ToF-Splatting: Dense SLAM using Sparse Time-of-Flight Depth and Multi-Frame Integration

• URL: http://arxiv.org/abs/2504.16545v1
• Date: Wed, 23 Apr 2025 09:19:43 GMT
• Title: ToF-Splatting: Dense SLAM using Sparse Time-of-Flight Depth and Multi-Frame Integration
• Authors: Andrea Conti, Matteo Poggi, Valerio Cambareri, Martin R. Oswald, Stefano Mattoccia,
• Abstract summary: We propose ToF-Splatting, the first 3D Gaussian Splatting-based SLAM pipeline tailored for using effectively very sparse ToF input data.<n>Our approach improves upon the state of the art by introducing a multi-frame integration module, which produces dense depth maps by merging cues from extremely sparse ToF depth, monocular color, and multi-view geometry.
• Score: 40.16200204154956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time-of-Flight (ToF) sensors provide efficient active depth sensing at relatively low power budgets; among such designs, only very sparse measurements from low-resolution sensors are considered to meet the increasingly limited power constraints of mobile and AR/VR devices. However, such extreme sparsity levels limit the seamless usage of ToF depth in SLAM. In this work, we propose ToF-Splatting, the first 3D Gaussian Splatting-based SLAM pipeline tailored for using effectively very sparse ToF input data. Our approach improves upon the state of the art by introducing a multi-frame integration module, which produces dense depth maps by merging cues from extremely sparse ToF depth, monocular color, and multi-view geometry. Extensive experiments on both synthetic and real sparse ToF datasets demonstrate the viability of our approach, as it achieves state-of-the-art tracking and mapping performances on reference datasets.

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