Reconstructing Emissive Sources from Low Dynamic Range Multi-view Images using Neural Radiance Fields

Centrala begrepp

A novel approach, ESR-NeRF, that can reconstruct emissive sources within a scene from low dynamic range (LDR) multi-view images, addressing the ambiguity and computational challenges posed by the presence of emissive sources.

Sammanfattning

The paper presents ESR-NeRF, a method for reconstructing emissive sources from LDR multi-view images. The key contributions are:

ESR-NeRF is the first NeRF-based inverse rendering approach that can handle scenes with any number of emissive sources, going beyond the assumption of distant light sources in previous work.

Unlike existing mesh-based methods that rely on high dynamic range (HDR) images, ESR-NeRF uses LDR images, overcoming the poor representation of emissive sources in standard photographs.

The method introduces a learnable tone-mapper to extract HDR values from LDR images and leverages neural networks to represent ray-traced fields, avoiding the high computational cost of explicit ray tracing.

ESR-NeRF employs a reflection-aware progressive approach to precisely identify emissive sources, addressing the ambiguity between emission and reflection in LDR images.

The reconstructed emissive sources can be used for scene editing, enabling users to modify the color and intensity of the sources.

Experiments on synthetic and real scenes demonstrate the superiority of ESR-NeRF over state-of-the-art NeRF-based re-lighting methods in both qualitative and quantitative evaluations.

ESR-NeRF also achieves competitive performance in surface reconstruction on the DTU dataset, even in the absence of emissive sources.

Statistik

The contrast between light on and off pixel values is more pronounced in surroundings than emissive sources.
Relying solely on pixel value thresholding is insufficient for differentiating between emissive sources and their reflections.
The computational cost of explicit ray tracing rises exponentially with the number of ray bounces.

Citat

"Existing NeRF-based inverse rendering methods suppose that scenes are exclusively illuminated by distant light sources, neglecting the potential influence of emissive sources within a scene."
"Two key issues must be addressed: 1) ambiguity arising from the limited dynamic range along with unknown lighting details, and 2) the expensive computational cost in volume rendering to backtrack the paths leading to final object colors."

Viktiga insikter från

ESR-NeRF: Emissive Source Reconstruction Using LDR Multi-view Images

by Jinseo Jeong... på arxiv.org 04-25-2024

https://arxiv.org/pdf/2404.15707.pdf

Djupare frågor

How could the proposed method be extended to handle scenes with a single lighting condition, rather than requiring both on and off images

To extend the proposed method to handle scenes with a single lighting condition, one approach could involve training the neural networks to learn the relationships between emissive sources, environmental lighting, and object textures under a unified lighting setting. By incorporating additional constraints or regularization techniques during training, the networks could potentially disentangle the effects of different lighting components within a scene. This could involve adjusting the loss functions or introducing specific architectural modifications to the neural networks to better capture the nuances of a single lighting condition.

What alternative approaches could be explored to address the challenge of representing new colors that traverse unobserved light paths during training

One alternative approach to address the challenge of representing new colors that traverse unobserved light paths during training could be to incorporate additional data augmentation techniques or synthetic data generation methods. By introducing variations in the training data that simulate different lighting conditions or color transformations, the neural networks could learn to generalize better to unseen color variations. Additionally, techniques such as domain adaptation or transfer learning could be explored to adapt the model to new color distributions encountered during inference, enhancing its ability to handle a wider range of color variations.

How might the method's performance be affected by the presence of complex geometry and materials in the scene, and what strategies could be employed to handle such scenarios

The performance of the method may be affected by the presence of complex geometry and materials in the scene, as these factors can introduce additional challenges in accurately reconstructing emissive sources and handling reflections. To address this, strategies such as incorporating more sophisticated neural network architectures capable of capturing intricate scene details, leveraging multi-scale representations to handle complex geometry, and integrating advanced material models for more accurate surface reconstruction could be beneficial. Additionally, employing techniques like hierarchical feature learning, attention mechanisms, or incorporating domain-specific priors could help the model better understand and represent complex scene characteristics, leading to improved performance in challenging scenarios.