6 citations · 10 across the 2 of their papers we have counts for
12 papers
Driv3R: Learning Dense 4D Reconstruction for Autonomous Driving
Xin Fei, Wenzhao Zheng, Yueqi Duan +4
Realtime 4D reconstruction for dynamic scenes remains a crucial challenge for autonomous driving perception. Most existing methods rely on depth estimation through self-supervision…
DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion
Wenqiang Sun, Shuo Chen, Fangfu Liu +4
In this paper, we introduce \textbf{DimensionX}, a framework designed to generate photorealistic 3D and 4D scenes from just a single image with video diffusion. Our approach begins…
Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
Fangfu Liu, Hanyang Wang, Shunyu Yao +3
In recent years, there has been rapid development in 3D generation models, opening up new possibilities for applications such as simulating the dynamic movements of 3D objects and…
Learning a Category-level Object Pose Estimator without Pose Annotations
Fengrui Tian, Yaoyao Liu, Adam Kortylewski +4
3D object pose estimation is a challenging task. Previous works always require thousands of object images with annotated poses for learning the 3D pose correspondence, which is lab…
Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos
Fengrui Tian, Yueqi Duan, Angtian Wang +2
In this work, we pioneer Semantic Flow, a neural semantic representation of dynamic scenes from monocular videos. In contrast to previous NeRF methods that reconstruct dynamic scen…
GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds
Shengjun Zhang, Xin Fei, Yueqi Duan
Point clouds captured by different sensors such as RGB-D cameras and LiDAR possess non-negligible domain gaps. Most existing methods design different network architectures and trai…