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Wenjie Chang

7 accepted papers

2026

Articulation in Motion: Prior-free Part Mobility Analysis for Articulated Objects By Dynamic-Static Disentanglement

ICLR 2026poster

Articulated objects are ubiquitous in daily life. Our goal is to achieve a high-quality reconstruction, segmentation of independent moving parts, and analysis of articulation. Recent methods analyse two different articulation states and perform per-point part segmentation, optimising per-part articu…

Cited by 0SourcecodeScholar
2026

ExMesh: EXplicit Mesh Reconstruction with Topology Adaptation

CVPR 2026

Reconstructing surface meshes from multi-view images has remained a core challenge in recent years. Most existing methods, whether implicit or explicit, depend on intermediate representations and post-processing steps like Marching Cubes or TSDF fusion, often resulting in artifacts and fragmented ge

Cited by 0SourceScholar
2026

MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting

AAAI 2026technical

Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the input views are extremely sparse. To address this issue, we propose MeshSplat, a generalizable sparse-view surface recon

Cited by 0SourcePDFScholar
2026

SunFaded: Illumination-Aware Gaussian Splatting for Dark Scenes with Camera-Mounted Active Lighting

CVPR 2026

Gaussian Splatting has emerged as a popular 3D representation technique, but still struggles with appearance inconsistencies, especially in dark scenes that require active illumination (e.g., camera flashes or co-moving light sources) to capture usable images, leading to dramatic local appearance fl

Cited by 0SourceScholar
2025

CUBE360: Learning Cubic Field Representation for Monocular Panoramic Depth Estimation

RA-L 2025

Panoramic depth estimation presents significant challenges due to the severe distortion caused by equirectangular projection (ERP) and the limited availability of panoramic RGB-D datasets. Inspired by the recent success of neural rendering, we propose a self-supervised method, named CUBE360, that le

Cited by 0SourceScholar
2025

Learning Neural Scene Representation from iToF Imaging

ICCV 2025poster

Indirect Time-of-Flight (iToF) cameras are popular for 3D perception because they are cost-effective and easy to deploy. They emit modulated infrared signals to illuminate the scene and process the received signals to generate amplitude and phase images. The depth is calculated from the phase using…

Cited by 0SourcePDFScholar
2023

Depth Estimation From Indoor Panoramas With Neural Scene Representation

CVPR 2023poster

Depth estimation from indoor panoramas is challenging due to the equirectangular distortions of panoramas and inaccurate matching. In this paper, we propose a practical framework to improve the accuracy and efficiency of depth estimation from multi-view indoor panoramic images with the Neural Radian…