Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation
This work addresses the challenge of intrinsic image decomposition and highlight separation for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the problem of separating object appearance into highlight, shading, and albedo layers using an unsupervised approach trained on multi-view real images, achieving state-of-the-art results across a broad range of objects.
We present an unsupervised approach for factorizing object appearance into highlight, shading, and albedo layers, trained by multi-view real images. To do so, we construct a multi-view dataset by collecting numerous customer product photos online, which exhibit large illumination variations that make them suitable for training of reflectance separation and can facilitate object-level decomposition. The main contribution of our approach is a proposed image representation based on local color distributions that allows training to be insensitive to the local misalignments of multi-view images. In addition, we present a new guidance cue for unsupervised training that exploits synergy between highlight separation and intrinsic image decomposition. Over a broad range of objects, our technique is shown to yield state-of-the-art results for both of these tasks.