Method: SAM3 + FoundationPose (single-view RGB-D).

A training-free pipeline combining two published, off-the-shelf models. No network was trained or fine-tuned; all weights are used as released, and the same models handle all objects without per-object training.

Codebase: https://github.com/nvidia-isaac/foundationpose_perception_pipeline

Pipeline (per BOP24 target image):

1. Input — a single target RGB-D view. The provided sensor depth is used directly; no additional views or depth reconstruction are used.
2. Detection and segmentation (SAM3) — text-prompted, open-vocabulary detection on the target RGB image, with a confidence threshold of 0.10.
3. 6D pose estimation (FoundationPose) — model-based registration using the target RGB image, sensor depth, and provided CAD models. FoundationPose uses 128 pose hypotheses and five refinement iterations. All pose proposals are submitted without a score cutoff; the score column contains FoundationPose’s reranking score.

Differences with respect to the linked publications:

• SAM3 and FoundationPose are composed into an integration pipeline.
• The individual networks are otherwise unmodified from their released versions.

Training images and rendering: None. No training or offline rendering was performed. During inference, FoundationPose renders CAD-model hypotheses online for pose refinement and scoring. The provided models_eval CAD meshes are used directly.

Networks: One set of networks is used for all objects—a single SAM3 model and a single FoundationPose model. No per-object models are used.