Sparse 3D Reconstruction via Object-Centric Ray Sampling
This addresses the problem of efficient and accurate 3D reconstruction from limited camera views for applications in computer vision and graphics, though it appears incremental as it builds on existing neural and mesh representations.
The paper tackles 3D object reconstruction from sparse views by proposing a hybrid model with an object-centric ray sampling scheme, which achieves state-of-the-art results on datasets like Google's Scanned Objects without needing segmentation masks.
We propose a novel method for 3D object reconstruction from a sparse set of views captured from a 360-degree calibrated camera rig. We represent the object surface through a hybrid model that uses both an MLP-based neural representation and a triangle mesh. A key contribution in our work is a novel object-centric sampling scheme of the neural representation, where rays are shared among all views. This efficiently concentrates and reduces the number of samples used to update the neural model at each iteration. This sampling scheme relies on the mesh representation to ensure also that samples are well-distributed along its normals. The rendering is then performed efficiently by a differentiable renderer. We demonstrate that this sampling scheme results in a more effective training of the neural representation, does not require the additional supervision of segmentation masks, yields state of the art 3D reconstructions, and works with sparse views on the Google's Scanned Objects, Tank and Temples and MVMC Car datasets. Code available at: https://github.com/llukmancerkezi/ROSTER