1"use strict";(self.webpackChunkmy_website=self.webpackChunkmy_website||[]).push([[4414],{5274:(e,s,n)=>{n.r(s),n.d(s,{assets:()=>o,contentTitle:()=>a,default:()=>d,frontMatter:()=>l,metadata:()=>i,toc:()=>c});const i=JSON.parse('{"id":"community_projects","title":"Community Projects","description":"<meta","source":"@site/docs/20_community_projects.md","sourceDirName":".","slug":"/community_projects","permalink":"/community_projects","draft":false,"unlisted":false,"tags":[],"version":"current","sidebarPosition":20,"frontMatter":{"title":"Community Projects","hide_title":true},"sidebar":"overviewSidebar","previous":{"title":"First word discovered","permalink":"/firstletters"},"next":{"title":"The Villa dei Papiri in LEGO\xae","permalink":"/villa_model"}}');var t=n(4848),r=n(8453);const l={title:"Community Projects",hide_title:!0},a="\ud83d\udcdc Awesome Scroll Tools Awesome",o={},c=[{value:"\ud83d\udcca Data 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Reports",id:"-reports-3",level:3}];function h(e){const s={a:"a",code:"code",em:"em",h1:"h1",h2:"h2",h3:"h3",h4:"h4",header:"header",img:"img",li:"li",p:"p",strong:"strong",ul:"ul",...(0,r.R)(),...e.components},{Head:n}=s;return n||function(e,s){throw new Error("Expected "+(s?"component":"object")+" `"+e+"` to be defined: you likely forgot to import, pass, or provide it.")}("Head",!0),(0,t.jsxs)(t.Fragment,{children:[(0,t.jsxs)(n,{children:[(0,t.jsx)("html",{"data-theme":"dark"}),(0,t.jsx)("meta",{name:"description",content:"Open-source tools built by the Vesuvius Challenge community for data access, segmentation, and ink detection to help read the buried Herculaneum scrolls."}),(0,t.jsx)("meta",{property:"og:type",content:"website"}),(0,t.jsx)("meta",{property:"og:url",content:"https://scrollprize.org"}),(0,t.jsx)("meta",{property:"og:title",content:"Vesuvius Challenge"}),(0,t.jsx)("meta",{property:"og:description",content:"Open-source tools built by the Vesuvius Challenge community 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",(0,t.jsx)(s.em,{children:"Visualization"}),"."]}),"\n",(0,t.jsx)(s.p,{children:"Some highlighted contributions are added to this repository as submodules."}),"\n",(0,t.jsxs)(s.p,{children:["We keep this repository updated as much as we can, but research moves ",(0,t.jsx)(s.em,{children:"fast"}),"! \ud83c\udfc3\ud83d\udca8"]}),"\n",(0,t.jsxs)(s.p,{children:["For state-of-the-art updates j
1oin our ",(0,t.jsx)(s.a,{href:"https://discord.com/invite/uTfNwwecCQ",children:"Discord server"})," \ud83d\udcac\u23f0"]}),"\n",(0,t.jsx)(s.h2,{id:"-data-accessvisualization",children:"\ud83d\udcca Data access/visualization"}),"\n",(0,t.jsx)(s.h3,{id:"-highlighted",children:"\ud83c\udf1f Highlighted"}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/scrollprize/vesuvius",children:"vesuvius"}),": Python library for accessing Vesuvius Challenge data. 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By Johannes Rudolph"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/mvrcii/phalanx",children:"vesuvius-phalanx"}),": Python library / CLI for accessing Vesuvius data. Allows flexible access to volume and fragment scroll data. By Marcel Roth"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/climbmax123/LLFIOCunkloadingTestingAndBenching",children:"llfio-chunkloader"}),": A method to access data in chunks of (x,y,z) that is much faster and more compute-efficient than Zarr. (Written in C++ but it is possible to integrate in Python)."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/usc-caisplusplus/scroll-data-preprocessing",children:"preprocessed-data"}),": Data preprocessing code and a fully processed version of the dataset in .zarr format to allow for faster training of ink detection models."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/Bullo27/scroll-data-audit",children:"scroll-data-audit"})," by Matteo Bulloni. Integrity auditor for the open-data: reconciles the catalog (",(0,t.jsx)(s.code,{children:"metadata.json"}),") against the actual Zarr arrays, filenames and scan metadata, and verifies multiscale pyramid value-correctness. Reported a Scroll 5 (PHerc0172) catalog shape error (",(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/issues/1211",children:"#1211"}),") and certified the rest of the open-data consistent."