# 3D Gated Recurrent Fusion for Semantic Scene Completion

arXiv: https://arxiv.org/abs/2002.07269 · HTML: https://mlsanity.com/paper/2002.07269/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

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

- [3D Semantic Scene Completion: a Survey](https://mlsanity.com/paper/2103.07466/) (2021)
- [SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion](https://mlsanity.com/paper/2306.15349/) (2023)
- [Semantic Scene Completion with Cleaner Self](https://mlsanity.com/paper/2303.09977/) (2023)
- [IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement](https://mlsanity.com/paper/2106.15413/) (2021)
- [Semantic Complete Scene Forecasting from a 4D Dynamic Point Cloud Sequence](https://mlsanity.com/paper/2312.08054/) (2023)
- [CVSformer: Cross-View Synthesis Transformer for Semantic Scene Completion](https://mlsanity.com/paper/2307.07938/) (2023)
- [Unleashing Network Potentials for Semantic Scene Completion](https://mlsanity.com/paper/2403.07560/) (2024)
- [P2M2-Net: Part-Aware Prompt-Guided Multimodal Point Cloud Completion](https://mlsanity.com/paper/2312.17611/) (2023)

## Abstract (arXiv)

This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion. RGB images contain texture details of the object(s) which are vital for semantic scene understanding. Meanwhile, depth images capture geometric clues of high relevance for shape completion. Using both RGB and depth images can further boost the accuracy of SSC over employing one modality in isolation. We propose a 3D gated recurrent fusion network (GRFNet), which learns to adaptively select and fuse the relevant information from depth and RGB by making use of the gate and memory modules. Based on the single-stage fusion, we further propose a multi-stage fusion strategy, which could model the correlations among different stages within the network. Extensive experiments on two benchmark datasets demonstrate the superior performance and the effectiveness of the proposed GRFNet for data fusion in SSC. Code will be made available.