# αSurf: Implicit Surface Reconstruction for Semi-Transparent and Thin Objects with Decoupled Geometry and Opacity

arXiv: https://arxiv.org/abs/2303.10083 · HTML: https://mlsanity.com/paper/2303.10083/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details](https://mlsanity.com/paper/2206.07850/) (2022)
- [HDR-NeRF: High Dynamic Range Neural Radiance Fields](https://mlsanity.com/paper/2111.14451/) (2021)
- [Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance](https://mlsanity.com/paper/2003.09852/) (2020)

## Built on by (most cited first, 4 of 4)

- [Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects](https://mlsanity.com/paper/2305.17929/) (2023)
- [Volumetric Rendering with Baked Quadrature Fields](https://mlsanity.com/paper/2312.02202/) (2023)
- [From Transparent to Opaque: Rethinking Neural Implicit Surfaces with α-NeuS](https://mlsanity.com/paper/2411.05362/) (2024)
- [CeRF: Convolutional Neural Radiance Fields for New View Synthesis with Derivatives of Ray Modeling](https://mlsanity.com/paper/2307.07125/) (2023)

## Abstract (arXiv)

Implicit surface representations such as the signed distance function (SDF) have emerged as a promising approach for image-based surface reconstruction. However, existing optimization methods assume solid surfaces and are therefore unable to properly reconstruct semi-transparent surfaces and thin structures, which also exhibit low opacity due to the blending effect with the background. While neural radiance field (NeRF) based methods can model semi-transparency and achieve photo-realistic quality in synthesized novel views, their volumetric geometry representation tightly couples geometry and opacity, and therefore cannot be easily converted into surfaces without introducing artifacts. We present αSurf, a novel surface representation with decoupled geometry and opacity for the reconstruction of semi-transparent and thin surfaces where the colors mix. Ray-surface intersections on our representation can be found in closed-form via analytical solutions of cubic polynomials, avoiding Monte-Carlo sampling and is fully differentiable by construction. Our qualitative and quantitative evaluations show that our approach can accurately reconstruct surfaces with semi-transparent and thin parts with fewer artifacts, achieving better reconstruction quality than state-of-the-art SDF and NeRF methods. Website: https://alphasurf.netlify.app/