Search Results for author:
Found 9 papers, 4 papers with code
Diverse Part Synthesis for 3D Shape Creation
Specifically, we experiment with multimodal deep generative models that allow sampling diverse suggestions for shape parts and focus on models which have not been considered in previous work on shape synthesis.
Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation
Since the preparation of labeled data for training semantic segmentation networks of point clouds is a time-consuming process, weakly supervised approaches have been introduced to learn from only a small fraction of data.
Photo-to-Shape Material Transfer for Diverse Structures
The image translation network translates the color from the exemplar to a projection of the 3D shape and the part segmentation from the projection to the exemplar.
AutoPoly: Predicting a Polygonal Mesh Construction Sequence from a Silhouette Image
The key idea of our method is the use of the Monte Carlo tree search (MCTS) algorithm and differentiable rendering to separately predict sequential topological actions and geometric actions.
Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning
Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set.
Predictive and Generative Neural Networks for Object Functionality
Given a 3D object in isolation, our functional similarity network (fSIM-NET), a variation of the triplet network, is trained to predict the functionality of the object by inferring functionality-revealing interaction contexts.
RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud
We show results of simultaneous motion and part predictions from synthetic and real scans of 3D objects exhibiting a variety of part mobilities, possibly involving multiple movable parts.
FAME: 3D Shape Generation via Functionality-Aware Model Evolution
Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.
Graphics
Graph2Plan: Learning Floorplan Generation from Layout Graphs
The core component of our learning framework is a deep neural network, Graph2Plan, which converts a layout graph, along with a building boundary, into a floorplan that fulfills both the layout and boundary constraints.
