638 research outputs found
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go
We present NeuroMorph, a new neural network architecture that takes as input
two 3D shapes and produces in one go, i.e. in a single feed forward pass, a
smooth interpolation and point-to-point correspondences between them. The
interpolation, expressed as a deformation field, changes the pose of the source
shape to resemble the target, but leaves the object identity unchanged.
NeuroMorph uses an elegant architecture combining graph convolutions with
global feature pooling to extract local features. During training, the model is
incentivized to create realistic deformations by approximating geodesics on the
underlying shape space manifold. This strong geometric prior allows to train
our model end-to-end and in a fully unsupervised manner without requiring any
manual correspondence annotations. NeuroMorph works well for a large variety of
input shapes, including non-isometric pairs from different object categories.
It obtains state-of-the-art results for both shape correspondence and
interpolation tasks, matching or surpassing the performance of recent
unsupervised and supervised methods on multiple benchmarks.Comment: Published at the IEEE/CVF Conference on Computer Vision and Pattern
Recognition 202
Measuring the Discrepancy between 3D Geometric Models using Directional Distance Fields
Qualifying the discrepancy between 3D geometric models, which could be
represented with either point clouds or triangle meshes, is a pivotal issue
with board applications. Existing methods mainly focus on directly establishing
the correspondence between two models and then aggregating point-wise distance
between corresponding points, resulting in them being either inefficient or
ineffective. In this paper, we propose DirDist, an efficient, effective,
robust, and differentiable distance metric for 3D geometry data. Specifically,
we construct DirDist based on the proposed implicit representation of 3D
models, namely directional distance field (DDF), which defines the directional
distances of 3D points to a model to capture its local surface geometry. We
then transfer the discrepancy between two 3D geometric models as the
discrepancy between their DDFs defined on an identical domain, naturally
establishing model correspondence. To demonstrate the advantage of our DirDist,
we explore various distance metric-driven 3D geometric modeling tasks,
including template surface fitting, rigid registration, non-rigid registration,
scene flow estimation and human pose optimization. Extensive experiments show
that our DirDist achieves significantly higher accuracy under all tasks. As a
generic distance metric, DirDist has the potential to advance the field of 3D
geometric modeling. The source code is available at
\url{https://github.com/rsy6318/DirDist}
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