852 citations · 1.5k across the 82 of their papers we have counts for
90 papers
MotionSync: Non-Causal Refinement of Causal Tracker for Label-Efficient 3D Perception
Rahul Ahuja, Bala Murali Manoghar Sai Sudhakar, Shashwata Gupta +3
Three-dimensional box-and-track annotation is the cost bottleneck in autonomous-driving data engines, and the offline systems built to relieve it replace the online perception stac…
RedLight-VLA: Models for traffic-rule grounding and behavioral emphasis in driving policies
Bala Murali Manoghar Sai Sudhakar, Sourab Bapu Sridhar, Sandipan Das +5
Behavior-cloned Vision-Language-Action (VLA) driving policies struggle with rare rule-governed maneuvers at signalized intersections. Braking and launching examples contribute litt…
Multi-Modal Traffic Sign Detection with Semantic Attributes for Autonomous Driving
Meda Lazar, Sourab Sridhar, Shashwata Gupta +3
Reliable traffic sign detection is a prerequisite for the global deployment of autonomous driving systems, where regulatory compliance and road safety depend on perceiving signs co…
Neuromorphic LiDAR-based Bird's Eye View Object Detection using Energy-efficient Spiking Neural Networks
Sambit Mohapatra, Senthil Yogamani, Heinrich Gotzig +1
Autonomous driving perception demands accurate and efficient processing of three-dimensional sensor data under strict power constraints. Traditional convolutional neural networks a…
FishRoPE: Projective Rotary Position Embeddings for Omnidirectional Visual Perception
Rahul Ahuja, Mudit Jain, Bala Murali Manoghar Sai Sudhakar +4
Vision foundation models (VFMs) and Bird's Eye View (BEV) representation have advanced visual perception substantially, yet their internal spatial representations assume the rectil…
Sparse3DTrack: Monocular 3D Object Tracking Using Sparse Supervision
Nikhil Gosala, B. Ravi Kiran, Senthil Yogamani +1
Monocular 3D object tracking aims to estimate temporally consistent 3D object poses across video frames, enabling autonomous agents to reason about scene dynamics. However, existin…
Class-Incremental Motion Forecasting
Nicolas Schischka, Nikhil Gosala, B Ravi Kiran +2
Motion forecasting enables autonomous vehicles to anticipate scene evolution by predicting the future trajectories of dynamic agents. However, existing approaches typically assume…
MambaFusion: Adaptive State-Space Fusion for Multimodal 3D Object Detection
Venkatraman Narayanan, Bala Sai, Rahul Ahuja +3
Reliable 3D object detection is fundamental to autonomous driving, and multimodal fusion algorithms using cameras and LiDAR remain a persistent challenge. Cameras provide dense vis…
Dataset Safety in Autonomous Driving: Requirements, Risks, and Assurance
Alireza Abbaspour, Tejaskumar Balgonda Patil, B Ravi Kiran +2
Dataset integrity is fundamental to the safety and reliability of AI systems, especially in autonomous driving. This paper presents a structured framework for developing safe datas…
CleverDistiller: Simple and Spatially Consistent Cross-modal Distillation
Hariprasath Govindarajan, Maciej K. Wozniak, Marvin Klingner +3
Vision foundation models (VFMs) such as DINO have led to a paradigm shift in 2D camera-based perception towards extracting generalized features to support many downstream tasks. Re…
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving
Maciej K. Wozniak, Hariprasath Govindarajan, Marvin Klingner +3
Recent self-supervised clustering-based pre-training techniques like DINO and Cribo have shown impressive results for downstream detection and segmentation tasks. However, real-wor…
LetsMap: Unsupervised Representation Learning for Semantic BEV Mapping
Nikhil Gosala, Kürsat Petek, B Ravi Kiran +4
Semantic Bird's Eye View (BEV) maps offer a rich representation with strong occlusion reasoning for various decision making tasks in autonomous driving. However, most BEV mapping a…
FisheyeDetNet: 360° Surround view Fisheye Camera based Object Detection System for Autonomous Driving
Ganesh Sistu, Senthil Yogamani
Object detection is a mature problem in autonomous driving with pedestrian detection being one of the first deployed algorithms. It has been comprehensively studied in the literatu…
DaF-BEVSeg: Distortion-aware Fisheye Camera based Bird's Eye View Segmentation with Occlusion Reasoning
Senthil Yogamani, David Unger, Venkatraman Narayanan +1
Semantic segmentation is an effective way to perform scene understanding. Recently, segmentation in 3D Bird's Eye View (BEV) space has become popular as its directly used by drive…
Impact of Video Compression Artifacts on Fisheye Camera Visual Perception Tasks
Madhumitha Sakthi, Louis Kerofsky, Varun Ravi Kumar +1
Autonomous driving systems require extensive data collection schemes to cover the diverse scenarios needed for building a robust and safe system. The data volumes are in the order…
BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation
Jonas Schramm, Niclas Vödisch, Kürsat Petek +4
Semantic scene segmentation from a bird's-eye-view (BEV) perspective plays a crucial role in facilitating planning and decision-making for mobile robots. Although recent vision-onl…
Neural Rendering based Urban Scene Reconstruction for Autonomous Driving
Shihao Shen, Louis Kerofsky, Varun Ravi Kumar +1
Dense 3D reconstruction has many applications in automated driving including automated annotation validation, multimodal data augmentation, providing ground truth annotations for s…
Multi-camera Bird's Eye View Perception for Autonomous Driving
David Unger, Nikhil Gosala, Varun Ravi Kumar +3
Most automated driving systems comprise a diverse sensor set, including several cameras, Radars, and LiDARs, ensuring a complete 360°coverage in near and far regions. Unlike Radar…
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving
Sambit Mohapatra, Senthil Yogamani, Varun Ravi Kumar +3
LiDAR is crucial for robust 3D scene perception in autonomous driving. LiDAR perception has the largest body of literature after camera perception. However, multi-task learning acr…
X-Align++: cross-modal cross-view alignment for Bird's-eye-view segmentation
Shubhankar Borse, Senthil Yogamani, Marvin Klingner +4
Bird's-eye-view (BEV) grid is a typical representation of the perception of road components, e.g., drivable area, in autonomous driving. Most existing approaches rely on cameras on…
XKD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection
Marvin Klingner, Shubhankar Borse, Varun Ravi Kumar +4
Recent advances in 3D object detection (3DOD) have obtained remarkably strong results for LiDAR-based models. In contrast, surround-view 3DOD models based on multiple camera images…
Optical Flow for Autonomous Driving: Applications, Challenges and Improvements
Shihao Shen, Louis Kerofsky, Senthil Yogamani
Optical flow estimation is a well-studied topic for automated driving applications. Many outstanding optical flow estimation methods have been proposed, but they become erroneous w…
X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation
Shubhankar Borse, Marvin Klingner, Varun Ravi Kumar +4
Bird's-eye-view (BEV) grid is a common representation for the perception of road components, e.g., drivable area, in autonomous driving. Most existing approaches rely on cameras on…
Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge
Saravanabalagi Ramachandran, Ganesh Sistu, Varun Ravi Kumar +2
Object detection is a comprehensively studied problem in autonomous driving. However, it has been relatively less explored in the case of fisheye cameras. The strong radial distort…