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HDR NERF: Neural Scene reconstruction in low light
Implicit scene representations, particularly Neural Radiance Fields (NeRF), have significantly advanced scene reconstruction and synthesis, surpassing traditional methods in creating photorealistic renderings from sparse images. However, the potential of integrating these methods with advanced sensor technologies that measure light at the granularity of a photon remains largely unexplored. These sensors, known for their exceptional low-light sensitivity and high dynamic range, could address the limitations of current NeRF implementations in challenging lighting conditions, offering a novel approach to neural-based scene reconstruction.
Keywords: NERF, computer vision, HDR imaging, event camera
Implicit scene representations, particularly Neural Radiance Fields (NeRF), have significantly advanced scene reconstruction and synthesis, surpassing traditional methods in creating photorealistic renderings from sparse images. However, the potential of integrating these methods with advanced sensor technologies that measure light at the granularity of a photon remains largely unexplored. These sensors, known for their exceptional low-light sensitivity and high dynamic range, could address the limitations of current NeRF implementations in challenging lighting conditions, offering a novel approach to neural-based scene reconstruction.
Implicit scene representations, particularly Neural Radiance Fields (NeRF), have significantly advanced scene reconstruction and synthesis, surpassing traditional methods in creating photorealistic renderings from sparse images. However, the potential of integrating these methods with advanced sensor technologies that measure light at the granularity of a photon remains largely unexplored. These sensors, known for their exceptional low-light sensitivity and high dynamic range, could address the limitations of current NeRF implementations in challenging lighting conditions, offering a novel approach to neural-based scene reconstruction.
his project aims to pioneer the integration of SPAD sensors with neural-based scene reconstruction frameworks, specifically focusing on enhancing Neural Radiance Fields. The primary objective is to investigate how photon derived data can be utilized to improve scene reconstruction fidelity, depth accuracy, and rendering quality under diverse lighting conditions. By extending NeRF to incorporate event-based data from SPADs, we anticipate a significant leap in the performance of neural scene synthesis methodologies, particularly in challenging environments where traditional sensors falter.
his project aims to pioneer the integration of SPAD sensors with neural-based scene reconstruction frameworks, specifically focusing on enhancing Neural Radiance Fields. The primary objective is to investigate how photon derived data can be utilized to improve scene reconstruction fidelity, depth accuracy, and rendering quality under diverse lighting conditions. By extending NeRF to incorporate event-based data from SPADs, we anticipate a significant leap in the performance of neural scene synthesis methodologies, particularly in challenging environments where traditional sensors falter.