TensoIR: Tensorial Inverse Rendering Approach for Scene Reconstruction

Concepts de base

TensoIR proposes a novel approach based on tensor factorization and neural fields for efficient and accurate scene reconstruction, achieving state-of-the-art results in inverse rendering.

Résumé

TensoIR introduces a tensor factorization-based framework that combines radiance field rendering with physically-based model estimation to reconstruct scene geometry, materials, and illumination accurately. The method efficiently models secondary shading effects and supports input images captured under various lighting conditions. By jointly optimizing radiance field reconstruction and physically-based rendering, TensoIR achieves high-quality scene reconstruction results outperforming previous methods qualitatively and quantitatively.

Stats

Our method can achieve good quality in a much shorter training period (25 minutes).
NeRFactor takes days to compute because of visibility pre-computation.
InvRender is faster than NeRFactor but fails on challenging scenes like the Lego.
Our approach leverages ray marching-based radiance rendering for accurate shadowing.
Multi-light settings greatly improve geometry reconstruction without adding computational costs.

Citations

"Our approach models a scene as both a radiance field and a physically-based model with density, normals, lighting, and material properties."
"TensoIR achieves high-quality scene reconstruction results outperforming previous methods qualitatively and quantitatively."

Idées clés tirées de

TensoIR

by Haian Jin,Is... à arxiv.org 03-08-2024

https://arxiv.org/pdf/2304.12461.pdf

Questions plus approfondies

How does TensoIR's joint optimization framework contribute to the efficiency of the reconstruction process?

TensoIR's joint optimization framework plays a crucial role in enhancing the efficiency of the reconstruction process by simultaneously estimating scene geometry, materials, and illumination from multi-view images captured under unknown lighting conditions. By jointly reconstructing both radiance field and physically-based models through an end-to-end per-scene optimization, TensoIR is able to achieve high-quality geometry and reflectance reconstruction efficiently. This approach allows for accurate computation of shadowing and indirect lighting effects while optimizing the scene representation with tensor factors and MLPs. The joint optimization ensures that all components are reconstructed together, leading to faster convergence during training compared to disjoint methods that pre-train neural networks separately.

How does TensoIR's ability to support multi-light capture scenarios impact the accuracy of inverse rendering?

TensoIR's ability to support multi-light capture scenarios has significant implications on the accuracy of inverse rendering. By extending its tensor factorization-based representation to model appearance properties under different lighting conditions, TensoIR can effectively leverage additional input data in a multi-light setup. This capability enhances geometry reconstruction and helps resolve color ambiguities between lighting and materials by providing useful photometric cues. Multi-light settings improve material estimation accuracy and reduce ambiguity in predicting surface properties, ultimately leading to more precise scene reconstructions. Additionally, capturing scenes under multiple unknown lighting conditions enables better modeling of complex light interactions such as shadows and indirect illumination, resulting in higher-quality inverse rendering outcomes.

How does TensoIR's approach compare to traditional mesh or volume-based representations in terms of flexibility and accuracy?

In comparison to traditional mesh or volume-based representations, TensoIR offers superior flexibility and accuracy in scene reconstruction tasks. Traditional representations like meshes or volumes have limitations in accurately reproducing complex geometries or capturing fine details due to their rigid structures. In contrast, TensoIR leverages tensor factorization combined with neural fields for a more flexible representation that can model intricate shapes with high fidelity.
Furthermore, traditional methods may struggle with handling varying material properties or unknown lighting conditions effectively. TensoIR excels at estimating scene geometry, materials, and illumination concurrently from multi-view images captured under diverse lighting environments using its efficient tensor factorized representation.
Overall,

Flexibility: Traditional approaches are often constrained by predefined structures like meshes or volumes; however,

Tensor Factorization: Allows for a more adaptable representation capable of capturing detailed geometric features.

Accuracy: While traditional methods may lack precision when dealing with complex scenes,

Joint Optimization: Enables accurate modeling of secondary shading effects like shadows & indirect lighting for high-quality reconstructions.
Therefore,TensoIR outperforms traditional techniques by offering enhanced flexibility along with improved accuracy in inverse rendering tasks through its innovative approach combining tensorial representations & neural fields.