Search Results for author:
Found 5 papers, 3 papers with code
DC-PCN: Point Cloud Completion Network with Dual-Codebook Guided Quantization
Despite achieving encouraging results, a significant issue remains: these methods often overlook the variability in point clouds sampled from a single 3D object surface.
RI-MAE: Rotation-Invariant Masked AutoEncoders for Self-Supervised Point Cloud Representation Learning
Finally, the predictor predicts the latent features of the masked patches using the output latent embeddings from the student, supervised by the outputs from the teacher.
SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph Attention
3D visual grounding aims to automatically locate the 3D region of the specified object given the corresponding textual description.
SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation
Specifically, we achieve this by proposing (1) Collaborative Prompts that describe instrument structures via collaborating category-level and part-level texts; (2) Cross-Modal Prompt Encoder that encodes text prompts jointly with visual embeddings into discriminative part-level representations; and (3) Part-to-Whole Adaptive Fusion and Hierarchical Decoding that adaptively fuse the part-level representations into a whole for accurate instrument segmentation in surgical scenarios.
Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation
Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS).
