351 citations · 431 across the 31 of their papers we have counts for
8 papers · 1 filter
DAGMapper: Learning to Map by Discovering Lane Topology
Namdar Homayounfar, Wei-Chiu Ma, Justin Liang +3
One of the fundamental challenges to scale self-driving is being able to create accurate high definition maps (HD maps) with low cost. Current attempts to automate this process typ…
Hierarchical Recurrent Attention Networks for Structured Online Maps
Namdar Homayounfar, Wei-Chiu Ma, Shrinidhi Kowshika Lakshmikanth +1
In this paper, we tackle the problem of online road network extraction from sparse 3D point clouds. Our method is inspired by how an annotator builds a lane graph, by first identif…
Convolutional Recurrent Network for Road Boundary Extraction
Justin Liang, Namdar Homayounfar, Wei-Chiu Ma +2
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper…
Recovering and Simulating Pedestrians in the Wild
Ze Yang, Siva Manivasagam, Ming Liang +3
Sensor simulation is a key component for testing the performance of self-driving vehicles and for data augmentation to better train perception systems. Typical approaches rely on a…
Conditional Entropy Coding for Efficient Video Compression
Jerry Liu, Shenlong Wang, Wei-Chiu Ma +4
We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. Unlike prior learning-based approaches, we…
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…
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong +6
We tackle the problem of producing realistic simulations of LiDAR point clouds, the sensor of preference for most self-driving vehicles. We argue that, by leveraging real data, we…