Existing deep learning-based approaches for monocular 3D object detection in autonomous driving often model the as a rotated cuboid while object’s geometric shape has been ignored. In this work, we propose an approach incorporating shape-aware 2D/3D constraints into framework. Specifically, employ neural network to learn distinguished 2D keypoints image domain and regress their corresponding coordinates local coordinate first. Then are built by these correspondences each boost performance....

An autonomous excavator system (AES) operates in the real world over long time periods.

To understand human behaviors, action recognition based on videos is a common approach. Compared with image-based recognition, provide much more information, reducing the ambiguity of actions. In last decade, many works focus datasets, novel models and learning approaches have improved video to higher level. However, there are challenges unsolved problems, in particular sports analytics where data collection labeling sophisticated, requiring people domain knowledge even sport professionals...

Previous garment modeling techniques mainly focus on designing novel garments to dress up virtual characters. We study the of real and develop a system that is intuitive use even for novice users. Our includes component detectors design attribute classifiers learned from manually labeled image database. In time, we scan with Kinect build rough shape by KinectFusion raw RGBD sequence. The will identify components (e.g. collar, sleeve, pockets, belt, buttons) their attributes falbala collar or...

AbstractThis study investigates the superposition law of blasting damage between double cylindrical charges in PMMA through laboratory experiments and numerical simulation. The results showed that evolution is consistent with fractal law. For a single charge, crushing area distributed throughout charge section, whereas stemming section consists primarily sparse cracks contains no area. peak concentrated 5 12.5 times borehole's diameter from initiation point. distance boreholes does not...

To study the interaction of moving cracks and pre-cracks plexiglass (PMMA) under impact loading, a three-point bending experiment was carried out using dynamic caustics experimental system. Research showed that when offset distance between pre-crack defect is 9 mm, failure mode crack most complicated. When 0 experiences attraction force defect, stress intensity factor growth speed increase gradually, peak value reached intersect. greater than 6 effect on no longer significant, propagates...

While deep learning has been widely used for video analytics, such as classification and action detection, dense detection with fast-moving subjects from sports videos is still challenging. In this work, we release yet another benchmark P 2 ANet ing ong- A ction which consists of 2,721 clips collected the broadcasting professional table tennis matches in World Table Tennis Championships Olympiads. We work a crew professionals referees on specially designed annotation toolbox to obtain...

Holistically understanding an object with its 3D movable parts is essential for visual models of a robot to interact the world. For example, only by many possible part dynamics other vehicles (e.g., door or trunk opening, taillight blinking changing lane), self-driving vehicle can be success in dealing emergency cases. However, existing tackle rarely on these situations, but focus bounding box detection. In this paper, we fill important missing piece autonomous driving solving two critical...

Small object detection in traffic sign applications often faces challenges like complex backgrounds, blurry samples, and multi-scale variations. Existing solutions tend to complicate the algorithms. In this study, we designed an efficient simple algorithm network called StarCAN-PFD, based on single-stage YOLOv8 framework, accurately recognize small objects scenarios. We proposed StarCAN feature extraction network, which was enhanced with Context Anchor Attention (CAA). Pyramid Focus...

Abstract We present InstanceFusion, a robust real‐time system to detect, segment, and reconstruct instance‐level 3D objects of indoor scenes with hand‐held RGBD camera. It combines the strengths deep learning traditional SLAM techniques produce visually compelling semantic models. The key success comes from our novel segmentation scheme efficient data fusion, which are both implemented on GPU. Specifically, for each incoming frame, we take advantages features, point cloud, reconstructed...

Part information has been shown to be resistant occlusions and viewpoint changes, which is beneficial for various vision-related tasks. However, we found very limited work in car pose estimation reconstruction from street views leveraging the part information. There are two major contributions this paper. Firstly, make first attempt build a framework simultaneously estimate shape, translation, orientation, semantic parts of cars 3D space single view. As it labor-intensive annotate on real...

We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from single image for autonomous driving. Our combines the strengths deep learning elegance traditional techniques part-based deformable model representation produce high-quality in presence severe occlusions. new vehicle that is used instance segmentation automatically generate dataset contains dense correspondences between 2D images models. also end-to-end neural network predict 2D/3D...

We present a novel approach to robustly detect and perceive vehicles in different camera views as part of cooperative vehicle-infrastructure system (CVIS). Our formulation is designed for arbitrary makes no assumptions about intrinsic or extrinsic parameters. First, deal with multi-view data scarcity, we propose part-assisted view synthesis algorithm augmentation. train part-based texture inpainting network self-supervised manner. Then render the textured model into background image target...

This paper analyzes the problem of campus green space in relieving college students’ psychological pressure. It discusses this issue from five aspects: source pressure, significance alleviating theoretical analysis how spaces can relieve principles to be followed when using alleviate and selection appropriate spaces. Furthermore, it examines role pressure for students proposes methods utilizing The aim is summarize analyze recent developments accomplishments stress relief, thereby further...

Excavators are widely used for material-handling applications in unstructured environments, including mining and construction. The size of the global market excavators is 44.12 Billion USD 2018 predicted to grow 63.14 by 2026. Operating a real-world environment can be challenging due extreme conditions rock sliding, ground collapse, or exceeding dust. Multiple fatalities injuries occur each year during excavations. An autonomous excavator that substitute human operators these hazardous...

Understanding an articulated 3D object with its movable parts is essential skill for intelligent agent. This paper presents a novel approach to parse part mobility from point cloud sequences. The key innovation learning explicit correspondence raw unordered sequence. We propose deep network called P^3-Net parallelize trajectory feature extraction and establishment, performing joint optimization between them. Specifically, we design Match-LSTM module reaggregate features among different...