DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image

• URL: http://arxiv.org/abs/2209.13362v1
• Date: Tue, 27 Sep 2022 13:11:37 GMT
• Title: DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image
• Authors: Yijin Li, Xinyang Liu, Wenqi Dong, Han Zhou, Hujun Bao, Guofeng Zhang, Yinda Zhang, Zhaopeng Cui
• Abstract summary: We propose DELTAR, a novel method to empower light-weight ToF sensors with the capability of measuring high resolution and accurate depth. As the core of DELTAR, a feature extractor customized for depth distribution and an attention-based neural architecture is proposed to fuse the information from the color and ToF domain efficiently. Experiments show that our method produces more accurate depth than existing frameworks designed for depth completion and depth super-resolution and achieves on par performance with a commodity-level RGB-D sensor.
• Score: 39.389538555506256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Light-weight time-of-flight (ToF) depth sensors are small, cheap, low-energy and have been massively deployed on mobile devices for the purposes like autofocus, obstacle detection, etc. However, due to their specific measurements (depth distribution in a region instead of the depth value at a certain pixel) and extremely low resolution, they are insufficient for applications requiring high-fidelity depth such as 3D reconstruction. In this paper, we propose DELTAR, a novel method to empower light-weight ToF sensors with the capability of measuring high resolution and accurate depth by cooperating with a color image. As the core of DELTAR, a feature extractor customized for depth distribution and an attention-based neural architecture is proposed to fuse the information from the color and ToF domain efficiently. To evaluate our system in real-world scenarios, we design a data collection device and propose a new approach to calibrate the RGB camera and ToF sensor. Experiments show that our method produces more accurate depth than existing frameworks designed for depth completion and depth super-resolution and achieves on par performance with a commodity-level RGB-D sensor. Code and data are available at https://zju3dv.github.io/deltar/.

Related papers

• RGB Guided ToF Imaging System: A Survey of Deep Learning-based Methods [30.34690112905212]
  Integrating an RGB camera into a ToF imaging system has become a significant technique for perceiving the real world. This paper comprehensively reviews the works related to RGB guided ToF imaging, including network structures, learning strategies, evaluation metrics, benchmark datasets, and objective functions.
  arXiv  Detail & Related papers  (2024-05-16T17:59:58Z)
• Robust Depth Enhancement via Polarization Prompt Fusion Tuning [112.88371907047396]
  We present a framework that leverages polarization imaging to improve inaccurate depth measurements from various depth sensors. Our method first adopts a learning-based strategy where a neural network is trained to estimate a dense and complete depth map from polarization data and a sensor depth map from different sensors. To further improve the performance, we propose a Polarization Prompt Fusion Tuning (PPFT) strategy to effectively utilize RGB-based models pre-trained on large-scale datasets.
  arXiv  Detail & Related papers  (2024-04-05T17:55:33Z)
• 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)
• Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution [52.582632746409665]
  We propose a novel Symmetric Uncertainty-aware Feature Transmission (SUFT) for color-guided DSR. Our method achieves superior performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-01T06:35:59Z)
• Consistent Direct Time-of-Flight Video Depth Super-Resolution [9.173767380836852]
  Direct time-of-flight (dToF) sensors are promising for next-generation on-device 3D sensing. We propose the first multi-frame fusion scheme to mitigate the spatial ambiguity resulting from the low-resolution dToF imaging. We introduce DyDToF, the first synthetic RGB-dToF video dataset that features dynamic objects and a realistic dToF simulator.
  arXiv  Detail & Related papers  (2022-11-16T04:16:20Z)
• FloatingFusion: Depth from ToF and Image-stabilized Stereo Cameras [37.812681878193914]
  smartphones now have multimodal camera systems with time-of-flight (ToF) depth sensors and multiple color cameras. producing accurate high-resolution depth is still challenging due to the low resolution and limited active illumination power of ToF sensors. We propose an automatic calibration technique based on dense 2D/3D matching that can estimate camera parameters from a single snapshot.
  arXiv  Detail & Related papers  (2022-10-06T09:57:09Z)
• Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction [91.43066633305662]
  We propose a novel multi-task and multi-modal filtered transformer (MMFT) network for RGB-D salient object detection (SOD) Specifically, we unify three complementary tasks: depth estimation, salient object detection and contour estimation. The multi-task mechanism promotes the model to learn the task-aware features from the auxiliary tasks. Experiments show that it not only significantly surpasses the depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts a high-quality depth map and salient contour at the same time.
  arXiv  Detail & Related papers  (2022-03-09T17:20:18Z)
• 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)

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.