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 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 44	96.26 27	72.84 27	99.38 1	92.64 6	95.93 10	97.08 9

DELS-MVS		90.05 7	90.09 10	89.94 4	93.14 77	73.88 8	97.01 3	94.40 48	88.32 2	85.71 46	94.91 69	74.11 20	98.91 17	87.26 51	95.94 9	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
EPNet		87.84 22	88.38 17	86.23 78	93.30 70	66.05 133	95.26 31	94.84 25	87.09 3	88.06 26	94.53 77	66.79 58	97.34 75	83.89 78	91.68 80	95.29 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CANet		89.61 11	89.99 11	88.46 20	94.39 44	69.71 43	96.53 11	93.78 65	86.89 4	89.68 19	95.78 34	65.94 65	99.10 9	92.99 4	93.91 44	96.58 17

patch_mono-2		89.71 10	90.99 5	85.85 89	96.04 26	63.70 199	95.04 41	95.19 13	86.74 5	91.53 11	95.15 59	73.86 21	97.58 63	93.38 3	92.00 75	96.28 32

DeepPCF-MVS	81.17 1	89.72 9	91.38 3	84.72 127	93.00 80	58.16 292	96.72 7	94.41 46	86.50 6	90.25 18	97.83 1	75.46 14	98.67 23	92.78 5	95.49 13	97.32 6

CANet_DTU		84.09 82	83.52 76	85.81 90	90.30 148	66.82 115	91.87 156	89.01 261	85.27 7	86.09 41	93.74 101	47.71 258	96.98 99	77.90 127	89.78 102	93.65 133

CLD-MVS		82.73 104	82.35 104	83.86 149	87.90 205	67.65 90	95.45 27	92.18 137	85.06 8	72.58 178	92.27 135	52.46 215	95.78 140	84.18 74	79.06 181	88.16 228
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 5	90.89 6	88.61 19	96.76 10	70.65 25	96.47 12	94.83 26	84.83 9	89.07 23	96.80 15	70.86 34	99.06 15	92.64 6	95.71 11	96.12 35

NCCC		89.07 14	89.46 14	87.91 24	96.60 12	69.05 54	96.38 13	94.64 36	84.42 10	86.74 34	96.20 28	66.56 61	98.76 22	89.03 34	94.56 33	95.92 42

PS-MVSNAJ		88.14 16	87.61 26	89.71 7	92.06 103	76.72 1	95.75 19	93.26 92	83.86 11	89.55 20	96.06 31	53.55 205	97.89 48	91.10 18	93.31 55	94.54 97

DeepC-MVS_fast	79.48 2	87.95 20	88.00 20	87.79 27	95.86 29	68.32 70	95.74 20	94.11 60	83.82 12	83.49 69	96.19 29	64.53 82	98.44 30	83.42 81	94.88 24	96.61 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		87.92 21	87.38 32	89.55 12	91.41 129	76.43 3	95.74 20	93.12 101	83.53 13	89.55 20	95.95 32	53.45 209	97.68 54	91.07 19	92.62 64	94.54 97

TSAR-MVS + MP.		88.11 18	88.64 16	86.54 65	91.73 118	68.04 79	90.36 215	93.55 79	82.89 14	91.29 12	92.89 120	72.27 30	96.03 134	87.99 40	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 2	90.52 7	91.24 1	89.68 159	76.68 2	97.29 1	95.35 10	82.87 15	91.58 10	97.22 4	79.93 5	99.10 9	83.12 82	97.64 2	97.94 1

WTY-MVS		86.32 45	85.81 52	87.85 25	92.82 85	69.37 49	95.20 33	95.25 12	82.71 16	81.91 79	94.73 73	67.93 47	97.63 60	79.55 110	82.25 159	96.54 18

lupinMVS		87.74 23	87.77 23	87.63 33	89.24 171	71.18 20	96.57 10	92.90 109	82.70 17	87.13 30	95.27 52	64.99 75	95.80 139	89.34 28	91.80 78	95.93 41

HPM-MVS++		89.37 13	89.95 12	87.64 29	95.10 34	68.23 75	95.24 32	94.49 41	82.43 18	88.90 24	96.35 24	71.89 33	98.63 24	88.76 36	96.40 6	96.06 36

PVSNet_Blended		86.73 42	86.86 40	86.31 76	93.76 58	67.53 93	96.33 14	93.61 76	82.34 19	81.00 91	93.08 112	63.19 101	97.29 78	87.08 52	91.38 86	94.13 114

MSP-MVS		90.38 4	91.87 1	85.88 86	92.83 83	64.03 191	93.06 105	94.33 52	82.19 20	93.65 3	96.15 30	85.89 1	97.19 84	91.02 20	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
DWT-MVSNet_test		83.95 85	82.80 95	87.41 37	92.90 82	70.07 32	89.12 247	94.42 45	82.15 21	77.64 128	91.77 142	70.81 35	96.22 124	65.03 234	81.36 167	95.94 40

Regformer-1		87.24 30	87.60 27	86.15 80	95.14 32	65.83 141	93.95 71	95.12 16	82.11 22	84.25 61	95.73 36	67.88 48	98.35 33	85.60 63	88.64 111	94.26 106

PAPM		85.89 53	85.46 57	87.18 42	88.20 198	72.42 14	92.41 134	92.77 112	82.11 22	80.34 97	93.07 114	68.27 40	95.02 174	78.39 122	93.59 51	94.09 116

jason		86.40 44	86.17 47	87.11 44	86.16 234	70.54 27	95.71 23	92.19 136	82.00 24	84.58 58	94.34 87	61.86 112	95.53 160	87.76 42	90.89 92	95.27 67
jason: jason.
baseline1		81.84 120	81.03 120	84.28 142	91.60 121	66.62 121	91.08 194	91.66 159	81.87 25	74.86 156	91.67 146	69.98 37	94.92 179	71.76 172	64.75 279	91.29 191

CHOSEN 1792x2688		84.98 65	83.45 79	89.57 11	89.94 154	75.14 5	92.07 146	92.32 127	81.87 25	75.68 148	88.27 189	60.18 126	98.60 25	80.46 106	90.27 99	94.96 81

Regformer-2		87.00 37	87.43 30	85.71 96	95.14 32	64.73 170	93.95 71	94.95 23	81.69 27	84.03 66	95.73 36	67.35 52	98.19 37	85.40 65	88.64 111	94.20 108

h-mvs33		83.01 100	82.56 100	84.35 139	89.34 166	62.02 233	92.72 119	93.76 68	81.45 28	82.73 74	92.25 136	60.11 127	97.13 88	87.69 43	62.96 290	93.91 125

hse-mvs2		81.12 131	81.11 119	81.16 219	86.52 227	57.48 302	89.40 240	91.16 178	81.45 28	82.73 74	90.49 162	60.11 127	94.58 188	87.69 43	60.41 317	91.41 185

ET-MVSNet_ETH3D		84.01 83	83.15 89	86.58 63	90.78 142	70.89 23	94.74 47	94.62 37	81.44 30	58.19 308	93.64 103	73.64 24	92.35 269	82.66 86	78.66 186	96.50 23

casdiffmvs		85.37 59	84.87 65	86.84 51	88.25 196	69.07 53	93.04 107	91.76 153	81.27 31	80.84 93	92.07 138	64.23 85	96.06 132	84.98 69	87.43 121	95.39 55

ETV-MVS		86.01 51	86.11 48	85.70 97	90.21 150	67.02 110	93.43 96	91.92 144	81.21 32	84.13 65	94.07 96	60.93 120	95.63 150	89.28 29	89.81 100	94.46 104

Regformer-3		85.80 54	85.92 50	85.46 103	94.17 48	65.09 163	92.95 112	95.11 17	81.13 33	81.68 81	95.04 60	65.82 67	98.32 34	83.02 83	84.36 145	92.97 153

DeepC-MVS	77.85 3	85.52 57	85.24 59	86.37 73	88.80 181	66.64 120	92.15 140	93.68 73	81.07 34	76.91 139	93.64 103	62.59 106	98.44 30	85.50 64	92.84 62	94.03 120
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 64	84.44 69	86.71 57	88.33 193	68.73 61	90.24 220	91.82 152	81.05 35	81.18 87	92.50 127	63.69 93	96.08 131	84.45 73	86.71 129	95.32 61

PC_three_1452									80.91 36	94.07 2	96.83 14	83.57 4	99.12 5	95.70 2	97.42 4	97.55 4

IU-MVS					96.46 13	69.91 37		95.18 14	80.75 37	95.28 1				92.34 8	95.36 14	96.47 24

Regformer-4		85.45 58	85.69 55	84.73 125	94.17 48	63.23 209	92.95 112	94.83 26	80.66 38	81.29 84	95.04 60	65.12 73	98.08 40	82.74 85	84.36 145	92.88 157

diffmvs		84.28 76	83.83 74	85.61 99	87.40 214	68.02 80	90.88 200	89.24 248	80.54 39	81.64 82	92.52 126	59.83 132	94.52 195	87.32 50	85.11 139	94.29 105

VPNet		78.82 172	77.53 176	82.70 177	84.52 259	66.44 125	93.93 73	92.23 130	80.46 40	72.60 177	88.38 187	49.18 244	93.13 236	72.47 164	63.97 287	88.55 220

canonicalmvs		86.85 39	86.25 46	88.66 18	91.80 117	71.92 15	93.54 91	91.71 156	80.26 41	87.55 28	95.25 54	63.59 96	96.93 105	88.18 39	84.34 147	97.11 8

CS-MVS-test		86.14 49	87.01 37	83.52 159	92.63 91	59.36 281	95.49 26	91.92 144	80.09 42	85.46 50	95.53 43	61.82 113	95.77 142	86.77 56	93.37 54	95.41 54

CS-MVS		85.74 55	86.65 43	82.98 171	92.00 107	58.07 293	95.32 30	91.87 148	79.98 43	84.86 56	95.38 45	62.27 108	95.52 161	86.12 60	92.08 74	95.36 57

MVSTER		82.47 108	82.05 105	83.74 151	92.68 90	69.01 55	91.90 155	93.21 93	79.83 44	72.14 186	85.71 221	74.72 16	94.72 184	75.72 137	72.49 227	87.50 233

HQP-NCC					87.54 210		94.06 63		79.80 45	74.18 160

ACMP_Plane					87.54 210		94.06 63		79.80 45	74.18 160

HQP-MVS		81.14 129	80.64 125	82.64 179	87.54 210	63.66 202	94.06 63	91.70 157	79.80 45	74.18 160	90.30 166	51.63 222	95.61 152	77.63 128	78.90 182	88.63 218

baseline2		83.68 92	83.42 82	84.48 135	87.37 215	66.00 135	90.06 224	95.93 7	79.71 48	69.08 221	90.39 164	77.92 6	96.28 122	78.91 117	81.38 166	91.16 192

EI-MVSNet-Vis-set		83.77 89	83.67 75	84.06 145	92.79 88	63.56 205	91.76 163	94.81 28	79.65 49	77.87 125	94.09 94	63.35 99	97.90 47	79.35 111	79.36 178	90.74 196

