HDR-NSFF: High Dynamic Range Neural Scene Flow Fields
Radiance of real-world scenes typically spans a much wider dynamic range than what standard cameras can capture. While conventional HDR methods merge alternating-exposure frames, these approaches are inherently constrained to 2D pixel-level alignment, often leading to ghosting artifacts and temporal inconsistency in dynamic scenes. To address these limitations, we present HDR-NSFF, a paradigm shift from 2D-based merging to 4D spatio-temporal modeling. Our framework reconstructs dynamic HDR radiance fields from alternating-exposure monocular videos by representing the scene as a continuous function of space and time, and is compatible with both neural radiance field and 4D Gaussian Splatti...
| Authors | Shin Dong-Yeon, Kim Jun-Seong, Kwon Byung-Ki et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
Models and datasets referencing this paper
- SHlNDY/HDR-NSFFDataset
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