# CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion arXiv: https://arxiv.org/abs/2211.13702 ยท HTML: https://mlsanity.com/paper/2211.13702/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 47 references that are in the map. - First posted on arXiv 24 Nov 2022. ## Builds on (most cited first, 30 of 35) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017) - [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018) - [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020) - [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016) - [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018) - [PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers](https://mlsanity.com/paper/2108.08839/) (2021) - [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017) - [GRNet: Gridding Residual Network for Dense Point Cloud Completion](https://mlsanity.com/paper/2006.03761/) (2020) - [Morphing and Sampling Network for Dense Point Cloud Completion](https://mlsanity.com/paper/1912.00280/) (2019) - [Anisotropic Convolutional Networks for 3D Semantic Scene Completion](https://mlsanity.com/paper/2004.02122/) (2020) - [3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior](https://mlsanity.com/paper/2003.14052/) (2020) - [Point Cloud Completion by Skip-attention Network with Hierarchical Folding](https://mlsanity.com/paper/2005.03871/) (2020) - [Variational Relational Point Completion Network](https://mlsanity.com/paper/2104.10154/) (2021) - [PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths](https://mlsanity.com/paper/2012.03408/) (2020) - [Shape Completion Enabled Robotic Grasping](https://mlsanity.com/paper/1609.08546/) (2016) - [Efficient Semantic Scene Completion Network with Spatial Group Convolution](https://mlsanity.com/paper/1907.05091/) (2019) - [PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths](https://mlsanity.com/paper/2202.09507/) (2022) - [RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion](https://mlsanity.com/paper/1903.00620/) (2019) - [Semantic Scene Completion via Integrating Instances and Scene in-the-Loop](https://mlsanity.com/paper/2104.03640/) (2021) - [Two Stream 3D Semantic Scene Completion](https://mlsanity.com/paper/1804.03550/) (2018) - [View-Guided Point Cloud Completion](https://mlsanity.com/paper/2104.05666/) (2021) - [SSCNav: Confidence-Aware Semantic Scene Completion for Visual Semantic Navigation](https://mlsanity.com/paper/2012.04512/) (2020) - [Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer](https://mlsanity.com/paper/2202.09367/) (2022) - [Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion](https://mlsanity.com/paper/2006.03762/) (2020) - [SceneNet: Understanding Real World Indoor Scenes With Synthetic Data](https://mlsanity.com/paper/1511.07041/) (2015) - [Attention-based Multi-modal Fusion Network for Semantic Scene Completion](https://mlsanity.com/paper/2003.13910/) (2020) - [ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion](https://mlsanity.com/paper/2104.09587/) (2021) - [View-volume Network for Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1806.05361/) (2018) - [Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspective](https://mlsanity.com/paper/2112.12925/) (2021) ## Built on by (most cited first, 11 of 11) - [SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion](https://mlsanity.com/paper/2306.15349/) (2023) - [PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completion](https://mlsanity.com/paper/2309.12708/) (2023) - [Skip Mamba Diffusion for Monocular 3D Semantic Scene Completion](https://mlsanity.com/paper/2501.07260/) (2025) - [PointSea: Point Cloud Completion via Self-structure Augmentation](https://mlsanity.com/paper/2502.17053/) (2025) - [ProxyTransformation: Preshaping Point Cloud Manifold With Proxy Attention For 3D Visual Grounding](https://mlsanity.com/paper/2502.19247/) (2025) - [Deep Learning for 3D Point Cloud Enhancement: A Survey](https://mlsanity.com/paper/2411.00857/) (2024) - [PRENet: A Plane-Fit Redundancy Encoding Point Cloud Sequence Network for Real-Time 3D Action Recognition](https://mlsanity.com/paper/2405.06929/) (2024) - [RWKV-PCSSC: Exploring RWKV Model for Point Cloud Semantic Scene Completion](https://mlsanity.com/paper/2511.09878/) (2025) - [Joint Learning for Scattered Point Cloud Understanding with Hierarchical Self-Distillation](https://mlsanity.com/paper/2312.16902/) (2023) - [Preserving Topological and Geometric Embeddings for Point Cloud Recovery](https://mlsanity.com/paper/2507.19121/) (2025) - [LiGuard: A Streamlined Open-Source Framework for Rapid & Interactive Lidar Research](https://mlsanity.com/paper/2509.02902/) (2025) ## Abstract (arXiv) Semantic scene completion (SSC) aims to complete a partial 3D scene and predict its semantics simultaneously. Most existing works adopt the voxel representations, thus suffering from the growth of memory and computation cost as the voxel resolution increases. Though a few works attempt to solve SSC from the perspective of 3D point clouds, they have not fully exploited the correlation and complementarity between the two tasks of scene completion and semantic segmentation. In our work, we present CasFusionNet, a novel cascaded network for point cloud semantic scene completion by dense feature fusion. Specifically, we design (i) a global completion module (GCM) to produce an upsampled and completed but coarse point set, (ii) a semantic segmentation module (SSM) to predict the per-point semantic labels of the completed points generated by GCM, and (iii) a local refinement module (LRM) to further refine the coarse completed points and the associated labels from a local perspective. We organize the above three modules via dense feature fusion in each level, and cascade a total of four levels, where we also employ feature fusion between each level for sufficient information usage. Both quantitative and qualitative results on our compiled two point-based datasets validate the effectiveness and superiority of our CasFusionNet compared to state-of-the-art methods in terms of both scene completion and semantic segmentation. The codes and datasets are available at: https://github.com/JinfengX/CasFusionNet.