EIA-MVS		84.84 67	84.88 64	84.69 128	91.30 130	62.36 228	93.85 78	92.04 139	79.45 50	79.33 109	94.28 90	62.42 107	96.35 121	80.05 107	91.25 89	95.38 56

plane_prior						62.42 226	93.85 78		79.38 51						78.80 184

dcpmvs_2		87.37 28	87.55 28	86.85 50	95.04 36	68.20 76	90.36 215	90.66 198	79.37 52	81.20 86	93.67 102	74.73 15	96.55 118	90.88 21	92.00 75	95.82 45

alignmvs		87.28 29	86.97 38	88.24 23	91.30 130	71.14 22	95.61 24	93.56 78	79.30 53	87.07 33	95.25 54	68.43 39	96.93 105	87.87 41	84.33 148	96.65 13

TESTMET0.1,1		82.41 109	81.98 107	83.72 154	88.08 199	63.74 196	92.70 121	93.77 67	79.30 53	77.61 130	87.57 201	58.19 149	94.08 209	73.91 151	86.68 130	93.33 141

EI-MVSNet-UG-set		83.14 98	82.96 90	83.67 156	92.28 98	63.19 211	91.38 180	94.68 34	79.22 55	76.60 141	93.75 100	62.64 105	97.76 53	78.07 125	78.01 189	90.05 204

PVSNet	73.49 8	80.05 149	78.63 157	84.31 140	90.92 137	64.97 166	92.47 133	91.05 187	79.18 56	72.43 183	90.51 161	37.05 316	94.06 211	68.06 201	86.00 134	93.90 127

HY-MVS	76.49 5	84.28 76	83.36 85	87.02 47	92.22 100	67.74 87	84.65 288	94.50 40	79.15 57	82.23 77	87.93 196	66.88 56	96.94 103	80.53 105	82.20 160	96.39 28

PVSNet_BlendedMVS		83.38 94	83.43 80	83.22 167	93.76 58	67.53 93	94.06 63	93.61 76	79.13 58	81.00 91	85.14 225	63.19 101	97.29 78	87.08 52	73.91 217	84.83 286

plane_prior3						61.95 236			79.09 59	72.53 179

MVS_111021_HR		86.19 48	85.80 53	87.37 38	93.17 76	69.79 40	93.99 67	93.76 68	79.08 60	78.88 117	93.99 97	62.25 109	98.15 38	85.93 62	91.15 90	94.15 113

MSLP-MVS++		86.27 46	85.91 51	87.35 39	92.01 106	68.97 57	95.04 41	92.70 114	79.04 61	81.50 83	96.50 20	58.98 144	96.78 109	83.49 80	93.93 43	96.29 30

IB-MVS	77.80 4	82.18 112	80.46 129	87.35 39	89.14 173	70.28 30	95.59 25	95.17 15	78.85 62	70.19 208	85.82 219	70.66 36	97.67 55	72.19 168	66.52 266	94.09 116
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	82.69 107	80.82 121	88.31 22	89.57 161	71.26 19	92.60 127	94.39 49	78.84 63	67.89 240	92.48 130	48.42 250	98.52 26	68.80 198	94.40 37	95.15 72

HQP_MVS		80.34 144	79.75 139	82.12 197	86.94 221	62.42 226	93.13 103	91.31 172	78.81 64	72.53 179	89.14 180	50.66 229	95.55 158	76.74 131	78.53 187	88.39 224

plane_prior2							93.13 103		78.81 64

MG-MVS		87.11 33	86.27 44	89.62 8	97.79 1	76.27 4	94.96 44	94.49 41	78.74 66	83.87 68	92.94 117	64.34 84	96.94 103	75.19 140	94.09 40	95.66 48

gm-plane-assit					88.42 189	67.04 109			78.62 67		91.83 141		97.37 72	76.57 133

VNet		86.20 47	85.65 56	87.84 26	93.92 54	69.99 33	95.73 22	95.94 6	78.43 68	86.00 42	93.07 114	58.22 148	97.00 95	85.22 67	84.33 148	96.52 19

tpm		78.58 180	77.03 184	83.22 167	85.94 239	64.56 171	83.21 301	91.14 181	78.31 69	73.67 167	79.68 295	64.01 87	92.09 275	66.07 223	71.26 237	93.03 151

xxxxxxxxxxxxxcwj		87.14 32	87.19 34	86.99 48	93.84 56	67.89 83	95.05 39	84.72 318	78.19 70	86.25 36	96.44 21	66.98 54	97.79 51	88.68 37	94.56 33	95.28 65

save fliter					93.84 56	67.89 83	95.05 39	92.66 117	78.19 70

TSAR-MVS + GP.		87.96 19	88.37 18	86.70 59	93.51 67	65.32 152	95.15 35	93.84 64	78.17 72	85.93 43	94.80 72	75.80 13	98.21 35	89.38 27	88.78 108	96.59 15

FIs		79.47 161	79.41 147	79.67 254	85.95 237	59.40 278	91.68 167	93.94 62	78.06 73	68.96 224	88.28 188	66.61 60	91.77 281	66.20 222	74.99 211	87.82 230

sss		82.71 106	82.38 103	83.73 153	89.25 170	59.58 276	92.24 138	94.89 24	77.96 74	79.86 103	92.38 132	56.70 168	97.05 90	77.26 130	80.86 171	94.55 95

PMMVS		81.98 118	82.04 106	81.78 204	89.76 158	56.17 311	91.13 193	90.69 195	77.96 74	80.09 100	93.57 105	46.33 268	94.99 175	81.41 98	87.46 120	94.17 111

DROMVSNet		84.53 72	85.04 62	83.01 170	89.34 166	61.37 246	94.42 50	91.09 183	77.91 76	83.24 70	94.20 92	58.37 147	95.40 163	85.35 66	91.41 85	92.27 173

test1111		80.84 136	80.02 133	83.33 165	87.87 206	60.76 257	92.62 126	86.86 298	77.86 77	75.73 147	91.39 149	46.35 266	94.70 187	72.79 159	88.68 110	94.52 99

MVS_Test		84.16 81	83.20 86	87.05 46	91.56 123	69.82 39	89.99 229	92.05 138	77.77 78	82.84 73	86.57 211	63.93 89	96.09 129	74.91 146	89.18 106	95.25 70

SteuartSystems-ACMMP		86.82 41	86.90 39	86.58 63	90.42 145	66.38 126	96.09 15	93.87 63	77.73 79	84.01 67	95.66 38	63.39 98	97.94 43	87.40 48	93.55 52	95.42 53
Skip Steuart: Steuart Systems R&D Blog.
EPNet_dtu		78.80 173	79.26 151	77.43 282	88.06 200	49.71 343	91.96 154	91.95 143	77.67 80	76.56 142	91.28 151	58.51 146	90.20 303	56.37 278	80.95 170	92.39 165
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test2506		83.29 95	82.92 92	84.37 138	88.39 191	63.18 212	92.01 149	91.35 171	77.66 81	78.49 122	91.42 147	64.58 81	95.09 172	73.19 153	89.23 104	94.85 83

ECVR-MVS		81.29 126	80.38 130	84.01 147	88.39 191	61.96 235	92.56 132	86.79 299	77.66 81	76.63 140	91.42 147	46.34 267	95.24 170	74.36 150	89.23 104	94.85 83

tpmrst		80.57 139	79.14 154	84.84 123	90.10 151	68.28 72	81.70 309	89.72 236	77.63 83	75.96 145	79.54 297	64.94 77	92.71 252	75.43 138	77.28 201	93.55 135

testdata1							89.21 244		77.55 84

UniMVSNet_NR-MVSNet		78.15 187	77.55 175	79.98 245	84.46 261	60.26 266	92.25 137	93.20 95	77.50 85	68.88 225	86.61 210	66.10 63	92.13 273	66.38 219	62.55 294	87.54 232

UA-Net		80.02 150	79.65 140	81.11 222	89.33 168	57.72 297	86.33 282	89.00 262	77.44 86	81.01 90	89.15 179	59.33 139	95.90 137	61.01 259	84.28 150	89.73 208

PVSNet_Blended_VisFu		83.97 84	83.50 77	85.39 107	90.02 152	66.59 123	93.77 83	91.73 154	77.43 87	77.08 138	89.81 174	63.77 92	96.97 100	79.67 109	88.21 114	92.60 161

ETH3 D test6400		90.27 6	90.44 8	89.75 6	96.82 9	74.33 7	95.89 16	94.80 29	77.13 88	89.13 22	97.38 2	74.49 18	98.48 28	92.32 11	95.98 8	96.46 25

NR-MVSNet		76.05 217	74.59 213	80.44 234	82.96 281	62.18 232	90.83 202	91.73 154	77.12 89	60.96 294	86.35 212	59.28 140	91.80 280	60.74 260	61.34 309	87.35 238

RRT_test8_iter05		80.61 138	79.62 141	83.60 158	91.87 116	66.90 113	93.42 98	93.68 73	77.09 90	68.83 227	85.63 222	66.82 57	95.42 162	76.46 135	62.74 293	88.48 221

FC-MVSNet-test		77.99 189	78.08 165	77.70 277	84.89 254	55.51 316	90.27 218	93.75 71	76.87 91	66.80 255	87.59 200	65.71 69	90.23 302	62.89 249	73.94 216	87.37 237

SD-MVS		87.49 26	87.49 29	87.50 35	93.60 63	68.82 60	93.90 75	92.63 120	76.86 92	87.90 27	95.76 35	66.17 62	97.63 60	89.06 32	91.48 84	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
UGNet		79.87 153	78.68 156	83.45 164	89.96 153	61.51 243	92.13 141	90.79 193	76.83 93	78.85 119	86.33 214	38.16 302	96.17 126	67.93 203	87.17 122	92.67 159
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		82.02 117	81.52 112	83.51 161	88.42 189	62.88 221	89.77 232	88.93 263	76.78 94	75.55 152	93.10 110	50.31 232	95.38 165	83.82 79	87.02 123	92.26 174

TranMVSNet+NR-MVSNet		75.86 222	74.52 216	79.89 248	82.44 284	60.64 262	91.37 181	91.37 170	76.63 95	67.65 243	86.21 215	52.37 216	91.55 285	61.84 255	60.81 312	87.48 234

PAPR		85.15 62	84.47 67	87.18 42	96.02 27	68.29 71	91.85 158	93.00 106	76.59 96	79.03 113	95.00 62	61.59 114	97.61 62	78.16 124	89.00 107	95.63 49

UniMVSNet (Re)		77.58 196	76.78 188	79.98 245	84.11 267	60.80 254	91.76 163	93.17 98	76.56 97	69.93 214	84.78 230	63.32 100	92.36 268	64.89 235	62.51 296	86.78 247

DU-MVS		76.86 204	75.84 201	79.91 247	82.96 281	60.26 266	91.26 186	91.54 162	76.46 98	68.88 225	86.35 212	56.16 174	92.13 273	66.38 219	62.55 294	87.35 238

OPM-MVS		79.00 167	78.09 164	81.73 205	83.52 275	63.83 193	91.64 169	90.30 211	76.36 99	71.97 189	89.93 173	46.30 269	95.17 171	75.10 141	77.70 192	86.19 260
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
WR-MVS		76.76 208	75.74 203	79.82 250	84.60 257	62.27 231	92.60 127	92.51 124	76.06 100	67.87 241	85.34 223	56.76 166	90.24 301	62.20 253	63.69 289	86.94 245

GA-MVS		78.33 185	76.23 196	84.65 129	83.65 273	66.30 129	91.44 173	90.14 218	76.01 101	70.32 206	84.02 237	42.50 285	94.72 184	70.98 176	77.00 203	92.94 154

