FF3R: Feedforward Feature 3D Reconstruction from Unconstrained views

• URL: http://arxiv.org/abs/2604.09862v1
• Date: Fri, 10 Apr 2026 19:45:24 GMT
• Title: FF3R: Feedforward Feature 3D Reconstruction from Unconstrained views
• Abstract summary: This paper introduces FF3R, a fully annotation-free feed-forward framework that unifies geometric and semantic reasoning from unconstrained multi-view image sequences.<n>Unlike previous methods, FF3R does not require camera poses, depth maps, or semantic labels, relying solely on rendering supervision for RGB and feature maps.<n>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.
• Score: 34.14517479047999
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• OpenGaFF: Open-Vocabulary Gaussian Feature Field with Codebook Attention [80.51557267896938]
  We present OpenGaFF, a novel framework for open-vocabulary 3D scene understanding built upon 3D Gaussian Splatting.<n>At the core of our method is a Gaussian Feature Field that models semantics as a continuous function of Gaussian geometry and appearance.<n>To further enforce object-level semantic consistency, we introduce a structured codebook that serves as a set of shared semantic primitives.
  arXiv  Detail & Related papers  (2026-05-07T12:10:07Z)
• Learning 3D Representations for Spatial Intelligence from Unposed Multi-View Images [81.94999489820974]
  UniSplat is a feed-forward framework for learning 3D representations from unposed multi-view images.<n>We introduce a dual-masking strategy that strengthens geometry induction in the encoder.<n>Second, we develop a coarse-to-fine Gaussian splatting strategy that reduces appearance-semantics inconsistencies.<n>Third, we introduce a pose-conditioned recalibration mechanism that interrelates predicted 3D point and semantic maps into the image plane.
  arXiv  Detail & Related papers  (2026-04-12T10:36:18Z)
• Enhancing MLLM Spatial Understanding via Active 3D Scene Exploration for Multi-Perspective Reasoning [10.542346290671114]
  We propose a textittraining-free framework that introduces a Visual Chain-of-Thought mechanism grounded in explicit 3D reconstruction.<n>The framework outperforms specialized spatial models and general-purpose MLLMs on major benchmarks such as 3DSRBench and Rel3D.
  arXiv  Detail & Related papers  (2026-04-08T06:47:55Z)
• Contrastive Multi-Modal Hypergraph Reasoning for 3D Crowd Mesh Recovery [12.116457701406047]
  Multi-person 3D reconstruction is pivotal for real-world interaction analysis.<n>Current approaches rely on single-modality inputs, which inherently lack geometric guidance.<n>We propose Contrastive Multi-modal Hypergraph Reasoning to synergize semantic, geometric, and pose cues for crowd reconstruction.
  arXiv  Detail & Related papers  (2026-04-01T09:39:01Z)
• Reliev3R: Relieving Feed-forward Reconstruction from Multi-View Geometric Annotations [98.66466590444553]
  We propose Reliev3R, a weakly-supervised paradigm for training FFRMs from scratch without cost-prohibitive multi-view geometric annotations.<n>At the core of Reliev3R, we design an ambiguity-aware relative depth loss and a trigonometry-based reprojection loss to facilitate supervision for multi-view geometric consistency.
  arXiv  Detail & Related papers  (2026-04-01T06:46:54Z)
• SemGS: Feed-Forward Semantic 3D Gaussian Splatting from Sparse Views for Generalizable Scene Understanding [18.889530477440793]
  SemGS is a feed-forward framework for reconstructing generalizable semantic fields from image inputs.<n>We introduce a camera-aware attention mechanism into the feature extractor to explicitly model geometric relationships between camera viewpoints.<n>Experiments show that SemGS achieves state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2026-03-03T03:06:37Z)
• IGGT: Instance-Grounded Geometry Transformer for Semantic 3D Reconstruction [82.53307702809606]
  Humans naturally perceive the geometric structure and semantic content of a 3D world as intertwined dimensions.<n>We propose InstanceGrounded Geometry Transformer (IGGT) to unify the knowledge for both spatial reconstruction and instance-level contextual understanding.
  arXiv  Detail & Related papers  (2025-10-26T14:57:44Z)
• Cross-Modal Geometric Hierarchy Fusion: An Implicit-Submap Driven Framework for Resilient 3D Place Recognition [9.411542547451193]
  We propose a novel framework that redefines 3D place recognition through density-agnostic geometric reasoning.<n>Specifically, we introduce an implicit 3D representation based on elastic points, which is immune to the interference of original scene point cloud density.<n>With the aid of these two types of information, we obtain descriptors that fuse geometric information from both bird's-eye view and 3D segment perspectives.
  arXiv  Detail & Related papers  (2025-06-17T07:04:07Z)
• Hierarchical Context Alignment with Disentangled Geometric and Temporal Modeling for Semantic Occupancy Prediction [61.484280369655536]
  Camera-based 3D Semantic Occupancy Prediction (SOP) is crucial for understanding complex 3D scenes from limited 2D image observations.<n>Existing SOP methods typically aggregate contextual features to assist the occupancy representation learning.<n>We introduce a new Hierarchical context alignment paradigm for a more accurate SOP (Hi-SOP)
  arXiv  Detail & Related papers  (2024-12-11T09:53:10Z)
• GOV-NeSF: Generalizable Open-Vocabulary Neural Semantic Fields [50.68719394443926]
  Generalizable Open-Vocabulary Neural Semantic Fields (GOV-NeSF) is a novel approach offering a generalizable implicit representation of 3D scenes with open-vocabulary semantics. GOV-NeSF exhibits state-of-the-art performance in both 2D and 3D open-vocabulary semantic segmentation.
  arXiv  Detail & Related papers  (2024-04-01T05:19:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.