Unseen Object 6D Pose Estimation: A Benchmark and Baselines

Gou, Minghao; Pan, Haolin; Fang, Haoshu; Liu, Ziyuan; Lu, Cewu; Tan, P.

doi:10.48550/arxiv.2206.11808

Cited by 4 publications

(4 citation statements)

References 46 publications

(83 reference statements)

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“…Current state-of-the-art pose estimation methods can only handle previously trained objects [36]. Instead of predicting pose directly, [36] proposed a learning-based method that finds corresponding points between an unseen object and an RGBD image, which can be transferred to nonlearned objects. However, it depends on the object 3D model and the point cloud of the scene.…”

Section: Discussionmentioning

confidence: 99%

Bio-Inspired Proprioceptive Touch of a Soft Finger with Inner-Finger Kinesthetic Perception

Liu,

Han,

Guo

et al. 2023

Biomimetics

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In-hand object pose estimation is challenging for humans and robots due to occlusion caused by the hand and object. This paper proposes a soft finger that integrates inner vision with kinesthetic sensing to estimate object pose inspired by human fingers. The soft finger has a flexible skeleton and skin that adapts to different objects, and the skeleton deformations during interaction provide contact information obtained by the image from the inner camera. The proposed framework is an end-to-end method that uses raw images from soft fingers to estimate in-hand object pose. It consists of an encoder for kinesthetic information processing and an object pose and category estimator. The framework was tested on seven objects, achieving an impressive error of 2.02 mm and 11.34 degrees for pose error and 99.05% for classification.

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Section: Discussionmentioning

confidence: 99%

Bio-Inspired Proprioceptive Touch of a Soft Finger with Inner-Finger Kinesthetic Perception

Liu,

Han,

Guo

et al. 2023

Biomimetics

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“…Model-agnostic methods focus on estimating the poses of objects unseen during training, regardless of their category (Wen and Bekris 2021;He et al 2022;Cai, Heikkilä, and Rahtu 2022;Gou et al 2022;Shugurov et al 2022;Liu et al 2023;Sun et al 2022;He et al 2023). These methods assume that some additional input about the object at hand is provided at test time in order to define a reference pose (otherwise, the pose estimation problem would be illdefined).…”

Section: Related Workmentioning

confidence: 99%

MFOS: Model-Free & One-Shot Object Pose Estimation

Lee,

Cabon,

Brégier

et al. 2024

AAAI

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Existing learning-based methods for object pose estimation in RGB images are mostly model-specific or category based. They lack the capability to generalize to new object categories at test time, hence severely hindering their practicability and scalability. Notably, recent attempts have been made to solve this issue, but they still require accurate 3D data of the object surface at both train and test time. In this paper, we introduce a novel approach that can estimate in a single forward pass the pose of objects never seen during training, given minimum input. In contrast to existing state-of-the-art approaches, which rely on task-specific modules, our proposed model is entirely based on a transformer architecture, which can benefit from recently proposed 3D-geometry general pretraining. We conduct extensive experiments and report state-of-the-art one-shot performance on the challenging LINEMOD benchmark. Finally, extensive ablations allow us to determine good practices with this relatively new type of architecture in the field.

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“…MegaPose6D [12] is a notable example where the network is trained on a huge dataset with 2 million images. The method most similar to ours is a point cloud based method [3]. It also uses the object model as input.…”

Section: Related Workmentioning

confidence: 99%

“…2 the features computed from object point cloud are independent of the features computed from the scene point cloud. This is opposed to [3]. This allow us to use the same object feature for multiple pose estimations.…”

Section: Computing Object Features Off-linementioning

confidence: 99%

GP3D: Generalized Pose Estimation in 3D Point Clouds: A case study on bin picking

Hagelskjær¹

2023

Preprint

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In this paper, we present GP3D, a novel network for generalized pose estimation in 3D point clouds. The method generalizes to new objects by using both the scene point cloud and the object point cloud with keypoint indexes as input. The network is trained to match the object keypoints to scene points.To address the pose estimation of novel objects we also present a new approach for training pose estimation. The typical solution is a single model trained for pose estimation of a specific object in any scenario. This has several drawbacks: training a model for each object is time-consuming, energy consuming, and by excluding the scenario information the task becomes more difficult.In this paper, we present the opposite solution; a scenariospecific pose estimation method for novel objects that do not require retraining. The network is trained on 1500 objects and is able to learn a generalized solution. We demonstrate that the network is able to correctly predict novel objects, and demonstrate the ability of the network to perform outside of the trained class. We believe that the demonstrated method is a valuable solution for many real-world scenarios. Code and trained network will be made available after publication.

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Unseen Object 6D Pose Estimation: A Benchmark and Baselines

Cited by 4 publications

References 46 publications

Bio-Inspired Proprioceptive Touch of a Soft Finger with Inner-Finger Kinesthetic Perception

Bio-Inspired Proprioceptive Touch of a Soft Finger with Inner-Finger Kinesthetic Perception

MFOS: Model-Free & One-Shot Object Pose Estimation

GP3D: Generalized Pose Estimation in 3D Point Clouds: A case study on bin picking

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