  Image-based geometric modeling and novel view synthesis based on sparse,
large-baseline samplings are challenging but important tasks for emerging
multimedia applications such as virtual reality and immersive telepresence.
Existing methods fail to produce satisfactory results due to the limitation on
inferring reliable depth information over such challenging reference
conditions. With the popularization of commercial light field (LF) cameras,
capturing LF images (LFIs) is as convenient as taking regular photos, and
geometry information can be reliably inferred. This inspires us to use a sparse
set of LF captures to render high-quality novel views globally. However, fusion
of LF captures from multiple angles is challenging due to the scale
inconsistency caused by various capture settings. To overcome this challenge,
we propose a novel scale-consistent volume rescaling algorithm that robustly
aligns the disparity probability volumes (DPV) among different captures for
scale-consistent global geometry fusion. Based on the fused DPV projected to
the target camera frustum, novel learning-based modules have been proposed
(i.e., the attention-guided multi-scale residual fusion module, and the
disparity field guided deep re-regularization module) which comprehensively
regularize noisy observations from heterogeneous captures for high-quality
rendering of novel LFIs. Both quantitative and qualitative experiments over the
Stanford Lytro Multi-view LF dataset show that the proposed method outperforms
state-of-the-art methods significantly under different experiment settings for
disparity inference and LF synthesis.
