Robust and Accurate Superquadric Recovery: a Probabilistic Approach

Liu, Weixiao; Wu, Yuwei; Ruan, Sipu; Chirikjian, Gregory S.

doi:10.1109/cvpr52688.2022.00270

Cited by 24 publications

(18 citation statements)

References 33 publications

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“…The authors introduce an auxiliary function in the unstable region and receive a better abstraction accuracy. More recently, Liu et al [24] formulate the problem in a probabilistic fashion and propose a geometric strategy to avoid local optimum, bringing a significant improvement in robustness to outlier and fitting accuracy. Wu et al [47] extend and recast the work as a nonparametric Bayesian inference problem so as to improve the applicability on complex shapes.…”

Section: Related Workmentioning

confidence: 99%

“…Note that the shape parameters can exceed 2, resulting in nonconvex shapes. However, practically in most studies [24,31] and also in our paper, we limit them within the convex region as defined above. The points x = [x, y, z] ∈ R 3 satisfying Eq.…”

Section: Preliminarymentioning

confidence: 99%

“…Later in [7,22], their method is extended to represent more complex objects with multiple primitives. More recently, the authors of [24,47] reformulate the task as a probabilistic inference problem with enhanced accuracy and robustness to noise and outliers. At the same time, with the surge of data-driven techniques, researchers attempt to train neural networks to infer cuboids [27,38,41,48,50] and superquadrics [29,31] representations in an end-to-end fashion.…”

Section: Introductionmentioning

confidence: 99%

“…The computational methods are vulnerable to the inherent ambiguity of the point-to-surface relationship. For exam-ple, the algorithms tend to fill empty spaces of a non-convex object with primitives by mistake, due to the inside/outside ambiguity of a surface depicted by a set of points [24,48]. The main drawback of the learning approaches lies in the lack of generalizability beyond the object category on which the model is trained [24,31,47,48].…”

Section: Introductionmentioning

confidence: 99%

“…For exam-ple, the algorithms tend to fill empty spaces of a non-convex object with primitives by mistake, due to the inside/outside ambiguity of a surface depicted by a set of points [24,48]. The main drawback of the learning approaches lies in the lack of generalizability beyond the object category on which the model is trained [24,31,47,48]. Also, the shape abstraction accuracy is inferior to the computational methods.…”

Section: Introductionmentioning

confidence: 99%

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Marching-Primitives: Shape Abstraction from Signed Distance Function

Liu¹,

Wu²,

Ruan³

et al. 2023

Preprint

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Representing complex objects with basic geometric primitives has long been a topic in computer vision. Primitive-based representations have the merits of compactness and computational efficiency in higher-level tasks such as physics simulation, collision checking, and robotic manipulation. Unlike previous works which extract polygonal meshes from a signed distance function (SDF), in this paper, we present a novel method, named Marching-Primitives, to obtain a primitive-based abstraction directly from an SDF. Our method grows geometric primitives (such as superquadrics) iteratively by analyzing the connectivity of voxels while marching at different levels of signed distance. For each valid connected volume of interest, we march on the scope of voxels from which a primitive is able to be extracted in a probabilistic sense and simultaneously solve for the parameters of the primitive to capture the underlying local geometry. We evaluate the performance of our method on both synthetic and real-world datasets. The results show that the proposed method outperforms the state-of-the-art in terms of accuracy, and is directly generalizable among different categories and scales.The code is open-sourced at https://github.com/ ChirikjianLab/Marching-Primitives.git.

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Section: Related Workmentioning

confidence: 99%

Section: Preliminarymentioning

confidence: 99%