Ghost Object

"Ghost objects," spurious detections in various sensor modalities (e.g., radar, LiDAR, cameras), represent a significant challenge across diverse applications, from autonomous driving to image processing. Current research focuses on developing methods to identify and mitigate these artifacts, employing techniques such as asymmetric blending in video interpolation, 3D diffusion models for NeRF refinement, and novel loss functions in object detection training to improve robustness against noisy data. Addressing ghost objects is crucial for enhancing the reliability and safety of AI systems, particularly in safety-critical applications like autonomous vehicles, where false positives can have severe consequences.