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Trajectory Flow: Geometry-Constrained Surface Reconstruction via Unsigned Distance Fields
IEEE Transactions on Visualization and Computer Graphics
|August 6, 2026
Summary
This study introduces a new method for reconstructing 3D surfaces from point clouds using trajectory flow, improving accuracy and robustness in unsigned distance field (UDF) reconstruction.
Area of Science:
- Computer Vision
- Geometric Deep Learning
- 3D Reconstruction
Background:
- Neural implicit methods reconstruct surfaces using distance fields.
- Current unsigned distance field (UDF) methods struggle with sparse data due to local error accumulation.
Purpose of the Study:
- To develop a more robust and accurate method for UDF reconstruction from point clouds.
- To overcome limitations of local regression in current self-supervised UDF methods.
Main Methods:
- Reformulated UDF learning as a geometrically constrained trajectory-evolution problem.
- Introduced a bidirectional linear flow for shortest-path trajectory modeling.
- Developed a flow-guided iterative densification strategy for improved point-to-surface projection.
Main Results:
- Achieved state-of-the-art performance in reconstruction accuracy.
- Demonstrated superior robustness against sparse and non-uniform sampling.
- Validated through extensive experiments on synthetic and real-world data.
Conclusions:
- The trajectory-flow approach offers a globally consistent and geometrically stable alternative to local regression for UDF learning.
- This method effectively addresses challenges posed by sparse sampling in 3D reconstruction.
- The proposed technique advances the field of neural implicit surface reconstruction.
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