26,624 research outputs found
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
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
As machine learning systems move from computer-science laboratories into the
open world, their accountability becomes a high priority problem.
Accountability requires deep understanding of system behavior and its failures.
Current evaluation methods such as single-score error metrics and confusion
matrices provide aggregate views of system performance that hide important
shortcomings. Understanding details about failures is important for identifying
pathways for refinement, communicating the reliability of systems in different
settings, and for specifying appropriate human oversight and engagement.
Characterization of failures and shortcomings is particularly complex for
systems composed of multiple machine learned components. For such systems,
existing evaluation methods have limited expressiveness in describing and
explaining the relationship among input content, the internal states of system
components, and final output quality. We present Pandora, a set of hybrid
human-machine methods and tools for describing and explaining system failures.
Pandora leverages both human and system-generated observations to summarize
conditions of system malfunction with respect to the input content and system
architecture. We share results of a case study with a machine learning pipeline
for image captioning that show how detailed performance views can be beneficial
for analysis and debugging
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
History of art paintings through the lens of entropy and complexity
Art is the ultimate expression of human creativity that is deeply influenced
by the philosophy and culture of the corresponding historical epoch. The
quantitative analysis of art is therefore essential for better understanding
human cultural evolution. Here we present a large-scale quantitative analysis
of almost 140 thousand paintings, spanning nearly a millennium of art history.
Based on the local spatial patterns in the images of these paintings, we
estimate the permutation entropy and the statistical complexity of each
painting. These measures map the degree of visual order of artworks into a
scale of order-disorder and simplicity-complexity that locally reflects
qualitative categories proposed by art historians. The dynamical behavior of
these measures reveals a clear temporal evolution of art, marked by transitions
that agree with the main historical periods of art. Our research shows that
different artistic styles have a distinct average degree of entropy and
complexity, thus allowing a hierarchical organization and clustering of styles
according to these metrics. We have further verified that the identified groups
correspond well with the textual content used to qualitatively describe the
styles, and that the employed complexity-entropy measures can be used for an
effective classification of artworks.Comment: 10 two-column pages, 5 figures; accepted for publication in PNAS
[supplementary information available at
http://www.pnas.org/highwire/filestream/824089/field_highwire_adjunct_files/0/pnas.1800083115.sapp.pdf
Graph-based Object Understanding
Computer Vision algorithms become increasingly prevalent in our everyday lives. Especially recognition systems are often employed to automatize certain tasks (i.e. quality control). In State-of-the-Art approaches global shape char acteristics are leveraged, discarding nuanced shape varieties in the individual parts of the object. Thus, these systems fall short on both learning and utilizing the inherent underlying part structures of objects. By recognizing common substructures between known and queried objects, part-based systems may identify objects more robustly in lieu of occlusion or redundant parts. As we observe these traits, there are theories that such part-based approaches are indeed present in humans. Leveraging abstracted representations of decomposed objects may additionally offer better generalization on less training data. Enabling computer systems to reason about objects on the basis of their parts is the focus of this dissertation. Any part-based method first requires a segmentation approach to assign object regions to individual parts. Therefore, a 2D multi-view segmentation approach for 3D mesh segmentation is extended. The approach uses the normal and depth information of the objects to reliably extract part boundary contours. This method significantly reduces training time of the segmentation model compared to other segmentation approaches while still providing good segmentation results on the test data. To explore the benefits of part-based systems, a symbolic object classification dataset is created that inherently adheres to underlying rules made of spatial relations between part entities. This abstract data is also transformed into 3D point clouds. This enables us to benchmark conventional 3D point cloud classification models against the newly developed model that utilizes ground truth symbol segmentations for the classification task. With the new model, improved classification performance can be observed. This offers empirical evidence that part segmentation may boost classification accuracy if the data obey part-based rules. Additionally, prediction results of the model on segmented 3D data are compared against a modified variant of the model that directly uses the underlying symbols. The perception gap, representing issues with extracting the symbols from the segmented point clouds, is quantified. Furthermore, a framework for 3D object classification on real world objects is developed. The designed pipeline automatically segments an object into its parts, creates the according part graph and predicts the object class based on the similarity to graphs in the training dataset. The advantage of subgraph similarity is utilized in a second experiment, where out-of-distribution samples ofobjects are created, which contain redundant parts. Whereas traditional classification methods working on the global shape may misinterpret extracted feature vectors, the model creates robust predictions. Lastly, the task of object repairment is considered, in which a single part of the given object is compromised by a certain manipulation. As human-made objects follow an underlying part structure, a system to exploit this part structure in order to mend the object is developed. Given the global 3D point cloud of a compromised object, the object is automatically segmented, the shape features are extracted from the individual part clouds and are fed into a Graph Neural Network that predicts a manipulation action for each part. In conclusion, the opportunities of part-graph based methods for object understanding to improve 3D classification and regression tasks are explored. These approaches may enhance robotic computer vision pipelines in the future.2021-06-2
Online Context-based Object Recognition for Mobile Robots
This work proposes a robotic object recognition
system that takes advantage of the contextual information latent
in human-like environments in an online fashion. To fully leverage
context, it is needed perceptual information from (at least) a
portion of the scene containing the objects of interest, which could
not be entirely covered by just an one-shot sensor observation.
Information from a larger portion of the scenario could still
be considered by progressively registering observations, but this
approach experiences difficulties under some circumstances, e.g.
limited and heavily demanded computational resources, dynamic
environments, etc. Instead of this, the proposed recognition
system relies on an anchoring process for the fast registration
and propagation of objects’ features and locations beyond the
current sensor frustum. In this way, the system builds a graphbased
world model containing the objects in the scenario (both
in the current and previously perceived shots), which is exploited
by a Probabilistic Graphical Model (PGM) in order to leverage
contextual information during recognition. We also propose a
novel way to include the outcome of local object recognition
methods in the PGM, which results in a decrease in the usually
high CRF learning complexity. A demonstration of our proposal
has been conducted employing a dataset captured by a mobile
robot from restaurant-like settings, showing promising results.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Grounding semantics in robots for Visual Question Answering
In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning
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