**PolarNet** is an improved grid representation for online, single-scan LiDAR point clouds. Instead of using common spherical or bird's-eye-view projection, the polar bird's-eye-view representation balances the points across grid cells in a polar coordinate system, indirectly aligning a segmentation network's attention with the long-tailed distribution of the points along the radial axis.

**Fast AutoAugment** is an image data augmentation algorithm that finds effective augmentation policies via a search strategy based on density matching, motivated by Bayesian DA. The strategy is to improve the generalization performance of a given network by learning the augmentation policies which treat augmented data as missing data points of training data. However, different from Bayesian DA, the proposed method recovers those missing data points by the exploitation-and-exploration of a family of inference-time augmentations via Bayesian optimization in the policy search phase. This is realized by using an efficient density matching algorithm that does not require any back-propagation for network training for each policy evaluation.

Fast AutoAugment

PolarNet

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

In the context of image enhancement, maximizing NIMA score as a prior can increase the likelihood of enhancing perceptual quality of an image.

Source	PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com