Cargando…
MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images
Monocular 3D object detection is very challenging in autonomous driving due to the lack of depth information. This paper proposes a one-stage monocular 3D object detection network (MDS Net), which uses the anchor-free method to detect 3D objects in a per-pixel prediction. Firstly, a novel depth-base...
Autores principales: | Xie, Zhouzhen, Song, Yuying, Wu, Jingxuan, Li, Zecheng, Song, Chunyi, Xu, Zhiwei |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9415185/ https://www.ncbi.nlm.nih.gov/pubmed/36015965 http://dx.doi.org/10.3390/s22166197 |
Ejemplares similares
-
GFI-Net: Global Feature Interaction Network for Monocular Depth Estimation
por: Zhang, Cong, et al.
Publicado: (2023) -
Deep Learning-Based Monocular 3D Object Detection with Refinement of Depth Information
por: Hu, Henan, et al.
Publicado: (2022) -
eGAC3D: enhancing depth adaptive convolution and depth estimation for monocular 3D object pose detection
por: Ngo, Duc Tuan, et al.
Publicado: (2022) -
Unsupervised Monocular Depth Estimation Method Based on Uncertainty Analysis and Retinex Algorithm
por: Song, Chuanxue, et al.
Publicado: (2020) -
Uncertainty Prediction for Monocular 3D Object Detection
por: Mun, Junghwan, et al.
Publicado: (2023)