3D object detection is vital for many robotics applications. For tasks where
a 2D perspective range image exists, we propose to learn a 3D representation
directly from this range image view. To this end, we designed a 2D
convolutional network architecture that carries the 3D spherical coordinates of
each pixel throughout the network. Its layers can consume any arbitrary
convolution kernel in place of the default inner product kernel and exploit the
underlying local geometry around each pixel. We outline four such kernels: a
dense kernel according to the bag-of-words paradigm, and three graph kernels
inspired by recent graph neural network advances: the Transformer, the
PointNet, and the Edge Convolution. We also explore cross-modality fusion with
the camera image, facilitated by operating in the perspective range image view.
Our method performs competitively on the Waymo Open Dataset and improves the
state-of-the-art AP for pedestrian detection from 69.7% to 75.5%. It is also
efficient in that our smallest model, which still outperforms the popular
PointPillars in quality, requires 180 times fewer FLOPS and model parameters

该研究提出了一种基于 2D 透视范围图像的卷积神经网络架构方法，可用于直接从范围图像视图学习 3D 表示。通过利用本地几何和跨模态融合处理，该方法在行人检测方面的 AP 从 69.7％提高到 75.5％，同时模型参数和操作速度均表现良好。