Seeing Through the Invisible: AI Completes Depth for Transparent Objects

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Author: Denis Avetisyan

Researchers have developed a self-supervised learning technique that allows robots and machines to accurately estimate the depth of transparent objects like glass or plastic, enhancing their ability to interact with the world.

The system leverages masked input data, originally intended for supervised learning, to perform self-supervised learning, achieving functionality without requiring complete, transparent object depth maps-a pragmatic approach acknowledging the inevitable complexities of real-world production environments.
The system leverages masked input data, originally intended for supervised learning, to perform self-supervised learning, achieving functionality without requiring complete, transparent object depth maps-a pragmatic approach acknowledging the inevitable complexities of real-world production environments.

A novel approach leverages depth information from non-transparent regions to complete depth maps for transparent objects using self-supervised learning and transformer networks.

Accurately perceiving depth remains a core challenge in computer vision, particularly with transparent objects which confound conventional depth sensors due to light refraction and reflection. This paper, ‘Self-Supervised Learning for Transparent Object Depth Completion Using Depth from Non-Transparent Objects’, addresses this limitation by introducing a novel self-supervised learning approach that leverages depth information from non-transparent regions to complete depth maps for transparent objects. The proposed method achieves performance comparable to fully supervised techniques, and importantly, demonstrates improved model performance when training data is limited. Could this self-supervised paradigm unlock more robust and data-efficient depth perception for robotics and augmented reality applications?

The Illusion of Depth: Why Sensors Fail Us

Many technologies, from self-driving cars to surgical robots, rely on depth sensing to perceive the three-dimensional world, yet current methods often falter when encountering transparent materials like glass or water. These systems typically operate under the assumption that light reflects diffusely off surfaces – known as the Lambertian reflectance model – allowing devices to calculate distance based on the intensity of returned light. However, transparency fundamentally alters this process; light passes through the object rather than being readily reflected, creating a signal that doesn’t conform to the expected Lambertian behavior. This mismatch leads to inaccurate depth calculations, hindering the reliable operation of applications that demand precise environmental understanding and robust object manipulation. Consequently, advancements in depth sensing must address the challenges posed by transparency to unlock the full potential of these increasingly prevalent technologies.

Depth sensors commonly rely on the Lambertian assumption – the principle that light diffusely reflects from a matte surface in all directions – to calculate distance. However, this foundational concept falters when encountering transparent materials like glass or water. Unlike opaque objects which scatter light, transparent surfaces allow a significant portion of light to pass through, altering the reflected light distribution and creating a signal that depth sensors misinterpret. This leads to inaccurate depth estimations because the sensor expects a diffuse reflection, but receives a combination of transmitted and minimally reflected light. Consequently, the calculated distance is often skewed, rendering the depth map unreliable for applications requiring precise spatial understanding and hindering the functionality of systems dependent on accurate depth perception.

The inability of current depth sensors to accurately perceive transparent surfaces presents a significant obstacle to advancements in several key technological areas. Robotic grasping, for example, requires precise depth information to reliably identify and manipulate objects – a task severely compromised when dealing with glass or plastic. Similarly, the immersive experiences promised by augmented reality rely on convincingly overlaying digital content onto the real world, which demands an accurate understanding of scene geometry, including transparent elements. Perhaps most critically, autonomous navigation systems – essential for self-driving cars and delivery robots – depend on reliable depth data to avoid collisions and plan safe paths; a failure to detect transparent obstacles like glass doors or windows could have catastrophic consequences, highlighting the urgent need for more robust depth sensing solutions.

Simulating the depth deficits caused by transparent objects is achieved by artificially masking regions of non-transparent objects as observed by an RGB-D sensor.
Simulating the depth deficits caused by transparent objects is achieved by artificially masking regions of non-transparent objects as observed by an RGB-D sensor.

Engineering the Illusion: Simulating Transparency’s Effects

The performance of depth completion algorithms is directly impacted by their ability to handle data deficits caused by transparent objects, which introduce unique challenges due to light refraction and transmission. These objects do not produce consistent depth returns for standard depth sensors like LiDAR or stereo cameras, resulting in missing or inaccurate depth values. Consequently, training data must explicitly incorporate these types of errors to ensure the algorithm learns to infer depth information in the presence of transparency. Simulating these deficits, rather than relying solely on real-world data which may lack sufficient examples of these specific errors, allows for the creation of a more robust and generalized depth completion model capable of accurate performance in diverse scenarios involving transparent materials such as glass, water, or acrylic.

