Point Cloud Pre-training

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

15 shown of 15 papers with code (26 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
  28 May 2022 3 repositories listed
  By fine-tuning on downstream tasks, Point-M2AE achieves 86.
• PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
  21 Jul 2020 2 repositories listed Syntology ran 3 of 4 samples · 1 unverified · 2 pointer-only (licence)
  To this end, we select a suite of diverse datasets and tasks to measure the effect of unsupervised pre-training on a large source set of 3D scenes.
• Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning
  8 Jul 2024 1 repository listed Syntology ran 6 of 8 samples · 2 unverified
  To answer this question, we first empirically validate that integrating MAE-based point cloud pre-training with the standard contrastive learning paradigm, even with meticulous design, can lead to a decrease in…
• PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm
  12 Oct 2023 1 repository listed
  In this paper, we introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation, thereby establishing a pathway to 3D foundational models.
• Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio Masking
  8 Jun 2023 1 repository listed
  In our work, we present Point-LGMask, a novel method to embed both local and global contexts with multi-ratio masking, which is quite effective for self-supervised feature learning of point clouds but is unfortunately…
• AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset
  1 Jun 2023 1 repository listed Syntology ran 4 of 9 samples · 5 unverified
  It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that can achieve promising results on…
• GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training
  15 May 2023 1 repository listed
  This paper tries to address a fundamental question in point cloud self-supervised learning: what is a good signal we should leverage to learn features from point clouds without annotations?
• PointClustering: Unsupervised Point Cloud Pre-Training Using Transformation Invariance in Clustering
  1 Jan 2023 1 repository listed
  Feature invariance under different data transformations, i.
• BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios
  12 Dec 2022 1 repository listed
  Based on the property of outdoor point clouds in autonomous driving scenarios, i.
• Boosting Point-BERT by Multi-choice Tokens
  27 Jul 2022 1 repository listed
  Masked language modeling (MLM) has become one of the most successful self-supervised pre-training task.
• ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection
  26 Jul 2022 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  Existing approaches for unsupervised point cloud pre-training are constrained to either scene-level or point/voxel-level instance discrimination.
• POS-BERT: Point Cloud One-Stage BERT Pre-Training
  3 Apr 2022 1 repository listed
  We propose to use the dynamically updated momentum encoder as the tokenizer, which is updated and outputs the dynamic supervision signal along with the training process.
• Point Cloud Pre-Training With Natural 3D Structures
  1 Jan 2022 1 repository listed
  Moreover, the PC-FractalDB pre-trained model is especially effective in training with limited data.
• Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes
  1 Sep 2021 1 repository listed
  Albeit simple, the pre-trained encoder can capture the key points of an unseen point cloud and surpasses the encoder trained from scratch on downstream tasks.
• Unsupervised Point Cloud Pre-Training via Occlusion Completion
  2 Oct 2020 1 repository listed
  We find that even when we construct a single pre-training dataset (from ModelNet40), this pre-training method improves accuracy across different datasets and encoders, on a wide range of downstream tasks.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.