Adaptive Multi-Exposure Regularization For Dynamic Hdr Scene Reconstruction

Dynamic HDR reconstruction with Gaussian Splatting relies on neural tone mappers to model the nonlinear relationship between reconstructed HDR radiance and multi-exposure observations. Under varying exposure conditions, the pre-activation distribution within the tone mapper can shift substantially, altering its activation behavior and limiting the nonlinear responses effectively involved in reconstruction. To address this issue, we propose Adaptive Multi-Exposure Regularization (\method), which calibrates the tone mapper's pre-activation distribution across exposure conditions using exposure-conditioned residual offsets. Extensive experiments across multiple dynamic HDR Gaussian Splatting pipelines demonstrate consistent improvements in HDR reconstruction quality, supporting the general applicability of our method.