9 accepted papers
The growing adoption of compact and cost-effective solid-state LiDARs has greatly advanced robotics. However, their inherently limited Field-of-View (FOV) hinders their application in tasks requiring wide-range depth perception. To overcome this limitation, we introduce the Low Coverage Depth Comple
Parameter-efficient fine-tuning has emerged as a promising paradigm in RGB-T tracking, enabling downstream task adaptation by freezing pretrained parameters and fine-tuning only a small set of parameters. This set forms a rank space made up of multiple individual ranks, whose expressiveness directly
3D object detection is a critical component of autonomous driving, yet its performance degrades severely in adverse weather due to the degradation of LiDAR point clouds. While existing LiDAR-4D radar fusion methods enhance robustness by incorporating weather-robust 4D radar data, they often depend o
Real-time dense depth completion has become a critical enabling technology for autonomous systems requiring instant environment perception, such as self-driving vehicles and robotics. While recent depth completion methods achieve remarkable accuracy, their computational complexity often exceeds the
"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 geometric information from a small number of points. Although increasing the spatial resolution of feature representation…
Visual localization, also known as camera pose estimation, is a crucial component of many applications, such as robotics, autonomous driving, and augmented reality. Traditional visual localization algorithms typically run on point cloud maps generated by algorithms such as Structure-from-Motion (SfM
Vision Transformer (ViT) has gained increasing attention in the computer vision community in recent years. However the core component of ViT Self-Attention lacks explicit spatial priors and bears a quadratic computational complexity thereby constraining the applicability of ViT. To alleviate these i…
Thanks to the excellent global modeling capability of attention mechanisms, the Vision Transformer has achieved better results than ConvNet in many computer tasks. However, in generating hierarchical feature maps, the Transformer still adopts the ConvNet feature aggregation scheme. This leads to the…
Visual localization plays a key role in various robot perception systems. Robust visual localization relies on reliable and repeatable local features to establish high quality point correspondences among images. This letter focuses on addressing two limitations of joint learning detector and descrip