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

https://github.com/JiehongLin/SAM-6D (a) (b) (d) (e) (c) Figure 1. We present SAM-6D for zero-shot 6D object pose estimation. SAM-6D takes an RGB image (a) and a depth map (b) of a cluttered scene as inputs, and performs instance segmentation (d) and pose estimation (e) for novel objects (c). We present the qualitative results of SAM-6D on the seven core datasets of the BOP benchmark [54], including YCB-V, LM-O, HB, T-LESS, IC-BIN, ITODD and TUD-L, arranged from left to right. Best view in the electronic version.