Learning to Predict Indoor Illumination from a Single Image (1704.00090v3)

Abstract: We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that directly regresses a limited field-of-view photo to HDR illumination, without strong assumptions on scene geometry, material properties, or lighting. We show that this can be accomplished in a three step process: 1) we train a robust lighting classifier to automatically annotate the location of light sources in a large dataset of LDR environment maps, 2) we use these annotations to train a deep neural network that predicts the location of lights in a scene from a single limited field-of-view photo, and 3) we fine-tune this network using a small dataset of HDR environment maps to predict light intensities. This allows us to automatically recover high-quality HDR illumination estimates that significantly outperform previous state-of-the-art methods. Consequently, using our illumination estimates for applications like 3D object insertion, we can achieve results that are photo-realistic, which is validated via a perceptual user study.

• The paper introduces an end-to-end deep learning framework that accurately predicts HDR illumination from a single indoor LDR image without specialized capture methods.
• It employs a three-phase approach combining lighting classification, deep light source prediction, and fine-tuning with HDR maps to enhance estimation precision.
• The method outperforms state-of-the-art techniques, offering improved realism for photorealistic 3D object insertion and advancing AR/VR lighting applications.

Learning to Predict Indoor Illumination from a Single Image

The paper presents a comprehensive approach to estimating high dynamic range (HDR) illumination from a single, limited field-of-view, low dynamic range (LDR) indoor photograph. This is a notable contribution to the field of scene illumination estimation, especially since previous approaches have often required either specialized image capture techniques, substantial user input, or simplified models of the scene. Here, the authors propose an end-to-end deep learning framework that effectively predicts HDR illumination without stringent assumptions on the scene's geometry, material properties, or lighting conditions.

Methodology

The proposed method is structured into three key phases:

1. Lighting Classification and Annotation: The authors start by training a robust lighting classifier to annotate light source locations within a vast dataset of LDR environment maps. This classifier forms the basis for associating specific image regions with potential light sources.
2. Deep Learning for Light Source Prediction: Utilizing the annotations from the first step, a deep neural network is trained to predict the spatial locations of light sources from a single limited field-of-view image. This step leverages the annotated dataset to enable learning of spatial lighting patterns and properties.
3. Network Fine-Tuning for HDR Predictions: The network is then fine-tuned using a smaller dataset of HDR environment maps, enabling it to predict light intensities accurately. This step is critical to bridge the gap between LDR-based training and HDR illumination prediction, thereby enhancing the precision of lighting estimation.

Results

The research delivers significant advancements over existing state-of-the-art methods, specifically in applications such as photo-realistic 3D object insertion. The paper validates the performance of its illumination estimates through a perceptual user paper, underscoring the effectiveness and realism of the method. While it avoids using physical proxies or requiring user engagement for geometry or light source estimation, the proposed framework demonstrates substantial benefits in contexts requiring realistic rendering and virtual object integration.

Implications and Future Directions

This research opens several avenues for further paper:

• Enhanced Data Sets: The authors note the lack of extensive HDR datasets during training. Future work could focus on developing larger annotated HDR datasets to further enhance model performance.
• Precision Improvements: Addressing the challenges associated with predicting the precise spatial extent and orientation of out-of-view lights could be a focus area. Developing algorithms capable of inferring spatially varying illumination from diverse and complex scenes might enhance the model's robustness.
• Color Prediction: The paper touches upon intensity prediction, but future work might explore extending the capabilities of the network to predict light color accurately. Given the critical role of color in image realism, joint learning of light intensity and color could significantly improve realism in virtual renderings.
• Application Expansion: Beyond image-based object insertion, these models could find applications in virtual and augmented reality settings, where real-time environmental lighting estimation is crucial for seamless integration of virtual elements.

Overall, this paper represents a meaningful contribution to the field of computational photography and graphics by advancing the accuracy and applicability of lighting estimation methods. The approach sets the stage for future research that can enhance the synthesis of realistically rendered scenes under varying and complex lighting conditions.