High‑resolution three‑dimensional (3D) tissue atlases promise to redefine how we study cellular architecture‑function relationships in human tissues. Large-scale consortia such as the Human Bimolecular Atlas Program (HuBMAP) systematically build detailed 3D organ maps by profiling serial tissue sections with single-cell spatial technologies. However, an accurate and efficient reconstruction method that can handle atlas-scale datasets remains elusive. We introduce Space-map, an open-source method that integrates single‑cell coordinates with optional histological image features to assemble serial sections into 3D models. Space‑map combines multi‑scale feature matching with large‑deformation diffeomorphic metric mapping, delivering global reconstructions while preserving local micro‑anatomy. To demonstrate the capability of Space-map, we generated a serially sectioned spatial transcriptomics (Xenium, ~2.9M cells) dataset and a spatial proteomics dataset (CODEX, ~2.4M cells). Applying Space-map to these single-cell spatial maps, we built three 3D models for both diseased (colon polyp) and reference colon tissues. These high-resolution 3D models showcase the intricate structure of the human colon across different states. Space-map is fast and highly efficient. We demonstrated its performance and accuracy using in‑house and public datasets. The result shows that Space‑map is 10 times faster and ~2‑fold more accurate than PASTE and STalign, making 3D atlas reconstruction more accessible. Our study provides a new robust and user-friendly software available at https://github.com/a12910/spacemap that can be easily applied for constructing molecular 3D tissue maps of human organs at single-cell resolution.