PVSNet_0	68.08 15	71.81 260	68.32 272	82.27 189	84.68 255	62.31 230	88.68 254	90.31 210	75.84 102	57.93 313	80.65 283	37.85 307	94.19 205	69.94 185	29.05 367	90.31 201

CDS-MVSNet		81.43 124	80.74 122	83.52 159	86.26 232	64.45 176	92.09 144	90.65 199	75.83 103	73.95 166	89.81 174	63.97 88	92.91 245	71.27 175	82.82 156	93.20 146
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CostFormer		82.33 110	81.15 115	85.86 88	89.01 176	68.46 67	82.39 306	93.01 104	75.59 104	80.25 98	81.57 266	72.03 32	94.96 176	79.06 115	77.48 198	94.16 112

nrg030		80.93 134	79.86 137	84.13 144	83.69 272	68.83 59	93.23 101	91.20 176	75.55 105	75.06 155	88.22 193	63.04 104	94.74 183	81.88 92	66.88 263	88.82 216

VDD-MVS		83.06 99	81.81 110	86.81 53	90.86 140	67.70 88	95.40 28	91.50 165	75.46 106	81.78 80	92.34 134	40.09 293	97.13 88	86.85 55	82.04 161	95.60 50

Effi-MVS+-dtu		76.14 213	75.28 207	78.72 268	83.22 277	55.17 318	89.87 230	87.78 289	75.42 107	67.98 237	81.43 268	45.08 275	92.52 261	75.08 142	71.63 232	88.48 221

mvs-test1		78.74 176	77.95 168	81.14 220	83.22 277	57.13 306	93.96 68	87.78 289	75.42 107	72.68 175	90.80 156	45.08 275	94.54 193	75.08 142	77.49 197	91.74 179

test_prior3		87.38 27	87.70 24	86.42 70	94.71 39	67.35 98	95.10 37	93.10 102	75.40 109	85.25 54	95.61 40	67.94 45	96.84 107	87.47 46	94.77 25	95.05 77

test_prior2							95.10 37		75.40 109	85.25 54	95.61 40	67.94 45		87.47 46	94.77 25

zzz-MVS		84.73 68	84.47 67	85.50 101	91.89 113	65.16 157	91.55 171	92.23 130	75.32 111	80.53 95	95.21 56	56.06 177	97.16 86	84.86 71	92.55 66	94.18 109

MTAPA		83.91 86	83.38 84	85.50 101	91.89 113	65.16 157	81.75 308	92.23 130	75.32 111	80.53 95	95.21 56	56.06 177	97.16 86	84.86 71	92.55 66	94.18 109

EPMVS		78.49 182	75.98 199	86.02 82	91.21 132	69.68 44	80.23 321	91.20 176	75.25 113	72.48 181	78.11 305	54.65 191	93.69 227	57.66 275	83.04 154	94.69 89

miper_enhance_ethall		78.86 171	77.97 167	81.54 210	88.00 203	65.17 156	91.41 174	89.15 254	75.19 114	68.79 228	83.98 238	67.17 53	92.82 247	72.73 160	65.30 270	86.62 253

v2v482		77.42 198	75.65 204	82.73 176	80.38 300	67.13 106	91.85 158	90.23 215	75.09 115	69.37 217	83.39 245	53.79 203	94.44 197	71.77 171	65.00 276	86.63 252

VPA-MVSNet		79.03 166	78.00 166	82.11 200	85.95 237	64.48 175	93.22 102	94.66 35	75.05 116	74.04 165	84.95 228	52.17 217	93.52 230	74.90 147	67.04 262	88.32 226

ACMMP_NAP		86.05 50	85.80 53	86.80 54	91.58 122	67.53 93	91.79 160	93.49 83	74.93 117	84.61 57	95.30 49	59.42 137	97.92 45	86.13 59	94.92 19	94.94 82

thres200		79.66 156	78.33 160	83.66 157	92.54 93	65.82 142	93.06 105	96.31 3	74.90 118	73.30 169	88.66 182	59.67 134	95.61 152	47.84 310	78.67 185	89.56 211

TAMVS		80.37 143	79.45 146	83.13 169	85.14 249	63.37 206	91.23 187	90.76 194	74.81 119	72.65 176	88.49 184	60.63 122	92.95 240	69.41 190	81.95 162	93.08 150

MP-MVS-pluss		85.24 61	85.13 60	85.56 100	91.42 127	65.59 147	91.54 172	92.51 124	74.56 120	80.62 94	95.64 39	59.15 141	97.00 95	86.94 54	93.80 45	94.07 118
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
mvs_anonymous		81.36 125	79.99 135	85.46 103	90.39 147	68.40 68	86.88 279	90.61 200	74.41 121	70.31 207	84.67 231	63.79 91	92.32 270	73.13 154	85.70 135	95.67 47

MAR-MVS		84.18 80	83.43 80	86.44 69	96.25 23	65.93 138	94.28 54	94.27 54	74.41 121	79.16 112	95.61 40	53.99 200	98.88 20	69.62 188	93.26 56	94.50 101
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		80.49 142	79.30 150	84.05 146	90.83 141	64.36 183	93.60 88	89.42 243	74.35 123	69.09 220	90.15 169	55.23 185	95.61 152	64.61 236	86.43 133	92.17 175

thisisatest0515		83.41 93	82.49 101	86.16 79	89.46 165	68.26 73	93.54 91	94.70 33	74.31 124	75.75 146	90.92 153	72.62 28	96.52 119	69.64 186	81.50 165	93.71 131

bset_n11_16_dypcd		75.95 221	74.16 222	81.30 215	76.91 336	65.14 159	88.89 250	87.48 292	74.30 125	69.90 215	83.40 244	42.16 288	92.42 264	78.39 122	66.03 267	86.32 256

Vis-MVSNet (Re-imp)		79.24 164	79.57 142	78.24 274	88.46 187	52.29 330	90.41 213	89.12 256	74.24 126	69.13 219	91.91 140	65.77 68	90.09 305	59.00 271	88.09 116	92.33 167

SMA-MVS		88.14 16	88.29 19	87.67 28	93.21 74	68.72 62	93.85 78	94.03 61	74.18 127	91.74 9	96.67 16	65.61 70	98.42 32	89.24 30	96.08 7	95.88 44
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		78.37 183	77.43 177	81.17 218	86.60 226	57.45 303	89.46 239	91.16 178	74.11 128	74.40 159	90.49 162	55.52 182	94.57 189	74.73 149	60.43 316	91.48 183

3Dnovator+	73.60 7	82.10 116	80.60 126	86.60 61	90.89 139	66.80 117	95.20 33	93.44 86	74.05 129	67.42 246	92.49 129	49.46 240	97.65 59	70.80 178	91.68 80	95.33 59

XVS		83.87 87	83.47 78	85.05 115	93.22 72	63.78 194	92.92 114	92.66 117	73.99 130	78.18 123	94.31 89	55.25 183	97.41 70	79.16 113	91.58 82	93.95 123

X-MVStestdata		76.86 204	74.13 224	85.05 115	93.22 72	63.78 194	92.92 114	92.66 117	73.99 130	78.18 123	10.19 375	55.25 183	97.41 70	79.16 113	91.58 82	93.95 123

MS-PatchMatch		77.90 193	76.50 192	82.12 197	85.99 236	69.95 36	91.75 165	92.70 114	73.97 132	62.58 288	84.44 234	41.11 290	95.78 140	63.76 242	92.17 72	80.62 330

LCM-MVSNet-Re		72.93 251	71.84 249	76.18 296	88.49 185	48.02 348	80.07 324	70.17 360	73.96 133	52.25 330	80.09 292	49.98 235	88.24 318	67.35 207	84.23 151	92.28 170

Vis-MVSNet		80.92 135	79.98 136	83.74 151	88.48 186	61.80 237	93.44 95	88.26 283	73.96 133	77.73 126	91.76 143	49.94 236	94.76 181	65.84 225	90.37 98	94.65 93
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test-mter		79.96 151	79.38 149	81.72 206	86.93 223	61.17 247	92.70 121	91.54 162	73.85 135	75.62 149	86.94 208	49.84 238	92.38 266	72.21 166	84.76 143	91.60 180

OMC-MVS		78.67 179	77.91 170	80.95 229	85.76 241	57.40 304	88.49 257	88.67 271	73.85 135	72.43 183	92.10 137	49.29 243	94.55 192	72.73 160	77.89 190	90.91 195

Fast-Effi-MVS+		81.14 129	80.01 134	84.51 134	90.24 149	65.86 139	94.12 60	89.15 254	73.81 137	75.37 154	88.26 190	57.26 156	94.53 194	66.97 212	84.92 140	93.15 147

ZNCC-MVS		85.33 60	85.08 61	86.06 81	93.09 79	65.65 145	93.89 76	93.41 88	73.75 138	79.94 101	94.68 75	60.61 123	98.03 41	82.63 88	93.72 48	94.52 99

V42		76.46 211	74.55 215	82.19 193	79.14 317	67.82 85	90.26 219	89.42 243	73.75 138	68.63 231	81.89 259	51.31 224	94.09 208	71.69 173	64.84 277	84.66 287

v1144		76.73 209	74.88 209	82.27 189	80.23 305	66.60 122	91.68 167	90.21 217	73.69 140	69.06 222	81.89 259	52.73 213	94.40 198	69.21 193	65.23 273	85.80 271

v148		76.19 212	74.47 217	81.36 213	80.05 306	64.44 177	91.75 165	90.23 215	73.68 141	67.13 250	80.84 279	55.92 180	93.86 225	68.95 196	61.73 305	85.76 274

CR-MVSNet		73.79 245	70.82 257	82.70 177	83.15 279	67.96 81	70.25 345	84.00 326	73.67 142	69.97 212	72.41 335	57.82 152	89.48 309	52.99 291	73.13 221	90.64 198

XXY-MVS		77.94 191	76.44 193	82.43 183	82.60 283	64.44 177	92.01 149	91.83 151	73.59 143	70.00 211	85.82 219	54.43 196	94.76 181	69.63 187	68.02 256	88.10 229

#test#		84.98 65	84.74 66	85.72 94	93.75 60	65.01 164	94.09 61	93.19 96	73.55 144	79.22 110	94.93 66	59.04 142	97.67 55	82.66 86	92.21 69	94.49 102

tfpn200view9		78.79 174	77.43 177	82.88 173	92.21 101	64.49 173	92.05 147	96.28 4	73.48 145	71.75 192	88.26 190	60.07 129	95.32 166	45.16 320	77.58 194	88.83 214

thres400		78.68 177	77.43 177	82.43 183	92.21 101	64.49 173	92.05 147	96.28 4	73.48 145	71.75 192	88.26 190	60.07 129	95.32 166	45.16 320	77.58 194	87.48 234

FMVSNet3		77.73 194	76.04 198	82.80 174	91.20 133	68.99 56	91.87 156	91.99 141	73.35 147	67.04 251	83.19 247	56.62 170	92.14 272	59.80 267	69.34 245	87.28 240

GST-MVS		84.63 71	84.29 71	85.66 98	92.82 85	65.27 153	93.04 107	93.13 100	73.20 148	78.89 114	94.18 93	59.41 138	97.85 50	81.45 97	92.48 68	93.86 128

USDC		67.43 294	64.51 293	76.19 295	77.94 330	55.29 317	78.38 331	85.00 316	73.17 149	48.36 344	80.37 286	21.23 357	92.48 263	52.15 292	64.02 286	80.81 328

