Parameter-Efficient CLIP Adaptation for 3D Understanding via Unified Tokenization

Vision-language models, such as CLIP, encode rich semantic knowledge through large-scale image-text pretraining. Reusing these models for 3D understanding is highly desirable, because 3D-text pairs and dense point-level annotations are far scarcer and more difficult to obtain than their 2D counterparts. However, CLIP is trained on regular 2D image patches, whereas point clouds are unordered, sparse, and irregular. The primary challenge lies in constructing 3D token sequences whose geometry, position, and local relations can be interpreted by a frozen, pretrained vision transformer. Moreover, coordinate-scale variations across heterogeneous 3D domains make unified training particularly challenging. To bridge these gaps, we propose a parameter-efficient framework that learns a scale-normalized 3D tokenizer, enabling the CLIP visual encoder to be reused for point-cloud understanding. Our tokenizer, named UTok3D, estimates an input-adaptive geometric scale to calibrate sparse voxelization, coordinate normalization, token-center computation, and 3D positional encoding, and serializes the resulting tokens by Hilbert ordering. This shared geometric convention makes joint training feasible across point clouds with substantially different metric scales, including object-level shapes, indoor scenes, and outdoor LiDAR scans. The tokenizer is trained without 3D annotations through self-supervised cross-modal distillation from foundation-model features extracted from multi-view images, combining local superpoint alignment with the proposed Sinkhorn Ranked Contrastive distillation. Experiments on ShapeNetPart, ScanNetV2, S3DIS, SemanticKITTI, and nuScenes show that, when equipped with a scale-consistent and CLIP-interpretable token interface, a frozen CLIP visual backbone can be effectively reused for annotation-free 3D segmentation.