mlsanityData to 7 Oct 2026

Exploring Easy Boosts for Lidar Semantic Scene Completion

arXiv 2606.03992 ↗First posted 2 Jun 2026cs.CV cs.ROcited by 2

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Abstract (arXiv)

This paper investigates "free lunch" strategies to boost the performance of lidar semantic scene completion (SSC) without requiring complex architectural redesigns. We first demonstrate that endowing input point clouds with semantic pseudo-labels from off-the-shelf segmentors significantly improves the performance of existing architectures. By evaluating these models against an oracle, we establish that high-quality semantic priors are a primary driver of mIoU gains. Furthermore, we equip the input lidar scan with visibility information that distinguishes between empty and unknown spaces, which provides a secondary performance boost across the tested architectures. Using these simple enhancements, we observe that older models remain competitive with state-of-the-art systems, and can even outperform them. Our code is available at https://github.com/astra-vision/SSC-Priors.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 25 references that are in the map. First posted on arXiv 2 Jun 2026.

Builds on

22 of 25 references.

References · 22

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,201
  3. 2016Semantic Scene Completion from a Single Depth Imagecited by 468
  4. 2023Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Drivingcited by 325
  5. 2020Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completioncited by 222
  6. 2020LMSCNet: Lightweight Multiscale 3D Semantic Completioncited by 187
  7. 2020S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Cloudscited by 102
  8. 2023SCPNet: Semantic Scene Completion on Point Cloudcited by 80
  9. 2023UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generationcited by 76
  10. 2020Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Datacited by 74
  11. 2023SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Drivingcited by 72
  12. 2019What You See is What You Get: Exploiting Visibility for 3D Object Detectioncited by 69
10 more
  1. 2023Using a Waffle Iron for Automotive Point Cloud Semantic Segmentationcited by 53
  2. 2024SemCity: Semantic Scene Generation with Triplane Diffusioncited by 53
  3. 2018Two Stream 3D Semantic Scene Completioncited by 48
  4. 2021Semantic Segmentation-assisted Scene Completion for LiDAR Point Cloudscited by 37
  5. 2023LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDARcited by 31
  6. 2023SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusioncited by 30
  7. 2018DeepMapping: Unsupervised Map Estimation From Multiple Point Cloudscited by 20
  8. 2024DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Modelscited by 16
  9. 2024TALoS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of Sightcited by 12
  10. 2024Deep Learning on 3D Semantic Segmentation: A Detailed Reviewcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Generative Semantic Scene Completioncited by 0
  2. 2026SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completioncited by 0