Fin3R: Fine-Tuning Feed-Foward 3D Reconstruction Models via Monocular Knowledge Distillation
Weining Ren, Hongjun Wang, Xiao Tan, Kai Han
We present Fin3R, a simple, effective, and general fine-tuning method for feed-forward 3D reconstruction networks. This family of models regresses the pointmap of all input images under the reference frame coordinate within a single forward pass. However, current models struggle with fine geometry due to (i) the scarcity of high-fidelity depth-and-pose supervision and (ii) the inherent geometric misalignment from multi-view pointmap regression. Fin3R tackles both issues with an extra lightweight fine-tuning step. We freeze the decoder that handles view matching and fine-tune only the image encoder, the component dedicated to feature extraction. The encoder is enriched with fine geometry distilled from a strong monocular teacher on large unlabeled datasets using a custom, lightweight LoRA adapter. We test our method on three representative models—DUSt3R (two-view), CUT3R (sequential multi-view), and VGGT (parallel multi-view)—and fine-tuned models deliver sharper boundaries, recover complex structures, and achieve higher geometry accuracy in both single- and multi-view settings. At test time, Fin3R adds only the tiny LoRA weights, leaving memory and latency virtually unchanged.