Jin Liu

Jin Liu, Huiyuan Fu, Chuanming Wang et al.

Exposure correction aims to enhance images suffering from improper exposure to achieve satisfactory visual effects. Despite recent progress, existing methods generally mitigate either overexposure or underexposure in input images, and they still struggle to handle images with mixed exposure, i.e., one image incorporates both overexposed and underexposed regions. The mixed exposure distribution is non-uniform and leads to varying representation, which makes it challenging to address in a unified process. In this paper, we introduce an effective Region-aware Exposure Correction Network (RECNet) that can handle mixed exposure by adaptively learning and bridging different regional exposure representations. Specifically, to address the challenge posed by mixed exposure disparities, we develop a region-aware de-exposure module that effectively translates regional features of mixed exposure scenarios into an exposure-invariant feature space. Simultaneously, as de-exposure operation inevitably reduces discriminative information, we introduce a mixed-scale restoration unit that integrates exposure-invariant features and unprocessed features to recover local information. To further achieve a uniform exposure distribution in the global image, we propose an exposure contrastive regularization strategy under the constraints of intra-regional exposure consistency and inter-regional exposure continuity. Extensive experiments are conducted on various datasets, and the experimental results demonstrate the superiority and generalization of our proposed method. The code is released at: https://github.com/kravrolens/RECNet.

Jin Liu, Sheng He

In this paper, we propose an efficient end-to-end algorithm to tackle the problem of estimating the 6D pose of objects from a single RGB image. Our system trains a fully convolutional network to regress the 3D rotation and the 3D translation in region layer. On this basis, a special layer, Collinear Equation Layer, is added next to region layer to output the 2D projections of the 3D bounding boxs corners. In the back propagation stage, the 6D pose network are adjusted according to the error of the 2D projections. In the detection phase, we directly output the position and pose through the region layer. Besides, we introduce a novel and concise representation of 3D rotation to make the regression more precise and easier. Experiments show that our method outperforms base-line and state of the art methods both at accuracy and efficiency. In the LineMod dataset, our algorithm achieves less than 18 ms/object on a GeForce GTX 1080Ti GPU, while the translational error and rotational error are less than 1.67 cm and 2.5 degree.