A Segmentation Transfer Approach for Rigid Models

International audienceIn this paper, we propose using a segmented example model to perform a semantic oriented segmentation of rigid 3D models of the same class (tables, chairs, etc.). For this, we introduce an alignment method that maps the meaningful parts of the models and we develop a novel approach based on random walks to transfer a consistent segmentation from the example to the target model. The example-driven segmentation is fast and entirely automatic. We demonstrate the effectiveness of our approach through multiple results of inter-shape segmentation transfer presented for different classes of rigid models

Alignment of 3D models

International audienceIn this paper we present a new method for alignment of 3D models. This approach is based on two types of symmetries of the models: the reflective symmetry and the local translational symmetry along a direction. Inspired by the work on the principal component analysis (PCA), we select the best optimal alignment axes within the PCA-axes, the plane reflection symmetry being used as a selection criterion. This pre-processing transforms the alignment problem into an indexing scheme based on the number of the retained PCA-axes. In order to capture the local translational symmetry of a shape along a direction, we introduce a new measure we call the local translational invariance cost (LTIC). The mirror planes of a model are also used to reduce the number of candidate coordinate frames when looking for the one which corresponds to the user's perception. Experimental results show that the proposed method finds the rotation that best aligns a 3D mesh

A Novel Method for Alignment of 3D Models

In this paper we present a new method for alignment of 3D models. This approach is based on symmetry properties, and uses the fact that PCA techniques have good properties with respect to the planar reflective symmetry. The fast search of the best optimal alignment axes within the PCA-eigenvectors is an essential first step in our alignment process. The plane reflection symmetry is used as a criterion for selection. This pre-processing transforms the alignment problem into an indexing scheme based on the number of the retained PCA-axes. We also introduce a local translational invariance cost (LTIC) that captures a measure of the local translational symmetries of a shape with respect to a given direction. Experimental results show that the proposed method finds the rotation that best aligns a 3D mesh

Enhanced 2D/3D Approaches Based on Relevance Index for 3D-Shape Retrieval

International audienceWe present a new approach for 3D model indexing and retrieval using 2D/3D shape descriptors based on silhouettes or depth-buffer images. To take into account the dispersion of information in the views, we associate to each view a relevance index which will be afterward used in the dissimilarity computation. The performance of this new approach is evaluated on the Princeton 3D Shape Benchmark database

LMSCNet: Lightweight Multiscale 3D Semantic Completion

We introduce a new approach for multiscale 3D semantic scene completion from sparse 3D occupancy grid like voxelized LiDAR scans. As opposed to the literature, we use a 2D UNet backbone with comprehensive multiscale skip connections to enhance feature flow, along with 3D segmentation heads. On the SemanticKITTI benchmark, our method performs on par on semantic completion and better on completion than all other published methods - while being significantly lighter and faster. As such it provides a great performance/speed trade-off for mobile-robotics applications. The ablation studies demonstrate our method is robust to lower density inputs, and that it enables very high speed semantic completion at the coarsest level. Qualitative results of our approach are provided at http://tiny.cc/lmscnet.Comment: For a demo video, see http://tiny.cc/lmscne

A Segmentation Transfer Method for Articulated Models

The definitive version is available at digilib.eg.orgInternational audienceIn this paper, we propose using a pre-segmented example model to perform semantic-oriented segmentation of non-rigid 3D models of the same class (human, octopus, quadrupeds, etc.). Using the fact that the same type of non-rigid models share the same global topological structure, we exploit coarse topological shape attributes in conjunction with a seed-based segmentation approach to transfer a meaningful and consistent segmentation from the example to the target models. Promising results of inter-shape segmentation transfer are shown and discussed for different classes of models

Interpretable Goal-Based model for Vehicle Trajectory Prediction in Interactive Scenarios

The abilities to understand the social interaction behaviors between a vehicle and its surroundings while predicting its trajectory in an urban environment are critical for road safety in autonomous driving. Social interactions are hard to explain because of their uncertainty. In recent years, neural network-based methods have been widely used for trajectory prediction and have been shown to outperform hand-crafted methods. However, these methods suffer from their lack of interpretability. In order to overcome this limitation, we combine the interpretability of a discrete choice model with the high accuracy of a neural network-based model for the task of vehicle trajectory prediction in an interactive environment. We implement and evaluate our model using the INTERACTION dataset and demonstrate the effectiveness of our proposed architecture to explain its predictions without compromising the accuracy.Comment: arXiv admin note: text overlap with arXiv:2105.03136 by other author

Results on hypergraph planarity

Unpublished manuscriptUnpublished manuscriptUsing the notion of planarity and drawing for hypergraphs introduced respectively by Johnson and Pollak [JP87] and Mäkinen [Ma90], we show in this paper that any hypergraph having less than nine hyperedges is vertex-planar and can be drawn in the edge standard and in the subset standard without edge crossing