# Point Cloud Colorization Based on Densely Annotated 3D Shape Dataset

arXiv: https://arxiv.org/abs/1810.05396 · HTML: https://mlsanity.com/paper/1810.05396/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2018.

## Built on by (most cited first, 7 of 7)

- [Advances in 3D Neural Stylization: A Survey](https://mlsanity.com/paper/2311.18328/) (2023)
- [Pre-Training LiDAR-Based 3D Object Detectors Through Colorization](https://mlsanity.com/paper/2310.14592/) (2023)
- [Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes](https://mlsanity.com/paper/2503.10055/) (2025)
- [CU-Net: Real-Time High-Fidelity Color Upsampling for Point Clouds](https://mlsanity.com/paper/2209.06112/) (2022)
- [HyperColor: A HyperNetwork Approach for Synthesizing Auto-colored 3D Models for Game Scenes Population](https://mlsanity.com/paper/2108.01411/) (2021)
- [LiDAR Point Cloud Colourisation Using Multi-Camera Fusion and Low-Light Image Enhancement](https://mlsanity.com/paper/2509.25859/) (2025)
- [LoGoColor: Local-Global 3D Colorization for 360deg Scenes](https://mlsanity.com/paper/2512.09278/) (2025)

## Abstract (arXiv)

This paper introduces DensePoint, a densely sampled and annotated point cloud dataset containing over 10,000 single objects across 16 categories, by merging different kind of information from two existing datasets. Each point cloud in DensePoint contains 40,000 points, and each point is associated with two sorts of information: RGB value and part annotation. In addition, we propose a method for point cloud colorization by utilizing Generative Adversarial Networks (GANs). The network makes it possible to generate colours for point clouds of single objects by only giving the point cloud itself. Experiments on DensePoint show that there exist clear boundaries in point clouds between different parts of an object, suggesting that the proposed network is able to generate reasonably good colours. Our dataset is publicly available on the project page.