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

Neural Radiance Fields (NeRF) coupled with GANs represent a promisingdirection in the area of 3D reconstruction from a single view, owing to theirability to efficiently model arbitrary topologies. Recent work in this area,however, has mostly focused on synthetic datasets where exact ground-truthposes are known, and has overlooked pose estimation, which is important forcertain downstream applications such as augmented reality (AR) and robotics. Weintroduce a principled end-to-end reconstruction framework for natural images,where accurate ground-truth poses are not available. Our approach recovers anSDF-parameterized 3D shape, pose, and appearance from a single image of anobject, without exploiting multiple views during training. More specifically,we leverage an unconditional 3D-aware generator, to which we apply a hybridinversion scheme where a model produces a first guess of the solution which isthen refined via optimization. Our framework can de-render an image in as fewas 10 steps, enabling its use in practical scenarios. We demonstratestate-of-the-art results on a variety of real and synthetic benchmarks.

Shape, Pose, and Appearance from a Single Image via Bootstrapped Radiance Field Inversion