Multi-HexPlanes: A Lightweight Map Representation for Rendering and 3D Reconstruction

Jianhao Zheng, Gábor Valasek, Daniel Barath, Iro Armeni; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 2021-2031

Creating maps of the world around us is paramount to many applications including those related to robotics such as navigation and inspection. Given the computational resource limitations typical of robotic platforms there is a pressing need for lightweight 3D representations that capture detailed texture and geometric information with minimal storage. Traditional voxel-based approaches require substantial memory resources. On the other hand neural implicit and 3D Gaussian splatting representations require significant computational power (GPUs) and can hardly run in real-time. In this paper we introduce a novel scene representation Multi-HexPlanes that divides 3D environments into large boxes and utilizes the faces of the boxes to encapsulate texture and geometric information. This representation reduces the memory requirement to store the map making our approach especially suitable for systems with limited memory. Through extensive evaluations on large-scale datasets we find that our method achieves better performance on rendering and more complete 3D reconstruction. We also demonstrate that our map representation can output dense feature points with rich geometric information for downstream tasks such as training 3D Gaussian splats. The proposed technique promises substantial improvements in real-time 3D mapping applications particularly for devices constrained by processing power and storage.