Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting (2404.19758v1)

Abstract: 3D scene generation has quickly become a challenging new research direction, fueled by consistent improvements of 2D generative diffusion models. Most prior work in this area generates scenes by iteratively stitching newly generated frames with existing geometry. These works often depend on pre-trained monocular depth estimators to lift the generated images into 3D, fusing them with the existing scene representation. These approaches are then often evaluated via a text metric, measuring the similarity between the generated images and a given text prompt. In this work, we make two fundamental contributions to the field of 3D scene generation. First, we note that lifting images to 3D with a monocular depth estimation model is suboptimal as it ignores the geometry of the existing scene. We thus introduce a novel depth completion model, trained via teacher distillation and self-training to learn the 3D fusion process, resulting in improved geometric coherence of the scene. Second, we introduce a new benchmarking scheme for scene generation methods that is based on ground truth geometry, and thus measures the quality of the structure of the scene.

• The paper introduces a context-aware depth inpainting model that fills missing depth data for improved geometric coherence in 3D scene generation.
• It establishes a new benchmark focusing on structural accuracy by comparing generated depth maps against ground truth geometries.
• Experiments demonstrate a significant reduction in visual artifacts, paving the way for more immersive applications in VR, gaming, and simulation.

Insights on Advanced 3D Scene Generation and Geometric Consistency

Introduction to 3D Scene Generation Challenges

3D scene generation is an exciting progression in computer vision that challenges the community to not just create new visual content but to construct complete, navigable 3D environments. This typically starts from a single image or a textual description and involves a complex synthesis process. Traditionally, depth estimation models are used to convert these 2D images to 3D scenes. However, inconsistencies often arise since these models tend to ignore the existing geometrical details of the scene.

This approach of using generic depth prediction often results in visual and geometric discontinuities which diminish the quality and immersion of the generated 3D scene. There's also a significant gap in how these scenes are evaluated, with current methods focusing more on image quality rather than the geometrical accuracy of the scene.

Moving Towards Geometrical Coherence

A Novel Approach to Depth Estimation:

The paper introduces a new method that integrates the geometry of the existing scene into the generation process. This method uses a depth completion model that is trained to consider parts of the scene already generated, improving the geometric coherence significantly. This model is particularly tailored to handle incomplete depth maps that arise when viewing the scene from new perspectives, filling in missing details in a context-sensitive manner.

Benchmarking Geometric Quality:

One of the standout contributions of this paper is the development of a new benchmark for evaluating the geometric structure of 3D scenes. This benchmark doesn't just look at the visual or textural fidelity but focuses on the structural accuracy of a scene by comparing generated depth maps against ground truth geometries.

Comprehensive Experiments and Results

The experiments demonstrate a clear advantage of the proposed method over traditional scene generation techniques. Specifically, it deals much better with geometric inconsistencies, which older models, reliant on unconditioned depth estimation, often struggled with. Through a series of rigorous tests, this novel approach not only aligns better with existing scene components but also reduces artefacts dramatically, pointing towards a more reliable and robust system for 3D scene construction.

Practical Implications and Future Perspectives

The advancements discussed could greatly enhance applications in VR, gaming, and simulation training by providing a tool to create more realistic and navigable 3D environments from minimal input. Academically, it sets a precedent for future research to prioritize geometric consistency in 3D scene generation.

As we look towards future developments, continuing to refine depth estimation models and developing more sophisticated benchmarks will be key. This could involve greater emphasis on handling dynamic elements within scenes or improving the scalability of these methods to handle more complex scenes without compromising on speed or accuracy.

Conclusion

The step towards integrating depth completion models trained with context-awareness, and the emphasis on geometric rather than just visual fidelity marks a significant point in 3D scene generation research. As 3D scene generation continues to evolve, focusing on these aspects will be crucial in bridging the gap between visually appealing and geometrically coherent scene generation. This research not only enhances our current capabilities but also sets the stage for more immersive and realistic applications in the future.