Common interview questions include: Explain point cloud preprocessing steps, How do you handle occlusion in 3D detection, What are the differences between 2D and 3D object detection, Describe sensor fusion techniques, How do you optimize detection models for real-time applications, and What evaluation metrics are most relevant for 3D detection systems.

Technical skills include Python programming, deep learning frameworks, point cloud processing, computer vision fundamentals, and sensor calibration. Soft skills encompass problem-solving abilities, analytical thinking, attention to detail, collaboration for interdisciplinary projects, and continuous learning to keep pace with rapidly evolving detection technologies and methodologies.

Autonomous driving teams use these skills for pedestrian and vehicle detection, Robotics teams implement object avoidance and manipulation, AR/VR teams create spatial interaction systems, Security teams develop surveillance solutions, and Industrial automation teams build quality inspection and sorting systems using 3D perception technologies.

The report provides detailed skill scores across point cloud processing, algorithm implementation, and optimization techniques. It includes benchmarking against industry standards, identifies strengths and weaknesses in specific detection methodologies, offers performance predictions for real-world applications, and features proctoring insights ensuring assessment integrity and candidate authenticity.