Search
Results
-
Reflection Separation using a Pair of Unpolarized and Polarized Images
(2020)Advances in Neural Information Processing Systems 32When we take photos through glass windows or doors, the transmitted background scene is often blended with undesirable reflection. Separating two layers apart to enhance the image quality is of vital importance for both human and machine perception. In this paper, we propose to exploit physical constraints from a pair of unpolarized and polarized images to separate reflection and transmission layers. Due to the simplified capturing setup, ...Conference Paper -
Compression and Completion of Animated Point Clouds using Topological Properties of the Manifold
(2020)2020 International Conference on 3D Vision (3DV)Recent progress in consumer hardware allowed for the collection of a large amount of animated point cloud data, which is on the one hand highly redundant and on the other hand incomplete. Our goal is to bridge this gap and find a low dimensional representation capable of approximation to a desired precision and completion of missing data. Model-less non-rigid 3D reconstruction algorithms, formulated as a linear factorization of observed ...Conference Paper -
Probabilistic 3D surface reconstruction from sparse MRI information
(2020)Lecture Notes in Computer Science ~ Medical Image Computing and Computer Assisted Intervention – MICCAI 2020Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool should: be fast in prediction of accurate well localised and high resolution models, evaluate prediction uncertainty, work with as little input data as possible. Current deep learning state of the art (SOTA) 3D reconstruction methods, however, often only produce ...Conference Paper -
Online Invariance Selection for Local Feature Descriptors
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020To be invariant, or not to be invariant: that is the question formulated in this work about local descriptors. A limitation of current feature descriptors is the trade-off between generalization and discriminative power: more invariance means less informative descriptors. We propose to overcome this limitation with a disentanglement of invariance in local descriptors and with an online selection of the most appropriate invariance given ...Conference Paper -
Geometry-Aware Satellite-to-Ground Image Synthesis for Urban Areas
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We present a novel method for generating panoramic street-view images which are geometrically consistent with a given satellite image. Different from existing approaches that completely rely on a deep learning architecture to generalize cross-view image distributions, our approach explicitly loops in the geometric configuration of the ground objects based on the satellite views, such that the produced ground view synthesis preserves the ...Conference Paper -
DIST: Rendering Deep Implicit Signed Distance Function With Differentiable Sphere Tracing
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We propose a differentiable sphere tracing algorithm to bridge the gap between inverse graphics methods and the recently proposed deep learning based implicit signed distance function. Due to the nature of the implicit function, the rendering process requires tremendous function queries, which is particularly problematic when the function is represented as a neural network. We optimize both the forward and backward pass of our rendering ...Conference Paper -
Learned Semantic Multi-Sensor Depth Map Fusion
(2020)2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)Conference Paper -
Learned Multi-View Texture Super-Resolution
(2019)2019 International Conference on 3D Vision (3DV)Conference Paper -
Efficient 2D-3D Matching for Multi-Camera Visual Localization
(2019)2019 International Conference on Robotics and Automation (ICRA)Conference Paper -
Real-Time Dense Mapping for Self-Driving Vehicles using Fisheye Cameras
(2019)2019 International Conference on Robotics and Automation (ICRA)Conference Paper