363 research outputs found
Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps
This paper addresses the problem of single image depth estimation (SIDE),
focusing on improving the quality of deep neural network predictions. In a
supervised learning scenario, the quality of predictions is intrinsically
related to the training labels, which guide the optimization process. For
indoor scenes, structured-light-based depth sensors (e.g. Kinect) are able to
provide dense, albeit short-range, depth maps. On the other hand, for outdoor
scenes, LiDARs are considered the standard sensor, which comparatively provides
much sparser measurements, especially in areas further away. Rather than
modifying the neural network architecture to deal with sparse depth maps, this
article introduces a novel densification method for depth maps, using the
Hilbert Maps framework. A continuous occupancy map is produced based on 3D
points from LiDAR scans, and the resulting reconstructed surface is projected
into a 2D depth map with arbitrary resolution. Experiments conducted with
various subsets of the KITTI dataset show a significant improvement produced by
the proposed Sparse-to-Continuous technique, without the introduction of extra
information into the training stage.Comment: Accepted. (c) 2019 IEEE. Personal use of this material is permitted.
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this work in other work
Im2Pano3D: Extrapolating 360 Structure and Semantics Beyond the Field of View
We present Im2Pano3D, a convolutional neural network that generates a dense
prediction of 3D structure and a probability distribution of semantic labels
for a full 360 panoramic view of an indoor scene when given only a partial
observation (<= 50%) in the form of an RGB-D image. To make this possible,
Im2Pano3D leverages strong contextual priors learned from large-scale synthetic
and real-world indoor scenes. To ease the prediction of 3D structure, we
propose to parameterize 3D surfaces with their plane equations and train the
model to predict these parameters directly. To provide meaningful training
supervision, we use multiple loss functions that consider both pixel level
accuracy and global context consistency. Experiments demon- strate that
Im2Pano3D is able to predict the semantics and 3D structure of the unobserved
scene with more than 56% pixel accuracy and less than 0.52m average distance
error, which is significantly better than alternative approaches.Comment: Video summary: https://youtu.be/Au3GmktK-S
Optimized Data Representation for Interactive Multiview Navigation
In contrary to traditional media streaming services where a unique media
content is delivered to different users, interactive multiview navigation
applications enable users to choose their own viewpoints and freely navigate in
a 3-D scene. The interactivity brings new challenges in addition to the
classical rate-distortion trade-off, which considers only the compression
performance and viewing quality. On the one hand, interactivity necessitates
sufficient viewpoints for richer navigation; on the other hand, it requires to
provide low bandwidth and delay costs for smooth navigation during view
transitions. In this paper, we formally describe the novel trade-offs posed by
the navigation interactivity and classical rate-distortion criterion. Based on
an original formulation, we look for the optimal design of the data
representation by introducing novel rate and distortion models and practical
solving algorithms. Experiments show that the proposed data representation
method outperforms the baseline solution by providing lower resource
consumptions and higher visual quality in all navigation configurations, which
certainly confirms the potential of the proposed data representation in
practical interactive navigation systems
Raw Depth Image Enhancement Using a Neural Network
The term image is often used to denote a data format that records information about a scene’s color. This dissertation object focuses on a similar format for recording distance information about a scene, “depth images”. Depth images have been used extensively in consumer-level applications, such as Apple’s Face ID, based on depth images for face recognition.
However, depth images suffer from low precision and high errors, and some post-processing techniques need to be utilized to improve their quality. Deep learning, or neural networks, are frameworks that use a series of hierarchically arranged nonlinear networks to process input data. Although each layer of the network is limited in its capabilities, the learning capacity accumulated by the multilayer network becomes very powerful. This dissertation assembles two different deep learning frameworks to solve two different types of raw image preprocessing problems. The first network is the super-resolution network, a nonlinear interpolation of low-resolution deep images through the deep network to obtain high-resolution images. The second network is the inpainting network, which is used to mitigate the problem of losing specific pixel data in the original depth image for various reasons.
This dissertation presents deep images processed by these two frameworks, and the quality of the processed images is significantly improved compared to the original images. The great potential of deep learning techniques in the field of deep image processing is shown
Towards Robust Blind Face Restoration with Codebook Lookup Transformer
Blind face restoration is a highly ill-posed problem that often requires
auxiliary guidance to 1) improve the mapping from degraded inputs to desired
outputs, or 2) complement high-quality details lost in the inputs. In this
paper, we demonstrate that a learned discrete codebook prior in a small proxy
space largely reduces the uncertainty and ambiguity of restoration mapping by
casting blind face restoration as a code prediction task, while providing rich
visual atoms for generating high-quality faces. Under this paradigm, we propose
a Transformer-based prediction network, named CodeFormer, to model the global
composition and context of the low-quality faces for code prediction, enabling
the discovery of natural faces that closely approximate the target faces even
when the inputs are severely degraded. To enhance the adaptiveness for
different degradation, we also propose a controllable feature transformation
module that allows a flexible trade-off between fidelity and quality. Thanks to
the expressive codebook prior and global modeling, CodeFormer outperforms the
state of the arts in both quality and fidelity, showing superior robustness to
degradation. Extensive experimental results on synthetic and real-world
datasets verify the effectiveness of our method.Comment: Accepted by NeurIPS 2022. Code: https://github.com/sczhou/CodeForme
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