TIDES: Time-Derivative Event Simulation via Deformable Reconstruction

• URL: http://arxiv.org/abs/2606.02058v1
• Date: Mon, 01 Jun 2026 10:49:13 GMT
• Title: TIDES: Time-Derivative Event Simulation via Deformable Reconstruction
• Abstract summary: We present TIDES, a continuous-time event simulator built on dynamic splatting.<n> TIDES operates on an explicit 3D representation with learnt geometry and motion.<n>It can derive per-pixel intensity dynamics directly from the scene, rather than by differencing rendered frames.
• Score: 10.871120244987805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event cameras emit asynchronous events in response to environmental appearance changes. The scarcity of real-world event datasets makes simulation essential. However, most simulators infer event timestamps from frame sequences, forcing many threshold crossings to share a small set of discrete times; a failure mode we term timestamp batching that worsens under fast motion and occlusion. We present TIDES, a continuous-time event simulator built on dynamic Gaussian splatting. Because TIDES operates on an explicit 3D scene representation with learnt geometry and motion, it can derive per-pixel intensity dynamics directly from the scene, rather than by differencing rendered frames. This enables accurate threshold-crossing prediction, including multiple crossings per rendering step, without temporal upsampling or frame interpolation. The same 3D scene model reveals where objects partially occlude one another; TIDES uses this to guide adaptive time stepping, concentrating computation only in regions where occlusion dynamics make simple models of brightness change unreliable. Finally, we model finite sensor bandwidth using a tile-level arbiter whose throughput, jitter, and event drops reproduce realistic sensor artifacts. Across paired RGB-event benchmarks, TIDES attains state-of-the-art event-stream fidelity. We also show that events simulated by TIDES transfer more effectively to real downstream tasks than competitors'.

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