Dual-S3D: Hierarchical Dual-Path Selective SSM-CNN for High-Fidelity Implicit Reconstruction

Single-view 3D reconstruction aims to recover the complete 3D geometry and appearance of objects from a single RGB image and its corresponding camera parameters. Yet, the task remains challenging due to incomplete image information and inherent ambiguity. Existing methods primarily encounter two issues: balancing extracting local details with the construction of global topology and the interference caused by the early fusion of RGB and depth features in high-texture regions, destabilizing SDF optimization. We propose Dual-S3D, a novel single-view 3D reconstruction framework to address these challenges. Our method employs a hierarchical dual-path feature extraction strategy based on stages that utilize CNNs to anchor local geometric details. In contrast, subsequent stages leverage a Transformer integrated with selective SSM to capture global topology, enhancing scene understanding and feature representation. Additionally, we design an auxiliary branch that progressively fuses precomputed depth features with pixel-level features to decouple visual and geometric cues effectively. Extensive experiments on the 3D-FRONT and Pix3D datasets demonstrate that our approach significantly outperforms existing methods—reducing Chamfer distance by 51%, increasing F-score by 33.6%, and improving normal consistency by 10.3%—thus achieving state-of-the-art reconstruction quality.