Complete point clouds with geometry-aware transformers with AdaPoinTr
Complete point clouds with geometry-aware transformers with AdaPoinTr
AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformers
arXiv paper abstract https://arxiv.org/abs/2301.04545
arXiv PDF paper https://arxiv.org/pdf/2301.04545.pdf
... present a new method that reformulates point cloud completion as a set-to-set translation problem and design a new model, called PoinTr, which adopts a Transformer encoder-decoder architecture for point cloud completion.
By representing the point cloud as a set of unordered groups of points with position embeddings, ... convert the input data to a sequence of point proxies and employ the Transformers for generation.
... The migration of Transformers enables ... model to better learn structural knowledge and preserve detailed information for point cloud completion.
Taking a step towards more complicated and diverse situations, ... further propose AdaPoinTr by developing an adaptive query generation mechanism and designing a novel denoising task during completing a point cloud.
Coupling these two techniques enables us to train the model efficiently and effectively: ... reduce training time (by 15x or more) and improve completion performance (over 20%).
... demonstrate the effectiveness ... surpassing other work by a large margin and establishing new state-of-the-arts ... with higher throughputs and fewer FLOPs ...
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