Hey stell mich dar!Andreas Eitel Autonomous Intelligent Systems University of Freiburg

Info DeepTemporalSeg: Temporally Consistent Semantic Segmentation of 3D LiDAR Scans This website presents our work on semantic segmentation of a 3D LiDAR scan. We propose a new architecture called DBLiDARNet. The architecture is based on dense blocks and to limit the number of learnable parameters we use depth separable convolution in the decoder. To make predictions temporally consistent we propose a Bayes filter approach. The filter recursively estimates the current semantic state of a point by using prediction from current and previous scans. Your browser does not support the video tag. Publication Ayush Dewan, Wolfram Burgard

DeepTemporalSeg: Temporally Consistent Semantic Segmentation of 3D LiDAR Scans



Download BibTeX @article{dewan-deeptemporalseg, author = {Ayush Dewan, Wolfram Burgard}, title = {DeepTemporalSeg: Temporally Consistent Semantic Segmentation of 3D LiDAR Scans }, booktitle = {https://arxiv.org/abs/1906.06962}, year = {2019}, url = {http://deep-temporal-seg.informatik.uni-freiburg.de/dewan_deep_temporal_seg.pdf} } Code and Dataset We provide the code and the datasets for our proposed architecture. For more details please refer to the We provide the code and the datasets for our proposed architecture. For more details please refer to the GitHub page.