  3D point cloud understanding is an important component in autonomous driving
and robotics. In this paper, we present a novel Embedding-Querying paradigm
(EQ- Paradigm) for 3D understanding tasks including detection, segmentation,
and classification. EQ-Paradigm is a unified paradigm that enables the
combination of any existing 3D backbone architectures with different task
heads. Under the EQ-Paradigm, the input is firstly encoded in the embedding
stage with an arbitrary feature extraction architecture, which is independent
of tasks and heads. Then, the querying stage enables the encoded features to be
applicable for diverse task heads. This is achieved by introducing an
intermediate representation, i.e., Q-representation, in the querying stage to
serve as a bridge between the embedding stage and task heads. We design a novel
Q- Net as the querying stage network. Extensive experimental results on various
3D tasks, including object detection, semantic segmentation and shape
classification, show that EQ-Paradigm in tandem with Q-Net is a general and
effective pipeline, which enables a flexible collaboration of backbones and
heads, and further boosts the performance of the state-of-the-art methods.
Codes and models are available at
https://github.com/dvlab-research/DeepVision3D.
