mlsanityData to 7 Oct 2026

DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models

arXiv 2409.18092 ↗First posted 26 Sep 2024cs.CV cs.AI cs.ROcited by 16

On the map: World models → World modeling from LiDAR generation

also in 1 more
Abstract (arXiv)

Perception systems play a crucial role in autonomous driving, incorporating multiple sensors and corresponding computer vision algorithms. 3D LiDAR sensors are widely used to capture sparse point clouds of the vehicle's surroundings. However, such systems struggle to perceive occluded areas and gaps in the scene due to the sparsity of these point clouds and their lack of semantics. To address these challenges, Semantic Scene Completion (SSC) jointly predicts unobserved geometry and semantics in the scene given raw LiDAR measurements, aiming for a more complete scene representation. Building on promising results of diffusion models in image generation and super-resolution tasks, we propose their extension to SSC by implementing the noising and denoising diffusion processes in the point and semantic spaces individually. To control the generation, we employ semantic LiDAR point clouds as conditional input and design local and global regularization losses to stabilize the denoising process. We evaluate our approach on autonomous driving datasets, and it achieves state-of-the-art performance for SSC, surpassing most existing methods.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in World modeling from LiDAR generation, one of 11 directions in World models. Builds on 32 of its 50 references that are in the map. First posted on arXiv 26 Sep 2024.

Builds on

32 of 50 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion20 Mar
  2. 2023LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models17 Sep
  3. 2022Learning to Generate Realistic LiDAR Point Clouds8 Sep

Other references · 29

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  4. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  5. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,201
  6. 2021Variational Diffusion Modelscited by 1,145
  7. 2021KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3Dcited by 570
  8. 2016Semantic Scene Completion from a Single Depth Imagecited by 468
  9. 2020Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentationcited by 388
  10. 2021MonoScene: Monocular 3D Semantic Scene Completioncited by 357
  11. 2023VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completioncited by 306
  12. 2020Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completioncited by 222
17 more
  1. 2016Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesiscited by 219
  2. 2022Scalable Adaptive Computation for Iterative Generationcited by 190
  3. 2020LMSCNet: Lightweight Multiscale 3D Semantic Completioncited by 187
  4. 2022KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Waycited by 132
  5. 2020S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Cloudscited by 102
  6. 2021A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completioncited by 100
  7. 2023SCPNet: Semantic Scene Completion on Point Cloudcited by 80
  8. 20213D Semantic Scene Completion: a Surveycited by 74
  9. 2023SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Drivingcited by 72
  10. 2020Generative Sparse Detection Networks for 3D Single-shot Object Detectioncited by 69
  11. 2022SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representationscited by 44
  12. 2021Semantic Segmentation-assisted Scene Completion for LiDAR Point Cloudscited by 37
  13. 2023LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDARcited by 31
  14. 2020High-dimensional Convolutional Networks for Geometric Pattern Recognitioncited by 24
  15. 2018Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mappingcited by 23
  16. 2024SLCF-Net: Sequential LiDAR-Camera Fusion for Semantic Scene Completion using a 3D Recurrent U-Netcited by 12
  17. 2021Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimizationcited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 8; 2026: 7.

16 papers.

Sort:

By area:

  1. 20253D and 4D World Modeling: A Surveycited by 64
  2. 2024Distilling Diffusion Models to Efficient 3D LiDAR Scene Completioncited by 10
  3. 2025LiDPM: Rethinking Point Diffusion for Lidar Scene Completioncited by 10
  4. 2025Diffusion Distillation With Direct Preference Optimization For Efficient 3D LiDAR Scene Completioncited by 4
  5. 2025Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendationcited by 2
  6. 2026Exploring Easy Boosts for Lidar Semantic Scene Completioncited by 2
  7. 2025Octree Diffusion for Semantic Scene Generation and Completioncited by 1
  8. 2025SWA-SOP: Spatially-aware Window Attention for Semantic Occupancy Prediction in Autonomous Drivingcited by 0
  9. 2025OC-SOP: Enhancing Vision-Based 3D Semantic Occupancy Prediction by Object-Centric Awarenesscited by 0
  10. 2025C3-GS: Learning Context-aware, Cross-dimension, Cross-scale Feature for Generalizable Gaussian Splattingcited by 0
  11. 2026Adaptive Multi-Scale Channel-Spatial Attention Aggregation Framework for 3D Indoor Semantic Scene Completion Toward Assisting Visually Impairedcited by 0
  12. 2026PatchScene: Patch-based Voxel Diffusion for Large-Scale Scene Completioncited by 0
4 more
  1. 2026EditSSC: Toward Editable Semantic Occupancy Scenes with Unconditional Diffusion Modelscited by 0
  2. 2026Iterate or Widen? When Test-Time Refinement Helps LiDAR Scene Completion: A Controlled Study of Evidence Geometry, Training Coverage, and Computecited by 0
  3. 2026Generative Semantic Scene Completioncited by 0
  4. 2026SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026CloudDiffusion: Diffusion-Based Scene Completion in the Point Cloud Domain
  2. 2025LiNeXt: Revisiting LiDAR Completion with Efficient Non-Diffusion Architectures
  3. 2026ZVeC: A Zero-Shot Framework for Instance-Level Vehicle Extraction and Generative Point Cloud Completion
  4. 2025LiDAR Point Cloud Image-based Generation Using Denoising Diffusion Probabilistic Models
  5. 2026LiFlow: Flow Matching for 3D LiDAR Scene Completion
  6. 2025Range-Edit: Semantic Mask Guided Outdoor LiDAR Scene Editing
  7. 2024Simultaneous Diffusion Sampling for Conditional LiDAR Generation
  8. 2024Label-efficient Semantic Scene Completion with Scribble Annotations