Virtual Line Descriptor and Semi-Local Matching Method for Reliable Feature Correspondence

International audienceFinding reliable correspondences between sets of feature points in two images remains challenging in case of ambiguities or strong transformations. In this paper, we define a photometric descriptor for virtual lines that join neighbouring feature points. We show that it can be used in the second-order term of existing graph matchers to significantly improve their accuracy. We also define a semi-local matching method based on this descriptor. We show that it is robust to strong transformations and more accurate than existing graph matchers for scenes with significant occlusions, including for very low inlier rates. Used as a preprocessor to filter outliers from match candidates, it significantly improves the robustness of RANSAC and reduces camera calibration errors

Statistical Criteria for Shape Fusion and Selection

International audience—Surface reconstruction from point clouds often relies on a primitive extraction step, that may be followed by a merging step because of a possible over-segmentation. We present two statistical criteria to decide whether or not two surfaces are to be considered as the same, and thus can be merged. They are based on the statistical tests of Kolmogorov-Smirnov and Mann-Whitney for comparing distributions. Moreover, computation time can be significantly cut down using a reduced sampling based on the Dvoretzky-Keifer-Wolfowitz inequality. The strength of our approach is that it relies in practice on a single intuitive parameter (homogeneous to a distance) and that it can be applied to any shape, including meshes, not just geometric primitives. It also enables the comparison of shapes of different kinds, providing a way to choose between different shape candidates. We show several applications of our method, experimenting geometric primitive (planeand cylinder) detection, selection and fusion, both on precise laser scans and noisy photogrammetric 3D data

Fast and Robust Normal Estimation for Point Clouds with Sharp Features

Proceedings of the 10th Symposium of on Geometry Processing (SGP 2012), Tallinn, Estonia, July 2012.International audienceThis paper presents a new method for estimating normals on unorganized point clouds that preserves sharp fea- tures. It is based on a robust version of the Randomized Hough Transform (RHT). We consider the filled Hough transform accumulator as an image of the discrete probability distribution of possible normals. The normals we estimate corresponds to the maximum of this distribution. We use a fixed-size accumulator for speed, statistical exploration bounds for robustness, and randomized accumulators to prevent discretization effects. We also propose various sampling strategies to deal with anisotropy, as produced by laser scans due to differences of incidence. Our experiments show that our approach offers an ideal compromise between precision, speed, and robustness: it is at least as precise and noise-resistant as state-of-the-art methods that preserve sharp features, while being almost an order of magnitude faster. Besides, it can handle anisotropy with minor speed and precision losses

Tempo Documentation - Interacting with a C Program Specializer

Tempo is a program specializer for C programs. It has been developed at IRISA / INRIA - University of Rennes 1 (1994-2000), and then at LaBRI / INRIA - University of Bordeaux 1 (since 2000). This technical report puts together a cleaned-up and reformatted version of the various on-line manuals and other useful documents that have been written on Tempo for its distribution, but that used to exist only as separate and sometimes mobile HTML pages. Grouping them and giving them a technical report number make it easy to reference them in a publication. Although it is not developed and maintained anymore, Tempo is still distributed. It can be downloaded from the Phoenix project-team web pages (http://phoenix.inria.fr/). Publications concerning Tempo as well as tutorial slides are also available on this web site. Technical information in this report is (theoretically) up to date with respect to the last official release of Tempo, dated February 11th, 2003.Tempo est un spécialiseur de programmes C. Il a été développé à l'IRISA / INRIA - University of Rennes 1 (1994-2000), puis au LaBRI / INRIA - University of Bordeaux 1 (à partir de 2000). Ce rapport technique rassemble des versions « nettoyées » et remise en forme des divers manuels et autres documents pratiques qui ont été écrits pour la distribution de Tempo, mais qui n'existaient que sous forme de pages HTML séparées et parfois mobiles. Les regrouper et leur donner un numéro de rapport technique permet d'y faire proprement référence dans des publications. Bien qu'il ne soit désormais plus développé et maintenu, Tempo est toujours distribué. Il peut être téléchargé sur le site web de l'équipe-projet (http://phoenix.inria.fr/). Les publications de l'équipe sur Tempo ainsi que les transparents d'un tutoriel sont également disponibles sur ce site. Les informations techniques dans ce rapport sont (en théorie) à jour par rapport à la dernière version officielle de Tempo, qui date du 11 février 2003

