BG-Triangle: Bézier Gaussian Triangle for 3D Vectorization and Rendering
This addresses the challenge of accurate shape modeling in 3D reconstruction and rendering for computer vision and graphics applications, representing an incremental improvement over existing methods.
The paper tackles the problem of preserving sharp edges in differentiable rendering by introducing BG-Triangle, a hybrid representation combining Bézier triangles and Gaussian models, which achieves comparable rendering quality to 3DGS with superior boundary preservation and uses fewer primitives.
Differentiable rendering enables efficient optimization by allowing gradients to be computed through the rendering process, facilitating 3D reconstruction, inverse rendering and neural scene representation learning. To ensure differentiability, existing solutions approximate or re-formulate traditional rendering operations using smooth, probabilistic proxies such as volumes or Gaussian primitives. Consequently, they struggle to preserve sharp edges due to the lack of explicit boundary definitions. We present a novel hybrid representation, Bézier Gaussian Triangle (BG-Triangle), that combines Bézier triangle-based vector graphics primitives with Gaussian-based probabilistic models, to maintain accurate shape modeling while conducting resolution-independent differentiable rendering. We present a robust and effective discontinuity-aware rendering technique to reduce uncertainties at object boundaries. We also employ an adaptive densification and pruning scheme for efficient training while reliably handling level-of-detail (LoD) variations. Experiments show that BG-Triangle achieves comparable rendering quality as 3DGS but with superior boundary preservation. More importantly, BG-Triangle uses a much smaller number of primitives than its alternatives, showcasing the benefits of vectorized graphics primitives and the potential to bridge the gap between classic and emerging representations.