OmniSDF: Efficient Omnidirectional Scene Reconstruction using Adaptive Spherical Binoctrees and Neural Signed Distance Functions

Core Concepts

We present a memory-efficient neural 3D reconstruction method that uses an adaptive spherical binoctree data structure and signed distance functions to accurately reconstruct large-scale unbounded scenes from short egocentric omnidirectional video inputs.

Abstract

The paper introduces a novel method for 3D reconstruction of large-scale unbounded scenes from short egocentric omnidirectional video inputs. The key ideas are:

Representation: The geometry is represented using a neural signed distance function (SDF) within an adaptive spherical binoctree data structure. This allows efficient partitioning of the reconstruction space to focus sampling on areas with more detail.

Adaptive Subdivision: The spherical binoctree is adaptively subdivided during the optimization process, starting from a coarse initial structure and refining it based on the estimated SDF values. This enables the method to capture high-frequency details while maintaining memory efficiency.

Sampling Strategies: The paper proposes three levels of sampling: whole-space spherical sampling, coarse sphoxel (spherical voxel) sampling, and adaptive fine sphoxel sampling. This hierarchical sampling approach guides the optimization towards plausible surface regions.

Intersection and Surface Detection: Novel techniques are introduced to handle the irregular shape of sphoxels, including a ray-triangle intersection test and a surface existence test based on the SDF value and sphoxel radius.

The method is evaluated on both synthetic and real-world datasets, showing improved reconstruction accuracy and memory efficiency compared to state-of-the-art neural and classical 3D reconstruction approaches.

Stats

The number of voxels required for a dense regular Cartesian grid to achieve the same minimum voxel size as our adaptive spherical binoctree is significantly higher:

Sponza: 33,335,054,331 vs 4,346,041 voxels
Lone-monk: 2,234,638,740 vs 231,237 voxels
San Miguel: 1,953,273,076 vs 951,703 voxels

Quotes

"Our approach allows for more efficient and accurate 3D surface reconstruction of large-scale unbounded scenes using omnidirectional video inputs."
"We show that our method outperforms other state-of-the-art 3D reconstruction methods in balancing detail and memory cost."

Key Insights Distilled From

OmniSDF

by Hakyeong Kim... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00678.pdf

Deeper Inquiries

How could the proposed adaptive binoctree structure be extended to handle dynamic scenes or incorporate additional sensor modalities beyond video?

The adaptive binoctree structure proposed in the OmniSDF method could be extended to handle dynamic scenes by incorporating real-time updates based on changing inputs. For dynamic scenes, the binoctree could be updated continuously as new data comes in, allowing for adaptive subdivision and sampling strategies to adjust to the evolving scene geometry. Additionally, incorporating additional sensor modalities beyond video, such as depth sensors or LiDAR data, could enhance the accuracy and robustness of the reconstruction. By integrating data from multiple sensors, the binoctree could adapt to different types of input information, leading to more comprehensive and detailed scene reconstructions.

What are the potential limitations of the neural SDF representation, and how could it be further improved to handle challenging scene geometries or materials?

One potential limitation of the neural SDF representation is its sensitivity to noisy or sparse input data, which can lead to inaccuracies in reconstructing complex scene geometries or materials. To address this limitation, the neural SDF representation could be further improved by incorporating uncertainty estimation techniques to account for data variability and noise. By modeling uncertainty in the neural network predictions, the reconstruction can be more robust to challenging scene geometries and materials. Additionally, exploring multi-scale approaches or hierarchical neural networks could help capture fine details and intricate structures in the scene, improving the overall reconstruction quality.

Could the insights from this work on efficient spatial partitioning be applied to other 3D reconstruction or scene understanding tasks beyond omnidirectional inputs?

Yes, the insights from the efficient spatial partitioning techniques developed in the OmniSDF method can be applied to various other 3D reconstruction and scene understanding tasks beyond omnidirectional inputs. For example, in robotics applications, efficient spatial partitioning can aid in mapping and navigation tasks by optimizing the representation of the environment for path planning algorithms. In augmented reality and virtual reality applications, efficient spatial partitioning can enhance the realism and accuracy of virtual scenes. By adapting the principles of adaptive subdivision and sampling strategies to different domains, such as medical imaging or cultural heritage preservation, the efficiency and accuracy of 3D reconstruction tasks can be significantly improved.