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Found 6 papers, 5 papers with code
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.
Unsupervised Continual Semantic Adaptation through Neural Rendering
Due to the mismatch between training and deployment data, adapting the model on the new scenes is often crucial to obtain good performance.
Self-Improving Semantic Perception for Indoor Localisation
We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments.
Primal-Dual Mesh Convolutional Neural Networks
Recent works in geometric deep learning have introduced neural networks that allow performing inference tasks on three-dimensional geometric data by defining convolution, and sometimes pooling, operations on triangle meshes.
