LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual
  Preferences
Authors: name: Hongyan Zhi, name: Peihao Chen, name: Junyan Li, name: Shuailei Ma, name: Xinyu Sun, name: Tianhang Xiang, name: Yinjie Lei, name: Mingkui Tan, name: Chuang Gan

Research on 3D Vision-Language Models (3D-VLMs) is gaining increasing
attention, which is crucial for developing embodied AI within 3D scenes, such
as visual navigation and embodied question answering. Due to the high density
of visual features, especially in large 3D scenes, accurately locating
task-relevant visual information is challenging. Existing works attempt to
segment all objects and consider their features as scene representations.
However, these task-agnostic object features include much redundant information
and missing details for the task-relevant area. To tackle these problems, we
propose LSceneLLM, an adaptive framework that automatically identifies
task-relevant areas by leveraging LLM's visual preference for different tasks,
followed by a plug-and-play scene magnifier module to capture fine-grained
details in focused areas. Specifically, a dense token selector examines the
attention map of LLM to identify visual preferences for the instruction input.
It then magnifies fine-grained details of the focusing area. An adaptive
self-attention module is leveraged to fuse the coarse-grained and selected
fine-grained visual information. To comprehensively evaluate the large scene
understanding ability of 3D-VLMs, we further introduce a cross-room
understanding benchmark, XR-Scene, which contains a series of large scene
understanding tasks including XR-QA, XR-EmbodiedPlanning, and XR-SceneCaption.
Experiments show that our method surpasses existing methods on both large scene
understanding and existing scene understanding benchmarks. Plunging our scene
magnifier module into the existing 3D-VLMs also brings significant improvement.