Detecting defects buried inside chips is critical for failure analysis in semiconductor manufacturing. In this paper, we perform 3D voxel segmentation on 2.5D semicon chips to locate and identify defects that may be present in them. We integrate tree based Ensemble method with the Cascaded Anisotropic Convolutional Neural Networks to employ component structural characteristics of semicon 3D object in voxel segmentation process. We fabricate custom 2.5D chips purposely creating defective regions by using a specific fabrication and assembling process. Thereafter, use commercial 3D XRM tools for 3D imaging of these chips. We perform accurate 3D Object localization for each 3D x-ray scan by using a slice and fuse approach. Then, we perform voxel segmentation on logic die (integral component of semicon chip) to detect Cu-pillar, solder, and void regions (if any). The results show that we achieve state-of-the-art voxel segmentation dice scores for all three sub-components.

The development of X-Ray microscopy (XRM) technology has enabled non-destructive inspection of semiconductor structures for defect identification. Deep learning is widely used as the state-of-the-art approach to perform visual analysis tasks. However, deep learning based models require large amount of annotated data to train. This can be time-consuming and expensive to obtain especially for dense prediction tasks like semantic segmentation. In this work, we explore active learning (AL) as a potential solution to alleviate the annotation burden. We identify two unique challenges when applying AL on semiconductor XRM scans: large domain shift and severe class-imbalance. To address these challenges, we propose to perform contrastive pretraining on the unlabelled data to obtain the initialization weights for each AL cycle, and a rareness-aware acquisition function that favors the selection of samples containing rare classes. We evaluate our method on a semiconductor dataset that is compiled from XRM scans of high bandwidth memory structures composed of logic and memory dies, and demonstrate that our method achieves state-of-the-art performance.

This paper demonstrates the use of 3D Anisotropic Convolutional Neural Network (CNN) with predict-refine mechanism for 3D brain tumor segmentation. We propose two networks that utilize multi-scale feedback and saliency maps respectively to segment three critical regions involved in automated brain tumor segmentation. The proposed networks are formulated to predict feature maps at different resolutions during the prediction phase. These networks perform refinement process using the saliency or feature maps as feedback information for the refinement process. The recurrent architecture allows the network to automatically rectify errors in saliency map of the previous prediction phase resulting in more reliable final predictions. Our experimental results on the BraTS2017 dataset demonstrate the superior performance of our proposed predict-refine architecture than current state of the art approaches improving results by up to 8% without any additional increase in the 1.9M model parameters.

With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tasks such as 3D object detection. Existing datasets either represent simple scenarios or provide only day-time data. In this paper, we introduce a new challenging A*3D dataset which consists of RGB images and LiDAR data with a significant diversity of scene, time, and weather. The dataset consists of high-density images (≈ 10 times more than the pioneering KITTI dataset), heavy occlusions, a large number of nighttime frames (≈ 3 times the nuScenes dataset), addressing the gaps in the existing datasets to push the boundaries of tasks in autonomous driving research to more challenging highly diverse environments. The dataset contains 39K frames, 7 classes, and 230K 3D object annotations. An extensive 3D object detection benchmark evaluation on the A*3D dataset for various attributes such as high density, day-time/night-time, gives interesting insights into the advantages and limitations of training and testing 3D object detection in real-world setting.

Advances in Unmanned Aerial Vehicle (UAV) opens venues for application such as tunnel inspection. Owing to its versatility to fly inside the tunnels, it can quickly identify defects and potential problems related to safety. However, long tunnels, especially with repetitive or uniform structures pose a significant problem for UAV navigation. Furthermore, post-processing visual data from the camera mounted on the UAV is required to generate useful information for the inspection task. In this work, we design a UAV with a single rotating camera to accomplish the task. Compared to other platforms, our solution can fit the stringent requirement for tunnel inspection, in terms of battery life, size and weight. While the current state-of-the-art can estimate camera pose and 3D geometry from a sequence of images, they assume large overlap, small rotational motion, and many distinct matching points between images. These assumptions severely limit their effectiveness in tunnel-like scenarios where the camera has erratic or large rotational motion, such as the one mounted on the UAV. This paper presents a novel solution which exploits Structure-from-Motion, Bundle Adjustment, and available geometry priors to robustly estimate camera pose and automatically reconstruct a fully-dense 3D scene using the least possible number of images in various challenging tunnel-like environments. We validate our system with both Virtual Reality application and experimentation with a real dataset. The results demonstrate that the proposed reconstruction along with texture mapping allows for remote navigation and inspection of tunnel-like environments, even those which are inaccessible for humans.

