1 citations · 4 across the 23 of their papers we have counts for
13 papers · 1 filter
Anchor3R: Streaming 3D Reconstruction with Transient Anchors for Long-Horizon Visual Mapping
Peilin Tao, Chong Cheng, Yuansen Du +8
Long-horizon online visual mapping requires continuous camera-motion and scene-geometry estimation under bounded computation. Recent feed-forward 3D reconstruction models provide s…
Anchored, Not Graded: Vision-Language Models Fail at Slant-from-Texture Perception
Qian Zhang, Michal Golovanevsky, Fulvio Domini +1
Human perception of surface slant from texture exhibits systematic, graded biases that emerge reliably in psychophysical experiments. Prior work showed that unsupervised CNNs repro…
HorizonStream: Long-Horizon Attention for Streaming 3D Reconstruction
Chong Cheng, Peilin Tao, Nanjie Yao +9
Online 3D reconstruction requires estimating camera pose and scene geometry under strict causal and bounded-memory constraints. Existing methods often suffer from drift, jitter, or…
Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach
Qian Zhang, George Em Karniadakis
Reconstructing flow fields from sparse measurements is a fundamental problem in fluid mechanics with broad implications for modeling, control, and design. In this work, we propose…
HorizonDrive: Self-Corrective Autoregressive World Model for Long-horizon Driving Simulation
Conglang Zhang, Yifan Zhan, Qingjie Wang +10
Closed-loop driving simulation requires real-time interaction beyond short offline clips, pushing current driving world models toward autoregressive (AR) rollout. Existing AR disti…
EponaV2: Driving World Model with Comprehensive Future Reasoning
Jiawei Xu, Zhizhou Zhong, Zhijian Shu +8
Data scaling plays a pivotal role in the pursuit of general intelligence. However, the prevailing perception-planning paradigm in autonomous driving relies heavily on expensive man…
REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer
Changze Li, Zhe Chen, Shaoyu Chen +7
In recent years, autonomous parking has made significant advances, yet parking tasks still face challenges in extreme scenarios such as mechanical and dead-end parking slots, often…
Scal3R: Scalable Test-Time Training for Large-Scale 3D Reconstruction
Tao Xie, Peishan Yang, Yudong Jin +8
This paper addresses the task of large-scale 3D scene reconstruction from long video sequences. Recent feed-forward reconstruction models have shown promising results by directly r…
How Far Are Large Multimodal Models from Human-Level Spatial Action? A Benchmark for Goal-Oriented Embodied Navigation in Urban Airspace
Baining Zhao, Ziyou Wang, Jianjie Fang +8
Large multimodal models (LMMs) show strong visual-linguistic reasoning but their capacity for spatial decision-making and action remains unclear. In this work, we investigate wheth…
Specializing Large Models for Oracle Bone Script Interpretation via Component-Grounded Multimodal Knowledge Augmentation
Jianing Zhang, Runan Li, Honglin Pang +5
Deciphering ancient Chinese Oracle Bone Script (OBS) is a challenging task that offers insights into the beliefs, systems, and culture of the ancient era. Existing approaches treat…
Composing Driving Worlds through Disentangled Control for Adversarial Scenario Generation
Yifan Zhan, Zhengqing Chen, Qingjie Wang +7
A major challenge in autonomous driving is the "long tail" of safety-critical edge cases, which often emerge from unusual combinations of common traffic elements. Synthesizing thes…
LongStream: Long-Sequence Streaming Autoregressive Visual Geometry
Chong Cheng, Xianda Chen, Tao Xie +5
Long-sequence streaming 3D reconstruction remains a significant open challenge. Existing autoregressive models often fail when processing long sequences because they anchor poses t…
Entropy-Guided k-Guard Sampling for Long-Horizon Autoregressive Video Generation
Yizhao Han, Tianxing Shi, Zhao Wang +6
Autoregressive (AR) architectures have achieved significant successes in LLMs, inspiring explorations for video generation. In LLMs, top-p/top-k sampling strategies work exceptiona…