**Fast Voxel Query** is a module used in the [Voxel Transformer](https://paperswithcode.com/method/votr) 3D object detection model implementation of self-attention, specifically Local and Dilated Attention. For each querying index $v\_{i}$, an attending voxel index $v\_{j}$ is determined by Local and Dilated Attention. Then we can lookup the non-empty index $j$ in the hash table with hashed $v\_{j}$ as the key. Finally, the non-empty index $j$ is used to gather the attending feature $f\_{j}$ from $\mathcal{F}$ for [multi-head attention](https://paperswithcode.com/method/multi-head-attention).

A **Region Proposal Network**, or **RPN**, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals. RPN and algorithms like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) can be merged into a single network by sharing their convolutional features - using the recently popular terminology of neural networks with attention mechanisms, the RPN component tells the unified network where to look.

RPNs are designed to efficiently predict region proposals with a wide range of scales and aspect ratios. RPNs use anchor boxes that serve as references at multiple scales and aspect ratios. The scheme can be thought of as a pyramid of regression references, which avoids enumerating images or filters of multiple scales or aspect ratios.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Fast Voxel Query

Voxel Transformer for 3D Object Detection

**Fast R-CNN** is an object detection model that improves in its predecessor [R-CNN](https://paperswithcode.com/method/r-cnn) in a number of ways. Instead of extracting CNN features independently for each region of interest, Fast R-CNN aggregates them into a single forward pass over the image; i.e. regions of interest from the same image share computation and memory in the forward and backward passes.

Source	Voxel Transformer for 3D Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com