Teleoperated open-pit excavation requires fast and reliable 3D scene modeling under lightweight sensor configurations. To this end, this paper proposes a monocular camera–LiDAR fusion-based fast 3D Gaussian reconstruction method tailored for teleoperated open-pit excavation. The proposed approach uses only two sensors, a monocular camera and LiDAR, and integrates SPNet, a depth completion network, to improve the geometric completeness of the reconstructed scene. It further introduces a stride-aware initialization strategy that leverages the depth–stride correlation to jointly construct the initial Gaussian set and estimate the initial scales. During optimization, scale and color regularization are applied to prevent uncontrolled growth of Gaussians. Experiments in a Carla-simulated open-pit excavation scenario show that, under high-resolution input of 1920 × 1080, the proposed method achieves a stable 3D model update rate of approximately 2.5 Hz. The reconstruction quality under training viewpoints reaches PSNR 30.5388, SSIM 0.9161, and LPIPS 0.1333. Compared with 4DTAM and MonoGS, the proposed method achieves better overall reconstruction quality. It also maintains a much higher update rate than 4DTAM and a comparable update rate to MonoGS. Ablation studies further verify the critical contribution of the depth completion module and the stride-aware initialization strategy to the overall reconstruction performance. In addition, preliminary validation on field data further demonstrates the applicability of the proposed method under real-world open-pit excavation-loading conditions. The proposed method generates stable and usable 3D models of rock-pile working face under a lightweight sensor configuration, providing a reliable geometric basis for remote situational awareness and excavation assistance.
Bi et al. (Thu,) studied this question.