Dynamic Inverse Rendering for Enhanced Material-Lighting Decomposition
- URL: http://arxiv.org/abs/2607.09329v1
- Date: Fri, 10 Jul 2026 12:09:53 GMT
- Title: Dynamic Inverse Rendering for Enhanced Material-Lighting Decomposition
- Abstract summary: We introduce a relightable approach that marries object tracking and reconstruction with inverse rendering for general rigidly moving objects.<n>Results on RGB videos of real hand-held objects show that our pipeline preserves this advantage even under noisy real-world conditions.
- Score: 23.732861610774133
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Decomposing outgoing surface radiance into material and illumination during inverse rendering is essential for applications such as relighting and augmented reality, yet it is severely ill-posed since multiple combinations can result in the same observed colour. Capturing an object under multiple lighting conditions usually helps resolve this ambiguity as it constrains the optimization towards correct solutions. In this work, we explore the potential of reconstructing rigidly moving objects -- which provides observations of diverse light-surface interactions -- to resolve the material-lighting ambiguity in inverse rendering. For this purpose, we introduce a relightable approach that marries object tracking and reconstruction with inverse rendering for general rigidly moving objects. Our experimental analysis on synthetic data demonstrates that motion can be an advantage for disentangling material and lighting: the reconstructed material is significantly more accurate when the object is observed under rigid motion than when it is static. Moreover, results on RGB videos of real hand-held objects show that our pipeline preserves this advantage even under noisy real-world conditions.
Related papers
- RT-GS: Gaussian Splatting with Reflection and Transmittance Primitives [52.10688811929143]
We propose a unified framework that integrates a microfacet material model and ray tracing to jointly model specular reflection and transmittance in Gaussian Splatting.<n>Our experiments demonstrate that our method successfully produces reflections and recovers objects behind transparent surfaces in complex environments.
arXiv Detail & Related papers (2026-04-01T05:50:03Z) - DiffTrans: Differentiable Geometry-Materials Decomposition for Reconstructing Transparent Objects [53.83670041249326]
Reconstructing transparent objects from a set of multi-view images is a challenging task due to the complicated nature and indeterminate behavior of light propagation.<n>We propose a differentiable rendering framework for transparent objects, dubbed DiffTrans, which allows for efficient decomposition and reconstruction of the geometry and materials of transparent objects.
arXiv Detail & Related papers (2026-02-28T02:21:31Z) - MaterialRefGS: Reflective Gaussian Splatting with Multi-view Consistent Material Inference [83.38607296779423]
We show that multi-view consistent material inference with more physically-based environment modeling is key to learning accurate reflections with Gaussian Splatting.<n>Our method faithfully recovers both illumination and geometry, achieving state-of-the-art rendering quality in novel views synthesis.
arXiv Detail & Related papers (2025-10-13T13:29:20Z) - IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination [37.96484120807323]
This paper aims to recover object materials from posed images captured under an unknown static lighting condition.
We learn the material prior with a generative model for regularizing the optimization process.
Experiments on real-world and synthetic datasets demonstrate that our approach achieves state-of-the-art performance on material recovery.
arXiv Detail & Related papers (2024-04-17T17:45:08Z) - Neural Relighting with Subsurface Scattering by Learning the Radiance
Transfer Gradient [73.52585139592398]
We propose a novel framework for learning the radiance transfer field via volume rendering.
We will release our code and a novel light stage dataset of objects with subsurface scattering effects publicly available.
arXiv Detail & Related papers (2023-06-15T17:56:04Z) - NDJIR: Neural Direct and Joint Inverse Rendering for Geometry, Lights,
and Materials of Real Object [5.665283675533071]
We propose neural direct and joint inverse rendering, NDJIR.
Our proposed method can decompose semantically well for real object in photogrammetric setting.
arXiv Detail & Related papers (2023-02-02T13:21:03Z) - Physics-based Indirect Illumination for Inverse Rendering [70.27534648770057]
We present a physics-based inverse rendering method that learns the illumination, geometry, and materials of a scene from posed multi-view RGB images.
As a side product, our physics-based inverse rendering model also facilitates flexible and realistic material editing as well as relighting.
arXiv Detail & Related papers (2022-12-09T07:33:49Z) - DIB-R++: Learning to Predict Lighting and Material with a Hybrid
Differentiable Renderer [78.91753256634453]
We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiables.
In this work, we propose DIBR++, a hybrid differentiable which supports these effects by combining specularization and ray-tracing.
Compared to more advanced physics-based differentiables, DIBR++ is highly performant due to its compact and expressive model.
arXiv Detail & Related papers (2021-10-30T01:59:39Z) - PhySG: Inverse Rendering with Spherical Gaussians for Physics-based
Material Editing and Relighting [60.75436852495868]
We present PhySG, an inverse rendering pipeline that reconstructs geometry, materials, and illumination from scratch from RGB input images.
We demonstrate, with both synthetic and real data, that our reconstructions not only enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination.
arXiv Detail & Related papers (2021-04-01T17:59:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.