Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images. However, existing NeRF approaches often encounter challenges due to the lack of explicit 3D supervision and imprecise camera poses, resulting in suboptimal outcomes. To tackle these issues, we propose AltNeRF---a novel framework designed to create resilient NeRF representations using self-supervised monocular depth estimation (SMDE) from monocular videos, without relying on known camera poses. SMDE in AltNeRF masterfully learns depth and pose priors to regulate NeRF training. The depth prior enriches NeRF's capacity for precise scene geometry depiction, while the pose prior provides a robust starting point for subsequent pose refinement. Moreover, we introduce an alternating algorithm that harmoniously melds NeRF outputs into SMDE through a consistence-driven mechanism, thus enhancing the integrity of depth priors. This alternation empowers AltNeRF to progressively refine NeRF representations, yielding the synthesis of realistic novel views. Extensive experiments showcase the compelling capabilities of AltNeRF in generating high-fidelity and robust novel views that closely resemble reality.

Visual anomaly detection has been an active topic in industrial applications. In particular, it aims to classify anomalies and precisely locate defective areas in the printed products. To the best of our knowledge, there is no anomaly detection dataset for industrial printings. In this paper, we are the first to introduce a Printed Products with Templates (PPT) dataset, which contains large templates and sliced images collected from industry scene images. PPT is a challenging dataset with more variable surface defects and more disturbing background than existing related benchmarks. Furthermore, we propose a template matching method for anomaly detection of printed products, which consists of a fast template matching block with a convolutional operation using the test sliced image as its kernel, and a prediction network for generating an anomaly map of the test sliced image. Experimental results show that our method achieves state-of-the-art performance compared to the related anomaly detection approaches.