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Depth-discriminative Metric Learning for Monocular 3D Object Detection

Wonhyeok Choi, Mingyu Shin, Sunghoon Im

TL;DR

This work tackles depth ambiguity in monocular 3D object detection by learning depth-discriminative features through a local, depth-guided metric learning framework. It introduces a $(K,B,\epsilon)$-quasi-isometric loss that aligns depth space with feature space while preserving the manifold's non-linear structure via local neighborhood constraints, plus an auxiliary object-wise depth map head that enhances depth quality without increasing inference time. The method demonstrates broad compatibility by boosting performance across multiple baselines on KITTI and Waymo, with consistent gains particularly for data-efficient models; ablations show the quasi-isometric loss contributes more than the depth-map loss, and SupCR comparisons highlight the advantages of preserving local geometry over forcibly shaping the entire feature space. Overall, this approach provides a practical, scalable path to improve depth discrimination in monocular 3D detection with minimal computation and augmentation overhead, potentially extending to multi-camera setups and other regression tasks.

Abstract

Monocular 3D object detection poses a significant challenge due to the lack of depth information in RGB images. Many existing methods strive to enhance the object depth estimation performance by allocating additional parameters for object depth estimation, utilizing extra modules or data. In contrast, we introduce a novel metric learning scheme that encourages the model to extract depth-discriminative features regardless of the visual attributes without increasing inference time and model size. Our method employs the distance-preserving function to organize the feature space manifold in relation to ground-truth object depth. The proposed (K, B, eps)-quasi-isometric loss leverages predetermined pairwise distance restriction as guidance for adjusting the distance among object descriptors without disrupting the non-linearity of the natural feature manifold. Moreover, we introduce an auxiliary head for object-wise depth estimation, which enhances depth quality while maintaining the inference time. The broad applicability of our method is demonstrated through experiments that show improvements in overall performance when integrated into various baselines. The results show that our method consistently improves the performance of various baselines by 23.51% and 5.78% on average across KITTI and Waymo, respectively.

Depth-discriminative Metric Learning for Monocular 3D Object Detection

TL;DR

This work tackles depth ambiguity in monocular 3D object detection by learning depth-discriminative features through a local, depth-guided metric learning framework. It introduces a -quasi-isometric loss that aligns depth space with feature space while preserving the manifold's non-linear structure via local neighborhood constraints, plus an auxiliary object-wise depth map head that enhances depth quality without increasing inference time. The method demonstrates broad compatibility by boosting performance across multiple baselines on KITTI and Waymo, with consistent gains particularly for data-efficient models; ablations show the quasi-isometric loss contributes more than the depth-map loss, and SupCR comparisons highlight the advantages of preserving local geometry over forcibly shaping the entire feature space. Overall, this approach provides a practical, scalable path to improve depth discrimination in monocular 3D detection with minimal computation and augmentation overhead, potentially extending to multi-camera setups and other regression tasks.

Abstract

Monocular 3D object detection poses a significant challenge due to the lack of depth information in RGB images. Many existing methods strive to enhance the object depth estimation performance by allocating additional parameters for object depth estimation, utilizing extra modules or data. In contrast, we introduce a novel metric learning scheme that encourages the model to extract depth-discriminative features regardless of the visual attributes without increasing inference time and model size. Our method employs the distance-preserving function to organize the feature space manifold in relation to ground-truth object depth. The proposed (K, B, eps)-quasi-isometric loss leverages predetermined pairwise distance restriction as guidance for adjusting the distance among object descriptors without disrupting the non-linearity of the natural feature manifold. Moreover, we introduce an auxiliary head for object-wise depth estimation, which enhances depth quality while maintaining the inference time. The broad applicability of our method is demonstrated through experiments that show improvements in overall performance when integrated into various baselines. The results show that our method consistently improves the performance of various baselines by 23.51% and 5.78% on average across KITTI and Waymo, respectively.
Paper Structure (36 sections, 1 theorem, 11 equations, 4 figures, 13 tables, 1 algorithm)

This paper contains 36 sections, 1 theorem, 11 equations, 4 figures, 13 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. Monocular 3D object detection.
  4. Manifold geometry preservation.
  5. Metric learning.
  6. Method
  7. Preliminary
  8. Metric space.
  9. Quasi-isometry.
  10. Problem Definition
  11. Methodology
  12. (K, B, TEXT)-Quasi-isometric loss.
  13. Object-wise depth map loss.
  14. Total loss.
  15. Experiments
  16. ...and 21 more sections

Key Result

Theorem 1

Given that $B={B'}/{|\mathbf{P}|}$ and $B' \geq 0$, the two metric spaces $(\mathbf{Z}, |\cdot,\cdot|)$ and $(\mathbf{P}, \hat{\mathcal{G}}(\cdot,\cdot))$ are quasi-isometric.

Figures (4)

  • Figure 1: Illustration of our quasi-isometric loss $\mathcal{L}_{qi}$.
  • Figure 2: Loss scale (log-scaled) with respect to property-violated pairs ratio and performance.
  • Figure 3: Example of non-linearity preservation by using local-constraint.
  • Figure 4: Comparison of qualitative results between MonoCon and MonoCon + ours. Yellow circles highlight accurately estimated parts compared to the MonoCon. (GT: green, Prediction of MonoCon: blue, Prediction of MonoCon + Ours: red)

Theorems & Definitions (1)

  • Theorem