YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression

• URL: http://arxiv.org/abs/2205.02536v1
• Date: Thu, 5 May 2022 09:51:39 GMT
• Title: YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression
• Authors: Arash Amini, Arul Selvam Periyasamy, Sven Behnke
• Abstract summary: We propose YOLOPose, a Transformer-based multi-object 6D pose estimation method based on keypoint regression. Our method is suitable for real-time applications and achieves results comparable to state-of-the-art methods.
• Score: 44.282841879849244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications. The state-of-the-art models for pose estimation are convolutional neural network (CNN)-based. Lately, Transformers, an architecture originally proposed for natural language processing, is achieving state-of-the-art results in many computer vision tasks as well. Equipped with the multi-head self-attention mechanism, Transformers enable simple single-stage end-to-end architectures for learning object detection and 6D object pose estimation jointly. In this work, we propose YOLOPose (short form for You Only Look Once Pose estimation), a Transformer-based multi-object 6D pose estimation method based on keypoint regression. In contrast to the standard heatmaps for predicting keypoints in an image, we directly regress the keypoints. Additionally, we employ a learnable orientation estimation module to predict the orientation from the keypoints. Along with a separate translation estimation module, our model is end-to-end differentiable. Our method is suitable for real-time applications and achieves results comparable to state-of-the-art methods.

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)
• YOLOPose V2: Understanding and Improving Transformer-based 6D Pose Estimation [36.067414358144816]
  YOLOPose is a Transformer-based multi-object 6D pose estimation method. We employ a learnable orientation estimation module to predict the orientation from the keypoints. Our method is suitable for real-time applications and achieves results comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-21T12:53:54Z)
• OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models [51.68715543630427]
  OnePose relies on detecting repeatable image keypoints and is thus prone to failure on low-textured objects. We propose a keypoint-free pose estimation pipeline to remove the need for repeatable keypoint detection. A 2D-3D matching network directly establishes 2D-3D correspondences between the query image and the reconstructed point-cloud model.
  arXiv  Detail & Related papers  (2023-01-18T17:47:13Z)
• RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild [73.1276968007689]
  We describe a data-driven method for inferring the camera viewpoints given multiple images of an arbitrary object. We show that our approach outperforms state-of-the-art SfM and SLAM methods given sparse images on both seen and unseen categories.
  arXiv  Detail & Related papers  (2022-08-11T17:59:59Z)
• 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)
• T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression [40.90172673391803]
  T6D-Direct is a real-time single-stage direct method with a transformer-based architecture built on DETR to perform 6D multi-object pose direct estimation. Our method achieves the fastest inference time, and the pose estimation accuracy is comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-09-22T18:13:33Z)
• 6D Object Pose Estimation using Keypoints and Part Affinity Fields [24.126513851779936]
  The task of 6D object pose estimation from RGB images is an important requirement for autonomous service robots to be able to interact with the real world. We present a two-step pipeline for estimating the 6 DoF translation and orientation of known objects.
  arXiv  Detail & Related papers  (2021-07-05T14:41:19Z)
• End-to-End Trainable Multi-Instance Pose Estimation with Transformers [68.93512627479197]
  We propose a new end-to-end trainable approach for multi-instance pose estimation by combining a convolutional neural network with a transformer. Inspired by recent work on end-to-end trainable object detection with transformers, we use a transformer encoder-decoder architecture together with a bipartite matching scheme to directly regress the pose of all individuals in a given image. Our model, called POse Estimation Transformer (POET), is trained using a novel set-based global loss that consists of a keypoint loss, a keypoint visibility loss, a center loss and a class loss.
  arXiv  Detail & Related papers  (2021-03-22T18:19:22Z)
• PrimA6D: Rotational Primitive Reconstruction for Enhanced and Robust 6D Pose Estimation [11.873744190924599]
  We introduce a rotational primitive prediction based 6D object pose estimation using a single image as an input. We leverage a Variational AutoEncoder (VAE) to learn this underlying primitive and its associated keypoints. When evaluated over public datasets, our method yields a notable improvement over LINEMOD, Occlusion LINEMOD, and the Y-induced dataset.
  arXiv  Detail & Related papers  (2020-06-14T03:55:42Z)

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.