Learnable Burst-Encodable Time-of-Flight Imaging for High-Fidelity Long-Distance Depth Sensing

• URL: http://arxiv.org/abs/2505.22025v1
• Date: Wed, 28 May 2025 06:46:43 GMT
• Title: Learnable Burst-Encodable Time-of-Flight Imaging for High-Fidelity Long-Distance Depth Sensing
• Authors: Manchao Bao, Shengjiang Fang, Tao Yue, Xuemei Hu,
• Abstract summary: Long-distance depth imaging holds great promise for applications such as autonomous driving and robotics.<n>Direct time-of-flight (dToF) imaging offers high-precision, long-distance depth sensing, yet demands ultra-short pulse light sources and high-resolution time-to-digital converters.<n>We introduce a novel ToF imaging paradigm, termed Burst-Encodable Time-of-Flight (BE-ToF), which facilitates high-fidelity, long-distance depth imaging.
• Score: 7.645012220983793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-distance depth imaging holds great promise for applications such as autonomous driving and robotics. Direct time-of-flight (dToF) imaging offers high-precision, long-distance depth sensing, yet demands ultra-short pulse light sources and high-resolution time-to-digital converters. In contrast, indirect time-of-flight (iToF) imaging often suffers from phase wrapping and low signal-to-noise ratio (SNR) as the sensing distance increases. In this paper, we introduce a novel ToF imaging paradigm, termed Burst-Encodable Time-of-Flight (BE-ToF), which facilitates high-fidelity, long-distance depth imaging. Specifically, the BE-ToF system emits light pulses in burst mode and estimates the phase delay of the reflected signal over the entire burst period, thereby effectively avoiding the phase wrapping inherent to conventional iToF systems. Moreover, to address the low SNR caused by light attenuation over increasing distances, we propose an end-to-end learnable framework that jointly optimizes the coding functions and the depth reconstruction network. A specialized double well function and first-order difference term are incorporated into the framework to ensure the hardware implementability of the coding functions. The proposed approach is rigorously validated through comprehensive simulations and real-world prototype experiments, demonstrating its effectiveness and practical applicability.

Related papers

• FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion [63.87313550399871]
  Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability.<n>We propose Self-supervised Transfer (PST) and FrequencyDe-coupled Fusion module (FreDF)<n>PST establishes cross-modal knowledge transfer through latent space alignment with image foundation models.<n>FreDF explicitly decouples high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches.
  arXiv  Detail & Related papers  (2025-03-25T15:04:53Z)
• Multi-photon enhanced resolution for Superconducting Nanowire Single-Photon Detector-based Time-of-Flight lidar systems [0.0]
  We report a lidar system based on waveguide-integrated SNSPDs that excels in temporal accuracy, which translates into high range resolution.<n>For single-shot measurements, we find resolution in the millimeter regime, resulting from the jitter of the time-of-flight signal of 21$,$ps for low photon numbers.<n>For multi-shot measurements we find sub-millimeter range-accuracy of 0.75$,$mm and reveal additional surface information of scanned objects.
  arXiv  Detail & Related papers  (2025-03-19T15:47:16Z)
• FoveaSPAD: Exploiting Depth Priors for Adaptive and Efficient Single-Photon 3D Imaging [7.350208716861244]
  Single-photon avalanche diodes (SPADs) are an emerging image-sensing technology that offer many advantages such as extreme sensitivity and time resolution.<n>In this paper, we propose new algorithms and sensing policies that improve signal-to-noise ratio (SNR) and increase computing and memory efficiency.
  arXiv  Detail & Related papers  (2024-12-03T00:20:01Z)
• PASTA: Towards Flexible and Efficient HDR Imaging Via Progressively Aggregated Spatio-Temporal Alignment [91.38256332633544]
  PASTA is a Progressively Aggregated Spatio-Temporal Alignment framework for HDR deghosting. Our approach achieves effectiveness and efficiency by harnessing hierarchical representation during feature distanglement. Experimental results showcase PASTA's superiority over current SOTA methods in both visual quality and performance metrics.
  arXiv  Detail & Related papers  (2024-03-15T15:05:29Z)
• Multi-Modal Neural Radiance Field for Monocular Dense SLAM with a Light-Weight ToF Sensor [58.305341034419136]
  We present the first dense SLAM system with a monocular camera and a light-weight ToF sensor. We propose a multi-modal implicit scene representation that supports rendering both the signals from the RGB camera and light-weight ToF sensor. Experiments demonstrate that our system well exploits the signals of light-weight ToF sensors and achieves competitive results.
  arXiv  Detail & Related papers  (2023-08-28T07:56:13Z)
• FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring [72.43250555622254]
  We propose a lightweight and real-time unsupervised BID baseline, termed Frequency-domain Contrastive Loss Constrained Lightweight CycleGAN. FCL-GAN has attractive properties, i.e., no image domain limitation, no image resolution limitation, 25x lighter than SOTA, and 5x faster than SOTA. Experiments on several image datasets demonstrate the effectiveness of FCL-GAN in terms of performance, model size and reference time.
  arXiv  Detail & Related papers  (2022-04-16T15:08:03Z)
• Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments [56.306567220448684]
  We propose a new learning based end-to-end depth prediction network which takes noisy raw I-ToF signals as well as an RGB image. We show more than 40% RMSE improvement on the final depth map compared to the baseline approach.
  arXiv  Detail & Related papers  (2021-12-07T15:04:14Z)
• Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution [51.274657266928315]
  We propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors. Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters.
  arXiv  Detail & Related papers  (2021-04-07T12:00:38Z)
• iToF2dToF: A Robust and Flexible Representation for Data-Driven Time-of-Flight Imaging [26.17890136713725]
  Indirect Time-of-Flight (iToF) cameras are a promising depth sensing technology. They are prone to errors caused by multi-path interference (MPI) and low signal-to-noise ratio (SNR)
  arXiv  Detail & Related papers  (2021-03-12T04:57:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.