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/TAUIL-Abd-Elilah/vesuvius-repro",children:"vesuvius-repro"}),": Regional reproducibility spot-checks of one selected 256\xb3 region (central 128\xb3 scored) from each of 41 m7 artifacts across 36 scrolls. Forty TTA-off checks match at Dice 0.9983\u20131.0000; PHerc. Paris 4 matches at 0.9999 with TTA on. The audit exposed previously missing configuration provenance; ",(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/pull/1253",children:"#1253"})," now records TTA, and maintainers backfilled existing artifacts. Also resolves each artifact's CT level. By TAUIL Abd Elilah"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/Schurkai/vesuvius-catalog",children:"vesuvius-catalog"}),": scriptable catalog CLI/library for the open-data bucket - answers which samples have segments, ink outputs or surface predictions at which resolutions, resolves S3/HTTPS data URLs (JSON/CSV output for scripting), and includes working openers for the bucket's OME-Zarr v2 stores under zarr-python 3."]}),"\n"]}),"\n"]}),"\n",(0,t.jsx)(s.h2,{id:"segmentation",children:"Segmentation"}),"\n",(0,t.jsx)(s.h3,{id:"-highlighted-1",children:"\ud83c\udf1f Highlighted"}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsxs)(s.li,{children:[(0,t.jsx)(s.a,{href:"https://github.com/KhartesViewer/khartes",children:"Khartes"})," by Chuck is a tool to manually create and visualize segment meshes, while also visualizing a preview of the rendered segment."]}),"\n"]}),"\n",(0,t.jsx)(s.h3,{id:"\ufe0f-tools-1",children:"\ud83d\udee0\ufe0f Tools"}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/educelab/volume-cartographer",children:"Volume Cartographer"}),": the OG virtual unwrapping toolkit. 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1)," is an automatic tool that combines classical methods such as threshold gradient operator based edge detectors and Deep Learning based instance segmentation of point clouds to detect, merge and render segments. It was built by Julian Schilliger (part of Grand Prize winning submission)."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/schillij95/ThaumatoAnakalyptor/blob/main/ThaumatoAnakalyptor/sheet_to_mesh.py",children:"Fast Segment Rendering"})," by Julian Schilliger. Fast rendering of segments with GPU acceleration. Capable of saving the surface volume to multiple file formats."]}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsxs)(s.li,{children:[(0,t.jsx)(s.a,{href:"https://github.com/schillij95/ThaumatoAnakalyptor/commit/bcd382a0ef59b2a8566ec62a474479ea9d1bb8c2",children:"CPU rendering"})," by Julian Schilliger and Giorgio Angelotti"]}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/JamesDarby345/Volumetric_Vesuvius_Labelling",children:"Volumetric Vesuvius Labelling"})," by James Darby. Provide custom tooling the ",(0,t.jsx)(s.a,{href:"https://napari.org/stable/",children:"napari"})," 3d viewer that will help manually annotate volumetric masks of the scrolls to train ML models for 3D segmentation."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/giorgioangel/vesuvius_autoseg_preprocess",children:"Autosegmentation preprocessing pipeline"})," (work in progress) collection of scripts to pre-process volumes for autosegmentation. By Giorgio Angelotti"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/giorgioangel/vesuvius-segment2voxel",children:"Segment2Voxel"})," by Giorgio Angelotti. Tool to create 1-voxel thick volumetric segment labels starting from mesh .obj files."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/JamesDarby345/Volumetric_Instance_to_Mesh/tree/main",children:"Volumetric Instance Labels to obj"})," by James Darby. Tools to create .obj mesh files from volumetric instance labels."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/SuperOptimizer/Hraun",children:"Hraun"})," is a collection of python tools for handling volumetric scroll data by Forrest McDonald."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/joe-carr-data/windcheck",children:"windcheck"}
