Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 12 of 12 for “"3D object detection"”.
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3D Object Detection from Images
… Such actions require to identify and localize objects in the environment, effectively building a robust understanding of the scene. Humans easily gain this understanding thanks to their binocular vision, which provides an high-resolution and continuous stream of information to our brain that …
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3D Object Detection for Road Safety at Urban Intersections
… was to evaluate the error propagation of 2D and 3D object detection models for SSA. To achieve this, I built a bespoke conflict simulator to generate ground-truth conflicts that can be extracted by each object detector method. My research identified how minor inaccuracies can dramatically skew …
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The quaternion Bingham Distribution, 3D object detection, and dynamic manipulation
… 3-D rotational data. To specify "where" an object is in space, one must provide both a position and an orientation for the object. Noise and ambiguity in the robot's sensory data necessitate a robust model for representing uncertainty on the space of 3-D orientations. This is given by the …
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A Real-Time 3D Object Detection, Recognition and Presentation System on a Mobile Device for Assistive Navigation
… thesis proposes an integrated solution for 3D object detection, recognition, and presentation to increase accessibility for various user groups in indoor areas through a mobile application. The system has three major components: a 3D object detection module, an object tracking and update …
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Depth Correction++ for pseudo-LiDAR
In recent years, objection detection is one of the most important tasks in the autonomous driving area. There are many research studies done for 2D object detection that have achieved very high detection accuracy. However, the recent trend is 3D object detection. 3D object detection can provide …
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Pose Estimation and 3D Bounding Box Prediction for Autonomous Vehicles Through Lidar and Monocular Camera Sensor Fusion
… and compares its performance with VGG-19 for 3D object detection in autonomous vehicles. ResNet-101 is a deep Convolutional Neural Network with 101 layers and VGG-19 is a one with 19 layers. The research emphasizes the fusion of camera and lidar outputs to enhance the accuracy of 3D bounding …
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Integrative and Multi-scale Deep Learning for 3D Point Cloud Transmission Corridor Scene Segmentation: Noise Filtering, Attention-Fused Feature Integration, and Panoptic Network
… It enables the acquisition of high-density 3D point clouds with pulse repetition frequencies ranging from 100Hz to 2MHz. However, the increased overlap with atmospheric points has posed challenges in noise filtering and 3D point cloud quality. This dissertation proposes the Noise Seeking …
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SMALL AND LARGE PERCEPTION MODELS FOR ROBOTIC NAVIGATION
… typically focus on specialized tasks, such as object detection, segmentation, and terrain classification. On the other hand, large-scale vision models, particularly vision-language models (VLMs), leverage extensive training data to capture richer contextual information and enhance …
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3D diffusion based generation model for point cloud annotation and generation
… with the rapid advancement of applications and 3D scanning sensors, the demand for 3D deep learning based technology and data has increased dramatically. Especially 3D shape with semantic labels plays a significant role in 3D vision problems, such as auto-driven, 3D object detection and 3D scene …
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Learning Birds-Eye View Representations for Autonomous Driving
… notably that 2D images do not provide explicit 3D structure. We overcome this limitation by applying a combination of deep learning and geometry to transform image-based features into an orthographic birds-eye view representation of the scene, allowing algorithms to reason in a metric, 3D space. …
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Learning 3D Representations from Data
… algorithms are extremely good at recognizing objects in an image, but they fail to reason about 3D geometry. Second, the current success in the 2D domain is mainly due to the advance in convolutional neural networks (CNNs). However, CNNs do not generalize to arbitrary data modalities such as …
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Resource-efficient optimizations of 3D vision models for segmentation and detection
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01