21 305	75.18 161	90.17 175	20.40 378	85.76 348	84.59 83	74.42 227	89.87 214

ACMM	69.62 13	74.34 248	72.73 251	79.17 273	84.25 272	57.87 304	90.36 224	89.93 224	63.17 306	65.64 274	86.04 237	37.79 323	94.10 216	65.89 240	71.52 250	85.55 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmvs		72.88 265	69.76 281	82.22 199	90.98 125	67.05 112	78.22 352	88.30 288	63.10 307	64.35 289	74.98 348	55.09 195	94.27 209	43.25 347	69.57 260	85.34 296

SixPastTwentyTwo		64.92 323	61.78 330	74.34 321	78.74 335	49.76 353	83.42 311	79.51 357	62.86 308	50.27 359	77.35 330	30.92 357	90.49 308	45.89 339	47.06 370	82.78 324

SR-MVS-dyc-post		81.06 138	80.70 131	82.15 202	92.02 97	58.56 298	90.90 206	90.45 199	62.76 309	78.89 120	94.46 78	51.26 234	95.61 156	78.77 130	86.77 123	92.28 174

RE-MVS-def			80.48 137		92.02 97	58.56 298	90.90 206	90.45 199	62.76 309	78.89 120	94.46 78	49.30 250		78.77 130	86.77 123	92.28 174

TAPA-MVS	70.22 12	74.94 244	73.53 240	79.17 273	90.40 136	52.07 342	89.19 255	89.61 237	62.69 311	70.07 222	92.67 124	48.89 257	94.32 205	38.26 367	79.97 180	91.12 200
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Anonymous202405211		77.96 196	75.33 215	85.87 85	93.73 52	64.52 168	94.85 44	85.36 323	62.52 312	76.11 150	90.18 174	29.43 360	97.29 76	68.51 213	77.24 209	95.81 45

pmmvs-eth3d		65.53 322	62.32 327	75.19 313	69.39 377	59.59 283	82.80 319	83.43 340	62.52 312	51.30 356	72.49 353	32.86 345	87.16 343	55.32 300	50.73 364	78.83 364

AdaColmap		78.94 176	77.00 192	84.76 126	96.34 17	65.86 141	92.66 129	87.97 299	62.18 314	70.56 214	92.37 132	43.53 293	97.35 72	64.50 253	82.86 152	91.05 201

FOURS1					93.95 45	61.77 244	93.96 70	91.92 141	62.14 315	86.57 44

无先验							92.71 124	92.61 118	62.03 316				97.01 93	66.63 230		93.97 124

XVG-ACMP-BASELINE		68.04 305	65.53 305	75.56 310	74.06 362	52.37 340	78.43 349	85.88 319	62.03 316	58.91 325	81.21 296	20.38 379	91.15 304	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	62.15 307	79.77 315	38.14 318	91.44 302	68.90 210	67.45 278	83.21 320

tpm cat1		75.30 239	72.21 258	84.58 136	88.52 178	67.77 92	78.16 353	88.02 296	61.88 319	68.45 246	76.37 341	60.65 130	94.03 225	53.77 307	74.11 229	91.93 184

FMVSNet5		68.04 305	65.66 304	75.18 314	84.43 268	57.89 303	83.54 307	86.26 314	61.83 320	53.64 347	73.30 352	37.15 329	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	88.23 269	87.32 303	61.75 321	58.07 329	77.29 332	37.79 323	87.29 342	42.91 349	63.71 309	83.48 315

PatchMatch-RL		72.06 273	69.98 276	78.28 283	89.51 155	55.70 326	83.49 308	83.39 342	61.24 322	63.72 293	82.76 269	34.77 339	93.03 247	53.37 309	77.59 201	86.12 278

tt0805		73.07 260	70.73 272	80.07 251	78.37 340	57.05 316	87.78 277	92.18 133	61.23 323	67.04 265	86.49 229	31.35 354	94.58 194	65.06 250	67.12 279	88.57 233

