FF3R: Feedforward Feature 3D Reconstruction from Unconstrained views

TL;DR

FF3R is a novel, fully annotation-free framework that unifies geometric and semantic 3D reasoning from unconstrained multi-view images, improving various vision tasks without requiring camera poses or labels.

Contribution

It introduces a scalable, annotation-free method combining geometric and semantic reasoning with innovative modules for global and local consistency.

Findings

Outperforms previous methods in novel-view synthesis

Achieves superior open-vocabulary semantic segmentation

Demonstrates strong generalization to in-the-wild scenarios

Abstract

Recent advances in vision foundation models have revolutionized geometry reconstruction and semantic understanding. Yet, most of the existing approaches treat these capabilities in isolation, leading to redundant pipelines and compounded errors. This paper introduces FF3R, a fully annotation-free feed-forward framework that unifies geometric and semantic reasoning from unconstrained multi-view image sequences. Unlike previous methods, FF3R does not require camera poses, depth maps, or semantic labels, relying solely on rendering supervision for RGB and feature maps, establishing a scalable paradigm for unified 3D reasoning. In addition, we address two critical challenges in feedforward feature reconstruction pipelines, namely global semantic inconsistency and local structural inconsistency, through two key innovations: (i) a Token-wise Fusion Module that enriches geometry tokens with semantic context via cross-attention, and (ii) a Semantic-Geometry Mutual Boosting mechanism combining geometry-guided feature warping for global consistency with semantic-aware voxelization for local coherence. Extensive experiments on ScanNet and DL3DV-10K demonstrate FF3R's superior performance in novel-view synthesis, open-vocabulary semantic segmentation, and depth estimation, with strong generalization to in-the-wild scenarios, paving the way for embodied intelligence systems that demand both spatial and semantic understanding.