mlsanityData to 14 Oct 2026

Spectral World Renderer: Unified 3D Hyperspectral Unmixing

arXiv 2610.19520 ↗First posted 14 Oct 2026cs.CV cs.GRcited by 0

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Abstract (arXiv)

Cameras and the foundation models built on them recognize what a 3D scene contains, but not what it is made of: surfaces of the same colour can be different materials. Hyperspectral unmixing explains each spectrum as a mixture of material signatures, yet in 3D it is applied to rendered images after the views are composited, or tied to slow implicit fields, and no multi-view data with known composition exist to check the result. We present the Spectral World Renderer (SWR), which unifies world generation and unmixing. A coding agent builds executable 3D worlds that are rendered with measured spectra, so every hyperspectral image comes with exact labels and sub-pixel proportions; the resulting SWR-Bench provides eight indoor and outdoor worlds in which decompositions can be verified. SWR then unmixes inside rendering: each Gaussian of a compact 3D representation is decoded into material abundances before compositing, so one render yields spectra and material maps, and any surface can be queried by material name, a clicked pixel or a reference spectrum. Without material labels, SWR identifies materials best in all eight worlds, has the best mean PSNR on real captures, trains 3.7-12.9x faster than unmixing baselines, renders 34.9 labelled views/s and separates same-coloured materials.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 53 of its 93 references that are in the map. First posted on arXiv 14 Oct 2026.

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53 of 93 references.

References · 53

  1. 2023Segment Anythingcited by 8,234
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38 more
  1. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 575
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  23. 20263DCodeBench: Benchmarking Agentic Procedural 3D Modeling Via Codecited by 15
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  27. 2023Neural Spectro-polarimetric Fieldscited by 10
  28. 2024SAMa: Material-aware 3D Selection and Segmentationcited by 10
  29. 2026Generative World Renderercited by 10
  30. 2026Programmable World Modelcited by 10
  31. 2024SpectralGaussians: Semantic, spectral 3D Gaussian splatting for multi-spectral scene representation, visualization and analysiscited by 8
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  36. 2025Towards Integrating Multi-Spectral Imaging with Gaussian Splattingcited by 3
  37. 2026Recursive Code World Models: Building Complex Worlds through Recursive Scene Programscited by 3
  38. 20264DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenescited by 3

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  1. 2025Hyperspectral Image Generation with Unmixing Guided Diffusion Model
  2. 2024Many-Worlds Inverse Rendering
  3. 2023Material Palette: Extraction of Materials from a Single Image
  4. 2026HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone
  5. 2026WorldFlow3D: Flowing Through 3D Distributions for Unbounded World Generation
  6. 2023Diffusion Reflectance Map: Single-Image Stochastic Inverse Rendering of Illumination and Reflectance
  7. 2024MaterialFusion: Enhancing Inverse Rendering with Material Diffusion Priors
  8. 2023HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a Single Image