Enhanced Indoor 3D Scene Reconstruction with Occluded Surface Completion

Conceptos Básicos

A novel indoor 3D reconstruction method that completes occluded surfaces in addition to reconstructing visible surfaces, using a hierarchical octree representation and a dual-decoder architecture.

Resumen

The paper presents a novel indoor 3D scene reconstruction method that addresses the challenge of completing occluded surfaces, in addition to reconstructing visible surfaces. The key contributions are:

A hierarchical octree representation that separates the scene into fine-level features for visible surfaces and coarse-level features for occluded surfaces.
A dual-decoder architecture, where the Geo-decoder reconstructs visible surfaces using the fine-level features, and the 3D Inpainter completes the occluded surfaces using the coarse-level features.
The 3D Inpainter is trained offline on complete 3D scene meshes from multiple scenes, enabling it to generalize and complete occluded surfaces in unseen scenes.
During online optimization, the Geo-decoder and octree features are jointly optimized using depth observations from the test scene, allowing the method to specialize for the individual scene.

The proposed method is evaluated on the 3D-CRS and iTHOR datasets, significantly outperforming state-of-the-art methods in terms of the completeness of 3D reconstruction, particularly for occluded surfaces.

Estadísticas

The 3D-CRS dataset includes a complete 3D mesh of each scene.
The proposed method outperforms the baselines by a gain of 16.8% and 24.2% in terms of the completeness of 3D reconstruction on the 3D-CRS and iTHOR datasets, respectively.

Citas

"Our method uniquely treats visible and occluded surfaces from two different aspects: 1) We separately represent visible and occluded regions by fine-level and coarse-level features: Fine features are expected to encode high-frequency detailed geometry of visible surfaces, while coarse features are anticipated to represent contextual structure information, which is more generalizable for occluded surface completion."
"We design a scene-specific visible decoder, and a generalizable cross-scene occlusion decoder: The visible surface decoder is optimized online using depth readings in a testing scene, whereas the occluded surface decoder is trained offline with multiple scenes."

Ideas clave extraídas de

Behind the Veil

by Su Sun,Cheng... a las arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03070.pdf

Consultas más profundas

How can the proposed method be extended to handle dynamic scenes with moving objects

To extend the proposed method to handle dynamic scenes with moving objects, several modifications and additions can be made. One approach could involve incorporating a dynamic object detection and tracking system to identify and track moving objects within the scene. This information can then be used to update the 3D scene reconstruction in real-time as objects move. Additionally, the method could be enhanced to predict the trajectory of moving objects based on their previous movements, allowing for more accurate reconstruction of dynamic scenes. By integrating temporal information and predictive modeling, the system can adapt to changes in the scene and maintain an up-to-date 3D reconstruction despite the presence of moving objects.

What are the limitations of the current approach, and how can it be improved to handle more complex indoor environments

The current approach has limitations in handling more complex indoor environments, particularly in scenarios with intricate furniture arrangements, cluttered spaces, and varying lighting conditions. To improve the method for such environments, several enhancements can be considered. One key improvement could involve incorporating semantic segmentation to better understand the scene layout and differentiate between different objects and surfaces. This would enable more accurate reconstruction of occluded surfaces and improve the overall completeness of the 3D scene. Additionally, integrating multi-modal sensor data, such as RGB images and depth information, can provide richer input for the reconstruction process, enhancing the system's ability to handle complex indoor environments. Furthermore, refining the training data to include a wider variety of indoor scenes with diverse layouts and configurations can help the model generalize better to different environments.

How can the learned 3D geometry priors from the 3D Inpainter be leveraged for other tasks, such as object manipulation or scene understanding

The learned 3D geometry priors from the 3D Inpainter can be leveraged for various tasks beyond surface completion. One potential application is object manipulation, where the learned priors can assist in understanding the spatial relationships between objects in a scene. By utilizing the inferred 3D geometry, the system can predict how objects interact and move within the environment, enabling more accurate and realistic object manipulation. Additionally, the learned priors can be valuable for scene understanding tasks, such as scene segmentation and classification. By leveraging the 3D geometry priors, the system can better interpret and analyze indoor scenes, leading to improved scene understanding and context-aware applications.