1,113 research outputs found
Modelling and unsupervised learning of symmetric deformable object categories
We propose a new approach to model and learn, without manual supervision, the symmetries of natural objects, such as faces or flowers, given only images as input. It is well known that objects that have a symmetric structure do not usually result in symmetric images due to articulation and perspective effects. This is often tackled by seeking the intrinsic symmetries of the underlying 3D shape, which is very difficult to do when the latter cannot be recovered reliably from data. We show that, if only raw images are given, it is possible to look instead for symmetries in the space of object deformations. We can then learn symmetries from an unstructured collection of images of the object as an extension of the recently-introduced object frame representation, modified so that object symmetries reduce to the obvious symmetry groups in the normalized space. We also show that our formulation provides an explanation of the ambiguities that arise in recovering the pose of symmetric objects from their shape or images and we provide a way of discounting such ambiguities in learning
Modelling and unsupervised learning of symmetric deformable object categories
Touzot Charlotte. J.-M. Pontier et E. Roux (dir.), Droit nucléaire – Les déchets nucléaires, Presses universitaires d’Aix-Marseille, 2014. In: Revue Juridique de l'Environnement, n°3, 2015. pp. 586-587
MagicPony: Learning Articulated 3D Animals in the Wild
We consider the problem of learning a function that can estimate the 3D
shape, articulation, viewpoint, texture, and lighting of an articulated animal
like a horse, given a single test image. We present a new method, dubbed
MagicPony, that learns this function purely from in-the-wild single-view images
of the object category, with minimal assumptions about the topology of
deformation. At its core is an implicit-explicit representation of articulated
shape and appearance, combining the strengths of neural fields and meshes. In
order to help the model understand an object's shape and pose, we distil the
knowledge captured by an off-the-shelf self-supervised vision transformer and
fuse it into the 3D model. To overcome common local optima in viewpoint
estimation, we further introduce a new viewpoint sampling scheme that comes at
no added training cost. Compared to prior works, we show significant
quantitative and qualitative improvements on this challenging task. The model
also demonstrates excellent generalisation in reconstructing abstract drawings
and artefacts, despite the fact that it is only trained on real images.Comment: Project Page: https://3dmagicpony.github.io
Recovering 6D Object Pose: A Review and Multi-modal Analysis
A large number of studies analyse object detection and pose estimation at
visual level in 2D, discussing the effects of challenges such as occlusion,
clutter, texture, etc., on the performances of the methods, which work in the
context of RGB modality. Interpreting the depth data, the study in this paper
presents thorough multi-modal analyses. It discusses the above-mentioned
challenges for full 6D object pose estimation in RGB-D images comparing the
performances of several 6D detectors in order to answer the following
questions: What is the current position of the computer vision community for
maintaining "automation" in robotic manipulation? What next steps should the
community take for improving "autonomy" in robotics while handling objects? Our
findings include: (i) reasonably accurate results are obtained on
textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy
existence of occlusion and clutter severely affects the detectors, and
similar-looking distractors is the biggest challenge in recovering instances'
6D. (iii) Template-based methods and random forest-based learning algorithms
underlie object detection and 6D pose estimation. Recent paradigm is to learn
deep discriminative feature representations and to adopt CNNs taking RGB images
as input. (iv) Depending on the availability of large-scale 6D annotated depth
datasets, feature representations can be learnt on these datasets, and then the
learnt representations can be customized for the 6D problem
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