Motion- and Gravity-Aware 3D Reconstruction: Do Offline Priors Help?
Can optical flow and gravity improve camera-based 3D reconstruction without becoming permanent parts of the inference pipeline? This episode explains the training design, benchmark evidence, and practical implications of motion- and gravity-aware feed-forward reconstruction. We cover three questions: how training-only geometric hints enter VGGT and Pi3, why residual flow is a useful alternative to full optical flow, and what architecture-dependent injection points reveal about geometric representations. The discussion connects these choices to privileged-information learning and to the…
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