VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving (2407.06516v2)

Abstract: Generating 3D vehicle assets from in-the-wild observations is crucial to autonomous driving. Existing image-to-3D methods cannot well address this problem because they learn generation merely from image RGB information without a deeper understanding of in-the-wild vehicles (such as car models, manufacturers, etc.). This leads to their poor zero-shot prediction capability to handle real-world observations with occlusion or tricky viewing angles. To solve this problem, in this work, we propose VQA-Diff, a novel framework that leverages in-the-wild vehicle images to create photorealistic 3D vehicle assets for autonomous driving. VQA-Diff exploits the real-world knowledge inherited from the LLM in the Visual Question Answering (VQA) model for robust zero-shot prediction and the rich image prior knowledge in the Diffusion model for structure and appearance generation. In particular, we utilize a multi-expert Diffusion Models strategy to generate the structure information and employ a subject-driven structure-controlled generation mechanism to model appearance information. As a result, without the necessity to learn from a large-scale image-to-3D vehicle dataset collected from the real world, VQA-Diff still has a robust zero-shot image-to-novel-view generation ability. We conduct experiments on various datasets, including Pascal 3D+, Waymo, and Objaverse, to demonstrate that VQA-Diff outperforms existing state-of-the-art methods both qualitatively and quantitatively.

• The paper introduces a novel VQA-Diff framework that integrates VQA processing and multi-expert Diffusion Models for zero-shot 3D vehicle asset generation.
• It leverages textual descriptions and edge-controlled appearance generation to overcome occlusion and unconventional viewing angles in real-world images.
• The approach outperforms state-of-the-art methods on benchmarks like Pascal 3D+ and Waymo, offering high-fidelity outputs for autonomous driving simulations.

Exploiting VQA and Diffusion Models for Zero-Shot Image-to-3D Vehicle Asset Generation

The paper "VQA-Diff: Exploiting VQA and Diffusion for Zero-Shot Image-to-3D Vehicle Asset Generation in Autonomous Driving" focuses on the generation of photorealistic 3D vehicle assets from single in-the-wild RGB images, which is a pivotal task in autonomous driving applications. The presented approach, VQA-Diff, significantly advances upon existing image-to-3D methods by incorporating Visual Question Answering (VQA) models and Diffusion Models (DMs) to enhance zero-shot prediction capability, addressing complexities such as occlusion and unconventional viewing angles common in real-world scenes.

Methodological Insights

The paper introduces the VQA-Diff framework, which consists of three key components: VQA processing, multi-expert Diffusion Models, and appearance generation via ControlNet. Key insights are as follows:

1. VQA Processing: Unlike existing methodologies that primarily rely on RGB data, VQA-Diff leverages the textual understanding from VQA models to extract detailed vehicle descriptions — such as model, manufacturer, and production year — from sparse data observations. Employing this strategy, the model benefits from the extensive real-world knowledge embedded within LLMs, significantly boosting its zero-shot innovative view synthesis abilities.
2. Multi-Expert Diffusion Models: Diverging from traditional methods that generate both structure and appearance simultaneously, VQA-Diff independently handles the vehicle’s geometry. A multi-expert strategy is employed, wherein multiple DMs are trained to predict the structure of unseen vehicles from different fixed poses. This design surpasses the structural learning achievable by a single DM, overcoming limitations posed by the inadequacy of existing datasets to cover diverse vehicle models and views.
3. Subject-Driven Structure-Controlled Generation: The method utilizes extracted appearance information and integrates it into an edge-to-image ControlNet setup. Such integration allows the control of output structures using geometry data derived by Canny edge transformation and produces photorealistic novel views that align closely with the original image's appearances.

Comparative Performance and Implications

VQA-Diff demonstrates superior performance in generating unseen views of vehicles when evaluated against state-of-the-art methods including NFI, Zero123XL, and DG, across multiple datasets such as Pascal 3D+, Waymo, and Objaverse. On metrics such as ITC score, CLIP similarity, FID, and VQA score, VQA-Diff consistently outperforms competing methodologies, affirming its robustness in handling occlusions and achieving high fidelity in asset generation.

The practical implications of VQA-Diff are notable in the field of autonomous driving, where such photorealistic and structurally accurate 3D assets are essential for simulation, data augmentation, and development of real-to-simulation technologies. Theoretically, the paper contributes to the evolving understanding of cross-modal information fusion, demonstrating how linguistic data can enhance visual task outcomes.

Future Perspectives

Future developments could explore extending the VQA-Diff approach to a broader range of object categories beyond vehicles, which may involve advancing VQA models to accommodate a wider array of object cues and improving the VQA-Diff architecture to handle more generic geometries. Additionally, integrating this approach with real-time processing capabilities could further benefit its application in live autonomous driving systems. The methodology also has potential synergies with expanding fields such as mixed reality modeling, where the photorealistic simulation of diverse objects from minimal data may redefine interactive experiences.

In conclusion, the VQA-Diff framework represents a significant stride in zero-shot image-to-3D vehicle asset generation, leveraging cutting-edge cross-modal techniques to enhance prediction robustness and output fidelity in complex real-world scenarios.