28 citations · 61 across the 11 of their papers we have counts for
11 papers
Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds
Haoran Zhou, Honghua Chen, Yingkui Zhang +6
Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional geometric tasks such as surface consolidation and reconstruction, but also facilitates…
Image-Goal Navigation in Complex Environments via Modular Learning
Qiaoyun Wu, Jun Wang, Jing Liang +2
We present a novel approach for image-goal navigation, where an agent navigates with a goal image rather than accurate target information, which is more challenging. Our goal is to…
Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning
Qiaoyun Wu, Xiaoxi Gong, Kai Xu +3
We present a target-driven navigation system to improve mapless visual navigation in indoor scenes. Our method takes a multi-view observation of a robot and a target as inputs at e…
Deep Feature-preserving Normal Estimation for Point Cloud Filtering
Dening Lu, Xuequan Lu, Yangxing Sun +1
Point cloud filtering, the main bottleneck of which is removing noise (outliers) while preserving geometric features, is a fundamental problem in 3D field. The two-step schemes inv…
MLCVNet: Multi-Level Context VoteNet for 3D Object Detection
Qian Xie, Yu-Kun Lai, Jing Wu +4
In this paper, we address the 3D object detection task by capturing multi-level contextual information with the self-attention mechanism and multi-scale feature fusion. Most existi…
Reinforcement Learning-based Visual Navigation with Information-Theoretic Regularization
Qiaoyun Wu, Kai Xu, Jun Wang +3
To enhance the cross-target and cross-scene generalization of target-driven visual navigation based on deep reinforcement learning (RL), we introduce an information-theoretic regul…