Geometry-Aware Instance Segmentation with Disparity Maps

Most previous works of outdoor instance segmentation for images only use color information. We explore a novel direction of sensor fusion to exploit stereo cameras. Geometric information from disparities helps separate overlapping objects of the same or different classes. Moreover, geometric information penalizes region proposals with unlikely 3D shapes thus suppressing false positive detections. Mask regression is based on 2D, 2.5D, and 3D ROI using the pseudo-lidar and image-based representations. These mask predictions are fused by a mask scoring process. However, public datasets only adopt stereo systems with shorter baseline and focal legnth, which limit measuring ranges of stereo cameras. We collect and utilize High-Quality Driving Stereo (HQDS) dataset, using much longer baseline and focal length with higher resolution. Our performance attains state of the art. Please refer to our project page. The full paper is available here.Comment: CVPR 2020 Workshop of Scalability in Autonomous Driving (WSAD). Please refer to WSAD site for detail

InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation

Indoor monocular depth estimation has attracted increasing research interest. Most previous works have been focusing on methodology, primarily experimenting with NYU-Depth-V2 (NYUv2) Dataset, and only concentrated on the overall performance over the test set. However, little is known regarding robustness and generalization when it comes to applying monocular depth estimation methods to real-world scenarios where highly varying and diverse functional \textit{space types} are present such as library or kitchen. A study for performance breakdown into space types is essential to realize a pretrained model's performance variance. To facilitate our investigation for robustness and address limitations of previous works, we collect InSpaceType, a high-quality and high-resolution RGBD dataset for general indoor environments. We benchmark 11 recent methods on InSpaceType and find they severely suffer from performance imbalance concerning space types, which reveals their underlying bias. We extend our analysis to 4 other datasets, 3 mitigation approaches, and the ability to generalize to unseen space types. Our work marks the first in-depth investigation of performance imbalance across space types for indoor monocular depth estimation, drawing attention to potential safety concerns for model deployment without considering space types, and further shedding light on potential ways to improve robustness. See \url{https://depthcomputation.github.io/DepthPublic} for data

Composite type A thymoma and diffuse large B-cell lymphoma

AbstractThe concurrent occurrence of thymoma and diffuse large B-cell lymphoma in the thymus has not been previously reported. We describe a 74-year-old man who presented with general weakness, neck lymphadenopathy, night sweats, and body weight loss. A right anterior mediastinal mass was found on computed tomography of the chest. The immunohistochemical stains AE1/AE3, CD20, CD3, and MUM-1 confirmed the different components of the mediastinal tumor. A heavy-chain gene clonality assay and light-chain gene clonality assay confirmed the B-cell clonality of the mediastinal tumor and neck lymph node. The patient had received a complete course of chemotherapy, and the result of positron emission tomography–computed tomography showed complete remission. The pathologic report of this mass revealed composite type A thymoma and diffuse large B-cell lymphoma. If concurrent or composite thymoma and lymphoma are suspected, a thorough examination of the thymoma with a combination of ancillary studies is recommended to rule out the possibility of concurrent lymphoma