English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Existing learning-based methods for point cloud rendering adopt various 3Drepresentations and feature querying mechanisms to alleviate the sparsityproblem of point clouds. However, artifacts still appear in rendered images,due to the challenges in extracting continuous and discriminative 3D featuresfrom point clouds. In this paper, we present a dense while lightweight 3Drepresentation, named TriVol, that can be combined with NeRF to renderphoto-realistic images from point clouds. Our TriVol consists of triple slimvolumes, each of which is encoded from the point cloud. TriVol has twoadvantages. First, it fuses respective fields at different scales and thusextracts local and non-local features for discriminative representation.Second, since the volume size is greatly reduced, our 3D decoder can beefficiently inferred, allowing us to increase the resolution of the 3D space torender more point details. Extensive experiments on different benchmarks withvarying kinds of scenes/objects demonstrate our framework's effectivenesscompared with current approaches. Moreover, our framework has excellentgeneralization ability to render a category of scenes/objects withoutfine-tuning.

TriVol: Point Cloud Rendering via Triple Volumes