EyeExpand: A Low‐Burden and Accurate 3D Object Selection Method With Gaze and Raycasting

Abstract Raycasting is a widely used object selection technique in virtual reality. However, in dense scenes, it becomes difficult for users to accurately select targets when objects are partially or fully occluded. While recent studies have introduced progressive refinement techniques based on raycasting to address these limitations, they still suffer from challenges such as high interaction complexity and difficulties in preserving the relative spatial relationships between objects within the scene. In this paper, we present a simple and efficient 3D progressive refinement technique for object selection in dense scenes while maintaining the relative spatial positions of selected objects. We compare our technique with other progressive refinement techniques and evaluate their performance and user experience in a target selection task within dense VR environments. The results show that in low‐ and medium‐density scenarios, our technique outperforms existing progressive refinement techniques in terms of selection time. In high‐density scenarios, the proposed technique significantly reduces physical effort while maintaining comparable selection times, thereby offering an improved overall interactive experience.