Distortion-Aware Room Layout Estimation from A Single Fisheye Image

Omnidirectional images of 180 degrees or 360 degrees field of view provide the entire visual content around the capture cameras, giving rise to more sophisticated scene understanding and reasoning and bringing broad application prospects for VR/AR/MR. As a result, researches on omni-directional image layout estimation have sprung up in recent years. However, existing layout estimation methods designed for panorama images cannot perform well on fisheye images, mainly due to lack of public fisheye dataset as well as the significantly differences in the positions and degree of distortions caused by different projection models. To fill theses gaps, in this work we first reuse the released large-scale panorama datasets and reproduce them to fisheye images via projection conversion, thereby circumventing the challenge of obtaining high-quality fisheye datasets with ground truth layout annotations. Then, we propose a distortion-aware module according to the distortion of the orthographic projection (i.e., OrthConv) to perform effective features extraction from fisheye images. Additionally, we exploit bidirectional LSTM with two-dimensional step mode for horizontal and vertical prediction to capture the long-range geometric pattern of the object for the global coherent predictions even with occlusion and cluttered scenes. We extensively evaluate our deformable convolution for room layout estimation task. In comparison with state-of-the-art approaches, our approach produces considerable performance gains in real-world dataset as well as in synthetic dataset. This technology provides high-efficiency and low-cost technical implementations for VR house viewing and MR video surveillance. We present an MR-based building video surveillance scene equipped with nine fisheye lens can achieve an immersive hybrid display experience, which can be used for intelligent building management in the future.