  We present an inverse image-formation module that can enhance the robustness
of existing visual SLAM pipelines for casually captured scenarios. Casual video
captures often suffer from motion blur and varying appearances, which degrade
the final quality of coherent 3D visual representation. We propose integrating
the physical imaging into the SLAM system, which employs linear HDR radiance
maps to collect measurements. Specifically, individual frames aggregate images
of multiple poses along the camera trajectory to explain prevalent motion blur
in hand-held videos. Additionally, we accommodate per-frame appearance
variation by dedicating explicit variables for image formation steps, namely
white balance, exposure time, and camera response function. Through joint
optimization of additional variables, the SLAM pipeline produces high-quality
images with more accurate trajectories. Extensive experiments demonstrate that
our approach can be incorporated into recent visual SLAM pipelines using
various scene representations, such as neural radiance fields or Gaussian
splatting.
