# EdgeNet: Semantic Scene Completion from a Single RGB-D Image

arXiv: https://arxiv.org/abs/1908.02893 · HTML: https://mlsanity.com/paper/1908.02893/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2019.

## Built on by (most cited first, 13 of 13)

- [MonoScene: Monocular 3D Semantic Scene Completion](https://mlsanity.com/paper/2112.00726/) (2021)
- [OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception](https://mlsanity.com/paper/2303.03991/) (2023)
- [SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction](https://mlsanity.com/paper/2311.12754/) (2023)
- [PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness](https://mlsanity.com/paper/2312.02158/) (2023)
- [CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion](https://mlsanity.com/paper/2211.13702/) (2022)
- [Data Augmented 3D Semantic Scene Completion with 2D Segmentation Priors](https://mlsanity.com/paper/2111.13309/) (2021)
- [Semi-supervised 3D Semantic Scene Completion with 2D Vision Foundation Model Guidance](https://mlsanity.com/paper/2408.11559/) (2024)
- [OccuFly: A 3D Vision Benchmark for Semantic Scene Completion from the Aerial Perspective](https://mlsanity.com/paper/2512.20770/) (2025)
- [Unleashing Network Potentials for Semantic Scene Completion](https://mlsanity.com/paper/2403.07560/) (2024)
- [Deep Learning for 3D Point Cloud Enhancement: A Survey](https://mlsanity.com/paper/2411.00857/) (2024)
- [RWKV-PCSSC: Exploring RWKV Model for Point Cloud Semantic Scene Completion](https://mlsanity.com/paper/2511.09878/) (2025)
- [GoToNet: Fast Monocular Scene Exposure and Exploration](https://mlsanity.com/paper/2206.05967/) (2022)
- [Semantic Scene Completion with Multi-Feature Data Balancing Network](https://mlsanity.com/paper/2412.01431/) (2024)

## Abstract (arXiv)

Semantic scene completion is the task of predicting a complete 3D representation of volumetric occupancy with corresponding semantic labels for a scene from a single point of view. Previous works on Semantic Scene Completion from RGB-D data used either only depth or depth with colour by projecting the 2D image into the 3D volume resulting in a sparse data representation. In this work, we present a new strategy to encode colour information in 3D space using edge detection and flipped truncated signed distance. We also present EdgeNet, a new end-to-end neural network architecture capable of handling features generated from the fusion of depth and edge information. Experimental results show improvement of 6.9% over the state-of-the-art result on real data, for end-to-end approaches.