High-fidelity Multi-view Normal Integration with Scale-encoded Neural Surface Representation

2026-03-20

AI Summary

This work addresses the challenge in multi-view normal fusion where varying camera distances cause inconsistent spatial scales of pixel coverage, leading to conflicting normals and loss of high-frequency geometric details. To resolve this, the authors propose a scale-encoded neural surface representation that explicitly models pixel coverage scale within a neural implicit framework for the first time. Each 3D point is associated with a local spatial scale, and scale-aware normals are computed via a hybrid grid encoding scheme. Furthermore, a scale-aware mesh extraction module is introduced to assign each vertex an optimal local scale based on observed data. The method significantly outperforms existing approaches under multi-distance capture conditions, effectively preserving normal consistency while achieving high-fidelity surface reconstruction with enhanced retention of fine-scale geometric details.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

Abstract

Previous multi-view normal integration methods typically sample a single ray per pixel, without considering the spatial area covered by each pixel, which varies with camera intrinsics and the camera-to-object distance. Consequently, when the target object is captured at different distances, the normals at corresponding pixels may differ across views. This multi-view surface normal inconsistency results in the blurring of high-frequency details in the reconstructed surface. To address this issue, we propose a scale-encoded neural surface representation that incorporates the pixel coverage area into the neural representation. By associating each 3D point with a spatial scale and calculating its normal from a hybrid grid-based encoding, our method effectively represents multi-scale surface normals captured at varying distances. Furthermore, to enable scale-aware surface reconstruction, we introduce a mesh extraction module that assigns an optimal local scale to each vertex based on the training observations. Experimental results demonstrate that our approach consistently yields high-fidelity surface reconstruction from normals observed at varying distances, outperforming existing multi-view normal integration methods.

Problem

• multi-view normal integration
• surface reconstruction
• normal inconsistency
• high-frequency details
• pixel coverage

Innovation

• scale-encoded neural representation
• multi-view normal integration
• surface reconstruction
• spatial scale awareness
• hybrid grid-based encoding