HccePose(BF): Predicting Front & Back Surfaces to Construct Ultra-Dense 2D-3D Correspondences for Pose Estimation

• URL: http://arxiv.org/abs/2510.10177v2
• Date: Tue, 14 Oct 2025 07:12:01 GMT
• Title: HccePose(BF): Predicting Front & Back Surfaces to Construct Ultra-Dense 2D-3D Correspondences for Pose Estimation
• Authors: Yulin Wang, Mengting Hu, Hongli Li, Chen Luo,
• Abstract summary: In estimation for seen objects, a prevalent pipeline involves using neural networks to predict dense 3D coordinates of the object surface on 2D images.<n>This study predicts 3D coordinates both the object's front and back surfaces densely densely 3D coordinates between pose poses.<n>Results show that the proposed approach outperforms seven classic BOP core datasets.
• Score: 37.50046993538762
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In pose estimation for seen objects, a prevalent pipeline involves using neural networks to predict dense 3D coordinates of the object surface on 2D images, which are then used to establish dense 2D-3D correspondences. However, current methods primarily focus on more efficient encoding techniques to improve the precision of predicted 3D coordinates on the object's front surface, overlooking the potential benefits of incorporating the back surface and interior of the object. To better utilize the full surface and interior of the object, this study predicts 3D coordinates of both the object's front and back surfaces and densely samples 3D coordinates between them. This process creates ultra-dense 2D-3D correspondences, effectively enhancing pose estimation accuracy based on the Perspective-n-Point (PnP) algorithm. Additionally, we propose Hierarchical Continuous Coordinate Encoding (HCCE) to provide a more accurate and efficient representation of front and back surface coordinates. Experimental results show that, compared to existing state-of-the-art (SOTA) methods on the BOP website, the proposed approach outperforms across seven classic BOP core datasets. Code is available at https://github.com/WangYuLin-SEU/HCCEPose.

Related papers

• CVCP-Fusion: On Implicit Depth Estimation for 3D Bounding Box Prediction [2.0375637582248136]
  Cross-View Center Point-Fusion is a state-of-the-art model to perform 3D object detection. Our architecture utilizes aspects from previously established algorithms, Cross-View Transformers and CenterPoint.
  arXiv  Detail & Related papers  (2024-10-15T02:55:07Z)
• NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized Device Coordinates Space [77.6067460464962]
  Monocular 3D Semantic Scene Completion (SSC) has garnered significant attention in recent years due to its potential to predict complex semantics and geometry shapes from a single image, requiring no 3D inputs. We identify several critical issues in current state-of-the-art methods, including the Feature Ambiguity of projected 2D features in the ray to the 3D space, the Pose Ambiguity of the 3D convolution, and the Imbalance in the 3D convolution across different depth levels. We devise a novel Normalized Device Coordinates scene completion network (NDC-Scene) that directly extends the 2
  arXiv  Detail & Related papers  (2023-09-26T02:09:52Z)
• 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)
• Neural Correspondence Field for Object Pose Estimation [67.96767010122633]
  We propose a method for estimating the 6DoF pose of a rigid object with an available 3D model from a single RGB image. Unlike classical correspondence-based methods which predict 3D object coordinates at pixels of the input image, the proposed method predicts 3D object coordinates at 3D query points sampled in the camera frustum.
  arXiv  Detail & Related papers  (2022-07-30T01:48:23Z)
• Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps [66.24554680709417]
  Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications. We propose a non-invasive framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera.
  arXiv  Detail & Related papers  (2022-07-06T08:52:12Z)
• Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection [70.71934539556916]
  We learn geometry-guided depth estimation with projective modeling to advance monocular 3D object detection. Specifically, a principled geometry formula with projective modeling of 2D and 3D depth predictions in the monocular 3D object detection network is devised. Our method remarkably improves the detection performance of the state-of-the-art monocular-based method without extra data by 2.80% on the moderate test setting.
  arXiv  Detail & Related papers  (2021-07-29T12:30:39Z)
• GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimator [51.89441403642665]
  6D pose estimation of rigid objects is a long-standing and challenging task in computer vision.<n>Recently, the emergence of deep learning reveals the potential of Convolutional Neural Networks (CNNs) to predict reliable 6D poses.<n>This paper introduces a fully learning-based object pose estimator.
  arXiv  Detail & Related papers  (2021-02-24T09:11:31Z)
• Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking [34.40019455462043]
  We propose a joint spatial-temporal optimization-based stereo 3D object tracking method. From the network, we detect corresponding 2D bounding boxes on adjacent images and regress an initial 3D bounding box. Dense object cues that associating to the object centroid are then predicted using a region-based network.
  arXiv  Detail & Related papers  (2020-04-20T13:59:46Z)

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.