430 citations
- University of TorontoCA5 papers
- University of WaterlooCA4 papers
- West Virginia High Technology Consortium FoundationUS2 papers
- Columbia UniversityUS1 paper
- École Polytechnique Fédérale de LausanneCH1 paper
- Massachusetts Institute of TechnologyUS1 paper
- Microsoft Research (India)IN1 paper
- Microsoft (United States)US1 paper
- NSF National Center for Atmospheric ResearchUS1 paper
- NSF NCAR High Altitude ObservatoryUS1 paper
- University of California San DiegoUS1 paper
- University of PaviaIT1 paper
16 papers
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…
Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
Wenjie Luo, Bin Yang, Raquel Urtasun
In this paper we propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. By joint…
Deep Continuous Fusion for Multi-Sensor 3D Object Detection
Ming Liang, Bin Yang, Shenlong Wang +1
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-en…
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars
Julieta Martinez, Sasha Doubov, Jack Fan +4
We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M,…
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
Sourav Biswas, Jerry Liu, Kelvin Wong +2
We present a novel compression algorithm for reducing the storage of LiDAR sensor data streams. Our model exploits spatio-temporal relationships across multiple LiDAR sweeps to red…
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…