Methods › Computer Vision › Point Cloud Models › PQ-Transformer
PointQuad-Transformer
PQ-Transformer
Introduced by Xiaoxue Chen et al. in PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
PQ-Transformer, or PointQuad-Transformer, is a 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 (3+C) dimensions, through sampling and grouping. A voting module and a farthest point sampling (FPS) module are used to generate K₁ object proposals and K₂ 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.
Papers archive 2025-07-28
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PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds 12 Sep 2021 · 1 repository · arXiv:2109.05566
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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