Annotation-Free Curb Detection Leveraging Altitude Difference Image
<|reference_start|>Annotation-Free Curb Detection Leveraging Altitude Difference Image: Road curbs are considered as one of the crucial and ubiquitous traffic features, which are essential for ensuring the safety of autonomous vehicles. Current methods for detecting curbs primarily rely on camera imagery or LiDAR point clouds. Image-based methods are vulnerable to fluctuations in lighting conditions and exhibit poor robustness, while methods based on point clouds circumvent the issues associated with lighting variations. However, it is the typical case that significant processing delays are encountered due to the voluminous amount of 3D points contained in each frame of the point cloud data. Furthermore, the inherently unstructured characteristics of point clouds poses challenges for integrating the latest deep learning advancements into point cloud data applications. To address these issues, this work proposes an annotation-free curb detection method leveraging Altitude Difference Image (ADI), which effectively mitigates the aforementioned challenges. Given that methods based on deep learning generally demand extensive, manually annotated datasets, which are both expensive and labor-intensive to create, we present an Automatic Curb Annotator (ACA) module. This module utilizes a deterministic curb detection algorithm to automatically generate a vast quantity of training data. Consequently, it facilitates the training of the curb detection model without necessitating any manual annotation of data. Finally, by incorporating a post-processing module, we manage to achieve state-of-the-art results on the KITTI 3D curb dataset with considerably reduced processing delays compared to existing methods, which underscores the effectiveness of our approach in curb detection tasks.<|reference_end|>
@article{ma2024annotation-free,
    title={Annotation-Free Curb Detection Leveraging Altitude Difference Image},
    author={Fulong Ma, Peng Hou, Yuxuan Liu, Ming Liu, and Jun Ma},
    journal={arXiv preprint arXiv:2409.20171},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20171},
    primaryClass={cs.CV}
}