SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation

arXiv:2311.15707v2 Announce Type: replace
Abstract: Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D poses in cluttered scenes, presenting significant challenges for model generalizability. Fortunately, the recent Segment Anything Model (SAM) has showcased remarkable zero-shot transfer performance, which provides a promising solution to tackle this task. Motivated by this, we introduce SAM-6D, a novel framework designed to realize the task through two steps, including instance segmentation and pose estimation. Given the target objects, SAM-6D employs …

abstract arxiv challenges cs.cv detection novel object objects performance presenting sam segment segment anything segment anything model solution transfer type zero-shot