1)," by Josep Carreras. Finds where a traced surface passes through itself, from mesh geometry alone, and emits transverse-clean ",(0,t.jsx)(s.code,{children:"tifxyz"})," outputs plus a VC3D overlay."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/OliverDaubney/vesuvius_basic_compression",children:"Scroll compression and masking"})," by Olivier Daubney. Script to compress and mask scroll data, greatly reducing storage requirements!"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/schillij95/ThaumatoAnakalyptor/blob/main/ThaumatoAnakalyptor/mesh_merger.py",children:"Mesh merging"})," by Julian Schilliger. Merges multiple overlapping meshes into one continuous mesh. Flattening not included."]}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsxs)(s.li,{children:[(0,t.jsx)(s.a,{href:"https://gist.github.com/giorgioangel/b4cc56a5514335a2947adb058af2982b",children:"Mesh merging prototype"})," by Giorgio Angelotti. Different attempt to merge existing mesh of segments by projecting them in 2D and retriangulating in the plane."]}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://discord.com/channels/1079907749569237093/1232307086952501313",children:"Meshing and chunking"})," by Santiago Pelufo"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/tspersonalgithub/march_2024_progress_submission",children:"Volumetric segmentation model with labels"}),", deep learning 3D model to separate papyrus from air, by Tim Skinner"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://discord.com/channels/1079907749569237093/1221902373887279226",children:"Superpixels and cells"})," by Santiago Pelufo"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/schillij95/ThaumatoAnakalyptor/blob/main/ThaumatoAnakalyptor/slim_uv.py",children:"Segment Flattening"})," by Julian Schilliger and Giorgio Angelotti. Improved flattening of scroll segments."]}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsxs)(s.li,{children:[(0,t.jsx)(s.a,{href:"https://github.com/giorgioangel/slim-flatboi",children:"Slim-Flatboi"})," previous implementation of the SLIM algorithm with minimization of isometric distortion to flatten scroll segments. Later included in ThaumatoAnakalyptor. By Giorgio Angelotti."]}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://discord.com/channels/1079907749569237093/1179216516697296906/1179216516697296906",children:"Single Sheet Segmentation attempt"})," by Brett Olsen"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/spelufo/vesuvius-blender",children:"vesuvius-blender"})," by Santiago Pelufo. Explore the X-ray scans in Blender."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/spelufo/vesuvius-build/tree/main",children:"vesuvius-build"})," by Santiago Pelufo. Scripts to build files for progressive loading of the data. Convert the tif stack to grid cells or to h5 format that can be used by Ilastik."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/MosheLevy20/VolumeAnnotate",children:"Volume Annotate"})," A partial reimplementation of Volume Cartographer in Python by Moshe Levy."]}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsxs)(s.li,{children:[(0,t.jsx)(s.a,{href:"https://github.com/teeohem96/VA-Sheet-Tracer",children:"VA-Sheet Tracer"})," by Trevor, Tom, Babak and Boaz"]}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/caethan/vesuvius_image",children:"vesuvius-image"})," by Brett Olsen. Tool for storing and viewing data, including efficient Zarr loading of stack of tif images later included in Khartes."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/educelab/quick-segment",children:"Qu
