4 papers
DrivingDepth: Sparse-Prompted Pixel-wise Scale Correction for Driving Depth Estimation
Chi Huang, Wenhao Zhang, Hang Yin +5
Dense depth estimation for autonomous driving faces a geometry-scale conflict: depth foundation models deliver pixel-aligned dense visual geometry without reliable metric scale, wh…
Monte Carlo Energy Aggregation for Mobile 3D Gaussian Splatting
Xiaobiao Du, YuAn Wang, Hao Li +3
Recent advances in 3D Gaussian Splatting have demonstrated unprecedented success in novel view synthesis. However, the substantial inference and storage overhead driven by high-ord…
Beyond the Dirac Delta: Mitigating Diversity Collapse in Reinforcement Fine-Tuning for Versatile Image Generation
Jinmei Liu, Haoru Li, Zhenhong Sun +6
Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow models, to align with complex human prefere…
FaithFusion: Harmonizing Reconstruction and Generation via Pixel-wise Information Gain
YuAn Wang, Xiaofan Li, Chi Huang +5
In controllable driving-scene reconstruction and 3D scene generation, maintaining geometric fidelity while synthesizing visually plausible appearance under large viewpoint shifts i…