no code implementations • 17 Jul 2023 • Sambit Mohapatra, Senthil Yogamani, Varun Ravi Kumar, Stefan Milz, Heinrich Gotzig, Patrick Mäder
We achieve state-of-the-art results for two tasks, semantic and motion segmentation, and close to state-of-the-art performance for 3D object detection.
no code implementations • 6 Jun 2022 • Sambit Mohapatra, Thomas Mesquida, Mona Hodaei, Senthil Yogamani, Heinrich Gotzig, Patrick Mader
Spiking Neural Networks are a recent and new neural network design approach that promises tremendous improvements in power efficiency, computation efficiency, and processing latency.
no code implementations • 8 Nov 2021 • Sambit Mohapatra, Mona Hodaei, Senthil Yogamani, Stefan Milz, Heinrich Gotzig, Martin Simon, Hazem Rashed, Patrick Maeder
To the best of our knowledge, this is the first work directly performing motion segmentation in LiDAR BEV space.
no code implementations • 21 Apr 2021 • Sambit Mohapatra, Senthil Yogamani, Heinrich Gotzig, Stefan Milz, Patrick Mader
Most of the research is focused on achieving higher accuracy and these models are not optimized for deployment on embedded systems from the perspective of latency and power efficiency.
no code implementations • 11 Jan 2019 • Sambit Mohapatra, Heinrich Gotzig, Senthil Yogamani, Stefan Milz, Raoul Zollner
Neural networks have become the standard model for various computer vision tasks in automated driving including semantic segmentation, moving object detection, depth estimation, visual odometry, etc.