Search Results for author:
Found 9 papers, 1 papers with code
RESSCAL3D: Resolution Scalable 3D Semantic Segmentation of Point Clouds
To the best of our knowledge, the proposed method is the first to propose a resolution-scalable approach for 3D semantic segmentation of point clouds based on deep learning.
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers
In this paper we delve into the properties of transformers, attained through self-supervision, in the point cloud domain.
Ranked #11 on
3D Point Cloud Classification
on ScanObjectNN
(OBJ-ONLY (OA) metric, using extra
training data)
3D Point Cloud Classification
Explainable artificial intelligence
3DBodyNet: Fast Reconstruction of 3D Animatable Human Body Shape from a Single Commodity Depth Camera
As easy-to-use as taking a photo using a mobile phone, our algorithm only needs two depth images of the front-facing and back-facing bodies.
Method for Registration of 3D Shapes Without Overlap for Known 3D Priors
In this letter, to the best of knowledge, the first method for the registration of 3D shapes without overlap, assuming that the shapes correspond to partial views of a known semi-rigid 3D prior is presented.
MaskLayer: Enabling scalable deep learning solutions by training embedded feature sets
To the best of our knowledge, this is the first work presenting a generic solution able to achieve quality scalable results within the deep learning framework.
Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization
Among the existing GCNNs, many methods can be viewed as instances of a neural message passing motif; features of nodes are passed around their neighbors, aggregated and transformed to produce better nodes' representations.
Learning to Estimate the Body Shape Under Clothing from a Single 3D Scan
In this paper, we propose the first learning-based approach to estimate the human body shape under clothing from a single dressed-human scan, dubbed Body PointNet.
Multi-modal deep network for RGB-D segmentation of clothes
In this Letter, the authors propose a deep learning based method to perform semantic segmentation of clothes from RGB-D images of people.
A generic method of wearable items virtual try-on
In this paper, we present the first general method for virtual try-ons that is fully automatic and suitable for many items including garments, hair, shoes, watches, necklaces, hats, and so on.