1ick Segment"})," Created by EduceLab for annotating a large air gap in Scroll 1, and then projecting from that gap to either side to create two large segments, colloquially referred to as the \u201cMonster Segment\u201d. Hasn\u2019t been used for more segmentation, since it was the only large air gap we could find."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/WillStevens/scrollreading",children:"scrollreading"})," by Will Stevens. Experiments with using algorithms based on flood-fill to extract non-intersecting surfaces from scrolls."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/hendrikschilling/volume-cartographer",children:"VC with OME-Zarr & more"})," by Hendrik Schilling:"]}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsxs)(s.li,{children:["fast interactive OME-Zarr access and live slicing & flattening ",(0,t.jsx)(s.a,{href:"https://discord.com/channels/1079907749569237093/1286341523570688121",children:"thread"})]}),"\n",(0,t.jsxs)(s.li,{children:["instant flattening from VC segments without meshing (10s for one slice) ",(0,t.jsx)(s.a,{href:"https://discord.com/channels/1079907749569237093/1289946915269509251",children:"thread"})]}),"\n",(0,t.jsxs)(s.li,{children:["segment surface refinement (also works on obj segments) ",(0,t.jsx)(s.a,{href:"https://discord.com/channels/1079907749569237093/1290364437836075231",children:"thread"})]}),"\n",(0,t.jsxs)(s.li,{children:["fiber based segmentation efforts using an optimizing physics inspired surface meshing approach based on ceres-solver ",(0,t.jsx)(s.a,{href:"https://discord.com/channels/1079907749569237093/1301139262422646926",children:"thread"})]}),"\n",(0,t.jsxs)(s.li,{children:["non-destructive large scale interactive segment viewing and editing ",(0,t.jsx)(s.a,{href:"https://discord.com/channels/1079907749569237093/1294185795221065802",children:"thread"})]}),"\n",(0,t.jsxs)(s.li,{children:["automatic patch generation pipeline: vc_grow_seg_from_seed, vc_render_tifxyz, vc_tifxyz2obj: ",(0,t.jsx)(s.a,{href:"https://discord.com/channels/1079907749569237093/1312490723001499808",children:"thread"})]}),"\n",(0,t.jsxs)(s.li,{children:["segment tagging, segment masking, POIs, segment filters (all/filter by focus point/filter by POIs), display intersections scaling to thousands of segments ",(0,t.jsx)(s.a,{href:"https://discord.com/channels/1079907749569237093/1286341523570688121/1312537855846907974",children:"message"})]}),"\n",(0,t.jsxs)(s.li,{children:["low memory tiled rendering to enable GP-sized an full scroll rendering ",(0,t.jsx)(s.a,{href:"https://github.com/hendrikschilling/volume-cartographer/blob/dev-zarr/apps/src/vc_render_tifxyz.cpp",children:"https://github.com/hendrikschilling/volume-cartographer/blob/dev-zarr/apps/src/vc_render_tifxyz.cpp"})]}),"\n",(0,t.jsxs)(s.li,{children:["large segment tracing based on patch consensus: vc_grow_seg_from_segments, as documented in the ",(0,t.jsx)(s.a,{href:"https://github.com/hendrikschilling/FASP?tab=readme-ov-file#vc_grow_seg_from_segments",children:"FASP submission"})]}),"\n",(0,t.jsxs)(s.li,{children:["consistent winding number estimation by winding number diffusion: ",(0,t.jsx)(s.a,{href:"https://github.com/hendrikschilling/FASP?tab=readme-ov-file#51-winding-number-assignment",children:"vc_tifxyz_winding"})]}),"\n",(0,t.jsxs)(s.li,{children:["segment fusion & inpainting: ",(0,t.jsx)(s.a,{href:"https://github.com/hendrikschilling/FASP?tab=readme-ov-file#vc_fill_quadmesh",children:"vc_fill_quadmesh"})]}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://discord.com/channels/1079907749569237093/1315006782191570975",children:"fast and low memory inference for the GP ink detection"})," 1/5 the memory consumption and 20x the speed compared to the baseline GP ink detection for large segments to allow GP and full scroll size ink detection and fast preview."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/jrudolph/vesuvius-gui?tab=readme-ov-file#vesuvius-render",children:"vesuvius-render"})," by Johannes Rudolph:"]}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsx)(s.li,{children:"Fast self
