3D Point Cloud Linear Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

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

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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

Most implemented papers archive 2025-07-28

17 shown of 17 papers with code (21 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.

• Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining
  5 Feb 2023 5 repositories listed Syntology ran 2 of 5 samples · 3 unverified
  This motivates us to learn 3D representations by sharing the merits of both paradigms, which is non-trivial due to the pattern difference between the two paradigms.
• ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
  27 Feb 2024 3 repositories listed Syntology ran 9 of 17 samples · 8 unverified
  This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.
• 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.
• Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
  29 Nov 2021 3 repositories listed Syntology ran 4 of 6 samples · 2 unverified
  Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers.
• SO-Net: Self-Organizing Network for Point Cloud Analysis
  12 Mar 2018 3 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  This paper presents SO-Net, a permutation invariant architecture for deep learning with orderless point clouds.
• FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
  19 Dec 2017 3 repositories listed
  Recent deep networks that directly handle points in a point set, e.
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
  24 Oct 2016 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  We study the problem of 3D object generation.
• Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
  13 Dec 2022 2 repositories listed Syntology ran 4 of 6 samples · 2 unverified
  Pre-training by numerous image data has become de-facto for robust 2D representations.
• AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud Learning
  2 Jan 2025 1 repository listed
  We propose AdaCrossNet, a novel self-supervised learning framework for point cloud understanding that utilizes a dynamic weight adjustment mechanism for IM and CM contrastive learning.
• Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point Cloud
  25 Apr 2024 1 repository listed
  To this end, we introduce a sequencer that orders point cloud patch embeddings to efficiently compute and utilize their proximity based on the indices during target and context selection.
• CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud
  8 Jun 2023 1 repository listed
  In this study, we introduce a novel selfsupervised method called CrossMoCo, which learns the representations of unlabelled point cloud data in a multi-modal setup that also utilizes the 2D rendered images of the point…
• CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
  1 Mar 2022 1 repository listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds.
• Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
  3 Jan 2022 1 repository listed
  The most popular and accessible 3D representation, i.
• Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
  1 Sep 2021 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  To date, various 3D scene understanding tasks still lack practical and generalizable pre-trained models, primarily due to the intricate nature of 3D scene understanding tasks and their immense variations introduced by…
• 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.
• Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
  3 Aug 2020 1 repository listed Syntology ran 0 of 9 samples · 9 unverified
  Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic…
• Self-supervised Learning of Point Clouds via Orientation Estimation
  1 Aug 2020 1 repository listed
  A point cloud can be rotated in infinitely many ways, which provides a rich label-free source for self-supervision.

Syntology lines on 9 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.