Decomposed 3D Scene Reconstruction with Minimal Human Interaction

Centrala begrepp

A novel method for decomposing 3D scenes into individual objects and backgrounds with minimal human interaction, by integrating the Segment Anything Model (SAM) with hybrid implicit-explicit neural surface representations and a mesh-based region-growing technique.

Sammanfattning

The paper presents Total-Decom, a novel method for decomposed 3D scene reconstruction from multi-view images with minimal human interaction. The key aspects are:

1. Implicit Neural Surface Reconstruction:

• Employs an implicit neural surface representation to achieve dense and complete 3D reconstruction from images.
• Integrates object-aware information by distilling image features from the Segment Anything Model (SAM).
• Disentangles foreground and background geometry using geometric priors and regularization.

2. Interactive Decomposition:

• Extracts an explicit mesh surface to provide geometry information for better decomposition and efficient rendering.
• Leverages the SAM decoder and rendered SAM features to convert a single user click into a dense object mask, enabling interactive control over decomposition granularity.

3. Mesh-based Region Growing:

• Proposes a new mesh-based region-growing algorithm that leverages feature similarities, geometry topology, and object boundaries derived from SAM to accurately extract object surfaces.
• Requires minimal human annotations, typically just one click per object on average.

The method is extensively evaluated on benchmark datasets, demonstrating its ability to decompose complex scenes into individual objects with high accuracy, outperforming state-of-the-art approaches. The decomposed 3D reconstruction also enables various downstream applications such as animation and scene editing.

Statistik

The paper does not provide any specific numerical data or statistics. The key results are presented through qualitative comparisons and quantitative evaluations of reconstruction accuracy.

Citat

"Our approach seamlessly integrates the Segment Anything Model (SAM) with hybrid implicit-explicit neural surface representations and a mesh-based region-growing technique for accurate 3D object decomposition."
"Total-Decom requires minimal human annotations while providing users with real-time control over the granularity and quality of decomposition."

Viktiga insikter från

Total-Decom

by Xiaoyang Lyu... på arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19314.pdf

Djupare frågor

How can the proposed method be extended to handle more challenging scenarios, such as highly occluded objects or dynamic scenes

To handle more challenging scenarios like highly occluded objects or dynamic scenes, the proposed method can be extended in several ways. One approach could involve incorporating generative methods to complete invisible 3D objects in occluded areas. By leveraging generative models, the system could predict the missing parts of objects based on the visible information, enhancing the completeness of the reconstructed scene. Additionally, integrating temporal information from multiple frames could help in capturing dynamic scenes more accurately. By considering the evolution of objects over time, the system could improve the reconstruction of moving or changing elements within the scene.

What are the potential limitations of the current approach, and how could they be addressed in future work

While the current approach shows promising results in decomposed 3D reconstruction, there are potential limitations that could be addressed in future work. One limitation is the handling of complex object shapes or textures that may not be accurately captured by the implicit surface representation. To address this, incorporating more sophisticated neural network architectures or hybrid models that combine implicit and explicit representations could enhance the reconstruction quality. Additionally, improving the region-growing algorithm to handle more intricate object boundaries and occlusions could further refine the decomposition process. Addressing these limitations could lead to more precise and detailed reconstructions in challenging scenarios.

How could the decomposed 3D reconstruction be leveraged to enable more advanced applications in areas like robotics, virtual reality, or digital content creation

The decomposed 3D reconstruction enabled by the proposed method has significant potential for advanced applications in various fields. In robotics, the detailed object-level reconstructions could enhance robot perception and manipulation tasks, allowing robots to interact more effectively with their environment. In virtual reality, the decomposed scenes could be utilized for immersive experiences, enabling realistic interactions with virtual objects. For digital content creation, the ability to extract individual objects from a scene could streamline the process of creating animations, simulations, or virtual environments. By leveraging the decomposed 3D reconstructions, these applications could benefit from more accurate and detailed scene representations, leading to enhanced user experiences and improved workflow efficiency.