UniGeo: A Unified 3D Indoor Object Detection Framework Integrating Geometry-Aware Learning and Dynamic Channel Gating

TL;DR

UniGeo is a novel 3D indoor object detection framework that combines geometry-aware learning and dynamic channel gating to improve feature representation and detection accuracy in sparse point cloud scenes.

Contribution

It introduces a geometry-aware learning module and a dynamic channel gating mechanism, addressing geometric modeling and feature enhancement in 3D point cloud detection.

Findings

Outperforms existing methods on six indoor datasets.

Effectively models geometric relationships in sparse scenes.

Enhances feature representation with adaptive channel weighting.

Abstract

The growing adoption of robotics and augmented reality in real-world applications has driven considerable research interest in 3D object detection based on point clouds. While previous methods address unified training across multiple datasets, they fail to model geometric relationships in sparse point cloud scenes and ignore the feature distribution in significant areas, which ultimately restricts their performance. To deal with this issue, a unified 3D indoor detection framework, called UniGeo, is proposed. To model geometric relations in scenes, we first propose a geometry-aware learning module that establishes a learnable mapping from spatial relationships to feature weights, which enabes explicit geometric feature enhancement. Then, to further enhance point cloud feature representation, we propose a dynamic channel gating mechanism that leverages learnable channel-wise weighting. This mechanism adaptively optimizes features generated by the sparse 3D U-Net network, significantly enhancing key geometric information. Extensive experiments on six different indoor scene datasets clearly validate the superior performance of our method.