2022 |
Šircelj, Jaka; Peer, Peter; Solina, Franc; Štruc, Vitomir Hierarchical Superquadric Decomposition with Implicit Space Separation Proceedings Article V: Proceedings of ERK 2022, str. 1-4, 2022. Povzetek | Povezava | BibTeX | Oznake: 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 Članek v strokovni reviji V: Sensors, vol. 22, iss. 4, no. 5332, 2022. Povzetek | Povezava | BibTeX | Oznake: 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. |
Objave
2022 |
Hierarchical Superquadric Decomposition with Implicit Space Separation Proceedings Article V: Proceedings of ERK 2022, str. 1-4, 2022. |
Reconstructing Superquadrics from Intensity and Color Images Članek v strokovni reviji V: Sensors, vol. 22, iss. 4, no. 5332, 2022. |