**Panoptic-PolarNet** is a point cloud segmentation framework for LiDAR point clouds. It learns both semantic segmentation and class-agnostic instance clustering in a single inference network using a polar Bird's Eye View (BEV) representation, enabling the authors to circumvent the issue of occlusion among instances in urban street scenes. We first encode the raw point cloud data with $K$ features into a fixed-size representation on the polar BEV map. Next, we use a single backbone encoder-decoder network to generate semantic prediction, center [heatmap](https://paperswithcode.com/method/heatmap) and offset regression. Finally, we merge these outputs via a voting-based fusion to yield the panoptic segmentation result.

**TSRUp**, or **Transformation-based Spatial Recurrent Unit p**, is a modification of a [ConvGRU](https://paperswithcode.com/method/cgru) used in the [TriVD-GAN](https://paperswithcode.com/method/trivd-gan) architecture for video generation.

It largely follows [TSRUc](https://paperswithcode.com/method/tsruc), but computes $\theta$, $u$ and $c$ in parallel given $x\_{t}$ and $h\_{t−1}$, yielding the following replacement for the $c$ update equation:

$$ c = \rho\left(W\_{c} \star\_{n}\left[h\_{t-1}; x\_{t}\right] + b\_{c} \right) $$

In these equations $\sigma$ and $\rho$ are the elementwise sigmoid and [ReLU](https://paperswithcode.com/method/relu) functions respectively and the $\star\_{n}$ represents a [convolution](https://paperswithcode.com/method/convolution) with a kernel of size $n \times n$. Brackets are used to represent a feature concatenation.

TSRUp

Transformation-based Adversarial Video Prediction on Large-Scale Data

Panoptic-PolarNet

Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation

**GPT-2** is a [Transformer](https://paperswithcode.com/methods/category/transformers) architecture that was notable for its size (1.5 billion parameters) on its release. The model is pretrained on a WebText dataset - text from 45 million website links. It largely follows the previous [GPT](https://paperswithcode.com/method/gpt) architecture with some modifications:

- [Layer normalization](https://paperswithcode.com/method/layer-normalization) is moved to the input of each sub-block, similar to a
pre-activation residual network and an additional layer normalization was added after the final self-attention block. 

- A modified initialization which accounts for the accumulation on the residual path with model depth
is used. Weights of residual layers are scaled at initialization by a factor of $1/\sqrt{N}$ where $N$ is the number of residual layers. 

- The vocabulary is expanded to 50,257. The context size is expanded from 512 to 1024 tokens and
a larger batch size of 512 is used.

Source	Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com