WACV 2022 Papers

Quality scores provide a measure to evaluate the utility of biometric samples for biometric recognition. Biometric recognition systems require high-quality samples to achieve optimal performance. This paper focuses on face images and the measurement of face image utility with general and face-specific image quality metrics. While face-specific metrics rely on features of aligned face images, general image quality metrics can be used on the global image and relate to human perceptions. In this paper, we analyze the gap between the general image quality metrics and the face image quality metrics. Our contribution lies in a thorough examination of how different the image quality assessment algorithms relate to the utility for the face recognition task. The results of image quality assessment algorithms are further compared with those of dedicated face image quality assessment algorithms. In total, 25 different quality metrics are evaluated on three face image databases, BioSecure, LFW, and VGGFace2 using three open-source face recognition solutions, SphereFace, ArcFace, and FaceNet. Our results reveal a clear correlation between learned image metrics to face image utility even without being specifically trained as a face utility measure. Individual handcrafted features lack general stability and perform significantly worse than general face-specific quality metrics. We additionally provide a visual insight into the image areas contributing to the quality score of a selected set of quality assessment methods.

Automatic detection of geoinformation from satellite images has been a fundamental yet challenging problem, which aims to reduce the manual effort of human annotators in maintaining an up-to-date digital map. There are currently several high-resolution satellite imagery datasets that are publicly available. However, the associated ground-truth annotations are limited to road, building, and land use, while the annotations of other geographic objects or attributes are mostly not available. To bridge the gap, we present Grab-Pklot, the first high-resolution and context-enriched satellite imagery dataset for parking lot detection. Our dataset consists of 1344 satellite images with the ground-truth annotations of carparks in Singapore. Motivated by the observation that carparks are mostly co-appear with other geographic objects, we associate each satellite image in our dataset with the surrounding contextual information of road and building, given in the format of multi-channel images. As a side contribution, we present a fusion-based segmentation approach to demonstrate that the parking lot detection accuracy can be improved by modeling the correlations between parking lots and other geographic objects. Experiments on our dataset provide baseline results as well as new insights into the challenges and opportunities in parking lot detection from satellite images.

In this paper, we study fine-grained 3D object identification in real-world scenes described by a textual query. The task aims to discriminatively understand an instance of a particular 3D object described by natural language utterances among other instances of 3D objects of the same class appearing in a visual scene. We introduce the 3DRefTransformer net, a transformer-based neural network that identifies 3D objects described by linguistic utterances in real-world scenes. The network's input is 3D object segmented point cloud images representing a real-world scene and a language utterance that refers to one of the scene objects. The goal is to identify the referred object. Compared to the state-of-the-art models that are mostly based on graph convolutions and LSTMs, our 3DRefTransformer net offers two key advantages. First, it is an end-to-end transformer model that operates both on language and 3D visual objects. Second, it has a natural ability to ground textual terms in the utterance to the learning representation of 3D objects in the scene. We further incorporate object pairwise spatial relation loss and contrastive learning during model training. We show in our experiments that our model improves the performance upon the current SOTA significantly on Referit3D Nr3D and Sr3D datasets. Code and Models will be made publicly available.

Existing face completion solutions are primarily driven by end-to-end models that directly generate 2D completions of 2D masked faces. By having to implicitly account for geometric and photometric variations in facial shape and appearance, such approaches result in unrealistic completions, especially under large variations in pose, shape, illumination and mask sizes. To alleviate these limitations, we introduce 3DFaceFill, an analysis-by-synthesis approach for face completion that explicitly considers the image formation process. It comprises three components, (1) an encoder that disentangles the face into its constituent 3D mesh, 3D pose, illumination and albedo factors, (2) an autoencoder that inpaints the UV representation of facial albedo, and (3) a renderer that resynthesizes the completed face. By operating on the UV representation, 3DFaceFill affords the power of correspondence and allows us to naturally enforce geometrical priors (e.g. facial symmetry) more effectively. Quantitatively, 3DFaceFill improves the state-of-the-art by up to 4dB higher PSNR and 25% better LPIPS for large masks. And, qualitatively, it leads to demonstrably more photorealistic face completions over a range of masks and occlusions while preserving consistency in global and component-wise shape, pose, illumination and eye-gaze.

3D object reconstruction based on deep neural networks has been gaining attention in recent years. However, recovering 3D shapes of hidden and buried objects remains to be a challenge. Ground Penetrating Radar (GPR) is among the most powerful and widely used instruments for detecting and locating underground objects such as plant roots and pipes, with affordable prices and continually evolving technology. This paper first proposes a deep convolution neural network-based anchor-free GPR curve signal detection network utilizing B-scans from a GPR sensor. The detection results can help obtain precisely fitted parabola curves. Furthermore, a graph neural network-based root shape reconstruction network is designated in order to progressively recover major taproot and then fine root branches' geometry. Our results on the gprMax simulated root data as well as the real-world GPR data collected from apple orchards demonstrate the potential of using the proposed framework as a new approach for fine-grained underground object shape reconstruction in a non-destructive way.