PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervision

no code implementations • 16 Mar 2023 • Konstantinos Tertikas, Despoina Paschalidou, Boxiao Pan, Jeong Joon Park, Mikaela Angelina Uy, Ioannis Emiris, Yannis Avrithis, Leonidas Guibas

Evaluations on various ShapeNet categories demonstrate the ability of our model to generate editable 3D objects of improved fidelity, compared to previous part-based generative approaches that require 3D supervision or models relying on NeRFs.

Generating Part-Aware Editable 3D Shapes Without 3D Supervision

1 code implementation • CVPR 2023 • Konstantinos Tertikas, Despoina Paschalidou, Boxiao Pan, Jeong Joon Park, Mikaela Angelina Uy, Ioannis Emiris, Yannis Avrithis, Leonidas Guibas

Evaluations on various ShapeNet categories demonstrate the ability of our model to generate editable 3D objects of improved fidelity, compared to previous part-based generative approaches that require 3D supervision or models relying on NeRFs.

Making Better Mistakes: Leveraging Class Hierarchies with Deep Networks

1 code implementation • CVPR 2020 • Luca Bertinetto, Romain Mueller, Konstantinos Tertikas, Sina Samangooei, Nicholas A. Lord

Deep neural networks have improved image classification dramatically over the past decade, but have done so by focusing on performance measures that treat all classes other than the ground truth as equally wrong.

Image Classification

A Dataset for Lane Instance Segmentation in Urban Environments

no code implementations • ECCV 2018 • Brook Roberts, Sebastian Kaltwang, Sina Samangooei, Mark Pender-Bare, Konstantinos Tertikas, John Redford

Autonomous vehicles require knowledge of the surrounding road layout, which can be predicted by state-of-the-art CNNs.

Autonomous Vehicles Instance Segmentation +2