arXiv Computer Vision

TRACKGRAPH: Online Open-Vocabulary 3D Scene Graphs via Image-Space Tracking

TRACKGRAPH is an online open‑vocabulary 3D mapping system that tracks 2D masks in the image stream before fusing them into a class‑agnostic 3D segment layer within a hierarchical scene graph. It uses FastSAM and CLIP for sparse keyframes, DINOv3 for dense mask propagation, and compact multi‑view CLIP embeddings for open‑vocabulary retrieval. The method outperforms state‑of‑the‑art mapping techniques on Replica, ScanNet++, and HM3D, achieving higher synonym frequency, faster processing, and lower GPU memory usage, and has been deployed on quadruped robots and drones at real‑time rates.

arXiv Computer Vision

Sep 21

A Scene Language Model for Open-Vocabulary Scene Mapping

The paper introduces SceneLM, a vision‑language model that maintains an open‑vocabulary 3D scene map using only a structured text list of objects as persistent memory. The model updates this textual map by adding, editing, and removing objects for each input image, learning the process through supervision tasks and an automatic annotation pipeline. Evaluations on language‑grounded retrieval and localization benchmarks show competitive performance with traditional mapping systems while producing a 6‑12× more compact representation, and the model can run online on an edge device such as a quadruped robot.

By Adam Lilja, Fabio H\"ubel, Siming He, Junsheng Fu, Claire Tomlin, Lars Hammarstrand, Jitendra Malik, Jonas Frey, Marco Pavone

llms rag multimodal benchmarks

arXiv AI

Sep 10

GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting

GoDeep is an annotation‑free method for open‑vocabulary 3D scene understanding that uses a vision‑language model solely as a translator to generate structured, entity‑level descriptions of each image. These descriptions are projected and aggregated in a language‑only embedding space, eliminating the need for a 3D training corpus or domain‑specific encoder. The approach achieves competitive performance on ScanNet++ and a cultural heritage benchmark, accurately localizes out‑of‑vocabulary objects, and offers explainable, point‑level predictions.

By Thodoris Betsas, Anastasios Doulamis, Andreas Georgopoulos

llms rag computer-vision multimodal benchmarks

arXiv AI

Aug 13

CORE-3D: Context-aware Open-vocabulary Retrieval by Embeddings in 3D

arXiv:2509. 24528v4 Announce Type: replace-cross Abstract: Object retrieval from a scene has become a new trend of research due to its numerous applications.

By Mohamad Amin Mirzaei, Pantea Amoie, Ali Ekhterachian, Matin Mirzababaei, Babak Khalaj

llms rag computer-vision multimodal benchmarks

arXiv AI

Jun 19

QueryGaussian: Scalable and Training-Free Open-Vocabulary 3D Instance Retrieval

arXiv:2606. 19733v1 Announce Type: cross Abstract: Efficiently retrieving specific 3D instances from large-scale scenes via natural language prompts remains a formidable challenge in multimedia analysis.

By Xiuyuan Zhu, Ke Lu, Zijie Yang, Chao Yue, Jian Xue, Dongming Zhang

rag computer-vision efficiency benchmarks

arXiv Computer Vision

Sep 2

Ov3R: Open-Vocabulary Semantic 3D Reconstruction from RGB Videos

arXiv:2507.22052v3 Announce Type: replace Abstract: We present Ov3R, a novel framework for open-vocabulary semantic 3D reconstruction from RGB video streams, designed to advance Spatial AI. The syste...

By Ziren Gong, Xiaohan Li, Fabio Tosi, Jiawei Han, Stefano Mattoccia, Jianfei Cai, Matteo Poggi

rag computer-vision benchmarks safety

Hugging Face Trending Papers

Jul 30

MonoVoc: Decoupling Geometry and Semantics for Lightweight Monocular Open-Vocabulary 3D Gaussians

Open vocabulary 3D scene understanding is essential for next-generation interactive systems, empowering users to intuitively query and navigate reconstructed environments using natural language. However, current 3D Gaussian frameworks are often bottlenecked by restrictive multiview capture requirements, costly scene-specific optimization, and the massive memory overhead of storing dense language features.

rag computer-vision nlp benchmarks

arXiv AI

Jul 10

VocaDet: Sample-Driven Open-Vocabulary Object Detection and Segmentation via Visual Tokenization and Vector Database Retrieval

arXiv:2607. 08541v1 Announce Type: cross Abstract: Open-vocabulary object detection and segmentation aim to recognize arbitrary objects beyond predefined categories.

By ZhiXin Sun

rag computer-vision nlp multimodal

arXiv Computer Vision

Sep 18

SenseFuse: Label-Free Fusion of Image and Shape Encoders for Open-Vocabulary 3D Instance Segmentation

SenseFuse introduces a label‑free fusion approach that balances 2D image and 3D shape encoders for open‑vocabulary 3D instance segmentation. By selecting a scene‑level fusion weight through an adaptive, sensitivity‑based mechanism, it improves mask labeling accuracy across multiple datasets, recovering up to 93% of the potential gain from an oracle weight. The method demonstrates that image and shape encoders have complementary failure patterns, leading to higher instance AP in most evaluated settings.

By Euiseok Han, Tri Ton, Hwanhee Kim, Seungyeon Ryu, Chang D. Yoo

computer-vision robotics benchmarks

arXiv AI

Sep 25

TrackEverything: Long Horizon Dense Tracking via De-Duplicating 3D Scene Representations

TrackEverything is a 3D point tracker that overcomes the trade‑off between sparse long‑horizon tracking and dense short‑clip tracking by representing videos as persistent 3D scene tracks in world coordinates. It introduces voxel‑based de‑duplication at sliding‑window boundaries, a two‑stage refinement process (endpoint refiner and lightweight trajectory refiner), and a 3D WAFT module that replaces memory‑heavy 4D correlation volumes with efficient feature sampling. The method can track all visible points in videos longer than 1000 frames using only 40 GB of GPU memory, outperforming existing dense trackers on short clips and matching sparse trackers on long sequences.

By Ayush Jain, Sreeharsha Paruchuri, Ishita Gupta, Fan Zhang, Tanner Schmidt, Jakob Engel, Katerina Fragkiadaki, Adam W. Harley

benchmarks

arXiv Machine Learning

Jun 24

Open-Vocabulary BEV Segmentation with 3D-Aware Geometric Constraints

arXiv:2606. 24353v1 Announce Type: cross Abstract: Bird's-eye view (BEV) perception fuses multi-camera images into a unified top-down representation for autonomous driving.

By Hojun Choi, Seulbin Hwang, Dae Jung Kim, Kisung Kim, Hyunjung Shim, Jinhan Lee

llms agents computer-vision robotics efficiency multimodal benchmarks

arXiv Computer Vision

Aug 24

Stream3Dv2: Geometric-Semantic Fusion Enhanced Streaming Zero-Shot 3D Scene Understanding

arXiv:2608.21136v1 Announce Type: new Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supe...

By Jie Xu, Na Zhao

llms agents computer-vision robotics

arXiv AI

Jul 21

OV-MAP: Open-Vocabulary Zero-Shot 3D Instance Segmentation Map for Robots

arXiv:2506. 11585v2 Announce Type: replace-cross Abstract: We introduce OV-MAP, a novel approach to open-world 3D mapping for mobile robots by integrating open-features into 3D maps to enhance object recognition capabilities.

By Juno Kim, Yesol Park, Hye-Jung Yoon, Byoung-Tak Zhang

computer-vision robotics

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