MP-MVS		85.02 63	84.97 63	85.17 114	92.60 92	64.27 187	93.24 100	92.27 129	73.13 150	79.63 106	94.43 80	61.90 111	97.17 85	85.00 68	92.56 65	94.06 119
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
xiu_mvs_v1_base_debu		82.16 113	81.12 116	85.26 111	86.42 228	68.72 62	92.59 129	90.44 204	73.12 151	84.20 62	94.36 82	38.04 304	95.73 144	84.12 75	86.81 124	91.33 186

xiu_mvs_v1_base		82.16 113	81.12 116	85.26 111	86.42 228	68.72 62	92.59 129	90.44 204	73.12 151	84.20 62	94.36 82	38.04 304	95.73 144	84.12 75	86.81 124	91.33 186

xiu_mvs_v1_base_debi		82.16 113	81.12 116	85.26 111	86.42 228	68.72 62	92.59 129	90.44 204	73.12 151	84.20 62	94.36 82	38.04 304	95.73 144	84.12 75	86.81 124	91.33 186

D2MVS		73.80 244	72.02 248	79.15 265	79.15 316	62.97 215	88.58 256	90.07 220	72.94 154	59.22 302	78.30 302	42.31 287	92.70 254	65.59 229	72.00 230	81.79 321

BH-RMVSNet		79.46 162	77.65 173	84.89 120	91.68 120	65.66 144	93.55 90	88.09 285	72.93 155	73.37 168	91.12 152	46.20 270	96.12 128	56.28 279	85.61 137	92.91 155

ETH3D-3000-0.1		87.61 24	87.89 21	86.75 56	93.58 64	67.21 102	94.31 53	94.14 59	72.92 156	87.13 30	96.62 17	67.81 49	97.94 43	90.13 24	94.42 36	95.09 75

IS-MVSNet		80.14 147	79.41 147	82.33 187	87.91 204	60.08 270	91.97 153	88.27 281	72.90 157	71.44 196	91.73 145	61.44 115	93.66 228	62.47 252	86.53 131	93.24 144

PS-MVSNAJss		77.26 201	76.31 195	80.13 242	80.64 298	59.16 283	90.63 210	91.06 186	72.80 158	68.58 232	84.57 233	53.55 205	93.96 219	72.97 155	71.96 231	87.27 241

9.14			87.63 25		93.86 55		94.41 51	94.18 56	72.76 159	86.21 38	96.51 19	66.64 59	97.88 49	90.08 25	94.04 41

v1192		75.98 219	73.92 227	82.15 194	79.73 307	66.24 131	91.22 188	89.75 231	72.67 160	68.49 233	81.42 269	49.86 237	94.27 202	67.08 210	65.02 275	85.95 268

Effi-MVS+		83.82 88	82.76 96	86.99 48	89.56 162	69.40 46	91.35 182	86.12 307	72.59 161	83.22 71	92.81 124	59.60 135	96.01 136	81.76 93	87.80 118	95.56 51

UnsupCasMVSNet_eth		65.79 301	63.10 301	73.88 309	70.71 354	50.29 341	81.09 314	89.88 228	72.58 162	49.25 342	74.77 329	32.57 330	87.43 328	55.96 280	41.04 357	83.90 293

1112_ss		80.56 140	79.83 138	82.77 175	88.65 183	60.78 255	92.29 136	88.36 278	72.58 162	72.46 182	94.95 64	65.09 74	93.42 233	66.38 219	77.71 191	94.10 115

test_part1		79.63 157	77.86 171	84.93 118	92.50 94	71.43 17	94.15 58	91.08 185	72.51 164	70.66 200	84.98 227	59.84 131	95.07 173	72.07 169	62.94 291	88.30 227

DVP-MVS++		90.53 3	91.09 4	88.87 14	97.31 4	69.91 37	93.96 68	94.37 50	72.48 165	92.07 6	96.85 12	83.82 2	99.15 2	91.53 16	97.42 4	97.55 4

test_0728_THIRD									72.48 165	90.55 16	96.93 10	76.24 11	99.08 11	91.53 16	94.99 17	96.43 26

cl22		77.94 191	76.78 188	81.42 212	87.57 209	64.93 168	90.67 206	88.86 266	72.45 167	67.63 244	82.68 251	64.07 86	92.91 245	71.79 170	65.30 270	86.44 254

thres600view7		78.00 188	76.66 190	82.03 202	91.93 110	63.69 200	91.30 185	96.33 1	72.43 168	70.46 203	87.89 197	60.31 124	94.92 179	42.64 332	76.64 205	87.48 234

IterMVS-LS		76.49 210	75.18 208	80.43 235	84.49 260	62.74 223	90.64 208	88.80 267	72.40 169	65.16 264	81.72 262	60.98 119	92.27 271	67.74 204	64.65 281	86.29 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		78.97 168	78.22 163	81.25 216	85.33 245	62.73 224	89.53 237	93.21 93	72.39 170	72.14 186	90.13 170	60.99 118	94.72 184	67.73 205	72.49 227	86.29 257

miper_ehance_all_eth		77.60 195	76.44 193	81.09 226	85.70 242	64.41 180	90.65 207	88.64 273	72.31 171	67.37 249	82.52 252	64.77 79	92.64 258	70.67 180	65.30 270	86.24 259

v144192		76.05 217	74.03 225	82.12 197	79.50 311	66.55 124	91.39 178	89.71 237	72.30 172	68.17 235	81.33 271	51.75 220	94.03 216	67.94 202	64.19 283	85.77 272

thres100view900		78.37 183	77.01 185	82.46 182	91.89 113	63.21 210	91.19 191	96.33 1	72.28 173	70.45 204	87.89 197	60.31 124	95.32 166	45.16 320	77.58 194	88.83 214

RRT_MVS		77.38 199	76.59 191	79.77 252	90.91 138	63.61 204	91.15 192	90.91 191	72.28 173	72.06 188	87.28 206	43.92 280	89.04 312	73.32 152	67.47 260	86.67 248

PatchmatchNet		77.46 197	74.63 212	85.96 84	89.55 163	70.35 29	79.97 325	89.55 239	72.23 175	70.94 198	76.91 316	57.03 160	92.79 250	54.27 285	81.17 168	94.74 88
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
HFP-MVS		84.73 68	84.40 70	85.72 94	93.75 60	65.01 164	93.50 93	93.19 96	72.19 176	79.22 110	94.93 66	59.04 142	97.67 55	81.55 95	92.21 69	94.49 102

ACMMPR		84.37 73	84.06 72	85.28 110	93.56 65	64.37 182	93.50 93	93.15 99	72.19 176	78.85 119	94.86 70	56.69 169	97.45 67	81.55 95	92.20 71	94.02 121

1314		80.70 137	78.95 155	85.94 85	87.77 208	67.56 91	87.91 265	92.55 123	72.17 178	67.44 245	93.09 111	50.27 233	97.04 92	71.68 174	87.64 119	93.23 145

region2R		84.36 74	84.03 73	85.36 108	93.54 66	64.31 184	93.43 96	92.95 107	72.16 179	78.86 118	94.84 71	56.97 164	97.53 65	81.38 99	92.11 73	94.24 107

Test_1112_low_res		79.56 159	78.60 158	82.43 183	88.24 197	60.39 265	92.09 144	87.99 287	72.10 180	71.84 190	87.42 203	64.62 80	93.04 237	65.80 226	77.30 200	93.85 129

v1921920		75.63 227	73.49 232	82.06 201	79.38 312	66.35 127	91.07 196	89.48 240	71.98 181	67.99 236	81.22 274	49.16 246	93.90 222	66.56 215	64.56 282	85.92 270

DVP-MVS		89.41 12	89.73 13	88.45 21	96.40 17	69.99 33	96.64 8	94.52 39	71.92 182	90.55 16	96.93 10	73.77 22	99.08 11	91.91 14	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 17	69.99 33	96.76 6	94.33 52	71.92 182	91.89 8	97.11 7	73.77 22

Fast-Effi-MVS+-dtu		75.04 233	73.37 233	80.07 243	80.86 294	59.52 277	91.20 190	85.38 312	71.90 184	65.20 263	84.84 229	41.46 289	92.97 239	66.50 218	72.96 223	87.73 231

LFMVS		84.34 75	82.73 97	89.18 13	94.76 37	73.25 10	94.99 43	91.89 147	71.90 184	82.16 78	93.49 107	47.98 255	97.05 90	82.55 89	84.82 141	97.25 7

eth_miper_zixun_eth		75.96 220	74.40 218	80.66 232	84.66 256	63.02 214	89.28 242	88.27 281	71.88 186	65.73 259	81.65 263	59.45 136	92.81 248	68.13 200	60.53 314	86.14 261

train_agg		87.21 31	87.42 31	86.60 61	94.18 46	67.28 100	94.16 56	93.51 80	71.87 187	85.52 48	95.33 47	68.19 41	97.27 82	89.09 31	94.90 21	95.25 70

test_8					94.19 45	67.19 103	94.15 58	93.42 87	71.87 187	85.38 51	95.35 46	68.19 41	96.95 102

agg_prior1		87.02 36	87.26 33	86.28 77	94.16 50	66.97 111	94.08 62	93.31 90	71.85 189	84.49 59	95.39 44	68.91 38	96.75 111	88.84 35	94.32 38	95.13 73

MDTV_nov1_ep13			72.61 241		89.06 174	68.48 66	80.33 319	90.11 219	71.84 190	71.81 191	75.92 324	53.01 211	93.92 221	48.04 307	73.38 219

ab-mvs		80.18 146	78.31 161	85.80 91	88.44 188	65.49 151	83.00 303	92.67 116	71.82 191	77.36 133	85.01 226	54.50 192	96.59 114	76.35 136	75.63 210	95.32 61

ACMMP		81.49 123	80.67 124	83.93 148	91.71 119	62.90 220	92.13 141	92.22 134	71.79 192	71.68 194	93.49 107	50.32 231	96.96 101	78.47 121	84.22 152	91.93 177
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 40	86.85 41	86.78 55	93.47 68	65.55 148	95.39 29	95.10 18	71.77 193	85.69 47	96.52 18	62.07 110	98.77 21	86.06 61	95.60 12	96.03 38

TEST9					94.18 46	67.28 100	94.16 56	93.51 80	71.75 194	85.52 48	95.33 47	68.01 44	97.27 82

c3_l		76.83 207	75.47 205	80.93 230	85.02 252	64.18 189	90.39 214	88.11 284	71.66 195	66.65 256	81.64 264	63.58 97	92.56 259	69.31 192	62.86 292	86.04 265

SED-MVS		89.94 8	90.36 9	88.70 16	96.45 14	69.38 47	96.89 4	94.44 43	71.65 196	92.11 4	97.21 5	76.79 9	99.11 6	92.34 8	95.36 14	97.62 2

test_241102_TWO								94.41 46	71.65 196	92.07 6	97.21 5	74.58 17	99.11 6	92.34 8	95.36 14	96.59 15

test_241102_ONE					96.45 14	69.38 47		94.44 43	71.65 196	92.11 4	97.05 8	76.79 9	99.11 6

v8		75.35 229	73.26 234	81.61 209	80.67 297	66.82 115	89.54 236	89.27 247	71.65 196	63.30 282	80.30 288	54.99 189	94.06 211	67.33 209	62.33 297	83.94 292

v1240		75.21 232	72.98 236	81.88 203	79.20 314	66.00 135	90.75 205	89.11 257	71.63 200	67.41 247	81.22 274	47.36 260	93.87 223	65.46 231	64.72 280	85.77 272

