Enhancing Indoor Scene Reconstruction Using Neural Signed Distance Fields with View-Dependent Normal Compensation

Keskeiset käsitteet

A neural signed distance field (SDF) 3D reconstruction framework with view-dependent normal compensation that enhances the quality of indoor scene reconstructions by addressing multi-view inconsistency between monocular normal priors.

Tiivistelmä

The paper presents NC-SDF, a neural SDF 3D reconstruction framework that focuses on enhancing indoor scene reconstruction by addressing multi-view inconsistency between monocular normal priors. The key contributions are:

View-dependent normal compensation model: The framework integrates view-dependent biases in monocular normal priors into the neural implicit representation of the scene, enabling adaptive compensation for the biases and resulting in globally consistent and locally detailed reconstructions.

Informative pixel sampling strategy: An informative pixel sampling strategy is proposed to pay more attention to intricate geometry by prioritizing pixels with higher information content, further enhancing the reconstruction of geometric details.

Hybrid geometry modeling: A hybrid geometry model is introduced, combining the strengths of multi-layer perceptrons (MLPs) and voxel grids to strike a balance between modeling low-frequency structures and high-frequency details.

Comprehensive experiments on both synthetic and real-world datasets demonstrate that NC-SDF achieves state-of-the-art performance in indoor scene reconstruction, outperforming existing methods in terms of reconstruction quality.

Tilastot

The paper reports the following key metrics:

Accuracy (Acc): Lower is better
Completeness (Comp): Lower is better
Precision (Prec): Higher is better
Recall (Recall): Higher is better
F-score (F-score): Higher is better

Lainaukset

"Our NC-SDF excels in capturing intricate geometry while producing smooth surfaces in texture-less regions."
"The combination of our three designs ensures consistent and smooth surfaces while enabling sharp details in the reconstructions."

Tärkeimmät oivallukset

NC-SDF: Enhancing Indoor Scene Reconstruction Using Neural SDFs with View-Dependent Normal Compensation

by Ziyi Chen,Xi... klo arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00340.pdf

Syvällisempiä Kysymyksiä

How can the proposed view-dependent normal compensation model be extended to handle other types of geometric priors beyond monocular normals

The view-dependent normal compensation model proposed in the NC-SDF framework can be extended to handle other types of geometric priors beyond monocular normals by incorporating additional sources of geometric information. For example, depth priors from multi-view stereo methods or sensor depths could be integrated into the model to provide more comprehensive supervision. By adapting the normal compensation model to consider these different types of geometric priors, the framework can enhance its ability to handle a wider range of geometric complexities and improve the overall reconstruction quality. Additionally, the model could be extended to incorporate semantic priors or structural constraints to further refine the reconstruction process and ensure consistency in the scene representation.

What are the potential limitations of the hybrid geometry modeling approach, and how could it be further improved to handle more complex indoor scenes

The hybrid geometry modeling approach, while effective in balancing the representation of low-frequency and high-frequency structures, may have limitations in handling extremely complex indoor scenes with intricate details and diverse geometry. One potential limitation is the scalability of the model to capture fine details in large-scale environments. To address this limitation, the hybrid geometry model could be further improved by incorporating hierarchical feature representations or adaptive resolution mechanisms. By dynamically adjusting the level of detail in different regions of the scene based on the complexity of the geometry, the model can better capture intricate structures while maintaining efficiency and scalability. Additionally, exploring advanced feature fusion techniques, such as attention mechanisms or graph neural networks, could enhance the model's ability to capture complex spatial relationships and improve reconstruction accuracy in challenging indoor scenes.

Given the focus on indoor scene reconstruction, how could the proposed techniques be adapted to handle outdoor environments with more diverse and challenging geometry

To adapt the proposed techniques for handling outdoor environments with more diverse and challenging geometry, several modifications and extensions can be considered. Firstly, the view-dependent normal compensation model could be enhanced to account for outdoor lighting conditions, varying textures, and dynamic elements present in outdoor scenes. By incorporating environmental factors and scene dynamics into the compensation process, the model can adapt to the unique challenges of outdoor reconstruction tasks. Additionally, the informative pixel sampling strategy could be optimized for outdoor scenes by considering factors such as vegetation, terrain variations, and weather conditions to prioritize sampling in regions with significant geometric details. Furthermore, the hybrid geometry modeling approach could be extended to incorporate multi-modal data sources, such as LiDAR or aerial imagery, to capture the complex geometry and spatial relationships characteristic of outdoor environments. By integrating diverse data modalities and refining the modeling techniques for outdoor scenes, the proposed techniques can be effectively adapted to handle the complexities of outdoor scene reconstruction.