Reconstruction of 3D flight trajectories from ad-hoc camera networks

• URL: http://arxiv.org/abs/2003.04784v2
• Date: Wed, 29 Jul 2020 09:40:39 GMT
• Title: Reconstruction of 3D flight trajectories from ad-hoc camera networks
• Authors: Jingtong Li, Jesse Murray, Dorina Ismaili, Konrad Schindler, Cenek Albl
• Abstract summary: We present a method to reconstruct the 3D trajectory of an airborne robotic system only from videos recorded with cameras that are unsynchronized. Our approach enables robust and accurate outside-in tracking of dynamically flying targets, with cheap and easy-to-deploy equipment.
• Score: 19.96488566402593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a method to reconstruct the 3D trajectory of an airborne robotic system only from videos recorded with cameras that are unsynchronized, may feature rolling shutter distortion, and whose viewpoints are unknown. Our approach enables robust and accurate outside-in tracking of dynamically flying targets, with cheap and easy-to-deploy equipment. We show that, in spite of the weakly constrained setting, recent developments in computer vision make it possible to reconstruct trajectories in 3D from unsynchronized, uncalibrated networks of consumer cameras, and validate the proposed method in a realistic field experiment. We make our code available along with the data, including cm-accurate ground-truth from differential GNSS navigation.

Related papers

• Learning Camera Movement Control from Real-World Drone Videos [25.10006841389459]
  Existing AI videography methods struggle with limited appearance diversity in simulation training. We propose a scalable method that involves collecting real-world training data. We show that our system effectively learns to perform challenging camera movements.
  arXiv  Detail & Related papers  (2024-12-12T18:59:54Z)
• Stereo4D: Learning How Things Move in 3D from Internet Stereo Videos [76.07894127235058]
  We present a system for mining high-quality 4D reconstructions from internet stereoscopic, wide-angle videos. We use this method to generate large-scale data in the form of world-consistent, pseudo-metric 3D point clouds. We demonstrate the utility of this data by training a variant of DUSt3R to predict structure and 3D motion from real-world image pairs.
  arXiv  Detail & Related papers  (2024-12-12T18:59:54Z)
• Street Gaussians without 3D Object Tracker [86.62329193275916]
  Existing methods rely on labor-intensive manual labeling of object poses to reconstruct dynamic objects in canonical space and move them based on these poses during rendering. We propose a stable object tracking module by leveraging associations from 2D deep trackers within a 3D object fusion strategy. We address inevitable tracking errors by further introducing a motion learning strategy in an implicit feature space that autonomously corrects trajectory errors and recovers missed detections.
  arXiv  Detail & Related papers  (2024-12-07T05:49:42Z)
• DynOMo: Online Point Tracking by Dynamic Online Monocular Gaussian Reconstruction [65.46359561104867]
  We target the challenge of online 2D and 3D point tracking from unposed monocular camera input. We leverage 3D Gaussian splatting to reconstruct dynamic scenes in an online fashion. We aim to inspire the community to advance online point tracking and reconstruction, expanding the applicability to diverse real-world scenarios.
  arXiv  Detail & Related papers  (2024-09-03T17:58:03Z)
• Reconstructing Satellites in 3D from Amateur Telescope Images [42.850623200702394]
  This paper proposes a framework for the 3D reconstruction of satellites in low-Earth orbit, utilizing videos captured by small amateur telescopes. The video data obtained from these telescopes differ significantly from data for standard 3D reconstruction tasks, characterized by intense motion blur, atmospheric turbulence, pervasive background light pollution, extended focal length and constrained observational perspectives. We apply a customized Structure from Motion (SfM) approach, followed by an improved 3D Gaussian splatting algorithm, to achieve high-fidelity 3D model reconstruction.
  arXiv  Detail & Related papers  (2024-04-29T03:13:09Z)
• 3D Data Augmentation for Driving Scenes on Camera [50.41413053812315]
  We propose a 3D data augmentation approach termed Drive-3DAug, aiming at augmenting the driving scenes on camera in the 3D space. We first utilize Neural Radiance Field (NeRF) to reconstruct the 3D models of background and foreground objects. Then, augmented driving scenes can be obtained by placing the 3D objects with adapted location and orientation at the pre-defined valid region of backgrounds.
  arXiv  Detail & Related papers  (2023-03-18T05:51:05Z)
• Aerial Monocular 3D Object Detection [67.20369963664314]
  DVDET is proposed to achieve aerial monocular 3D object detection in both the 2D image space and the 3D physical space. To address the severe view deformation issue, we propose a novel trainable geo-deformable transformation module. To encourage more researchers to investigate this area, we will release the dataset and related code.
  arXiv  Detail & Related papers  (2022-08-08T08:32:56Z)
• Neural Scene Representation for Locomotion on Structured Terrain [56.48607865960868]
  We propose a learning-based method to reconstruct the local terrain for a mobile robot traversing urban environments. Using a stream of depth measurements from the onboard cameras and the robot's trajectory, the estimates the topography in the robot's vicinity. We propose a 3D reconstruction model that faithfully reconstructs the scene, despite the noisy measurements and large amounts of missing data coming from the blind spots of the camera arrangement.
  arXiv  Detail & Related papers  (2022-06-16T10:45:17Z)
• Real-time dense 3D Reconstruction from monocular video data captured by low-cost UAVs [0.3867363075280543]
  Real-time 3D reconstruction enables fast dense mapping of the environment which benefits numerous applications, such as navigation or live evaluation of an emergency. In contrast to most real-time capable approaches, our approach does not need an explicit depth sensor. By exploiting the self-motion of the unmanned aerial vehicle (UAV) flying with oblique view around buildings, we estimate both camera trajectory and depth for selected images with enough novel content.
  arXiv  Detail & Related papers  (2021-04-21T13:12:17Z)
• Integration of the 3D Environment for UAV Onboard Visual Object Tracking [7.652259812856325]
  Single visual object tracking from an unmanned aerial vehicle poses fundamental challenges. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. By representing the position of the target in 3D space rather than in image space, we stabilize the tracking during ego-motion.
  arXiv  Detail & Related papers  (2020-08-06T18:37:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.