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

RGB-D-based Action Recognition Datasets: A Survey

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state-of-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-\'{a}-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols

Neural Network Fusion of Color, Depth and Location for Object Instance Recognition on a Mobile Robot

International audienceThe development of mobile robots for domestic assistance re-quires solving problems integrating ideas from different fields of research like computer vision, robotic manipulation, localization and mapping. Semantic mapping, that is, the enrichment a map with high-level infor-mation like room and object identities, is an example of such a complex robotic task. Solving this task requires taking into account hard software and hardware constraints brought by the context of autonomous mobile robots, where short processing times and low energy consumption are mandatory. We present a light-weight scene segmentation and object in-stance recognition algorithm using an RGB-D camera and demonstrate it in a semantic mapping experiment. Our method uses a feed-forward neural network to fuse texture, color and depth information. Running at 3 Hz on a single laptop computer, our algorithm achieves a recognition rate of 97% in a controlled environment, and 87% in the adversarial con-ditions of a real robotic task. Our results demonstrate that state of the art recognition rates on a database does not guarantee performance in a real world experiment. We also show the benefit in these conditions of fusing several recognition decisions and data from different sources. The database we compiled for the purpose of this study is publicly available