Springer Science and Business Media LLC

This paper presents a method for finding an $$L_q$$ -closest-point to a set of affine subspaces, that is a point for which the sum of the q-th power of orthogonal distances to all the subspaces is minimized, where $$1 \le q < 2$$ . We give a theoretical proof for the convergence of the proposed algorithm to a unique $$L_q$$ minimum. The proposed method is motivated by the $$L_q$$ Weiszfeld algorithm, an extremely simple and rapid averaging algorithm, that finds the $$L_q$$ mean of a set of given points in a Euclidean space. The proposed algorithm is applied to the triangulation problem in computer vision by finding the $$L_q$$ -closest-point to a set of lines in 3D. Our experimental results for the triangulation problem confirm that the $$L_q$$ -closest-point method, for $$1 \le q < 2$$ , is more robust to outliers than the $$L_2$$ -closest-point method.

In this paper, we describe a data-driven approach to leverage repositories of 3D models for scene understanding. Our ability to relate what we see in an image to a large collection of 3D models allows us to transfer information from these models, creating a rich understanding of the scene. We develop a framework for auto-calibrating a camera, rendering 3D models from the viewpoint an image was taken, and computing a similarity measure between each 3D model and an input image. We demonstrate this data-driven approach in the context of geometry estimation and show the ability to find the identities, poses and styles of objects in a scene. The true benefit of 3DNN compared to a traditional 2D nearest-neighbor approach is that by generalizing across viewpoints, we free ourselves from the need to have training examples captured from all possible viewpoints. Thus, we are able to achieve comparable results using orders of magnitude less data, and recognize objects from never-before-seen viewpoints. In this work, we describe the 3DNN algorithm and rigorously evaluate its performance for the tasks of geometry estimation and object detection/segmentation, as well as two novel applications: affordance estimation and photorealistic object insertion.

This work presents an automatic algorithm for extracting vectorial land registers from altimetric data in dense urban areas. We focus on elementary shape extraction and propose a method that extracts rectangular buildings. The result is a vectorial land register that can be used, for instance, to perform precise roof shape estimation. Using a spatial point process framework, we model towns as configurations of and unknown number of rectangles. An energy is defined, which takes into account both low level information provided by the altimetry of the scene, and geometric knowledge about the disposition of buildings in towns. Estimation is done by minimizing the energy using simulated annealing. We use an MCMC sampler that is a combination of general Metropolis Hastings Green techniques and the Geyer and Møller algorithm for point process sampling. We define some original proposition kernels, such as birth or death in a neighborhood and define the energy with respect to an inhomogeneous Poisson point process. We present results on real data provided by the IGN (French National Geographic Institute). Results were obtained automatically. These results consist of configurations of rectangles describing a dense urban area.

In this paper, we propose a new variational model to segment an object belonging to a given shape space using the active contour method, a geometric shape prior and the Mumford-Shah functional. The core of our model is an energy functional composed by three complementary terms. The first one is based on a shape model which constrains the active contour to get a shape of interest. The second term detects object boundaries from image gradients. And the third term drives globally the shape prior and the active contour towards a homogeneous intensity region. The segmentation of the object of interest is given by the minimum of our energy functional. This minimum is computed with the calculus of variations and the gradient descent method that provide a system of evolution equations solved with the well-known level set method. We also prove the existence of this minimum in the space of functions with bounded variation. Applications of the proposed model are presented on synthetic and medical images.

We propose a 3D environment modelling method using multiple pairs of high-resolution spherical images. Spherical images of a scene are captured using a rotating line scan camera. Reconstruction is based on stereo image pairs with a vertical displacement between camera views. A 3D mesh model for each pair of spherical images is reconstructed by stereo matching. For accurate surface reconstruction, we propose a PDE-based disparity estimation method which produces continuous depth fields with sharp depth discontinuities even in occluded and highly textured regions. A full environment model is constructed by fusion of partial reconstruction from spherical stereo pairs at multiple widely spaced locations. To avoid camera calibration steps for all camera locations, we calculate 3D rigid transforms between capture points using feature matching and register all meshes into a unified coordinate system. Finally a complete 3D model of the environment is generated by selecting the most reliable observations among overlapped surface measurements considering surface visibility, orientation and distance from the camera. We analyse the characteristics and behaviour of errors for spherical stereo imaging. Performance of the proposed algorithm is evaluated against ground-truth from the Middlebury stereo test bed and LIDAR scans. Results are also compared with conventional structure-from-motion algorithms. The final composite model is rendered from a wide range of viewpoints with high quality textures.

Zero-Shot Learning (ZSL) involves transferring knowledge from seen classes to unseen classes by establishing connections between visual and semantic spaces. Traditional ZSL methods identify novel classes by class-level attribute vectors, which implies an information bottleneck. These approaches often use class-level attribute vectors as the fitting target during training, disregarding the individual variations within a class. Moreover, the attributes used for training lack location information and are prone to mismatch with local regions of visual features. To this end, we introduce a Near-Instance-Level Attribute Bottleneck (IAB) to alter class-level attribute vectors as well as visual features throughout the training phase to better reflect their naturalistic correspondences. Specifically, our Near-Instance-Wise Attribute Adaptation (NAA) modifies class attribute vectors to obtain multiple attribute basis vectors, generating a subspace that is more relevant to instance-level samples. Additionally, our Vision Attribute Relation Strengthening (VARS) module searches for attribute-related regions within the features, offering additional location information during the training phase. The proposed method is evaluated on four ZSL benchmarks, revealing that it is superior or competitive to the state-of-the-art methods on ZSL and the more challenging Generalized Zero-Shot Learning (GZSL) settings. Extensive experiments corroborate the sustainability of this study as one of the most potential directions for ZSL, i.e., the effectiveness of enhancing the visual-semantic relationships formed during training using a simple model structure. Code is available at: https://github.com/LanchJL/IAB-GZSL .

Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. Over the last two decades, LiDAR scanners have become the standard sensor for robot localization and mapping. This article aims to provide an overview of recent progress and advancements in LiDAR-based global localization. We begin by formulating the problem and exploring the application scope. We then present a review of the methodology, including recent advancements in several topics, such as maps, descriptor extraction, and cross-robot localization. The contents of the article are organized under three themes. The first theme concerns the combination of global place retrieval and local pose estimation. The second theme is upgrading single-shot measurements to sequential ones for sequential global localization. Finally, the third theme focuses on extending single-robot global localization to cross-robot localization in multi-robot systems. We conclude the survey with a discussion of open challenges and promising directions in global LiDAR localization. To our best knowledge, this is the first comprehensive survey on global LiDAR localization for mobile robots.