arXiv (All)
open access
RoomLight: A 2.5D Illumination Prior for Indoor Environments
Ill-posed inverse problems require priors to constrain the solution space toward plausible outcomes. In inverse rendering, learned priors modeling the distribution of natural illumination improve the recovery of scene properties. However, existing models rely on the distant-illumination assumption,
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Perturb-and-Solve: Efficient Learned-Operator Conditioning for Latent Diffusion Inverse Problems#1117883
- ANaLOG: Anisotropic Native-Latent Operator Guidance for Solving Inverse Problems#1115889
- Differentiable Objectives for 3D Scene Relighting via Gradient Descent on OLAT Basis Coefficients#168631
- Coordinate-Residual Physics-Driven Neural Network for Inverse Scattering Imaging#820600
- Consistency as Regularization for Unsupervised Shadow Removal#822232
Record · ID 1064844
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive