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LaMAR: Benchmarking Localization and Mapping for Augmented Reality
(2022)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2022Localization and mapping is the foundational technology for augmented reality (AR) that enables sharing and persistence of digital content in the real world. While significant progress has been made, researchers are still mostly driven by unrealistic benchmarks not representative of real-world AR scenarios. In particular, benchmarks are often based on small-scale datasets with low scene diversity, captured from stationary cameras, and ...Conference Paper -
Learning to Find Good Models in RANSAC
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We propose the Model Quality Network, MQ-Net in short, for predicting the quality, e.g. the pose error of essential matrices, of models generated inside RANSAC. It replaces the traditionally used scoring techniques, e.g., inlier counting of RANSAC, truncated loss of MSAC, and the marginalization-based loss of MAGSAC++. Moreover, Minimal samples Filtering Network (MF-Net) is proposed for the early rejection of minimal samples that likely ...Conference Paper -
Privacy Preserving Partial Localization
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Recently proposed privacy preserving solutions for cloud-based localization rely on lifting traditional point-based maps to randomized 3D line clouds. While the lifted representation is effective in concealing private information, there are two fundamental limitations. First, without careful construction of the line clouds, the representation is vulnerable to density-based inversion attacks. Secondly, after successful localization, the ...Conference Paper -
Visual Localization via Few-Shot Scene Region Classification
(2022)2022 International Conference on 3D Vision (3DV)Visual (re)localization addresses the problem of estimating the 6-DoF (Degree of Freedom) camera pose of a query image captured in a known scene, which is a key building block of many computer vision and robotics applications. Recent advances in structure-based localization solve this problem by memorizing the mapping from image pixels to scene coordinates with neural networks to build 2D-3D correspondences for camera pose optimization. ...Conference Paper -
Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. To this end, we model the blurred appearance of a fast moving object in a generative fashion by parametrizing its 3D position, rotation, velocity, acceleration, bounces, shape, and texture over the duration of a predefined time window spanning multiple frames. Using differentiable rendering, we are able to ...Conference Paper -
Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency
(2022)2022 International Conference on Robotics and Automation (ICRA)For robotic interaction in environments shared with other agents, access to volumetric and semantic maps of the scene is crucial. However, such environments are inevitably subject to long-term changes, which the map needs to account for. We thus propose panoptic multi-TSDFs as a novel representation for multi-resolution volumetric mapping in changing environments. By leveraging high-level information for 3D reconstruction, our proposed ...Conference Paper -
Quantification of Predictive Uncertainty via Inference-Time Sampling
(2022)Lecture Notes in Computer Science ~ Uncertainty for Safe Utilization of Machine Learning in Medical ImagingPredictive variability due to data ambiguities has typically been addressed via construction dedicated models with built-in probabilistic capabilities that are trained to predict uncertainty estimates as variables of interest. These approaches require distinct architectural components and training mechanisms, may include restrictive assumptions and exhibit overconfidence, i.e., high confidence in imprecise predictions. In this work, we ...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 -
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