1-contained CPU-based rendering of segments from obj files downloading data on-the-fly."}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/sgoutteb/segmata",children:"segmata"})," by Stephane Gouttebroze:"]}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsx)(s.li,{children:"Improve the segmentation process by sharpening the layers rendering, this is based on optimizing the layer 32, a further objective is to link this optimization on a inference loop (optimizing on the detected ink instead of only layers)"}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://lcparker/synthetic-pages",children:"Synthetic instance labels and volume generation"})," by lcparker"]}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsx)(s.li,{children:"Generate artificial 3D volumes with corresponding instance labels for use in pretraining instance segmentation networks"}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://lcparker/Mask3D",children:"Mask3D for instance segmentation on scroll volumes"})," by lcparker"]}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsx)(s.li,{children:"SOTA instance segmentation network, configured to work with scroll volumes"}),"\n",(0,t.jsxs)(s.li,{children:[(0,t.jsx)(s.a,{href:"https://github.com/lcparker/pretraining-advantage",children:"Effects of pretraining on synthetically generated data"}),", plus pretrained and finetuned weights for the Mask3D network"]}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://discordapp.com/channels/1079907749569237093/1407379961417039953",children:"Affinity Prediction with Unet"})," by Ayush Mishra"]}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsx)(s.li,{children:"Unet trained on affinity labels using pytorch_connectomics and experiments with watershed"}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://www.kaggle.com/code/bluetriad/scroll4-gaborfilters/notebook?scriptVersionId=265957590",children:"Gabor Filter for surface prediction"})," by Ayush Mishra"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/Hob3rMallow/scrollfiesta_public",children:"ScrollFiesta -- virtual meshing & unwrapping for the Herculaneum papyri"})," by HariSeldon and friends - now with parameterization and GPU acceleration!"]}),"\n",(0,t.jsxs)(s.ul,{children:["\n",(0,t.jsxs)(s.li,{children:[(0,t.jsx)(s.a,{href:"https://github.com/pscamillo/scrollfiesta_public/blob/cuda-mls/BENCHMARKS.md",children:"GPU-accelerated MLS projection for ScrollFiesta"})," by pscamillo \u2014 OpenMP + CUDA FP32 acceleration, byte-identical, ~6x throughput. (Note: GPU acceleration through CubeCL is now part of the main ScrollFiesta repository)"]}),"\n"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/pull/997",children:"Scroll-specific augmentations"})," by pscamillo. GPU-native training augmentations modeling real scroll/CT distortions: Squeeze (",(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/pull/997",children:"#997"}),"), Decohesion + Warp (",(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/pull/999",children:"#999"}),"), Ring (",(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/pull/1020",children:"#1020"}),"), Streak (",(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/pull/1021",children:"#1021"}),"), Warp edge-padding fix (",(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/pull/1025",children:"#1025"}),") (#201), each validated with ablation + benchmark."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/pscamillo/winding-ruler",children:"winding-ruler"})," by pscamillo. Measures winding evidence for the spiral fit: where human annotations matter, why generation fails at the published resolution, and a collection-wide winding-pitch atlas (36 scrolls). Includes ",(0,t.jsx)(s.a,{href:"https://github.com/pscamillo/winding-ruler/blob/main/concordance/qa_holescan.py",children:"qa_holescan"}),", a content-level detector for silent z-slice loss in predict3d output (",(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/issues/1183",children:"#1183"}),")."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/altommo/scrollfiesta-mls-hip",children:"HIP/ROCm port of ScrollFiesta's MLS for AMD GPUs"})," by Alan Thompson (altommo) \u2014 clean-room HIP port of the MLS-midpoint kernel, validated on RX 9070 / gfx1201: ~16.8x kernel, ~5.8x multi-cube, topology-equivalent weld-safe mesh. (",(0,t.jsx)(s.a,{href:"https://github.com/Hob3rMallow/scrollfiesta_public/pull/4",children:"upstream PR"}),")"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/altommo/scrollfiesta-mls-cubecl",children:"CubeCL port of ScrollFiesta's MLS (portable GPU: AMD/NVIDIA/WGPU)"})," by Alan Thompson (altommo) \u2014 Rust/CubeCL reimplementation of the same MLS kernel from a single source; validated on RX 9070 / gfx1201 against the HIP port, passing the 0.25-voxel weld-safety gate and 5-pass strict parity vs clean HIP."