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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.
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.
Frustum PointNets for 3D Object Detection from RGB-D Data
TLDR
This work directly operates on raw point clouds by popping up RGBD scans and leverages both mature 2D object detectors and advanced 3D deep learning for object localization, achieving efficiency as well as high recall for even small objects.
KPConv: Flexible and Deformable Convolution for Point Clouds
TLDR
KPConv is a new design of point convolution, i.e. that operates on point clouds without any intermediate representation, that outperform state-of-the-art classification and segmentation approaches on several datasets.
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.
Deep Hough Voting for 3D Object Detection in Point Clouds
TLDR
This work proposes VoteNet, an end-to-end 3D object detection network based on a synergy of deep point set networks and Hough voting that achieves state-of-the-art 3D detection on two large datasets of real 3D scans, ScanNet and SUN RGB-D with a simple design, compact model size and high efficiency.
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.
FlowNet3D: Learning Scene Flow in 3D Point Clouds
TLDR
This work proposes a novel deep neural network named FlowNet3D that learns scene flow from point clouds in an end-to-end fashion and successfully generalizes to real scans, outperforming various baselines and showing competitive results to the prior art.
Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis
TLDR
A data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis and a 3D-Encoder-Predictor Network (3D-EPN) which is composed of 3D convolutional layers.
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
TLDR
This work aims at facilitating research on 3D representation learning by selecting a suite of diverse datasets and tasks to measure the effect of unsupervised pre-training on a large source set of 3D scenes and achieving improvement over recent best results in segmentation and detection across 6 different benchmarks.
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