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…tile meshes Literature review (mesh segmentation, LoD2 reconstruction, procedural inversion, shadow/albedo recovery), open reference datasets, a sketch pipeline and the licensing constraint on commercial photorealistic tiles. Nothing implemented. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UhevLiW3dUtveoxxmG9fg3
Raster imagery + terrain tile -> plausible 3D scene (relief, buildings, trees, structures), judged on credibility rather than accuracy. Covers monocular height, off-nadir and shadow cues, satellite neural fields, global height products, footprints and roof structure, LoD2, completion trained with holes, facades, vegetation, shadow removal, satellite-to-city generation, inverse procedural modeling and 3D foundation models; plus reference datasets, licensing constraints, credibility metrics, a sketch pipeline and experiments. Nothing implemented. The earlier mesh-based note now points to it. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UhevLiW3dUtveoxxmG9fg3
…nable in Tuile Detail and super-resolution, cross-zoom consistency, hallucination measurement, deblurring/MTF, denoising, JPEG artifact removal, all-in-one restoration, white balance, exposure/tone (LUT and bilateral-grid fields), colour transfer and multi-source harmonisation, colour science, haze, atmospheric correction, cloud removal and compositing, seasons, transient objects and inpainting, and quality assessment. Complements doc 18. Opens with what is actionable: three render-side fixes in our own chain (anisotropic sampling in the viewer, linear-light mips, reprojection kernel), then offline enhancement stored as a derived layer. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UhevLiW3dUtveoxxmG9fg3
…ramids Neural texture compression (NTC and its BC-latent descendants), filtering of neural textures, the GPU substrate (cooperative vectors/matrices, wgpu 29, WebGPU f16 and subgroups), learned image codecs and scalable coding, implicit neural representations and overfitted codecs, 2D Gaussian images, geospatial fields and embedding tiles. Judged against tile constraints (borders, parent/child consistency, never black, determinism, static view, RGB only, encode throughput), with tracks and experiments for Tuile. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UhevLiW3dUtveoxxmG9fg3
webgpu-neural-networks.md: platform support (Linux the gap), JS runtimes (ORT-Web, Transformers.js, LiteRT.js, WebLLM, WebNN), the Rust ecosystem (Burn/CubeCL device sharing, versions checked on crates.io), compilers emitting WGSL, interop, published trials by model class, dispatch overhead as the bottleneck, limits and features, kernels, numerics, deployment, and guidance and experiments for a Rust/wgpu engine. onnx-webgpu-wgpu.md: ONNX essentials (opsets, contrib ops, external data, quantization, export), ONNX Runtime Web and native WebGPU EP (Dawn), Rust ONNX paths (burn-onnx, ort/ort-web, tract, RTen, candle, wonnx), ONNX-to-WGSL compilers, pitfalls, and a recommendation per target. Checked in published sources: wgpu 29.0.3 exposes no way to extract or adopt a JS GPUDevice (backend module private), so JS runtimes cannot share Tuile's device; cubecl-wgpu 0.10 is on wgpu 29, 0.11 on wgpu 30. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UhevLiW3dUtveoxxmG9fg3
…yers A derived layer is a pure function of source tiles (tile, halo, parent, other dates) and a versioned model, computed on first request, stored and served like any layer. In tuile-tile-server the GPU worker is just another Upstream; the client always shows the raw tile and swaps when the derived one is resident. Covers the three tiers (browser, proxy, farm), naming, determinism and provenance, licences (stored derivatives for open or own sources only), a cost model to fill by measurement, and open questions. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01UhevLiW3dUtveoxxmG9fg3
…deepest imagery level, measured on the pyramid's own (z, z+1) pairs
…ithout the URL list
…eepest imagery level — dataset of damaged and cross-level pairs, before/after browser
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What
Seven research and architecture notes in
docs/explorations/, written in French like the other R&D docs. This is documentation only: no code, and nothing here is proposed for implementation.3d-from-imagery-tiles.mdshape-from-photorealistic-tiles.mdimagery-tile-enhancement.mdneural-texture-representations.mdwebgpu-neural-networks.mdonnx-webgpu-wgpu.mdgpu-tile-proxy.mdtuile-tile-serverit is just anotherUpstream. The client always shows the raw tile and swaps it once the derived one is ready. Also covers the three tiers (browser, proxy, farm), determinism and provenance, licences, a cost model to fill by measurement, and open questions.Facts checked directly in published sources
GPUDevicethrough its public API.mod backend;is private (src/lib.rs:114) andWebDevice.innerispub(crate)(src/backend/webgpu.rs:1202). As a result, JS ML runtimes cannot share Tuile's device on the web.cubecl-wgpu0.10 (Burn 0.21) depends on wgpu ^29, which matches Tuile.cubecl-wgpu0.11 (Burn 0.22) depends on wgpu ^30.Why
These notes scope research directions for an open-source tile engine before any code is written: what the literature provides, which open data supports it, what licences rule out, and what is actionable with a few GPUs.
Caveats
docs/20-conventions.md, which targets code PRs. I can split it if preferred.cargochecks are unaffected.🤖 Generated with Claude Code
https://claude.ai/code/session_01UhevLiW3dUtveoxxmG9fg3