TASE: Truncation-Aware Semantic Embeddings for 3D Scene Understanding and Editing

High-fidelity semantic 3D scene representations are crucialfor numerous applications, including robotics, autonomous driving, andsimulation. Beyond this, the ability to edit such representations enablesdevelopers to adapt these applications more easily to specific target sce-narios. Current approaches provide limited support for controllable edit-ing. We introduce TASE, a method that projects pretrained 2D seman-tic features into a truncation-aware embedding space to enable flexi-ble 3D scene editing. Our method explicitly optimizes a feature spacein which progressively reducing feature channels yields increasingly ab-stract semantic representations, while retaining more channels preservesfine-grained detail. Additionally, we improve multi-view consistency ofthe features using a scale- and translation-consistency loss. The result-ing truncation-aware embedding space enables text-driven edits to 3Dscenes, providing explicit control over how strongly edits adhere to theoriginal scene content and allowing more substantial modifications thanprior methods. Moreover, we propose a finetuning stage for the edit-ing image generation model to mitigate artifacts caused by geometricchanges. Experimental results demonstrate competitive performance in3D scene editing, substantially outperforming prior methods on editsinvolving large geometric modifications.