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Renjie Chen

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

TrackGS: Optimizing COLMAP-Free 3D Gaussian Splatting with Global Track Constraints

Dongbo Shi
Shen Cao
Lubin Fan
Bojian Wu
Jinhui Guo
Ligang Liu
Renjie Chen

We present TrackGS, a novel method to integrate global feature tracks with 3D Gaussian Splatting (3DGS) for COLMAP-free novel view synthesis. While 3DGS delivers impressive rendering quality, its reliance on accurate precomputed camera parameters remains a significant limitation. Existing COLMAP-free approaches depend on local constraints that fail in complex scenarios. Our key innovation lies in leveraging feature tracks to establish global geometric constraints, enabling simultaneous optimization of camera parameters and 3D Gaussians. Specifically, we: (1) introduce track-constrained Gaussians that serve as geometric anchors, (2) propose novel 2D and 3D track losses to enforce multi-view consistency, and (3) derive differentiable formulations for camera intrinsics optimization. Extensive experiments on challenging real-world and synthetic datasets demonstrate state-of-the-art performance, with much lower pose error than previous methods while maintaining superior rendering quality. Our approach eliminates the need for COLMAP preprocessing, making 3DGS more accessible for practical applications.

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ICML Conference 2025 Conference Paper

Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability

Yicheng Pan 0001
Renjie Chen
Pengyu Long
Bingchen Fan

Hierarchical and overlapping clustering are two prevalent phenomena that often coexist in real-world system. While numerous studies have examined these two structures separately, characterizing and evaluating their hybrid forms remains an open challenge. To bridge this gap, we initiate the study of hierarchical overlapping clustering on graphs by introducing a new cost function and establishing its rationality through several intuitive properties. We further develop an approximation algorithm that achieves a constant approximation factor for its dual version. Our approach employs a recursive overlapping bipartition framework based on local search, enabling a highly scalable speed-up variant. Experimental results demonstrate that this speed-up algorithm outperforms all baseline methods significantly in both effectiveness (across synthetic and real datasets) and scalability.

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AAAI Conference 2018 Short Paper

A Stratified Feature Ranking Method for Supervised Feature Selection

Renjie Chen
Xiaojun Chen
Guowen Yuan
Wenya Sun
Qingyao Wu

Most feature selection methods usually select the highest rank features which may be highly correlated with each other. In this paper, we propose a Stratiﬁed Feature Ranking (SFR) method for supervised feature selection. In the new method, a Subspace Feature Clustering (SFC) is proposed to identify feature clusters, and a stratiﬁed feature ranking method is proposed to rank the features such that the high rank features are lowly correlated. Experimental results show the superiority of SFR.

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