Diffusion Priors for 3D Scene Completion

1. The Challenge of Sparse Views and the Need for a Smarter Prior

When you try to train a NeRF on just a handful of images (say, 3 to 9), the optimization problem is severely underconstrained. The model will perfectly reconstruct the input views, but any unseen viewpoint will likely be a mess of "floater" artifacts and blurry geometry because the model has no information to guide it.

The SDS approach from our last lesson is a starting point, but it's not ideal for scenes. A generic prompt like "a living room" doesn't capture the specific details of the parts we can see. We need a prior that is both generative (to fill in the blanks) and conditional (to respect the input data).

2. Method 1: Regularization with a Multi-View Conditioned Prior

The first approach, exemplified by the paper ReconFusion, is to build and use a diffusion model that is an expert at novel view synthesis. Instead of a general-purpose text-to-image model, this prior is specifically trained to answer the question: "Given this set of input images and their camera poses, what would the scene look like from this new camera pose?"

Let's dive into the paper to see how this is done.

The core idea is to augment the standard NeRF training loss with a diffusion-based loss that penalizes unrealistic renderings from novel viewpoints. This requires two main components: a specialized diffusion model and a reconstruction pipeline that uses it.

2.1. The Multi-View Diffusion Prior

To build this prior, the authors fine-tune a pre-trained latent diffusion model. The key innovation is how they condition it on the available input views.

This conditioning mechanism is powerful. The CLIP embeddings tell the model what is in the scene (e.g., "a chair, a table"), while the PixelNeRF feature map tells it where things are relative to the novel camera view. This allows the model to generate a view that is consistent with the geometry of the input images.

2.2. The Reconstruction Pipeline

With this trained prior, how do we use it to optimize our main NeRF? Instead of the single-step SDS loss from the last lesson, ReconFusion uses a multi-step sampling approach that we can think of as generating a "pseudo-target" image.

The complete pipeline is a clever two-part optimization:

• Reconstruction Loss ${\mathcal{L}}_{\text{Recon}}$: For the known input camera views, use a standard photometric loss to ensure the NeRF matches the input images.
• Diffusion Loss ${\mathcal{L}}_{\text{sample}}$: For randomly sampled novel camera views, use the multi-step diffusion sampling process to generate a target image and compute a loss that pushes the NeRF's rendering towards this more plausible target.

This approach effectively regularizes the empty spaces in the scene, encouraging the NeRF to fill them with geometry and texture that the diffusion prior considers realistic, given the input views.

3. Method 2: Scene Completion via Inpainting

Training a specialized multi-view diffusion model like in ReconFusion is effective but requires significant data and computation. A different and very clever approach is to re-purpose an off-the-shelf 2D inpainting diffusion model. This is the core idea behind methods like RealmDreamer.

The workflow is conceptually simple and powerful:

1. Generate an initial, incomplete 3D representation from your partial data.
2. When rendering from a novel view, this incomplete model will have holes.
3. Use a 2D inpainting model to fill these holes in image space.
4. Use this completed image as the supervisory signal to optimize and fill in the 3D model.

Let's explore the RealmDreamer paper, which uses this technique for text-to-scene generation but whose mechanism is perfectly suited for scene completion.

3.1. Inpainting Distillation

The core of this method is using the inpainting model to guide the 3D optimization. Let's say our partial data is a single image.

1. Initialization: We lift the single image to a partial point cloud using a monocular depth estimator. Crucially, we also compute the "occlusion volume"—the 3D space that is hidden behind the visible points. This defines the region that needs to be "dreamed up."

2. Inpainting Loop: For a novel view:

   • The current 3D model is rendered, producing an image with holes.
   • A mask is generated corresponding to where the occlusion volume is visible.
   • This masked, incomplete render is fed to a 2D inpainting diffusion model.
   • The model fills in the masked region, producing a complete, plausible 2D image. This completed image becomes the target for an SDS-style loss that updates the 3D model.

3.2. Adding a Geometric Prior

A brilliant addition in RealmDreamer is the use of a second diffusion prior—this time for geometry. They use a monocular depth diffusion model to further regularize the optimization, ensuring the generated scene isn't just texturally plausible but also geometrically sound.

This combination of an appearance prior (inpainting model) and a geometry prior (depth model) provides very strong supervision, allowing for the generation of high-quality, coherent 3D scenes from very minimal input.

Conclusion

We've now seen two sophisticated and powerful ways to apply diffusion priors for the challenging task of 3D scene completion from partial data. Both methods use the prior to regularize an underconstrained optimization problem, but they do so in distinct ways.

Key Takeaways:

• Underconstrained Problem: Reconstructing a full 3D scene from sparse views (partial data) is ill-posed, leading to artifacts in standard NeRF optimization.
• Diffusion Priors as Regularizers: We can distill the knowledge of large-scale 2D generative models to fill in the missing information in a plausible way.
• Strategy 1: Multi-View Priors (ReconFusion): Train a specialized diffusion model for novel view synthesis, conditioned on multiple input views. Use this model to generate targets for a multi-step distillation loss.
• Strategy 2: Inpainting Priors (RealmDreamer): Re-purpose an off-the-shelf 2D inpainting model. Render the incomplete scene, identify holes, and use the inpainting model's output as the target to fill in the 3D representation.
• Geometric Priors: The generated appearance can be complemented with a geometric prior, often from a depth diffusion model, to ensure a higher quality and more realistic 3D structure.

Next Lesson Preview:

In both ReconFusion and RealmDreamer, a critical, though sometimes implicit, component is the ability to reason about camera poses. The priors need to know the pose of the input views and the target view. In our next lesson, we will focus specifically on this: "Explain how camera pose information is encoded and used as a conditioning signal in 3D generative models."