65,476 research outputs found
Local wavelet features for statistical object classification and localisation
This article presents a system for texture-based
probabilistic classification and localisation of 3D objects in 2D digital images and discusses selected applications. The objects are described by local feature vectors computed using the wavelet transform. In the training phase, object features are statistically modelled as normal density functions. In the recognition phase, a maximisation algorithm compares the learned density functions
with the feature vectors extracted from a real scene and yields the classes and poses of objects found in it. Experiments carried out on a real dataset of over 40000 images demonstrate the robustness of the system in terms of classification and localisation accuracy. Finally, two important application scenarios are discussed, namely classification of museum artefacts and classification of
metallography images
Shape Generation using Spatially Partitioned Point Clouds
We propose a method to generate 3D shapes using point clouds. Given a
point-cloud representation of a 3D shape, our method builds a kd-tree to
spatially partition the points. This orders them consistently across all
shapes, resulting in reasonably good correspondences across all shapes. We then
use PCA analysis to derive a linear shape basis across the spatially
partitioned points, and optimize the point ordering by iteratively minimizing
the PCA reconstruction error. Even with the spatial sorting, the point clouds
are inherently noisy and the resulting distribution over the shape coefficients
can be highly multi-modal. We propose to use the expressive power of neural
networks to learn a distribution over the shape coefficients in a
generative-adversarial framework. Compared to 3D shape generative models
trained on voxel-representations, our point-based method is considerably more
light-weight and scalable, with little loss of quality. It also outperforms
simpler linear factor models such as Probabilistic PCA, both qualitatively and
quantitatively, on a number of categories from the ShapeNet dataset.
Furthermore, our method can easily incorporate other point attributes such as
normal and color information, an additional advantage over voxel-based
representations.Comment: To appear at BMVC 201
Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors
We present a method to infer 3D pose and shape of vehicles from a single
image. To tackle this ill-posed problem, we optimize two-scale projection
consistency between the generated 3D hypotheses and their 2D
pseudo-measurements. Specifically, we use a morphable wireframe model to
generate a fine-scaled representation of vehicle shape and pose. To reduce its
sensitivity to 2D landmarks, we jointly model the 3D bounding box as a coarse
representation which improves robustness. We also integrate three task priors,
including unsupervised monocular depth, a ground plane constraint as well as
vehicle shape priors, with forward projection errors into an overall energy
function.Comment: Proc. of the AAAI, September 201
Deep Shape Matching
We cast shape matching as metric learning with convolutional networks. We
break the end-to-end process of image representation into two parts. Firstly,
well established efficient methods are chosen to turn the images into edge
maps. Secondly, the network is trained with edge maps of landmark images, which
are automatically obtained by a structure-from-motion pipeline. The learned
representation is evaluated on a range of different tasks, providing
improvements on challenging cases of domain generalization, generic
sketch-based image retrieval or its fine-grained counterpart. In contrast to
other methods that learn a different model per task, object category, or
domain, we use the same network throughout all our experiments, achieving
state-of-the-art results in multiple benchmarks.Comment: ECCV 201
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