Single image 3D reconstruction is an important but challenging task that requires extensive
knowledge of our natural world. Many existing methods solve this problem by optimizing a …

Natural language processing and 2D vision models have attained remarkable proficiency on
many tasks primarily by escalating the scale of training data. However, 3D vision tasks have …

The unprecedented advancements in Large Language Models (LLMs) have shown a
profound impact on natural language processing but are yet to fully embrace the realm of 3D …

Recent advancements in multimodal pre-training have shown promising efficacy in 3D
representation learning by aligning multimodal features across 3D shapes their 2D …

Large-kernel convolutional neural networks (ConvNets) have recently received extensive
research attention but two unresolved and critical issues demand further investigation. 1) …

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

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Large vision-language models have achieved outstanding performance, but their size and
computational requirements make their deployment on resource-constrained devices and …

Abstract 3D vision-language (3D-VL) grounding, which aims to align language with 3D
physical environments, stands as a cornerstone in develo** embodied agents. In …

We present a novel alignment-before-generation approach to tackle the challenging task of
generating general 3D shapes based on 2D images or texts. Directly learning a conditional …