3,075 research outputs found
Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
In this paper we introduce Co-Fusion, a dense SLAM system that takes a live
stream of RGB-D images as input and segments the scene into different objects
(using either motion or semantic cues) while simultaneously tracking and
reconstructing their 3D shape in real time. We use a multiple model fitting
approach where each object can move independently from the background and still
be effectively tracked and its shape fused over time using only the information
from pixels associated with that object label. Previous attempts to deal with
dynamic scenes have typically considered moving regions as outliers, and
consequently do not model their shape or track their motion over time. In
contrast, we enable the robot to maintain 3D models for each of the segmented
objects and to improve them over time through fusion. As a result, our system
can enable a robot to maintain a scene description at the object level which
has the potential to allow interactions with its working environment; even in
the case of dynamic scenes.Comment: International Conference on Robotics and Automation (ICRA) 2017,
http://visual.cs.ucl.ac.uk/pubs/cofusion,
https://github.com/martinruenz/co-fusio
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Object-Aware Tracking and Mapping
Reasoning about geometric properties of digital cameras and optical physics enabled
researchers to build methods that localise cameras in 3D space from a video
stream, while – often simultaneously – constructing a model of the environment.
Related techniques have evolved substantially since the 1980s, leading to increasingly
accurate estimations. Traditionally, however, the quality of results is strongly
affected by the presence of moving objects, incomplete data, or difficult surfaces
– i.e. surfaces that are not Lambertian or lack texture. One insight of this work is
that these problems can be addressed by going beyond geometrical and optical constraints,
in favour of object level and semantic constraints. Incorporating specific
types of prior knowledge in the inference process, such as motion or shape priors,
leads to approaches with distinct advantages and disadvantages.
After introducing relevant concepts in Chapter 1 and Chapter 2, methods for building
object-centric maps in dynamic environments using motion priors are investigated
in Chapter 5. Chapter 6 addresses the same problem as Chapter 5, but presents
an approach which relies on semantic priors rather than motion cues. To fully exploit
semantic information, Chapter 7 discusses the conditioning of shape representations
on prior knowledge and the practical application to monocular, object-aware
reconstruction systems
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Learning Material-Aware Local Descriptors for 3D Shapes
Material understanding is critical for design, geometric modeling, and
analysis of functional objects. We enable material-aware 3D shape analysis by
employing a projective convolutional neural network architecture to learn
material- aware descriptors from view-based representations of 3D points for
point-wise material classification or material- aware retrieval. Unfortunately,
only a small fraction of shapes in 3D repositories are labeled with physical
mate- rials, posing a challenge for learning methods. To address this
challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material
labels. We focus on furniture models which exhibit interesting structure and
material variabil- ity. In addition, we also contribute a high-quality expert-
labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We
further apply a mesh-aware con- ditional random field, which incorporates
rotational and reflective symmetries, to smooth our local material predic-
tions across neighboring surface patches. We demonstrate the effectiveness of
our learned descriptors for automatic texturing, material-aware retrieval, and
physical simulation. The dataset and code will be publicly available.Comment: 3DV 201
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