23 citations · 50 across the 19 of their papers we have counts for
20 papers · 1 filter
Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends
Jiuming Liu, Chaojun Ni, Mengmeng Liu +7
With rapid development of large language models and diffusion-based content generation, world modeling has attracted increasing research attention, benefiting various downstream do…
Mamba-VGGT: Persistent Long-Sequence Video Geometry Grounded Transformer via External Sliding Window Mamba Memory
Tianchen Deng, Zhenxiang Xiong, Nailin Wang +4
Visual Geometry Grounded Transformers (VGGT) have set new benchmarks in high-fidelity 3D scene reconstruction. However, as the sequence length increases, these models suffer from c…
VGGT-Occ: Geometry-Grounded and Density-Aware Gated Fusion for 3D Occupancy Prediction
Xun Chen, Tianchen Deng, Rui Wang +5
3D semantic occupancy prediction requires accurate 2D-to-3D feature lifting, yet current methods restrict camera geometry to initial projections. Subsequent operations like offset…
RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization
Junwei Zheng, Ruize Dai, Ruiping Liu +7
Metric Cross-View Geo-Localization (MCVGL) aims to estimate the 3-DoF camera pose (position and heading) by matching ground and satellite images. In this work, instead of pinhole a…
MegaFlow: Zero-Shot Large Displacement Optical Flow
Dingxi Zhang, Fangjinhua Wang, Marc Pollefeys +1
Accurate estimation of large displacement optical flow remains a critical challenge. Existing methods typically rely on iterative local search or/and domain-specific fine-tuning, w…
Controllable Egocentric Video Generation via Occlusion-Aware Sparse 3D Hand Joints
Chenyangguang Zhang, Botao Ye, Boqi Chen +4
Controllable video generation for complex hand-object interactions is a critical step toward building visual world models. However, existing methods often struggle to achieve fine-…
ImLoc: Revisiting Visual Localization with Image-based Representation
Xudong Jiang, Fangjinhua Wang, Silvano Galliani +2
Existing visual localization methods are typically either 2D image-based, which are easy to build and maintain but limited in effective geometric reasoning, or 3D structure-based,…
From Indoor to Open World: Revealing the Spatial Reasoning Gap in MLLMs
Mingrui Wu, Zhaozhi Wang, Fangjinhua Wang +3
While Multimodal Large Language Models (MLLMs) have achieved impressive performance on semantic tasks, their spatial intelligence--crucial for robust and grounded AI systems--remai…
Lightweight and Accurate Multi-View Stereo with Confidence-Aware Diffusion Model
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong +1
To reconstruct the 3D geometry from calibrated images, learning-based multi-view stereo (MVS) methods typically perform multi-view depth estimation and then fuse depth maps into a…
Open-Vocabulary Functional 3D Scene Graphs for Real-World Indoor Spaces
Chenyangguang Zhang, Alexandros Delitzas, Fangjinhua Wang +4
We introduce the task of predicting functional 3D scene graphs for real-world indoor environments from posed RGB-D images. Unlike traditional 3D scene graphs that focus on spatial…
SG-Tailor: Inter-Object Commonsense Relationship Reasoning for Scene Graph Manipulation
Haoliang Shang, Hanyu Wu, Guangyao Zhai +4
Scene graphs capture complex relationships among objects, serving as strong priors for content generation and manipulation. Yet, reasonably manipulating scene graphs -- whether by…
R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization
Xudong Jiang, Fangjinhua Wang, Silvano Galliani +2
Learning-based visual localization methods that use scene coordinate regression (SCR) offer the advantage of smaller map sizes. However, on datasets with complex illumination chang…
DepthSplat: Connecting Gaussian Splatting and Depth
Haofei Xu, Songyou Peng, Fangjinhua Wang +4
Gaussian splatting and single-view depth estimation are typically studied in isolation. In this paper, we present DepthSplat to connect Gaussian splatting and depth estimation and…
Learning-based Multi-View Stereo: A Survey
Fangjinhua Wang, Qingtian Zhu, Di Chang +5
3D reconstruction aims to recover the dense 3D structure of a scene. It plays an essential role in various applications such as Augmented/Virtual Reality (AR/VR), autonomous drivin…
GLACE: Global Local Accelerated Coordinate Encoding
Fangjinhua Wang, Xudong Jiang, Silvano Galliani +2
Scene coordinate regression (SCR) methods are a family of visual localization methods that directly regress 2D-3D matches for camera pose estimation. They are effective in small-sc…
UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections
Fangjinhua Wang, Marie-Julie Rakotosaona, Michael Niemeyer +3
Neural 3D scene representations have shown great potential for 3D reconstruction from 2D images. However, reconstructing real-world captures of complex scenes still remains a chall…
VolRecon: Volume Rendering of Signed Ray Distance Functions for Generalizable Multi-View Reconstruction
Yufan Ren, Fangjinhua Wang, Tong Zhang +2
The success of the Neural Radiance Fields (NeRF) in novel view synthesis has inspired researchers to propose neural implicit scene reconstruction. However, most existing neural imp…
3D Textured Shape Recovery with Learned Geometric Priors
Lei Li, Zhizheng Liu, Weining Ren +4
3D textured shape recovery from partial scans is crucial for many real-world applications. Existing approaches have demonstrated the efficacy of implicit function representation, b…
IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo
Fangjinhua Wang, Silvano Galliani, Christoph Vogel +1
We present IterMVS, a new data-driven method for high-resolution multi-view stereo. We propose a novel GRU-based estimator that encodes pixel-wise probability distributions of dept…
PatchmatchNet: Learned Multi-View Patchmatch Stereo
Fangjinhua Wang, Silvano Galliani, Christoph Vogel +2
We present PatchmatchNet, a novel and learnable cascade formulation of Patchmatch for high-resolution multi-view stereo. With high computation speed and low memory requirement, Pat…