NEAT: A Rendering-Distilling Approach for Efficient 3D Wireframe Reconstruction from Multi-View Images

Alapfogalmak

This paper presents NEAT, a novel rendering-distilling formulation using neural fields to represent 3D line segments and junctions, enabling matching-free 3D wireframe reconstruction from multi-view images.

Kivonat

The paper proposes a novel approach called NEAT (NEural Attraction) for 3D wireframe reconstruction from multi-view images. The key ideas are:

1. Rendering 3D Line Segments: NEAT leverages neural fields to implicitly represent 3D line segments from 2D wireframe observations, without relying on explicit feature matching across views.

2. Global 3D Junction Perceiving: NEAT jointly optimizes a set of learnable global 3D junctions to distill the sparse wireframe structure from the dense neural fields of 3D line segments.

3. Wireframe Distillation: The perceived 3D junctions are used to index and group the rendered 3D line segments, forming the final 3D wireframe representation.

The authors demonstrate that NEAT significantly outperforms state-of-the-art matching-based approaches on the DTU and BlendedMVS datasets, handling both straight-line dominated and curve-rich scenes. Additionally, the distilled 3D junctions by NEAT can serve as better initialization than SfM points for the 3D Gaussian Splatting framework, using about 20 times fewer initial 3D points.

Statisztikák

The paper reports the following key statistics:

NEAT achieves an average junction accuracy (ACC-J) of 0.7718 and line segment accuracy (ACC-L) of 0.8002 on the DTU dataset.
NEAT reconstructs an average of 624 3D line segments and 503 3D junctions on the DTU dataset.
On the BlendedMVS dataset, NEAT achieves an ACC-J of 0.1949 and ACC-L of 0.1802, reconstructing an average of 602 3D line segments and 514 3D junctions.

Idézetek

"Our NEAT enjoys the joint optimization of the neural fields and the global junctions from scratch, using view-dependent 2D observations without precomputed cross-view feature matching."
"Comprehensive experiments on the DTU and BlendedMVS datasets demonstrate our NEAT's superiority over state-of-the-art alternatives for 3D wireframe reconstruction."
"Moreover, the distilled 3D global junctions by NEAT, are a better initialization than SfM points, for the recently-emerged 3D Gaussian Splatting for high-fidelity novel view synthesis using about 20 times fewer initial 3D points."

Főbb Kivonatok

NEAT

by Nan Xue,Bin ... : arxiv.org 04-04-2024

https://arxiv.org/pdf/2307.10206.pdf

Mélyebb kérdések

How can the NEAT approach be extended to handle large-scale scenes with a potentially unbounded number of 3D junctions

To extend the NEAT approach for handling large-scale scenes with an unbounded number of 3D junctions, a scalable strategy can be implemented. One approach could involve dynamically adjusting the number of global junctions based on the complexity and size of the scene. Instead of predefining the number of junctions, an adaptive mechanism could be introduced that automatically determines the optimal number of junctions needed for accurate reconstruction. This adaptive mechanism could be guided by scene characteristics, such as density of features, complexity of geometry, and distribution of line segments. Additionally, a hierarchical or multi-resolution approach could be employed to divide the scene into manageable sub-regions, each with its set of junctions, allowing for efficient processing of large-scale scenes while maintaining reconstruction accuracy.

What other applications beyond 3D wireframe reconstruction could benefit from the NEAT formulation of jointly learning dense neural fields and sparse geometric primitives

The NEAT formulation of jointly learning dense neural fields and sparse geometric primitives can benefit various applications beyond 3D wireframe reconstruction. One potential application is in robotics for environment perception and navigation. By leveraging the learned dense neural fields for scene representation and sparse geometric primitives for structural information, robots can navigate complex environments more effectively. Another application could be in augmented reality (AR) for real-time scene understanding and object interaction. The ability to reconstruct 3D scenes accurately and efficiently using NEAT can enhance AR experiences by providing a more realistic and interactive environment. Furthermore, in medical imaging, NEAT could be utilized for reconstructing anatomical structures from 2D medical images, aiding in diagnosis and treatment planning.

Can the NEAT approach be further improved by incorporating additional cues, such as semantic information or physical constraints, to enhance the robustness and accuracy of 3D wireframe reconstruction

The NEAT approach can be further improved by incorporating additional cues such as semantic information and physical constraints to enhance the robustness and accuracy of 3D wireframe reconstruction. Semantic information, such as object categories or scene labels, can guide the reconstruction process by providing context and priors for the arrangement of geometric primitives. By integrating semantic cues, NEAT can improve the understanding of the scene structure and enhance the fidelity of the reconstructed wireframes. Moreover, incorporating physical constraints, such as depth constraints or geometric relationships between objects, can help in refining the reconstruction by ensuring geometric consistency and adherence to real-world constraints. By integrating these additional cues, NEAT can achieve more accurate and contextually meaningful 3D wireframe reconstructions.