Abstract: 

In this paper, we present a novel approach called FaRT (Fast and Robust Transfer) for view synthesis in computer graphics. Our method leverages a continuous volumetric function represented by multilayer perceptrons to capture the scene's appearance. However, traditional FaRT techniques often struggle with accurately rendering glossy surfaces. To address this limitation, we propose FaRT, which introduces a new parameterization of reflected radiance and utilizes spatially-varying scene properties. By incorporating a regularizer on normal vectors, our model significantly enhances the realism and accuracy of specular reflections. Moreover, our model's internal representation of outgoing radiance proves to be interpretable and useful for scene editing.

Introduction: 

View synthesis plays a crucial role in computer graphics applications, enabling the generation of realistic images from novel viewpoints. The NeRF technique has shown remarkable success in representing fine geometric structures and view-dependent appearance. However, NeRF-based methods often struggle to accurately render glossy surfaces, leading to unrealistic artifacts. In this paper, we introduce FaRT as a solution to this limitation. By replacing the parameterization of view-dependent outgoing radiance with a representation of reflected radiance and leveraging spatially-varying scene properties, our approach significantly improves the rendering quality of glossy surfaces. We also demonstrate the interpretability and scene editing capabilities of our model's internal representation.

Related Work: 

Previous research in view synthesis has focused on capturing fine geometric details and view-dependent appearance using techniques like NeRF. While NeRF excels in many aspects, it falls short when it comes to accurately representing glossy surfaces. To overcome this limitation, several methods have been proposed, such as XYZ and ABC. These approaches attempt to enhance the rendering quality of glossy surfaces by incorporating additional constraints or introducing novel parameterizations. However, these methods still suffer from certain limitations, such as high computational costs or limited interpretability. In contrast, our approach, FaRT, offers a robust and efficient solution that addresses these challenges.

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Methodology: 

The core idea of FaRT lies in the parameterization of outgoing radiance. Unlike traditional approaches that rely on view-dependent radiance, we introduce a representation of reflected radiance and utilize a collection of spatially-varying scene properties. By structuring the radiance function in this manner, we achieve improved interpolation and accurate rendering of glossy surfaces. Additionally, we incorporate a regularizer on normal vectors to enhance the accuracy of specular reflections. The integration of these components enables FaRT to capture the complex appearance of glossy surfaces with high fidelity.

Experiments:

To evaluate the effectiveness of FaRT, we conducted a series of comprehensive experiments on various scenes with glossy surfaces. Our goal was to assess the performance of FaRT in terms of visual quality, accuracy, and robustness compared to state-of-the-art view synthesis techniques.

We collected a diverse dataset comprising scenes with a range of reflective materials, including metals, glass, and polished surfaces. The scenes were captured from multiple viewpoints to create a comprehensive set of training and testing data. For evaluation purposes, we used objective metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and perceptual quality assessment metrics to quantitatively analyze the rendered images.

In addition to quantitative evaluations, we conducted a user study to gather subjective feedback on the visual quality of the rendered images. A group of participants with expertise in computer graphics and image synthesis were asked to rate the realism and fidelity of the generated views. The study also included comparisons with other state-of-the-art methods to gauge the superiority of FaRT in terms of glossy surface rendering.

The results of our experiments demonstrated the superior performance of FaRT across multiple evaluation metrics. The rendered images exhibited improved visual quality with accurate representation of glossy surfaces, including realistic specular reflections and highlights. The quantitative evaluations consistently showed higher PSNR and SSIM scores for FaRT compared to other methods, indicating better fidelity to ground truth images.

Furthermore, the user study confirmed the perceptual superiority of FaRT, as the participants consistently rated the rendered views from FaRT as more visually pleasing and realistic compared to the alternative methods. They specifically highlighted the improved handling of glossy materials, noting the accurate depiction of specular reflections and the overall high level of detail.

Overall, the extensive experiments and evaluations demonstrated that FaRT excels in capturing the appearance of glossy surfaces, producing visually appealing and highly realistic synthesized views. The combination of quantitative metrics and subjective assessments confirmed the superiority of FaRT over existing state-of-the-art techniques in the domain of view synthesis.

Conclusion: 

In this paper, we have presented FaRT, a novel approach for view synthesis that addresses the challenges of accurately rendering glossy surfaces. By introducing a parameterization of reflected radiance and leveraging spatially-varying scene properties, FaRT achieves improved interpolation and realistic rendering of glossy surfaces. Our experiments have demonstrated the superior performance of FaRT compared to state-of-the-art methods. The interpretability and scene editing capabilities of our model's internal representation further enhance its practical utility. FaRT opens up new possibilities for high-quality view synthesis and scene editing in computer graphics applications.

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