Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detection (2406.00830v1)

Abstract: Open-vocabulary 3D Object Detection (OV-3DDet) addresses the detection of objects from an arbitrary list of novel categories in 3D scenes, which remains a very challenging problem. In this work, we propose CoDAv2, a unified framework designed to innovatively tackle both the localization and classification of novel 3D objects, under the condition of limited base categories. For localization, the proposed 3D Novel Object Discovery (3D-NOD) strategy utilizes 3D geometries and 2D open-vocabulary semantic priors to discover pseudo labels for novel objects during training. 3D-NOD is further extended with an Enrichment strategy that significantly enriches the novel object distribution in the training scenes, and then enhances the model's ability to localize more novel objects. The 3D-NOD with Enrichment is termed 3D-NODE. For classification, the Discovery-driven Cross-modal Alignment (DCMA) module aligns features from 3D point clouds and 2D/textual modalities, employing both class-agnostic and class-specific alignments that are iteratively refined to handle the expanding vocabulary of objects. Besides, 2D box guidance boosts the classification accuracy against complex background noises, which is coined as Box-DCMA. Extensive evaluation demonstrates the superiority of CoDAv2. CoDAv2 outperforms the best-performing method by a large margin (AP_Novel of 9.17 vs. 3.61 on SUN-RGBD and 9.12 vs. 3.74 on ScanNetv2). Source code and pre-trained models are available at the GitHub project page.

An Analysis of "Collaborative Novel Object Discovery and Box-Guided Cross-Modal Alignment for Open-Vocabulary 3D Object Detection"

The paper introduces CoDAv2, an innovative framework for Open-Vocabulary 3D Object Detection (OV-3DDet) designed to detect objects from non-fixed, expansive vocabularies. This research addresses prevalent challenges in OV-3DDet by introducing effective strategies for localizing and classifying novel objects using minimal base category annotations.

Key Contributions

1. 3D Novel Object Discovery (3D-NOD): The paper proposes a 3D-NOD strategy for localizing novel objects using 3D geometry and 2D semantic priors, drawn from the CLIP model. This method transcends traditional base category limitations by incorporating semantic probabilities derived from projected 2D object features on image planes. By iteratively refining object discovery, the model can identify a broader range of novel objects.
2. 3D Novel Object Enrichment: Extending the capabilities of 3D-NOD, the 3D Novel Object Enrichment strategy aims to address the data scarcity of novel objects by maintaining an updated pool of discovered objects. This pool is used to augment training data dynamically, promoting better generalization and increased detection probability for novel objects.
3. Discovery-Driven Cross-Modal Alignment (DCMA): For classification, DCMA aligns features of 3D point clouds with those from 2D images and text mods of CLIP. This involves a dual strategy: a class-agnostic approach aligns features across all object boundaries, while class-specific contrastive learning enhances discrimination within a large vocabulary space.
4. Box-Guided Cross-Modal Alignment (Box-DCMA): To improve background-foreground object discrimination, Box-DCMA employs 2D box-guided strategies. This methodology, which integrates 2D open vocabulary detections, refines background object classification, mitigating misclassification imbalances noted when utilizing CLIP-derived priors alone.

Results and Evaluation

CoDAv2 is experimentally validated against other techniques on standard 3D object detection datasets, SUN-RGBD, and ScanNetv2. The framework achieves superior performance, with notable improvements in average precision (AP) for novel categories, surpassing existing benchmarks by a significant margin (150% on SUN-RGBD and 140% on ScanNetv2). The iterative 3D-NOD approach coupled with Box-DCMA's solidifies the model’s open-vocabulary capabilities, enabling it to adeptly localize and classify novel objects within cluttered scenes.

Theoretical and Practical Implications

The proposed methodologies provide a new perspective for mitigating the sample scarcity issue in OV-3DDet without extensive annotations. The dual alignment strategy within DCMA, emphasizes the effectiveness of leveraging both class-agnostic and class-specific priors, contributing to the field's understanding of cross-modal feature alignment.

Practically, this framework is poised to enhance capabilities across fields necessitating flexible object detection systems, such as autonomous robotics and AR/VR environments. The integration of multimodal clues into 3D scenes offers improved scene understanding, particularly in settings marked by rapid evolution and expansion of object vocabularies.

Future Directions

This paper opens several avenues for future exploration. The integration of more varied multimodal inputs could further enhance the robustness and precision of novel object detection. Expanding experiments to outdoor or mixed-environment datasets could verify the framework’s adaptability in diverse real-world contexts. Additionally, exploring the impacts of more sophisticated LLMs beyond CLIP for 2D-3D cross-modal understanding may offer performance increments.

In conclusion, the CoDAv2 framework significantly advances the field of OV-3DDet by providing a structured approach to addressing the complex challenges of novel object localization and classification within expansive category vocabularies. Its efficient blend of 3D and 2D modal priors and alignment strategies make substantial strides toward practical deployment in dynamic environments.