Search Results for author:
Found 10 papers, 7 papers with code
PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction
In our experiments, we apply our algorithm to reconstruct 3D objects in the ScanNet dataset and evaluate our results against CAD model retrieval-based reconstructions.
HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans
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.
Automatically Annotating Indoor Images with CAD Models via RGB-D Scans
We present an automatic method for annotating images of indoor scenes with the CAD models of the objects by relying on RGB-D scans.
MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud
We present MonteBoxFinder, a method that, given a noisy input point cloud, fits cuboids to the input scene.
MCTS with Refinement for Proposals Selection Games in Scene Understanding
We also introduce a novel differentiable method for rendering the polygonal shapes of these proposals.
MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans
For this step, we propose a novel differentiable method for rendering the polygonal shapes of these proposals.
Monte Carlo Scene Search for 3D Scene Understanding
We explore how a general AI algorithm can be used for 3D scene understanding to reduce the need for training data.
General 3D Room Layout from a Single View by Render-and-Compare
In order to deal with occlusions between components of the layout, which is a problem ignored by previous works, we introduce an analysis-by-synthesis method to iteratively refine the 3D layout estimate.
Casting Geometric Constraints in Semantic Segmentation as Semi-Supervised Learning
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 dataset, can perform poorly when applied to images that are not part of the dataset, because of the dataset bias, a common phenomenon in computer vision.
S4-Net: Geometry-Consistent Semi-Supervised Semantic Segmentation
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.
