mlsanityData to 7 Oct 2026

Differentiable Refraction-Tracing for Mesh Reconstruction of Transparent Objects

arXiv 2009.09144 ↗First posted 19 Sep 2020cs.GRcited by 23

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Abstract (arXiv)

Capturing the 3D geometry of transparent objects is a challenging task, ill-suited for general-purpose scanning and reconstruction techniques, since these cannot handle specular light transport phenomena. Existing state-of-the-art methods, designed specifically for this task, either involve a complex setup to reconstruct complete refractive ray paths, or leverage a data-driven approach based on synthetic training data. In either case, the reconstructed 3D models suffer from over-smoothing and loss of fine detail. This paper introduces a novel, high precision, 3D acquisition and reconstruction method for solid transparent objects. Using a static background with a coded pattern, we establish a mapping between the camera view rays and locations on the background. Differentiable tracing of refractive ray paths is then used to directly optimize a 3D mesh approximation of the object, while simultaneously ensuring silhouette consistency and smoothness. Extensive experiments and comparisons demonstrate the superior accuracy of our method.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 11; 2024: 2; 2025: 4; 2026: 1.

23 papers.

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  1. 2022Multi-View Mesh Reconstruction with Neural Deferred Shadingcited by 44
  2. 2022Eikonal Fields for Refractive Novel-View Synthesiscited by 30
  3. 2023MEGANE: Morphable Eyeglass and Avatar Networkcited by 24
  4. 2023NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fieldscited by 16
  5. 2025TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussianscited by 16
  6. 2022Hybrid Mesh-neural Representation for 3D Transparent Object Reconstructioncited by 15
  7. 2023NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objectscited by 14
  8. 2025TSGS: Improving Gaussian Splatting for Transparent Surface Reconstruction via Normal and De-lighting Priorscited by 14
  9. 2023Seeing Through the Glass: Neural 3D Reconstruction of Object Inside a Transparent Containercited by 10
  10. 2023NeTO:Neural Reconstruction of Transparent Objects with Self-Occlusion Aware Refraction-Tracingcited by 9
  11. 2022Polarimetric Inverse Rendering for Transparent Shapes Reconstructioncited by 8
  12. 2023Mesh Density Adaptation for Template-based Shape Reconstructioncited by 7
11 more
  1. 2023RFTrans: Leveraging Refractive Flow of Transparent Objects for Surface Normal Estimation and Manipulationcited by 6
  2. 2024AniSDF: Fused-Granularity Neural Surfaces with Anisotropic Encoding for High-Fidelity 3D Reconstructioncited by 5
  3. 2025Glossy Object Reconstruction with Cost-effective Polarized Acquisitioncited by 3
  4. 2023REF2-NeRF: Reflection and Refraction aware Neural Radiance Fieldcited by 2
  5. 2024From Transparent to Opaque: Rethinking Neural Implicit Surfaces with α-NeuScited by 2
  6. 2023Neural Radiance Fields for Transparent Object Using Visual Hullcited by 1
  7. 2025RefRef: A Dataset and Benchmark for Reconstructing Refractive and Reflective Objectscited by 1
  8. 2021N-SfC: Robust and Fast Shape Estimation from Caustic Imagescited by 0
  9. 2023Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Priorcited by 0
  10. 2023EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstructioncited by 0
  11. 2026DiffTrans: Differentiable Geometry-Materials Decomposition for Reconstructing Transparent Objectscited by 0