ForeSplat: Optimization-Aware Foresight for Feed-Forward 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2605.22020v2
• Date: Fri, 22 May 2026 02:28:30 GMT
• Title: ForeSplat: Optimization-Aware Foresight for Feed-Forward 3D Gaussian Splatting
• Abstract summary: ForeSplat is an optimization-aware training framework for feed-forward 3DGS models.<n>ForeSplat substantially reduces the capacity pressure on the feed-forward model.<n>It bridges the gap between amortized prediction and per-scene optimization.
• Score: 65.02700555625749
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Feed-forward 3D Gaussian Splatting models offer fast single-pass reconstruction,but scaling them to match per-scene optimization quality is fundamentally hindered by the scarcity of large-scale 3D annotations. A practical compromise is predict-then-refine,where post-prediction optimization compensates for the limited capacity of the feed-forward network. However,standard feed-forward 3DGS is trained solely for zero-step rendering error,ignoring whether its output constitutes a good initialization for the downstream optimizer. We present ForeSplat,an optimization-aware training framework that equips feed-forward 3DGS models to produce initializations explicitly designed for rapid,effective refinement. By offloading part of the scene-modeling burden to the optimizer,ForeSplat substantially reduces the capacity pressure on the feed-forward model,making high-quality reconstruction feasible even with compact networks. At its core is MetaGrad,a lightweight multi-anchor meta-gradient training rule that bypasses costly higher-order differentiation through the 3DGS optimizer. MetaGrad unrolls a short inner-loop refinement trajectory,samples anchor states,and back-propagates aggregated first-order gradients to the prediction head as a surrogate optimization-aware signal. This fine-tuning adds no inference cost and enables high-quality reconstruction within seconds after a few refinement steps. We instantiate ForeSplat on diverse backbones,including AnySplat,Pi3X,and a distilled variant tailored for edge deployment. Across all tested architectures,a ForeSplat-trained initialization converges in fewer refinement steps and reaches a higher peak reconstruction quality than its vanilla counterpart,even fully converged. The framework consistently bridges the gap between amortized prediction and per-scene optimization,establishing a practical path toward lightweight,high-fidelity 3D reconstruction.

Related papers

• Learn2Splat: Extending the Horizon of Learned 3DGS Optimization [53.913063313338874]
  We introduce a learned for 3DGS that avoids degradation over extended horizons without auxiliary mechanisms.<n>Results improved novel view quality while remaining stable over long horizons, with zero-shot generalization to unseen reconstruction settings.
  arXiv  Detail & Related papers  (2026-05-15T09:21:29Z)
• Diff3R: Feed-forward 3D Gaussian Splatting with Uncertainty-aware Differentiable Optimization [76.38917994186733]
  Diff3R is a novel framework that bridges feed-forward prediction and test-time optimization.<n>We show that it can be seamlessly integrated into existing feed-forward 3DGS architectures for both pose-given and pose-free methods.
  arXiv  Detail & Related papers  (2026-04-01T15:40:20Z)
• A Step to Decouple Optimization in 3DGS [38.797134528503015]
  3D Gaussian Splatting (3DGS) has emerged as a powerful technique for real-time novel view synthesis.<n>In this paper, we take a step to decouple the process into: Sparse Adam, Re-State Regularization and Decoupled Attribute Regularization.
  arXiv  Detail & Related papers  (2026-01-23T13:34:39Z)
• 3DGS Compression with Sparsity-guided Hierarchical Transform Coding [19.575833741231953]
  Sparsity-guided Hierarchical Transform Coding (SHTC) is first end-to-end optimized transform coding framework for 3DGS compression.<n>SHTC jointly optimize the 3DGS, transforms and a lightweight context model.<n>This novel design significantly improves R-D performance with minimal additional parameters and computational overhead.
  arXiv  Detail & Related papers  (2025-05-28T22:17:24Z)
• QuickSplat: Fast 3D Surface Reconstruction via Learned Gaussian Initialization [69.50126552763157]
  Surface reconstruction is fundamental to computer vision and graphics, enabling applications in 3D modeling, mixed reality, robotics, and more.<n>Existing approaches based on rendering obtain promising results, but optimize on a per-scene basis, resulting in a slow optimization that can struggle to model textureless regions.<n>We introduce QuickSplat, which learns data-driven priors to generate dense initializations for 2D gaussian splatting optimization of large-scale indoor scenes.
  arXiv  Detail & Related papers  (2025-05-08T18:43:26Z)
• FreeSplat++: Generalizable 3D Gaussian Splatting for Efficient Indoor Scene Reconstruction [50.534213038479926]
  FreeSplat++ is an alternative approach to large-scale indoor whole-scene reconstruction.<n>Our method with depth-regularized per-scene fine-tuning demonstrates substantial improvements in reconstruction accuracy and a notable reduction in training time.
  arXiv  Detail & Related papers  (2025-03-29T06:22:08Z)
• InstantSplat: Sparse-view Gaussian Splatting in Seconds [91.77050739918037]
  We introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed.<n>InstantSplat employs a self-supervised framework that optimize 3D scene representation and camera poses.<n>It achieves an acceleration of over 30x in reconstruction and improves visual quality (SSIM) from 0.3755 to 0.7624 compared to traditional SfM with 3D-GS.
  arXiv  Detail & Related papers  (2024-03-29T17:29:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.