HVLF: A Holistic Visual Localization Framework Across Diverse Scenes.

Area of Science:

• Computer Vision
• Machine Learning

Background:

• Multitask learning (MTL) in scene coordinate regression (SCoRe) shows promise for visual localization.
• Existing frameworks struggle with rigid weight activation, limiting concurrent capture of universal and specific scene features.
• Simple network architectures contribute to insufficient feature representation.

Purpose of the Study:

• To develop a holistic framework (HVLF) for flexible identification of scene-universal and scene-specific attributes.
• To enhance feature representation using integrated attention mechanisms for improved visual localization.
• To address limitations in feature extraction and network architecture in current SCoRe techniques.

Main Methods:

• HVLF employs a soft weight activation strategy (SWAS) with polyhedral convolution for concurrent optimization of scene-shared and scene-specific weights.
• A mixed attention perception module (MAPM) integrates channelwise, spatialwise, and elementwise attention for multilevel feature fusion.
• The framework aims to improve the network's comprehensive scene perception and feature discrimination capabilities.

Main Results:

• HVLF achieves impressive localization performance on both indoor and outdoor datasets.
• Experiments demonstrate the universality of SWAS and MAPM, showing they can be integrated into other methods.
• The proposed techniques contribute to more precise scene coordinate regression.

Conclusions:

• HVLF effectively addresses limitations in existing SCoRe frameworks by enabling flexible attribute identification and enhanced feature representation.
• The developed soft weight activation strategy and mixed attention perception module offer significant improvements in visual localization.
• The universality of HVLF's components suggests broad applicability in computer vision tasks like 3-D object detection and feature matching.