Object detection is a fundamental task in remote sensing image analysis and scene understanding. Previous remote sensing object detectors are typically based on convolutional neural networks (CNNs), whose performance is significantly limited by the intrinsic locality of convolution operations. The emergence of vision Transformers brings potential solutions to this problem, which have the capability to be a solid alternative to CNNs. However, three crucial obstacles hinder the application and performance of Transformers in the task of remote sensing object detection, i.e., 1) high computational complexity, especially for high-resolution remote sensing images, 2) training-and sample-inefficiency caused by lack of inductive bias, and 3) difficulty in learning arbitrary orientation knowledge of geospatial objects. To address these issues, in this paper, a novel efficient inductive vision Transformer framework is proposed for oriented object detection in remote sensing imagery. This framework follows the hierarchical feature pyramid structure and makes threefold contributions, as follows. 1) Spatial redundancy in remote sensing images is fully explored and an adaptive multi-grained routing mechanism is proposed to facilitate token sparsity in Transformers, which can dramatically reduce the computational cost without comprising the accuracy. 2) A compact dual-path encoding architecture, where both global long-range dependencies and local semantic relations are jointly and complementarily captured, is proposed to enhance inductive bias in Transformers. 3) An angle tokenization technique is proposed to promote the encoding, embedding, and learning of direction knowledge for oriented objects in remote sensing scenarios. In this work, the above three contributions are instantiated in an advanced Transformer-based object detector, namely EIA-PVT. Comprehensive experiments on two publicly available datasets have demonstrated its effectiveness and superiority for oriented object detection in remote sensing images.

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models.

Photometric stereo recovers three-dimensional (3D) object surface normal from multiple images under different illumination directions. Traditional photometric stereo methods suffer from the problem of non-Lambertian surfaces with general reflectance. By leveraging deep neural networks, learning-based methods are capable of improving the surface normal estimation under general non-Lambertian surfaces. These state-of-the-art learning-based methods however do not associate surface normal with reconstructed images and, therefore, they cannot explore the beneficial effect of such association on the estimation of the surface normal. In this paper, we specifically exploit the positive impact of this association and propose a novel dual regression network for both fine surface normals and arbitrary reconstructed images in calibrated photometric stereo. Our work unifies the 3D reconstruction and rendering tasks in a deep learning framework, with the explorations including: 1. generating specified reconstructed images under arbitrary illumination directions, which provides more intuitive perception of the reflectance and is extremely useful for visual applications, such as virtual reality, and 2. our dual regression scheme introduces an additional constraint on observed images and reconstructed images, which forms a closed-loop to provide additional supervision. Experiments show that our proposed method achieves accurate reconstructed images under arbitrarily specified illumination directions and it significantly outperforms the state-of-the-art learning-based single regression methods in calibrated photometric stereo.

Acquiring high-resolution 3D surface structures is a crucial task in computer vision as it provides more detailed surface textures and clearer structures. Photometric stereo can measure per-pixel surface normals of a 3D object using various shading cues. However, obtaining high-resolution images in a linear response photometric stereo imaging system can be challenging. Additionally, photometric stereo, as a per-pixel reconstruction method, requires higher-resolution surface normal maps to accurately depict complex surface structures, particularly in regions that demand more attention and precise reconstruction. Therefore, measuring high-resolution surface normals via low-resolution photometric stereo images is of great importance. Motivated by these, we propose a Super-resolution Photometric Stereo Network, namely SR-PSN. In order to address the issues of measuring the high-resolution surface normals from low-resolution photometric images, we mainly (1) apply a dual-position threshold normalization pre-processing scheme to effectively handle the spatially-varying reflectance of non-Lambertian surfaces, (2) adopt a local affinity feature module to learn the rich structural representation by explicitly revealing the neighbor relationships, (3) employ a parallel multi-scale feature extractor, which preserves high-resolution representations and deep feature extraction, and (4) propose a shared-weight regressor to handle the multi-scale features, to prevent the model collapsing into learning non-important features related to a certain fixed scale. Extensive ablation experiments validate the effectiveness of our proposed modules. Furthermore, quantitative experiments conducted on public benchmarks demonstrate that SR-PSN outperforms state-of-the-art calibrated photometric stereo methods. Notably, SR-PSN achieves superior results while utilizing photometric stereo images with only half the resolution of other methods. It effectively restores the structure of complex surfaces, producing a high-resolution normal map.