The ability to detect objects that are not prevalent in the training set is a critical capability in many 3D applications, including autonomous driving. Machine learning methods for object recognition often assume that all object categories encountered during inference belong to a closed set of classes present in the training data. This assumption limits generalization to the real world, as objects not seen during training may be misclassified or entirely ignored. As part of reliable AI, OOD detection identifies inputs that deviate significantly from the training distribution. This paper provides a comprehensive overview of OOD detection within the broader scope of trustworthy and uncertain AI. We begin with key use cases across diverse domains, introduce benchmark datasets spanning multiple modalities, and discuss evaluation metrics. Next, we present a comparative analysis of OOD detection methodologies, exploring model structures, uncertainty indicators, and distributional distance taxonomies, alongside uncertainty calibration techniques. Finally, we highlight promising research directions, including adversarially robust OOD detection and failure identification, particularly relevant to 3D applications. The paper offers both theoretical and practical insights into OOD detection, showcasing emerging research opportunities such as 3D vision integration. These insights help new researchers navigate the field more effectively, contributing to the development of reliable, safe, and robust AI systems.

Recent progress in novel view synthesis of indoor scenes using diffusion models has attracted significant attention, particularly for generating desired poses from a source image. Existing methods can produce plausible views near the input view, but they often fail to extrapolate views far beyond the input perspective. Additionally, achieving a multiview consistent diffusion model typically requires training of computationally intensive 3D priors, limiting scalability to long-range generation. In this paper, we present a transformer-based latent diffusion model that leverages view geometry constraints, including explicitly warped feature maps of the input view as the denoised target view and a conditioning combination of epipolar-weighted source image feature map, Plücker raymap, and camera poses. This approach allows for the extrapolation of consistent novel views, both semantically and geometrically, over long-range trajectories in a single-shot manner. Our model is evaluated on two indoor datasets, ScanNet and RealEstate10K, using a diverse set of metrics for view quality and consistency evaluation. Experimental results demonstrate the superiority of our approach over existing models, showcasing its potential for semantically and geometrically consistent novel view synthesis, scalable in video generation applications.

Novel view synthesis (NVS) from a single image is highly ill-posed due to large unobserved regions, especially for views that deviate significantly from the input. While existing methods focus on consistency between the source and generated views, they often fail to maintain coherence and correct view alignment across long-range or looped trajectories. We propose a model that addresses this by decomposing single-view NVS into a 360-degree scene extrapolation followed by novel view interpolation. This design ensures long-term view and scene consistency by conditioning on keyframes extracted and warped from a generated panoramic representation. In the first stage, a panorama diffusion model learns the scene prior from the input perspective image. Perspective keyframes are then sampled and warped from the panorama and used as anchor frames in a pre-trained video diffusion model, which generates novel views through a proposed spatial noise diffusion process. Compared to the prior work, our method produces globally consistent novel views-even in loop-closure scenarios, while enabling flexible camera control. Experiments on diverse scene datasets demonstrate that our approach outperforms existing methods in generating coherent views along user-defined trajectories. Our implementation is available at https://github.com/YiGuYT/LookBeyond.

In this paper, we present a Computer Vision (CV) based tracking and fusion algorithm, dedicated to a 3D printed gimbal system on drones flying in nature. The whole gimbal system can stabilize the camera orientation robustly in challenging environments by using skyline and ground plane as references. Our main contributions are the following: a) a light-weight Resnet-18 backbone network model was trained from scratch, and deployed onto the Jetson Nano platform to segment the image specifically into binary parts (ground and sky); b) our geometry assumption from the skyline and ground cues delivers the potential for robust visual tracking in the wild by using the skyline and ground plane as references; c) a manifold surface-based adaptive particle sampling can fuse orientation from multiple sensor sources flexibly. The whole algorithm pipeline is tested on our 3D-printed gimbal module with Jetson Nano. The experiments were performed on top of a building in a real landscape. The public code link: https://github.com/alexandor91/gimbal-fusion.git.