19 citations · 43 across the 12 of their papers we have counts for
14 papers
LifePlanner: Evaluating LLM Agents for Geo-spatial Planning with Social Media Data
Zhen Dong, Yuning Peng, Yutao Shi +4
Geo-spatial planning, like trip design, is a realistic testbed for LLM agents because it requires grounded tool use, noisy evidence retrieval, and multi-constraint reasoning. Most…
LEGO: Leveled Language Gaussian Splatting
Yuning Peng, Haiping Wang, Yuan Liu +3
We introduce LEGO for advanced open-vocabulary scene understanding. Beyond basic concept recognition, its core innovation lies in capturing the intrinsic semantic hierarchies withi…
SpatialLLM: From Multi-modality Data to Urban Spatial Intelligence
Jiabin Chen, Haiping Wang, Jinpeng Li +3
We propose SpatialLLM, a novel approach advancing spatial intelligence tasks in complex urban scenes. Unlike previous methods requiring geographic analysis tools or domain expertis…
Align3R: Aligned Monocular Depth Estimation for Dynamic Videos
Jiahao Lu, Tianyu Huang, Peng Li +7
Recent developments in monocular depth estimation methods enable high-quality depth estimation of single-view images but fail to estimate consistent video depth across different fr…
GAGS: Granularity-Aware Feature Distillation for Language Gaussian Splatting
Yuning Peng, Haiping Wang, Yuan Liu +3
3D open-vocabulary scene understanding, which accurately perceives complex semantic properties of objects in space, has gained significant attention in recent years. In this paper,…
VistaDream: Sampling multiview consistent images for single-view scene reconstruction
Haiping Wang, Yuan Liu, Ziwei Liu +3
In this paper, we propose VistaDream a novel framework to reconstruct a 3D scene from a single-view image. Recent diffusion models enable generating high-quality novel-view images…
Exploiting Motion Prior for Accurate Pose Estimation of Dashboard Cameras
Yipeng Lu, Yifan Zhao, Haiping Wang +4
Dashboard cameras (dashcams) record millions of driving videos daily, offering a valuable potential data source for various applications, including driving map production and updat…
Explicitly Guided Information Interaction Network for Cross-modal Point Cloud Completion
Hang Xu, Chen Long, Wenxiao Zhang +4
In this paper, we explore a novel framework, EGIInet (Explicitly Guided Information Interaction Network), a model for View-guided Point cloud Completion (ViPC) task, which aims to…
SparseDC: Depth Completion from sparse and non-uniform inputs
Chen Long, Wenxiao Zhang, Zhe Chen +5
We propose SparseDC, a model for Depth Completion of Sparse and non-uniform depth inputs. Unlike previous methods focusing on completing fixed distributions on benchmark datasets (…
FreeReg: Image-to-Point Cloud Registration Leveraging Pretrained Diffusion Models and Monocular Depth Estimators
Haiping Wang, Yuan Liu, Bing Wang +4
Matching cross-modality features between images and point clouds is a fundamental problem for image-to-point cloud registration. However, due to the modality difference between ima…
Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting
Haiping Wang, Yuan Liu, Zhen Dong +4
In this paper, we present a new method for the multiview registration of point cloud. Previous multiview registration methods rely on exhaustive pairwise registration to construct…
You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
Haiping Wang, Yuan Liu, Zhen Dong +1
In this paper, we propose a novel local descriptor-based framework, called You Only Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to most…
AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds
Runsong Zhu, Yuan Liu, Zhen Dong +4
This paper presents a neural network for robust normal estimation on point clouds, named AdaFit, that can deal with point clouds with noise and density variations. Existing works u…
Learnable Motion Coherence for Correspondence Pruning
Yuan Liu, Lingjie Liu, Cheng Lin +2
Motion coherence is an important clue for distinguishing true correspondences from false ones. Modeling motion coherence on sparse putative correspondences is challenging due to th…