# Semantic Scene Completion Combining Colour and Depth: preliminary experiments

arXiv: https://arxiv.org/abs/1802.04735 · HTML: https://mlsanity.com/paper/1802.04735/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2018.

## Built on by (most cited first, 8 of 8)

- [MonoScene: Monocular 3D Semantic Scene Completion](https://mlsanity.com/paper/2112.00726/) (2021)
- [3D Semantic Scene Completion: a Survey](https://mlsanity.com/paper/2103.07466/) (2021)
- [Semantic Scene Completion via Integrating Instances and Scene in-the-Loop](https://mlsanity.com/paper/2104.03640/) (2021)
- [Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspective](https://mlsanity.com/paper/2112.12925/) (2021)
- [Data Augmented 3D Semantic Scene Completion with 2D Segmentation Priors](https://mlsanity.com/paper/2111.13309/) (2021)
- [Skip Mamba Diffusion for Monocular 3D Semantic Scene Completion](https://mlsanity.com/paper/2501.07260/) (2025)
- [Deep Learning for 3D Point Cloud Enhancement: A Survey](https://mlsanity.com/paper/2411.00857/) (2024)
- [Towards Balanced RGB-TSDF Fusion for Consistent Semantic Scene Completion by 3D RGB Feature Completion and a Classwise Entropy Loss Function](https://mlsanity.com/paper/2403.16888/) (2024)

## Abstract (arXiv)

Semantic scene completion is the task of producing a complete 3D voxel representation of volumetric occupancy with semantic labels for a scene from a single-view observation. We built upon the recent work of Song et al. (CVPR 2017), who proposed SSCnet, a method that performs scene completion and semantic labelling in a single end-to-end 3D convolutional network. SSCnet uses only depth maps as input, even though depth maps are usually obtained from devices that also capture colour information, such as RGBD sensors and stereo cameras. In this work, we investigate the potential of the RGB colour channels to improve SSCnet.