Generate What You Can't See - a View-dependent Image Generation

Abstract: In order to operate autonomously, a robot should explore the environment and build a model of each of the surrounding objects. A common approach is to carefully scan the whole workspace. This is time-consuming. It is also often impossible to reach all the viewpoints required to acquire full knowledge about the environment. Humans can perform shape completion of occluded objects by relying on past experience. Therefore, we propose a method that generates images of an object from various viewpoints using a single input RGB image. A deep neural network is trained to imagine the object appearance from many viewpoints. We present the whole pipeline, which takes a single RGB image as input and returns a sequence of RGB and depth images of the object. The method utilizes a CNN-based object detector to extract the object from the natural scene. Then, the proposed network generates a set of RGB and depth images. We show the results both on a synthetic dataset and on real images.

• The paper introduces a complete pipeline that synthesizes view-dependent RGB and depth images from a single input, improving object modeling for robotic manipulation.
• It employs a CNN-based object detection using Mask R-CNN and a U-Net inspired generative network to predict images from new viewpoints with high interpolation accuracy.
• The approach leverages both synthetic data from the ShapeNet dataset and real-world imagery, training specialized class-specific models to reduce the need for exhaustive scanning.

Overview of "Generate What You Can't See - a View-dependent Image Generation"

The paper "Generate What You Can't See - a View-dependent Image Generation" (1903.06814) introduces a novel approach to object model generation using view-dependent image generation from a single RGB image. This method is particularly tailored for autonomous mobile manipulation robots that require complete models of objects for tasks like manipulation without needing exhaustive scanning, which is often impractical.

Core Contributions and Methodology

The primary contribution of this paper is a complete pipeline capable of generating a sequence of RGB and depth images of an object from a single input image. This process mimics human perception capabilities, where an object can be visualized from different viewpoints without a complete sensory scan. The methodology involves a combination of deep learning techniques, primarily using CNNs for object detection and a U-Net architecture for generating the view-dependent images.

Object Detection and Extraction

The initial stage of the pipeline involves an object detection mechanism utilizing Mask R-CNN, which is a CNN-based method for detecting objects and extracting masks. The extracted object is then processed to fit into a standard format (128x128 pixels) to be fed into the generative model.

Generative Network Architecture

The core of this approach relies on a CNN-based generative network designed to predict images from multiple viewpoints. The architecture builds upon the U-Net model, a proven design for tasks demanding precise spatial predictions. The network takes a concatenated RGB and depth input and synthesizes images corresponding to new target viewpoints, thus allowing the network to "hallucinate" the hidden features of objects.

A noteworthy aspect is the dedicated model per object class, ensuring specialized models that leverage class-specific properties to improve generation quality. Training the models on the ShapeNet dataset enabled utilization of synthetic data where each object is captured from various angles, addressing the limitation of real-world data availability.

Implementation Details

For practical implementation, the system utilizes both synthetically generated data and real-world imagery to train and evaluate the models. The generative networks are trained independently with a focus on minimizing mean square error between predicted and ground-truth images. An important practical aspect is data handling, where instance-specific training pairs are created to capture unique instance features, avoiding cross-instance pairings that can degrade performance.

Performance Evaluation

The performance was rigorously evaluated using metrics like image error (in terms of pixel difference) and accuracy, highlighting the effectiveness of image interpolation and viewpoint extrapolation capabilities. Specifically, the network's ability to generate unseen viewpoints and objects was demonstrated with high accuracy across a diverse set of objects.

Implications and Future Work

This research holds significant implications for enhancing the autonomy of robots in dynamic environments, where rapid adaptation to new object configurations is crucial. The ability to confidently extrapolate views from limited visual data reduces the need for complex sensory equipment and scanning behaviors.

Future work as proposed in the paper includes improvements in texture generation and integration of all class-specific models into a unified scalable framework. Enhancements in computational resources are anticipated to refine model accuracy further. The development of comprehensive methods for deriving consistent 3D models from generated 2D viewpoints is also a proposed area of exploration.

Conclusion

The work detailed in the paper provides a robust framework for advancing view-dependent object model generation with potential applications in robotic vision, rapid prototyping, and interactive AI systems. By leveraging deep learning methodologies, the pipeline presents a scalable solution to image generation based on limited visual input, positioning it as a valuable tool for next-gen robotic systems.