PLANA3R: Zero-shot Metric Planar 3D Reconstruction via Feed-Forward Planar Splatting
Changkun Liu, Bin Tan, Zeran Ke, Shangzhan Zhang, Jiachen Liu, Ming Qian, Nan Xue, Yujun Shen, Tristan Braud

TL;DR
PLANA3R is a novel feedforward framework that leverages planar primitives and vision transformers to achieve scalable, zero-shot metric 3D reconstruction of indoor scenes from unposed images without explicit plane supervision.
Contribution
It introduces PLANA3R, a pose-free, planar primitive-based 3D reconstruction method that learns without explicit plane annotations, enabling scalable training and strong generalization.
Findings
Effective 3D reconstruction and depth estimation on indoor datasets
Strong generalization to out-of-domain environments
Ability to perform accurate plane segmentation
Abstract
This paper addresses metric 3D reconstruction of indoor scenes by exploiting their inherent geometric regularities with compact representations. Using planar 3D primitives - a well-suited representation for man-made environments - we introduce PLANA3R, a pose-free framework for metric Planar 3D Reconstruction from unposed two-view images. Our approach employs Vision Transformers to extract a set of sparse planar primitives, estimate relative camera poses, and supervise geometry learning via planar splatting, where gradients are propagated through high-resolution rendered depth and normal maps of primitives. Unlike prior feedforward methods that require 3D plane annotations during training, PLANA3R learns planar 3D structures without explicit plane supervision, enabling scalable training on large-scale stereo datasets using only depth and normal annotations. We validate PLANA3R on…
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Taxonomy
TopicsAdvanced Vision and Imaging · Robotics and Sensor-Based Localization · Optical measurement and interference techniques
