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Yushi Guan

4 accepted papers

2026

MERG3R: A Divide-and-Conquer Approach to Large-Scale Neural Visual Geometry

CVPR 2026

Recent advancements in neural visual geometry, including transformer-based models such as VGGT and Pi3, have achieved impressive accuracy on 3D reconstruction tasks. However, their reliance on full attention makes them fundamentally limited by GPU memory capacity, preventing them from scaling to lar

Cited by 0SourceScholar
2025

ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction

ICCV 2025accepted

3D Gaussian Splatting (3DGS) is a state-of-art technique to model real-world scenes with high quality and real-time rendering.Typically, a higher quality representation can be achieved by using a large number of 3D Gaussians. However, using large 3D Gaussian counts significantly increases the GPU de…

Cited by 0SourcePDFScholar
2025

Retri3D: 3D Neural Graphics Representation Retrieval

ICLR 2025spotlight

Learnable 3D Neural Graphics Representations (3DNGR) have emerged as promising 3D representations for reconstructing 3D scenes from 2D images. Numerous works, including Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and their variants, have significantly enhanced the quality of these r…

Cited by 0SourcePDFScholar
2023

Ev-Conv: Fast CNN Inference on Event Camera Inputs for High-Speed Robot Perception

RA-L 2023

Event cameras capture visual information with a high temporal resolution and a wide dynamic range. This enables capturing visual information at fine time granularities (e.g., microseconds) in rapidly changing environments. This makes event cameras highly useful for high-speed robotics tasks involvin

Cited by 1SourceScholar