We propose SNI-SLAM a semantic SLAM system utilizing neural implicit representation that
simultaneously performs accurate semantic mapping high-quality surface reconstruction and …

3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving and robotics. It has received significant attention from the …

Multi-modal 3D scene understanding has gained considerable attention due to its wide
applications in many areas, such as autonomous driving and human-computer interaction …

Image modality is not perfect as it often fails in certain conditions, eg, night and fast motion.
This significantly limits the robustness and versatility of existing multi-modal (ie, Image+ X) …

Fusing an arbitrary number of modalities is vital for achieving robust multi-modal fusion of
semantic segmentation yet remains less explored to date. Recent endeavors regard RGB …

Abstract 4D panoptic segmentation is a challenging but practically useful task that requires
every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and …

Training deep models for LiDAR semantic segmentation is challenging due to the inherent
sparsity of point clouds. Utilizing temporal data is a natural remedy against the sparsity …

Combining LiDAR points and images for robust semantic segmentation has shown great
potential. However, the heterogeneity between the two modalities (eg the density, the field of …

Leveraging information across diverse modalities is known to enhance performance on
multimodal segmentation tasks. However, effectively fusing information from different …

Abstract Self-Driving Vehicles or Autonomous Driving (AD) have emerged as the prime field
of research in Artificial Intelligence and Machine Learning of late. The indicated market …