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Found 6 papers, 4 papers with code
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
In this work, we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image.
U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds
In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly match the target.
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection
Despite monocular 3D object detection having recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery, such two-stage methods typically suffer from overfitting and are incapable of explicitly encapsulating the geometric relation between depth and object bounding box.
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Dense methods also improved pose estimation in the presence of occlusion.
IKEA Object State Dataset: A 6DoF object pose estimation dataset and benchmark for multi-state assembly objects
Utilizing 6DoF(Degrees of Freedom) pose information of an object and its components is critical for object state detection tasks.
TGA: Two-level Group Attention for Assembly State Detection
Assembly state detection, i. e., object state detection, has a critical meaning in computer vision tasks, especially in AR assisted assembly.
