Neural Volumes: Learning Dynamic Renderable Volumes from Images (1906.07751v1)

Abstract: Modeling and rendering of dynamic scenes is challenging, as natural scenes often contain complex phenomena such as thin structures, evolving topology, translucency, scattering, occlusion, and biological motion. Mesh-based reconstruction and tracking often fail in these cases, and other approaches (e.g., light field video) typically rely on constrained viewing conditions, which limit interactivity. We circumvent these difficulties by presenting a learning-based approach to representing dynamic objects inspired by the integral projection model used in tomographic imaging. The approach is supervised directly from 2D images in a multi-view capture setting and does not require explicit reconstruction or tracking of the object. Our method has two primary components: an encoder-decoder network that transforms input images into a 3D volume representation, and a differentiable ray-marching operation that enables end-to-end training. By virtue of its 3D representation, our construction extrapolates better to novel viewpoints compared to screen-space rendering techniques. The encoder-decoder architecture learns a latent representation of a dynamic scene that enables us to produce novel content sequences not seen during training. To overcome memory limitations of voxel-based representations, we learn a dynamic irregular grid structure implemented with a warp field during ray-marching. This structure greatly improves the apparent resolution and reduces grid-like artifacts and jagged motion. Finally, we demonstrate how to incorporate surface-based representations into our volumetric-learning framework for applications where the highest resolution is required, using facial performance capture as a case in point.

• The paper presents a learning-based volumetric method that accurately renders dynamic 3D scenes from image data.
• It uses an encoder-decoder architecture with differentiable ray marching for efficient gradient propagation and robust convergence.
• The approach generalizes to novel viewpoints and adeptly handles complexities such as translucency, occlusions, and changing topology.

Exploring Neural Volumes for Dynamic 3D Scene Reconstruction and Rendering

This paper titled "Neural Volumes: Learning Dynamic Renderable Volumes from Images" proposes a method for modeling and rendering dynamic scenes using a neural network-based volumetric representation. The authors address the challenges associated with traditional mesh-based reconstruction methods, particularly in handling complex phenomena such as translucent materials, changing topology, and occlusions found in natural scenes.

Key Methodological Contributions

The central innovation presented in this paper is a learning-based approach that circumvents the constraints of explicit geometric tracking and reconstruction. The method involves two primary components: an encoder-decoder architecture that transforms input images into a 3D volume representation and a differentiable ray-marching algorithm that facilitates end-to-end training. The encoder-decoder network functions to learn a latent representation of dynamic scenes, essential for extrapolating to novel viewpoints and generating unseen sequences. This is in contrast to screen-space rendering methods, which do not extrapolate well to new viewpoints due to their lack of 3D awareness.

The paper introduces a unique use of a volumetric representation characterized by an opacity and color field at each position in the 3D space. Semi-transparent rendering through differentiable ray marching aids in distributing gradient information along each ray, thus improving convergence during optimization. This technique is powerful in handling the complexities introduced by dynamic scenes without significant memory overhead—a common limitation of voxel-based representations.

Additionally, the authors have developed an irregular grid structure with a warp field that enables higher resolution apparent outputs, reducing typical artifacts and enhancing the fluidity of motion portrayal. There’s also the integration of surface-based reconstruction into volumetric learning for domains requiring high-resolution realism, such as facial and performance capture.

Numerical and Comparative Analysis

In their experiments, the authors demonstrated the efficacy of their approach across various complex scene types, including challenging materials like hair and smoke. The results indicate improved generalization to unseen viewpoints and fidelity in dynamic scene reconstruction when compared to traditional mesh methods and other volumetric approaches.

Implications and Future Directions

The proposed neural volume framework broadens the scope of data-driven rendering methods, emphasizing applications in virtual reality and other interactive settings that demand real-time processing and high-quality visual fidelity. It represents a notable step forward in computational rendering paradigms by moving away from explicit geometric constraints towards learned representations.

Future work can focus on extending this framework to account for refractive and reflective materials seamlessly, further enhancing the model's ability to capture and reproduce natural appearances. Additionally, integrating temporal dynamics into the latent space could facilitate more realistic animations and interactions in unstructured environments. This also opens up possibilities for more sophisticated control over volumetric models through direct user input and other real-world applications.

The paper meticulously outlines an innovative approach that maintains computational efficiency and memory usage while providing robust solutions to the nuanced problems of dynamic scene representation with neural networks.