236 citations · 459 across the 16 of their papers we have counts for
16 papers · 1 filter
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting
Kan Chen, Runzhou Ge, Hang Qiu +12
Widely adopted motion forecasting datasets substitute the observed sensory inputs with higher-level abstractions such as 3D boxes and polylines. These sparse shapes are inferred th…
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds
Zhaoqi Leng, Shuyang Cheng, Benjamin Caine +5
Data augmentation is an important technique to improve data efficiency and save labeling cost for 3D detection in point clouds. Yet, existing augmentation policies have so far been…
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds
Pei Sun, Mingxing Tan, Weiyue Wang +4
3D object detection in point clouds is a core component for modern robotics and autonomous driving systems. A key challenge in 3D object detection comes from the inherent sparse na…
Multi-Class 3D Object Detection with Single-Class Supervision
Mao Ye, Chenxi Liu, Maoqing Yao +4
While multi-class 3D detectors are needed in many robotics applications, training them with fully labeled datasets can be expensive in labeling cost. An alternative approach is to…
SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation
Qiangeng Xu, Yin Zhou, Weiyue Wang +2
In autonomous driving, a LiDAR-based object detector should perform reliably at different geographic locations and under various weather conditions. While recent 3D detection resea…
To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels
Yuning Chai, Pei Sun, Jiquan Ngiam +5
3D object detection is vital for many robotics applications. For tasks where a 2D perspective range image exists, we propose to learn a 3D representation directly from this range i…
RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
Pei Sun, Weiyue Wang, Yuning Chai +5
The detection of 3D objects from LiDAR data is a critical component in most autonomous driving systems. Safe, high speed driving needs larger detection ranges, which are enabled by…
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
Qiangeng Xu, Weiyue Wang, Duygu Ceylan +2
Reconstructing 3D shapes from single-view images has been a long-standing research problem. In this paper, we present DISN, a Deep Implicit Surface Network which can generate a hig…
3DN: 3D Deformation Network
Weiyue Wang, Duygu Ceylan, Radomir Mech +1
Applications in virtual and augmented reality create a demand for rapid creation and easy access to large sets of 3D models. An effective way to address this demand is to edit or d…
Stochastic Dynamics for Video Infilling
Qiangeng Xu, Hanwang Zhang, Weiyue Wang +2
In this paper, we introduce a stochastic dynamics video infilling (SDVI) framework to generate frames between long intervals in a video. Our task differs from video interpolation w…
Recurrent Slice Networks for 3D Segmentation of Point Clouds
Qiangui Huang, Weiyue Wang, Ulrich Neumann
Point clouds are an efficient data format for 3D data. However, existing 3D segmentation methods for point clouds either do not model local dependencies \cite{pointnet} or require…
Depth-aware CNN for RGB-D Segmentation
Weiyue Wang, Ulrich Neumann
Convolutional neural networks (CNN) are limited by the lack of capability to handle geometric information due to the fixed grid kernel structure. The availability of depth data ena…
Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks
Weiyue Wang, Qiangui Huang, Suya You +2
Recent advances in convolutional neural networks have shown promising results in 3D shape completion. But due to GPU memory limitations, these methods can only produce low-resoluti…
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
Weiyue Wang, Ronald Yu, Qiangui Huang +1
We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a single networ…
Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning
Qiangui Huang, Weiyue Wang, Kevin Zhou +2
Recurrent neural network (RNN), as a powerful contextual dependency modeling framework, has been widely applied to scene labeling problems. However, this work shows that directly a…
A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes
Weiyao Lin, Yang Mi, Weiyue Wang +3
This paper addresses the problem of detecting coherent motions in crowd scenes and presents its two applications in crowd scene understanding: semantic region detection and recurre…