Learned Optimal Visual Time-of-Flight Imaging With Fisher Information Guidance

Indirect time-of-flight (iToF) imaging provides absolute depth by encoding light transport delays into correlation measurements via active illumination modulation and demodulation. This technology plays a crucial role in object detection and scene understanding and has been widely adopted in applications such as robotics, automotive driving, and augmented reality. However, iToF systems often suffer from low signal-to-noise ratio (SNR), particularly under strong ambient illumination. Signal attenuation and noise-induced phase errors severely degrade depth accuracy, while reliable depth discontinuity and edge reconstruction remain challenging. In this paper, we introduce an optimal visual ToF imaging scheme, optimized through end-to-end learning of iToF coding functions and depth reconstruction, guided by discriminative Fisher information supervision. By integrating vision information from RGB images, we facilitate the convergence of the optimization for iToF coding functions and enhance robustness under noisy inputs. Moreover, we design a dual-branch visual ToF reconstruction network that estimates depth from iToF measurements while exploiting edge guidance derived from visual information to preserve fine geometric details. Extensive experiments on both synthetic and real-world datasets verify the effectiveness of the proposed approach, particularly in challenging low-SNR conditions.