Exploring the Potential of Encoder-free Architectures in 3D LMMs
Authors: name: Yiwen Tang, name: Zoey Guo, name: Zhuhao Wang, name: Ray Zhang, name: Qizhi Chen, name: Junli Liu, name: Delin Qu, name: Zhigang Wang, name: Dong Wang, name: Xuelong Li, name: Bin Zhao

Encoder-free architectures have been preliminarily explored in the 2D visual
domain, yet it remains an open question whether they can be effectively applied
to 3D understanding scenarios. In this paper, we present the first
comprehensive investigation into the potential of encoder-free architectures to
overcome the challenges of encoder-based 3D Large Multimodal Models (LMMs).
These challenges include the failure to adapt to varying point cloud
resolutions and the point features from the encoder not meeting the semantic
needs of Large Language Models (LLMs). We identify key aspects for 3D LMMs to
remove the encoder and enable the LLM to assume the role of the 3D encoder: 1)
We propose the LLM-embedded Semantic Encoding strategy in the pre-training
stage, exploring the effects of various point cloud self-supervised losses. And
we present the Hybrid Semantic Loss to extract high-level semantics. 2) We
introduce the Hierarchical Geometry Aggregation strategy in the instruction
tuning stage. This incorporates inductive bias into the LLM early layers to
focus on the local details of the point clouds. To the end, we present the
first Encoder-free 3D LMM, ENEL. Our 7B model rivals the current
state-of-the-art model, ShapeLLM-13B, achieving 55.0%, 50.92%, and 42.7% on the
classification, captioning, and VQA tasks, respectively. Our results
demonstrate that the encoder-free architecture is highly promising for
replacing encoder-based architectures in the field of 3D understanding. The
code is released at https://github.com/Ivan-Tang-3D/ENEL