RLSplat: Resource-Linear Budget Control for Feed-forward 3D Gaussian Splatting
RLSplat: Resource-Linear Budget Control for Feed-forward 3D Gaussian Splatting
RLSplat achieves linear reduction in latency, memory, and model size for feedforward 3D Gaussian splatting by assigning Gaussians through regional allocation and using local attention decoding. In zero-shot evaluation, RLSplat outperforms previous budget-controlled methods under all budgets, surpassing the strongest baseline even with a 4x reduction in Gaussian count, and achieves a 4.1x speedup and a 9.6x reduction in peak memory at low budgets.
Published on Oct 20
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Abstract
RLSplat enables feed-forward 3D Gaussian splatting with linear reductions in latency, memory, and model size by allocating Gaussians regionally and decoding them via local attention.
Feed-forward 3D Gaussian Splatting (FF3DGS) reconstructs 3D scenes from images in a single forward pass, making it well-suited to applications that must operate within limited resource budgets. To further improve its practicality, recent budget-controllable FF3DGS methods allow the number of Gaussians to be adjusted at inference, so that the output meets a given storage limit or rendering-latency constraint. However, these methods overlook the cost of inference itself: to produce a representation with the target number of Gaussians, they first predict a dense set of Gaussians and then prune or select from it, requiring nearly constant latency and peak memory regardless of the budget. We therefore propose RLSplat, a resource-linear budget-controllable FF3DGS framework whose representation size, inference latency, and peak memory all decrease linearly as the Gaussian budget is reduced. Instead of pruning or selection, RLSplat learns a Gaussian allocation policy that distributes the budget across local scene regions, so that exactly the requested number of Gaussians is generated. These Gaussians are then refined through resource-linear Gaussian decoding, in which each Gaussian attends only to nearby Gaussians found via an efficient hierarchical neighbor search, so that the decoding cost decreases linearly with fewer Gaussians. In zero-shot evaluation, RLSplat outperforms prior budget-controllable methods at every budget, surpassing the strongest baseline even with 4times fewer Gaussians, while running up to 4.1times faster with 9.6times less peak memory at low budgets.
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