arXiv subjects

Christopher Zach

Publications and source records attributed to Christopher Zach.

28 records · Page 2Linked to original sources

arXiv2019

Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions

Semantic segmentation and instance level segmentation made substantial progress in recent years due to the emergence of deep neural networks (DNNs). A number of deep architectures with Convolution Neural Networks (CNNs) were proposed that surpass the traditional machine learning approaches for segmentation by a large margin. These architectures predict the directly observable semantic category of each pixel by usually optimizing a cross entropy loss. In this work we push the limit of semantic segmentation towards predicting semantic labels of directly visible as well as occluded objects or objects parts, where the network's input is a single depth image. We group the semantic categories into one background and multiple foreground object groups, and we propose a modification of the standard cross-entropy loss to cope with the settings. In our experiments we demonstrate that a CNN trained by minimizing the proposed loss is able to predict semantic categories for visible and occluded object parts without requiring to increase the network size (compared to a standard segmentation task). The results are validated on a newly generated dataset (augmented from SUNCG) dataset.

cs.CV↗

arXiv2019

Contrastive Learning for Lifted Networks

In this work we address supervised learning of neural networks via lifted network formulations. Lifted networks are interesting because they allow training on massively parallel hardware and assign energy models to discriminatively trained neural networks. We demonstrate that the training methods for lifted networks proposed in the literature have significant limitations and show how to use a contrastive loss to address those limitations. We demonstrate that this contrastive training approximates back-propagation in theory and in practice and that it is superior to the training objective regularly used for lifted networks.

cs.CV↗

arXiv2019

Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss

Three-dimensional object detection from a single view is a challenging task which, if performed with good accuracy, is an important enabler of low-cost mobile robot perception. Previous approaches to this problem suffer either from an overly complex inference engine or from an insufficient detection accuracy. To deal with these issues, we present SS3D, a single-stage monocular 3D object detector. The framework consists of (i) a CNN, which outputs a redundant representation of each relevant object in the image with corresponding uncertainty estimates, and (ii) a 3D bounding box optimizer. We show how modeling heteroscedastic uncertainty improves performance upon our baseline, and furthermore, how back-propagation can be done through the optimizer in order to train the pipeline end-to-end for additional accuracy. Our method achieves SOTA accuracy on monocular 3D object detection, while running at 20 fps in a straightforward implementation. We argue that the SS3D architecture provides a solid framework upon which high performing detection systems can be built, with autonomous driving being the main application in mind.

cs.CV↗

arXiv2018

ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time

Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets. State-of-the-art methods are, however, not directly transferable to real-time applications or embedded devices, since naive adaptation of such systems to reduce computational cost (speed, memory and energy) causes a significant drop in accuracy. We propose ContextNet, a new deep neural network architecture which builds on factorized convolution, network compression and pyramid representation to produce competitive semantic segmentation in real-time with low memory requirement. ContextNet combines a deep network branch at low resolution that captures global context information efficiently with a shallow branch that focuses on high-resolution segmentation details. We analyse our network in a thorough ablation study and present results on the Cityscapes dataset, achieving 66.1% accuracy at 18.3 frames per second at full (1024x2048) resolution (41.9 fps with pipelined computations for streamed data).

cs.CV↗

arXiv2018

Weakly supervised learning of indoor geometry by dual warping

A major element of depth perception and 3D understanding is the ability to predict the 3D layout of a scene and its contained objects for a novel pose. Indoor environments are particularly suitable for novel view prediction, since the set of objects in such environments is relatively restricted. In this work we address the task of 3D prediction especially for indoor scenes by leveraging only weak supervision. In the literature 3D scene prediction is usually solved via a 3D voxel grid. However, such methods are limited to estimating rather coarse 3D voxel grids, since predicting entire voxel spaces has large computational costs. Hence, our method operates in image-space rather than in voxel space, and the task of 3D estimation essentially becomes a depth image completion problem. We propose a novel approach to easily generate training data containing depth maps with realistic occlusions, and subsequently train a network for completing those occluded regions. Using multiple publicly available dataset~\cite{song2017semantic,Silberman:ECCV12} we benchmark our method against existing approaches and are able to obtain superior performance. We further demonstrate the flexibility of our method by presenting results for new view synthesis of RGB-D images.

