Foundation models are trained on increasingly immense and opaque datasets.

Relabelingmethods typically pose the question: if, in hindsight, we assume that our experience was optimal for some task, for what task was it optimal?

Deep learning methods have boosted the adoption of NLP systems in real-life applications.

We present radiance field propagation (RFP), a novel approach to segmenting objects in 3D during reconstruction given only unlabeled multi-view images of a scene. RFP is derived from emerging neural radiance field-based techniques, whichjointlyencodes semantics withappearance andgeometry.

However, if intrinsic parameters change, neural computations are altered drastically.