Learning Stixel-based Instance Segmentation
| Autor: | Monty Santarossa, Claudius Zelenka, Uwe Franke, Lukas Schneider, Lars Schmarje, Reinhard Koch |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Pixel business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Deep learning Computer Science - Computer Vision and Pattern Recognition Point cloud Pattern recognition Unstructured data Image segmentation Image (mathematics) Minimum bounding box Segmentation Artificial intelligence business |
| Zdroj: | 2021 IEEE Intelligent Vehicles Symposium (IV). |
| Popis: | Stixels have been successfully applied to a wide range of vision tasks in autonomous driving, recently including instance segmentation. However, due to their sparse occurrence in the image, until now Stixels seldomly served as input for Deep Learning algorithms, restricting their utility for such approaches. In this work we present StixelPointNet, a novel method to perform fast instance segmentation directly on Stixels. By regarding the Stixel representation as unstructured data similar to point clouds, architectures like PointNet are able to learn features from Stixels. We use a bounding box detector to propose candidate instances, for which the relevant Stixels are extracted from the input image. On these Stixels, a PointNet models learns binary segmentations, which we then unify throughout the whole image in a final selection step. StixelPointNet achieves state-of-the-art performance on Stixel-level, is considerably faster than pixel-based segmentation methods, and shows that with our approach the Stixel domain can be introduced to many new 3D Deep Learning tasks. Comment: Accepted for publication in IEEE Intelligent Vehicles Symposium |
| Databáze: | OpenAIRE |
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