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Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)This work addresses the challenging task of LiDAR-based 3D object detection in foggy weather. Collecting and annotating data in such a scenario is very time, labor and cost intensive. In this paper, we tackle this problem by simulating physically accurate fog into clear-weather scenes, so that the abundant existing real datasets captured in clear weather can be repurposed for our task. Our contributions are twofold: 1) We develop a ...Conference Paper -
Multi-View Stereo and LIDAR for Outdoor Scene Modelling
(2007)International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesIn this paper we want to start the discussion on whether image based3-D modelling techniques and especially multi-view stereo canpossibly be used to replaceLIDARsystems for outdoor 3D data acquisition. Two main issues have to be addressed in this context:(i) camera self-calibration and (ii) dense multi-view depth estimation. To investigate both, we have acquired test data from outdoorscenes withLIDARand cameras. Using theLIDARdata as ...Conference Paper -
3D Reconstruction with a Collaborative Approach Based on Smartphones and a Cloud-Based Server
(2017)International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesConference Paper -
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. Semantic segmentation and monocular depth estimation are shown to be complementary tasks; in a multi-task learning setting, a proper encoding of their relationships can further improve performance on both tasks. Motivated by this observation, we propose a novel Cross-Task Relation Layer (CTRL), ...Conference Paper -
Robust Aerial Object Tracking in High Dynamic Flight Maneuvers
(2015)ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information SciencesIntegrating drones into the civil airspace is one of the biggest challenges for civil aviation, responsible authorities and involved com- panies around the world in the upcoming years. For a full integration into non-segregated airspace such a system has to provide the capability to automatically detect and avoid other airspace users. Electro-optical cameras have proven to be an adequate sensor to detect all types of aerial objects, ...Conference Paper -
A smartphone-based 3D pipeline for the creative industry - The replicate eu project
(2017)International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ~ 3D Virtual Reconstruction and Visualization of Complex ArchitecturesDuring the last two decades we have witnessed great improvements in ICT hardware and software technologies. Three-dimensional content is starting to become commonplace now in many applications. Although for many years 3D technologies have been used in the generation of assets by researchers and experts, nowadays these tools are starting to become commercially available to every citizen. This is especially the case for smartphones, that ...Conference Paper -
L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research. In particular, event cameras have emerged as a compelling alternative to frame-based cameras in low-power and latency-demanding applications. To enable event cameras to operate alongside staple sensors like lidar in perception tasks, we propose a direct, temporally-decoupled extrinsic calibration method ...Conference Paper -
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take
(2023)2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Neural-network-based single image depth prediction (SIDP) is a challenging task where the goal is to predict the scene's per-pixel depth at test time. Since the problem, by definition, is ill-posed, the fundamental goal is to come up with an approach that can reliably model the scene depth from a set of training examples. In the pursuit of perfect depth estimation, most existing state-of-the-art learning techniques predict a single scalar ...Conference Paper -
The Interestingness of Images
(2013)2013 IEEE International Conference on Computer Vision (ICCV 2013)Conference Paper -
P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior
(2022)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Monocular depth estimation is vital for scene understanding and downstream tasks. We focus on the supervised setup, in which ground-truth depth is available only at training time. Based on knowledge about the high regularity of real 3D scenes, we propose a method that learns to selectively leverage information from coplanar pixels to improve the predicted depth. In particular, we introduce a piecewise planarity prior which states that for ...Conference Paper