Steel scrap composition estimation is an important part of yield planning and fair supplier compensation. Measuring the composition of a multi-ton pile is an inherently laborious process, so the assessment is often left to trained human operators.

Recent works tackle composition-level tasks, but require structured acquisition and multi-stage preprocessing. We study a simpler approach to steel scrap load assessment by aggregating features from sparse, handheld views of the pile.

On 600 out-of-fold piles, frozen DINOv2 tokens aggregated uniformly across views reach 13.4% total variation (TV) distance against operator estimates. Most remaining error lies in allocation among four thickness-defined heavy melting scrap (HMS) classes, yet image-dependent allocation reduces within-HMS TV from 25.1% under a training-set split prior to 11.2%.

Due to the lack of public datasets and benchmarks for predicting composition, external evaluation relies on the public DOES dataset intended for classification. A classifier using the same frozen features reaches 93.97% accuracy, exceeding the published results we compare against.

The composition model also aligns with related DOES categories without adaptation.