27 citations · 59 across the 19 of their papers we have counts for
9 papers · 1 filter
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…
Efficient Brood Cell Detection in Layer Trap Nests for Bees and Wasps: Balancing Labeling Effort and Species Coverage
Chenchang Liu, Felix Fornoff, Annika Grasreiner +3
Monitoring cavity-nesting wild bees and wasps is vital for biodiversity research and conservation. Layer trap nests (LTNs) are emerging as a valuable tool to study the abundance an…
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…
SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving
Sambit Mohapatra, Thomas Mesquida, Mona Hodaei +3
Spiking Neural Networks are a recent and new neural network design approach that promises tremendous improvements in power efficiency, computation efficiency, and processing latenc…
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…