Where to drive: free space detection with one fisheye camera

• URL: http://arxiv.org/abs/2011.05822v1
• Date: Wed, 11 Nov 2020 14:36:45 GMT
• Title: Where to drive: free space detection with one fisheye camera
• Authors: Tobias Scheck, Adarsh Mallandur, Christian Wiede, Gangolf Hirtz
• Abstract summary: We propose to use synthetic training data based on Unity3D. A five-pass algorithm is used to create a virtual fisheye camera. The results indicate that synthetic fisheye images can be used in deep learning context.
• Score: 1.7499351967216341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development in the field of autonomous driving goes hand in hand with ever new developments in the field of image processing and machine learning methods. In order to fully exploit the advantages of deep learning, it is necessary to have sufficient labeled training data available. This is especially not the case for omnidirectional fisheye cameras. As a solution, we propose in this paper to use synthetic training data based on Unity3D. A five-pass algorithm is used to create a virtual fisheye camera. This synthetic training data is evaluated for the application of free space detection for different deep learning network architectures. The results indicate that synthetic fisheye images can be used in deep learning context.

Related papers

• Motion Degeneracy in Self-supervised Learning of Elevation Angle Estimation for 2D Forward-Looking Sonar [4.683630397028384]
  This study aims to realize stable self-supervised learning of elevation angle estimation without pretraining using synthetic images. We first analyze the motion field of 2D forward-looking sonar, which is related to the main supervision signal.
  arXiv  Detail & Related papers  (2023-07-30T08:06:11Z)
• Neural Implicit Dense Semantic SLAM [83.04331351572277]
  We propose a novel RGBD vSLAM algorithm that learns a memory-efficient, dense 3D geometry, and semantic segmentation of an indoor scene in an online manner. Our pipeline combines classical 3D vision-based tracking and loop closing with neural fields-based mapping. Our proposed algorithm can greatly enhance scene perception and assist with a range of robot control problems.
  arXiv  Detail & Related papers  (2023-04-27T23:03:52Z)
• Markerless Camera-to-Robot Pose Estimation via Self-supervised Sim-to-Real Transfer [26.21320177775571]
  We propose an end-to-end pose estimation framework that is capable of online camera-to-robot calibration and a self-supervised training method. Our framework combines deep learning and geometric vision for solving the robot pose, and the pipeline is fully differentiable.
  arXiv  Detail & Related papers  (2023-02-28T05:55:42Z)
• Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing [77.34726150561087]
  This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks.
  arXiv  Detail & Related papers  (2022-11-02T11:10:42Z)
• Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network [70.53093934205057]
  3D object detection task from lidar or camera sensors is essential for autonomous driving. We propose a novel semantic passing framework, named SPNet, to boost the performance of existing lidar-based 3D detection models.
  arXiv  Detail & Related papers  (2022-07-12T12:35:34Z)
• Stereo Matching by Self-supervision of Multiscopic Vision [65.38359887232025]
  We propose a new self-supervised framework for stereo matching utilizing multiple images captured at aligned camera positions. A cross photometric loss, an uncertainty-aware mutual-supervision loss, and a new smoothness loss are introduced to optimize the network. Our model obtains better disparity maps than previous unsupervised methods on the KITTI dataset.
  arXiv  Detail & Related papers  (2021-04-09T02:58:59Z)
• FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images [2.187613144178315]
  Keypoint detection and description is a commonly used building block in computer vision systems. SuperPoint is a self-supervised keypoint detector and descriptor that has achieved state-of-the-art results on homography estimation. We introduce a fisheye adaptation pipeline to enable training on undistorted fisheye images.
  arXiv  Detail & Related papers  (2021-02-27T11:26:34Z)
• Supervised Training of Dense Object Nets using Optimal Descriptors for Industrial Robotic Applications [57.87136703404356]
  Dense Object Nets (DONs) by Florence, Manuelli and Tedrake introduced dense object descriptors as a novel visual object representation for the robotics community. In this paper we show that given a 3D model of an object, we can generate its descriptor space image, which allows for supervised training of DONs. We compare the training methods on generating 6D grasps for industrial objects and show that our novel supervised training approach improves the pick-and-place performance in industry-relevant tasks.
  arXiv  Detail & Related papers  (2021-02-16T11:40:12Z)
• PennSyn2Real: Training Object Recognition Models without Human Labeling [12.923677573437699]
  We propose PennSyn2Real - a synthetic dataset consisting of more than 100,000 4K images of more than 20 types of micro aerial vehicles (MAVs) The dataset can be used to generate arbitrary numbers of training images for high-level computer vision tasks such as MAV detection and classification. We show that synthetic data generated using this framework can be directly used to train CNN models for common object recognition tasks such as detection and segmentation.
  arXiv  Detail & Related papers  (2020-09-22T02:53:40Z)
• Deep Traffic Sign Detection and Recognition Without Target Domain Real Images [52.079665469286496]
  We propose a novel database generation method that requires no real image from the target-domain, and (ii) templates of the traffic signs. The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available. On large data sets, training with a fully synthetic data set almost matches the performance of training with a real one.
  arXiv  Detail & Related papers  (2020-07-30T21:06:47Z)
• DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization [21.096166727043077]
  We propose a real-time deep learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image. An image-to-image translation network is employed to bridge the gap between the rendered real images producing synthetic images for training.
  arXiv  Detail & Related papers  (2020-03-11T21:11:05Z)

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.