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Updated: Sep 11, 2025
11:34
High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
Published on: December 3, 2013
15.7K
Propagating Sparse Depth via Depth Foundation Model for Out-of-Distribution Depth Completion
Summary
This study introduces a robust depth completion framework using foundation models to enhance sparse depth maps. It achieves high performance in out-of-distribution scenarios without extensive training.
Area of Science:
- Computer Vision
- Machine Learning
Background:
- Depth completion is crucial for reconstructing dense depth maps from sparse data.
- Existing methods struggle with out-of-distribution (OOD) scenarios due to limited training data.
Purpose of the Study:
- To develop a robust depth completion framework leveraging foundation models.
- To improve performance in OOD scenarios without requiring large-scale training.
Main Methods:
- Utilized a depth foundation model to extract environmental cues (structural, semantic) from RGB images.
- Implemented a parameter-free dual-space propagation (3D and 2D) for accurate depth information transfer.
- Introduced a learnable correction module for refining intricate structures and depth predictions.
Main Results:
- Achieved remarkable robustness in OOD scenarios.
- Outperformed state-of-the-art depth completion methods on 16 diverse datasets.
- Demonstrated effective guidance of sparse depth information using environmental cues.
Conclusions:
- The proposed framework offers a robust and efficient solution for depth completion.
- Leveraging foundation models is a promising direction for enhancing model generalization.
- The dual-space propagation and correction module effectively maintain geometric structure and accuracy.
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