651 research outputs found
Simultaneous Stereo Video Deblurring and Scene Flow Estimation
Videos for outdoor scene often show unpleasant blur effects due to the large
relative motion between the camera and the dynamic objects and large depth
variations. Existing works typically focus monocular video deblurring. In this
paper, we propose a novel approach to deblurring from stereo videos. In
particular, we exploit the piece-wise planar assumption about the scene and
leverage the scene flow information to deblur the image. Unlike the existing
approach [31] which used a pre-computed scene flow, we propose a single
framework to jointly estimate the scene flow and deblur the image, where the
motion cues from scene flow estimation and blur information could reinforce
each other, and produce superior results than the conventional scene flow
estimation or stereo deblurring methods. We evaluate our method extensively on
two available datasets and achieve significant improvement in flow estimation
and removing the blur effect over the state-of-the-art methods.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
OutCast: Outdoor Single-image Relighting with Cast Shadows
We propose a relighting method for outdoor images. Our method mainly focuses
on predicting cast shadows in arbitrary novel lighting directions from a single
image while also accounting for shading and global effects such the sun light
color and clouds. Previous solutions for this problem rely on reconstructing
occluder geometry, e.g. using multi-view stereo, which requires many images of
the scene. Instead, in this work we make use of a noisy off-the-shelf
single-image depth map estimation as a source of geometry. Whilst this can be a
good guide for some lighting effects, the resulting depth map quality is
insufficient for directly ray-tracing the shadows. Addressing this, we propose
a learned image space ray-marching layer that converts the approximate depth
map into a deep 3D representation that is fused into occlusion queries using a
learned traversal. Our proposed method achieves, for the first time,
state-of-the-art relighting results, with only a single image as input. For
supplementary material visit our project page at:
https://dgriffiths.uk/outcast.Comment: Eurographics 2022 - Accepte
ConstScene: Dataset and Model for Advancing Robust Semantic Segmentation in Construction Environments
The increasing demand for autonomous machines in construction environments
necessitates the development of robust object detection algorithms that can
perform effectively across various weather and environmental conditions. This
paper introduces a new semantic segmentation dataset specifically tailored for
construction sites, taking into account the diverse challenges posed by adverse
weather and environmental conditions. The dataset is designed to enhance the
training and evaluation of object detection models, fostering their
adaptability and reliability in real-world construction applications. Our
dataset comprises annotated images captured under a wide range of different
weather conditions, including but not limited to sunny days, rainy periods,
foggy atmospheres, and low-light situations. Additionally, environmental
factors such as the existence of dirt/mud on the camera lens are integrated
into the dataset through actual captures and synthetic generation to simulate
the complex conditions prevalent in construction sites. We also generate
synthetic images of the annotations including precise semantic segmentation
masks for various objects commonly found in construction environments, such as
wheel loader machines, personnel, cars, and structural elements. To demonstrate
the dataset's utility, we evaluate state-of-the-art object detection algorithms
on our proposed benchmark. The results highlight the dataset's success in
adversarial training models across diverse conditions, showcasing its efficacy
compared to existing datasets that lack such environmental variability.Comment: 9 page
Article img2ndsm:Height estimation from single airborne rgb images with deep learning
Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.</p
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