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 …

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 …

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 …

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 …

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 …

Deep neural network models have achieved remarkable progress in 3D scene
understanding while trained in the closed-set setting and with full labels. However, the major …

In this paper, we investigate Open-Vocabulary 3D Instance Segmentation (OV-3DIS) with
free-form language instructions. Earlier works that rely on only annotated base categories for …