Triplex4DGS: High-Speed 4D Gaussian Splatting with Complementary Vision Sensors

High-speed dynamic scene 4D reconstruction is crucial for real-world applications. Conventional RGB-based reconstruction suffers from limited temporal resolution to record fast-moving objects, while high-speed RGB cameras incur prohibitive bandwidth and data overhead. As an alternative, the complementary vision sensor (CVS) simultaneously captures synchronized 30-fps RGB frames alongside high-frequency, sparse and multi-bit Temporal Difference (TD) and Spatial Difference (SD) signals, enabling high-speed imaging with much lower bandwidth than traditional sensors. However, directly reconstructing RGB frames from TD/SD may introduce temporal inconsistencies that propagate into subsequent 4D reconstruction. Moreover, TD and SD are monochromatic and follow photometric characteristics distinct from RGB pixels, making joint optimization challenging. Meanwhile, existing 4D Gaussian Splatting frameworks lack differentiable rendering models for TD and SD supervision, so such data still fails to yield improvements for high-speed 4D reconstruction. To tackle the above challenges, we propose Triplex4DGS, a two-stage framework that fully exploits CVS' three complementary data modalities, achieving the capability of ultra-high-speed 4D scene reconstruction with extremely low hardware overhead. In the first stage of Triplex4DGS, a continuous dynamic Gaussian representation is initialized from sparse RGB keyframes. The second stage builds unified supervision within the TD–SD response space via a differentiable spatio-temporal difference simulator, enabling hybrid observation-guided optimization, where SD refines fine-grained spatial geometry, TD regularizes temporal motion trajectories, and RGB frames maintain consistent visual appearance. Furthermore, we build a multi-CVS data acquisition system and construct the first simulated and real-world CVS 4D reconstruction benchmark. Extensive experiments on both datasets demonstrate state-of-the-art performance, validating the effectiveness of Triplex4DGS with CVS for high-speed 4D reconstruction.