9 papers · 2 filters
Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation
Shaocong Xu, Songlin Wei, Qizhe Wei +12
Transparent objects remain notoriously hard for perception systems: refraction, reflection and transmission break the assumptions behind stereo, ToF and purely discriminative monoc…
Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method
Bohan Li, Xin Jin, Hu Zhu +9
Driving scene generation is a critical domain for autonomous driving, enabling downstream applications, including perception and planning evaluation. Occupancy-centric methods have…
One View, Many Worlds: Single-Image to 3D Object Meets Generative Domain Randomization for One-Shot 6D Pose Estimation
Zheng Geng, Nan Wang, Shaocong Xu +5
Estimating the 6D pose of arbitrary unseen objects from a single reference image is critical for robotics operating in the long-tail of real-world instances. However, this setting…
Light of Normals: Unified Feature Representation for Universal Photometric Stereo
Houyuan Chen, Hong Li, Chongjie Ye +11
Universal photometric stereo (PS) is defined by two factors: it must (i) operate under arbitrary, unknown lighting conditions and (ii) avoid reliance on specific illumination model…
Unifying Appearance Codes and Bilateral Grids for Driving Scene Gaussian Splatting
Nan Wang, Yuantao Chen, Lixing Xiao +11
Neural rendering techniques, including NeRF and Gaussian Splatting (GS), rely on photometric consistency to produce high-quality reconstructions. However, in real-world scenarios,…
ORV: 4D Occupancy-centric Robot Video Generation
Xiuyu Yang, Bohan Li, Shaocong Xu +9
Recent embodied intelligence suffers from data scarcity, while conventional simulators lack visual realism. Controllable video generation is emerging as a promising data engine, ye…
Challenger: Affordable Adversarial Driving Video Generation
Zhiyuan Xu, Bohan Li, Huan-ang Gao +7
Generating photorealistic driving videos has seen significant progress recently, but current methods largely focus on ordinary, non-adversarial scenarios. Meanwhile, efforts to gen…
DiST-4D: Disentangled Spatiotemporal Diffusion with Metric Depth for 4D Driving Scene Generation
Jiazhe Guo, Yikang Ding, Xiwu Chen +8
Current generative models struggle to synthesize dynamic 4D driving scenes that simultaneously support temporal extrapolation and spatial novel view synthesis (NVS) without per-sce…
MuDG: Taming Multi-modal Diffusion with Gaussian Splatting for Urban Scene Reconstruction
Yingshuang Zou, Yikang Ding, Chuanrui Zhang +6
Recent breakthroughs in radiance fields have significantly advanced 3D scene reconstruction and novel view synthesis (NVS) in autonomous driving. Nevertheless, critical limitations…