Tempo Documentation - Interacting with a C Program Specializer

Tempo is a program specializer for C programs. It has been developed at IRISA / INRIA - University of Rennes 1 (1994-2000), and then at LaBRI / INRIA - University of Bordeaux 1 (since 2000). This technical report puts together a cleaned-up and reformatted version of the various on-line manuals and other useful documents that have been written on Tempo for its distribution, but that used to exist only as separate and sometimes mobile HTML pages. Grouping them and giving them a technical report number make it easy to reference them in a publication. Although it is not developed and maintained anymore, Tempo is still distributed. It can be downloaded from the Phoenix project-team web pages (http://phoenix.inria.fr/). Publications concerning Tempo as well as tutorial slides are also available on this web site. Technical information in this report is (theoretically) up to date with respect to the last official release of Tempo, dated February 11th, 2003.Tempo est un spécialiseur de programmes C. Il a été développé à l'IRISA / INRIA - University of Rennes 1 (1994-2000), puis au LaBRI / INRIA - University of Bordeaux 1 (à partir de 2000). Ce rapport technique rassemble des versions « nettoyées » et remise en forme des divers manuels et autres documents pratiques qui ont été écrits pour la distribution de Tempo, mais qui n'existaient que sous forme de pages HTML séparées et parfois mobiles. Les regrouper et leur donner un numéro de rapport technique permet d'y faire proprement référence dans des publications. Bien qu'il ne soit désormais plus développé et maintenu, Tempo est toujours distribué. Il peut être téléchargé sur le site web de l'équipe-projet (http://phoenix.inria.fr/). Les publications de l'équipe sur Tempo ainsi que les transparents d'un tutoriel sont également disponibles sur ce site. Les informations techniques dans ce rapport sont (en théorie) à jour par rapport à la dernière version officielle de Tempo, qui date du 11 février 2003

Un sens logique pour les graphes sémantiques

posterInternational audienceWe discuss the meaning of semantic graphs, in particular of those used in Meaning-Text Theory. We provide a precise, possibly underspecified, meaning to such graphs through a simple translation into a Minimal Recursion Semantics formula. This translation covers cases of multiple predications over several entities, higher order predication and modalities.Nous discutons du sens des graphes sémantiques, notamment de ceux utilisés en Théorie Sens-Texte. Nous leur donnons un sens précis, éventuellement sous-spécifié, grâce à une traduction simple vers une formule de Minimal Recursion Semantics qui couvre les cas de prédications multiples sur plusieurs entités, de prédication d'ordre supérieur et de modalités

Learning Grammars for Architecture-Specific Facade Parsing

International audienceParsing facade images requires optimal handcrafted grammar for a given class of buildings. Such a handcrafted grammar is often designed manually by experts. In this paper, we present a novel framework to learn a compact grammar from a set of ground-truth images. To this end, parse trees of ground-truth annotated images are obtained running existing inference algorithms with a simple, very general grammar. From these parse trees, repeated subtrees are sought and merged together to share derivations and produce a grammar with fewer rules. Furthermore, unsupervised clustering is performed on these rules, so that, rules corresponding to the same complex pattern are grouped together leading to a rich compact grammar. Experimental validation and comparison with the state-of-the-art grammar-based methods on four diff erent datasets show that the learned grammar helps in much faster convergence while producing equal or more accurate parsing results compared to handcrafted grammars as well as grammars learned by other methods. Besides, we release a new dataset of facade images from Paris following the Art-deco style and demonstrate the general applicability and extreme potential of the proposed framework

Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation

Semantic segmentation of point clouds in autonomous driving datasets requires techniques that can process large numbers of points efficiently. Sparse 3D convolutions have become the de-facto tools to construct deep neural networks for this task: they exploit point cloud sparsity to reduce the memory and computational loads and are at the core of today's best methods. In this paper, we propose an alternative method that reaches the level of state-of-the-art methods without requiring sparse convolutions. We actually show that such level of performance is achievable by relying on tools a priori unfit for large scale and high-performing 3D perception. In particular, we propose a novel 3D backbone, WaffleIron, made almost exclusively of MLPs and dense 2D convolutions and present how to train it to reach high performance on SemanticKITTI and nuScenes. We believe that WaffleIron is a compelling alternative to backbones using sparse 3D convolutions, especially in frameworks and on hardware where those convolutions are not readily available.Comment: Accepted at ICCV23. Code available at https://github.com/valeoai/WaffleIro