Name
Title | ||
|---|---|---|
Improving Lidar-Based Semantic Segmentation of Top-View Grid Maps by Learning Features in Complementary Representations. |
Abstract | ||
|---|---|---|
In this paper we introduce a novel way to predict semantic information from sparse, single-shot LiDAR measurements in the context of autonomous driving. In particular, we fuse learned features from complementary representations. The approach is aimed specifically at improving the semantic segmentation of top-view grid maps. Towards this goal the 3D LiDAR point cloud is projected onto two orthogonal 2D representations. For each representation a tailored deep learning architecture is developed to effectively extract semantic information which are fused by a superordinate deep neural network. The contribution of this work is threefold: (1) We examine different stages within the segmentation network for fusion. (2) We quantify the impact of embedding different features. (3) We use the findings of this survey to design a tailored deep neural network architecture leveraging respective advantages of different representations. Our method is evaluated using the SemanticKITTI dataset which provides a point-wise semantic annotation of more than 23.000 LiDAR measurements. |
Year | Venue | DocType |
|---|---|---|
2021 | FUSION | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Bieder | 1 | 0 | 1.01 |
| Maximilian Link | 2 | 0 | 0.34 |
| Simon Romanski | 3 | 0 | 0.34 |
| Haohao Hu | 4 | 0 | 1.01 |
| Christoph Stiller | 5 | 0 | 0.68 |