Search Results for author:
Found 9 papers, 5 papers with code
GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis
We introduce GEM3D -- a new deep, topology-aware generative model of 3D shapes.
FacadeNet: Conditional Facade Synthesis via Selective Editing
We introduce FacadeNet, a deep learning approach for synthesizing building facade images from diverse viewpoints.
Efficient Deduplication and Leakage Detection in Large Scale Image Datasets with a focus on the CrowdAI Mapping Challenge Dataset
The CrowdAI Mapping Challenge Dataset is one of these datasets that has been used extensively in recent years to train deep neural networks.
Projective Urban Texturing
Re-targeting such 2D datasets to 3D geometry is challenging because the underlying shape, size, and layout of the urban structures in the photos do not correspond to the ones in the target meshes.
BuildingNet: Learning to Label 3D Buildings
We introduce BuildingNet: (a) a large-scale dataset of 3D building models whose exteriors are consistently labeled, (b) a graph neural network that labels building meshes by analyzing spatial and structural relations of their geometric primitives.
Ranked #1 on
3D Building Mesh Labeling
on BuildingNet-Mesh
Learning Part Boundaries from 3D Point Clouds
We present a method that detects boundaries of parts in 3D shapes represented as point clouds.
Cross-Shape Attention for Part Segmentation of 3D Point Clouds
The mechanism assesses both the degree of interaction between points and also mediates feature propagation across shapes, improving the accuracy and consistency of the resulting point-wise feature representations for shape segmentation.
Ranked #1 on
3D Semantic Segmentation
on PartNet
Learning Material-Aware Local Descriptors for 3D Shapes
Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical mate- rials, posing a challenge for learning methods.
3D Shape Segmentation with Projective Convolutional Networks
Our architecture combines image-based Fully Convolutional Networks (FCNs) and surface-based Conditional Random Fields (CRFs) to yield coherent segmentations of 3D shapes.
