SwitchSplat: Learning Discrete Object States from Casually Captured Scenes

Feed-forward 3D Gaussian Splatting reconstructs scenes from images in seconds. Pixel-aligned methods keep every Gaussian grounded in an observation, but produce redundant, potentially misaligned Gaussians across views. Token-based methods instead aggregate all views into a fixed set of learned tokens but lack explicit observation support and use a fixed reconstruction budget. We introduce ZipSplat, a method that predicts a compact, observation-grounded scene representation whose size is set at inference. It derives each scene token from an image patch rather than a learned query, and decodes it into freely placed 3D Gaussians. Tokens across views are merged into a compact set whose size is adjustable at inference. However, without geometric supervision, decoded Gaussians can drift from their source patch, and merging can mix tokens from different surfaces. We therefore introduce two geometric losses to ground and shape this representation. A cone loss ties each token's Gaussians to its source patch, and GeoCE aligns token similarity with 3D proximity so that tokens observing the same surface merge together. Building on these observation--geometry associations, we further introduce TokenBA, a token-level bundle adjustment that refines scene tokens and camera poses in a few seconds. ZipSplat requires neither camera poses nor intrinsics, yet outperforms pixel-aligned and token-based baselines on DL3DV and RealEstate10K, even those given ground-truth cameras. It further generalizes zero-shot to Mip-NeRF360 and ScanNet++. Our project page is at https://veichta.com/zipsplat.

Reconstructing dynamic 3D scenes from multi-view videos is a foundational task for robotics, AR/VR, and digital twins. While 3D Gaussian Splatting (3DGS) provides state-of-the art scene representation, its dynamic adaptations (4DGS) face significant challenges. Early MLP-based deformation models excel at interpolating within training frames but fail at temporal extrapolation --- important feature for forecasting in predictive systems. To address this, physically grounded approaches have been proposed to enable robust future forecasting, while, concurrently, semantic 4DGS methods have been developed for scene analysis and language-driven prompting. However, there is currently no framework that unites both physical consistency and semantic understanding. In this paper, we propose the first, to the best of our knowledge, unified 4DGS approach that is both physically grounded and semantically aware. Furthermore, to optimize computational efficiency, we introduce a novel rigid-body constraint mechanism. Instead of predicting the physical dynamics for every Gaussian particle, our model predicts the motion of a single anchor Gaussian per semantic object, updating the remaining Gaussians via relative transformations. This architectural design significantly accelerates novel view synthesis compared to an unconstrained baseline, while enabling object-accurate extrapolation and intuitive semantic interaction. Our project page: https://be2rlab.github.io/r5dgs/.

Feed-forward 3D Gaussian Splatting enables efficient novel-view synthesis without per-scene optimization, but most existing methods assume a fixed set of context views and process them jointly. This limits their applicability to online scenarios where calibrated views arrive sequentially and the scene must be updated causally. We present StreamSplat, a streaming feed-forward 3DGS framework that incrementally maintains a persistent geometry-grounded scene state and decodes it into renderable 3D Gaussians after each input chunk. StreamSplat centers on a Voxel-Aligned Causal Cache (VACC), which stores historical 3D tokens in a memory-bounded voxel structure so that memory grows with explored scene geometry rather than stream length. To better reuse history during causal prediction, we introduce History-Projected Depth Anchoring (HPDA) to project cached geometry as depth guidance for current cost-volume estimation, and Cache-Guided Feature Injection (CGFI) to inject cached latent evidence into Gaussian-token regression. Experiments on DL3DV, RealEstate10K, and ScanNet show that StreamSplat remains competitive with state-of-the-art feed-forward 3DGS methods under sparse causal inputs, despite not using future views or full-scene context. More importantly, it scales to long input streams with 256, 512, and 1024 views where fixed-view baselines run out of memory, yielding sustained improvements in novel-view synthesis quality as more observations arrive. The code will be made publicly available upon acceptance.