Recently MLP-based methods have shown strong performance in point cloud
analysis. Simple MLP architectures are able to learn geometric features in
local point groups yet fail to model long-range dependencies directly. In this
paper, we propose Point Deformable Network (PDNet), a concise MLP-based network
that can capture long-range relations with strong representation ability.
Specifically, we put forward Point Deformable Aggregation Module (PDAM) to
improve representation capability in both long-range dependency and adaptive
aggregation among points. For each query point, PDAM aggregates information
from deformable reference points rather than points in limited local areas. The
deformable reference points are generated data-dependent, and we initialize
them according to the input point positions. Additional offsets and modulation
scalars are learned on the whole point features, which shift the deformable
reference points to the regions of interest. We also suggest estimating the
normal vector for point clouds and applying Enhanced Normal Embedding (ENE) to
the geometric extractors to improve the representation ability of single-point.
Extensive experiments and ablation studies on various benchmarks demonstrate
the effectiveness and superiority of our PDNet.

该论文提出了一种基于简洁的 MLP 网络的 Point Deformable Network (PDNet)，它能够捕捉到具有强大表征能力的长程关系。论文还提出了 Point Deformable Aggregation Module (PDAM)，通过对可变形参考点的聚合，改进了长程依赖和点之间的自适应聚合的表示能力。在各种基准测试中进行了大量实验和消融研究，证明了 PDNet 的有效性和优越性。