In this content, the authors discuss the challenges and advancements in 3D scene generation. They introduce a novel depth completion network to smoothly integrate newly hallucinated regions into existing scenes, ensuring geometric consistency. The content emphasizes the limitations of existing monocular depth estimation models and presents a new benchmarking scheme based on ground truth geometry for evaluating scene generation methods. Furthermore, the authors highlight their innovative training procedure, which involves depth inpainting and prediction without sparse depth input. Overall, this work makes significant contributions to the field of 3D scene generation by enhancing geometric coherence and providing a new evaluation framework.
https://research.paulengstler.com/invisible-stitch/