Publications

@article{TomasevicSensors,

title = {Reconstructing Superquadrics from Intensity and Color Images},

author = {Darian Tomašecić and Peter Peer and Franc Solina and Aleš Jaklič and Vitomir Štruc},

url = {https://www.mdpi.com/1424-8220/22/14/5332/pdf?version=1658380987},

doi = {https://doi.org/10.3390/s22145332},

year  = {2022},

date = {2022-07-16},

journal = {Sensors},

volume = {22},

number = {5332},

issue = {4},

abstract = {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.},

keywords = {arrs, CNN, depth data, depth estimation, depth sensing, intensity images, superquadric, superquadrics},

pubstate = {published},

tppubtype = {article}

}

@article{Oblak2021,

title = {Learning to predict superquadric parameters from depth images with explicit and implicit supervision},

author = {Tim Oblak and Jaka Šircelj and Vitomir Struc and Peter Peer and Franc Solina and Aleš Jaklic

},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9274424},

doi = {10.1109/ACCESS.2020.3041584},

issn = {2169-3536},

year  = {2021},

date = {2021-01-01},

journal = {IEEE Access},

pages = {1-16},

abstract = {Reconstruction of 3D space from visual data has always been a significant challenge in

the field of computer vision. A popular approach to address this problem can be found in the form of

bottom-up reconstruction techniques which try to model complex 3D scenes through a constellation of

volumetric primitives. Such techniques are inspired by the current understanding of the human visual

system and are, therefore, strongly related to the way humans process visual information, as suggested

by recent visual neuroscience literature. While advances have been made in recent years in the area of

3D reconstruction, the problem remains challenging due to the many possible ways of representing 3D

data, the ambiguity of determining the shape and general position in 3D space and the difficulty to train

efficient models for the prediction of volumetric primitives. In this paper, we address these challenges and

present a novel solution for recovering volumetric primitives from depth images. Specifically, we focus on

the recovery of superquadrics, a special type of parametric models able to describe a wide array of 3D

shapes using only a few parameters. We present a new learning objective that relies on the superquadric

(inside-outside) function and develop two learning strategies for training convolutional neural networks

(CNN) capable of predicting superquadric parameters. The first uses explicit supervision and penalizes the

difference between the predicted and reference superquadric parameters. The second strategy uses implicit

supervision and penalizes differences between the input depth images and depth images rendered from

the predicted parameters. CNN predictors for superquadric parameters are trained with both strategies and

evaluated on a large dataset of synthetic and real-world depth images. Experimental results show that both

strategies compare favourably to the existing state-of-the-art and result in high quality 3D reconstructions

of the modelled scenes at a much shorter processing time.},

keywords = {3d, computer vision, depth images, differential renderer, recovery, superquadric},

pubstate = {published},

tppubtype = {article}

}