20,477 research outputs found
Deep Generative Modeling of LiDAR Data
Building models capable of generating structured output is a key challenge
for AI and robotics. While generative models have been explored on many types
of data, little work has been done on synthesizing lidar scans, which play a
key role in robot mapping and localization. In this work, we show that one can
adapt deep generative models for this task by unravelling lidar scans into a 2D
point map. Our approach can generate high quality samples, while simultaneously
learning a meaningful latent representation of the data. We demonstrate
significant improvements against state-of-the-art point cloud generation
methods. Furthermore, we propose a novel data representation that augments the
2D signal with absolute positional information. We show that this helps
robustness to noisy and imputed input; the learned model can recover the
underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks
While recent deep monocular depth estimation approaches based on supervised
regression have achieved remarkable performance, costly ground truth
annotations are required during training. To cope with this issue, in this
paper we present a novel unsupervised deep learning approach for predicting
depth maps and show that the depth estimation task can be effectively tackled
within an adversarial learning framework. Specifically, we propose a deep
generative network that learns to predict the correspondence field i.e. the
disparity map between two image views in a calibrated stereo camera setting.
The proposed architecture consists of two generative sub-networks jointly
trained with adversarial learning for reconstructing the disparity map and
organized in a cycle such as to provide mutual constraints and supervision to
each other. Extensive experiments on the publicly available datasets KITTI and
Cityscapes demonstrate the effectiveness of the proposed model and competitive
results with state of the art methods. The code and trained model are available
on https://github.com/andrea-pilzer/unsup-stereo-depthGAN.Comment: To appear in 3DV 2018. Code is available on GitHu
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
Facial landmarks constitute the most compressed representation of faces and
are known to preserve information such as pose, gender and facial structure
present in the faces. Several works exist that attempt to perform high-level
face-related analysis tasks based on landmarks. In contrast, in this work, an
attempt is made to tackle the inverse problem of synthesizing faces from their
respective landmarks. The primary aim of this work is to demonstrate that
information preserved by landmarks (gender in particular) can be further
accentuated by leveraging generative models to synthesize corresponding faces.
Though the problem is particularly challenging due to its ill-posed nature, we
believe that successful synthesis will enable several applications such as
boosting performance of high-level face related tasks using landmark points and
performing dataset augmentation. To this end, a novel face-synthesis method
known as Gender Preserving Generative Adversarial Network (GP-GAN) that is
guided by adversarial loss, perceptual loss and a gender preserving loss is
presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN
that leverages advantages of U-Net and DenseNet architectures. Extensive
experiments and comparison with recent methods are performed to verify the
effectiveness of the proposed method.Comment: 6 pages, 5 figures, this paper is accepted as 2018 24th International
Conference on Pattern Recognition (ICPR2018
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