SPAC-Net: Rethinking Point Cloud Completion With Structural Prior.

Area of Science:

• Computer Vision
• 3D Shape Analysis
• Geometric Deep Learning

Background:

• Point cloud completion aims to reconstruct complete 3D shapes from partial data.
• Existing encoder-decoder methods often lose fine details due to feature abstraction.
• A need exists for methods that better preserve structural information during completion.

Purpose of the Study:

• To propose a novel framework, SPAC-Net, for improved point cloud completion.
• To introduce and leverage a new structural prior called 'interface' for guiding the completion process.
• To enhance the preservation of fine details in reconstructed 3D shapes.

Main Methods:

• Developed the Marginal Detector (MAD) module to identify the 'interface' between observed and missing parts.
• Proposed a coarse shape prediction method based on interface point displacement.
• Introduced a Structure Supplement (SSP) module to enhance structural details before upsampling.

Main Results:

• SPAC-Net significantly outperforms existing state-of-the-art methods on challenging benchmarks.
• The proposed interface prior effectively guides the completion process.
• The MAD and SSP modules contribute to superior detail preservation.

Conclusions:

• SPAC-Net offers a more effective approach to 3D point cloud completion by utilizing structural priors.
• The framework successfully addresses the detail loss issue prevalent in prior methods.
• The method demonstrates strong performance and potential for various 3D reconstruction applications.