We propose NeRFiller, an approach that completes missing portions of a 3D capture via generative 3D inpainting using off-the-shelf 2D visual generative models. Often parts of a captured 3D scene or object are missing due to mesh reconstruction failures or a lack of observations (e.g., contact regions, such as the bottom of objects, or hard-to-reach areas). We approach this challenging 3D inpainting problem by leveraging a 2D inpainting diffusion model. We identify a surprising behavior of these models, where they generate more 3D consistent inpaints when images form a 2$\times$2 grid, and show how to generalize this behavior to more than four images. We then present an iterative framework to distill these inpainted regions into a single consistent 3D scene. In contrast to related works, we focus on completing scenes rather than deleting foreground objects, and our approach does not require tight 2D object masks or text. We compare our approach to relevant baselines adapted to our setting on a variety of scenes, where NeRFiller creates the most 3D consistent and plausible scene completions. Our project page is at https://ethanweber.me/nerfiller.

The above is also true for an image of a works assume either ground-truth keypoint annotations or "never-before-seen" object (as opposed to a common object a large dataset of images of a single object category. However, category such as cars) where humans demonstrate surprisingly neither of the above assumptions hold true for the longtail robust generalization despite lacking an object or category of the objects present in the world. We present a selfsupervised specific priors [6]. These correspondences in turn inform technique that directly optimizes on a sparse collection downstream inferences about the object such as shape, of images of a particular object/object category to affordances, and more. In this work, we tackle this problem obtain consistent dense correspondences across the collection. of "low-shot dense correspondence" - i.e. given only a small We use pairwise nearest neighbors obtained from deep in-the-wild image collection ( 10-30 images) of an object features of a pre-trained vision transformer (ViT) model as or object category, we recover dense and consistent correspondences noisy and sparse keypoint matches and make them dense across the entire collection.

We present a learnt system for multi-view stereopsis. In contrast to recent learning based methods for 3D reconstruction, we leverage the underlying 3D geometry of the problem through feature projection and unprojection along viewing rays. By formulating these operations in a differentiable manner, we are able to learn the system end-to-end for the task of metric 3D reconstruction. End-to-end learning allows us to jointly reason about shape priors while conforming to geometric constraints, enabling reconstruction from much fewer images (even a single image) than required by classical approaches as well as completion of unseen surfaces. We thoroughly evaluate our approach on the ShapeNet dataset and demonstrate the benefits over classical approaches and recent learning based methods.