  Laparoscopic liver surgery poses a complex intraoperative dynamic environment
for surgeons, where remains a significant challenge to distinguish critical or
even hidden structures inside the liver. Liver anatomical landmarks, e.g.,
ridge and ligament, serve as important markers for 2D-3D alignment, which can
significantly enhance the spatial perception of surgeons for precise surgery.
To facilitate the detection of laparoscopic liver landmarks, we collect a novel
dataset called L3D, which comprises 1,152 frames with elaborated landmark
annotations from surgical videos of 39 patients across two medical sites. For
benchmarking purposes, 12 mainstream detection methods are selected and
comprehensively evaluated on L3D. Further, we propose a depth-driven geometric
prompt learning network, namely D2GPLand. Specifically, we design a Depth-aware
Prompt Embedding (DPE) module that is guided by self-supervised prompts and
generates semantically relevant geometric information with the benefit of
global depth cues extracted from SAM-based features. Additionally, a
Semantic-specific Geometric Augmentation (SGA) scheme is introduced to
efficiently merge RGB-D spatial and geometric information through reverse
anatomic perception. The experimental results indicate that D2GPLand obtains
state-of-the-art performance on L3D, with 63.52% DICE and 48.68% IoU scores.
Together with 2D-3D fusion technology, our method can directly provide the
surgeon with intuitive guidance information in laparoscopic scenarios.
