AI RESEARCH
Few-Shot Incremental 3D Object Detection in Dynamic Indoor Environments
arXiv CS.CV
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ArXi:2604.07997v1 Announce Type: new Incremental 3D object perception is a critical step toward embodied intelligence in dynamic indoor environments. However, existing incremental 3D detection methods rely on extensive annotations of novel classes for satisfactory performance. To address this limitation, we propose FI3Det, a Few-shot Incremental 3D Detection framework that enables efficient 3D perception with only a few novel samples by leveraging vision-language models (VLMs) to learn knowledge of unseen categories. FI3Det