SCA		75.82 223	72.76 238	85.01 117	86.63 225	70.08 31	81.06 315	89.19 251	71.60 201	70.01 210	77.09 314	45.53 272	90.25 298	60.43 262	73.27 220	94.68 90

BH-untuned		78.68 177	77.08 183	83.48 163	89.84 156	63.74 196	92.70 121	88.59 274	71.57 202	66.83 254	88.65 183	51.75 220	95.39 164	59.03 270	84.77 142	91.32 189

IterMVS		72.65 258	70.83 256	78.09 275	82.17 285	62.96 216	87.64 270	86.28 303	71.56 203	60.44 296	78.85 300	45.42 274	86.66 331	63.30 245	61.83 302	84.65 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mPP-MVS		82.96 102	82.44 102	84.52 133	92.83 83	62.92 219	92.76 117	91.85 150	71.52 204	75.61 151	94.24 91	53.48 208	96.99 98	78.97 116	90.73 93	93.64 134

test-LLR		80.10 148	79.56 143	81.72 206	86.93 223	61.17 247	92.70 121	91.54 162	71.51 205	75.62 149	86.94 208	53.83 201	92.38 266	72.21 166	84.76 143	91.60 180

test0.0.03 1		72.76 254	72.71 240	72.88 317	80.25 304	47.99 349	91.22 188	89.45 241	71.51 205	62.51 289	87.66 199	53.83 201	85.06 337	50.16 297	67.84 259	85.58 275

ETH3D cwj APD-0.16		87.06 34	87.18 35	86.71 57	91.99 108	67.48 96	92.97 110	94.21 55	71.48 207	85.72 45	96.32 26	68.13 43	98.00 42	89.06 32	94.70 31	94.65 93

test_one_0601					96.32 20	69.74 42		94.18 56	71.42 208	90.67 15	96.85 12	74.45 19

PGM-MVS		83.25 96	82.70 98	84.92 119	92.81 87	64.07 190	90.44 211	92.20 135	71.28 209	77.23 135	94.43 80	55.17 187	97.31 77	79.33 112	91.38 86	93.37 138

thisisatest0530		81.15 128	80.07 131	84.39 137	88.26 195	65.63 146	91.40 176	94.62 37	71.27 210	70.93 199	89.18 178	72.47 29	96.04 133	65.62 228	76.89 204	91.49 182

cl____		76.07 214	74.67 210	80.28 238	85.15 248	61.76 239	90.12 222	88.73 269	71.16 211	65.43 261	81.57 266	61.15 116	92.95 240	66.54 216	62.17 298	86.13 263

DIV-MVS_self_test		76.07 214	74.67 210	80.28 238	85.14 249	61.75 240	90.12 222	88.73 269	71.16 211	65.42 262	81.60 265	61.15 116	92.94 244	66.54 216	62.16 300	86.14 261

dp		75.01 234	72.09 247	83.76 150	89.28 169	66.22 132	79.96 326	89.75 231	71.16 211	67.80 242	77.19 313	51.81 219	92.54 260	50.39 296	71.44 236	92.51 164

CP-MVS		83.71 91	83.40 83	84.65 129	93.14 77	63.84 192	94.59 48	92.28 128	71.03 214	77.41 132	94.92 68	55.21 186	96.19 125	81.32 100	90.70 94	93.91 125

v10		74.77 236	72.54 243	81.46 211	80.33 303	66.71 119	89.15 246	89.08 258	70.94 215	63.08 283	79.86 293	52.52 214	94.04 214	65.70 227	62.17 298	83.64 294

CDPH-MVS		85.71 56	85.46 57	86.46 68	94.75 38	67.19 103	93.89 76	92.83 111	70.90 216	83.09 72	95.28 50	63.62 94	97.36 73	80.63 104	94.18 39	94.84 86

GBi-Net		75.65 225	73.83 228	81.10 223	88.85 178	65.11 160	90.01 226	90.32 207	70.84 217	67.04 251	80.25 289	48.03 252	91.54 286	59.80 267	69.34 245	86.64 249

test1		75.65 225	73.83 228	81.10 223	88.85 178	65.11 160	90.01 226	90.32 207	70.84 217	67.04 251	80.25 289	48.03 252	91.54 286	59.80 267	69.34 245	86.64 249

FMVSNet2		76.07 214	74.01 226	82.26 191	88.85 178	67.66 89	91.33 183	91.61 160	70.84 217	65.98 258	82.25 255	48.03 252	92.00 277	58.46 272	68.73 251	87.10 242

SF-MVS		87.03 35	87.09 36	86.84 51	92.70 89	67.45 97	93.64 86	93.76 68	70.78 220	86.25 36	96.44 21	66.98 54	97.79 51	88.68 37	94.56 33	95.28 65

ZD-MVS					96.63 11	65.50 150		93.50 82	70.74 221	85.26 53	95.19 58	64.92 78	97.29 78	87.51 45	93.01 59

HyFIR lowres test		81.03 133	79.56 143	85.43 105	87.81 207	68.11 78	90.18 221	90.01 225	70.65 222	72.95 172	86.06 217	63.61 95	94.50 196	75.01 144	79.75 176	93.67 132

MVP-Stereo		77.12 203	76.23 196	79.79 251	81.72 289	66.34 128	89.29 241	90.88 192	70.56 223	62.01 291	82.88 248	49.34 241	94.13 206	65.55 230	93.80 45	78.88 343
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMP	71.68 10	75.58 228	74.23 221	79.62 256	84.97 253	59.64 274	90.80 203	89.07 259	70.39 224	62.95 284	87.30 205	38.28 301	93.87 223	72.89 156	71.45 235	85.36 280
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
HPM-MVS		83.25 96	82.95 91	84.17 143	92.25 99	62.88 221	90.91 197	91.86 149	70.30 225	77.12 136	93.96 98	56.75 167	96.28 122	82.04 91	91.34 88	93.34 139
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
GeoE		78.90 170	77.43 177	83.29 166	88.95 177	62.02 233	92.31 135	86.23 305	70.24 226	71.34 197	89.27 177	54.43 196	94.04 214	63.31 244	80.81 172	93.81 130

tpm2		79.80 155	77.95 168	85.34 109	88.28 194	68.26 73	81.56 311	91.42 168	70.11 227	77.59 131	80.50 284	67.40 51	94.26 204	67.34 208	77.35 199	93.51 136

TR-MVS		78.77 175	77.37 182	82.95 172	90.49 144	60.88 253	93.67 85	90.07 220	70.08 228	74.51 158	91.37 150	45.69 271	95.70 149	60.12 265	80.32 173	92.29 169

CL-MVSNet_self_test		69.92 271	68.09 273	75.41 299	73.25 347	55.90 314	90.05 225	89.90 227	69.96 229	61.96 292	76.54 317	51.05 227	87.64 324	49.51 301	50.59 343	82.70 311

PAPM_NR		82.97 101	81.84 108	86.37 73	94.10 52	66.76 118	87.66 269	92.84 110	69.96 229	74.07 164	93.57 105	63.10 103	97.50 66	70.66 181	90.58 96	94.85 83

PCF-MVS	73.15 9	79.29 163	77.63 174	84.29 141	86.06 235	65.96 137	87.03 275	91.10 182	69.86 231	69.79 216	90.64 157	57.54 155	96.59 114	64.37 238	82.29 158	90.32 200
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
miper_lstm_enhance		73.05 249	71.73 251	77.03 287	83.80 270	58.32 291	81.76 307	88.88 264	69.80 232	61.01 293	78.23 304	57.19 157	87.51 327	65.34 232	59.53 319	85.27 283

MIMVSNet		71.64 261	68.44 270	81.23 217	81.97 288	64.44 177	73.05 341	88.80 267	69.67 233	64.59 268	74.79 328	32.79 328	87.82 322	53.99 286	76.35 207	91.42 184

LPG-MVS_test		75.82 223	74.58 214	79.56 258	84.31 264	59.37 279	90.44 211	89.73 234	69.49 234	64.86 265	88.42 185	38.65 298	94.30 200	72.56 162	72.76 224	85.01 284

LGP-MVS_train				79.56 258	84.31 264	59.37 279		89.73 234	69.49 234	64.86 265	88.42 185	38.65 298	94.30 200	72.56 162	72.76 224	85.01 284

APDe-MVS		87.54 25	87.84 22	86.65 60	96.07 25	66.30 129	94.84 46	93.78 65	69.35 236	88.39 25	96.34 25	67.74 50	97.66 58	90.62 22	93.44 53	96.01 39

tttt0517		79.50 160	78.53 159	82.41 186	87.22 217	61.43 245	89.75 233	94.76 30	69.29 237	67.91 239	88.06 195	72.92 26	95.63 150	62.91 248	73.90 218	90.16 202

Patchmatch-RL test		68.17 286	64.49 294	79.19 262	71.22 352	53.93 323	70.07 347	71.54 359	69.22 238	56.79 317	62.89 354	56.58 171	88.61 313	69.53 189	52.61 338	95.03 80

test_yl		84.28 76	83.16 87	87.64 29	94.52 42	69.24 50	95.78 17	95.09 19	69.19 239	81.09 88	92.88 121	57.00 162	97.44 68	81.11 102	81.76 163	96.23 33

DCV-MVSNet		84.28 76	83.16 87	87.64 29	94.52 42	69.24 50	95.78 17	95.09 19	69.19 239	81.09 88	92.88 121	57.00 162	97.44 68	81.11 102	81.76 163	96.23 33

jajsoiax		73.05 249	71.51 253	77.67 278	77.46 332	54.83 319	88.81 252	90.04 224	69.13 241	62.85 286	83.51 242	31.16 336	92.75 251	70.83 177	69.80 241	85.43 279

DP-MVS Recon		82.73 104	81.65 111	85.98 83	97.31 4	67.06 107	95.15 35	91.99 141	69.08 242	76.50 143	93.89 99	54.48 195	98.20 36	70.76 179	85.66 136	92.69 158

Baseline_NR-MVSNet		73.99 242	72.83 237	77.48 281	80.78 295	59.29 282	91.79 160	84.55 321	68.85 243	68.99 223	80.70 280	56.16 174	92.04 276	62.67 250	60.98 311	81.11 324

CHOSEN 280x420		77.35 200	76.95 187	78.55 269	87.07 220	62.68 225	69.71 348	82.95 334	68.80 244	71.48 195	87.27 207	66.03 64	84.00 342	76.47 134	82.81 157	88.95 213

DPE-MVS		88.77 15	89.21 15	87.45 36	96.26 22	67.56 91	94.17 55	94.15 58	68.77 245	90.74 14	97.27 3	76.09 12	98.49 27	90.58 23	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		72.71 256	71.11 254	77.52 279	77.41 333	54.52 321	88.45 259	89.76 230	68.76 246	62.70 287	83.26 246	29.49 340	92.71 252	70.51 183	69.62 243	85.34 281

MVS		84.66 70	82.86 94	90.06 2	90.93 136	74.56 6	87.91 265	95.54 9	68.55 247	72.35 185	94.71 74	59.78 133	98.90 18	81.29 101	94.69 32	96.74 12

EPP-MVSNet		81.79 121	81.52 112	82.61 180	88.77 182	60.21 268	93.02 109	93.66 75	68.52 248	72.90 173	90.39 164	72.19 31	94.96 176	74.93 145	79.29 180	92.67 159

