3DGeoDet: General-purpose Geometry-aware Image-based 3D Object Detection
- URL: http://arxiv.org/abs/2506.09541v1
- Date: Wed, 11 Jun 2025 09:18:36 GMT
- Title: 3DGeoDet: General-purpose Geometry-aware Image-based 3D Object Detection
- Authors: Yi Zhang, Yi Wang, Yawen Cui, Lap-Pui Chau,
- Abstract summary: 3DGeoDet is a novel geometry-aware 3D object detection approach.<n>It effectively handles single- and multi-view RGB images in indoor and outdoor environments.
- Score: 17.502554516157893
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper proposes 3DGeoDet, a novel geometry-aware 3D object detection approach that effectively handles single- and multi-view RGB images in indoor and outdoor environments, showcasing its general-purpose applicability. The key challenge for image-based 3D object detection tasks is the lack of 3D geometric cues, which leads to ambiguity in establishing correspondences between images and 3D representations. To tackle this problem, 3DGeoDet generates efficient 3D geometric representations in both explicit and implicit manners based on predicted depth information. Specifically, we utilize the predicted depth to learn voxel occupancy and optimize the voxelized 3D feature volume explicitly through the proposed voxel occupancy attention. To further enhance 3D awareness, the feature volume is integrated with an implicit 3D representation, the truncated signed distance function (TSDF). Without requiring supervision from 3D signals, we significantly improve the model's comprehension of 3D geometry by leveraging intermediate 3D representations and achieve end-to-end training. Our approach surpasses the performance of state-of-the-art image-based methods on both single- and multi-view benchmark datasets across diverse environments, achieving a 9.3 mAP@0.5 improvement on the SUN RGB-D dataset, a 3.3 mAP@0.5 improvement on the ScanNetV2 dataset, and a 0.19 AP3D@0.7 improvement on the KITTI dataset. The project page is available at: https://cindy0725.github.io/3DGeoDet/.
Related papers
- BIP3D: Bridging 2D Images and 3D Perception for Embodied Intelligence [11.91274849875519]
We introduce a novel image-centric 3D perception model, BIP3D, to overcome the limitations of point-centric methods.<n>We leverage pre-trained 2D vision foundation models to enhance semantic understanding, and introduce a spatial enhancer module to improve spatial understanding.<n>In our experiments, BIP3D outperforms current state-of-the-art results on the EmbodiedScan benchmark, achieving improvements of 5.69% in the 3D detection task and 15.25% in the 3D visual grounding task.
arXiv Detail & Related papers (2024-11-22T11:35:42Z) - Enhancing Single Image to 3D Generation using Gaussian Splatting and Hybrid Diffusion Priors [17.544733016978928]
3D object generation from a single image involves estimating the full 3D geometry and texture of unseen views from an unposed RGB image captured in the wild.
Recent advancements in 3D object generation have introduced techniques that reconstruct an object's 3D shape and texture.
We propose bridging the gap between 2D and 3D diffusion models to address this limitation.
arXiv Detail & Related papers (2024-10-12T10:14:11Z) - GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation [65.33726478659304]
We introduce the Geometry-Aware Large Reconstruction Model (GeoLRM), an approach which can predict high-quality assets with 512k Gaussians and 21 input images in only 11 GB GPU memory.
Previous works neglect the inherent sparsity of 3D structure and do not utilize explicit geometric relationships between 3D and 2D images.
GeoLRM tackles these issues by incorporating a novel 3D-aware transformer structure that directly processes 3D points and uses deformable cross-attention mechanisms.
arXiv Detail & Related papers (2024-06-21T17:49:31Z) - DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data [50.164670363633704]
We present DIRECT-3D, a diffusion-based 3D generative model for creating high-quality 3D assets from text prompts.
Our model is directly trained on extensive noisy and unaligned in-the-wild' 3D assets.
We achieve state-of-the-art performance in both single-class generation and text-to-3D generation.
arXiv Detail & Related papers (2024-06-06T17:58:15Z) - 3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features [70.50665869806188]
3DiffTection is a state-of-the-art method for 3D object detection from single images.
We fine-tune a diffusion model to perform novel view synthesis conditioned on a single image.
We further train the model on target data with detection supervision.
arXiv Detail & Related papers (2023-11-07T23:46:41Z) - Cross3DVG: Cross-Dataset 3D Visual Grounding on Different RGB-D Scans [6.936271803454143]
We present a novel task for cross-dataset visual grounding in 3D scenes (Cross3DVG)
We created RIORefer, a large-scale 3D visual grounding dataset.
It includes more than 63k diverse descriptions of 3D objects within 1,380 indoor RGB-D scans from 3RScan.
arXiv Detail & Related papers (2023-05-23T09:52:49Z) - Voxel-based 3D Detection and Reconstruction of Multiple Objects from a
Single Image [22.037472446683765]
We learn a regular grid of 3D voxel features from the input image which is aligned with 3D scene space via a 3D feature lifting operator.
Based on the 3D voxel features, our novel CenterNet-3D detection head formulates the 3D detection as keypoint detection in the 3D space.
We devise an efficient coarse-to-fine reconstruction module, including coarse-level voxelization and a novel local PCA-SDF shape representation.
arXiv Detail & Related papers (2021-11-04T18:30:37Z) - AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection [15.244852122106634]
We propose an approach for incorporating the shape-aware 2D/3D constraints into the 3D detection framework.
Specifically, we employ the deep neural network to learn distinguished 2D keypoints in the 2D image domain.
For generating the ground truth of 2D/3D keypoints, an automatic model-fitting approach has been proposed.
arXiv Detail & Related papers (2021-08-25T08:50:06Z) - 3D-to-2D Distillation for Indoor Scene Parsing [78.36781565047656]
We present a new approach that enables us to leverage 3D features extracted from large-scale 3D data repository to enhance 2D features extracted from RGB images.
First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training.
Second, we design a two-stage dimension normalization scheme to calibrate the 2D and 3D features for better integration.
Third, we design a semantic-aware adversarial training model to extend our framework for training with unpaired 3D data.
arXiv Detail & Related papers (2021-04-06T02:22:24Z) - ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object
Detection [69.68263074432224]
We present a novel framework named ZoomNet for stereo imagery-based 3D detection.
The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes.
To further exploit the abundant texture cues in RGB images for more accurate disparity estimation, we introduce a conceptually straight-forward module -- adaptive zooming.
arXiv Detail & Related papers (2020-03-01T17:18:08Z) - DSGN: Deep Stereo Geometry Network for 3D Object Detection [79.16397166985706]
There is a large performance gap between image-based and LiDAR-based 3D object detectors.
Our method, called Deep Stereo Geometry Network (DSGN), significantly reduces this gap.
For the first time, we provide a simple and effective one-stage stereo-based 3D detection pipeline.
arXiv Detail & Related papers (2020-01-10T11:44:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.