Sparse Color-Code Net: Real-Time RGB-Based 6D Object Pose Estimation on Edge Devices

TL;DR

Sparse Color-Code Net (SCCN) enables real-time 6D object pose estimation on edge devices by leveraging sparse geometry features and a novel symmetry representation, achieving high accuracy and speed.

Contribution

The paper introduces SCCN, a novel RGB-based pipeline that improves real-time 6D pose estimation efficiency and handles symmetric objects effectively on edge hardware.

Findings

Achieves 19 FPS on LINEMOD dataset with high accuracy.

Maintains real-time performance at 6 FPS on Occlusion LINEMOD.

Effectively addresses symmetric object ambiguities.

Abstract

As robotics and augmented reality applications increasingly rely on precise and efficient 6D object pose estimation, real-time performance on edge devices is required for more interactive and responsive systems. Our proposed Sparse Color-Code Net (SCCN) embodies a clear and concise pipeline design to effectively address this requirement. SCCN performs pixel-level predictions on the target object in the RGB image, utilizing the sparsity of essential object geometry features to speed up the Perspective-n-Point (PnP) computation process. Additionally, it introduces a novel pixel-level geometry-based object symmetry representation that seamlessly integrates with the initial pose predictions, effectively addressing symmetric object ambiguities. SCCN notably achieves an estimation rate of 19 frames per second (FPS) and 6 FPS on the benchmark LINEMOD dataset and the Occlusion LINEMOD dataset, respectively, for an NVIDIA Jetson AGX Xavier, while consistently maintaining high estimation accuracy at these rates.