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Found 8 papers, 1 papers with code
DiffAnt: Diffusion Models for Action Anticipation
However, the majority of existing action anticipation models adhere to a deterministic approach, neglecting to account for future uncertainties.
Attention-based Part Assembly for 3D Volumetric Shape Modeling
Modeling a 3D volumetric shape as an assembly of decomposed shape parts is much more challenging, but semantically more valuable than direct reconstruction from a full shape representation.
Attention-based Point Cloud Edge Sampling
Point cloud sampling is a less explored research topic for this data representation.
Ranked #31 on
3D Point Cloud Classification
on ModelNet40
Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly
On robotics computer vision tasks, generating and annotating large amounts of data from real-world for the use of deep learning-based approaches is often difficult or even impossible.
SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-task Learning
Our work is a sub-task in the framework of a remanufacturing project, in which small electric motors are used as fundamental objects.
MotorFactory: A Blender Add-on for Large Dataset Generation of Small Electric Motors
To enable automatic disassembly of different product types with uncertain conditions and degrees of wear in remanufacturing, agile production systems that can adapt dynamically to changing requirements are needed.
Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding
We present an improved approach for 3D object detection in point cloud data based on the Frustum PointNet (F-PointNet).
Generative-Contrastive Learning for Self-Supervised Latent Representations of 3D Shapes from Multi-Modal Euclidean Input
We propose a combined generative and contrastive neural architecture for learning latent representations of 3D volumetric shapes.
