FishFormer: Annulus Slicing-based Transformer for Fisheye Rectification with Efficacy Domain Exploration

Numerous significant progress on fisheye image rectification has been achieved through CNN. Nevertheless, constrained by a fixed receptive field, the global distribution and the local symmetry of the distortion have not been fully exploited. To leverage these two characteristics, we introduced Fishformer that processes the fisheye image as a sequence to enhance global and local perception. We tuned the Transformer according to the structural properties of fisheye images. First, the uneven distortion distribution in patches generated by the existing square slicing method confuses the network, resulting in difficult training. Therefore, we propose an annulus slicing method to maintain the consistency of the distortion in each patch, thus perceiving the distortion distribution well. Second, we analyze that different distortion parameters have their own efficacy domains. Hence, the perception of the local area is as important as the global, but Transformer has a weakness for local texture perception. Therefore, we propose a novel layer attention mechanism to enhance the local perception and texture transfer. Our network simultaneously implements global perception and focused local perception decided by the different parameters. Extensive experiments demonstrate that our method provides superior performance compared with state-of-the-art methods.

FisheyeEX: Polar Outpainting for Extending the FoV of Fisheye Lens

Fisheye lens gains increasing applications in computational photography and assisted driving because of its wide field of view (FoV). However, the fisheye image generally contains invalid black regions induced by its imaging model. In this paper, we present a FisheyeEX method that extends the FoV of the fisheye lens by outpainting the invalid regions, improving the integrity of captured scenes. Compared with the rectangle and undistorted image, there are two challenges for fisheye image outpainting: irregular painting regions and distortion synthesis. Observing the radial symmetry of the fisheye image, we first propose a polar outpainting strategy to extrapolate the coherent semantics from the center to the outside region. Such an outpainting manner considers the distribution pattern of radial distortion and the circle boundary, boosting a more reasonable completion direction. For the distortion synthesis, we propose a spiral distortion-aware perception module, in which the learning path keeps consistent with the distortion prior of the fisheye image. Subsequently, a scene revision module rearranges the generated pixels with the estimated distortion to match the fisheye image, thus extending the FoV. In the experiment, we evaluate the proposed FisheyeEX on three popular outdoor datasets: Cityscapes, BDD100k, and KITTI, and one real-world fisheye image dataset. The results demonstrate that our approach significantly outperforms the state-of-the-art methods, gaining around 27% more content beyond the original fisheye image.

JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation

Video frame interpolation (VFI) aims to generate predictive frames by warping learnable motions from the bidirectional historical references. Most existing works utilize spatio-temporal semantic information extractor to realize motion estimation and interpolation modeling, not enough considering with the real mechanistic rationality of generated middle motions. In this paper, we reformulate VFI as a multi-variable non-linear (MNL) regression problem, and a Joint Non-linear Motion Regression (JNMR) strategy is proposed to model complicated motions of inter-frame. To establish the MNL regression, ConvLSTM is adopted to construct the distribution of complete motions in temporal dimension. The motion correlations between the target frame and multiple reference frames can be regressed by the modeled distribution. Moreover, the feature learning network is designed to optimize for the MNL regression modeling. A coarse-to-fine synthesis enhancement module is further conducted to learn visual dynamics at different resolutions through repetitive regression and interpolation. Highly competitive experimental results on frame interpolation show that the effectiveness and significant improvement compared with state-of-the-art performance, and the robustness of complicated motion estimation is improved by the MNL motion regression.

Unsupervised Homography Estimation with Coplanarity-Aware GAN

Estimating homography from an image pair is a fundamental problem in image alignment. Unsupervised learning methods have received increasing attention in this field due to their promising performance and label-free training. However, existing methods do not explicitly consider the problem of plane-induced parallax, which will make the predicted homography compromised on multiple planes. In this work, we propose a novel method HomoGAN to guide unsupervised homography estimation to focus on the dominant plane. First, a multi-scale transformer network is designed to predict homography from the feature pyramids of input images in a coarse-to-fine fashion. Moreover, we propose an unsupervised GAN to impose coplanarity constraint on the predicted homography, which is realized by using a generator to predict a mask of aligned regions, and then a discriminator to check if two masked feature maps are induced by a single homography. To validate the effectiveness of HomoGAN and its components, we conduct extensive experiments on a large-scale dataset, and the results show that our matching error is 22% lower than the previous SOTA method. Code is available at https://github.com/megvii-research/HomoGAN.

