3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment (2308.04352v1)

Abstract: 3D vision-language grounding (3D-VL) is an emerging field that aims to connect the 3D physical world with natural language, which is crucial for achieving embodied intelligence. Current 3D-VL models rely heavily on sophisticated modules, auxiliary losses, and optimization tricks, which calls for a simple and unified model. In this paper, we propose 3D-VisTA, a pre-trained Transformer for 3D Vision and Text Alignment that can be easily adapted to various downstream tasks. 3D-VisTA simply utilizes self-attention layers for both single-modal modeling and multi-modal fusion without any sophisticated task-specific design. To further enhance its performance on 3D-VL tasks, we construct ScanScribe, the first large-scale 3D scene-text pairs dataset for 3D-VL pre-training. ScanScribe contains 2,995 RGB-D scans for 1,185 unique indoor scenes originating from ScanNet and 3R-Scan datasets, along with paired 278K scene descriptions generated from existing 3D-VL tasks, templates, and GPT-3. 3D-VisTA is pre-trained on ScanScribe via masked language/object modeling and scene-text matching. It achieves state-of-the-art results on various 3D-VL tasks, ranging from visual grounding and dense captioning to question answering and situated reasoning. Moreover, 3D-VisTA demonstrates superior data efficiency, obtaining strong performance even with limited annotations during downstream task fine-tuning.

Analysis of "3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment"

The paper presents "3D-VisTA," a pre-trained transformer-based model for 3D vision and text alignment, aiming to bridge the gap between the 3D physical world and natural language. This work emerges in the context of the imperative need for embodied intelligence, where intelligent systems can understand and execute human instructions in three-dimensional environments. Traditionally, 3D vision-language (3D-VL) models have been characterized by complex architectures and task-specific tuning, often necessitating multiple optimization methods and auxiliary loss functions. In contrast, 3D-VisTA offers a simplified architecture using self-attention layers, allowing it to adapt flexibly across a range of tasks without the need for sophisticated elements or tricks.

Core Contributions

The authors’ primary contribution is the development of the 3D-VisTA model, which leverages a multi-layer transformer for both single-modal and multi-modal tasks without intricate architectural modifications. Its performance enhancement is significantly grounded in the pre-training phase, which uses "ScanScribe," a novel dataset crafted for this research. ScanScribe is notable for its scale and diversity, comprising approximately 3,000 RGB-D scans of indoor scenes with paired text descriptions, derived from integrating existing 3D models and generated textual data.

3D-VisTA’s efficacy is demonstrated on six benchmark 3D-VL tasks, where it sets new performance standards. Key tasks include visual grounding, dense captioning, question answering, and situated reasoning. Impressive results, such as an 8.1% increase over state-of-the-art (SOTA) accuracy in ScanRefer tasks and substantial gains in other datasets, underscore the model’s capabilities. Importantly, 3D-VisTA also exhibits strong performance under constrained data conditions, showing resilience and adaptability when fine-tuned with limited annotations.

Implications and Future Directions

This work makes bold strides in the pursuit of developing a generalized framework for 3D vision-language interplay. The simplification brought by 3D-VisTA distinctly enhances the model's versatility across diverse tasks, potentially alleviating some of the complexity-induced inefficiencies in model training and deployment. Furthermore, the introduction of ScanScribe as a pre-training dataset sets a precedent for future explorations in 3D-VL tasks, emphasizing the value of large-scale, diverse, and contextually rich 3D-scene-text pairings.

The scalability of 3D-VisTA, coupled with its ability to handle a wide range of intricate 3D-VL tasks, opens pathways for future research to build even larger and more comprehensive datasets. This, in turn, could lead to more robust pre-training methods that facilitate zero-shot and few-shot learning capabilities in 3D environments, akin to the advances witnessed in NLP and 2D vision-language integrations.

Looking ahead, integrating joint object detection and feature learning within the 3D-VisTA framework during pre-training could further enhance its performance. This paper lays a strong foundation for such endeavours, encouraging the continuation of research towards more unified, efficient, and effective approaches to 3D vision-language tasks.

In conclusion, the insights and innovations presented in this paper advance the field by reducing complexity while improving adaptability and performance. The work establishes a critical framework not just for current practices but for guiding future developments in the rapidly evolving domain of 3D vision-language systems.