# Imagine3D-LLM

The work introduces Imagine3D-LLM: a small set of learnable Gaussian summary tokens is appended after the image tokens, decoded into a compact 3D Gaussian Splatting representation, and trained jointly with a photometric reconstruction loss and standard next-token prediction, so that a multi-view multimodal LLM assembles a coarse 3D scene representation before answering; it consistently outperforms prior approaches across seven spatial reasoning and 3D scene understanding benchmarks, reaching 68.5 average on SPAR-Bench and surpassing the strongest spatially-aware baseline 3DThinker-7B by 5.2 points.

The work introduces Imagine3D-LLM: a small set of learnable Gaussian summary tokens is appended after the image tokens, decoded into a compact 3D Gaussian Splatting representation, and trained jointly with a photometric reconstruction loss and standard next-token prediction, so that a multi-view multimodal LLM assembles a coarse 3D scene representation before answering; it consistently outperforms prior approaches across seven spatial reasoning and 3D scene understanding benchmarks, reaching 68.5 average on SPAR-Bench and surpassing the strongest spatially-aware baseline 3DThinker-7B by 5.2 points.