Point Ladder Tuning: Parameter-Efficient Hierarchical Adaptation for 3D Point Cloud Understanding

Fine-tuning pre-trained point-cloud backbones typically up-dates all parameters, resulting in substantial computation and memoryoverhead. More importantly, modern point backbones rely on aggressivetokenization and downsampling, which yields compact global tokens butirreversibly discards fine-grained local geometry, an inherent bottleneckfor parameter-efficient adaptation. Consequently, existing PEFT meth-ods that operate only on these coarsened tokens can modulate globalsemantics but struggle to recover the missing multi-scale locality. Wepresent Point Ladder Tuning (PLT), a locality-aware PEFT frameworkthat performs hierarchical, instance-conditioned adaptation while keep-ing the backbone frozen. PLT forms a lightweight closed loop: (i) a Hier-archical Ladder Network (HLN) constructs a multi-resolution local fea-ture pyramid directly from raw points; (ii) a Local–Global Fusion (LGF)aligns and fuses local pyramids with intermediate backbone semantics;and (iii) a Dynamic Prompt Generator produces instance-aware multi-scale prompts to modulate the frozen backbone effectively. For denseprediction, we further introduce a lightweight segmentation head thatprogressively upsamples fused features and leverages backbone priors torefine fine structures. Extensive experiments on classification and denseprediction show that PLT consistently surpasses prior PEFT baselineswith minimal tunable parameters. PLT achieves state-of-the-art perfor-mance using only 2.71% trainable parameters for classification and 7.69%for dense prediction, and scales favorably to larger backbones, requiringmerely 0.36% parameters on PointGPT-L.