1,557 research outputs found
3D Shape Estimation from 2D Landmarks: A Convex Relaxation Approach
We investigate the problem of estimating the 3D shape of an object, given a
set of 2D landmarks in a single image. To alleviate the reconstruction
ambiguity, a widely-used approach is to confine the unknown 3D shape within a
shape space built upon existing shapes. While this approach has proven to be
successful in various applications, a challenging issue remains, i.e., the
joint estimation of shape parameters and camera-pose parameters requires to
solve a nonconvex optimization problem. The existing methods often adopt an
alternating minimization scheme to locally update the parameters, and
consequently the solution is sensitive to initialization. In this paper, we
propose a convex formulation to address this problem and develop an efficient
algorithm to solve the proposed convex program. We demonstrate the exact
recovery property of the proposed method, its merits compared to alternative
methods, and the applicability in human pose and car shape estimation.Comment: In Proceedings of CVPR 201
Semantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a
fundamental problem in computer vision. Handling the difficult cases of this
problem is not only very challenging but also of high practical relevance,
e.g., in the context of life-long localization for augmented reality or
autonomous robots. In this paper, we propose a novel approach based on a joint
3D geometric and semantic understanding of the world, enabling it to succeed
under conditions where previous approaches failed. Our method leverages a novel
generative model for descriptor learning, trained on semantic scene completion
as an auxiliary task. The resulting 3D descriptors are robust to missing
observations by encoding high-level 3D geometric and semantic information.
Experiments on several challenging large-scale localization datasets
demonstrate reliable localization under extreme viewpoint, illumination, and
geometry changes
Continuous Action Recognition Based on Sequence Alignment
Continuous action recognition is more challenging than isolated recognition
because classification and segmentation must be simultaneously carried out. We
build on the well known dynamic time warping (DTW) framework and devise a novel
visual alignment technique, namely dynamic frame warping (DFW), which performs
isolated recognition based on per-frame representation of videos, and on
aligning a test sequence with a model sequence. Moreover, we propose two
extensions which enable to perform recognition concomitant with segmentation,
namely one-pass DFW and two-pass DFW. These two methods have their roots in the
domain of continuous recognition of speech and, to the best of our knowledge,
their extension to continuous visual action recognition has been overlooked. We
test and illustrate the proposed techniques with a recently released dataset
(RAVEL) and with two public-domain datasets widely used in action recognition
(Hollywood-1 and Hollywood-2). We also compare the performances of the proposed
isolated and continuous recognition algorithms with several recently published
methods
Real-Time Seamless Single Shot 6D Object Pose Prediction
We propose a single-shot approach for simultaneously detecting an object in
an RGB image and predicting its 6D pose without requiring multiple stages or
having to examine multiple hypotheses. Unlike a recently proposed single-shot
technique for this task (Kehl et al., ICCV'17) that only predicts an
approximate 6D pose that must then be refined, ours is accurate enough not to
require additional post-processing. As a result, it is much faster - 50 fps on
a Titan X (Pascal) GPU - and more suitable for real-time processing. The key
component of our method is a new CNN architecture inspired by the YOLO network
design that directly predicts the 2D image locations of the projected vertices
of the object's 3D bounding box. The object's 6D pose is then estimated using a
PnP algorithm.
For single object and multiple object pose estimation on the LINEMOD and
OCCLUSION datasets, our approach substantially outperforms other recent
CNN-based approaches when they are all used without post-processing. During
post-processing, a pose refinement step can be used to boost the accuracy of
the existing methods, but at 10 fps or less, they are much slower than our
method.Comment: CVPR 201
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
Representing 3D shape in sparse range images for urban object classification
This thesis develops techniques for interpreting 3D range images acquired in outdoor environments at a low resolution. It focuses on the task of robustly capturing the shapes that comprise objects, in order to classify them. With the recent development of 3D sensors such as the Velodyne, it is now possible to capture range images at video frame rates, allowing mobile robots to observe dynamic scenes in 3D. To classify objects in these scenes, features are extracted from the data, which allows different regions to be matched. However, range images acquired at this speed are of low resolution, and there are often significant changes in sensor viewpoint and occlusion. In this context, existing methods for feature extraction do not perform well. This thesis contributes algorithms for the robust abstraction from 3D points to object classes. Efficient region-of-interest and surface normal extraction are evaluated, resulting in a keypoint algorithm that provides stable orientations. These build towards a novel feature, called the ‘line image,’ that is designed to consistently capture local shape, regardless of sensor viewpoint. It does this by explicitly reasoning about the difference between known empty space, and space that has not been measured due to occlusion or sparse sensing. A dataset of urban objects scanned with a Velodyne was collected and hand labelled, in order to compare this feature with several others on the task of classification. First, a simple k-nearest neighbours approach was used, where the line image showed improvements. Second, more complex classifiers were applied, requiring the features to be clustered. The clusters were used in topic modelling, allowing specific sub-parts of objects to be learnt across multiple scales, improving accuracy by 10%. This work is applicable to any range image data. In general, it demonstrates the advantages in using the inherent density and occupancy information in a range image during 3D point cloud processing
Representing 3D shape in sparse range images for urban object classification
This thesis develops techniques for interpreting 3D range images acquired in outdoor environments at a low resolution. It focuses on the task of robustly capturing the shapes that comprise objects, in order to classify them. With the recent development of 3D sensors such as the Velodyne, it is now possible to capture range images at video frame rates, allowing mobile robots to observe dynamic scenes in 3D. To classify objects in these scenes, features are extracted from the data, which allows different regions to be matched. However, range images acquired at this speed are of low resolution, and there are often significant changes in sensor viewpoint and occlusion. In this context, existing methods for feature extraction do not perform well. This thesis contributes algorithms for the robust abstraction from 3D points to object classes. Efficient region-of-interest and surface normal extraction are evaluated, resulting in a keypoint algorithm that provides stable orientations. These build towards a novel feature, called the ‘line image,’ that is designed to consistently capture local shape, regardless of sensor viewpoint. It does this by explicitly reasoning about the difference between known empty space, and space that has not been measured due to occlusion or sparse sensing. A dataset of urban objects scanned with a Velodyne was collected and hand labelled, in order to compare this feature with several others on the task of classification. First, a simple k-nearest neighbours approach was used, where the line image showed improvements. Second, more complex classifiers were applied, requiring the features to be clustered. The clusters were used in topic modelling, allowing specific sub-parts of objects to be learnt across multiple scales, improving accuracy by 10%. This work is applicable to any range image data. In general, it demonstrates the advantages in using the inherent density and occupancy information in a range image during 3D point cloud processing
- …