Zhihao Zhang, Abhinav Kumar, Huaizhi Qu, Tianlong Chen, Xiaoming Liu
原文摘要
Monocular 3D detection (Mono3D) hinges on recovering depth from a single image, for which the geometric formulation $z \approx f \cdot H / h_{2D}$ is widely adopted due to its simplicity and direct grounding in projective geometry. We show that this formulation harbors a systematic, projection-induced bias: substituting the 2D bounding-box height $h_{2D}$ recovers the depth of an object's visible front surface rather than its center, causing size-dependent underestimation that persists even under ground-truth inputs. We address this front-surface bias with MonoPCA, a unified framework built on a projection-consistent reformulation. At its core, PCDepth retains the measurable $h_{2D}$ and closes the gap to center depth through an orientation- and size-aware correction derived from perspective geometry, eliminating the bias without introducing ill-posed regression targets. Because PCDepth couples depth to predicted object size and orientation, attribute perception becomes essential. We therefore introduce GeoAlign, a teacher-student framework that transfers geometric representations from Vision Foundation Models into a lightweight student, strengthening attribute perception at no inference cost. Across four challenging benchmarks, MonoPCA achieves SoTA performance, including a +1.44% AP$_{3D}$ gain on the KITTI leaderboard and +2.80% on Omni3D$_{OUT}$. Code is available at https://github.com/alanzhangcs/MonoPCA.
Personal AI agents often act on underspecified instructions and draw on context such as their user's messages and calendar to decide how to proceed. This context can contain private preferences that interfere with the task, with other people's interests, or with confidentiality norms. Prior work has studied deception under goal conflicts and the leakage of private information, but largely as separate problems. We study both problems together in a simulated company Slack workspace in which four employees delegate sprint staffing to personal agents. Two agents learn from private messages that their employee avoids working with a particular colleague. Because the other agents request justifications for task preferences and the only true justification is confidential, the agents cannot simultaneously remain honest, preserve confidentiality, and complete the staffing. We evaluate seven open-weight models and Claude and GPT models. Of the 135/140 open-weight agents that pursue the avoidance preference, 107 either make false claims about task fit or logistics or cite a true consideration as if it were their actual reason. In a three-model comparison, confidentiality instructions reduce disclosure of the preference from 83% (none) to 31% (medium) to 2% (strong). However, the task is often not completed anyway; in the main experiment (medium confidentiality instructions), only 16 of 70 runs end with a valid staffing. In their reasoning traces, agents frequently acknowledge evidence against their claims and still conclude that the claims are not false; they engage in motivated reasoning. Our results indicate that agents can strategically make false or misleading claims in a day-to-day task without being instructed to; the environment provides a controlled testbed for studying how agents resolve such conflicts and for developing deception monitors.