3D Part Segmentation
65 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
PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation
In this paper, we address the problem of segmentation and labelling of the 3D point clouds by proposing a inception based deep network architecture called PIG-Net, that effectively characterizes the local and global geometric details of the point clouds.
Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences
Specifically, 2D image features of rendered images from different views are extracted by a 2D convolutional neural network, and 3D point cloud features are extracted by a graph convolution neural network.
LE-HGR: A Lightweight and Efficient RGB-based Online Gesture Recognition Network for Embedded AR Devices
Finally, we provide a variety of experimental results to show that the proposed framework is able to achieve state-of-the-art accuracy with significantly reduced computational cost, which are the key properties for enabling real-time applications in low-cost commercial devices such as mobile devices and AR/VR headsets.
Interpolated Convolutional Networks for 3D Point Cloud Understanding
Our InterpConv is shown to be permutation and sparsity invariant, and can directly handle irregular inputs.
Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition
We present a generic, flexible and 3D rotation invariant framework based on spherical symmetry for point cloud recognition.
Structural Relational Reasoning of Point Clouds
The symmetry for the corners of a box, the continuity for the surfaces of a monitor, the linkage between the torso and other body parts --- it suggests that 3D objects may have common and underlying inner relations between local structures, and it is a fundamental ability for intelligent species to reason for them.
PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation
Meanwhile, to increase the segmentation accuracy at each node, we enhance the recursive contextual feature with the shape feature extracted for the corresponding part.
Octree guided CNN with Spherical Kernels for 3D Point Clouds
We propose an octree guided neural network architecture and spherical convolutional kernel for machine learning from arbitrary 3D point clouds.
Multiview Based 3D Scene Understanding On Partial Point Sets
Deep learning within the context of point clouds has gained much research interest in recent years mostly due to the promising results that have been achieved on a number of challenging benchmarks, such as 3D shape recognition and scene semantic segmentation.
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network
However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features.
