CubicSplat: Differentiable Vector Graphics via Error-Bounded Forward Relaxation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2608.22344v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) achieves state-of-the-art rendering quality at real-time speeds but suffers from "model bloat" - a large number of redunda...
arXiv:2506. 07069v2 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, combining high-quality reconstruction with efficient rendering.
Windfoil is a GPU-friendly algorithm that unifies rasterisation and differentiable vector graphics by evaluating the box-filtered winding number of quadratic Bézier contours in closed form. Implemented in WebGPU, it runs across diverse environments—from web browsers on consumer laptops to high-resolution print media—and supports real-time 2D rendering, high‑resolution rasterisation, and differentiable rendering. Compared to production engines like Skia and game‑oriented rasterisers such as Slug, Windfoil achieves closer fidelity to a reference coverage while maintaining comparable performance, and it outperforms DiffVG and Bézier Splatting in reconstruction quality at a fraction of the per‑step cost, scaling to tens of thousands of shapes at interactive rates.
3D Gaussian Splatting (3DGS) has recently enabled real-time novel view synthesis with impressive quality. However, it struggles to recover accurate surfaces under limited viewpoints and due to the inherent irregularity of Gaussian primitives.
arXiv:2609.14521v1 Announce Type: new Abstract: Recovering editable 3D parametric curves from 2D images is a fundamental challenge in computer graphics, bridging pixel-based perception and vector-bas...
arXiv:2607. 00885v1 Announce Type: cross Abstract: Recent advances in neural rendering have established 3D Gaussian Splatting (3DGS) as a highly efficient representation for novel view synthesis, enabling fast training and real-time rendering with strong fidelity.