Advancements and Challenges in Computer Vision: From Pixels to Perception

Engineering notes

Key topics: autonomous driving, perception. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Computer vision enables machines to analyse and understand visual information obtained from images, videos, and sensor data. Over the past decade, the field has experienced significant progress, driven mainly by advances in deep learning. These developments have improved performance in many areas, including healthcare, autonomous driving, robotics, manufacturing, and security. Despite these achievements, high accuracy alone does not guarantee reliable performance in real-world environments. This paper reviews the major developments and challenges in modern computer vision. The discussion focuses on deep learning architectures, transfer learning, three-dimensional vision, generative models, data quality, explainability, robustness, and ethical issues. It highlights the need for computer vision systems that are not only accurate but also transparent, reliable, and trustworthy. In addition, a conceptual framework is presented to illustrate how these challenges interact across different stages of the computer vision pipeline.

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