Facial geometry and appearance capture have demonstrated tremendous success in 3D scanning real humans in studios. Recent works propose to democratize this technique while keeping the results high quality. However, they are still inconvenient for daily usage. In addition, they focus on an easier problem of only capturing facial skin. This paper proposes a novel method for high-quality face capture, featuring an easy-to-use system and the capability to model the complete face with skin, mouth interior, hair, and eyes. We reconstruct facial geometry and appearance from a single co-located smartphone flashlight sequence captured in a dim room where the flashlight is the dominant light source (e.g. rooms with curtains or at night). To model the complete face, we propose a novel hybrid representation to effectively model both eyes and other facial regions, along with novel techniques to learn it from images. We apply a combined lighting model to compactly represent real illuminations and exploit a morphable face albedo model as a reflectance prior to disentangle diffuse and specular. Experiments show that our method can capture high-quality 3D relightable scans.
With the continuous maturation and application of autonomous driving technology, a systematic examination of open-source autonomous driving datasets becomes instrumental in fostering the robust evolution of the industry ecosystem. Current autonomous driving datasets can broadly be categorized into two generations. The first-generation autonomous driving datasets are characterized by relatively simpler sensor modalities, smaller data scale, and is limited to perception-level tasks. KITTI, introduced in 2012, serves as a prominent representative of this initial wave. In contrast, the second-generation datasets exhibit heightened complexity in sensor modalities, greater data scale and diversity, and an expansion of tasks from perception to encompass prediction and control. Leading examples of the second generation include nuScenes and Waymo, introduced around 2019. This comprehensive review, conducted in collaboration with esteemed colleagues from both academia and industry, systematically assesses over seventy open-source autonomous driving datasets from domestic and international sources. It offers insights into various aspects, such as the principles underlying the creation of high-quality datasets, the pivotal role of data engine systems, and the utilization of generative foundation models to facilitate scalable data generation. Furthermore, this review undertakes an exhaustive analysis and discourse regarding the characteristics and data scales that future third-generation autonomous driving datasets should possess. It also delves into the scientific and technical challenges that warrant resolution. These endeavors are pivotal in advancing autonomous innovation and fostering technological enhancement in critical domains. For further details, please refer to https://github.com/OpenDriveLab/DriveAGI.
Previous head avatar methods have mostly relied on fixed explicit primitives (mesh, point) or implicit surfaces (Sign Distance Function) and volumetric neural radiance field, it challenging to strike a balance among high fidelity, training speed, and resource consumption. The recent popularity of hybrid field has brought novel representation, but is limited by relying on parameterization factors obtained through fixed mappings. We propose GaussianHead: an head avatar algorithm based on anisotropic 3D gaussian primitives. We leverage canonical gaussians to represent dynamic scenes. Using explicit "dynamic" tri-plane as an efficient container for parameterized head geometry, aligned well with factors in the underlying geometry and tri-plane, we obtain aligned canonical factors for the canonical gaussians. With a tiny MLP, factors are decoded into opacity and spherical harmonic coefficients of 3D gaussian primitives. Finally, we use efficient differentiable gaussian rasterizer for rendering. Our approach benefits significantly from our novel representation based on 3D gaussians, and the proper alignment transformation of underlying geometry structures and factors in tri-plane eliminates biases introduced by fixed mappings. Compared to state-of-the-art techniques, we achieve optimal visual results in tasks such as self-reconstruction, novel view synthesis, and cross-identity reenactment while maintaining high rendering efficiency (0.12s per frame). Even the pores around the nose are clearly visible in some cases. Code and additional video can be found on the project homepage.
Either RGB images or inertial signals have been used for the task of motion capture (mocap), but combining them together is a new and interesting topic. We believe that the combination is complementary and able to solve the inherent difficulties of using one modality input, including occlusions, extreme lighting/texture, and out-of-view for visual mocap and global drifts for inertial mocap. To this end, we propose a method that fuses monocular images and sparse IMUs for real-time human motion capture. Our method contains a dual coordinate strategy to fully explore the IMU signals with different goals in motion capture. To be specific, besides one branch transforming the IMU signals to the camera coordinate system to combine with the image information, there is another branch to learn from the IMU signals in the body root coordinate system to better estimate body poses. Furthermore, a hidden state feedback mechanism is proposed for both two branches to compensate for their own drawbacks in extreme input cases. Thus our method can easily switch between the two kinds of signals or combine them in different cases to achieve a robust mocap. %The two divided parts can help each other for better mocap results under different conditions. Quantitative and qualitative results demonstrate that by delicately designing the fusion method, our technique significantly outperforms the state-of-the-art vision, IMU, and combined methods on both global orientation and local pose estimation. Our codes are available for research at https://shaohua-pan.github.io/robustcap-page/.
