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360-degree surround view systems play a critical role in driver assistance across low-speed maneuvering scenarios. However, conventional rendering pipelines often suffer from artifacts such as the Manhattan effect, missing pixels, and non-photorealistic rendering, leading to diminished perception. To address these challenges, we propose a novel image synthesis pipeline leveraging generative modeling to enhance rendering quality. Surround-view RGBD images are fused into a point cloud and rendered from alternate viewpoints, formulating novel view synthesis as an image-to-image translation problem. A denoising diffusion model refines the generated views and suppresses rendering artifacts, producing outputs with significantly improved perceptual realism. Qualitative evaluations on diverse driving scenarios showcase the effectiveness of the proposed pipeline.

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Point Cloud Fusion with Diffusion Models: An Integrated Pipeline for High-Quality Surround View Rendering

  • 会议论文

  • 发表日期：

    2026年

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  • Vinod Rajendran

摘要

360-degree surround view systems play a critical role in driver assistance across low-speed maneuvering scenarios. However, conventional rendering pipelines often suffer from artifacts such as the Manhattan effect, missing pixels, and non-photorealistic rendering, leading to diminished perception. To address these challenges, we propose a novel image synthesis pipeline leveraging generative modeling to enhance rendering quality. Surround-view RGBD images are fused into a point cloud and rendered from alternate viewpoints, formulating novel view synthesis as an image-to-image translation problem. A denoising diffusion model refines the generated views and suppresses rendering artifacts, producing outputs with significantly improved perceptual realism. Qualitative evaluations on diverse driving scenarios showcase the effectiveness of the proposed pipeline.