429 citations · 608 across the 22 of their papers we have counts for
17 papers · 1 filter
Towards Unsupervised Object Detection From LiDAR Point Clouds
Lunjun Zhang, Anqi Joyce Yang, Yuwen Xiong +4
In this paper, we study the problem of unsupervised object detection from 3D point clouds in self-driving scenes. We present a simple yet effective method that exploits (i) point c…
UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation
Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang +1
LiDAR provides accurate geometric measurements of the 3D world. Unfortunately, dense LiDARs are very expensive and the point clouds captured by low-beam LiDAR are often sparse. To…
LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
Anqi Joyce Yang, Sergio Casas, Nikita Dvornik +5
A major bottleneck to scaling-up training of self-driving perception systems are the human annotations required for supervision. A promising alternative is to leverage "auto-labell…
Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
Lunjun Zhang, Yuwen Xiong, Ze Yang +3
Learning world models can teach an agent how the world works in an unsupervised manner. Even though it can be viewed as a special case of sequence modeling, progress for scaling wo…
Network Automatic Pruning: Start NAP and Take a Nap
Wenyuan Zeng, Yuwen Xiong, Raquel Urtasun
Network pruning can significantly reduce the computation and memory footprint of large neural networks. To achieve a good trade-off between model size and performance, popular prun…
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
Katie Luo, Sergio Casas, Renjie Liao +4
In this paper, we address the important problem in self-driving of forecasting multi-pedestrian motion and their shared scene occupancy map, critical for safe navigation. Our contr…
Self-Supervised Representation Learning from Flow Equivariance
Yuwen Xiong, Mengye Ren, Wenyuan Zeng +1
Self-supervised representation learning is able to learn semantically meaningful features; however, much of its recent success relies on multiple crops of an image with very few ob…
Weakly-supervised 3D Shape Completion in the Wild
Jiayuan Gu, Wei-Chiu Ma, Sivabalan Manivasagam +5
3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned. Different from pr…
LevelSet R-CNN: A Deep Variational Method for Instance Segmentation
Namdar Homayounfar, Yuwen Xiong, Justin Liang +2
Obtaining precise instance segmentation masks is of high importance in many modern applications such as robotic manipulation and autonomous driving. Currently, many state of the ar…
PolyTransform: Deep Polygon Transformer for Instance Segmentation
Justin Liang, Namdar Homayounfar, Wei-Chiu Ma +3
In this paper, we propose PolyTransform, a novel instance segmentation algorithm that produces precise, geometry-preserving masks by combining the strengths of prevailing segmentat…
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Ajay Jain, Sergio Casas, Renjie Liao +4
Self-driving vehicles plan around both static and dynamic objects, applying predictive models of behavior to estimate future locations of the objects in the environment. However, f…
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
Xiaohui Zeng, Renjie Liao, Li Gu +3
In this paper, we propose the differentiable mask-matching network (DMM-Net) for solving the video object segmentation problem where the initial object masks are provided. Relying…
Deformable Filter Convolution for Point Cloud Reasoning
Yuwen Xiong, Mengye Ren, Renjie Liao +2
Point clouds are the native output of many real-world 3D sensors. To borrow the success of 2D convolutional network architectures, a majority of popular 3D perception models voxeli…
Deep Rigid Instance Scene Flow
Wei-Chiu Ma, Shenlong Wang, Rui Hu +2
In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application dom…
UPSNet: A Unified Panoptic Segmentation Network
Yuwen Xiong, Renjie Liao, Hengshuang Zhao +4
In this paper, we propose a unified panoptic segmentation network (UPSNet) for tackling the newly proposed panoptic segmentation task. On top of a single backbone residual network,…
Deformable Convolutional Networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong +4
Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we intro…
Deep Feature Flow for Video Recognition
Xizhou Zhu, Yuwen Xiong, Jifeng Dai +2
Deep convolutional neutral networks have achieved great success on image recognition tasks. Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to vi…