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Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction
The ultimate goal of many image-based modeling systems is to render
photo-realistic novel views of a scene without visible artifacts. Existing
evaluation metrics and benchmarks focus mainly on the geometric accuracy of the
reconstructed model, which is, however, a poor predictor of visual accuracy.
Furthermore, using only geometric accuracy by itself does not allow evaluating
systems that either lack a geometric scene representation or utilize coarse
proxy geometry. Examples include light field or image-based rendering systems.
We propose a unified evaluation approach based on novel view prediction error
that is able to analyze the visual quality of any method that can render novel
views from input images. One of the key advantages of this approach is that it
does not require ground truth geometry. This dramatically simplifies the
creation of test datasets and benchmarks. It also allows us to evaluate the
quality of an unknown scene during the acquisition and reconstruction process,
which is useful for acquisition planning. We evaluate our approach on a range
of methods including standard geometry-plus-texture pipelines as well as
image-based rendering techniques, compare it to existing geometry-based
benchmarks, and demonstrate its utility for a range of use cases.Comment: 10 pages, 12 figures, paper was submitted to ACM Transactions on
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