169 citations · 255 across the 8 of their papers we have counts for
8 papers
Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation
Dengsheng Chen, Jun Li, Zheng Wang +1
We present a novel approach to category-level 6D object pose and size estimation. To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified represent…
Preliminary study on the modal decomposition of Hermite Gaussian beams via deep learning
Yi An, Tianyue Hou, Jun Li +4
The Hermite-Gaussian (HG) modes make up a complete and orthonormal basis, which have been extensively used to describe optical fields. Here, we demonstrate, for the first time to o…
Learning Part Generation and Assembly for Structure-aware Shape Synthesis
Jun Li, Chengjie Niu, Kai Xu
Learning powerful deep generative models for 3D shape synthesis is largely hindered by the difficulty in ensuring plausibility encompassing correct topology and reasonable geometry…
Deep learning enabled superfast and accurate M^2 evaluation for fiber beams
Yi An, Jun Li, Liangjin Huang +3
We introduce deep learning technique to predict the beam propagation factor M^2 of the laser beams emitting from few-mode fiber for the first time, to the best of our knowledge. Th…
Deep learning-based phase control method for coherent beam combining and its application in generating orbital angular momentum beams
Tianyue Hou, Yi An, Qi Chang +8
We incorporate deep learning (DL) into coherent beam combining (CBC) systems for the first time, to the best of our knowledge. Using a well-trained convolutional neural network DL…
Learning to decompose the modes in few-mode fibers with deep convolutional neural network
Yi An, Liangjin Huang, Jun Li +3
We introduce deep learning technique to perform complete mode decomposition for few-mode optical fiber for the first time. Our goal is to learn a fast and accurate mapping from nea…