Generating Smooth 3D Scenes with Depth Inpainting

Core Concepts

Depth inpainting is crucial for generating geometrically consistent 3D scenes from a single image or text prompt.

Abstract

The paper introduces two key contributions to the field of 3D scene generation:

1. A novel depth completion model that learns to predict depth maps conditioned on the existing scene geometry, resulting in improved geometric coherence of the generated scenes. This model is trained in a self-supervised manner using teacher distillation and self-training.

2. A new benchmarking scheme for evaluating the geometric quality of scene generation methods, based on ground truth depth data. This allows assessing the consistency and accuracy of the generated 3D structure, going beyond visual quality metrics.

The authors show that existing scene generation methods suffer from geometric inconsistencies, which are uncovered by the proposed benchmark. Their depth inpainting model significantly outperforms prior approaches in terms of geometric fidelity, while also maintaining high visual quality.

The pipeline first uses a generative model like Stable Diffusion to hallucinate new scene content beyond the initial input. It then leverages the depth inpainting model to predict depth maps that are consistent with the existing scene geometry, seamlessly integrating the new content. Additional support views are generated to further constrain the scene and fill in occluded regions. Finally, the point cloud representation is converted to a smooth Gaussian splat optimization to produce the final 360-degree scene.

Stats

The paper does not provide specific numerical data, but rather focuses on qualitative and benchmarking results.

Quotes

"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."

Key Insights Distilled From

Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting

by Paul Engstle... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19758.pdf

Deeper Inquiries

How could the proposed depth inpainting model be extended to handle dynamic scenes or videos?

The proposed depth inpainting model could be extended to handle dynamic scenes or videos by incorporating temporal information into the inpainting process. This could involve leveraging techniques from video inpainting or video prediction models to predict depth information across frames. By considering the temporal coherence of the scene, the model can better inpaint missing depth values in dynamic scenes where objects are moving or the camera viewpoint is changing. Additionally, the model could be trained on video datasets to learn the dynamics of the scene and improve the accuracy of depth inpainting over time.

What are the limitations of the current benchmarking approach, and how could it be further improved to better capture the nuances of scene generation quality?

The current benchmarking approach, which focuses on evaluating the geometric quality of generated scenes based on ground truth depth maps, has limitations in capturing the full spectrum of scene generation quality. One limitation is that it may not fully account for the visual realism or perceptual quality of the generated scenes, as it primarily focuses on geometric consistency. To improve the benchmarking approach, additional metrics related to visual fidelity, realism, and perceptual quality could be incorporated. This could involve using image quality assessment metrics, perceptual similarity metrics, or user studies to evaluate the overall quality of the generated scenes beyond just geometric accuracy. Additionally, considering the semantic consistency of the scenes and how well they align with the given text prompts could provide a more comprehensive evaluation of scene generation quality.

What other applications beyond 3D scene generation could benefit from the depth inpainting capabilities demonstrated in this work?

The depth inpainting capabilities demonstrated in this work have potential applications beyond 3D scene generation. One such application is in autonomous driving systems, where accurate depth estimation is crucial for obstacle detection and scene understanding. The depth inpainting model could be used to fill in missing depth information in LiDAR or camera sensor data, improving the overall perception capabilities of autonomous vehicles. Additionally, in augmented reality (AR) and virtual reality (VR) applications, the depth inpainting model could enhance the realism of virtual scenes by filling in missing depth information and creating more immersive environments. Furthermore, in medical imaging, the model could be utilized for reconstructing 3D structures from incomplete or noisy medical scans, aiding in diagnosis and treatment planning.