Pöllabauer, ThomasThomasPöllabauerEmrich, JanJanEmrichKnauthe, VolkerVolkerKnautheKuijper, ArjanArjanKuijper2024-07-292024-07-292024https://publica.fraunhofer.de/handle/publica/47220710.48550/arXiv.2402.05610Estimating the 6D pose of objects accurately, quickly, and robustly remains a difficult task. However, recent methods for directly regressing poses from RGB images using dense features have achieved state-of-the-art results. Stereo vision, which provides an additional perspective on the object, can help reduce pose ambiguity and occlusion. Moreover, stereo can directly infer the distance of an object, while mono-vision requires internalized knowledge of the object's size. To extend the state-of-the-art in 6D object pose estimation to stereo, we created a BOP compatible stereo version of the YCB-V dataset. Our method outperforms state-of-the-art 6D pose estimation algorithms by utilizing stereo vision and can easily be adopted for other dense feature-based algorithms.enBranche: Automotive IndustryBranche: HealthcareBranche: BioeconomicsBranche: Cultural und Creative EconomyResearch Line: Computer graphics (CG)Research Line: Computer vision (CV)Research Line: Human computer interaction (HCI)Research Line: Machine learning (ML)LTA: Monitoring and control of processes and systemsLTA: Machine intelligence, algorithms, and data structures (incl. semantics)Computer visionMachine learningRobot visionObject pose estimationpaper