Masking strategies are employed in depth completion training to simulate data deficits caused by transparent objects. These strategies function by selectively removing ground truth depth values in areas of an image corresponding to transparent surfaces, effectively creating incomplete or missing depth information. This process introduces controlled imperfections into the training data, forcing the depth completion algorithm to learn to infer depth in the presence of occlusion and incomplete observations. The resulting simulated deficits mimic the challenges presented by real-world transparent materials like glass or water, and are crucial for developing robust and accurate depth estimation systems.

Localized masking and global random masking represent distinct approaches to simulating data deficits during depth completion algorithm training. Localized masking specifically targets regions identified as containing transparent objects, removing depth information only from these areas. This targeted removal mimics the real-world data loss experienced when depth sensors struggle with transparency. In contrast, global random masking introduces data deficits randomly across the entire scene, serving as a baseline for evaluating the effectiveness of more sophisticated techniques like localized masking. By comparing performance against this baseline, researchers can quantitatively assess whether localized masking improves robustness to transparency-induced errors and whether the algorithm effectively utilizes available data in non-occluded regions.

To generate more realistic training data for depth completion algorithms, advanced masking techniques leverage the Segment Anything Model (SAM) and morphological erosion. SAM is employed to automatically and accurately segment transparent objects within a scene, providing precise masks for depth data removal. Following segmentation, morphological erosion is applied to these masks, subtly shrinking the masked areas and simulating the partial visibility and imperfect boundary detection inherent in real-world sensing. This process creates more nuanced depth deficits than simple binary masking, accounting for factors like refraction, reflection, and sensor limitations, and thereby improving the robustness of trained algorithms to challenging transparent object scenarios.

Our masking strategy effectively isolates relevant image regions for improved performance.
Our masking strategy effectively isolates relevant image regions for improved performance.

TDCNet: A Pragmatic Approach to Transparent Depth Completion

TDCNet is a network architecture developed to specifically address the challenges inherent in completing depth data for transparent objects, a task complicated by the lack of direct depth returns from these surfaces. Unlike standard depth completion methods, TDCNet is designed to infer depth information in these areas by leveraging contextual information from surrounding pixels and features. This targeted approach distinguishes it from general-purpose depth completion networks and allows it to achieve improved performance on datasets containing a significant proportion of transparent materials, such as glass or water. The network’s design prioritizes accurate reconstruction of depth even where conventional depth sensors struggle, offering a solution for applications requiring complete and accurate 3D scene understanding.

TDCNet utilizes a U-Net architecture to efficiently process input imagery, enabling rapid feature extraction and depth map reconstruction. This architecture, characterized by its encoder-decoder structure with skip connections, facilitates the preservation of fine-grained details crucial for transparent object representation. Complementing the U-Net, the Swin Transformer module is integrated to enhance feature extraction capabilities, particularly in capturing long-range dependencies within the input data. The Swin Transformer’s window-based self-attention mechanism allows for efficient processing of high-resolution images while maintaining a strong ability to model contextual relationships, ultimately improving the accuracy of depth completion, especially in challenging scenarios with limited or noisy depth information.

TDCNet addresses limitations in depth sensing reliability by leveraging a U-Net architecture combined with Swin Transformer blocks. This integration allows the network to effectively infer depth information in areas where direct depth measurements are compromised, such as transparent surfaces or regions with low texture. The U-Net provides efficient image processing, while the Swin Transformer enhances feature extraction capabilities, enabling robust depth completion even with incomplete or noisy input data. This approach is particularly valuable in scenarios where accurate depth perception is critical despite challenging sensing conditions.

TDCNet’s performance was quantitatively assessed using standard depth completion metrics: $RMSE$ (Root Mean Squared Error), $REL$ (Relative Error), $MAE$ (Mean Absolute Error), and a threshold-based accuracy metric denoted as Threshold-σ. Evaluation results demonstrate that the proposed self-supervised learning approach achieves 70% of the performance level attained by fully supervised methods when measured against these established metrics. This indicates a substantial level of performance is achievable without the need for extensive labeled datasets, representing a key advantage of the TDCNet architecture and training methodology.

Our self-supervised learning method demonstrates significantly lower relative error-indicated by colors closer to the background-than competing methods when reconstructing the TransCG dataset, with red representing the largest errors.
Our self-supervised learning method demonstrates significantly lower relative error-indicated by colors closer to the background-than competing methods when reconstructing the TransCG dataset, with red representing the largest errors.

