Name: ◉⌖✦⟡ more experiments with radial and pinch ui
Uploaded: 2025-08-31T16:38:51.000Z
Duration: PT11.485S
Channel: Kat ⊷ the Poet Engineer
Description: Kat ⊷ the Poet Engineer shorts video about ◉⌖✦⟡ more experiments with radial and pinch ui

◉⌖✦⟡ more experiments with radial and pinch ui

Kat ⊷ the Poet Engineer

371,441 Aufrufe • vor 10 Monaten

bars, ticks, noise - continuing experiments with radial layout 💫

Kat ⊷ the Poet Engineer

56,184 Aufrufe • vor 5 Monaten

some more experiments with css and js

Shiv

213,612 Aufrufe • vor 10 Monaten

WIP Radial event selection UI for 'The Gunter Records'... show more

green271

11,232 Aufrufe • vor 7 Monaten

More experiments with gaussian splats and AR image targets

Ian Curtis

10,735 Aufrufe • vor 1 Jahr

More experiments with Pika labs “additions”

Jon Finger

457,698 Aufrufe • vor 1 Jahr

NeuRBF: A Neural Fields Representation with Adaptive Radial Basis... Functions paper page: present a novel type of neural fields that uses general radial bases for signal representation. State-of-the-art neural fields typically rely on grid-based representations for storing local neural features and N-dimensional linear kernels for interpolating features at continuous query points. The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals. Our method instead builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. To further improve the channel-wise capacity of radial basis functions, we propose to compose them with multi-frequency sinusoid functions. This technique extends a radial basis to multiple Fourier radial bases of different frequency bands without requiring extra parameters, facilitating the representation of details. Moreover, by marrying adaptive radial bases with grid-based ones, our hybrid combination inherits both adaptivity and interpolation smoothness. We carefully designed weighting schemes to let radial bases adapt to different types of signals effectively. Our experiments on 2D image and 3D signed distance field representation demonstrate the higher accuracy and compactness of our method than prior arts. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed.show more