Traditional image stitching algorithms use transforms such as homography to combine different views of a scene. They usually work well when the scene is planar or when the camera is only rotated, keeping its position static. This severely limits their use in real world scenarios where an unmanned aerial vehicle (UAV) potentially hovers around and flies in an enclosed area while rotating to capture a video sequence. We utilize known scene geometry along with recorded camera trajectory to create cylindrical images captured in a given environment such as a tunnel where the camera rotates around its center. The captured images of the inner surface of the given scene are combined to create a composite panoramic image that is textured onto a 3D geometrical object in Unity graphical engine to create an immersive environment for end users.

2D object proposals, quickly detected regions in an image that likely contain an object of interest, are an effective approach for improving the computational efficiency and accuracy of object detection in color images. In this paper, we propose a novel online method that generates 3D object proposals in an RGB-D video sequence. Our main observation is that depth images provide important information about the geometry of the scene. Diverging from the traditional goal of 2D object proposals to provide a high recall, we aim for precise 3D proposals. We leverage on depth information per frame and multiview scene information to obtain accurate 3D object proposals. Using efficient but robust registration enables us to combine multiple frames of a scene in near real time and generate 3D bounding boxes for potential 3D regions of interest. Using standard metrics, such as precision-recall (P-R) curves and F-measure, we show that the proposed approach is significantly more accurate than the current state-of-the-art techniques. Our online approach can be integrated into simultaneous localization and mapping-based video processing for quick 3D object localization. Our method takes less than a second in MATLAB on the UW-RGBD scene data set on a single thread CPU and, thus, has potential to be used in low-power chips in unmanned aerial vehicles, quadcopters, and drones.

Depth sensing devices have created various new applications in scientific and commercial research with the advent of Microsoft Kinect and PMD (Photon Mixing Device) cameras. Most of these applications require the depth cameras to be pre-calibrated. However, traditional calibration methods using a checkerboard do not work very well for depth cameras due to the low image resolution. In this paper, we propose a depth calibration scheme which excels in estimating camera calibration parameters when only a handful of corners and calibration images are available. We exploit the noise properties of PMD devices to denoise depth measurements and perform camera calibration using the denoised depth as additional set of measurements. Our synthetic and real experiments show that our depth denoising and depth based calibration scheme provides significantly better results than traditional calibration methods.

This paper explores the possibility of acquiring inverse li ght transport directly. The current strategy of obtaining an inverse light transport ma trix involves two steps: First, acquire the forward light transport matrix (f-LTM) and then calculate the inverse of the f-LTM. Both steps of the strategy requires considerable computational power. In addition to computational cost, the measurement error incurred at the first step inevitably propagates to or potentially gets amplified in the matrix inv ersion step. In this paper, we propose a sensing strategy that acquires the inverse light transport matrix (i-LTM) directly, without reconstructing the f-LTM. Our direct strategy reduces both computational error and cost of acquiring i-LTM. For that, we propose a compressive inverse theory. Following the compressible property of i-LTM, a reconstruction condition for i-LTM is introduced. This new framework implies a trade-off between two factors: condition numbers of submatrices of f-LTM and the isometry constant of the illumination pattern. Our direct i-LTM reconstruction method is then demonstrated with a 2nd-bounce separation experiment on an M-shaped panel scene. Finally by quantitatively comparing our method with the existing two-stage approach, our method shows higher accuracy with lower complexity. The proofs of main theorem/lemma are contained in the supplementary material. The compressive inverse theory is general and potentially useful for wider application.

This paper presents a Markov Random Field (MRF)-based approach for depth map super-resolution and enhancement. Given a low-resolution or moderate quality depth map, we study the problem of enhancing its resolution or quality with a registered high-resolution color image. Different from the previous methods, this MRF-based approach is based on a novel data term formulation that fits well to the unique characteristics of depth maps. We also discuss a few important design choices that boost the performance of general MRF-based methods. Experimental results show that our proposed approach achieves high resolution depth maps at more desirable quality, both qualitatively and quantitatively. It can also be applied to enhance the depth maps derived with state-of-the-art stereo methods, resulting in the raised ranking based on the Middlebury benchmark.

Through radiometric compensation, a projector-camera system can project a desired image onto a non-flat and non-white surface. This can be achieved by computing the inverse light transport of a scene. A light transport matrix is in general large and on the order of 106× 106elements. Therefore, computing the inverse light transport matrix is computationally and memory intensive. Two prior methods were proposed to simplify matrix inversion by ignoring scene inter-reflection between individual or clusters of camera pixels. However, compromising scene inter-reflection in spatial domain introduces spatial artifacts and how to systematically adjust the compensation quality is not obvious. In this work, we show how scene inter-reflection can be systematically approximated by stratifying the light transport of a scene. The stratified light transport enables a similar stratification in the inverse light transport. We can show that the stratified inverse light transport converges to the true inverse. For radiometric compensation, the set of stratified inverse light transport provides a systematic way of quantifying the tradeoff between computational efficiency and accuracy. The framework of stratified matrix inversion is general and can have other applications, especially for applications that involve large-size sparse matrices.