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/spencerdavis-tx/vesuvius-automesh",children:"vesuvius-automesh"})," by spencerdavis-tx. Fully automated, QC-gated surface harvest: masks the public surface predictions against the CT, seed-sweeps the villa tracer, and accepts output per 25 mm window under a two-part quality gate plus an independent topology check - 279 cm2 of verified Scroll 3 surface with zero manual annotation, CPU only. Ships per-window QC records for all 157 windows (passes and failures) as a labeled set of tracer failure modes."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/axiosdevs/herculaneum-scroll-tools",children:"Herculaneum Scroll Tools"})," by axiosdevs \u2014 CT-consistency QA for the published m7 surface predictions: voxel-exact phantom fractions measured for all 36 samples incl. all 13 grand-prize scrolls (43.3% of positives sit outside the masked CT), with a one-pass ",(0,t.jsx)(s.code,{children:"clean"})," mode (",(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/issues/1114",children:"villa#1114"}),"). ",(0,t.jsx)(s.code,{children:"audit_ct_support"})," audits any prediction volume in two modes: a ",(0,t.jsx)(s.strong,{children:"zero-download"})," chunk triage that reads only stored zarr chunk keys (~10 s per scroll, no voxel transfer) and classifies every prediction chunk as CT-supported, inside the one-chunk blend margin, or beyond it; and an exact voxel-level survey over chunk-aligned slabs. Across the batch: 1,662,405 prediction chunks, 83.1% supported, 16.9% one-c
1hunk halo, ",(0,t.jsx)(s.strong,{children:"0 beyond the margin in every sample"})," \u2014 consistent with the blend-margin mechanism and with no second source. Plus cross-scan registration (2023\u21922025 rescan, MAD 29 \xb5m), a winding-constraint annotator/verifier in native spiral-input format, and dual-energy high-Z ink-candidate rendering."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/Nieuwlaar/tifxyz-repair",children:"tifxyz-repair"})," by Nieuwlaar. Validates and repairs ",(0,t.jsx)(s.code,{children:"tifxyz"})," patch metadata against VC3D's exact loader semantics: detects the stale-bbox corruption of ",(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/issues/1272",children:"villa#1272"})," (106 of 4,922 verified PHercParis4 spiral-input patches affected, independently reproducing the issue's counts), rewrites corrected bboxes in place (atomic, ",(0,t.jsx)(s.code,{children:"--dry-run"}),", backups) and ships ready-to-apply boxes for every flagged patch; full-corpus audits certify all 40,782 unverified patches and all 817 published segment meshes clean. Companion PR ",(0,t.jsx)(s.a,{href:"https://github.com/ScrollPrize/villa/pull/1285",children:"#1285"})," stops the corruption at the source."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/abundantjoe/winding-sync",children:"winding-sync"})," by Joseph Balmaceda. Generates relative winding constraints directly from CT via structure-tensor lamina orientation, then reconciles contradictions globally as L1 integer synchronization (totally unimodular LP, exact integer solutions). Complements winding-number diffusion over existing surfaces by working from the raw volume before a surface exists. Tolerates 15% gross measurement error before dropping below 95% accuracy, versus 2% for spanning-tree propagation. Absolute winding counts are not yet calibrated; see README."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/7jycwjmbfn-eng/spiral-fit-consumer-gpu",children:"spiral-fit-consumer-gpu"})," by Shuhan Yang runs the spiral fitter in this repo on a 12 GB consumer GPU without the native VC extension. Drop-in replacements for the sparse CUDA cache, which otherwise raises before the first step, and for point-to-patch linking, where a measured 41-hour ETA drops to minutes. Both are checked for bitwise-identical output against the originals. Includes a scaling ladder and pool-tuning measurements from one gaming laptop."