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Bayer pattern is a widely used Color Filter Array (CFA) for digital image sensors, efficiently capturing different light wavelengths on different pixels without the need for a costly ISP pipeline. The resulting single-channel raw Bayer images offer benefits such as spectral wavelength sensitivity and low time latency. However, object detection based on Bayer images has been underexplored due to challenges in human observation and algorithm design caused by the discontinuous color channels in adjacent pixels. To address this issue, we propose the BayerDetect network, an end-to-end deep object detection framework that aims to achieve fast, accurate, and memory-efficient object detection. Unlike RGB color images, where each pixel encodes spectral context from adjacent pixels during ISP color interpolation, raw Bayer images lack spectral context. To enhance the spectral context, the BayerDetect network introduces a spectral frequency attention block, transforming the raw Bayer image pattern to the frequency domain. In object detection, clear object boundaries are essential for accurate bounding box predictions. To handle the challenges posed by alternating spectral channels and mitigate the influence of discontinuous boundaries, the BayerDetect network incorporates a spatial attention scheme that utilizes deformable convolutional kernels in multiple scales to explore spatial context effectively. The extracted convolutional features are then passed through a sparse set of proposal boxes for detection and classification. We conducted experiments on both public and self-collected raw Bayer images, and the results demonstrate the superb performance of the BayerDetect network in object detection tasks. 

Unsupervised visual odometry as an active topic has attracted extensive attention, benefiting from its label-free practical value and robustness in real-world scenarios. However, the performance of camera pose estimation and tracking through deep neural network is still not as ideal as most other tasks, such as detection, segmentation and depth estimation, due to the lack of drift correction in the estimated trajectory and map optimization in the recovered 3D scenes. In this work, we introduce pose graph and bundle adjustment optimization to our network training process, which iteratively updates both the motion and depth estimations from the deep learning network, and enforces the refined outputs to further meet the unsupervised photometric and geometric constraints. The integration of pose graph and bundle adjustment is easy to implement and significantly enhances the training effectiveness. Experiments on KITTI dataset demonstrate that the introduced method achieves a significant improvement in motion estimation compared with other recent unsupervised monocular visual odometry algorithms. 

Single image 3D face reconstruction with accurate geometric details is a critical and challenging task due to the similar appearance on the face surface and fine details in organs. In this work, we introduce a self-supervised 3D face reconstruction approach from a single image that can recover detailed textures under different camera settings. The proposed network learns high-quality disparity maps from stereo face images during the training stage, while just a single face image is required to generate the 3D model in real applications. To recover fine details of each organ and facial surface, the framework introduces facial landmark spatial consistency to constrain the face recovering learning process in local point level and segmentation scheme on facial organs to constrain the correspondences at the organ level. The face shape and textures will further be refined by establishing holistic constraints based on the varying light illumination and shading information. The proposed learning framework can recover more accurate 3D facial details both quantitatively and qualitatively compared with state-of-the-art 3DMM and geometry-based reconstruction algorithms based on a single image. 

Single image depth estimation is a critical issue for robot vision, augmented reality, and many other applications when an image sequence is not available. Self-supervised single image depth estimation models target at predicting accurate disparity map just from one single image without ground truth supervision or stereo image pair during real applications. Compared with direct single image depth estimation, single image stereo algorithm can generate the depth from different camera perspectives. In this paper, we propose a novel architecture to infer accurate disparity by leveraging both spectral-consistency based learning model and view-prediction based stereo reconstruction algorithm. Direct spectral-consistency based method can avoid false positive matching in smooth regions. Single image stereo can preserve more distinct boundaries from another camera perspective. By learning confidence maps and designing a fusion strategy, the two disparities from the two approaches are able to be effectively fused to produce the refined disparity. Extensive experiments and ablations indicate that our method exploits both advantages of spectral consistency and view prediction, especially in constraining object boundaries and correcting wrong predicting regions. 

Visual odometry (VO) and single image depth estimation are critical for robot vision, 3D reconstruction, and camera pose estimation that can be applied to autonomous driving, map building, augmented reality and many other applications. Various supervised learning models have been proposed to train the VO or single image depth estimation framework for each targeted scene to improve the performance recently. However, little effort has been made to learn these separate tasks together without requiring the collection of a significant number of labels. This paper proposes a novel unsupervised learning approach to simultaneously perceive VO and single image depth estimation. In our framework, either of these tasks can benefit from each other through simultaneously learning these two tasks. We correlate these two tasks by enforcing depth consistency between VO and single image depth estimation. Based on the single image depth estimation, we can resolve the most common and challenging scaling issue of monocular VO. Meanwhile, through training from a sequence of images, VO can enhance the single image depth estimation accuracy. The effectiveness of our proposed method is demonstrated through extensive experiments compared with current state-of-the-art methods on the benchmark datasets. 

Self-supervised depth estimation has recently demonstrated promising performance compared to the supervised methods on challenging indoor scenes. However, the majority of efforts mainly focus on exploiting photometric and geometric consistency via forward image warping and backward image warping, based on monocular videos or stereo image pairs. The influence of defocus blur to depth estimation is neglected, resulting in a limited performance for objects and scenes in out of focus. In this work, we propose the first framework for simultaneous depth estimation from a single image and image focal stacks using depth-from-defocus and depth-from-focus algorithms. The proposed network is able to learn optimal depth mapping from the information contained in the blur of a single image, generate a simulated image focal stack and all-in-focus image, and train a depth estimator from an image focal stack. In addition to the validation of our method on both synthetic NYUv2 dataset and real DSLR dataset, we also collect our own dataset using a DSLR camera and further verify on it. Experiments demonstrate that our system surpasses the state-of-the-art supervised depth estimation method over 4% in accuracy and achieves superb performance among the methods without direct supervision on the synthesized NYUv2 dataset, which has been rarely explored. 

Despite deep learning approaches have achieved promising successes in 2D optical flow estimation, it is a challenge to accurately estimate scene flow in 3D space as point clouds are inherently lacking topological information. In this paper, we aim at handling the problem of self-supervised 3D scene flow estimation based on dynamic graph convolutional neural networks (GCNNs), namely 3D SceneFlowNet. To better learn geometric relationships among points, we introduce EdgeConv to learn multiple-level features in a pyramid from point clouds and a self-attention mechanism to apply the multi-level features to predict the final scene flow. Our trained model can efficiently process a pair of adjacent point clouds as input and predict a 3D scene flow accurately without any supervision. The proposed approach achieves superior performance on both synthetic ModelNet40 dataset and real LiDAR scans from KITTI Scene Flow 2015 datasets.