End-to-end Learning Improves Static Object Geo-localization in Monocular Video
Accurately estimating the position of static objects, such as traffic lights, from the moving camera of a self-driving car is a challenging problem. In this work, we present a system that improves the localization of static objects by jointly-optimizing the components of the system via learning. Our system is comprised of networks that perform: 1) 5DoF object pose estimation from a single image, 2) association of objects between pairs of frames, and 3) multi-object tracking to produce the final geo-localization of the static objects within the scene. We evaluate our approach using a publicly-available data set, focusing on traffic lights due to data availability. For each component, we compare against contemporary alternatives and show significantly-improved performance. We also show that the end-to-end system performance is further improved via joint-training of the constituent models.
Code (1)
Tasks
geo-localizationMulti-Object TrackingObject TrackingPose EstimationPositionSimilar Papers 제목 키워드 기반
CoGS: Compositional Dynamic Human-Object Scenes Gaussian Splatting from Monocular Video
Reconstructing dynamic human--object interaction scenes from monocular video is difficult because the human, manipulated object, and background obey different motion models while sharing the same pixels. Existing dynamic…
Kinematic 3D Object Detection in Monocular Video
Perceiving the physical world in 3D is fundamental for self-driving applications. Although temporal motion is an invaluable resource to human vision for detection, tracking, and depth perception, such features have not b…
3D Object DetectionMonocular 3D Object DetectionObjectobject-detection+2D$^2$NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video
Given a monocular video, segmenting and decoupling dynamic objects while recovering the static environment is a widely studied problem in machine intelligence. Existing solutions usually approach this problem in the imag…
Image SegmentationNeRFSemantic SegmentationShadow RemovalDyBluRF: Dynamic Deblurring Neural Radiance Fields for Blurry Monocular Video
Neural Radiance Fields (NeRF), initially developed for static scenes, have inspired many video novel view synthesis techniques. However, the challenge for video view synthesis arises from motion blur, a consequence of ob…
DeblurringNeRFNovel View SynthesisLearning Geocentric Object Pose in Oblique Monocular Images
An object's geocentric pose, defined as the height above ground and orientation with respect to gravity, is a powerful representation of real-world structure for object detection, segmentation, and localization tasks usi…
Earth ObservationObjectobject-detectionObject Detection+2