Deep Outdoor Illumination Estimation (1611.06403v3)

Abstract: We present a CNN-based technique to estimate high-dynamic range outdoor illumination from a single low dynamic range image. To train the CNN, we leverage a large dataset of outdoor panoramas. We fit a low-dimensional physically-based outdoor illumination model to the skies in these panoramas giving us a compact set of parameters (including sun position, atmospheric conditions, and camera parameters). We extract limited field-of-view images from the panoramas, and train a CNN with this large set of input image--output lighting parameter pairs. Given a test image, this network can be used to infer illumination parameters that can, in turn, be used to reconstruct an outdoor illumination environment map. We demonstrate that our approach allows the recovery of plausible illumination conditions and enables photorealistic virtual object insertion from a single image. An extensive evaluation on both the panorama dataset and captured HDR environment maps shows that our technique significantly outperforms previous solutions to this problem.

• The paper presents a CNN approach that infers HDR outdoor lighting from single LDR images with improved accuracy in sun positioning.
• The method leverages a large dataset of 360° panoramas and fits a physically-based sky model to extract compact lighting parameters.
• Results show significant improvements measured by RMSE metrics, enabling more realistic photorealistic virtual object integrations.

Overview of "Deep Outdoor Illumination Estimation"

The paper "Deep Outdoor Illumination Estimation" presents a technique based on convolutional neural networks (CNNs) for estimating high-dynamic range (HDR) outdoor lighting from a single low-dynamic range (LDR) image. The primary challenge addressed in the paper is the complexity of disentangling illumination from scene geometry and material properties, particularly in outdoor environments where control over scene elements is limited. This research contributes to the field by proposing a method for robust outdoor illumination inference without requiring explicit geometric details or strong priors about the scene.

Methodology

The authors leverage a large dataset of 360-degree outdoor panoramas to train their CNN model. By fitting a parametric, physically-based sky model (the Hošek-Wilkie model) to these panoramas, they derive a compact set of lighting parameters, including sun position and atmospheric conditions. This enables the construction of HDR environment maps from limited field-of-view images. The CNN is trained to regress LDR images to these combined lighting parameters, allowing the extraction of realistic illumination conditions that can be used for photorealistic virtual object insertion.

Results

The authors report significant improvements over existing methods, particularly in terms of the fidelity of sun position estimates and general lighting conditions. Quantitative evaluations demonstrate that their approach yields a more accurate HDR lighting approximation, useful for image-based lighting and relighting tasks. Using metrics such as RMSE and scale-invariant RMSE, the paper illustrates strong performance across a variety of environmental conditions, evidenced by comprehensive testing on the SUN360 dataset and a smaller dataset of captured HDR panoramas.

Implications and Future Directions

Practically, the results of this research are applicable to augmented reality, virtual reality, and any application requiring realistic virtual integration into real-world scenes. Theoretically, this work opens discussions on improving sky models to better capture complex atmospheric conditions, such as overcast skies, which are currently challenging for physically-based models.

Future directions might explore the integration of alternate sky models to better represent diffuse illumination scenarios or extend the approach to account for ground-level reflections, enhancing realism in specular object renderings. Additionally, applying this technique to indoor lighting estimation, where structured geometry could potentially assist in refining the illumination model, presents an intriguing avenue.

Overall, the paper advances the understanding of outdoor lighting estimation with deep learning, introducing a robust approach that effectively harnesses the power of CNNs to navigate the problems posed by natural outdoor scenes.