English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

3D scene understanding has gained significant attention due to its wide rangeof applications. However, existing methods for 3D scene understanding arelimited to specific downstream tasks, which hinders their practicality inreal-world applications. This paper presents Chat-3D, which combines the 3Dvisual perceptual ability of pre-trained 3D representations and the impressivereasoning and conversation capabilities of advanced LLMs to achieve the firstuniversal dialogue systems for 3D scenes. Specifically, we align 3Drepresentations into the feature space of LLMs, thus enabling LLMs to perceivethe 3D world. Given the scarcity of 3D scene-text data, we propose athree-stage training strategy to efficiently utilize the available data forbetter alignment. To enhance the reasoning ability and develop a user-friendlyinteraction scheme, we further construct a high-quality object-centric 3Dinstruction dataset and design an associated object-centric prompt. Ourexperiments show that Chat-3D achieves an impressive ability to comprehenddiverse instructions for 3D scenes, engage in intricate spatial reasoning, andincorporate external knowledge into its responses. Chat-3D achieves a 75.6%relative score compared with GPT-4 on the constructed instruction dataset.

Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes