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Zero-shot 6D object pose estimation involves the detection of novel objectswith their 6D poses in cluttered scenes, presenting significant challenges formodel generalizability. Fortunately, the recent Segment Anything Model (SAM)has showcased remarkable zero-shot transfer performance, which provides apromising solution to tackle this task. Motivated by this, we introduce SAM-6D,a novel framework designed to realize the task through two steps, includinginstance segmentation and pose estimation. Given the target objects, SAM-6Demploys two dedicated sub-networks, namely Instance Segmentation Model (ISM)and Pose Estimation Model (PEM), to perform these steps on cluttered RGB-Dimages. ISM takes SAM as an advanced starting point to generate all possibleobject proposals and selectively preserves valid ones through meticulouslycrafted object matching scores in terms of semantics, appearance and geometry.By treating pose estimation as a partial-to-partial point matching problem, PEMperforms a two-stage point matching process featuring a novel design ofbackground tokens to construct dense 3D-3D correspondence, ultimately yieldingthe pose estimates. Without bells and whistles, SAM-6D outperforms the existingmethods on the seven core datasets of the BOP Benchmark for both instancesegmentation and pose estimation of novel objects.

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