Manufacturing industries are currently experiencing the fourth revolution with penetration of
new immersive technologies extremely focused on the flexible manufacturing and human …

Manufacturing industries are currently experiencing the fourth industrial revolution with the
rapid advancements in immersive technologies for human–machine interaction (HMI) and …

The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted
descriptors. However, it is widely despised by recent deep matchers that obtain the rotation …

We propose 3DSmoothNet, a full workflow to match 3D point clouds with a siamese deep
learning architecture and fully convolutional layers using a voxelized smoothed density …

We present a novel method for detecting 3D model instances and estimating their 6D poses
from RGB data in a single shot. To this end, we extend the popular SSD paradigm to cover …

Abstract We present PPFNet-Point Pair Feature NETwork for deeply learning a globally
informed 3D local feature descriptor to find correspondences in unorganized point clouds …

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process
sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule …

We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point
cloud geometry. Based on the folding-based auto-encoding of well known point pair …

Features that are equivariant to a larger group of symmetries have been shown to be more
discriminative and powerful in recent studies. However, higher-order equivariant features …

We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.
Registration of multiple scans typically follows a two-stage pipeline: the initial pairwise …