DEPTHOR++: Robust Depth Enhancement from a Real-World Lightweight dToF and RGB Guidance

• URL: http://arxiv.org/abs/2509.26498v1
• Date: Tue, 30 Sep 2025 16:41:11 GMT
• Title: DEPTHOR++: Robust Depth Enhancement from a Real-World Lightweight dToF and RGB Guidance
• Authors: Jijun Xiang, Longliang Liu, Xuan Zhu, Xianqi Wang, Min Lin, Xin Yang,
• Abstract summary: DEPTHOR++ is a practical and novel depth completion framework.<n>It enhances robustness to noisy dToF inputs from three key aspects.<n>On the ZJU-L5 dataset and real-world samples, our training strategy significantly boosts existing depth completion models.
• Score: 14.818201604060144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depth enhancement, which converts raw dToF signals into dense depth maps using RGB guidance, is crucial for improving depth perception in high-precision tasks such as 3D reconstruction and SLAM. However, existing methods often assume ideal dToF inputs and perfect dToF-RGB alignment, overlooking calibration errors and anomalies, thus limiting real-world applicability. This work systematically analyzes the noise characteristics of real-world lightweight dToF sensors and proposes a practical and novel depth completion framework, DEPTHOR++, which enhances robustness to noisy dToF inputs from three key aspects. First, we introduce a simulation method based on synthetic datasets to generate realistic training samples for robust model training. Second, we propose a learnable-parameter-free anomaly detection mechanism to identify and remove erroneous dToF measurements, preventing misleading propagation during completion. Third, we design a depth completion network tailored to noisy dToF inputs, which integrates RGB images and pre-trained monocular depth estimation priors to improve depth recovery in challenging regions. On the ZJU-L5 dataset and real-world samples, our training strategy significantly boosts existing depth completion models, with our model achieving state-of-the-art performance, improving RMSE and Rel by 22% and 11% on average. On the Mirror3D-NYU dataset, by incorporating the anomaly detection method, our model improves upon the previous SOTA by 37% in mirror regions. On the Hammer dataset, using simulated low-cost dToF data from RealSense L515, our method surpasses the L515 measurements with an average gain of 22%, demonstrating its potential to enable low-cost sensors to outperform higher-end devices. Qualitative results across diverse real-world datasets further validate the effectiveness and generalizability of our approach.

Related papers

• Pseudo Depth Meets Gaussian: A Feed-forward RGB SLAM Baseline [64.42938561167402]
  We propose an online 3D reconstruction method using 3D Gaussian-based SLAM, combined with a feed-forward recurrent prediction module.<n>This approach replaces slow test-time optimization with fast network inference, significantly improving tracking speed.<n>Our method achieves performance on par with the state-of-the-art SplaTAM, while reducing tracking time by more than 90%.
  arXiv  Detail & Related papers  (2025-08-06T16:16:58Z)
• DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB Image [8.588871458005114]
  We propose a novel completion-based method, named DEPTHOR, for depth enhancement in computer vision.<n>First, we simulate real-world dToF data from the accurate ground truth in synthetic datasets to enable noise-robust training.<n>Second, we design a novel network that incorporates monocular depth estimation (MDE), leveraging global depth relationships and contextual information to improve prediction in challenging regions.
  arXiv  Detail & Related papers  (2025-04-02T11:02:21Z)
• Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian [49.21866794516328]
  3D Gaussian splatting has demonstrated impressive performance in real-time novel view synthesis. Previous approaches have incorporated depth supervision into the training of 3D Gaussians to mitigate overfitting. We introduce a novel method to supervise the depth distribution of 3D Gaussians, utilizing depth priors with integrated uncertainty estimates.
  arXiv  Detail & Related papers  (2024-05-30T03:18:30Z)
• 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)
• Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis [48.59382455101753]
  2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose. Recent studies focus on RGB-D face recognition to improve robustness by incorporating depth information. In this work, we first construct a diverse depth dataset generated by 3D Morphable Models for depth model pre-training. Then, we propose a domain-independent pre-training framework that utilizes readily available pre-trained RGB and depth models to separately perform face recognition without needing additional paired data for retraining.
  arXiv  Detail & Related papers  (2024-03-11T09:12:24Z)
• Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration [20.82054596017465]
  Recent camera-based 3D object detection is limited by the precision of transforming from image to 3D feature spaces. This paper aims to address such a fundamental problem of camera-based 3D object detection: How to effectively learn depth information for accurate feature lifting and object localization.
  arXiv  Detail & Related papers  (2024-02-07T14:21:26Z)
• Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation [12.843938169660404]
  RGB-based surface anomaly detection methods have advanced significantly. Certain surface anomalies remain practically invisible in RGB alone, necessitating the incorporation of 3D information. Re-training RGB backbones on industrial depth datasets is hindered by the limited availability of sufficiently large datasets. We propose a new surface anomaly detection method 3DSR, which outperforms all existing state-of-the-art on the challenging MVTec3D anomaly detection benchmark.
  arXiv  Detail & Related papers  (2023-11-02T09:44:21Z)
• UncLe-SLAM: Uncertainty Learning for Dense Neural SLAM [60.575435353047304]
  We present an uncertainty learning framework for dense neural simultaneous localization and mapping (SLAM) We propose an online framework for sensor uncertainty estimation that can be trained in a self-supervised manner from only 2D input data.
  arXiv  Detail & Related papers  (2023-06-19T16:26:25Z)
• 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)
• Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth [90.33296913575818]
  In some video-based scenarios such as video depth estimation and 3D scene reconstruction from a video, the unknown scale and shift residing in per-frame prediction may cause the depth inconsistency. We propose a locally weighted linear regression method to recover the scale and shift with very sparse anchor points. Our method can boost the performance of existing state-of-the-art approaches by 50% at most over several zero-shot benchmarks.
  arXiv  Detail & Related papers  (2022-02-03T08:52:54Z)
• 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)
• Project to Adapt: Domain Adaptation for Depth Completion from Noisy and Sparse Sensor Data [26.050220048154596]
  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.
  arXiv  Detail & Related papers  (2020-08-03T17:21:57Z)

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.