**Point-GNN** is a graph neural network for detecting objects from a LiDAR point cloud. It predicts the category and shape of the object that each vertex in the graph belongs to. In Point-GNN, there is an auto-registration mechanism to reduce translation variance, as well as a box merging and scoring operation to combine detections from multiple vertices accurately.

**$n$-step Returns** are used for value function estimation in reinforcement learning. Specifically, for $n$ steps we can write the complete return as:

$$ R\_{t}^{(n)} = r\_{t+1} + \gamma{r}\_{t+2} + \cdots + \gamma^{n-1}\_{t+n} + \gamma^{n}V\_{t}\left(s\_{t+n}\right) $$

We can then write an $n$-step backup, in the style of TD learning, as:

$$ \Delta{V}\_{r}\left(s\_{t}\right) = \alpha\left[R\_{t}^{(n)} - V\_{t}\left(s\_{t}\right)\right] $$

Multi-step returns often lead to faster learning with suitably tuned $n$.

Image Credit: Sutton and Barto, Reinforcement Learning

N-step Returns

Point-GNN

Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud

A **R(2+1)D** convolutional neural network is a network for action recognition that employs [R(2+1)D](https://paperswithcode.com/method/2-1-d-convolution) convolutions in a [ResNet](https://paperswithcode.com/method/resnet) inspired architecture. The use of these convolutions over regular [3D Convolutions](https://paperswithcode.com/method/3d-convolution) reduces computational complexity, prevents overfitting, and introduces more non-linearities that allow for a better functional relationship to be modeled.

Source	Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com