We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points. We argue that the organization of 3D point clouds can be efficiently captured by a structure called superpoint graph (SPG), derived from a partition of the scanned scene into geometrically homogeneous elements. SPGs offer a compact yet rich representation of contextual relationships between object parts, which is then exploited by a graph convolutional network. Our framework sets a new state of the art for segmenting outdoor LiDAR scans (+11.9 and +8.8 mIoU points for both Semantic3D test sets), as well as indoor scans (+12.4 mIoU points for the S3DIS dataset).

We propose working set/greedy algorithms to efficiently solve problems penalized, respectively, by the total variation on a general weighted graph and its $\ell_0$ counterpart the total level-set boundary size when the piecewise constant solutions have a small number of distinct level sets; this is typically the case when the total level-set boundary size is small, which is encouraged by these two forms of penalization. Our algorithms exploit this structure by recursively splitting the level sets of a piecewise constant candidate solution using graph cuts. We obtain significant speedups over state-of-the-art algorithms for images that are well approximated with few level sets.

We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets.

We present a preconditioning of a generalized forward-backward splitting algorithm for finding a zero of a sum of maximally monotone operators $\sum_{i=1}^{n} A_i + B$ with $B$ cocoercive, involving only the computation of $B$ and of the resolvent of each $A_i$ separately. This allows us in particular to minimize functionals of the form $\sum_{i=1}^n g_i + f$ with $f$ smooth, using only the gradient of $f$ and the proximity operator of each $g_i$ separately. By adapting the underlying metric, such preconditioning can serve two practical purposes: first, it might accelerate the convergence or, second, it might simplify the computation of the resolvent of $A_i$ for some $i$. In addition, in many cases of interest, our preconditioning strategy allows the economy of storage and computation concerning some auxiliary variables. In particular, we show how this approach can handle large-scale, nonsmooth, convex optimization problems structured on graphs, which arise in many image processing or learning applicatio...

In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respective importance of spatial and temporal structures in such data. With this objective, we consider several designs of convolutional, recurrent, and hybrid neural networks, and assess their performance on a large dataset of freely available Sentinel-2 imagery. We find that the best-performing approaches are hybrid configurations for which most of the parameters (up to 90%) are allocated to modeling the temporal structure of the data. Our results thus constitute a set of guidelines for the design of bespoke deep learning models for crop type classification.