SAGA: Spectral Adversarial Geometric Attack on 3D Meshes

A triangular mesh is one of the most popular 3D data representations. As such, the deployment of deep neural networks for mesh processing is widely spread and is increasingly attracting more attention. However, neural networks are prone to adversarial attacks, where carefully crafted inputs impair the model's functionality. The need to explore these vulnerabilities is a fundamental factor in the future development of 3D-based applications. Recently, mesh attacks were studied on the semantic level, where classifiers are misled to produce wrong predictions. Nevertheless, mesh surfaces possess complex geometric attributes beyond their semantic meaning, and their analysis often includes the need to encode and reconstruct the geometry of the shape. We propose a novel framework for a geometric adversarial attack on a 3D mesh autoencoder. In this setting, an adversarial input mesh deceives the autoencoder by forcing it to reconstruct a different geometric shape at its output. The malicious input is produced by perturbing a clean shape in the spectral domain. Our method leverages the spectral decomposition of the mesh along with additional mesh-related properties to obtain visually credible results that consider the delicacy of surface distortions. Our code is publicly available at https://github.com/StolikTomer/SAGA

SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow

Scene flow estimation is a long-standing problem in computer vision, where the goal is to find the 3D motion of a scene from its consecutive observations. Recently, there have been efforts to compute the scene flow from 3D point clouds. A common approach is to train a regression model that consumes source and target point clouds and outputs the per-point translation vectors. An alternative is to learn point matches between the point clouds concurrently with regressing a refinement of the initial correspondence flow. In both cases, the learning task is very challenging since the flow regression is done in the free 3D space, and a typical solution is to resort to a large annotated synthetic dataset. We introduce SCOOP, a new method for scene flow estimation that can be learned on a small amount of data without employing ground-truth flow supervision. In contrast to previous work, we train a pure correspondence model focused on learning point feature representation and initialize the flow as the difference between a source point and its softly corresponding target point. Then, in the run-time phase, we directly optimize a flow refinement component with a self-supervised objective, which leads to a coherent and accurate flow field between the point clouds. Experiments on widespread datasets demonstrate the performance gains achieved by our method compared to existing leading techniques while using a fraction of the training data. Our code is publicly available at https://github.com/itailang/SCOOP

Expansion of immunoglobulin-secreting cells and defects in B cell tolerance in Rag-dependent immunodeficiency

The contribution of B cells to the pathology of Omenn syndrome and leaky severe combined immunodeficiency (SCID) has not been previously investigated. We have studied a mut/mut mouse model of leaky SCID with a homozygous Rag1 S723C mutation that impairs, but does not abrogate, V(D)J recombination activity. In spite of a severe block at the pro–B cell stage and profound B cell lymphopenia, significant serum levels of immunoglobulin (Ig) G, IgM, IgA, and IgE and a high proportion of Ig-secreting cells were detected in mut/mut mice. Antibody responses to trinitrophenyl (TNP)-Ficoll and production of high-affinity antibodies to TNP–keyhole limpet hemocyanin were severely impaired, even after adoptive transfer of wild-type CD4+ T cells. Mut/mut mice produced high amounts of low-affinity self-reactive antibodies and showed significant lymphocytic infiltrates in peripheral tissues. Autoantibody production was associated with impaired receptor editing and increased serum B cell–activating factor (BAFF) concentrations. Autoantibodies and elevated BAFF levels were also identified in patients with Omenn syndrome and leaky SCID as a result of hypomorphic RAG mutations. These data indicate that the stochastic generation of an autoreactive B cell repertoire, which is associated with defects in central and peripheral checkpoints of B cell tolerance, is an important, previously unrecognized, aspect of immunodeficiencies associated with hypomorphic RAG mutations

Change detection from multiple camera images extended to non-stationary cameras

We describe an approach for analysis of surveillance video taken from moving vehicles making repeated passes through a specific, well-defined corridor. Our goal is to detect stationary objects which have appeared in scenes along the established route. Our motivation is to address security concerns in hostile theaters where stationary surveillance cameras would be destroyed almost immediately; yet mobile camera platforms; i.e., group transport vehicles/convoys are plentiful. Challenges include illumination changes from different time/day, and handling parallax resulting from our non-stationary camera. We provide an example using artificial surveillance images taken on the Stanford University campus. Scale-up to critical security theaters would be straightforward. The approach is equally applicable to images collected by aircraft