Jianfei Jiang

Haochen Yu, Qiankun Liu, Hongyuan Liu et al.

Recently, more attention has been paid to feedforward reconstruction paradigms, which mainly learn a fixed view transformation implicitly and reconstruct the scene with a single representation. However, their generalization capability and reconstruction accuracy are still limited while reconstructing driving scenes, which results from two aspects: (1) The fixed view transformation fails when the camera configuration changes, limiting the generalization capability across different driving scenes equipped with different camera configurations. (2) The small overlapping regions between sparse views of the $360^\circ$ panorama and the complexity of driving scenes increase the learning difficulty, reducing the reconstruction accuracy. To handle these difficulties, we propose \textbf{XYZCylinder}, a feedforward model based on a unified cylinder lifting method which involves camera modeling and feature lifting. Specifically, to improve the generalization capability, we design a Unified Cylinder Camera Modeling (UCCM) strategy, which avoids the learning of viewpoint-dependent spatial correspondence and unifies different camera configurations with adjustable parameters. To improve the reconstruction accuracy, we propose a hybrid representation with several dedicated modules based on newly designed Cylinder Plane Feature Group (CPFG) to lift 2D image features to 3D space. Experimental results show that XYZCylinder achieves state-of-the-art performance under different evaluation settings, and can be generalized to other driving scenes in a zero-shot manner. Project page: \href{https://yuyuyu223.github.io/XYZCYlinder-projectpage/}{here}.

Hongyuan Liu, Haochen Yu, Bochao Zou et al.

Reconstructing dynamic driving scenes from dashcam videos has attracted increasing attention due to its significance in autonomous driving and scene understanding. While recent advances have made impressive progress, most methods still unify all background elements into a single representation, hindering both instance-level understanding and flexible scene editing. Some approaches attempt to lift 2D segmentation into 3D space, but often rely on pre-processed instance IDs or complex pipelines to map continuous features to discrete identities. Moreover, these methods are typically designed for indoor scenes with rich viewpoints, making them less applicable to outdoor driving scenarios. In this paper, we present InstDrive, an instance-aware 3D Gaussian Splatting framework tailored for the interactive reconstruction of dynamic driving scene. We use masks generated by SAM as pseudo ground-truth to guide 2D feature learning via contrastive loss and pseudo-supervised objectives. At the 3D level, we introduce regularization to implicitly encode instance identities and enforce consistency through a voxel-based loss. A lightweight static codebook further bridges continuous features and discrete identities without requiring data pre-processing or complex optimization. Quantitative and qualitative experiments demonstrate the effectiveness of InstDrive, and to the best of our knowledge, it is the first framework to achieve 3D instance segmentation in dynamic, open-world driving scenes.More visualizations are available at our project page.