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Evin Pinar Örnek

4 accepted papers

2023

CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion

NeurIPS 2023poster

Controllable scene synthesis aims to create interactive environments for numerous industrial use cases. Scene graphs provide a highly suitable interface to facilitate these applications by abstracting the scene context in a compact manner. Existing methods, reliant on retrieval from extensive databa…

2023

SupeRGB-D: Zero-Shot Instance Segmentation in Cluttered Indoor Environments

RA-L 2023

Object instance segmentation is a key challenge for indoor robots navigating cluttered environments with many small objects. Limitations in 3D sensing capabilities often make it difficult to detect every possible object. While deep learning approaches may be effective for this problem, manually anno

Cited by 14SourcecodeScholar
2022

Object-Aware Monocular Depth Prediction With Instance Convolutions

RA-L 2022

With the advent of deep learning, estimating depth from a single RGB image has recently received a lot of attention, being capable of empowering many different applications ranging from path planning for robotics to computational cinematography. Nevertheless,while the depth maps are in their entiret

Cited by 3SourcecodeScholar
2020

Co-Planar Parametrization for Stereo-SLAM and Visual-Inertial Odometry

RA-L 2020

This letter proposes a novel SLAM framework for stereo and visual inertial odometry estimation. It builds an efficient and robust parametrization of co-planar points and lines which leverages specific geometric constraints to improve camera pose optimization in terms of both efficiency and accuracy.

Cited by 28SourcecodeScholar