SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes
Cheng-De Fan, Chen-Wei Chang, Yi-Ruei Liu, Jie-Ying Lee, Jiun-Long, Huang, Yu-Chee Tseng, Yu-Lun Liu
TL;DR
SpectroMotion is a new method that combines 3D Gaussian Splatting with physically-based rendering and deformation fields to accurately reconstruct and synthesize dynamic scenes with complex specular surfaces in photorealistic detail.
Contribution
It introduces residual correction and deformable environment maps to improve dynamic specular scene reconstruction, outperforming existing 3DGS methods.
Findings
Achieves photorealistic rendering of dynamic specular scenes
Outperforms state-of-the-art methods in complex scene synthesis
Enhances geometry and color prediction through coarse-to-fine training
Abstract
We present SpectroMotion, a novel approach that combines 3D Gaussian Splatting (3DGS) with physically-based rendering (PBR) and deformation fields to reconstruct dynamic specular scenes. Previous methods extending 3DGS to model dynamic scenes have struggled to represent specular surfaces accurately. Our method addresses this limitation by introducing a residual correction technique for accurate surface normal computation during deformation, complemented by a deformable environment map that adapts to time-varying lighting conditions. We implement a coarse-to-fine training strategy significantly enhancing scene geometry and specular color prediction. It is the only existing 3DGS method capable of synthesizing photorealistic real-world dynamic specular scenes, outperforming state-of-the-art methods in rendering complex, dynamic, and specular scenes.
Peer Reviews
Decision·ICLR 2025 Conference Withdrawn Submission
This work introduces an interesting solution for modeling both dynamic and specular objects, incorporating an improved normal estimation technique alongside normal regularization, which are particularly noteworthy contributions. According to the reported results, this method demonstrates enhanced performance in novel view synthesis across multiple datasets.
1. While the method claims to model both specular and dynamic objects, the results presented from NeRF-DS appear relatively static to me, despite the significant camera motion in these data. I would be particularly interested in seeing results that exhibit greater dynamics, similar to those from D-NeRF Synthetic (e.g., Lego and Jumpingjacks) or HyperNeRF (e.g., cut-lemon and expresso). 2. The paper introduces a deformable environment map to account for dynamic lighting; however, the visualizatio
1. The paper is generally well-written, easy to follow and understand. 2. The proposed deformable environment map and physical normal estimation is reasonable and meaningful. 3. The experimental results show improvements over baselines.
1. Complex pipeline. The proposed pipeline consists of 3 stages, which may limit its performance and scalability. 2. The proposed physical normal estimation from 3D Gaussians is good, but recent methods (e.g. 2D Gaussian Splatting) have proposed a more general and intuitive solution for well-defined Gaussian normals. With these more simple and effective representations, I doubt the necessity of designing complex corrections for the ill-defined 3D Gaussian normal estimation problem. 3. Missing ab
**Motivation** * This paper correctly identifies the challenging moving specular scene reconstruction using the existing methods. It extends the NeRF-DS problem settings using the latest deformable 3DGS methods to improve the performance. **Method** * The proposed method utilizes the PBR to improve the modeling of specular colors under motion. This is very challenging because of the difficulty of accurately reconstructing the surface's normal direction and environment lighting under a monocular
**Method** * My main concern regarding the proposed method is the theoretical validity of the deformable environment map. * First, an important assumption of using an environment map to model a specular surface is that the environment should be far away from the surface. However, the reflective surface present in the datasets is mainly reflecting objects close to it. This assumption is further violated when this specular surface starts moving. This is all because the query of environment mappin
- This paper is well-written and easy to understand. - Using zero-order spherical harmonics (SH) and Cook-Torrance BRDF to model diffuse and specular respectively makes sense. - Using normal residuals to flatten the Gaussian, thereby improving the accuracy of normal estimation in dynamic scenes, is insightful for the community. - The visual results have improved on the NeRF-DS and HyperNeRF datasets.
1. Inappropriate reference: - In Line 103, strictly speaking, Gaussian-DR is not a PBR method, because its approach does not incorporate the rendering equation. - Missing reference: Relightable 3D Gaussians: Realistic Point Cloud Relighting with BRDF Decomposition and Ray Tracing, by Gao et al., ECCV 2024. 2. This method, in principle, lacks novelty and can be seen as a combination of Deformable-GS and Gaussian-Shader to some extent. 3. The introduction of BRDF calculations and the deforma
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Taxonomy
TopicsColor Science and Applications · Computer Graphics and Visualization Techniques · 3D Surveying and Cultural Heritage