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Found 7 papers, 3 papers with code
GS-Pose: Cascaded Framework for Generalizable Segmentation-based 6D Object Pose Estimation
At inference, GS-Pose operates sequentially by locating the object in the input image, estimating its initial 6D pose using a retrieval approach, and refining the pose with a render-and-compare method.
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis
Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e. g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism.
Swapped goal-conditioned offline reinforcement learning
Offline goal-conditioned reinforcement learning (GCRL) can be challenging due to overfitting to the given dataset.
MSDA: Monocular Self-supervised Domain Adaptation for 6D Object Pose Estimation
Though massive amounts of synthetic RGB images are easy to obtain, the models trained on them suffer from noticeable performance degradation due to the synthetic-to-real domain gap.
SC6D: Symmetry-agnostic and Correspondence-free 6D Object Pose Estimation
The pose estimation is decomposed into three sub-tasks: a) object 3D rotation representation learning and matching; b) estimation of the 2D location of the object center; and c) scale-invariant distance estimation (the translation along the z-axis) via classification.
OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation
This paper proposes a universal framework, called OVE6D, for model-based 6D object pose estimation from a single depth image and a target object mask.
Convolutional Low-Resolution Fine-Grained Classification
Successful fine-grained image classification methods learn subtle details between visually similar (sub-)classes, but the problem becomes significantly more challenging if the details are missing due to low resolution.
