Compact Neural Graphics Primitives with Learned Hash Probing

Neural graphics primitives are faster and achieve higher quality when their neural networks are augmented by spatial data structures that hold trainable features arranged in a grid. However, existing feature grids either come with a large memory footprint (dense or factorized grids, trees, and hash tables) or slow performance (index learning and vector quantization). In this paper, we show that a hash table with learned probes has neither disadvantage, resulting in a favorable combination of size and speed. Inference is faster than unprobed hash tables at equal quality while training is only 1.2-2.6x slower, significantly outperforming prior index learning approaches. We arrive at this formulation by casting all feature grids into a common framework: they each correspond to a lookup function that indexes into a table of feature vectors. In this framework, the lookup functions of existing data structures can be combined by simple arithmetic combinations of their indices, resulting in Pareto optimal compression and speed.Comment: Project Page: https://research.nvidia.com/labs/toronto-ai/compact-ng

Compressió geomètrica de núvols de punts amb MPEG G-PCC

MPEG G-PCC (Geometric Point Cloud Compression) uses technologies like octrees, rasterization and arithmetic coding for compression for non-projectable 3D point clouds. A reference implementation is available as G-PCC Test Model v11. The aim of this project is to explore the performance of G-PCC. Finding different coding tools that propose various ways to perform this geometric compression is part of the challenge. Obtaining datasets and being able to test the efficiency of these software is key to understand the idea behind all these techniques, which are part of a research field in which the breakthroughs are still to come.MPEG G-PCC (Geometric Point Cloud Compression) utiliza tecnologías como octrees, rasterización i codificación aritmética para la compresión de nubes de puntos 3D no proyectables. Una implementación de referencia está disponible como modelo de test de MPEG. El objetivo de éste proyecto es explorar las técnicas de G-PCC. Encontrar diferentes codificadores que propongan varias maneras de realizar esta compresión geométrica forma parte del reto. Obtener datasets i poder realizar pruebas de la eficacia de estos softwares es clave para comprender la idea que hay detrás de todas estas técnicas, que forman parte de un ámbito de investigación en el cual los grandes avances están aún por venir.MPEG G-PPC (Geometric Point Cloud Compression) utilitza tecnologies com octrees, rasterització i codificació aritmètica per a la compressió de núvols de punts 3D no projectables. Una implementació de referència està disponible com a model de test de MPEG. L'objectiu d'aquest projecte és explorar les tècniques de G-PCC. Fer recerca de diferents codificadors que proposin diverses maneres de realitzar aquesta compressió geomètrica forma part del repte. Obtenir datasets i poder fer proves de l'eficàcia d'aquests software és clau per comprendre la idea que hi ha darrere de totes aquestes tècniques, que formen part d'un àmbit d'investigació en el qual els grans avenços encara han de venir

HUMAN4D: A human-centric multimodal dataset for motions and immersive media

We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing vari