DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image (2209.13362v1)

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/.

• The paper presents an innovative attention-based approach to fuse lightweight ToF sensor data with RGB images for enhanced depth estimation.
• It introduces a custom feature extractor with a cross-domain attention module to transform depth distributions into high-resolution maps.
• Experimental results show significant improvements in accuracy and RMSE, validating its potential for advanced 3D reconstruction and navigation applications.

DELTAR: Enhancing Depth Estimation with Lightweight ToF Sensors and RGB Images

This paper introduces DELTAR, a novel approach designed to enhance the depth measurement capabilities of lightweight time-of-flight (ToF) sensors when used in conjunction with RGB images. Lightweight ToF sensors, significantly deployed in mobile devices due to their cost-effectiveness and low energy consumption, suffer from limitations in resolution and accuracy, having inherently low spatial resolution and yielding depth measurements in a distribution form rather than definitive depth values. These constraints restrict their usage in advanced applications requiring high-fidelity depth data, such as 3D reconstruction and simultaneous localization and mapping (SLAM).

The DELTAR framework aims to address these limitations by fusing data from both ToF sensors and RGB images, driving a combined estimation mechanism for accurate and dense depth maps. The authors propose a two-pronged approach, meticulously designed to handle the complexities and characteristics inherent in each data type. The core contribution lies in an attention-based neural architecture, featuring a specialized feature extractor that translates depth distributions from ToF sensors into usable data while synchronously employing RGB information to achieve high-resolution depth predictions.

Methodology

The methodological advances in this paper stem from several key innovations:

1. Feature Extraction Customization: A novel feature extractor is engineered specifically for processing and interpreting the depth distributions provided by the ToF sensor. This module utilizes a sampling approach on the distribution, followed by feature extraction using PointNet architecture, which allows the model to effectively encode the depth information.
2. Cross-Domain Attention-Based Fusion: Integrating data from distinct sensors is realized through an advanced Transformer-based module. This fusion mechanism leverages attention mechanisms, facilitating coherent assimilation of image and distribution features across multiple scales. A cross-attention mechanism is employed, which is informed by learned patch-distribution correspondence, ensuring efficient localization of relevant depth information.
3. Calibration Method: A sophisticated calibration technique aligns RGB camera outputs with ToF measurements. This method compensates for the inherent differences in data acquisition modes, resolving correspondences through an EM-like calibration algorithm that establishes accurate spatial alignments.
4. Dataset Collection: With no existing datasets matching the unique setup of their sensor suite, the researchers meticulously construct a real-world dataset, ZJU-L5. This collection encompasses hundreds of L5-image pairs, facilitating comprehensive model evaluation and training.

Experimental Evaluation

Empirical assessments underscore the superior performance of DELTAR in comparison to extant approaches in monocular depth estimation, depth completion, and depth super-resolution. The DELTAR model demonstrates marked improvements across standard metrics, such as accuracy and RMSE, achieving depth quality on par with commodity-level RGB-D sensors, like Intel RealSense. Quantitative metrics aside, the qualitative evaluations reveal a model adept at maintaining fine spatial details and accurate depth boundaries, a critical improvement over rival methods.

Implications and Future Directions

The implications of this work are significant, reflecting both practical and theoretical advancements in depth estimation methodologies. Practically, DELTAR enables the deployment of commonplace mobile ToF sensors in demanding applications which were previously unfeasible due to hardware limitations. Theoretically, the integration of attention-based mechanisms tailored to sensor-specific characteristics opens avenues for cross-disciplinary research, potentially informing future AI developments across sensory fusion tasks.

Looking forward, the authors acknowledge potential paths for optimization, particularly concerning the computational demands of their model. Real-time processing remains a particular focus, suggesting further refinement of network architectures could enhance efficiency without compromising depth fidelity. Additionally, DELTAR's application could extend into dynamic scenes within robotics and autonomous navigation, leveraging its robust depth estimation capabilities.

In conclusion, DELTAR represents a significant contribution to the field of computer vision, enabling high-resolution depth estimation by smartly integrating lightweight ToF sensor data with the comprehensive context provided by RGB imagery. The paper presents a compelling model that navigates existing limitations of depth sensing technologies and serves as a catalyst for future innovation.