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

Data-driven shape analysis and processing

Data-driven methods serve an increasingly important role in discovering geometric, structural, and semantic relationships between shapes. In contrast to traditional approaches that process shapes in isolation of each other, data-driven methods aggregate information from 3D model collections to improve the analysis, modeling and editing of shapes. Through reviewing the literature, we provide an overview of the main concepts and components of these methods, as well as discuss their application to classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing

View subspaces for indexing and retrieval of 3D models

View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithmsComment: Three-Dimensional Image Processing (3DIP) and Applications (Proceedings Volume) Proceedings of SPIE Volume: 7526 Editor(s): Atilla M. Baskurt ISBN: 9780819479198 Date: 2 February 201

VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Thus, we propose a full convolutional volumetric auto encoder that learns volumetric representation from noisy data by estimating the voxel occupancy grids. The proposed method outperforms prior work on challenging tasks like denoising and shape completion. We also show that the obtained deep embedding gives competitive performance when used for classification and promising results for shape interpolation

Is the voice an auditory face?: An ALE meta-analysis comparing vocal and facial emotion processing

This meta-analysis compares the brain structures and mechanisms involved in facial and vocal emotion recognition. Neuroimaging studies contrasting emotional with neutral (face: N = 76, voice: N = 34) and explicit with implicit emotion processing (face: N = 27, voice: N = 20) were collected to shed light on stimulus and goal-driven mechanisms, respectively. Activation likelihood estimations were conducted on the full data sets for the separate modalities and on reduced, modality-matched data sets for modality comparison. Stimulus-driven emotion processing engaged large networks with significant modality differences in the superior temporal (voice-specific) and the medial temporal (face-specific) cortex. Goal-driven processing was associated with only a small cluster in the dorsomedial prefrontal cortex for voices but not faces. Neither stimulus- nor goal-driven processing showed significant modality overlap. Together, these findings suggest that stimulus-driven processes shape activity in the social brain more powerfully than goal-driven processes in both the visual and the auditory domains. Yet, whereas faces emphasize subcortical emotional and mnemonic mechanisms, voices emphasize cortical mechanisms associated with perception and effortful stimulus evaluation (e.g. via subvocalization). These differences may be due to sensory stimulus properties and highlight the need for a modality-specific perspective when modeling emotion processing in the brain

Eksplorasi material daur ulang sampah polystyrene (PS) menggunakan metode material-driven design

Every year the Indonesian people are estimated to contribute 0.48-1.29 million metric tons of plastic waste to the oceans. Polystyrene (PS) takes more than 500 years to decompose naturally among the types of trash thrown away. For local producers, PS waste recycling requires more processing than other thermoplastic waste processing. On the other hand, PS waste processing is interesting to develop because of its shiny and transparent nature, so it is used as a jewelry product that cannot absorb a lot of waste. Therefore, this study aims to explore the uniqueness of PS recycling further so that it can be utilized optimally by local plastic waste recycling business actors. This study uses primary data collection methods such as observation, focus group discussions, and experiments. Experimental stages are carried out to find the proper treatment and get a unique texture and shape different from other materials. Furthermore, an analysis is made using the material-driven design method to see the value of the material. This analysis produces material visualization concepts such as uneven, imperfect, artless, translucent, and luxurious, which are suitable to function as vocal points for room styling. This study found that deficiencies in processing existing PS recycled materials can be overcome effectively and efficiently by melting 75 percent of the shredded plastic

Watching plants grow:A position paper on computer vision and Arabidopsis thaliana

The authors present a comprehensive overview of image processing and analysis work done to support research into the model flowering plant Arabidopsis thaliana. Beside the plant's importance in biological research, using image analysis to obtain experimental measurements of it is an interesting vision problem in its own right, involving the segmentation and analysis of sequences of images of objects whose shape varies between individual specimens and also changes over time. While useful measurements can be obtained by segmenting a whole plant from the background, they suggest that the increased range and precision of measurements made available by leaf‐level segmentation makes this a problem well worth solving. A variety of approaches have been tried by biologists as well as computer vision researchers. This is an interdisciplinary area and the computer vision community has an important contribution to make. They suggest that there is a need for publicly available datasets with ground truth annotations to enable the evaluation of new approaches and to support the building of training data for modern data‐driven computer vision approaches, which are those most likely to result in the kind of fully automated systems that will be of use to biologists

Automated computation of arbor densities: a step toward identifying neuronal cell types

The shape and position of a neuron convey information regarding its molecular and functional identity. The identification of cell types from structure, a classic method, relies on the time-consuming step of arbor tracing. However, as genetic tools and imaging methods make data-driven approaches to neuronal circuit analysis feasible, the need for automated processing increases. Here, we first establish that mouse retinal ganglion cell types can be as precise about distributing their arbor volumes across the inner plexiform layer as they are about distributing the skeletons of the arbors. Then, we describe an automated approach to computing the spatial distribution of the dendritic arbors, or arbor density, with respect to a global depth coordinate based on this observation. Our method involves three-dimensional reconstruction of neuronal arbors by a supervised machine learning algorithm, post-processing of the enhanced stacks to remove somata and isolate the neuron of interest, and registration of neurons to each other using automatically detected arbors of the starburst amacrine interneurons as fiducial markers. In principle, this method could be generalizable to other structures of the CNS, provided that they allow sparse labeling of the cells and contain a reliable axis of spatial reference