TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction

Sparse-view 3D reconstruction is increasingly addressed with feed-forward networks. Yet most methods use Gaussian primitives whose surfaces are only implicit. Obtaining a mesh for simulation or embodied interaction still requires costly post-hoc extraction such as TSDF fusion or Poisson reconstruction, breaking the feed-forward promise in pose-free settings where structure and pose are estimated jointly. We present TriSplat, a feed-forward network that represents scenes with oriented triangle primitives and exports meshes from a single forward pass, jointly predicting point maps, triangle attributes, camera poses, and optional intrinsics. Triangle orientation is anchored to geometry rather than regressed freely. Normals from the predicted point maps are refined by an image-conditioned head and converted into stable local frames, while a mono-normal bootstrap and opacity and blur scheduling stabilize early training and sharpen the surface. Experiments on RealEstate10K and DL3DV show more geometry-faithful reconstructions than Gaussian baselines and competitive novel-view rendering. Because the primitives are surface triangles, the output loads directly into physics engines, collision detectors, and rasterization pipelines without conversion, making it a practical simulation-ready solution for feed-forward 3D scene reconstruction.