PLC	68.80 14	75.23 240	73.68 239	79.86 260	92.93 73	58.68 297	90.64 217	88.30 288	60.90 324	64.43 288	90.53 165	42.38 298	94.57 196	56.52 295	76.54 213	86.33 269
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	80.03 253	85.22 253	63.25 211	87.72 278	84.66 329	60.83 325	51.57 354	79.43 319	27.29 365	94.96 179	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	77.96 287	78.69 336	62.93 220	89.86 240	89.17 253	60.55 326	50.27 359	77.73 329	22.60 374	94.06 220	47.18 333	72.65 242	76.88 371

VDDNet		80.50 147	78.26 169	87.21 44	86.19 235	69.79 44	94.48 50	91.31 170	60.42 327	79.34 115	90.91 160	38.48 314	96.56 117	82.16 98	81.05 172	95.27 67

CPTT-MVS		79.59 164	79.16 159	80.89 236	91.54 115	59.80 281	92.10 150	88.54 283	60.42 327	72.96 183	93.28 110	48.27 259	92.80 259	78.89 129	86.50 128	90.06 211

our_test_3		68.29 303	64.69 311	79.11 276	78.92 331	64.85 166	88.40 268	85.06 325	60.32 329	52.68 349	76.12 343	40.81 303	89.80 320	44.25 346	55.65 351	82.67 331

ITE_SJBPF				70.43 345	74.44 360	47.06 369		77.32 358	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	79.77 263	78.92 331	66.04 136	88.68 263	82.90 345	60.11 331	55.45 339	75.96 344	39.19 308	90.55 306	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	71.56 206	90.26 173	43.22 295	75.05 381	74.26 161	62.70 314	87.25 256

新几何1				84.73 127	92.32 89	64.28 182		91.46 166	59.56 334	79.77 109	92.90 118	56.95 172	96.57 116	63.40 259	92.91 56	93.34 142

旧先验2							92.00 159		59.37 335	87.54 38			93.47 241	75.39 150

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	84.00 275	51.55 344	67.08 379	83.53 339	58.78 337	54.94 341	80.31 307	34.54 340	93.23 244	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				81.34 221	89.02 169	57.72 306		89.84 227	58.65 338	85.32 58	94.09 94	57.03 167	93.28 243	69.34 203	90.56 89	93.03 153

ACMH+	65.35 16	67.65 308	64.55 312	76.96 302	84.59 264	57.10 315	88.08 270	80.79 351	58.59 339	53.00 348	81.09 298	26.63 367	92.95 250	46.51 335	61.69 328	80.82 346

ADS-MVSNet2		66.90 313	63.44 320	77.26 297	88.06 195	60.70 268	68.01 376	75.56 364	57.57 340	64.48 285	69.87 365	38.68 309	84.10 356	40.87 358	67.89 275	86.97 258

ADS-MVSNet		68.54 301	64.38 316	81.03 232	88.06 195	66.90 116	68.01 376	84.02 334	57.57 340	64.48 285	69.87 365	38.68 309	89.21 323	40.87 358	67.89 275	86.97 258

MDA-MVSNet-bldmvs		61.54 337	57.70 341	73.05 329	79.53 322	57.00 319	83.08 316	81.23 349	57.57 340	34.91 388	72.45 354	32.79 346	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	84.70 328	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	88.60 325	50.08 318	38.90 384	79.63 357

MSDG		69.54 292	65.73 302	80.96 233	85.11 257	63.71 197	84.19 303	83.28 343	56.95 345	54.50 342	84.03 256	31.50 352	96.03 138	42.87 351	69.13 266	83.14 322

F-COLMAP		70.66 281	68.44 289	77.32 295	86.37 233	55.91 324	88.00 273	86.32 312	56.94 346	57.28 335	88.07 207	33.58 344	92.49 273	51.02 313	68.37 271	83.55 312

test222					89.77 148	61.60 249	89.55 245	89.42 243	56.83 347	77.28 140	92.43 130	52.76 220			91.14 83	93.09 150

