707 research outputs found
SHREC’20 Track:Retrieval of digital surfaces with similar geometric reliefs
International audienceThis paper presents the methods that have participated in the SHREC'20 contest on retrieval of surface patches with similar geometric reliefs and 1 the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge, it is necessary to characterize the local "geometric pattern" information, possibly forgetting model size and bending. Seven groups participated in this contest and twenty runs were submitted for evaluation. The performances of the methods reveal that good results are achieved with a number of techniques that use different approaches
Academic competitions
Academic challenges comprise effective means for (i) advancing the state of
the art, (ii) putting in the spotlight of a scientific community specific
topics and problems, as well as (iii) closing the gap for under represented
communities in terms of accessing and participating in the shaping of research
fields. Competitions can be traced back for centuries and their achievements
have had great influence in our modern world. Recently, they (re)gained
popularity, with the overwhelming amounts of data that is being generated in
different domains, as well as the need of pushing the barriers of existing
methods, and available tools to handle such data. This chapter provides a
survey of academic challenges in the context of machine learning and related
fields. We review the most influential competitions in the last few years and
analyze challenges per area of knowledge. The aims of scientific challenges,
their goals, major achievements and expectations for the next few years are
reviewed
Geometric Deep Learned Descriptors for 3D Shape Recognition
The availability of large 3D shape benchmarks has sparked a flurry of research activity in the development of efficient techniques for 3D shape recognition, which is a fundamental problem in a variety of domains such as pattern recognition, computer vision, and geometry processing. A key element in virtually any shape recognition method is to represent a 3D shape by a concise and compact shape descriptor aimed at facilitating the recognition tasks.
The recent trend in shape recognition is geared toward using deep neural networks to learn features at various levels of abstraction, and has been driven, in large part, by a combination of affordable computing hardware, open source software, and the availability of large-scale datasets. In this thesis, we propose deep learning approaches to 3D shape classification and retrieval. Our approaches inherit many useful properties from the geodesic distance, most notably the capture of the intrinsic geometric structure of 3D shapes and the invariance to isometric deformations. More specifically, we present an integrated framework for 3D shape classification that extracts discriminative geometric shape descriptors with geodesic moments. Further, we introduce a geometric framework for unsupervised 3D shape retrieval using geodesic moments and stacked sparse autoencoders. The key idea is to learn deep shape representations in an unsupervised manner. Such discriminative shape descriptors can then be used to compute pairwise dissimilarities between shapes in a dataset, and to find the retrieved set of the most relevant shapes to a given shape query. Experimental evaluation on three standard 3D shape benchmarks demonstrate the competitive performance of our approach in comparison with existing techniques.
We also introduce a deep similarity network fusion framework for 3D shape classification using a graph convolutional neural network, which is an efficient and scalable deep learning model for graph-structured data. The proposed approach coalesces the geometrical discriminative power of geodesic moments and similarity network fusion in an effort to design a simple, yet discriminative shape descriptor. This geometric shape descriptor is then fed into the graph convolutional neural network to learn a deep feature representation of a 3D shape. We validate our method on ModelNet shape benchmarks, demonstrating that the proposed framework yields significant performance gains compared to state-of-the-art approaches
SHREC 2022 track on online detection of heterogeneous gestures
This paper presents the outcomes of a contest organized to evaluate methods for the online recognition of heterogeneous gestures from sequences of 3D hand poses. The task is the detection of gestures belonging to a dictionary of 16 classes characterized by different pose and motion features. The dataset features continuous sequences of hand tracking data where the gestures are interleaved with non-significant motions. The data have been captured using the Hololens 2 finger tracking system in a realistic use-case of mixed reality interaction. The evaluation is based not only on the detection performances but also on the latency and the false positives, making it possible to understand the feasibility of practical interaction tools based on the algorithms proposed. The outcomes of the contest's evaluation demonstrate the necessity of further research to reduce recognition errors, while the computational cost of the algorithms proposed is sufficiently low
SHREC 2022 Track on Online Detection of Heterogeneous Gestures
This paper presents the outcomes of a contest organized to evaluate methods
for the online recognition of heterogeneous gestures from sequences of 3D hand
poses. The task is the detection of gestures belonging to a dictionary of 16
classes characterized by different pose and motion features. The dataset
features continuous sequences of hand tracking data where the gestures are
interleaved with non-significant motions. The data have been captured using the
Hololens 2 finger tracking system in a realistic use-case of mixed reality
interaction. The evaluation is based not only on the detection performances but
also on the latency and the false positives, making it possible to understand
the feasibility of practical interaction tools based on the algorithms
proposed. The outcomes of the contest's evaluation demonstrate the necessity of
further research to reduce recognition errors, while the computational cost of
the algorithms proposed is sufficiently low.Comment: Accepted on Computer & Graphics journa
Deep Shape Representations for 3D Object Recognition
Deep learning is a rapidly growing discipline that models high-level features in data as multilayered
neural networks. The recent trend toward deep neural networks has been driven, in large part, by
a combination of affordable computing hardware, open source software, and the availability of
pre-trained networks on large-scale datasets.
In this thesis, we propose deep learning approaches to 3D shape recognition using a multilevel
feature learning paradigm. We start by comprehensively reviewing recent shape descriptors,
including hand-crafted descriptors that are mostly developed in the spectral geometry setting and
also the ones obtained via learning-based methods. Then, we introduce novel multi-level feature
learning approaches using spectral graph wavelets, bag-of-features and deep learning. Low-level
features are first extracted from a 3D shape using spectral graph wavelets. Mid-level features are
then generated via the bag-of-features model by employing locality-constrained linear coding as a
feature coding method, in conjunction with the biharmonic distance and intrinsic spatial pyramid
matching in a bid to effectively measure the spatial relationship between each pair of the bag-offeature
descriptors.
For the task of 3D shape retrieval, high-level shape features are learned via a deep auto-encoder
on mid-level features. Then, we compare the deep learned descriptor of a query shape to the
descriptors of all shapes in the dataset using a dissimilarity measure for 3D shape retrieval. For the
task of 3D shape classification, mid-level features are represented as 2D images in order to be fed
into a pre-trained convolutional neural network to learn high-level features from the penultimate
fully-connected layer of the network. Finally, a multiclass support vector machine classifier is
trained on these deep learned descriptors, and the classification accuracy is subsequently computed.
The proposed 3D shape retrieval and classification approaches are evaluated on three standard 3D
shape benchmarks through extensive experiments, and the results show compelling superiority of
our approaches over state-of-the-art methods
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