Large-format single-photon avalanche diode (SPAD)-based direct time of flight (dToF) sensors are expected to be widely applied in future L5 full driving automation. However, the high-power in-pixel TDCs and the huge amount of data generated by multiframe histogram sampling impose limitations on the pixel format of SPAD-based dToF sensors. To tackle this challenge, we proposed the Computing-in-pixel Edge-aware Detection and Reconstruction (CEDAR) architecture. In this architecture, edge pixels are recognized by charge-domain convolution (CDC) computing, and noise pixels are eliminated by in-memory denoising (IMD). Only few TDCs in these edge pixels are activated, resulting in significant power and data savings. Afterward, the full-format image is reconstructed by a U-Net using the obtained depth information from these edge pixels. For the first time, we proposed a high-resolution 512 × 512 SPAD-based dToF sensor with a low power of 83.3 mW, a distance accuracy of 0.9 cm, and a frame rate of 60 fps. The high-resolution 3D image can be reconstructed by only 3.5% sparse edge pixels, achieving a PSNR of 35.2 dB. The CEDAR architecture can achieve 16× pixel format and image resolution improvement under the same constraint of power dissipation.