This benchmark ranks image-to-3D models on how faithfully they rebuild an object as a textured 3D asset from a single image. The rankings come from … pairwise matches, decided by … votes. Quality is evaluated in two dimensions: Geometry and Faithfulness. All rankings are fitted with a Bradley–Terry model and reported on the Elo scale. All data and judgments are public; see the notes for details.
We evaluate image-to-3D models purely on human feedback. Humans perform inconsistently on absolute judgements, but much better in a comparative setting. Our rating is therefore based on pair-wise matches, the results of which are jointly fit using the Bradley–Terry model where the probability of model m beating model m′ is given by:
A match pairs the 3D assets two models generated from the same input image. As evaluating all permutations is infeasible, matches are sampled without replacement, ensuring broad coverage per model. The resulting scores are scaled to the familiar Elo range via Rm = (400 / ln 10) βm + 1000 (2).
We curated 357 input images, each showing an object to reconstruct, held fixed to ensure reproducibility of the results. Every asset is shown to annotators as a turntable video rendered identically for every model, next to the input image.
To ensure trustworthy ratings we employ several measures, notably annotator pre-filtering via gold-standard 3D comparisons, and redundancy, collecting a minimum of m responses per match (m = 5) and deciding the winner by majority. Read more
We use crowd intelligence to deliver real, diverse human feedback at scale for model evaluation and post-training.
Figure A3. Each model's standing within each input image category, scored on overall preference. Read a column: who is best in that category. Darkest is first. Hover or tap a cell to read its score, 95% interval and sample size; click a category to sort the rows by it. The number beside each model is its overall rank, so a jump reads as a jump. A cell reports a rank only above 500 matches for that model and is hatched below it, a rank on a handful of matchups is noise, not a result. How many input images a category rests on is in its header and every tooltip: a narrow category asks a narrower question.
