Related papers: Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation

Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation

URL: http://arxiv.org/abs/2210.02009v1
Date: Wed, 5 Oct 2022 03:44:34 GMT
Title: Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation
Authors: Jialei Xu, Xianming Liu, Yuanchao Bai, Junjun Jiang, Kaixuan Wang, Xiaozhi Chen, Xiangyang Ji
Abstract summary: We propose a novel multi-camera collaborative depth prediction method. It does not require large overlapping areas while maintaining structure consistency between cameras. Experimental results on DDAD and NuScenes datasets demonstrate the superior performance of our method.
Score: 75.99435808648784
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Depth map estimation from images is an important task in robotic systems. Existing methods can be categorized into two groups including multi-view stereo and monocular depth estimation. The former requires cameras to have large overlapping areas and sufficient baseline between cameras, while the latter that processes each image independently can hardly guarantee the structure consistency between cameras. In this paper, we propose a novel multi-camera collaborative depth prediction method that does not require large overlapping areas while maintaining structure consistency between cameras. Specifically, we formulate the depth estimation as a weighted combination of depth basis, in which the weights are updated iteratively by a refinement network driven by the proposed consistency loss. During the iterative update, the results of depth estimation are compared across cameras and the information of overlapping areas is propagated to the whole depth maps with the help of basis formulation. Experimental results on DDAD and NuScenes datasets demonstrate the superior performance of our method.

Related papers

Robust and Flexible Omnidirectional Depth Estimation with Multiple 360° Cameras [8.850391039025077]
We use geometric constraints and redundant information of multiple 360-degree cameras to achieve robust and flexible omnidirectional depth estimation. Our two algorithms achieve state-of-the-art performance, accurately predicting depth maps even when provided with soiled panorama inputs.
arXiv Detail & Related papers (2024-09-23T07:31:48Z)
GEDepth: Ground Embedding for Monocular Depth Estimation [4.95394574147086]
This paper proposes a novel ground embedding module to decouple camera parameters from pictorial cues. A ground attention is designed in the module to optimally combine ground depth with residual depth. Experiments reveal that our approach achieves the state-of-the-art results on popular benchmarks.
arXiv Detail & Related papers (2023-09-18T17:56:06Z)
FS-Depth: Focal-and-Scale Depth Estimation from a Single Image in Unseen Indoor Scene [57.26600120397529]
It has long been an ill-posed problem to predict absolute depth maps from single images in real (unseen) indoor scenes. We develop a focal-and-scale depth estimation model to well learn absolute depth maps from single images in unseen indoor scenes.
arXiv Detail & Related papers (2023-07-27T04:49:36Z)
SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation [101.55622133406446]
We propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras. Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views. In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets.
arXiv Detail & Related papers (2022-04-07T17:58:47Z)
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging [14.279471205248534]
We show how a consistent scene structure and high-frequency details affect depth estimation performance. We present a double estimation method that improves the whole-image depth estimation and a patch selection method that adds local details. We demonstrate that by merging estimations at different resolutions with changing context, we can generate multi-megapixel depth maps with a high level of detail.
arXiv Detail & Related papers (2021-05-28T17:55:15Z)
Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo [71.59494156155309]
Existing approaches for multi-view 3D pose estimation explicitly establish cross-view correspondences to group 2D pose detections from multiple camera views. We present our multi-view 3D pose estimation approach based on plane sweep stereo to jointly address the cross-view fusion and 3D pose reconstruction in a single shot.
arXiv Detail & Related papers (2021-04-06T03:49:35Z)
Robust Consistent Video Depth Estimation [65.53308117778361]
We present an algorithm for estimating consistent dense depth maps and camera poses from a monocular video. Our algorithm combines two complementary techniques: (1) flexible deformation-splines for low-frequency large-scale alignment and (2) geometry-aware depth filtering for high-frequency alignment of fine depth details. In contrast to prior approaches, our method does not require camera poses as input and achieves robust reconstruction for challenging hand-held cell phone captures containing a significant amount of noise, shake, motion blur, and rolling shutter deformations.
arXiv Detail & Related papers (2020-12-10T18:59:48Z)
Video Depth Estimation by Fusing Flow-to-Depth Proposals [65.24533384679657]
We present an approach with a differentiable flow-to-depth layer for video depth estimation. The model consists of a flow-to-depth layer, a camera pose refinement module, and a depth fusion network. Our approach outperforms state-of-the-art depth estimation methods, and has reasonable cross dataset generalization capability.
arXiv Detail & Related papers (2019-12-30T10:45:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.