6 citations · 8 across the 10 of their papers we have counts for
12 papers · 1 filter
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…
MCTS with Refinement for Proposals Selection Games in Scene Understanding
Sinisa Stekovic, Mahdi Rad, Alireza Moradi +2
We propose a novel method applicable in many scene understanding problems that adapts the Monte Carlo Tree Search (MCTS) algorithm, originally designed to learn to play games of hi…
MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans
Sinisa Stekovic, Mahdi Rad, Friedrich Fraundorfer +1
We propose a novel method for reconstructing floor plans from noisy 3D point clouds. Our main contribution is a principled approach that relies on the Monte Carlo Tree Search (MCTS…
Monte Carlo Scene Search for 3D Scene Understanding
Shreyas Hampali, Sinisa Stekovic, Sayan Deb Sarkar +3
We explore how a general AI algorithm can be used for 3D scene understanding to reduce the need for training data. More exactly, we propose a modification of the Monte Carlo Tree S…
General 3D Room Layout from a Single View by Render-and-Compare
Sinisa Stekovic, Shreyas Hampali, Mahdi Rad +3
We present a novel method to reconstruct the 3D layout of a room (walls, floors, ceilings) from a single perspective view in challenging conditions, by contrast with previous singl…
Casting Geometric Constraints in Semantic Segmentation as Semi-Supervised Learning
Sinisa Stekovic, Friedrich Fraundorfer, Vincent Lepetit
We propose a simple yet effective method to learn to segment new indoor scenes from video frames: State-of-the-art methods trained on one dataset, even as large as the SUNRGB-D dat…
S4-Net: Geometry-Consistent Semi-Supervised Semantic Segmentation
Sinisa Stekovic, Friedrich Fraundorfer, Vincent Lepetit
We show that it is possible to learn semantic segmentation from very limited amounts of manual annotations, by enforcing geometric 3D constraints between multiple views. More exact…