Reconstruct 3D objects even with open surfaces using unsigned distance functions with NeuralUDF

Reconstruct 3D objects even with open surfaces using unsigned distance functions with NeuralUDF

NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies
arXiv paper abstract https://arxiv.org/abs/2211.14173
arXiv PDF paper https://arxiv.org/pdf/2211.14173.pdf
Project page https://www.xxlong.site/NeuralUDF

… present a novel method, called NeuralUDF, for reconstructing surfaces with arbitrary topologies from 2D images via volume rendering.

Recent advances in neural rendering based reconstruction have achieved compelling results.

However, these methods are limited to objects with closed surfaces since they adopt Signed Distance Function (SDF) as surface representation which requires the target shape to be divided into inside and outside.

… propose to represent surfaces as the Unsigned Distance Function (UDF) and develop a new volume rendering scheme to learn the neural UDF representation.

Specifically, a new density function that correlates the property of UDF with the volume rendering scheme is introduced for robust optimization of the UDF fields.

… show that … method not only enables high-quality reconstruction of non-closed shapes with complex typologies, but also achieves comparable performance to the SDF based methods on the reconstruction of closed surfaces.

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I apply innovative technologies like machine learning, computer vision, and physics to further an organization's goals. Am recognized innovator with 66 patents.