3 papers
cs.GR2025
ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction
Sankeerth Durvasula, Sharanshangar Muhunthan, Zain Moustafa +7
3D Gaussian Splatting (3DGS) is a state-of-art technique to model real-world scenes with high quality and real-time rendering. Typically, a higher quality representation can be ach…
cs.LG2025
INRet: A General Framework for Accurate Retrieval of INRs for Shapes
Yushi Guan, Daniel Kwan, Ruofan Liang +4
Implicit neural representations (INRs) have become an important method for encoding various data types, such as 3D objects or scenes, images, and videos. They have proven to be par…
cs.DC2024
ARGO: An Auto-Tuning Runtime System for Scalable GNN Training on Multi-Core Processor
Yi-Chien Lin, Yuyang Chen, Sameh Gobriel +3
As Graph Neural Networks (GNNs) become popular, libraries like PyTorch-Geometric (PyG) and Deep Graph Library (DGL) are proposed; these libraries have emerged as the de facto stand…