6 citations · 8 across the 10 of their papers we have counts for
12 papers
GuideFlow3D: Optimization-Guided Rectified Flow For Appearance Transfer
Sayan Deb Sarkar, Sinisa Stekovic, Vincent Lepetit +1
Transferring appearance to 3D assets using different representations of the appearance object - such as images or text - has garnered interest due to its wide range of applications…
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…
MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud
Michaël Ramamonjisoa, Sinisa Stekovic, Vincent Lepetit
We present MonteBoxFinder, a method that, given a noisy input point cloud, fits cuboids to the input scene. Our primary contribution is a discrete optimization algorithm that, from…