Search Results for author: Stefan Milz

Found 19 papers, 3 papers with code

BEVDetNet: Bird's Eye View LiDAR Point Cloud based Real-time 3D Object Detection for Autonomous Driving

no code implementations21 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.

3D Object Detection Autonomous Driving +2

Improving Map Re-localization with Deep 'Movable' Objects Segmentation on 3D LiDAR Point Clouds

no code implementations8 Oct 2019 Victor Vaquero, Kai Fischer, Francesc Moreno-Noguer, Alberto Sanfeliu, Stefan Milz

Results show that we are able to accurately re-locate over a filtered map, consistently reducing trajectory errors between an average of 35. 1% with respect to a non-filtered map version and of 47. 9% with respect to a standalone map created on the current session.

Autonomous Vehicles

Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds

no code implementations16 Apr 2019 Martin Simon, Karl Amende, Andrea Kraus, Jens Honer, Timo Sämann, Hauke Kaulbersch, Stefan Milz, Horst Michael Gross

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 robotics.

3D Object Detection Autonomous Driving +3

Points2Pix: 3D Point-Cloud to Image Translation using conditional Generative Adversarial Networks

no code implementations26 Jan 2019 Stefan Milz, Martin Simon, Kai Fischer, Maximillian Pöpperl

The generator is capable of processing three conditions, whereas the point-cloud is encoded as raw point-set and camera projection.

object-detection Object Detection +1

Exploring Deep Spiking Neural Networks for Automated Driving Applications

no code implementations11 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.

Depth Estimation Moving Object Detection +3

Efficient Semantic Segmentation for Visual Bird's-eye View Interpretation

no code implementations29 Nov 2018 Timo Sämann, Karl Amende, Stefan Milz, Christian Witt, Martin Simon, Johannes Petzold

The ability to perform semantic segmentation in real-time capable applications with limited hardware is of great importance.

Semantic Segmentation

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

9 code implementations16 Mar 2018 Martin Simon, Stefan Milz, Karl Amende, Horst-Michael Gross

We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only.

3D Object Detection Autonomous Driving +4

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