8 papers
GeoFlow: Efficient Driving Video Generation via Geometry-Aligned Priors
Jiazheng Liu, Hang Li, Jiawei Zhang +5
Generative models like Diffusion Models and Flow Matching have demonstrated remarkable capabilities in synthesizing high-fidelity driving videos, but are severely constrained by hi…
Learning Gaussian Structure: Intervention-Guided Density Control for Feed-Forward Driving Reconstruction
Hang Li, Jiahe Li, Meiying Gu +3
Feed-forward Gaussian reconstruction has recently emerged as an efficient approach for driving scene reconstruction. However, prevailing LiDAR-based methods preserve the initial co…
Revisiting Photometric Ambiguity for Accurate Gaussian-Splatting Surface Reconstruction
Jiahe Li, Jiawei Zhang, Xiao Bai +4
Surface reconstruction with differentiable rendering has achieved impressive performance in recent years, yet the pervasive photometric ambiguities have strictly bottlenecked exist…
SCE-SLAM: Scale-Consistent Monocular SLAM via Scene Coordinate Embeddings
Yuchen Wu, Jiahe Li, Xiaohan Yu +3
Monocular visual SLAM enables 3D reconstruction from internet video and autonomous navigation on resource-constrained platforms, yet suffers from scale drift, i.e., the gradual div…
FoundationSLAM: Unleashing the Power of Depth Foundation Models for End-to-End Dense Visual SLAM
Yuchen Wu, Jiahe Li, Fabio Tosi +3
We present FoundationSLAM, a learning-based monocular dense SLAM system that addresses the absence of geometric consistency in previous flow-based approaches for accurate and robus…
SparseSurf: Sparse-View 3D Gaussian Splatting for Surface Reconstruction
Meiying Gu, Jiawei Zhang, Jiahe Li +4
Recent advances in optimizing Gaussian Splatting for scene geometry have enabled efficient reconstruction of detailed surfaces from images. However, when input views are sparse, su…