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Bird's Eye View (BEV) offers a panoramic, god's-eye perspective by consolidating data from various vehicle sensors into a unified model. This high-level view mitigates error accumulation in data fusion, a crucial aspect for autonomous driving. Aligning coordinate systems within BEV creates a 4D space, integrating the time dimension to enhance object trajectory tracking.
However, annotating 4D BEV data presents challenges due to its complexity. Enter 4D-BEV annotation technology—an innovative solution pioneered by Nexdata. This groundbreaking approach introduces the time axis into ai data annotation, facilitating more robust AI systems. Annotators leverage BEV to label static objects like vehicles and pedestrians, recording their attributes alongside entry and exit times. This detailed ai data service significantly bolsters safety and decision-making algorithms in autonomous vehicles.
Nexdata's 4D annotation tool operates in a 3D space with an added temporal dimension. It seamlessly integrates data from lidar, radar, cameras, and overhead views while ensuring precise alignment and fusion. Notably, the tool's pre-recognition annotation technology substantially boosts both efficiency and accuracy in labeling.
Key Features of Nexdata's Annotation Tool
One standout feature is its ability to handle massive point cloud data collection and annotation seamlessly. The utilization of Potree, a WebGL-based framework, enables interactive visualization of extensive point cloud data on web platforms. To avoid biases, the tool references mapping parameters from AI data collections. Additionally, customizable color settings facilitate accurate identification of point cloud targets, while the preloading function further enhances annotation efficiency.
Case Studies Illustrating Utility
4D Lane Marking Annotation: This process involves annotating lidar point cloud data annotaion services with global pose information, specifically focusing on lane markings, including solid, dashed, double lines, and guide lines.
4D Segmentation Annotation: Reconstruction of sequential frames to annotate semantic segmentation, categorizing areas like vegetation, drivable zones, and unknown obstacles.
Nexdata's extensive collaborations with global autonomous driving entities, spanning OEMs, startups, tech giants, and Tier1 manufacturers, underscore its expertise in this field. Their commitment to continuous technical development aims to refine AI infrastructure, simplifying the deployment of AI applications.
Looking ahead, Nexdata remains dedicated to advancing technical research, further improving AI infrastructure, and supporting users in deploying AI applications more conveniently.
In the ever-evolving landscape of Artificial Intelligence (AI), the utilization of speech data stands as a pivotal force in training AI models, enabling advancements in natural language understanding, human-computer interaction, and diverse applications across industries. Speech data, comprising diverse spoken language samples, serves as the linchpin in empowering AI systems to comprehend and interact with human language.
Text-to-Speech (TTS) technology has undergone remarkable advancements, enabling machines to communicate seamlessly through voice, transforming how we interact with technology. From voice assistants to intelligent customer service and smart homes, TTS has woven itself into our daily lives. In the latest ChatGPT update, the inclusion of voice conversation functionality stands out as a revolutionary feature. Users can now engage in real-time conversations with ChatGPT using synthesized voices, mirroring natural phone conversations with instantaneous responses.