Search Results for author:
Found 8 papers, 6 papers with code
SD-Net: Symmetric-Aware Keypoint Prediction and Domain Adaptation for 6D Pose Estimation In Bin-picking Scenarios
Specifically, at the keypoint prediction stage, we designe a robust 3D keypoints selection strategy considering the symmetry class of objects and equivalent keypoints, which facilitate locating 3D keypoints even in highly occluded scenes.
NormNet: Scale Normalization for 6D Pose Estimation in Stacked Scenarios
Existing Object Pose Estimation (OPE) methods for stacked scenarios are not robust to changes in object scale.
Medical Image Segmentation via Sparse Coding Decoder
Transformers have achieved significant success in medical image segmentation, owing to its capability to capture long-range dependencies.
3DFill:Reference-guided Image Inpainting by Self-supervised 3D Image Alignment
We then fill the hole in the target image with the contents of the aligned image.
KRF: Keypoint Refinement with Fusion Network for 6D Pose Estimation
The CIKP method introduces color information into registration and registers point cloud around each keypoint to increase stability.
Class-Aware Contrastive Semi-Supervised Learning
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization.
PPR-Net:Point-wise Pose Regression Network for Instance Segmentation and 6D Pose Estimation in Bin-picking Scenarios
For each point in the point cloud, the network regresses a 6D pose of the object instance that the point belongs to.
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds.
Ranked #6 on
3D Part Segmentation
on IntrA
