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Scalable Point Cloud-based Reconstruction with Local Implicit Functions
(2020)2020 International Conference on 3D Vision (3DV)Surface reconstruction from point clouds has been a well-studied research topic with applications in computer vision and computer graphics. Recently, several learningbased methods were proposed for 3D shape representation through implicit functions which among others can be used for point cloud-based reconstruction. Although delivering compelling results for synthetic object datasets of overseeable size, they fail to represent larger ...Conference Paper -
KAPLAN: A 3D Point Descriptor for Shape Completion
(2020)2020 International Conference on 3D Vision (3DV)We present a novel 3D shape completion method that operates directly on unstructured point clouds, thus avoiding resource-intensive data structures like voxel grids. To this end, we introduce KAPLAN, a 3D point descriptor that aggregates local shape information via a series of 2D convolutions. The key idea is to project the points in a local neighborhood onto multiple planes with different orientations. In each of those planes, point ...Conference Paper -
Self-Supervised Learning of Non-Rigid Residual Flow and Ego-Motion
(2020)2020 International Conference on 3D Vision (3DV)Most of the current scene flow methods choose to model scene flow as a per point translation vector without differentiating between static and dynamic components of 3D motion. In this work we present an alternative method for end-to-end scene flow learning by joint estimation of non-rigid residual flow and ego-motion flow for dynamic 3D scenes. We propose to learn the relative rigid transformation from a pair of point clouds followed by ...Conference Paper -
RoutedFusion: Learning Real-Time Depth Map Fusion
(2020)2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)The efficient fusion of depth maps is a key part of most state-of-the-art 3D reconstruction methods. Besides requiring high accuracy, these depth fusion methods need to be scalable and real-time capable. To this end, we present a novel real-time capable machine learning-based method for depth map fusion. Similar to the seminal depth map fusion approach by Curless and Levoy, we only update a local group of voxels to ensure real-time ...Conference Paper -
Handcrafted Outlier Detection Revisited
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020Local feature matching is a critical part of many computer vision pipelines, including among others Structure-from-Motion, SLAM, and Visual Localization. However, due to limitations in the descriptors, raw matches are often contaminated by a majority of outliers. As a result, outlier detection is a fundamental problem in computer vision and a wide range of approaches, from simple checks based on descriptor similarity to geometric verification, ...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 -
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 -
Learning 3D Semantic Reconstruction on Octrees
(2019)Lecture Notes in Computer Science ~ Pattern RecognitionConference Paper