LAMP: 3D Source-Aware Lighting Estimation at Multiple Points

Key points

• Existing illumination estimation methods primarily recover lighting distributions for rendering, but typically leave the contributions of individual physical light sources at specific scene locations implicit.1
• We address this gap by leveraging the scene understanding capabilities of vision-language models (VLMs) to connect local illumination with its physical sources.1
• We supervise these relationships using procedurally controlled indoor scenes, reusing independently rendered source responses to derive contribution targets across lighting configurations and obtaining exact directions from source and query positions.1
• Combined with a lightweight network predicting dense RGB irradiance, LAMP achieves probe-rendering quality comparable to prior lighting-estimation methods while additionally localizing individual physical sources in 3D and explicitly attributing local illumination to them.1

Sources (1)

• [1]LAMP: 3D Source-Aware Lighting Estimation at Multiple Points
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 10:36 PM

  We introduce LAMP, a framework for point-conditioned, source-aware illumination reasoning from a single RGB image with marked query points.

  Existing illumination estimation methods primarily recover lighting distributions for rendering, but typically leave the contributions of individual physical light sources at specific scene locations implicit.

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