Point Deformable Network with Enhanced Normal Embedding for Point Cloud
  Analysis
Xingyilang Yin Xi Yang Liangchen Liu Nannan Wang Xinbo Gao
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.