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

Potential Mechanisms Responsible for the Antinephrolithic Effects of an Aqueous Extract of Fructus Aurantii

The potential effects of Fa extract on the prevention and treatment of CaOx nephrolithiasis were analyzed in an ethylene glycol- (EG-) induced CaOx crystallization model in rats and an in vitro assay. Multiple biochemical variables were measured in the urine and kidney. Kidney sections were subjected to histopathological and immunohistochemical analyses. Urolithiasis-related osteopontin (OPN) was evaluated by Western blotting. The in vitro assay revealed the significant inhibition of crystal formation (3.50±1.43) and dilution of formed crystals (12.20±3.35) in the group treated with 1 mg/mL Fa extract compared with the control group (52.30±4.71 and 53.00±4.54, resp.) (p<0.05). The in vivo experiments showed that prophylactic treatment with Fa aqueous extract significantly prevented EG-induced renal crystallization and pathological alterations compared with nephrolithic rats (p<0.05). Significantly lower levels of oxidative stress, oxalate, and OPN expression as well as increased citrate and urine output levels were observed in both the low- and high-dose prophylactic groups (p<0.05). However, in the low- and high-dose therapeutic groups, none of these indexes were significantly improved (p>0.05) except for urinary oxalate in the high-dose therapeutic groups (p<0.05). Fa extract prevented CaOx crystallization and promoted crystal dissolution in vitro. Additionally, it was efficacious in preventing the formation of CaOx nephrolithiasis in rats