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

TL;DR
HPR3D introduces a hierarchical proxy node framework for 3D reconstruction that balances fidelity, compactness, and editability, enabling high-quality rendering and intuitive editing of 3D objects.
Contribution
The paper presents a novel hierarchical proxy node representation for 3D data, improving universality, editability, and scalability over existing methods.
Findings
Achieves high-fidelity 3D reconstruction and rendering.
Enables direct, intuitive editing of 3D shapes.
Demonstrates superior performance in reconstruction and editing tasks.
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…
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Taxonomy
TopicsImage Processing and 3D Reconstruction · Computer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis
