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11/24/2020
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
Extracting robust and general 3D local features is key to downstream tas...
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10/19/2020
Learning to Reconstruct and Segment 3D Objects
To endow machines with the ability to perceive the real-world in a three...
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10/09/2020
GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering
We present a simple yet powerful implicit neural function that can repre...
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09/07/2020
Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges
An essential prerequisite for unleashing the potential of supervised dee...
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03/05/2020
PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization
In this paper, we present a novel end-to-end learning-based LiDAR reloca...
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11/25/2019
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
We study the problem of efficient semantic segmentation for large-scale ...
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10/13/2019
DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network
Odometry is of key importance for localization in the absence of a map. ...
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06/04/2019
Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
We propose a novel, conceptually simple and general framework for instan...
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08/02/2018
Attentional Aggregation of Deep Feature Sets for Multi-view 3D Reconstruction
We study the problem of recovering an underlying 3D shape from a set of ...
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04/25/2018
3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations
The ability to interact and understand the environment is a fundamental ...
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04/16/2018
Defo-Net: Learning Body Deformation using Generative Adversarial Networks
Modelling the physical properties of everyday objects is a fundamental p...
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02/01/2018
3D Object Dense Reconstruction from a Single Depth View
In this paper, we propose a novel approach, 3D-RecGAN++, which reconstru...
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08/26/2017