3DCFS : Fast and robust joint 3D semantic-instance segmentation via coupled feature selection

We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation. Inspired by the human scene perception process, we design a novel coupled feature selection module, named CFSM, that adaptively selects and fuses the reciprocal semantic and instance features from two tasks in a coupled manner. To further boost the performance of the instance segmentation task in our 3DCFS, we investigate a loss function that helps the model learn to balance the magnitudes of the output embedding dimensions during training, which makes calculating the Euclidean distance more reliable and enhances the generalizability of the model. Extensive experiments demonstrate that our 3DCFS outperforms state-of-the-art methods on benchmark datasets in terms of accuracy, speed and computational cost

SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation

Recently, self-supervised monocular depth estimation has gained popularity with numerous applications in autonomous driving and robotics. However, existing solutions primarily seek to estimate depth from immediate visual features, and struggle to recover fine-grained scene details with limited generalization. In this paper, we introduce SQLdepth, a novel approach that can effectively learn fine-grained scene structures from motion. In SQLdepth, we propose a novel Self Query Layer (SQL) to build a self-cost volume and infer depth from it, rather than inferring depth from feature maps. The self-cost volume implicitly captures the intrinsic geometry of the scene within a single frame. Each individual slice of the volume signifies the relative distances between points and objects within a latent space. Ultimately, this volume is compressed to the depth map via a novel decoding approach. Experimental results on KITTI and Cityscapes show that our method attains remarkable state-of-the-art performance (AbsRel = $0.082$ on KITTI, $0.052$ on KITTI with improved ground-truth and $0.106$ on Cityscapes), achieves $9.9\%$, $5.5\%$ and $4.5\%$ error reduction from the previous best. In addition, our approach showcases reduced training complexity, computational efficiency, improved generalization, and the ability to recover fine-grained scene details. Moreover, the self-supervised pre-trained and metric fine-tuned SQLdepth can surpass existing supervised methods by significant margins (AbsRel = $0.043$, $14\%$ error reduction). self-matching-oriented relative distance querying in SQL improves the robustness and zero-shot generalization capability of SQLdepth. Code and the pre-trained weights will be publicly available. Code is available at \href{https://github.com/hisfog/SQLdepth-Impl}{https://github.com/hisfog/SQLdepth-Impl}.Comment: 14 pages, 9 figure

Expectation-Maximization Algorithm with Local Adaptivity ∗

Abstract. We develop an expectation-maximization algorithm with local adaptivity for image segmentation and classification. The key idea of our approach is to combine global statistics extracted from the Gaussian mixture model or other proper statistical models with local statistics and geometrical information, such as local probability distribution, orientation, and anisotropy. The combined information is used to design an adaptive local classification strategy that improves the robustness of the algorithm and also keeps fine features in the image. The proposed methodology is flexible and can be easily generalized to deal with other inferred information/quantities and statistical methods/models

Exogenous Hydrogen Sulfide Activates PI3K/Akt/eNOS Pathway to Improve Replicative Senescence in Human Umbilical Vein Endothelial Cells

Background. Endothelial cell senescence is one of the key mechanistic factors in the pathogenesis of atherosclerosis. In terms of molecules, the phosphatidylinositol 3-kinase/protein kinase B/endothelial nitric oxide synthase (PI3K/Akt/eNOS) signaling plays an important role in the prevention and control of endothelial cell senescence, while hydrogen sulfide (H2S) improves the induced precocious senescence of endothelial cells through the PI3K/Akt/eNOS pathway. Comparatively, replicative senescence in endothelial cells is more in line with the actual physiological changes of human aging. This study aims to investigate the mechanism by which H2S improves endothelial cell replicative senescence and the involvement of the PI3K/Akt/eNOS pathway. Methods. we established a model of replicative senescence in human umbilical vein endothelial cells (HUVECs) and explored the effect of 200 μmol/L sodium hydrosulfide (NaHS; a donor of H2S) on senescence, which was determined by cell morphology, the expression level of plasminogen activator inhibitor 1 (PAI-1), and the positive rate of senescence-associated β-galactosidase (SA-β-Gal) staining. Cell viability was detected by MTT assay to evaluate the effect of NaHS and the PI3K inhibitor, LY294002. Meanwhile, the protein expression of PI3K, Akt, p-Akt, and eNOS in endothelial cells of each group was detected by Western blot. Results. the replicative senescence model was established in HUVECs at the passage of 16 cumulative cell population doubling values (CPDL). Treatment with NaHS not only significantly reduced the expression of PAI-1 and the positive rate of SA-β-Gal in HUVEC’s replicative senescence model but also notably increased the expression of PI3K, p-Akt, p-eNOS, and the content of nitric oxide(NO). However, the effects of NaHS on the expression of the pathway and the content of NO in HUVECs were abolished when LY294002 specifically inhibited PI3K. Conclusion. NaHS improves the replicative senescence of HUVECs with the contribution of the PI3K/Akt/eNOS pathway