# Rendering and Tracking the Directional TSDF: Modeling Surface Orientation for Coherent Maps

arXiv: https://arxiv.org/abs/2108.08115 · HTML: https://mlsanity.com/paper/2108.08115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 10 of 10)

- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Neural RGB-D Surface Reconstruction](https://mlsanity.com/paper/2104.04532/) (2021)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure](https://mlsanity.com/paper/1708.00783/) (2017)
- [PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction](https://mlsanity.com/paper/1807.11034/) (2018)
- [An Efficient Volumetric Mesh Representation for Real-time Scene Reconstruction using Spatial Hashing](https://mlsanity.com/paper/1803.03949/) (2018)
- [Freetures: Localization in Signed Distance Function Maps](https://mlsanity.com/paper/2010.09378/) (2020)
- [stdgpu: Efficient STL-like Data Structures on the GPU](https://mlsanity.com/paper/1908.05936/) (2019)
- [Directional TSDF: Modeling Surface Orientation for Coherent Meshes](https://mlsanity.com/paper/1908.05146/) (2019)

## Abstract (arXiv)

Dense real-time tracking and mapping from RGB-D images is an important tool for many robotic applications, such as navigation or grasping. The recently presented Directional Truncated Signed Distance Function (DTSDF) is an augmentation of the regular TSDF and shows potential for more coherent maps and improved tracking performance. In this work, we present methods for rendering depth- and color maps from the DTSDF, making it a true drop-in replacement for the regular TSDF in established trackers. We evaluate and show, that our method increases re-usability of mapped scenes. Furthermore, we add color integration which notably improves color-correctness at adjacent surfaces.