SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks

Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries rather than pixel-wise raster images. This need contrasts with the majority of existing methods, which typically generate over-smoothed footprint polygons. Editing these automatically produced polygons can be inefficient, if not more time-consuming than manual digitization. This paper introduces a semi-automatic approach for building footprint extraction through semantically-sensitive superpixels and neural graph networks. Drawing inspiration from object-based classification techniques, we first learn to generate superpixels that are not only boundary-preserving but also semantically-sensitive. The superpixels respond exclusively to building boundaries rather than other natural objects, while simultaneously producing semantic segmentation of the buildings. These intermediate superpixel representations can be naturally considered as nodes within a graph. Consequently, graph neural networks are employed to model the global interactions among all superpixels and enhance the representativeness of node features for building segmentation. Classical approaches are utilized to extract and regularize boundaries for the vectorized building footprints. Utilizing minimal clicks and straightforward strokes, we efficiently accomplish accurate segmentation outcomes, eliminating the necessity for editing polygon vertices. Our proposed approach demonstrates superior precision and efficacy, as validated by experimental assessments on various public benchmark datasets. A significant improvement of 8% in AP50 was observed in vector graphics evaluation, surpassing established techniques. Additionally, we have devised an optimized and sophisticated pipeline for interactive editing, poised to further augment the overall quality of the results

Meta Architecture for Point Cloud Analysis

Recent advances in 3D point cloud analysis bring a diverse set of network architectures to the field. However, the lack of a unified framework to interpret those networks makes any systematic comparison, contrast, or analysis challenging, and practically limits healthy development of the field. In this paper, we take the initiative to explore and propose a unified framework called PointMeta, to which the popular 3D point cloud analysis approaches could fit. This brings three benefits. First, it allows us to compare different approaches in a fair manner, and use quick experiments to verify any empirical observations or assumptions summarized from the comparison. Second, the big picture brought by PointMeta enables us to think across different components, and revisit common beliefs and key design decisions made by the popular approaches. Third, based on the learnings from the previous two analyses, by doing simple tweaks on the existing approaches, we are able to derive a basic building block, termed PointMetaBase. It shows very strong performance in efficiency and effectiveness through extensive experiments on challenging benchmarks, and thus verifies the necessity and benefits of high-level interpretation, contrast, and comparison like PointMeta. In particular, PointMetaBase surpasses the previous state-of-the-art method by 0.7%/1.4/%2.1% mIoU with only 2%/11%/13% of the computation cost on the S3DIS datasets

AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation

The robotics community is increasingly interested in autonomous aerial transportation. Unmanned aerial vehicles with suspended payloads have advantages over other systems, including mechanical simplicity and agility, but pose great challenges in planning and control. To realize fully autonomous aerial transportation, this paper presents a systematic solution to address these difficulties. First, we present a real-time planning method that generates smooth trajectories considering the time-varying shape and non-linear dynamics of the system, ensuring whole-body safety and dynamic feasibility. Additionally, an adaptive NMPC with a hierarchical disturbance compensation strategy is designed to overcome unknown external perturbations and inaccurate model parameters. Extensive experiments show that our method is capable of generating high-quality trajectories online, even in highly constrained environments, and tracking aggressive flight trajectories accurately, even under significant uncertainty. We plan to release our code to benefit the community.Comment: Accepted by IEEE Robotics and Automation Letter

A Unified Framework for 3D Point Cloud Visual Grounding

Thanks to its precise spatial referencing, 3D point cloud visual grounding is essential for deep understanding and dynamic interaction in 3D environments, encompassing 3D Referring Expression Comprehension (3DREC) and Segmentation (3DRES). We argue that 3DREC and 3DRES should be unified in one framework, which is also a natural progression in the community. To explain, 3DREC help 3DRES locate the referent, while 3DRES also facilitate 3DREC via more fine-grained language-visual alignment. To achieve this, this paper takes the initiative step to integrate 3DREC and 3DRES into a unified framework, termed 3D Referring Transformer (3DRefTR). Its key idea is to build upon a mature 3DREC model and leverage ready query embeddings and visual tokens from the 3DREC model to construct a dedicated mask branch. Specially, we propose Superpoint Mask Branch, which serves a dual purpose: i) By harnessing on the inherent association between the superpoints and point cloud, it eliminates the heavy computational overhead on the high-resolution visual features for upsampling; ii) By leveraging the heterogeneous CPU-GPU parallelism, while the GPU is occupied generating visual and language tokens, the CPU concurrently produces superpoints, equivalently accomplishing the upsampling computation. This elaborate design enables 3DRefTR to achieve both well-performing 3DRES and 3DREC capacities with only a 6% additional latency compared to the original 3DREC model. Empirical evaluations affirm the superiority of 3DRefTR. Specifically, on the ScanRefer dataset, 3DRefTR surpasses the state-of-the-art 3DRES method by 12.43% in mIoU and improves upon the SOTA 3DREC method by 0.6% [email protected]. The codes and models will be released soon

