Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving

no code implementations • 18 Jan 2021 • Min Bai, Shenlong Wang, Kelvin Wong, Ersin Yumer, Raquel Urtasun

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).

Auto4D: Learning to Label 4D Objects from Sequential Point Clouds

no code implementations • 17 Jan 2021 • Bin Yang, Min Bai, Ming Liang, Wenyuan Zeng, Raquel Urtasun

The key idea is to decompose the 4D object label into two parts: the object size in 3D that's fixed through time for rigid objects, and the motion path describing the evolution of the object's pose through time.

3D Object Detection

Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

no code implementations • 8 Aug 2019 • Wei-Chiu Ma, Ignacio Tartavull, Ioan Andrei Bârsan, Shenlong Wang, Min Bai, Gellert Mattyus, Namdar Homayounfar, Shrinidhi Kowshika Lakshmikanth, Andrei Pokrovsky, Raquel Urtasun

In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters.

Self-Driving Cars

Deep Multi-Sensor Lane Detection

no code implementations • 4 May 2019 • Min Bai, Gellert Mattyus, Namdar Homayounfar, Shenlong Wang, Shrinidhi Kowshika Lakshmikanth, Raquel Urtasun

Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving.

Autonomous Driving Lane Detection +1

UPSNet: A Unified Panoptic Segmentation Network

1 code implementation • CVPR 2019 • Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun

More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification.

Instance Segmentation Panoptic Segmentation

Learning deep structured active contours end-to-end

1 code implementation • CVPR 2018 • Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun

The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications.

Instance Segmentation Semantic Segmentation

TorontoCity: Seeing the World with a Million Eyes

no code implementations • ICCV 2017 • Shenlong Wang, Min Bai, Gellert Mattyus, Hang Chu, Wenjie Luo, Bin Yang, Justin Liang, Joel Cheverie, Sanja Fidler, Raquel Urtasun

In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712. 5 $km^2$ of land, 8439 $km$ of road and around 400, 000 buildings.

Instance Segmentation Semantic Segmentation

Deep Watershed Transform for Instance Segmentation

3 code implementations • CVPR 2017 • Min Bai, Raquel Urtasun

Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes.

Instance Segmentation Semantic Segmentation +1

Exploiting Semantic Information and Deep Matching for Optical Flow

no code implementations • 6 Apr 2016 • Min Bai, Wenjie Luo, Kaustav Kundu, Raquel Urtasun

We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving.

Autonomous Driving Optical Flow Estimation