Semantic segmentation of 3D point cloud is a key task in numerous intelligent transportation system applications, e.g. self-driving vehicles, traffic monitoring. Due to the sparsity and varying density of points in the outdoor point clouds, it becomes particularly challenging to extract object-centric features from data. This leads to poor semantic segmentation, especially for the rare object classes. To address that, we introduce the first-ever Slot Attention Transformer based technique to effectively model object-centric features in point cloud data. Our method uses cylindrical splits of space for voxelization and computes channel-wise positional embeddings before repetitively encoding the point cloud with slot attentions. Our second major contribution is a Large-Scale Outdoor Point Cloud dataset (SWAN), collected in a dense urban environment, driving 150km distance. It provides 16 billion points in more than 200K frames. The dataset also provides annotations for 10K frames for 24 classes. We also contribute a data augmentation scheme to handle rare object classes in real-world point clouds. Besides benchmarking popular existing methods on SWAN for the first time, we thoroughly evaluate our technique on the existing large-scale datasets, Semantic KITTI and nuScenes. Our results demonstrate a consistent performance gain for our technique, and verify the need of the more challenging SWAN dataset.
|Number of pages||11|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Publication status||Published - 1 May 2023|