5 research outputs found
Deep Depth From Focus
Depth from focus (DFF) is one of the classical ill-posed inverse problems in
computer vision. Most approaches recover the depth at each pixel based on the
focal setting which exhibits maximal sharpness. Yet, it is not obvious how to
reliably estimate the sharpness level, particularly in low-textured areas. In
this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end
learning approach to this problem. One of the main challenges we face is the
hunger for data of deep neural networks. In order to obtain a significant
amount of focal stacks with corresponding groundtruth depth, we propose to
leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us
to digitally create focal stacks of varying sizes. Compared to existing
benchmarks our dataset is 25 times larger, enabling the use of machine learning
for this inverse problem. We compare our results with state-of-the-art DFF
methods and we also analyze the effect of several key deep architectural
components. These experiments show that our proposed method `DDFFNet' achieves
state-of-the-art performance in all scenes, reducing depth error by more than
75% compared to the classical DFF methods.Comment: accepted to Asian Conference on Computer Vision (ACCV) 201
SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentation for Accurate Freespace Detection
Freespace detection is an essential component of visual perception for
self-driving cars. The recent efforts made in data-fusion convolutional neural
networks (CNNs) have significantly improved semantic driving scene
segmentation. Freespace can be hypothesized as a ground plane, on which the
points have similar surface normals. Hence, in this paper, we first introduce a
novel module, named surface normal estimator (SNE), which can infer surface
normal information from dense depth/disparity images with high accuracy and
efficiency. Furthermore, we propose a data-fusion CNN architecture, referred to
as RoadSeg, which can extract and fuse features from both RGB images and the
inferred surface normal information for accurate freespace detection. For
research purposes, we publish a large-scale synthetic freespace detection
dataset, named Ready-to-Drive (R2D) road dataset, collected under different
illumination and weather conditions. The experimental results demonstrate that
our proposed SNE module can benefit all the state-of-the-art CNNs for freespace
detection, and our SNE-RoadSeg achieves the best overall performance among
different datasets.Comment: ECCV 202
We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation
Manual visual inspection performed by certified inspectors is still the main
form of road pothole detection. This process is, however, not only tedious,
time-consuming and costly, but also dangerous for the inspectors. Furthermore,
the road pothole detection results are always subjective, because they depend
entirely on the individual experience. Our recently introduced disparity (or
inverse depth) transformation algorithm allows better discrimination between
damaged and undamaged road areas, and it can be easily deployed to any semantic
segmentation network for better road pothole detection results. To boost the
performance, we propose a novel attention aggregation (AA) framework, which
takes the advantages of different types of attention modules. In addition, we
develop an effective training set augmentation technique based on adversarial
domain adaptation, where the synthetic road RGB images and transformed road
disparity (or inverse depth) images are generated to enhance the training of
semantic segmentation networks. The experimental results demonstrate that,
firstly, the transformed disparity (or inverse depth) images become more
informative; secondly, AA-UNet and AA-RTFNet, our best performing
implementations, respectively outperform all other state-of-the-art
single-modal and data-fusion networks for road pothole detection; and finally,
the training set augmentation technique based on adversarial domain adaptation
not only improves the accuracy of the state-of-the-art semantic segmentation
networks, but also accelerates their convergence.Comment: 16 pages, 7 figures and 2 tables. This paper is accepted by ECCV
Workshops 202