Unseen Object 6D Pose Estimation: A Benchmark and Baselines

• URL: http://arxiv.org/abs/2206.11808v1
• Date: Thu, 23 Jun 2022 16:29:53 GMT
• Title: Unseen Object 6D Pose Estimation: A Benchmark and Baselines
• Authors: Minghao Gou, Haolin Pan, Hao-Shu Fang, Ziyuan Liu, Cewu Lu, Ping Tan
• Abstract summary: We propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing. We collect a dataset with both real and synthetic images and up to 48 unseen objects in the test set. By training an end-to-end 3D correspondences network, our method finds corresponding points between an unseen object and a partial view RGBD image accurately and efficiently.
• Score: 62.8809734237213
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating the 6D pose for unseen objects is in great demand for many real-world applications. However, current state-of-the-art pose estimation methods can only handle objects that are previously trained. In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing. We collect a dataset with both real and synthetic images and up to 48 unseen objects in the test set. In the mean while, we propose a new metric named Infimum ADD (IADD) which is an invariant measurement for objects with different types of pose ambiguity. A two-stage baseline solution for this task is also provided. By training an end-to-end 3D correspondences network, our method finds corresponding points between an unseen object and a partial view RGBD image accurately and efficiently. It then calculates the 6D pose from the correspondences using an algorithm robust to object symmetry. Extensive experiments show that our method outperforms several intuitive baselines and thus verify its effectiveness. All the data, code and models will be made publicly available. Project page: www.graspnet.net/unseen6d

Related papers

• ZS6D: Zero-shot 6D Object Pose Estimation using Vision Transformers [9.899633398596672]
  We introduce ZS6D, for zero-shot novel object 6D pose estimation. Visual descriptors, extracted using pre-trained Vision Transformers (ViT), are used for matching rendered templates. Experiments are performed on LMO, YCBV, and TLESS datasets.
  arXiv  Detail & Related papers  (2023-09-21T11:53:01Z)
• PoseMatcher: One-shot 6D Object Pose Estimation by Deep Feature Matching [51.142988196855484]
  We propose PoseMatcher, an accurate model free one-shot object pose estimator. We create a new training pipeline for object to image matching based on a three-view system. To enable PoseMatcher to attend to distinct input modalities, an image and a pointcloud, we introduce IO-Layer.
  arXiv  Detail & Related papers  (2023-04-03T21:14:59Z)
• NOPE: Novel Object Pose Estimation from a Single Image [67.11073133072527]
  We propose an approach that takes a single image of a new object as input and predicts the relative pose of this object in new images without prior knowledge of the object's 3D model. We achieve this by training a model to directly predict discriminative embeddings for viewpoints surrounding the object. This prediction is done using a simple U-Net architecture with attention and conditioned on the desired pose, which yields extremely fast inference.
  arXiv  Detail & Related papers  (2023-03-23T18:55:43Z)
• LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation [69.70498875887611]
  LocPoseNet is able to robustly learn location prior for unseen objects. Our method outperforms existing works by a large margin on LINEMOD and GenMOP.
  arXiv  Detail & Related papers  (2022-11-29T15:21:34Z)
• Coupled Iterative Refinement for 6D Multi-Object Pose Estimation [64.7198752089041]
  Given a set of known 3D objects and an RGB or RGB-D input image, we detect and estimate the 6D pose of each object. Our approach iteratively refines both pose and correspondence in a tightly coupled manner, allowing us to dynamically remove outliers to improve accuracy.
  arXiv  Detail & Related papers  (2022-04-26T18:00:08Z)
• ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation [76.31125154523056]
  We present a discrete descriptor, which can represent the object surface densely. We also propose a coarse to fine training strategy, which enables fine-grained correspondence prediction.
  arXiv  Detail & Related papers  (2022-03-17T16:16:24Z)
• NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation [44.42449011619408]
  We present a weakly-supervised reconstruction-based pipeline, named NeRF-Pose, which needs only 2D object segmentation and known relative camera poses during training. A NeRF-enabled RAN+SAC algorithm is used to estimate stable and accurate pose from the predicted correspondences. Experiments on LineMod-Occlusion show that the proposed method has state-of-the-art accuracy in comparison to the best 6D pose estimation methods.
  arXiv  Detail & Related papers  (2022-03-09T15:28:02Z)
• EfficientPose: An efficient, accurate and scalable end-to-end 6D multi object pose estimation approach [0.0]
  We introduce EfficientPose, a new approach for 6D object pose estimation. It is highly accurate, efficient and scalable over a wide range of computational resources. It can detect the 2D bounding box of multiple objects and instances as well as estimate their full 6D poses in a single shot.
  arXiv  Detail & Related papers  (2020-11-09T10:23:55Z)
• Single Shot 6D Object Pose Estimation [11.37625512264302]
  We introduce a novel single shot approach for 6D object pose estimation of rigid objects based on depth images. A fully convolutional neural network is employed, where the 3D input data is spatially discretized and pose estimation is considered as a regression task. With 65 fps on a GPU, our Object Pose Network (OP-Net) is extremely fast, is optimized end-to-end, and estimates the 6D pose of multiple objects in the image simultaneously.
  arXiv  Detail & Related papers  (2020-04-27T11:59:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.