Efficient 3D Semantic Segmentation with Superpoint Transformer

We introduce a novel superpoint-based transformer architecture for efficient semantic segmentation of large-scale 3D scenes. Our method incorporates a fast algorithm to partition point clouds into a hierarchical superpoint structure, which makes our preprocessing 7 times faster than existing superpoint-based approaches. Additionally, we leverage a self-attention mechanism to capture the relationships between superpoints at multiple scales, leading to state-of-the-art performance on three challenging benchmark datasets: S3DIS (76.0% mIoU 6-fold validation), KITTI-360 (63.5% on Val), and DALES (79.6%). With only 212k parameters, our approach is up to 200 times more compact than other state-of-the-art models while maintaining similar performance. Furthermore, our model can be trained on a single GPU in 3 hours for a fold of the S3DIS dataset, which is 7x to 70x fewer GPU-hours than the best-performing methods. Our code and models are accessible at github.com/drprojects/superpoint_transformer.Comment: Accepted at ICCV 2023. Camera-ready version with Appendix. Code available at github.com/drprojects/superpoint_transforme

Spatially Structured Sparse Morphological Component Separation for Voltage-Sensitive Dye Optical Imaging

International audienceBackground. Voltage-sensitive dye optical imaging is a promising technique for studying in vivo neural assemblies dynamics where functional clustering can be visualized in the imaging plane. Its practical potential is however limited by many artifacts. New Method. We present a novel method, that we call "SMCS" (Spatially Structured Sparse Morphological Component Separation), to separate the relevant biological signal from noise and artifacts. It extends Generalized Linear Models (GLM) by using a set of convex non-smooth regularization priors adapted to the morphology of the sources and artifacts to capture. Results. We make use of first order proximal splitting algorithms to solve the corresponding large scale optimization problem. We also propose an automatic parameters selection procedure based on statistical risk estimation methods. Comparison with Existing Methods. We compare this method with blank subtraction and GLM methods on both synthetic and real data. It shows encouraging perspectives for the observation of complex cortical dynamics. Conclusions. This work shows how recent advances in source separation can be integrated into a biophysical model of VSDOI. Going beyond GLM methods is important to capture transient cortical events such as propagating waves

Generalized Forward-Backward Splitting

This paper introduces the generalized forward-backward splitting algorithm for minimizing convex functions of the form $F + \sum_{i=1}^n G_i$, where $F$ has a Lipschitz-continuous gradient and the $G_i$'s are simple in the sense that their Moreau proximity operators are easy to compute. While the forward-backward algorithm cannot deal with more than $n = 1$ non-smooth function, our method generalizes it to the case of arbitrary $n$. Our method makes an explicit use of the regularity of $F$ in the forward step, and the proximity operators of the $G_i$'s are applied in parallel in the backward step. This allows the generalized forward backward to efficiently address an important class of convex problems. We prove its convergence in infinite dimension, and its robustness to errors on the computation of the proximity operators and of the gradient of $F$. Examples on inverse problems in imaging demonstrate the advantage of the proposed methods in comparison to other splitting algorithms.Comment: 24 pages, 4 figure

Une approche mathématique de l'imagerie optique par colorant potentiométrique

Voltage-sensitive dye optical imaging is a promising recording modality for the cortical activity, but its practical potential is limited by many artefacts and interferences in the acquisitions. Inspired by existing models in the literature, we propose a generative model of the signal, based on an additive mixtures of components, each one being constrained within an union of linear spaces, determined by its biophysical origin. Motivated by the resulting component separation problem, which is an underdetermined linear inverse problem, we develop: (1) convex, spatially structured regularizations, enforcing in particular sparsity on the solutions; (2) a new rst-order proximal algorithm for minimizing e›ciently the resulting functional; (3) statistical methods for automatic parameters selection, based on Stein’s unbiased risk estimate.We study thosemethods in a general framework, and discuss their potential applications in variouselds of applied mathematics, in particular for large scale inverse problems or regressions. We develop subsequently a soŸware for noisy component separation, in an integrated environment adapted to voltage-sensitive dye optical imaging. Finally, we evaluate this soŸware on dišerent data set, including synthetic and real data, showing encouraging perspectives for the observation of complex cortical dynamics.L’imagerie optique par colorant potentiométrique est une méthode d’enregistrement de l’activité corticale prometteuse, mais dont le potentiel réel est limité par la présence d’artefacts et d’interférences dans les acquisitions. À partir de modèles existant dans la littérature, nous proposons un modèle génératif du signal basé sur un mélange additif de composantes, chacune contrainte dans une union d’espaces linéaires déterminés par son origine biophysique. Motivés par le problème de séparation de composantes qui en découle, qui est un problème inverse linéaire sous-déterminé, nous développons : (1) des régularisations convexes structurées spatialement, favorisant en particulier des solutions parcimonieuses ; (2) un nouvel algorithme proximal de premier ordre pour minimiser efficacement la fonctionnelle qui en résulte ; (3) des méthodes statistiques de sélection de paramètre basées sur l’estimateur non biaisé du risque de Stein. Nous étudions ces outils dans un cadre général, et discutons leur utilité pour de nombreux domaines des mathématiques appliqués, en particulier pour les problèmes inverses ou de régression en grande dimension. Nous développons par la suite un logiciel de séparation de composantes en présence de bruit, dans un environnement intégré adapté à l’imagerie optique par colorant potentiométrique. Finalement, nous évaluons ce logiciel sur différentes données, synthétiques et réelles, montrant des résultats encourageants quant à la possibilité d’observer des dynamiques corticales complexes

Préconditionnement d'une généralisation de l'algorithme explicite-implicite, et application à l'optimisation sur des graphes

International audienceWe present a preconditioning of a generalized forward-backward splitting algorithm for nding 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. is allows in particular to minimize functionals of the form \sum_{i=1}^n g_i + f with f smooth. By adapting the underlying metric, such preconditioning can serve two practical purposes: rst, 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 arises in many image processing or learning applications, and that it compares favorably to alternatives in the literature

Target and conditional sensitivity analysis with emphasis on dependence measures

In the context of sensitivity analysis of complex phenomena in presence of uncertainty, we motivate and precise the idea of orienting the analysis towards a critical domain of the studied phenomenon. We make a brief history of related approaches in the literature, and propose a more general and systematic approach. Nonparametric measures of dependence being well-suited to this approach, we also make a review of available methods and of their use for sensitivity analysis, and clarify some of their properties. As a byproduct, we notably describe a new way of computing correlation ratios for Sobol' indices, which does not require specific experience plans nor rely on independence of the input factors. Finally, we show on synthetic numerical experiments both the interest of target and conditional sensitivity analysis, and the relevance of the dependence measures