CSCG		86.87 38	86.26 45	88.72 15	95.05 35	70.79 24	93.83 82	95.33 11	68.48 249	77.63 129	94.35 86	73.04 25	98.45 29	84.92 70	93.71 49	96.92 11

CP-MVSNet		70.50 267	69.91 263	72.26 322	80.71 296	51.00 337	87.23 274	90.30 211	67.84 250	59.64 299	82.69 250	50.23 234	82.30 354	51.28 293	59.28 320	83.46 299

testtj		86.62 43	86.66 42	86.50 67	96.95 8	65.70 143	94.41 51	93.45 84	67.74 251	86.19 39	96.39 23	64.38 83	97.91 46	87.33 49	93.14 58	95.90 43

pmmvs5		73.35 247	71.52 252	78.86 267	78.64 324	60.61 263	91.08 194	86.90 297	67.69 252	63.32 281	83.64 240	44.33 279	90.53 295	62.04 254	66.02 268	85.46 278

pm-mvs1		72.89 252	71.09 255	78.26 273	79.10 318	57.62 299	90.80 203	89.30 246	67.66 253	62.91 285	81.78 261	49.11 247	92.95 240	60.29 264	58.89 322	84.22 290

MDTV_nov1_ep13_2view						59.90 272	80.13 323		67.65 254	72.79 174		54.33 198		59.83 266		92.58 162

pmmvs4		73.92 243	71.81 250	80.25 240	79.17 315	65.24 154	87.43 272	87.26 296	67.64 255	63.46 280	83.91 239	48.96 248	91.53 289	62.94 247	65.49 269	83.96 291

WR-MVS_H		70.59 266	69.94 262	72.53 319	81.03 293	51.43 334	87.35 273	92.03 140	67.38 256	60.23 297	80.70 280	55.84 181	83.45 346	46.33 316	58.58 324	82.72 309

KD-MVS_2432*1600		69.03 278	66.37 280	77.01 288	85.56 243	61.06 250	81.44 312	90.25 213	67.27 257	58.00 311	76.53 318	54.49 193	87.63 325	48.04 307	35.77 362	82.34 315

miper_refine_blended		69.03 278	66.37 280	77.01 288	85.56 243	61.06 250	81.44 312	90.25 213	67.27 257	58.00 311	76.53 318	54.49 193	87.63 325	48.04 307	35.77 362	82.34 315

PS-CasMVS		69.86 273	69.13 267	72.07 325	80.35 302	50.57 339	87.02 276	89.75 231	67.27 257	59.19 303	82.28 254	46.58 264	82.24 355	50.69 295	59.02 321	83.39 301

PEN-MVS		69.46 275	68.56 269	72.17 324	79.27 313	49.71 343	86.90 278	89.24 248	67.24 260	59.08 304	82.51 253	47.23 261	83.54 345	48.42 305	57.12 325	83.25 302

cascas		78.18 186	75.77 202	85.41 106	87.14 219	69.11 52	92.96 111	91.15 180	66.71 261	70.47 202	86.07 216	37.49 310	96.48 120	70.15 184	79.80 175	90.65 197

APD-MVS		85.93 52	85.99 49	85.76 93	95.98 28	65.21 155	93.59 89	92.58 122	66.54 262	86.17 40	95.88 33	63.83 90	97.00 95	86.39 58	92.94 60	95.06 76
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
OpenMVS	70.45 11	78.54 181	75.92 200	86.41 72	85.93 240	71.68 16	92.74 118	92.51 124	66.49 263	64.56 269	91.96 139	43.88 281	98.10 39	54.61 283	90.65 95	89.44 212

DTE-MVSNet		68.46 284	67.33 276	71.87 327	77.94 330	49.00 346	86.16 283	88.58 275	66.36 264	58.19 308	82.21 256	46.36 265	83.87 343	44.97 323	55.17 332	82.73 308

IterMVS-SCA-FT		71.55 262	69.97 261	76.32 294	81.48 290	60.67 261	87.64 270	85.99 308	66.17 265	59.50 300	78.88 299	45.53 272	83.65 344	62.58 251	61.93 301	84.63 289

TransMVSNet (Re)		70.07 270	67.66 274	77.31 285	80.62 299	59.13 284	91.78 162	84.94 317	65.97 266	60.08 298	80.44 285	50.78 228	91.87 278	48.84 303	45.46 351	80.94 326

MVSFormer		83.75 90	82.88 93	86.37 73	89.24 171	71.18 20	89.07 248	90.69 195	65.80 267	87.13 30	94.34 87	64.99 75	92.67 255	72.83 157	91.80 78	95.27 67

test_djsdf		73.76 246	72.56 242	77.39 283	77.00 335	53.93 323	89.07 248	90.69 195	65.80 267	63.92 275	82.03 258	43.14 284	92.67 255	72.83 157	68.53 252	85.57 276

API-MVS		82.28 111	80.53 127	87.54 34	96.13 24	70.59 26	93.63 87	91.04 188	65.72 269	75.45 153	92.83 123	56.11 176	98.89 19	64.10 239	89.75 103	93.15 147

原ACMM1				84.42 136	93.21 74	64.27 187		93.40 89	65.39 270	79.51 107	92.50 127	58.11 150	96.69 113	65.27 233	93.96 42	92.32 168

testgi		64.48 306	62.87 304	69.31 332	71.24 351	40.62 363	85.49 284	79.92 342	65.36 271	54.18 323	83.49 243	23.74 353	84.55 338	41.60 334	60.79 313	82.77 307

QAPM		79.95 152	77.39 181	87.64 29	89.63 160	71.41 18	93.30 99	93.70 72	65.34 272	67.39 248	91.75 144	47.83 256	98.96 16	57.71 274	89.81 100	92.54 163

HPM-MVS_fast		80.25 145	79.55 145	82.33 187	91.55 124	59.95 271	91.32 184	89.16 253	65.23 273	74.71 157	93.07 114	47.81 257	95.74 143	74.87 148	88.23 113	91.31 190

tfpnnormal		70.10 269	67.36 275	78.32 271	83.45 276	60.97 252	88.85 251	92.77 112	64.85 274	60.83 295	78.53 301	43.52 283	93.48 231	31.73 362	61.70 306	80.52 331

SR-MVS		82.81 103	82.58 99	83.50 162	93.35 69	61.16 249	92.23 139	91.28 175	64.48 275	81.27 85	95.28 50	53.71 204	95.86 138	82.87 84	88.77 109	93.49 137

K. test v3		63.09 312	59.61 316	73.53 312	76.26 339	49.38 345	83.27 299	77.15 346	64.35 276	47.77 346	72.32 337	28.73 342	87.79 323	49.93 299	36.69 361	83.41 300

test1172		81.90 119	81.83 109	82.13 196	93.23 71	57.52 301	91.61 170	90.98 190	64.32 277	80.20 99	95.00 62	51.26 225	95.61 152	81.73 94	88.13 115	93.26 143

v7n		71.31 263	68.65 268	79.28 261	76.40 338	60.77 256	86.71 280	89.45 241	64.17 278	58.77 307	78.24 303	44.59 278	93.54 229	57.76 273	61.75 304	83.52 297

FMVSNet1		72.71 256	69.91 263	81.10 223	83.60 274	65.11 160	90.01 226	90.32 207	63.92 279	63.56 279	80.25 289	36.35 319	91.54 286	54.46 284	66.75 264	86.64 249

XVG-OURS		74.25 240	72.46 244	79.63 255	78.45 326	57.59 300	80.33 319	87.39 293	63.86 280	68.76 229	89.62 176	40.50 292	91.72 282	69.00 195	74.25 213	89.58 209

UniMVSNet_ETH3D		72.74 255	70.53 258	79.36 260	78.62 325	56.64 309	85.01 286	89.20 250	63.77 281	64.84 267	84.44 234	34.05 325	91.86 279	63.94 240	70.89 239	89.57 210

114514_t		79.17 165	77.67 172	83.68 155	95.32 31	65.53 149	92.85 116	91.60 161	63.49 282	67.92 238	90.63 159	46.65 263	95.72 148	67.01 211	83.54 153	89.79 206

APD-MVS_3200maxsize		81.64 122	81.32 114	82.59 181	92.36 95	58.74 286	91.39 178	91.01 189	63.35 283	79.72 105	94.62 76	51.82 218	96.14 127	79.71 108	87.93 117	92.89 156

test20.03		63.83 309	62.65 305	67.38 338	70.58 356	39.94 364	86.57 281	84.17 323	63.29 284	51.86 331	77.30 310	37.09 315	82.47 352	38.87 345	54.13 336	79.73 337

XVG-OURS-SEG-HR		74.70 237	73.08 235	79.57 257	78.25 327	57.33 305	80.49 317	87.32 294	63.22 285	68.76 229	90.12 172	44.89 277	91.59 284	70.55 182	74.09 215	89.79 206

ACMM	69.62 13	74.34 238	72.73 239	79.17 263	84.25 266	57.87 295	90.36 215	89.93 226	63.17 286	65.64 260	86.04 218	37.79 308	94.10 207	65.89 224	71.52 234	85.55 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmvs		72.88 253	69.76 265	82.22 192	90.98 135	67.05 108	78.22 333	88.30 279	63.10 287	64.35 274	74.98 327	55.09 188	94.27 202	43.25 326	69.57 244	85.34 281

SixPastTwentyTwo		64.92 303	61.78 310	74.34 307	78.74 322	49.76 342	83.42 298	79.51 344	62.86 288	50.27 338	77.35 309	30.92 338	90.49 296	45.89 318	47.06 348	82.78 306

SR-MVS-dyc-post		81.06 132	80.70 123	82.15 194	92.02 104	58.56 288	90.90 198	90.45 201	62.76 289	78.89 114	94.46 78	51.26 225	95.61 152	78.77 119	86.77 127	92.28 170

RE-MVS-def			80.48 128		92.02 104	58.56 288	90.90 198	90.45 201	62.76 289	78.89 114	94.46 78	49.30 242		78.77 119	86.77 127	92.28 170

TAPA-MVS	70.22 12	74.94 235	73.53 231	79.17 263	90.40 146	52.07 331	89.19 245	89.61 238	62.69 291	70.07 209	92.67 125	48.89 249	94.32 199	38.26 346	79.97 174	91.12 193
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
abl_6		79.82 154	79.20 152	81.70 208	89.85 155	58.34 290	88.47 258	90.07 220	62.56 292	77.71 127	93.08 112	47.65 259	96.78 109	77.94 126	85.45 138	89.99 205

Anonymous202405211		77.96 190	75.33 206	85.87 87	93.73 62	64.52 172	94.85 45	85.36 313	62.52 293	76.11 144	90.18 168	29.43 341	97.29 78	68.51 199	77.24 202	95.81 46

pmmvs-eth3d		65.53 302	62.32 307	75.19 300	69.39 358	59.59 275	82.80 304	83.43 330	62.52 293	51.30 335	72.49 333	32.86 327	87.16 330	55.32 282	50.73 342	78.83 344

AdaColmap		78.94 169	77.00 186	84.76 124	96.34 19	65.86 139	92.66 125	87.97 288	62.18 295	70.56 201	92.37 133	43.53 282	97.35 74	64.50 237	82.86 155	91.05 194

