AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views

Lihan Jiang, Yucheng Mao, Linning Xu, Tao Lu, Kerui Ren, Yichen Jin, Xudong Xu, Mulin Yu, Jiangmiao Pang, Feng Zhao†, Dahua Lin, Bo Dai†

Project Code Paper

• We introduce AnySplat, a feed‑forward network for novel‑view synthesis from uncalibrated image collections in both sparse‑ and dense‑view scenarios.

MV-CoLight: Efficient Object Compositing with Consistent Lighting and Shadow Generation

Kerui Ren, Jiayang Bai, Linning Xu, Lihan Jiang, Jiangmiao Pang, Mulin Yu†, Bo Dai†

Project Paper

• We introduce MV-CoLight, a two-stage framework for illumination-consistent object compositing in both 2D images and 3D scenes.

HaloGS: Loose Coupling of Compact Geometry and Gaussian Splats for 3D Scenes

Changjian Jiang*, Kerui Ren*, Linning Xu, Jiong Chen, Jiangmiao Pang, Yu Zhang, Bo Dai, Mulin Yu†

Project Paper

• We introduce HaloGS, a dual-representation that loosely couples triangles for geometry with Gaussians for appearance, enabling high-fidelity rendering with compact geometry.

Novel Demonstration Generation with Gaussian Splatting Enables Robust One-Shot Manipulation

Sizhe Yang, Wenye Yu, Jia Zeng, Jun Lv, Kerui Ren, Cewu Lu, Dahua Lin, Jiangmiao Pang†

Project Code Paper

• We introduce RoboSplat, a framework that leverages 3D Gaussian Splatting (3DGS) to generate novel demonstrations for RGB-based policy learning.

Horizon-GS: Unified 3D Gaussian Splatting for Large-Scale Aerial-to-Ground Scenes

Lihan Jiang*, Kerui Ren*, Mulin Yu, Linning Xu, Junting Dong, Tao Lu, Feng Zhao, Dahua Lin, Bo Dai†

Project Code Paper

• We introduce Horizon-GS, tackles the unified reconstruction and rendering for aerial and street views with a new training strategy, overcoming viewpoint discrepancies to generate high-fidelity scenes.

Bootstrap-GS: Self-Supervised Augmentation for High-Fidelity Gaussian Splatting

Yifei Gao*, Kerui Ren*, Jie Ou, Lei Wang, Jiaji Wu, Jun Cheng

Code Paper

• We introduce a bootstrapping framework that generates pseudo-ground truth data from novel viewpoints compatible with the original training set and feeds it back into training, which reduces artifacts, improves metrics, and is adaptable to other Gaussian-based methods.

Pay Attention to What You Need

Yifei Gao, Shaohong Chen, Lei Wang, Ruiting Dai, Ziyun Zhang, Kerui Ren, Jiaji Wu, Jun Cheng

Code Paper

• We propose Scaled ReAttention (SRA), a lightweight method that boosts LLMs’ long-context understanding without fine-tuning or retraining.

Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians

Kerui Ren*, Lihan Jiang*, Tao Lu, Mulin Yu, Linning Xu, Zhangkai Ni, Bo Dai†

Project Code Paper

• We introduce Octree-GS, featuring an LOD-structured 3D Gaussian approach supporting level-of-detail decomposition for scene representation that contributes to the final rendering results.

Semantic Masking with Curriculum Learning for Robust HDR Image Reconstruction

Zhangkai Ni, Yang Zhang, Kerui Ren, Wenhan Yang†, Hanli Wang†, Sam Kwong

Code

• We propose a novel SAM-guided MIM for HDR reconstruction (SMHDR). The combination of curriculum learning strategy and masked image modeling bridges the gap between pixel fitting and semantic understanding, which successfully turns the knowledge of SAM into the intrinsic understanding of the quality issues of HDR images.