91 citations · 152 across the 40 of their papers we have counts for
12 papers · 1 filter
Evaluating the Impact of Weather-Induced Sensor Occlusion on BEVFusion for 3D Object Detection
Sanjay Kumar, Tim Brophy, Eoin Martino Grua +3
Accurate 3D object detection is essential for automated vehicles to navigate safely in complex real-world environments. Bird's Eye View (BEV) representations, which project multi-s…
SuperQuadricOcc: Real-Time Self-Supervised Semantic Occupancy Estimation with Superquadric Volume Rendering
Seamie Hayes, Alexandre Boulch, Andrei Bursuc +4
Self-supervision for semantic occupancy estimation is appealing as it removes the labour-intensive manual annotation, thus allowing one to scale to larger autonomous driving datase…
Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA)
Nikos Theodoridis, Tim Brophy, Reenu Mohandas +4
The remarkable progress of Vision-Language Models (VLMs) on a variety of tasks has raised interest in their application to automated driving. However, for these models to be truste…
Evaluating Small Vision-Language Models on Distance-Dependent Traffic Perception
Nikos Theodoridis, Tim Brophy, Reenu Mohandas +4
Vision-Language Models (VLMs) are becoming increasingly powerful, demonstrating strong performance on a variety of tasks that require both visual and textual understanding. Their s…
FIN: Fast Inference Network for Map Segmentation
Ruan Bispo, Tim Brophy, Reenu Mohandas +2
Multi-sensor fusion in autonomous vehicles is becoming more common to offer a more robust alternative for several perception tasks. This need arises from the unique contribution of…
PAN: Pillars-Attention-Based Network for 3D Object Detection
Ruan Bispo, Dane Mitrev, Letizia Mariotti +4
Camera-radar fusion offers a robust and low-cost alternative to Camera-lidar fusion for the 3D object detection task in real-time under adverse weather and lighting conditions. How…
Easy3D-Labels: Supervising Semantic Occupancy Estimation with 3D Pseudo-Labels for Automotive Perception
Seamie Hayes, Ganesh Sistu, Tim Brophy +1
In perception for automated vehicles, safety is critical not only for the driver but also for other agents in the scene, particularly vulnerable road users such as pedestrians and…
BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation
Hiep Truong Cong, Ajay Kumar Sigatapu, Arindam Das +4
Accurate motion understanding of the dynamic objects within the scene in bird's-eye-view (BEV) is critical to ensure a reliable obstacle avoidance system and smooth path planning f…
Image Segmentation: Inducing graph-based learning
Aryan Singh, Pepijn Van de Ven, Ciarán Eising +1
This study explores the potential of graph neural networks (GNNs) to enhance semantic segmentation across diverse image modalities. We evaluate the effectiveness of a novel GNN-bas…
SOLAS: Superpositioning an Optical Lens in Automotive Simulation
Daniel Jakab, Julian Barthel, Alexander Braun +7
Automotive Simulation is a potentially cost-effective strategy to identify and test corner case scenarios in automotive perception. Recent work has shown a significant shift in cre…
Revisiting Birds Eye View Perception Models with Frozen Foundation Models: DINOv2 and Metric3Dv2
Seamie Hayes, Ganesh Sistu, Ciarán Eising
Birds Eye View perception models require extensive data to perform and generalize effectively. While traditional datasets often provide abundant driving scenes from diverse locatio…
Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with Multi-Sensor Fusion
Sanjay Kumar, Hiep Truong, Sushil Sharma +4
Autonomous driving technology is rapidly evolving, offering the potential for safer and more efficient transportation. However, the performance of these systems can be significantl…