**3DSSD** is a point-based 3D single stage object detection detector. In this paradigm, all upsampling layers and refinement stage, which are indispensable in all existing point-based methods, are abandoned to reduce the large computation cost. The authors propose a fusion sampling strategy in the downsampling process to make detection on less representative points feasible. A delicate box prediction network including a candidate generation layer, an anchor-free regression head with a 3D center-ness assignment strategy is designed to meet the needs of accuracy and speed.

**Q-Learning** is an off-policy temporal difference control algorithm:

$$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} + \gamma\max\_{a}Q\left(S\_{t+1}, a\right) - Q\left(S\_{t}, A\_{t}\right)\right] $$

The learned action-value function $Q$ directly approximates $q\_{*}$, the optimal action-value function, independent of the policy being followed.

Source: Sutton and Barto, Reinforcement Learning, 2nd Edition

Q-Learning

3DSSD

3DSSD: Point-based 3D Single Stage Object Detector

**TradeS** is an online joint detection and tracking model, coined as TRACK to DEtect and Segment, exploiting tracking clues to assist detection end-to-end. TraDeS infers object tracking offset by a cost volume, which is used to propagate previous object features for improving current object detection and segmentation.

Source	3DSSD: Point-based 3D Single Stage Object Detector
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com