235 citations · 850 across the 47 of their papers we have counts for
15 papers · 1 filter
Object Re-Identification from Point Clouds
Benjamin Thérien, Chengjie Huang, Adrian Chow +1
Object re-identification (ReID) from images plays a critical role in application domains of image retrieval (surveillance, retail analytics, etc.) and multi-object tracking (autono…
FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs
Luke Rowe, Martin Ethier, Eli-Henry Dykhne +1
Predicting the future motion of road agents is a critical task in an autonomous driving pipeline. In this work, we address the problem of generating a set of scene-level, or joint,…
XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection
Sunsheng Gu, Vahdat Abdelzad, Krzysztof Czarnecki
Explainable AI (XAI) methods are frequently applied to obtain qualitative insights about deep models' predictions. However, such insights need to be interpreted by a human observer…
Interpretable Deep Tracking
Benjamin Thérien, Krzysztof Czarnecki
Imagine experiencing a crash as the passenger of an autonomous vehicle. Wouldn't you want to know why it happened? Current end-to-end optimizable deep neural networks (DNNs) in 3D…
Out-of-Distribution Detection for LiDAR-based 3D Object Detection
Chengjie Huang, Van Duong Nguyen, Vahdat Abdelzad +5
3D object detection is an essential part of automated driving, and deep neural networks (DNNs) have achieved state-of-the-art performance for this task. However, deep models are no…
SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving
Prarthana Bhattacharyya, Chengjie Huang, Krzysztof Czarnecki
Self-supervised learning (SSL) is an emerging technique that has been successfully employed to train convolutional neural networks (CNNs) and graph neural networks (GNNs) for more…
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection
Matthew Pitropov, Chengjie Huang, Vahdat Abdelzad +2
The estimation of uncertainty in robotic vision, such as 3D object detection, is an essential component in developing safe autonomous systems aware of their own performance. Howeve…
SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection
Prarthana Bhattacharyya, Chengjie Huang, Krzysztof Czarnecki
Existing point-cloud based 3D object detectors use convolution-like operators to process information in a local neighbourhood with fixed-weight kernels and aggregate global context…
Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations
Prarthana Bhattacharyya, Krzysztof Czarnecki
We present Deformable PV-RCNN, a high-performing point-cloud based 3D object detector. Currently, the proposal refinement methods used by the state-of-the-art two-stage detectors c…
Canadian Adverse Driving Conditions Dataset
Matthew Pitropov, Danson Garcia, Jason Rebello +4
The Canadian Adverse Driving Conditions (CADC) dataset was collected with the Autonomoose autonomous vehicle platform, based on a modified Lincoln MKZ. The dataset, collected durin…
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation
Matt Angus, Krzysztof Czarnecki, Rick Salay
The detection of out of distribution samples for image classification has been widely researched. Safety critical applications, such as autonomous driving, would benefit from the a…
MLOD: A multi-view 3D object detection based on robust feature fusion method
Jian Deng, Krzysztof Czarnecki
This paper presents Multi-view Labelling Object Detector (MLOD). The detector takes an RGB image and a LIDAR point cloud as input and follows the two-stage object detection framewo…
FANTrack: 3D Multi-Object Tracking with Feature Association Network
Erkan Baser, Venkateshwaran Balasubramanian, Prarthana Bhattacharyya +1
We propose a data-driven approach to online multi-object tracking (MOT) that uses a convolutional neural network (CNN) for data association in a tracking-by-detection framework. Th…
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
Braden Hurl, Krzysztof Czarnecki, Steven Waslander
We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed wor…
Unlimited Road-scene Synthetic Annotation (URSA) Dataset
Matt Angus, Mohamed ElBalkini, Samin Khan +5
In training deep neural networks for semantic segmentation, the main limiting factor is the low amount of ground truth annotation data that is available in currently existing datas…