EditSSC: Toward Editable Semantic Occupancy Scenes with Unconditional Diffusion Models

• URL: http://arxiv.org/abs/2606.09273v1
• Date: Mon, 08 Jun 2026 09:41:37 GMT
• Title: EditSSC: Toward Editable Semantic Occupancy Scenes with Unconditional Diffusion Models
• Abstract summary: We propose EditSSC, an editing-ready method for 3D semantic scene generation.<n>Our approach reshapes semantic occupancy grids into multi-channel BEV images.<n>By exploiting class-to-code correspondences in the codebook, our method supports sketch-guided generation, inpainting, and outpainting without any retraining.
• Score: 58.566303405645726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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