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arXiv · 2507.11971

HPR3D: Hierarchical Proxy Representation for High-Fidelity 3D Reconstruction and Controllable Editing

Tielong Wang·Yuxuan Xiong·Jinfan Liu·Zhifan Zhang·Ye Chen·Yue Shi·Bingbing Ni
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Abstract

Current 3D representations like meshes, voxels, point clouds, and NeRF-based neural implicit fields exhibit significant limitations: they are often task-specific, lacking universal applicability across reconstruction, generation, editing, and driving. While meshes offer high precision, their dense vertex data complicates editing; NeRFs deliver excellent rendering but suffer from structural ambiguity, hindering animation and manipulation; all representations inherently struggle with the trade-off between data complexity and fidelity. To overcome these issues, we introduce a novel 3D Hierarchical Proxy Node representation. Its core innovation lies in representing an object's shape and texture via a sparse set of hierarchically organized (tree-structured) proxy nodes distributed on its surface and interior. Each node stores local shape and texture information (implicitly encoded by a small MLP) within its neighborhood. Querying any 3D coordinate's properties involves efficient neural interpolation and lightweight decoding from relevant nearby and parent nodes. This framework yields a highly compact representation where nodes align with local semantics, enabling direct drag-and-edit manipulation, and offers scalable quality-complexity control. Extensive experiments across 3D reconstruction and editing demonstrate our method's expressive efficiency, high-fidelity rendering quality, and superior editability.

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Tielong Wang, Yuxuan Xiong, Jinfan Liu, Zhifan Zhang, Ye Chen, Yue Shi, Bingbing Ni. 2025-07-16. HPR3D: Hierarchical Proxy Representation for High-Fidelity 3D Reconstruction and Controllable Editing. https://arxiv.org/abs/2507.11971

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