3D Point Cloud Classification

Benchmarks archive 2025-07-28

5 leaderboard tables shown for this task, 5 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

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

Subtasks archive 2025-07-28

4 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 150 papers with code (202 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.

• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
  7 Jun 2017 68 repositories listed Syntology ran 36 of 67 samples · 31 unverified · 26 pointer-only (licence)
  By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
• Point Transformer
  16 Dec 2020 24 repositories listed
  For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70.
• Dynamic Graph CNN for Learning on Point Clouds
  24 Jan 2018 21 repositories listed Syntology ran 16 of 44 samples · 28 unverified · 31 pointer-only (licence)
  Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices.
• PointCNN: Convolution On 𝒳-Transformed Points
  23 Jan 2018 16 repositories listed
  The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN.
• Perceiver: General Perception with Iterative Attention
  4 Mar 2021 12 repositories listed Syntology ran 41 of 55 samples · 14 unverified · 11 pointer-only (licence)
  The perception models used in deep learning on the other hand are designed for individual modalities, often relying on domain-specific assumptions such as the local grid structures exploited by virtually all existing…
• PCT: Point cloud transformer
  17 Dec 2020 11 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning.
• KPConv: Flexible and Deformable Convolution for Point Clouds
  18 Apr 2019 10 repositories listed Syntology ran 5 of 12 samples · 7 unverified · 3 pointer-only (licence)
  Furthermore, these locations are continuous in space and can be learned by the network.
• PointConv: Deep Convolutional Networks on 3D Point Clouds
  17 Nov 2018 9 repositories listed Syntology ran 11 of 15 samples · 4 unverified · 1 pointer-only (licence)
  Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.
• Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
  28 Jan 2022 6 repositories listed Syntology ran 14 of 29 samples · 15 unverified
  Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications.
• 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.
• Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
  16 Dec 2022 4 repositories listed
  The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natural languages.
• Masked Autoencoders for Point Cloud Self-supervised Learning
  13 Mar 2022 4 repositories listed Syntology ran 7 of 9 samples · 2 unverified · 1 pointer-only (licence)
  Then, a standard Transformer based autoencoder, with an asymmetric design and a shifting mask tokens operation, learns high-level latent features from unmasked point patches, aiming to reconstruct the masked point…
• DeepGCNs: Making GCNs Go as Deep as CNNs
  15 Oct 2019 4 repositories listed Syntology ran 1 of 9 samples · 8 unverified · 1 pointer-only (licence)
  This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.
• Relation-Shape Convolutional Neural Network for Point Cloud Analysis
  16 Apr 2019 4 repositories listed Syntology ran 3 of 5 samples · 2 unverified
  Specifically, the convolutional weight for local point set is forced to learn a high-level relation expression from predefined geometric priors, between a sampled point from this point set and the others.
• 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.
• Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
  14 Apr 2023 3 repositories listed Syntology ran 9 of 14 samples · 5 unverified · 14 pointer-only (licence)
  To conquer this limitation, we propose a novel Instance-aware Dynamic Prompt Tuning (IDPT) strategy for pre-trained point cloud models.
• Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis
  14 Mar 2023 3 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with…
• PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
  9 Jun 2022 3 repositories listed Syntology ran 7 of 17 samples · 10 unverified
  In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions.
• 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.
• PointMixer: MLP-Mixer for Point Cloud Understanding
  22 Nov 2021 3 repositories listed
  MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer.
• Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline
  9 Jun 2021 3 repositories listed Syntology ran 4 of 12 samples · 8 unverified
  It also outperforms state-of-the-art methods on ScanObjectNN, a real-world point cloud benchmark, and demonstrates better cross-dataset generalization.
• Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
  4 May 2021 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Discrete point cloud objects lack sufficient shape descriptors of 3D geometries.
• Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud
  20 Dec 2020 3 repositories listed
  GDANet introduces Geometry-Disentangle Module to dynamically disentangle point clouds into the contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components.
• Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
  13 Aug 2019 3 repositories listed Syntology ran 2 of 6 samples · 4 unverified
  From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial…
• 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.
• 3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks
  22 Nov 2017 3 repositories listed
  The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods.
• 3D ShapeNets: A Deep Representation for Volumetric Shapes
  22 Jun 2014 3 repositories listed
  Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically.
• FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes
  7 May 2024 2 repositories listed
  To bridge this data gap, we introduce the FRench ALS Clouds from TArgeted Landscapes (FRACTAL) dataset: an ultra-large-scale aerial Lidar dataset made of 100, 000 dense point clouds with high quality labels for 7…
• SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification
  16 May 2023 2 repositories listed
  In this paper, different from previous 2D DG works, we focus on the 3D DG problem and propose a Single-dataset Unified Generalization (SUG) framework that only leverages a single source dataset to alleviate the…

Syntology lines on 20 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.