TRACE: Ergodic Trajectory Optimization for Active Scene Reconstruction

Key points

On all eight Replica scenes, it achieves higher PSNR than ActiveGS, with an average gain of 1.5 dB. The image-quality metrics LPIPS and SSIM move in the same direction.
The gains differ by scene type. In office scenes, SSIM is similar but LPIPS improves by 6% to 19%, while in room and hotel scenes the SSIM gap widens from 0.012 to 0.017.
The gain does not come simply from taking more observation frames. A variant that added 10 random shots on the ActiveGS path matched the original within 0.08 dB, while a variant with 10 evenly interpolated shots was actually 1.7 dB worse.
If the footstep-depletion trick is removed and only the original kernel ergodic objective is used, performance drops by 2.1 dB and becomes worse than ActiveGS on 6 of the 8 scenes. This shows that suppressing redundant observations is the key.
On a Unitree Go2 quadruped robot with an Intel D435 RGB-D camera, the system was run on a 42-square-meter space for a 5-minute mission, and it executed without infeasible commands at the planned horizons.
The real-world execution success rate was 100%, while the compared NBV baselines lightly touched the environment in every attempt. Because the trajectory itself is the optimization variable, waypoints can be passed directly to the controller.

External research summaries. These are not HDATF publications or measured product results.