LATIDIA · Robótica

LLM2R: De grandes modelos de lenguaje a reensambladores 3D

arXiv: 2610.18604v1Announce Type: cross Resumen: El ensamblaje de piezas 3D tiene como objetivo reconstruir una forma completa a partir de un conjunto de piezas no ensambladas mediante la predicción de una transformación SE(3) rígida para cada pieza. Porque la colocación de un

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arXiv:2610.18604v1 Announce Type: cross Abstract: 3D part assembly aims to reconstruct a complete shape from a set of unassembled parts by predicting a rigid SE(3) transformation for each part. Because the placement of a part is rarely determined by its own geometry, the task demands reasoning over the entire part set, and existing methods, which rely on pairwise relations or iterative generation and verification, scale poorly to objects with many parts. We present LLM2R, which casts part assembly as sequence modeling with a pretrained decoder-only large language model (LLM): a Point-BERT encoder and a Q-Former compress each part into a block of geometric tokens, the blocks of all parts are