BitC-3DGS: High-Capacity 3D Gaussian Splatting Watermarking via Bit Compression
Organizations: School of Cyber Science and Engineering, Southeast University, Nanjing, China · Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore · College of Electronic Engineer, National University of Defense and Technology, Hefei, 230000, China · Institute of AI for Industries, Chinese Academy of Sciences, Nanjing, China · School of Computer Science and Engineering, Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, Southeast University, Nanjing 210096, China · School of Cyber Science and Engineering, Southeast University, and also with Purple Mountain Laboratories, and also with Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education, Nanjing 210000, China · Department of Computer and Information Science, University of Macau, Macau, China, and also with Faculty of Science and Technology, UOW College Hong Kong, Hong Kong, China · Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
Abstract
High-capacity watermarking is necessary for 3D Gaussian Splatting (3DGS) assets to embed rich information (e.g., ownership, provenance, and authentication codes), enabling reliable identification and integrity verification in large-scale 3D asset pipelines. Existing bit-to-token watermarking methods based on a pre-trained text encoder are limited to 77-bit messages due to CLIP's fixed 77-token context length, as tokens beyond this limit are unsupported by learned positional embeddings. To address this limitation, we introduce BitC-3DGS, a bit-compression framework that encodes multiple message bits per token. It employs a bit-compressed tokenization scheme that encodes multiple bits within the same chunk into a single semantic token. To enable recovery of the compressed information, it further introduces a dual-branch architecture for joint chunk decompression and bit decoding, along with a hard-message sampling strategy to improve combinatorial coverage during decoder training. Extensive experiments on the Blender and LLFF datasets demonstrate the effectiveness of BitC-3DGS for high-capacity watermarking, achieving high message recovery accuracy and rendering fidelity. For example, it supports 128-bit message capacity with recovery accuracy comparable to that of 64-bit messages in recent state-of-the-art methods.