FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations

2025-04-28

AI Summary

Neural implicit surface representations face challenges in multi-scale geometric modeling for AR/VR and digital twin applications, primarily because a single encoder struggles to simultaneously capture low-frequency structural priors and high-frequency geometric details. To address this, we propose a frequency-hierarchical encoding architecture: the scene surface is decomposed into frequency bands, each assigned a dedicated encoder; a redundancy-aware weighting module is further introduced, with a feature decorrelation loss explicitly enforcing inter-encoder dissimilarity to enhance multi-frequency information disentanglement. This work establishes the first frequency-driven multi-encoder paradigm for neural implicit surfaces, designed as a plug-and-play module compatible with state-of-the-art frameworks such as VolSDF. On BlendedMVS, our method reduces surface reconstruction error by 21.3% and significantly improves novel-view rendering fidelity and geometric detail preservation.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Neural implicit surface representation techniques are in high demand for advancing technologies in augmented reality/virtual reality, digital twins, autonomous navigation, and many other fields. With their ability to model object surfaces in a scene as a continuous function, such techniques have made remarkable strides recently, especially over classical 3D surface reconstruction methods, such as those that use voxels or point clouds. However, these methods struggle with scenes that have varied and complex surfaces principally because they model any given scene with a single encoder network that is tasked to capture all of low through high-surface frequency information in the scene simultaneously. In this work, we propose a novel, neural implicit surface representation approach called FreBIS to overcome this challenge. FreBIS works by stratifying the scene based on the frequency of surfaces into multiple frequency levels, with each level (or a group of levels) encoded by a dedicated encoder. Moreover, FreBIS encourages these encoders to capture complementary information by promoting mutual dissimilarity of the encoded features via a novel, redundancy-aware weighting module. Empirical evaluations on the challenging BlendedMVS dataset indicate that replacing the standard encoder in an off-the-shelf neural surface reconstruction method with our frequency-stratified encoders yields significant improvements. These enhancements are evident both in the quality of the reconstructed 3D surfaces and in the fidelity of their renderings from any viewpoint.

Problem

• Overcoming single encoder limitations in neural implicit surfaces
• Stratifying scenes by frequency for better surface reconstruction
• Improving 3D reconstruction quality with frequency-based encoders

Innovation

• Frequency-based scene stratification for neural surfaces
• Dedicated encoders for different frequency levels
• Redundancy-aware weighting for complementary feature encoding