What question did this study set out to answer?

This research aims to improve neural implicit surface reconstruction in complex, uncontrolled environments.

December 21, 2025Open Access

Geometric Prior-Guided Neural Implicit Surface Reconstruction in the Wild

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Our method improves surface reconstruction accuracy in uncontrolled environments using digital preservation techniques and geometric constraints.

This research aims to improve neural implicit surface reconstruction in complex, uncontrolled environments.
Applied geometric constraints to implicit surface optimization.
Utilized sparse 3D points from structure-from-motion for signed distance function refinement.
Employed normal priors and filtering techniques for improved surface alignment.
Achieved superior accuracy and granularity in surface reconstructions compared to existing methods.
Successfully reconstructed 3D geometries of various landmarks from in-the-wild images.

Xiang et al. (2025) studied this question.

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December 21, 2025Open Access

View Full Paper

Ask AI

Bookmark

Authors

Our method improves surface reconstruction accuracy in uncontrolled environments using digital preservation techniques and geometric constraints.

This research aims to improve neural implicit surface reconstruction in complex, uncontrolled environments.
Applied geometric constraints to implicit surface optimization.
Utilized sparse 3D points from structure-from-motion for signed distance function refinement.
Employed normal priors and filtering techniques for improved surface alignment.
Achieved superior accuracy and granularity in surface reconstructions compared to existing methods.
Successfully reconstructed 3D geometries of various landmarks from in-the-wild images.

Xiang et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark