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Source note3 min read

Open-Vocabulary 3D Object Detection with Co-Distillation Discovery and Dual Guidance Robust Training

Source

arXiv

arxiv.org/abs/2608.19973v1

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Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Shangbo Yuan, Jie Xu, Xiaofeng Zhu, Na Zhao
  • Venue: arXiv: cs.CV
  • Published: 2026-08-20
  • Collected: 2026-08-21
  • URL: http://arxiv.org/abs/2608.19973v1
  • PDF: https://arxiv.org/pdf/2608.19973v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

The authors propose a new framework for open-vocabulary 3D object detection that addresses the limitations of existing methods by improving both the reliability of novel object discovery and the robustness of model training. The co-distillation strategy ensures high-quality novel objects using geometric consistency, structural objectness, and semantic certainty. A dual-guidance learning scheme is also introduced to mitigate the negative effects of imprecise 3D bounding boxes and semantic ambiguity.

What the Document Says

  • The work focuses on open-vocabulary 3D object detection (3D-OVD) and addresses its limitations in existing approaches.
  • A two-stage pipeline is typically used: discovery of novel objects using foundation models, followed by training a 3D-OVD model based on these discovered objects.
  • This pipeline often suffers from inaccurate localization and mismatched classification during the discovery stage, which limits performance.
  • The authors propose an innovative framework for reliable object discovery through co-distillation strategy with Hungarian matching.
  • They also introduce a dual-guidance learning scheme for robust model training.
  • The co-distillation strategy incorporates geometric consistency, structural objectness, and semantic certainty.
  • Dual-guidance learning includes scene-awareness-guided uncertainty regularization for the regression head and an LLM-guided hierarchical alignment for the classification head.
  • Experiments on SUN RGB-D and ScanNetV2 show significant performance gains over state-of-the-art approaches.
  • Code for the method is available at a specific GitHub repository.

Key Concepts Mentioned

  • open-vocabulary 3D object detection
  • co-distillation strategy
  • dual-guidance learning
  • geometric consistency
  • structural objectness
  • semantic certainty
  • scene-awareness-guided uncertainty regularization
  • LLM-guided hierarchical alignment

Claims Made by the Document

  • The proposed method improves the reliability of novel object discovery and the robustness of model training.
  • The co-distillation strategy ensures high-quality novel objects.
  • Dual-guidance learning effectively mitigates the negative effects of imprecise 3D bounding boxes and semantic ambiguity.

Evidence Used

  • Experiments on SUN RGB-D and ScanNetV2 demonstrate significant performance gains over state-of-the-art approaches.
  • The code for the method is available at a specific GitHub repository.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Recently, open-vocabulary 3D object detection (3D-OVD) has gained increasing attention for its ability to detect unseen objects in 3D scenes. Existing approaches typically adopt a two-stage pipeline that first discovers novel objects using foundation models and then trains a 3D-OVD model based on these discovered objects. Although effective, this pipeline often suffers from inaccurate localization and mismatched classification during the discovery stage, which subsequently limits the performance of the model training stage. To address these limitations, we advocate for improving both the reliability of novel object discovery and the robustness of model training, and propose an innovative framework. Specifically, for reliable discovery, our co-distillation strategy distills high-quality novel objects by applying Hungarian matching over a comprehensive score that incorporates geometric consistency, structural objectness, and semantic certainty. To enhance robust model training, we further propose a dual-guidance learning scheme, incorporating a scene-awareness-guided uncertainty regularization for the regression head and an LLM-guided hierarchical alignment for the classification head, effectively mitigating the negative effects of imprecise 3D bounding boxes and semantic ambiguity. Extensive experiments on SUN RGB-D and ScanNetV2 demonstrate that our method achieves significant performance gains over state-of-the-art approaches. Code is available at https://github.com/shangboyuan/Co-3DGT