Not All Points Are Equal: Uncertainty-Aware 4D LiDAR Scene Synthesis

• URL: http://arxiv.org/abs/2606.02510v1
• Date: Mon, 01 Jun 2026 17:24:14 GMT
• Title: Not All Points Are Equal: Uncertainty-Aware 4D LiDAR Scene Synthesis
• Abstract summary: We present U4D, a new framework that explicitly leverages spatial uncertainty to guide LiDAR scene generation.<n>Experiments on nuScenes and Semantic KITTI demonstrate state-of-the-art scene fidelity, temporal consistency, and downstream performance.
• Score: 63.07333625429548
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Constructing faithful 4D worlds from LiDAR-acquired sequences is crucial for embodied AI, yet current generative frameworks apply uniform modeling capacity across all spatial regions. This ignores that perceptual difficulty varies dramatically within a single scan: distant surfaces, occluded boundaries, and small-scale objects carry far higher uncertainty than well-observed structures. We present U4D, a new framework that explicitly leverages spatial uncertainty to guide LiDAR scene generation in a "hard-to-easy" schedule. U4D derives per-point uncertainty maps via Shannon Entropy from a pretrained segmentor, then applies an unconditional diffusion stage to synthesize high-entropy areas with precise geometry, followed by a conditional completion stage that fills in the remaining regions using these structures as priors. A MoST (Mixture of Spatio-Temporal) block further maintains cross-frame coherence by dynamically balancing spatial detail and temporal continuity. Extensive experiments on nuScenes and SemanticKITTI demonstrate state-of-the-art scene fidelity, temporal consistency, and downstream performance.

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