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Found 9 papers, 1 papers with code
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
3D Shape Segmentation via Shape Fully Convolutional Networks
3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations, which are similar to convolution and pooling operations used on images.
Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines
We present a semi-supervised co-analysis method for learning 3D shape styles from projected feature lines, achieving style patch localization with only weak supervision.
PartNet: A Recursive Part Decomposition Network for Fine-grained and Hierarchical Shape Segmentation
Meanwhile, to increase the segmentation accuracy at each node, we enhance the recursive contextual feature with the shape feature extracted for the corresponding part.
Ranked #14 on
3D Part Segmentation
on ShapeNet-Part
(Class Average IoU metric)
RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenes
We introduce RaidaR, a rich annotated image dataset of rainy street scenes, to support autonomous driving research.
CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies.
HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling
We present the first active learning tool for fine-grained 3D part labeling, a problem which challenges even the most advanced deep learning (DL) methods due to the significant structural variations among the small and intricate parts.
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images
We introduce the first active learning (AL) framework for high-accuracy instance segmentation of moveable parts from RGB images of real indoor scenes.
DPA-Net: Structured 3D Abstraction from Sparse Views via Differentiable Primitive Assembly
We present a differentiable rendering framework to learn structured 3D abstractions in the form of primitive assemblies from sparse RGB images capturing a 3D object.
