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Automatic 3D reconstruction of manifold meshes via delaunay triangulation and mesh sweeping
(2016)2016 IEEE Winter Conference on Applications of Computer Vision (WACV)In this paper we propose a new approach to incrementally initialize a manifold surface for automatic 3D reconstruction from images. More precisely we focus on the automatic initialization of a 3D mesh as close as possible to the final solution; indeed many approaches require a good initial solution for further refinement via multi-view stereo techniques. Our novel algorithm automatically estimates an initial manifold mesh for surface ...Conference Paper -
Shape As Points: A Differentiable Poisson Solver
(2021)Advances in Neural Information Processing Systems 34In recent years, neural implicit representations gained popularity in 3D reconstruction due to their expressiveness and flexibility. However, the implicit nature of neural implicit representations results in slow inference times and requires careful initialization. In this paper, we revisit the classic yet ubiquitous point cloud representation and introduce a differentiable point-to-mesh layer using a differentiable formulation of Poisson ...Conference Paper -
Augmenting crowd-sourced 3D reconstructions using semantic detections
(2018)2018 IEEE/CVF Conference on Computer Vision and Pattern RecognitionImage-based 3D reconstruction for Internet photo collections has become a robust technology to produce impressive virtual representations of real-world scenes. However, several fundamental challenges remain for Structure-from-Motion (SfM) pipelines, namely: the placement and reconstruction of transient objects only observed in single views, estimating the absolute scale of the scene, and (suprisingly often) recovering ground surfaces in ...Conference Paper -
Semantic Visual Localization
(2018)2018 IEEE/CVF Conference on Computer Vision and Pattern RecognitionRobust visual localization under a wide range of viewing conditions is a fundamental problem in computer vision. Handling the difficult cases of this problem is not only very challenging but also of high practical relevance, e.g., in the context of life-long localization for augmented reality or autonomous robots. In this paper, we propose a novel approach based on a joint 3D geometric and semantic understanding of the world, enabling it ...Conference Paper -
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
(2018)2018 IEEE/CVF Conference on Computer Vision and Pattern RecognitionWe seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a new large-scale visual localization method targeted for indoor environments. The method proceeds along three steps: (i) efficient retrieval of candidate poses that ensures scalability to large-scale environments, (ii) pose estimation using dense matching ...Conference Paper -
LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies
(2021)2021 International Conference on 3D Vision (3DV)3D representation and reconstruction of human bodies have been studied for a long time in computer vision. Traditional methods rely mostly on parametric statistical linear models, limiting the space of possible bodies to linear combinations. It is only recently that some approaches try to leverage neural implicit representations for human body modeling, and while demonstrating impressive results, they are either limited by representation ...Conference Paper -
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis
(2021)2021 International Conference on 3D Vision (3DV)Building upon the recent progress in novel view synthesis, we propose its application to improve monocular depth estimation. In particular, we propose a novel training method split in three main steps. First, the prediction results of a monocular depth network are warped to an additional view point. Second, we apply an additional image synthesis network, which corrects and improves the quality of the warped RGB image. The output of this ...Conference Paper -
Privacy Preserving Localization and Mapping from Uncalibrated Cameras
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Recent works on localization and mapping from privacy preserving line features have made significant progress towards addressing the privacy concerns arising from cloud-based solutions in mixed reality and robotics. The requirement for calibrated cameras is a fundamental limitation for these approaches, which prevents their application in many crowd-sourced mapping scenarios. In this paper, we propose a solution to the uncalibrated privacy ...Conference Paper -
Holistic 3D Scene Understanding From a Single Image With Implicit Representation
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We present a new pipeline for holistic 3D scene understanding from a single image, which could predict object shapes, object poses, and scene layout. As it is a highly ill-posed problem, existing methods usually suffer from inaccurate estimation of both shapes and layout especially for the cluttered scene due to the heavy occlusion between objects. We propose to utilize the latest deep implicit representation to solve this challenge. We ...Conference Paper -
LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Multi-sensor fusion of multi-modal measurements from commodity inertial, visual and LiDAR sensors to provide robust and accurate 6DOF pose estimation holds great potential in robotics and beyond. In this paper, building upon our prior work (i.e., LIC-Fusion), we develop a sliding-window filter based LiDAR-Inertial-Camera odometry with online spatiotemporal calibration (i.e., LIC-Fusion 2.0), which introduces a novel sliding-window ...Conference Paper