CNLPA		74.31 249	72.30 257	80.32 242	91.49 116	61.66 248	90.85 209	80.72 352	56.67 348	63.85 292	90.64 162	46.75 271	90.84 305	53.79 306	75.99 218	88.47 237

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	88.56 326	53.60 308	53.63 358	80.71 348

YYNet1		63.76 331	60.14 334	74.62 318	78.06 344	60.19 277	83.46 310	83.99 337	56.18 350	39.25 384	71.56 362	37.18 328	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	78.15 343	60.41 272	83.49 308	84.03 333	56.17 351	39.17 385	71.59 361	37.22 327	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	89.22 253	83.41 341	55.48 352	53.86 346	77.84 328	26.28 368	93.95 229	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	84.21 331	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	67.91 252	84.54 252	15.35 385	81.22 376	75.65 148	66.16 286	82.88 323

CVMVSNet		74.04 252	74.27 229	73.33 327	85.33 250	43.94 377	89.53 247	88.39 285	54.33 355	70.37 218	90.13 177	49.17 253	84.05 357	61.83 273	79.36 186	91.99 183

Anonymous20240529		76.84 215	74.15 231	84.88 119	91.02 124	64.95 164	93.84 80	91.09 181	53.57 356	73.00 182	87.42 217	35.91 335	97.32 74	69.14 207	72.41 245	92.36 170

pmmvs6		67.57 309	64.76 310	76.00 309	72.82 367	53.37 337	88.71 262	86.78 311	53.19 357	57.58 334	78.03 327	35.33 338	92.41 275	55.56 299	54.88 355	82.21 335

TinyColmap		60.32 339	56.42 346	72.00 341	78.78 334	53.18 338	78.36 351	75.64 363	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	84.10 274	60.75 265	87.95 274	79.71 356	52.03 359	52.41 350	77.20 333	32.21 350	91.64 293	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	71.21 185	62.79 313	67.07 385

Anonymous20231211		73.08 259	70.39 275	81.13 226	90.62 132	63.33 210	91.40 183	90.06 220	51.84 361	64.46 287	80.67 302	36.49 333	94.07 219	63.83 257	64.17 304	85.98 281

AllTest		61.66 335	58.06 339	72.46 334	79.57 320	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	79.57 320	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	80.32 242	82.07 297	63.68 200	67.96 378	87.62 301	50.86 364	69.37 229	65.18 373	57.09 166	88.53 327	41.59 356	66.60 283	88.74 230

Anonymous20240521		62.09 334	59.08 337	71.10 343	67.19 380	48.72 360	83.91 305	85.23 324	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	80.14 249	95.36 28	62.93 220	89.56 244	76.11 360	50.27 366	57.69 333	85.23 242	39.68 306	95.73 148	33.35 377	71.05 254	81.78 339

gg-mvs-nofinetune		77.18 207	74.31 228	85.80 89	91.42 117	68.36 75	71.78 366	94.72 34	49.61 367	77.12 142	45.92 390	77.41 8	93.98 227	67.62 221	93.16 53	95.05 76

JIA-IIPM		66.06 317	62.45 326	76.88 303	81.42 302	54.45 334	57.49 391	88.67 278	49.36 368	63.86 291	46.86 389	56.06 184	90.25 310	49.53 320	68.83 267	85.95 282

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	81.44 301	53.00 339	83.75 306	75.53 365	48.34 371	48.81 365	81.40 290	24.14 370	90.30 309	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	78.34 281	82.12 296	64.38 177	68.72 373	84.00 335	48.23 372	59.24 320	72.41 355	57.82 160	89.27 322	46.10 338	56.68 350	81.36 340

LS3D		69.17 294	66.40 298	77.50 291	91.92 103	56.12 323	85.12 298	80.37 354	46.96 373	56.50 337	87.51 216	37.25 326	93.71 235	32.52 383	79.40 185	82.68 330

