With the rapid development and widespread adoption of virtual reality （VR），360° omnidirectional image （ODI） and omnidirectional video （ODV） have become important media for entertainment，education，healthcare，gaming，and immersive training. By capturing a full 360° field of view，they enable users to navigate and interact with content freely，offering a sense of presence that conventional 2D media cannot provide. However，ODI and ODV data also introduce distinctive challenges，including spherical distortion caused by projection methods，uneven viewpoint distributions arising from user-controlled perspectives，and strict real-time processing requirements needed to preserve immersion. These characteristics make saliency detection—the task of identifying regions that attract human attention—particularly important in VR environments，while also rendering traditional 2D saliency models inadequate for full-sphere attention prediction. This survey provides a comprehensive review of recent advances in saliency detection for VR 360° content，spanning traditional feature-based methods and modern deep-learning approaches，including convolutional neural network （CNN），Transformers for long-range dependency modeling，and long short-term memory （LSTM） networks for temporal modeling in video. The survey begins by outlining the fundamental principles of VR 360° content，including imaging mechanisms，geometric properties of spherical representations，and key characteristics such as boundary discontinuity and projection-induced artifacts that distinguish it from conventional 2D imagery and necessitate specialized saliency models. Existing methods are then organized into several major categories： traditional handcrafted approaches based on low-level cues such as color，contrast，and texture； advanced deep architectures designed to learn robust features under spherical constraints； multi-projection modeling strategies for equirectangular，cubemap，and spherical domains； and multimodal fusion methods that integrate visual，auditory，and depth information. Traditional methods are computationally efficient and conceptually simple，but they often struggle with spherical distortion and spatial discontinuity. For example，early superpixel-based segmentation methods are sensitive to boundary effects，and color-dictionary sparse-representation approaches do not adapt well to dynamic VR content. In contrast，deep-learning methods have transformed the field by enabling end-to-end optimization and better handling of dynamic viewpoints and multimodal information. Representative examples include CNN-based models such as SalGCN，which use spherical graph convolution to mitigate projection distortion； Transformer-based models such as SalViT360，which capture global dependencies in 360° video effectively； and LSTM-based models such as HiBayes-LSTM，which improve temporal modeling for scanpath prediction by incorporating user-behavior cues. A major part of this survey is devoted to datasets and evaluation metrics. We review prominent ODI and ODV saliency datasets，including Salient360！，HTRO，and PAVS10K，and summarize their scale，annotation types （such as fixation maps and scanpaths），and applicability to tasks including saliency prediction and salient-object detection. We also analyze commonly used metrics，including Pearson correlation coefficient （CC），normalized scanpath saliency （NSS），Kullback–Leibler divergence （KLD），F-measure，mean absolute error （MAE），S-measure，and E-measure，discussing both their usefulness and their limitations in the presence of 360°-specific distortions，especially near the poles in equirectangular projection. Comparative studies indicate that deep-learning methods，particularly those using multimodal fusion and advanced projection strategies，consistently outperform traditional approaches on key indicators such as AUC and CC. The survey further examines practical applications of saliency detection in VR，including image and video quality assessment，adaptive compression for bandwidth optimization，and virtual cinematography. In quality assessment，saliency-guided models such as SG360BIQA improve prediction accuracy by emphasizing user-attended regions and thereby aligning objective metrics more closely with subjective experience. In compression，approaches such as RoSal360 allocate more bitrate to salient regions to improve transmission efficiency in resource-constrained VR systems. In virtual cinematography，attention-driven deep reinforcement learning models simulate human gaze patterns to support smoother and more intuitive camera control. These examples highlight the broader importance of saliency detection in optimizing VR systems and improving user immersion across applications from immersive storytelling to teleconferencing. Despite this progress，substantial challenges remain，including geometric distortion in spherical representations，the need for real-time processing under strict latency constraints，and the scarcity of unified multimodal datasets that combine visual，auditory，and interaction signals. Future work should therefore explore richer multimodal integration，lightweight and unsupervised learning frameworks，and large-scale standardized benchmarks. Emerging directions such as transfer learning for cross-domain adaptation，contrastive learning for more discriminative representations，and spherical-geometry-aware data augmentation may also help improve generalization and alleviate data scarcity. In summary，this survey offers a detailed analysis of saliency detection for VR 360° content，bridging traditional heuristic methods and modern data-driven approaches. By reviewing algorithms，datasets，evaluation metrics，applications，and open challenges，it aims to support future advances toward more efficient，immersive，and user-centered VR systems. The algorithms and datasets discussed in this article have been summarized at https://github.com/jiacongyu/PVSD.