Research map / Vision-language and multimodal / Human and embodied modeling
Beyond Human Perception: Understanding Multi-Object World from Monocular View
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Introduces MonoMulti-3DVG, a multi-object 3D visual grounding task from monocular RGB, with the MonoMulti3D-ROPE dataset (built on Rope3D) and CyclopsNet, a two-stage model using state-prompt encoding and denoising alignment fusion.
Problem
Multi-object 3D visual grounding from monocular RGB images is unexplored, since existing 3DVG datasets are single-object and rely on costly LiDAR or point-cloud sensors.
Approach
A two-stage pipeline: a monocular 3D detector (MonoUNI) proposes objects, then CyclopsNet matches them to text. It adds a State-Prompt Visual Encoder that injects 3D orientation/dimension/position into visual features, and a Denoising Alignment Fusion module that models embeddings as Gaussians and aligns them with KL divergence.
Key results
- On MonoMulti3D-ROPE with GT boxes, CyclopsNet reaches F1 69.09 vs 63.70 for the second-best M3DRef-CLIP.
- With predicted boxes, CyclopsNet reaches F1 60.16 vs 54.82 for M3DRef-CLIP.
- Ablation on GT boxes: F1 rises from 51.20 (no components) to 69.09 with the State-Prompt, VTC and QGJE modules.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CyclopsNet instead of M3DRef-CLIP | MonoMulti3D-ROPE 3D visual grounding · MonoMulti3D-ROPE test set (GT bboxes) | F1: 63.70 -> 69.09 | Helped |
| Use CyclopsNet instead of M3DRef-CLIP | MonoMulti3D-ROPE 3D visual grounding · MonoMulti3D-ROPE test set (GT bboxes) | Precision: 49.82 -> 54.46 | Helped |
| Use CyclopsNet instead of M3DRef-CLIP | MonoMulti3D-ROPE 3D visual grounding · MonoMulti3D-ROPE test set (GT bboxes) | Recall: 88.30 -> 94.47 | Helped |
| Use CyclopsNet instead of M3DRef-CLIP | MonoMulti3D-ROPE 3D visual grounding · MonoMulti3D-ROPE test set (Pred bboxes) | F1: 54.82 -> 60.16 | Helped |
| Use CyclopsNet instead of M3DRef-CLIP | MonoMulti3D-ROPE 3D visual grounding · MonoMulti3D-ROPE test set (Pred bboxes) | Precision: 48.78 -> 51.98 | Helped |
| Use CyclopsNet instead of M3DRef-CLIP | MonoMulti3D-ROPE 3D visual grounding · MonoMulti3D-ROPE test set (Pred bboxes) | Recall: 62.55 -> 71.40 | Helped |
| Add State-Prompt (ablation) | CyclopsNet · MonoMulti3D-ROPE test set (GT bboxes) | F1: 51.20 -> 64.10 | Helped |
| Add VTC (ablation) | CyclopsNet with State-Prompt · MonoMulti3D-ROPE test set (GT bboxes) | F1: 64.10 -> 65.60 | Helped |
| Add QGJE (ablation) | CyclopsNet with State-Prompt and VTC · MonoMulti3D-ROPE test set (GT bboxes) | F1: 65.60 -> 69.09 | Helped |
Surprising findings
- With predicted boxes, the model produces fewer false positives than with GT boxes, because the detector misses objects.
Limitations
- Descriptions are generated by ChatGPT from attribute templates, with only manual validation for hallucinations.
- Performance drops substantially when relying on predicted 3D boxes from the detector.
- Evaluated only on a single outdoor driving dataset (Rope3D-derived).
Experimental setup
Datasets: Rope3D MonoMulti3D-ROPE
Benchmarks: MonoMulti3D-ROPE
Metrics: F1 precision recall TP FP FN
Baselines: ScanRefer ReferIt3D InstanceRefer 3DVG-Trans 3DJCG M3DRef-CLIP
Builds on: ALBEF MonoUNI Kolmogorov-Arnold Network
Closest papers by meaning
- MonoVLM: Monocular 3D Visual Grounding with Vision Language Models (CVPR 2026 · similarity 0.90)
- Mono3DVG: 3D Visual Grounding in Monocular Images (AAAI 2024 · similarity 0.89)
- Unifying 2D and 3D Vision-Language Understanding (ICML 2025 · similarity 0.89)
- ImageNet3D: Towards General-Purpose Object-Level 3D Understanding (NeurIPS 2024 · similarity 0.88)
- 360+x: A Panoptic Multi-modal Scene Understanding Dataset (CVPR 2024 · similarity 0.87)
- The 3D-PC: a benchmark for visual perspective taking in humans and machines (ICLR 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.