L. Guibas

Publications754
h-index124
Citations76,024
Highly Influential Citations9,161
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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.
View on IEEE
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.
View PDF on arXiv
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.
View PDF on arXiv
The Earth Mover's Distance as a Metric for Image Retrieval
TLDR
This paper investigates the properties of a metric between two distributions, the Earth Mover's Distance (EMD), for content-based image retrieval, and compares the retrieval performance of the EMD with that of other distances.
View on Springer
A Concise and Provably Informative Multi‐Scale Signature Based on Heat Diffusion
TLDR
The Heat Kernel Signature, called the HKS, is obtained by restricting the well‐known heat kernel to the temporal domain and shows that under certain mild assumptions, HKS captures all of the information contained in the heat kernel, and characterizes the shape up to isometry.
View on Wiley
A metric for distributions with applications to image databases
TLDR
This paper uses the Earth Mover's Distance to exhibit the structure of color-distribution and texture spaces by means of Multi-Dimensional Scaling displays, and proposes a novel approach to the problem of navigating through a collection of color images, which leads to a new paradigm for image database search.
View on IEEE
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.
View on IEEE
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.
View on IEEE
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.
View on IEEE
Learning Representations and Generative Models for 3D Point Clouds
TLDR
A deep AutoEncoder network with state-of-the-art reconstruction quality and generalization ability is introduced with results that outperform existing methods on 3D recognition tasks and enable shape editing via simple algebraic manipulations.
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