  Robust 3D perception under corruption has become an essential task for the
realm of 3D vision. While current data augmentation techniques usually perform
random transformations on all point cloud objects in an offline way and ignore
the structure of the samples, resulting in over-or-under enhancement. In this
work, we propose an alternative to make sample-adaptive transformations based
on the structure of the sample to cope with potential corruption via an
auto-augmentation framework, named as AdaptPoint. Specially, we leverage a
imitator, consisting of a Deformation Controller and a Mask Controller,
respectively in charge of predicting deformation parameters and producing a
per-point mask, based on the intrinsic structural information of the input
point cloud, and then conduct corruption simulations on top. Then a
discriminator is utilized to prevent the generation of excessive corruption
that deviates from the original data distribution. In addition, a
perception-guidance feedback mechanism is incorporated to guide the generation
of samples with appropriate difficulty level. Furthermore, to address the
paucity of real-world corrupted point cloud, we also introduce a new dataset
ScanObjectNN-C, that exhibits greater similarity to actual data in real-world
environments, especially when contrasted with preceding CAD datasets.
Experiments show that our method achieves state-of-the-art results on multiple
corruption benchmarks, including ModelNet-C, our ScanObjectNN-C, and
ShapeNet-C.
