Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection

• URL: http://arxiv.org/abs/2305.12853v1
• Date: Mon, 22 May 2023 09:24:55 GMT
• Title: Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection
• Authors: Jinglin Zhan, Tiejun Liu, Rengang Li, Jingwei Zhang, Zhaoxiang Zhang, Yuntao Chen
• Abstract summary: We study the synthesis-based LiDAR data augmentation approach (so-called GT-Aug) which offers maxium controllability over generated data samples. We propose Real-Aug, a synthesis-based augmentation method which prioritizes on generating realistic LiDAR scans. We achieve a state-of-the-art 0.744 NDS and 0.702 mAP on nuScenes test set.
• Score: 45.102312149413855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data and model are the undoubtable two supporting pillars for LiDAR object detection. However, data-centric works have fallen far behind compared with the ever-growing list of fancy new models. In this work, we systematically study the synthesis-based LiDAR data augmentation approach (so-called GT-Aug) which offers maxium controllability over generated data samples. We pinpoint the main shortcoming of existing works is introducing unrealistic LiDAR scan patterns during GT-Aug. In light of this finding, we propose Real-Aug, a synthesis-based augmentation method which prioritizes on generating realistic LiDAR scans. Our method consists a reality-conforming scene composition module which handles the details of the composition and a real-synthesis mixing up training strategy which gradually adapts the data distribution from synthetic data to the real one. To verify the effectiveness of our methods, we conduct extensive ablation studies and validate the proposed Real-Aug on a wide combination of detectors and datasets. We achieve a state-of-the-art 0.744 NDS and 0.702 mAP on nuScenes test set. The code shall be released soon.

Related papers

• Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene [22.297964850282177]
  We propose LiDAR-2D Self-paced Learning (LiSe) for unsupervised 3D detection. RGB images serve as a valuable complement to LiDAR data, offering precise 2D localization cues. Our framework devises a self-paced learning pipeline that incorporates adaptive sampling and weak model aggregation strategies.
  arXiv  Detail & Related papers  (2024-07-11T14:58:49Z)
• 4D Contrastive Superflows are Dense 3D Representation Learners [62.433137130087445]
  We introduce SuperFlow, a novel framework designed to harness consecutive LiDAR-camera pairs for establishing pretraining objectives. To further boost learning efficiency, we incorporate a plug-and-play view consistency module that enhances alignment of the knowledge distilled from camera views.
  arXiv  Detail & Related papers  (2024-07-08T17:59:54Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• Just Add $100 More: Augmenting NeRF-based Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem [12.26293873825084]
  We propose to leverage pseudo-LiDAR point clouds generated from videos capturing a surround view of miniatures or real-world objects of minor classes. Our method, called Pseudo Ground Truth Augmentation (PGT-Aug), consists of three main steps: (i) volumetric 3D instance reconstruction using a 2D-to-3D view synthesis model, (ii) object-level domain alignment with LiDAR intensity estimation, and (iii) a hybrid context-aware placement method from ground and map information.
  arXiv  Detail & Related papers  (2024-03-18T08:50:04Z)
• LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models [1.1965844936801797]
  Generative modeling of 3D LiDAR data is an emerging task with promising applications for autonomous mobile robots. We present R2DM, a novel generative model for LiDAR data that can generate diverse and high-fidelity 3D scene point clouds. Our method is built upon denoising diffusion probabilistic models (DDPMs), which have shown impressive results among generative model frameworks.
  arXiv  Detail & Related papers  (2023-09-17T12:26:57Z)
• Neural LiDAR Fields for Novel View Synthesis [80.45307792404685]
  We present Neural Fields for LiDAR (NFL), a method to optimise a neural field scene representation from LiDAR measurements. NFL combines the rendering power of neural fields with a detailed, physically motivated model of the LiDAR sensing process. We show that the improved realism of the synthesized views narrows the domain gap to real scans and translates to better registration and semantic segmentation performance.
  arXiv  Detail & Related papers  (2023-05-02T17:55:38Z)
• A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation [42.2398858786125]
  Deep learning in computer vision has achieved great success with the price of large-scale labeled training data. The uncontrollable data collection process produces non-IID training and test data, where undesired duplication may exist. To circumvent them, an alternative is to generate synthetic data via 3D rendering with domain randomization.
  arXiv  Detail & Related papers  (2023-03-16T09:03:52Z)
• Learning to Simulate Realistic LiDARs [66.7519667383175]
  We introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces. We use our technique to learn models of two distinct LiDAR sensors and use them to improve simulated LiDAR data accordingly.
  arXiv  Detail & Related papers  (2022-09-22T13:12:54Z)
• Boosting 3D Object Detection by Simulating Multimodality on Point Clouds [51.87740119160152]
  This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to simulate features and responses that follow a multi-modality (LiDAR-image) detector. The approach needs LiDAR-image data only when training the single-modality detector, and once well-trained, it only needs LiDAR data at inference. Experimental results on the nuScenes dataset show that our approach outperforms all SOTA LiDAR-only 3D detectors.
  arXiv  Detail & Related papers  (2022-06-30T01:44:30Z)

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.