Cargando…
DeepHMap++: Combined Projection Grouping and Correspondence Learning for Full DoF Pose Estimation
In recent years, estimating the 6D pose of object instances with convolutional neural network (CNN) has received considerable attention. Depending on whether intermediate cues are used, the relevant literature can be roughly divided into two broad categories: direct methods and two-stage pipelines....
Autores principales: | Fu, Mingliang, Zhou, Weijia |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427142/ https://www.ncbi.nlm.nih.gov/pubmed/30823451 http://dx.doi.org/10.3390/s19051032 |
Ejemplares similares
-
Robust 6-DoF Pose Estimation under Hybrid Constraints
por: Ren, Hong, et al.
Publicado: (2022) -
Fault-Tolerant Six-DoF Pose Estimation for Tendon-Driven Continuum Mechanisms
por: Raffin, Antonin, et al.
Publicado: (2021) -
Single-Camera Multi-View 6DoF pose estimation for robotic grasping
por: Yuan, Shuangjie, et al.
Publicado: (2023) -
6DoF Pose Estimation of Transparent Object from a Single RGB-D Image
por: Xu, Chi, et al.
Publicado: (2020) -
Viscoelastic Mechanical Responses of HMAP under Moving Load
por: Sun, Yazhen, et al.
Publicado: (2018)