6D Pose Estimation
96 papers with code • 5 benchmarks • 16 datasets
Image: Zeng et al
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Latest papers
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.
GBOT: Graph-Based 3D Object Tracking for Augmented Reality-Assisted Assembly Guidance
Augmented reality assembly guidance requires 6D object poses of target objects in real time.
STAR: Shape-focused Texture Agnostic Representations for Improved Object Detection and 6D Pose Estimation
To achieve a focus on learning shape features, the textures are randomized during the rendering of the training data.
FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups.
PViT-6D: Overclocking Vision Transformers for 6D Pose Estimation with Confidence-Level Prediction and Pose Tokens
In the current state of 6D pose estimation, top-performing techniques depend on complex intermediate correspondences, specialized architectures, and non-end-to-end algorithms.
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.
Enhancing 6-DoF Object Pose Estimation through Multiple Modality Fusion: A Hybrid CNN Architecture with Cross-Layer and Cross-Modal Integration
To tackle this challenge, we proposed a pioneering two-stage hybrid Convolutional Neural Network (CNN) architecture, which connects segmentation and pose estimation in tandem.
Revisiting Fully Convolutional Geometric Features for Object 6D Pose Estimation
Recent works on 6D object pose estimation focus on learning keypoint correspondences between images and object models, and then determine the object pose through RANSAC-based algorithms or by directly regressing the pose with end-to-end optimisations.
SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D Object Pose Estimation
Detecting objects and estimating their 6D poses is essential for automated systems to interact safely with the environment.
Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose
Domain gap between synthetic and real data in visual regression (e. g. 6D pose estimation) is bridged in this paper via global feature alignment and local refinement on the coarse classification of discretized anchor classes in target space, which imposes a piece-wise target manifold regularization into domain-invariant representation learning.