For robots to perform a wide variety of tasks, they require a 3D representation of the world
that is semantically rich, yet compact and efficient for task-driven perception and planning …

This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM)
designed for embodied interaction, exploring a universal 3D object understanding with 3D …

We introduce Open3DIS a novel solution designed to tackle the problem of Open-
Vocabulary Instance Segmentation within 3D scenes. Objects within 3D environments …

We propose a lightweight and scalable Regional Point-Language Contrastive learning
framework namely RegionPLC for open-world 3D scene understanding aiming to identify …

In this work, we introduce OpenIns3D, a new 3D-input-only framework for 3D open-
vocabulary scene understanding. The OpenIns3D framework employs a “Mask-Snap …

Prior studies on 3D scene understanding have primarily developed specialized models for
specific tasks or required task-specific fine-tuning. In this study, we propose Grounded 3D …

There is a sensory gulf between the Earth that humans inhabit and the digital realms in
which modern AI agents are created. To develop AI agents that can sense, think, and act as …

In this paper, we investigate the use of diffusion models which are pre-trained on large-scale
image-caption pairs for open-vocabulary 3D semantic understanding. We propose a novel …

Rapid advancements in 3D vision-language (3D-VL) tasks have opened up new avenues
for human interaction with embodied agents or robots using natural language. Despite this …

3D open-vocabulary scene understanding aims to recognize arbitrary novel categories
beyond the base label space. However, existing works not only fail to fully utilize all the …