ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation (1910.02550v2)

Abstract: Transparent objects are a common part of everyday life, yet they possess unique visual properties that make them incredibly difficult for standard 3D sensors to produce accurate depth estimates for. In many cases, they often appear as noisy or distorted approximations of the surfaces that lie behind them. To address these challenges, we present ClearGrasp -- a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation. Given a single RGB-D image of transparent objects, ClearGrasp uses deep convolutional networks to infer surface normals, masks of transparent surfaces, and occlusion boundaries. It then uses these outputs to refine the initial depth estimates for all transparent surfaces in the scene. To train and test ClearGrasp, we construct a large-scale synthetic dataset of over 50,000 RGB-D images, as well as a real-world test benchmark with 286 RGB-D images of transparent objects and their ground truth geometries. The experiments demonstrate that ClearGrasp is substantially better than monocular depth estimation baselines and is capable of generalizing to real-world images and novel objects. We also demonstrate that ClearGrasp can be applied out-of-the-box to improve grasping algorithms' performance on transparent objects. Code, data, and benchmarks will be released. Supplementary materials available on the project website: https://sites.google.com/view/cleargrasp

• The paper presents ClearGrasp, a novel method that estimates the 3D shape of transparent objects from a single RGB-D image using deep learning.
• It employs deep networks to derive surface normals, masks, and occlusion boundaries, then refines depth maps via global optimization.
• Experimental results show significant RMSE and accuracy improvements over baselines, advancing the capability of robotic manipulation.

ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation

The paper presents ClearGrasp, a profound methodological innovation aimed at estimating the 3D geometry of transparent objects from a single RGB-D image, solving a predominant issue faced by robotic manipulation in realistic environments. Transparent objects, such as plastic containers and glassware, pose significant challenges to traditional 3D sensors due to their refractive and specular properties, which can lead to distorted depth perception. The ClearGrasp algorithm integrates deep learning with synthetic training data to improve manipulation algorithms' ability to handle such items with precision.

Methodology Overview

ClearGrasp leverages deep convolutional networks to extract and utilize critical structural information from RGB-D images. The method focuses on three primary outputs: surface normals, masks of transparent surfaces, and occlusion boundaries, which serve as inputs for global optimization to refine depth estimations. Notably, it includes masked pixels where depth is unreliable, addressing the sensor inaccuracies associated with transparent materials.

Key Components:

• Surface Normal Estimation: This is accomplished using a deep neural network trained to capture the curvature of transparent objects, offering more reliable insights than direct depth inference.
• Boundary Detection: It involves predicting occlusion and contact edges, essential for understanding depth discontinuities, thereby distinguishing between different types of boundaries critical for global optimization.
• Global Optimization: The algorithm refines the depth map by adjusting sensor data using the predicted normals and boundaries, leveraging commodity RGB-D cameras' initial estimates for non-transparent surfaces to inform surrounding transparent areas.

Dataset Development and Evaluation

The researchers constructed a large-scale synthetic dataset consisting of over 50,000 images alongside a real-world benchmark with 286 images. The synthetic data is derived from high-quality rendered scenes using Blender's rendering capabilities, simulating critical effects like refraction and reflection, while the real dataset captures transparent objects through a meticulous overlay method with spray-painted equivalents. Results demonstrated substantial improvements over monocular depth estimation baselines, showcasing the efficacy of ClearGrasp in both synthetic and real-world tests, with generalization across unseen objects.

Key Results:

• ClearGrasp achieved superior results in depth estimation metrics, showing marked improvements in root mean squared error (RMSE) and accuracy compared to DenseDepth and DeepCompletion alternatives.
• Inclusion of occlusion contact edges and weighted loss terms significantly refined depth completion outcomes, highlighted within the conducted ablative studies.

Implications and Future Directions

ClearGrasp represents a significant advancement in translating 3D geometric estimation to applications requiring precise manipulation of transparent objects, such as robotics in manufacturing and logistics. Practically, it enhances robotic systems' ability to handle tasks like dishwashing or sorting items with irregular shapes or sizes. Theoretically, ClearGrasp provides insights into leveraging synthetic data effectively to train models for real-world applications, thus further bridging the domain gap.

Future work may focus on enhancing robustness under variable lighting conditions or in cluttered environments, as well as addressing sharp caustic patterns which currently challenge the system's integrity. Greater emphasis on learning from mixed training datasets may also improve the generality and applicability of similar approaches across diverse transparent object categories.

Overall, ClearGrasp exemplifies significant progress in overcoming longstanding challenges in robotic vision, paving the way for increasingly sophisticated AI-driven manipulation capabilities.