Pith. sign in

REVIEW 5 cited by

Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2305.04691 v1 pith:GYSKHTAI submitted 2023-05-08 cs.CV

Ben Fei , Weidong Yang , Liwen Liu , Tianyue Luo , Rui Zhang , Yixuan Li , Ying He This is my paper
classification cs.CV
keywords pointcloudlearningself-supervisedcloudsdatarepresentationchallenges
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Point cloud data has been extensively studied due to its compact form and flexibility in representing complex 3D structures. The ability of point cloud data to accurately capture and represent intricate 3D geometry makes it an ideal choice for a wide range of applications, including computer vision, robotics, and autonomous driving, all of which require an understanding of the underlying spatial structures. Given the challenges associated with annotating large-scale point clouds, self-supervised point cloud representation learning has attracted increasing attention in recent years. This approach aims to learn generic and useful point cloud representations from unlabeled data, circumventing the need for extensive manual annotations. In this paper, we present a comprehensive survey of self-supervised point cloud representation learning using DNNs. We begin by presenting the motivation and general trends in recent research. We then briefly introduce the commonly used datasets and evaluation metrics. Following that, we delve into an extensive exploration of self-supervised point cloud representation learning methods based on these techniques. Finally, we share our thoughts on some of the challenges and potential issues that future research in self-supervised learning for pre-training 3D point clouds may encounter.

Discussion (0). Sign in to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density

    cs.LG 2026-08 conditional novelty 6.0 of 10

    ED-DiT pretrains a diffusion transformer on electron-density point clouds with a physical electron-number constraint, and the resulting encoder outperforms scratch models across six molecular tasks.

  2. Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations

    cs.CV 2025-07 conditional novelty 6.0 of 10

    LiMA distills long-term multi-camera image features into LiDAR backbones and reports consistent gains on segmentation and detection benchmarks.

  3. Asymmetric Dual Self-Distillation for 3D Self-Supervised Representation Learning

    cs.CV 2025-06 reject novelty 6.0 of 10

    AsymDSD unifies latent masked point modeling and cross-view invariance self-distillation to learn 3D representations, reporting 90.53% on ScanObjectNN and 93.72% with 930k-shape pretraining.

  4. Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Self-supervised pre-training with 3D Gaussian Splatting supervision improves downstream 3D object detection over a masked-autoencoder baseline on SUN RGB-D and ScanNetV2.

  5. Multi-modal Multi-task Pre-training for Improved Point Cloud Understanding

    cs.CV 2025-07 reject novelty 4.0 of 10

    MMPT combines three existing self-supervised tasks for point cloud pre-training and reports improved results across several benchmarks.

Pith tools