Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

Existing state-of-the-art 3D instance segmentation methods perform semantic segmentation
followed by grouping. The hard predictions are made when performing semantic …

In this work, we present a new multi-view depth estimation method that utilizes both
conventional SfM reconstruction and learning-based priors over the recently proposed …

Arguably one of the top success stories of deep learning is transfer learning. The finding that
pre-training a network on a rich source set (eg, ImageNet) can help boost performance once …

Feature learning for 3D object detection from point clouds is very challenging due to the
irregularity of 3D point cloud data. In this paper, we propose Pointformer, a Transformer …

We present a novel two-stage fully sparse convolutional 3D object detection framework,
named CAGroup3D. Our proposed method first generates some high-quality 3D proposals …

Abstract We present NeRF-Det, a novel method for indoor 3D detection with posed RGB
images as input. Unlike existing indoor 3D detection methods that struggle to model scene …

The rapid progress in 3D scene understanding has come with growing demand for data;
however, collecting and annotating 3D scenes (eg point clouds) are notoriously hard. For …

3D object detection from LiDAR point cloud is a challenging problem in 3D scene
understanding and has many practical applications. In this paper, we extend our preliminary …