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20232026
most citedEntangled View-Epipolar Information Aggregation for Generalizable Neural Radiance Fields

2 citations · 4 across the 6 of their papers we have counts for

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cs.CV2026

LATO: 3D Mesh Flow Matching with Structured TOpology Preserving LAtents

Tianhao Zhao, Youjia Zhang, Hang Long +7

In this paper, we introduce LATO, a novel topology-preserving latent representation that enables scalable, flow matching-based synthesis of explicit 3D meshes. LATO represents a me…

cs.CV2024

Ref-GS: Directional Factorization for 2D Gaussian Splatting

Youjia Zhang, Anpei Chen, Yumin Wan +4

In this paper, we introduce Ref-GS, a novel approach for directional light factorization in 2D Gaussian splatting, which enables photorealistic view-dependent appearance rendering…

cs.CV2024

MaGS: Reconstructing and Simulating Dynamic 3D Objects with Mesh-adsorbed Gaussian Splatting

Shaojie Ma, Yawei Luo, Wei Yang +1

3D reconstruction and simulation, although interrelated, have distinct objectives: reconstruction requires a flexible 3D representation that can adapt to diverse scenes, while simu…

cs.CV2024★ 1 cited

EfficientGS: Streamlining Gaussian Splatting for Large-Scale High-Resolution Scene Representation

Wenkai Liu, Tao Guan, Bin Zhu +5

In the domain of 3D scene representation, 3D Gaussian Splatting (3DGS) has emerged as a pivotal technology. However, its application to large-scale, high-resolution scenes (exceedi…

cs.CV2023★ 2 cited

Entangled View-Epipolar Information Aggregation for Generalizable Neural Radiance Fields

Zhiyuan Min, Yawei Luo, Wei Yang +2

Generalizable NeRF can directly synthesize novel views across new scenes, eliminating the need for scene-specific retraining in vanilla NeRF. A critical enabling factor in these ap…

cs.CV2023★ 1 cited

DiffusionTrack: Diffusion Model For Multi-Object Tracking

Run Luo, Zikai Song, Lintao Ma +3

Multi-object tracking (MOT) is a challenging vision task that aims to detect individual objects within a single frame and associate them across multiple frames. Recent MOT approach…