On the Evaluation of RGB-D-based Categorical Pose and Shape Estimation

• URL: http://arxiv.org/abs/2202.10346v1
• Date: Mon, 21 Feb 2022 16:31:18 GMT
• Title: On the Evaluation of RGB-D-based Categorical Pose and Shape Estimation
• Authors: Leonard Bruns, Patric Jensfelt
• Abstract summary: In this work we take a critical look at this predominant evaluation protocol including metrics and datasets. We propose a new set of metrics, contribute new annotations for the Redwood dataset and evaluate state-of-the-art methods in a fair comparison.
• Score: 5.71097144710995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, various methods for 6D pose and shape estimation of objects have been proposed. Typically, these methods evaluate their pose estimation in terms of average precision, and reconstruction quality with chamfer distance. In this work we take a critical look at this predominant evaluation protocol including metrics and datasets. We propose a new set of metrics, contribute new annotations for the Redwood dataset and evaluate state-of-the-art methods in a fair comparison. We find that existing methods do not generalize well to unconstrained orientations, and are actually heavily biased towards objects being upright. We contribute an easy-to-use evaluation toolbox with well-defined metrics, method and dataset interfaces, which readily allows evaluation and comparison with various state-of-the-art approaches (see https://github.com/roym899/pose_and_shape_evaluation ).

Related papers

• 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)
• RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recovery [72.13154206106259]
  We propose a novel pipeline that decouples the 6D pose and size estimation to mitigate the influence of imperfect scales on rigid transformations. Specifically, we leverage a pre-trained monocular estimator to extract local geometric information. A separate branch is designed to directly recover the metric scale of the object based on category-level statistics.
  arXiv  Detail & Related papers  (2023-09-19T02:20:26Z)
• For A More Comprehensive Evaluation of 6DoF Object Pose Tracking [22.696375341994035]
  We contribute a unified benchmark to address the above problems. For more accurate annotation of YCBV, we propose a multi-view multi-object global pose refinement method. In experiments, we validate the precision and reliability of the proposed global pose refinement method with a realistic semi-synthesized dataset.
  arXiv  Detail & Related papers  (2023-09-14T15:35:08Z)
• RGB-D-Based Categorical Object Pose and Shape Estimation: Methods, Datasets, and Evaluation [5.71097144710995]
  This work provides an overview of the field in terms of methods, datasets, and evaluation protocols. We take a critical look at the predominant evaluation protocol, including metrics and datasets. We propose a new set of metrics, contribute new annotations for the Redwood dataset, and evaluate state-of-the-art methods in a fair comparison.
  arXiv  Detail & Related papers  (2023-01-19T15:59:10Z)
• 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)
• Evaluating Feature Attribution Methods in the Image Domain [7.852862161478641]
  We investigate existing metrics and propose new variants of metrics for the evaluation of attribution maps. We find that different attribution metrics seem to measure different underlying concepts of attribution maps. We propose a general benchmarking approach to identify the ideal feature attribution method for a given use case.
  arXiv  Detail & Related papers  (2022-02-22T15:14:33Z)
• TISE: A Toolbox for Text-to-Image Synthesis Evaluation [9.092600296992925]
  We conduct a study on state-of-the-art methods for single- and multi-object text-to-image synthesis. We propose a common framework for evaluating these methods.
  arXiv  Detail & Related papers  (2021-12-02T16:39:35Z)
• Estimating leverage scores via rank revealing methods and randomization [50.591267188664666]
  We study algorithms for estimating the statistical leverage scores of rectangular dense or sparse matrices of arbitrary rank. Our approach is based on combining rank revealing methods with compositions of dense and sparse randomized dimensionality reduction transforms.
  arXiv  Detail & Related papers  (2021-05-23T19:21:55Z)
• A Hybrid Approach for 6DoF Pose Estimation [4.200736775540874]
  We propose a method for 6DoF pose estimation using a state-of-the-art deep learning based instance detector. We additionally use an automatic method selection that chooses the instance detector and the training set as that with the highest performance on the validation set. This hybrid approach leverages the best of learning and classic approaches, using CNNs to filter highly unstructured data and cut through the clutter, and a local geometric approach with proven convergence for robust pose estimation.
  arXiv  Detail & Related papers  (2020-11-11T09:58:23Z)
• Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation [85.96410825961966]
  We argue that the image features extracted at a central point contain limited information for predicting distant keypoints or bounding box boundaries. To facilitate inference, we propose to instead perform regression from a set of points placed at more advantageous positions. We apply this proposed framework, called Point-Set Anchors, to object detection, instance segmentation, and human pose estimation.
  arXiv  Detail & Related papers  (2020-07-06T15:59:56Z)
• CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus [62.86856923633923]
  We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-08T17:37:01Z)

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.