Resolution Where It Counts: Hash-based GPU-Accelerated 3D Reconstruction via Variance-Adaptive Voxel Grids

2025-11-26

AI Summary

To address low memory efficiency, high computational overhead, and limited GPU support in real-time, resource-constrained 3D surface reconstruction, this paper proposes a variance-adaptive multi-resolution voxel grid. It dynamically adjusts voxel size based on local Signed Distance Function (SDF) observation variance and employs a flattened spatial hash for constant-time memory access and full GPU parallelism. Unlike conventional octrees, it integrates a parallel quadtree to regulate Gaussian lattice density. The key innovation lies in the first tight integration of variance-driven adaptive voxel partitioning with hash-based indexing, significantly improving scalability and rendering efficiency. Our open-source implementation achieves up to 13× speedup and 4× memory reduction over fixed-resolution baselines, while maintaining comparable reconstruction accuracy—enabling real-time, high-performance 3D reconstruction and rendering.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

Efficient and scalable 3D surface reconstruction from range data remains a core challenge in computer graphics and vision, particularly in real-time and resource-constrained scenarios. Traditional volumetric methods based on fixed-resolution voxel grids or hierarchical structures like octrees often suffer from memory inefficiency, computational overhead, and a lack of GPU support. We propose a novel variance-adaptive, multi-resolution voxel grid that dynamically adjusts voxel size based on the local variance of signed distance field (SDF) observations. Unlike prior multi-resolution approaches that rely on recursive octree structures, our method leverages a flat spatial hash table to store all voxel blocks, supporting constant-time access and full GPU parallelism. This design enables high memory efficiency and real-time scalability. We further demonstrate how our representation supports GPU-accelerated rendering through a parallel quad-tree structure for Gaussian Splatting, enabling effective control over splat density. Our open-source CUDA/C++ implementation achieves up to 13x speedup and 4x lower memory usage compared to fixed-resolution baselines, while maintaining on par results in terms of reconstruction accuracy, offering a practical and extensible solution for high-performance 3D reconstruction.

Problem

• Dynamic voxel size adjustment using local SDF variance
• Eliminating memory inefficiency in fixed-resolution volumetric methods
• Enabling GPU parallelism through flat spatial hash tables

Innovation

• Variance-adaptive voxel grid adjusting resolution dynamically
• Flat spatial hash table enabling constant-time GPU access
• Parallel quad-tree structure for GPU-accelerated Gaussian Splatting