2 research outputs found
Learning to Fuse Local Geometric Features for 3D Rigid Data Matching
This paper presents a simple yet very effective data-driven approach to fuse
both low-level and high-level local geometric features for 3D rigid data
matching. It is a common practice to generate distinctive geometric descriptors
by fusing low-level features from various viewpoints or subspaces, or enhance
geometric feature matching by leveraging multiple high-level features. In prior
works, they are typically performed via linear operations such as concatenation
and min pooling. We show that more compact and distinctive representations can
be achieved by optimizing a neural network (NN) model under the triplet
framework that non-linearly fuses local geometric features in Euclidean spaces.
The NN model is trained by an improved triplet loss function that fully
leverages all pairwise relationships within the triplet. Moreover, the fused
descriptor by our approach is also competitive to deep learned descriptors from
raw data while being more lightweight and rotational invariant. Experimental
results on four standard datasets with various data modalities and application
contexts confirm the advantages of our approach in terms of both feature
matching and geometric registration
A comprehensive survey on point cloud registration
Registration is a transformation estimation problem between two point clouds,
which has a unique and critical role in numerous computer vision applications.
The developments of optimization-based methods and deep learning methods have
improved registration robustness and efficiency. Recently, the combinations of
optimization-based and deep learning methods have further improved performance.
However, the connections between optimization-based and deep learning methods
are still unclear. Moreover, with the recent development of 3D sensors and 3D
reconstruction techniques, a new research direction emerges to align
cross-source point clouds. This survey conducts a comprehensive survey,
including both same-source and cross-source registration methods, and summarize
the connections between optimization-based and deep learning methods, to
provide further research insight. This survey also builds a new benchmark to
evaluate the state-of-the-art registration algorithms in solving cross-source
challenges. Besides, this survey summarizes the benchmark data sets and
discusses point cloud registration applications across various domains.
Finally, this survey proposes potential research directions in this rapidly
growing field.Comment: review pape