Building Scene Models by Completing and Hallucinating Depth and Semantics

Building 3D scene models has been a longstanding goal of computer vision. The great progress in depth sensors brings us one step closer to achieving this in a single shot. However, depth sensors still produce imperfect measurements that are sparse and contain holes. While depth completion aims at tackling this issue, it ignores the fact that some regions of the scene are occluded by the foreground objects. Building a scene model would therefore require to hallucinate the depth behind these objects. In contrast with existing methods that either rely on manual input, or focus on the indoor scenario, we introduce a fully-automatic method to jointly complete and hallucinate depth and semantics in challenging outdoor scenes. To this end, we develop a two-layer model representing both the visible information and the hidden one. At the heart of our approach lies a formulation based on the Mumford-Shah functional, for which we derive an effective optimization strategy. Our experiments evidence that our approach can accurately fill the large holes in the input depth maps, segment the different kinds of objects in the scene, and hallucinate the depth and semantics behind the foreground objects

Deep Industrial Image Anomaly Detection: A Survey

The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection

Development of Correspondence Field and Its Application to Effective Depth Estimation in Stereo Camera Systems

Stereo camera systems are still the most widely used apparatus for estimating 3D or depth information of a scene due to their low-cost. Estimation of depth using a stereo camera requires first estimating the disparity map using stereo matching algorithms and calculating depth via triangulation based on the camera arrangement (their locations and orientations with respect to the scene). In almost all cases, the arrangement is determined based on human experience since there lacks an effective theoretical tool to guide the design of the camera arrangement. This thesis presents the development of a novel tool, called correspondence field (CF), and its application to optimize the stereo camera arrangement for depth estimation

Diminished Reality based on Kinect Fusion

While Augmented Reality allows adding virtual objects over a real scene, Diminished Reality can remove real objects from a scene. Currently existing multi-view based approaches and image completion based approaches to DR with 2D sensors. In this paper, we propose a practical approach to diminished reality for indoor environment with 3D model creation based on Kinect 3D sensors. Kinect can capture RGB information and per-pixel depth information of scenes at same time, and it can use the depth information to create 3D model. Our approach is based on the 3D model to search the region of the objects which will be removed. First we get the RGB and depth information of a scene from Kinect and get 3D model based on Kinect Fusion. After that an object is added into the scene. We compare the scene with original scene and process surface images acquired from Kinect fusion to find the accurate region of the object. At last we replace the data and diminish the object in real time. The solution uses Kinect fusion technique to create models and OpenCV for image processing. Our approach achieves a good result that we can diminish the objects clearly and restore the scene. Our approach provides real-time Diminished Reality for indoor environment

Semantic Segmentation and Completion of 2D and 3D Scenes

Semantic segmentation is one of the fundamental problems in computer vision. This thesis addresses various tasks, all related to the fine-grained, i.e. pixel-wise or voxel-wise, semantic understanding of a scene. In the recent years semantic segmentation by 2D convolutional neural networks has become as much as a default pre-processing step for many other computer vision tasks, since it outputs very rich spatially resolved feature maps and semantic labels that are useful for many higher level recognition tasks. In this thesis, we make several contributions to the field of semantic scene understanding using an image or a depth measurement, recorded by different types of laser sensors, as input. Firstly, we propose a new approach to 2D semantic segmentation of images. It consists of an adaptation of an existing approach for real time capability under constrained hardware demands that are required by a real life drone. The approach is based on a highly optimized implementation of random forests combined with a label propagation strategy. Next, we shift our focus to what we believe is one of the important next forefronts in computer vision: To give machines the ability to anticipate and extrapolate beyond what is captured in a single frame by a camera or depth sensor. This anticipation capability is what allows humans to efficiently interact with their environment. The need for this ability is most prominently displayed in the behaviour of today's autonomous cars. One of their shortcomings is that they only interpret the current sensor state, which prevents them from anticipating events which would require an adaptation of their driving policy. The result is a lot of sudden breaks and non-human-like driving behaviour, which can provoke accidents or negatively impact the traffic flow. Therefore we first propose a task to spatially anticipate semantic labels outside the field of view of an image. The task is based on the Cityscapes dataset, where each image has been center cropped. The goal is to train an algorithm that predicts the semantic segmentation map in the area outside the cropped input region. Along with the task itself, we propose an efficient iterative approach based on 2D convolutional neural networks by designing a task adapted loss function. Afterwards, we switch to the 3D domain. In three dimensions the goal shifts from assigning pixel-wise labels towards the reconstruction of the full 3D scene using a grid of labeled voxels. Thereby one has to anticipate the semantics and geometry in the space that is occluded by the objects themselves from the viewpoint of an image or laser sensor. The task is known as 3D semantic scene completion and has recently caught a lot of attention. Here we propose two new approaches that advance the performance of existing 3D semantic scene completion baselines. The first one is a two stream approach where we leverage a multi-modal input consisting of images and Kinect depth measurements in an early fusion scheme. Moreover we propose a more memory efficient input embedding. The second approach to semantic scene completion leverages the power of the recently introduced generative adversarial networks (GANs). Here we construct a network architecture that follows the GAN principles and uses a discriminator network as an additional regularizer in the 3D-CNN training. With our proposed approaches in semantic scene completion we achieve a new state-of-the-art performance on two benchmark datasets. Finally we observe that one of the shortcomings in semantic scene completion is the lack of a realistic, large scale dataset. We therefore introduce the first real world dataset for semantic scene completion based on the KITTI odometry benchmark. By semantically annotating alls scans of a 10 Hz Velodyne laser scanner, driving through urban and countryside areas, we obtain data that is valuable for many tasks including semantic scene completion. Along with the data we explore the performance of current semantic scene completion models as well as models for semantic point cloud segmentation and motion segmentation. The results show that there is still a lot of space for improvement for either tasks so our dataset is a valuable contribution for future research into these directions