5 accepted papers
Recently, open-vocabulary semantic segmentation has garnered growing attention. Most current methods leverage vision-language models like CLIP to recognize unseen categories through their zero-shot capabilities. However, CLIP struggles to establish potential spatial dependencies among scene objects…
Enhancing the cross-domain generalization of 3D semantic segmentation is a pivotal task in computer vision that has recently gained increasing attention. Most existing methods, whether using consistency regularization or cross-modal feature fusion, focus solely on individual objects while overlookin…
Visual simultaneous localization and mapping (VSLAM) has broad applications, with state-of-the-art methods leveraging deep neural networks for better robustness and applicability. However, there is a lack of research in fusing these learning-based methods with multi-sensor information, which could b
Accurate, continuous and seamless state estimation is the fundamental module for intelligent navigation applications, such as self-driving cars and autonomous robots. However, it is often difficult for a standalone sensor to fulfill the demanding requirements of precise navigation in complex scenari
High-definition (HD) map is crucial for intelligent vehicles to perform high-level localization and navigation. To improve the availability and usability of HD map, it is meaningful to investigate crowd-sourced mapping solutions and low-cost map-aided localization schemes which don't rely on high-en