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1. ReShader: View-Dependent Highlights for Single Image View-Synthesis

   https://doi.org/10.1145/3618393  

   Paliwal, Avinash; Nguyen, Brandon G; Tsarov, Andrii; Kalantari, Nima Khademi (December 2023, ACM Transactions on Graphics)

   In recent years, novel view synthesis from a single image has seen significant progress thanks to the rapid advancements in 3D scene representation and image inpainting techniques. While the current approaches are able to synthesize geometrically consistent novel views, they often do not handle the view-dependent effects properly. Specifically, the highlights in their synthesized images usually appear to be glued to the surfaces, making the novel views unrealistic. To address this major problem, we make a key observation that the process of synthesizing novel views requires changing the shading of the pixels based on the novel camera, and moving them to appropriate locations. Therefore, we propose to split the view synthesis process into two independent tasks of pixel reshading and relocation. During the reshading process, we take the single image as the input and adjust its shading based on the novel camera. This reshaded image is then used as the input to an existing view synthesis method to relocate the pixels and produce the final novel view image. We propose to use a neural network to perform reshading and generate a large set of synthetic input-reshaded pairs to train our network. We demonstrate that our approach produces plausible novel view images with realistic moving highlights on a variety of real world scenes. 

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2. A View-Adversarial Framework for Multi-View Network Embedding

   https://doi.org/10.1145/3340531.3412127  

   Fu, Dongqi; Xu, Zhe; Li, Bo; Tong, Hanghang; He, Jingrui (October 2020, The 29th ACM International Conference on Information and Knowledge Management)

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3. A View-Adversarial Framework for Multi-View Network Embedding

   Dongqi Fu, Zhe Xu (October 2020, CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge)

   null (Ed.)

   Network embedding has demonstrated effective empirical performance for various network mining tasks such as node classification, link prediction, clustering, and anomaly detection. However, most of these algorithms focus on the single-view network scenario. From a real-world perspective, one individual node can have different connectivity patterns in different networks. For example, one user can have different relationships on Twitter, Facebook, and LinkedIn due to varying user behaviors on different platforms. In this case, jointly considering the structural information from multiple platforms (i.e., multiple views) can potentially lead to more comprehensive node representations, and eliminate noises and bias from a single view. In this paper, we propose a view-adversarial framework to generate comprehensive and robust multi-view network representations named VANE, which is based on two adversarial games. The first adversarial game enhances the comprehensiveness of the node representation by discriminating the view information which is obtained from the subgraph induced by neighbors of that node. The second adversarial game improves the robustness of the node representation with the challenging of fake node representations from the generative adversarial net. We conduct extensive experiments on downstream tasks with real-world multi-view networks, which shows that our proposed VANE framework significantly outperforms other baseline methods. 

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4. Camera-view supervision for bird's-eye-view semantic segmentation

   https://doi.org/10.3389/fdata.2024.1431346  

   Yang, Bowen; Yu, LinLin; Chen, Feng (November 2024, Frontiers in Big Data)

   Bird's-eye-view Semantic Segmentation (BEVSS) is a powerful and crucial component of planning and control systems in many autonomous vehicles. Current methods rely on end-to-end learning to train models, leading to indirectly supervised and inaccurate camera-to-BEV projections. We propose a novel method of supervising feature extraction with camera-view depth and segmentation information, which improves the quality of feature extraction and projection in the BEVSS pipeline. Our model, evaluated on the nuScenes dataset, shows a 3.8% improvement in Intersection-over-Union (IoU) for vehicle segmentation and a 30-fold reduction in depth error compared to baselines, while maintaining competitive inference times of 32 FPS. This method offers more accurate and reliable BEVSS for real-time autonomous driving systems. The codes and implementation details and code can be found athttps://github.com/bluffish/sucam. 

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5. View Synthesis with Scene Recognition for Cross-View Image Localization

   https://doi.org/10.3390/fi15040126  

   Lee, Uddom; Jiang, Peng; Wu, Hongyi; Xin, Chunsheng (April 2023, Future Internet)

   Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with features from the images containing seasonal changes. Our design features a unique scheme to ensure that the synthesized images contain the important features from both reference and patch images, covering seasonable features and minimizing the gap for the image-based localization tasks. The performance evaluation shows that the proposed framework can synthesize the views in various weather and lighting conditions. 

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