Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data

• URL: http://arxiv.org/abs/2008.01034v2
• Date: Wed, 5 Aug 2020 14:46:40 GMT
• Title: Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data
• Authors: Adrian Lopez-Rodriguez and Benjamin Busam and Krystian Mikolajczyk
• Abstract summary: We propose a domain adaptation approach for sparse-to-dense depth completion that is trained from synthetic data, without annotations in the real domain or additional sensors. Our approach simulates the real sensor noise in an RGB+LiDAR set-up, and consists of three modules: simulating the real LiDAR input in the synthetic domain via projections, filtering the real noisy LiDAR for supervision and adapting the synthetic RGB image using a CycleGAN approach.
• Score: 26.050220048154596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depth completion aims to predict a dense depth map from a sparse depth input. The acquisition of dense ground truth annotations for depth completion settings can be difficult and, at the same time, a significant domain gap between real LiDAR measurements and synthetic data has prevented from successful training of models in virtual settings. We propose a domain adaptation approach for sparse-to-dense depth completion that is trained from synthetic data, without annotations in the real domain or additional sensors. Our approach simulates the real sensor noise in an RGB+LiDAR set-up, and consists of three modules: simulating the real LiDAR input in the synthetic domain via projections, filtering the real noisy LiDAR for supervision and adapting the synthetic RGB image using a CycleGAN approach. We extensively evaluate these modules against the state-of-the-art in the KITTI depth completion benchmark, showing significant improvements.

Related papers

• Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation [9.812476193015488]
  We propose a method of data generation in simulation using 3D synthetic environments and CycleGAN domain transfer. We compare this method of data generation to the popular NYUDepth V2 dataset by training a depth estimation model based on the DenseDepth structure using different training sets of real and simulated data. We evaluate the performance of the models on newly collected images and LiDAR depth data from a Husky robot to verify the generalizability of the approach and show that GAN-transformed data can serve as an effective alternative to real-world data, particularly in depth estimation.
  arXiv  Detail & Related papers  (2024-05-02T09:21:10Z)
• Robust Depth Enhancement via Polarization Prompt Fusion Tuning [112.88371907047396]
  We present a framework that leverages polarization imaging to improve inaccurate depth measurements from various depth sensors. Our method first adopts a learning-based strategy where a neural network is trained to estimate a dense and complete depth map from polarization data and a sensor depth map from different sensors. To further improve the performance, we propose a Polarization Prompt Fusion Tuning (PPFT) strategy to effectively utilize RGB-based models pre-trained on large-scale datasets.
  arXiv  Detail & Related papers  (2024-04-05T17:55:33Z)
• Q-SLAM: Quadric Representations for Monocular SLAM [85.82697759049388]
  We reimagine volumetric representations through the lens of quadrics. We use quadric assumption to rectify noisy depth estimations from RGB inputs. We introduce a novel quadric-decomposed transformer to aggregate information across quadrics.
  arXiv  Detail & Related papers  (2024-03-12T23:27:30Z)
• Ternary-Type Opacity and Hybrid Odometry for RGB NeRF-SLAM [58.736472371951955]
  We introduce a ternary-type opacity (TT) model, which categorizes points on a ray intersecting a surface into three regions: before, on, and behind the surface. This enables a more accurate rendering of depth, subsequently improving the performance of image warping techniques. Our integrated approach of TT and HO achieves state-of-the-art performance on synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-12-20T18:03:17Z)
• 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)
• Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects [28.84776177634971]
  We propose a powerful RGBD fusion network, SwinDRNet, for depth restoration. We also propose Domain Randomization-Enhanced Depth Simulation (DREDS) approach to simulate an active stereo depth system. We show that our depth restoration effectively boosts the performance of downstream tasks.
  arXiv  Detail & Related papers  (2022-08-07T19:17:16Z)
• Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction [91.43066633305662]
  We propose a novel multi-task and multi-modal filtered transformer (MMFT) network for RGB-D salient object detection (SOD) Specifically, we unify three complementary tasks: depth estimation, salient object detection and contour estimation. The multi-task mechanism promotes the model to learn the task-aware features from the auxiliary tasks. Experiments show that it not only significantly surpasses the depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts a high-quality depth map and salient contour at the same time.
  arXiv  Detail & Related papers  (2022-03-09T17:20:18Z)
• Consistent Depth Prediction under Various Illuminations using Dilated Cross Attention [1.332560004325655]
  We propose to use internet 3D indoor scenes and manually tune their illuminations to render photo-realistic RGB photos and their corresponding depth and BRDF maps. We perform cross attention on these dilated features to retain the consistency of depth prediction under different illuminations. Our method is evaluated by comparing it with current state-of-the-art methods on Vari dataset and a significant improvement is observed in experiments.
  arXiv  Detail & Related papers  (2021-12-15T10:02:46Z)
• Sparse Depth Completion with Semantic Mesh Deformation Optimization [4.03103540543081]
  We propose a neural network with post-optimization, which takes an RGB image and sparse depth samples as input and predicts the complete depth map. Our evaluation results outperform the existing work consistently on both indoor and outdoor datasets.
  arXiv  Detail & Related papers  (2021-12-10T13:01:06Z)
• ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation [111.56730703473411]
  Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations. Simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels. ePointDA consists of three modules: self-supervised dropout noise rendering, statistics-invariant and spatially-adaptive feature alignment, and transferable segmentation learning.
  arXiv  Detail & Related papers  (2020-09-07T23:46:08Z)
• Decoder Modulation for Indoor Depth Completion [2.099922236065961]
  Depth completion recovers a dense depth map from sensor measurements. Current methods are mostly tailored for very sparse depth measurements from LiDARs in outdoor settings. We propose a new model that takes into account the statistical difference between such regions.
  arXiv  Detail & Related papers  (2020-05-18T11:42:42Z)

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.