841 citations · 7.7k across the 158 of their papers we have counts for
30 papers · 1 filter
NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation
Xiaohui Zeng, Raquel Urtasun, Richard Zemel +2
In this paper, we present a non-parametric structured latent variable model for image generation, called NP-DRAW, which sequentially draws on a latent canvas in a part-by-part fash…
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
Sean Segal, Nishanth Kumar, Sergio Casas +4
Self-driving vehicles must perceive and predict the future positions of nearby actors in order to avoid collisions and drive safely. A learned deep learning module is often respons…
IntentNet: Learning to Predict Intention from Raw Sensor Data
Sergio Casas, Wenjie Luo, Raquel Urtasun
In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants. We define intent as a combination of discrete high-level behavi…
Deep Feedback Inverse Problem Solver
Wei-Chiu Ma, Shenlong Wang, Jiayuan Gu +3
We present an efficient, effective, and generic approach towards solving inverse problems. The key idea is to leverage the feedback signal provided by the forward process and learn…
Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving
Min Bai, Shenlong Wang, Kelvin Wong +2
In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV). Our re…
MP3: A Unified Model to Map, Perceive, Predict and Plan
Sergio Casas, Abbas Sadat, Raquel Urtasun
High-definition maps (HD maps) are a key component of most modern self-driving systems due to their valuable semantic and geometric information. Unfortunately, building HD maps has…
Deep Parametric Continuous Convolutional Neural Networks
Shenlong Wang, Simon Suo, Wei-Chiu Ma +2
Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. This limits their applica…
End-to-end Interpretable Neural Motion Planner
Wenyuan Zeng, Wenjie Luo, Simon Suo +4
In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interaction…
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting
Wenyuan Zeng, Ming Liang, Renjie Liao +1
Forecasting the future behaviors of dynamic actors is an important task in many robotics applications such as self-driving. It is extremely challenging as actors have latent intent…
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…
Cost-Efficient Online Hyperparameter Optimization
Jingkang Wang, Mengye Ren, Ilija Bogunovic +2
Recent work on hyperparameters optimization (HPO) has shown the possibility of training certain hyperparameters together with regular parameters. However, these online HPO algorith…
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
Ze Yang, Shenlong Wang, Sivabalan Manivasagam +5
Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation.…
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
Simon Suo, Sebastian Regalado, Sergio Casas +1
Simulation has the potential to massively scale evaluation of self-driving systems enabling rapid development as well as safe deployment. To close the gap between simulation and th…
Diverse Complexity Measures for Dataset Curation in Self-driving
Abbas Sadat, Sean Segal, Sergio Casas +4
Modern self-driving autonomy systems heavily rely on deep learning. As a consequence, their performance is influenced significantly by the quality and richness of the training data…
VideoClick: Video Object Segmentation with a Single Click
Namdar Homayounfar, Justin Liang, Wei-Chiu Ma +1
Annotating videos with object segmentation masks typically involves a two stage procedure of drawing polygons per object instance for all the frames and then linking them through t…
SceneGen: Learning to Generate Realistic Traffic Scenes
Shuhan Tan, Kelvin Wong, Shenlong Wang +3
We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics…
End-to-End Deep Structured Models for Drawing Crosswalks
Justin Liang, Raquel Urtasun
In this paper we address the problem of detecting crosswalks from LiDAR and camera imagery. Towards this goal, given multiple LiDAR sweeps and the corresponding imagery, we project…
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…
Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild
Shivam Duggal, Zihao Wang, Wei-Chiu Ma +4
Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and grap…
Deep Structured Reactive Planning
Jerry Liu, Wenyuan Zeng, Raquel Urtasun +1
An intelligent agent operating in the real-world must balance achieving its goal with maintaining the safety and comfort of not only itself, but also other participants within the…
Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving
James Tu, Huichen Li, Xinchen Yan +6
Modern self-driving perception systems have been shown to improve upon processing complementary inputs such as LiDAR with images. In isolation, 2D images have been found to be extr…
Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
John Phillips, Julieta Martinez, Ioan Andrei Bârsan +3
Over the last few years, we have witnessed tremendous progress on many subtasks of autonomous driving, including perception, motion forecasting, and motion planning. However, these…
PLUMENet: Efficient 3D Object Detection from Stereo Images
Yan Wang, Bin Yang, Rui Hu +2
3D object detection is a key component of many robotic applications such as self-driving vehicles. While many approaches rely on expensive 3D sensors such as LiDAR to produce accur…
Auto4D: Learning to Label 4D Objects from Sequential Point Clouds
Bin Yang, Min Bai, Ming Liang +2
In the past few years we have seen great advances in object perception (particularly in 4D space-time dimensions) thanks to deep learning methods. However, they typically rely on l…