LiDAR Enhanced Structure-from-Motion

Although Structure-from-Motion (SfM) as a maturing technique has been widely used in many applications, state-of-the-art SfM algorithms are still not robust enough in certain situations. For example, images for inspection purposes are often taken in close distance to obtain detailed textures, which will result in less overlap between images and thus decrease the accuracy of estimated motion. In this paper, we propose a LiDAR-enhanced SfM pipeline that jointly processes data from a rotating LiDAR and a stereo camera pair to estimate sensor motions. We show that incorporating LiDAR helps to effectively reject falsely matched images and significantly improve the model consistency in large-scale environments. Experiments are conducted in different environments to test the performance of the proposed pipeline and comparison results with the state-of-the-art SfM algorithms are reported.Comment: 6 pages plus reference. Work has been submitted to ICRA 202

A novel method for high-throughput detection and quantification of neutrophil extracellular traps reveals ROS-independent NET release with immune complexes

AbstractA newly-described first-line immune defence mechanism of neutrophils is the release of neutrophil extracellular traps (NETs). Immune complexes (ICxs) induce low level NET release. As such, the in vitro quantification of NETs is challenging with current methodologies. In order to investigate the role of NET release in ICx-mediated autoimmune diseases, we developed a highly sensitive and automated method for quantification of NETs. After labelling human neutrophils with PKH26 and extracellular DNA with Sytox green, cells are fixed and automatically imaged with 3-dimensional confocal laser scanning microscopy (3D-CLSM). NET release is then quantified with digital image analysis whereby the NET amount (Sytox green area) is corrected for the number of imaged neutrophils (PKH26 area). A high sensitivity of the assay is achieved by a) significantly augmenting the area of the well imaged (11%) as compared to conventional assays (0.5%) and b) using a 3D imaging technique for optimal capture of NETs, which are topologically superimposed on neutrophils. In this assay, we confirmed low levels of NET release upon human ICx stimulation which were positive for citrullinated histones and neutrophil elastase. In contrast to PMA-induced NET release, ICx-induced NET release was unchanged when co-incubated with diphenyleneiodonium (DPI). We were able to quantify NET release upon stimulation with serum from RA and SLE patients, which was not observed with normal human serum. To our knowledge, this is the first semi-automated assay capable of sensitive detection and quantification of NET release at a low threshold by using 3D CLSM. The assay is applicable in a high-throughput manner and allows the in vitro analysis of NET release in ICx-mediated autoimmune diseases

VoxDet: Voxel Learning for Novel Instance Detection

Detecting unseen instances based on multi-view templates is a challenging problem due to its open-world nature. Traditional methodologies, which primarily rely on 2D representations and matching techniques, are often inadequate in handling pose variations and occlusions. To solve this, we introduce VoxDet, a pioneer 3D geometry-aware framework that fully utilizes the strong 3D voxel representation and reliable voxel matching mechanism. VoxDet first ingeniously proposes template voxel aggregation (TVA) module, effectively transforming multi-view 2D images into 3D voxel features. By leveraging associated camera poses, these features are aggregated into a compact 3D template voxel. In novel instance detection, this voxel representation demonstrates heightened resilience to occlusion and pose variations. We also discover that a 3D reconstruction objective helps to pre-train the 2D-3D mapping in TVA. Second, to quickly align with the template voxel, VoxDet incorporates a Query Voxel Matching (QVM) module. The 2D queries are first converted into their voxel representation with the learned 2D-3D mapping. We find that since the 3D voxel representations encode the geometry, we can first estimate the relative rotation and then compare the aligned voxels, leading to improved accuracy and efficiency. Exhaustive experiments are conducted on the demanding LineMod-Occlusion, YCB-video, and the newly built RoboTools benchmarks, where VoxDet outperforms various 2D baselines remarkably with 20% higher recall and faster speed. To the best of our knowledge, VoxDet is the first to incorporate implicit 3D knowledge for 2D tasks.Comment: 17 pages, 10 figure

The heterogeneous effect of software patents on expected returns: evidence from India

We contribute to the literature on the role of patenting for economic development by analyzing the impact of patent protection for software in India. We find that a proposed broadening of patent eligibility to include software in 2004 had a large positive effect on average returns for listed software companies in India. An unanticipated reversal of this proposed policy change in 2005 resulted in substantial negative returns. We illustrate substantial heterogeneity in the dynamics of these effects across the sequence of events. We also find smaller firms to have been systematically and most significantly affected by the tightening of patent law with regard to software patents

FoundLoc: Vision-based Onboard Aerial Localization in the Wild

Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GNSS, face limitations in visual-based localization due to appearance variances and stylistic dissimilarities between camera and reference imagery, or operate under the assumption of a known initial pose. In this paper, we developed a GNSS-denied localization approach for UAVs that harnesses both Visual-Inertial Odometry (VIO) and Visual Place Recognition (VPR) using a foundation model. This paper presents a novel vision-based pipeline that works exclusively with a nadir-facing camera, an Inertial Measurement Unit (IMU), and pre-existing satellite imagery for robust, accurate localization in varied environments and conditions. Our system demonstrated average localization accuracy within a $20$-meter range, with a minimum error below $1$ meter, under real-world conditions marked by drastic changes in environmental appearance and with no assumption of the vehicle's initial pose. The method is proven to be effective and robust, addressing the crucial need for reliable UAV localization in GNSS-denied environments, while also being computationally efficient enough to be deployed on resource-constrained platforms

Modified reaction centers from Rhodobacter sphaeroides R26

Incubation of photosynthetic reaction centers from Rhodobacter sphaeroides R26 with exogenous 132-OH-bacteriochlorophyll ap or aGG according to Scheer et al. (1987) results in the exchange of endogenous bacteriochlorophyll ap. The exchange amounts to less-than-or-equals, slant 50% according to HPLC analysis, corresponding to a complete replacement of the ‘monomeric’ bacteriochlorophylls, bm and bl, by exogenous pigment. The absorption spectra show small, but distinct changes in the Qx-region of the bacteriochlorophylls, and bleaching of the modified reaction centers is retained. The corresponding binding sites must be accessible from the exterior, and allow for the introduction of a polar residue at C-132. This is supported by the observation of side reactions of the endogenous ‘monomeric’ bacteriochlorophylls within the reaction center pigments, e.g. epimerization and hydroxylation at C-132