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cs.CV2026

InfiniSplat: Implicit Gaussian Decoding for Large-Baseline Monocular View Synthesis

Jiawei Wang, Hao Yu, Yongzhen Hu +7

Single-image feed-forward 3D Gaussian Splatting (3DGS) aims to directly generate a renderable 3D scene representation from one input image, avoiding the cost of multi-view capture…

cs.CV2026

Uncertainty-Aware Gaussian Map for Vision-Language Navigation

Jianzhe Gao, Rui Liu, Yuxuan Xu +6

Vision-Language Navigation (VLN) requires an agent to navigate 3D environments following natural language instructions. During navigation, existing agents commonly encounter percep…

cs.CV2026

PointForward: Feedforward Driving Reconstruction through Point-Aligned Representations

Cheng Chi, Xianqi Wang, Hongcheng Luo +9

High-fidelity reconstruction of driving scenes is crucial for autonomous driving. While recent feedforward 3D Gaussian Splatting (3DGS) methods enable fast reconstruction, their pe…

cs.CV2026

Pixel-Perfect Visual Geometry Estimation

Gangwei Xu, Haotong Lin, Hongcheng Luo +6

Recovering clean and accurate geometry from images is essential for robotics and augmented reality. However, existing geometry foundation models still suffer severely from flying p…

cs.CV2026

InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields

Hao Yu, Haotong Lin, Jiawei Wang +7

Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolut…

cs.CV2026

UniSH: Unifying Scene and Human Reconstruction in a Feed-Forward Pass

Mengfei Li, Peng Li, Zheng Zhang +9

We present UniSH, a unified, feed-forward framework for joint metric-scale 3D scene and human reconstruction. A key challenge in this domain is the scarcity of large-scale, annotat…