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Perception is the main bottleneck to perform autonomous mobile manipulationtasks, especially in cluttered and unstructured environment. In this paper, wepropose a novel two-stage paradigm that leverage both CNN object prior andgenerative sampling to perform object detection and 6D pose estimation. Ourtwo-stage approach builds upon both CNN and generative sampling-based localsearch method to achieve sampling the network density, or SAND filter. We showthe quantitative results that SAND effectively improve object detection resultby reducing false positive and false negative recognitions, and furtherproduces accurate pose estimation. We also conduct extensive categorical objectsorting experiments to show our method is able to produce accurate and reliabledetections and object poses.

Never Mind the Bounding Boxes, Here's the SAND Filters