GS-Pose: Category-Level Object Pose Estimation via Geometric and Semantic Correspondence

• URL: http://arxiv.org/abs/2311.13777v1
• Date: Thu, 23 Nov 2023 02:35:38 GMT
• Title: GS-Pose: Category-Level Object Pose Estimation via Geometric and Semantic Correspondence
• Authors: Pengyuan Wang, Takuya Ikeda, Robert Lee, Koichi Nishiwaki
• Abstract summary: Category-level pose estimation is a challenging task with many potential applications in computer vision and robotics. We propose to utilize both geometric and semantic features obtained from a pre-trained foundation model. This requires significantly less data to train than prior methods since the semantic features are robust to object texture and appearance.
• Score: 5.500735640045456
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Category-level pose estimation is a challenging task with many potential applications in computer vision and robotics. Recently, deep-learning-based approaches have made great progress, but are typically hindered by the need for large datasets of either pose-labelled real images or carefully tuned photorealistic simulators. This can be avoided by using only geometry inputs such as depth images to reduce the domain-gap but these approaches suffer from a lack of semantic information, which can be vital in the pose estimation problem. To resolve this conflict, we propose to utilize both geometric and semantic features obtained from a pre-trained foundation model.Our approach projects 2D features from this foundation model into 3D for a single object model per category, and then performs matching against this for new single view observations of unseen object instances with a trained matching network. This requires significantly less data to train than prior methods since the semantic features are robust to object texture and appearance. We demonstrate this with a rich evaluation, showing improved performance over prior methods with a fraction of the data required.

Related papers

• ShapeICP: Iterative Category-level Object Pose and Shape Estimation from Depth [15.487722156919988]
  Category-level object pose and shape estimation from a single depth image has recently drawn research attention due to its wide applications in robotics and self-driving. We propose an iterative estimation method that does not require learning from any pose-annotated data. Our algorithm, named ShapeICP, has its foundation in the iterative closest point (ICP) algorithm but is equipped with additional features for the category-level pose and shape estimation task.
  arXiv  Detail & Related papers  (2024-08-23T15:12:55Z)
• FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects [55.77542145604758]
  FoundationPose is a unified foundation model for 6D object pose estimation and tracking. Our approach can be instantly applied at test-time to a novel object without fine-tuning.
  arXiv  Detail & Related papers  (2023-12-13T18:28:09Z)
• 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)
• FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models [67.96827539201071]
  We propose a novel test-time optimization approach for 3D scene reconstruction. Our method achieves state-of-the-art cross-dataset reconstruction on five zero-shot testing datasets.
  arXiv  Detail & Related papers  (2023-08-10T17:55:02Z)
• 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)
• 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)
• Category-Agnostic 6D Pose Estimation with Conditional Neural Processes [19.387280883044482]
  We present a novel meta-learning approach for 6D pose estimation on unknown objects. Our algorithm learns object representation in a category-agnostic way, which endows it with strong generalization capabilities across object categories.
  arXiv  Detail & Related papers  (2022-06-14T20:46:09Z)
• Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning [12.697842097171119]
  We present a curriculum learning mechanism that adaptively augments the generated regions, which allows the model to consistently acquire a useful learning signal. Our experiments show that our approach improves on the MoCo v2 baseline by a large margin on multiple object-level tasks when pre-training on multi-object scene image datasets.
  arXiv  Detail & Related papers  (2021-11-26T18:29:57Z)
• Category Level Object Pose Estimation via Neural Analysis-by-Synthesis [64.14028598360741]
  In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module. The image synthesis network is designed to efficiently span the pose configuration space. We experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone.
  arXiv  Detail & Related papers  (2020-08-18T20:30:47Z)

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.