FOURS1					93.95 53	61.77 238	93.96 68	91.92 144	62.14 296	86.57 35

无先验							92.71 120	92.61 121	62.03 297				97.01 93	66.63 213		93.97 122

XVG-ACMP-BASELINE		68.04 287	65.53 286	75.56 298	74.06 346	52.37 329	78.43 330	85.88 309	62.03 297	58.91 306	81.21 276	20.38 359	91.15 292	60.69 261	68.18 254	83.16 304

anonymousdsp		71.14 264	69.37 266	76.45 293	72.95 348	54.71 320	84.19 290	88.88 264	61.92 299	62.15 290	79.77 294	38.14 303	91.44 291	68.90 197	67.45 261	83.21 303

tpm cat1		75.30 230	72.21 246	84.58 132	88.52 184	67.77 86	78.16 334	88.02 286	61.88 300	68.45 234	76.37 320	60.65 121	94.03 216	53.77 288	74.11 214	91.93 177

FMVSNet5		68.04 287	65.66 285	75.18 301	84.43 262	57.89 294	83.54 294	86.26 304	61.83 301	53.64 326	73.30 332	37.15 314	85.08 336	48.99 302	61.77 303	82.56 314

Anonymous20231206		67.53 292	65.78 282	72.79 318	74.95 342	47.59 351	88.23 261	87.32 294	61.75 302	58.07 310	77.29 311	37.79 308	87.29 329	42.91 328	63.71 288	83.48 298

PatchMatch-RL		72.06 259	69.98 260	78.28 272	89.51 164	55.70 315	83.49 295	83.39 332	61.24 303	63.72 278	82.76 249	34.77 323	93.03 238	53.37 290	77.59 193	86.12 264

PLC	68.80 14	75.23 231	73.68 230	79.86 249	92.93 81	58.68 287	90.64 208	88.30 279	60.90 304	64.43 273	90.53 160	42.38 286	94.57 189	56.52 277	76.54 206	86.33 255
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH	63.93 17	68.62 281	64.81 289	80.03 244	85.22 247	63.25 208	87.72 268	84.66 320	60.83 305	51.57 333	79.43 298	27.29 346	94.96 176	41.76 333	64.84 277	81.88 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 282	65.41 287	77.96 276	78.69 323	62.93 217	89.86 231	89.17 252	60.55 306	50.27 338	77.73 308	22.60 355	94.06 211	47.18 313	72.65 226	76.88 350

VDDNet		80.50 141	78.26 162	87.21 41	86.19 233	69.79 40	94.48 49	91.31 172	60.42 307	79.34 108	90.91 154	38.48 300	96.56 117	82.16 90	81.05 169	95.27 67

CPTT-MVS		79.59 158	79.16 153	80.89 231	91.54 125	59.80 273	92.10 143	88.54 276	60.42 307	72.96 171	93.28 109	48.27 251	92.80 249	78.89 118	86.50 132	90.06 203

our_test_3		68.29 285	64.69 291	79.11 266	78.92 319	64.85 169	88.40 260	85.06 315	60.32 309	52.68 328	76.12 322	40.81 291	89.80 308	44.25 325	55.65 330	82.67 313

ITE_SJBPF				70.43 330	74.44 344	47.06 353		77.32 345	60.16 310	54.04 324	83.53 241	23.30 354	84.01 341	43.07 327	61.58 308	80.21 336

ppachtmachnet_test		67.72 289	63.70 298	79.77 252	78.92 319	66.04 134	88.68 254	82.90 335	60.11 311	55.45 319	75.96 323	39.19 295	90.55 294	39.53 341	52.55 339	82.71 310

new-patchmatchnet		59.30 323	56.48 324	67.79 335	65.86 362	44.19 357	82.47 305	81.77 336	59.94 312	43.65 357	66.20 350	27.67 345	81.68 357	39.34 342	41.40 356	77.50 349

MVS_0304		68.99 280	67.23 277	74.28 308	80.36 301	52.54 328	87.01 277	86.36 301	59.89 313	66.22 257	73.56 331	24.25 350	88.03 320	57.34 276	70.11 240	82.27 317

新几何1				84.73 125	92.32 96	64.28 186		91.46 167	59.56 314	79.77 104	92.90 119	56.95 165	96.57 116	63.40 243	92.91 61	93.34 139

1121		81.25 127	80.05 132	84.87 122	92.30 97	64.31 184	87.91 265	91.39 169	59.44 315	79.94 101	92.91 118	57.09 158	97.01 93	66.63 213	92.81 63	93.29 142

旧先验2							92.00 152		59.37 316	87.54 29			93.47 232	75.39 139

PM-MVS		59.40 322	56.59 323	67.84 334	63.63 363	41.86 360	76.76 336	63.22 368	59.01 317	51.07 336	72.27 338	11.72 368	83.25 348	61.34 257	50.28 344	78.39 347

LTVRE_ROB	59.60 19	66.27 298	63.54 299	74.45 305	84.00 269	51.55 333	67.08 355	83.53 329	58.78 318	54.94 320	80.31 287	34.54 324	93.23 235	40.64 339	68.03 255	78.58 346
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				81.34 214	89.02 175	57.72 297		89.84 229	58.65 319	85.32 52	94.09 94	57.03 160	93.28 234	69.34 191	90.56 97	93.03 151

ACMH+	65.35 16	67.65 290	64.55 292	76.96 290	84.59 258	57.10 307	88.08 262	80.79 339	58.59 320	53.00 327	81.09 278	26.63 348	92.95 240	46.51 314	61.69 307	80.82 327

ADS-MVSNet2		66.90 295	63.44 300	77.26 286	88.06 200	60.70 260	68.01 352	75.56 350	57.57 321	64.48 270	69.87 344	38.68 296	84.10 339	40.87 337	67.89 257	86.97 243

ADS-MVSNet		68.54 283	64.38 296	81.03 227	88.06 200	66.90 113	68.01 352	84.02 325	57.57 321	64.48 270	69.87 344	38.68 296	89.21 311	40.87 337	67.89 257	86.97 243

MDA-MVSNet-bldmvs		61.54 317	57.70 321	73.05 315	79.53 310	57.00 308	83.08 302	81.23 337	57.57 321	34.91 363	72.45 334	32.79 328	86.26 334	35.81 350	41.95 355	75.89 352

KD-MVS_self_test		60.87 318	58.60 318	67.68 336	66.13 361	39.93 365	75.63 339	84.70 319	57.32 324	49.57 341	68.45 347	29.55 339	82.87 350	48.09 306	47.94 347	80.25 335

UnsupCasMVSNet_bld		61.60 316	57.71 320	73.29 314	68.73 359	51.64 332	78.61 329	89.05 260	57.20 325	46.11 347	61.96 356	28.70 343	88.60 314	50.08 298	38.90 359	79.63 338

MSDG		69.54 274	65.73 283	80.96 228	85.11 251	63.71 198	84.19 290	83.28 333	56.95 326	54.50 321	84.03 236	31.50 334	96.03 134	42.87 330	69.13 248	83.14 305

F-COLMAP		70.66 265	68.44 270	77.32 284	86.37 231	55.91 313	88.00 263	86.32 302	56.94 327	57.28 316	88.07 194	33.58 326	92.49 262	51.02 294	68.37 253	83.55 295

test222					89.77 157	61.60 242	89.55 235	89.42 243	56.83 328	77.28 134	92.43 131	52.76 212			91.14 91	93.09 149

CNLPA		74.31 239	72.30 245	80.32 236	91.49 126	61.66 241	90.85 201	80.72 340	56.67 329	63.85 277	90.64 157	46.75 262	90.84 293	53.79 287	75.99 209	88.47 223

OurMVSNet-221017-0		64.68 304	62.17 308	72.21 323	76.08 341	47.35 352	80.67 316	81.02 338	56.19 330	51.60 332	79.66 296	27.05 347	88.56 315	53.60 289	53.63 337	80.71 329

YYNet1		63.76 311	60.14 314	74.62 304	78.06 329	60.19 269	83.46 297	83.99 328	56.18 331	39.25 360	71.56 342	37.18 313	83.34 347	42.90 329	48.70 346	80.32 333

MDA-MVSNet_test_wron		63.78 310	60.16 313	74.64 303	78.15 328	60.41 264	83.49 295	84.03 324	56.17 332	39.17 361	71.59 341	37.22 312	83.24 349	42.87 330	48.73 345	80.26 334

OpenMVS_ROB	61.12 18	66.39 297	62.92 303	76.80 292	76.51 337	57.77 296	89.22 243	83.41 331	55.48 333	53.86 325	77.84 307	26.28 349	93.95 220	34.90 353	68.76 250	78.68 345

MIMVSNet1		60.16 321	57.33 322	68.67 333	69.71 357	44.13 358	78.92 328	84.21 322	55.05 334	44.63 354	71.85 339	23.91 352	81.54 358	32.63 360	55.03 333	80.35 332

CVMVSNet		74.04 241	74.27 220	73.33 313	85.33 245	43.94 359	89.53 237	88.39 277	54.33 335	70.37 205	90.13 170	49.17 245	84.05 340	61.83 256	79.36 178	91.99 176

Anonymous20240529		76.84 206	74.15 223	84.88 121	91.02 134	64.95 167	93.84 81	91.09 183	53.57 336	73.00 170	87.42 203	35.91 320	97.32 76	69.14 194	72.41 229	92.36 166

pmmvs6		67.57 291	64.76 290	76.00 297	72.82 350	53.37 325	88.71 253	86.78 300	53.19 337	57.58 315	78.03 306	35.33 322	92.41 265	55.56 281	54.88 334	82.21 318

TinyColmap		60.32 319	56.42 325	72.00 326	78.78 321	53.18 326	78.36 332	75.64 349	52.30 338	41.59 359	75.82 325	14.76 365	88.35 317	35.84 349	54.71 335	74.46 354

test_0402		64.54 305	61.09 311	74.92 302	84.10 268	60.75 258	87.95 264	79.71 343	52.03 339	52.41 329	77.20 312	32.21 332	91.64 283	23.14 365	61.03 310	72.36 357

Anonymous20231211		73.08 248	70.39 259	81.13 221	90.62 143	63.33 207	91.40 176	90.06 223	51.84 340	64.46 272	80.67 282	36.49 318	94.07 210	63.83 241	64.17 284	85.98 267

AllTest		61.66 315	58.06 319	72.46 320	79.57 308	51.42 335	80.17 322	68.61 362	51.25 341	45.88 348	81.23 272	19.86 360	86.58 332	38.98 343	57.01 327	79.39 339

TestCases				72.46 320	79.57 308	51.42 335		68.61 362	51.25 341	45.88 348	81.23 272	19.86 360	86.58 332	38.98 343	57.01 327	79.39 339

PatchT		69.11 277	65.37 288	80.32 236	82.07 287	63.68 201	67.96 354	87.62 291	50.86 343	69.37 217	65.18 351	57.09 158	88.53 316	41.59 335	66.60 265	88.74 217

Anonymous20240521		62.09 314	59.08 317	71.10 328	67.19 360	48.72 347	83.91 292	85.23 314	50.38 344	47.84 345	71.22 343	20.74 358	85.51 335	46.47 315	58.75 323	79.06 342

DP-MVS		69.90 272	66.48 278	80.14 241	95.36 30	62.93 217	89.56 234	76.11 347	50.27 345	57.69 314	85.23 224	39.68 294	95.73 144	33.35 356	71.05 238	81.78 322

gg-mvs-nofinetune		77.18 202	74.31 219	85.80 91	91.42 127	68.36 69	71.78 342	94.72 32	49.61 346	77.12 136	45.92 363	77.41 8	93.98 218	67.62 206	93.16 57	95.05 77

