VoxRep: Enhancing 3D Spatial Understanding in 2D Vision-Language Models via Voxel Representation
This addresses the problem of 3D environment comprehension for domains like robotics and autonomous navigation, representing an incremental improvement by adapting existing 2D VLMs to voxel data.
The paper tackled the challenge of extracting high-level semantic meaning from voxel grids for 3D spatial understanding by proposing a method that slices voxel data into 2D slices and processes them with a Vision-Language Model, enabling efficient 3D semantic understanding without complex 3D networks.
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.