3D Part Segmentation
64 papers with code • 2 benchmarks • 6 datasets
Segmenting 3D object parts
( Image credit: MeshCNN: A Network with an Edge )
Libraries
Use these libraries to find 3D Part Segmentation models and implementations
ShapeNet
IntrA
LiDAR-MOS
Teeth3DS
Workshop Tools Dataset
TomoSAM
Latest papers with no code
PartSTAD: 2D-to-3D Part Segmentation Task Adaptation
Our proposed task adaptation method finetunes a 2D bounding box prediction model with an objective function for 3D segmentation.
PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation
This paper proposes a cross-modal distillation framework, PartDistill, which transfers 2D knowledge from vision-language models (VLMs) to facilitate 3D shape part segmentation.
ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation
The main idea of our approach is to explore the natural relationship between multi-view correspondences and the prompt mechanism of foundational models and build bridges on it.
APPT : Asymmetric Parallel Point Transformer for 3D Point Cloud Understanding
To tackle these problems, we propose Asymmetric Parallel Point Transformer (APPT).
SegNeRF: 3D Part Segmentation with Neural Radiance Fields
The predicted semantic fields allow SegNeRF to achieve an average mIoU of $\textbf{30. 30%}$ for 2D novel view segmentation, and $\textbf{37. 46%}$ for 3D part segmentation, boasting competitive performance against point-based methods by using only a few posed images.
CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning
However, the disadvantage is that the resulting models from the fully-supervised learning methodology are highly reliant on the completeness of the available dataset, and its generalization ability is relatively poor to new unknown segmentation types (i. e. further additional novel classes).
PointVector: A Vector Representation In Point Cloud Analysis
In point cloud analysis, point-based methods have rapidly developed in recent years.
3D Meta-Segmentation Neural Network
Based on the learned information of task distribution, our meta part segmentation learner is able to dynamically update the part segmentation learner with optimal parameters which enable our part segmentation learner to rapidly adapt and have great generalization ability on new part segmentation tasks.
Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis
In this work we propose PointDisc, a point discriminative learning method to leverage self-supervisions for data-efficient 3D point cloud classification and segmentation.
MKConv: Multidimensional Feature Representation for Point Cloud Analysis
In this paper, we present Multidimensional Kernel Convolution (MKConv), a novel convolution operator that learns to transform the point feature representation from a vector to a multidimensional matrix.
