DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge
- URL: http://arxiv.org/abs/2405.17102v1
- Date: Mon, 27 May 2024 12:21:31 GMT
- Title: DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge
- Authors: Yifan Mao, Ming Li, Jian Liu, Jiayang Liu, Zihan Qin, Chunxi Chu, Jialei Xu, Wenbo Zhao, Junjun Jiang, Xianming Liu,
- Abstract summary: In this report, we introduce the DINO-SD, a novel surround-view depth estimation model.
Our DINO-SD does not need additional data and has strong robustness.
Our DINO-SD get the best performance in the track4 of ICRA 2024 RoboDepth Challenge.
- Score: 54.71866583204417
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Surround-view depth estimation is a crucial task aims to acquire the depth maps of the surrounding views. It has many applications in real world scenarios such as autonomous driving, AR/VR and 3D reconstruction, etc. However, given that most of the data in the autonomous driving dataset is collected in daytime scenarios, this leads to poor depth model performance in the face of out-of-distribution(OoD) data. While some works try to improve the robustness of depth model under OoD data, these methods either require additional training data or lake generalizability. In this report, we introduce the DINO-SD, a novel surround-view depth estimation model. Our DINO-SD does not need additional data and has strong robustness. Our DINO-SD get the best performance in the track4 of ICRA 2024 RoboDepth Challenge.
Related papers
- DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications [21.066770408683265]
DurLAR is a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery.
Our evaluation shows our joint use supervised and self-supervised loss terms, enabled via the superior ground truth resolution.
arXiv Detail & Related papers (2024-06-14T14:24:05Z) - RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and
Comfortable Autonomous Driving [67.09546127265034]
Road surface reconstruction helps to enhance the analysis and prediction of vehicle responses for motion planning and control systems.
We introduce the Road Surface Reconstruction dataset, a real-world, high-resolution, and high-precision dataset collected with a specialized platform in diverse driving conditions.
It covers common road types containing approximately 16,000 pairs of stereo images, original point clouds, and ground-truth depth/disparity maps.
arXiv Detail & Related papers (2023-10-03T17:59:32Z) - 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) - NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation [58.21817572577012]
Video depth estimation aims to infer temporally consistent depth.
We introduce NVDS+ that stabilizes inconsistent depth estimated by various single-image models in a plug-and-play manner.
We also elaborate a large-scale Video Depth in the Wild dataset, which contains 14,203 videos with over two million frames.
arXiv Detail & Related papers (2023-07-17T17:57:01Z) - One Million Scenes for Autonomous Driving: ONCE Dataset [91.94189514073354]
We introduce the ONCE dataset for 3D object detection in the autonomous driving scenario.
The data is selected from 144 driving hours, which is 20x longer than the largest 3D autonomous driving dataset available.
We reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
arXiv Detail & Related papers (2021-06-21T12:28:08Z) - ODIN: Automated Drift Detection and Recovery in Video Analytics [7.292916882993351]
ODIN is a visual data analytics system that automatically detects and recovers from drift.
We present an unsupervised algorithm for detecting drift by comparing the distributions of the given data against that of previously seen data.
specialized models outperform their non-specialized counterpart on accuracy, performance, and memory footprint.
arXiv Detail & Related papers (2020-09-09T12:13:40Z) - DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention
and Alertness Analysis [54.198237164152786]
Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS)
The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development.
In this paper, we introduce the Driver Monitoring dataset (DMD), an extensive dataset which includes real and simulated driving scenarios.
arXiv Detail & Related papers (2020-08-27T12:33:54Z) - RGB-D Salient Object Detection: A Survey [195.83586883670358]
We provide a comprehensive survey of RGB-D based SOD models from various perspectives.
We also review SOD models and popular benchmark datasets from this domain.
We discuss several challenges and open directions of RGB-D based SOD for future research.
arXiv Detail & Related papers (2020-08-01T10:01:32Z)
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.