2022 |
Šircelj, Jaka; Peer, Peter; Solina, Franc; Štruc, Vitomir Hierarchical Superquadric Decomposition with Implicit Space Separation Proceedings Article In: Proceedings of ERK 2022, pp. 1-4, 2022. Abstract | Links | BibTeX | Tags: CNN, deep learning, depth estimation, iterative procedure, model fitting, recursive model, superquadric, superquadrics, volumetric primitive @inproceedings{SirceljSuperQuadrics,We introduce a new method to reconstruct 3D objects using a set of volumetric primitives, i.e., superquadrics. The method hierarchically decomposes a target 3D object into pairs of superquadrics recovering finer and finer details. While such hierarchical methods have been studied before, we introduce a new way of splitting the object space using only properties of the predicted superquadrics. The method is trained and evaluated on the ShapeNet dataset. The results of our experiments suggest that reasonable reconstructions can be obtained with the proposed approach for a diverse set of objects with complex geometry. |
Tomašecić, Darian; Peer, Peter; Solina, Franc; Jaklič, Aleš; Štruc, Vitomir Reconstructing Superquadrics from Intensity and Color Images Journal Article In: Sensors, vol. 22, iss. 4, no. 5332, 2022. Abstract | Links | BibTeX | Tags: arrs, CNN, depth data, depth estimation, depth sensing, intensity images, superquadric, superquadrics @article{TomasevicSensors,The task of reconstructing 3D scenes based on visual data represents a longstanding problem in computer vision. Common reconstruction approaches rely on the use of multiple volumetric primitives to describe complex objects. Superquadrics (a class of volumetric primitives) have shown great promise due to their ability to describe various shapes with only a few parameters. Recent research has shown that deep learning methods can be used to accurately reconstruct random superquadrics from both 3D point cloud data and simple depth images. In this paper, we extended these reconstruction methods to intensity and color images. Specifically, we used a dedicated convolutional neural network (CNN) model to reconstruct a single superquadric from the given input image. We analyzed the results in a qualitative and quantitative manner, by visualizing reconstructed superquadrics as well as observing error and accuracy distributions of predictions. We showed that a CNN model designed around a simple ResNet backbone can be used to accurately reconstruct superquadrics from images containing one object, but only if one of the spatial parameters is fixed or if it can be determined from other image characteristics, e.g., shadows. Furthermore, we experimented with images of increasing complexity, for example, by adding textures, and observed that the results degraded only slightly. In addition, we show that our model outperforms the current state-of-the-art method on the studied task. Our final result is a highly accurate superquadric reconstruction model, which can also reconstruct superquadrics from real images of simple objects, without additional training. |
2020 |
Šircelj, Jaka; Oblak, Tim; Grm, Klemen; Petković, Uroš; Jaklič, Aleš; Peer, Peter; Štruc, Vitomir; Solina, Franc Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks Proceedings Article In: 25th Computer Vision Winter Workshop (CVWW 2020), 2020. Abstract | Links | BibTeX | Tags: CNN, convolutional neural networks, segmentation, superquadrics, volumetric data @inproceedings{sircelj2020sqcnn,In this paper we address the problem of representing 3D visual data with parameterized volumetric shape primitives. Specifically, we present a (two-stage) approach built around convolutional neural networks (CNNs) capable of segmenting complex depth scenes into the simpler geometric structures that can be represented with superquadric models. In the first stage, our approach uses a Mask RCNN model to identify superquadric-like structures in depth scenes and then fits superquadric models to the segmented structures using a specially designed CNN regressor. Using our approach we are able to describe complex structures with a small number of interpretable parameters. We evaluated the proposed approach on synthetic as well as real-world depth data and show that our solution does not only result in competitive performance in comparison to the state-of-the-art, but is able to decompose scenes into a number of superquadric models at a fraction of the time required by competing approaches. We make all data and models used in the paper available from https://lmi.fe.uni-lj.si/en/research/resources/sq-seg. |
2019 |
Oblak, Tim; Grm, Klemen; Jaklič, Aleš; Peer, Peter; Štruc, Vitomir; Solina, Franc Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study Proceedings Article In: 2019 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), pp. 45-52, IEEE, 2019. Abstract | Links | BibTeX | Tags: CNN, convolutional neural networks, superquadrics, volumetric data @inproceedings{oblak2019recovery,It has been a longstanding goal in computer vision to describe the 3D physical space in terms of parameterized volumetric models that would allow autonomous machines to understand and interact with their surroundings. Such models are typically motivated by human visual perception and aim to represents all elements of the physical word ranging from individual objects to complex scenes using a small set of parameters. One of the de facto standards to approach this problem are superquadrics - volumetric models that define various 3D shape primitives and can be fitted to actual 3D data (either in the form of point clouds or range images). However, existing solutions to superquadric recovery involve costly iterative fitting procedures, which limit the applicability of such techniques in practice. To alleviate this problem, we explore in this paper the possibility to recover superquadrics from range images without time consuming iterative parameter estimation techniques by using contemporary deep-learning models, more specifically, convolutional neural networks (CNNs). We pose the superquadric recovery problem as a regression task and develop a CNN regressor that is able to estimate the parameters of a superquadric model from a given range image. We train the regressor on a large set of synthetic range images, each containing a single (unrotated) superquadric shape and evaluate the learned model in comparative experiments with the current state-of-the-art. Additionally, we also present a qualitative analysis involving a dataset of real-world objects. The results of our experiments show that the proposed regressor not only outperforms the existing state-of-the-art, but also ensures a 270x faster execution time. |