Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer (1908.01210v2)

Abstract: Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering. Enabling ML models to understand image formation might be key for generalization. However, due to an essential rasterization step involving discrete assignment operations, rendering pipelines are non-differentiable and thus largely inaccessible to gradient-based ML techniques. In this paper, we present {\emph DIB-R}, a differentiable rendering framework which allows gradients to be analytically computed for all pixels in an image. Key to our approach is to view foreground rasterization as a weighted interpolation of local properties and background rasterization as a distance-based aggregation of global geometry. Our approach allows for accurate optimization over vertex positions, colors, normals, light directions and texture coordinates through a variety of lighting models. We showcase our approach in two ML applications: single-image 3D object prediction, and 3D textured object generation, both trained using exclusively using 2D supervision. Our project website is: https://nv-tlabs.github.io/DIB-R/

• The paper introduces DIB-R, a differentiable rendering framework that computes analytical gradients for robust 3D object prediction.
• The framework leverages weighted interpolation for foreground pixels and distance-based aggregation for background to enable precise gradient flow.
• DIB-R demonstrates superior performance on 3D IOU and F-score metrics, enhancing 3D reconstruction from single 2D images.

Insights into Differentiable Rendering for 3D Object Prediction

The paper "Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer" introduces a differentiable rendering framework named DIB-R. This framework addresses the challenges of integrating the rendering process with gradient-based machine learning models. Traditional rendering pipelines involve non-differentiable operations, which limit their integration with deep learning methodologies. By viewing foreground rasterization as a weighted interpolation and background rasterization as a distance-based aggregation, the authors propose a method to compute gradients analytically across the entire image.

The central contribution of DIB-R is its ability to handle a comprehensive set of vertex attributes, allowing for optimized predictions over vertex positions, colors, textures, and various lighting models. This capability is showcased in two primary applications: 3D object prediction from a single image and 3D textured object generation, both relying solely on 2D supervision.

Methodological Advancements

The paper advances the field of differentiable rendering by providing a robust pipeline where gradients can be computed with respect to various scene properties without the approximations that typically limit existing approaches. This is achieved through differentiable rasterization, where foreground pixels are treated through interpolation—assigning them values based on nearest enclosing face vertex attributes, thus enabling analytical gradient computation. Conversely, background pixels benefit from a soft assignment that leverages a distance-based aggregation, adding depth to the learning signal and utilizing occluded information efficiently.

Furthermore, the authors implement support for multiple rendering models, underscoring the flexibility of DIB-R. By integrating traditional graphics lighting models such as Phong and Lambertian, along with Spherical Harmonics, the framework allows gradient optimization for all vertex attributes under different lighting conditions, thereby enhancing the realism of rendered outputs.

Numerical Results and Implications

DIB-R demonstrates superior quantitative performance in the task of single-image 3D object prediction. The method achieves notable improvements in 3D Intersection-Over-Union (IOU) and F-score metrics across diverse ShapeNet categories compared to existing methods like N3MR and SoftRas-Mesh. The framework not only achieves state-of-the-art results in 3D IOU but also enables finer geometric and color details in reconstructions, as evidenced by qualitative visualizations.

The implications of this work are multifold. The development of a fully differentiable rendering pipeline opens avenues for more robust and integrated learning pipelines in computer vision and graphics. The versatility of DIB-R in handling complex 3D geometry and textures suggests potential applications in augmented reality, virtual reality, and autonomous driving, where precise 3D understanding from 2D images is crucial.

Future Directions

The proposed framework sets the stage for several future research directions. Exploring the integration of more complex lighting models could lead to even more realistic renderings. Additionally, applying DIB-R to dynamic scenes and real-time applications could leverage its fast rasterization capabilities. Moreover, adapting the framework for other neural network architectures or combining it with generative adversarial networks (GANs) for unsupervised learning tasks holds promise.

In conclusion, the paper presents a significant contribution to the field of differentiable rendering. By facilitating direct gradient flow through rendering processes, DIB-R stands as a promising tool for advanced 3D prediction tasks, offering new insights and opportunities for the integration of traditional graphics with modern deep learning techniques.