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Virtually Enriched NYU Depth V2 Dataset for Improving Monocular Depth Estimation


Core Concepts
Virtually augmenting the NYU Depth V2 dataset with randomly generated 3D objects improves the performance and generalization of deep neural networks for monocular depth estimation.
Abstract
The paper presents ANYU, a new virtually augmented version of the NYU Depth V2 dataset, designed for improving monocular depth estimation. Unlike previous approaches that use full 3D scenes of a virtual world, ANYU was created by incorporating RGB-D representations of virtual reality objects into the original NYU Depth V2 images. The authors deliberately did not match each virtual object with an appropriate texture and location within the real-world image. Instead, they used randomness in generating virtual objects and choosing their placement to maximize the diversity of the training data. Extensive experiments on the original and virtually enriched NYU Depth V2 datasets, as well as the iBims-1 benchmark, show that ANYU improves the monocular depth estimation performance and generalization of deep neural networks with different architectures, especially the state-of-the-art VPD model. The key findings are: Supplementing the NYU Depth V2 dataset with 5-40% of virtually augmented RGB-D image pairs yields the best depth estimation results, with further increase in augmentation leading to a slight degradation but still better performance than the original dataset. Reducing the size of the original NYU Depth V2 training set and expanding it with virtually enriched images significantly mitigates the performance drop, demonstrating the importance of data diversity. The VPD and PixelFormer models trained on ANYU achieve new state-of-the-art results on the NYU Depth V2 test set and improved cross-dataset generalization on the iBims-1 benchmark. The authors make the ANYU dataset publicly available in two configurations with 10% and 100% additional synthetically enriched RGB-D pairs for efficient training and empirical exploration of virtual augmentation.
Stats
Reducing the NYU Depth V2 training set from 100% to 1% increases the RMSE by a factor of 2, while expanding the remaining 1% with virtually enriched images improves the RMSE by 25%. Training the VPD model on the full NYU Depth V2 dataset with 10% virtually augmented images achieves the lowest RMSE of 0.248, compared to 0.254 without augmentation. The PixelFormer model trained on ANYU improves the RMSE from 0.322 to 0.320 on the NYU Depth V2 test set, and from 1.595 to 1.539 on the iBims-1 benchmark. The VPD model trained on ANYU achieves a new state-of-the-art RMSE of 1.365 on the iBims-1 dataset, compared to 1.493 without augmentation.
Quotes
"In contrast to the well-known approach where full 3D scenes of a virtual world are utilized to generate artificial datasets, ANYU was created by incorporating RGB-D representations of virtual reality objects into the original NYU depth v2 images." "We specifically did not match each generated virtual object with an appropriate texture and a suitable location within the real-world image. Instead, an assignment of texture, location, lighting, and other rendering parameters was randomized to maximize a diversity of the training data, and to show that it is randomness that can improve the generalizing ability of a dataset."

Deeper Inquiries

How can the virtual augmentation process be further improved to better match the characteristics of real-world scenes and objects?

To enhance the virtual augmentation process and better align it with real-world scenes and objects, several improvements can be implemented: Texture Matching: Instead of random assignment, a more sophisticated algorithm can be developed to match textures of virtual objects with real-world scenes. This can involve using advanced texture mapping techniques to ensure a more seamless integration. Lighting Consistency: Implementing algorithms to match the lighting conditions of virtual objects with those in the real-world images can improve the realism of the augmented dataset. Shadow Generation: Developing algorithms to accurately generate shadows of virtual objects based on the lighting in the real-world scenes can enhance the visual coherence of the augmented images. Object Occlusion: Incorporating algorithms to simulate object occlusion in the augmented dataset can add another layer of realism, making the virtual objects interact more realistically with the real-world scenes.

How can the virtual augmentation process be further improved to better match the characteristics of real-world scenes and objects?

To enhance the diversity and realism of the augmented dataset beyond random placement and rendering of virtual objects, the following techniques can be explored: Semantic Understanding: Incorporating semantic understanding of the scenes can help in placing virtual objects more intelligently. By analyzing the content of the real-world scenes, virtual objects can be placed in contextually relevant positions. Physics-based Simulation: Introducing physics-based simulations for the virtual objects can add a layer of realism. Simulating interactions such as collisions, gravity effects, and object dynamics can make the augmented dataset more realistic. Generative Adversarial Networks (GANs): Utilizing GANs to generate virtual objects that closely resemble real-world objects can improve the quality of the augmented dataset. GANs can learn the distribution of real-world objects and generate synthetic objects that blend seamlessly with the real scenes. Style Transfer: Applying style transfer techniques to virtual objects can help in matching their appearance with the real-world scenes. By transferring the style of real objects to virtual ones, a more cohesive and realistic dataset can be created.

Could the virtual augmentation approach be extended to other computer vision tasks beyond monocular depth estimation, such as object detection or semantic segmentation?

Yes, the virtual augmentation approach can be extended to other computer vision tasks such as object detection and semantic segmentation. By incorporating virtual objects into real-world scenes, the augmented dataset can provide a more diverse and challenging training environment for these tasks. Object Detection: Virtual objects can be added to real scenes to create complex and cluttered environments for training object detection models. This can help in improving the robustness of the models to various scenarios and occlusions. Semantic Segmentation: Augmenting real-world scenes with virtual objects can aid in training semantic segmentation models to accurately classify pixels in the presence of additional objects. This can enhance the model's ability to segment objects in complex scenes. Instance Segmentation: By incorporating virtual objects with different instance labels into real scenes, the augmented dataset can be used to train instance segmentation models to segment individual objects within a scene accurately. Scene Understanding: Virtual augmentation can also be beneficial for tasks like scene understanding, where the goal is to comprehend the entire scene. By adding diverse virtual elements, the models can learn to interpret complex scenes more effectively.
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