Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects (2208.03792v2)

Abstract: Commercial depth sensors usually generate noisy and missing depths, especially on specular and transparent objects, which poses critical issues to downstream depth or point cloud-based tasks. To mitigate this problem, we propose a powerful RGBD fusion network, SwinDRNet, for depth restoration. We further propose Domain Randomization-Enhanced Depth Simulation (DREDS) approach to simulate an active stereo depth system using physically based rendering and generate a large-scale synthetic dataset that contains 130K photorealistic RGB images along with their simulated depths carrying realistic sensor noises. To evaluate depth restoration methods, we also curate a real-world dataset, namely STD, that captures 30 cluttered scenes composed of 50 objects with different materials from specular, transparent, to diffuse. Experiments demonstrate that the proposed DREDS dataset bridges the sim-to-real domain gap such that, trained on DREDS, our SwinDRNet can seamlessly generalize to other real depth datasets, e.g. ClearGrasp, and outperform the competing methods on depth restoration with a real-time speed. We further show that our depth restoration effectively boosts the performance of downstream tasks, including category-level pose estimation and grasping tasks. Our data and code are available at https://github.com/PKU-EPIC/DREDS

• The paper introduces a novel Domain Randomization-Enhanced Depth Simulation (DREDS) technique integrated with SwinDRNet to restore noisy depth data for specular and transparent objects.
• It employs a synthetic dataset of 130K RGB-depth images with realistic sensor noise, effectively bridging the gap between simulation and real-world scenarios.
• SwinDRNet achieves 30 FPS real-time performance and superior accuracy in tasks like 6D pose estimation and robotic grasping, outperforming state-of-the-art methods.

Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects: A Review

The paper presents an approach that addresses the prevalent issue of noisy and incomplete depth data acquisition, especially when handling specular and transparent objects through the depth restoration network, SwinDRNet, enhanced with a domain randomization technique. This entails several advancements in simulation-based depth dataset generation and real-world applicability, particularly benefiting object recognition and robotic manipulation tasks.

Key Contributions

1. Depth Sensor Simulation and Domain Randomization:
   • The authors have developed a novel Domain Randomization-Enhanced Depth Simulation (DREDS) approach, which employs a simulation technique based on active stereo depth sensor systems to generate synthetic depth data.
   • The DREDS dataset comprises 130K photorealistic RGB and corresponding depth images with simulated sensor noise, effectively bridging the gap between simulated and real environments. Domain randomization is utilized to attribute realistic variability to object textures, materials (ranging from specular to transparent), and environmental factors, thus enhancing the generalization of models trained on these synthetic datasets.
2. SwinDRNet: Depth Restoration Network:
   • The SwinDRNet, based on the Swin Transformer architecture, acts as a robust two-stream RGB-D fusion network for depth restoration. The network addresses the inadequacies in depth data, significantly improving upon standard restoration techniques by incorporating a cross-attention mechanism to effectively fuse multi-modal features.
   • A noteworthy aspect is the real-time processing capability, achieving a frame rate of 30 FPS, which makes it applicable to real-world scenarios.
3. Real-world Benchmark Dataset (STD):
   • In addition to synthetic data, the paper introduces the STD real-world dataset comprising 27K depth images from various scenes involving specular, transparent, and diffuse materials, enabling extensive evaluation of the proposed depth restoration methods.

Experimental Evaluation

Experiments conducted demonstrate the efficacy of SwinDRNet in restoring depth maps in simulated and real-world contexts. The depth restoration network shows superior performance, as evidenced by metrics such as RMSE, REL, and accuracy within thresholds (e.g., $\delta_{1.05}$), comparing favorably to state-of-the-art baselines like LIDF and NLSPN. Additionally, its deployment in downstream tasks like category-level 6D pose estimation and robotic grasping further validates the robustness of depth restoration, where restored depths enhance pose accuracy and success rates in grasping tasks.

Practical and Theoretical Implications

Practically, the paper presents critical advancements for AR/VR applications and robot vision systems, offering enhanced object recognition accuracy and interaction capabilities with complex, specular, or transparent objects. Theoretically, this research poses intriguing questions about the further application of domain randomization in other perception-based tasks and the potential improvements in sim-to-real transfer learning paradigms.

Future Directions

Exploring the broader implications of domain randomization in different sensory modalities could further improve model robustness. Moreover, refining the SwinDRNet architecture or developing new training regimes to handle extremely rare or unconventional materials could extend its applicability. Additionally, examining cross-application synergies within AI by coupling depth perception with, for example, tactile sensing could pave the way for more adaptable robotic systems.

In summary, this research provides a solid foundation for advancing robotic perception techniques, particularly regarding specular and transparent materials, and establishes valuable datasets for the community that can expedite further exploration in depth-based navigation and interaction tasks. It exemplifies the benefits of integrating synthetic data with real-world utilization, promoting a comprehensive understanding of object-based perception systems.