5 accepted papers
Audio serves as an important bridge connecting humans to their surroundings, providing a unique modality for perceiving the world. For embodied AI systems, such as robots and autonomous vehicles, enabling them to understand the world through sound is a promising and significant research direction. I…
Vision-Language Pre-training models (VLMs) have emerged as a highly promising solution to the generative problem, achieving remarkable success in the field of 2D image generation. However, extending these 2D paradigms to 3D domains is still unexplored due to the scarcity of text-3D pairs and shape a…
3D open-vocabulary semantic segmentation is a challenge in the task of 3D scene understanding, as most current models trained on closed-set datasets struggle to effectively identify categories that were not seen during training. To address this, we introduce a framework called LSWKD. It distills kno…
Fast and efficient semantic segmentation of large-scale LiDAR point clouds is a fundamental problem in autonomous driving. To achieve this goal, the existing point-based methods mainly choose to adopt Random Sampling strategy to process large-scale point clouds. However, our quantative and qualitati…
Self-attention mechanism recently achieves impressive advancement in Natural Language Processing (NLP) and Image Processing domains. Its permutation invariance property makes it ideally suitable for point cloud processing. Inspired by this remarkable success, we propose an end-to-end architecture, d