JIA-IIPM		66.06 299	62.45 306	76.88 291	81.42 292	54.45 322	57.49 363	88.67 271	49.36 347	63.86 276	46.86 362	56.06 177	90.25 298	49.53 300	68.83 249	85.95 268

N_pmnet		50.55 328	49.11 331	54.88 346	77.17 334	4.02 382	84.36 289	2.00 381	48.59 348	45.86 350	68.82 346	32.22 331	82.80 351	31.58 363	51.38 341	77.81 348

ANet_high		40.27 333	35.20 336	55.47 344	34.74 378	34.47 370	63.84 358	71.56 358	48.42 349	18.80 369	41.08 367	9.52 371	64.45 370	20.18 366	8.66 374	67.49 361

COLMAP_ROB	57.96 20	62.98 313	59.65 315	72.98 316	81.44 291	53.00 327	83.75 293	75.53 351	48.34 350	48.81 343	81.40 270	24.14 351	90.30 297	32.95 358	60.52 315	75.65 353
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmtry		67.53 292	63.93 297	78.34 270	82.12 286	64.38 181	68.72 349	84.00 326	48.23 351	59.24 301	72.41 335	57.82 152	89.27 310	46.10 317	56.68 329	81.36 323

LS3D		69.17 276	66.40 279	77.50 280	91.92 111	56.12 312	85.12 285	80.37 341	46.96 352	56.50 318	87.51 202	37.25 311	93.71 226	32.52 361	79.40 177	82.68 312

RPSCF		64.24 307	61.98 309	71.01 329	76.10 340	45.00 356	75.83 338	75.94 348	46.94 353	58.96 305	84.59 232	31.40 335	82.00 356	47.76 311	60.33 318	86.04 265

RPMNet		70.42 268	65.68 284	84.63 131	83.15 279	67.96 81	70.25 345	90.45 201	46.83 354	69.97 212	65.10 352	56.48 173	95.30 169	35.79 351	73.13 221	90.64 198

CMPMVS	48.56 21	66.77 296	64.41 295	73.84 310	70.65 355	50.31 340	77.79 335	85.73 311	45.54 355	44.76 353	82.14 257	35.40 321	90.14 304	63.18 246	74.54 212	81.07 325
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		64.01 308	63.01 302	67.02 339	74.40 345	38.86 368	83.27 299	86.19 306	45.11 356	54.27 322	81.15 277	36.91 317	80.01 359	48.79 304	57.02 326	82.19 319

TDRefinement		55.28 326	51.58 329	66.39 340	59.53 368	46.15 355	76.23 337	72.80 355	44.60 357	42.49 358	76.28 321	15.29 363	82.39 353	33.20 357	43.75 353	70.62 359

Patchmatch-test		65.86 300	60.94 312	80.62 233	83.75 271	58.83 285	58.91 362	75.26 352	44.50 358	50.95 337	77.09 314	58.81 145	87.90 321	35.13 352	64.03 285	95.12 74

LF4IMVS		54.01 327	52.12 328	59.69 342	62.41 366	39.91 366	68.59 350	68.28 364	42.96 359	44.55 355	75.18 326	14.09 367	68.39 365	41.36 336	51.68 340	70.78 358

DSMNet-mixed		56.78 324	54.44 327	63.79 341	63.21 364	29.44 372	64.43 357	64.10 367	42.12 360	51.32 334	71.60 340	31.76 333	75.04 361	36.23 348	65.20 274	86.87 246

pmmvs3		55.51 325	51.50 330	67.53 337	57.90 369	50.93 338	80.37 318	73.66 354	40.63 361	44.15 356	64.75 353	16.30 362	78.97 360	44.77 324	40.98 358	72.69 355

new_pmnet		49.31 329	46.44 332	57.93 343	62.84 365	40.74 362	68.47 351	62.96 369	36.48 362	35.09 362	57.81 358	14.97 364	72.18 363	32.86 359	46.44 349	60.88 363

MVS-HIRNet		60.25 320	55.55 326	74.35 306	84.37 263	56.57 310	71.64 343	74.11 353	34.44 363	45.54 352	42.24 366	31.11 337	89.81 306	40.36 340	76.10 208	76.67 351

DeepMVS_CX				34.71 354	51.45 371	24.73 376		28.48 380	31.46 364	17.49 370	52.75 359	5.80 375	42.60 375	18.18 367	19.42 368	36.81 367

FPMVS		45.64 330	43.10 333	53.23 348	51.42 372	36.46 369	64.97 356	71.91 357	29.13 365	27.53 365	61.55 357	9.83 370	65.01 369	16.00 369	55.58 331	58.22 364

PMMVS2		37.93 335	33.61 338	50.92 349	46.31 374	24.76 375	60.55 361	50.05 373	28.94 366	20.93 367	47.59 361	4.41 378	65.13 368	25.14 364	18.55 369	62.87 362

LCM-MVSNet		40.54 332	35.79 335	54.76 347	36.92 377	30.81 371	51.41 364	69.02 361	22.07 367	24.63 366	45.37 364	4.56 377	65.81 367	33.67 355	34.50 364	67.67 360

PMVS	26.43 22	31.84 337	28.16 340	42.89 352	25.87 380	27.58 373	50.92 365	49.78 374	21.37 368	14.17 372	40.81 368	2.01 379	66.62 366	9.61 372	38.88 360	34.49 368
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.91 336	31.44 339	45.30 351	70.99 353	39.64 367	19.85 371	72.56 356	20.10 369	16.16 371	21.47 372	5.08 376	71.16 364	13.07 370	43.70 354	25.08 369
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		24.61 338	24.00 342	26.45 355	43.74 375	18.44 379	60.86 359	39.66 375	15.11 370	9.53 374	22.10 371	6.52 374	46.94 373	8.31 373	10.14 371	13.98 371

EMVS		23.76 340	23.20 344	25.46 356	41.52 376	16.90 380	60.56 360	38.79 378	14.62 371	8.99 375	20.24 374	7.35 373	45.82 374	7.25 374	9.46 372	13.64 372

MVE	24.84 23	24.35 339	19.77 345	38.09 353	34.56 379	26.92 374	26.57 369	38.87 377	11.73 372	11.37 373	27.44 369	1.37 380	50.42 372	11.41 371	14.60 370	36.93 366
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		38.59 334	35.16 337	48.89 350	54.33 370	21.35 377	45.32 367	53.71 372	7.41 373	28.74 364	51.62 360	8.70 372	52.87 371	33.73 354	32.89 365	72.47 356

wuyk23d		11.30 343	10.95 346	12.33 358	48.05 373	19.89 378	25.89 370	1.92 382	3.58 374	3.12 376	1.37 376	0.64 381	15.77 377	6.23 375	7.77 375	1.35 373

tmp_tt		22.26 341	23.75 343	17.80 357	5.23 381	12.06 381	35.26 368	39.48 376	2.82 375	18.94 368	44.20 365	22.23 356	24.64 376	36.30 347	9.31 373	16.69 370

EGC-MVSNET		42.35 331	38.09 334	55.11 345	74.57 343	46.62 354	71.63 344	55.77 371	0.04 376	0.24 377	62.70 355	14.24 366	74.91 362	17.59 368	46.06 350	43.80 365

testmvs		7.23 345	9.62 348	0.06 360	0.04 382	0.02 384	84.98 287	0.02 383	0.03 377	0.18 378	1.21 377	0.01 383	0.02 378	0.14 376	0.01 376	0.13 375

test123		6.92 346	9.21 349	0.08 359	0.03 383	0.05 383	81.65 310	0.01 384	0.02 378	0.14 379	0.85 378	0.03 382	0.02 378	0.12 377	0.00 377	0.16 374

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

cdsmvs_eth3d_5k		19.86 342	26.47 341	0.00 361	0.00 384	0.00 385	0.00 372	93.45 84	0.00 379	0.00 380	95.27 52	49.56 239	0.00 380	0.00 378	0.00 377	0.00 376

pcd_1.5k_mvsjas		4.46 347	5.95 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	53.55 205	0.00 380	0.00 378	0.00 377	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

ab-mvs-re		7.91 344	10.55 347	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	94.95 64	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

MSC_two_6792asdad				89.60 9	97.31 4	73.22 11		95.05 21					99.07 13	92.01 12	94.77 25	96.51 20

No_MVS				89.60 9	97.31 4	73.22 11		95.05 21					99.07 13	92.01 12	94.77 25	96.51 20

eth-test2					0.00 384

eth-test					0.00 384

OPU-MVS				89.97 3	97.52 3	73.15 13	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

test_0728_SECOND				88.70 16	96.45 14	70.43 28	96.64 8	94.37 50					99.15 2	91.91 14	94.90 21	96.51 20

GSMVS																94.68 90

test_part2					96.29 21	68.16 77				90.78 13

sam_mvs1												57.85 151				94.68 90

sam_mvs												54.91 190

ambc				69.61 331	61.38 367	41.35 361	49.07 366	85.86 310		50.18 340	66.40 349	10.16 369	88.14 319	45.73 319	44.20 352	79.32 341

MTGPA								92.23 130

test_post1							78.95 327				20.70 373	53.05 210	91.50 290	60.43 262

test_post											23.01 370	56.49 172	92.67 255

patchmatchnet-post											67.62 348	57.62 154	90.25 298

GG-mvs-BLEND				86.53 66	91.91 112	69.67 45	75.02 340	94.75 31		78.67 121	90.85 155	77.91 7	94.56 191	72.25 165	93.74 47	95.36 57

MTMP							93.77 83	32.52 379

test9_res														89.41 26	94.96 18	95.29 63

agg_prior2														86.41 57	94.75 30	95.33 59

agg_prior					94.16 50	66.97 111		93.31 90		84.49 59			96.75 111

test_prior4						67.18 105	93.92 74

test_prior				86.42 70	94.71 39	67.35 98		93.10 102					96.84 107			95.05 77

新几何2							91.41 174

旧先验1					91.94 109	60.74 259		91.50 165			94.36 82	65.23 72			91.84 77	94.55 95

原ACMM2							92.01 149

testdata2													96.09 129	61.26 258

segment_acmp												65.94 65

test12				87.09 45	94.60 41	68.86 58		92.91 108		82.67 76		65.44 71	97.55 64		93.69 50	94.84 86

plane_prior7					86.94 221	61.51 243

plane_prior6					87.23 216	62.32 229						50.66 229

plane_prior5								91.31 172					95.55 158	76.74 131	78.53 187	88.39 224

plane_prior4											89.14 180

plane_prior1					87.15 218

n2								0.00 385

nn								0.00 385

door-mid								66.01 366

lessismore_v0				73.72 311	72.93 349	47.83 350		61.72 370		45.86 350	73.76 330	28.63 344	89.81 306	47.75 312	31.37 366	83.53 296

test11								93.01 104

door								66.57 365

HQP5-MVS						63.66 202

BP-MVS														77.63 128

HQP4-MVS										74.18 160			95.61 152			88.63 218

HQP3-MVS								91.70 157							78.90 182

HQP2-MVS												51.63 222

NP-MVS					87.41 213	63.04 213					90.30 166

ACMMP++_ref															71.63 232

ACMMP++															69.72 242

Test By Simon												54.21 199

High-resolution multi-view benchmark