ArbiViewGen: Controllable Arbitrary Viewpoint Camera Data Generation for Autonomous Driving via Stable Diffusion Models
Yatong Lan, Jingfeng Chen, Yiru Wang, Lei He

TL;DR
ArbiViewGen introduces a diffusion-based framework that enables controllable generation of arbitrary viewpoint images for autonomous driving, overcoming the lack of ground-truth data through innovative self-supervised learning and view stitching techniques.
Contribution
The paper presents ArbiViewGen, a novel controllable image generation method for autonomous driving that does not require ground-truth extrapolated views, using feature-aware stitching and self-supervised learning.
Findings
First controllable arbitrary view generation method for autonomous driving.
Effective self-supervised training without ground-truth extrapolated data.
High-quality, multi-view consistent images from multi-camera setups.
Abstract
Arbitrary viewpoint image generation holds significant potential for autonomous driving, yet remains a challenging task due to the lack of ground-truth data for extrapolated views, which hampers the training of high-fidelity generative models. In this work, we propose Arbiviewgen, a novel diffusion-based framework for the generation of controllable camera images from arbitrary points of view. To address the absence of ground-truth data in unseen views, we introduce two key components: Feature-Aware Adaptive View Stitching (FAVS) and Cross-View Consistency Self-Supervised Learning (CVC-SSL). FAVS employs a hierarchical matching strategy that first establishes coarse geometric correspondences using camera poses, then performs fine-grained alignment through improved feature matching algorithms, and identifies high-confidence matching regions via clustering analysis. Building upon this,…
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