cs.CV↗

arXiv2018

Maximum Consensus Parameter Estimation by Reweighted $\ell_1$ Methods

Robust parameter estimation in computer vision is frequently accomplished by solving the maximum consensus (MaxCon) problem. Widely used randomized methods for MaxCon, however, can only produce {random} approximate solutions, while global methods are too slow to exercise on realistic problem sizes. Here we analyse MaxCon as iterative reweighted algorithms on the data residuals. We propose a smooth surrogate function, the minimization of which leads to an extremely simple iteratively reweighted algorithm for MaxCon. We show that our algorithm is very efficient and in many cases, yields the global solution. This makes it an attractive alternative for randomized methods and global optimizers. The convergence analysis of our method and its fundamental differences from the other iteratively reweighted methods are also presented.

cs.CV↗

arXiv2017

SPP-Net: Deep Absolute Pose Regression with Synthetic Views

Image based localization is one of the important problems in computer vision due to its wide applicability in robotics, augmented reality, and autonomous systems. There is a rich set of methods described in the literature how to geometrically register a 2D image w.r.t.\ a 3D model. Recently, methods based on deep (and convolutional) feedforward networks (CNNs) became popular for pose regression. However, these CNN-based methods are still less accurate than geometry based methods despite being fast and memory efficient. In this work we design a deep neural network architecture based on sparse feature descriptors to estimate the absolute pose of an image. Our choice of using sparse feature descriptors has two major advantages: first, our network is significantly smaller than the CNNs proposed in the literature for this task---thereby making our approach more efficient and scalable. Second---and more importantly---, usage of sparse features allows to augment the training data with synthetic viewpoints, which leads to substantial improvements in the generalization performance to unseen poses. Thus, our proposed method aims to combine the best of the two worlds---feature-based localization and CNN-based pose regression--to achieve state-of-the-art performance in the absolute pose estimation. A detailed analysis of the proposed architecture and a rigorous evaluation on the existing datasets are provided to support our method.

cs.CV↗

arXiv2017

Minimal Solvers for Monocular Rolling Shutter Compensation under Ackermann Motion

Modern automotive vehicles are often equipped with a budget commercial rolling shutter camera. These devices often produce distorted images due to the inter-row delay of the camera while capturing the image. Recent methods for monocular rolling shutter motion compensation utilize blur kernel and the straightness property of line segments. However, these methods are limited to handling rotational motion and also are not fast enough to operate in real time. In this paper, we propose a minimal solver for the rolling shutter motion compensation which assumes known vertical direction of the camera. Thanks to the Ackermann motion model of vehicles which consists of only two motion parameters, and two parameters for the simplified depth assumption that lead to a 4-line algorithm. The proposed minimal solver estimates the rolling shutter camera motion efficiently and accurately. The extensive experiments on real and simulated datasets demonstrate the benefits of our approach in terms of qualitative and quantitative results.

cs.CV↗

arXiv2017

Compact Relaxations for MAP Inference in Pairwise MRFs with Piecewise Linear Priors

Label assignment problems with large state spaces are important tasks especially in computer vision. Often the pairwise interaction (or smoothness prior) between labels assigned at adjacent nodes (or pixels) can be described as a function of the label difference. Exact inference in such labeling tasks is still difficult, and therefore approximate inference methods based on a linear programming (LP) relaxation are commonly used in practice. In this work we study how compact linear programs can be constructed for general piecwise linear smoothness priors. The number of unknowns is O(LK) per pairwise clique in terms of the state space size $L$ and the number of linear segments K. This compares to an O(L^2) size complexity of the standard LP relaxation if the piecewise linear structure is ignored. Our compact construction and the standard LP relaxation are equivalent and lead to the same (approximate) label assignment.

cs.CV↗

arXiv2015

Multi-modal Image Registration for Correlative Microscopy

Correlative microscopy is a methodology combining the functionality of light microscopy with the high resolution of electron microscopy and other microscopy technologies. Image registration for correlative microscopy is quite challenging because it is a multi-modal, multi-scale and multi-dimensional registration problem. In this report, I introduce two methods of image registration for correlative microscopy. The first method is based on fiducials (beads). I generate landmarks from the fiducials and compute the similarity transformation matrix based on three pairs of nearest corresponding landmarks. A least-squares matching process is applied afterwards to further refine the registration. The second method is inspired by the image analogies approach. I introduce the sparse representation model into image analogies. I first train representative image patches (dictionaries) for pre-registered datasets from two different modalities, and then I use the sparse coding technique to transfer a given image to a predicted image from one modality to another based on the learned dictionaries. The final image registration is between the predicted image and the original image corresponding to the given image in the different modality. The method transforms a multi-modal registration problem to a mono-modal one. I test my approaches on Transmission Electron Microscopy (TEM) and confocal microscopy images. Experimental results of the methods are also shown in this report.

cs.CV↗