Nitrogen isotope evidence for expanded ocean suboxia in the early Cenozoic

The million-year variability of the marine nitrogen cycle is poorly understood. Before 57 million years (Ma) ago, the ^(15)N/^(14)N ratio (δ^(15)N) of foraminifera shell-bound organic matter from three sediment cores was high, indicating expanded water column suboxia and denitrification. Between 57 and 50 Ma ago, δ^(15)N declined by 13 to 16 per mil in the North Pacific and by 3 to 8 per mil in the Atlantic. The decline preceded global cooling and appears to have coincided with the early stages of the Asia-India collision. Warm, salty intermediate-depth water forming along the Tethys Sea margins may have caused the expanded suboxia, ending with the collision. From 50 to 35 Ma ago, δ^(15)N was lower than modern values, suggesting widespread sedimentary denitrification on broad continental shelves. Δ^(15)N rose at 35 Ma ago, as ice sheets grew, sea level fell, and continental shelves narrowed

A Frustum-based probabilistic framework for 3D object detection by fusion of LiDAR and camera data

Abstract(#br)This paper presents a real-time 3D object detector based on LiDAR based Simultaneous Localization and Mapping (LiDAR-SLAM). The 3D point clouds acquired by mobile LiDAR systems, within the environment of buildings, are usually highly sparse, irregularly distributed, and often contain occlusion and structural ambiguity. Existing 3D object detection methods based on Convolutional Neural Networks (CNNs) rely heavily on both the stability of the 3D features and a large amount of labelling. A key challenge is efficient detection of 3D objects in point clouds of large-scale building environments without pre-training the 3D CNN model. To project image-based object detection results and LiDAR-SLAM results onto a 3D probability map, we combine visual and range information into a frustum-based probabilistic framework. As such, we solve the sparse and noise problem in LiDAR-SLAM data, in which any point cloud descriptor can hardly be applied. The 3D object detection results, obtained using both backpack LiDAR dataset and the well-known KITTI Vision Benchmark Suite, show that our method outperforms the state-of-the-art methods for object localization and bounding box estimation

Prevention of Wogonin on Colorectal Cancer Tumorigenesis by Regulating p53 Nuclear Translocation

The tumor suppressor protein p53 plays an important role in the development and progression of colon cancer, and the subcellular organelle localization directly affects its function. Wogonin (5,7-dihydroxy-8-methoxyflavone), a mono-flavonoid extracted from root of Scutellaria baicalensis Georgi, possesses acceptable toxicity and has been used in colorectal cancer (CRC) chemoprevention in pre-clinical trials by oncologist. However, the underlying anti-colon cancer mechanisms of wogonin are not yet fully understood. In the present study, the effect of wogonin on the initiation and development of colitis-associated cancer through p53 nuclear translocation was explored. AOM-DSS CRC animal model and human CRC HCT-116 cell model were used to evaluate the in vivo and in vitro anti-colon cancer action of wogonin. We observed that wogonin showed a dramaticlly preventive effect on colon cancer. Our results showed that wogonin caused apoptotic cell death in human CRC HCT-116 cell through increased endoplasmic reticulum (ER) stress. Meanwhile, excessive ER stress facilitated the cytoplasmic localization of p53 through increasing phosphor-p53 at S315 and S376 sites, induced caspase-dependent apoptosis and inhibited autophagy. Furthermore, we verified the chemoprevention effect and toxicity of wogonin in vivo by utilizing an AOM-DSS colon cancer animal model. We found that wogonin not only reduced tumor multiplicity, preserved colon length to normal (6.79 ± 0.34 to 7.41 ± 0.56, P < 0.05) but also didn’t induce side effects on various organs. In conclusion, these results explain the anti-tumor effect of wogonin in CRC and suggest wogonin as a potential therapeutic candidate for the therapeutic strategy in CRC treatment