SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D
Object Pose Estimation
- URL: http://arxiv.org/abs/2307.00306v1
- Date: Sat, 1 Jul 2023 11:28:53 GMT
- Title: SyMFM6D: Symmetry-aware Multi-directional Fusion for Multi-View 6D
Object Pose Estimation
- Authors: Fabian Duffhauss, Sebastian Koch, Hanna Ziesche, Ngo Anh Vien and
Gerhard Neumann
- Abstract summary: We present a novel symmetry-aware multi-view 6D pose estimator called SyMFM6D.
Our approach efficiently fuses the RGB-D frames from multiple perspectives in a deep multi-directional fusion network.
We show that our approach is robust towards inaccurate camera calibration and dynamic camera setups.
- Score: 16.460390441848464
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Detecting objects and estimating their 6D poses is essential for automated
systems to interact safely with the environment. Most 6D pose estimators,
however, rely on a single camera frame and suffer from occlusions and
ambiguities due to object symmetries. We overcome this issue by presenting a
novel symmetry-aware multi-view 6D pose estimator called SyMFM6D. Our approach
efficiently fuses the RGB-D frames from multiple perspectives in a deep
multi-directional fusion network and predicts predefined keypoints for all
objects in the scene simultaneously. Based on the keypoints and an instance
semantic segmentation, we efficiently compute the 6D poses by least-squares
fitting. To address the ambiguity issues for symmetric objects, we propose a
novel training procedure for symmetry-aware keypoint detection including a new
objective function. Our SyMFM6D network significantly outperforms the
state-of-the-art in both single-view and multi-view 6D pose estimation. We
furthermore show the effectiveness of our symmetry-aware training procedure and
demonstrate that our approach is robust towards inaccurate camera calibration
and dynamic camera setups.
Related papers
- RelPose++: Recovering 6D Poses from Sparse-view Observations [66.6922660401558]
We address the task of estimating 6D camera poses from sparse-view image sets (2-8 images)
We build on the recent RelPose framework which learns a network that infers distributions over relative rotations over image pairs.
Our final system results in large improvements in 6D pose prediction over prior art on both seen and unseen object categories.
arXiv Detail & Related papers (2023-05-08T17:59:58Z) - Ambiguity-Aware Multi-Object Pose Optimization for Visually-Assisted
Robot Manipulation [17.440729138126162]
We present an ambiguity-aware 6D object pose estimation network, PrimA6D++, as a generic uncertainty prediction method.
The proposed method shows a significant performance improvement in T-LESS and YCB-Video datasets.
We further demonstrate real-time scene recognition capability for visually-assisted robot manipulation.
arXiv Detail & Related papers (2022-11-02T08:57:20Z) - MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep
Point-wise Voting Network [14.754297065772676]
We present a novel multi-view 6D pose estimation method called MV6D.
We base our approach on the PVN3D network that uses a single RGB-D image to predict keypoints of the target objects.
In contrast to current multi-view pose detection networks such as CosyPose, our MV6D can learn the fusion of multiple perspectives in an end-to-end manner.
arXiv Detail & Related papers (2022-08-01T23:34:43Z) - 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) - FS6D: Few-Shot 6D Pose Estimation of Novel Objects [116.34922994123973]
6D object pose estimation networks are limited in their capability to scale to large numbers of object instances.
In this work, we study a new open set problem; the few-shot 6D object poses estimation: estimating the 6D pose of an unknown object by a few support views without extra training.
arXiv Detail & Related papers (2022-03-28T10:31:29Z) - 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) - CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and
Categorical 6D Pose and Size Estimation [19.284468553414918]
This paper studies the complex task of simultaneous multi-object 3D reconstruction, 6D pose and size estimation from a single-view RGB-D observation.
Existing approaches mainly follow a complex multi-stage pipeline which first localizes and detects each object instance in the image and then regresses to either their 3D meshes or 6D poses.
We present a simple one-stage approach to predict both the 3D shape and estimate the 6D pose and size jointly in a bounding-box free manner.
arXiv Detail & Related papers (2022-03-03T18:59:04Z) - VIPose: Real-time Visual-Inertial 6D Object Pose Tracking [3.44942675405441]
We introduce a novel Deep Neural Network (DNN) called VIPose to address the object pose tracking problem in real-time.
The key contribution is the design of a novel DNN architecture which fuses visual and inertial features to predict the objects' relative 6D pose.
The approach presents accuracy performances comparable to state-of-the-art techniques, but with additional benefit to be real-time.
arXiv Detail & Related papers (2021-07-27T06:10:23Z) - Spatial Attention Improves Iterative 6D Object Pose Estimation [52.365075652976735]
We propose a new method for 6D pose estimation refinement from RGB images.
Our main insight is that after the initial pose estimate, it is important to pay attention to distinct spatial features of the object.
We experimentally show that this approach learns to attend to salient spatial features and learns to ignore occluded parts of the object, leading to better pose estimation across datasets.
arXiv Detail & Related papers (2021-01-05T17:18:52Z) - CosyPose: Consistent multi-view multi-object 6D pose estimation [48.097599674329004]
We present a single-view single-object 6D pose estimation method, which we use to generate 6D object pose hypotheses.
Second, we develop a robust method for matching individual 6D object pose hypotheses across different input images.
Third, we develop a method for global scene refinement given multiple object hypotheses and their correspondences across views.
arXiv Detail & Related papers (2020-08-19T14:11:56Z)
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.