HDR Scene Reconstruction from Resolution-Mismatched Event Streams and Single-Exposure Images

High dynamic range (HDR) radiance-field reconstruction from single-exposure low dynamic range (LDR) images is limited by the information loss in saturated regions, while event cameras provide complementary measurements whose dynamic range far exceeds that of conventional sensors. In practical hybrid sensor systems, however, the RGB camera usually has a higher spatial resolution than the event sensor, which makes existing event-aided HDR reconstruction methods difficult to apply directly. A straightforward solution is to super-resolve the event stream before reconstruction, but this 2D preprocessing introduces a global color cast and view-inconsistent high-frequency artifacts once the super-resolved events supervise a 3D radiance field. We propose a framework for HDR radiance-field reconstruction from resolution-mismatched event–image inputs. The framework incorporates a pretrained 2D event super-resolution prior and corrects its transfer to 3D reconstruction through a color correction module, which anchors the rendered radiance to the chrominance of the LDR images, and a dual-resolution event-stream constraint, which supervises the synthesized events at both the super-resolved and the native resolution. Experiments on the EvHDR-NeRF benchmark show that the proposed method achieves higher six-scene mean HDR fidelity than the strongest baseline while reducing the global color cast and alleviating multi-view artifacts.