Wireless communication plays an essential role in daily life and industry. For stable communication, pointing the antenna of the receiver in the optimal direction becomes crucial, especially in activity recognition through Wi-Fi sensing. In Wi-Fi sensing, receiver parameters such as antenna angles have traditionally been determined through calibration involving installed Wi-Fi transmitters and receivers. Knowing the received signal strength for each direction in advance would eliminate or minimize the need for calibration efforts. To address this problem, we propose Calibration-less Wi-Fi sensing with NeRF2 (CAWN), a method that estimates the optimal receiver antenna direction for Wi-Fi sensing by adapting NeRF2 to Wi-Fi environments. While NeRF2 has proven effective only for long-wavelength radio waves, we investigate its application to 5-GHz Wi-Fi short-wavelength radio waves and evaluates its effectiveness in activity recognition using Wi-Fi sensing.