Abstract: Systems and methods described herein support enhanced computer vision capabilities which may be applicable to, for example, autonomous vehicle operation. An example method includes generating a latent space and a decoder based on image data that includes multiple images, where each image has a different viewing frame of a scene. The method also includes generating a volumetric embedding that is representative of a novel viewing frame of the scene. The method includes decoding, with the decoder, the latent space using cross-attention with the volumetric embedding, and generating a novel viewing frame of the scene based on an output of the decoder.

Abstract: Systems and methods described herein support enhanced computer vision capabilities which may be applicable to, for example, autonomous vehicle operation. An example method includes generating, through training, a shared latent space based on (i) image data that include multiple images, where each image has a different viewing frame of a scene, and (ii) first and second types of embeddings, and training a decoder based on the first type of embeddings. The method also includes generating an embedding based on the first type of embeddings that is representative of a novel viewing frame of the scene, decoding, with the decoder, the shared latent space using cross-attention with the generated embedding, and generating the novel viewing frame of the scene based on an output of the decoder.

Abstract: Systems and methods described herein support enhanced computer vision capabilities which may be applicable to, for example, autonomous vehicle operation. An example method includes An example method includes training a shared latent space and a first decoder based on first image data that includes multiple images, and training the shared latent space and a second decoder based on second image data that includes multiple images. The method also includes generating a volumetric embedding that is representative of a novel viewing frame the first scene. Further, the method includes decoding, with the first decoders, the shared latent space with the volumetric embedding, and generating the novel viewing frame of the first scene based on the output of the first decoder.

Abstract: An example method includes generating embeddings of image data that includes multiple images, where each image has a different viewpoints of a scene, generating a latent space and a decoder, wherein the decoder receives embeddings as input to generate an output viewpoint, for each viewpoint in the image data, determining a volumetric rendering view synthesis loss and a multi-view photometric loss, and applying an optimization algorithm to the latent space and the decoder over a number of epochs until the volumetric rendering view synthesis loss is within a volumetric threshold and the multi-view photometric loss is within a multi-view threshold.

Abstract: The present invention relates to the liquid phase oxidation of aromatic hydrocarbons in the presence of at least one heavy metal oxidation catalyst and bromine, which is activated by at least one of anthracene or another polycyclic aromatic compound to produce aromatic carboxylic acids.