The massive use of data mining in industrial contexts faces the problem of the results understanding by users. For example, until now, geo-scientists showdown their adoptions of these systems because of the intuitive part of their work, which requires to well understand a geological study instead of only trusting a computational system. In this paper, a new supervised machine learning based approach to build classifier from 3D-objects is presented. In this approach, first of all, an extraction of pertinent subparts of the objects is performed to highlight subparts characterising the most each kind of 3D-objects. Afterwards, a classical machine learning model is applied to build a classifier based on these pertinent subparts. The main idea is to use discriminant subparts of 3D-objects for the supervised classification in order to take care of the local nature of pertinent elements. This allows the user to be aware of these subparts which have been useful to determine the corresponding class of the object. Two algorithms are presented the 3DRESC algorithm, and its extension, the 3DRESC-TF algorithm, based on the used of transfer learning to improve its performance. Final results are presented that highlight the advantages of this new approach to build description features of 3D-objects and of the introduction of transfer learning.

We present a cluster-based volume rendering system for roaming very large volumes. This system allows to move a gigabyte-sized probe inside a total volume of several tens or hundreds of gigabytes in real-time. While the size of the probe is limited by the total amount of texture memory on the cluster, the size of the total data set has no theoretical limit. The cluster is used as a distributed graphics processing unit that both aggregates graphics power and graphics memory. A hardware-accelerated volume renderer runs in parallel on the cluster nodes and the final image compositing is implemented using a pipelined sort-last rendering algorithm. Meanwhile, volume bricking and volume paging allow efficient data caching. On each rendering node, a distributed hierarchical cache system implements a global software-based distributed shared memory on the cluster. In case of a cache miss, this system first checks page residency on the other cluster nodes instead of directly accessing local disks. Using two Gigabit Ethernet network interfaces per node, we accelerate data fetching by a factor of 4 compared to directly accessing local disks. The system also implements asynchronous disk access and texture loading, which makes it possible to overlap data loading, volume slicing and rendering for optimal volume roaming.

In this paper, we present a volume roaming system dedicated to oil and gas exploration. Our system combines probe-based volume rendering with data processing and computing. The daily oil production and the estimation of the world proven-reserves directly affect the barrel price and have a strong impact on the economy. Among others, production and correct estimation are linked to the accuracy of the sub-surface model used for predicting oil reservoirs shape and size. Geoscientists build this model from the interpretation of seismic data, i.e. 3D images of the subsurface obtained from geophysical surveys. Our system couples visualization and data processing for the interpretation of seismic data. It is based on volume roaming along with efficient volume paging to manipulate the multi-gigabyte data sets commonly acquired during seismic surveys. Our volume rendering lenses implement high quality pre-integrated volume rendering with accurate lighting. They use a generic multi-modal volume rendering system that blends several volumes in the spirit of the "stencil" paradigm used in 2D painting programs. In addition, our system can interactively display non-polygonal isosurfaces painted with an attribute. Beside the visualization algorithms, automatic extraction of local features of the subsurface model also take full advantage of the volume paging.