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An integrated framework for surface reconstruction from incomplete point clouds based on constraint feedforward

Yuxiang Leng¹, Youyuan Wang¹, Yaqi Nie¹

¹State Key Laboratory of Power transmission equipment Technology, Chongqing University, 400044, Chongqing, China.

Neural Networks : the Official Journal of the International Neural Network Society

|June 23, 2026

Summary

This summary is machine-generated.

This study introduces a new framework for 3D point cloud surface reconstruction, improving accuracy for intelligent transportation and industrial uses by filling gaps and holes effectively.

Keywords:

Completion Incomplete point cloud Surface reconstruction UDF Upsampling

Related Experiment Videos

Area of Science:

Computer Vision
Geometric Modeling
3D Reconstruction

Background:

3D point cloud surface reconstruction is vital for intelligent transportation and industrial applications.
Existing methods struggle with local point cloud gaps caused by disturbances and intrinsic structural holes, impacting accuracy.

Purpose of the Study:

To develop an integrated framework for robust 3D point cloud surface reconstruction.
To enhance reconstruction accuracy and surface continuity in the presence of data gaps and holes.

Main Methods:

Propose an integrated framework using constraint feedforward migration for point completion and surface realization.
Introduce a gradient-constrained surface reconstruction approach utilizing unsigned distance function (UDF) level sets.

Main Results:

Demonstrated superior performance on public, electrical equipment, and autonomous driving (KITTI) datasets.
Validated robustness in handling local point cloud gaps and structural holes.

Conclusions:

The proposed method significantly improves 3D point cloud surface reconstruction accuracy and robustness.
The framework shows strong potential for real-world applications in smart electrical systems and intelligent transportation.