VAPO: Visibility-Aware Keypoint Localization for Efficient 6DoF Object Pose Estimation

• URL: http://arxiv.org/abs/2403.14559v3
• Date: Tue, 18 Feb 2025 21:16:27 GMT
• Title: VAPO: Visibility-Aware Keypoint Localization for Efficient 6DoF Object Pose Estimation
• Authors: Ruyi Lian, Yuewei Lin, Longin Jan Latecki, Haibin Ling,
• Abstract summary: Localizing 3D keypoints in a 2D image is an effective way to establish 3D-2D correspondences for 6DoF object pose estimation. In this paper, we address this issue by localizing the important keypoints in terms of visibility. We construct VAPO (Visibility-Aware POse estimator) by integrating the visibility-aware importance with a state-of-the-art pose estimation algorithm.
• Score: 52.81869878956534
• License:
• Abstract: Localizing predefined 3D keypoints in a 2D image is an effective way to establish 3D-2D correspondences for 6DoF object pose estimation. However, unreliable localization results of invisible keypoints degrade the quality of correspondences. In this paper, we address this issue by localizing the important keypoints in terms of visibility. Since keypoint visibility information is currently missing in the dataset collection process, we propose an efficient way to generate binary visibility labels from available object-level annotations, for keypoints of both asymmetric objects and symmetric objects. We further derive real-valued visibility-aware importance from binary labels based on the PageRank algorithm. Taking advantage of the flexibility of our visibility-aware importance, we construct VAPO (Visibility-Aware POse estimator) by integrating the visibility-aware importance with a state-of-the-art pose estimation algorithm, along with additional positional encoding. VAPO can work in both CAD-based and CAD-free settings. Extensive experiments are conducted on popular pose estimation benchmarks including Linemod, Linemod-Occlusion, and YCB-V, demonstrating that VAPO clearly achieves state-of-the-art performances. Our code is available at https://github.com/RuyiLian/VAPO.

Related papers

• PointOBB: Learning Oriented Object Detection via Single Point Supervision [55.88982271340328]
  This paper proposes PointOBB, the first single Point-based OBB generation method, for oriented object detection. PointOBB operates through the collaborative utilization of three distinctive views: an original view, a resized view, and a rotated/flipped (rot/flp) view. Experimental results on the DIOR-R and DOTA-v1.0 datasets demonstrate that PointOBB achieves promising performance.
  arXiv  Detail & Related papers  (2023-11-23T15:51:50Z)
• SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data [17.471342278936365]
  We propose a new method to infer keypoints from arbitrary object categories in practical scenarios where point cloud data (PCD) are noisy, down-sampled and arbitrarily rotated. We achieve these desiderata by proposing a new self-supervised training strategy for keypoints estimation. We compare the keypoints estimated by the proposed approach with those of the state-of-the-art unsupervised approaches.
  arXiv  Detail & Related papers  (2023-08-10T08:10:01Z)
• CheckerPose: Progressive Dense Keypoint Localization for Object Pose Estimation with Graph Neural Network [66.24726878647543]
  Estimating the 6-DoF pose of a rigid object from a single RGB image is a crucial yet challenging task. Recent studies have shown the great potential of dense correspondence-based solutions. We propose a novel pose estimation algorithm named CheckerPose, which improves on three main aspects.
  arXiv  Detail & Related papers  (2023-03-29T17:30:53Z)
• OA-BEV: Bringing Object Awareness to Bird's-Eye-View Representation for Multi-Camera 3D Object Detection [78.38062015443195]
  OA-BEV is a network that can be plugged into the BEV-based 3D object detection framework. Our method achieves consistent improvements over the BEV-based baselines in terms of both average precision and nuScenes detection score.
  arXiv  Detail & Related papers  (2023-01-13T06:02:31Z)
• Objects as Spatio-Temporal 2.5D points [5.588892124219713]
  We propose a weakly supervised method to estimate 3D position of objects by jointly learning to regress the 2D object detections scene's depth prediction in a single feed-forward pass of a network. Our proposed method extends a single-point based object detector, and introduces a novel object representation where each object is modeled as a BEV point-temporally, without the need of any 3D or BEV annotations for training and LiDAR data at query time.
  arXiv  Detail & Related papers  (2022-12-06T05:14:30Z)
• CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation [67.12857074801731]
  We introduce a novel method, CPPF++, designed for sim-to-real pose estimation. To address the challenge posed by vote collision, we propose a novel approach that involves modeling the voting uncertainty. We incorporate several innovative modules, including noisy pair filtering, online alignment optimization, and a feature ensemble.
  arXiv  Detail & Related papers  (2022-11-24T03:27:00Z)
• SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection [78.90102636266276]
  We propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA) Based on the estimated point-wise foreground scores, we then propose a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling. In practice, SASA shows to be effective in identifying valuable points related to foreground objects and improving feature learning for point-based 3D detection.
  arXiv  Detail & Related papers  (2022-01-06T08:54:47Z)
• 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)

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.