The proposed self-supervised geometric augmentation tasks can be effectively extended to other 3D vision tasks, such as 3D object detection and segmentation, by adapting the core principles of geometric invariance and relational learning to the specific requirements of these tasks.
3D Object Detection: In object detection, the goal is to identify and localize objects within a 3D space. The translation distance prediction task can be modified to predict the shifts in bounding box centers due to occlusions or noise, thereby enhancing the model's robustness against variations in object positioning. Additionally, the relational learning approach can be adapted to consider the spatial relationships between detected objects, allowing the model to learn from the interactions and relative positions of multiple objects in a scene. This can improve the accuracy of object localization and classification by providing contextual information.
3D Segmentation: For segmentation tasks, where the objective is to classify each point in a point cloud, the self-supervised geometric augmentation can be tailored to focus on local geometric features. The translation distance prediction can be applied to segments of point clouds, helping to maintain consistency in segment boundaries despite occlusions or noise. Furthermore, relational learning can be utilized to capture the relationships between points within the same segment and across different segments, enhancing the model's ability to distinguish between closely located objects or parts of the same object. This can lead to improved segmentation accuracy by leveraging the geometric context of points.
Generalization Across Tasks: The integration of self-supervised tasks that focus on geometric invariance can facilitate the transfer of learned representations across different 3D vision tasks. By training models on a variety of tasks using similar self-supervised techniques, the learned features can become more robust and generalizable, ultimately improving performance in diverse applications such as robotics, augmented reality, and autonomous driving.