Kelvin Wong

I am a PhD student at the University of Toronto and the Vector Institute, where I work on machine learning and self-driving cars. My advisor is Raquel Urtasun.

Previously, I was a research scientist at Uber Advanced Technologies Group. I earned my MSc from the University of Toronto, my BCS from the University of Waterloo, and my BBA from Wilfrid Laurier University. During my undergrad, I was fortunate to have worked with Will Evans and Peter van Beek.

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SceneGen: Learning to Generate Realistic Traffic Scenes
Shuhan Tan*, Kelvin Wong*, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun
Computer Vision and Pattern Recognition (CVPR), 2021

An autoregressive model of traffic scenes.

Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving
Min Bai, Shenlong Wang, Kelvin Wong, Ersin Yumer, Raquel Urtasun
arXiv, 2021

Spatio-temporal segmentation of construction zones.

MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models
Sourav Biswas, Jerry Liu, Kelvin Wong, Shenlong Wang, Raquel Urtasun
Neural Information Processing Systems (NeurIPS), 2020

Multi-sweep LiDAR compression using deep entropy models.

Simultaneous Visibility Representations of Undirected Pairs of Graphs
Ben Chugg, Will Evans, Kelvin Wong
Computational Geometry: Theory and Applications, 2021
Canadian Conference on Computational Geometry (CCCG), 2020

Visualize pairs of undirected graphs simulatenously.

Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
Kelvin Wong*, Qiang Zhang*, Ming Liang, Bin Yang, Renjie Liao, Abbas Sadat, Raquel Urtasun
European Conference on Computer Vision (ECCV), 2020

Test motion planning using simulated perception and prediction outputs.

OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
Lila Huang, Shenlong Wang, Kelvin Wong, Jerry Liu, Raquel Urtasun
Computer Vision and Pattern Recognition (CVPR), 2020   (Oral)

Single-sweep LiDAR compression using deep entropy models.

LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Wei-Chiu Ma, Raquel Urtasun
Computer Vision and Pattern Recognition (CVPR), 2020   (Oral)

Realistic LiDAR simulation for self-driving vehicles.

Deformable Filter Convolution for Point Cloud Reasoning
Yuwen Xiong*, Mengye Ren*, Renjie Liao, Kelvin Wong, Raquel Urtasun
NeurIPS Workshop on Sets and Partitions, 2019

Deformable convolution for 3D point clouds.

Identifying Unknown Instances for Autonomous Driving
Kelvin Wong, Shenlong Wang, Mengye Ren, Ming Liang, Raquel Urtasun
Conference on Robot Learning (CoRL), 2019   (Spotlight)

3D perception for the open-set world.


Conference Reviewer: AAAI 2021, CVPR 2021

Website shamelessly copied from Jon Barron.