43 citations · 47 across the 10 of their papers we have counts for
12 papers
FeCoSplat: Feedback-Guided Compression for Feed-Forward 3D Gaussian Splatting
Yuxuan Li, Yihang Chen, Yufeng Zhang +2
Feed-forward 3D Gaussian Splatting (3DGS) enables efficient novel-view synthesis from sparse multi-view images, yet its representations remain costly to store and transmit. Existin…
SPARE-GS: Structural Parsimony and Resource Efficiency for 3D Gaussian Splatting
Zhang Chen, Shuai Wan, Fuzheng Yang +3
3D Gaussian Splatting (3DGS) achieves high-fidelity novel view synthesis in real-time; however its training efficiency and representation compactness are hindered by excessive prim…
UnGAP: Uncertainty-Guided Affine Prompting for Real-Time Crack Segmentation
Conghui Li, Huanyu He, Xin Wang +2
Real-time crack segmentation is vital for structural health monitoring but is plagued by aleatoric uncertainties arising from varying lighting, blur, and texture ambiguity. Current…
Unleashing Diffusion Transformers for Visual Correspondence by Modulating Massive Activations
Chaofan Gan, Yuanpeng Tu, Xi Chen +4
Pre-trained stable diffusion models (SD) have shown great advances in visual correspondence. In this paper, we investigate the capabilities of Diffusion Transformers (DiTs) for acc…
PCGS: Progressive Compression of 3D Gaussian Splatting
Yihang Chen, Mengyao Li, Qianyi Wu +3
3D Gaussian Splatting (3DGS) achieves impressive rendering fidelity and speed for novel view synthesis. However, its substantial data size poses a significant challenge for practic…
HAC++: Towards 100X Compression of 3D Gaussian Splatting
Yihang Chen, Qianyi Wu, Weiyao Lin +2
3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel view synthesis, boasting rapid rendering speed with high fidelity. However, the substantial Gaussians an…
CSTA: Spatial-Temporal Causal Adaptive Learning for Exemplar-Free Video Class-Incremental Learning
Tieyuan Chen, Huabin Liu, Chern Hong Lim +4
Continual learning aims to acquire new knowledge while retaining past information. Class-incremental learning (CIL) presents a challenging scenario where classes are introduced seq…
MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning
Tieyuan Chen, Huabin Liu, Yi Wang +5
Video causal reasoning aims to achieve a high-level understanding of videos from a causal perspective. However, it exhibits limitations in its scope, primarily executed in a questi…
Fast Feedforward 3D Gaussian Splatting Compression
Yihang Chen, Qianyi Wu, Mengyao Li +3
With 3D Gaussian Splatting (3DGS) advancing real-time and high-fidelity rendering for novel view synthesis, storage requirements pose challenges for their widespread adoption. Alth…
MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning
Tieyuan Chen, Huabin Liu, Tianyao He +8
Video causal reasoning aims to achieve a high-level understanding of video content from a causal perspective. However, current video reasoning tasks are limited in scope, primarily…
Back to Newton's Laws: Learning Vision-based Agile Flight via Differentiable Physics
Yuang Zhang, Yu Hu, Yunlong Song +2
Swarm navigation in cluttered environments is a grand challenge in robotics. This work combines deep learning with first-principle physics through differentiable simulation to enab…
HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression
Yihang Chen, Qianyi Wu, Weiyao Lin +2
3D Gaussian Splatting (3DGS) has emerged as a promising framework for novel view synthesis, boasting rapid rendering speed with high fidelity. However, the substantial Gaussians an…