Neighborhood Normalization for Robust Geometric Feature Learning

Xingtong Liu

Abstract:

Extracting geometric features from 3D models is a
common first step in applications such as 3D registration,
tracking, and scene flow estimation. Many hand-crafted
and learning-based methods aim to produce consistent
and distinguishable geometric features for 3D models with
partial overlap. These methods work well in cases where
the point density and scale of the overlapping 3D objects
are similar, but struggle in applications where 3D data
are obtained independently with unknown global scale and
scene overlap. Unfortunately, instances of this resolution
mismatch are common in practice, e.g., when aligning
data from multiple sensors. In this work, we introduce
a new normalization technique, Batch-Neighborhood
Normalization, aiming to improve robustness to mean-std
variation of local feature distributions that presumably
can happen in samples with varying point density. We
empirically demonstrate that the presented normalization
method’s performance compares favorably to comparison
methods in indoor and outdoor environments, and on a
clinical dataset, on common point registration benchmarks
in both standard and, particularly, resolution-mismatch settings. The source code and clinical dataset are available at
https://github.com/lppllppl920/NeighborhoodNormalizationPytorch.