Chi Zhang

Zhaobing Kang, Wei Zou, Zheng Zhu et al.

This paper presents a generic 6DOF camera pose estimation method, which can be used for both the pinhole camera and the fish-eye camera. Different from existing methods, relative positions of 3D points rather than absolute coordinates in the world coordinate system are employed in our method, and it has a unique solution. The application scope of POSIT (Pose from Orthography and Scaling with Iteration) algorithm is generalized to fish-eye cameras by combining with the radially symmetric projection model. The image point relationship between the pinhole camera and the fish-eye camera is derived based on their projection model. The general pose expression which fits for different cameras can be acquired by four noncoplanar object points and their corresponding image points. Accurate estimation results are calculated iteratively. Experimental results on synthetic and real data show that the pose estimation results of our method are more stable and accurate than state-of-the-art methods. The source code is available at https://github.com/k032131/EPOSIT.

Feifei Li, Qi Song, Chi Zhang et al.

Novel View synthesis (NVS) techniques, notably Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), can augment camera pose estimation by extending training data with rendered images. However, the images rendered by these methods are often plagued by blurring, undermining their reliability as training data for camera pose estimation. This limitation is particularly critical for Scene Coordinate Regression (SCR) methods, which aim at pixel-level 3D coordinate estimation, because rendering artifacts directly lead to estimation inaccuracies. To address this challenge, we propose a dual-criteria filtering mechanism that dynamically identifies and discards suboptimal pixels during training. The dual-criteria filter evaluates two concurrent metrics: (1) real-time SCR reprojection error, and (2) gradient threshold, across the coordinate regression domain. In addition, for visual localization problems in sparse input scenarios, it will be even more necessary to use data generated by NVS to assist the localization task. We design a coarse-to-fine PoI variant using sparse input NVS to solve this problem. Experiments across indoor and outdoor benchmarks confirm our method's efficacy. It achieves state-of-the-art localization accuracy while maintaining computational efficiency.

Qi Song, Qingyong Hu, Chi Zhang et al.

3D perception tasks, such as 3D object detection and Bird's-Eye-View (BEV) segmentation using multi-camera images, have drawn significant attention recently. Despite the fact that accurately estimating both semantic and 3D scene layouts are crucial for this task, existing techniques often neglect the synergistic effects of semantic and depth cues, leading to the occurrence of classification and position estimation errors. Additionally, the input-independent nature of initial queries also limits the learning capacity of Transformer-based models. To tackle these challenges, we propose an input-aware Transformer framework that leverages Semantics and Depth as priors (named SDTR). Our approach involves the use of an S-D Encoder that explicitly models semantic and depth priors, thereby disentangling the learning process of object categorization and position estimation. Moreover, we introduce a Prior-guided Query Builder that incorporates the semantic prior into the initial queries of the Transformer, resulting in more effective input-aware queries. Extensive experiments on the nuScenes and Lyft benchmarks demonstrate the state-of-the-art performance of our method in both 3D object detection and BEV segmentation tasks.