6 research outputs found
Learning task-specific features for 3D pointcloud graph creation
Processing 3D pointclouds with Deep Learning methods is not an easy task. A
common choice is to do so with Graph Neural Networks, but this framework
involves the creation of edges between points, which are explicitly not related
between them. Historically, naive and handcrafted methods like k Nearest
Neighbors (k-NN) or query ball point over xyz features have been proposed,
focusing more attention on improving the network than improving the graph. In
this work, we propose a more principled way of creating a graph from a 3D
pointcloud. Our method is based on performing k-NN over a transformation of the
input 3D pointcloud. This transformation is done by an Multi-Later Perceptron
(MLP) with learnable parameters that is optimized through backpropagation
jointly with the rest of the network. We also introduce a regularization method
based on stress minimization, which allows to control how distant is the learnt
graph from our baseline: k-NN over xyz space. This framework is tested on
ModelNet40, where graphs generated by our network outperformed the baseline by
0.3 points in overall accuracy
SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration
Point cloud registration is to estimate a transformation to align point
clouds collected in different perspectives. In learning-based point cloud
registration, a robust descriptor is vital for high-accuracy registration.
However, most methods are susceptible to noise and have poor generalization
ability on unseen datasets. Motivated by this, we introduce SphereNet to learn
a noise-robust and unseen-general descriptor for point cloud registration. In
our method, first, the spheroid generator builds a geometric domain based on
spherical voxelization to encode initial features. Then, the spherical
interpolation of the sphere is introduced to realize robustness against noise.
Finally, a new spherical convolutional neural network with spherical integrity
padding completes the extraction of descriptors, which reduces the loss of
features and fully captures the geometric features. To evaluate our methods, a
new benchmark 3DMatch-noise with strong noise is introduced. Extensive
experiments are carried out on both indoor and outdoor datasets. Under
high-intensity noise, SphereNet increases the feature matching recall by more
than 25 percentage points on 3DMatch-noise. In addition, it sets a new
state-of-the-art performance for the 3DMatch and 3DLoMatch benchmarks with
93.5\% and 75.6\% registration recall and also has the best generalization
ability on unseen datasets.Comment: 15 pages, under review for IEEE Transactions on Circuits and Systems
for Video Technolog