6 papers
LiNeXt: Revisiting LiDAR Completion with Efficient Non-Diffusion Architectures
Wenzhe He, Xiaojun Chen, Ruiqi Wang +5
3D LiDAR scene completion from point clouds is a fundamental component of perception systems in autonomous vehicles. Previous methods have predominantly employed diffusion models f…
Self-Supervised Point Cloud Completion based on Multi-View Augmentations of Single Partial Point Cloud
Jingjing Lu, Huilong Pi, Yunchuan Qin +2
Point cloud completion aims to reconstruct complete shapes from partial observations. Although current methods have achieved remarkable performance, they still have some limitation…
VLScene: Vision-Language Guidance Distillation for Camera-Based 3D Semantic Scene Completion
Meng Wang, Huilong Pi, Ruihui Li +3
Camera-based 3D semantic scene completion (SSC) provides dense geometric and semantic perception for autonomous driving. However, images provide limited information making the mode…
Vision-based 3D Semantic Scene Completion via Capture Dynamic Representations
Meng Wang, Fan Wu, Yunchuan Qin +3
The vision-based semantic scene completion task aims to predict dense geometric and semantic 3D scene representations from 2D images. However, the presence of dynamic objects in th…
Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance
Meng Wang, Fan Wu, Ruihui Li +3
3D Semantic Scene Completion (SSC) provides comprehensive scene geometry and semantics for autonomous driving perception, which is crucial for enabling accurate and reliable decisi…
PAR: Prompt-Aware Token Reduction Method for Efficient Large Multimodal Models
Yingen Liu, Fan Wu, Ruihui Li +2
Multimodal large language models (MLLMs) demonstrate strong performance across visual tasks, but their efficiency is hindered by significant computational and memory demands from p…