# Semi-supervised Implicit Scene Completion from Sparse LiDAR

arXiv: https://arxiv.org/abs/2111.14798 · HTML: https://mlsanity.com/paper/2111.14798/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 35 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 26 of 26)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields](https://mlsanity.com/paper/2011.12100/) (2020)
- [Convolutional Occupancy Networks](https://mlsanity.com/paper/2003.04618/) (2020)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [In-Place Scene Labelling and Understanding with Implicit Scene Representation](https://mlsanity.com/paper/2103.15875/) (2021)
- [Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion](https://mlsanity.com/paper/2012.03762/) (2020)
- [NeX: Real-time View Synthesis with Neural Basis Expansion](https://mlsanity.com/paper/2103.05606/) (2021)
- [BSP-Net: Generating Compact Meshes via Binary Space Partitioning](https://mlsanity.com/paper/1911.06971/) (2019)
- [LMSCNet: Lightweight Multiscale 3D Semantic Completion](https://mlsanity.com/paper/2008.10559/) (2020)
- [MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis](https://mlsanity.com/paper/2103.14910/) (2021)
- [3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior](https://mlsanity.com/paper/2003.14052/) (2020)
- [Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds](https://mlsanity.com/paper/2007.08488/) (2020)
- [Holistic 3D Scene Understanding from a Single Image with Implicit Representation](https://mlsanity.com/paper/2103.06422/) (2021)
- [Efficient Semantic Scene Completion Network with Spatial Group Convolution](https://mlsanity.com/paper/1907.05091/) (2019)
- [RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion](https://mlsanity.com/paper/1903.00620/) (2019)
- [Depth Based Semantic Scene Completion with Position Importance Aware Loss](https://mlsanity.com/paper/2001.10709/) (2020)
- [RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction](https://mlsanity.com/paper/2011.14744/) (2020)
- [Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation](https://mlsanity.com/paper/1707.00383/) (2017)
- [PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds](https://mlsanity.com/paper/2109.05566/) (2021)

## Built on by (most cited first, 10 of 10)

- [VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion](https://mlsanity.com/paper/2302.12251/) (2023)
- [SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving](https://mlsanity.com/paper/2306.09001/) (2023)
- [RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar](https://mlsanity.com/paper/2405.14014/) (2024)
- [GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions](https://mlsanity.com/paper/2206.02780/) (2022)
- [S4C: Self-Supervised Semantic Scene Completion with Neural Fields](https://mlsanity.com/paper/2310.07522/) (2023)
- [Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion](https://mlsanity.com/paper/2507.06230/) (2025)
- [SNAKE: Shape-aware Neural 3D Keypoint Field](https://mlsanity.com/paper/2206.01724/) (2022)
- [Boosting Self-Supervision for Single-View Scene Completion via Knowledge Distillation](https://mlsanity.com/paper/2404.07933/) (2024)
- [IPFormer: Visual 3D Panoptic Scene Completion with Context-Adaptive Instance Proposals](https://mlsanity.com/paper/2506.20671/) (2025)
- [Decomposition of Neural Discrete Representations for Large-Scale 3D Mapping](https://mlsanity.com/paper/2407.15554/) (2024)

## Abstract (arXiv)

Recent advances show that semi-supervised implicit representation learning can be achieved through physical constraints like Eikonal equations. However, this scheme has not yet been successfully used for LiDAR point cloud data, due to its spatially varying sparsity. In this paper, we develop a novel formulation that conditions the semi-supervised implicit function on localized shape embeddings. It exploits the strong representation learning power of sparse convolutional networks to generate shape-aware dense feature volumes, while still allows semi-supervised signed distance function learning without knowing its exact values at free space. With extensive quantitative and qualitative results, we demonstrate intrinsic properties of this new learning system and its usefulness in real-world road scenes. Notably, we improve IoU from 26.3% to 51.0% on SemanticKITTI. Moreover, we explore two paradigms to integrate semantic label predictions, achieving implicit semantic completion. Code and models can be accessed at https://github.com/OPEN-AIR-SUN/SISC.