Point clouds captured by different sensors such as RGB-D cameras and LiDAR
possess non-negligible domain gaps. Most existing methods design different
network architectures and train separately on point clouds from various
sensors. Typically, point-based methods achieve outstanding performances on
even-distributed dense point clouds from RGB-D cameras, while voxel-based
methods are more efficient for large-range sparse LiDAR point clouds. In this
paper, we propose geometry-to-voxel auxiliary learning to enable voxel
representations to access point-level geometric information, which supports
better generalisation of the voxel-based backbone with additional
interpretations of multi-sensor point clouds. Specifically, we construct
hierarchical geometry pools generated by a voxel-guided dynamic point network,
which efficiently provide auxiliary fine-grained geometric information adapted
to different stages of voxel features. We conduct experiments on joint
multi-sensor datasets to demonstrate the effectiveness of GeoAuxNet. Enjoying
elaborate geometric information, our method outperforms other models
collectively trained on multi-sensor datasets, and achieve competitive results
with the-state-of-art experts on each single dataset.

使用几何 - 体素辅助学习的方法，通过提供对多传感器点云的附加解释，使得体素表示可以获取点级几何信息，并支持体素骨干在多传感器点云上更好的泛化能力。我们的方法在联合多传感器数据集上表现出色，超越了其他模型，并在每个单一数据集上与现有的专家方法取得了竞争性结果。