Search Results for author:
Found 7 papers, 3 papers with code
NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generation
3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints.
BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation
A variational auto-encoder is employed to compress the tri-planes into the latent tri-plane space, on which the denoising diffusion process is performed.
Adaptive Low Rank Adaptation of Segment Anything to Salient Object Detection
Foundation models, such as OpenAI's GPT-3 and GPT-4, Meta's LLaMA, and Google's PaLM2, have revolutionized the field of artificial intelligence.
P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds
Point cloud completion aims to recover the complete shape based on a partial observation.
Model Calibration in Dense Classification with Adaptive Label Perturbation
To improve model calibration, we propose Adaptive Stochastic Label Perturbation (ASLP) which learns a unique label perturbation level for each training image.
Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion
Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence.
Generalised Co-Salient Object Detection
We propose a new setting that relaxes an assumption in the conventional Co-Salient Object Detection (CoSOD) setting by allowing the presence of "noisy images" which do not show the shared co-salient object.
