mlsanityData to 14 Oct 2026

TULIP: Transformer for Upsampling of LiDAR Point Clouds

arXiv 2312.06733 ↗First posted 11 Dec 2023cs.CVcited by 12

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Abstract (arXiv)

LiDAR Upsampling is a challenging task for the perception systems of robots and autonomous vehicles, due to the sparse and irregular structure of large-scale scene contexts. Recent works propose to solve this problem by converting LiDAR data from 3D Euclidean space into an image super-resolution problem in 2D image space. Although their methods can generate high-resolution range images with fine-grained details, the resulting 3D point clouds often blur out details and predict invalid points. In this paper, we propose TULIP, a new method to reconstruct high-resolution LiDAR point clouds from low-resolution LiDAR input. We also follow a range image-based approach but specifically modify the patch and window geometries of a Swin-Transformer-based network to better fit the characteristics of range images. We conducted several experiments on three public real-world and simulated datasets. TULIP outperforms state-of-the-art methods in all relevant metrics and generates robust and more realistic point clouds than prior works.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 57 of its 70 references that are in the map. First posted on arXiv 11 Dec 2023.

Builds on

57 of 70 references.

References · 57

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50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 5.

12 papers.

  1. 2024Fast LiDAR Data Generation with Rectified Flowscited by 14
  2. 2025SRMamba: Mamba for Super-Resolution of LiDAR Point Cloudscited by 5
  3. 2024LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenescited by 4
  4. 2026Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Cloudscited by 4
  5. 2025SRMambaV2: Biomimetic Attention for Sparse Point Cloud Upsampling in Autonomous Drivingcited by 1
  6. 2025Real-Time LiDAR Super-Resolution via Frequency-Aware Multi-Scale Fusioncited by 1
  7. 2026Collaborative Learning for Semi-Supervised LiDAR Semantic Segmentationcited by 1
  8. 2026GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structurecited by 1
  9. 2024WHU-Synthetic: A Synthetic Perception Dataset for 3-D Multitask Model Researchcited by 0
  10. 2025PVNet: Point-Voxel Interaction LiDAR Scene Upsampling Via Diffusion Modelscited by 0
  11. 2026A Comprehensive Survey on Deep Learning-Based LiDAR Super-Resolution for Autonomous Drivingcited by 0
  12. 2026Ilov3Splat: Instance-Level Open-Vocabulary 3D Scene Understanding in Gaussian Splattingcited by 0