Being data-driven is one of the most iconic properties of deep learning algorithms. The birth
of ImageNet drives a remarkable trend of" learning from large-scale data" in computer vision …

Autonomous driving techniques have been flourishing in recent years while thirsting for
huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up …

Abstract We introduce Equivariant Neural Field Expectation Maximization (EFEM), a simple,
effective, and robust geometric algorithm that can segment objects in 3D scenes without …

Masked Modeling (MM) has demonstrated widespread success in various vision challenges,
by reconstructing masked visual patches. Yet, applying MM for large-scale 3D scenes …

We introduce SAMPro3D for zero-shot 3D indoor scene segmentation. Given the 3D point
cloud and multiple posed 2D frames of 3D scenes, our approach segments 3D scenes by …

Point cloud-based 3D small object detection is crucial for autonomous driving and smart
ships. The current 3D object detection mainly relies on object global features derived from …

Abstract 3D referring expression comprehension is a task to ground text representations
onto objects in 3D scenes. It is a crucial task for indoor household robots or augmented …

In this paper, we propose an efficient feature pruning strategy for 3D small object detection.
Conventional 3D object detection methods struggle on small objects due to the weak …

Adapting deep learning networks for point cloud data recognition in self-driving vehicles
faces challenges due to the variability in datasets and sensor technologies, emphasizing the …

3D object instance segmentation plays a vital role in various applications such as
autonomous driving, robotics and virtual reality. However, tabletop scenes exhibit diverse …