PlaneSeg: Building a Plug-In for Boosting Planar Region Segmentation.

Area of Science:

• Computer Vision
• Machine Learning
• Geometric Deep Learning

Background:

• Traditional planar region segmentation methods struggle with imprecise boundaries and difficulty in detecting small regions.
• These limitations hinder performance in downstream tasks like 3D reconstruction and depth prediction.

Purpose of the Study:

• To introduce PlaneSeg, an end-to-end framework designed to improve planar region segmentation.
• To address the limitations of existing methods by enhancing boundary accuracy and small object detection.

Main Methods:

• PlaneSeg integrates three modules: edge feature extraction for refined boundaries, a multiscale module for comprehensive spatial and semantic information, and a resolution-adaptation module for detailed feature fusion.
• Edge-aware feature maps are generated to constrain segmentation accuracy.
• Pairwise feature fusion is employed to resample pixels and extract finer details.

Main Results:

• PlaneSeg demonstrates superior performance compared to state-of-the-art methods on plane segmentation tasks.
• The framework achieves significant improvements in 3D plane reconstruction and depth prediction.
• Extensive experiments validate the effectiveness of PlaneSeg across multiple downstream applications.

Conclusions:

• PlaneSeg offers a robust and versatile solution for planar region segmentation.
• The framework effectively overcomes the challenges of vague boundaries and small object detection.
• PlaneSeg's integration capability and performance advancements make it a valuable tool for various computer vision applications.