Neural networks have come a long way in recent years and have become a use case in almost every application. One of the most interesting use cases is 3D modeling of the real world. We have seen Neural Radiation Fields (NeRFs) that can accurately capture the 3D geometry of a scene using normal everyday cameras. These advances opened a new page in 3D surface reconstruction.
The goal of 3D surface reconstruction is to recover detailed geometric structures from a scene by analyzing multiple images captured from various viewpoints. These reconstructed surfaces contain valuable structural information that can be applied to various applications, including generating 3D assets for augmented/virtual/mixed reality and mapping environments for autonomous robotic navigation. One particularly intriguing approach is photogrammetric surface reconstruction using a single RGB camera, as it allows users to easily create digital replicas of the real world using common mobile devices.
3D surface reconstruction plays a crucial role in generating dense geometric structures from multiple images, enabling a wide range of applications such as augmented/virtual/mixed reality and robotics. Although classical methods, such as multiview stereo algorithms, have been popular for sparse 3D reconstruction, they often have problems with ambiguous observations and produce inaccurate or incomplete results. Neural surface reconstruction methods have emerged as a promising solution by leveraging coordinate-based multilayer perceptrons (MLPs) to represent scenes as implicit functions. However, the fidelity of current methods is not well matched to the capability of MLP.
What if we could have a method that would solve the scaling problem? What if we could accurately generate 3D surface models just by using RGB inputs? time to meet neuralangelo.
neuralangelo is a framework that combines the power of Instant NGP (Neural Graphics Primitives) and neural SDF rendering to achieve high-fidelity surface reconstruction.
neuralangelo adopts Instant NGP as a neural Signed Distance Function (SDF) representation of the underlying 3D scene. Instant NGP features a hybrid 3D grid structure with multi-resolution hash encoding, along with a lightweight MLP that enhances expressivity while maintaining a log-linear memory footprint. This hybrid rendering significantly improves the rendering power of neural fields and excels at capturing fine-grained detail.
To further improve the quality of the hash-encoded surface reconstruction, neuralangelo introduces two key techniques. First, numerical gradients are used to compute higher order derivatives, such as surface normals, which help stabilize the optimization process. Second, a progressive optimization program is implemented to recover structures at different levels of detail, allowing for a comprehensive reconstruction approach. These techniques work in synergy, leading to substantial improvements in both reconstruction accuracy and view synthesis quality.
neuralangelo naturally incorporates the power of multi-resolution hashing into neural SDF representations, resulting in improved reconstruction capabilities. Second, the use of numerical gradients and eikonal regularization helps improve the quality of hash-encoded surface reconstruction by stabilizing the optimization process. Lastly, extensive experiments on standard landmarks and real-world scenes demonstrate the efficacy of Neuralangelo, showing significant improvements over previous image-based neural surface reconstruction methods in terms of reconstruction accuracy and view synthesis quality. .
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Ekrem Çetinkaya received his B.Sc. in 2018 and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. She wrote her M.Sc. thesis on denoising images using deep convolutional networks. She is currently pursuing a PhD. She graduated from the University of Klagenfurt, Austria, and working as a researcher in the ATHENA project. Her research interests include deep learning, computer vision, and multimedia networks.
