EditSSC: 基于无条件扩散模型的可编辑语义占用场景
EditSSC: Toward Editable Semantic Occupancy Scenes with Unconditional Diffusion Models
AI总结：
提出EditSSC方法，利用2D BEV表示和现成潜扩散网络实现3D语义场景生成与免训练编辑，在SemanticKITTI上优于现有3D专用基线。
摘要：
3D语义场景生成对于自动驾驶应用至关重要，但大多数方法依赖于复杂的3D专用架构，如三平面编码器和适配的扩散网络，限制了其简单性和编辑能力。我们提出EditSSC，一种使用2D鸟瞰图（BEV）表示和现成潜扩散网络的3D语义场景生成方法，支持编辑。我们的方法将3D语义占用网格重塑为多通道BEV图像，并利用Stable Diffusion的量化自编码器和UNet，仅做最小修改。我们在量化后的潜变量上进行扩散，从而实现了免训练的编辑能力。通过利用码本中的类到码对应关系，我们的方法支持草图引导生成、修补和外推，无需任何重新训练。在SemanticKITTI上，EditSSC在无条件生成方面优于现有的3D专用基线，表明成熟的2D架构可以有效地用于3D场景生成和编辑。
英文摘要：
3D semantic scene generation is crucial for autonomous driving applications, yet most methods rely on complex 3D-specific architectures such as triplane encoders and adapted diffusion networks, limiting both their simplicity and their editing capabilities. We propose EditSSC, an editing-ready method for 3D semantic scene generation using 2D Bird's Eye View (BEV) representations and off-the-shelf latent diffusion network. Our approach reshapes 3D semantic occupancy grids into multi-channel BEV images and leverages the quantized autoencoder and UNet from Stable Diffusion with minimal modifications. We perform diffusion on the latents after quantization, which enables training-free editing capabilities. By exploiting class-to-code correspondences in the codebook, our method supports sketch-guided generation, inpainting, and outpainting without any retraining. On SemanticKITTI, EditSSC outperforms existing 3D-specific baselines on unconditional generation, demonstrating that well-established 2D architectures can be effectively repurposed for 3D scene generation and editing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
