Pose-Free Radiance Reconstruction With Depth and Exposure Priors on a Cloud-Native Hybrid KAN Framework

Training neural radiance fields without pre-estimated camera poses remains challenging, particularly in the presence of complex illumination and large inter-frame motion. This work presents a self-calibrating radiance-field framework designed to address these difficulties through a unified optimization strategy. Reliable monocular depth priors from the Depth Anything V2 model are incorporated to provide stable geometric constraints, and multiview point-cloud alignment is enforced to prevent pose drift under wide baselines. Illumination-induced photometric inconsistencies are handled by a jointly optimized exposure-compensation module that separates transient brightness variations from the underlying scene radiance. To enhance the representational capacity beyond that of standard multilayer perceptrons, a Kolmogorov-Arnold Network is adopted as the radiance-field backbone, enabling improved recovery of fine geometric and appearance details. The complete pipeline is deployed on a cloud-native infrastructure that supports scalable optimization and interactive access through an LLM-assisted interface. Experiments on indoor and outdoor datasets demonstrate that the proposed method consistently improves pose accuracy and novel-view synthesis quality over existing self-calibrating approaches.