  Light field (LF) representations aim to provide photo-realistic,
free-viewpoint viewing experiences. However, the most popular LF
representations are images from multiple views. Multi-view image-based
representations generally need to restrict the range or degrees of freedom of
the viewing experience to what can be interpolated in the image domain,
essentially because they lack explicit geometry information. We present a new
surface light field (SLF) representation based on explicit geometry, and a
method for SLF compression. First, we map the multi-view images of a scene onto
a 3D geometric point cloud. The color of each point in the point cloud is a
function of viewing direction known as a view map. We represent each view map
efficiently in a B-Spline wavelet basis. This representation is capable of
modeling diverse surface materials and complex lighting conditions in a highly
scalable and adaptive manner. The coefficients of the B-Spline wavelet
representation are then compressed spatially. To increase the spatial
correlation and thus improve compression efficiency, we introduce a smoothing
term to make the coefficients more similar across the 3D space. We compress the
coefficients spatially using existing point cloud compression (PCC) methods. On
the decoder side, the scene is rendered efficiently from any viewing direction
by reconstructing the view map at each point. In contrast to multi-view
image-based LF approaches, our method supports photo-realistic rendering of
real-world scenes from arbitrary viewpoints, i.e., with an unlimited six
degrees of freedom (6DOF). In terms of rate and distortion, experimental
results show that our method achieves superior performance with lighter decoder
complexity compared with a reference image-plus-geometry compression (IGC)
scheme, indicating its potential in practical virtual and augmented reality
applications.
