A Unified Framework for Multi-Sensor HDR Video Reconstruction
One of the most successful approaches to modern high quality HDR-video capture is to use camera setups with multiple sensors imaging the scene through a common optical system. However, such systems pose several challenges for HDR reconstruction algorithms. Previous reconstruction techniques have considered debayering, denoising, resampling (align- ment) and exposure fusion as separate problems. In contrast, in this paper we present a unifying approach, performing HDR assembly directly from raw sensor data. Our framework includes a camera noise model adapted to HDR video and an algorithm for spatially adaptive HDR reconstruction based on fitting of local polynomial approximations to observed sensor data. The method is easy to implement and allows reconstruction to an arbitrary resolution and output mapping. We present an implementation in CUDA and show real-time performance for an experimental 4 Mpixel multi-sensor HDR video system. We further show that our algorithm has clear advantages over existing methods, both in terms of flexibility and reconstruction quality.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingHDR ReconstructionVideo ReconstructionSimilar Papers 제목 키워드 기반
A Unified Framework for Compressive Video Recovery from Coded Exposure Techniques
Several coded exposure techniques have been proposed for acquiring high frame rate videos at low bandwidth. Most recently, a Coded-2-Bucket camera has been proposed that can acquire two compressed measurements in a singl…
OmniGen: Unified Multimodal Sensor Generation for Autonomous Driving
Autonomous driving has seen remarkable advancements, largely driven by extensive real-world data collection. However, acquiring diverse and corner-case data remains costly and inefficient. Generative models have emerged …
Autonomous DrivingEVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction
Event cameras are a new type of vision sensor that incorporates asynchronous and independent pixels, offering advantages over traditional frame-based cameras such as high dynamic range and minimal motion blur. However, t…
Event-Based Video ReconstructionEvent-based visionVideo ReconstructionReconstruction of Videos Taken by a Non-Regular Sampling Sensor
Recently, it has been shown that a high resolution image can be obtained without the usage of a high resolution sensor. The main idea has been that a low resolution sensor is covered with a non-regular sampling mask foll…
Any4D: Unified Feed-Forward Metric 4D Reconstruction
We present Any4D, a scalable multi-view transformer for metric-scale, dense feed-forward 4D reconstruction. Any4D directly generates per-pixel motion and geometry predictions for N frames, in contrast to prior work that …
Point Tracking