Category-Level Articulated Object Pose Estimation

Li, Xiaolong; Wang, He; Yi, Li; Guibas, Leonidas J.; Abbott, A. Lynn; Song, Shuran

doi:10.1109/cvpr42600.2020.00376

Cited by 159 publications

(159 citation statements)

References 22 publications

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“…Our proposed OMADNet can address the category-level articulated object pose estimation task given a single depth image as input. Overall, our work is closely related to [15] with significant different on methodology. Firstly, [15] takes a part-centered perspective, the joint states are estimated from the predicted part segmentations and poses.…”

Section: Related Workmentioning

confidence: 99%

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OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval

Han¹,

Liu²,

Wang³

et al. 2021

Preprint

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Articulated objects are pervasive in daily life. However, due to the intrinsic high-DoF structure, the joint states of the articulated objects are hard to be estimated. To model articulated objects, two kinds of shape deformations namely the geometric and the pose deformation should be considered. In this work, we present a novel categoryspecific parametric representation called Object Model with Articulated Deformations (OMAD) to explicitly model the articulated objects. In OMAD, a category is associated with a linear shape function with shared shape basis and a non-linear joint function. Both functions can be learned from a large-scale object model dataset and fixed as categoryspecific priors. Then we propose an OMADNet to predict the shape parameters and the joint states from an object's single observation. With the full representation of the object shape and joint states, we can address several tasks including category-level object pose estimation and the articulated object retrieval. To evaluate these tasks, we create a synthetic dataset based on PartNet-Mobility. Extensive experiments show that our simple OMADNet can serve as a strong baseline for both tasks. Our code and dataset are available in https://sites.google.com/view/omad-bmvc/.

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Section: Related Workmentioning

confidence: 99%

“…Cateogry-level object pose estimation task aims at predicting poses of unseen objects in the same category, which is firstly introduced by NOCS [27]. A-NCSH [15] extends this task to articulated objects. Later, several works on this task are conducted on videos [11,16].…”

Section: Related Workmentioning

confidence: 99%

OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval

Han¹,

Liu²,

Wang³

et al. 2021

Preprint

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show abstract

“…Such approaches first recover the position of semantic keypoints [56] in the images with neural networks, and then recover the 3D pose of the object by solving a geometric optimization problem [31,53,56,57,64]. In some works, a canonical coordinate space is predicted by a network instead of relying on geometric reasoning [14,22,41,78]. Lim et al [42] establish 2D-3D correspondences between images and textureless CAD models by using HOG descriptors, and render edgemaps of the CAD models.…”

Section: Related Workmentioning

confidence: 99%

Optimal Pose and Shape Estimation for Category-level 3D Object Perception

Shi

Yang

Carlone

2021

Robotics: Science and Systems XVII

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We consider a category-level perception problem, where one is given 3D sensor data picturing an object of a given category (e.g., a car), and has to reconstruct the pose and shape of the object despite intra-class variability (i.e., different car models have different shapes). We consider an active shape model, where -for an object category-we are given a library of potential CAD models describing objects in that category, and we adopt a standard formulation where pose and shape estimation are formulated as a non-convex optimization. Our first contribution is to provide the first certifiably optimal solver for pose and shape estimation. In particular, we show that rotation estimation can be decoupled from the estimation of the object translation and shape, and we demonstrate that (i) the optimal object rotation can be computed via a tight (small-size) semidefinite relaxation, and (ii) the translation and shape parameters can be computed in closed-form given the rotation. Our second contribution is to add an outlier rejection layer to our solver, hence making it robust to a large number of misdetections. Towards this goal, we wrap our optimal solver in a robust estimation scheme based on graduated non-convexity. To further enhance robustness to outliers, we also develop the first graph-theoretic formulation to prune outliers in category-level perception, which removes outliers via convex hull and maximum clique computations; the resulting approach is robust to 70 − 90% outliers. Our third contribution is an extensive experimental evaluation. Besides providing an ablation study on a simulated dataset and on the PASCAL3D+ dataset, we combine our solver with a deep-learned keypoint detector, and show that the resulting approach improves over the state of the art in vehicle pose estimation in the ApolloScape datasets.

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“…Lu et al [47] proposed to train a probabilistic graphical model as a classifier to predict the appropriate grasp types (power grasp or precision grasp). Li et al [48] developed a deep network that uses a single depth point cloud to estimate the pose of an articulated object. Newbury et al [49] used two Convolutional Neural Networks (CNNs) to estimate both the placement rotations and stabilities and obtain the humanpreferred object placements and orientations.…”

Section: B Placement Estimationmentioning

confidence: 99%

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

Hu¹,

Wan²,

Koyama³

et al. 2021

Preprint

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This paper presented an regrasp planning method to eliminate grasp uncertainty while considering the geometric constraints of a fixture. The method automatically finds the Stable Placement Poses (SPPs) of an object on a Triangular Corner Fixture (TCF), elevates the object from its SPPs to dropping poses and finds the Deterministic Dropping Poses (DDPs), builds regrasp graphs by using the SPP-DDP pairs and their associated grasp configurations, and searches the graph to find regrasp motion sequences for precise assembly. Since the SPPs and their associated regrasps are constrained by the TCF's geometry and have high precision, the final object poses regrasped via it has low uncertainty and can be directly used for assembly by position control. In the experimental section, we study the performance of analytical and learning-based methods for estimating the DDPs of different objects and quantitatively examine the proposed method's ability to suppress uncertainty using assembly tasks like peg-in-hole insertion and sheathing tubes, aligning holes, mounting bearing housings, etc. The results demonstrate the method's robustness and efficacy.

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Category-Level Articulated Object Pose Estimation

Cited by 159 publications

References 22 publications

OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval

OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval

Optimal Pose and Shape Estimation for Category-level 3D Object Perception

Reducing Uncertainty Using Placement and Regrasp Planning on a Triangular Corner Fixture

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