Recent Trends in 3D Reconstruction of General Non-Rigid Scenes (2403.15064v2)

Abstract: Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision. It enables the synthesizing of photorealistic novel views, useful for the movie industry and AR/VR applications. It also facilitates the content creation necessary in computer games and AR/VR by avoiding laborious manual design processes. Further, such models are fundamental for intelligent computing systems that need to interpret real-world scenes and actions to act and interact safely with the human world. Notably, the world surrounding us is dynamic, and reconstructing models of dynamic, non-rigidly moving scenes is a severely underconstrained and challenging problem. This state-of-the-art report (STAR) offers the reader a comprehensive summary of state-of-the-art techniques with monocular and multi-view inputs such as data from RGB and RGB-D sensors, among others, conveying an understanding of different approaches, their potential applications, and promising further research directions. The report covers 3D reconstruction of general non-rigid scenes and further addresses the techniques for scene decomposition, editing and controlling, and generalizable and generative modeling. More specifically, we first review the common and fundamental concepts necessary to understand and navigate the field and then discuss the state-of-the-art techniques by reviewing recent approaches that use traditional and machine-learning-based neural representations, including a discussion on the newly enabled applications. The STAR is concluded with a discussion of the remaining limitations and open challenges.

• The paper introduces hybrid neural scene representations that combine voxel grids and feature planes with implicit neural models to enhance reconstruction speed and realism.
• The paper demonstrates that integrating classical non-neural approaches with modern techniques simplifies scene editing while improving real-time performance.
• The paper outlines future directions using generative models and data-driven priors to build generalizable systems for reconstructing diverse dynamic non-rigid scenes.

Recent Advancements in 3D Reconstruction of Dynamic Scenes

Introduction to 3D Reconstruction Challenges in Dynamic Environments

3D reconstruction of dynamic non-rigid scenes is a significantly challenging domain within computer vision and computer graphics. This complexity primarily arises due to the underconstrained nature of inferring 3D geometry and appearance from 2D observations in dynamically evolving scenes. Applications across movie production, augmented and virtual reality (AR/VR), and interaction design depend on robust 3D reconstruction techniques to interpret real-world dynamics accurately. The dynamic nature of these scenes introduces ambiguities in depth perception, occlusions, and deformation modeling, necessitating advanced computational approaches for accurate reconstruction.

Emergence of Neural Scene Representations

Neural scene representations have revolutionized 3D reconstruction methodologies by offering a flexible and unified framework for capturing complex scene dynamics. At the core of these advances is the concept of encoding scenes into implicit neural representations, typically utilizing Neural Radiance Fields (NeRFs) and their variants. These models have been extended to capture temporal variations, enabling dynamic scene reconstruction and novel view synthesis. Despite their potential, the computational overhead associated with training and inference in neural scene models posits significant challenges for real-time applications.

Hybrid Neural Scene Representations: Bridging Efficiency and Realism

Hybrid representations have emerged as a potent solution to the computational challenges posed by pure neural scene models. By integrating neural components with traditional data structures like voxel grids, feature planes, and point clouds, hybrid models achieve significant improvements in training and rendering speeds. These representations facilitate efficient querying and manipulation of scene features, enabling real-time applications in dynamic environments. Notably, such models allow decoupling scene representation from rendering, further enhancing editability and control over scene dynamics.

Navigating Challenges with Non-Neural Representations

Despite the advancements in neural and hybrid representations, non-neural approaches continue to play a crucial role in scenarios where data-driven methods face limitations. These methods, leveraging classical representations such as meshes, voxels, and surfels, offer direct control over geometric and appearance properties of scenes, simplifying edits and interactions. Real-time performance, a critical requirement in many applications, remains more achievable through these classical approaches, particularly when dealing with geometry reconstruction and tracking in dynamic scenes.

Towards Generalizable and Generative Modeling

A promising direction for future research is the development of generalizable and generative models for non-rigid scene reconstruction. Learning data-driven priors from large datasets has the potential to address the intrinsic challenges of scene dynamics, allowing models to generalize across different scenes and articulate objects. Generative models, leveraging techniques such as diffusion models, open new possibilities for scene synthesis and editing, enabling the generation of realistic and consistent dynamic scenes from sparse data or textual descriptions.

Open Challenges and Future Directions

The field of 3D reconstruction of dynamic scenes is ripe with open challenges and opportunities for innovation. Topics such as intrinsic decomposition and relighting, faster scene representations for real-time applications, reliable camera pose estimation in dynamic environments, and physics-based methods for enhanced realism are pivotal areas awaiting further exploration. Moreover, embracing compositionality and multi-object interaction, leveraging specialized sensors, and exploring the intersection with generative AI models present fertile grounds for advancing the state-of-the-art in dynamic scene reconstruction.

Conclusion

The advancements in 3D reconstruction of dynamic scenes highlight the field's rapid evolution, driven by the convergence of neural representations, hybrid models, and classical approaches. As the community continues to tackle the inherent challenges of dynamic environments, the development of efficient, generalizable, and generative models stands as a cornerstone for future breakthroughs. These advancements promise to revolutionize applications across entertainment, AR/VR, and interactive systems, offering unprecedented realism and interactivity in digital content creation and consumption.