2 citations · 4 across the 7 of their papers we have counts for
9 papers
LiFR v2: Completion-Augmented Event Propagation for High-Rate Dense Prediction
Tao Wan, Xiaoshan Wu, Yifei Yu +6
High-rate dense perception in dynamic environments is limited by the low update rate of RGB cameras, as rapid scene changes can occur between frames. Event cameras offer temporally…
Monocular Depth Estimation from a Single Image: Progress and Opportunities
Muxin Liu, Xiaoyang Lyu, Yang-Tian Sun +4
Monocular depth estimation has long stood as a fundamental challenge in computer vision, enabling a wide range of applications including 3D reconstruction, robotics, autonomous dri…
OptiGeo: Efficient Monocular Geometry for Embodied Perception in Optically Challenging Scenes
Muxin Liu, Tianbo Liu, Jing Xia +7
Monocular depth estimation has achieved strong open-domain generalization, yet reliable robotic deployment remains difficult in transparent, reflective, and specular environments,…
FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry
Muxin Liu, Xiaoyang Lyu, Tianhe Ren +7
We present FoundationGeo, a two-stage framework that explicitly bridges relative and metric prediction via spatial calibration and principled data design. Stage 1 learns a high-fid…
Stabilizing Streaming Video Geometry via Dynamic Feature Normalization
Xiaoyang Lyu, Muxin Liu, Xiaoshan Wu +5
Consistent 3D geometry estimation from streaming RGB input is crucial for real-world applications such as autonomous driving, embodied AI, and large-scale reconstruction. While mod…
PhysEditBench: A Protocol-Conditioned Benchmark for Dense Physical-Map Prediction with Image Editors
Jiaxin Yang, Yu Hou, Muxin Liu +5
Can general-purpose image editors predict physical maps from a single RGB image? General-purpose image editors differ from standard task-specific dense-prediction models: they do n…
When Verification Fails: How Compositionally Infeasible Claims Escape Rejection
Muxin Liu, Delip Rao, Grace Kim +1
Scientific claim verification, the task of determining whether claims are entailed by scientific evidence, is fundamental to establishing discoveries in evidence while preventing m…
Stream of Search (SoS): Learning to Search in Language
Kanishk Gandhi, Denise Lee, Gabriel Grand +4
Language models are rarely shown fruitful mistakes while training. They then struggle to look beyond the next token, suffering from a snowballing of errors and struggling to predic…
LILO: Learning Interpretable Libraries by Compressing and Documenting Code
Gabriel Grand, Lionel Wong, Maddy Bowers +4
While large language models (LLMs) now excel at code generation, a key aspect of software development is the art of refactoring: consolidating code into libraries of reusable and r…