76 citations · 102 across the 19 of their papers we have counts for
21 papers
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…
Continuous Online Extrinsic Calibration of Fisheye Camera and LiDAR
Jack Borer, Jeremy Tschirner, Florian Ölsner +1
Automated driving systems use multi-modal sensor suites to ensure the reliable, redundant and robust perception of the operating domain, for example camera and LiDAR. An accurate e…
LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation
Sambit Mohapatra, Mona Hodaei, Senthil Yogamani +5
Moving object detection and segmentation is an essential task in the Autonomous Driving pipeline. Detecting and isolating static and moving components of a vehicle's surroundings a…
SVDistNet: Self-Supervised Near-Field Distance Estimation on Surround View Fisheye Cameras
Varun Ravi Kumar, Marvin Klingner, Senthil Yogamani +4
A 360° perception of scene geometry is essential for automated driving, notably for parking and urban driving scenarios. Typically, it is achieved using surround-view fisheye camer…
BEVDetNet: Bird's Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving
Sambit Mohapatra, Senthil Yogamani, Heinrich Gotzig +2
3D object detection based on LiDAR point clouds is a crucial module in autonomous driving particularly for long range sensing. Most of the research is focused on achieving higher a…
OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving
Varun Ravi Kumar, Senthil Yogamani, Hazem Rashed +5
Surround View fisheye cameras are commonly deployed in automated driving for 360° near-field sensing around the vehicle. This work presents a multi-task visual perception network o…
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving
Varun Ravi Kumar, Marvin Klingner, Senthil Yogamani +3
State-of-the-art self-supervised learning approaches for monocular depth estimation usually suffer from scale ambiguity. They do not generalize well when applied on distance estima…
UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Models
Varun Ravi Kumar, Senthil Yogamani, Markus Bach +3
In classical computer vision, rectification is an integral part of multi-view depth estimation. It typically includes epipolar rectification and lens distortion correction. This pr…
StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks
Kai Fischer, Martin Simon, Florian Oelsner +3
Robust point cloud registration in real-time is an important prerequisite for many mapping and localization algorithms. Traditional methods like ICP tend to fail without good initi…
Improving Map Re-localization with Deep 'Movable' Objects Segmentation on 3D LiDAR Point Clouds
Victor Vaquero, Kai Fischer, Francesc Moreno-Noguer +2
Localization and Mapping is an essential component to enable Autonomous Vehicles navigation, and requires an accuracy exceeding that of commercial GPS-based systems. Current odomet…
FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving
Varun Ravi Kumar, Sandesh Athni Hiremath, Stefan Milz +4
Fisheye cameras are commonly used in applications like autonomous driving and surveillance to provide a large field of view (). However, they come at the cost of stro…
WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving
Senthil Yogamani, Ciaran Hughes, Jonathan Horgan +14
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of their prevalence…
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
Martin Simon, Karl Amende, Andrea Kraus +5
Accurate detection of 3D objects is a fundamental problem in computer vision and has an enormous impact on autonomous cars, augmented/virtual reality and many applications in robot…
Exploring Deep Spiking Neural Networks for Automated Driving Applications
Sambit Mohapatra, Heinrich Gotzig, Senthil Yogamani +2
Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, vi…
Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks
Maximilian Pöpperl, Raghavendra Gulagundi, Senthil Yogamani +1
Recently, realistic data augmentation using neural networks especially generative neural networks (GAN) has achieved outstanding results. The communities main research focus is vis…
Capsule Neural Network based Height Classification using Low-Cost Automotive Ultrasonic Sensors
Maximilian Pöpperl, Raghavendra Gulagundi, Senthil Yogamani +1
High performance ultrasonic sensor hardware is mainly used in medical applications. Although, the development in automotive scenarios is towards autonomous driving, the ultrasonic…
NeurAll: Towards a Unified Visual Perception Model for Automated Driving
Ganesh Sistu, Isabelle Leang, Sumanth Chennupati +4
Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SL…
Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks
Stefan Milz, Martin Simon, Kai Fischer +1
We present the first approach for 3D point-cloud to image translation based on conditional Generative Adversarial Networks (cGAN). The model handles multi-modal information sources…
Efficient Semantic Segmentation for Visual Bird's-eye View Interpretation
Timo Sämann, Karl Amende, Stefan Milz +3
The ability to perform semantic segmentation in real-time capable applications with limited hardware is of great importance. One such application is the interpretation of the visua…
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
Martin Simon, Stefan Milz, Karl Amende +1
Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and moti…
Monocular Fisheye Camera Depth Estimation Using Sparse LiDAR Supervision
Varun Ravi Kumar, Stefan Milz, Martin Simon +5
Near field depth estimation around a self driving car is an important function that can be achieved by four wide angle fisheye cameras having a field of view of over 180. Depth est…