3 citations · 5 across the 12 of their papers we have counts for
13 papers
Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving
Nikos Theodoridis, Reenu Mohandas, Ganesh Sistu +3
The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their reasoning and generalisation capabiliti…
FisheyeGaussianLift: BEV Feature Lifting for Surround-View Fisheye Camera Perception
Shubham Sonarghare, Prasad Deshpande, Ciaran Hogan +2
Accurate BEV semantic segmentation from fisheye imagery remains challenging due to extreme non-linear distortion, occlusion, and depth ambiguity inherent to wide-angle projections.…
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…
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…
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…
Reflective Teacher: Semi-Supervised Multimodal 3D Object Detection in Bird's-Eye-View via Uncertainty Measure
Saheli Hazra, Sudip Das, Rohit Choudhary +4
Applying pseudo labeling techniques has been found to be advantageous in semi-supervised 3D object detection (SSOD) in Bird's-Eye-View (BEV) for autonomous driving, particularly wh…
Deformable Convolution Based Road Scene Semantic Segmentation of Fisheye Images in Autonomous Driving
Anam Manzoor, Aryan Singh, Ganesh Sistu +4
This study investigates the effectiveness of modern Deformable Convolutional Neural Networks (DCNNs) for semantic segmentation tasks, particularly in autonomous driving scenarios w…
Optimizing Visual Question Answering Models for Driving: Bridging the Gap Between Human and Machine Attention Patterns
Kaavya Rekanar, Martin Hayes, Ganesh Sistu +1
Visual Question Answering (VQA) models play a critical role in enhancing the perception capabilities of autonomous driving systems by allowing vehicles to analyze visual inputs alo…
Depth Estimation using Weighted-loss and Transfer Learning
Muhammad Adeel Hafeez, Michael G. Madden, Ganesh Sistu +1
Depth estimation from 2D images is a common computer vision task that has applications in many fields including autonomous vehicles, scene understanding and robotics. The accuracy…
Beyond the Known: Adversarial Autoencoders in Novelty Detection
Muhammad Asad, Ihsan Ullah, Ganesh Sistu +1
In novelty detection, the goal is to decide if a new data point should be categorized as an inlier or an outlier, given a training dataset that primarily captures the inlier distri…