PIE-PS: Photometric Stereo from Physical Irradiance Event Streams
To address these limits, we introduce PIE-PS, a learning-based framework for dense surface normal reconstruction from raw event streams and known lighting.
ProofPaper ↗
Key points
- Event cameras record asynchronous log-image-irradiance changes with microsecond latency and high dynamic range.
- We form Physical Irradiance Events (PIEs) by pairing two adjacent events at the same pixel with their corresponding light directions.
- To share spatial and temporal context across nearby PIEs, we introduce PIE-GNN, which treats each PIE as a graph node and encodes it with its light-pair geometry.
- Experiments on synthetic and real data show that PIE-PS outperforms prior event-based photometric stereo methods and the direct solver baseline.
Sources (1)
- [1]PIE-PS: Photometric Stereo from Physical Irradiance Event StreamsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 11:41 AM
To address these limits, we introduce PIE-PS, a learning-based framework for dense surface normal reconstruction from raw event streams and known lighting.
Event cameras record asynchronous log-image-irradiance changes with microsecond latency and high dynamic range.
Extractive summary: sentences quoted from the sources.