# Full 3D Reconstruction of Transparent Objects

arXiv: https://arxiv.org/abs/1805.03482 · HTML: https://mlsanity.com/paper/1805.03482/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 24 of 24)

- [BOP Challenge 2022 on Detection, Segmentation and Pose Estimation of Specific Rigid Objects](https://mlsanity.com/paper/2302.13075/) (2023)
- [RGB-D Local Implicit Function for Depth Completion of Transparent Objects](https://mlsanity.com/paper/2104.00622/) (2021)
- [BOP Challenge 2023 on Detection, Segmentation and Pose Estimation of Seen and Unseen Rigid Objects](https://mlsanity.com/paper/2403.09799/) (2024)
- [Eikonal Fields for Refractive Novel-View Synthesis](https://mlsanity.com/paper/2202.00948/) (2022)
- [Transparent Shape from a Single View Polarization Image](https://mlsanity.com/paper/2204.06331/) (2022)
- [NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fields](https://mlsanity.com/paper/2309.13039/) (2023)
- [TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians](https://mlsanity.com/paper/2504.18768/) (2025)
- [Hybrid Mesh-neural Representation for 3D Transparent Object Reconstruction](https://mlsanity.com/paper/2203.12613/) (2022)
- [NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects](https://mlsanity.com/paper/2303.11963/) (2023)
- [TODE-Trans: Transparent Object Depth Estimation with Transformer](https://mlsanity.com/paper/2209.08455/) (2022)
- [Seeing Through the Glass: Neural 3D Reconstruction of Object Inside a Transparent Container](https://mlsanity.com/paper/2303.13805/) (2023)
- [NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracing](https://mlsanity.com/paper/2303.11219/) (2023)
- [Polarimetric Inverse Rendering for Transparent Shapes Reconstruction](https://mlsanity.com/paper/2208.11836/) (2022)
- [RFTrans: Leveraging Refractive Flow of Transparent Objects for Surface Normal Estimation and Manipulation](https://mlsanity.com/paper/2311.12398/) (2023)
- [Towards Monocular Shape from Refraction](https://mlsanity.com/paper/2305.19743/) (2023)
- [Glossy Object Reconstruction with Cost-effective Polarized Acquisition](https://mlsanity.com/paper/2504.07025/) (2025)
- [REF2-NeRF: Reflection and Refraction aware Neural Radiance Field](https://mlsanity.com/paper/2311.17116/) (2023)
- [From Transparent to Opaque: Rethinking Neural Implicit Surfaces with α-NeuS](https://mlsanity.com/paper/2411.05362/) (2024)
- [Shape and Reflectance Reconstruction in Uncontrolled Environments by Differentiable Rendering](https://mlsanity.com/paper/2110.12975/) (2021)
- [RefRef: A Dataset and Benchmark for Reconstructing Refractive and Reflective Objects](https://mlsanity.com/paper/2505.05848/) (2025)
- [NERFBK: A High-Quality Benchmark for NERF-Based 3D Reconstruction](https://mlsanity.com/paper/2306.06300/) (2023)
- [Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior](https://mlsanity.com/paper/2310.09956/) (2023)
- [HTMNet: A Hybrid Network with Transformer-Mamba Bottleneck Multimodal Fusion for Transparent and Reflective Objects Depth Completion](https://mlsanity.com/paper/2505.20904/) (2025)
- [DiffTrans: Differentiable Geometry-Materials Decomposition for Reconstructing Transparent Objects](https://mlsanity.com/paper/2603.00413/) (2026)

## Abstract (arXiv)

Numerous techniques have been proposed for reconstructing 3D models for opaque objects in past decades. However, none of them can be directly applied to transparent objects. This paper presents a fully automatic approach for reconstructing complete 3D shapes of transparent objects. Through positioning an object on a turntable, its silhouettes and light refraction paths under different viewing directions are captured. Then, starting from an initial rough model generated from space carving, our algorithm progressively optimizes the model under three constraints: surface and refraction normal consistency, surface projection and silhouette consistency, and surface smoothness. Experimental results on both synthetic and real objects demonstrate that our method can successfully recover the complex shapes of transparent objects and faithfully reproduce their light refraction properties.