SAR images are highly sensitive to observation configurations, and they exhibit significant variations across different viewing angles, making it challenging to represent and learn their anisotropic features. As a result, deep learning methods often generalize poorly across different view angles. Inspired by the concept of neural radiance fields (NeRF), this study combines SAR imaging mechanisms with neural networks to propose a novel NeRF model for SAR image generation. Following the mapping and projection pinciples, a set of SAR images is modeled implicitly as a function of attenuation coefficients and scattering intensities in the 3D imaging space through a differentiable rendering equation. SAR-NeRF is then constructed to learn the distribution of attenuation coefficients and scattering intensities of voxels, where the vectorized form of 3D voxel SAR rendering equation and the sampling relationship between the 3D space voxels and the 2D view ray grids are analytically derived. Through quantitative experiments on various datasets, we thoroughly assess the multi-view representation and generalization capabilities of SAR-NeRF. Additionally, it is found that SAR-NeRF augumented dataset can significantly improve SAR target classification performance under few-shot learning setup, where a 10-type classification accuracy of 91.6\% can be achieved by using only 12 images per class.
Radar imaging is crucial in remote sensing and has many applications in detection and autonomous driving. However, the received radar signal for imaging is enormous and redundant, which degrades the speed of real-time radar quantitative imaging and leads to obstacles in the downlink applications. In this paper, we propose a physics-assisted deep learning method for radar quantitative imaging with the advantage of compressed sensing (CS). Specifically, the signal model for frequency-modulated continuous-wave (FMCW) radar imaging which only uses four antennas and parts of frequency components is formulated in terms of matrices multiplication. The learned fast iterative shrinkage-thresholding algorithm with residual neural network (L-FISTA-ResNet) is proposed for solving the quantitative imaging problem. The L-FISTA is developed to ensure the basic solution and ResNet is attached to enhance the image quality. Simulation results show that our proposed method has higher reconstruction accuracy than the traditional optimization method and pure neural networks. The effectiveness and generalization performance of the proposed strategy is verified in unseen target imaging, denoising, and frequency migration tasks.
Multi-baseline synthetic aperture radar interferometry (MB-InSAR), capable of mapping 3D surface model with high precision, is able to overcome the ill-posed problem in the single-baseline InSAR by use of the baseline diversity. Single pass MB acquisition with the advantages of high coherence and simple phase components has a more practical capability in 3D reconstruction than conventional repeat-pass MB acquisition. Using an asymptotic 3D phase unwrapping (PU), it is possible to get a reliable 3D reconstruction using very sparse acquisitions but the interferograms should follow the optimal baseline design. However, current spaceborne SAR system doesn't satisfy this principle, inducing more difficulties in practical application. In this article, a new concept of Tandem Dual-Antenna SAR Interferometry (TDA-InSAR) system for single-pass reliable 3D surface mapping using the asymptotic 3D PU is proposed. Its optimal MB acquisition is analyzed to achieve both good relative height precision and flexible baseline design. Two indicators, i.e., expected relative height precision and successful phase unwrapping rate, are selected to optimize the system parameters and evaluate the performance of various baseline configurations. Additionally, simulation-based demonstrations are conducted to evaluate the performance in typical scenarios and investigate the impact of various error sources. The results indicate that the proposed TDA-InSAR is able to get the specified MB acquisition for the asymptotic 3D PU, which offers a feasible solution for single-pass 3D SAR imaging.
Currently, density-based clustering algorithms are widely applied because they can detect clusters with arbitrary shapes. However, they perform poorly in measuring global density, determining reasonable cluster centers or structures, assigning samples accurately and handling data with large density differences among clusters. To overcome their drawbacks, this paper proposes a granule fusion density-based clustering with evidential reasoning (GFDC). Both local and global densities of samples are measured by a sparse degree metric first. Then information granules are generated in high-density and low-density regions, assisting in processing clusters with significant density differences. Further, three novel granule fusion strategies are utilized to combine granules into stable cluster structures, helping to detect clusters with arbitrary shapes. Finally, by an assignment method developed from Dempster-Shafer theory, unstable samples are assigned. After using GFDC, a reasonable clustering result and some identified outliers can be obtained. The experimental results on extensive datasets demonstrate the effectiveness of GFDC.
For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from hematoxylin and eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation.
Human and environment sensing are two important topics in Computer Vision and Graphics. Human motion is often captured by inertial sensors, while the environment is mostly reconstructed using cameras. We integrate the two techniques together in EgoLocate, a system that simultaneously performs human motion capture (mocap), localization, and mapping in real time from sparse body-mounted sensors, including 6 inertial measurement units (IMUs) and a monocular phone camera. On one hand, inertial mocap suffers from large translation drift due to the lack of the global positioning signal. EgoLocate leverages image-based simultaneous localization and mapping (SLAM) techniques to locate the human in the reconstructed scene. On the other hand, SLAM often fails when the visual feature is poor. EgoLocate involves inertial mocap to provide a strong prior for the camera motion. Experiments show that localization, a key challenge for both two fields, is largely improved by our technique, compared with the state of the art of the two fields. Our codes are available for research at https://xinyu-yi.github.io/EgoLocate/.