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Found 8 papers, 4 papers with code
UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model
Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information.
A Multi-modal Garden Dataset and Hybrid 3D Dense Reconstruction Framework Based on Panoramic Stereo Images for a Trimming Robot
Next, the refined disparity maps are converted into full-view point clouds or single-view point clouds for the pose fusion module.
A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments
More specifically, in the automation stage, the robot navigates to the specified location without the need of a precise parking.
Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows
This paper presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors.
UDFNet: Unsupervised Disparity Fusion with Adversarial Networks
In this paper, a mathematical model for disparity fusion is proposed to guide an adversarial network to train effectively without ground truth disparity data.
DUGMA: Dynamic Uncertainty-Based Gaussian Mixture Alignment
Existing rigid registration methods failed to use the physical 3D uncertainty distribution of each point from a real sensor in the dynamic alignment process mainly because the uncertainty model for a point is static and invariant and it is hard to describe the change of these physical uncertainty models in the registration process.
Sdf-GAN: Semi-supervised Depth Fusion with Multi-scale Adversarial Networks
into a refiner network to better refine raw disparity inputs.
Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models
Matching 3D rigid point clouds in complex environments robustly and accurately is still a core technique used in many applications.
