2 research outputs found
Neural Subdivision
This paper introduces Neural Subdivision, a novel framework for data-driven
coarse-to-fine geometry modeling. During inference, our method takes a coarse
triangle mesh as input and recursively subdivides it to a finer geometry by
applying the fixed topological updates of Loop Subdivision, but predicting
vertex positions using a neural network conditioned on the local geometry of a
patch. This approach enables us to learn complex non-linear subdivision
schemes, beyond simple linear averaging used in classical techniques. One of
our key contributions is a novel self-supervised training setup that only
requires a set of high-resolution meshes for learning network weights. For any
training shape, we stochastically generate diverse low-resolution
discretizations of coarse counterparts, while maintaining a bijective mapping
that prescribes the exact target position of every new vertex during the
subdivision process. This leads to a very efficient and accurate loss function
for conditional mesh generation, and enables us to train a method that
generalizes across discretizations and favors preserving the manifold structure
of the output. During training we optimize for the same set of network weights
across all local mesh patches, thus providing an architecture that is not
constrained to a specific input mesh, fixed genus, or category. Our network
encodes patch geometry in a local frame in a rotation- and
translation-invariant manner. Jointly, these design choices enable our method
to generalize well, and we demonstrate that even when trained on a single
high-resolution mesh our method generates reasonable subdivisions for novel
shapes.Comment: 16 page
3D Point Cloud Processing and Learning for Autonomous Driving
We present a review of 3D point cloud processing and learning for autonomous
driving. As one of the most important sensors in autonomous vehicles, light
detection and ranging (LiDAR) sensors collect 3D point clouds that precisely
record the external surfaces of objects and scenes. The tools for 3D point
cloud processing and learning are critical to the map creation, localization,
and perception modules in an autonomous vehicle. While much attention has been
paid to data collected from cameras, such as images and videos, an increasing
number of researchers have recognized the importance and significance of LiDAR
in autonomous driving and have proposed processing and learning algorithms to
exploit 3D point clouds. We review the recent progress in this research area
and summarize what has been tried and what is needed for practical and safe
autonomous vehicles. We also offer perspectives on open issues that are needed
to be solved in the future.Comment: IEEE Signal Processing Magazine, Special Issue on Autonomous Drivin