This project develops a framework that jointly estimates scene geometry (depth) and camera motion directly from pairs of RGB images. We combine visual foundation models (e.g., SD3.5, DINOv2) with geometric optimization to refine predictions into a consistent 3D representation.

Our approach performs gradient descent over a simple least-squares objective, aligning predicted optical flow with correspondences from an off-the-shelf flow estimator. By fine-tuning pretrained foundation models to predict affine-invariant depth and optical flow jointly, we bridge geometry and motion estimation in a single unified system.

Contribution: A method that integrates learning-based priors with optimization, enabling robust 3D scene reconstruction from multiple RGB views.

This work aims to recover a full 3D representation of indoor scenes — geometry (depth and normals) and appearance (SVBRDF) — directly from RGB images.

We adapt the Stable Video Diffusion model to a multi-view setting, conditioning it on several images of a scene and training it to output depth maps, surface normals, and spatially varying reflectance properties. This approach leverages diffusion priors for learning consistent multi-view geometry and appearance estimation.

Contribution: A novel diffusion-based method that jointly estimates geometry and SVBRDFs, providing a complete 3D scene model. This enables downstream applications such as relighting, novel view synthesis, and 3D reconstruction.