MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences

• URL: http://arxiv.org/abs/2306.03206v1
• Date: Mon, 5 Jun 2023 19:28:19 GMT
• Title: MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences
• Authors: Yingwei Li, Charles R. Qi, Yin Zhou, Chenxi Liu, Dragomir Anguelov
• Abstract summary: We propose MoDAR, using motion forecasting outputs as a type of virtual modality, to augment LiDAR point clouds. A fused point cloud of both raw sensor points and the virtual points can then be fed to any off-the-shelf point-cloud based 3D object detector.
• Score: 38.7464958249103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Occluded and long-range objects are ubiquitous and challenging for 3D object detection. Point cloud sequence data provide unique opportunities to improve such cases, as an occluded or distant object can be observed from different viewpoints or gets better visibility over time. However, the efficiency and effectiveness in encoding long-term sequence data can still be improved. In this work, we propose MoDAR, using motion forecasting outputs as a type of virtual modality, to augment LiDAR point clouds. The MoDAR modality propagates object information from temporal contexts to a target frame, represented as a set of virtual points, one for each object from a waypoint on a forecasted trajectory. A fused point cloud of both raw sensor points and the virtual points can then be fed to any off-the-shelf point-cloud based 3D object detector. Evaluated on the Waymo Open Dataset, our method significantly improves prior art detectors by using motion forecasting from extra-long sequences (e.g. 18 seconds), achieving new state of the arts, while not adding much computation overhead.

Related papers

• Future Does Matter: Boosting 3D Object Detection with Temporal Motion Estimation in Point Cloud Sequences [25.74000325019015]
  We introduce a novel LiDAR 3D object detection framework, namely LiSTM, to facilitate spatial-temporal feature learning with cross-frame motion forecasting information. We have conducted experiments on the aggregation and nuScenes datasets to demonstrate that the proposed framework achieves superior 3D detection performance.
  arXiv  Detail & Related papers  (2024-09-06T16:29:04Z)
• PTT: Point-Trajectory Transformer for Efficient Temporal 3D Object Detection [66.94819989912823]
  We propose a point-trajectory transformer with long short-term memory for efficient temporal 3D object detection. We use point clouds of current-frame objects and their historical trajectories as input to minimize the memory bank storage requirement. We conduct extensive experiments on the large-scale dataset to demonstrate that our approach performs well against state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-13T18:59:13Z)
• DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds [55.755450273390004]
  Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D detectors is not explored mainly due to two reasons: (1) the onboard multi-object tracker cannot generate sufficient complete object trajectories, and (2) the motion state of objects poses an inevitable challenge for the object-centric refining stage. To tackle these problems, we propose a novel paradigm of offboard 3D object detection, named DetZero.
  arXiv  Detail & Related papers  (2023-06-09T16:42:00Z)
• MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection [8.305942415868042]
  Most LiDAR sensors generate a sequence of point clouds in real-time. Recent studies have revealed that substantial performance improvement can be achieved by exploiting the context present in a sequence of point sets. We propose a novel 3D object detection architecture, which can encode point cloud sequences acquired by multiple successive scans.
  arXiv  Detail & Related papers  (2022-12-01T11:24:47Z)
• D-Align: Dual Query Co-attention Network for 3D Object Detection Based on Multi-frame Point Cloud Sequence [8.21339007493213]
  Conventional 3D object detectors detect objects using a set of points acquired over a fixed duration. Recent studies have shown that the performance of object detection can be further enhanced by utilizing point cloud sequences. We propose D-Align, which can effectively produce strong bird's-eye-view (BEV) features by aligning and aggregating the features obtained from a sequence of point sets.
  arXiv  Detail & Related papers  (2022-09-30T20:41:25Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection [64.2159881697615]
  Object detection from 3D point clouds remains a challenging task, though recent studies pushed the envelope with the deep learning techniques. We propose a domain adaptation like approach to enhance the robustness of the feature representation. Our simple yet effective approach fundamentally boosts the performance of 3D point cloud object detection and achieves the state-of-the-art results.
  arXiv  Detail & Related papers  (2020-06-08T05:15:06Z)
• LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention [100.52873557168637]
  3D object detectors usually focus on the single-frame detection, while ignoring the information in consecutive point cloud frames. In this paper, we propose an end-to-end online 3D video object detector that operates on point sequences.
  arXiv  Detail & Related papers  (2020-04-03T06:06:52Z)
• Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds [32.916690488130506]
  We propose a universal module that helps 3D detectors focus on the densest region of the point clouds in a boundary-aware manner. Experiments on KITTI dataset show that DENFI improves the performance of the baseline single-stage detector remarkably.
  arXiv  Detail & Related papers  (2020-04-01T01:21:23Z)

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.