InstantHDR: Single-forward Gaussian Splatting for High Dynamic Range 3D Reconstruction

High dynamic range (HDR) novel view synthesis (NVS) aimsto reconstruct HDR scenes from multi-exposure low dynamic range (LDR)images. Existing HDR pipelines heavily rely on known camera poses,well-initialized dense point clouds, and time-consuming per-scene op-timization. Current feed-forward alternatives overlook the HDR prob-lem by assuming exposure-invariant appearance. To bridge this gap, wepropose InstantHDR, a feed-forward network that initializes 3D HDRscenes from uncalibrated multi-exposure LDR collections in a fast sin-gle forward pass. Specifically, we design a geometry-guided appearancemodeling for multi-exposure fusion, and a meta-network for generaliz-able scene-specific tone mapping. Due to the lack of HDR scene data,we build a pre-training dataset, called HDR-Pretrain, for generalizablefeed-forward HDR models, featuring 168 Blender-rendered scenes, di-verse lighting types, and multiple camera response functions. Compre-hensive experiments show that our InstantHDR delivers a single-forwardHDR initialization at ∼ 700× the speed of SoTA optimization-basedmethods, and reaches comparable quality in real settings after lightweightpost-optimization while remaining ∼ 20× faster. All code, models, anddatasets: https://github.com/Bugjudger/InstantHDR.