1,634 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
3D object detection with deep learning
Finding an appropriate environment representation is a crucial problem in robotics. 3D data has been recently used thanks to the advent of low cost RGB-D cameras. We propose a new way to represent a 3D map based on the information provided by an expert. Namely, the expert is the output of a Convolutional Neural Network trained with deep learning techniques. Relying on such information, we propose the generation of 3D maps using individual semantic labels, which are associated with environment objects or semantic labels. So, for each label we are provided with a partial 3D map whose data belong to the 3D perceptions, namely point clouds, which have an associated probability above a given threshold. The final map is obtained by registering and merging all these partial maps. The use of semantic labels provide us a with way to build the map while recognizing objects.This work has been supported by the Spanish Government TIN2016-76515-R Grant, supported with Feder funds, and by grant of Vicerrectorado de Investigación y Transferencia de Conocimiento para el fomento de la I+D+i en la Universidad de Alicante 2016
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