title: A Review of 3D Object Detection with Vision-Language Models
summary: This review provides a systematic analysis of comprehensive survey of 3D
  object detection with vision-language models(VLMs) , a rapidly advancing area
  at the intersection of 3D vision and multimodal AI. By examining over 100
  research papers, we provide the first systematic analysis dedicated to 3D
  object detection with vision-language models. We begin by outlining the unique
  challenges of 3D object detection with vision-language models, emphasizing
  differences from 2D detection in spatial reasoning and data complexity.
  Traditional approaches using point clouds and voxel grids are compared to
  modern vision-language frameworks like CLIP and 3D LLMs, which enable
  open-vocabulary detection and zero-shot generalization. We review key
  architectures, pretraining strategies, and prompt engineering methods that
  align textual and 3D features for effective 3D object detection with
  vision-language models. Visualization examples and evaluation benchmarks are
  discussed to illustrate performance and behavior. Finally, we highlight current
  challenges, such as limited 3D-language datasets and computational demands, and
  propose future research directions to advance 3D object detection with
  vision-language models. >Object Detection, Vision-Language Models, Agents,
  VLMs, LLMs, AI
paper_authors._id: 681179a26a014c6e057963d8
paper_authors.name: Ranjan Sapkota
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-30T07:56:21.388Z
paper_authors._id: 67ddd80896ac367438d400a6
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/C1NY6Nv5i0erwLnzCrTUM.png
paper_authors.fullname: Ranjan Sapkota
paper_authors.type: user
paper_authors.user: RanjanSapkota
paper_authors._id: 681179a26a014c6e057963d9
paper_authors.name: Konstantinos I Roumeliotis
paper_authors._id: 681179a26a014c6e057963da
paper_authors.name: Rahul Harsha Cheppally
paper_authors._id: 681179a26a014c6e057963db
paper_authors.name: Marco Flores Calero
paper_authors._id: 681179a26a014c6e057963dc
paper_authors.name: Manoj Karkee
paper_title: A Review of 3D Object Detection with Vision-Language Models
paper_summary: This review provides a systematic analysis of comprehensive survey of 3D
  object detection with vision-language models(VLMs) , a rapidly advancing area
  at the intersection of 3D vision and multimodal AI. By examining over 100
  research papers, we provide the first systematic analysis dedicated to 3D
  object detection with vision-language models. We begin by outlining the unique
  challenges of 3D object detection with vision-language models, emphasizing
  differences from 2D detection in spatial reasoning and data complexity.
  Traditional approaches using point clouds and voxel grids are compared to
  modern vision-language frameworks like CLIP and 3D LLMs, which enable
  open-vocabulary detection and zero-shot generalization. We review key
  architectures, pretraining strategies, and prompt engineering methods that
  align textual and 3D features for effective 3D object detection with
  vision-language models. Visualization examples and evaluation benchmarks are
  discussed to illustrate performance and behavior. Finally, we highlight current
  challenges, such as limited 3D-language datasets and computational demands, and
  propose future research directions to advance 3D object detection with
  vision-language models. >Object Detection, Vision-Language Models, Agents,
  VLMs, LLMs, AI
paper_ai_summary: A systematic review of 3D object detection using vision-language models highlights challenges, compares architectures, and discusses future research directions.
paper_ai_keywords: 3D object detection vision-language models CLIP 3D LLMs open-vocabulary detection zero-shot generalization point clouds voxel grids pretraining strategies prompt engineering 3D-language datasets computational demands