Core Concepts
Scene-LLM enhances interactive planning in 3D scenes by integrating egocentric and scene-level information.
Abstract
This paper introduces Scene-LLM, a model that combines egocentric and scene-level 3D visual information for interactive planning. It addresses the limitations of existing models in handling dynamic scenes. The model's architecture, training strategies, and performance on various benchmarks are discussed.
Introduction
- Scene-LLM integrates Large Language Models (LLMs) with 3D visual understanding.
- Existing models struggle with dynamic 3D scenes, prompting the need for Scene-LLM.
Data Extraction Techniques
- Hybrid 3D visual feature representation is used to align dense spatial information effectively.
- A projection layer is employed to integrate textual and visual features efficiently.
Performance Evaluation
- Scene-LLM excels in dense captioning, question answering, and interactive planning tasks.
- Empirical evaluations demonstrate state-of-the-art results on various benchmarks.
Stats
Scene-LLMは、密なキャプショニング、質問応答、およびインタラクティブな計画において強力な能力を示す。
ベンチマークで最先端の結果を達成した。
Quotes
"Scene-LLM demonstrates superior performance over other methods in most metrics."
"Empirical evaluations demonstrate that Scene-LLM excels in a wide range of 3D scene reasoning tasks."