Feiran Wang

We revisit and observe that RayMap-based predictions exhibit inherent static scene bias and propose RayMap3R, a training-free streaming framework for dynamic scene reconstruction.

We present LIST3R, an instance-aware framework for long-sequence 3D reconstruction inspired by the way humans organize spatial memory around stable and recognizable objects.

CIF formulates a continuous probabilistic field over object existence and identity in space-time, enabling consistent instance representations across views for dynamic scene understanding.

GPF reformulates photon mapping as a continuous radiance field of 3D Gaussian primitives, achieving photon-level accuracy for global illumination with greatly reduced computation.

We present CogniMap3D, a bioinspired framework that maintains a persistent memory bank of static scenes, enabling efficient spatial knowledge storage and rapid retrieval.

Rooted in the X-ray imaging process, X-Field presents a representation specifically for high-quality X-ray Novel View Synthesis and CT Reconstruction.

To mitigate medical data scarcity, ZECO synthesizes high-quality 3D MRI images across various modalities, conditioned on segmentation masks.

Point clouds captured by LiDAR are often colorless; PCCN-RE enables high-quality colorization with a relevance embedding module built on a Conditional GAN.

Understanding morphology and distribution of neurons remains a significant challenge in modern neuroscience. We aim to develop a unified framework for sparse-to-dense neural generation and unsupervised segmentation, providing deeper insights into neural activity and connectivity.