3D Object Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 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

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

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 47 papers with code (93 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.

• PointLLM: Empowering Large Language Models to Understand Point Clouds
  31 Aug 2023 3 repositories listed
  The unprecedented advancements in Large Language Models (LLMs) have shown a profound impact on natural language processing but are yet to fully embrace the realm of 3D understanding.
• 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.
• 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.
• Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
  20 Sep 2019 3 repositories listed Syntology ran 1 of 11 samples · 10 unverified
  We propose a spherical kernel for efficient graph convolution of 3D point clouds.
• 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…
• Open-Pose 3D Zero-Shot Learning: Benchmark and Challenges
  12 Dec 2023 2 repositories listed
  To this end, we propose a more realistic and challenging scenario named open-pose 3D zero-shot classification, focusing on the recognition of 3D objects regardless of their orientation.
• Uni3D: Exploring Unified 3D Representation at Scale
  10 Oct 2023 2 repositories listed Syntology ran 1 of 2 samples · 1 unverified
  Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language.
• SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
  27 Mar 2021 2 repositories listed
  Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks.
• Point Transformer
  2 Nov 2020 2 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets.
• 3D Point Capsule Networks
  27 Dec 2018 2 repositories listed
  In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
• Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
  10 Apr 2017 2 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  A number of problems can be formulated as prediction on graph-structured data.
• PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Sets
  14 Oct 2024 1 repository listed
  KANs have been integrated into various deep learning architectures such as convolutional neural networks, graph neural networks, and transformers, with their performance evaluated.
• PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
  16 Aug 2024 1 repository listed
  These methods typically include an encoder accepting visible patches (normalized) and corresponding patch centers (position) as input, with the decoder accepting the output of the encoder and the centers (position) of…
• DC3DO: Diffusion Classifier for 3D Objects
  13 Aug 2024 1 repository listed
  Inspired by Geoffrey Hinton emphasis on generative modeling, To recognize shapes, first learn to generate them, we explore the use of 3D diffusion models for object classification.
• MiniGPT-3D: Efficiently Aligning 3D Point Clouds with Large Language Models using 2D Priors
  2 May 2024 1 repository listed
  Notably, MiniGPT-3D gains an 8.
• Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach
  9 Mar 2024 1 repository listed
  The proposed approach is a combination of the GRU and LSTM.
• Point Cloud Self-supervised Learning via 3D to Multi-view Masked Autoencoder
  17 Nov 2023 1 repository listed
  However, a notable limitation of these approaches is that they do not fully utilize the multi-view attributes inherent in 3D point clouds, which is crucial for a deeper understanding of 3D structures.
• Extending Multi-modal Contrastive Representations
  13 Oct 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Inspired by recent C-MCR, this paper proposes Extending Multimodal Contrastive Representation (Ex-MCR), a training-efficient and paired-data-free method to flexibly learn unified contrastive representation space for…
• 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…
• Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation
  27 Apr 2023 1 repository listed
  Discovering inter-point connection for efficient high-dimensional feature extraction from point coordinate is a key challenge in processing point cloud.
• Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations
  8 Mar 2023 1 repository listed
  In contrast to other G-invariant architectures in the literature, the preactivations of the$G$-DNNs presented here are able to transform by \emph{signed} permutation representations (signed perm-reps) of G.
• PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration
  6 Feb 2023 1 repository listed
  To solve this problem, we propose a rotation-invariant geometric relation to restore the relative pose with equivariant information for patches defined over different scales.
• Improved Training for 3D Point Cloud Classification
  1 Jan 2023 1 repository listed
  PointNet is a pioneering approach in this direction that feeds the 3D point cloud data directly to a model.
• MATE: Masked Autoencoders are Online 3D Test-Time Learners
  21 Nov 2022 1 repository listed
  Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data.
• Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding
  6 Oct 2022 1 repository listed
  Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.
• 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.
• On Automatic Data Augmentation for 3D Point Cloud Classification
  11 Dec 2021 1 repository listed
  Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics.
• diffConv: Analyzing Irregular Point Clouds with an Irregular View
  29 Nov 2021 1 repository listed
  Standard spatial convolutions assume input data with a regular neighborhood structure.
• PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
  15 Oct 2021 1 repository listed
  3D shape representation and its processing have substantial effects on 3D shape recognition.
• Regularization Strategy for Point Cloud via Rigidly Mixed Sample
  3 Feb 2021 1 repository listed Syntology ran 2 of 3 samples · 1 unverified
  Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks.

Syntology lines on 8 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.