3D Part Segmentation

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

6 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.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 74 papers with code (99 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.
• 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.
• Submanifold Sparse Convolutional Networks
  5 Jun 2017 8 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc.
• 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…
• 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.
• 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.
• Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
  4 Jun 2021 3 repositories listed Syntology ran 10 of 30 samples · 20 unverified
  We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input.
• 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.
• 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.
• 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.
• SAMPart3D: Segment Any Part in 3D Objects
  11 Nov 2024 2 repositories listed Syntology ran 0 of 10 samples · 10 unverified
  For flexibility, we distill scale-conditioned part-aware 3D features for 3D part segmentation at multiple granularities.
• PartSTAD: 2D-to-3D Part Segmentation Task Adaptation
  11 Jan 2024 2 repositories listed
  Our proposed task adaptation method finetunes a 2D bounding box prediction model with an objective function for 3D segmentation.
• PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models
  3 Dec 2022 2 repositories listed
  Generalizable 3D part segmentation is important but challenging in vision and robotics.
• PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
  21 Nov 2022 2 repositories listed Syntology ran 5 of 12 samples · 7 unverified
  In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection.
• Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding
  30 Nov 2021 2 repositories listed Syntology ran 2 of 6 samples · 4 unverified
  To this end, we introduce the concept of the multi-view point cloud (Voint cloud), representing each 3D point as a set of features extracted from several view-points.
• AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud Segmentation
  25 Nov 2021 2 repositories listed
  To overcome these problems, we propose a) a graph convolutional network (GCN) in an adversarial learning scheme where a discriminator network provides a segmentation network with informative information to improve…
• PVT: Point-Voxel Transformer for Point Cloud Learning
  13 Aug 2021 2 repositories listed
  The recently developed pure Transformer architectures have attained promising accuracy on point cloud learning benchmarks compared to convolutional neural networks.
• 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.
• SPLATNet: Sparse Lattice Networks for Point Cloud Processing
  22 Feb 2018 2 repositories listed
  We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.
• Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
  4 Apr 2017 2 repositories listed
  We present a new deep learning architecture (called Kd-network) that is designed for 3D model recognition tasks and works with unstructured point clouds.
• 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.
• Rethinking Masked Representation Learning for 3D Point Cloud Understanding
  26 Dec 2024 1 repository listed
  Recently, masked point modeling-based methods have shown significant performance improvements for point cloud understanding, yet these methods rely on overlapping grouping strategies (k-nearest neighbor algorithm)…

Syntology lines on 17 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.