UniGeo通过几何感知与动态通道门控，提升点云场景下的3D物体检测精度。
UniGeo: A Unified 3D Indoor Object Detection Framework Integrating Geometry-Aware Learning and Dynamic Channel Gating
- 引入几何感知模块，将空间关系映射为特征权重，显式增强几何特征。
- 设计动态通道门控机制，自适应优化稀疏点云特征，提升关键信息表达。
- 在6个室内数据集上表现优异，适合高精度3D检测应用场景。
随着机器人和增强现实技术在真实场景中的广泛应用，基于点云的3D物体检测成为研究热点。尽管已有方法实现多数据集统一训练，但仍未能有效建模稀疏点云场景中的几何关系，且忽略显著区域的特征分布，制约了性能提升。为此，本文提出统一的3D室内检测框架UniGeo。首先，设计几何感知学习模块，建立空间关系到特征权重的可学习映射，实现显式的几何特征增强；其次，提出动态通道门控机制，通过可学习的通道加权策略，自适应优化由稀疏3D U-Net生成的特征，显著强化关键几何信息。在六个不同室内场景数据集上的大量实验验证了该方法的优越性能。
原文摘要 · Abstract (English)
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.
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