Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs (2506.05318v2)

Abstract: Remarkable progress in 2D Vision-LLMs (VLMs) has spurred interest in extending them to 3D settings for tasks like 3D Question Answering, Dense Captioning, and Visual Grounding. Unlike 2D VLMs that typically process images through an image encoder, 3D scenes, with their intricate spatial structures, allow for diverse model architectures. Based on their encoder design, this paper categorizes recent 3D VLMs into 3D object-centric, 2D image-based, and 3D scene-centric approaches. Despite the architectural similarity of 3D scene-centric VLMs to their 2D counterparts, they have exhibited comparatively lower performance compared with the latest 3D object-centric and 2D image-based approaches. To understand this gap, we conduct an in-depth analysis, revealing that 3D scene-centric VLMs show limited reliance on the 3D scene encoder, and the pre-train stage appears less effective than in 2D VLMs. Furthermore, we observe that data scaling benefits are less pronounced on larger datasets. Our investigation suggests that while these models possess cross-modal alignment capabilities, they tend to over-rely on linguistic cues and overfit to frequent answer distributions, thereby diminishing the effective utilization of the 3D encoder. To address these limitations and encourage genuine 3D scene understanding, we introduce a novel 3D Relevance Discrimination QA dataset designed to disrupt shortcut learning and improve 3D understanding. Our findings highlight the need for advanced evaluation and improved strategies for better 3D understanding in 3D VLMs.

Overview of 3D Vision-LLMs: Challenges and Future Directions

The paper "Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs" provides a comprehensive analysis of 3D Vision-LLMs (VLMs) and highlights the challenges in adapting 2D VLM architectures to 3D scenarios. This research is driven by the need to advance 3D Question Answering (QA), Dense Captioning (DC), and Visual Grounding (VG) tasks through effective integration of vision and language. Despite the structural similarities between 2D and 3D VLMs, the paper reveals that 3D scene-centric models fare poorly against 3D object-centric and 2D image-based approaches. The authors emphasize that cross-modal alignment capabilities of 3D VLMs are often compromised due to an over-reliance on linguistic data, detracting from effective utilization of 3D encoders.

Key Observations

The research identifies three crucial shortcomings of 3D scene-centric VLMs:

1. Encoder Dependence: The performance of these models is relatively unaffected by the absence of pre-trained weights or even the features produced by the 3D encoder. This indicates a reliance primarily on latent variables learned during the instruction tuning phase rather than rich 3D scene features.
2. Pre-training Efficacy: Unlike 2D VLMs, the pre-training process seems to have a negligible effect on performance in 3D settings. While pre-training typically prepares the model for more effective alignment and generalization, this advantage is not observed in 3D VLMs when using current scene encoders.
3. Data Scaling Limitations: The effectiveness of scaling datasets in improving model performance is limited to smaller datasets. Larger datasets do not translate to noticeable gains, suggesting that the models fail to learn effectively from vast amounts of data.

Novel Contributions

To address the aforementioned limitations, the authors introduce the 3D Relevance Discrimination QA (3D-RDQA) dataset. This dataset disrupts shortcut learning patterns and enhances genuine 3D scene understanding by presenting models with data pairs that challenge their reliance on linguistic cues. The 3D-RDQA is crafted to make 3D VLMs critically assess their understanding of spatial structures, ensuring decisions are informed by 3D scene representations rather than memorized linguistic correlations.

Implications and Future Directions

The findings offer several implications for future 3D VLM development. Models need redesigned training paradigms that emphasize 3D reasoning over text-based pattern recognition. Incorporating more advanced 3D encoders with semantic information and finer-grained spatial details is critical. The notion of disentangling scene understanding from text over-reliance is pivotal to the practical deployment of VLMs in applications like autonomous systems and virtual environments.

Furthermore, the paper suggests the potential replication of the relevance discrimination approach for tasks beyond QA, like Dense Captioning and Visual Grounding. Future exploration might include role isolation techniques or Direct Preference Optimization to further balance learning across diverse datasets and better align 3D scene understanding with specific task requirements.

In conclusion, while 3D VLMs currently face significant challenges in semantic understanding and data scale utilization, the insights and contributions of this research pave the way for more effective and robust 3D vision-language integration. Continued research, particularly with improved model architectures and dataset designs, could greatly enhance the field's capability to accurately link vision and language in complex 3D environments.