PQ-Transformer
PointQuad-Transformer
**PQ-Transformer**, or **PointQuad-Transformer**, is a [Transformer](https://paperswithcode.com/method/transformer)-based architecture that predicts 3D objects and layouts simultaneously, using point cloud inputs. Unlike existing methods that either estimate layout keypoints or edges, room layouts are directly parameterized as a set of quads. Along with the quad representation, a physical constraint loss function is used that discourages object-layout interference.

Given an input 3D point cloud of $N$ points, the point cloud feature learning backbone extracts $M$ context-aware point features of $\left(3+C\right)$ dimensions, through sampling and grouping. A voting module and a farthest point sampling (FPS) module are used to generate $K\_{1}$ object proposals and $K\_{2}$ quad proposals respectively. Then the proposals are processed by a transformer decoder to further refine proposal features. Through several feedforward layers and non-maximum suppression (NMS), the proposals become the final object bounding boxes and layout quads.
PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds
https://paperswithcode.com/paper/pq-transformer-jointly-parsing-3d-objects-and
PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds