  Point clouds captured in real-world applications are often incomplete due to
the limited sensor resolution, single viewpoint, and occlusion. Therefore,
recovering the complete point clouds from partial ones becomes an indispensable
task in many practical applications. 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 that 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
point cloud to a sequence of point proxies and employ the transformers for
point cloud 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. Furthermore, we propose two more challenging benchmarks with more
diverse incomplete point clouds that can better reflect the real-world
scenarios to promote future research. Experimental results show that our method
outperforms state-of-the-art methods by a large margin on both the new
benchmarks and the existing ones. Code is available at
https://github.com/yuxumin/PoinTr