RPSCF		64.24 327	61.98 329	71.01 344	76.10 355	45.00 374	75.83 361	75.94 361	46.94 374	58.96 324	84.59 250	31.40 353	82.00 373	47.76 331	60.33 339	86.04 279

RPMNet		70.42 284	65.68 303	84.63 134	83.15 284	67.96 88	70.25 369	90.45 199	46.83 375	69.97 225	65.10 374	56.48 180	95.30 171	35.79 372	73.13 236	90.64 205

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	35.54 336	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	85.73 321	45.54 377	44.76 376	82.14 277	35.40 337	90.14 316	63.18 263	74.54 225	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	86.19 316	45.11 378	54.27 343	81.15 297	36.91 332	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	80.62 239	83.75 277	58.83 295	58.91 390	75.26 366	44.50 380	50.95 358	77.09 335	58.81 152	87.90 332	35.13 373	64.03 306	95.12 74

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	34.42 341	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	86.87 261

pmmvs3		55.51 347	51.50 352	67.53 356	57.90 392	50.93 349	80.37 336	73.66 369	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	84.37 269	56.57 321	71.64 367	74.11 368	34.44 388	45.54 374	42.24 395	31.11 356	89.81 318	40.36 361	76.10 217	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 56	49.56 247	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	53.55 212	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 64	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 24	94.77 25	96.51 21

No_MVS				89.60 8	97.31 4	73.22 10		95.05 26					99.07 13	92.01 24	94.77 25	96.51 21

eth-test2					0.00 414

eth-test					0.00 414

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

test_0728_SECOND				88.70 16	96.45 12	70.43 32	96.64 9	94.37 52					99.15 2	91.91 27	94.90 21	96.51 21

GSMVS																94.68 92

test_part2					96.29 19	68.16 84				90.78 16

sam_mvs1												57.85 159				94.68 92

sam_mvs												54.91 197

ambc				69.61 347	61.38 389	41.35 382	49.07 396	85.86 320		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	53.05 217	91.50 301	60.43 279

test_post											23.01 400	56.49 179	92.67 265

patchmatchnet-post											67.62 370	57.62 162	90.25 310

GG-mvs-BLEND				86.53 68	91.91 104	69.67 49	75.02 363	94.75 33		78.67 127	90.85 161	77.91 7	94.56 198	72.25 175	93.74 43	95.36 59

MTMP							93.77 84	32.52 409

test9_res														89.41 39	94.96 18	95.29 64

agg_prior2														86.41 66	94.75 29	95.33 60

agg_prior					94.16 43	66.97 115		93.31 89		84.49 65			96.75 111

test_prior4						67.18 109	93.92 73

test_prior				86.42 71	94.71 35	67.35 104		93.10 99					96.84 108			95.05 76

新几何2							91.41 181

旧先验1					91.94 101	60.74 266		91.50 164			94.36 82	65.23 73			91.84 69	94.55 99

原ACMM2							92.01 156

testdata2													96.09 132	61.26 275

segment_acmp												65.94 66

test12				87.09 48	94.60 36	68.86 64		92.91 105		82.67 81		65.44 71	97.55 62		93.69 46	94.84 85

plane_prior7					86.94 223	61.51 250

plane_prior6					87.23 215	62.32 234						50.66 237

plane_prior5								91.31 170					95.55 161	76.74 140	78.53 195	88.39 238

plane_prior4											89.14 189

plane_prior1					87.15 217

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	89.81 318	47.75 332	31.37 392	83.53 313

test11								93.01 101

door								66.57 384

HQP5-MVS						63.66 201

BP-MVS														77.63 137

HQP4-MVS										74.18 170			95.61 156			88.63 231

HQP3-MVS								91.70 156							78.90 190

HQP2-MVS												51.63 230

NP-MVS					87.41 211	63.04 216					90.30 171

ACMMP++_ref															71.63 248

ACMMP++															69.72 258

Test By Simon												54.21 206

High-resolution multi-view benchmark