LOGen: Toward Lidar Object Generation by Point Diffusion

• URL: http://arxiv.org/abs/2412.07385v1
• Date: Tue, 10 Dec 2024 10:30:27 GMT
• Title: LOGen: Toward Lidar Object Generation by Point Diffusion
• Authors: Ellington Kirby, Mickael Chen, Renaud Marlet, Nermin Samet,
• Abstract summary: A common strategy to improve lidar segmentation results on rare semantic classes consists of pasting objects from one lidar scene into another. In this work, we explore how to enhance instance diversity using a lidar object generator.
• Score: 10.002129602976085
• License:
• Abstract: A common strategy to improve lidar segmentation results on rare semantic classes consists of pasting objects from one lidar scene into another. While this augments the quantity of instances seen at training time and varies their context, the instances fundamentally remain the same. In this work, we explore how to enhance instance diversity using a lidar object generator. We introduce a novel diffusion-based method to produce lidar point clouds of dataset objects, including reflectance, and with an extensive control of the generation via conditioning information. Our experiments on nuScenes show the quality of our object generations measured with new 3D metrics developed to suit lidar objects.

Related papers

• EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation [25.12999060040265]
  Learning to manipulate objects from high-dimensional observations presents significant challenges. Recent approaches have utilized large-scale offline data to train models from pixel observations. We propose a novel behavioral cloning (BC) approach that leverages object-centric representations and an entity-centric Transformer.
  arXiv  Detail & Related papers  (2024-12-25T13:50:15Z)
• Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange [50.45953583802282]
  We introduce a novel self-supervised learning (SSL) strategy for point cloud scene understanding. Our approach leverages both object patterns and contextual cues to produce robust features. Our experiments demonstrate the superiority of our method over existing SSL techniques.
  arXiv  Detail & Related papers  (2024-04-11T06:39:53Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• Learning Object-level Point Augmentor for Semi-supervised 3D Object Detection [85.170578641966]
  We propose an object-level point augmentor (OPA) that performs local transformations for semi-supervised 3D object detection. In this way, the resultant augmentor is derived to emphasize object instances rather than irrelevant backgrounds. Experiments on the ScanNet and SUN RGB-D datasets show that the proposed OPA performs favorably against the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-12-19T06:56:14Z)
• SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition [35.4163266882568]
  We introduce Self-Supervised Learning Over Sets (SOS) to pre-train a generic Objects In Contact (OIC) representation model. Our OIC significantly boosts the performance of multiple state-of-the-art video classification models.
  arXiv  Detail & Related papers  (2022-04-10T23:27:19Z)
• Fusing Local Similarities for Retrieval-based 3D Orientation Estimation of Unseen Objects [70.49392581592089]
  We tackle the task of estimating the 3D orientation of previously-unseen objects from monocular images. We follow a retrieval-based strategy and prevent the network from learning object-specific features. Our experiments on the LineMOD, LineMOD-Occluded, and T-LESS datasets show that our method yields a significantly better generalization to unseen objects than previous works.
  arXiv  Detail & Related papers  (2022-03-16T08:53:00Z)
• Diversity in deep generative models and generative AI [0.0]
  We introduce a kernel-based measure quantization method that can produce new objects from a given target measure by approximating it as a whole. This ensures a better diversity of the produced objects. The method is tested on classic machine learning benchmarks.
  arXiv  Detail & Related papers  (2022-02-19T10:52:52Z)
• Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation [53.49821324597837]
  Weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. We present a Context Decoupling Augmentation ( CDA) method to change the inherent context in which the objects appear. To validate the effectiveness of the proposed method, extensive experiments on PASCAL VOC 2012 dataset with several alternative network architectures demonstrate that CDA can boost various popular WSSS methods to the new state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-03-02T15:05:09Z)
• Object-Centric Image Generation from Layouts [93.10217725729468]
  We develop a layout-to-image-generation method to generate complex scenes with multiple objects. Our method learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We introduce SceneFID, an object-centric adaptation of the popular Fr'echet Inception Distance metric, that is better suited for multi-object images.
  arXiv  Detail & Related papers  (2020-03-16T21:40:09Z)

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.