Multi-view stereo with multiple projectors for oneshot entire shape scan based on Neural SDF and DSSS demultiplexing

3D reconstruction has been widely studied and applied in various fields. Multi-view stereo (MVS) methods can recover dense geometry from multiple views but often fail for texture-less objects due to unreliable feature matching. Active stereo with structured light (SL) addresses this limitation, however, when using multiple cameras and projectors for entire shape acquisition, overlapped SL patterns interfere with one another, leading to decoding failures.We propose a novel MVS framework based on neural signed distance fields (Neural SDF) with multiple projectors that employs Direct Sequence Spread Spectrum (DSSS) to separate multiplexed patterns. This approach enables robust and accurate 3D shape reconstruction through Neural SDF optimization with a photometric loss that accounts for both the positions and the patterns of the projectors. We built real scanning devices, and experiments on several objects demonstrated the effectiveness of the proposed method.