NOPE: Novel Object Pose Estimation from a Single Image

• URL: http://arxiv.org/abs/2303.13612v2
• Date: Fri, 29 Mar 2024 23:42:05 GMT
• Title: NOPE: Novel Object Pose Estimation from a Single Image
• Authors: Van Nguyen Nguyen, Thibault Groueix, Yinlin Hu, Mathieu Salzmann, Vincent Lepetit,
• Abstract summary: 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.
• Score: 67.11073133072527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The practicality of 3D object pose estimation remains limited for many applications due to the need for prior knowledge of a 3D model and a training period for new objects. To address this limitation, 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 and without requiring training time for new objects and categories. 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. We compare our approach to state-of-the-art methods and show it outperforms them both in terms of accuracy and robustness. Our source code is publicly available at https://github.com/nv-nguyen/nope

Related papers

• Learning a Category-level Object Pose Estimator without Pose Annotations [37.03715008347576]
  We propose to learn a category-level 3D object pose estimator without pose annotations. Instead of using manually annotated images, we leverage diffusion models to generate a set of images under controlled pose differences. We show that our method has the capability of category-level object pose estimation from a single shot setting.
  arXiv  Detail & Related papers  (2024-04-08T15:59:29Z)
• MFOS: Model-Free & One-Shot Object Pose Estimation [10.009454818723025]
  We introduce a novel approach that can estimate in a single forward pass the pose of objects never seen during training, given minimum input. We conduct extensive experiments and report state-of-the-art one-shot performance on the challenging LINEMOD benchmark.
  arXiv  Detail & Related papers  (2023-10-03T09:12:07Z)
• ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping [85.38689479346276]
  Current techniques heavily rely on a reference 3D object, limiting their generalizability and making it expensive to expand to new object categories. This paper proposes ShapeShift, a superquadric-based framework for object pose estimation that predicts the object's pose relative to a primitive shape which is fitted to the object.
  arXiv  Detail & Related papers  (2023-04-10T20:55:41Z)
• 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)
• MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare [84.80956484848505]
  MegaPose is a method to estimate the 6D pose of novel objects, that is, objects unseen during training. We present a 6D pose refiner based on a render&compare strategy which can be applied to novel objects. Second, we introduce a novel approach for coarse pose estimation which leverages a network trained to classify whether the pose error between a synthetic rendering and an observed image of the same object can be corrected by the refiner.
  arXiv  Detail & Related papers  (2022-12-13T19:30:03Z)
• Unseen Object 6D Pose Estimation: A Benchmark and Baselines [62.8809734237213]
  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.
  arXiv  Detail & Related papers  (2022-06-23T16:29:53Z)
• Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions [79.34847067293649]
  We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. It relies on a small set of training objects to learn local object representations. We are the first to show generalization without retraining on the LINEMOD and Occlusion-LINEMOD datasets.
  arXiv  Detail & Related papers  (2022-03-31T17:50:35Z)

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.