4 citations · 8 across the 7 of their papers we have counts for
7 papers
CogniMap3D: Cognitive 3D Mapping and Rapid Retrieval
Feiran Wang, Junyi Wu, Dawen Cai +2
We present CogniMap3D, a bioinspired framework for dynamic 3D scene understanding and reconstruction that emulates human cognitive processes. Our approach maintains a persistent me…
TraceFlow: Dynamic 3D Reconstruction of Specular Scenes Driven by Ray Tracing
Jiachen Tao, Junyi Wu, Haoxuan Wang +3
We present TraceFlow, a novel framework for high-fidelity rendering of dynamic specular scenes by addressing two key challenges: precise reflection direction estimation and physica…
ZECO: ZeroFusion Guided 3D MRI Conditional Generation
Feiran Wang, Bin Duan, Jiachen Tao +3
Medical image segmentation is crucial for enhancing diagnostic accuracy and treatment planning in Magnetic Resonance Imaging (MRI). However, acquiring precise lesion masks for segm…
Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation
Bin Xie, Hao Tang, Dawen Cai +2
Segment Anything Model (SAM) has demonstrated impressive zero-shot performance and brought a range of unexplored capabilities to natural image segmentation tasks. However, as a ver…
Online Multi-spectral Neuron Tracing
Bin Duan, Yuzhang Shang, Dawen Cai +1
In this paper, we propose an online multi-spectral neuron tracing method with uniquely designed modules, where no offline training are required. Our method is trained online to upd…
MaskSAM: Towards Auto-prompt SAM with Mask Classification for Volumetric Medical Image Segmentation
Bin Xie, Hao Tang, Bin Duan +3
Segment Anything Model (SAM), a prompt-driven foundation model for natural image segmentation, has demonstrated impressive zero-shot performance. However, SAM does not work when di…
Few-shot Medical Image Segmentation with Cycle-resemblance Attention
Hao Ding, Changchang Sun, Hao Tang +2
Recently, due to the increasing requirements of medical imaging applications and the professional requirements of annotating medical images, few-shot learning has gained increasing…