Robotic and autonomous systems need dense spatial cues, but many monocular depth models are heavy, task-specific, or hard to attach to an existing multimodal stack. CLIP offers strong semantic representations, yet most CLIP-based depth methods still depend on text prompts or backbone updates, which complicate deployment in integrated control pipelines. We present SPACE-CLIP, a decoder-only depth framework that reads geometric cues directly from a frozen CLIP vision encoder and bypasses the text encoder at inference time. The model combines FiLM-conditioned semantic features from deep layers with structural features from shallow layers to recover both global scene layout and local geometric detail. Under the TFI-FB constraint (text-free inference and frozen vision backbone), SPACE-CLIP achieves AbsRel 0.0901 on KITTI and 0.1042 on NYU Depth V2, and the same dual-pathway decoder transfers to a frozen SigLIP backbone with comparable results. These findings show that a compact decoder can turn a shared foundation-model backbone into a reusable spatial perception module for embodied AI and autonomous robotic systems. Our model is available at https://github.com/taewan2002/space-clip
@article{arxiv.2601.17657,
title = {SPACE-CLIP: Spatial Perception via Adaptive CLIP Embeddings for Monocular Depth Estimation},
author = {Taewan Cho and Taeryang Kim and Andrew Jaeyong Choi},
journal= {arXiv preprint arXiv:2601.17657},
year = {2026}
}