In this paper, we 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, we convert the input data to a sequence of
point proxies and employ the Transformers for generation. To facilitate
Transformers to better leverage the inductive bias about 3D geometric
structures of point clouds, we further devise a geometry-aware block that
models the local geometric relationships explicitly. The migration of
Transformers enables our model to better learn structural knowledge and
preserve detailed information for point cloud completion. Taking a step towards
more complicated and diverse situations, we 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: we reduce training
time (by 15x or more) and improve completion performance (over 20%). We also
show our method can be extended to the scene-level point cloud completion
scenario by designing a new geometry-enhanced semantic scene completion
framework. Extensive experiments on the existing and newly-proposed datasets
demonstrate the effectiveness of our method, which attains 6.53 CD on PCN, 0.81
CD on ShapeNet-55 and 0.392 MMD on real-world KITTI, surpassing other work by a
large margin and establishing new state-of-the-arts on various benchmarks. Most
notably, AdaPoinTr can achieve such promising performance with higher
throughputs and fewer FLOPs compared with the previous best methods in
practice. The code and datasets are available at
this https URL

本文提出了一种新的方法，将点云补全问题转化为集合转换问题，并设计了一种名为 PoinTr 的新模型，采用 Transformer 编码器 - 解码器架构进行点云补全。通过将点云表示为带有位置嵌入的无序点组的集合，我们将输入数据转换为点代理的序列，并使用 Transformer 进行生成。为了更好地利用点云的 3D 几何结构的归纳偏差，我们进一步设计了一个几何感知块，以显式模拟本地几何关系。我们还进一步提出了 AdaPoinTr，并在点云完成期间开发了自适应查询生成机制和新颖的去噪任务，实现了模型的高效和有效训练。我们还展示了我们的方法可以扩展到场景级点云完成方案，并设计了一种新的几何增强语义场景完成框架。我们的方法在现有和新提出的数据集上进行了大量实验证明其有效性，获得了 6.53CD 的 PCN，0.81CD 的 ShapeNet-55 和 0.392MMD 的真实 KITTI，在各种基准测试中均超过了其他工作，并建立了新的技术水平。值得注意的是，AdaPoinTr 可以以更高的吞吐量和更少的 FLOP 实现如此令人满意的性能，而不像以前的最佳方法。