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/aviad12g/tifxyz-doctor",children:"TIFXYZ Doctor"})," by Aviad Cohen \u2014 deterministic QA and triage for TIFXYZ surface grids, with sparse overlays and reproducible corpus/reader checks. v0.2 adds an overlap-component-isolated benchmark on 709 official human-reviewed PHercParis4 ",(0,t.jsx)(s.code,{children:"same_wrap"})," patches; its frozen cue localizes abrupt synthetic normal-offset steps while byte-identical null controls and the reported gradual-transition miss rate bound the claim."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/Nicodol/spiralcheck",children:"spiralcheck"})," by Nicolas Dolegieviez. Held-out evaluation for whole-scroll spiral fits: scores a finished run from its output meshes alone (CPU-only, no checkpoint, producer-agnostic) against verified patches withheld from that fit, and measures geometrically how much of the withheld evidence actually sits within touching distance of the fit's real inputs \u2014 on PHerc. Paris 4, 54.8% of a naive name-level split leaked that way, which no hash-level check can see. Also ships ground-truth-free winding-order checks around the umbilicus, a planted-defect matrix with computed null-control bounds, and ",(0,t.jsx)(s.code,{children:"spiralcheck demo"}),", which runs the whole pipeline on a synthetic scroll with planted defects and needs no data."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/ttendoscopie-creator/eligible-spiral-dataset",children:"eligible-spiral-dataset"})," by Thierry Tuszynski surveys what is actually published today for each of the 13 Grand-Prize-eligible scrolls \u2014 9 have tracks, 3 have an umbilicus \u2014 and assembles a ",(0,t.jsx)(s.code,{children:"fit_spiral"})," dataset from it. The layout and the ",(0,t.jsx)(s.code,{children:"spiral-scroll.json"}),' template are already published in the First Letters workflow post; this adds a survey that stops rather than printing "no" when a host does not answer, and restores the nanosecond ',(0,t.jsx)(s.code,{children:"st_mtime_ns"})," that ",(0,t.jsx)(s.code,{children:"_tracks_db_signature"})," fingerprints, so the published ",(0,t.jsx)(s.code,{children:".crossings.npz"})," is accepted instead of rebuilt \u2014 on PHerc. 0826 that is a whole-scroll cache, ",(0,t.jsx)(s.code,{children:"z_range [4500, 16919]"}),". It writes ",(0,t.jsx)(s.code,{children:"normal_zarr_group"})," and ",(0,t.jsx)(s.code,{children:"lasagna_scale"}),", checked field by field against the published template, and requires ",(0,t.jsx)(s.code,{children:"--outward-sense"})," rather than guessing it. 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Anton Repushko shared some ink labels for Scroll 1, these labels were used by many participants for their final submission in December 2023."]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://dl.ash2txt.org/community-uploads/bruniss/",children:"Crackle labels on Scroll 1"})," by Sean Johnson"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:["[Ink Generator]\n(",(0,t.jsx)(s.a,{href:"https://github.com/StewartSethA/VesuviusInkGenerator",children:"https://github.com/StewartSethA/VesuviusInkGenerator"}),") by Seth Stewart, ink volume sample patches generated using gradient ascent"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:["[Ink feature visualization]\n(",(0,t.jsx)(s.a,{href:"https://github.com/StewartSethA/ink-seer",children:"https://github.com/StewartSethA/ink-seer"}),") by Seth Stewart, ink feature vector clustering and visualization via t-SNE to distinguish ink from non-ink features"]}),"\n"]}),"\n",(0,t.jsxs)(s.li,{children:["\n",(0,t.jsxs)(s.p,{children:[(0,t.jsx)(s.a,{href:"https://github.com/Bodillium/Herculaneum-Scroll-Labels",children:"Scroll 5 Ink Labels"})," by Nicola Bodill. 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