From the 1 of 6 linked papers with an AI index.
6 papers
Beyond Visual Ambiguity: Guiding Robust Monocular Depth Estimation in Challenging Scenarios via Detailed Long Captions
Junrui Zhang, Jiaqi Li, Yiran Wang +2
The paper introduces CapDepth, a framework that uses detailed long textual captions to guide monocular depth estimation, improving robustness on non‑Lambertian surfaces and in adve…
BokehFlow: Depth-Free Controllable Bokeh Rendering via Flow Matching
Yachuan Huang, Xianrui Luo, Qiwen Wang +6
Bokeh rendering simulates the shallow depth-of-field effect in photography, enhancing visual aesthetics and guiding viewer attention to regions of interest. Although recent approac…
MuGS: Multi-Baseline Generalizable Gaussian Splatting Reconstruction
Yaopeng Lou, Liao Shen, Tianqi Liu +4
We present Multi-Baseline Gaussian Splatting (MuGS), a generalized feed-forward approach for novel view synthesis that effectively handles diverse baseline settings, including spar…
TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion
Yiran Wang, Jiaqi Li, Chaoyi Hong +6
Radar-Camera depth estimation aims to predict dense and accurate metric depth by fusing input images and Radar data. Model efficiency is crucial for this task in pursuit of real-ti…
DoF-Gaussian: Controllable Depth-of-Field for 3D Gaussian Splatting
Liao Shen, Tianqi Liu, Huiqiang Sun +4
Recent advances in 3D Gaussian Splatting (3D-GS) have shown remarkable success in representing 3D scenes and generating high-quality, novel views in real-time. However, 3D-GS and i…
Self-Distilled Depth Refinement with Noisy Poisson Fusion
Jiaqi Li, Yiran Wang, Jinghong Zheng +4
Depth refinement aims to infer high-resolution depth with fine-grained edges and details, refining low-resolution results of depth estimation models. The prevailing methods adopt t…