Kavli Affiliate: Yi Zhou
| First 5 Authors: Jan Held, Jan Held, , ,
| Summary:
Reconstructing 3D scenes and synthesizing novel views has seen rapid progress
in recent years. Neural Radiance Fields demonstrated that continuous volumetric
radiance fields can achieve high-quality image synthesis, but their long
training and rendering times limit practicality. 3D Gaussian Splatting (3DGS)
addressed these issues by representing scenes with millions of Gaussians,
enabling real-time rendering and fast optimization. However, Gaussian
primitives are not natively compatible with the mesh-based pipelines used in VR
headsets, and real-time graphics applications. Existing solutions attempt to
convert Gaussians into meshes through post-processing or two-stage pipelines,
which increases complexity and degrades visual quality. In this work, we
introduce Triangle Splatting+, which directly optimizes triangles, the
fundamental primitive of computer graphics, within a differentiable splatting
framework. We formulate triangle parametrization to enable connectivity through
shared vertices, and we design a training strategy that enforces opaque
triangles. The final output is immediately usable in standard graphics engines
without post-processing. Experiments on the Mip-NeRF360 and Tanks & Temples
datasets show that Triangle Splatting+achieves state-of-the-art performance in
mesh-based novel view synthesis. Our method surpasses prior splatting
approaches in visual fidelity while remaining efficient and fast to training.
Moreover, the resulting semi-connected meshes support downstream applications
such as physics-based simulation or interactive walkthroughs. The project page
is https://trianglesplatting2.github.io/trianglesplatting2/.
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