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

Indoor scene reconstruction from monocular images has long been sought afterby augmented reality and robotics developers. Recent advances in neural fieldrepresentations and monocular priors have led to remarkable results inscene-level surface reconstructions. The reliance on Multilayer Perceptrons(MLP), however, significantly limits speed in training and rendering. In thiswork, we propose to directly use signed distance function (SDF) in sparse voxelblock grids for fast and accurate scene reconstruction without MLPs. Ourglobally sparse and locally dense data structure exploits surfaces' spatialsparsity, enables cache-friendly queries, and allows direct extensions tomulti-modal data such as color and semantic labels. To apply thisrepresentation to monocular scene reconstruction, we develop a scalecalibration algorithm for fast geometric initialization from monocular depthpriors. We apply differentiable volume rendering from this initialization torefine details with fast convergence. We also introduce efficienthigh-dimensional Continuous Random Fields (CRFs) to further exploit thesemantic-geometry consistency between scene objects. Experiments show that ourapproach is 10x faster in training and 100x faster in rendering while achievingcomparable accuracy to state-of-the-art neural implicit methods.

Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids