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Found 3 papers, 1 papers with code
Modular Quantization-Aware Training: Increasing Accuracy by Decreasing Precision in 6D Object Pose Estimation
Edge applications, such as collaborative robotics and spacecraft rendezvous, demand efficient 6D object pose estimation on resource-constrained embedded platforms.
Fusing RGBD Tracking and Segmentation Tree Sampling for Multi-Hypothesis Volumetric Segmentation
In this work, we propose Multihypothesis Segmentation Tracking (MST), a novel method for volumetric segmentation in changing scenes, which allows scene ambiguity to be tracked and our estimates to be adjusted over time as we interact with the scene.
Inferring Occluded Geometry Improves Performance when Retrieving an Object from Dense Clutter
Object search -- the problem of finding a target object in a cluttered scene -- is essential to solve for many robotics applications in warehouse and household environments.
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