从「3D相册」到「物理底座」：CVPR 2026 开启 3DGS 的具身智能时代
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TL;DR - A CVPR 2026 research roundup argues that 3D Gaussian Splatting is evolving from a rendering technique into infrastructure for robotics, simulation, and embodied AI. The highlighted systems improve generation speed, deployment efficiency, sensor robustness, and physical interaction.
- EcoSplat and SparseSplat generate lightweight scenes rapidly while adapting Gaussian counts and density to compute budgets and scene complexity.
- CaT-GS accelerates large-scene rendering through inter-frame caching and GPU load balancing; EDGS reduces training through geometry-aware initialization.
- TokenGS decouples Gaussians from pixels, improving pose-noise robustness and enabling lightweight on-device token tuning.
- SGAD-SLAM corrects noisy depth during mapping, while ParticleGS models Gaussians as Neural ODE-driven physical particles for motion extrapolation.
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从「3D相册」到「物理底座」：CVPR 2026 开启 3DGS 的具身智能时代
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TL;DR - A CVPR 2026 research roundup argues that 3D Gaussian Splatting is evolving from a rendering technique into infrastructure for robotics, simulation, and embodied AI. The highlighted systems improve generation speed, deployment efficiency, sensor robustness, and physical interaction.
- EcoSplat and SparseSplat generate lightweight scenes rapidly while adapting Gaussian counts and density to compute budgets and scene complexity.
- CaT-GS accelerates large-scene rendering through inter-frame caching and GPU load balancing; EDGS reduces training through geometry-aware initialization.
- TokenGS decouples Gaussians from pixels, improving pose-noise robustness and enabling lightweight on-device token tuning.
- SGAD-SLAM corrects noisy depth during mapping, while ParticleGS models Gaussians as Neural ODE-driven physical particles for motion extrapolation.