FreeZe: Training-free zero-shot 6D pose estimation with geometric and vision foundation models

• URL: http://arxiv.org/abs/2312.00947v2
• Date: Wed, 3 Apr 2024 17:58:13 GMT
• Title: FreeZe: Training-free zero-shot 6D pose estimation with geometric and vision foundation models
• Authors: Andrea Caraffa, Davide Boscaini, Amir Hamza, Fabio Poiesi,
• Abstract summary: We show how to tackle the same task but without training on specific data. We propose FreeZe, a novel solution that harnesses the capabilities of pre-trained geometric and vision foundation models. FreeZe consistently outperforms all state-of-the-art approaches, including competitors extensively trained on synthetic 6D pose estimation data.
• Score: 5.754251195342313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Estimating the 6D pose of objects unseen during training is highly desirable yet challenging. Zero-shot object 6D pose estimation methods address this challenge by leveraging additional task-specific supervision provided by large-scale, photo-realistic synthetic datasets. However, their performance heavily depends on the quality and diversity of rendered data and they require extensive training. In this work, we show how to tackle the same task but without training on specific data. We propose FreeZe, a novel solution that harnesses the capabilities of pre-trained geometric and vision foundation models. FreeZe leverages 3D geometric descriptors learned from unrelated 3D point clouds and 2D visual features learned from web-scale 2D images to generate discriminative 3D point-level descriptors. We then estimate the 6D pose of unseen objects by 3D registration based on RANSAC. We also introduce a novel algorithm to solve ambiguous cases due to geometrically symmetric objects that is based on visual features. We comprehensively evaluate FreeZe across the seven core datasets of the BOP Benchmark, which include over a hundred 3D objects and 20,000 images captured in various scenarios. FreeZe consistently outperforms all state-of-the-art approaches, including competitors extensively trained on synthetic 6D pose estimation data. Code will be publicly available at https://andreacaraffa.github.io/freeze.

Related papers

• Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation [14.469317161361202]
  We propose a 6D object pose estimation method that can be trained with pure RGB images without any auxiliary information. We evaluate our method on three challenging datasets and demonstrate that it outperforms state-of-the-art self-supervised methods significantly.
  arXiv  Detail & Related papers  (2023-08-19T13:52:18Z)
• 3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation [50.15926681475939]
  Inverse graphics aims to infer the 3D scene structure from 2D images. We introduce probabilistic modeling to quantify uncertainty and achieve robustness in 6D pose estimation tasks. 3DNEL effectively combines learned neural embeddings from RGB with depth information to improve robustness in sim-to-real 6D object pose estimation from RGB-D images.
  arXiv  Detail & Related papers  (2023-02-07T20:48:35Z)
• Self-Supervised Geometric Correspondence for Category-Level 6D Object Pose Estimation in the Wild [47.80637472803838]
  We introduce a self-supervised learning approach trained directly on large-scale real-world object videos for category-level 6D pose estimation in the wild. Our framework reconstructs the canonical 3D shape of an object category and learns dense correspondences between input images and the canonical shape via surface embedding. Surprisingly, our method, without any human annotations or simulators, can achieve on-par or even better performance than previous supervised or semi-supervised methods on in-the-wild images.
  arXiv  Detail & Related papers  (2022-10-13T17:19:22Z)
• 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)
• 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)
• Weakly Supervised Learning of Keypoints for 6D Object Pose Estimation [73.40404343241782]
  We propose a weakly supervised 6D object pose estimation approach based on 2D keypoint detection. Our approach achieves comparable performance with state-of-the-art fully supervised approaches.
  arXiv  Detail & Related papers  (2022-03-07T16:23:47Z)
• SHREC 2020 track: 6D Object Pose Estimation [26.4781238445338]
  6D pose estimation is crucial for augmented reality, virtual reality, robotic manipulation and visual navigation. Different pose estimation methods have different strengths and weaknesses, depending on feature representations and scene contents. Existing 3D datasets that are used for data-driven methods to estimate 6D poses have limited view angles and low resolution.
  arXiv  Detail & Related papers  (2020-10-19T09:45:42Z)
• 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)
• CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning [74.53664270194643]
  Modern monocular 6D pose estimation methods can only cope with a handful of object instances. We propose a novel method for class-level monocular 6D pose estimation, coupled with metric shape retrieval. We experimentally demonstrate that we can retrieve precise 6D poses and metric shapes from a single RGB image.
  arXiv  Detail & Related papers  (2020-03-12T15:28:13Z)

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.