Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

Object detection, as of one the most fundamental and challenging problems in computer
vision, has received great attention in recent years. Over the past two decades, we have …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, eg, via
range projection, is an effective and popular approach. These projection-based methods …

Advanced 3D object detection methods usually rely on large-scale, elaborately labeled
datasets to achieve good performance. However, labeling the bounding boxes for the 3D …

Transformers have been at the heart of the Natural Language Processing (NLP) and
Computer Vision (CV) revolutions. The significant success in NLP and CV inspired exploring …

Abstract We present a Multimodal Interlaced Transformer (MIT) that jointly considers 2D and
3D data for weakly supervised point cloud segmentation. Research studies have shown that …

As a special type of transformer, vision transformers (ViTs) can be used for various computer
vision (CV) applications. Convolutional neural networks (CNNs) have several potential …

The success of the transformer architecture in natural language processing has recently
triggered attention in the computer vision field. The transformer has been used as a …

This paper describes an attention-driven approach to 3-D point cloud sampling. We
establish our method based on a structure-aware attention discriminant analysis that …