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VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection

Bonan Ding, Jin Xie, Jing Nie, Jiale Cao, Xuelong Li, Yanwei Pang

TL;DR

VFMM3D tackles monocular 3D object detection by converting single-view images into LiDAR-like point clouds using Depth Anything Model (DAM) for depth and the Segment Anything Model (SAM) for foreground segmentation. A text-prompt variant of SAM, along with a painting step, enriches the pseudo-LiDAR with semantic foreground information, and a sparsification stage reduces noise and computational load before feeding data into standard LiDAR-based detectors. The approach is dataset-agnostic and demonstrates state-of-the-art results on KITTI and Waymo while remaining compatible with multiple detectors (PV-RCNN, Voxel-RCNN, PointPillars). The work highlights the potential of Vision Foundation Models to unlock high-quality 3D perception from monocular imagery, with practical implications for autonomous driving and robotics, and suggests avenues for future improvements with broader VFMs and datasets.

Abstract

Due to its cost-effectiveness and widespread availability, monocular 3D object detection, which relies solely on a single camera during inference, holds significant importance across various applications, including autonomous driving and robotics. Nevertheless, directly predicting the coordinates of objects in 3D space from monocular images poses challenges. Therefore, an effective solution involves transforming monocular images into LiDAR-like representations and employing a LiDAR-based 3D object detector to predict the 3D coordinates of objects. The key step in this method is accurately converting the monocular image into a reliable point cloud form. In this paper, we present VFMM3D, an innovative framework that leverages the capabilities of Vision Foundation Models (VFMs) to accurately transform single-view images into LiDAR point cloud representations. VFMM3D utilizes the Segment Anything Model (SAM) and Depth Anything Model (DAM) to generate high-quality pseudo-LiDAR data enriched with rich foreground information. Specifically, the Depth Anything Model (DAM) is employed to generate dense depth maps. Subsequently, the Segment Anything Model (SAM) is utilized to differentiate foreground and background regions by predicting instance masks. These predicted instance masks and depth maps are then combined and projected into 3D space to generate pseudo-LiDAR points. Finally, any object detectors based on point clouds can be utilized to predict the 3D coordinates of objects. Comprehensive experiments are conducted on two challenging 3D object detection datasets, KITTI and Waymo. Our VFMM3D establishes a new state-of-the-art performance on both datasets. Additionally, experimental results demonstrate the generality of VFMM3D, showcasing its seamless integration into various LiDAR-based 3D object detectors.

VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection

TL;DR

VFMM3D tackles monocular 3D object detection by converting single-view images into LiDAR-like point clouds using Depth Anything Model (DAM) for depth and the Segment Anything Model (SAM) for foreground segmentation. A text-prompt variant of SAM, along with a painting step, enriches the pseudo-LiDAR with semantic foreground information, and a sparsification stage reduces noise and computational load before feeding data into standard LiDAR-based detectors. The approach is dataset-agnostic and demonstrates state-of-the-art results on KITTI and Waymo while remaining compatible with multiple detectors (PV-RCNN, Voxel-RCNN, PointPillars). The work highlights the potential of Vision Foundation Models to unlock high-quality 3D perception from monocular imagery, with practical implications for autonomous driving and robotics, and suggests avenues for future improvements with broader VFMs and datasets.

Abstract

Due to its cost-effectiveness and widespread availability, monocular 3D object detection, which relies solely on a single camera during inference, holds significant importance across various applications, including autonomous driving and robotics. Nevertheless, directly predicting the coordinates of objects in 3D space from monocular images poses challenges. Therefore, an effective solution involves transforming monocular images into LiDAR-like representations and employing a LiDAR-based 3D object detector to predict the 3D coordinates of objects. The key step in this method is accurately converting the monocular image into a reliable point cloud form. In this paper, we present VFMM3D, an innovative framework that leverages the capabilities of Vision Foundation Models (VFMs) to accurately transform single-view images into LiDAR point cloud representations. VFMM3D utilizes the Segment Anything Model (SAM) and Depth Anything Model (DAM) to generate high-quality pseudo-LiDAR data enriched with rich foreground information. Specifically, the Depth Anything Model (DAM) is employed to generate dense depth maps. Subsequently, the Segment Anything Model (SAM) is utilized to differentiate foreground and background regions by predicting instance masks. These predicted instance masks and depth maps are then combined and projected into 3D space to generate pseudo-LiDAR points. Finally, any object detectors based on point clouds can be utilized to predict the 3D coordinates of objects. Comprehensive experiments are conducted on two challenging 3D object detection datasets, KITTI and Waymo. Our VFMM3D establishes a new state-of-the-art performance on both datasets. Additionally, experimental results demonstrate the generality of VFMM3D, showcasing its seamless integration into various LiDAR-based 3D object detectors.
Paper Structure (16 sections, 3 equations, 4 figures, 3 tables, 2 algorithms)

This paper contains 16 sections, 3 equations, 4 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Task Definition
  5. Framework Overview
  6. Pseudo-LiDAR Generation with DAM
  7. Pseudo-LiDAR Painting with Text-Prompt SAM
  8. Sparsify Pseudo-LiDAR
  9. LiDAR-Based Detection
  10. Experiments
  11. Settings
  12. Waymo Results
  13. KITTI Results
  14. Visualization of Each Component Results.
  15. Ablation Study
  16. ...and 1 more sections

Figures (4)

  • Figure 1: The overall architecture of VFMM3D. Our model consists of four parts: (a) Pseudo-LiDAR generation by DAM, (b) Pseudo-LiDAR painting by text-prompt SAM, (c) Pseudo-LiDAR sparsification, (d) 3D object detection by LiDAR-based detectors.
  • Figure 2: The detailed architecture of Pseudo-LiDAR Painting Text-Prompt SAM.
  • Figure 3: Qualitative results of VFMM3D on KITTI val. set. We visualize our 3D bounding box estimates (in red) alongside ground truth annotations (in green) on front view images (1st and 3rd rows) and pseudo-LiDAR point clouds (2nd and 4th rows).
  • Figure 4: Visualization of segmentation results (2nd row) from text-prompt SAM, depth maps (3rd row) from DAM, raw Pseudo-LiDAR (4th row) from depth map and Painted Sparse Pseudo-LiDAR (5th row) results from each component of VFMM3D on KITTI.