理想DrivingScene：仅凭两帧图像即可实时重建动态驾驶场景

Group 1 - The article discusses the challenges in achieving real-time, high-fidelity, and multi-task output in autonomous driving systems, emphasizing the importance of 4D dynamic scene reconstruction [1][2] - It highlights the limitations of existing static and dynamic scene reconstruction methods, particularly their inability to handle moving objects effectively [3][4] Group 2 - The research introduces a two-phase training paradigm that first learns robust static scene priors before training the dynamic module, addressing the instability of end-to-end training [4][11] - A mixed shared architecture for the residual flow network is proposed, which allows for efficient dynamic modeling while maintaining cross-view consistency [4][14] - The method utilizes a pure visual online feed-forward framework that processes two consecutive panoramic images to output various results without offline optimization [4][18] Group 3 - The experimental results demonstrate significant improvements in novel view synthesis metrics, with the proposed method achieving a PSNR of 28.76, surpassing previous methods [13][20] - The efficiency analysis shows that the proposed method has a faster inference time of 0.21 seconds per frame, which is 38% faster than DrivingForward and 70% faster than Driv3R [18][19] - The qualitative results indicate that the proposed method effectively captures dynamic objects with clear edges and temporal consistency, outperforming existing methods in dynamic scene reconstruction [19][22]