5 papers
RGB-Pointmap Pretraining for Unified 3D Scene Understanding
Ye Mao, Weixun Luo, Ranran Huang +2
Pretraining 3D encoders through alignment with Contrastive Language-Image Pre-training (CLIP) has emerged as a promising direction for learning generalizable representations for 3D…
SPFSplatV2: Efficient Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views
Ranran Huang, Krystian Mikolajczyk
We introduce SPFSplatV2, an efficient feed-forward framework for 3D Gaussian splatting from sparse multi-view images, requiring no ground-truth poses during training or inference.…
From None to All: Self-Supervised 3D Reconstruction via Novel View Synthesis
Ranran Huang, Weixun Luo, Ye Mao +1
In this paper, we introduce NAS3R, a self-supervised feed-forward framework that jointly learns explicit 3D geometry and camera parameters with no ground-truth annotations and no p…
POMA-3D: The Point Map Way to 3D Scene Understanding
Ye Mao, Weixun Luo, Ranran Huang +2
In this paper, we introduce POMA-3D, the first self-supervised 3D representation model learned from point maps. Point maps encode explicit 3D coordinates on a structured 2D grid, p…
No Pose at All: Self-Supervised Pose-Free 3D Gaussian Splatting from Sparse Views
Ranran Huang, Krystian Mikolajczyk
We introduce SPFSplat, an efficient framework for 3D Gaussian splatting from sparse multi-view images, requiring no ground-truth poses during training or inference. It employs a sh…