5 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.CV2022
Hierarchical Superquadric Decomposition with Implicit Space Separation
Jaka Šircelj, Peter Peer, Franc Solina +1
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…
cs.CV2020★ 5 cited
Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks
Jaka Šircelj, Tim Oblak, Klemen Grm +5
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 con…
cs.CV2019
Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study
Tim Oblak, Klemen Grm, Aleš Jaklič +3
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…