Implement Multitexture obj point sampler

[justinhchae]

Pull Request from Esri's AI Prototypes Team. Seeking to implement multitexture support for OBJ WITH point sampling direct from OBJ data structures. This PR includes all features that exist in #1572.

We leverage PyTorch3D quite a bit on our applied research in mesh segmentation and often use obj datasets that depict entire cities or entire regions. They mostly all require multiple texture files and we eventually need to subset meshes for nearly all stages of the pipeline. In addition, we are experimenting with feature extraction methods that generally involve using point cloud based techniques - such approaches require sampling at least one point per face and linking all sampled points to their origin faces. These features and more are implemented in this branch.

We've written a story about the full scope of changes at GeoAI/PyTorch3D.

Key Excerpt from our article:

multitexture-obj-point-sampler: This branch includes all changes in multitexture-obj-io-support and adds support for sampling points directly from meshes in obj format. This branch introduces a new function, pytorch3d.ops.sample_points_from_obj, that leverages core functions that already exist in pytorch3d.ops.sample_points_from_meshes. Sampling points directly from an obj that has many large texture files can be advantageous over a Meshes data structure since the Meshes structure concatenates textures in memory. There are three key features to highlight. First, this branch allows both sample_points_from_meshes and sample_points_from_obj to return a mappers tensor that links each sampled point to the face it was sampled from. Second, this branch allows one to force sample_points_from_obj to return at least one point from all faces, regardless of face area with (sample_all_faces=True). Third, this branch allows a user to specify the density of the desired point cloud with min_sampling_factor rather than a fixed number of points. Linked to issue #1571.

Issues Addressed Include: #1571.