Awesome works and resources relevant to 360 vision. Suggestions or pull requests are welcome. Inspired by awesome-deep-vision, awesome-adversarial-machine-learning, awesome-deep-learning-papers, and awesome-architecture-search.

However, current MLaaS are mostly 2D image-designated and not optimized for the distinctive characteristics of raw 360° video frames. In this paper, we propose a novel MLaaS-based system to address this challenge.

We present a large-scale dataset that contains rich sensory information and full annotations. We recorded several suburbs of Karlsruhe, Germany, corresponding to over 320k images and 100k laser scans in a driving distance of 73.7km.

Jan 13, 2025 · 2D vision systems capture images in two dimensions, specifically along the X (width) and Y (height) axes. They produce flat, planar images that represent objects’ contours without depth information.

Jul 7, 2021 · 2D vision systems use digital cameras to capture the image of an object. With 2D machine vision, a two-dimensional map (X, Y) of reflected intensity is captured and processed. The 2D image, however, does not provide any height information.

In this paper, an experimental setup for real-time de-tection and localization for a 360°-camera using a YOLO algorithm will be presented.

• Coverage of image-acquisition modules and preprocessing functions within a unified framework. • Derivation of 2D image-processing functions from first principles by extending 1D signal-processing concepts.

Machine vision is a technology used to provide imaging-based automatic inspection and analysis. With 2D machine vision, a two dimensional map (X, Y) of reflected intensity is captured and processed. Processing typically consists of comparing variations in intensity (contrast).

There are four main techniques for achieving 3D machine vision systems: laser triangulation; stereo vision; time-of-flight and structured light. These we’ll discuss and compare in a separate article. The Zivid 3D color cameras employs the structured light technique.

2D Feature Tracking in Camera Images for Sensor Fusion Goal The goal of this project is to build a collision detection system by tracking the features in a image.