RGB-D cameras have a difficult time accurately capturing the depth of transparent objects because of the optical effects of reflection and refraction. Because of this, the depth maps these cameras produce frequently contain inaccurate or missing information. To overcome this problem, recent research has developed sophisticated network designs and advanced visual features intended to recreate the missing depth information. Though these methods can boost accuracy, they also pose difficulties with the connection of various visual data and significantly raise processing needs.
A unique method known as DistillGrasp has been put out in response to these difficulties. Its goal is to efficiently finish depth maps by transferring information from a high-capacity teacher network to a more efficient student network. A specifically created position correlation block (PCB) in the teacher network employs RGB images as reference points, also known as queries and keys, in order to determine associated values. This helps the model in accurately establishing correlations between various features, which it then applies to the transparent areas lacking depth information.
The method presents a consistent feature correlation module (CFCM) to students. This module saves consistent and dependable regions from the RGB images and the current depth maps. It then fills in the gaps in the depth of information by using a convolutional neural network (CNN) to identify the connections between these regions. A distillation loss is applied to make sure the student network doesn’t just replicate the regional features of the teacher network. This loss function promotes a more comprehensive knowledge of the scene by accounting for the object’s edge information and structure in addition to the difference between the expected and actual depth values.
Extensive experiments on the ClearGrasp dataset have proven the effectiveness of this methodology. According to the findings, the teacher network performs better in terms of accuracy and generalization than the most advanced techniques in use. The student network operates at a rapid 48 frames per second (FPS) and produces competitive results despite being more computationally efficient. Furthermore, DistillGrasp demonstrated notable improvements in speed when implemented on an actual robotic grasping system, demonstrating its usefulness and resilience in handling the intricacies of translucent objects.
The team has summarized their primary contributions as follows.
This work discusses the application of knowledge distillation to enhance the precision of depth completion, particularly for transparent objects. This new method trains a more effective student network by utilizing the advantages of a stronger teacher network.
The study presents two unique approaches to identifying connections between the characteristics of the student and teacher networks. In the student network, the Consistent Feature Correlation Module (CFCM) has been employed to maintain efficiency without losing performance, whereas the Position Correlation Block (PCB) has been used in the instructor network to align features precisely. These tactics guarantee both networks reach high levels of precision and velocity.
A composite distillation loss has been implemented to close the performance difference between the student and teacher networks. This loss function, which consists of distance loss, structure loss, and edge loss, enables the student network to efficiently learn both local and global features, guaranteeing that it can mimic the performance of the teacher network.
In terms of accuracy and generalization, extensive testing on the ClearGrasp dataset has demonstrated that the teacher network performs better than the state-of-the-art techniques. Even though it is faster, the student network produces competitive outcomes. The technique’s successful application on a UR10e robot for gripping transparent objects proves its effectiveness.
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