A Review of 3D Object Detection with Vision-Language Models
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
681179a26a014c6e057963d8
Ranjan Sapkota
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2025-04-30T07:56:21.388Z
67ddd80896ac367438d400a6
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Ranjan Sapkota
user
RanjanSapkota
681179a26a014c6e057963d9
Konstantinos I Roumeliotis
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2025-07-17T08:20:14.101Z
64bcce94f8f28a19b0ba6c86
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Dr. Konstantinos I. Roumeliotis
user
rkonstadinos
681179a26a014c6e057963da
Rahul Harsha Cheppally
681179a26a014c6e057963db
Marco Flores Calero
681179a26a014c6e057963dc
Manoj Karkee
A Review of 3D Object Detection with Vision-Language Models
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
A systematic review of 3D object detection using vision-language models highlights challenges, compares architectures, and discusses future research directions.
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