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Found 3 papers, 2 papers with code
3D Indoor Instance Segmentation in an Open-World
We argue that such a closed-world assumption is restrictive and explore for the first time 3D indoor instance segmentation in an open-world setting, where the model is allowed to distinguish a set of known classes as well as identify an unknown object as unknown and then later incrementally learning the semantic category of the unknown when the corresponding category labels are available.
Drawing Attention to Detail: Pose Alignment through Self-Attention for Fine-Grained Object Classification
Intra-class variations in the open world lead to various challenges in classification tasks.
3D Instance Segmentation via Enhanced Spatial and Semantic Supervision
Specifically, we provide the transformer block with spatial features to facilitate differentiation between similar object queries and incorporate semantic supervision to enhance prediction accuracy based on object class.
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