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Look, Cast and Mold: Learning 3D Shape Manifold from Single-view Synthetic Data
To generalize the model towards a real scenario, we propose to fulfill several aspects: (1) Look: visually incorporate spatial structure from the single view to enhance the expressiveness of representation; (2) Cast: perceptually align the 2D image features to the 3D shape priors with cross-modal semantic contrastive mapping; (3) Mold: reconstruct stereo-shape of target by transforming embeddings into the desired manifold.
Attention-Aware Multi-View Stereo
Multi-view stereo is a crucial task in computer vision, that requires accurate and robust photo-consistency among input images for depth estimation.
P-MVSNet: Learning Patch-Wise Matching Confidence Aggregation for Multi-View Stereo
Learning-based methods are demonstrating their strong competitiveness in estimating depth for multi-view stereo reconstruction in recent years.
