SynPick: A Dataset for Dynamic Bin Picking Scene Understanding
We present SynPick, a synthetic dataset for dynamic scene understanding in bin-picking scenarios. In contrast to existing datasets, our dataset is both situated in a realistic industrial application domain -- inspired by the well-known Amazon Robotics Challenge (ARC) -- and features dynamic scenes with authentic picking actions as chosen by our picking heuristic developed for the ARC 2017. The dataset is compatible with the popular BOP dataset format. We describe the dataset generation process in detail, including object arrangement generation and manipulation simulation using the NVIDIA PhysX physics engine. To cover a large action space, we perform untargeted and targeted picking actions, as well as random moving actions. To establish a baseline for object perception, a state-of-the-art pose estimation approach is evaluated on the dataset. We demonstrate the usefulness of tracking poses during manipulation instead of single-shot estimation even with a naive filtering approach. The generator source code and dataset are publicly available.
Code (1)
Tasks
ARCDataset GenerationPose EstimationScene UnderstandingSimilar Papers 제목 키워드 기반
Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking
In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial bin-picking. The dataset comprises both synthetic and real-world scenes. For both, point clouds, dep…
6D Pose Estimation using RGBInstance SegmentationObjectPose Estimation+3Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks
In this paper, we present a novel deep neural network architecture for joint class-agnostic object segmentation and grasp detection for robotic picking tasks using a parallel-plate gripper. We introduce depth-aware Coord…
Instance SegmentationObjectRobotic GraspingSegmentation+1FPCC: Fast Point Cloud Clustering based Instance Segmentation for Industrial Bin-picking
Instance segmentation is an important pre-processing task in numerous real-world applications, such as robotics, autonomous vehicles, and human-computer interaction. Compared with the rapid development of deep learning f…
3D Instance SegmentationClusteringDeep ClusteringSegmentationSDT-6D: Fully Sparse Depth-Transformer for Staged End-to-End 6D Pose Estimation in Industrial Multi-View Bin Picking
Accurately recovering 6D poses in densely packed industrial bin-picking environments remain a serious challenge, owing to occlusions, reflections, and textureless parts. We introduce a holistic depth-only 6D pose estimat…
6D Pose EstimationDemonstrating Multi-Suction Item Picking at Scale via Multi-Modal Learning of Pick Success
This work demonstrates how autonomously learning aspects of robotic operation from sparsely-labeled, real-world data of deployed, engineered solutions at industrial scale can provide with solutions that achieve improved …
Robot ManipulationSemantic Segmentation