Enhancing 3D semantic scene completion with refinement module.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Semantic Scene Completion (SSC) is crucial for understanding 3D environments.
• Existing SSC models often produce coarse predictions that require refinement.
• Integrating refinement modules into SSC pipelines can improve performance.

Purpose of the Study:

• To introduce ESSC-RM, a plug-and-play framework for enhancing Semantic Scene Completion.
• To improve the accuracy and performance of existing SSC models through a novel refinement process.
• To demonstrate the general applicability of ESSC-RM across different SSC architectures.

Main Methods:

• ESSC-RM employs a two-phase approach: initial coarse prediction followed by refinement.
• The refinement utilizes a 3D U-Net-based Prediction Noise-Aware Module (PNAM) and Voxel-level Local Geometry Module (VLGM).
• Multiscale supervision is applied during the refinement phase.

Main Results:

• ESSC-RM consistently improves semantic prediction performance on the SemanticKITTI dataset.
• Integration with CGFormer resulted in a mean IoU increase from 16.87% to 17.27%.
• Integration with MonoScene led to a mean IoU increase from 11.08% to 11.51%.

Conclusions:

• ESSC-RM effectively refines semantic scene completion predictions.
• The framework is a general solution applicable to various SSC models.
• ESSC-RM offers a significant improvement in semantic prediction accuracy.