The TransCG Dataset: A Necessary Foundation, Despite its Limitations

The TransCG Dataset is a foundational resource for the development and assessment of algorithms designed to complete depth information for transparent objects. It consists of a diverse collection of scenes with accurately annotated depth maps, specifically addressing the challenges posed by transparent materials like glass and water. This dataset facilitates both the training of supervised learning models and the quantitative evaluation of their performance on a standardized benchmark. The inclusion of realistic transparent objects, coupled with precise depth ground truth, allows researchers to rigorously test and compare the accuracy and robustness of different depth completion approaches in scenarios involving complex transparency effects.

The TransCG Dataset comprises a diverse collection of scenes specifically designed for training and evaluating transparent object depth completion algorithms. It features accurately labeled depth maps for a wide variety of environments and objects, with a particular emphasis on realistic representations of transparent materials like glass and water. This includes detailed annotations defining the depth values for each pixel in the images, allowing algorithms to learn to accurately reconstruct the 3D structure of transparent objects within complex scenes. The dataset’s diversity in scene types, object arrangements, and lighting conditions contributes to the development of more robust and generalizable depth completion models.

Self-Supervised Learning presents a viable alternative to traditional supervised methods for depth completion by leveraging unlabeled data. Techniques such as Masked Autoencoders (MAE) operate by intentionally masking portions of the input data and training the model to reconstruct the missing information. This process forces the network to learn robust feature representations and understand the underlying structure of the scene without relying on explicit ground truth labels. The resulting models demonstrate performance approaching 70% of that achieved with fully supervised learning, indicating the potential of self-supervised approaches to enhance depth completion systems, particularly when labeled data is scarce or expensive to obtain.

Combining labeled and unlabeled data in depth completion systems enhances robustness and generalization capabilities. Utilizing a self-supervised learning approach, specifically with techniques like Masked Autoencoders, allows training on datasets containing both labeled and unlabeled depth maps. Evaluation demonstrates this approach achieves 70% of the performance levels obtained through fully supervised methods, indicating a significant degree of effectiveness and potential for improvement through continued research and optimization of self-supervised techniques.

Self-supervised learning significantly reduces relative error in the TransCG dataset, as demonstrated by error maps where colors closer to the background indicate lower error compared to the red regions indicating higher error.
Self-supervised learning significantly reduces relative error in the TransCG dataset, as demonstrated by error maps where colors closer to the background indicate lower error compared to the red regions indicating higher error.

The pursuit of depth completion, particularly for transparent objects, feels less like innovation and more like delaying the inevitable technical debt. This work leverages self-supervised learning, masking depths from non-transparent regions, to sidestep the need for extensive labeled data. It’s a clever bandage, certainly, but one built on the assumption that current RGB-D sensors and semantic segmentation are stable enough foundations. As Andrew Ng once stated, “AI is magical, but it’s not a miracle.” The paper’s success in approaching supervised method performance highlights a pragmatic acceptance: perfect data is a myth, and a functional, if imperfect, solution is preferable to endless refinement. If a bug in depth estimation is reproducible, at least it’s a stable system, even if not a correct one.

What’s Next?

The pursuit of depth completion for transparent objects, cleverly sidestepping the need for exhaustive labeled data, feels…familiar. It’s a temporary reprieve. The current approach, masking depths from non-transparent regions as a self-supervisory signal, addresses a symptom, not the disease. Production environments rarely cooperate with neat object classifications. Expect the inevitable cascade of edge cases: partially transparent objects, complex reflections, and the delightful ambiguity of real-world sensor noise. These will reveal the brittleness inherent in any system relying on such clean separations.

The immediate future will likely see more sophisticated masking strategies, perhaps integrating uncertainty estimates or adversarial training to handle imperfect segmentations. However, a true leap forward requires moving beyond feature-level completion. The field needs to grapple with the semantics of transparency. Understanding how light interacts with a surface isn’t just about filling in missing depth values; it’s about modeling the physics of perception. That’s a significantly harder problem, and one that will inevitably expose the limitations of current transformer-based architectures.

Ultimately, this work feels less like a solution and more like a well-engineered delay of the inevitable. It buys time, offers a path forward in data-scarce scenarios, but it doesn’t fundamentally alter the reality that robust perception in complex environments demands more than clever self-supervision. It demands understanding-and that’s always the hardest part. Legacy systems will become memories of better times, and bugs will remain proof of life.

Original article: https://arxiv.org/pdf/2512.05006.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-07 00:25