arXiv Computer Vision
Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 11
InstantHDR: Single-forward Gaussian Splatting Initialization for HDR 3D Reconstruction
InstantHDR is a feed-forward network that initializes high dynamic range (HDR) 3D scenes from uncalibrated multi-exposure low dynamic range (LDR) image collections in a single forward pass. It uses geometry-guided appearance modeling for multi-exposure fusion and a meta-network for scene-specific tone mapping. The authors also created a pre-training dataset, HDR-Pretrain, with 168 Blender-rendered scenes to support generalizable HDR models, achieving a speedup of about 700× over state‑of‑the‑art optimization methods while maintaining comparable quality after lightweight post‑optimization.
arXiv Computer Vision
Sep 14
Recurrent Dynamic Range Extension
The paper introduces a method for progressively extending the dynamic range of an image by learning to increase it by a single exposure value first, then applying the network recurrently to achieve full HDR reconstruction. The approach is agnostic to input dynamic range, targets a bounded output domain, and utilizes RAW images with adversarial losses to produce realistic results. Memory Replay during backpropagation allows training over multiple inference stages, reducing reconstruction errors and enabling robust recovery of bright highlights in long‑tailed HDR scenes.
arXiv Computer Vision
Sep 1
Multi-exposure HDR Imaging: A Review of Pixel-level and Feature-level Reconstruction Methods
arXiv:2608.28674v1 Announce Type: new Abstract: Multi-exposure is an efficient way to capture real-world high-dynamic-range (HDR) scenes. However, HDR imaging suffers from severe ghosting artifacts i...
arXiv Computer Vision
Sep 18
RawSLAM: Online HDR Gaussian SLAM from Linear Radiance
RawSLAM introduces the first online Gaussian SLAM framework that operates directly on single‑exposure 16‑bit linear HDR images, overcoming the limitations of traditional 8‑bit LDR SLAM systems in extreme lighting. The approach combines an HDR Gaussian splatting module with a Reinhard‑compressed photometric objective and structure‑guided spatial weighting, achieving superior trajectory and reconstruction accuracy compared to a direct HDR adaptation of MonoGS. The same formulation also improves performance on standard 8‑bit inputs and can be transferred to other SLAM systems such as SplaTAM, Gaussian SLAM, and DROID‑W, eliminating tracking failures in challenging illumination sequences. Additionally, RawSLAM provides a new dataset of 10 real‑world indoor sequences with 16‑bit RAW imagery, depth, IMU, and OptiTrack poses.
Hugging Face Trending Papers
Aug 6
Dual-Output Multi-Exposure HDR Reconstruction via SDR Fusion and Gain Map Inverse Tone Mapping
We propose DOME-HDR, a dual-output multi-exposure HDR reconstruction framework that jointly produces a perceptually balanced SDR image and a consistent HDR image via gain map inverse tone mapping. Given three bracketed LDR inputs, DOME-HDR first synthesizes a base SDR using a LoRA-adapted latent diffusion model.
arXiv Computer Vision
Sep 4
Generating HDR Video from SDR Video
The paper presents a framework for converting standard dynamic range (SDR) videos into high dynamic range (HDR) videos using large-scale generative video models. It introduces a Multi-Exposure Video Model (MEVM) that predicts exposure-bracketed linear SDR sequences from a single nonlinear SDR input, and a Video Merging Model (VMM) that fuses these predictions into a high-quality HDR sequence while preserving detail in shadows and highlights. Experiments, qualitative evaluation, and a user study demonstrate robust HDR conversion for casual consumer footage and iconic films, and the approach can be integrated into existing SDR generative video pipelines.