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3D ShapeNets: A deep representation for volumetric shapes
TLDR
This work proposes to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network, and shows that this 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks. 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
Multi-Scale Context Aggregation by Dilated Convolutions
TLDR
This work develops a new convolutional network module that is specifically designed for dense prediction, and shows that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. Expand
LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
TLDR
This work proposes to amplify human effort through a partially automated labeling scheme, leveraging deep learning with humans in the loop, and constructs a new image dataset, LSUN, which contains around one million labeled images for each of 10 scene categories and 20 object categories. Expand
Semantic Scene Completion from a Single Depth Image
TLDR
The semantic scene completion network (SSCNet) is introduced, an end-to-end 3D convolutional network that takes a single depth image as input and simultaneously outputs occupancy and semantic labels for all voxels in the camera view frustum. Expand
BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling
TLDR
The design and implementation of a scalable annotation system that can provide a comprehensive set of image labels for large-scale driving datasets, and a new driving dataset, which is an order of magnitude larger than previous efforts. Expand
Dilated Residual Networks
TLDR
It is shown that dilated residual networks (DRNs) outperform their non-dilated counterparts in image classification without increasing the models depth or complexity and the accuracy advantage of DRNs is further magnified in downstream applications such as object localization and semantic segmentation. Expand
Deep Layer Aggregation
TLDR
This work augments standard architectures with deeper aggregation to better fuse information across layers and iteratively and hierarchically merge the feature hierarchy to make networks with better accuracy and fewer parameters. Expand
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
TLDR
This paper introduces the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems, and outperforms baselines across different settings on multiple large-scale datasets. Expand
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
TLDR
This work constructs BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving and shows that special training strategies are needed for existing models to perform such heterogeneous tasks. Expand
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