arXiv Machine Learning By Yicheng Zhan, Liang Shi, Wojciech Matusik, Qi Sun, Kaan Ak\c{s}it

Configurable Holography: Towards Display and Scene Adaptation

Read the original on arXiv Machine Learning →

arXiv:2405. 01558v4 Announce Type: replace-cross Abstract: Rendering holograms for holographic displays is often an iterative and computationally costly process.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

Hugging Face Trending Papers
Jul 20

FF-ProCams: Feed-Forward Gaussian Splatting for Projector-Camera System

Projector-camera (ProCams) systems achieve active scene perception and controllable appearance manipulation via structured illumination, serving as a core infrastructure for spatial augmented reality, projection mapping, and surface reflectance acquisition. Existing inverse-rendering methods for ProCams deliver high-fidelity results but rely on time-consuming per-scene optimization, while mainstream feed-forward 3D reconstruction models produce baked appearance that cannot adapt to spatially varying projector illumination.