• Publications
  • Influence
ImageNet Large Scale Visual Recognition Challenge
TLDR
The creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared. Expand
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
TLDR
This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Expand
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
TLDR
A hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set and proposes novel set learning layers to adaptively combine features from multiple scales to learn deep point set features efficiently and robustly. Expand
ShapeNet: An Information-Rich 3D Model Repository
TLDR
ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy, a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Expand
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
TLDR
This paper addresses the problem of 3D reconstruction from a single image, generating a straight-forward form of output unorthordox, and designs architecture, loss function and learning paradigm that are novel and effective, capable of predicting multiple plausible 3D point clouds from an input image. Expand
Volumetric and Multi-view CNNs for Object Classification on 3D Data
TLDR
This paper introduces two distinct network architectures of volumetric CNNs and examines multi-view CNNs, providing a better understanding of the space of methods available for object classification on 3D data. Expand
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
TLDR
A high-level image representation, called the Object Bank, is proposed, where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors, blind to the testing dataset or visual task. Expand
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
TLDR
A scalable and overfit-resistant image synthesis pipeline, together with a novel CNN specifically tailored for the viewpoint estimation task, is proposed that can significantly outperform state-of-the-art methods on PASCAL 3D+ benchmark. Expand
A scalable active framework for region annotation in 3D shape collections
TLDR
This work proposes a novel active learning method capable of enriching massive geometric datasets with accurate semantic region annotations, and demonstrates that incorporating verification of all produced labelings within this unified objective improves both accuracy and efficiency of the active learning procedure. Expand
PartNet: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level 3D Object Understanding
TLDR
This work presents PartNet, a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information, and proposes a baseline method for part instance segmentation that is superior performance over existing methods. Expand
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