311,505 research outputs found
Geometry meets semantics for semi-supervised monocular depth estimation
Depth estimation from a single image represents a very exciting challenge in
computer vision. While other image-based depth sensing techniques leverage on
the geometry between different viewpoints (e.g., stereo or structure from
motion), the lack of these cues within a single image renders ill-posed the
monocular depth estimation task. For inference, state-of-the-art
encoder-decoder architectures for monocular depth estimation rely on effective
feature representations learned at training time. For unsupervised training of
these models, geometry has been effectively exploited by suitable images
warping losses computed from views acquired by a stereo rig or a moving camera.
In this paper, we make a further step forward showing that learning semantic
information from images enables to improve effectively monocular depth
estimation as well. In particular, by leveraging on semantically labeled images
together with unsupervised signals gained by geometry through an image warping
loss, we propose a deep learning approach aimed at joint semantic segmentation
and depth estimation. Our overall learning framework is semi-supervised, as we
deploy groundtruth data only in the semantic domain. At training time, our
network learns a common feature representation for both tasks and a novel
cross-task loss function is proposed. The experimental findings show how,
jointly tackling depth prediction and semantic segmentation, allows to improve
depth estimation accuracy. In particular, on the KITTI dataset our network
outperforms state-of-the-art methods for monocular depth estimation.Comment: 16 pages, Accepted to ACCV 201
Learning Single-Image Depth from Videos using Quality Assessment Networks
Depth estimation from a single image in the wild remains a challenging
problem. One main obstacle is the lack of high-quality training data for images
in the wild. In this paper we propose a method to automatically generate such
data through Structure-from-Motion (SfM) on Internet videos. The core of this
method is a Quality Assessment Network that identifies high-quality
reconstructions obtained from SfM. Using this method, we collect single-view
depth training data from a large number of YouTube videos and construct a new
dataset called YouTube3D. Experiments show that YouTube3D is useful in training
depth estimation networks and advances the state of the art of single-view
depth estimation in the wild
A Variational Framework for Structure from Motion inOmnidirectional Image Sequences
We address the problem of depth and ego-motion estimation from omnidirectional images. We propose a correspondence-free structure-from-motion problem for sequences of images mapped on the 2-sphere. A novel graph-based variational framework is first proposed for depth estimation between pairs of images. The estimation is cast as a TV-L1 optimization problem that is solved by a fast graph-based algorithm. The ego-motion is then estimated directly from the depth information without explicit computation of the optical flow. Both problems are finally addressed together in an iterative algorithm that alternates between depth and ego-motion estimation for fast computation of 3D information from motion in image sequences. Experimental results demonstrate the effective performance of the proposed algorithm for 3D reconstruction from synthetic and natural omnidirectional image
Reducing “Structure from Motion”: a general framework for dynamic vision. 1. Modeling
The literature on recursive estimation of structure and motion from monocular image sequences comprises a large number of apparently unrelated models and estimation techniques. We propose a framework that allows us to derive and compare all models by following the idea of dynamical system reduction. The “natural” dynamic model, derived from the rigidity constraint and the projection model, is first reduced by explicitly decoupling structure (depth) from motion. Then, implicit decoupling techniques are explored, which consist of imposing that some function of the unknown parameters is held constant. By appropriately choosing such a function, not only can we account for models seen so far in the literature, but we can also derive novel ones
Lifting GIS Maps into Strong Geometric Context for Scene Understanding
Contextual information can have a substantial impact on the performance of
visual tasks such as semantic segmentation, object detection, and geometric
estimation. Data stored in Geographic Information Systems (GIS) offers a rich
source of contextual information that has been largely untapped by computer
vision. We propose to leverage such information for scene understanding by
combining GIS resources with large sets of unorganized photographs using
Structure from Motion (SfM) techniques. We present a pipeline to quickly
generate strong 3D geometric priors from 2D GIS data using SfM models aligned
with minimal user input. Given an image resectioned against this model, we
generate robust predictions of depth, surface normals, and semantic labels. We
show that the precision of the predicted geometry is substantially more
accurate other single-image depth estimation methods. We then demonstrate the
utility of these contextual constraints for re-scoring pedestrian detections,
and use these GIS contextual features alongside object detection score maps to
improve a CRF-based semantic segmentation framework, boosting accuracy over
baseline models
Towards Keypoint Guided Self-Supervised Depth Estimation
This paper proposes to use keypoints as a self-supervision clue for learning
depth map estimation from a collection of input images. As ground truth depth
from real images is difficult to obtain, there are many unsupervised and
self-supervised approaches to depth estimation that have been proposed. Most of
these unsupervised approaches use depth map and ego-motion estimations to
reproject the pixels from the current image into the adjacent image from the
image collection. Depth and ego-motion estimations are evaluated based on pixel
intensity differences between the correspondent original and reprojected
pixels. Instead of reprojecting the individual pixels, we propose to first
select image keypoints in both images and then reproject and compare the
correspondent keypoints of the two images. The keypoints should describe the
distinctive image features well. By learning a deep model with and without the
keypoint extraction technique, we show that using the keypoints improve the
depth estimation learning. We also propose some future directions for
keypoint-guided learning of structure-from-motion problems
Reducing "Structure From Motion": a General Framework for Dynamic Vision - Part 1: Modeling
The literature on recursive estimation of structure and motion from monocular image sequences comprises a large number of different models and estimation techniques. We propose a framework that allows us to derive and compare all models by following the idea of dynamical system reduction.
The "natural" dynamic model, derived by the rigidity constraint and the perspective projection, is first reduced by explicitly decoupling structure (depth) from motion. Then implicit decoupling techniques are explored, which consist of imposing that some function of the unknown parameters is held constant. By appropriately choosing such a function, not only can we account for all models seen so far in the literature, but we can also derive novel ones
A Kalman filter approach to direct depth estimation incorporating surface structure
The problem of depth-from-motion using a monocular image sequence is considered. A pixel-based model is developed for direct depth estimation within a Kaiman filtering framework. A method is proposed for incorporating local surface structure into the Kaiman filter. Experimental results are provided to illustrate the effect of structural information on depth estimation. ©1999 IEEE.published_or_final_versio
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