Learning Neural Implicit Representations with Surface Signal Parameterizations

Neural implicit surface representations have recently emerged as popular alternative to explicit 3D object encodings, such as polygonal meshes, tabulated points, or voxels. While significant work has improved the geometric fidelity of these representations, much less attention is given to their final appearance. Traditional explicit object representations commonly couple the 3D shape data with auxiliary surface-mapped image data, such as diffuse color textures and fine-scale geometric details in normal maps that typically require a mapping of the 3D surface onto a plane, i.e., a surface parameterization; implicit representations, on the other hand, cannot be easily textured due to lack of configurable surface parameterization. Inspired by this digital content authoring methodology, we design a neural network architecture that implicitly encodes the underlying surface parameterization suitable for appearance data. As such, our model remains compatible with existing mesh-based digital content with appearance data. Motivated by recent work that overfits compact networks to individual 3D objects, we present a new weight-encoded neural implicit representation that extends the capability of neural implicit surfaces to enable various common and important applications of texture mapping. Our method outperforms reasonable baselines and state-of-the-art alternatives.

VTAMIQ: Transformers for Attention Modulated Image Quality Assessment

Following the major successes of self-attention and Transformers for image analysis, we investigate the use of such attention mechanisms in the context of Image Quality Assessment (IQA) and propose a novel full-reference IQA method, Vision Transformer for Attention Modulated Image Quality (VTAMIQ). Our method achieves competitive or state-of-the-art performance on the existing IQA datasets and significantly outperforms previous metrics in cross-database evaluations. Most patch-wise IQA methods treat each patch independently; this partially discards global information and limits the ability to model long-distance interactions. We avoid this problem altogether by employing a transformer to encode a sequence of patches as a single global representation, which by design considers interdependencies between patches. We rely on various attention mechanisms -- first with self-attention within the Transformer, and second with channel attention within our difference modulation network -- specifically to reveal and enhance the more salient features throughout our architecture. With large-scale pre-training for both classification and IQA tasks, VTAMIQ generalizes well to unseen sets of images and distortions, further demonstrating the strength of transformer-based networks for vision modelling.