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.

The EPRCA (enhanced proportional rate control algorithm) is a leading congestion control protocol for available bit rate (ABR) service in ATM networks. Even though EPRCA has been adopted by the ATM Forum as the standard for ABR congestion control, we show that this protocol is only marginally stable. The stability analysis presented in this paper does not consider interactions among network components and should be considered as a bottle-neck stability result. We show that, if a queue is the bottleneck of a congested path, then its queue size will eventually decrease. However, if a queue is not congested, its queue size can actually increase and the congestion can be self-induced.