DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization (2108.10743v1)

Abstract: Panorama images have a much larger field-of-view thus naturally encode enriched scene context information compared to standard perspective images, which however is not well exploited in the previous scene understanding methods. In this paper, we propose a novel method for panoramic 3D scene understanding which recovers the 3D room layout and the shape, pose, position, and semantic category for each object from a single full-view panorama image. In order to fully utilize the rich context information, we design a novel graph neural network based context model to predict the relationship among objects and room layout, and a differentiable relationship-based optimization module to optimize object arrangement with well-designed objective functions on-the-fly. Realizing the existing data are either with incomplete ground truth or overly-simplified scene, we present a new synthetic dataset with good diversity in room layout and furniture placement, and realistic image quality for total panoramic 3D scene understanding. Experiments demonstrate that our method outperforms existing methods on panoramic scene understanding in terms of both geometry accuracy and object arrangement. Code is available at https://chengzhag.github.io/publication/dpc.

DeepPanoContext: Enhancing 3D Scene Understanding with Panoramic Imagery

The paper "DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization" introduces a comprehensive approach for understanding 3D scenes by leveraging panoramic images. It seeks to exploit the enriched scene context inherently present in panoramic images compared to standard images, addressing an area previously underutilized in scene understanding methodologies.

The primary contribution of this work lies in a novel framework that combines graph neural networks and relation-based optimization. This framework provides a holistic understanding of 3D scenes by recovering critical elements such as the room layout and the shape, pose, position, and semantic category of objects from a single panoramic image. The paper posits that traditional image-based scene parsing suffers due to the limited field of view offered by standard cameras. By contrast, panoramic images, with their 360\degree~field of view, encompass more contextual information, providing a richer scene description.

Methodology

The proposed methodology consists of several key components:

1. Graph Neural Network-Based Context Model: The authors employ a graph neural network to model the relationships among objects and between objects and the room layout. This model effectively harnesses the context available in a panoramic image, aiding in the accurate estimation of object poses and ensuring that object arrangements comply with typical scene layouts.
2. Differentiable Relationship-Based Optimization: To refine object arrangements, the authors introduce a novel optimization module. This differentiable model focuses on adjusting object positions based on predicted inter-object and object-to-layout relationships, helping prevent physical collisions, optimizing object rotation, and ensuring context-consistent object placements.
3. Synthetic Dataset: The paper addresses a crucial challenge in panoramic scene datasets: the lack of comprehensive ground truth for training and evaluation. Thus, a new synthetic dataset was created, featuring diverse room layouts, realistic image qualities, and complete 3D information. This dataset serves as a valuable training and evaluation resource.

Results and Discussion

The results demonstrate that the method achieves significant improvements over existing approaches in the domains of geometric accuracy and object arrangement. Among the impactful findings, the paper highlights:

• Superior 3D Detection Performance: The proposed method shows marked improvements in mean average precision for 3D detection tasks, outperforming existing state-of-the-art methods across various object categories.
• Contextual Plausibility: The relationship-based optimization reduces physical violations (e.g., object collisions) significantly, providing more human-perceivable realistic results in terms of object placement and orientation.
• Generalization Capability: By demonstrating effectiveness on real-world datasets like PanoContext, the paper asserts the framework's adaptability and robustness in diverse settings.

This method's implications are substantial, presenting a clear pathway for further research in scene understanding. Considering the ever-increasing deployment of AI systems in varied environments such as autonomous vehicles or virtual reality, the ability to parse complete scene contexts quickly and accurately from single images has enormous potential. Future research could delve into unifying network modules for efficiency gains or exploring other domains where panoramic data can improve contextual understanding.

In summary, the paper by Zhang et al. marks a significant stride in panoramic 3D scene understanding, demonstrating enhanced performance through the intelligent use of context and optimization models. The proposed solutions and dataset lay a strong foundation for advancing comprehensive 3D scene parsing methodologies.