Confidence Guided Stereo 3D Object Detection with Split Depth Estimation

• URL: http://arxiv.org/abs/2003.05505v1
• Date: Wed, 11 Mar 2020 20:00:11 GMT
• Title: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation
• Authors: Chengyao Li, Jason Ku and Steven L. Waslander
• Abstract summary: CG-Stereo is a confidence-guided stereo 3D object detection pipeline. It uses separate decoders for foreground and background pixels during depth estimation. Our approach outperforms all state-of-the-art stereo-based 3D detectors on the KITTI benchmark.
• Score: 10.64859537162938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and reliable 3D object detection is vital to safe autonomous driving. Despite recent developments, the performance gap between stereo-based methods and LiDAR-based methods is still considerable. Accurate depth estimation is crucial to the performance of stereo-based 3D object detection methods, particularly for those pixels associated with objects in the foreground. Moreover, stereo-based methods suffer from high variance in the depth estimation accuracy, which is often not considered in the object detection pipeline. To tackle these two issues, we propose CG-Stereo, a confidence-guided stereo 3D object detection pipeline that uses separate decoders for foreground and background pixels during depth estimation, and leverages the confidence estimation from the depth estimation network as a soft attention mechanism in the 3D object detector. Our approach outperforms all state-of-the-art stereo-based 3D detectors on the KITTI benchmark.

Related papers

• OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection [102.0744303467713]
  We propose a new multi-view 3D object detector named OPEN. Our main idea is to effectively inject object-wise depth information into the network through our proposed object-wise position embedding. OPEN achieves a new state-of-the-art performance with 64.4% NDS and 56.7% mAP on the nuScenes test benchmark.
  arXiv  Detail & Related papers  (2024-07-15T14:29:15Z)
• SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection [0.0]
  A recently proposed framework for monocular 3D detection based on Pseudo-Stereo has received considerable attention in the community. In this work, we propose an end-to-end, efficient pseudo-stereo 3D detection framework by introducing a Single-View Diffusion Model. SVDM allows the entire pseudo-stereo 3D detection pipeline to be trained end-to-end and can benefit from the training of stereo detectors.
  arXiv  Detail & Related papers  (2023-07-05T13:10:37Z)
• Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision [13.593246617391266]
  We propose a method to boost the RGB image-based 3D detector by jointly training the detection network with a depth prediction loss analogous to the depth estimation task. Our novel object-centric depth prediction loss focuses on depth around foreground objects, which is important for 3D object detection. Our depth regression model is further trained to predict the uncertainty of depth to represent the 3D confidence of objects.
  arXiv  Detail & Related papers  (2022-10-29T11:32:28Z)
• SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation [11.169586369931803]
  We propose a stereo-image based anchor-free 3D detection method, called structure-aware stereo 3D detector (termed as SIDE) We explore the instance-level depth information via constructing the cost volume from RoIs of each object. Our method achieves the state-of-the-art performance compared to existing methods without depth map supervision.
  arXiv  Detail & Related papers  (2021-08-22T08:09:38Z)
• LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector [80.7563981951707]
  We propose LIGA-Stereo to learn stereo-based 3D detectors under the guidance of high-level geometry-aware representations of LiDAR-based detection models. Compared with the state-of-the-art stereo detector, our method has improved the 3D detection performance of cars, pedestrians, cyclists by 10.44%, 5.69%, 5.97% mAP respectively.
  arXiv  Detail & Related papers  (2021-08-18T17:24:40Z)
• M3DSSD: Monocular 3D Single Stage Object Detector [82.25793227026443]
  We propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. The proposed M3DSSD achieves significantly better performance than the monocular 3D object detection methods on the KITTI dataset.
  arXiv  Detail & Related papers  (2021-03-24T13:09:11Z)
• Stereo CenterNet based 3D Object Detection for Autonomous Driving [2.508414661327797]
  We propose a 3D object detection method using geometric information in stereo images, called Stereo CenterNet. Stereo CenterNet predicts the four semantic key points of the 3D bounding box of the object in space and uses 2D left right boxes, 3D dimension, orientation and key points to restore the bounding box of the object in the 3D space. Experiments conducted on the KITTI dataset show that our method achieves the best speed-accuracy trade-off compared with the state-of-the-art methods based on stereo geometry.
  arXiv  Detail & Related papers  (2021-03-20T02:18:49Z)
• PLUME: Efficient 3D Object Detection from Stereo Images [95.31278688164646]
  Existing methods tackle the problem in two steps: first depth estimation is performed, a pseudo LiDAR point cloud representation is computed from the depth estimates, and then object detection is performed in 3D space. We propose a model that unifies these two tasks in the same metric space. Our approach achieves state-of-the-art performance on the challenging KITTI benchmark, with significantly reduced inference time compared with existing methods.
  arXiv  Detail & Related papers  (2021-01-17T05:11:38Z)
• Expandable YOLO: 3D Object Detection from RGB-D Images [64.14512458954344]
  This paper aims at constructing a light-weight object detector that inputs a depth and a color image from a stereo camera. By extending the network architecture of YOLOv3 to 3D in the middle, it is possible to output in the depth direction. Intersection over Uninon (IoU) in 3D space is introduced to confirm the accuracy of region extraction results.
  arXiv  Detail & Related papers  (2020-06-26T07:32:30Z)
• 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.