Toward Class-Agnostic Tracking Using Feature Decorrelation in Point Clouds.

Single object tracking in point clouds has been attracting more and more attention owing to the presence of LiDAR sensors in 3D vision. However, existing methods based on deep neural networks mainly focus on training different models for different categories, which makes them unable to perform well in real-world applications when encountering classes unseen during the training phase. In this work, we investigate a more challenging task in LiDAR point clouds, namely class-agnostic tracking, where a general model is supposed to be learned to handle targets of both observed and unseen categories.

Support-Free Hollowing.

Offsetting-based hollowing is a solid modeling operation widely used in 3D printing, which can change the model's physical properties and reduce the weight by generating voids inside a model. However, a hollowing operation can lead to additional supporting structures for fabrication in interior voids, which cannot be removed. As a consequence, the result of a hollowing operation is affected by these additional supporting structures when applying the operation to optimize physical properties of different models.