# Directional TSDF: Modeling Surface Orientation for Coherent Meshes

arXiv: https://arxiv.org/abs/1908.05146 · HTML: https://mlsanity.com/paper/1908.05146/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Rendering and Tracking the Directional TSDF: Modeling Surface Orientation for Coherent Maps](https://mlsanity.com/paper/2108.08115/) (2021)
- [Rendering the Directional TSDF for Tracking and Multi-Sensor Registration with Point-To-Plane Scale ICP](https://mlsanity.com/paper/2301.12796/) (2023)
- [Learning Scene-Level Signed Directional Distance Function with Ellipsoidal Priors and Neural Residuals](https://mlsanity.com/paper/2503.20066/) (2025)

## Abstract (arXiv)

Real-time 3D reconstruction from RGB-D sensor data plays an important role in many robotic applications, such as object modeling and mapping. The popular method of fusing depth information into a truncated signed distance function (TSDF) and applying the marching cubes algorithm for mesh extraction has severe issues with thin structures: not only does it lead to loss of accuracy, but it can generate completely wrong surfaces. To address this, we propose the directional TSDF - a novel representation that stores opposite surfaces separate from each other. The marching cubes algorithm is modified accordingly to retrieve a coherent mesh representation. We further increase the accuracy by using surface gradient-based ray casting for fusing new measurements. We show that our method outperforms state-of-the-art TSDF reconstruction algorithms in mesh accuracy.