Deformable NeRF using Recursively Subdivided Tetrahedra (2410.04402v1)

Abstract: While neural radiance fields (NeRF) have shown promise in novel view synthesis, their implicit representation limits explicit control over object manipulation. Existing research has proposed the integration of explicit geometric proxies to enable deformation. However, these methods face two primary challenges: firstly, the time-consuming and computationally demanding tetrahedralization process; and secondly, handling complex or thin structures often leads to either excessive, storage-intensive tetrahedral meshes or poor-quality ones that impair deformation capabilities. To address these challenges, we propose DeformRF, a method that seamlessly integrates the manipulability of tetrahedral meshes with the high-quality rendering capabilities of feature grid representations. To avoid ill-shaped tetrahedra and tetrahedralization for each object, we propose a two-stage training strategy. Starting with an almost-regular tetrahedral grid, our model initially retains key tetrahedra surrounding the object and subsequently refines object details using finer-granularity mesh in the second stage. We also present the concept of recursively subdivided tetrahedra to create higher-resolution meshes implicitly. This enables multi-resolution encoding while only necessitating the storage of the coarse tetrahedral mesh generated in the first training stage. We conduct a comprehensive evaluation of our DeformRF on both synthetic and real-captured datasets. Both quantitative and qualitative results demonstrate the effectiveness of our method for novel view synthesis and deformation tasks. Project page: https://ustc3dv.github.io/DeformRF/

• The paper presents DeformRF, a novel method that integrates recursive tetrahedral subdivision to enhance NeRF's deformation and rendering capabilities.
• It employs a two-stage training process that starts with a coarse mesh and refines details through recursive subdivision, boosting computational efficiency.
• Evaluation on synthetic and real-world datasets demonstrates superior rendering quality and deformation performance compared to traditional NeRF techniques.

Overview of "Deformable NeRF using Recursively Subdivided Tetrahedra"

The paper "Deformable NeRF using Recursively Subdivided Tetrahedra" presents DeformRF, a novel method to augment Neural Radiance Fields (NeRF) with explicit geometric control, enabling effective object manipulation. This technique enhances traditional NeRF methods which primarily focus on high-quality rendering without flexible deformation capability.

Motivation and Challenges

While NeRF excelled in novel view synthesis, its implicit representation hindered direct object manipulation. Traditional integrations of geometric proxies, like tetrahedral meshes, faced issues such as computational intensity and inefficiency when dealing with complex structures. DeformRF addresses these challenges by refining the intersection of tetrahedral mesh flexibility with grid-based rendering precision, optimizing both computational efficiency and rendering quality.

Methodology

DeformRF introduces two primary innovations:

1. Recursive Tetrahedral Subdivision: The method constructs a multi-resolution mesh hierarchy without storing high-resolution versions. Starting from a nearly regular grid, tetrahedra are recursively subdivided to enhance detail without the need to explicitly retain these finer meshes, using barycentric interpolation informed by an iterative barycentric computation algorithm.
2. Two-Stage Training Process: A novel training strategy is employed. Initially, a coarse mesh encapsulating the object is trained, followed by a refined training stage that increases subdivision levels to enrich visual fidelity. This approach sidesteps the need for complex surface mesh extraction and high-volume data storage.

Results and Evaluation

Comprehensive evaluations on synthetic and real-world datasets indicate that DeformRF surpasses existing methods in rendering quality and deformation performance, achieving peak metrics such as PSNR, SSIM, and LPIPS. These assessments reveal superior handling of intricate details and textures, maintaining photorealistic quality even under complex deformations or animations.

Implications and Future Work

The introduction of a deformable NeRF has significant implications for fields like animation, virtual reality, and computer graphics, offering improved precision in object manipulation and scene interactions. The hierarchical representation facilitates streamlined memory usage and optimized computational efficiency.

Potential future developments might explore enhancing the fidelity of the barycentric interpolation or extending the technique to more complex dynamic movements. Integration with additional physics-based simulation frameworks could further broaden application scenarios.

Conclusion

The paper contributes to the field of neural graphics by providing a more manipulable and efficient NeRF variant. DeformRF's combination of tetrahedral mesh manipulability with high-quality rendering delineates a step forward in adaptable 3D modeling and presents an enriched platform for both academic and practical advancements in computational graphics.