ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation

2022/03/17 by Yongzhi Su, Mahdi Saleh, Su, Yongzhi +13 · 15 citations
Computer Science · Engineering · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Robot Manipulation and Learning

paper · pdf · doi:10.48550/arxiv.2203.09418

openalex publication_date 2022/03/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the presence of occlusion. More recently researchers have shown improvements by learning object fragments as segmentation. In this work, we present a discrete descriptor, which can represent the object surface densely. By incorporating a hierarchical binary grouping, we can encode the object surface very efficiently. Moreover, we propose a coarse to fine training strategy, which enables fine-grained correspondence prediction. Finally, by matching predicted codes with object surface and using a PnP solver, we estimate the 6DoF pose. Results on the public LM-O and YCB-V datasets show major improvement over the state of the art w.r.t. ADD(-S) metric, even surpassing RGB-D based methods in some cases.

Cited by

• Eff-GRot: Efficient and Generalizable Rotation Estimation with Transformers
• ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors
• VFM-VLM: Vision Foundation Model and Vision Language Model based Visual Comparison for 3D Pose Estimation
• Is Image-based Object Pose Estimation Ready to Support Grasping?
• WALDO: Where Unseen Model-based 6D Pose Estimation Meets Occlusion
• OPFormer: Object Pose Estimation leveraging foundation model with geometric encoding
• STORM: Segment, Track, and Object Re-Localization from a Single Image
• VLM6D: VLM based 6Dof Pose Estimation based on RGB-D Images
• RayPose: Ray Bundling Diffusion for Template Views in Unseen 6D Object Pose Estimation
• DKPMV: Dense Keypoints Fusion from Multi-View RGB Frames for 6D Pose Estimation of Textureless Objects
• HccePose(BF): Predicting Front & Back Surfaces to Construct Ultra-Dense 2D-3D Correspondences for Pose Estimation
• Self-supervised Learning Of Visual Pose Estimation Without Pose Labels By Classifying LED States
• Generative Visual Foresight Meets Task-Agnostic Pose Estimation in Robotic Table-Top Manipulation
• FPG-NAS: FLOPs-Aware Gated Differentiable Neural Architecture Search for Efficient 6DoF Pose Estimation
• Unified Category-Level Object Detection and Pose Estimation from RGB Images using 3D Prototypes

Related

• HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
• DLTPose: 6DoF Pose Estimation From Accurate Dense Surface Point Estimates
• A Hybrid Approach for 6DoF Pose Estimation
• Co-op: Correspondence-based Novel Object Pose Estimation
• DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale Consistency