“…However, each of these methods has its own limitations. For example, MPI representation is lightweight and can capture specular surfaces, but its discretized representation can lead to suboptimal performance for sloped surfaces [66]. In addition, MPI representation with predetermined layer structures can suffer from abrupt layer changes across discontinuities in depth, leading to inferior preserved locality.…”
Section: Novel View Synthesis For Virtual Realitymentioning
Figure 1: We propose a learning-based approach for generating novel views from pre-captured omnidirectional inputs, which can adapt to the user's eye height during playback, resulting in an improved perceptive and immersive experience.
“…However, each of these methods has its own limitations. For example, MPI representation is lightweight and can capture specular surfaces, but its discretized representation can lead to suboptimal performance for sloped surfaces [66]. In addition, MPI representation with predetermined layer structures can suffer from abrupt layer changes across discontinuities in depth, leading to inferior preserved locality.…”
Section: Novel View Synthesis For Virtual Realitymentioning
Figure 1: We propose a learning-based approach for generating novel views from pre-captured omnidirectional inputs, which can adapt to the user's eye height during playback, resulting in an improved perceptive and immersive experience.
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