activity
20182022
most citedTAP-Net: Transport-and-Pack using Reinforcement Learning

46 citations · 101 across the 9 of their papers we have counts for

collaborators

12 papers

cs.CV20226 cited

3DRM:Pair-wise relation module for 3D object detection

Yuqing Lan, Yao Duan, Yifei Shi +2

Context has proven to be one of the most important factors in object layout reasoning for 3D scene understanding. Existing deep contextual models either learn holistic features for…

cs.CV2021

Consistent Two-Flow Network for Tele-Registration of Point Clouds

Zihao Yan, Zimu Yi, Ruizhen Hu +3

Rigid registration of partial observations is a fundamental problem in various applied fields. In computer graphics, special attention has been given to the registration between tw…

cs.CV2021

RPG: Learning Recursive Point Cloud Generation

Wei-Jan Ko, Hui-Yu Huang, Yu-Liang Kuo +3

In this paper we propose a novel point cloud generator that is able to reconstruct and generate 3D point clouds composed of semantic parts. Given a latent representation of the tar…

cs.CV20211 cited

VGF-Net: Visual-Geometric Fusion Learning for Simultaneous Drone Navigation and Height Mapping

Yilin Liu, Ke Xie, Hui Huang

The drone navigation requires the comprehensive understanding of both visual and geometric information in the 3D world. In this paper, we present a Visual-Geometric Fusion Network(…

cs.CV202111 cited

Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning

Ruizhen Hu, Bin Chen, Juzhan Xu +3

We present a neural optimization model trained with reinforcement learning to solve the coordinate ordering problem for sets of star glyphs. Given a set of star glyphs associated t…

cs.CV20202 cited

Hausdorff Point Convolution with Geometric Priors

Pengdi Huang, Liqiang Lin, Fuyou Xue +3

Without a shape-aware response, it is hard to characterize the 3D geometry of a point cloud efficiently with a compact set of kernels. In this paper, we advocate the use of Hausdor…