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Patch-Grid: An Efficient and Feature-Preserving Neural Implicit Surface Representation

delete2025-04-26
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PRE
AI
G
Guying Lin
L
Lei Yang
C
Congyi Zhang
H
Hao Pan
Y
Yuhan Ping
G
Guodong Wei
T
Taku Komura
J
John Keyser
W
Wenping Wang
DOI:10.1145/3727142delete
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Abstract

Abstract

En 中文
Neural implicit representations are increasingly used to depict threedimensional (3D) shapes owing to their inherent smoothness and compactness, contrasting with traditional discrete representations. Yet, the multilayer perceptron-based neural representation, because of its smooth nature, rounds sharp corners or edges, rendering it unsuitable for representing objects with sharp features like computer-aided design (CAD) models. Moreover, neural implicit representations need long training times to fit 3D shapes. While previous works address these issues separately, we present a unified neural implicit representation called Patch-Grid, which efficiently fits complex shapes, preserves sharp features delineating different patches, and can also represent surfaces with open boundaries and thin geometric features. Patch-Grid learns a signed distance field (SDF) to approximate an encompassing surface patch of the shape with a learnable patch feature volume. To form sharp edges and corners in a CAD model, Patch-Grid merges the learned SDFs via the constructive solid geometry (CSG) approach. Core to the merging process is a novel merge grid design that organizes different patch feature volumes in a common octree structure. This design choice ensures robust merging of multiple learned SDFs by confining the CSG operations to localized regions. Additionally, it drastically reduces the complexity of the CSG operations in each merging cell, allowing the proposed method to be trained in seconds to fit a complex shape at high fidelity. Experimental results demonstrate that the proposed Patch-Grid representation is capable of accurately reconstructing shapes with complex sharp features, open boundaries, and thin geometric elements, achieving state-of-the-art reconstruction quality with high computational efficiency within seconds.
Keywords:
Neural implicit surface representation
Neural signed distance field
Constructive solid geometry

Journal

ACM Transactions on Graphics cover
ACM Transactions on Graphics
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M
Microsoft Research Asia
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TransGP
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Univ Hong Kong
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