- The paper introduces DiffTrans, a framework that jointly optimizes geometry and material absorption for transparent objects using differentiable rendering and recursive ray tracing.
- It employs a three-phase pipelineโFlexiCubes for geometry initialization, environment radiance field recovery, and CUDA-accelerated recursive ray tracingโto robustly extract object properties.
- Experiments show significant gains in geometry accuracy, IoR regression, and relighting fidelity, outperforming state-of-the-art methods on both synthetic and real-world datasets.
DiffTrans: Differentiable Geometry-Materials Decomposition for Reconstructing Transparent Objects
Introduction and Context
The reconstruction of transparent objects from multi-view imagery constitutes a classically ill-posed challenge in computational visual computing, owing to the complexity of refractive light transport and the entanglement of geometry and materials in object appearance. Traditional solutions have relied on environment matting or hardware-specific acquisition, but these approaches have limited generality and are not well-suited for objects with intricate topology and heterogeneous interior materials. Recent inverse rendering techniques based on differentiable rendering and neural implicit or explicit representations have advanced the field, but prior methods have critical limitations: they often either neglect material absorption, are unable to represent complex textures and internal structures, or lack physically correct modeling of refractive transport.
Methodology
The paper introduces DiffTrans, a differentiable rendering framework that targets the joint decomposition and reconstruction of geometry and material (including internal absorption) for transparent objects from multi-view RGB images and object masks. The system is structured into three core phases:
- Geometry Initialization with FlexiCubes: DiffTrans employs FlexiCubes as the isosurface representation, optimizing a mesh proxy via differentiable rasterization against object silhouettes. Dilation and smoothness regularization ensure both topological expressiveness and geometric quality during initialization.
- Environment Recovery: Using pixels outside object masks, an environment radiance field (ERF) is reconstructed, leveraging a MERF-style architecture combining dense grids, triplanes, and proposal grids. This decoupling is crucial due to the highly entangled effects of environment and object on appearance in transparent objects.
- Recursive Differentiable Ray Tracing: The key technical contribution is a CUDA-accelerated recursive mesh-based differentiable ray tracer that simultaneously optimizes mesh geometry, spatially-varying index of refraction (IoR), and absorption coefficients. The system explicitly models optical transport under the assumption of constant IoR per object and no roughness, computing deterministic light paths via analytic solutions to interface boundary refraction and Fresnel blending, and integrating radiance decay in the absorptive interior.
Optimization spans a three-term objective: photometric loss, tone regularization to stabilize color ratios in challenging scenarios, and spatial smoothness regularizers on material parameters. AdamUniform mesh optimization and a suite of geometric regularizations mitigate self-intersection and excessive local distortion, supporting effective gradient flow across the complex loss landscape induced by refractive transport.
Numerical and Empirical Evaluation
Extensive evaluations were performed on both synthetic datasets (covering objects with and without internal absorption) and real-world captures, using COLMAP for pose estimation and a combination of automated and manual annotation for mask extraction. The evaluation metrics include Chamfer Distance (CD), F1-score for geometry accuracy, and image-space metrics for relighting (PSNR, SSIM, and LPIPS).
Key empirical findings:
- Geometry Reconstruction: DiffTrans delivers the lowest Chamfer Distance and highest F1-score across all tested scenarios, outperforming methods such as NeRO, Nu-NeRF, and NeRRF, particularly in cases with complex topology and material inhomogeneity.
- IoR and Material Recovery: In contrast to prior works that fix or ignore IoR, DiffTrans directly regresses IoR per object, achieving minimal error to ground truth values in synthetic cases. The system robustly captures spatially varying absorption consistent with ground truth for objects containing complex pigment or gradient textures.
- Relighting and View Synthesis: DiffTrans enables high-fidelity relighting under novel illumination, outperforming NeRRF and NeRO, which are unable to reconstruct internal material properties or correctly model refraction. Quantitative improvements in PSNR (e.g., outperforming NeRO and NeRRF by several dB) and lower LPIPS scores corroborate the improved perceptual accuracy.
- Ablation Studies: Removal of tone regularization slightly reduces SSIM in some cases but increases robustness and photometric fidelity in others, supporting the design's stability. DiffTrans is robust to mask noise, with minor degradation under moderate annotation errors.
- Computational Efficiency: The CUDA/OptiX implementation results in practical training wall times (typically 1โ2 hours with a modern high-end GPU) and scales well with scene complexity.
Theoretical Implications and Comparison to Prior Art
DiffTrans introduces two principal theoretical advancements over existing literature:
- Differentiable Joint Decomposition: By jointly optimizing geometry and physically based material absorption in an end-to-end framework, DiffTrans advances beyond models, such as NEMTO or TransparentGS, that either ignore absorption or treat it as a parameter tied solely to surface properties. The capacity to recover volumetric internal material properties allows faithful reconstruction of realistic transparent objects, substantially improving editability and physical correctness.
- Recursive Mesh-based Ray Tracing: The use of a mesh-based, recursive, analytically differentiable tracer is significant. Prior neural implicit methods (e.g., NeRF and derivatives) often underperform in geometry extraction due to the limitations of volume rendering and straight-ray assumptions. In contrast, DiffTrans's mesh-centric pipeline and recursive refraction/reflection computation (with correct Fresnel blending) ensures physically plausible, high-fidelity appearance, and enables reliable extraction of explicit 3D geometry for downstream applications.
Limitations and Future Directions
DiffTrans imposes several constraints for tractability: objects must have piecewise constant IoR, surfaces are assumed to be ideally specular (no roughness or surface micro-facet distribution), and radiative transport is approximated without spatially-varying scattering or in-scattering. While these assumptions admit efficient, stable inverse problem solution for a wide class of real-world objects (glass, crystal, resin), they limit applicability to highly scattering or rough transparent objects.
The methodology is also reliant on high-quality silhouette extraction and accurate pose estimation, and is primarily validated on scenes where the majority of radiometric ambiguity can be resolved by optimization. In scenarios with severe inter-object interactions or highly complex lighting, extensions to the environment modeling or multi-object transparency disambiguation are likely required.
Practical and Theoretical Impacts
Practical implications include improved asset digitization of decorative, product design, and scientific glassware, enabling realistic relighting, editability, and AR/VR integration for objects previously not amenable to photogrammetric capture. The coherent joint geometry/material representation enables physically based simulation and direct coupling with differentiable rendering for downstream inference tasks.
Theoretical impacts extend to the domain of physically-constrained inverse rendering, offering a strong baseline for integrating mesh-based and volumetric representations and suggesting a robust route for future methods incorporating spatially-varying microstructure, scattering, and non-linear refractive phenomena.
Ongoing research will be necessary to relax the assumptions on IoR constancy and purely specular surfaces, as well as to increase the efficiency and robustness of silhouette-based initialization in larger or partially occluded multi-object scenes.
Conclusion
DiffTrans establishes a new standard for accurate, interpretable, and efficient reconstruction of transparent objects with complex geometry and internal materials using differentiable rendering and mesh-based recursive ray tracing. Empirical results validate substantial superiority over prior art in both geometric and material fidelity, with practical and theoretical contributions of lasting relevance for computer vision, graphics, and 3D content creation. Future work should focus on extending the physical realism of the material model, accommodating rough and scattering materials, and further integrating uncertainty modeling for greater robustness in practical deployments.
Reference: "DiffTrans: Differentiable Geometry-Materials Decomposition for Reconstructing Transparent Objects" (2603.00413)