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Light field (LF) representations aim to provide photo-realistic,free-viewpoint viewing experiences. However, the most popular LFrepresentations are images from multiple views. Multi-view image-basedrepresentations generally need to restrict the range or degrees of freedom ofthe viewing experience to what can be interpolated in the image domain,essentially because they lack explicit geometry information. We present a newsurface light field (SLF) representation based on explicit geometry, and amethod for SLF compression. First, we map the multi-view images of a scene ontoa 3D geometric point cloud. The color of each point in the point cloud is afunction of viewing direction known as a view map. We represent each view mapefficiently in a B-Spline wavelet basis. This representation is capable ofmodeling diverse surface materials and complex lighting conditions in a highlyscalable and adaptive manner. The coefficients of the B-Spline waveletrepresentation are then compressed spatially. To increase the spatialcorrelation and thus improve compression efficiency, we introduce a smoothingterm to make the coefficients more similar across the 3D space. We compress thecoefficients spatially using existing point cloud compression (PCC) methods. Onthe decoder side, the scene is rendered efficiently from any viewing directionby reconstructing the view map at each point. In contrast to multi-viewimage-based LF approaches, our method supports photo-realistic rendering ofreal-world scenes from arbitrary viewpoints, i.e., with an unlimited sixdegrees of freedom (6DOF). In terms of rate and distortion, experimentalresults show that our method achieves superior performance with lighter decodercomplexity compared with a reference image-plus-geometry compression (IGC)scheme, indicating its potential in practical virtual and augmented realityapplications.

Surface Light Field Compression using a Point Cloud Codec