8 papers
Scene Generation at Absolute Scale: Utilizing Semantic and Geometric Guidance From Text for Accurate and Interpretable 3D Indoor Scene Generation
Stefan Ainetter, Thomas Deixelberger, Edoardo A. Dominici +3
We present GuidedSceneGen, a text-to-3D generation framework that produces metrically accurate, globally consistent, and semantically interpretable indoor scenes. Unlike prior text…
DreamAnywhere: Object-Centric Panoramic 3D Scene Generation
Edoardo Alberto Dominici, Jozef Hladky, Floor Verhoeven +9
Recent advances in text-to-3D scene generation have demonstrated significant potential to transform content creation across multiple industries. Although the research community has…
Leveraging Automatic CAD Annotations for Supervised Learning in 3D Scene Understanding
Yuchen Rao, Stefan Ainetter, Sinisa Stekovic +2
High-level 3D scene understanding is essential in many applications. However, the challenges of generating accurate 3D annotations make development of deep learning models difficul…
PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction
Sinisa Stekovic, Arslan Artykov, Stefan Ainetter +2
We propose PyTorchGeoNodes, a differentiable module for reconstructing 3D objects and their parameters from images using interpretable shape programs. Unlike traditional CAD model…
HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans
Stefan Ainetter, Sinisa Stekovic, Friedrich Fraundorfer +1
We present an automated and efficient approach for retrieving high-quality CAD models of objects and their poses in a scene captured by a moving RGB-D camera. We first investigate…
Automatically Annotating Indoor Images with CAD Models via RGB-D Scans
Stefan Ainetter, Sinisa Stekovic, Friedrich Fraundorfer +1
We present an automatic method for annotating images of indoor scenes with the CAD models of the objects by relying on RGB-D scans. Through a visual evaluation by 3D experts, we sh…
Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Tasks
Stefan Ainetter, Christoph Böhm, Rohit Dhakate +2
In this paper, we present a novel deep neural network architecture for joint class-agnostic object segmentation and grasp detection for robotic picking tasks using a parallel-plate…
End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB
Stefan Ainetter, Friedrich Fraundorfer
In this work, we introduce a novel, end-to-end trainable CNN-based architecture to deliver high quality results for grasp detection suitable for a parallel-plate gripper, and seman…