# Neural directional distance field object representation for uni-directional path-traced rendering

arXiv: https://arxiv.org/abs/2306.16142 · HTML: https://mlsanity.com/paper/2306.16142/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 28 Jun 2023.

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

- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing](https://mlsanity.com/paper/1911.13225/) (2019)
- [Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research](https://mlsanity.com/paper/1911.05063/) (2019)
- [Representing 3D Shapes with Probabilistic Directed Distance Fields](https://mlsanity.com/paper/2112.05300/) (2021)
- [A Deep Signed Directional Distance Function for Object Shape Representation](https://mlsanity.com/paper/2107.11024/) (2021)
- [Multi-View Reconstruction using Signed Ray Distance Functions (SRDF)](https://mlsanity.com/paper/2209.00082/) (2022)
- [TransPoser: Transformer as an Optimizer for Joint Object Shape and Pose Estimation](https://mlsanity.com/paper/2303.13477/) (2023)

## Built on by (most cited first, 2 of 2)

- [Neural Bounding](https://mlsanity.com/paper/2310.06822/) (2023)
- [Directed Distance Fields for Constant-Time Ray Queries on Gaussian Splatting](https://mlsanity.com/paper/2606.00817/) (2026)

## Abstract (arXiv)

Faster rendering of synthetic images is a core problem in the field of computer graphics. Rendering algorithms, such as path-tracing is dependent on parameters like size of the image, number of light bounces, number of samples per pixel, all of which, are fixed if one wants to obtain a image of a desired quality. It is also dependent on the size and complexity of the scene being rendered. One of the largest bottleneck in rendering, particularly when the scene is very large, is querying for objects in the path of a given ray in the scene. By changing the data type that represents the objects in the scene, one may reduce render time, however, a different representation of a scene requires the modification of the rendering algorithm. In this paper, (a) we introduce directed distance field, as a functional representation of a object; (b) how the directed distance functions, when stored as a neural network, be optimized and; (c) how such an object can be rendered with a modified path-tracing algorithm.