Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation (2203.01452v2)

Abstract: Panoramic images with their 360-degree directional view encompass exhaustive information about the surrounding space, providing a rich foundation for scene understanding. To unfold this potential in the form of robust panoramic segmentation models, large quantities of expensive, pixel-wise annotations are crucial for success. Such annotations are available, but predominantly for narrow-angle, pinhole-camera images which, off the shelf, serve as sub-optimal resources for training panoramic models. Distortions and the distinct image-feature distribution in 360-degree panoramas impede the transfer from the annotation-rich pinhole domain and therefore come with a big dent in performance. To get around this domain difference and bring together semantic annotations from pinhole- and 360-degree surround-visuals, we propose to learn object deformations and panoramic image distortions in the Deformable Patch Embedding (DPE) and Deformable MLP (DMLP) components which blend into our Transformer for PAnoramic Semantic Segmentation (Trans4PASS) model. Finally, we tie together shared semantics in pinhole- and panoramic feature embeddings by generating multi-scale prototype features and aligning them in our Mutual Prototypical Adaptation (MPA) for unsupervised domain adaptation. On the indoor Stanford2D3D dataset, our Trans4PASS with MPA maintains comparable performance to fully-supervised state-of-the-arts, cutting the need for over 1,400 labeled panoramas. On the outdoor DensePASS dataset, we break state-of-the-art by 14.39% mIoU and set the new bar at 56.38%. Code will be made publicly available at https://github.com/jamycheung/Trans4PASS.

• The paper introduces a novel Trans4PASS framework that uses deformable patch embedding and MLP to effectively manage panoramic distortions.
• It employs mutual prototypical adaptation to align pinhole and panoramic features, reducing the need for extensive labeled data.
• Experimental results demonstrate over 14% mIoU improvement on DensePASS and competitive performance on indoor segmentation tasks.

Overview of "Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation"

The paper "Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation" presents a novel framework tailored for the segmentation of 360-degree panoramas, a challenging domain due to the inherent distortions introduced by equirectangular projection. The authors propose a transformer-based architecture, Trans4PASS, enhanced with distortion-aware components designed to effectively parse panoramic imagery by adapting to its unique characteristics.

Key Contributions and Methodology

The Trans4PASS architecture introduces two critical components: the Deformable Patch Embedding (DPE) and the Deformable MLP (DMLP), which are instrumental in managing image distortions and capturing long-range dependencies:

• Deformable Patch Embedding (DPE): This module leverages learnable offsets to accommodate panoramic distortions, thus improving feature extraction by dynamically adjusting to the spatial variances present in 360-degree images. Unlike standard patch embeddings, which use fixed grid sampling, DPE adapts to the data’s inherent distortions, thus maintaining semantic fidelity.
• Deformable MLP (DMLP): Positioned within the feature parsing stage, this module mixes patches with learned spatial offsets, enhancing global context modeling necessary for panoramic scenes.

For domain adaptation, the authors explore the Pin2Pan scenario, where they align the semantically enriched pinhole features with panoramic data features. They introduce Mutual Prototypical Adaptation (MPA), which utilizes class-wise prototypical knowledge distilled from both pinhole and panoramic domains. This approach mitigates the necessity for copious labeled panoramic data, effectively utilizing labeled pinhole datasets for adaptation.

Experimental Results

The framework is evaluated on indoor and outdoor datasets, Stanford2D3D and DensePASS, respectively. Key findings include:

• Performance Metrics: On the DensePASS dataset, Trans4PASS with MPA achieves a mIoU of 56.38%, surpassing existing state-of-the-art methods by over 14.39%. In indoor scenarios, on the Stanford2D3D dataset, the framework achieves performance comparable to fully-supervised models without requiring extensive labeled data, marking a significant improvement in unsupervised domain adaptation scenarios.
• Qualitative Outcomes: The model demonstrates superior handling of distortions, effectively segmenting intricate features like roads, buildings, and sidewalks, which are typically challenging due to their distorted appearance in panoramic views.

Implications and Future Directions

The proposed Trans4PASS and MPA present substantive advancements in the segmentation of panoramic images, which are increasingly relevant in applications such as autonomous driving, virtual reality, and surveillance. By mitigating the dependency on large-scale annotated datasets specific to panoramic formats, this work supports scalable deployment of semantic segmentation systems in domains where capture devices primarily produce distorted wide-FoV content.

The theoretical implications extend to the design of vision transformers, illustrating the potential of spatially adaptive embeddings and projections in tackling other forms of distortion across different vision tasks. Future work could explore extending Trans4PASS to other modalities, such as video, or integrating additional sensory data, enhancing context-aware understanding in dynamic environments.

In summary, the paper provides robust methodologies and empirical evidence on the efficacy of transformer-based models augmented with distortion-aware components, setting a new benchmark for the fusion of Transformer architectures with spatial adaptation mechanisms in computer vision.