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Found 10 papers, 5 papers with code
Under pressure: learning-based analog gauge reading in the wild
We propose an interpretable framework for reading analog gauges that is deployable on real world robotic systems.
ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification
To build spatial AI systems that can quickly be taught about new objects, we need to effectively solve the problem of single-shot object detection, instance segmentation and re-identification.
Panoptic Vision-Language Feature Fields
In this paper, we propose to the best of our knowledge the first algorithm for open-vocabulary panoptic segmentation in 3D scenes.
Neural Implicit Vision-Language Feature Fields
In this work, we present a zero-shot volumetric open-vocabulary semantic scene segmentation method.
Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps
Methods have recently been proposed that densely segment 3D volumes into classes using only color images and expert supervision in the form of sparse semantically annotated pixels.
Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses
In this work, we present a full object keypoint tracking toolkit, encompassing the entire process from data collection, labeling, model learning and evaluation.
3D Annotation Of Arbitrary Objects In The Wild
Recent years have produced a variety of learning based methods in the context of computer vision and robotics.
Points2Vec: Unsupervised Object-level Feature Learning from Point Clouds
This, despite the fact that the physical 3D spaces have a similar semantic structure to bodies of text: words are surrounded by words that are semantically related, just like objects are surrounded by other objects that are similar in concept and usage.
Go Fetch: Mobile Manipulation in Unstructured Environments
With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved efficiency, quality, and safety of operations.
Deep convolutional Gaussian processes
We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure.
