Feature Recovery for Object Understanding Under Physical Transformation

Objects in post-fire environments often undergo irreversible physical transformations that change their geometry, material state, and visual appearance. Detecting and identifying these remnants is important for locating hazards, reconstructing pre-incident contents, and inventorying losses. Unlike standard image corruptions, fire damage changes the physical structure of the object itself. To study this setting, we introduce TRACE, a transformation-aware benchmark for post-fire object understanding. TRACE contains 21.4K real-image-grounded synthetic scenes and paired object-level pristine-to-degraded progressions spanning 499 object identities across 189 categories. We define five tasks that evaluate both localization and pre-degradation understanding: degraded-object detection, pristine-state recovery and retrieval, original material recovery, pristine description generation, and functional reasoning. Existing models degrade sharply as fire damage becomes more severe. From the least to the most severe degradation level, RF-DETR mAP decreases by 71% relative, while InternVL3.5 retrieval Recall@1 drops from 93.85 to 28.11. To address this, we propose the Feature Restoration Module, or FRM, a lightweight plug-and-play module that maps degraded encoder features toward pristine-aligned representations while keeping the host model frozen. FRM is trained only with paired feature supervision and improves scene-level detection, CLIP/SigLIP2 feature recovery, and all four object-level VLM tasks. Its gains are larger under more severe degradation. Across VLM hosts and severity levels, FRM improves retrieval by 12.5% on average, material recovery by 20.1%, description generation by 13.2%, and functional reasoning by 12.4%.