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Chengwei Ren

7 accepted papers

2025

Diff2I2P: Differentiable Image-to-Point Cloud Registration with Diffusion Prior

ICCV 2025poster

Learning cross-modal correspondences is essential for image-to-point cloud (I2P) registration. Existing methods achieve this mostly by utilizing metric learning to enforce feature alignment across modalities, disregarding the inherent modality gap between image and point data. Consequently, this par…

2025

EVOS: Efficient Implicit Neural Training via EVOlutionary Selector

CVPR 2025poster

We propose EVOlutionary Selector (EVOS), an efficient training paradigm for accelerating Implicit Neural Representation (INR). Unlike conventional INR training that feeds all samples through the neural network in each iteration, our approach restricts training to strategically selected points, reduc…

2025

Enhancing Implicit Neural Representations via Symmetric Power Transformation

AAAI 2025technical

We propose symmetric power transformation to enhance the capacity of Implicit Neural Representation (INR) from the perspective of data transformation. Unlike prior work utilizing random permutation or index rearrangement, our method features a reversible operation that does not require additional st…

2025

GauUpdate: New Object Insertion in 3D Gaussian Fields with Consistent Global Illumination

ICCV 2025poster

3D Gaussian Splatting (3DGS) is a prevailing technique to reconstruct large-scale 3D scenes from multiview images for novel view synthesis, like a room, a block, and even a city. Such large-scale scenes are not static with changes constantly happening in these scenes, like a new building being built…

Cited by 0SourcePDFScholar
2025

Understanding Bias Terms in Neural Representations

NeurIPS 2025poster

In this paper, we examine the impact and significance of bias terms in Implicit Neural Representations (INRs). While bias terms are known to enhance nonlinear capacity by shifting activations in typical neural networks, we discover their functionality differs markedly in neural representation networ…

Cited by 0SourceScholar
2024

Multi-scale Consistency for Robust 3D Registration via Hierarchical Sinkhorn Tree

NeurIPS 2024poster

We study the problem of retrieving accurate correspondence through multi-scale consistency (MSC) for robust point cloud registration. Existing works in a coarse-to-fine manner either suffer from severe noisy correspondences caused by unreliable coarse matching or struggle to form outlier-free coarse…

Cited by 0SourcePDFScholar