To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

We present Point-BERT, a novel paradigm for learning Transformers to generalize the
concept of BERT onto 3D point cloud. Following BERT, we devise a Masked Point Modeling …

LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point-
or voxel-based methods as they often yield better performance than the traditional range …

Neural networks are prone to overfitting and memorizing data patterns. To avoid over-fitting
and enhance their generalization and performance, various methods have been suggested …

Point clouds can be represented in many forms (views), typically, point-based sets, voxel-
based cells or range-based images (ie, panoramic view). The point-based view is …

Abstract 3D object detection is an important task in autonomous driving to perceive the
surroundings. Despite the excellent performance, the existing 3D detectors lack the …

At present, effectively aggregating and transferring the local features of point cloud is still an
unresolved technological conundrum. In this study, we propose a new space-cover …

Cattle detection is an important task in precision livestock farming, but it remains challenging
due to the varying appearance and poses of cattle in different scenarios. In this paper, we …

Deep neural networks on 3D point cloud data have been widely used in the real world,
especially in safety-critical applications. However, their robustness against corruptions is …

Majority of research in learning based methods has been towards designing and training
networks for specific tasks. However, many of the learning based tasks, across modalities …