Cylin-Painting: Seamless 360° Panoramic Image Outpainting and Beyond with Cylinder-Style Convolutions

Image outpainting gains increasing attention since it can generate the complete scene from a partial view, providing a valuable solution to construct 360{\deg} panoramic images. As image outpainting suffers from the intrinsic issue of unidirectional completion flow, previous methods convert the original problem into inpainting, which allows a bidirectional flow. However, we find that inpainting has its own limitations and is inferior to outpainting in certain situations. The question of how they may be combined for the best of both has as yet remained under-explored. In this paper, we provide a deep analysis of the differences between inpainting and outpainting, which essentially depends on how the source pixels contribute to the unknown regions under different spatial arrangements. Motivated by this analysis, we present a Cylin-Painting framework that involves meaningful collaborations between inpainting and outpainting and efficiently fuses the different arrangements, with a view to leveraging their complementary benefits on a consistent and seamless cylinder. Nevertheless, directly applying the cylinder-style convolution often generates visually unpleasing results as it could discard important positional information. To address this issue, we further present a learnable positional embedding strategy and incorporate the missing component of positional encoding into the cylinder convolution, which significantly improves the panoramic results. Note that while developed for image outpainting, the proposed solution can be effectively extended to other panoramic vision tasks, such as object detection, depth estimation, and image super resolution.

Towards Reliable Image Outpainting: Learning Structure-Aware Multimodal Fusion with Depth Guidance

Image outpainting technology generates visually reasonable content regardless of authenticity, making it unreliable to serve for practical applications even though introducing additional modalities eg. the sketch. Since sparse depth maps are widely captured in robotics and autonomous systems, together with RGB images, we combine the sparse depth in the image outpainting task to provide more reliable performance. Concretely, we propose a Depth-Guided Outpainting Network (DGONet) to model the feature representations of different modalities differentially and learn the structure-aware cross-modal fusion. To this end, two components are designed to implement: 1) The Multimodal Learning Module produces unique depth and RGB feature representations from the perspectives of different modal characteristics. 2) The Depth Guidance Fusion Module leverages the complete depth modality to guide the establishment of RGB contents by progressive multimodal feature fusion. Furthermore, we specially design an additional constraint strategy consisting of Cross-modal Loss and Edge Loss to enhance ambiguous contours and expedite reliable content generation. Extensive experiments on KITTI demonstrate our superiority over the state-of-the-art methods with more reliable content generation.

Deep Rectangling for Image Stitching: A Learning Baseline

Stitched images provide a wide field-of-view (FoV) but suffer from unpleasant irregular boundaries. To deal with this problem, existing image rectangling methods devote to searching an initial mesh and optimizing a target mesh to form the mesh deformation in two stages. Then rectangular images can be generated by warping stitched images. However, these solutions only work for images with rich linear structures, leading to noticeable distortions for portraits and landscapes with non-linear objects. In this paper, we address these issues by proposing the first deep learning solution to image rectangling. Concretely, we predefine a rigid target mesh and only estimate an initial mesh to form the mesh deformation, contributing to a compact one-stage solution. The initial mesh is predicted using a fully convolutional network with a residual progressive regression strategy. To obtain results with high content fidelity, a comprehensive objective function is proposed to simultaneously encourage the boundary rectangular, mesh shape-preserving, and content perceptually natural. Besides, we build the first image stitching rectangling dataset with a large diversity in irregular boundaries and scenes. Experiments demonstrate our superiority over traditional methods both quantitatively and qualitatively.

Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images Using Dual-cubemap

Estimating the depth of omnidirectional images is more challenging than that of normal field-of-view (NFoV) images because the varying distortion can significantly twist an object's shape. The existing methods suffer from troublesome distortion while estimating the depth of omnidirectional images, leading to inferior performance. To reduce the negative impact of the distortion influence, we propose a distortion-tolerant omnidirectional depth estimation algorithm using a dual-cubemap. It comprises two modules: Dual-Cubemap Depth Estimation (DCDE) module and Boundary Revision (BR) module. In DCDE module, we present a rotation-based dual-cubemap model to estimate the accurate NFoV depth, reducing the distortion at the cost of boundary discontinuity on omnidirectional depths. Then a boundary revision module is designed to smooth the discontinuous boundaries, which contributes to the precise and visually continuous omnidirectional depths. Extensive experiments demonstrate the superiority of our method over other state-of-the-art solutions.

PanoFormer: Panorama Transformer for Indoor 360° Depth Estimation

Existing panoramic depth estimation methods based on convolutional neural networks (CNNs) focus on removing panoramic distortions, failing to perceive panoramic structures efficiently due to the fixed receptive field in CNNs. This paper proposes the panorama transformer (named PanoFormer) to estimate the depth in panorama images, with tangent patches from spherical domain, learnable token flows, and panorama specific metrics. In particular, we divide patches on the spherical tangent domain into tokens to reduce the negative effect of panoramic distortions. Since the geometric structures are essential for depth estimation, a self-attention module is redesigned with an additional learnable token flow. In addition, considering the characteristic of the spherical domain, we present two panorama-specific metrics to comprehensively evaluate the panoramic depth estimation models' performance. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art (SOTA) methods. Furthermore, the proposed method can be effectively extended to solve semantic panorama segmentation, a similar pixel2pixel task. Code will be available.