# Point cloud A set of data points in 3D space, typically containing XYZ coordinates and possibly attributes such as intensity, color, or classification. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Supporter spotlight: Jochen Sprickerhof on reproducible builds DevFeed: [Supporter spotlight: Jochen Sprickerhof on reproducible builds]() Original publisher: [Read original article]() Published: 2026-09-15T03:54:00Z Content type: news Language: en Sources: [reproducible-builds.org]() Topics: [reproducible builds](), [Open Source](), [Debian](), [Programming](), [F-Droid](), [Robotics](), [Point cloud]() Tags: [debian](), [open-source](), [org](), [programming](), [reproducible-builds](), [robotics]() ### AI overview An interview with Jochen Sprickerhof, a freelance programmer and Reproducible Builds core team member, covering his work on Debian, F-Droid, software projects, and bit-for-bit reproduction of Debian packages. ### Source excerpt The Reproducible Builds project relies on several projects, supporters and sponsors for financial support, but they are also valued as ambassadors who spread the word about our project and the work that we do. This is the ninth installment in a series featuring the projects, companies and individuals who support the Reproducible Builds project. We started this series by featuring the Civil Infrastructure Platform project, and followed this up with a post about the Ford Foundation as well as recent ones about ARDC, the Google Open Source Security Team (GOSST), Bootstrappable Builds, the F-Droid project, David A. Wheeler, Simon Butler and Kees Cook. Today, however, we will be talking with Jochen Sprickerhof, one of the newer members of the Reproducible Builds project core team. Vagrant Cascadian: Could you tell me a bit about yourself? What sort of things do you work on? Jochen Sprickerhof: I am a freelance programmer working on Open Source. Mainly doing Debian, F-Droid and some smaller software projects. In general I made it a habit to look into every software I use and try to fix bugs or add features I need. In Debian, I maintain about 180 packages with topics covering home banking, build systems and robotics. Most of my time, I currently work on reproduce.debian.net, where we try to bit-for-bit reproduce the packages distributed by Debian. Vagrant: Could you describe the path that lead you to working on reproducible builds? Jochen: I started my Debian journey as a teenager, converting my school to Debian and serving as its system administrator for 13 years. After studying Applied System Science, I joined the university's robotics labs, where I worked on the Robot Operating System (ROS) and the Point Cloud Library (PCL). In the end, I enjoyed programming more than writing papers, so I eventually left academia for a robotics startup. Some years ago, I realized that the open source work I was doing in my spare time was actually the work I cared most about. Nowadays I ## AI-generated synthetic neurons speed up brain mapping DevFeed: [AI-generated synthetic neurons speed up brain mapping]() Original publisher: [Read original article]() Published: 2026-04-16T12:18:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [AI Models](), [Google](), [Point cloud](), [Training AI Models](), [datasets](), [neuron](), [data]() Tags: [3d](), [accelerate](), [ai](), [classification](), [errors](), [general-science](), [generation](), [google](), [health-bioscience](), [iclr](), [iclr-2026](), [images](), [machine-intelligence](), [mapping](), [model](), [models](), [neuron](), [partners](), [research](), [scale](), [science](), [synthetic](), [visualization]() ### AI overview Google Research describes how MoGen generates synthetic neuronal shapes to improve AI models that reconstruct brain wiring maps. Adding synthetic training examples reduced reconstruction errors by 4.4%, potentially saving 157 person-years of manual proofreading for a complete mouse brain. ### Source excerpt General Science ## Metric Depth from a Single Camera: How Robots See Without LiDAR DevFeed: [Metric Depth from a Single Camera: How Robots See Without LiDAR]() Original publisher: [Read original article]() Published: 2026-04-07T00:00:00Z Content type: tutorial Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Artificial Intelligence](), [Algorithms](), [systems](), [Point cloud](), [3D]() Tags: [3d](), [ai](), [algorithm](), [camera](), [lidar]() ### AI overview This article explains how robots can estimate dense, metric depth from a single camera by combining an AI depth-estimation model with a tracking algorithm that provides real-world scale. It contrasts this approach with LiDAR and describes the role of depth ambiguity in camera-based navigation. ### Source excerpt Take a photo of a coffee mug on your desk. Now look at that photo... ## The Čech Complex and the Vietoris-Rips Complex DevFeed: [The Čech Complex and the Vietoris-Rips Complex]() Original publisher: [Read original article]() Published: 2015-08-06T09:00:00Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [data](), [datasets](), [Point cloud](), [Computing]() Tags: [approximation](), [cech-complex](), [complex](), [computational-topology](), [data-mining](), [data-science](), [dataset](), [homology](), [linear-algebra](), [mathematics](), [persistent-homology](), [point](), [points](), [simplicial-complex](), [vietoris-rips-complex]() ### AI overview This article introduces computational topology for analyzing the shape of data. It explains how point clouds can be converted into simplicial complexes so homology and persistent homology can identify qualitative features such as connected components and holes, with some resistance to noise. ### Source excerpt It's about time we got back to computational topology. Previously in this series we endured a lightning tour of the fundamental group and homology, then we saw how to compute the homology of a simplicial complex using linear algebra. What we really want to do is talk about the inherent shape of data. Homology allows us to compute some qualitative features of a given shape, i.e., find and count the number of connected components or a given shape, or the number of "2-dimensional holes" it has.