VoxRep: Enhancing 3D Spatial Understanding in 2D Vision-Language Models via Voxel Representation

• URL: http://arxiv.org/abs/2503.21214v1
• Date: Thu, 27 Mar 2025 07:07:11 GMT
• Title: VoxRep: Enhancing 3D Spatial Understanding in 2D Vision-Language Models via Voxel Representation
• Authors: Alan Dao, Norapat Buppodom,
• Abstract summary: Voxel grids offer a structured representation of 3D space, but extracting high-level semantic meaning remains challenging.<n>This paper proposes a novel approach utilizing a Vision-Language Model (VLM) to extract "voxel semantics"-object identity, color, and location-from voxel data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Comprehending 3D environments is vital for intelligent systems in domains like robotics and autonomous navigation. Voxel grids offer a structured representation of 3D space, but extracting high-level semantic meaning remains challenging. This paper proposes a novel approach utilizing a Vision-Language Model (VLM) to extract "voxel semantics"-object identity, color, and location-from voxel data. Critically, instead of employing complex 3D networks, our method processes the voxel space by systematically slicing it along a primary axis (e.g., the Z-axis, analogous to CT scan slices). These 2D slices are then formatted and sequentially fed into the image encoder of a standard VLM. The model learns to aggregate information across slices and correlate spatial patterns with semantic concepts provided by the language component. This slice-based strategy aims to leverage the power of pre-trained 2D VLMs for efficient 3D semantic understanding directly from voxel representations.

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