SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving

• URL: http://arxiv.org/abs/2102.09883v1
• Date: Fri, 19 Feb 2021 11:56:44 GMT
• Title: SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving
• Authors: George Eskandar, Alexander Braun, Martin Meinke, Karim Armanious, Bin Yang
• Abstract summary: We propose a new LiDAR prediction framework that is based on generative models namely Variational Recurrent Neural Networks (VRNNs) Our algorithm is able to address the limitations of previous video prediction frameworks when dealing with sparse data by spatially inpainting the depth maps in the upcoming frames. We present a sparse version of VRNNs and an effective self-supervised training method that does not require any labels.
• Score: 63.87272273293804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting future 3D LiDAR pointclouds is a challenging task that is useful in many applications in autonomous driving such as trajectory prediction, pose forecasting and decision making. In this work, we propose a new LiDAR prediction framework that is based on generative models namely Variational Recurrent Neural Networks (VRNNs), titled Stochastic LiDAR Prediction and Completion (SLPC). Our algorithm is able to address the limitations of previous video prediction frameworks when dealing with sparse data by spatially inpainting the depth maps in the upcoming frames. Our contributions can thus be summarized as follows: we introduce the new task of predicting and completing depth maps from spatially sparse data, we present a sparse version of VRNNs and an effective self-supervised training method that does not require any labels. Experimental results illustrate the effectiveness of our framework in comparison to the state of the art methods in video prediction.

Related papers

• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• Self-supervised Multi-future Occupancy Forecasting for Autonomous Driving [45.886941596233974]
  LiDAR-generated occupancy grid maps (L-OGMs) offer a robust bird's-eye view for the scene representation. Our proposed framework performs L-OGM prediction in the latent space of a generative architecture. We decode predictions using either a single-step decoder, which provides high-quality predictions in real-time, or a diffusion-based batch decoder.
  arXiv  Detail & Related papers  (2024-07-30T18:37:59Z)
• GRANP: A Graph Recurrent Attentive Neural Process Model for Vehicle Trajectory Prediction [3.031375888004876]
  We propose a novel model named Graph Recurrent Attentive Neural Process (GRANP) for vehicle trajectory prediction. GRANP contains an encoder with deterministic and latent paths, and a decoder for prediction. We show that GRANP achieves state-of-the-art results and can efficiently quantify uncertainties.
  arXiv  Detail & Related papers  (2024-04-09T05:51:40Z)
• OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments [77.0399450848749]
  We propose an OccNeRF method for training occupancy networks without 3D supervision. We parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range. For semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model.
  arXiv  Detail & Related papers  (2023-12-14T18:58:52Z)
• SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations [76.45009891152178]
  Pretraining-finetuning approach can alleviate the labeling burden by fine-tuning a pre-trained backbone across various downstream datasets as well as tasks. We show, for the first time, that general representations learning can be achieved through the task of occupancy prediction. Our findings will facilitate the understanding of LiDAR points and pave the way for future advancements in LiDAR pre-training.
  arXiv  Detail & Related papers  (2023-09-19T11:13:01Z)
• Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction [63.3021778885906]
  3D bounding boxes are a widespread intermediate representation in many computer vision applications. We propose methods for leveraging our autoregressive model to make high confidence predictions and meaningful uncertainty measures. We release a simulated dataset, COB-3D, which highlights new types of ambiguity that arise in real-world robotics applications.
  arXiv  Detail & Related papers  (2022-10-13T23:57:40Z)
• LOPR: Latent Occupancy PRediction using Generative Models [49.15687400958916]
  LiDAR generated occupancy grid maps (L-OGMs) offer a robust bird's eye-view scene representation. We propose a framework that decouples occupancy prediction into: representation learning and prediction within the learned latent space.
  arXiv  Detail & Related papers  (2022-10-03T22:04:00Z)
• PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D [10.580548257913843]
  We propose a new pedestrian action prediction dataset created by adding per-frame 2D/3D bounding box and behavioral annotations to nuScenes. In addition, we propose a hybrid neural network architecture that incorporates various data modalities for predicting pedestrian crossing action.
  arXiv  Detail & Related papers  (2020-12-14T18:13:44Z)

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.