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Xiaoxu Meng

4 accepted papers

2026

DualPrim: Compact 3D Reconstruction with Positive and Negative Primitives

CVPR 2026

We present Compact 3D Reconstruction with Positive and Negative Primitives (DualPrim), a novel approach for reconstructing compact and topologically regular 3D meshes from multi-view images. Unlike traditional methods that rely on implicit representations such as signed distance functions, or explic

Cited by 0SourceScholar
2023

NeAT: Learning Neural Implicit Surfaces With Arbitrary Topologies From Multi-View Images

CVPR 2023poster

Recent progress in neural implicit functions has set new state-of-the-art in reconstructing high-fidelity 3D shapes from a collection of images. However, these approaches are limited to closed surfaces as they require the surface to be represented by a signed distance field. In this paper, we propos…

2023

NeUDF: Leaning Neural Unsigned Distance Fields With Volume Rendering

CVPR 2023poster

Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in neural implicit surface rendering. However, existing methods based on signed distance function (SDF) are limited to closed surfaces, failing to reconstruct a wide range of real-world objects that cont…

Cited by 58SourcePDFScholar
2022

HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies

NeurIPS 2022accept

Neural implicit function based on signed distance field (SDF) has achieved impressive progress in reconstructing 3D models with high fidelity. However, such approaches can only represent closed shapes. Recent works based on unsigned distance function (UDF) are proposed to handle both watertight and…

Cited by 21SourcePDFScholar